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2024 International conference on Mechatronics and Intelligent control, ICMIC 2024

作者： Zheng, Yi Zhang, Shengpeng Li, Limeng Zhang, Yuanjie Li, Xuemei National Geomatics Center of China Beijing China Qinghai Provincial Key Laboratory of Geomatics Technology and Application China Qinghai Provincial Basic Surveying and Mapping Institute China

ISBN: (数字)9781510686847

ISBN: (纸本)9781510686830

National fundamental geo-entity construction has been vigorously promoted by the Ministry of Natural Resources of the People's Republic of China. As one of the core products, it is a digital abstract expression of the real world in the form of 2 dimensions, which is difficult to fully meet the actual needs of analysis, calculation and use in the 3-dimensional digital space. This paper designs the process and key technologies for collecting elevation and height information of fundamental geo-entities to upgrade them from 2D to 3D form. This paper elaborates on the implementation process of key technologies such as elevation information acquisition based on DEM, stereo measurement of elevation information, height information acquisition. A practical and feasible process for elevation and height acquisition method of fundamental geo-entity has been formed, which effectively utilizes the advantages of geo-entities in information carrying, expression, sharing, and association. The experimental results show that the production process described in this paper can quickly and effectively obtain elevation and height information of geo-entities and result in 3D fundamental geo-entities, providing technical support and important references for upgrading fundamental geo-entities from 2D to 3D form. © 2025 SPIE.

关键词： Stereo image processing

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Triple Dynamic Graph Convolutional Recurrent Network for Traffic Prediction

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2025年

作者： Zhang, Xiaomei Jiang, Ziqin Lou, Ping Wuhan Univ Technol Sch Informat Engn Wuhan 430070 Hubei Peoples R China

Effective traffic prediction is a critical component of traffic management, especially long-term traffic prediction, as it holds significance for urban traffic planning, traffic warning, people's travel planning, etc. Traffic flow data usually include natural spatio-temporal characteristics, which means the spatial and temporal dependencies are both to be considered for building traffic prediction model. Hence, a triple dynamic graph convolutional recurrent network (TDGCRN) is proposed in this paper, in which a temporal segmentation-based triple spatio-temporal encoder-decoder module is used to capture correlation features of different periods contained in traffic dataset, such as hourly, daily, and weekly cycles;and then a dynamic fusion module is introduced to adaptively learn the weights of different periods to effectively fuse these different cycle features;finally a congestion index-based adjacency matrix update module is utilized to model the topology of graphs to capture the dynamic topology characteristics of the road network. In addition, the congestion index is presented to detect the topology changes caused by traffic congestion to reduce the number of dynamic graphs so as to save computational cost in the training process on our self-collected dataset WH-CN. Experiments on the public dataset (PEMS-BAY and METR-LA datasets) and WH-CN dataset show that our model achieved average improvements of 0.98%, 1.19%, and 1.17% in MAE, RMSE, and MAPE metrics by comparison with the suboptimal baseline, respectively. And on the WH-CN dataset, it also reduced the average training time per epoch by reducing the frequency of dynamic graph generation, thereby reducing the computational cost.

关键词： Roads Correlation Computational modeling Training Indexes Feature extraction Traffic control Traffic congestion Time series analysis Predictive models Dynamic graph convolutional network multi-view learning traffic congestion index traffic prediction

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analysis of Synchrophasor Measurements with Artificial Intelligence Techniques 3

Analysis of Synchrophasor Measurements with Artificial Intel...

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3rd International conference on Electronics and Renewable Systems, ICEARS 2025

作者： Kannan, M. Madhavan, P.M. Ravibharathi, M. Jaividhya, S. Sri Vidhya, D. K.S. Rangasamy College Of Technology Namakkkal Tamilnadu Tiruchengode India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509675

The integration of synchronous phasor measurement units (PMUs) has fundamentally transformed grid monitoring and control systems. These units capture time-synchronized, high-resolution data on key electrical parameters such as voltage, current, and frequency, enabling advanced analysis of grid dynamics. PMUs generate up to 70% of the high-resolution data used to monitor and manage power grids. However, the sheer volume of this data presents a significant challenge in terms of real-time processing and actionable insights. Artificial intelligence (AI), particularly machine learning and deep learning, has emerged as a powerful tool to address these challenges. AI enhances the ability to process and analyze vast amounts of data efficiently, achieving up to 85% accuracy in identifying patterns, detecting anomalies, and predicting failures. The synergy between PMUs and AI not only improves operational efficiency but also plays a critical role in grid stability. AI-driven systems can optimize power distribution by up to 50% and enhance fault tolerance by 60%, ensuring that the grid operates more reliably and efficiently. Further research and simulations suggest that AI-based approaches can increase overall grid reliability by up to 90%. This advancement opens new possibilities for transforming traditional energy infrastructure into smart, adaptive networks. © 2025 IEEE.

