The Ross procedure has long been seen as an optimal operation for a select few. The detractors of it highlight the issue of an additional harvesting of the pulmonary artery, subjecting the native PA to systemic pressures and the need for reintervention as reasons to avoid it. However, the PA is a living tissue and capable of adapting and remodeling to growth. We therefore review the current evidence available to discuss the indications, contraindications, harvesting techniques, and modifications in a state-of-the-art narrative review of the PA as an aortic conduit. Due to the lack of substantial well-designed randomized controlled trials (RCTs), we also highlight the areas of need to reiterate the importance of the Ross procedure as part of the surgical armamentarium. The use of the pulmonary autograft (PA) as a substitute for aortic valve and root replacement (AVR/AVRR) was initially proposed by Donald Ross, and subsequently, it has been the subject of large observational studies and randomized controlled trials (RCTs) –8.The PA is relatively easily harvested and can be inserted without significant mismatch in size with the aortic valve and leads to few early site-related adverse events, one of all being endocarditis –16. HoweDespite encouraging results, the use of the PA has been limited worldwide. This may reflect a resistance to change from the widely accepted gold standard of aortic valve replacement . However, evidence from one recent patient-level meta-analysis  stronglyp = 0.006). The effectiveness in using the PA to replace the aortic valve diseased is even more relevant if we consider that a large percentage of patients had received previous cardiac surgery and were managed with homograft aortic root replacements (42%) or underwent the Ross procedure for active endocarditis (8%). One of the most important statistics in this patient study was that the 13-year survival in recipients of the Ross procedure was uniform to that of the age- and sex-matched British general population [A growing number of studies comparing recipients of the Ross procedure and other AVR options in adults have been performed. 1 RCT  enrolledpulation . Althougn = 208) compared to those who underwent conventional mechanical AVR (n = 208)  [p = 0.19). To note, firstly, 43% of recipients of the Ross surgery had aortic insufficiency preoperatively, and secondly, reoperations in this group included any surgical or percutaneous reintervention on the aortic and/or pulmonary position. Another important statistic significantly in favor of the Ross procedure was represented by higher freedom from thromboembolic and/or major hemorrhagic complications  [Nowadays, there is a remarkable body of evidence to support the use of the Ross procedure, as it appears to offer an additional survival benefit over mechanical AVR. Indeed, more than 4 years ago, a propensity-matched cohort included 416 young and middle-aged adults with follow-up duration exceeding a mean of 14 years and reported a significant reduction in the hazard ratio (HR) for cardiac- and valve-related mortality in patients who received the Ross procedure ( = 0.03) . This st< 0.001) .p = 0.018) whereas 30-day mortality was similar  [Another study from Buratto included 1928 patients undergoing isolated mechanical AVR and 392 with the Ross procedure, which were evaluated with a risk-adjusted analysis and with follow-up duration exceeding 25 years . Among 2 > 0.99) . Importa > 0.99) .The Ross procedure is a valuable option to treat both congenital and acquired disease of aortic valve and the left ventricular outflow tract. The pulmonary autograft can be implanted using 2 methods: the subcoronary implantation or free-end technique , 3 and tIn the miniroot technique, the pulmonary valve is transposed into the aortic position with its pulmonary trunk so that the PA is withdrawn from the infundibulum of the right ventricle, scrupulously respecting its morphology. The pulmonary infundibulum consists mostly of the conal or infundibular septum, which separates the pulmonary valve from the aortic and tricuspid valves. We still recognize a second part which is the anterior extension or division of the trabecula septomarginalis while the third, smaller part is a superior extension of the trabecular septum.The miniroot technique is the most used procedure and included complete preservation of pulmonary valve and pulmonary trunk with the need to coronary artery reimplantation . The insDue to the different geometry and morphostructure of the aortic and pulmonary root , subcoronary implantation can be technically challenging, especially in patients with aortic insufficiency or bicuspid/unicuspid aortic valves. In addition, this approach is not possible when Ross's operation is the preferred operation for the congenital patients in the phase of pediatric growth. For this reason, the free-standing aortic root replacement (miniroot technique) has been the most used technique , 57.p < 0.001) and the sinotubular junction , but not at the level of the annulus  at mean follow-up of 5.1 years [The major concern with the use of pulmonary autograft used as miniroot implant is the increased risk of late pulmonary autograft dilatation due to the exposure of the full root to systemic pressures . This di.1 years , 11, 58.To overcome the PA expansion and avoid potential risk of reoperation for autograft failure, a number of technical modifications have been proposed, but there are currently no standard recommendations for their use. Three different approaches are customary in higher volume center, but data that support the effectiveness on the long-term results of these techniques are lacking. The approach of inclusion of the PA within the patient's aortic root allows autograft to have a protection against the adverse effect of systemic pressure over time , 57. AltHowever, concerns remain that any observational study is not without the potential risk of drawing skepticism for a surgical indication. In this regard, confusion can be generated in a form of selection bias that is independent of whether the elements provided in the study are aggregated data, propensity-matched, or processed with a multivariable analysis. It is logical to hypothesize that although the efficacy and safety of the Ross procedure can be partly explained by careful patient selection, it is more likely attributable to intrinsic characteristics of the PA that make it decidedly unique for the biomimetic characteristic of living tissue thus highlighting its hemodynamics and biological-adaptive behavior. Despite convergent evidence demonstrating superiority in long-term results with the use of the Ross procedure over the usage of other operation for AVR- including data from a randomized controlled trial , a systeThe Achilles heel of the PA replacing the AVR is the expansion of the vessel wall of the conduit after transposition into the left circulatory system where it is subjected to a higher arterial pressure, with a consequent decrease in the competence of the pulmonary valve and consequent increase in valve regurgitation , 33, 58.In young or middle-aged patients, the evidence has established that the Ross procedure offers a lasting solution, particularly in the female population , emphasiAlthough advances in prosthetic manufacturing have allowed the use of new-generation mechanical valves, it is potentially less thrombogenic, and lower international normalized ratio targets are required; however, self-monitoring of oral anticoagulants is still necessary , 70. TheFrom the results provided in the population of patients aged between 50 and 60 years, it is evident that the Ross procedure finds its optimal indication in people who, due to the absence of obvious comorbidities, are certainly more likely to benefit from the use of the pulmonary autograft. These patients had specific demographic, morphological characteristics and the absence of previous pathologies in their clinical history. In this respect, they had an expected life expectancy of 15 years, an active lifestyle, favorable anatomy, the absence of other major concomitant heart disease, and poor comorbidity.Contraindications to the use of the Ross procedure are preoperative aortic regurgitation, an aortic ring of ≥27 mm, and pulmonary size mismatch , 15, 59.What are then the modern indications for the use of the Ross procedure for AVD, and what do the recent studies add to our knowledge? We believe that the use of PA should be considered when planning any aortic valve and root surgery, given its characteristic of living tissue that corresponds to somatic growth, low risk of dimensional mismatch, versatility in achieving efficacy and safety for any type of operation on the aortic valve and on the aortic root, its perioperative safety, comparable to conventional mechanical and biological valves, and its late duration as a valve substitute, now clearly demonstrated, superior to traditionally used prostheses. Even the presence of a pathological bicuspid aortic valve today cannot be considered a contraindication. The study by Poh et al.  reportedWithout doubt, the option for Ross procedure, although performed with an external reinforcement, is indicated in young patients with BAV and an aorta with a maximum diameter of 40 mm who do not have a hereditary pathology of the connective tissue or of the aorta. As reported below, the condition for the choice of the PA as substitute is conditioned by the use of surgical techniques that allow modified Ross procedures with the use of various external supports aimed at stabilizing the sinotubular junction and thus minimizing the risk of late aortic insufficiency and PA dysfunction. Overall, the choice of PA and surgical strategy for Ross procedure is based in part on evidence of comorbidities, ethical consideration, skill, and experience. To optimize outcomes in the population receiving the use of the Ross procedure and avoid the autograft failure, some considerations are needed on the knowledge of coexisting conditions that limit life expectancy to <15 years such as chronic dialysis in renal failure or radiation-induced valve disease. Also, some autoimmune diseases, for example, lupus erythematosus or rheumatoid arthritis, can raise concerns about the functional duration of the PA. Unquestionably, today, we have gained adequate knowledge of when to choose the Ross procedure and when it is deemed appropriate for the right patient and how to perform it perfectly. For example, the data provided by the current literature do not identify the use of PA as an appropriate choice in patients with rheumatic disease . On the The concern related to the use of pulmonary autograft in aortic position is the potential long-term failure of the 2 valves, aortic and pulmonary. For many surgeons, this represents the Achilles heel of the Ross procedure and can certainly be deterrent to its widespread use. In fact, a patient who initially exhibits single-valve disease may subsequently require repeat surgery to treat two diseased valves. As noted by Stulak et al. , when chFor this reason, the problem of the longevity of the biological substitute is a priority, trying to give reasonable and favorable solutions to avoid