Lithium-ion batteries (LiBs) are seen as a viable option to meet the rising demand for energy storage. To meet this requirement, substantial research is being accomplished in battery materials as well as operational safety. LiBs are delicate and may fail if not handled properly. The failure modes and mechanisms for any system can be derived using different methodologies like failure mode effects analysis (FMEA) and failure mode methods effects analysis (FMMEA). FMMEA is used in this paper as it helps to identify the reliability of a system at the component level focusing on the physics causing the observed failures and should thus be superior to the more data-driven FMEA approach. Mitigation strategies in LiBs to overcome the failure modes can be categorized as intrinsic safety, additional protection devices, and fire inhibition and ventilation. Intrinsic safety involves modifications of materials in anode, cathode, and electrolyte. Additives added to the electrolyte enhance the properties assisting in the improvement of solid-electrolyte interphase and stability. Protection devices include vents, circuit breakers, fuses, current interrupt devices, and positive temperature coefficient devices. Battery thermal management is also a protection method to maintain the temperature below the threshold level, it includes air, liquid, and phase change material-based cooling. Fire identification at the preliminary stage and introducing fire suppressive additives is very critical. This review paper provides a brief overview of advancements in battery chemistries, relevant modes, methods, and mechanisms of potential failures, and finally the required mitigation strategies to overcome these failures. Internal combustion engines are a hundred-year-old technology and their development was backed by the stringent emission norms imposed by environmental pollution control boards in different countries. As the emission norms are becoming more stringent, internal combustion engines cannot meet the future norms. Hence, electric vehicles are the future automotive technologies with hybrid electric vehicles as a bridging technology. The electrification of the automotive sector is mainly driven by changes in climate-related policies, awareness of economic and security problems related to oil, a rise in pollution in the air which is depleting air quality, and rapid technology development in the field of batteries. Thus, there is a need to develop vehicles that run on sources other than fossil fuels. In electric vehicles, the internal combustion energy is replaced by a simpler and lighter electric motor that gets its power locally from the batteries, thereby, a wide variety of power sources including renewables can be used to supply the energy for the propulsion of the car. The electrification of the automotive sector, as well as ever-increasing demands for consumer electronics , have leA thorough understanding of each material will enable the researchers to improve the materials  accordinMitigation strategies are critical to reducing the risk of failures in LiBs as well as their consequences. They can thus be achieved in two steps. In the first step, strategies are implemented during the normal operation of batteries, to reduce the risk of a particular failure mode occurring in a device. Such strategies include material alterations, protection devices, and thermal management of batteries. In the second step, implemented strategies are the ones that are adopted when the LiBs have failed to aid in evading the disaster subsequently caused by the failure. This includes the isolation, diagnosis, and extinguishing of fires.The contents of this literature review have been organized into five sections. A schematic overview of the topics covered is also seen in A rechargeable battery is an energy storage component that reversibly converts the stored chemical energy into electrical energy. LiBs are a class of rechargeable batteries that are capable of undergoing numerous charging and discharging cycles. They have gained rapid popularity in recent times due to their superior performance. Similar to other batteries, they comprise important components such as electrolytes  , separat4Ti5O12 (LTO) O2 cathode material, the breakdown is already ob-served at around 140–160 °C [In the case of the Li[Ni0–160 °C ,157,158.yCoxMnzO2 is used as cathode material, then the breakdown occurs only at around 260 °C and is followed by reactions that are exothermic in nature leading to a sharp increase in temperature [When LiNiperature ,161,162.2O4 as the cathode material, the thermal breakdown is observed in-between 150 °C and 225 °C [When using LiMnd 225 °C ,164.LiPThere is also another phenomenon that affects the thermal performance of the battery, which is the oxidation of electrolytes. This oxidation is initiated due to the breakdown of metal oxide cathode material releasing oxygen or due to the electrolyte coming in contact with air due to a damaged battery case .(23)2.5Apart from these, even separators and collectors melt and thereby contribute to thermal runaway. In the case of separators, combinations of PP and PE are used, which have a melting temperature of 130–170 °C ,168,169.In this section, the possible mitigation strategies are discussed to overcome or restrict some specific modes and mechanisms of Lithium-ion battery failure. LiB safety is the prime focus, so multiple mitigation strategies are followed to keep the batteries safe. This can be done by two methods, one by avoiding operation conditions, which lead to heat and gas formation, the other being the process to control the heat and gas formation to avoid failure. More stable LiBs can be accomplished by alteration of the chemistry and/or component structures of the battery. The alteration can be made in electrodes and electrolytes . The eleThe inclusion of dedicated safety devices to the battery design is also one of the mitigation strategies. These safety devices should be economical and should not lead to packaging issues in the battery . The saf2O3 coating carried out by the ALD process, which results in the formation of ultrathin conformal film covering the anode composite which improves both cycle performance and inertness towards thermal attack [4Ti5O12) or Titanium niobates (TNO) instead of conventional carbon-based anodes improves the safety of the batteries [Surface coating is a popular method used for anode alteration. Among the coating technologies, atomic layer deposition (ALD) is widely used. A typical example is an All attack . The prol attack . Likewisatteries . Siliconatteries .Additives used in electrode composites enhance the temperature stability of the SEI layer built upon the anode surface. Higher temperature stability of the SEI layer, in turn, enhances the temperature activeness of the anode .4) coating improves the performance in electrochemical reactions and temperature stability of the cathode [2) coating on a Li cathode enhances the cycling stability and discharge strength [4 is a common coating material on LCO that improves overcharge tolerance and increases thermal strength [3, which improves cycling efficiency and reduces reactivity for side reactions under high-temperature stress [3-coated NMC cathodes, are also in use.For cathode alteration, both surface coatings and element substitution are widely explored. The use of surface coating is one of the common methods to enhance the electrochemical performance of the cathode material. The coating materials have to be thermally and chemically inactive . As per  cathode . Tin oxistrength . MnSiO4 strength . Fluoride stress . A few oThe other important means of modification is element substitution, which is very effective in enhancing the performance of cathodes. Aluminum is one of the most efficient substitutions or doping agents for transition metals such as Co, Ni, and Mn, which increases the structural stability under thermal stress as sketched in + and OH− ions) as indicated in + and OH− ions are formed due to the abstraction of hydrogen present in organic electrolytes. This process is driven by the highly reactive oxygen ions produced within the cell due to the high temperature caused during thermal runaway . Activetated in ). These e growth . Althouge growth . Zeng ete growth . The none growth . Additive growth . Alternae growth .The most common separators are made of Polyethylene (PE) and Polypropylene (PP). A shutdown type of separator is made up of multilayer films which help in closing the pores and prevent the ion transport across the separator during excess thermal runaway. The combination of PE and PP layers improves the safety of the separator, as the PE layer will melt and fill the pores of the PP layer by the creation of a protective film that limits the flow of current during cell abuse . This irDespite LiBs being the most viable option to power electric vehicles, they have issues that need to be tackled to ensure optimum performance. The battery works at peak efficiency only when maintained at a certain temperature range (20–40 °C (±5 °C)). Previously discussed issues such as ISCs and thermal runaways can be monitored as well as prevented to a certain extent by using Battery Management Systems (BMS). In the case of monitoring the battery’s health, the BMS plays a vital role. It monitors parameters and stats such as State of Charge (SOC), State of Health (SOH), Depth of Discharge (DOD), and State of Function (SOF) for the entire pack as well as for individual cells.BMS components can be divided into two major categories: software and hardware . The sofThe role of BMS for an easier understanding can be further classified as system management, identification, or prediction of an endangering scenario, and the response and decisions to be taken once the system’s abnormal behavior is observed . Firstly2 and solvents, but will also contain HF, POF3 and in the case of a fire, the combustion products are formed from above. A mechanism can be designed with a mechanical link using a metal disc, which is attached to the cathode. This breaks the circuit and makes way for the gases to escape. The protection vent can be seen in xCoO2 at higher voltage releases of carbon dioxide within the system and hence the pressure was built up. This actuated the aluminum disc, which disconnected the cathode from the circuit there by protecting the internal components. This protection vent acts as a backup safety device as it is irreversible and accompanies the release of organic compounds which may pose additional risks that need to be managed. Usually, the POTC will overrun the operation of the protection vent by reversibly limiting the current in the circuit under overheating conditions, as described below [Protection vents act as a response mechanism to release excess pressure developed inside the battery beyond the threshold by letting the gas escape into the atmosphere. The escaping gas is typically mainly COed below .POTCs typically consist of conductive polymers and operate on the principle that heating caused by the joule effect correspondingly increases the resistance in the device. This will strongly, but reversibly, inhibit the current passage if the cell is overheated. Under normal operation temperatures, the POTC should contribute only negligibly to the overall cell resistance . The POTSome of the other  circuit cut-off devices include magnetic switches, bimetallic thermostats, and electronic Protection Circuit Modules (PRCM), which can be used to shield the battery pack, preventing the need to trigger irreversible shutdown mechanisms. The PRCM is attached to the battery circuit independently. When there is a malfunction in the circuit, the PRCM will break and the circuit is shielded . Thus, LAs discussed in Air cooling is the most popular method for small battery packs where the heat generated is not so high. There are different methods of air cooling of LiBs in terms of contact, namely direct air cooling and indirect air fin cooling. Based on the type of energizing air to pass through, there is forced air and free air-cooling. A pump is utilized to force the air from the atmosphere to the battery packs in the forced mode of air-cooling. The forced mode of air cooling is mainly used when a large amount of heat is to be removed from the battery packs, or when there is no duct present in the vehicle for air passage from the atmosphere. In free air cooling, the vehicle motion drives the passage of air into the battery packs through proper ventilation .In the direct mode of air-cooling, the air is passed directly over the surface of the cells through the tubes or ducts. As the air passes the exposed surfaces of the battery, the heat is taken away by the airflow via convective heat transfer. The effectiveness of the cooling system is based on several parameters, such as the heat transfer coefficient of air and the battery surface, which exposed the face area of the batteries in contact with airflow . In indiLiquid cooling is always considered a better alternative to air cooling, especially for larger battery packs, because of their higher thermal conductivity and the high specific heat of the coolant compared to the air . Liquid Internal cooling is the process  where thExternal cooling refers to cooling by a liquid that is passed along the outer periphery of the battery pack. Among its advantages are low cost and flexibility in design. Different equipment used here can be in the form of tubes, jackets, and channels designed according to the battery layout ,208. The2O3, CuO, Fe3O4, TiO2, Al, Cu, Ni, Ag, and other metals or metal oxides. The thermal conductivity of metals is higher compared to standard cooling fluids. The addition of these particles into the cooling fluid can increase its thermal conductivity to a large extent. The impact of an additive on the thermal conductivity is related to the atomic size. In general, the lower the atomic radius, the higher the thermal conductivity [The selection of an appropriate coolant is very critical to obtain better efficiency. Major coolants include water, oil, nano-fluids, and liquid metals. Water is mainly utilized in various cases of cooling. It possesses better properties in terms of specific heat capacity, thermal conductivity, and viscosity. Water consumes less power and is circulated through pads, jackets, and tubes to avoid the short circuit that might occur if it comes in contact with the battery terminals . Oil is uctivity .PCM cooling is another alternative that is used for LiBs cooling. The PCM materials possess a high latent heat of fusion and ideal fusion point. The flow pathway of heat in the battery of PCM cooling is that the heat generated in the battery is passed to PCM and it reduces the maximum of the heat without passing it to further materials such as the battery casing. The concept of PCM is that when the heat is absorbed by the material there is a phase change from solid to liquid and as soon as that heat is pulled out of the system the PCM material changes its state back to solid .The key property of PCM material is a suitable melting temperature within the range of operation of the battery. This is very critical to achieve effective heat dissipation. The packaging of the battery pack has to be done carefully with additional volume available for the PCM material when it changes to the liquid state. To tailor the heat transfer properties of PCM materials, a metal matrix can be embedded, or high thermal conductivity additives and additional fins for heat removal can be added. The system can also be designed with a dual function as a latent heat storage system. The PCM approach has an advantage in terms of weight and flexibility in the packaging of the battery pack . Common The most basic mitigation strategies for different mechanical failures due to compression and penetration of the batteries are to optimize the battery casings. These casings can adopt different cross-sections like the double layer metal sheet, NavTruss  is a commercially used agent to enhance fire suppression. 6F12O, and a combination of both. It can be observed that the combination of C6F12O with the water mist will reduce the peak temperature of the surface and also reduce the temperature in the shortest interval of time [The gaseous suppression agent dodecafluoro-2-methylpentan-3-one (C of time . ABC pow of time . Halons  of time . Heptafl of time . Though This review summarizes materials, failure modes and mechanisms, and different mitigation strategies that can be adopted for the improvement of Lithium-ion battery safety. NMC and LFP are promising cathode materials. Moving forward, graphite is commercially widely used as an anode material. However, research is going on to implement lithium metal or silicon as an anode owing to its advantages. In the area of electrolytes, the focus is on solid and gel polymer electrolytes. Furthermore, different mechanisms and modes of failure were studied using the FMMEA method. It was observed that anode active material is more susceptible and plays a critical role in leading to system-wide failure. Therefore, this component needs to be re-worked and its safety should be improved. In the case of modes, mechanical is one of the most perilous, as it rapidly deteriorates the health of the battery, simultaneously leading to other modes of failure. Understanding failures will help us to formulate mitigation strategies. Among them, innate safety strategies include alterations made in cell materials where additives are coated on the electrodes which will aid the cell performance; additives are also added to electrolytes, which make the electrolytes fire-resistant. Protection devices are introduced to shield the battery pack from variation current passage. In the thermal management of battery packs, different strategies are used in different applications, such as air cooling used in small battery packs with less heat generation, liquid cooling used in large battery packs with higher heat generation and PCM cooling can be used in small battery packs with packaging constraints. All these strategies are used to evade the excessive heat generated in the batteries. The fire control strategy is used to suppress the fire after the lithium-ion battery fails. It includes fire diagnosis devices like smoke detectors, which have a better response time compared to fire detectors, and in extinguishing agents, water mist with suitable additives is a viable option as it is available in abundance and has fewer side effects during the process of extinguishing.


This paper presents automated harmonic removal as a desirable solution to effectively identify and discard the harmonic influence over the output signal by neglecting any user-defined parameter at start-up and automatically reconstruct back to become a useful output signal prior to system identification. Stochastic subspace-based algorithms (SSI) methods are the most practical tool due to the consistency in modal parameters estimation. However, it will be problematic when applied to structures with rotating machines and the presence of harmonic excitations. Difficulties arise when automating this procedure without any human interaction and the problem is still unresolved because stochastic subspace-based algorithms (SSI) methods still require parameters (the maximum within-cluster distance) that are compulsory to be defined at start-up for each analysis of the dataset. Thus, the use of image-based feature extraction for clustering and classification of harmonic components and structural poles directly from a stabilization diagram and for modal system identification is the focus of the present paper. As a fundamental necessary condition, the algorithm has been assessed first from computed numerical responses and then applied to the experimental dataset with the presence of harmonic excitation. Results of the proposed approach for estimating modal parameters demonstrated very high accuracy and exhibited consistent results before and after removing harmonic components from the response signal. The present structural modal identification method, operational modal analysis (OMA), is widely and commonly used within various engineering fields, such as mechanical, aerospace, electrical and civil, due to its capability to implement economical and fast tests that rely solely on structural response signals induced by undetermined ambient excitations  without affecting its operating conditions ,2. This In recent years, the development of automated procedures for identifying modal parameters in operating conditions has become increasingly popular and stochastic subspace-based algorithms (SSI) methods have been selected as the most practical tool for this procedure due to the consistency in modal parameters’ estimation, especially under non-stationary noise excitations ,13,14,15It should be noted that harmonic components cannot, in general, be removed by simple filtering, as this would in most practical cases significantly change the poles of the structural modes and thereby their natural frequency and modal damping. Here, several indicators for the separation of structural and harmonic modes in output-only modal identification are proposed in In the context of a system subjected to random excitation combined with the steady-state signal, the subspace-based methods identify the harmonics as very lightly damped modes that one can filter in the mode selection process . DifficuIn that case, a desirable solution is required to effectively identify and discard the harmonic influence over the output signal by neglecting any calibration or user-defined parameter at start-up and then automatically reconstructing back to become a useful output signal prior to system identification. Thus, the development of such a novel approach for an automated harmonic removal method in the SSI framework using image-based feature extraction for clustering and classification of harmonic components from structural poles and also for modal system identification is the focus of the present paper. As a fundamental necessary condition, the algorithm has been assessed first from computed numerical responses based on random white noise, acting on three shear-type frame structures, corrupted with noise and with the addition of harmonic excitation. Then, the original implementation is also applied to the experimental dataset with the presence of harmonic excitation.The rest of the paper is organized as follows. The automated harmonic signal removal technique in OMA based on parametric methods consists of the following steps, as shown in The steps required for the automated denoising technique are outlined in Stochastic Subspace Identification (SSI) has been a recognized approach since the previous decade, primarily because of its user-friendly execution . This pai based on discrete data, as follows: Y with the last block rows, i, removed and i, removed. Hence, each l*l. The computed correlations at different time lags are then stored in the block Toeplitz matrix. The size of the Toeplitz matrix becomes n*n when estimating modal parameter with model order n. Thus, the subsequent Equation (2) should correct for the number of block rows i:The initial step is to compute the output correlations as shown in Equation (1). x, and maximum system order, n, were set as 2 and 50 modes, respectively. The next step is to calculate the singular value decomposition (SVD) of the block Toeplitz that can provide the unitary matrices [U] and [V]. The positive singular values are ranked in descendant order of the diagonal matrix [Σ] as in Equation (3)  needs to be obtained. This is done for each order from 1 to Now that Resolving the eigenvalue problem for [A] produces the diagonal matrix [M] and the eigenvectors The mode shapes of the system The step of identifying the state matrix and the modal parameters are repeated for each order up until When the modal parameters are estimated using the SSI techniques, the stabilization diagram can be constructed in order to select the optimal State Space Dimension. This tool is a typical means to distinguish between stable, unstable and noise modes, which is performed by estimating poles with an increasing model order. The unstable and noise modes appear due to an oversized model system. The noise modes are caused by physical reasons, while the unstable modes are generated to ensure the mathematical description of the measured data. Theoretically, the stabilized physical modes can be identified by the vertical alignment of stable poles, while noise modes are scattered. This is based on the poles’ comparability with respect to the order of the given model with that obtained from a lower order model .The natural frequencies and damping ratio of poles from two orders are compared using Equations (12) and (13) as follows :(12)|f and (15) below:Then, the standardized image features from MATLAB software  were applied in this study to extract the image features of each image of stabilization diagrams that were