关键词： Phasor measurement units

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process Improvement Tools and Methods 12th

Process Improvement Tools and Methods

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12th International conference of Machine and Industrial Design in Mechanical Engineering

作者： Katarina, Skurkova Lestyanszka Prajova, Vanessa Slovak Univ Technol Bratislava Fac Mat Sci & Technol Trnava Trnava Slovakia

ISBN: (纸本)9783031805141;9783031805127;9783031805110

The ability to make timely and sound decisions is one of the key activities of managing an organisation. Organisations try to reduce production costs as much as possible, but this in turn brings the downside of products that do not meet customer expectations. However, this phenomenon also has other downstream processes that burden the organisation and make it more difficult to continue to function. These include customer complaints, the cost of rectifying the defects complained of, downtime, etc. The successful operation of any organisation therefore depends on its ability to adapt to changing customer requirements, as these are a priority in the market. Statistical methods and tools are an effective tool for quality improvement and it is not only advisable, but we can say essential, to introduce them into production processes. Without systematic quality management, an organisation is not able to grow, because it has to combine the enormous effort of developing and introducing new products with the maximum maintenance of the quality of the products already in place. The aim of this paper is to highlight selected tools and methods for process improvement in an organization operating in the automotive industry. The subject of observation is the process of tightening airbag joints. Using Pareto analysis, both bolt and nut tightening data was collected during 3 work shifts over a period of one month. One of the statistical tools and methods used in organizations are sophisticated methods - it includes operational research methods, multivariate analysis, etc. Statistical process control is essentially the maintenance of a process in a steady state. Based on the result, a conclusion is then drawn as to whether corrective action is to be taken to improve/remove the undesirable condition or, in the case of favourable results, to continue with the next production process.

关键词： Manufacturing equipment process Industrial engineering tools and methods Improvement

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Simplification process in Supply Chain Management Through Artificial Intelligence 4

Simplification Process in Supply Chain Management Through Ar...

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4th International conference on Sentiment analysis and Deep Learning, ICSADL 2025

作者： Sharma, Ayushi Kumar, Rakesh Kumar, Deepak Pandey, Purnendu Shekhar Awasthi, Shashank Uttaranchal Institute of Management Uttaranchal University Uttarakhand Dehradun India Graphic Era Deemed to Be University Department of Management Studies Dehradun India Army Institute of Technology Pune India G.L. Bajaj Institute of Technology and Management India

ISBN: (纸本)9798331523923

The aim of this study is to examine Artificial Intelligence(AI) applications in the supply Chain process. According to recent research on Supply Chain Management (SCM) in AI technology and various other technologies (machine learning, deep learning, neural network, expert vision, artificial neural network). Supply Chain Management (SCM) is transforming to artificial intelligence (AI), which improves sustainability, accuracy, and efficiency. This study explores AI in inventory management, warehouse management, customer satisfaction etc. AI Transportation, inventory control, and demand forecasting are all made easier by technologies like computer vision, robotics, and machine learning. Global sustainability goals are in line with AI, but adoption is hampered by issues like labour skill gaps, algorithmic biases, cybersecurity threats, and data quality. Training for the workforce, effective data management, and sustainable procedures are among the recommendations. AI is a key component of SCM's future since it can promote innovation, operational excellence, and sustainable development. The study contributes the objectives to analysis about AI reduced overall cost in supply chain management and also to study and analysis about AI enhanced quality and quantity of product in supply chain management. The study also discussed the SCM through the demand and supply concept economically. There are many challenges like biasness in the algorithm, skill person, and compromise with accuracy which need to be addressed. Finally, the study recommends suggestions and discusses the limitations of AI in supply chain management including sustainable future scope. © 2025 IEEE.