adverse complications. Primary leaflet failure and dilation of the annulus, Valsalva sinuses, or sinotubular junction may occur in the patient who has an indication for a second operation after a Ross procedure for pulmonary autograft failure , 78. SevAssociated with autograft failure, there is a risk of pulmonary homograft dysfunction which is used to reconstruct the ventricular-pulmonary outflow tract. The failure of pulmonary homograft is mainly manifested by the onset of an increasing valvular and supravalvular pulmonary stenosis which occurs more frequently at the level of the distal anastomosis and appears to be linked to an inflammatory activity . Insteadn = 212) included in the Toronto series [Evidence based on follow-up of patients  and 3.95 ± 0.64 mm (12.1%) in the nonreinforced group at the Valsalva sinus level  [We evaluated the effect of reinforced and nonreinforced Ross procedure on long-term echocardiographic outcomes in 66 patients who received a PA for aortic valve surgery. The results are clearly delineated in = 0.001) .The experimental project Ross is a European alliance of investigators who aim to provide the basis for studying how to prevent the expansion of pulmonary autograft used in aortic valve surgery. The project was initiated in January 2011 and is achieved with collaboration of the Department of Cardiac Surgery of Centre Cardiologique du Nord and la Pitie Salpetriere Hospital. The first results were presented and discussed in June 2013 at the annual meeting of the Heart Valve Society in Venice, Italy –89.The primary objective of Ross's experimental project was to combine the individual data of the experimental animal model by comparing nonreinforced and reinforced pulmonary autograft as ideal substitutes for aortic valve surgery. Using an experimental model of growing sheep based on the simulation of the Ross operation, the experimental project Ross has estimated that the analysis of the results would have detected significant differences in the pulmonary autograft morphostructure at 6-month follow-up. The pulmonary autograft was inserted in the descending aorta while the right ventricle outflow tract was reconstructed with a fresh homograft from another lamb of the same age and weight or native pericardial neoconduit , 90.To date, in all the reported studies, the comparison between reinforced and nonreinforced pulmonary autograft did not consider the somatic growth variable which is fundamental when the Ross operation is performed during the patient's growing age. Echocardiographic findings were largely undersized to assess differences in clinical events. Although the rate of superior expansion of the nonreinforced pulmonary autograft was established, there was no available evidence on any potential clinical benefit for the late outcomes.p = .4) but with the exponential increase in the expansion of pulmonary autograft (42%) in reinforced Ross with bioresorbable vascular scaffold (BVS/polydioxanone) and with semibioresorbable vascular scaffold, which combine the polydioxanone (PDS) and expanded polytetrafluoroethylene (e-PTFE) . These are depicted in Figures The results were presented and discussed in June 2013 at the annual meeting of the Heart Valve Society in Venice, Italy . At 6 moThe use of pulmonary autograft in aortic valve surgery preserves, as living tissue, the structural and functional unity of the neoaortic root favoring better long-term clinical outcomes. The PA offers a continuous mediated living tissue activity that cannot be ensured by any other valve substitute with nonliving tissue characteristics. Although homografts have shown long-term viability, from a mechanical standpoint, they have proven to be inferior to pulmonary autografts –6, 91. VThe increased distensibility of viable pulmonary autograft is associated with recognized adaptive remodeling when the PA is transposed to an aortic position under systemic loading, thus mimicking the highly sophisticated anatomy and function of the native aortic root –91. The We have enhanced the remodeling capacity of the PA and at the same time reduced the negative effect of systemic pressure on the vessel wall using a semibioresorbable vascular scaffold that combined the polydioxanone to expanded polytetrafluoroethylene (BVS/PDS-e-PTFE) , 93, 94.p < 0.05). These findings were coupled with a significant reduction in apoptosis in the reinforced PA supporting the idea of an active remodeling process in this group  [We have shown that the interaction between temporary bioresorbable reinforcement and pulmonary autograft has orchestrated a complex vascular remodeling process based on a balance between inflammation and production of extracellular matrix resulting after biomaterial resorption, in a “neovessel” which has characteristics similar to the aorta but is still biologically alive and capable of growing. The use of resorbable polyester was also associated with higher production of new extracellular matrix that was mainly characterized by a higher content of elastin fiber in the PA, as well as by a more compact organization of collagen fibers in the elastic zone of the vessel. Interestingly, the metalloprotease MMP-9 was found to be overexpressed indicating an ongoing matrix remodeling process. In parallel, cell proliferation was found to be increased in this group as testified by the significantly higher percentage of Ki67-positive cells  .These findings offered a plausible biologic and biomechanical explanation to the observed advantage in clinical outcomes. A biocompatible reinforcement of the PA would therefore allow induction of an in vivo creation of a PA with morphostructural characteristics that allow improved tolerance to the hemodynamic load of the arterial system and to guarantee a harmonic increase in size during somatic growth , 102.To further guide the choice of valve selection, we have formulated an algorithm to guide clinicians based on the evidence available as shown in Although the impact of the Ross procedure on long-term survival has been proven in young and middle-aged adults with significant impact when matching that of the age- and sex-matched general population, large propensity-matched observational studies and meta-analysis failed to cement its place as the ideal aortic valve substitute. The use of pulmonary autograft in aortic valve surgery has provided solid evidence of better long-term freedom from death and valvular complications compared to other conventional aortic valve prostheses used for AVR. Evidence has suggested that the Ross procedure provides better results when performed in centers of excellence where high volumes of aortic root surgery are performed. Much has been learned in the past 50 years of the Ross operation practice about the use and behavior of pulmonary autograft transposed in aortic position, but we are still in the process of iterative learning with more research needed. Unfortunately, there is a lack of evidence due to the absence of well-designed RCTs comparing the Ross procedure to mechanical or biological valves. Furthermore, greater confirmatory results can be provided by randomized clinical trials with the use of biocompatible external reinforcements able to stimulate, guide, and improve the natural processes of biological remodeling of the graft and reaction to foreign materials while respecting tissue growth. These studies could be a turning point in solving some of the drawbacks of the Ross procedure. Nevertheless, a significant impulse can be given by the biomechanical studies performed on the heart valves and on the extracellular matrix with the application of finite element analysis –112.


Industrial pipework maintenance inspection can be automated through machine vision-based effusion monitoring. However, colorless effusions such as water can be difficult to detect in a complex industrial environment due to weak illumination and poor visibility of the background. This paper deploys the reflective characteristics of effusion and its lower temperature compared to the environment in order to develop an automatic inspection system for power plant pipeworks’ maintenance. Such a system is aimed at detecting the colorless fluid effusion based on dual source images and a contour features algorithm. In this respect, a visible light source unit highlights the reflective features of the effusion edge. Meanwhile, high-definition images of the potential effusion are acquired under both visible and infrared lights. A customized image processing procedure extracts the potential effusion features from the infrared image to retrieve the region of interest for segmentation purposes and transfer such information to the visible light image to determine the effusion contour. Finally, a decision-making support tool based on the image contour closure is enabled for classification purposes. The implementation of the proposed system is tested on a real industrial environment. Experimental results show a classification accuracy up to 99%, demonstrating excellent suitability in meeting industrial requirements. Pipes are currently considered to be the safest and most cost-effective means of fluid conveyance in factories, especially for dangerous flammable fluids. However, liquid leakage is an inevitable problem in pipeline systems as a result of wear, corrosion, and damage of pipeline infrastructure . Liquid As a consequence, pipeline leakage is very likely to have an impact on the availability and productivity of the plants resulting in economic losses , health In such a context, this paper investigates a framework for an implementation procedure and experimental validation to automate maintenance tasks by identifying pipes defects such as leaking through a combined use of multi spectra imaging. This approach aims at detecting pipe leakages through the automatic recognition and characterization of water effusions utilizing a tailored hardware and image processing.The remainder of this paper is organized as follows. A literature review is carried out exploring state of the art of the main technologies adopted in leaking detection and maintenance automation along with the scientific and technological challenges. The experimental setup utilized to collect digital images of water effusion in a powerplant environment is reported. A procedure for the image processing is described with the aim of extracting significant features of the effusion contour. A classification-based decision-making support system is then illustrated. The proposed system is finally implemented to inspect a number of real power plant pieces of equipment, and the results are discussed in terms of accuracy and industrial suitability.The current literature and industrial practice surveys show that most of the advanced contributions are concentrated in three key enabling technologies, namely sensor monitoring, machine learning, and computer vision.As regards the use of sensors and sensor networks in leakage detection, the variety of devices ranges from water meters for flow rate measurement  to pressIntelligent decision-making systems on pipe leakage assessment has been successfully carried out by applying machine learning algorithms. In this respect, Abdulla and Herzallah  