This work demonstrates that in parallel with the one existed at high doping concentration, there also exists an optimal combination of the transport properties of a thermoelectric material at low doping concentration as the curve of the relation between electrical conductivity and doping concentration is rigidly shifted toward that direction without disturbing the Seebeck coefficient and the thermal conductivity. Based on this finding, a new thermocouple design that uses low doping legs and high doping semiconductors as the external carrier injectors surrounding the legs is developed. The analytical model developed for the new thermocouple indicated that its efficiency and power output could be more than tripled as compared to those of the original design. A single thermocouple made of Silicon semiconductors was simulated numerically using different sets of input parameters. The results showed that the density of the externally injected carriers played a significant role in enhancing the thermocouple’s efficiency and power output. The potential to convert the released heat into electricity through thermoelectic generator is significant. Figure R is the electrical resistance and K the thermal conductance, to take into account of the effects of its geometry and size.Enhancing energy-use efficiency through thermoelectric generator, a solid-state device that can reliably convert heat released from vehicle exhaust and various manufacturing processes directly into electricity without the need for any moving mechanical components, constitutes one of the important measures toward building a sustainable environment. Such a technique can not only reduce fuel consumption that is depleting the limited oil reserves but also lower greenhouse gas emissions that have a tremendous detrimental impact on our environment. More than half of the energy supplied to the current thermal-process-based power generation systems or combustion engines is lost mostly as heatzT by:zT of a thermoelectric leg is crucial for the efficiency of a generator and is the ultimate goal in the search for a good thermoelectric material besides its abundance and cost. “Untuned” bulk thermoelectric materials have the zTs much less than 1.0 and can hardly be useful for high-efficient power generation. These materials have to be “tuned” to have (1) large power factor zTs. However, it has proved to be a challenging task to meet both of these two requirements in a material since its transport properties are intrinsically related and are functions of the band structures, doping concentrations, and scattering processes among others. As it will be showed in later section that zT difficult.For a thermoelectric leg with temperature-independent transport properties, its efficiency under a given temperature difference can be expressed as a function of its 4. Through many decades’ search, a deep insight of the intrinsic nature of a good thermoelectric material has been gained, theories, various synthesis technologies, and treatment techniques have been developed toward realizing the ideal thermoelectric material. These include low-dimensional theory6, band engineering, structural modification, phase manipulation, etc. . These efforts have led to the developments of many new materials and the achievement of zT as high as 3.1. These advances in the quality of thermoelectric materials represented by the improvement made on zTs have been compiled into historical charts for typical materials8 . Based on these charts, the upper envelopes of the zTs of several classes of thermoelectric materials can be drawn as shown in Fig. Clearly, an ideal thermoelectric material should have a large Seebeck coefficient and behave like a crystalline solid for its charge carrier transport and like an amorphous solid for its phonon transport, so called “phonon glass/electron crystal, PGECzT thermoelectric materials into a high-efficient generator has proven to be challenging not only because the transport properties of both the n- and the p-type semiconductor legs are temperature-dependent but also because their optimum zTs are different both in the magnitude and at the temperature level and are only attainable within a small fraction of the operating temperature range of the device. Furthermore, construction of a generator needs other components such as the interconnectors, thermal insulating ceramics, heat exchangers, and electricity extracting unit. These components introduce additional electrical and thermal resistance that have to be minimized to make the device as efficient as its legs. Consequently, module design plays an important role in realizing the efficiency of thermoelectric legs at the generator level and currently lags far behind the advances of thermoelectric materials.Translating high ZT could be obtained when the relation 62, where A and L are, respectively, the cross-sectional area and length of the leg and the subscripts n and p are leg types. Numerical simulation showed that this analytical relation was approximately true even when the temperature-dependent transport properties of the materials and the electrical and thermal contact resistances were taken into account63. The numerical simulation further showed that a similar power output per unit module area obtained with a greater number of longer legs could be realized with a smaller number of shorter legs when the thermal contact resistance was neglected.Design optimizations so far are limited to the parameters including leg length, cross-sectional area, and shape; module fill factor and the electrical and thermal resistance induced by the interfaces between the leg and the interconnector and between the interconnector and the thermal insulator. It was found that an optimal 64 indicated that different shapes of a constant cross-sectional area along a leg had little effect on power generation and efficiency of the module. However, a leg with variable cross-sectional area showed improved power generation and efficiency66. Among different leg shapes investigated, pyramidal leg67 showed a higher output power density than cuboid, exponential69, and quadratic shaped legs70. Subsequently, a laboratory-scale thermoelectric module with pyramid-shaped leg was recently built for proof-of-concept71. The experimental data confirmed the analytical prediction. A pyramid-shaped leg can increase the temperature gradient along the leg, thus the Seebeck voltage, by lowering its thermal conductance. Furthermore, it can take into account of the Thomson effect which is usually ignored in the leg with a constant cross-sectional area. The laboratory-scale module showed a 67% increase in power generation compared with the module having cuboid legs. More leg shapes , including cuboid, trapezoid, hourglass, Y-shaped, and hollow legs and their different arrangements were modeled using the finite element method72. The numerical results showed that the hourglass shaped leg had more than doubled electrical potential and the maximum power output compared to the cuboid leg when a constant temperature is maintained at the hot end and a natural convection is applied at the cold end.Early investigationzT within a large operating temperature range. The fundamental concept of FGTEM is to synthesize a leg material to have an optimum zT all the way from the hot-end to the cold-end through locally selecting a particular material, microstructure, or composition. Several techniques, including tuning the amount of dopant at different spatial positions of73 and creating a carrier concentration or a composition gradient in a material82, have been proposed to synthesize FGTEMs. FGTEM has the advantages to have a gradual phase transition without inducing any interface resistances. However, the synthesizing technology so far is challenging. A segmented FGTEM that joins the existing thermoelectric materials to fit the zT profile of a thermoelectric leg with their optimum zTs was proposed by Ioffe in his patent application83 and gains increased interest since87. Several promising designs89 have been studied and showed improved efficiencies. A shortcoming of the segmented legs is the presence of the large interface resistances at the junctions between consecutive sections. Furthermore, the materials of different segments have to be compatible to make a high-efficient leg91. Cascade modules can avoid the electrical contact resistance problem by stacking multiple stages of thermocouples with different materials and using separated electric circuit for each stage to improve its performance within large temperature difference. Cascade module is complicated to implement.Three approaches, i.e., functionally graded thermoelectric material (FGTEM), segmented thermoelectric legs, and cascaded thermoelectric modules, are commonly used to improve the low average zT which is yet below the anticipated level, Clearly, current device design optimizations can only produce an optimal efficiency within the range limited by its leg’s 92. In this regard, using low doping concentration as a starting material to optimize its thermal-to-electrical conversion efficiency could have certain advantages over the high-doping concentration approach. However, low doping material has limited charge carriers to work with to improve its electrical conductivity. This could be a major barrier in searching for an ideal thermoelectric material with low doping concentration. In this section, it will be shown that an optimal combination of the transport properties also exists at lower doping concentration for certain materials with their transport properties have the similar trends as those shown in Fig. In addition to depicting the complex nature of the intrinsically dependent transport properties as stated in the Introduction, Fig. zT are extracted from those shifted curves shown in Fig. zT is also shifted toward low doping concentration, which indicates that the optimum zT can also be attainable in low doping materials through tuning the electrical conductivity.Assuming the electrical conductivity curve shown in Fig. mentclass2pt{minimmentclass2pt{minimn- or p-channel is injected into the drain to increase its electric current without altering its lattice structure. Injecting charge carriers into a thermoelectric leg without disturbing its lattice structure is one of the aspects on which the new design concept is based. The another aspect is to use lower doping concentration as the starting material that has both the best Seebeck coefficient and the best thermal conductivity, leaving the electrical conductivity to be improved by injecting carriers from the leg’s surrounding boundary using higher doping concentration materials. The theory behind this concept can be reasoned by the following three processes.Shifting q is electric charge, First, when a semiconductor with high carrier concentration is joined with a leg that has lower carrier concentration, the concentration gradient at the interface causes the carriers to diffuse from the higher concentration material to the lower concentration leg. As a result, the carrier concentration near the boundary of the leg increases, which leads to higher electric current density. From Drude’s model, n-type leg move toward the boundary where, otherwise, an infinitive carrier recombination rate exists. Consequently, this carrier concentration gradient reduces the carrier recombination rate at the boundary such that electron mobility near the boundary is improved. From Drude’s