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Proceedings of the 2023 4th International Conference on Machine Learning and Computer Application

作者： Mingchen Liu Jinheng Jiang Chun Huang School of Software Engineering Huazhong University of Science and Technology China School of Mechanical Science and Engineering Huazhong University of Science and Technology China School of Energy and Power Engineering Huazhong University of Science and Technology China

ISBN: (纸本)9798400709449

In recent years, a word guessing game called Wordle has gone viral all over the world. The daily updated word and players’ try times have become a popular topic among people. This paper investigates the correlation between word attributes and game results, aiming to enhance predictions and facilitate efficient word selection for game operators. Concurrently, it fosters optimization of machine learning techniques applied to the Wordle game, thus empowering the industry. For this objective, we posed three critical and insightful questions concerning the game: the temporal distribution of the number of reported results, predictions of player guess counts, and classification of target word difficulty based on game data. Subsequently, we designed and built three distinct optimized models to simulate and address these issues: Model I: Time Series Prediction Model, which achieved an R-square over 0.98, demonstrating a superior fit for the data; Model II: Results Distribution Regression Model, which accomplished a Mean Absolute Error (MAE) less than 5, reflecting the model's impressive precision; Model III: Difficulty Classification Model, boasting an accuracy surpassing 80%, which attests to its robust performance. In these three tasks, the models we constructed achieved impressive prediction results, showing considerable improvement in both accuracy and generalizability compared to traditional machine learning methods and mainstream time series prediction techniques.

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Research and development of an information system for speech recognition based on neural network algorithms metrics 4

Research and development of an information system for speech...

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4th IEEE International Conference on Smart Information Systems and Technologies, SIST 2024

作者： Kanashev, Maksat Mukasheva, Assel Yedilkhan, Didar Rakhmetulaeva, Sabina Gnatyuk, Sergiy Nazym, Mirambayeva Almaty University of Power Engineering and Telecommunications Almaty Kazakhstan The School of Information Technology and Engineering Kazakh-British Technical University Almaty Kazakhstan The Department of Computer Engineering Astana IT University Astana Kazakhstan The Department of Information Systems International University of Information Technologies Almaty Kazakhstan The Faculty of Cybersecurity Computer and Software Engineering National Aviation University National Aviation University Kyiv Ukraine

ISBN: (纸本)9798350374865

This article examines the parameters of speech recognition in audio recordings containing a variety of phrases uttered by different people with different accents and intonations. The aim of the study is to evaluate the recognition speed, the accuracy of audio-to-text conversion and resource consumption when using SpeechRecognition and Levenshtein libraries. During the analysis, the above libraries were used to record and recognize audio files, as well as to calculate the distance between two lines and determine the recognition accuracy. The findings of this study can be useful for improving speech recognition systems and creating more adaptive and efficient solutions for audio data processing. The results of comparing the accuracy of speech recognition models are recorded in JSON format, after which, using the matplotlib library, they are visualized as graphs, comparing the speed and accuracy of recognition. Visualization of data in this way contributes to their improvement and more effective integration into educational systems. In conclusion, the authors emphasize the prospects for further development of speech recognition technologies in different fields. © 2024 IEEE.

关键词： Audio recordings

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Magic Weapon for Wordle: Modeling and Analysis based on Word's Attributes 4

Magic Weapon for Wordle: Modeling and Analysis based on Word...

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4th International Conference on Machine Learning and Computer Application, ICMLCA 2023

作者： Liu, Mingchen Jiang, Jinheng Huang, Chun School of Software Engineering Huazhong University of Science and Technology 1037 Luo Yu Road Hubei Province Wuhan China School of Mechanical Science and Engineering Huazhong University of Science and Technology 1037 Luo Yu Road Hubei Province Wuhan China School of Energy and Power Engineering Huazhong University of Science and Technology 1037 Luo Yu Road Hubei Province Wuhan China