previously generated. These features specifically represent the characteristics of each parameter  for different conditions, either stable or unstable. The standardized image feature, Maximally Stable External Regions based on regions as the characteristic value, was used in this study in order to capture all the modes of interest particularly in terms of computational mode appearance. Generally, the image feature will provide a certain value based on its image characteristics. If the image is blank, the value will become zero, otherwise, if the pole appears, the value will increase. The increasing value of image feature extraction depends on the number of poles appeared in that particular image. It works well with stabilization diagram because the stabilize physical modes consist of the vertical alignment of stable poles, while noise modes are scattered. By generating the image of stabilization diagram according to 0.01 interval frequency , the poles can be clustered accordingly. Therefore, the generated input image based on the interval frequency of stabilization diagram plays a key role in the performance of the image feature extraction. Details explanation about the process of this procedure was shown in find and the threshold in order to discriminate the weak ones and leave only the dominant modes. The approach is based on excluding the weak modes with lower value than the threshold. The threshold is set to be 20 features in an image clustering plot (10 poles only considered as stable). The example of the generated images that have been classified as physical modes from the clustering plot are displayed in This algorithm was configured to group all the physical poles of the stabilization diagram by using image features extraction and then constructing an image clustering plot, as shown in The classification of structural modes and the harmonic component of the system was autonomously implemented in MATLAB using shape recognition process. The process of shape recognition starts with the reading input image from the graphics file. Then, the input RGB image (stands for “Red Green Blue” and refers to three hues of light that can be mixed together to create different colors image) is converted to the greyscale image by eliminating the hue and saturation information while retaining the luminance. The computation of a global threshold from the grayscale image is performed using Otsu’s method  before bHarmonic frequencies are determined based on the presence of square-shape poles recognized inside an image that represent a harmonic component. The square-shape pole is detected by a magenta square marker type as shown in nlinfit) of the responses input signal on the predictors in time using a sinusoidal model function, The next step is to remove the harmonic signal by applying nonlinear regression ys=AThen, the estimated regression coefficients are used to reconstruct the sinusoidal signal that represents harmonic frequency. Then, the generated sinusoidal signals, The identification of modal parameters is essential in order to determine the inherent dynamic characteristics of a system. For the estimation of natural frequencies, it has been discussed in the previous section. Therefore, this section only focuses on the estimation of modal damping ratios. Generally, the process of estimating the modal damping ratios depends on the identified natural frequencies which are based on the frequency versus modal damping ratios plot in the stabilization diagram. Only stable poles are chosen to be displayed in this plot, as shown in The process of extracting modal damping ratios from the frequency versus modal damping ratio plot involves the same process as the natural frequency, which uses image clustering, but in terms of the generated input images, it is quite different. The process of the generated input image for modal damping ratios from the frequency versus modal damping ratios plot requires the identified natural frequencies as a fixed x-axis and the value of modal damping ratios along the y-axis. In this case, the input image was generated and displayed separately into every value of the modal damping ratio according to a 0.002 interval along the y-axis with the fixed value of identified natural frequency in the x-axis, as characterized in Equations (18) and (19), respectively. In order to make image feature extraction more efficient, all axes and legends in the plot of these images must be removed.The standardized image features from MATLAB, Maximally Stable External Regions based on regions as the characteristic value, was applied to extract the image features of each image of stabilization diagrams that were previously generated. The image feature will provide a certain value based on its image characteristics. If the image is blank, the value will become zero, otherwise, if the pole appears, the value will increase. Each pole presents the value of 2, the increasing value of image feature extraction depends on the number of poles that appeared in that particular image. This identified image features extraction are then constructed in image clustering plot, as shown in Figure 21. The process is repeated for each mode because each mode has its own natural frequency. find and is based on the maximum number of images featured in image clustering plot. The estimation of the modal damping ratio for each mode is autonomously implemented, and involves the MATLAB command—As a fundamentally necessary condition, the algorithm has been assessed first from computed numerical responses based on random white noise, acting on different ideal shear-type frame structures. In this section, the proposed algorithms are validated using simulated data from a simple three-story shear-type model consisting of three degrees of freedom (DOF), as illustrated in The characteristics of the simulated three-story shear-type models which present well-separated modes are provided in To determine the coefficients analysis ,43,44,45γ = 12 and β = 14) [Input is taken as a stationary broadband ambient excitation with normally distributed random numbers assuming independent inputs for all DOF of the models. It has a constant power spectral density (PSD) which can cover a wide range of frequencies and is adequate to excite all the structural modes. The random input excitation, which is also known as zero-mean Gaussian white noise, takes the assumption of the excitation system to be linear and time-invariant. The response of the system is simulated using Newmark’s method with constant average acceleration  . The adoThe simulated outputs, which are time series with the accelerations of all the DOFs of the models, are corrupted with noise that mimics the influence of the sensors and measuring chain noise. This is simulated by normally distributed random numbers with a standard deviation equal to 10% of the standard deviation of the simulated outputs (this percentage of noise is quite conservative in the case of well-conducted ambient vibration tests ).In order to evaluate the influence of the presence of harmonic components on the exciting force, and to check the efficiency of the proposed automated harmonic signal removal, a combination of harmonic excitation and Gaussian white noise excitation with zero mean is applied to each mass separately. The frequencies of the applied harmonic excitation to structural modes are equal to 3 Hz and multiples of it (6 Hz and 9 Hz), as shown in The effectiveness of the proposed algorithm is verified by the target values of all the structural modal parameters on three-story frames using eigenvalue problem analysis and the assigned value for the modal damping ratio, as described in The results of the automated harmonic removal technique are characterized in Generated harmonic signals in the time and frequency domain are shown in The image clustering plots for identifying modal damping ratios for the three-story frame model from numerical simulations after removing the harmonic signal are displayed in Results obtained from the two different approaches are also presented in Based on the image clustering result, plotted by using image features extraction from Maximally Stable External Regions as displayed in In order to demonstrate the novel ability of the proposed SSI-Covariance with image-based feature extraction technique to eliminate harmonics, modal parameters are also identified using the SSI method, classical SSI-Covariance and SSI-Data. Results obtained from the two different approaches are also presented in Results of the proposed automated SSI-Covariance with a novel approach using image-based feature extraction in estimating modal parameters, particularly natural frequencies, demonstrated very high accuracy, exhibiting an average percentage deviation below 1% for all cases. In addition, results of the estimated modal damping ratio are quite consistent and provide limited errors with percentage deviation (error) below 4%, where classical SSI-Covariance can be up to 30% of percentage deviation (error) before removing harmonic components from the response signal as presented in By comparing the results of the estimated modal parameter using the proposed automated harmonic signal removal using SSI-Covariance with image-based feature extraction with other SSI methods, which are classical SSI-Covariance and SSI-Data, the results yield quite accurate and consistent results for before and after removing the harmonic component and it seems not very affected with corrupted response signal with unwanted mode. Moreover, this approach is also highly efficient in identifying and clearly separating closely spaced modes, as seen in The results of this study also prove that the implementation of image-based feature extraction for identification and classification of structural modes and harmonic component in a stabilization diagram and sinusoidal model fitting as a tool for filtering purposes are a good combination as well as an effective procedure, producing a good input signal that is free from unwanted modes.A three-story steel frame is set up to experimentally demonstrate the applicability of the proposed approach and to illustrate the theoretical concepts, described in this chapter. The masses on each story are dominant and it is composed of 3 rigid iron masses connected to each other and to the base through flexible aluminum columns by connection elements, which are clamped at nodes 1–6 and the base . HorizonIn order to illustrate the complex candidate models of OMA, a vibration experiment of the metallic frame was performed in the laboratory. It is assumed that the lateral response on nodes 1 and 4, 2 and 5, as well as on 3 and 6, are equal. So, only acceleration signals in nodes 1, 2 and 3 were measured.The signal acquisition system used in the experiment consists of wicoson accelerometers, signal conditioners and data acquisition equipment, as shown in In order to induce random input excitation, a slip shaker is used to excite the three-story steel frame. It has a constant PSD which can cover a wide range of frequencies and is adequate to excite all the structural modes . The ranIn addition to the random input excitation, the three-story steel frame is excited by harmonic excitations provided by a motor of rechargeable hair trimmer turning at a nominal rotation of 9000 revolutions per minute (rpm), peaks at harmonic excitation of 150 Hz and multiples of it are expected for top power motor with the shaft connecting the motor and blade. It has been placed on the second floor of the three-story steel frame, as shown in The duration of each setup was 5 min and the maximum frequency was 250 Hz. The DAQ employs some fixed signal processing filters which need to be addressed when taking measurements. The hardware does not actually sample input channels with the specified sample rate frequency sampling (fs) but samples it with 128 times the specified sample rate. This is to prevent aliasing effects. A sampled dataset can only represent a limited bandwidth of the input signal. It can only distinguish signal components with a maximum frequency of The estimated first singular value of the power spectral density (PSD) matrix and the spectrogram of the experimental temporal response are shown in The results of the automated harmonic removal technique are characterized in The proposed algorithm was analyzed under a controlled room that was carried out for experimental testing on a steel frame. The results are again consistent, even with the presence of harmonic