model, it is also known that the electrical conductivity increases with electron mobility.Second, as carriers continue to move across the interface into the leg, some uncompensated positive ions remain near the interface on the side of the material with high carrier concentration. An electric field with a direction from the positive charge toward the negative charge is build-up at the interface. This electric field creates a barrier potential preventing further carrier diffusion until an equilibrium state is reached. This electric field pulls the injected carriers toward leg’s exterior boundary. As a result, the carrier concentration at the boundary is significant higher than that of the leg, which can prevents electrons of a Third, as the higher carrier concentration material is joined with the leg at the leg’s exterior boundary, the originally free boundary of the leg becomes constrained. Thus, its phonon waves also become constrained at the boundary. Consequently, the higher carrier concentration material can be an additional freedom to modulate the lattice vibration mode of the leg to further lower its lattice thermal conductivity.n- and the p-type legs. Figure To investigate the effects of the injected carriers on the transport properties and on the efficiency of a thermocouple, a common-emitter bipolar junction transistor is used in this work to substitute the high-carrier-concentration semiconductor. The BJTs are joined or embedded on the exterior boundaries of both the n-p-n bipolar transistor is briefly introduced in this section (see Methods for details and variable definitions). A bipolar junction transistor is made of three regions and two p-n junctions as shown in Fig. n-p-n transistor and p-n-p transistor. These three regions are designated as: emitter, base, and collector with the base sandwiched between the emitter and the collector. The emitter and the base regions form the first p-n junction. The second p-n junction is formed between the base and the collector regions. The emitter has the highest doping, the collector has the lowest doping. The doping concentration in the base is smaller than that in the emitter but higher than that in the collector. Three terminals connect, respectively, to each region. Based on the bias applied at these terminals, a BJT can be operated in four different modes: forward active, saturation, cutoff, and inverted. For each operating mode, the bias sets the level of barrier potentials at the two metallurgical junctions to modulate the carrier flow from one region to another.Models to determine the current-voltage characteristics of an p-n junction theory, the emitter, base, and collector currents, 96In the new thermocouple design, the normal operating condition, i.e., the forward active mode is used, in which the emitter-base junction is forward biased, whereas the collector-based junction is reverse biased. Based on ideal entclass1pt{minimaThe current gain of a common-base bipolar junction transistor, denoted as sing Eq. , it can n-p-n bipolar transistor in a common-emitter configuration and two resistors to modulate the voltages at the base and the collector. 97:Voltages and currents can be amplified by a bipolar transistor in combination with other components. This amplification functionality of a bipolar transistor is used here to modulate the amount of charge carriers injected into the thermoelectric legs in the new thermocouple design. Figure 99: From heat and electric current coupled equations, heat balance equations can be reduced for a thermoelectric leg. The heat balance equations at the hot and the cold ends of the leg are, respectively, expressed asIntroducing Eq.  into Eq.Since both the second and the third terms of Eq.  are largEquation  can thenThe improvement in the output power can be measured by the power gain rate defined as:For a single thermocouple, c in Eq.  can be dThe ratio between the output power and the supplied power, i.e.,n-type Silicon semiconductor and has a cross-sectional area , ,  and the c of Eq.  into Eq.Figure Substituting ation of  and usination of  for \docSetting e of Eq.  equals te of Eq. , \documeFigure From the relations between thermoelectric properties  and doping concentration, an optimal figure of merit can be attainable by rigidly shifting the electrical conductivity curve toward low doping concentration without disturbing the Seebeck coefficient and the thermal conductivity curves. This is in contrast to the current high-doping concentration approach in finding an optimal figure of merit among various class of materials.The proposed new thermocouple design that uses low doping Silicon as the thermoelectric legs and bipolar junction transistors as the external carrier injectors can triple the power output and efficiency of the original design when the electric current in the leg contributed by the injected carriers is equal to or greater than the current induced by the Seebeck effect. The improvement on the power output and the efficiency of the new thermocouple significantly depends on the base current and the load line slope of the transistor.This work presents a typical example to improve the figure of merit of a thermocouple through its design such that its efficiency and power output can potentially exceed those of the materials of the thermoelectric legs. Using low-doping material as thermoelectric leg and high-doping semiconductors as external carrier injectors is a new concept for thermocouple design and can potentially lead to the development of new thermoelectric materials and high-efficient thermoelectric modules.p-n junction theory, the electron concentration in the base at the emitter-base junction can be expressed as:Under normal operating condition, i.e., the forward active mode, the emitter-base junction is forward biased, whereas the collector-based junction is reverse biased. In this mode, the electron concentration in the base at the emitter-base junction is much higher than the equilibrium concentration of minority carriers in the base. Based on ideal W is the base width. Here, it is assumed that the widths of the depletion regions at the emitter-base and collector-base interface are sufficiently small and can be neglected comparing to the base width.To the contrary, the electron concentration in the base at the collector-base junction is much smaller than the equilibrium concentration of minority carriers in the base and is given by:The distribution of minority carrier in the base region can be determined by the one-dimensional steady-state continuity equation:The solution of the continuity equation, Eq. , is straUsing a one-dimensional continuity equation identical to Eq.  and usinSimilarly, using the minority carrier concentrations at the boundaries of the collector: S is the cross-sectional area of the base region.Electric current induced by carrier diffusion is proportional to the spatial derivative of the carrier concentration. The diffusion current in the base at the emitter edge is thus given by:The diffusion current in the base at the collector edge is determined by:The electric current caused by hole diffusion in the emitter at the emitter-base junction is determined by:Similarly, the electric current caused by hole diffusion in the collector at the collector-base junction is calculated by:When rom Eqs. , 30b), , \documerom Eqs.  as:31\do


To establish a smart system for measuring the flowrate during urination without any temporospatial constraints for patients with a urinary disorder, the acoustic signatures from the uroflow of patients being treated for LUTS at a tertiary hospital were utilized. (2) Methods: Uroflowmetry data were collected for construction and verification of a long short-term memory (LSTM) deep-learning algorithm. The initial sample size comprised 34 patients; 27 patients were included in the final analysis. Uroflow sounds generated from flow impacts on a structure were analyzed by loudness and roughness parameters. (3) Results: A similar signal pattern to the clinical urological measurements was observed and applied for health diagnosis. (4) Conclusions: Consistent flowrate values were obtained by applying the uroflow sound samples from the randomly selected patients to the constructed model for validation. The flowrate predicted using the acoustic signature accurately demonstrated actual physical characteristics. This could be used for developing a new smart flowmetry device applicable in everyday life with minimal constraints from settings and enable remote diagnosis of urinary system diseases by objective continuous measurements of bladder emptying function. Urinary impairment, which causes a decline in the quality of life, increases with age. For diagnoses of the cause of LUTS, conventional weight transducer sensors and ultrasonography, as forms of uroflowmetry, have been the common non-invasive techniques for measuring bladder emptying ,2,3. UroUrination is influenced by the environment and emotions. Such a phenomenon is even more prominent in patients with an overactive bladder . BecauseDiagnosis of the bladder emptying pattern needs to have information about the flowrate and total flow volume. For outside flows, it is not straightforward to install instruments . The souIn this study, the quantification was preformatted using the sounds of urine flow with the statistical features trained by deep learning. The proposed model substitutes the classic uroflowmetry to be used in daily life. As an example, for application of the proposed methodology by the acoustic signals, the long short-term memory (LSTM) was used to classify LUTS, compared with those treated, by the information from the uroflowmetry. A dataset was constructed with loudness and roughness as the input coefficients. The flowrate measured using uroflowmetry was used as the output coefficient. The algorithm was constructed to classify the health status of the urinary system based on the prediction results of the flowrate sequence using the LSTM classification model. This allows applications to continuously monitor urination characteristics on an everyday basis.V, was calculated by differentiation of the urine weight as:W and ρ are the urine weight and the density of urine, respectively.When detailed information about the urination characteristics is required, the patient begins urinating into the uroflowmeter connected to a computer recorder for measuring the voided volume, voiding time, and urinary pattern. The test results assist the physician in identifying the causes of specific urological impairment and determining the efficiency of the bladder and the sphincter. In this study, the sounds from the interaction of uroflow with the test setup was utilized as a new non-invasive method. p and N are the discrete sound pressure to be transformed and the number of periodic frequency samples, respectively (herein N = 4096). This transformation allowed an investigation of the influence of background noise on the experimental environment. The Hanning window was applied for leakage reduction as:m is the shifting length of the window function. The transformation was performed with a 25% overlap and m = 4096. To examine the real-time fluctuations in the sound pressure according to the urine velocity, short-time Fourier transform (STFT) was performed to study the variation in spectral components depending on the flowrates as: With the increase in outflow velocity, the uroflow sound generated from flow impact