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In recent years, with the increasing demand for high-quality images in various fields, more and more attention has been focused on noise removal techniques for image processing. The effective elimination of unwanted noise plays a crucial role in improving image quality. To meet this challenge, many noise removal methods have been proposed, among which the diffusion model has become one of the focuses of many researchers. In order to make the restored image closer to the real image and retain more features of the image, this paper proposes a DIR-SDE method with reference to the diffusion models of IR-SDE and IDM, which improve the feature retention of the image in the de-raining process, and then improve the realism of the image for the image de-raining task. In this study, IR-SDE was used as the base structure of the diffusion model, IR-SDE was improved, and DINO-ViT was combined to enhance the image features. During the diffusion process, the image features were extracted using DINO-ViT, and these features were fused with the original images to enhance the learning effect of the model. The model was also trained and validated with the Rain100H dataset. Compared with the IR-SDE method, it improved 0.003 in the SSIM, 0.003 in the LPIPS, and 1.23 in the FID. The experimental results show that the diffusion model proposed in this study can effectively improve the image restoration performance. [ABSTRACT FROM AUTHOR]

A positioning system in a specific space is for the purpose of determining the location of an object. The Global Positioning System (GPS) is the most popular and valuable development for classical navigation. However, it may not always be precise and available due to the effects of multi-path propagation and signal attenuation. Thus, we need some additional skills to keep its stability and accuracy or improve its performance. Based on the technique of neural networks, the positioning information from multiple GPS receivers is composed to obtain a better version which is more accurate and stable and thus can be applied to advanced applications. Particularly, the concepts of shallow and asymmetric neural networks are used in this work. Our design possesses fewer hidden layers via the former property and further reduces the connections of classical Fully Connected Neural Networks (FCNNs) via the latter property. Hence, it takes very little time to realize the training and predicting procedures. Also, it will help promote several works in practice, such as the implementation of embedded systems. Finally, a practical test, called the vehicular road test, is utilized to guarantee the level of confidence in the improvement of our algorithm. [ABSTRACT FROM AUTHOR]

Lumbar disc bulging or herniation (LDBH) is one of the major causes of spinal stenosis and related nerve compression, and its severity is the major determinant for spine surgery. MRI of the spine is the most important diagnostic tool for evaluating the need for surgical intervention in patients with LDBH. However, MRI utilization is limited by its low accessibility. Spinal X-rays can rapidly provide information on the bony structure of the patient. Our study aimed to identify the factors associated with LDBH, including disc height, and establish a clinical diagnostic tool to support its diagnosis based on lumbar X-ray findings. In this study, a total of 458 patients were used for analysis and 13 clinical and imaging variables were collected. Five machine-learning (ML) methods, including LASSO regression, MARS, decision tree, random forest, and extreme gradient boosting, were applied and integrated to identify important variables for predicting LDBH from lumbar spine X-rays. The results showed L4-5 posterior disc height, age, and L1-2 anterior disc height to be the top predictors, and a decision tree algorithm was constructed to support clinical decision-making. Our study highlights the potential of ML-based decision tools for surgeons and emphasizes the importance of L1-2 disc height in relation to LDBH. Future research will expand on these findings to develop a more comprehensive decision-supporting model. [ABSTRACT FROM AUTHOR]

Convolutional neural networks (CNNs) have shown promise in accurately diagnosing coronavirus disease 2019 (COVID-19) and bacterial pneumonia using chest X-ray images. However, determining the optimal feature extraction approach is challenging. This study investigates the use of fusion-extracted features by deep networks to improve the accuracy of COVID-19 and bacterial pneumonia classification with chest X-ray radiography. A Fusion CNN method was developed using five different deep learning models after transferred learning to extract image features (Fusion CNN). The combined features were used to build a support vector machine (SVM) classifier with a RBF kernel. The performance of the model was evaluated using accuracy, Kappa values, recall rate, and precision scores. The Fusion CNN model achieved an accuracy and Kappa value of 0.994 and 0.991, with precision scores for normal, COVID-19, and bacterial groups of 0.991, 0.998, and 0.994, respectively. The results indicate that the Fusion CNN models with the SVM classifier provided reliable and accurate classification performance, with Kappa values no less than 0.990. Using a Fusion CNN approach could be a possible solution to enhance accuracy further. Therefore, the study demonstrates the potential of deep learning and fusion-extracted features for accurate COVID-19 and bacterial pneumonia classification with chest X-ray radiography. [ABSTRACT FROM AUTHOR]