excitation or noise mode.From the stabilization diagram plot in Based on the image clustering result, a plot using image features extraction from Maximally Stable External Regions was displayed in In order to demonstrate the novel ability of the proposed SSI-Covariance with the image-based feature extraction technique to eliminate harmonics in realistic cases, modal parameters are also analyzed. The proposed method is validated by using stochastic subspace identification-data (SSI-Data), one of the well-known time-domain methods, mainly due to how robust and powerful it is in extracting structural modal parameters. Then, the proposed algorithm is also compared with the classical SSI-Covariance algorithm. Dealing with real or complex structures faces difficulties, especially in validating data for output-only data by using floor mass and stiffness properties of the structure. Besides that, the use of the EMA method with complete input-output data to validate data for the whole structure seems to impose additional challenges as the forced vibration testing requires the use of controlled and measurable dynamic excitation, which is very heavy and expensive devices for artificially generated vibrations. This may also cause damage to a structure when subjected to severe dynamic loading. In addition, the dynamic properties of a structure at a low level of vibration may be different compared to artificially generated vibrations which are normally between 10% and 20% for the natural periods of vibration but can exceed 30% for the reinforced concrete structure due to the crack of the structural elements . The besResults obtained from the two different approaches are also presented in In this paper, an operational modal identification method was proposed for ambient vibration testing in the presence of harmonics. This operational modal identification method, based on proposed automated harmonic signal removal using SSI-Covariance with image-based feature extraction, was first applied to a three-degrees-of-freedom system in order to validate its numerical implementation, and then investigated via an experimental steel frame. Identified modal parameters were compared to those found by a classical OMA approach based on the white noise excitation assumption, namely the SSI method, classical SSI-Covariance and SSI Data. Results of the proposed approach in estimating modal parameters demonstrated very high accuracy and exhibited consistent results for before and after removing the harmonic components from the response signal compared to other comparison methods.The main advantage of the proposed automated harmonic signal removal using SSI-Covariance with image-based feature extraction is its independence from the nature of the excitation and its novel ability to eliminate harmonic components. Consequently, it provides an accurate prediction of modal parameters in the presence of harmonic excitations. Thus, it opens possibilities for application to more complicated structures in the presence of harmonic excitations under operational conditions. The use of an image clustering rather than a normal clustering algorithm can neglect any calibration or user-defined parameter at start-up and any supplementary adaptive approach for cluster validation criteria.In the present day, image-based vibration measurement has brought great attention to civil engineering communities and is increasingly being used in the area of structural dynamics, particularly for modal analysis and damage identification ,49,50,51This also verifies that the developed algorithm can be used for continuous structural health monitoring by taking advantage of ambient excitation, which is always present. This allows for tracking the evolution of modal parameters over time and can be used to detect structural integrity or problems due to structural deterioration, or the occurrence of damage to the structure. For example, when the structure gets old, the value of a modal parameter such as natural frequency will reduce over time due to loss of stiffness, while the modal damping ratio will increase over time due to rusted steel. In general, the variation of natural frequencies over time is more apparent to be adopted as a parameter for damage detection due to consistent trends but, it has a somewhat low sensitivity unless severe damage happens and is reported to be a less than 5% change in frequency associated with critical damage. Conversely, the use of a modal damping ratio as a practical parameter or indicator for structural damage detection is more ideal and suitable due to its sensitivity and is sufficiently responsive to damage compared to natural frequency and mode shape, even though it is less popular among the engineering community because of inconsistent trends ,45. Ther


Tracking moving objects is one of the most promising yet the most challenging research areas pertaining to computer vision, pattern recognition and image processing. The challenges associated with object tracking range from problems pertaining to camera axis orientations to object occlusion. In addition, variations in remote scene environments add to the difficulties related to object tracking. All the mentioned challenges and problems pertaining to object tracking make the procedure computationally complex and time-consuming. In this paper, a stochastic gradient-based optimization technique has been used in conjunction with particle filters for object tracking. First, the object that needs to be tracked is detected using the Maximum Average Correlation Height (MACH) filter. The object of interest is detected based on the presence of a correlation peak and average similarity measure. The results of object detection are fed to the tracking routine. The gradient descent technique is employed for object tracking and is used to optimize the particle filters. The gradient descent technique allows particles to converge quickly, allowing less time for the object to be tracked. The results of the proposed algorithm are compared with similar state-of-the-art tracking algorithms on five datasets that include both artificial moving objects and humans to show that the gradient-based tracking algorithm provides better results, both in terms of accuracy and speed. Object recognition and tracking is still a major area of interest when it comes to digital image processing, pattern recognition, convolution neural networks and artificial intelligence . The appTraining tracking algorithms, such as approximate proximal gradient methods  and rapiTypically, object tracking is associated with several state-of-the-art techniques that are based on deep neural networks, artificial neural networks and convolution neural networks (CNNs) ,17. All One of the most famous types of gradient descent techniques is known as stochastic gradient descent (SGD). SGD is known to combine the benefits of basic gradient descent algorithms, i.e., the stochastic strategy and the backpropagation . SGD canIn the previous decade, lots of visual tracking methods have been implemented, each having its share of pros and cons. The visual tracking methods can be categorized into two main classes, i.e., discriminative algorithms and generative algorithms. The generative methods are known to classify the object of interest by convolving it with a kernel first, followed by the tracking process that selects the most suitable candidate with an appearance model most suited to the chosen template. The most popular generative methods are particle-Kalman filters , Kalman In , first, An eigenspace model for object tracking employs feature vectors linked with pixels in the target template, which are regarded as discrete observations of the target object . To arriIn this paper, an optimized algorithm for object recognition and tracking is proposed using SGD. The aim is also to develop a tracking routine that is able to track the object of interest under different scenarios. The proposed tracker will be tested on the target objects which undergo changes in appearance, scale and camera axis orientations. First, the object of interest is detected using the maximum average correlation height (MACH) filter. The object is detected using different parameters of the MACH filters. The detected object is then tracked in successive video frames using the SGD-based particle filters, which are enhanced forms of the conventional particle filter, providing better convergence than their predecessors. A comparison with similar state-of-the-art algorithms is performed to prove the effectiveness of the algorithm.The proposed methodology followed for the implementation of the algorithm is shown in Preprocessing is considered one of the most fundamental and very important steps in any image processing application. The step ensures that all the images possess similar dimensions and properties before the actual algorithm is applied to them. For preprocessing of the images, the difference of Gaussian (DoG) has been applied before the actual preprocessing of the images. The DoG filter not only reduces the noise by applying the Gaussian motion blur but also enhances the edges, which can be considered a major advantage when it comes to an image processing application . The DoGThe DoG is a bandpass filter that is used to compute wavelets that are symmetric in nature. By changing the standard deviation in the equation of DoG, the bandpass frequency can be altered. The value of the bandpass frequency must be selected so that it can provide the best tradeoff between intra-class distortion and inter-class discrimination. Combining DoG with the MACH filter results in much sharper correlation peaks because of the built-in tendency of DoG to detect edges. The DoG filter enhances the edges by approximating the Mexican hat wavelet. The DoG is actually a variance between two scaled Gaussian functions  i=1,2,… :(1)g1x,ySimilarly, for The DoG filter is applied by taking the difference between Equations (1) and (2).By combining Equations (1)–(3), we obtainThe rule of thumb for a successful DoG application is to choose the bandpass of the Mexican wavelet as the ratio of Object recognition has been performed using the MACH filter, which is a class of correlation filters. The identification of an object is a straightforward approach, but identifying an object in the case of change in camera axis orientations or in the presence of noise is a challenging task. The correlation filters are chosen because of their ability to detect and identify the object of interest under challenging circumstances. Another advantage of correlation filters is they are computationally less expensive as compared to their counterparts. The MACH filter uses a set of training images for the computation of correlation peaks. The MACH distinguishes the training images into true and false classes, where the true class of training images depicts the set of images that the filter retains, while the false class represents the discarded set of images.The MACH filters are known for their ability to suppress noise as well. The noise suppression ability comes with their ability to maximize average similarity measure (ASM) and reduce distortion. The MACH filter is based on enhancing the four main parameters, i.e., average correlation energy (ACE), average similarity measure (ASM), output noise variance (ONV) and average correlation energy (ACE). However, the most important aspect is the ASM, which is directly associated with the correlation peak. As mentioned above, the energy equation associated with MACH is based on four parameters.For the MACH implementation, Equation (5) needs to be minimized. The “T” sign in superscript represents the transpose of the functionAs stated earlier, MACH is known to recognize the object of interest by minimizing the ACE and ASM while maximizing the average correlation height. The minimization of ASM is achieved using Equation (7). “*” represents the complex conjugate of the function.h. The same h is also used for the minimization of ASM and ACE.The average correlation intensity, which is depicted by While minimizing Equation (5), Equation (10) can easily be extracted after calculating the ASM and ACE.The three parameters mentioned in the denominator of Equation (10) are considered crucial in the performance of MACH. The optimal value selection of these parameters is called optimal tradeoff (OT), which was initially proposed by Bone et al. . The OT α, β and γ for the COPI cost function may be considered optimal.Bone et al.  suggeste−5 and 