increased over the entire frequency range correspondingly. With the constant dimension of the patient’s urinary track, a rapid urinary speed generated uroflow sound with high energy at high frequencies. To enhance the accuracy of the LSTM predictions, a dataset was constructed with loudness and roughness as the input coefficients, and the flowrate measured using uroflowmetry as the output coefficient. The loudness represented the volume of acoustic signals measured by a microphone. Roughness reflected the pattern of variation in the spectrum level. These two parameters were used as the input features of LSTM for the training process. This study included patients being treated for LUTS at the Department of Urology, Hanyang University Medical Center, who were admitted between April and May 2019. All study participants provided informed consent, and the study design was approved by the appropriate ethics review board. The patients’ information is shown in The urinary flowrate pattern calculated by Equation 1 was used to divide the patients into 3 groups as shown in V, A, and rand were the original flowrate measured by uroflowmetry, the amplitude, and the random signal, respectively. One hundred flowrate data (V′) per patient unit were additionally constructed for training the LSTM classification model; this was verified with the raw flowrate data (V) measured by uroflowmetry. The classification results of the patients’ health status with the LSTM model were compared with the doctor’s diagnosis.Because of the limitation of the quantity of patient samples, an additional dataset was required to establish an LSTM classification model. The interpolation method and random signals were applied to obtain an additional flowrate dataset as:N) was calculated as the integral of the specific loudness (N’) at a critical band rate as  and the previous output values (th−1) are given, the first step in the forget gate layer of the LSTM cell is to determine which information is to be thrown away, which is carried out as follows:LSTM is based on the architecture of an upgraded recurrent neural network (RNN) that includes the preservation of previous information. The cell state in LSTM only needs linear summation to pass through the hidden layer, and the gradient moves between networks without attenuation. LSTM makes the neural network switch between memorizing the latest information and the information from a long time ago, so that the data can decide which information to keep and which to forget. This makes it suitable for classifying, processing, and predicting time series data . Figure he cells . When thThe next step is to decide what information is to be saved in the input gate layer. A vector of new candidate values is added to the state as follows:tf) is multiplied to forget a certain amount of the cell state values (tc−1), whereas the input (tx) and previous output values (th−1) are multiplied by the processed output values of the input gate (ti) to accept a certain amount as input values, from which a new cell state (tc) is created. The output value (to) under this cell state is multiplied as the output of the LSTM cell (th) to determine how much to forget from the cell state value and how much of the input value should be newly accepted, as follows:The output value from the forget gate (tV).The present study proposed an LSTM for real-time flowrate estimation of the external flow and carried out training of the LSTM with the loudness and roughness of the uroflow sounds as the inputs, and the mass flowrate measured by the weight balance as the output (th) from the last instance expresses the entire sequence. The softmax non-linear layer predicts the probability distribution of the fully connected layer and health status as:y, W, σ, tanh, and b represent the probability of health status classification, the weight, the sigmoid function, the hyperbolic tangent, and the bias term, respectively.To check the health status of the urinary system in a comfortable environment, the health status classification utilizing the flowrate was further proposed. The input node, batch size, hidden unit parameters were set to one, three, and five, respectively. The optimizer function was set as Adam. Epoch was set to 40 because any change above this did not affect the model’s performance. To make the output layer predict only the flowrate, a dense layer with just one neuron was used to carry out the training of the LSTM network. To verify the accuracy of the flowrate predictions through the constructed LSTM, As a data-driven estimation method, LSTM has feedback connections and it processes not only a single data point but also entire sequences of data. This study developed a new LSTM estimation methodology using uroflow sound signals as a replacement for conventional flowmetry. The proposed LSTM model allowed physical flowrate measurements with a high level of accuracy. The diagnosis of urinary impairment using only the sound pressure is not a novel idea. In this study, acoustic responses generated during urination in arbitrary environment were used for an LSTM algorithm to help the accurate diagnosis of urinary impairment. Many urologists have taken interest in and conducted various studies on the diagnosis of urinary impairment using sounds. Koiso et al.  attachedThe sound level is a factor expressing the loudness of the sound. Differences may occur depending on the height of the urine fluid. The present study showed its applicability in several forms such as panels in public smart toilets. The present study used time-dependent variations in the loudness and roughness of the acoustic response generated during urination as input values. The LSTM predictions closely followed the measurements of conventional flowmetry and allowed accurate prediction of the urination patterns. By utilizing the pattern variations, the presented identification was robust to differences in the height of the urinary tract. This study classified the patients into groups from the predicted flowrate patterns based on uroflow sound. Group A represented normal urinary activity with a bell-shaped urinary pattern, Group B represented LUTS or IDC with a staccato pattern, and Group C represented BPH/urethral stenosis or compression by adenoma with an interrupted pattern. These groupings allow the monitoring of health status through the flowrate patterns, and enable suspicion in addition to early diagnosis of LUTS. This procedure would be useful in clinical practices, homes, and offices. Our algorithm is applicable to portable devices for use in various environments with minimal influence from the surrounding noise.As the clinical data were measured on patients suffering from LTUS, the data used were collected almost exclusively from elderly males  in this study. The focus was on securing the possibility of classification techniques for LUTS patient states using the deep-learning algorithm from the engineering perspective. For mass application in the other fields, a larger number of samples in different circumstances  should be secured. The multipath channel effects need to be overcome to reduce the signal distortion due to reflections and reverberations. Further statistical analysis needs to be conducted in the future for more robust and precise diagnoses of LUTS in various environments.Repeated flowmetry at home or office with the user in a stable condition and a comfortable environment is relevant to the accurate measurement of urinary patterns. Such potential can be recognized despite the white coat syndrome often noticed during blood pressure measurements . The meaQmax and reported the findings from the analysis after using the device on 59 male patients. This study realized that 81% of the patients preferred measurement at home. Despite the advantages of measurement at home, there are limitations in using the device on females or children. The device developed in the present study allowed accurate measurements in both males and females.Devices for home-based uroflowmetry have been developed in various forms ,18,19. TThe LSTM flowrate classification model proposed in the present study illustrated accurate diagnosis for LUTS and early diagnosis of abnormal symptoms. By integrating linkage and wireless communication technologies for IoT-based metrological, analytical, and monitoring devices, a fun device for male and female public restrooms could be developed to realize LUTS health diagnoses of abnormal signs for the public, while also making a significant contribution to accumulating national health data, which is our next activity for further research.


The deployment of modern applications, like massive Internet of Things (IoT), poses a combination of challenges that service providers need to overcome: high availability of the offered services, low latency, and low energy consumption. To overcome these challenges, service providers have been placing computing infrastructure close to the end users, at the edge of the network. In this vein, single board computer (SBC) clusters have gained attention due to their low cost, low energy consumption, and easy programmability. A subset of IoT applications requires the deployment of battery-powered SBCs, or clusters thereof. More recently, the deployment of services on SBC clusters has been automated through the use of containers. The management of these containers is performed by orchestration platforms, like Kubernetes. However, orchestration platforms do not consider remaining energy levels for their placement decisions and therefore are not optimized for energy-constrained environments. In this study, we propose a scheduler that is optimised for energy-constrained SBC clusters and operates within Kubernetes. Through comparison with the available schedulers we achieved 23% fewer event rejections, 83% less deadline violations, and approximately a 59% reduction of the consumed energy throughout the cluster. Modern services, such as the Internet of Things (IoT), cooperative sensing, augmented reality, and Industry 4.0 have stringent requirements in terms of latency, availability, resilience and scalability . To accoEfficient management of the deployed clusters and seamless scalability is achieved by coupling network paradigms like edge computing (EC) and fog computing (FC) with containerization technologies and network function virtualization (NFV) . ContainSingle-board computers (SBCs) have become a mainstream choice for IoT environments . In the SBC clusters offer connectivity with a wide range of sensors and devices and therefore present new opportunities for the deployment of modern applications and services . Many use cases require location-specific measurements to meet latency-bound tasks, as the devices are co-located with a control device or within a monitored environment . Such usDeploying and managing several applications in a cluster can be a laborious and error-prone task, even if the application is containerized. Therefore, a platform capable of orchestrating, managing, and ensuring the life cycle of containers and applications is necessary. One of the most prominent platforms for managing containers is Kubernetes . KuberneKubernetes’ scheduling process, being primarily designed for data center environments, does not integrate energy measurements of the participating devices in the placement decisions. This can result in either under-utilizing the available energy resources or attempting deployments that are bound to fail due to