Autonomous vehicle systems rely heavily upon depth estimation, which facilitates the improvement of precision and stability in automated decision-making systems. Noteworthily, the technique of monocular depth estimation is critical for one of these feasible implementations. In the area of segmentation of medical images, UNet is a well-known encoder–decoder structure. Moreover, several studies have proven its further potential for monocular depth estimation. Similarly, based on UNet, we aim to propose a novel model of monocular depth estimation, which is constructed from the benefits of classical UNet and residual learning mechanisms and named URNet. Particularly, we employ the KITTI dataset in conjunction with the Eigen split strategy to determine the efficacy of our model. Compared with other studies, our URNet is significantly better, on the basis of higher the precision and lower error rate. Hence, it can deal properly with the depth estimation issue for autonomous driving systems. [ABSTRACT FROM AUTHOR]

Advanced Driver Assistance Systems (ADAS) are only applied to relatively simple scenarios, such as highways. If there is an emergency while driving, the driver should take control of the car to deal properly with the situation at any time. Obviously, this incurs the uncertainty of safety. Recently, in the literature, several studies have been proposed for the above-mentioned issue via Artificial Intelligence (AI). The achievement is exactly the aim that we look forward to, i.e., the autonomous vehicle. In this paper, we realize the autonomous driving control via Deep Reinforcement Learning (DRL) based on the CARLA (Car Learning to Act) simulator. Specifically, we use the ordinary Red-Green-Blue (RGB) camera and semantic segmentation camera to observe the view in front of the vehicle while driving. Then, the captured information is utilized as the input for different DRL models so as to evaluate the performance, where the DRL models include DDPG (Deep Deterministic Policy Gradient) and RDPG (Recurrent Deterministic Policy Gradient). Moreover, we also design an appropriate reward mechanism for these DRL models to realize efficient autonomous driving control. According to the results, only the RDPG strategies can finish the driving mission with the scenario that does not appear/include in the training scenario, and with the help of the semantic segmentation camera, the RDPG control strategy can further improve its efficiency. [ABSTRACT FROM AUTHOR]

While many monocular depth estimation methods have been proposed, determining depth variations in outdoor scenes remains challenging. Accordingly, this paper proposes an image segmentation-based monocular depth estimation model with attention mechanisms that can address outdoor scene variations. The segmentation model segments images into foreground and background regions and individually predicts depth maps. Moreover, attention mechanisms are also adopted to extract meaningful features from complex scenes to improve foreground and background depth map prediction via a multi-scale decoding scheme. From our experimental results, we observed that our proposed model outperformed previous methods by 27.5 % on the KITTI dataset. [ABSTRACT FROM AUTHOR]

Given virtual reality (VR)'s popularity, VR already impacts various applications such as education and tourism. In the above applications, users usually need to follow a specific target, such as a teacher or a tour guide, to explore and learn from the environment. However, because of the constraint with visual senses and unintuitive locomotion, following a target in VR is not as easy as doing it in real life. As a result, the user may not concentrate on the audio narration or the surroundings. Therefore, finding a following mechanism that can help the user concentrate and learn in VR is important. In this paper, we focus on the following techniques in VR. We propose four types of following mechanisms: limited (which restricts the user's ability to move), semi-limited (which constrains the user's range of activity), improved semi-limited (with visual assistance), and not limited (user's movement is not constrained). This study consisted of 29 participants divided into four groups and aimed to evaluate how the following mechanisms affect user experience and make users more concentrated in the virtual world. The experiment shows that the semi-limited following mechanism with visual assistance is superior to the other three types in the performance of the touring experience and helping users pay attention to the tour content. [ABSTRACT FROM AUTHOR]

An ultra-wideband (UWB) positioning system consists of at least three anchors and a tag for the positioning procedure. Via the UWB transceivers mounted on all devices in the system, we can obtain the distance information between each pair of devices and further realize the tag localization. However, the uncertain measurement in the real world may introduce incorrect measurement information, e.g., time, distance, positioning, and so on. Therefore, we intend to incorporate the technique of ensemble learning with UWB positioning to improve its performance. In this paper, we present two methods. The experimental results show that our ideas can be applied to different scenarios and work well. Of note, compared with the existing research in the literature, our first algorithm was more accurate and stable. Further, our second algorithm possessed even better performance than the first. Moreover, we also provide a comprehensive discussion for an ill-advised point, which is often used to evaluate the positioning efficiency in the literature. [ABSTRACT FROM AUTHOR]