32.1376.As mentioned earlier, MACH has the ability to detect the object of interest even if it undergoes changes in scaling, shifting or camera axis orientations. Once the object of interest is detected using MACH, a bounding box is used to encapsulate the object of interest, as mentioned in The goal of object tracking is to track the recognized object of interest in successive frames. The main object tracking algorithm employs the gradient descent-based particle filters. The gradient descent technique allows the particle filters to track the object of interest in less time as compared to the conventional particle filters.Gradient descent technique is one of the most famous methods used for optimizing algorithms. They have the natural tendency to converge when used together with other deep learning-based algorithms. In this paper, the gradient descent technique is used in conjunction with particle filters in order to improve the efficiency of the particle filters. The gradient descent technique optimizes the algorithms by working on their loss functions. The loss function can be described as the apparent difference between the function output and the samples. For this purpose, a hypothesis function can be assigned to a linear regression mathematical model, such as The optimization of the loss function and the mathematical model is the main goal of the gradient descent technique. This means that the gradient descent technique can be used to amend or modify the mathematical model of a particle filter by reducing the eventual loss function.In the case of particle filters, the hypothesis function can be very complex. Therefore, there is a need to define a more complicated hypothesis function. Adding more factors to the hypothesis function makes it more complex such that To minimize the cumulative loss function based on the ensemble of particle filters and the gradient descent technique, partial derivatives can be applied. Gradients are generically applied for measuring the trend of the aim function. Therefore, it is possible to associate the The precision of the function, i.e., the difference between the samples and the mathematical model, is represented by ϵ, which is known as the terminal function. The estimation process of particle filters will stop when the estimated difference becomes less or equal to ϵ. The particle filters are based on the prediction, estimation and upgrade regimes. The percentage of gradient used to upgrade the state of particle filters is determined by the controlling parameter α, which is the step size. The amended update expression for particle filters becomes:Since the function pertaining to the particle filters is convex in nature, it can be optimized using gradient descent. Another important aspect is the selection of the step size. Selecting a smaller step size may result in identifying the most optimal solution, but the downside is a very slow convergence speed. On the contrary, a larger step size may result in an elevated speed but does not ensure an optimal solution.Considering the pros and cons of both approaches, in this paper, the gradient descent technique is used for updating the probabilities and assigned weights to the particles using the steps mentioned in Algorithm 1. The conventional particle filtering process involves the states Algorithm 1: Gradient Descent AlgorithmInitializing the aim function parameters: ϵ;Initialize step size α and terminal distance of recursion parameter Gradient calculation of the function using the loss functions’ partial derivative using Equation (15);Apply the gradient descent algorithm on particle filters;If the gradient descents, i.e., Otherwise, continue the process;Employ the gradient descent process by multiplying the step size α with the gradient;θ using Equation (17).Renew all the values of Using the steps (5) and (6) of the gradient descent algorithm, new weights are assigned to the particles using Equation (17).Equation (17) shows the working particle filters after the implementation of gradient descent. The particle filters are based on two steps, i.e., prediction and then update. Equation (17) basically merges the two steps. The part of the equation involving the Five different data sets have been used for testing the results of the gradient-based tracking routine. Since the gradient descent technique is known as the optimization algorithm, the results of the proposed algorithm will be compared with other state-of-the-art algorithms.The first employed data set is the Blur Car, which shows a white vehicle moving on a road . The datThe second data set is Running Dog, which shows a dog running on a floor . The datThe third data set is Vehicle at Night, which shows a vehicle travelling at night . The datThe fourth data set is a grayscale vehicle traveling on the road . The datThe fifth data set utilized is called “Singer” , and it To prove the efficiency of the algorithm with other state-of-the-art similar algorithms, the gradient-based tracking technique has been compared with four other recently proposed techniques. The algorithm has been compared with a target tracking algorithm based on Convolution Neural Network (TTACNN) , ADT: obThe comparison between different state-of-the-art recent algorithms was performed in terms of execution time (in seconds), average tracking errors, precision, mean average precision (MAP) and the precision recall for a minimum of 300 frames of five different image processing datasets. The results depicted in The paper presents a tracking routine that tracks the object of interest (such as an object or a subject) using the gradient descent approach. The algorithm detects an object using the MACH filter, which recognizes the object of interest using ASM and generates a correlation peak depicting the presence of an object. A bounding box is constructed around the object once MACH recognizes it. The coordinates of the bounding box are then fed to the gradient descent-based object tracking routine, which tracks the object of interest in successive frames using a step size and the terminal function. The presence of the terminal function enables a much faster convergence of particles in the gradient-based algorithm compared to the conventional state-of-the-art algorithms. The proposed algorithm has a significant scope to improve in the future, as the gradient descent algorithm can be further improved and its ensemble with particle swarm optimization can yield even better convergence results for object and human recognition ,50,51. M


Coronavirus disease 2019 (COVID-19) is a novel disease that affects healthcare on a global scale and cannot be ignored because of its high fatality rate. Computed tomography (CT) images are presently being employed to assist doctors in detecting COVID-19 in its early stages. In several scenarios, a combination of epidemiological criteria (contact during the incubation period), the existence of clinical symptoms, laboratory tests (nucleic acid amplification tests), and clinical imaging-based tests are used to diagnose COVID-19. This method can miss patients and cause more complications. Deep learning is one of the techniques that has been proven to be prominent and reliable in several diagnostic domains involving medical imaging. This study utilizes a convolutional neural network (CNN), stacked autoencoder, and deep neural network to develop a COVID-19 diagnostic system. In this system, classification undergoes some modification before applying the three CT image techniques to determine normal and COVID-19 cases. A large-scale and challenging CT image dataset was used in the training process of the employed deep learning model and reporting their final performance. Experimental outcomes show that the highest accuracy rate was achieved using the CNN model with an accuracy of 88.30%, a sensitivity of 87.65%, and a specificity of 87.97%. Furthermore, the proposed system has outperformed the current existing state-of-the-art models in detecting the COVID-19 virus using CT images. Coronavirus disease 2019 (COVID-19) is a novel disease that rapidly spreads and affects the daily patterns of millions of people in the world. . The utiThe most effective technique for detecting lung COVID-19 is CT because of its 3D image-forming capability of the chest, which enables it to produce lung pathology in greater resolution. Computer processing of a CT image is a popularly used technique in aiding clinical lung COVID-19 diagnostics. The use of a computer-supported identification process for lung COVID-19 can be divided into two: a detection system and an identification approach . The CAD approach categorizes the previously identified candidate lung COVID-19 as normal or nonnormal COVID-19 cases . The detected lung inflammation is classified as normal or nonnormal as COVID-19 by the CAD approach . The CADWe showed that the proposed classification techniques and their implemented models can effectively improve the performance of COVID-19 diagnosis by using limited computational resources, few parameters, and only 746 chest CT scan samplesWe proposed a new COVID-19 diagnostic model to perform explainable detection and precise COVID-19 case classification and show significant improvement over previous systemsThe proposed CNN, DNN, and SAE use CT scans as a direct input to limit the complexity involved with data reconstruction during the deep learning and classification processesDeep learning enhances the accuracy of the computerized process and assessment of CT image segmentation and identification and accelerates the critical task. The problem associated with lung inflammation classification into normal or nonnormal COVID-19 cases is considered in this study. However, several existing CT scan datasets for COVID-19 comprise hundreds of CT images and fall short of the demand . SeveralThis paper is structured in five sections, and the following section evaluates the previous studies, while The complexity in the diagnostic of inflammatory and infectious lung disease using visual examination is still a challenge in developing an automated system for lung CT scan classification. The large number of patients requiring diagnosis is a source of errors for an acceptable standard as visual examination. Wang et al.  proposedFeature extraction complexity is one of some limitations in present classification models. Feature extraction algorithms play a vital role in gathering the significant variations in the spatial scattering of image pixels. The powerful discriminative capability of AI technologies, especially deep CNNs, is improving medical imaging solutions. However, deep learning algorithms, including CNNs need to be trained on large-scale datasets to show their capability. The majority of presently available CT scan datasets for COVID-19 , 26 compThe lung CT scan classification models in existing systems depend on deep learning methods to extract the best features. The classification performance between healthy and COVID-19 cases will improve further as a result of deep learning features. Proposing an efficient COVID-19, healthy, and COVID-19 lung classification in CT images applying the deep learning models is the motivation for this study. CNN, DNN, and SAE provide the means of developing a COVID-19 diagnosis method that can perform supervised learning and produce accurate detection.There is a significant require for speedy testing and diagnostic of patients amid the COVID-19 widespread. Lung CT is an images methodology that is widely accessible and cost-effective and can analyze and diagnose intense respiratory trouble disorder in patients with COVID-19. It can be utilized to discover imperative features within the medical samples that present all the clinician information that can be used to diagnose and monitor the COVID patents. With the usage of versatile ultrasound transducers, lung CT samples can be effortlessly obtained. In any case, the CT image is frequently of destitute quality. It frequently needs a specialist clinician elucidation, which may be highly subjective and time-consuming. The main goal of this work is to develop a new COVID-19 identification framework proposed to automatically distinguish between COVID-19 infected subjects and healthy one via CT images using different deep learning methods.The dataset consists of 746 COVID-19 patient cases with their CT scan images and full clinical description  . Free puThis research aims to enhance the process of COVID-19 identification and diagnosis by minimizing the diagnostic