关键词： Customer satisfaction

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Research on model risk of power supply chain based on neural network 3

Research on model risk of power supply chain based on neural...

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3rd International conference on Algorithms, Network, and Communication Technology, ICANCT 2024

作者： Chen, Yu Wang, Li Wang, Liujun Wang, Yong Cai, Lianxing State Grid Zhejiang Electric Power Co. Ltd. Material Management Department Bidding Management Center Hangzhou310000 China State Grid Zhejiang Producement Company Supply Department Hangzhou310000 China State Grid Jiaxing Electric Power Supply Company Materials Management Department Hangzhou314000 China

ISBN: (数字)9781510688902

ISBN: (纸本)9781510688896

The power system is a vital component of modern society's core infrastructure, as it plays a crucial role in ensuring a steady supply of energy and facilitating the smooth operation of society. The optimization of power supply chain management can effectively reduce power grid operating costs, improve supply chain flexibility, and reduce energy waste. The model based on neural network can learn complex characteristics of power system from large-scale historical data, and predict power demand and supply more accurately, so as to realize rational distribution and efficient utilization of power resources. Traditional methods for optimizing the power supply chain are often hindered by intricate mathematical models and computational efficiency issues. Consequently, they struggle to address the growing operational challenges faced by the increasingly complex power system. The model based on neural network can automatically capture the nonlinear relationship between various links in the power grid through end-to-end learning, so as to better deal with the dynamic changes and uncertainties of the power system. This helps to improve the robustness of the grid and reduce operational risks due to changes in external factors. Based on this, under the guidance of supply chain collaborative innovation theory and supply chain risk management theory, this paper uses process analysis method and other methods to initially identify the risk factors in the whole process of supply chain collaborative innovation, combines BP neural network, builds an evaluation model, and introduces MATLAB to learn and verify the model. It is proved that the evaluation model has application value to the risk prediction and evaluation of supply chain collaborative innovation. The experimental verification ultimately confirms the reliability of the model. © 2025 SPIE.

关键词： Neural networks Neurons Power grids Education and training data modeling Power supplies Industry Mathematical modeling Risk assessment Artificial neural networks

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Mining analysis of traffic accident features based on fp-growth algorithm and apriori algorithm 4

Mining analysis of traffic accident features based on fp-gro...

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4th International conference on Computer Vision, Application, and Algorithm, CVAA 2024

作者： Bao, Chun Tang, Yang Yang, Bingheng Wang, Xiang Chen, Jianqiu Xiong, Huan Lou, Benxia Zhou, Shixuan Cao, Xiaoli Gu, Guobin Guangxi Key Laboratory of International Join for China-ASEAN Comprehensive Transportation Nanning University Nanning541699 China School of Architecture and Transportation Engineering Guilin University of Electronic Technology Guilin541004 China

ISBN: (数字)9781510687622

ISBN: (纸本)9781510687615

In this paper, based on data mining technology, Fp-growth algorithm and Apriori algorithm are used to mine and analyze traffic accident features. The correlation between the causes and severity of traffic accidents is revealed through association rule mining. Apriori algorithm is less computationally efficient in mining frequent itemsets through the generation and pruning steps of candidate itemsets, though with high accuracy. In contrast, the Fp-growth algorithm avoids multiple database scans by constructing an Fp-tree, which saves memory resources and improves efficiency. The model construction and evaluation part uses a multi-type variable classification model to predict the accident severity level, and evaluates the model performance through a test set, which shows excellent performance in category 1. Ultimately, this study verifies the effectiveness of Fp-growth and Apriori algorithm in traffic accident factor mining and provides a decision support tool for traffic safety management. © 2025 SPIE.

关键词： Mining Roads data mining databases Safety Analytical research Transportation Performance modeling Mathematics process modeling

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BiLSTM-Attention-Delta: A Novel Framework for Predicting Dropout in MOOCs Within Big data Environments 17

BiLSTM-Attention-Delta: A Novel Framework for Predicting Dro...

