Search Results (407)

Rail traction and resistance play an essential role in the efficient operation of rail systems. The nature of traction is based on the balance between static friction and generated force at the perimeter of the driving wheels. The main objective of this paper is the development of a methodology and a modeling procedure for the design of train speed trajectory diagrams for the suburban railway. The model is applied to the Athens–Chalkida railway line (in Greece). For this purpose, geometric data for the above-mentioned railway line is collected from the Hellenic Railways Organization (OSE) and then recorded and digitized. A code is developed in MATLAB to calculate the total resistance of the railway line at each kilometer position. The traction elements of the trains operating on the Athens–Chalkida–Athens line, as well as other representative trains, and the magnitudes of their mechanical-aerodynamic resistances are recorded. The MATLAB program generates and compiles the train speed trajectory diagrams and the traction-resistance matrices. Finally, a comparison is made between the time, energy, CO₂ emissions, and fuel costs of the rail in relation to the competing mode of transportation, which, for the specific line studied, is the tourist bus. Full article

Multi-motor servo systems are widely used in industrial control. However, the single-core microprocessor architecture based on the microcontroller unit (MCU) and digital signal processor (DSP) is not well suited for high-performance multi-motor servo systems due to the inherent limitations in computing performance and serial execution of code. The bus-based distributed architecture formed by interconnecting multiple unit controllers increases system communication complexity, reduces system integration, and incurs additional hardware and software costs. Field programmable gate array (FPGA) possesses the characteristics of high real-time performance, parallel processing, and modularity. A single FPGA can integrate multiple motor servo controllers. This research uses MCU + FPGA as the core to realize high-precision multi-axis real-time control, combining the powerful performance of the MCU processor and the high-speed parallelism of FPGA. The MCU serves as the central processor and facilitates data interaction with the host computer through the controller area network (CAN). After data parsing and efficient computation, MCU communicates with the FPGA through flexible static memory controller (FSMC). A motor servo controller intellectual property (IP) core is designed and packaged for easy reuse within the FPGA. A 38-axis micro direct current (DC) motor control system is constructed to test the performance of the IP core and the heterogeneous embedded platforms. The experimental results show that the designed IP core exhibits robust functionality and scalability. The system exhibits high real-time performance and reliability. Full article

This paper proposes a load flow model to estimate the actual power output by incorporating time series data for solar irradiance and wind speed at a specific location. The integration of this time series data into the network is carried out in three distinct scenarios: considering only solar output, only wind output, and the combined contribution of solar and wind. These data integration processes are followed by load flow analysis conducted on the standard IEEE 33Bus radial distribution system. The time series simulations are executed using OpenDSS (Open Distribution System Simulator) software, which utilizes a COM (Common Object Model) interface to display results in MATLAB. Full article

The estimation of energy consumption under real-world driving conditions is a prerequisite for optimizing bus scheduling and meeting the requirements of route operation, thereby promoting the large-scale application of battery electric buses. However, the limitation of data accuracy and the uncertainty of many factors, such as weather conditions, traffic conditions, and driving styles, etc. make accurate energy consumption estimation complicated. In response to these challenges, a new method for estimating the energy consumption of battery electric buses (BEBs) is proposed in this research. This method estimates the speed profiles of different driving styles and the energy consumption extremes using real-world driving data. First, this research provides the constraints on speed formed by environmental factors including weather conditions, route characteristics, and traffic characteristics. On this basis, there are two levels of estimation for energy consumption. The first level classifies different driving styles and constructs the corresponding speed profiles with the time interval (10 s), the same as real-world driving data. The second level further constructs the speed profiles with the time interval of 1 s by filling in the first-level speed profiles and estimating the energy consumption extremes. Finally, the estimated maximum and minimum value of energy consumption were compared with the true value and the results showed that the real energy consumption did not exceed the extremes we estimated, which proves the method we proposed is reasonable and useful. Therefore, this research can provide a theoretical foundation for the deployment of battery electric buses. Full article

This study addresses the prediction of CAN bus data, a lesser-explored aspect within unsupervised anomaly detection research. We propose the Fast-Gated Attention (FGA) Transformer, a novel approach designed for accurate and efficient prediction of CAN bus data. This model utilizes a cross-attention window to optimize computational scale and feature extraction, a gated single-head attention mechanism in place of multi-head attention, and shared parameters to minimize model size. Additionally, a generalized unbiased linear attention approximation technique speeds up attention block computation. On three datasets—Car-Hacking, SynCAN, and Automotive Sensors—the FGA Transformer achieves predicted root mean square errors of 1.86 × 10⁻³, 3.03 × 10⁻³, and 30.66 × 10⁻³, with processing speeds of 2178, 2768, and 3062 frames per second, respectively. The FGA Transformer provides the best or comparable accuracy with a speed improvement ranging from 6 to 170 times over existing methods, underscoring its potential for CAN bus data prediction. Full article