This study examines the carbon reduction investment decisions of the electric power energy supply chain considering carbon trading and carbon subsidy policies in China's "dual carbon" context. By building a three-level supply chain system including electric power producers, retailers, and consumers, we discuss the optimal decision-making problem of the supply chain for three models of decentralized supply chain decision making without government subsidies, centralized supply chain decision making with government subsidies, and centralized supply chain decision making with government subsidies and carbon emission reduction cost sharing. Through model solving and further numerical simulations, the results showed that the increase in carbon emission reduction investment cost has a significant negative impact on power price and the total expected income of the supply chain. However, a reasonable level of government carbon emission reduction subsidy can effectively alleviate the increase in power price and improve the total expected income of supply chain. In addition, carbon mission reduction investment and supply chain cost allocation can effectively improve the carbon emission reduction level of the power supply chain, improve the income and enthusiasm of electric power producers, and realize the sustainable development of electric power energy consumption and the environment. [ABSTRACT FROM AUTHOR]

Recently, many artificial intelligence applications in smart cars have been utilized in real life. Making an unmanned ground vehicle (UGV) capable of moving autonomously has become a critical topic. Hence, in this work, a novel method for a UGV to realize path planning and obstacle avoidance is presented using a deep deterministic policy gradient approach (DDPG). More specifically, the lidar sensor mounted on the vehicle is utilized to measure the distance between the vehicle and the surrounding obstacles, and the odometer measures the mileage of the vehicle for the purpose of estimating the current location. Then, the above sensed data are treated as the training data for the DDPG training procedure, and several experiments are performed in different settings utilizing the robot operating system (ROS) and the Gazebo simulator with a real robot module, TurtleBot3, to present a comprehensive discussion. The simulation results show that using the presented design and reward architecture, the DDPG method is better than the classic deep Q-network (DQN) method, e.g., taking fewer steps to reach the goal, less training time to find the smallest number of steps for reaching the goal, and so on. [ABSTRACT FROM AUTHOR]

China is currently the world's largest energy consumer and carbon emitter. In order to reduce the harm of carbon dioxide to the global ecological environment, the use of natural gas instead of coal is a realistic choice for China to achieve the "dual carbon" goal. Opportunities also bring new challenges, and the price of natural gas is an important method of promoting the upstream and downstream industrial chains of natural gas, so it is of great practical significance to study the price of natural gas. This paper builds a three-level supply chain model consisting of suppliers in the natural gas market, city gas companies and consumers in the market and uses the Stackelberg game to study the decision-making models of different subjects under their own dominance and centralized decision-making; it also considers the pricing mechanism and profit situation of stakeholders in the natural gas market under the low-carbon preference of consumers and the level of corporate carbon emission reduction. The research results show that when considering consumers' low-carbon preferences, the sales prices of various stakeholders in the market have increased, which is beneficial for all entities in the natural gas industry chain. At the same time, with the low-carbon transformation of energy companies, the production method drives the price of raw materials to rise in the process of low-carbon innovation, which, in turn, makes the price of various stakeholders in the natural gas market and the level of carbon emission reduction per unit show a positive relationship; in order to maximize the overall profit of the supply chain, the natural gas market should adopt a centralized decision-making method to further promote the reform of China's natural gas marketization. [ABSTRACT FROM AUTHOR]

Dermatophytes are the group of keratinophilic fungi that cause superficial cutaneous infection, which traditionally belong to the genera Trichophyton, Microsporum, and Epidermophyton. Dermatophyte infection is not only a threat to the health of small animals, but also an important zoonotic and public health issue because of the potential transmission from animals to humans. Rabbit dermatophytosis is often clinically identified; however, limited information was found in Asia. The aims of this study are to investigate the prevalence and to evaluate the risk factors of dermatophytosis in pet rabbits in Northern Taiwan. Between March 2016 and October 2018, dander samples of pet rabbits were collected for fungal infection examination by Wood's lamp, microscopic examination (KOH preparation), fungal culture, and PCR assay (molecular identification). Z test and Fisher's exact test were performed to evaluate the potential risk factors, and logistic regression analysis was then performed to build the model of risk factors related to dermatophyte infection. Of the collected 250 dander samples of pet rabbits, 29 (11.6%) samples were positive for dermatophytes by molecular identification. In those samples, 28 samples were identified as the T. mentagrophytes complex and 1 sample was identified as M. canis. Based on the results of the Firth's bias reduction logistic analyses, animal source (rabbits purchased from pet shops) and number of rearing rabbits (three rabbits or more) were shown as the main risks for dermatophyte infection in the pet rabbits in Taiwan. The results of the present study elucidate the prevalence of rabbit dermatophyte infection, pathogens, and risk factors in Taiwan, and provide an important reference for the prevention and control of rabbit dermatophytosis. [ABSTRACT FROM AUTHOR]