used watRecent developments in visual detection systems involve the use of high-definition under fully visible light and infrared for the detection of leakage. In order to meet the needs of industry 4.0, Durga Prasad Penumuru et al. proposed an automatic material identification methodology based on machine vision and machine learning technology, which can successfully identify four different materials on the surface of a machine in the actual industry . Je-KangAs regards computer vision, the primary problem to address is the gap between high level concepts and low-level visual cues. In this context, illumination is one of the most effective factors in visual detection . High-deInfrared imaging is often used for effusion detection since the temperature of effusion is usually lower than that of the surrounding environment. Relevant research contributions, ranging from civil to industrial engineering applications, involve the use of a single infrared camera to detect the leakage areas in buildings due to infiltration deploying the temperature difference . Terumi Waterwall boiler tube leakage is the most frequent cause of failure in thermal power plants . One speIn order to characterize the operating conditions, an industrial survey of temperature measurements was carried out at HUANENG power plant, Shantou, China, with the aim of defining the temperature ranges of the equipment to be inspected. The visual detection technology survey suggests that the two major characteristics of colorless effusion, namely the reflective characteristic under full lighting and the low temperature characteristics in the environment, can be deployed by existing technology for detection purposes. However, the design and implementation of a comprehensive detection system results difficult in complex industrial environments. With reference to a generic thermal power plant, on the one hand, the site environment is complex and characterized by crisscrossing pipelines, with significant environmental interference problems occurring during maintenance inspection such as humidity and cold areas , visible light camera, two flash lights, an infrared camera, and an industrial personal computer (IPC). The visible light camera and infrared camera can capture high-definition images in two different light spectra at the same time . In ordeThe wireless transmission unit, made of an access point and a wireless network bridge, is responsible for establishing the wireless transmission channel from image acquisition unit to the data processing unit. The background processing unit includes hardware devices such as computing servers, computer monitor, and intelligent algorithms contained therein. The unit is used for image processing, data storage, and decision-making.In order to overcome the interference and occlusions caused by complex background, this paper proposes an automatic inspection system for colorless effusion contour detection method based on the sequential analysis of multi-source images. The framework flow chart of the proposed system is reported in The first step is the image acquisition, which consists in the simultaneous generation of three digital images, respectively consisting in an infrared image and two visible light images using a left and a right light source separately.The second step involves the infrared image processing. In this respect, according to the low temperature characteristics of the effusion, the potential effusion is identified and its position is determined by the definition of a region of interest (ROI).The third step is the visible light image processing in order to define the effusion contour. Then in the fourth step, the ‘top hat’ algorithm  is used To summarize, the overall process first uses infrared imaging to locate a potential effusion and then conducts a secondary assessment for the final effusion characterization. In this way, the leakage identification efficiency can be improved, and environmental interference can be reduced at the same time, thus improving the recognition accuracy.With reference to The temperature inside the power plant furnace reaches more than 1700 °C , requiriAlthough a colorless effusion has a lower temperature compared to the environment, the recognition of fluid accumulation within an infrared image could be potentially affected by water condensation phenomena on pipes, equipment, and floor. To overcome this issue, this paper combines the infrared imaging with visible light imaging. In this context, an infrared image is used to capture the potential effusion area. Subsequently, two visible light images are used to verify and assess the actual presence of the leakage/effusion as a classification task.The specific process is as follows: for a single detection task, the system will acquire a set of three digital images . Initially the images need to be aligned to compensate the offset between the two cameras. Subsequently, an infrared image pre-processing is performed and aimed at the ROI extraction  transform is an image processing procedure aimed at enhancing the areas with higher brightness in the image on the premise of maintaining the brightness distribution of the image . ConversIn this paper, both WHat and BHat transforms have been configured with a 35 × 35 pixel neighborhood size and a diamond-shaped morphological structuring element  FollowinDue to the light sources’ configuration and the water mass position, a single visible light source image can produce some discontinuities in the contour definition  The input image, shown in (2) The gradient intensity and direction are computed for each pixel as per Equations (1) and (2).(3) An edge thinning procedure based on non-maximum suppression is applied. In this respect, a generic pixel is considered an edge point if its gradient intensity is the largest compared to the intensities of the two adjacent pixels along the positive and negative gradient directions. Otherwise, it is discarded.(4) Two thresholds, namely high and low threshold, respectively, are defined to assess the strength of the edges. In order to be classified as a strong edge, the gradient intensity of a pixel must be higher than the high threshold. A gradient intensity value included between the two thresholds identifies a pixel as weak edge. A pixel is eventually discarded if its gradient intensity is lower than the low threshold.(5) According to the above four steps, pixels classified as strong edges are identified as edges, and then weak edge pixels need to be further confirmed. In this respect, the weak edge pixels connections are explored within an eight-neighborhood space. If in such neighborhood there is at least a strong edge pixel, the weak edge point can be confirmed as a real edge. In this way, it is possible to completely map the effusion edges, as shown in After having detected the complete effusion contour, the Moore-neighbor tracing (MNT) algorithm was used to assess its closure. MNT finds the next contour pixel using eight connected chain codes with a clockwise sequence scanning ,22. StarWith reference to 2. A total number of 461 effusions were placed for the experimental program.The AGV moves along the inspection path and reaches an inspection point. Here, the system performs the image acquisition and processing algorithm described in The inspection task in these experimental tests consists in a classification problem in correspondence to each inspection point. If the system correctly detects an effusion, the test result is recorded as a true positive. Alternatively, if the system detects an effusion where there is none, the test result is recorded as a false positive. Conversely, if the system does not detect any effusion where actually there is none, the test result is reported as a true negative, whereas a missed effusion detection is reported as a false negative.Following the classification task, the AGV moves to the next inspection point. Each inspection point was tested from 34 to 54 times to increase the statistical reliability of the experimental tests. In this way, a total number of 1367 tests were carried out.Out of 1367 experimental tests, 12 samples were misclassified, yielding a total detection error rate of 0.88%. Six false positives instances were recorded: false detection on 2_5 due to the protection cover; false detection (twice) on 5_4 due to flashlight failure; false detection on 6_3 due to strong light interference; and false detection (twice) on 4_2 due to light changes.The misclassification can be attributed to the system sensitivity which does not allow for the detection of small effusions which create water masses too small or too scattered to be detected. This issue can be tackled by acquiring higher resolution images and by performing periodical inspection rounds, allowing the water to form a detectable effusion. Analogously, effusions larger than the camera field of view are likely to be misclassified and, hence, not detected. In this case, specific wide-angle lenses should be utilized and the image processing procedure should be re-calibrated.Moreover, the cluttering effect of complex equipment (such as pipework), although minimized, still badly affects the classification performances. In this respect, a more agile image acquisition system should be developed in order to acquire digital images in difficult-to-reach areas. Additionally, a denser inspection path could be designed by increasing the number of inspection points.However, it is worth to highlight that the experimental setup designed in this paper, including the size of the tested effusions, has been defined based on the industrial experience, guidelines and requirements provided by the industrial partner.The experimental results show that the proposed method can deploy the reflective and low temperature characteristics of water effusion for an accurate detection system which has the potential to boost the automation of the maintenance operations. In this paper, the potential water effusion area is preliminary identified via infrared imaging and further assessed by the subsequent method of water effusion identification based on the reflective and shape characteristics of the water mass in the corresponding area. Such an approach can effectively identify leakage water in the actual environment of a thermal power plant.The overall classification success rate of the method proposed in this paper is above 99%. The experimental results show excellent suitability for the implementation in complex environmental conditions to detect the leakage water of different degrees. Other advantages include the detection capabilities of early micro-leakage water phenomena, strong environmental adaptability, and wide application range.Future research efforts should be focused on strengthening the optimal path planning for AGV inspection path optimization, as well as the optimal design of environmental inspection points for the whole plant . In this respect, equipment endowed with multiple pipes scanning will be divided into central and secondary pipes for which tailored customized inspection paths should be defined with different schedules to optimize the system coverage along with the inspection and maintenance costs.


