Train Monitoring System Research Articles

Teknologi Artificial Intelligence (AI) Vision Swift dalam Sistem Pemantauan Latihan Bulu Tangkis dengan Algoritma Optical Flow

Badminton is one of the most popular sports in Indonesia. In fact, Indonesia often wins various badminton competitions at the international level. Many people enjoy playing badminton, but many of them do not know whether their shots are good or whether they understand the basic techniques of badminton correctly. Additionally, many of them want to improve their skills but do not have enough time to train with a professional coach. This research aims to develop a badminton training monitoring system based on AI Vision technology using the Swift programming language. This system is expected to help badminton players evaluate and improve the quality of their shots independently. The main focus of this research is to optimize computational accuracy by using the Optical Flow algorithm to track the movement of the shuttlecock during training. In developing this system, the Optical Flow algorithm is used to analyze the shuttlecock's trajectory and its drop points. The results of this research show that testing 20 shots using shuttlecock trajectory can be accurately detected by the system with an accuracy of 97.22%. Meanwhile, the system's accuracy in tracking the placement of the shuttlecock in the opponent's service area is 94.50%.

MITIGASI RESIKO RANTAI PASOK PADA PASOKAN PENGGUNAAN BBM DI DEPO LOKOMOTIF PT KAI SIDOTOPO

Risk is a factor that always exists in every sector of life incluidng suppky usage in PT KAI. Based on the results and discussion of this research, the conclusion is that there are 20 risk agents and 20 risk events, of which 4 priority risk agents are lack of control over fuel hoses (A13), inaccurate measuring instruments (A7), Over limit Policy (A2) and lack of clear and measurable coordination (A1). Second, there are 6 mitigation actions, namely Modification of the queue and filling system (P1), Improvement of the train supervision and monitoring system with (P2), Training and coordination staff ( P3), Training of machinists and system machinists and managemen update (P4), Modification of fuel control data with by internet (P5), and Maintenance engginer properly and scheduled ( P6). Where the most effective solutions based on HOR Phase 2 are respectively PA 2, PA 3, PA 4 PA 6, PA 5 and PA 1. Meanwhile based on AHP they are PA 2, PA 1, PA 5, PA 6, PA 3 and PA 4. So the alternative solutions developed are PA 2, PA 3 and PA 4.

Determining Smart Phone Sensing and K-Means Clustering for Accurate and Timely Railway Track Joint Fault Diagnosis

The railway track joint is an important component that connects two sections of the rail and ensures a smooth and safe operation of trains. However, the joint is also a critical point of failure that can lead to train derailments and accidents. Therefore, accurate and timely detection of joint faults is crucial for ensuring the safety and reliability of railway transportation. In this paper, we propose a novel approach for railway track joint fault diagnosis using smart phone sensing and k-means clustering. Our approach utilizes the accelerometer sensor of a smart phone to measure the vibrations and movements of a specifically developed railway shoe stick that is employed on an actual railway track for the condition monitoring of the railway tracks. More than 60000 data values are collected and are then processed and analysed using k-means clustering, a popular unsupervised machine learning technique that groups similar data points together. The K means clustering in this study forms 3 clusters as a result. The 3 clusters after being validated on the track by virtue of visual inspection are determined to be acceleration values of the healthy track, track with higher joint gap than the standardized value and super-elevated railway track joint fault(s), respectively. In addition to its high accuracy and efficiency, our approach has several advantages over traditional methods, such as low cost, easy deployment, and high scalability. Moreover, the smart phone sensing technology can be easily integrated with existing train monitoring systems, making it a useful tool for real-time joint fault diagnosis and maintenance. Overall, this study demonstrates the potential of smart phone sensing and k-means clustering for railway track joint fault diagnosis and highlights the need for further research in this field.