A self-compensating control strategy for harmonic parameters based on the crown porcupine optimization algorithm is proposed for the single-phase rectifier and two-phase inverter operation mode of the bidirectional converter. In order to improve the response speed of the inverter voltage, the instantaneous expressions of the phase angle coefficient and amplitude coefficient of the dc-side voltage doubling fluctuation are derived, and the third harmonic is calculated based on the crown porcupine optimization algorithm according to the Proportional Integral (PI) + Quasi-Proportional Resonance (QPR) double closed-loop control method and injected into the input voltage of the inverter side to offset the influence of the bus-doubling fluctuation on the output voltage of the two-phase inverters of B and C so that the total harmonic content of the two-phase output voltages of the two-phase inverters of B and C can be reduced. The total harmonic content of the B and C inverter output voltages is reduced. The effective control of the control method for single-phase rectifier two-phase inverter mode is verified through simulation. Finally, the effectiveness of the control strategy is verified by experimenting with a 15 kW LCL-type bi-directional converter prototype. Full article

With its technical advantages of high speed, low latency, and broad connectivity, fifth-generation mobile communication technology has brought about unprecedented development in numerous vertical application scenarios. However, the high energy consumption and expansion difficulties of 5G infrastructure have become the main obstacles restricting its widespread application. Therefore, aiming to optimize the energy utilization efficiency of 5G base stations, a novel distributed photovoltaic 5G base station DC microgrid structure and an energy management strategy based on the Curve Fitting–Perturb and Observe–Incremental Conductance (CF-P&O-INC) Maximum Power Point Tracking (MPPT) algorithm from the perspectives of energy and information flows are proposed. Simulation results show that the proposed MPPT algorithm can increase the efficiency to 99.95% and 99.82% under uniform irradiation and partial shading, respectively. Under the proposed strategy, when the base station load changes drastically, the voltage fluctuation of the DC bus is less than 1.875%, and returns to a steady state within 0.07s, alleviating the high energy consumption of 5G base stations effectively and achieving coordinated optimization management of various types of energy in multi-source power supply systems. Full article

This paper describes an effective operating strategy for electric vehicles (EVs) in a hybrid facility that leverages renewable energy sources. The method is to enhance the profit of the wind–thermal–EV hybrid plant while maintaining the grid frequency (f_PG) and energy level of the EV battery storage system. In a renewable-associated power network, renewable energy producers must submit power supply proposals to the system operator at least one day before operations begin. The market managers then combine the power plans for the next several days based on bids from both power providers and distributors. However, due to the unpredictable nature of renewable resources, the electrical system cannot exactly adhere to the predefined power supply criteria. When true and estimated renewable power generation diverges, the electrical system may experience an excess or shortage of electricity. If there is a disparity between true and estimated wind power (TWP, EWP), the EV plant operates to minimize this variation. This lowers the costs associated with the discrepancy between actual and projected wind speeds (TWS, EWS). The proposed method effectively reduces the uncertainty associated with wind generation while being economically feasible, which is especially important in a deregulated power market. This study proposes four separate energy levels for an EV battery storage system (E_EV,max, E_EV,opt, E_EV,low, and E_EV,min) to increase system profit and revenue, which is unique to this work. The optimum operating of these EV battery energy levels is determined by the present electric grid frequency and the condition of TWP and EWP. The proposed approach is tested on a modified IEEE 30 bus system and compared to an existing strategy to demonstrate its effectiveness and superiority. The entire work was completed using the optimization technique called sequential quadratic programming (SQP). Full article

With the rapid development of vehicle electronic communication technology, in-vehicle bus network system communicates with external electronic devices such as mobile phones and OBD II, causing in-vehicle bus networks to face severe network security threats. This study aims to explore the security scheme of in-vehicle bus networks based on a key management system to ensure the confidentiality, integrity, authenticity, and availability of vehicle communication, and innovatively propose a key management system. This key management system uses data encryption and signature algorithms based on the elliptic curve cryptographic domain, which is mainly composed of key generation and key distribution modules. By designing a key life cycle management strategy for In-Vehicle Ethernet and using the digital envelope technique, data encryption and digital signatures are combined to ensure the secure generation and distribution of keys. Experimental simulation results show that the session key negotiation speed of the proposed key management system for In-Vehicle Ethernet in this study is 1.533 ms, which improves the speed by 80.5% compared with the traditional key management system. The key management system proposed in this study improves the real-time information processing efficiency in In-Vehicle Ethernet and lays a solid foundation for the stable development of intelligent connected vehicles. Full article