There are multifarious stationary vehicles in urban driving environments. Autonomous vehicles need to make appropriate overtaking maneuver decisions to navigate through the stationary vehicles. In literature, overtaking maneuver decision problems have been addressed in the perspective of either discretionary lane-change or parked vehicle classification. While the former approaches are prone to generating undesired overtaking maneuvers in urban traffic scenarios, the latter approaches induce deadlock situations behind a stationary vehicle which is not distinctly classified as a parked vehicle. To overcome the limitations, we analyzed the significant decision factors in the traffic scenes and designed a Deep Neural Network (DNN) model to make human-like overtaking maneuver decisions. The significant traffic-related and intention-related decision factors were harmoniously extracted in the traffic scene interpretation process and were utilized as the inputs of the model to generate overtaking maneuver decisions in the same manner with the human driver. The overall validation results convinced that the extracted decision factors contributed to increasing the learning performance of the model, and consequently, the proposed decision-making system enabled the autonomous vehicles to generate more human-like overtaking maneuver decisions in various urban traffic scenarios. With the advancement of autonomous driving technologies, various decision-making systems for driving maneuvers have been developed to navigate in dynamic environments ,2,3,4,5.In literature, overtaking decision problems have been addressed mainly from the perspective of lane-change decision-making. A lane-change has been generally considered as a maneuver to overtake a slow-moving vehicle ahead in multi-lane environments  suggesteRecently, some previous studies have specialized in decision problems of overtaking stationary vehicles  suggesteTo overcome the limitations of the previous studies, this paper proposes a learning-based decision-making system for overtaking stationary vehicles. This work aims to design and validate a decision-making system to generate human-like overtaking decisions in various urban traffic scenarios with stationary vehicles. The entire scheme of the proposed system is established based on Deep Neural Network (DNN) model in order to learn the decision strategy in complex situations from human demonstration data. In this scheme, the input of the DNN model needs to be carefully determined because it restricts how the model can interpret the perceived situation; otherwise, a large amount of data is required to automatically weigh the important factors from the raw input data. In the input selection process, we analyzed and selected significant factors which integrally involve the traffic-related and intention-related factors. This consequently enhanced the performance of the decision-making system to cope with various types of stationary vehicles on roads. To validate the performance, the proposed system was evaluated in various scenarios by comparing the results with the previous approaches.This paper has been organized into the following contents. A decision-making system for overtaking maneuvers aims to generate behavioral decisions on whether to overtake the preceding vehicle in the driving situation. To fulfill this goal, the proposed decision-making system involves two sequential processes as shown in First, the driving scene needs to be interpreted in terms of the desirability of the overtaking maneuver. Decision factors that contain the information that should be utilized in making decisions are extracted from the entire perceived data in this process. Whereas some of the factors can be measured directly from in-vehicle sensors, there are factors that require additional calculation to estimate from the measurements. Thus, this scene interpretation process is responsible for post-processing the measured sensor data and estimating the values of the decision factors.Second, a Deep Neural Network model is used to make a maneuver decision whether or not to overtake the preceding vehicle from the extracted factors. The DNN model is trained by demonstration data, which contains actual maneuver decision results of an expert driver in various situations. Since a DNN can model the nonlinear relationships between the input and the output, it is generally considered as a simple and effective method suitable for learning the complex policies of human drivers. The trained DNN model consequently predicts the maneuver decision of the driver in a certain situation, and the prediction result is used as an overtaking maneuver decision of the entire system.Human drivers make maneuver decisions based on how they interpret the scene from what they perceive. To imitate the decision-making strategy of a driver by learning-based methods, it is important to figure out which factors in the perceived scene dominantly influence the driver’s decision. The importance of each factor can be estimated by analyzing the effect of the factor on the performance of the learning model. To analyze and select the optimal decision factors from all relevant factors, the series of processes is conducted as follows. First, the general characteristics of the human drivers’ overtaking maneuver decisions are examined with the domain knowledge in driving. Second, all relevant factors are listed up and categorized based on the general characteristics. Third, the dominant factors underlying a particular driver’s decisions are obtained by analyzing the importance of each factor in the decisions. Consequently, the obtained factors are considered as important factors in the scene interpretation, and thereby used as the input of the decision-making model.There are three general characteristics of drivers’ decisions to overtake a stationary vehicle. First, when it is possible to clearly identify the reason why the vehicle is stopped on the road, the decision is made without much concern . For exaSecond, when it is difficult to correctly infer the intention of the preceding vehicle as shown in Third, the driving conditions of the ego vehicle may affect the decisions of the drivers. For example, if a driver discovers a stationary vehicle in the distance while driving at high speeds, it is sometimes preferred to steer slightly to the left to overtake the vehicle before getting closer to it. Additionally, the decision may change over time in the same situation. The increase in the time duration of waiting behind a stationary vehicle might induce a desire to escape from the situation, and the degree of tolerance depends on the aggressiveness of drivers . These tTo sum up, drivers commonly exhibit the characteristic of making situation-awareness decisions. This means that decisions depend not only on the intention of the preceding vehicle but also on traffic-related factors such as the states of the surrounding vehicles and the ego vehicle itself. The characteristics mentioned above need to be taken into account in selecting the decision factors to be used as the input of the decision-making model. Therefore, they are used as the basis for gathering relevant factors in the overtaking maneuver decisions.To collect relevant factors in a systematic manner, a categorization of factors is implemented in advance. The principle of categorization is inspired by the general characteristics of drivers mentioned previously. Thus, it aims to synthetically involve the intention-related factors and the traffic-related factors. Factors are finally divided into three groups as shown in Category A. Preceding vehicle1lateral distance to the right boundary of the road (’closeness to the curb’)2lateral distance to the left boundary of the road (’room for overtaking’)3duration of time it has been detected as stationary (’moving confidence’)4velocity5acceleration6yaw angle7yaw rate8lane occupancy rate9object width/lengthCategory B. Surrounding vehicles-Nearest vehicle1position2velocity3acceleration-Vehicles on surrounding lanes (Traffic flow)1number of vehicles2free space rate3spatial gaps4time/space mean speedCategory C. Ego vehicle1relative speed2relative distance3time-to-collision/time-headway (’collision risk’)4waiting time durationAll relevant factors are listed based on the general characteristics of human drivers. Even though these factors are expected to be relevant in general decisions, there might be less relevant or less influential factors in a certain driver’s decisions. It is necessary to select influential factors for the decision-making model to learn the driver’s policy efficiently. Therefore, each factor should be analyzed to confirm that it is beneficial to be used in the decision-making model.In this work, we adopt a sequential backward feature selection (SBS) method  to analyTo train and evaluate the proposed system, we acquired the demonstration data by using the autonomous vehicle A1 of ACE Lab, Hanyang University. The sensor configuration of A1 is as shown in To acquire the demonstration data, the test vehicle was driven by a certain driver, who is assumed to be an expert driver. The target route for the data acquisition is selected as shown in The collected driving data contains recorded scenes that are snapshots taken every 50 ms, which is the fixed sampling period. Each scene includes the perceived data and the decision signal of the driver. A scenario consists of a series of scenes, and the scenario can be classified into two categories: overtaking scenario and non-overtaking scenario. Although every scenario begins by detecting a preceding vehicle that is stationary regardless of the scenario category, how a scenario ends differs depending on the category. The not-overtaking scenarios usually end when the preceding vehicle starts moving and keep accelerating. On the other hand, the end of the overtaking scenarios is the moment when the driver begins executing the overtaking action; the execution procedure of the overtaking maneuver is not considered in this work.The summary of the acquired dataset is shown in The SBS method-based factor importance analysis method, illustrated in the previous section, is applied to find the dominant factors in learning a driver’s overtaking maneuver decisions. The results are shown in First, the most important factor in the decision-making model is the lateral position of the preceding vehicle which generally represents the closeness to the curb of the road. As closeness is a powerful feature mainly used in parking classification in literature, it gives substantial information about the intention of the preceding vehicle. Second, the waiting time of the ego vehicle in a given situation is selected as the second most important factor. This means that even if the situation is exactly the same, the decision of the driver may vary over time. If this factor is applied in the decision-making model, it is expected to be able to represent the driver’s individual characteristics such as aggressiveness or patience when driving. Third, the time-mean speed and the number of vehicles is selected as primary factors beyond many other factors in Category B. Since other additional factors such as space mean speed does not visibly improve the performance in the results, these two factors have been proven to be sufficient to interpret the surrounding traffic situation.Overall, the analysis results show that the intention-related factors in Category A and traffic-related factors in B or C are both influential in decision-making; thereby, they should collectively be extracted in the scene interpretation. According to the results, the eight most important factors are determined as the optimal input set of the proposed model in learning the driver’s decisions as shown in The process of abstracting the factors from the perceived sensor data of the autonomous vehicle is carried out in the scene interpretation module. Details on how to calculate and extract factors in this module are given below; some of the factors that can be measured directly from in-vehicle sensors are excluded in the explanation.The time-mean speed and the number of vehicles in the neighboring lane are simply obtained by averaging the speed and counting the vehicles in the region of interest (ROI) on the left lane as shown in There are two factors that contain temporal information: the waiting time of the ego vehicle and the moving confidence of the preceding vehicle. The former simply calculates the duration since the preceding vehicle in the ROI is detected as a stationary vehicle. This term only increases over time and does not stop or decrease in a scenario. On the other hand, the latter calculates the duration of which the preceding vehicle has been stationary. This term increases if the vehicle remains stationary, but may also drop to zero in the middle of the scenario if the vehicle begins moving. The difference between the two factors becomes obvious in the stop-and-go situations; the latter term would fluctuate whereas the former would keep increasing.The proposed model is trained by the acquired dataset. The input contains the data that is extracted from the recorded dataset by the scene interpretation module, and the output labels for training are the recorded binary decision signals of the driver. A cross-validation method has been used to prevent the overfitting problem of the trained model. For cross-validation, the training dataset is split into four parts so that three of them are used as the actual training set and the remaining one is used as the validation set. Then by repeatedly rotating the validation part four times, we are able to validate the model efficiently with a small amount of the dataset.The performance when learning the complex relationship between the input and the output in the DNN model depends on the design of the hidden layers of the model. It is important to optimize the parameters of the hidden layers to fit into the particular problem to be solved, and this is conducted with various hyper-parameter optimization methods. In this research, hyper-parameters are optimized in the validation through the grid search algorithm. The results of the hyper-parameter optimization are shown in We