Faktor Geografi Tempat Tinggal, Sosial dan Kultural terhadap Pernikahan Dini

Early marriage can have negative impacts on reproductive health and the lives of those involved. This study in South Bangka Regency aimed to analyze the relationship between geographic and socio-cultural factors and early marriage. Using a cross-sectional design and Chi-Square statistical test with a 95% confidence level (ρ ≤0.05), the study found that geographic factors did not significantly relate to early marriage (p=0.102). However, logistic regression analysis indicated that female adolescents living in coastal areas had a 1.49 times higher risk of early marriage compared to those living on the mainland. Socio-cultural factors, particularly education, showed a significant relationship (p=0.016), with lower education levels correlating with a higher likelihood of early marriage. Conversely, ethnicity did not show a significant relationship (p=0.147). Although cultural factors were not statistically significant, traditions and cultural norms still influence marriage decisions. Therefore, enhancing education, increasing awareness of the negative impacts of early marriage, and community interventions are crucial. Health professionals are advised to improve education and counseling on reproductive health and the effects of early marriage, adopt a proactive approach in communities, especially in coastal and isolated areas, and collaborate with schools and community organizations. Regular training and effective monitoring systems are also necessary to ensure the success of early marriage prevention programs

Improving the pragmatic usefulness of the scoring matrix for the Consolidated Framework for Implementation Research (CFIR). A proposal for a more frequency-based approach: The CFIR-f.

The Consolidated Framework for Implementation Research (CFIR) is a well-established framework for systematically identifying key factors influencing the implementation of programs. To enhance the classification of existing CFIR definitions, as well as its +2/-2 scoring system, this study incorporated the views of relevant experts to: i) improve how themes are scored; and ii) utilise more information regarding the frequency with which themes are identified. This structured, frequency-based approach to the CFIR's scoring process has been trialled as the CFIR-frequency (CFIR-f). Researchers thematically analysed semi-structured interview data from four groups of policy and program experts (N = 24) delivering two family-based therapies in New South Wales (NSW), Australia. Themes identified by less than 50% were excluded from further analysis. Themes identified by 50% or more of expert participants in the four groups were classified as enablers or barriers using clearly defined criteria. Each theme was allocated a score according to how many experts identified it as an enabler or barrier, and then mapped back onto the latest adaptation of the CFIR comprising 67 constructs. The CFIR-f successfully determined three enablers of, and six barriers to, implementation. Enablers included the family-based therapy programs, therapist training and participant monitoring systems. Barriers included referral, data collection and staffing difficulties, NSW adaptation issues and the suitability and safety of the programs for Aboriginal families. The same enablers and barriers were identified using both the CFIR-f and the original scoring approach, and the identified themes were successfully mapped to almost all CFIR constructs (65/67). This paper proposes a more frequency-based approach to CFIR's scoring process (the CFIR-f). By specifically utilising the frequency with which these barriers and enablers are identified, the CFIR-f engenders a list of ranked themes that service providers and policymakers can use to inform their decisions about program modification and implementation.

DEEP LEARNING FRAMEWORK FOR HUMAN ACTIVITY DETECTION BASED ON WIRELESS SENSORS.

Recognizing human athletic behaviors has grown in importance as a component of analysis during the last few years. For upcoming training, FPGA-based recognition has been built to perfectly predict human actions. Intelligent environments where human athletic movements may be automatically recognized are required to ensure the viability of such long-term athletic training monitoring systems. The system discussed here employs an Artificial Neural Network (ANN) based Field-Programmable Gate Array (FPGA). In order to infer human behavior from the data collected by a smart environment, machine learning algorithms must first be trained on annotated datasets. To effectively extract characteristics from unprocessed data and develop a machine learning model for anticipating an individual's movement, conventional signal processing techniques and domain knowledge are required. The purpose of this work is to show how a hybrid deep learning model may be applied to recognize human behavior. Convolutional neural networks and continuous neural networks, for example, are deep learning techniques that will extract the features and help with categorization. The proposed model forecasts human activity using wireless sensor data mining datasets. Accuracy, training loss, testing loss, the confusion matrix, and other metrics have all been used to evaluate the model's performance.