error rate associated with human error in reading chest CT scans. In addition, it aims to help medical practitioners conduct fast identification to distinguish among the patients with confirmed COVID-19 infection, other pneumonia infections, and healthy people. The proposed system can be divided into three major phases: preprocessing of CT images, extraction of dominant features, and classification of selected features by DNN, CNN, and SAE classifiers to make the final decision. The quality of the image scan depends on the accuracy of the metrics, intensity, and variations of the reconstructed and consecutive slices of CT scan . NormaliThe use of handcrafted features as input to the classifier with expert guidance may affect its performance. However, many deep learning techniques perform more successfully in extracting features, particularly in the computer vision domain. They also show efficiency in improving the accuracies of classification. In this study, the CNN technique is used to extract features efficiently. CNN is one of the successful classification methods in the computer vision domain. CNN is an improved version of the classical neural network by introducing new layers  . The conThe filters change the entire input size to be specific amount known as stride(s) to yield integer dimensions of output size . As a reCNN is a neural network based on a multilayer approach. It consists of a set of convolution and fully connected layers; the latter acts as the standard layer of the neural network. The idea of CNN can be traced back to the 1960s . It is bThe CNN architecture is illustrated in f(x). This condition is opposite to the individual hidden layer in the neural network, given that the hidden layer depth has no ability to improve the expression. The aspect of various sizes of the lung nodes is extracted from different network layers in the DNN processing phase. DNN has some issues with respect to problems associated with the local boundary and gradient spread. The original image is fed as the input in the training process to preserve more image information.A DNN is a basic neural network with more hidden nodes. An intensive computation of the input is conducted in the neural network because of the nonlinear transformation from each hidden layer to the output layer . HoweverThe DNN architecture comprises the input, hidden, and output layers. All these layers are interconnected to one another. DNN has no convolution layer. Training and label images are fed to the DNN. In the initial training, the random weight is assigned to every layer using Gauss distribution with bias equals to zero. Forward propagation is used for output calculation, while backpropagation is used for parameter updating. The depth structure of the neural network is illustrated in An SAE is an unsupervised learning algorithm having a multilayer neural network with SAE . It consThis research uses a combination of multiple autoencoders and softmax as a classification method to build an SAE network. The SAE network has several hidden layers and softmax as a final layer . Sparse The architecture of the SAE neural network is shown in n multiplied by 2. Dropout is also used to restrain overfitting. Random neurons are protected prior to backpropagation. Thus, updating parameters of shaded neurons is not allowed. DNN requires a large number of data as input for the neural network, and this condition requires large memory storage. Min_batch is used in the backpropagation to accelerate the updating process of parameters for solving the aforementioned issue. The loss function is formulated and explained as follows:w represents the weight, b represents the bias, n represents the training dataset instance numbers, and x is an input parameter that represents the values of the image pixel and output. The backpropagation method is adopted in DNN to update the b weight wand for enhancing accuracy by reducing the difference between the expected and actual values.In the loss function, the last element is used to restrain overfitting for the training procedure, and the aggregate of weights is distributed by x represents the weighted priority multiplication result and weight wand added to the y element to obtain the activation function output. The ReLU derivative can be 0 if x < 0; otherwise, it can be 1. ReLU can remove the gradient sigmoid activation function problem. However, the phenomenon of neuronal death can occur as the updating weight is stopped because of the continuous updating in the training process. In this situation, the ReLU output can be more than zero, which means the neural network output is offset.The leaky ReLU represents a neural network's activation function that helps improve the nonlinear modeling ability. The ReLU activation function is formulated as follows:This problem can be solved by applying the leaky ReLU method. This method is an activation function that can be represented as follows:Data augmentation refers to the differences in sizes of lung patterns. The size of lung patterns is regularly set to 28 × 28 to extract the aspects of the lung patterns, including the size and texture. Binary processing is used to obtain lung pattern images . They caThis section presents the experiment configuration settings and the outcomes of the proposed systems based on the classification of the CT scan images. Two main subsections are presented as follows:Caffe, a deep learning model based on expressiveness, modularity, and speed, is used in this research. A total of 746 CT scans are used, which comprise 349 CT scans for COVID-19 hospitalized individuals' CT scans for infected pneumonia patients, and 397 CT scans for healthy persons with no apparent chest infection. The first website publicly offers reference documents, radiology images, and patients' cases. The latter contains open access datasets with a wide archive of medical images with public viewing. The second website is a partnership on radiology. A total of 20% of the training data, which comprise approximately 448 images, are utilized for cross-validation. The same set of data applies to the three distinct architectures of the network.The learning rate of the CNN is set to 0.02, and the batch size is set to 24 during network training to obtain the best results. The process of convolution and sampling is performed twice in the network. The kernel size is 6, and two layers of convolution are considered and consist of 36 filters. The pooling layer has a kernel size of two. The purpose of a dropout layer is being used to avoid overfitting. At least a softmax function with two fully connected layers is considered. The DNN contains a completely connected layer.w and b are the crankiness and weight, respectively; x is the input factor. The neural networks correspond to the input image coding. The self-encoder creates the hidden layer, and it is primarily utilized in the identification process. This way cancels the self-encoder decoding portion. The coding factor of the SAE is utilized during training. The encoder creates a stack encoding component to add a confident number of training courses to the identification of the initializing neural network. The artifact characteristics are not evident after the encoder the image edge is formed. However, this condition triggers some reduction in classification accuracy. The 2D data input of 24 × 24 mapped into 576 × 1 is presented as an input image in the first layer. An entirely connected layer of 304 × 1 is used in the second layer. The third layer is fully connected to 256 × 1. A dropout layer will be considered after the third layer, which includes a parameter of 0.5. Units will also be hidden in 30%. The fully connected layer is the fourth layer of 128 × 1 with ReLU as an activation function. The SAE structure also consists of a completely connected layer. The input and output neurons of the automotive encoder are the same. Thus, the autoencoder is the same asThis section presents the classification of CT COVID-19 and normal cases. The classification results are based on the experiment outcomes of three deep learning models: CNN, DNN, and SAE. Accuracy, sensitivity, and specificity are used as evaluation indicators for the performance of the three mentioned models. The classification results of the three models are presented in The results show that the CNN has a sensitivity of 87.65%, a specificity ratio of 87.97%, and an accuracy of 88.30%. The accuracy, sensitivity, and specificity of DNN are 86.23%, 84.41%, and 86.77%, respectively. The good result is due to the fact that the operation of the convolution layer can be defined by the texture and shape of two distinct dimensions. A convolution kernel shares constraints throughout the procedure of convolution in diverse weights for diverse image features with a convolution kernel. Thus, the convolution operation is less than the fully connected operating conditions in terms of parameter number. The DNN has poor precision and sensitivity than the SAE. However, the former is better than the latter, given that it has a specificity ratio of 87.84%.Better specificity provides a greater degree of detection of COVID-19 within a single set of data, and this feature can be more useful in slightly earlier diagnosis of pulmonary pathways. However, DNN increases the number of false-positive COVID-19 cases to a certain degree. The DNN and SAE are completely connected networks, but different generating methods are possible. In encoder training, SAE is generated by parsing; the DNN is generated directly from formation through the completely connected layer.Unlike the previously published works that mainly depend on employing pretrained models in identifying the COIVD-19, we proposed to build a customized CNN model composed of two convulsion layers to reduce the number of hyperparameters and the model's complexity. On the contrary, the pretrained models such as ResNet and DenseNet have more complex architecture, which can present high computational load and execution time during training and validation processes. Furthermore, it is very difficult to change the main structure of the pretrained model by adding or removing some layers to find an optimal model's structure.Certainly, the computing time of the model is an important problem in identifying the COVID-19 virus, where having a customized CNN model that required fewer CT images can significantly help the doctors in the early diagnosis of the COVID-19, especially when there is a very limited number of CT images with confirmed COVID-19 infection.Finally, to compare the testing time required to produce the final decision per image of the proposed models and the current state-of-the-art works. The evaluation scenario with other benchmarked studies is presented in This study aims to explore and extensively test three deep learning models. The prediction is compared for normal and COVID-19 cases by using chest CT images. Several preprocessing stages are applied to CT images. First, data segmentation is applied to isolate the region of interest from the CT image background. Second, a data augmentation technique is used to increase the volume of CT images. Accordingly, adequate input can be provided for the deep learning models for directly affecting the performance of classification. The test outcomes indicate that CNN achieves the ideal execution compared with the DNN and SAE. Moreover, the CNN is superior to other models in terms of performance with an accuracy of 88.30%, a sensitivity of 87.65%, and a specificity ratio of 87.97%. Neural network layers in this study are comparatively small due to the constraints of the datasets. The proposed approach is anticipated to increase the accuracy of the other database. Furthermore, deep learning approaches can be used to assist physicians in mitigating this newly spreading disease with CT images to identify and distinguish early COVID-19 patients, recognize and segment images in the medical field, and predict the results of treating them. We also show that too excellent sensitivity can be obtained from CT images, which can address the need for early-stage detection of infected persons to limit the progression of the virus. We will further validate the performance of our technique on a large dataset. The process can be generalized for the good implementation of CAD frameworks for future medical image processes. This study is limited to three models as diagnosis systems. However, additional deep learning models will be explored in the future.