adopted several evaluation metrics which are the representative metrics for evaluating the prediction accuracy of the trained model: Precision, Recall, and F1 Score. The practical meanings of the metrics in this maneuver decision-making problem are explained in The evaluation of the proposed model and the other models was executed on the entire test dataset, of which acquisition environment was introduced in the previous section. The evaluation results are shown in Since the three metrics evaluated the prediction accuracy at every scene, of which period is 50 ms, they showed the scene-wise performance of the learning model. To additively evaluate the scenario-wise performance, we defined a new metric which is Success Rate. As shown in The success rate of the proposed DNN model was 13–15% higher than the other DNN models, and 5–9% higher than other training models with the proposed factors. The higher success rate in the test datasets proved that the proposed model as a decision-making system was able to reflect the driver’s maneuver decisions. In fact, the test dataset includes various situations where the preceding vehicle is stationary for an uncertain reason. Thus, the proposed model for overtaking maneuvers was proven to be capable of handling uncertain situations in a similar way to the driver.We further illustrate the decision-making results of the proposed system in some specific scenarios. The scenarios include various traffic situations. Through the presented scenarios, we aimed to analyze the influence of the selected decision factors and the ability of the proposed model.We evaluated the proposed model in a non-overtaking scenario at first. The proposed model did not make an overtaking decision within the whole scenario, as shown in We evaluated the model in an overtaking scenario. The light traffic scenario in The third scenario also involves a preceding vehicle stopped in the middle of the lane. The difference of this scenario from the previous one lies in the traffic flow of the left lane. As in This paper addressed the limitations of the previous approaches for overtaking maneuver decision-making systems, which have highly depended either on spatial gap-oriented lane-change decisions in multi-lane environments or parking classification of stationary vehicles. The paper presented a DNN-based decision-making system that can be utilized in generalized situations by learning the strategy of a human driver to navigate complex driving situations. The entire system consists of two modules: Scene interpretation and maneuver decision-making module. In the scene interpretation module, the dominant decision factors for the driver’s decisions, which integrally contain intention-related and traffic-related information, are extracted. The selected factors are used as the input of the DNN-model, and the model is trained by the driver’s demonstration data. The trained model is expected to make human-like overtaking maneuver decisions in generalized situations, including the situation where the preceding vehicle is stationary with an uncertain intention.The training results were analyzed and compared with the trained models representing previous approaches. The learning performance of the proposed DNN model was higher in all metrics than not only the DNN models trained by either intention-related or traffic-related decision factors but also other learning models utilizing the proposed decision factors. It showed that the proposed DNN model is more suitable to predict the overtaking maneuver decisions of the driver. Furthermore, we analyzed the decision-making results of the system in various traffic scenarios for evaluation. The experimental results in the presented scenarios verified that the proposed model decides to overtake a stationary vehicle by considering not only the intention of the preceding vehicle but also the traffic situations from the perceived scene. Overall, the proposed system showed 97.5% of the successful decision-making results in the entire scenarios. The results demonstrate convincingly that the proposed decision factors and the trained model contributed to making more human-like overtaking maneuver decisions in various scenarios than the previous approaches.Although the proposed model showed the improvement of the decision-making performance in uncertain situations compared to the previous approaches, there remains the necessity of further works. By acquiring more data, the designed model should be trained and validated for more reliability. Furthermore, by utilizing the demonstration data from multiple drivers, the research should involve evaluating the capability of the proposed model to reflect the characteristics of the individual drivers.


In view of the fact that vibration signals of rolling bearings are much contaminated by noise in the early failure period, this paper presents a new denoising SVD-VMD method by combining singular value decomposition (SVD) and variational mode decomposition (VMD). SVD is used to determine the structure of the underlying model, which is referred to as signal and noise subspaces, and VMD is used to decompose the original signal into several band-limited modes. Then the effective components are selected from these modes to reconstruct the denoised signal according to the difference spectrum (DS) of singular values and kurtosis values. Simulated signals and experimental signals of roller bearing faults have been analyzed using this proposed method and compared with SVD-DS. The results demonstrate that the proposed method can effectively retain the useful signals and denoise the bearing signals in extremely noisy backgrounds. As a basic supporting component, rolling element bearings are widely used in rotating mechanical systems, and play an important role in a variety of industries. As a matter of fact, more than 50% of rotating machinery failures are related to bearing faults ,2. ThereThere are many signal decomposition methods proposed for signal denoising, such as wavelet transform (WT), empirical mode decomposition (EMD), variational mode decomposition (VMD), and so on. WT can deal with signals mixed with non-stationary noise signals by decomposing them into non-overlapping frequency bands, but its accuracy depends excessively on the selection of wavelet basis function and the decomposition scale ,15. EMD,(1)SVD and difference spectrum (DS) of singular value are proposed to determine the number of intrinsic mode functions (IMFs) of VMD;(2)The effective order of SVD and the kurtosis of VMD are used to select the IMFs of VMD, and non-stationary signals are denoised by reconstructing the selected components of VMD;(3)The effectiveness and performance of the proposed method is verified using simulation bearing data and real experimental bearing data. These results are compared with those from SVD-DS.Singular value decomposition (SVD) is a powerful representation which can decompose a matrix into three component-matrices, exposing many of the useful and interesting properties of the original matrix. SVD can be used to denoise signals  as a kinThe rest of this paper is arranged as follows. In Singular value decomposition (SVD) has attracted a lot of attention because of its ability of noise filtering, disturbance in-sensitivity and high-resolution spectral factorization . As poinA] of dimension p × q, there exists a p × p orthogonal matrix [U], a q × q orthogonal matrix [V] and a p × q diagonal matrix [Σ] , so that the following decomposition holds:U and V consist of the left and right singular vectors of A.For a real matrix [k of A. if we choose a number k and set SVD reveals useful information about A and such a SVD is called truncated SVD. The value designed for r is very important because it will be a tradeoff between information loss and sensitivity to noise, which is called the number of effective singular values, and also called the effective order of the SVD.Difference spectrum of singular values  describeThen, the sequence ku has a central frequency and a finite bandwidth. To evaluate the bandwidth of each mode, the corresponding constrained variational problem is given as follows:VMD can effectively decompose non-stationary and nonlinear signals into a discrete set of quasi-orthogonal band limited intrinsic mode functions (IMFs) . Each IMTo solve the above constrained problem, the quadratic penalty factor In detail, the implementation process of the VMD is described as follows.n.Step 1: Initialize mode n = n + 1.Step 2: Execution cycle: Step 3: For all Step 4: Repeat steps (2) to (3), until the convergence stop condition is satisfied. The stop condition is given as follows:Step 5: Stop the iterations and obtain the IMF components.More details of the VMD algorithm can be found in .Combining the above theoretical basis, a novel denoising method for non-stationary signals based on SVD-VMD algorithm is proposed. There are two stages in the algorithm. In the first stage, the effective singular value order is obtained which reveals the underlying model structure of the signals. The detailed process is shown in the left part of the Then, SVD is used to decompose the Hankel matrix and obtain two orthogonal matrices containing the left and right singular vectors, respectively, as well as a diagonal matrix containing the singular values ordered in descending order magnitude. Then, the difference spectrum of singular values is calculated, and the effective order r of singular values is determined according to the maximum mutation position of difference spectrum.In the second stage, we use the obtained order to instruct VMD to decompose the original signal and select the relevant components to reconstruct denoised signal. The detailed process is shown in the right part of the n(t) is the Gaussian noise signal, where SNR is −13 dB. C is the attenuation coefficient of 700 and resonance frequency is set to 4 KHz. The characteristic frequency of inner ring failure is 120 Hz. sampling frequency is 12 KHz and the signal length is 4096.To verify the effectiveness of the proposed method, we used a simulated signal  which siWhen the impulsive signal is weak or it is covered by strong background noise, the impulsive frequency may be hidden by noise. The simulated signal is formed by adding the Gaussian noise signal with SNR −13 dB to the impulsive signal. Comparing Now we use the proposed SVD-VMD method to denoise the simulated signal. Firstly, we convert the simulated signal into the Hankel matrix and use SVD to decompose the Hankel matrix. The difference spectrum of singular values is shown in According to the above description of the SVD-VMD method, the IMFs number of VMD is set to be four according to the effective order r of singular values. The decomposition result of VMD for the simulated signal is shown in In order to select the component with the useful information, the kurtosis value is calculated for each IMF, which are recorded in In As we know, SVD itself can also be used for noise reduction if the effective order of singular values is found. The components corresponding to the effective order are useful and the other components are noise. There are many methods to determine the effective order of singular values such as the difference spectrum of singular values, singular value mean and singular value median. In this paper, we use difference spectrum (DS) of singular values to determine the effective order. This denoising method is called SVD-DS method. The denoised result is achieved by using SVD-DS is shown in In order to evaluate quantitatively the noise reduction performance of the denoising methods, two indicators, namely, the output signal-to-noise ratio (outSNR and RMSE by the abovementioned methods for the noisy simulated non-stationary signal with different The denoising performance of In 5 points. The experimental rotating frequency is about 30 Hz. The vibration signal of the rolling bearing with the inner ring failure is shown in The real roller bearing vibration signal data are used to verify further the effectiveness of the proposed SVD-VMD method; these experimental data come from the electrical engineering laboratory of Case Western Reserve University . The tesFrom The denoising results of the proposed SVD-VMD method is shown The collected vibration signal in To use the proposed SVD-VMD method, we construct Hankel matrix and decompose the matrix by SVD, then calculate the difference spectrum of singular which is shown in According to the effective order, we decompose the vibration signal with strong noise into eight intrinsic mode functions. The kurtosis of each mode is calculated in We also reconstructed the components corresponding to the effective order of singular values. The denoised results are shown in This paper has proposed a novel denoising method which utilizes both SVD and VMD. SVD is used to expose the underlying model of the signal, where we obtain the effective order of singular values by calculating the difference spectrum of singular values. The obtained effective order guides VMD to set the appropriate numbers of IMFs. Calculating the kurtosis value for each IMF, the IMF corresponding to the maximum value is selected to reconstruct the denoised signal. Compared with the evolutionary algorithm, this method is not a black box, and the results can be explained. Moreover, it does not require a lot of computation and time. Compared with SVD-DS, the proposed method has superior denoising performance when it is applied to the simulation signals and the real experimental signals of the roller bearing faults. The results demonstrate that, whether it is for high signal-to-noise ratio signals or low signal-to-noise ratio signals, the proposed SVD-VMD performs better.It should be noted that there are other denoising techniques, e.g., K-SVD and compressed sensing, which have not been studied here. We consider the proposed denoising technique in this paper will be effective for vibration signals from rotating machines. We also anticipate that this technique will work with some other types of signals. In the future, we may use this new denoising method in the acoustic analysis and compare the advantages/disadvantages between vibration analysis and acoustic analysis.