Recently, autonomous driving has become one of the most popular topics for smart vehicles. However, traditional control strategies are mostly rule-based, which have poor adaptability to the time-varying traffic conditions. Similarly, they have difficulty coping with unexpected situations that may occur any time in the real-world environment. Hence, in this paper, we exploited Deep Reinforcement Learning (DRL) to enhance the quality and safety of autonomous driving control. Based on the road scenes and self-driving simulation modules provided by AirSim, we used the Deep Deterministic Policy Gradient (DDPG) and Recurrent Deterministic Policy Gradient (RDPG) algorithms, combined with the Convolutional Neural Network (CNN), to realize the autonomous driving control of self-driving cars. In particular, by using the real-time images of the road provided by AirSim as the training data, we carefully formulated an appropriate reward-generation method to improve the convergence speed of the adopted DDPG and RDPG models and the control performance of moving driverless cars. [ABSTRACT FROM AUTHOR]

For some IoV-based collision-avoidance architectures, it is not necessary that all vehicles have communication abilities. Hence, they need some particular designs and extra components. In the literature, one of them uses a camera mounted onto the infrastructure at an intersection to realize collision detection. Consequently, technologies for real-time object detection and dynamic prediction are required for the purposes of collision avoidance. In this paper, we propose an interesting method to predict the future position of a vehicle based on a well-known, real-time object detection project, YOLOv3. Our algorithm utilizes the concept of vehicle dynamics and the confidence region to predict the future position on vehicles. This will help us to realize real-time dynamic prediction and Internet of Vehicles (IoV)-based collision detection. Lastly, in accordance with the experimental results, our design shows the performance for predicting the future position of a vehicle. [ABSTRACT FROM AUTHOR]

This study focuses on the development of a novel polishing pad for SiC wafers. Fe and Al2O3 particles were impregnated in a polyurethane matrix, thus forming a fixed abrasive polishing pad. Four types of pads with different compositions of Fe and Al2O3 were fabricated. A combination of loose and fixed polishing methods was used for polishing with the fabricated pads and was investigated to improve the polishing process. The surface characteristics of the polished SiC wafer and the SiC removal rate during polishing using the designed pads were examined and compared with those for SiC polished with a conventional polyurethane pad. Experimental results showed that the removal rate for SiC in the case of polishing with the pads consisting 1 wt % Fe and 3 wt % Al2O3 particles was approximately 73% higher than that observed when polishing using the conventional polyurethane polishing pad. Additionally, the surface roughness of the resulting SiC wafers after polishing with the Fe and Al2O3-impregnated pads was identical to that when using the conventional polyurethane pad, without any surface damage. The results indicated that the Fe and Al2O3-impregnated pads can be effectively used for SiC wafer polishing. When the proposed process was employed for polishing single-crystal SiC, both the polishing time and cost were reduced. This novel design can facilitate the extensive use of single-crystal SiC wafers in the future. [ABSTRACT FROM AUTHOR]

Microfluidic devices provide low sample consumption, high throughput, high integration, and good environment controllability advantages. An alternative to conventional bioreactors, microfluidic devices are a simple and effective platform for stem cell investigations. In this study, we describe the design of a microfluidic device as a chemical and mechanical shear stress bioreactor to stimulate rat bone marrow stromal cells (rBMSCs) into neuronal cells. 1-methyl-3-isobutylxanthine (IBMX) was used as a chemical reagent to induce rBMSCs differentiation into neurons. Furthermore, the shear stress applied to rBMSCs was generated by laminar microflow in the microchannel. Four parallel microfluidic chambers were designed to provide a multiplex culture platform, and both the microfluidic chamber-to-chamber, as well as microfluidic device-to-device, culture stability were evaluated. Our research shows that rBMSCs were uniformly cultured in the microfluidic device and differentiated into neuronal cells with IBMX induction. A three-fold increase in the neuronal cell differentiation ratio was noted when rBMSCs were subjected to both IBMX and fluid flow shear stress stimulation. Here, we propose a microfluidic device which is capable of providing chemical and physical stimulation, and could accelerate neuronal cell differentiation from bone marrow stromal cells. [ABSTRACT FROM AUTHOR]