insufficient resources. Moreover, the resources of the node that performs the scheduling decisions, known as the master node, are not included in the pool of resources for pod deployment. This results in under-utilized computational and energy capacity.A regression model able to establish the relationship between battery consumption and used resources in an SBC cluster;A study of the impact of including the controller node in the scheduling process in an energy-constrained SBC cluster;A scheduling algorithm that assigns events to cluster nodes based on expected battery consumption and used resources;Real-world evaluation of the proposed scheduler on an energy-constrained SBC cluster.Given these shortcomings of a prominent orchestration platform, this paper proposes a scheduler that improves the resource utilization of energy-constrained SBC clusters by guaranteeing high levels of acceptance ratios, low overhead in the scheduling process, and better utilization of the available resources. To achieve these, we contribute the following:The remainder of this paper is structured as follows. This section provides a literature review of existing research works related to workflow scheduling in cloud computing. We have focused our attention on papers that consider energy as a primary factor in their solutions. In addition, our study analyzes several methods to estimate the state of charge (SOC) of battery-powered devices. This parameter is crucial for automating service deployment and extending the lifetime of the cluster.Although workload scheduling is a well-studied field in cloud and fog computing, there is a lack of practical implementation and prototypes in the reviewed literature. This work addresses this gap by testing the suggested solutions against a testbed comprised of representative SBC devices and batteries.The authors of  have proIn ,13, the A deep reinforcement learning (DRL) approach is used in  to addreIn , VarasteDespite the plethora of papers addressing energy efficient scheduling algorithms in cloud and fog computing, they lack a practical evaluation using real-world testbeds comprised of resource-constrained nodes. All the approaches in the referenced works are implemented and evaluated in simulation environments, which require great CPU and memory capacity to execute and are therefore difficult to use and test.To fill the identified gaps in literature, this paper proposes a scheduler which assigns events to resource-constrained nodes while guaranteeing low energy consumption and increasing resilience. The set of nodes that we used are off-the-shelf SBCs, typically found in use cases like unmanned aerial vehicles (UAV) and WSN. The proposed scheduler takes into account the controller node in the scheduling process, thus increasing the number of scheduled events and the acceptance ratio of the system. In this vein, our solution makes better use of the cluster’s available resources by considering not only the computing nodes but also the controller node.SOC by using different methods.In battery-powered devices, knowing the remaining battery capacity helps avoid service disruption due to battery depletion. To this end, several works aim to estimate battery SOC estimation algorithm. The researchers evaluate the proposed estimator using two types of lithium-ion (Li-ion) batteries under different loading profiles and temperatures. The optimal model parameters used in the EKF are obtained from generic functions for battery modeling that combine several degrees of polynomials. Since the resulting model depends on the training datasets, the update of model parameters due to changes in these datasets may not be trivial in real-time scenarios.In , Hu et aSOC and the state of health (SOH) of lithium-ion batteries. They improve the estimation accuracy by considering the correction of the operating efficiency and the impact in the SOH. The authors’ proposed method can be easily implemented in all portable devices, such as SBCs, due to their simple calculation and low hardware requirements. Sagkriotis et al. [SOC algorithm by combining direct measurement of the electro-motive force (EMF) and Coulomb counting. They demonstrate the effectiveness of their approach by improving the SOC and accuracy of the remaining run time.The authors in  have pros et al.  have alss et al. , Pop et SOC estimation. The results of their model show a significant improvement in comparison with the baseline models  models) while being able to maintain similar computational efficiency.In contrast to the previous work, the authors of  have preSOC estimation algorithms regarding battery capacity predictions. However, these papers use battery operation parameters such as current, voltage and temperature to make their estimations. The main focus in this paper is to establish a relationship between previous SOC measurements and utilization of resources  to train a regression model which predicts energy consumption. By doing so, we further contribute towards automating the placement process in such environments. By obtaining a model that is able to predict energy consumption, network-wide placement decisions are enabled. Such decisions can improve the total lifetime of the network and optimize the management of the available resources.The aforementioned papers have proven the accuracy of In the last years, the applicability of SBCs has increased and now covers a wide range of use cases. In , JohnstoTo improve the resilience and performance of SBCs, the authors in ,21,24 haTaking the PaaS concept and the above papers as a reference, we developed an SBC cluster to deploy services and tasks while adhering to their requirements. We extend the Kubernetes scheduler by including energy measurements and estimations to deploy services and tasks. We improved the cluster’s resilience by extending its lifetime while adapting Kubernetes energy-constrained devices.In this section, we formally present the problems we address as well as the notation and system model of use.To capture the aforementioned environment characteristics and formally define the problems we investigate, we consider the reference architecture depicted in We treat the cluster as a pool of available resources which has to be managed in a way that does not violate deadlines or energy constraints. We consider two types of events that are scheduled in the cluster: tasks and network services. The former is a set of instructions that require a pre-defined and fixed amount of time for their execution. Examples of tasks are: log rotation (the process of compressing log files that are older than a particular time and deleting ancient ones) associated with a particular running service, processing rows from a service database table, backing up a service database before its deletion, etc. The resources required for the execution of a task are released upon completion. A service event is composed of a set of VNFs that can reserve resources for an arbitrary amount of time that is not known in advance.To include use cases that require fog-to-fog communication or other geo-distributed applications, the event requests are considered to be distributed among computing nodes that are geographically distant across multiple locations. An example use case would be on-boarding a set of drones to enable emergency service communications like telco services, such as IP telephony, between islands during a natural disaster . In this situation, each drone would run different VNFs: an access point (AP) VNF, a domain name system (DNS) VNF, and an access router VNF ,26.P). P represents all the virtual nodes (p)  are equivalent to Kubernetes pods) created on all the physical nodes where events can be scheduled (N). Each virtual node n, where S) is formed by a sequence of VNFs (F), where each function f must be processed on a set of physical nodes. These functions must be scheduled one after the other in a specified sequence. Each created virtual node p can only process one function at a time. Similar to the network functions, any given task (T) will be processed on the selected physical node where a virtual node p will be created to execute the requested task. Each event has a demanded rate . The priority queue introduces a certain delay in the assignation node procedure, since the controller node schedules events one by one. In the case of the arrival event rate becoming higher than the scheduling rate, we define the waiting time  is defined for processing a given event. In the case of a network service, the processing of its last function must be completed by this time. Otherwise, the scheduler incurs a service level agreement (SLA) violation. A list of notations related to the system model is provided in Finally, a deadline (SOC and capacity-based scheduler (SOCCS). It processes event requests and determines the best node in an SBC cluster to run them based on the remaining battery estimations and CPU usage in the nodes. The proposed scheduler is formed by three main elements: the SOC estimator, the monitor and the scheduler. The relation among these blocks and how they communicate with the orchestrator (represented by blue lines) as well as the communication between the orchestrator and the cluster elements (represented by green lines) is depicted in N) comprises the controller node and the computing nodes  by taking into account the used estimation model [The on model . Such faSOC can be achieved by dividing the percentage of the released electric charge in the battery by the one that entered it. Denoting the released capacity when the battery is completely discharged as SOC percentage can be obtained as follows:The estimation of the The proposed estimator block adopts the coulomb counting method because of its simple yet accurate approach. To get more accurate results, the posed in , we can Equation  is adjusSOC is given by Equation (DOD). The When the battery is fully charged, the Equation . HoweverSOC of the battery through Equation . Line 5 checks for the opposite condition. It verifies that the current node’s usage is below its maximum value. The node’s status is set to scheduled in line 6 if it was previously marked as unscheduled. The updating nodes procedure is used by the monitor block, and its behavior is described in Algorithm 1.This procedure updates the resource utilization of each node within the SBC cluster. It determines a node’s resource utilization by calculating the whole usage of its virtual nodes in terms of CPU and memory (line 2). Considering all the cluster nodes as candidates to place a created virtual node by default, this procedure checks if a current node’s utilization has not reached its defined maximum capacity (line 3). If the maximum capacity has been reached, the node’s status is marked as succeeded or failed. In the case that an event has completed its execution, the virtual node where it was running is marked as succeeded. If the event has been completed past its deadline (line 9), the algorithm updates the amount of deadline violations in line 10. The other state is related to the failed virtual nodes (line 11). When this condition is satisfied, the virtual nodes monitor updates the amount of rejected events in line 12. Afterwards, the algorithm releases the used resources and updates the respective parameters (lines 13–15). Finally, the algorithm calls the updating nodes procedure in line 16. The rejected events and deadline violations parameters are later used as evaluation metrics of