The primary goal of this study is to develop a mathematical model that can establish a transfer function relationship between the “external” pulse pressures measured by a tonometer and the “internal” pulse pressure in the artery. The purpose of the model is to accurately estimate and rebuild the internal pulse pressure waveforms using arterial tonometry measurements. To develop and validate a model without human subjects and operators for consistency, this study employs a radial pulse generation system, a robotic tonometry system, and a write model with an artificial skin and vessel. A transfer function model is developed using the results of the pulse testing and the mechanical characterization testing of the skin and vessel. To evaluate the model, the pulse waveforms are first reconstructed for various reference pulses using the model with tonometry data. They are then compared with pulse waveforms acquired by internal measurement (by the built-in pressure sensor in the vessel) the external measurement (the on-skin measurement by the robotic tonometry system). The results show that the model-produced pulse waveforms coinciding well with the internal pulse waveforms with small relative errors, indicating the effectiveness of the model in reproducing the actual pulse pressures inside the vessel. Arterial pulse pressure is an important biomarker for vascular diseases. In particular, being close to the heart, the pressure in aorta or the central aortic blood pressure (cBP) is strongly related to cardiovascular diseases, such as a heart attack. Thus, accurate measurements and assessments of cBP are critically important in predicting potential cardiovascular events and providing proper proactive medical treatments. Currently, the most accurate way to measure the aortic pressure for clinical reference is considered to be a direct blood pressure (BP) measurement method using an arterial catheter. For this invasive method, an arterial catheter is inserted into the artery of interest  to measure and monitor continuous BP and hemodynamics. While it is the most accurate, the invasive BP monitoring method with arterial catheters causes minor complications, such as local paresthesia, and carries the risk of serious complications, such as infection and nerve damage, particularly for children who have smaller arteries . Thus, iAlternative to the direct continuous BP measurement method with arterial catheters, various non-invasive BP monitoring methods are available. They include continuous or intermittent methods with their own advantages and disadvantages ,3. InterNon-invasive radial artery applanation tonometry (AT) is suitable for the estimation of cBP waveforms produced from inverse transformed radial waveforms . Radial Radial artery pulse waveforms provide useful clinical information related to cardiovascular risk factors ,2,3,4. PIn recent years, with the advancement of wearable technologies, more radial artery AT devices are being developed for continuous, real-time, and non-invasive monitoring of vital signs. For wearable radial artery AT devices (such as smart watches and bands) or radial tonometers, the radial artery pulse pressure is recorded by a sensor that directly contacts and presses down on the skin. The collected data are then processed using sophisticated mathematical models/algorithms to derive or calculate the cBP waveforms . SeveralDespite the importance of obtaining radial intravascular pressure waveforms from radial artery tonometry, limited studies investigated the mathematical transfer modeling between the tonometry pulse data and the pulse pressure inside the radial artery. Perhaps, the need for clinical trials has hampered active investigations into the radial artery transfer function studies. While it is one of the most essential components for the clinical verification process, clinical trials involving human subject testing demands tremendous resources, cost, time, and efforts. Moreover, it can overlook person-to-person variations or human errors that can cause misleading information. In the case of radial pulse measurements, several factors can affect the radial pulse measurements (such as variation of skin properties among patients), and the accuracy of measurement is sensitive to patient variations, such as the posture of the patient. Thus, the needs exist for alternative methods that can perform radial pulse research, eliminating or minimizing human variations.This research proposes to study a transfer function of radial pulse pressures using a robotic arterial tonometry measuring system and pulse generation system. The application of a pulse generation system capable of generating a radial pulse in an artificial wrist can significantly contribute to accurate transfer function modeling by offsetting various and unpredictable human factors. In addition to the need for mathematical modeling of arterial tonometry without human subject testing, the proposed research is anticipated to contribute to evaluation and validation of the accuracy, reliability, and usability of wearable sensors and wearable tonometry devices for clinical applications.The primary goal of this study is to develop a mathematical model that relates the radial pulse pressure measured on the skin surface and the pressure inside the radial artery based on the measurements obtained by a robotic tonometry system. To this end, a test set up is created with three main components that include an artificial wrist model (consisting of synthetic skin and blood vessel), a pulse generation system, and a robotic tonometry system. Based on the cam mechanism, the radial artery pulse-generation system (RAPS) can reproduce in vivo radial pulse waveforms consistently and repeatedly so that it can act as a reference (or input) pulse pressure. In order to quantitatively measure the pulse pressure, the robotic tonometry system (RTS) is equipped with highly sensitive pulse sensors (an array of piezo sensors) and employs dexterous robotic manipulation technology to control the measuring direction and pressing force of the sensor with a laser guide. The RTS can automatically detect and compress the radial artery as well as collect the (output) pulse signals on the skin surface. Thus, RAPS and RTS systems can ensure the consistent testing conditions for pulse measurement, eliminating potential human-induced errors or variations. Using the test setup, a series of pulse tests are performed on the wrist model with various input pulse waveforms.In addition to the pulsation testing, a separate DMA testing is conducted in order to characterize the mechanical properties of the skin and skin-vessel model. The results of the DMA testing are then used to develop a transfer function model along with the pulse data. This study intends to apply a modified Kelvin–Voigt model for estimating the radial skin dynamics caused by subcutaneous tissue, skin, and radial artery. The constants of the Kelvin–Voigt model are determined through an optimization algorithm based on the comparison of the signal measured on the skin with the robotic tonometry system (RTS) and the signal of the pressure sensor embedded in the vessel tube of the RAPS. In order to establish the transfer function that can be commonly applied to age-dependent waveforms with clinically different trends, the age-specific cams are designed based on the average waveforms of 10 s, 30 s, 60 s, and 80 s obtained through extensive clinical trials using RTS . The ageAfter developing the model, it is validated with age-related in vivo pulse inputs. For this verification study, the pulse waveforms that are reconstructed the model are compared and analyzed with those from the on-skin tonometry measurement data. The remaining major issue for the clinical application of the developed tonometry system is the ability to derive intravascular pressure waveforms using the tonometry results. Since the intravascular pressure waveforms are distorted by the stiffness and damping characteristics of skin layers and radial artery vessel , it is nThe next section provides a description of the configuration and operation principle of the RTS and the cam-based RAPS device. After describing the overall process of establishing the transfer function based on the comparison of the blood pressure waveform measured by built-in pressure sensor in the blood vessel tube and by RTS using a tonometry method, this paper presents the error analysis results between the waveform and the blood pressure waveform measured inside the blood vessel.The purpose of this section is to describe the proposed transfer function modeling method for this study. This section first presents a brief summary of the radial artery pulse-generation system and the robotic tonometry system. These are essential systems to obtain experimental data under consistent testing conditions for the modeling study. It then offers a description of the modeling process that is used in this study along with the experimental validation and analysis of the proposed model.Reproducing arterial pulse waveforms consistently and continuously is crucial to carrying out the proposed study. The authors developed a radial artery pulse-generation system or RAPS in a previous study . ConsistIn order to measure radial pulses without a human operator, this study will use a robotic tonometry pulse measurement system (RTS), which was developed in an earlier study by the authors. While the detailed design and performance of the RTS can be found in the study ,16, thisThis section explains the whole process of establishing a transfer function that can accurately predict the blood pressure waveform inside the radial artery from the pulse pressure waveform measured on the skin above the simulator’s wrist by using the RTS. In order to develop a transfer function model for the internal pressure in the artery and the externally measured tonometry pressure, the properties of the skin and the vessel need to be characterized. To determine the mechanical properties of the artificial skin and vessel, this study preformed compression testing using a dynamic mechanical analyze or DMA . The DMA ensures the purest mechanical data through independent control of deformation and measurement of stress by using separate motors and transducers.This part of the study intends to construct a mathematical model that combines the elasticity of the radial artery and the physical properties of the skin in order to estimate the waveform in the blood vessel using a blood pressure waveform measured externally by a tonometry method. To construct a transfer function model capable of reconstructing intravascular pressure, in this section, the radial artery and skin of the wrist were modeled using the Kelvin–Voigt model with an additional serial spring in the parallel structure of a spring and a dashpot. Next, the mechanical properties of artificial skin were extracted through optimization based on the overall stress–strain measurement results using DMA. A model capable of regenerating intravascular pressure waveforms was then completed by applying the determined physical property values to the Kelvin–Voigt model.Human skins exhibit complex physical behaviors, and many different models have been studied to capture them. One of the common mathematical methods that is used to model human skin behaviors is to use the Kelvin–Voigt model, in which Hookean springs (elastic elements) and