Wearable rehabilitation wristband for distal radius fractures.

Distal radius fractures are a common type of fracture. For patients treated with closed reduction with splinting, a period of rehabilitation is still required after the removal of the splint. However, there is a general lack of attention and low compliance to rehabilitation training during this period, so it is necessary to build a rehabilitation training monitoring system to improve the efficiency of patients' rehabilitation. A wearable rehabilitation training wristband was proposed, which could be used in the patient's daily rehabilitation training scenario and could recognize four common wrist rehabilitation actions in real-time by using three thin film pressure sensors to detect the pressure change curve at three points on the wrist. An algorithmic framework for classifying rehabilitation training actions was proposed. In our framework, an action pre-detection strategy was designed to exclude false detections caused by switching initial gestures during rehabilitation training and wait for the arrival of the complete signal. To classify the action signals into four categories, firstly an autoencoder was used to downscale the original signal. Six SVMs were then used for evaluation and voting, and the final action with the highest number of votes would be used as the prediction result. Experimental results showed that the proposed algorithmic framework achieved an average recognition accuracy of 89.62%, an average recognition recall of 88.93%, and an f1 score of 89.27% on the four rehabilitation training actions. The developed device has the advantages of being small size and easy to wear, which can quickly and accurately identify and classify four common rehabilitation training actions. It can easily be combined with peripheral devices and technologies (e.g., cell phones, computers, Internet) to build different rehabilitation training scenarios, making it worthwhile to use and promote in clinical settings.

Development and Evaluation of a Full-Waveform Resistance Training Monitoring System Based on a Linear Position Transducer

Recent advances in training monitoring are centered on the statistical indicators of the concentric phase of the movement. However, those studies lack consideration of the integrity of the movement. Moreover, training performance evaluation needs valid data on the movement. Thus, this study presents a full-waveform resistance training monitoring system (FRTMS) as a whole-movement-process monitoring solution to acquire and analyze the full-waveform data of resistance training. The FRTMS includes a portable data acquisition device and a data processing and visualization software platform. The data acquisition device monitors the barbell's movement data. The software platform guides users through the acquisition of training parameters and provides feedback on the training result variables. To validate the FRTMS, we compared the simultaneous measurements of 30-90% 1RM of Smith squat lifts performed by 21 subjects with the FRTMS to similar measurements obtained with a previously validated three-dimensional motion capture system. Results showed that the FRTMS produced practically identical velocity outcomes, with a high Pearson's correlation coefficient, intraclass correlation coefficient, and coefficient of multiple correlations and a low root mean square error. We also studied the applications of the FRTMS in practical training by comparing the training results of a six-week experimental intervention with velocity-based training (VBT) and percentage-based training (PBT). The current findings suggest that the proposed monitoring system can provide reliable data for refining future training monitoring and analysis.

Design and Implementation of Urodynamic Monitoring System

In order to assess and diagnose lower urinary tract dysfunction in patients and help patients with lower urinary tract rehabilitation training, an accurate and reliable urodynamic monitoring and automatic voiding system was designed. The system realizes the signal acquisition circuit of bladder pressure, abdominal pressure and urine volume based on the pressure sensor of urinary catheter and the load sensor. Meanwhile, the dynamic waveforms of urinary flow rate, bladder pressure and abdominal pressure are drawn in real time on the software of urodynamic monitoring. Signal processing and analysis of each signal are carried out, and the system performance is verified by building a simulation experiment. The experimental results show that the system is stable, reliable, accurate and meets the expected design goals, which can provide support for subsequent engineering design and clinical applications.

Security Architecture for Secure Train Control and Monitoring System.