ISBN: (纸本)9798400709449

In recent years, a word guessing game called Wordle has gone viral all over the world. The daily updated word and players' try times have become a popular topic among people. This paper investigates the correlation between word attributes and game results, aiming to enhance predictions and facilitate efficient word selection for game operators. Concurrently, it fosters optimization of machine learning techniques applied to the Wordle game, thus empowering the industry. For this objective, we posed three critical and insightful questions concerning the game: the temporal distribution of the number of reported results, predictions of player guess counts, and classification of target word difficulty based on game data. Subsequently, we designed and built three distinct optimized models to simulate and address these issues: Model I: Time Series Prediction Model, which achieved an R-square over 0.98, demonstrating a superior fit for the data;Model II: Results Distribution Regression Model, which accomplished a Mean Absolute Error (MAE) less than 5, reflecting the model's impressive precision;Model III: Difficulty Classification Model, boasting an accuracy surpassing 80%, which attests to its robust performance. In these three tasks, the models we constructed achieved impressive prediction results, showing considerable improvement in both accuracy and generalizability compared to traditional machine learning methods and mainstream time series prediction techniques. © 2023 ACM.

关键词： Forecasting

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Short-term power load forecasting based on multi-step probability distribution deep learning network 6

Short-term power load forecasting based on multi-step probab...

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6th International Conference on Computer Information Science and Application technology, CISAT 2023

作者： Chen, Ming Gao, Huaijin Chen, Kun Du, Chong Yang, Zhijie Computer Technology and Software Engineering Wuhan Polytechnic Wuhan430074 China Biomedical Engineering and Instrument Science Zhejiang University Hangzhou310027 China Electric Power Research Institute State Grid Hubei Electric Power Co. Ltd Wuhan430077 China Huanggang Power Supply Company State Grid Hubei Electric Power Co. Ltd Huanggang438021 China Wuhan Marine College Wuhan430062 China

ISBN: (纸本)9781510668546

With the improvement of renewable energy utilization rate, its proportion in power generation continues to increase. However, the uncertainty of renewable energy power generation has always been an urgent problem. Accurate forecast of short-term power load can assist modern power systems in accurately allocating the proportion of traditional and renewable energy generation. This is crucial for the safety and economic operation of the power grid. power load forecasting can be seen as a multivariate time series forecasting problem. Most of existing studies have focused on predicting the next moment power load. However, a great number of decisions in power planning scenarios require predictive models that can provide a complete conditional distribution with richer information, namely probability distribution prediction. To address this limitation, we applied a multi-step probability prediction model based on recurrent neural networks on short-term power load forecasting. Experiments conducted on the annual power load of multiple regions in the United States and comparison with some classic models verify the superiority of the applied model. The results indicate that the model applied in the article has a promising predictive performance. © 2023 SPIE.

关键词： Forecasting

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Cost-Aware Flexibility Evaluation for Microgrids

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Journal of Modern power Systems and Clean Energy 2025年第3期13卷 1003-1013页

作者： Alipour, Manijeh Gharehpetian, Gevork B. Ahmadiahangar, Roya Rosin, Argo University of Calgary Department of Electrical and Software Engineering CalgaryABT2N 1N4 Canada Amirkabir University of Technology Department of Electrical Engineering Tehran Iran Tallinn University of Technology Department of Electrical Power Engineering and Mechatronics Tallinn Estonia

As the penetration of intermittent renewable energy resources in microgrids (MGs) continues to grow globally, optimal operation management becomes increasingly crucial due to the variability of these sources. One potential solution to this challenge is the use of demand response (DR) programs, which are practical and relatively low-cost options. However, ensuring the security of MG operation also requires evaluating its flexibility by determining the acceptable boundaries of uncertain variables. Additionally, in real-world operational decision-making problems, there is a simultaneous optimization of multiple objectives, including the maximization of system flexibility and the minimization of system cost. This paper presents a methodology for developing a cost-aware flexibility evaluation method for MGs connected to the upstream grid, which are subject to volatile market prices. The model is based on the feasibility analysis of the uncertain space of wind power generation and load, and it also investigates the level of inflexibility present in the system. The impact of the DR program on the flexibility of MGs is quantified through a case study. The case study confirms the success of the proposed method and underscores the significance of cost modeling in flexibility evaluation problems. © 2013 State Grid Electric power Research Institute.

关键词： Risk perception

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Decoupled 2S-AGCN Human Behavior Recognition Based on New Partition Strategy 13th

Decoupled 2S-AGCN Human Behavior Recognition Based on New Pa...