Newtonian dashpots (viscous elements) are connected in parallel ,20,21. Rη, and the elastic modulus parameter, The current study aims at developing a transfer function model that is capable of reconstructing the internal pulse waveform using the tonometry data. The model can be used to estimate the arterial pressure in the vessel. Moreover, the reconstructed pulse waveforms can be used to obtain clinically meaningful information, such as an augmentation index. The augmentation index is often used to indirectly measure vascular stiffness. To attain the goal, this study employs a two-layer Kelvin–Voigt model and an optimization algorithm that determines the spring element ). In the equation, σ is stress and ϵ is strain. The values of the parameters To determine skin parameters, the modulus, Through dividing Equation (1) by One observes that as the damping parameter , one can determine C calculated by (6) to Equation (4), we can determine reconstructed deformation, Thus, by substituting the value of When the objective function is small enough for particular parameters, it means that the reconstructed waveform generated by the corresponding parameter is similar to the targeted waveform. Thus, parameters Analyzing how the waveform changes as it passes through the viscoelasticity material, a method to reconstruct the waveform of pulse pressure in an artificial vessel from the waveform measured on the skin is proposed. The process is based on the Kelvin–Voigt model together with the concept that a change of strain is proportional to the change of pressure.C in Equation (4), a different boundary condition must be used. Since a waveform is periodic with a period The process of reconstructing the strain variable from the measured stress on the skin is similar to that described in By the above condition, the constant In summary, the explicit equation for the reconstructed strain of the artificial vessel is obtained asr is the radius of the vessel, one can derive the relation thatTo reconstruct the waveforms of pressure inside the artificial vessel based on the strain variable obtained above, the relationships between the pressure inside the vessel and the strain of the tube was derived from the collapsible tube modeling in  where thFinally, noticing that The goal of this section is to evaluate the performance of the skin-vessel transfer function model developed in the previous section. To this end, a set of age-related radial pulse waveforms are used as reference waveforms, and they are compared with the pulse waveforms that are reconstructed by the transfer function model using tonometry data. In addition to the different pulse waveforms, pulse rates are changed to further evaluate the performance and the reliability of the model. To quantitatively evaluate the performance, the error analysis is conducted between the model generated pulse waveforms and those measured by the built-in pressure sensor for the reference pulse waveforms. Moreover, the augmentation index is compared between the sensor measured pulses waveforms and the reconstructed pulse waveforms.To generate age-dependent radial pulse pressure waveforms using the cam-based RAPS, the study uses the cams designed and fabricated based on in vivo data in an earlier study by the authors. While details of the human subject testing and cam designs can be found in ,14, thisTo analyze the results quantitatively, relative least square error  is defined as the ratio of the late systolic pressure of P to the early systolic pressure of P or the ratio of the second peak of the pulse waveform to the first peak of the waveform. P between the reconstructed pulse waveforms and the intravascular pulse waveforms. As shown in Beyond the mathematical error analysis, it is important to analyze how the reconstructed waveform captures physiological information of arterial pulses. Thus, this study uses the augmentation index (AI) as another measure for the evaluation of reconstructed waveforms. AI is one of the frequently used parameters for vascular aging ,24. The This study has presented a skin-vessel transfer function model that is capable of estimating the blood pulse pressure inside a radial artery and reconstructing arterial pulse pressure waveforms using the tonometry data. As described earlier, most transfer function studies involve human subject testing. On one hand, they are immensely valuable because clinical trials involving human subjects can offer more direct and significant clinical reference results. On the other hand, human subject testing can be less desirable as it can be resource intensive. Moreover, it is subject to human errors originated from various factors . Thus, the transfer function research involving human subject testing may not be suitable for investigating how the test subject’s variations  affect the accuracy of pulse pressure estimations. Not accounting for such realistic variations can result in misleading information when it comes to developing transfer function relationships between the internal and external pressures. The current work, therefore, has focused on developing a transfer function model accounting for the mechanical properties of skin and blood vessel. In developing such a model, it is important to have consistent input and output data without variations. To this end, the current study has employed a radial pulse generator (RAPS) to produce reference radial pulse waveforms that contain in vivo radial pulse characteristics. The reference pulses were measured on the surface of an artificial skin using a robotic tonometry pulse measurement system (RTS). The RAPS and RTS systems ensured consistent and repeatable results without involving human subjects and operators, eliminating man-made errors or uncertainties.2L-errors between the reconstructed pulse waveforms by the model and the intravascular pulse waveforms are smaller than those for the measured and the intravascular pulse waveforms, indicating that the reconstrued pulse waveforms can more accurately represent the intravascular pulse waveforms as compared with the measured pulse waveforms. For the cases considered in this study, the relative 2L-error between the on-skin measurement and the intravascular pulse waveforms are reduced by 11.58% on average with the transfer function model developed, demonstrating the effectiveness of the model in estimating intravascular pulse pressures. For further physiological analysis, the augmentation index (AI) was employed, which is a commonly used measure for vascular aging and arterial stiffness. The relative AI error analysis between the pulse waveforms obtained using the tonometry method and those obtained inside the vessel exhibit noticeable discrepancies. This is because the properties of the skin and the vessel distort the internal pulse pressures as they travel through them and are measured on the surface of the skin. The results further show that the relative AI error between the model-generated pulse waveforms and the internal ones are small (about 6% to less than 1%), indicating that the model can compensate the effect of the skin-vessel and effectively capture the AI of the internal pulse waveforms. This study has demonstrated the effectiveness of the transfer function model in producing the pulse waveforms that are close to the actual internal pulse waveforms. In particular, the model can reconstruct the internal pulse pressure waveforms so that they can be used for more meaningful analysis of the waveforms. Thus, the model is anticipated to apply to non-invasive radial pulse pressure measurements to monitor the “true” arterial pressure using the tonometry sensor measurements.This study adopted a modified Kelvin–Voigt model in order to reconstruct internal pulse pressures using the RTS data. After performing a series of characterization testing on the skin-vessel model using a dynamic mechanical analyzer, the results of the characterization testing along with an optimization algorithm were used to build the transfer function model. To evaluate the performance of the model, this study compared and analyzed the pulse waveforms by the model and those of measured by the embedded pressure sensor for four different age groups’ average pulse waveforms with two different rotational speeds. The results show that the relative In future study, the methodology used in this study for the development of transfer function models is further anticipated to expand by incorporating the variation of the skin properties and a wide range of pulse frequencies or heart beats. Various artificial wrist models for each skin and blood vessel characteristic of a person will be created and evaluated using the methodology developed in this study with an aim to infer the intravascular pressure of a real person in a non-invasive way. To this end, a library of skin data considering various parameters, such as gender, age, and body mass index (BMI) will be created, and an enhanced transfer function model using the library of data will be developed. Moreover, since the proposed transfer function method with the robotic tonometry system could reconstruct pulse waveforms in real time after measuring pulses remotely, future research will explore its application for emerging healthcare technologies, such as remote patient monitoring based on Internet of Things ,26. Thes


The paper examines the AQM mechanism based on neural networks. The active queue management allows packets to be dropped from the router’s queue before the buffer is full. The aim of the work is to use machine learning to create a model that copies the behavior of the AQM  Cisco predicts that by 2022, the Internet traffic will increase to 77 exabytes per month due to the rapid development of mobile technologies. The mobile data transfer will increase sevenfold compared to 2017, with an average annual growth of 46% [Wide area networks have their origins in the 1970s and were created for the American army. Thus, the most important aspect of the network based on a distributed architecture was to deliver reliable transmission of data and low connection costs. Unfortunately, the design assumptions proposed at the beginning turned out to be insufficient over the years.Initially, IP routers handled packets according to the FIFO (First In First Out) rule (the first incoming packet in the queue is the first one to be served) . For sucTo solve this problem, the Internet Engineering Task Force (IETF) proposed Active Queue Management (AQM) mechanisms . These mOne of the first active queue management algorithm—Random Early Detection (RED) —was propThe literature states that non-integer order controllers may have better performance than classic integer order ones. The first implementation of the fractional order PI controller used in queue management was presented in . Our preH. This approach also uses Convolutional Neural Networks. The promising results obtained in this work prompted us to create an efficient adaptive algorithm of Active Queue Management based on Convolutional Neural Networks.Increase in popularity of machine learning methods may enable the creation of a more efficient AQM mechanism. Artificial Neural Networks (ANNs) are a powerful tool with high ability to recognize patterns, even in the case of incomplete and partially distorted training data . One of Mechanisms that select AQM parameters based on the decisions of neural networks have been proposed in the