Interval programs have been developed for multiple sports, allowing athletes to return to sport-specific activity in a graded fashion, minimizing the risk of reinjury. However, there currently exists a gap in the literature surrounding the use of interval programs for the rehabilitation of punting and place-kicking athletes. We aim to perform a systematic review of the literature examining the use of interval kicking programs to aid punting and place-kicking athletes following a lower-extremity injury. Following PRISMA guidelines, a review was performed using PubMed and MEDLINE databases to evaluate the literature surrounding interval kicking programs for punting and place-kicking athletes. Search terms were combined using Boolean operators of “AND” and “OR”. Articles included in this review met these criteria: 1) included patients with lower-extremity pain/injury, 2) reported a return to sport progressive program, and 3) analyzed the measure’s ability to predict a successful return to sport. The initial search returned 115 articles. Seventy-nine of these articles were excluded after initial screening, leaving 36 full-text articles for final review. Of these final articles, there were no studies outlining the use of interval kicking programs by punting or place-kicking athletes. Of the articles reviewed, the most relevant was an interval kicking program developed by Arundale et al. specifically for the soccer athlete. Punting and place-kicking use biomechanically distinct patterns of movement, warranting a specific interval program. This review identified a gap in knowledge surrounding the use of interval programs in the rehabilitation of punting and place-kicking athletes. This review will now describe what is currently known regarding biomechanics of punting and place kicking, the injuries experienced by these athletes, and the benefit an individualized interval program could provide. There currently exists a gap in the literature surrounding the use of interval programs for the rehabilitation of punting and place-kicking athletes. The biomechanics and application of these skills are distinct, and an interval program designed specifically for these athletes is warranted. Future research should be dedicated to the development, implementation, and analysis of an interval kicking program designed for these athletes.  Punting and place kicking are a part of regular sport-specific activity within American football, rugby, and Australian rules football. Injuries to American kickers, and rugby and Australian rules football players predominantly affect the lower limb -3. AfterA systematic review of the literature evaluating the presence and use of interval programs in regard to punting and place kicking was performed using the PRISMA guidelines . A searcResultsThe search was performed in February 2020 and returned an initial 115 articles. After an initial screening by abstract and title for relevance to the research question, 36 articles remained for consideration. After final review, no article could be found that presented an interval program that was tailored for the punting and kicking athlete in a non-soccer sport. The most relevant article was the interval kicking program that was published by Arundale et al.; however, as previously stated, this program was developed for the soccer athlete. The search mainly returned articles that describe the epidemiology of injuries in American football, rugby, and Australian rules football as well as the kinematics of punting and place kicking. As the mechanics of punting and place kicking are different in football or rugby than kicking in soccer and given that in-game applications of kicking are unique to each of these sports, a specific interval program for these athletes is needed. The remainder of this article will describe what is currently known regarding kicking mechanics of punting and place kicking, the injuries experienced by these athletes, and further elaboration on the benefit that an individualized interval program could provide. Currently, the lack of a return-to-kicking program specific for American football athletes could reduce optimal recovery of players, increasing the burden of injury faced by both the individual player and the team. Interval programs that focus on injury-specific training, plyometrics, and neuromuscular control drills can enhance player recovery and reduce days of play lost ,7. Many Injuries to the punting and place-kicking athlete in American football, Australian rules football, and rugbyIn the National Football League (NFL), the population of professional kickers experiences injuries at a rate of 24 per year, while the total number of kickers in the position was only 45 in the 2020 team rosters ,14. ThisStudies have shown that NFL kicker injury occurs at significantly higher rates during games (17.7 per 1000) than during practice (1.91 per 1000). Australian Rugby League (ARL) players experience a similar pattern of increased injury incidence during matches than in practice. Many of the injuries experienced during games of NFL kickers and ARL players are non-contact mechanisms [In professional American football, muscle strains account for over 70% of kicker-related injuries . Most muIn the same group, ligament sprains make up a smaller portion of the total group’s injuries, accounting for approximately 18% of all injuries of NFL kickers ,3,31-33.The leading anatomic areas of injury in descending order for the American football kicking athletes were the pelvis/hip, thigh, and, finally, knee, while the leading types of injury were muscle-tendon followed by ligament/joint . AdditioPunting and place-kicking biomechanicsKicking kinematics require sufficient range of motion through the movement, muscle strength, and neuromuscular control, all of which can be hindered by a lower-extremity injury. The planted foot during the kicking movement is of equal importance to the striking foot, as a balanced and stable base is necessary to develop force and accuracy when kicking ,36. KickThe punt kick starts with a six-to-eight-step approach and ends with a strong concentric contraction of the kicking leg hamstring to begin the backswing. During this phase, the ball is dropped and the upper extremity contralateral to the kicking leg is abducted to provide balance during the kick. The backswing is characterized by full extension of the hip of the kicking leg as well as increased activity of the kicking hamstring and increased activity of the gluteal muscles in the support leg. During the wind-up phase, the kicking hip begins to flex and continues to flex throughout the remainder of the kicking motion, while the kicking knee begins to flex in preparation for a rapid extension in the following forward swing phase. Although the right knee is still flexing, the quadriceps replace the hamstring as the most active muscle group, presumably to decelerate the knee through eccentric contraction in order to begin the forward swing. The forward swing is a phase of continued hip flexion as well as rapid knee extension and ends with ball contact. The rectus abdominis, as well as the quadriceps of the kicking leg, sees substantial increase in activity during this phase. Follow-through includes the point of ball contact where the knee is still partially flexed to approximately 50 degrees. Follow-through lasts until the knee reaches full extension. During recovery, the right hip reaches full flexion and both feet typically leave the ground .Punt kicking is analogous to a throwing motion with much of the work of the kick performed eccentrically in the early phases of the kick by proximal muscle groups and the momentum transferred to the distal segments of the leg just before ball contact . ThroughIt is also necessary to evaluate the joint kinematics of a punt kick, in addition to the muscle activity. Individual analysis of the joints involved in the punt kick showed that the ankle of the support leg began in dorsiflexion and progressed into plantarflexion. The kicking ankle was not studied. The knee experienced varying degrees of flexion depending on whether the kicking or support leg was observed. The hip experienced flexion throughout the movement, and the pelvis moved from neutral to posterior tilt during the last half of the movement . GreaterIn addition to the described kicking phases, muscle activity, and joint kinematics, there is a need for the kicker to minimize the ground reaction forces of the support leg. Minimizing the ground reaction forces of the support leg is associated with a greater predicted distance when punting . IncreasPlace kicking is another in-game application of kicking that is common in rugby and American football. This kick is a method of scoring by either field goal or the point after touchdown in American football and is responsible for 45% of points scored in rugby union ,44. In cNeed for kicking program for punting and place-kicking athletesAs the American football kicking athlete injury profile is unique to the role, interval programs currently developed that are aimed toward European football athletes or for throwing sports will not have the position-specific modifications and exercises for optimal recovery in an American football kicking athlete. Hip injuries make up the largest proportion of the group’s injuries, while in European football, injuries to the thigh region, including the hamstrings and quadriceps, are the most common ,33. TherAlthough place-kicking and punting biomechanics are similar among all kicking athletes, each individual will likely have developed some unique mechanics that will not be accounted for perfectly by a general interval rehabilitation program. These athletes’ movement patterns should be assessed when rehabilitation begins to properly address their individual needs in addition to the use of the interval programs. LimitationsThe authors acknowledge limitations to the present work. This review showed a clear lack of sport-specific return-to-kicking interval therapy program for American football, rugby, and Australian rules football athletes. The efficacy of these interval recovery programs that have been used in soccer players and for throwing athletes has not been well studied for their improvement in player health over other recovery programs. To best test the hypothesis that these interval training programs decrease recovery time, improve kicking performance, and assist in player readiness for full-contact play in the injured athletes, future studies should be conducted to compare the athlete progression with various recovery programs implemented under circumstances of similar injury.The injury studies of the American football kicking athletes used were predominantly from the professional level, limiting the applicability of these studies for the use of rehabilitation of injury in recreational, high school, or collegiate athletes. This may cause a discrepancy due to the physical demands and conditioning requirements required of professional-level kicking athletes, diminishing the effectiveness and application of a rehabilitation program tailored to professionals for players of lower caliber. More rigorous and high-powered studies should investigate this discordance between professional and adolescent, recreational, and collegiate-level players. As such, providers must demonstrate increased clinical vigilance when applying these interval programs developed for professional athletes to others. Furthermore, the average age of professional athletes in American football is far greater than that of adolescent and collegiate leagues, confounding the relationship of injuries per player, as increased age was shown to be in association with increased risk of muscle strain and time to return to play following injury. This may also change the injury profile, as professional-level kicking athletes may experience different injuries during their careers than players in other leagues . Thus, iPunting and place kicking are physically demanding activities that are performed in sports where the main injury risk is to the lower extremities such as American football, rugby, and Australian rules football. Interval programs have been developed with the goal of ensuring athlete readiness for return to play in various sports such as baseball, golf, tennis, and soccer. Arundale et al. developed an interval kicking program to be used for the soccer athlete; however, at the time the authors conducted this review of the literature there was no interval kicking program designed to aid the rehabilitation of the punting or place-kicking athlete. Punting and place-kicking have both kinematic and in-game application differences that warrant their own interval kicking program to ensure these athletes can return to play as safely as possible. The purpose of this literature review is to establish the current status of interval kicking programs for the punting and place-kicking athlete. During this review it became apparent that there is a gap in the literature surrounding programs for these athletes. To the authors’ best knowledge, there are no specific interval programs published for the punting and place-kicking athlete. Utilizing an interval kicking program in conjunction with physical therapy could represent a modality of rehabilitation that allows athletes to achieve a safer return to sport and decrease the rate of reinjury. As such, there is a need for the development of interval progression programs for the punting and place-kicking athlete following rehabilitation to ensure physical readiness to return to sport in a state that will minimize risk of repeat injury. Future research should be focused on the development and implementation of such a program with a focus on the ability of players to return to their level of play prior to injury as well as the reducing rates of return injury.