A Train Control and Monitoring System (TCMS) is a vital part of monitoring sensors in a train. The data output of sensors is sent wirelessly to the data server for monitoring. However, as the wireless channel used to send the data is a shared public network, the transmitted data are prone to hackers and attacks. This paper proposes the Securebox architecture to manage secure data transfer from the onboard Vehicle Control Unit (VCU) to the data server in TCMS. The architecture is comprised of four main functions: network management, buffer management, data management, and security management. The architecture has been successfully developed in an HSM (Hardware Security Modul) and verified using alpha and beta software testing to form a secure TCMS. From the real-time testing phase in an electric-diesel train, the average performance of the AES-based HSM showed 55% faster time processing with unnoticed 0.1% added memory usage compared to the 3DES. The secure TCMS also withstands MITM attack and provides end-to-end data security compared to the (Mobile Station) MS to Base Station (BS) only in GSM-R.

The sensors-based artificial intelligence Train Control and Monitoring System (TCMS) for managing the railway transport fleet

Railways deliver a safe and sustainable form of transport and are typically pointed as one the safest form of transportation. Nevertheless, train accidents still happen, and when they happen, the consequences concern serious fatalities and injuries. Since every case is unique, the most frequent causes of train accidents are mechanical derailments, failures, as well as human errors and ignorance. In order to mitigate the risks posed by both physical and human related factors, various technological advancements have been designed and implemented. Among many existing Train Control and Monitoring Systems (TCMS), one can observe that recently developed artificial intelligence (AI) methods are also considered to be integrated part of the modern TCMS solutions. Following recent AI improvements and trends, in this paper we aim to present and discuss our newly developed TCMS system. In particular, both the system architecture and features are described along with the expected benefits of its implementation.

Safe4Rail-3: Advanced safety architecture and components for Next Generation Train Control and Monitoring System in Railways

The Train Control and Monitoring System (TCMS) is considered the brain and the communications backbone of the train with an important role on vehicle performances. The Next Generation TCMS (NG-TCMS) aims to overcome the embedded limitations of the existing TCMS concepts and standards from the 1990s to increase the capacity, trustworthiness, reliability, while reducing the Life Cycle Costs of trains. This paper presents an overview of the Safe4Rail-3 project activities and expected contributions to the development of the NG-TCMS, including device development and antenna simulations.

МОДЕЛЬ ИНТЕЛЛЕКТУАЛЬНОЙ ИНФОРМАЦИОННООБРАЗОВАТЕЛЬНОЙ СИСТЕМЫ ВОЕННОГО ВУЗА В УСЛОВИЯХ ЦИФРОВОЙ ТРАНСФОРМАЦИИ ОБРАЗОВАНИЯ

The article reveals the purpose, functions and components of the model of the intellectual information and educational system of the military university. Peculiarities of the components of the intellectual information and educational system providing its functioning are described: digital educational platform; training management and monitoring system; analytical system of training support; intellectual system, providing personalized training; cyber security system. Opportunities of intellectualization of training in the implementation of the model in the electronic environment of the university are presented.

Applying Artificial Intelligence Technology to Analyze the Athletes’ Training Under Sports Training Monitoring System

With the rapid development of artificial intelligence, the related technologies and applications that accompany it emerge as the times require. The industry based on artificial intelligence is booming. Image recognition and target tracking technology are widely used in various fields, especially in the fields of security monitoring and augmented reality. Combined with the characteristics of athletes’ sports, an auxiliary information system is developed to supervise and guide the training in real time. It can track and analyze the characteristics of individual athletes’ sports function, the arrangement of coaches’ training plan, the state of brain function, the index of routine physiology and biochemistry, nutrition regulation, and the condition of injuries and injuries in the middle of the day, so as to reveal the athletes’ training in the middle of the day the changing rule of various indexes in the training state. Based on the mobile artificial intelligence terminal technology, this paper develops and designs a monitoring system for athletes’ training process in C/S mode. GPS is used to obtain athletes’ position information in real time and provide real-time guidance for athletes.

Study on Strength and Quality Training of Youth Basketball Players.