Myasthenia gravis (MG), an acquired autoimmune-related neuromuscular disorder that causes muscle weakness, presents with varying severity, including myasthenic crisis (MC). Although MC can cause significant morbidity and mortality, specialized neuro-intensive care can produce a good long-term prognosis. Considering the outcomes of MG during hospitalization, it is critical to conduct risk assessments to predict the need for intensive care. Evidence and valid tools for the screening of critical patients with MG are lacking. We used three machine learning-based decision tree algorithms, including a classification and regression tree, C4.5, and C5.0, for predicting intensive care unit (ICU) admission of patients with MG. We included 228 MG patients admitted between 2015 and 2018. Among them, 88.2% were anti-acetylcholine receptors antibody positive and 4.7% were anti-muscle-specific kinase antibody positive. Twenty clinical variables were used as predictive variables. The C5.0 decision tree outperformed the other two decision tree and logistic regression models. The decision rules constructed by the best C5.0 model showed that the Myasthenia Gravis Foundation of America clinical classification at admission, thymoma history, azathioprine treatment history, disease duration, sex, and onset age were significant risk factors for the development of decision rules for ICU admission prediction. The developed machine learning-based decision tree can be a supportive tool for alerting clinicians regarding patients with MG who require intensive care, thereby improving the quality of care. [ABSTRACT FROM AUTHOR]

Sarcopenia and obesity can negatively impact quality of life and cause chronic fragility, and are associated with neuromuscular diseases, including myasthenia gravis (MG). The long-term consequences of body composition changes in chronic MG remain unknown; we therefore evaluated changes in body composition, including sarcopenia, obesity, lean body mass, and the prevalence of sarcopenic obesity in patients. In this cross-sectional study, 35 patients with MG (mean age: 56.1 years) and 175 matched controls were enrolled. Body fat mass and skeletal muscle mass were measured using whole body dual-energy X-ray absorptiometry. Patients with MG exhibited a higher prevalence of obesity and higher android adiposity and total body fat percentage than those of controls. Although the prevalence of sarcopenia and sarcopenic obesity did not increase with age, there was a decrease in arm and android muscle mass in patients with MG compared with controls. Lower muscle mass percentages were correlated with increased age and MG severity, but not with corticosteroid use. Thus, MG is associated with increased risk for obesity and decreased muscle mass with aging, regardless of corticosteroid use. Therefore, accurate diagnosis of body composition changes in MG could facilitate the application of appropriate therapies to promote health, improve quality of life, and prevent fragility. [ABSTRACT FROM AUTHOR]

Myasthenia gravis (MG) is an autoimmune disorder that causes muscle weakness. Although the management is well established, some patients are refractory and require prolonged hospitalization. Our study is aimed to identify the important factors that predict the duration of hospitalization in patients with MG by using machine learning methods. A total of 21 factors were chosen for machine learning analyses. We retrospectively reviewed the data of patients with MG who were admitted to hospital. Five machine learning methods, including stochastic gradient boosting (SGB), least absolute shrinkage and selection operator (Lasso), ridge regression (Ridge), eXtreme gradient boosting (XGboost), and gradient boosting with categorical features support (Catboost), were used to construct models for identify the important factors affecting the duration of hospital stay. A total of 232 data points of 204 hospitalized MG patients admitted were enrolled into the study. The MGFA classification, treatment of high-dose intravenous corticosteroid, age at admission, treatment with intravenous immunoglobulins, and thymoma were the top five significant variables affecting prolonged hospitalization. Our findings from machine learning will provide physicians with information to evaluate the potential risk of MG patients having prolonged hospital stay. The use of high-dose corticosteroids is associated with prolonged hospital stay and to be used cautiously in MG patients. [ABSTRACT FROM AUTHOR]