the proposed scheduler. The metrics are analyzed in detail in The monitor block’s procedure begins by initializing two parameters (lines 1–2) that run during the whole lifetime of the system (line 3). It checks several parameters . Next, the procedure checks if the current node is the controller node, because the data used by the model is determined by the node’s type (line 2). In SOC regression model considering the stored data (line 10).Procedure 2 uses a trained prediction model to forecast the Algorithm 2 is executed during the whole lifetime of the scheduling algorithm while there are existing elements in the priority queue (line 1). Before analyzing any possible node to assign an event, the algorithm obtains the node’s capacity from the monitor block (line 2). After gathering the usage of the nodes, the algorithm takes the first element in P, If the previous condition is not met (line 9), we analyze two possibilities with the same outcome (line 11). The first possibility corresponds to the case when no node can host a virtual node. The second possibility is more complex, since the only available node is the controller and the event to schedule will run during the whole lifetime of the system. For both cases the associated event  is rejected in lines 12–22, and SOC predictor model exists. If it does, the predictor model calculates the SOC of the current candidate node using Procedure 2 (line 27).The process of Algorithm 2 continues, and in the next step the first element in SOC prediction procedure is used in Algorithm 2 to calculate the node score through Equation  or a real measured value from the SOC estimator block. The expected CPU usage (SOC of the node when the SOC predictor model is not available (lines 29–30). After calculating In Equation , the valAlgorithm 2: Event Scheduler.SOC prediction model used in Algorithm 2 and the data set to train the model in Algorithm 4 (line 4). The Algorithm 3 represents the main process of the scheduler application which initializes the other processes, i.e., the monitor process, the event scheduler and the regression model handler see  (lines 5des line . After cy) lines –13 is deBased on the previous definitions, we calculate the ranking score for the virtual node where an event runs, denoted by Algorithm 3: Main process.SOC through sampled data by finding a relation between the data and the SOC measurements. Methods of this kind include autoregression moving average (ARMA), artificial neural network (ANN), support vector machine (SVR) and others [In contrast to the described methodology in d others . These mIn the case of regression models, the model coefficients are determined from available training data by minimizing the root mean square error (RMSE) between the predicted and real values. The RMSE represents the standard deviation of the prediction errors, thus showing how concentrated the data is around the line of best fit . In geneSOC estimation, since the computational characteristics of SBCs might not be capable of supporting complex algorithms, such as ARMA, ANN and SVR. The regression model is trained with an initial dataset formed by collected metrics during a defined period. To improve its accuracy, it is updated when the RMSE metric is above a pre-defined threshold. The model coefficients are calculated and updated through Algorithm 4.In this paper, we adopt a regression model for Algorithm 4: Regression Model Handler.SOC regression model after waiting a pre-defined time (line 2). After that, it verifies if the regression model has not been training (line 3). Then, the model is trained with the existing training dataset (SOC predictions from the trained model (line 7). Then, it determines the RMSE between the predictions and the measured SOC in SOC regression model using the existing dataset (line 10). After this process, the training dataset is restarted, and newly gathered data is added through the monitoring process (line 11).This algorithm checks the To evaluate the performance of our proposed scheduling algorithm, we have used a testbed formed by a cluster of four Raspberry Pi 4 Model B units . These dIn our evaluation scenarios, the services and tasks to be scheduled arrive one at a time following a Poisson distribution. We explore different event arrival rates that range from 2 to 12 events per time unit. The main parameters used for creating the services and tasks are selected randomly from the list of values shown in This section analyzes the impact of deploying selected events to the controller. Different from previous works in cloud and edge computing ,31,32), ,32, 31,3To study the impact of this decision, we ran several experiments for both types of scheduling (including/excluding the controller). The results are presented with a confidence interval of 95%. Notably, the number of successfully scheduled events was relatively similar for both criteria, i.e., deploying and not deploying in the controller node, and all the event generation rates. However, there is a noticeable difference in the number of rejections and deadline violations for generation rates of 8, 10 and 12. For the case of services and tasks that are assigned to the controller, we observe a reduction of 50%, 61% and 66% in the amount of rejected events, respectively. These reductions are more significant than the increments in the deadline violations, which can be up to 16%, 42% and 49% for the same generation rates.By using the controller node to deploy selected services and tasks, we minimize rejections and effectively utilize all available resources. In Based upon these results, we can confirm that using the controller node to deploy specific events guarantees a higher event acceptance ratio than using its resources solely for scheduling tasks. The higher acceptance ratio was evidenced by a significant reduction in the number of rejected events, although it was at the cost of greater deadline violations. Overall, the advantages outweigh the drawbacks when deploying services and tasks in the controller node in a resource-constrained environment. Thus, the proposed scheduler has been implemented by taking these results into account.SOC in a computing node. To describe the accuracy of the models, we included in the graphic the adjusted Before starting the training phase, several regression models were studied to choose the one that best fit our case of study. SOC begins to decline. From the adjusted The controller node has a different behavior with regards to the computing nodes, since its main function is to schedule service and task requests. This does not demand as much CPU usage as the deployment of services and tasks. The CPU usage represents a boundary parameter for the SBCs, because when a node reaches its maximum value, it cannot process new requests which would affect its performance. As a result, we must also consider CPU usage in the regression model for the controller node. In this regard, SOC regression model for any node n in the SBC cluster  and the native Kubernetes scheduler (KS). The former algorithm aims to allocate the different events to the node with the highest available buffer capacity. Thus, the node with the least CPU usage is chosen. The latter algorithm ranks the feasible nodes and selects the one with the highest-ranking score. In other words, the more resources the services and tasks use in a node, the lower its ranking is. These algorithms were analyzed through the following metrics: scheduled and rejected events, deadline violations, waiting and total time, acceptance ratio and battery consumption. To ensure the reliability of the results, we ran several experiments for each generation rate for all the analyzed scheduling algorithms and show results with a confidence interval of 95%.Since the generated services are formed by several network functions (VNFs), The acceptance ratio represents the proportion between scheduled and requested events. As seen in SOC in each experiment. SOC and minimum CPU usage. Additionally, the previous subsection revealed that our scheduler performs faster than the baseline algorithms, since the total time of the events in the system was notably reduced. Thus, our scheduler reduced the cluster’s operation time to process the requested events, which indisputably had a lower impact on battery consumption with regards to the other schedulers. By taking a closer look at the highest generation rate, our scheduler is shown to have saved up to 39% and 59% of the battery consumption in the controller node with regards to LLS and KS, respectively. Likewise, it decreased the consumption in Worker1 by 36% and 51% in comparison with the same algorithms. Regarding the workers’ imbalance in terms of battery consumption, we found that our proposed algorithm had the best performance. For a generation rate of 12 events per time unit, SOCCS presented an imbalance of 0.25 between the maximum and minimum average battery consumption. This value was much lower than the ones obtained in the LLS and KS algorithms .To obtain the battery consumption, we calculated the difference between the initial and final measured SOC and the CPU usage. The proposed scheduler was implemented and evaluated in a Raspberry Pi cluster running Kubernetes.In this paper, we proposed a scheduling algorithm, SOCCS, that assigns events to an SBC cluster by considering predicted battery consumption and used resources. The predictions were obtained through a regression model that establishes the relation between the Additionally, we analyzed the case of using the unused controller resources to deploy certain services and tasks. The obtained results confirmed that such consideration is a good criterion in a resource-constrained environment. By deploying events in the controller node, we increased the acceptance ratio by around 30% for the highest generation rate with regards to not deploying requests. Similarly, the acceptance ratio increment was related to a significant reduction in the rejected events, which could be up to 66% for a generation rate of 12 events per unit of time. Thus, the proposed scheduler could make better use of the available resources in the cluster.The evaluation results revealed that the presented solution outperformed the baseline algorithms with regards to analyzed metrics, such as rejected and scheduled events, deadline violations in scheduled events and battery consumption. Specifically, the SOCCS algorithm reduced the rejected VNFs for the highest event generation rate by around 23%, which was evidenced by its high acceptance ratio values. Additionally, our proposed algorithm decreased the number of deadline violations in the scheduled events by 83%. This result was confirmed by the low values in the waiting and total time of the events when the algorithm was used. Finally, our proposed scheduler saved around 59% of battery consumption in the controller node and 51% in the computing nodes for the highest generation rate.In terms of future work, we intend to provide our proposed scheduler with a mechanism to migrate events and functionalities to other nodes. This strategy would improve the fault tolerance of the cluster, since we could reassign demanding events in critical nodes to available ones in other clusters before the system shuts down due to battery depletion. In this regard, we also need to provide our scheduler with a communication channel to exchange information with other entities with a global network view, such as SDN controllers.