This paper presents a polar-format algorithm (PFA)-based synthetic-aperture radar (SAR) processor that can be mounted on a small drone to support video SAR (ViSAR) imaging. For drone mounting, it requires miniaturization, low power consumption, and high-speed performance. Therefore, to meet these requirements, the processor design was based on a field-programmable gate array (FPGA), and the implementation results are presented. The proposed PFA-based SAR processor consists of both an interpolation unit and a fast Fourier transform (FFT) unit. The interpolation unit uses linear interpolation for high speed while occupying a small space. In addition, the memory transfer is minimized through optimized operations using SAR system parameters. The FFT unit uses a base-4 systolic array architecture, chosen from among various fast parallel structures, to maximize the processing speed. Each unit is designed as a reusable block (IP core) to support reconfigurability and is interconnected using the advanced extensible interface (AXI) bus. The proposed PFA-based SAR processor was designed using Verilog-HDL and implemented on a Xilinx UltraScale+ MPSoC FPGA platform. It generates an image 2048 × 2048 pixels in size within 0.766 s, which is 44.862 times faster than that achieved by the ARM Cortex-A53 microprocessor. The speed-to-area ratio normalized by the number of resources shows that it achieves a higher speed at lower power consumption than previous studies. Full article

This study introduces an innovative algorithm for classifying transportation modes. It categorizes modes such as walking, biking, tram, bus, taxi, and private vehicles based on data collected through sensors embedded in smartphones. The data include date, time, latitude, longitude, altitude, and speed, gathered using a mobile application specifically designed for this project. These data were collected through the smartphone’s GPS to enhance the accuracy of the analysis. The stopping times of each transport mode, as well as the distance traveled and average speed, are analyzed to identify patterns and distinctive features. Conducted in Cuenca, Ecuador, the study aims to develop and validate an algorithm to enhance urban planning. It extracts significant features from mobility patterns, including speed, acceleration, and over-acceleration, and applies longitudinal dynamics to train the classification model. The classification algorithm relies on a decision tree model, achieving a high accuracy of 94.6% in validation and 94.9% in testing, demonstrating the effectiveness of the proposed approach. Additionally, the precision metric of 0.8938 signifies the model’s ability to make correct positive predictions, with nearly 90% of positive instances correctly identified. Furthermore, the recall metric at 0.83084 highlights the model’s capability to identify real positive instances within the dataset, capturing over 80% of positive instances. The calculated F1-score of 0.86117 indicates a harmonious balance between precision and recall, showcasing the models robust and well-rounded performance in classifying transport modes effectively. The study discusses the potential applications of this method in urban planning, transport management, public transport route optimization, and urban traffic monitoring. This research represents a preliminary stage in generating an origin–destination (OD) matrix to better understand how people move within the city. Full article

The energy consumption profiles of conventional fuelled and electric vehicles are different due to the fundamental differences in the driving characteristics of these vehicles, which have been actively researched elsewhere but mostly on the basis of uncommon geographical contexts. This study, therefore, collected driving data on electric and conventional diesel buses running along exactly the same set of bus routes in Hong Kong during normal daily revenue operations. This enabled a fair comparison of driving characteristics for both types of bus under identical real-life, on-road driving conditions, which highlighted the originality and contributions of this study. A three-step approach was adopted to carry out detailed driving pattern analyses, which included key driving parameters, speed–acceleration probability distributions (SAPDs), and vehicle-specific power (VSP) distributions. Results found that route-based comparisons did highlight important differences in driving patterns between electric and diesel buses that might have been smoothed out by analyses with mixed-route datasets. In particular, the spread, intensity, and directions of these differences were found to be exaggerated at the route-based level. The differences in driving patterns varied across different routes, which has significant implications on vehicle energy consumption. Government agencies and/or bus operators should make references to these results in formulating electric bus deployment plans. Full article