A data-driven approach is developed to predict the future capacity of lithium-ion batteries (LIBs) in this work. The empirical mode decomposition (EMD), kernel recursive least square tracker (KRLST), and long short-term memory (LSTM) are used to derive the proposed approach. First, the LIB capacity data is split into local regeneration and monotonic global degradation using the EMD approach. Next, the KRLST is used to track the decomposed intrinsic mode functions, and the residual signal is predicted using the LSTM sub-model. Finally, all the predicted intrinsic mode functions and the residual are ensembled to get the future capacity. The experimental and comparative analysis validates the high accuracy (RMSE of 0.00103) of the proposed ensemble approach compared to Gaussian process regression and LSTM fused model. Furthermore, two times lesser error than other fused models makes this approach an efficient tool for battery health prognostics.  Ensemble KRLST-LSTM framework for battery capacity predictionKRLST trained to address the local regeneration issue of batteriesLSTM utilized to predict the residualEnsemble KRLST-LSTM increases the prediction accuracy two to three-time Computer systems organization; Energy engineering; Energy systems Deterioration in the fossil fuel resources and problems related to climate change provides an excellent stimulus for the developers for focusing on green energy resources, green transportation , hybrid EVs, etc.), and smart grids . EVs andCapacity monitoring and RUL prediction are one of the main functions of SBHPS. As the LIB is a highly non-linear system, it is not easy to estimate these parameters. Till to date, several methodologies for RUL and capacity prediction have been reported . Based oIn the model-based method, algebraic and differential equations define the physics and the failure mode of LIBs. In a study , the autThe data-driven approaches do not need any prior information on the degradation process. To build the model, it only extracts the useful feature from measurable battery states . The machine learning models   and residual .2.The KRLST based model is designed for the q-steps ahead prediction of the IMF signals.3.The residual signal is captured using the LSTM trained sub-model.4.In the end, both the predictions are ensembled to get the predicted capacity and RUL.5.Various data-driven models are analyzed and compared to check the performance of the proposed model. The results prove the viability of the proposed ensemble model for SBHPS design.Driven by the benefits of LSTM and KRLST, this paper proposed a new approach for estimating and predicting the capacity and RUL of the LIB. To be specific, the major contributions of the proposed methodology are the following:The available online datasets  are utilized to validate the proposed scheme . The detFor all the NASA batteries , the cyclic aging tests were performed with a programmable electric load, controllable temperature chamber, and power supply . The opeThe capacity prediction result of the LIB (B0018) using GPR + LSTM and the proposed technique (KRLST + LSTM) are presented in To further validate the superiority of the proposed technique, other capacities discharging profiles were also predicted using the proposed and GPR + LSTM approach. The tracking ability and their corresponding RMSE have been shown in The quantitative analysis  of the capacity prediction for the proposed and GPR + LSTM is presented in For further validation of the proposed methodology, a recently published dataset is also utilized to check the effectiveness . Three bIn this study, an SBHPS is proposed to avoid the unwanted failure of the LIBs. The SBHPS must be capable enough to predict the accurate RUL of the LIB well before its end of life.The degradation curves of different online available LIB datasets are illustrated in Furthermore, a recently published dataset is also used to prove the performance of the proposed model. The dataset contains three different batteries  cyclic data. The 70% of capacity data is utilized to train the models, and the remaining dataset is used to predict the model. In th cycle. If the starting point of RUL prediction is earlier, then prediction performance decreases, as reported by  is tested, and the findings are presented in orted by . The valtion see , a signition see . The uncAfter extensive testing, it can be concluded that the proposed approach has high adaptability and shows high performance for all types of dataset. The reason is that the KRLST has the best tracking capability for time-varying regression. Furthermore, it explicitly handles uncertainty about the data based on the probabilistic GP framework; therefore, it handles the variation in IMFs well. Similarly, LSTM has also shown promising results in the prediction of residual value. Thus, the fusion of the characteristics of these two methods yielded the least RMSE compared to other methods. Moreover, the training time of the presented ensembled model is less than one minute, which means that the ensembled model can be beneficial to design a battery management system for a real-time application. As the Li-ion 18,650 and CS2 batteries are used in cell phones, notebook, and pads; therefore, the proposed model can be used to enhance the prediction of their capacity and RUL. The proposed methodology can be implemented to predict the RUL of the battery in EVs, which leads us toward our goal of zero-carbon cities.This study presents a data-driven technique to enable an accurate health prognosis for LIBs. A whole health prediction model of LIB is developed, where various algorithms  are ensembled to perform multiple tasks. The EMD approach decomposes the battery capacity data in IMFs and monotonic residual signals. The KRLST trained model is used to track the local regeneration/fluctuation (IMF), whereas global capacity degradation is predicted using the LSTM trained model. For validation, the proposed and GPR + LSTM approaches are implemented on three different types of available online datasets. The comparative analysis shows that the KRLST has better IMFs tracking ability as compared to the GPR model. The maximum RMSE of 0.00243 for 1-step ahead future capacities are noted against the NASA and CALCE datasets. The proposed model has shown high accuracy for 18-steps ahead of prediction with a maximum RMSE of only 0.00355. The ensembled model also shows high RUL prediction accuracy.The developed battery health prediction can be utilized to design a battery management system. However, the proposed prediction model is validated through constant conditions . Whereas, in most battery-powered applications, the operation conditions vary greatly during cycles, resulting in the battery's multiple degradation modes. So, further research is needed for RUL prediction in a dynamic environment. Furthermore, this work only focuses on the RUL prediction of a single battery cell. In contrast, in many applications such as EV, there are many cells connected in series, parallel, or series and parallel to form a battery pack. The battery pack RUL prediction must be explored because of the uneven aging of the battery cells because of the temperature gradient. In addition, the effects of incorporating optimization algorithms and the variant of EMD in the proposed model can be checked in the future.amad.zafar@ee.uol.edu.pk.Further requests for information should be directed and will be handled by the corresponding author and lead contact, Prof. Dr. Amad Zafar, email: This study did not generate new materials.The proposed approach consists of EMD, KRLST, and LSTM explained in detail in this section.The regeneration and global degradations are the high frequency (IMFs) and low-frequency signal  for LIB capacity. The EMD is a powerful and effective signal processing tool that analyzes the dynamical signal's local characteristics. It decomposes the nonlinear signal into IMFs and residual signals. The conditions for IMFs signal are following; i) the upper and lower envelopes' mean must be equal to zero, ii) the no. of zero crossings, and the no. of extremes must be equal to one or zero.xt) extreme values, the lower  and upper  envelopes are developed using a spline line. Calculate the mean using the following:After computing the input signal's (diff) between input and means can be calculated as;The difference , x, and f(x) of the input data in terms of kernel function can be expressed using Representer theorem as . The inner product in the feature space can be easily represented using Mercer's condition .. m(x) and εj are the mean and additive noise (with 0 mean and variance m(x) function is mostly assumed 0.The KRLST algorithm basically works on the Bayesian inference framework . The outBy using Bayesian and conditional probability rules:t is a known Gaussian distribution If the posterior at time step tAt time step Using The latent function's new predictive variance for new input is estimated as:By using For the new input, the inverse of the kernel matrix can be calculated as:For initialization of some parameters following formulas can be used.kt(x) represents the vector of covariance between x and all the other bases t. This posterior is added to another new GP, which does not have any capacity information and independent of a and b are the constant, which are used to balance the trade-off between The most crucial feature of KRLST is the forgetting strategy because the most recent data points contain relevant information, and older points may mislead. In this paper, B2P forgetting strategy is utilized . The posfined as :, output at the previous time step it) is to decide about the receiving of new information ft) and the output gate (ot)are responsible for deciding whether to forget about the previous state (ct−1) in the hidden layers and determine output (ht), respectively. The following mathematical equations can be used to model the system.All the states can be determined by input at the current time step  of LIBs are filtered through the Savitzky-Golay filter . The MAT