Secondly, equivalent fuel consumption minimization strategies (ECMS) combining different vehicle speed prediction methods are constructed. After verification by simulation, the equivalent fuel consumption of the simulation increases by only 4.89% compared with dynamic-programming-based energy management strategy and decreased by 5.40% compared with the speed prediction method with low accuracy.With the development of technology, speed prediction has become an important part of intelligent vehicle control strategies. However, the time-varying and nonlinear nature of vehicle speed increases the complexity and difficulty of prediction. Therefore, a CNN-based neural network architecture with two channel input (DICNN) is proposed in this paper. With two inputs and four channels, DICNN can predict the speed changes in the next 5 s by extracting the temporal information of 10 vehicle signals and the driver’s intention. The prediction performances of DICNN are firstly examined. The best RMSE, MAE, ME and R Vehicle speed prediction refers to the estimation of future vehicle speed with available information. By utilizing the predicted vehicle speed, the vehicle controller can adopt appropriate control strategies and the driver can adjust the manipulation in advance ,2. At prThe current research methods for vehicle speed prediction can be generally divided into a model-based method and a data-based method. Model-based methods include the microscopic model, macroscopic model, etc. The microscopic model focuses more on the speed variation of each vehicle. Differential equations are established to simulate vehicle traveling. The speed can be updated by continuously solving the differential equations. Ghandour et al. proposed a new method for predicting the future vehicle motion state, including longitudinal speed, which was applied to predict potential danger and achieved satisfactory results . Ma, Z eThe model-based speed prediction method has fast parameter learning speed and does not require large data sets. However, its weak fitting ability leads to difficulties in constructing accurate mathematical models for highly stochastic vehicle speed.The data-based speed prediction method can improve the ability to fit functional relationships between variables by utilizing large amounts of data. It does not make excessive assumptions about the prediction model and is free to learn arbitrary forms of mapping relationships from the training data. The most commonly applied data-based methods include stochastic prediction , shallowThe main purpose of shallow learning is to construct a data-driven mathematical model that finds the mapping between the input set and the output set. Common shallow learning methods are neural networks ,6,16, suThe purpose of deep learning is the same as shallow learning. The difference is that deep learning enables complex function approximation by building a deep nonlinear network structure. Commonly applied deep learning methods include the convolutional neural network (CNN) and the long short-term memory network (LSTM). Song et al. constructed a CNN-based traffic prediction model utilizing time data and speed data collected on the main roads in Seoul, Korea, and verified that the prediction performance of the model was higher than that of the other two multi-layer perceptron models . Han et The data-based speed prediction method can theoretically fit a highly nonlinear mapping. However, low-value data can cause the model to learn wrong mappings. Therefore, it is important to collect diverse experimental data and explore the deep value of the data.In this context, a dual-input CNN model (DICNN) is proposed in this paper. DICNN can extract features from vehicle signal sequences and driver intent to predict future speed changes. Firstly, road experiments are designed to collect rich vehicle data for a parallel plug-in hybrid electrical vehicle (PHEV). Secondly, the performance of DICNN is discussed compared with the other three prediction models. Finally, the prediction models are combined with the energy management strategy to further verify the relationship between the accuracy of prediction and the effect of energy saving.(1)Deep learning models are used to build the speed prediction model. The deep architecture has more tunable parameters, which can lead to better prediction results.(2)The training set is constructed based on rich vehicle data obtained from road experiments. The various vehicle signal sequences provide a more detailed picture of the current operating status of the vehicle.(3)The second type of input for the model characterizes the pedal signal. As a direct representation of driver intent in vehicle signals, the second type of input enables the prediction model to more sensitively capture trends in vehicle speed changes.The main innovations and contributions of this work are concluded as follows:The paper is organized as follows. Road experiments are designed to collect vehicle traveling data. The CAN bus signals of the vehicle are obtained from the devices of data collection. From all signals, 10 vehicle signals are selected and converted into a format that meets the input requirements of DICNN. The predicted vehicle speed is output based on the model. The whole flow is shown in The research object of this paper is a parallel PHEV. According to the placement of the electrification component, the vehicle belongs to the P2 type, i.e., the motor is placed behind the clutch 0 and before the transmission . The vehWe apply the on-board recorder to collect vehicle signals. In order to evaluate the data quality of the on-board recorder and provide an accurate basis for processing, VBOX is applied to evaluate and calibrate the key information such as vehicle speed and acceleration. The hardware scheme is shown in In order to reduce the stress on data storage, computation and signal filtering, the signal list was improved as shown in Due to the different signal frequencies, noise, abnormal data, etc., the collected data need to be processed first. The basic steps include data synchronization, filtering, etc. Due to the different sampling frequencies, the different signals are not sampled at strictly the same time. The time stamp of the VCU vehicle speed is used as the reference to normalize the sampled time of other signals.The vehicle system is a continuous system. The derivatives of vehicle signals such as vehicle speed, for example, are still physically significant and should not have abrupt changes. The Hermite interpolation polynomial ensures that the polynomial has equal function values and derivative values at the interpolation nodes. For long time sequences, the degree of the polynomial obtained by Hermite interpolation is high, and there is also the Runge phenomenon. Therefore, the segmented cubic Hermite interpolation is adopted in this paper, and its formula is shown as follows.Algorithm 1. Recursive mean filterData: origin signal vector Result: filtered signal vector 1 for todo2if then break  3Else then  4end  5end In the process of data processing, some data are beyond a reasonable range, such as the acceleration calculated from the vehicle speed. They generally behave as non-operational achievable high frequency jitter. Therefore, a recursive mean filter is adopted for the data process. Its filtering rule is that low frequency signals can pass normally, while high frequency signals exceeding the set frequency threshold are blocked and attenuated. The pseudo code of the recursive mean filter is shown in the Algorithm 1.In 1962, Hubel and Wiesel discovered that the unique network structure of neurons used for local sensitivity and orientation selection in the cat cerebral cortex could effectively reduce the complexity of feedback neural networks, and then proposed CNN. In recent times, CNN has become a key focus for research in many scientific fields. Its layout is closer to that of an actual biological neural network, and weight sharing reduces the complexity of the network. In particular, images with multidimensional inputs can be fed directly to the network, which avoids the complex data reconstruction during feature extraction. Its structure consists of an input layer, an intermediate layer and an output layer, where the intermediate layer includes a convolutional layer, pooling layer, activation function and batch normalization layer.The role of the convolution layer is compression and purification in order to enhance input signals and reduce noise. The equation for the convolution layer is described by weights . The chaTo speed up the training of convolutional neural networks and reduce the sensitivity to network initialization, a batch normalization layer is employed, which normalizes each input channel in a small batch . The strThe activation layer is responsible for activating the features extracted from the convolution layer. Since the convolution operation is a linear transformation, it is necessary to introduce an activation layer belonging to a nonlinear function for nonlinear mapping. The ReLU and the Softsign functions are applied. The ReLU function performs a threshold operation to set any input less than zero to zero . The SofThe pooling layer, also known as the subsampling layer, serves to perform feature selection and reduces the number of features. The pooling layer itself does not require any learning, but it reduces the number of parameters to be learned in the subsequent layers and also helps to reduce overfitting. The pooling function adopted in this paper is average pooling. For a region, the average pooling calculates the mean of all neurons in this region as the representation of this region, as shown in Equation (9).Each node in the fully connected layer is connected to all nodes in the previous layer, which combines the features extracted from the previous layer. For the vehicle speed prediction model belonging to the regression model, the output size of the fully connected layer is set to 1 which is the future speed of the vehicle. In this paper, five models with different parameters are trained to predict the future speed from 1 to 5 s in turn.The architecture of DICNN is shown in The first type of input consists of two channels. Each channel contains the 10*10 fake images. The first type of input reflects the characteristics of multiple vehicle signals at short-term adjacent moments. Its first channel adopts a fake image composed of 10 s timing vectors of 10 vehicle signals. The signals selected in this paper are VCU vehicle speed, VBOX vehicle speed, longitudinal acceleration, motor speed, motor torque, DC bus voltage, DC bus current, state of charge, BMS voltage and BMS current. The second channel adopts the horizontal gradient map of the fake image. The gradient map can describe the local intensity gradient or image edge well, and is a common method in image feature extraction. It is obtained by performing gradient operation on the image. For the fake image, the gradient in the horizontal direction has practical physical meaning. Extracting the characteristics of the signal gradient changes helps to provide a reference for future vehicle speed changes. Therefore, the horizontal gradient map of the first channel is used as the input of the second channel.The second type of input also consists of two channels, each containing a 1 × 10 vector to characterize the driver’s driving intentions. The driver’s intention is directly represented in the vehicle signal by the drive pedal opening and brake pedal opening. The drive pedal on is scaled from 0 to 1 and the brake pedal on is scaled from −1 to 0. The drive pedal opening and brake pedal opening are combined into one vehicle signal named pedal opening. The temporal pedal openings and their temporal accumulation values are used as the two channels of the second type of input to extract driving intentions and predict speed changes. The format of the input to DICNN is shown byPHEV improves the fuel economy by applying both fuel and electricity. In order to meet the vehicle’s power requirements while optimizing the vehicle economy, the control strategy needs to adjust the power distribution between the engine and electrical energy at any given moment according to the real-time road conditions. The development of a reasonable energy management strategy is the basis for achieving better economy for PHEV. This section analyzes the calculation principle of the equivalent fuel consumption minimization strategy (ECMS) and combines it with vehicle speed prediction for optimization.ECMS is derived from Pontryagin’s minimum principle (PMP) . CombineThe control objective is to minimize the equivalent fuel consumption at each moment. Therefore, the dependent variable of the Hamiltonian function should be transformed into the instantaneous equivalent fuel consumption. By dividing both sides of Equation (14) by the calorific value of the fuel at the same time, Equation (15) is obtained.The optimization objective of the conventional ECMS is to minimize the equivalent fuel consumption at the current moment. The optimization objective of the ECMS with speed prediction is to minimize the sum of the fuel consumption of the engine and the equivalent fuel consumption of the motor at each moment in the predicted time domain. At each sampling moment, an open-loop optimization problem in a finite time domain is solved based on the obtained vehicle information. The first element of the obtained control sequence is applied to the controlled object. At the next sampling moment, the above process is repeated to complete the control of the energy management strategy.In this paper, a dynamic programming (DP) algorithm is adopted to solve the optimization problem. The basic idea of the dynamic programming algorithm is to transform a multi-stage problem into multiple sub-problems of the same type, and gradually recursively search for optimization by solving the optimal solution of the sub-problems . The algIn this paper, the optimization task is described in terms of travel time. The sampling interval is set to 1 s, i.e., In this paper, the prediction effectiveness of DICNN is examined from both performance evaluation and vehicle simulation in turn. Three other speed prediction methods are selected for comparison and validation, which are Markov chain combined with Monte Carlo (MCMC), support vector machine (SVM) and single input CNN (SICNN). Both MCMC and SVM only use the vehicle speed with a history of 10 s as the model input. SICNN only usea the first type of input of DICNN as model input. The driving cycle adopted for validation is the worldwide harmonized light vehicles test cycle (WLTC).The black line in Box plots are used to reflect the characteristics of the data distribution. 2) are popular evaluation indicators for the prediction results. When the predicted speed is close to the actual speed, RMSE, MAE, ME and R2 are close to zero. Conversely, R2 close to 1 indicates an excellent match. It can be precisely analyzed that DICNN outperforms the other three speed prediction methods in terms of MAE, RMSE and R2 at all horizons except the third second ME.Simulation validation is performed under the Matlab/Simulink platform. This paper establishes a rule-based energy management strategy (RB), a DP-based energy management strategy and ECMS-based energy management strategies adopting the vehicle speed prediction methods of MCMC, SVM, SICNN and DICNN. As can be seen in The changes of fuel consumption in In order to further analyze the working condition of components, distributions of engine operating points and motor operating points are analyzed. From 2. We also construct ECMS energy management strategies with speed prediction for simulation and analysis. With the increase of prediction accuracy of vehicle speed, the difference between ECMS and DP can be reduced from 10.87% to 4.89%. More operating points of the power components are distributed in the high efficiency area to achieve lower equivalent fuel consumption.In conclusion, we propose a novel CNN-based neural network architecture named DICNN. With two inputs and four channels, DICNN can predict the speed change in the next 5 s by extracting the temporal information of 10 vehicle signals and the driver’s intention. It is compared with representative methods in stochastic prediction, machine learning and deep learning. The results show that DICNN outperforms other methods in terms of performance evaluation metrics including RMSE, MAE, ME and RIn the future, we will explore the impact of the type and number of vehicle signals on the prediction effect. In addition, more deep-learning-based models will be combined to further extract features from the training data and improve the accuracy of prediction.