In order to scientifically explore the effective path of strength quality training of basketball players and improve the effect of strength quality training of basketball players, this paper takes young basketball players as the research object and comprehensively observes the changes and improvement of strength quality by building a strength training monitoring system for basketball players. On this basis, it is proposed to integrate blood flow restriction and basketball players' special strength training. Through the comparison with the traditional resistance strength training method, it is found that after 8 weeks of experimental comparison, the athletes' strength quality test indicators show that the average 3RM of the experimental group 1 bench press is 65.2 kg, the experimental group 2 is 65.7 kg, and the experimental group 3 is 72.2 kg. The average performance of the traditional control group was 55.4 kg. Compared with the traditional group, the average performance of the three experimental groups in bench press was significantly improved, which also verified the feasibility of this method in strength quality training.

Driver injuries in heavy vs. light and medium truck local crashes, 2010–2019

Objective: Multiple heavy truck driver injury studies exist, but there is a paucity of research on light and medium truck driver injuries. The objective of this study was to use first report of injury (FROI) data to: (a) compare demographic and injury characteristics; (b) assess workers’ compensation (WC) claim disposition and lost work time status; and (c) describe injury scenarios by vehicle type for heavy truck and light/medium truck driver local crashes. Method: Kentucky Department of Workers’ Claims FROI quantitative and free text data were analyzed for years 2010–2019. Of 800 total FROIs, 451 involved heavy trucks and 349 involved light or medium trucks. Results: There was a higher light/medium truck driver crash FROI rate compared to the heavy truck driver crash FROI rate. There was a higher proportion of younger light/medium truck driver crash FROIs compared to younger heavy truck driver crash FROIs. The retail trade industry made up the largest percentage of light/medium truck local crash FROIs (47%); the transportation and warehousing industry was most frequently cited in heavy truck FROIs (46%). The heavy truck types most frequently identified in FROIs were semi-trucks (13%) and dump trucks (11%). The most common light/medium truck type identified was delivery trucks (30%). Most commonly, heavy truck crash FROIs involved rollovers, driving off/overcorrecting on narrow roadways, and driving downhill/unable to downshift. Light/medium truck crash FROIs most frequently involved being rear-ended, running red lights, and turning in front of other vehicles. Conclusions: The utilization of WC FROI data highlighted top injury scenarios and specific vehicle types for targeting driver safety training among truck drivers, particularly light/medium truck drivers. Road safety policies regarding driver training, crash reviews, and in-vehicle monitoring systems are needed for truck drivers with previous crash injuries, especially for light and medium truck drivers. Practical applications: Enhanced safety training on speeding on narrow roadways, on nearing intersections, and on downshifting on hills is needed for semi-truck, dump truck, and coal truck drivers with previous crash injuries. Rear-end crash prevention training (e.g., gradual stopping and checking mirrors) is needed for drivers of furniture, automotive parts and accessories, and groceries and soft drink delivery trucks with previous crash injuries.

Monitoring of Physical Fitness and Tactical Ability Based on Data-Driven Technology

Tennis is a competitive sport, which requires players to have good physical fitness and long-lasting endurance. Monitoring the physical training and technical and tactical abilities of tennis players is a necessary work to improve the physical fitness and competitive status of tennis players. Monitoring the physical training and technical and tactical capabilities of tennis players is necessary to improve their physical fitness and competitive status. Most of the traditional monitoring systems are incomplete, inaccurate, and inefficient, which can no longer meet the requirements of tennis training monitoring in the new era. Therefore, it is urgent to establish an efficient and more accurate tennis training monitoring system. With the rise of big data, data-driven technology has been concerned about and frequently used. It means using data as a production material, applying it in a scientific way to business operations, and giving continuous positive feedback to facilitate business optimisation. This study selected tennis sports training, physical fitness, and technical and tactical abilities as the monitoring object, and it analyzed the monitoring system combined with the grey relation algorithm and TOPSIS algorithm. The experiment showed that the monitoring system based on data-driven technology can comprehensively and accurately monitor the physical ability and technical and tactical abilities of tennis sports training, and the accuracy improves by 5.9%.