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13th EAI International Conference on Mobile Networks and Management, MONAMI 2023

作者： Qiuming, Liu Longping, Chen Da, Wang He, Xiao Yang, Zhou Dong, Wu School of Software Engineering Jiangxi University of Science and Technology Nanchang330013 China Nanchang Key Laboratory of Virtual Digital Factory and Cultural Communications Nanchang330013 China Information and Communication Branch of Jiangxi Electric Power Co. Ltd. Nanchang330095 China

ISBN: (纸本)9783031554704

Human skeleton point data has better environmental adaptability and motion expression ability than RGB video data. Therefore, the action recognition algorithm based on skeletal point data has received more and more attention and research. In recent years, skeletal point action recognition models based on graph convolutional networks (GCN) have demonstrated outstanding performance. However, most GCN-based skeletal action recognition models use three stable spatial configuration partitions, and manually set the connection relationship between each skeletal joint point. Resulting in an inability to better adapt to varying characteristics of different actions. And all channels of the input X features use the same graph convolution kernel, resulting in coupling aggregation. Contrary to the above problems, this paper proposes a new division strategy, which can better extract the feature information of neighbor nodes of nodes in the skeleton graph and adaptively obtain the connection relationship of joint nodes. And introduce Decoupled Graph Convolution (DC-GCN) to each partition to solve the coupled aggregation problem. Experiments on the NTU-RGB+D dataset show that the proposed method can achieve higher action recognition accuracy than most current methods. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering 2024.

关键词： Convolution

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Single Cell Energy Router Justification for Three Phase Near Zero Energy Buildings 14

Single Cell Energy Router Justification for Three Phase Near...

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14th Annual IEEE Applied power Electronics Conference and Exposition, APEC 2025

作者： Hokmabad, Hossein Nourollahi Shahsavar, Tala Hemmati Matiushkin, Oleksandr Jalakas, Tanel Husev, Oleksandr Belikov, Juri Tallinn University of Technology Department of Electrical Power Engineering and Mechatronics Tallinn Estonia Gdansk University of Technology Faculty of Electrical and Automation Engineering Gdansk Poland Tallinn University of Technology Department of Software Science Tallinn Estonia

ISBN: (纸本)9798331516116

The rise of building-integrated photovoltaics and distributed electric vehicle charging has led to significant phase imbalances in utility grids, challenging service providers due to limited behind-the-meter visibility. This paper introduces a novel Single-Cell Three-Phase (SC-TP) Energy Router (ER) that accesses all phases and balances them without the complexities and costs associated with conventional three-phase systems. Our comparative analysis shows that the SC-TP ER reduces phase unbalancing by 16% and achieves cost savings, offering a viable alternative to three-phase solutions with 25% reduction in cost. © 2025 IEEE.

关键词： power grids

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Content Prediction for Proactive Tile-Based VR Video Streaming in Mobile Edge Caching System 13th

Content Prediction for Proactive Tile-Based VR Video Stream...

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13th EAI International Conference on Mobile Networks and Management, MONAMI 2023

作者： Liu, Qiuming Chen, Hao Zhou, Yang Wu, Dong Li, Zihui Bai, Yaxin School of Software Engineering Jiangxi University of Science and Technology Nanchang330013 China Nanchang Key Laboratory of Virtual Digital Factory and Cultural Communications Nanchang330013 China Information and Communication Branch State Grid Jiangxi Electric Power Co. Nanchang330095 China

ISBN: (纸本)9783031554704

Content prediction can avoid VR video streaming delay in mobile edge caching system. To reduce request delay, popular content should be cached on edge server. Existing work either focuses on content prediction or on caching algorithms. However, in the end-edge-cloud system, prediction and caching should be considered together. In this paper, we jointly optimize the four stages of prediction, caching, computing and transmission in mobile edge caching system, aimed to maximize the user’s quality of experience. We propose a progressive policy to optimize the four steps of VR video streaming. Since the user’s QoE is determined by the performance of the resource allocation and caching algorithm, we design a caching algorithm with unknown future request content, which can efficiently improve the content hit rate, as well as the durations for prediction, computing and transmission. We optimize the four stages under arbitrary resource allocation and simulate the proposed algorithm according to the degree of overlap, as well as completion rate. Finally, under the real scenario, the proposed algorithm is verified by comparing with several other caching algorithms, simulation results show that the user’s QoE is improved under the progressive policy and the proposed algorithm. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering 2024.