We present a theoretical investigation of the magnetisation reversal process in CoFeB-based magnetic tunnel junctions (MTJs). We perform atomistic spin simulations of magnetisation dynamics induced by combination of spin orbit torque (SOT) and spin transfer torque (STT). Within the model the effect of SOT is introduced as a Slonczewski formalism, whereas the effect of STT is included via a spin accumulation model. We investigate a system of CoFeB/MgO/CoFeB coupled with a heavy metal layer where the charge current is injected into the plane of the heavy metal meanwhile the other charge current flows perpendicular into the MTJ structure. Our results reveal that SOT can assist the precessional switching induced by spin polarised current within a certain range of injected current densities yielding an efficient and fast reversal on the sub-nanosecond timescale. The combination of STT and SOT gives a promising pathway to improve high performance CoFeB-based devices with high speed and low power consumption. STT-MRAMs are based on magnetic tunnel junctions (MTJs), that is a trilayer structure consisting of two conductive ferromagnets and a thin insulator in the middle, usually CoFeB-based and MgO respectively7. One ferromagnet, termed the reference layer (RL), has pinned magnetisation and polarises the spin current flowing into the layer. The other ferromagnet, whose magnetisation can be freely rotated, is known as the free layer (FL). It can be reversed by a large enough excitation of the spin polarised current. In the case of STT-MRAM this is achieved by injecting a perpendicular current into the MTJ and exploiting the torque arising from the s-d exchange interaction between the conduction electrons and the local magnetisation. The information is retrieved by sensing the magnetisation state of the latter via the tunnel magnetoresistance (TMR) effect. These devices can yield fast switching, hence fast writing operations, on the order of a few nanoseconds. However, to achieve faster switching large current densities are required8. This undermines the potential for low power applications. Moreover, since the injected current and the magnetisation of the free layer share the same direction at rest, it is necessary to induce misalignment of the magnetisation in order to have a non-zero torque. This might cause long incubation times thereby reducing the speed. STT-MRAM is a two terminal device, i.e. the same electrical path is used to write and read. This offers the opportunity of reducing the cell dimension of the memory, thus going beyond the current silicon-based technology. On the other hand, the thin oxide that serves as tunnelling barrier in the MTJ may suffer damage when large current densities or voltages are applied. This can cause the breakdown of the device and result in durability issues8.In today’s information based society enormous amounts of data are stored and processed daily. Current spintronic devices such as spin transfer torque magnetic random access memories (STT-MRAMs) are among the most promising emerging memory technologies. STT-MRAMs have been considered as candidates for universal memories thanks to their potential for fast read/write operations, low power consumption, non-volatility and durability10. SOT can be generated by various mechanisms11 such as spin Hall effect (SHE), Rashba effect, inverse spin Galvanic effect, anomalous Hall effect and interfacial spin-precessional scattering. SOT has recently sparked interest in both theoretical and experimental research due to its application diversity. For example, SOT switching driven by mechanism of spin-processional scattering and anomalous Hall effect have been experimentally demonstrated by Baek et al.12 and Ma et al.13 respectively. Interestingly, generation of unconventional spin current in symmetry-reduced system such as CuPt14 and spin-orbit torque generated by the broken magnetic symmetry in the antiferromagnetic IrMn15 have been reported. One of the most known and studied mechanisms is SHE, which arises when a current is injected into a non-magnetic metal and spin up and spin down electrons are scattered to opposite directions. This creates an accumulation of spin density in the directions orthogonal to the current flow. This effect is caused by spin-orbit coupling and thus is larger in heavy metals11. SOT-MRAMs are devices based on MTJs that utilise SOT for the writing process, while using the TMR effect to retrieve the information as in STT-MRAM. SOT can be achieved by placing a non-magnetic heavy metal (HM), Pt, Ta or W generally, in contact with the ferromagnetic layer and injecting an in-plane electrical current into the HM. Spin-orbit coupling is responsible for the generation of a pure spin current orthogonal to the current direction. This is absorbed in the ferromagnet and exerts a torque on the magnetisation of the ferromagnet that can reverse its polarisation11. Due to the nature of the process, reversal occurs in a few hundreds of picoseconds. SOT does not suffer the incubation time issue, however it requires a way to break the symmetry so that the magnetisation ends in the desired final state. The traditional approach is to apply an in-plane magnetic field in the direction of the injected current density. The application of a field has the downside of increasing the power consumption of the device and it might affect the magnetisation of the MTJ itself or nearby cells. Nonetheless, SOT-MRAMs are characterised by a slightly lower power consumption than STT-MRAM17. SOT-MRAM is a three terminal device in which the writing and reading paths are separated: the writing current is injected into the metallic contact and does not flow across the MTJ, whereas a weak current injected into the MTJ is used to read. This ensures that only weak currents cross the MTJ avoiding the risk of damaging the barrier, hence improving the endurance. Moreover, it increases the reliability by avoiding undesired writing while reading since the read and write path are separated16. This comes at the cost of larger memory cells than STT-MRAM. Nonetheless, their fast operation, on the sub-nanosecond timescale, make these devices suitable for CPU cache memories17.Apart from the conventional STT switching by injecting a current perpendicular to the MTJ, the current-induced magnetisation switching can be achieved by spin-orbit torque (SOT), in SOT-MRAM devices6, such as dual MgO MTJs19, have been proposed. However, the major limitation affecting these solutions is the minimum MTJ size achievable. Voltage control of magnetic anisotropy (VCMA) MTJs21 offer the potential of low power magnetisation operations. In practice however, VCMA requires the assist from a charged current or a very precise control of the voltage pulses to yield a reliable deterministic switching22. Shape anisotropy MTJs25, whose free layer is patterned into a tall cylindrical ferromagnet, have been identified as pathway to scale the MTJ size down to or below 10 nm. However, efficient magnetisation dynamics and transport properties in such structures are yet to be fully demonstrated. To overcome the limitation of an external magnetic field in SOT-MRAMs, field-free switching has been proposed by controlling the geometry of the device26, introducing a tilted magnetic anisotropy27, using in-plane MTJs28 and T-type structures29, exploiting the exchange bias via coupling of the ferromagnetic layer with an antiferromagnet30, combining VCMA with SHE-induced SOT in in-plane MTJs31, using a magnetic hard mask to shape the SOT track32. However, downsides of these solutions are the scaling of the cell size and the limited efficiency30 and in some cases difficulties of integration in the current CMOS technology. Recently, Wu et al.33 proposed a voltage-gate-assisted SOT-MRAM that combining VCMA and SOT offers low energy consumption, fast operations and a high storage density. However, the design requires MTJ in-plane dimensions to be reduced and the writing still requires the application of an-plane magnetic field.Various alterations to the standard STT-MTJ stack43. This opens two scenarios: STT assisted by SOT and SOT assisted by STT. In the first case SOT acts by inducing an initial misalignment of the magnetisation of the ferromagnet. This would eliminate the incubation time seen in STT-MRAMs leading to significant reduction of the switching time. When STT assists SOT-driven dynamics, STT can provide the necessary bias to break the symmetry and provide deterministic switching of the magnetisation. This removes the necessity of an external field or to resort to engineered magnetic structures46 reducing the required current density. Most of the studies on SOT-MRAM assisted by STT focused on three-terminal devices46, even though there are reports of two-terminal devices39. SOT-assisted STT-MRAMs are investigated in Ref.41 showing improvements in the switching time and power consumption, however in these works long switching times are considered. Wang and collaborators40 propose a toggle spin torque MTJ where SOT and STT current pulses are sequentially applied, whilst Xu et al.42 propose a triple level cell combining STT and SOT-based MTJs where the writing mechanism is achieved in two steps.Another possible solution to overcome the intrinsic limitations affecting STT- and SOT-MRAMs is to combine these two effects in a single device53. These face limitations when dealing with the miniaturisation