Transformation and sustainable development must be undertaken in accordance with the trends of the times, which presents challenges to rural areas worldwide. In addition to preserving rural food specialties and presenting them in new ways to attract consumers, these areas must link farmers' production, processing, sales, and management. It is imperative to sustainably pass on rural foods and theircooking techniques and integrate them into innovative business strategies so that delicious rural foods can be sold on the consumer market, boosting rural economies and their development. The main objective of this research was to conduct indicator modeling and empirical analysis for the sustainable inheritance of Taiwan's rural cooking techniques and the development of innovative marketing strategies. The Fuzzy Delphi Technique was used as the main research method to select agricultural experts and rural household economy organizations for indicator modeling and empirical analysis. The results of the research indicate that agricultural experts believe that market operation is the primary developmental focus of cultural inheritance and innovation, whereas household economy organizations believe that education, training, promotion, and development are the primary developmental focuses. The greatest contribution and innovation of this research are the findings that culinary education and training, organized by the farmers' association, can sustainably pass on traditional rural cooking techniques, and the process of incorporating local ingredients into commercial gourmet food should also consider aspects of the economic and marketing strategies of market operation, facilitating the sustainable inheritance of unique, traditional, local, and rural food culture. [ABSTRACT FROM AUTHOR]

There is a lack of guidelines for physical exercise in patients with myasthenia gravis (MG). A few pilot studies have shown that exercise can be safely applied to patients with MG. However, how physical exercise affects body composition, disease function, and disease severity remains unknown. In this prospective study, we enrolled 34 patients with MG with stable condition and evaluated the disease severity, physical fitness parameters, and body composition (measured using whole-body dual-energy X-ray absorptiometry (DXA)), before and after conducting a 24-week physical exercise regimen of aerobic and resistance strength training. The outcomes were measured by DXA, quantitative MG (QMG) score, quality of life score, handgrip strength and walking speed. During the training regimen, participants were free to decide how many exercise sessions per week and regularly reported their weekly exercise time. The physical exercise program was well tolerated by the participants, the parameters of the QMG score and handgrip strength improved, and participants' body composition did not change significantly. The high exercise group experienced greater deterioration in muscle mass in the arms, but exhibited a greater improvement in forced vital capacity, walking speed, and symptom severity. The group with low QMG scores improved more in terms of physical fitness, including walking speed. These findings indicate that physical exercise is well tolerated by patients with MG, and is accompanied by improved muscular and physical functions. We propose that physical exercise is safe, effective, and appropriate for patients with well-regulated MG. [ABSTRACT FROM AUTHOR]

By establishing a two-level symbiotic supply chain system consisting of one supplier and one manufacturer, we use Stackelberg method to analyze the optimal price and revenue model of supplier and manufacturer in the symbiotic supply chain under two power structures in which the supplier and manufacturer are dominant respectively, and analyze the influence of the degree of symbiosis and power structure on the model. Through comparative analysis, we find that: There is a relationship between the income level and the degree of symbiosis in the symbiotic supply chain. The change of power structure will affect the relative benefits of suppliers and manufacturers in the symbiotic supply chain. The manufacturer's expected unit product revenue will affect the supply chain revenue when the manufacturer is dominant. Finally, the sensitivity analysis of relevant parameters is carried out through an example analysis, and the validity of the conclusion is verified. This paper has a guiding significance for the behavior of enterprises in the cogeneration supply chain. [ABSTRACT FROM AUTHOR]

This paper proposes a centralized-decentralized control strategy for regenerative braking energy utilization and power quality improvement in the modified AC-fed railway system with energy-storage-based smart electrical infrastructure. The proposal of a centralized-decentralized control strategy can enhance the ability to withstand and rapidly recover from disruptions, thus providing further guarantees for safe and reliable operation and energy conservation for railway systems. First of all, the description and control strategy of the modified railway system are outlined, and then the control principles and implementation process of the centralized control and decentralized control strategies are given. Moreover, a method of load power detection and regulated power reference calculation is proposed. Finally, the effectiveness of the proposed strategy is verified in a case of a modified railway system consisting of four traction substations and eight power supply sections. The results demonstrate that regenerative braking energy can be efficiently utilized in railways and that power quality can be improved using the proposed centralized-decentralized control strategy. [ABSTRACT FROM AUTHOR]

With respect to the problems of great impact on the utility grid, the increasing catenary voltage, the limited utilization rate of the regenerative braking energy (RBE) and the irreducible cost of electricity for locomotives caused by the RBE generated by electric locomotives of the whole railway line cannot be fully utilized in traction power supply systems. An energy management system (EMS) integrating electrical energy and information is proposed in this paper. A corresponding optimization algorithm is also proposed to calculate the distribution of the regenerative braking power in the whole railway line. The structure and working principle of the EMS are introduced. The basic principle and detailed steps of the proposed algorithm are given. Finally, the optimization calculation and simulation are carried out, the effectiveness and feasibility of the proposed EMS and the optimization algorithm are verified. [ABSTRACT FROM AUTHOR]