This paper explores the contribution of custom-trained Large Language Models (LLMs) to developing Open Education Resources (OERs) in higher education. Our empirical analysis is based on the case of a custom LLM specialized for teaching business management in higher education. This custom LLM has been conceptualized as a virtual teaching companion, aimed to serve as an OER, and trained using the authors’ licensed educational materials. It has been designed without coding or specialized machine learning tools using the commercially available ChatGPT Plus tool and a third-party Artificial Intelligence (AI) chatbot delivery service. This new breed of AI tools has the potential for wide implementation, as they can be designed by faculty using only conventional LLM prompting techniques in plain English. This paper focuses on the opportunities for custom-trained LLMs to create Open Educational Resources (OERs) and democratize academic teaching and learning. Our approach to AI chatbot evaluation is based on a mixed-mode approach, combining a qualitative analysis of expert opinions with a subsequent (quantitative) student survey. We have collected and analyzed responses from four subject experts and 204 business students at the Faculty of Economics, Business and Tourism Split (Croatia) and Faculty of Economics Mostar (Bosnia and Herzegovina). We used thematic analysis in the qualitative segment of our research. In the quantitative segment of empirical research, we used statistical methods and the SPSS 25 software package to analyze student responses to the modified BUS-15 questionnaire. Research results show that students positively evaluate the business management learning chatbot and consider it useful and responsive. However, interviewed experts raised concerns about the adequacy of chatbot answers to complex queries. They suggested that the custom-trained LLM lags behind the generic LLMs (such as ChatGPT, Gemini, and others). These findings suggest that custom LLMs might be useful tools for developing OERs in higher education. However, their training data, conversational capabilities, technical execution, and response speed must be monitored and improved. Since this research presents a novelty in the extant literature on AI in education, it requires further research on custom GPTs in education, including their use in multiple academic disciplines and contexts. Full article

Rapid urbanization has resulted in various challenges, including a decline in environmental quality, traffic congestion, housing tensions, and employment difficulties. To address these issues, the United Nations introduced the “2030 Agenda for Sustainable Development”. One of the specific targets, 11.2.1, aims to tackle transportation problems. This study focuses on Guilin City, which is designated as an innovation demonstration zone for the national sustainable development agenda. The research conducted in this study examines the state of public transportation in six urban areas of Guilin City from 2015 to 2021, utilizing the United Nations Sustainable Development Goals (SDGs) indicator system, evaluation method, geospatial analysis, and entropy value method. The findings reveal that the coverage area of public transportation in the six urban areas of Guilin City expanded from 147.98 km² in 2015 to 259.18 km² in 2021. The percentage of the population with access to public transportation increased from 69.06% in 2015 to 71.63% in 2018 and further to 75.60% in 2021. While the accessibility of public transportation in the other four districts exceeds 90%, Lingui District and Yanshan District have lower accessibility, but it is gradually improving. The center of gravity for public transportation is also shifting towards the southwest, with Lingui District and Yanshan District experiencing gradual development. The evaluation score for sustainable development increased from 64.30 to 74.48, indicating a transition from a low sustainable development level to medium sustainable development level. Significant progress has been made in the indicators of the share of new energy buses, the rate of bus sharing, the coverage rate of bus stops, and the number of public transportation vehicles per 10,000 people. However, the indicators for the average distance between bus stops, the average speed of public transportation, and the density of public transportation routes are growing at a slower pace. The development of urban public transportation continues to improve, and the overall trend is positive. The sustainable development evaluation framework and positioning method proposed in this study serve as a reference for the sustainable development of Guilin City. Additionally, it provides insights for evaluating the sustainable development goals of public transportation in tourist cities like Guilin in China and worldwide. Full article

Once a fire breaks out in an electric bus, it can easily lead to mass casualties and severe injuries, resulting in significant property damage and social impact. The high-temperature smoke and toxic gases in an electric bus fire are key factors that cause a large number of casualties, both of which are closely related to ventilation conditions. In view of this, this study utilized the Fire Dynamics Simulator (FDS 6) software to establish a three-dimensional experimental model of an electric bus. Numerical simulations of the fire combustion process in the electric bus under different ventilation conditions were conducted. Multiple fire scenes were established based on varying ventilation areas, different wind speeds, and diverse window opening positions. This study specifically analyzed the temperature and CO concentration variations under different fire scenes. By comparing the simulation results under different ventilation conditions, it can be concluded that when an electric bus catches fire, opening 100% of the windows, the wind speed is 8 m/s, and opening the rear window of the electric bus first can minimize the fire risk. Through the numerical simulation of electric bus fires under various conditions, this study analyzed the impact of different ventilation conditions on electric bus fires, providing a theoretical basis for firefighting and rescue efforts as well as personnel evacuation in electric bus fire incidents, with the ultimate goal of maximizing public safety. Full article