关键词： Forecasting

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Tightly-Coupled Lidar-Based Lifelong Mapping using Wheels and a MEMS Gyroscope for Mobile Robots in Dynamic Environment 24

Tightly-Coupled Lidar-Based Lifelong Mapping using Wheels an...

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3rd International Symposium on Robotics, Artificial Intelligence and Information engineering, RAIIE 2024

作者： Hu, Lizhi Chen, Huilin Li, Xiang Lu, Wei Zhao, Yifan Hu, Songyu Navigation and Localization Algorithm Department Zhejiang HuaRay Technology Co. Lt. Hangzhou China Software Research and Development Center Zhejiang HuaRay Technology Co. Lt. Hangzhou China State Key Laboratory of Fluid Power and Mechatronic Systems College of Mechanical Engineering Zhejiang University Hangzhou China

ISBN: (纸本)9798400718311

Localization and mapping technology of mobile robots is important in production and warehousing. The changing warehousing environment always result in poor accuracy and robustness of the mobile robot's localization. It is necessary to detect the degree of environmental changes and update the map. This paper proposes a novel lifelong mapping method fusing measurements from the lidar, wheels and a MEMS gyroscope, which can create a reliable map corresponding to the current working environment. Firstly, we use the wheels and gyroscopes measures for pre-integration to provide relative motion constraints, while integrating wheels and gyroscopes to provide initial pose for lidar matching. We also establish lidar odometry by submaps to provide another type of relative motion constraints. The absolute constraints are obtained by matching the lidar measurements with prior map. These three types of constraints are optimized in a factor graph. At the same time, during the process of updating the map, detect newly added artificial markers such as reflective columns or strips, and update them into the map timely. Then, in order to delete redundant lidar data keyframes for building map, we propose a determine redundancy method based on submaps. Finally, we propose a method for calculating the degree of map changes, which can update the map timely. Experimental results prove that our method can establish higher quality maps, optimize lidar external parameters timely during the mapping process, delete redundant lidar keyframes more efficiently, and update the current environmental map more accurately and timely. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Gyroscopes

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An Energy-Aware UAVs Path Coverage for Critical Infrastructure Inspections 12

An Energy-Aware UAVs Path Coverage for Critical Infrastructu...

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12th International Conference on Advanced Cloud and Big Data, CBD 2024

作者： Wang, Yifan Zeng, Wei Li, Guanyu Xiong, Chenglong Wang, Zicheng Mao, Yingchi College of Computer Science and Software Engineering Hohai University Nanjing China Hohai University Key Laboratory of Water Big Data Technology of Ministry of Water Resources Nanjing China Huaneng Lancang River Hydropower Co. CHINA HUANENG Group Kunming China Power China Kunming Engineering Co. Yunnan China

ISBN: (纸本)9798331511074

Critical infrastructures increasingly rely on unmanned aerial vehicles (UAVs) for inspection tasks. The significance of different components within these infrastructures is subject to variability and can be influenced by external factors. The principal aim of routine UAV inspections is to ensure differentiated coverage of pivotal sections with minimal energy consumption. However, extant research on UAV path coverage fails to fully account for the variability and dynamic shifts in regional significance and their impact on coverage efficacy. This paper presents an energy-aware collaborative coverage policy for UAVs, designated as ***-MATD3 employs a dynamic weight region partitioning method tailored to real-world environments and addresses the action selection challenge for UAVs using a discrete Partially Observable Markov Decision Process (Dec-POMDP). By amalgamating MATD3 with stacked LSTM, this approach mitigates redundant path overlaps and unnecessary action replication across multiple agents, thus optimizing coverage and diminishing energy usage. Simulation outcomes demonstrate that EA-MATD3 reduces energy consumption by an average of 9.65% relative to the Greedy, MADDPG, and MATD3 algorithms while sustaining a superior coverage rate. © 2024 IEEE.

关键词： Markov processes

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