Information Collection, Analysis, and Monitoring System of Children's Physical Training Based on Multisensor.

In order to obtain more children's physical training information and improve the accuracy of children's physical training monitoring, a multisensor-based children's physical training information collection, analysis, and monitoring system is proposed. In the process of physical training and sports training, people's physical training information collection is directly related to the level and effectiveness of physical training. With the combination of multisensor concept and sports training information collection, it can collect the key index data of sports mobilization in real time with the help of multiple sensors and information technology. Taking children's physical training as the object, this paper designs a multisensor physical training data information acquisition terminal, collects different training characteristic data with the help of multisensor equipment, and then comprehensively analyzes and monitors the physical information with the help of certain fusion technology, so as to construct a human posture recognition algorithm based on children's physical training information acquisition. Support vector machine and decision tree are used to classify children's different physical exercise states, and a relatively perfect algorithm architecture of human posture recognition is constructed. The results show that for two decision trees, each decision tree is trained with a total of 675 groups of data, and a total of 342 groups of data are verified and pruned. The two decision trees take 7.17 s and 7.32 s to complete the training process, respectively. It can be seen that when the number of training groups is equal, the training time of the two placement methods is close, so it can be considered that the two placement methods have little effect on the training speed of decision tree. The experimental data show that the design of children's physical training monitoring system in this paper has a certain market value.

A novel vibration energy harvesting system integrated with an inertial pendulum for zero-energy sensor applications in freight trains

With the development of the global economy, the demand for freight trains continues to rise, and the safety and maintenance of freight trains are essential. Therefore, sensors used for onboard monitoring of freight trains are vital components. However, the lack of onboard energy sources on freight trains makes the self-powering of sensors an urgent problem. In this paper, a novel renewable vibration energy harvesting system (VEHS) has been designed to provide electricity to sensors in freight train monitoring systems. The proposed VEHS mainly consists of three components: an energy input module, a motion transformation module, and an energy conversion module. The energy input module is composed of an inertial pendulum and a mass block. The movement transformation module integrates a novel and compact mechanical rectifier mechanism. The energy conversion module includes generators and energy storage devices. The wheel-rail coupled vibration is transformed into mechanical reciprocating rotation via the energy input module, and then into unidirectional mechanical rotation via the motion transformation module. Finally, the energy conversion module converts that mechanical energy of unidirectional rotation into electrical energy, and stores this micro-energy in a capacitor after rectification and boosting. Based on multi-body dynamics software and Simulink, a dynamic model was established to evaluate the vibration response of the system under different axle load and speed. Experiments revealed that the peak output power and energy harvesting efficiency of the VEHS were 1.04 W and 68.29 %, respectively. The RMS value of output power reaches 102.4 mW. In addition, this paper studies the application of the proposed VEHS to the China-Europe Express train. The results show that the system can be used to self-supply energy for freight train monitoring systems.

Safety enhancement design method and control strategy for CCU of high-speed train

The Train Control and Monitoring System (TCMS) is the communication command center of the train, and the Central Control Unit (CCU) is its core component, which should achieve SIL4 safety integrity level defined in standard EN50129. Comparing the current CCU safety mode of dual-machine hot standby to the double 2 out of 2 (2oo2) structure, this paper find that the latter performances better in security. So a new CCU architecture with enhanced safety is proposed, the double 2oo2 structure is applied to the CCU design, and the Markov failure probability model is established to analyze its safety quantitatively. The CCU based on double 2oo2 redundant structure could meet the failure safety principle of composite fail-safety and reactive fail-safety stipulated in the SIL4 safety integrity level, and its Tolerable Functional Failure Rate (TFFR) could reach the requirements of the SIL4. As another important part of RAMS (Reliability, Availability, Maintainability, and Safety), the reliability of this CCU architecture is evaluated both qualitatively and quantitively. According to the results, the double 2oo2 structure could greatly improve the safety of the TCMS and can be used in railway signal system with SIL4 safety integrity requirements.