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17th International conference on Computer Supported Education, CSEDU 2025

作者： Nguyen, Thu Nguyen, Hong-Tri Nguyen-Hoang, Tu-Anh Faculty of Information Science and Engineering University of Information Technology Ho Chi Minh City Viet Nam Vietnam National University Ho Chi Minh City Viet Nam Aalto University Finland

ISBN: (纸本)9789897587467

The high dropout rate on online education platforms like MOOCs is a significant challenge for modern education systems. This wastes resources and diminishes the course's credibility, impacting educational goals and limiting learners' personal development opportunities. Research on predicting dropout rates in MOOCs has achieved significant milestones, with effective predictive models and analysis of influencing factors to reduce dropout rates. However, challenges remain in ensuring data quality, safeguarding personal information, enhancing model interpretability, and addressing implementation difficulties, especially in the context of big data. This study focuses on analyzing big data to develop an AI-powered intelligent education system capable of monitoring and predicting student learning behavior to reduce dropout rates, while also personalizing the learning process and improving the learner's experience. However, the process of extracting big data from MOOCs poses numerous challenges, including ensuring data quality, integrity, and the ability to handle diverse and massive data. Model interpretability and deployment are also complex, requiring rigorous technical solutions and data management to optimize learning quality and experience. To tackle data processing and deployment challenges, the study introduces the BiLSTM-Attention-Delta framework. This model improves dropout prediction by over 10% compared to baselines, optimizes training and prediction times, and leverages the Delta big data architecture (BDA) for effective deployment in MOOCs. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： data quality

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Design and Comparative analysis of SRM Drive based on Two Novel Power Converter Topologies

Design and Comparative Analysis of SRM Drive based on Two No...

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Power, control and Computing Technologies (ICPC2T), conference on

作者： Sandeep Kumar Dehuri Makarand M. Lokhande Dept of Electrical Engineering Visvesvaraya National Institute of Technology Nagpur India

ISBN: (数字)9798331530402

ISBN: (纸本)9798331530419

The paper presents, two-novel current control converter topologies for 4-phase Switched Reluctance Motors (SRMs). The first converter is based on a reduced switch model, significantly decreasing the number of switching devices by inducting six IGBT power transistors and three modes of operation for minimizing torque ripple, thus providing a compact, lightweight, and inexpensive design. The second design is based on a MOSFET-switching enhanced four-level converter providing four modes of operation for minimizing torque ripple. Using MATLAB/SIMULINK Software, a 4-phase 8/6 SRM is simulated using the proposed converters. The simulation data validates the torque ripple-reducing capability of the suggested converters in the control process of SRM. Based on simulation outcomes a comparative analysis is performed.

关键词： Torque Computational modeling Switches Switched reluctance motors Mathematical models Topology Torque measurement Voltage control Load modeling Switching circuits

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CSGNet: A network with attention mechanism for automatic modulation recognition 5

CSGNet: A network with attention mechanism for automatic mod...

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5th International conference on Signal processing and Computer Science, SPCS 2024

作者： Li, Xianyi School of Control Science and Engineering Shandong University Shandong Jinan250002 China

ISBN: (数字)9781510686731

ISBN: (纸本)9781510686724

In this paper, a new network called CSGNet is introduced for automatic modulation recognition, designed to improve recognition accuracy in complex electromagnetic environments. This innovative network leverages the strengths of both Convolutional Neural Networks (CNN) and Gate Recurrent Units (GRU) while mitigating their inherent weaknesses. Additionally, it incorporates a Self-Attention mechanism to further enhance its perceptual abilities. The primary advantage of this method is its superior recognition accuracy under low SNR conditions compared to traditional methods. The process starts by extracting basic I/Q data from the RadioML 2018.01a dataset as input. Subsequently, the CSGNet model performs an end-to-end modulation recognition task. The results demonstrate that this method excels at multiple SNR levels, highlighting the significant potential of deep learning in solving automatic modulation recognition challenges. This study underscores the effectiveness of integrating CNN, GRU, and Self-Attention mechanisms in enhancing the robustness and accuracy of modulation recognition systems. Furthermore, it demonstrates how deep learning techniques can be effectively applied to complex signal processing tasks. The findings suggest that CSGNet could be a valuable tool for improving communication systems, especially in challenging environments with low SNR. © 2025 SPIE.

关键词： Modulation Signal to noise ratio Feature extraction Deep learning Matrices data modeling Machine learning data processing Neural networks Quadrature amplitude modulation

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