and down-scaling of the the device dimensions to few nanometre due to the continuum assumption that the magnetisation varies smoothly with position. In particular the macrospin approach assumes uniform magnetic properties over the whole ferromagnetic layer and therefore it is constrained to uniform magnetic configurations. On the other hand, the discrete nature of the atomistic model allows to have spatially varying magnetic properties over different lattice sites, something hard for micromagnetic simulations even with the finest discretisation, enabling the description of interfaces, surface and finite size effects. These features make possible to describe the anisotropy in CoFeB/MgO MTJs more accurately, one of the most important parameter for technological applications, that arises from and is localised at the CoFeB|MgO interface or the increase of the magnetic damping at the same interface54. For these reasons, in this work we investigate the influence of the combined STT and SOT phenomena on the magnetisation switching dynamics of CoFeB/MgO-based MTJs at the atomistic level, whose details can be found in the “To develop the performance of MTJ, it is important to understand the magnetisation dynamics of the magnetic material used in devices, since the critical current density for the reversal process, operating speed and power consumption are closely related to the dynamic behaviour. This work aims to develop a computational model which combines the atomistic spin model and a spin transport model for MTJ design. The majority of MTJ models employed to study magnetic systems are generally based on the macrospin and micromagnetic formalism55, where the number in brackets is the thickness, and FL and RL refer to free and reference layer, respectively. A list of the parameters used in the simulations is presented in Table y-direction. As a consequence the spin polarisation z-axis. The system is initialised in an antiparallel state with the RL magnetisation aligned along the positive z-axis and the FL polarised along the opposite direction. We would like to point out that the realisation of SOT and STT-induced magnetisation reversal in MTJs with current perpendicular to the stack where, in absence of the HM, SOT is solely provided by Rashba effect, is not a viable solution. In fact, to have a non-zero Rashba effect the current should be applied in an in-plane configuration, as it has been reported in literature57, due to symmetry requirements. Thus this solution cannot be applied to the MTJ stack considered in the present study. To evaluate the dynamical response of the combined SOT+STT protocol we perform three investigations triggered by: (A) pure spin transfer torque, (B) pure spin orbit torque and (C) the combination of both STT and SOT as detailed in the following sections.In this work we simulate the magnetisation dynamics induced by a combination of STT and SOT in CoFeB/MgO/CoFeB MTJs by means of an atomistic spin model. Figure mentclass2pt{minimz-axis. Charge currents with different densities are perpendicularly injected into the MTJ structure. As demonstrated in Fig. xy plane before reaching the steady state in the z direction. Increasing charge current density results in higher spin transfer torque acting on the magnetisation and subsequently reduces the switching time. In the case of pure STT the system exhibits the expected dynamics with a slow rise of the precession until 58, STT-induced magnetisation dynamics is characterised by a rotation of the in-plane component and by either coherent reversal or non-collinear reversal of the out-of-plane component. The former is always observed and is caused by the STT symmetry, the latter depends on the size and magnitude of 58 for strong 58, where for large We initially investigate the effect of the transverse injected current density entclass1pt{minima58, resulting in larger critical current densities. Thus, STT-induced precessional switching sets a limitation to size scaling in a low temperature regime.We can extract the critical current density for STT switching z-axis. However, this misalignment from the in-plane direction alone cannot yield the complete reversal of the magnetisation. Pure SOT dynamics is characterised by coherent rotation of the magnetisation, even for the largest size investigated. We attribute the coherent behaviour to the symmetry of SOT that tends to favour in-plane configurations and to suppress higher excitation modes.We next investigate the effect of the in-plane current density 41. The plot of the size dependence of In the case of pure SOT, since we cannot use the switching time to define the critical current density z-component of the magnetisation as a function of 41, we can distinguish different scenarios depending on the combination of Small Intermediate Intermediate Large The following analysis of the magnitudes of the current densities shows that both conditions 1 and 4 are not efficient in terms of energy requirements. Those could be employed in high speed devices where the power consumption is not an issue. On the other hand, the scenario depicted in 3 would reduce the total switching time without increasing the energy requirements. Spin transfer torque switching assisted by spin orbit torque could prove useful for applications, in particular those where the balance between the efficiency and the power consumption is essential, such as in portable and smart devices.Having fully characterised the dynamical response to STT and SOT individually, we now study the combination of both the effects using the protocol shown in Fig t, respectively; similarly 60:i, t is the thickness of the regions the system is discretised into, e is the electron charge and 61. We compute the write energy consumption y and 35. For Ta if we assume 62 and take a HM contact of dimensions 64. Increasing The resistance of the HM contact z-axis. Conversely, increasing the magnitude of z-component of the FL magnetisation in these same figures. While for pure STT we can clearly observe the formation of a domain at the edge of the disk that propagates through the system accompanied by rotation around the z-axis, the magnetic texture becomes uniform for 46 and/or strong current densities would be required. As such, our assumption of spatially uniform current densities should not impact the results presented here.Finally we investigate the reversal mechanism resulting from the combined application of STT and SOT to the system to evaluate the differences from the individual cases. The analysis involves calculation of the lowest-frequency modes  equation of motion68:i, i. In this work we use an atomistic spin model as implemented in the open source software package i and j, i along the easy-axis i and 69 based on the work of Bowden et al.70. The second term of Eq. , we can write:11. y-axis x and z-direction. If the stack is piled along the z-direction, in first instance we can neglect the effect of The SOT contribution is introduced as an additional term to in LLG equation, following a similar approach to Slonczewski11, where the Rashba or inverse spin galvanic effect (iSGE) should be accounted for. However, due to the complexity in determining the strength of the Rashba effect, we have decided to apply the aforementioned approximation. In presence of such SOT, the solved LLG equation can be expressed explicitly as:Here, Even though this form of the LLG equation is not computationally efficient, it demonstrates that if both damping-like and field-like contributions are included they intermix. If we consider an effective field n as Eq.  where \dbcc) crystal structure with lattice constant 2.86Å. CoFeB/MgO is characterised by a large perpendicular single-ion uniaxial interfacial anisotropy with zero anisotropy in the bulk75. The CoFeB atomic layers opposite to MgO are assumed to have no particular interfacial properties and we neglect the small bulk anisotropy of CoFeB7. We use a mean field approximation that depends on the Curie temperature 75. We describe CoFeB as having an atomic spin moment 7. The CoFeB/MgO interface has a larger damping than the rest of the CoFeB, in agreement with both experimental76 and theoretical54 works. Boron is not directly included in the simulations because it is not magnetic and because tends to diffuse out of the CoFe layer during annealing. The FL is capped by a heavy metal (HM) layer, which we take to be Ta. The role of HM is to convert electrical current into spin current that flows in the adjacent ferromagnet, whilst no magnetisation dynamics is assumed occurring within the material itself. Hence, we account for the effect of HM via the spin Hall angle 79, and we do not include it in the simulations explicitly.We model CoFeB as cylindrical alloy with a body-centred cubic (50 based on the stability of the lowest frequency normal modes, which are a small perturbation of the equilibrium magnetic state. They classify these modes by the winding number w, an integer index that can be understood as the number of times the magnetisation of the disk winds around a symmetry axis perpendicular to the plane of the disk when the disk is revolved once. The amplitudes of the three lowest-frequency modes (50:x, y, i is the imaginary unit, w is the surface of the disk and dxdy is the surface element over which the integration is carried out. For the In order to characterise the reversal process we employ the method of Visscher et al.