literature ,27. NeveOur contribution. The aim of the work is to propose an algorithm for Active Queue Management based on supervised learning paradigm. We use a previously trained Convolutional Network to manage the queue. The ANN is trained based on the data obtained in simulations. We observe the impact on the behavior of the AQM mechanism based on the The remainder of the paper is organized as follows: There are many works regarding new AQM algorithms. These mechanisms are compared with existing solutions in terms of transmission parameters such as total number of dropped packets, average queue length, or transmission delays. In the article  passive Additionally, the fractional order PI controller  is used A separate group includes studies that have used neural networks to improve the queue management mechanism in TCP networks. The article  proposedANNs were also used to create a new algorithm—Adaptive Neuron Proportional Integral Differential (ANPID) . This meIn article  authors The research presented in XuIeee is an example of an attempt to use unsupervised learning to create a more efficient AQM mechanism. For that purpose, the Hebbian Learning rule is used and a new adaptive PHAQM algorithm is presented.Bisoy and Pattnaik  used feeZhou et al.  also preThere are many works on the topic of AQMs based on neural networks. However, in these works, in contrast to our approach, the neural networks were mainly created using reinforcement learning. In addition, the research results did not consider the analysis of the effect of traffic self-similarity and long-term dependence on transmission efficiency.Self-similarity is widely observed in nature, but the term itself was introduced by Mandelbrot in 1960s and it generally means that the portion of the whole object can be considered an image of the whole in a reduced scale. The object is self-similar, when it exhibits the same statistical properties independently of the scale. Mandelbrot described it on the example of the scaled coastlines, which also exhibited self-similarity. This property can also be used in the case of time-series analysis. The degree of self-similarity in this case determines whether Long-Range Dependence (LRD) and Short-Range Dependence (SRD) occur in data. These relationships were observed as early as the middle of the twentieth century, when Sir H. E. Hurst described the occurrence of long-range dependence based on the value of water level fluctuations in the Nile River. Although the terms of self-similarity and LRD are sometimes used interchangeably, they are not the same .H is usually used to denote the Hurst exponent/parameter, which expresses the degree of self-similarity. The parameter can take values from range A continuous-time series It was first proven in  that actArtificial Neural Networks have found application in many different domains, e.g., image classification, natural language processing, signal processing etc. Additionally, Deep Learning approaches have become a solution to many problems due to their better ability to extract patterns than shallow learning . The verNetwork traffic and its features are often represented as a time series. To process time-series data with Artificial Neural Networks, different types of networks  and layers can be used, namely Recurrent layers (including Long-Short Term Memory (LSTM) layers and Gated Recurrent Unit (GRU) Layers) and 1D Convolutional Neural Networks.Autoencoders can be built using different types of layers, e.g., Dense Layers or Convolutional layers. The goal of this type of network is to compress input data and then reconstruct it on output . It can LSTM layers are often used for the purpose of time-series data processing. Single LSTM units solve the gradient vanishing and exploding issues typical for simple Recurrent Layers and are able to propagate gradients over a long period of time . The keyThe alternative for LSTM layers is a GRU layer. It is very similar to LSTM layer, also stores the Long-Time memory of the past information, which is vital for time-series processing. Nevertheless, it is simpler to implement and compute than the LSTM layer, thus more efficient .Additionally, convolutional layers can be used to process time-series data. In this case, time has to be treated as a spatial dimension . In factIn this paper, we used artificial neural network models to develop an active queue management mechanism. The neural networks were trained to mimic the operation of the AQM based on the fractional order The neural network model was based on four convolutional layers and two dense layers. After each convolutional layer, the data were normalized and the results were averaged. Additionally, a dropout layer was placed to prevent over-fitting to the learning data. Python and Keras libraries were used to implement the model. The conceptual structure of the model used in this paper is presented in In order to prepare the training set for the proposed neural network model, network simulations were performed, reflecting the queueing behavior of a fractional order controller n papers . The conTo obtain training data for an AQM model based on Convolutional Networks, network simulations were performed using the AQM mechanism. For this purpose, the discrete event simulator SimPy (written in Python) was used. This software is available under the MIT License and has been used in our previous works regarding the evaluation of AQMs ,26.Our simulation model was a discrete model of a articles ,15,25.All experiments considered different degrees of traffic self-similarity expressed using Hurst parameter. In experiments the Hurst parameter changed between The input intensity coefficient was set to a constant value In our experiments, we considered different numbers of items from queue occupancy history taken into consideration in the samples used to train Convolutional Networks. For simplicity, we refer to this number of samples as ’CNN History’. This length corresponded to the number of time slots in the simulation model that were used as training data for the network. For example Learning features:(a)n items from the queue’s occupancy history (CNN History).The last (b)n last queue statesHistory of packet rejections in where Classes:(a)11 labels that mapped the probability of packet rejection to the current transmission conditions, according to the principle shown in Thus, the training data consisted of:n last items of the queue’s occupancy history.Therefore, we considered different lengths of queue occupancy history, because from the perspective of the router, which is a low resource device, minimizing the length of the history would be beneficial. In our study, we tried to determine the minimum acceptable length of For each probability interval, one million one-dimensional learning records were prepared. Therefore, the training set consisted of 11 million records. They contained transmission information such as the length of the queue in each consecutive time slot, the number of dropped packets, and the value of the v is the first moment, which resembles momentum that records the past normalized gradient, s is the second moment and g denotes the gradient descent.Input data prepared in such a manner were used in the process of supervised learning of the neural network models. In order to train the model and minimize the cost function, the optimizer Adaptive Moment Estimation (Adam) was used with the following parameters:equation :(3)vt=β1In both the four convolutional layers and the two dense layers, ReLU was used as the activation function and Sigmoid/Softmax functions were used to determine the activation of the output layer. Categorical cross-entropy was used as a cost function. We limited the training process to 10 epochs. This value was sufficient, since the values start to stabilize after only 5–6 epochs, as confirmed by the results in In the case of Softmax function , the minory <100 .In the case of the model trained on data representing the behavior of three controllers simultaneously and the use of the Sigmoid activation function of the output layer, the maximum accuracy was 72.1% for the CNN History ≥500 see .This section presents the behavior of the trained neural network  were considered. Regardless of the value of n, the resulting queue length distributions are similar to the queue length distribution of the For the CNN model, different lengths of the last kets see . For hig 147 see . In thisIt should be noted that for all three CNN-based AQM models, a more efficient AQM model was obtained compared to the controllers that were used to create the test data. Even for the model that obtained the smallest accuracy during the learning process (48.77%, see  LRD see  and for  LRD see .In the next simulation step, we evaluated the AQM-CNN mechanism whose learning data were generated from the behavior of all three n elements of queue occupancy history taken as a CNN input is too small (CNN History The results show that when the number of last PIα3 see .n queue occupancy history elements is larger (CNN History On the other hand, when the considered number of last ler PIα1 .This feature indicates that for the AQM model based on Convolutional Networks, as the number of story elements used increases, the ability of the mechanism to adapt to current Internet transmission conditions also improves.The paper presents a new Active Queue Management mechanism based on Convolutional Neural Networks and supervised learning.To train the Convolutional Networks used in the experiments, data obtained through simulation have been used. The training data of the CNN model reflect the behavior of the AQM mechanism, based on a fractional order controller In our experiments, we took into account the effect of the degree of traffic self-similarity and long-term dependence on the performance of the proposed mechanism.n elements of the queue occupancy history, used as input of the neural network, on the efficiency of the proposed mechanism. The best results were obtained for CNN History = 500. The minimum length of CNN History for which results are still acceptable is 100.We also considered the effect of the number of last In the experiments, neural networks with different number of convolutional layers and different optimizers and cost functions were considered to build the AQM model. After comparing the results obtained with different activation functions, the results have shown that the most efficient model used Sigmoid activation function in the output layer, therefore we chose this function for further experiments. The decisions made in this work were also influenced by our previous work regarding traffic classification in terms of the degree of self-similarity .The most efficient AQM obtained in our study was based on the Convolutional Neural Network model, trained using the data reflecting the behavior of all three The results confirmed that the model based on Convolutional Neural Networks can effectively reproduce the results of the classical AQM algorithm and effectively manage the data transmission. Such a model maintains the assumed average number of packets in the queue and reduces the total number of dropped packets, independent of the degree of traffic self-similarity.It seems that the proposed mechanism exhibits some advantages over previously proposed mechanisms encountered in the literature. Our previous study  demonstrIn our previous study , we used