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27th International Conference on Knowledge Based and Intelligent Information and engineering Sytems, KES 2023

作者： Akber, Syed Muhammad Abrar Kazmi, Sadia Nishat Szczesna, Agnieszka Department of Computer Graphics Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Gliwice44-100 Poland

In the modern digital world, the use of animated applications has increased significantly and such applications have quietly become an integral part of life. Capturing human motions is a fundamental aspect of these applications which is performed by motion-capturing (MoCap) systems. Due to the heterogeneity of MoCap systems and marker configurations, the captured skeletons may vary in their structure, number of joints, and bone lengths. The need for motion retargeting arises to transfer motion captured from one articulated actor to another. Although motion retargeting is a long-standing issue, current methods cannot automatically execute retargeting between skeletons. This paper presents the retraining of a popular motion retargeting network with a new large-scale motion dataset. The retraining aims to demonstrate the capabilities of the motion retargeting network to handle the problem of motion retargeting between skeletons that may have different structures. The paper first, presents the motion retargeting network that employs an encoder-decoder architecture. Afterward, we re-train the motion retargeting network with a new MoCap dataset. To validate the effectiveness of the network, we visually compare the generated results with the existing retargeting model. The results show that the motion retargeting network effectively retargets the different motions. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： Decoder Deep neural network Encoder Motion retargeting

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Fauno: The Italian Large Language Model that will leave you senza parole! 13

Fauno: The Italian Large Language Model that will leave you ...

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13th Italian Information Retrieval Workshop, IIR 2023

作者： Bacciu, Andrea Trappolini, Giovanni Santilli, Andrea Rodolà, Emanuele Silvestri, Fabrizio Sapienza University Department of Computer Control and Management Engineering Italy Sapienza University Computer Science Department Italy

This paper presents Fauno, the first and largest open-source Italian conversational Large Language Model (LLM). Our goal with Fauno is to democratize the study of LLMs in Italian, demonstrating that obtaining a fine-tuned conversational bot with a single GPU is possible. In addition, we release a collection of datasets for conversational AI in Italian. The datasets on which we fine-tuned Fauno include various topics such as general question answering, computer science, and medical questions. We release our code and datasets on https://***/RSTLess-research/Fauno-Italian-LLM. © 2023 Copyright for this paper by its authors.

关键词： Computational linguistics

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Intelligent Predetermination of Generator Tripping Scheme: Knowledge Fusion-based Deep Reinforcement Learning Framework

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CSEE Journal of Power and Energy Systems 2024年第1期10卷 66-75页

作者： Lingkang Zeng Wei Yao Ze Hu Hang Shuai Zhouping Li Jinyu Wen Shijie Cheng State Key Laboratory of Advanced Electromagnetic Engineering and Technology School of Electrical and Electronics EngineeringHuazhong University of Science and TechnologyWuhan 430074China Dispatching and Control Center Central China Branch of State Grid Corporation of ChinaWuhan 430077China. Department of Electrical Engineering and Computer Science University of TennesseeKnoxvilleTN 37996USA

Generator tripping scheme(GTS)is the most commonly used scheme to prevent power systems from losing safety and ***,GTS is composed of offline predetermination and real-time scenario ***,it is extremely time-consuming and labor-intensive for manual predetermination for a large-scale modern power *** improve efficiency of predetermination,this paper proposes a framework of knowledge fusion-based deep reinforcement learning(KF-DRL)for intelligent predetermination of ***,the Markov Decision Process(MDP)for GTS problem is formulated based on transient instability ***,linear action space is developed to reduce dimensionality of action space for multiple controllable ***,KF-DRL leverages domain knowledge about GTS to mask invalid actions during the decision-making *** can enhance the efficiency and learning ***,the graph convolutional network(GCN)is introduced to the policy network for enhanced learning *** simulation results obtained on New England power system demonstrate superiority of the proposed KF-DRL framework for GTS over the purely data-driven DRL method.

关键词： Deep reinforcement learning generator tripping scheme graph convolutional network invalid action masking knowledgefusion

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A Novel Cutting Fluids Performance Assurance System for CNC Machine Tools

A Novel Cutting Fluids Performance Assurance System for CNC ...

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2024 International automatic control Conference, CACS 2024

作者： Chou, You-Syuan Chen, Yi-Shiang Chiang, Fu-Yu Yang, Chen-Yen Shi, Zhe-Xun Lin, Yu-Sheng Lin, Shang-Chih Su, Shun-Feng Huang, Yennun Feng Chia University Institute of Automatic Control Engineering Taichung407 Taiwan Feng Chia University Department of Automatic Control Engineering Taichung407 Taiwan National Taiwan University of Science and Technology Department of Electrical Engineering Taipei106 Taiwan Research Center for Information Technology Innovation Academia Sinica Taipei115 Taiwan

ISBN: (纸本)9798350354904

In the CNC machine tool industry, cutting fluid is a crucial component of the machining process. Nevertheless, as the duration of cutting fluid use increases, its performance progressively deteriorates, presenting a significant challenge that necessitates resolution. This study introduces an innovative intelligent monitoring system designed to optimize the preparation of cutting fluid in CNC machine tools. The aim is to extend the fluid's service life, mitigate tool wear, and address issues arising from fluid degradation. The system integrates a heating plate, sensors, pumping motors, and embedded systems to monitor and manage the cutting fluid effectively. Testing was conducted using a customdesigned container, and data analysis identified the optimal supply strategy, thereby extending the usable lifespan of the cutting fluid. This system enhances production longevity and allows for the configuration of containers with appropriate capacities based on specific requirements, ensuring consistent and high-quality production. © 2024 IEEE.

关键词： Cutting fluids

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Mining Erasable Itemsets from a Product Database with Quantitative Components 24

Mining Erasable Itemsets from a Product Database with Quanti...

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11th Multidisciplinary International Social Networks Conference, MISNC 2024

作者： Hong, Tzung-Pei Chen, Da Huang, Wei-Ming Tsai, Yu-Chuan Department of Computer Science and Information Engineering National University of Kaohsiung Taiwan Department of Computer Science and Engineering National Sun Yat-Sen University Taiwan Department of Computer Science and Engineering Department of Electrical and Control China Steel Inc. China Library and Information Center National University of Kaohsiung Taiwan

ISBN: (纸本)9798400717550

Erasable itemset mining is one of the most well-known methods in data mining for optimizing limited materials. After mining erasable itemsets, the manager can rearrange the production plan effectively. However, in real applications, traditional mining methods neglect some critical factors, such as costs or quantities of components forming a product. This thesis thus considers mining erasable itemsets from a product database with quantitative components. An erasable profit-based itemset mining (EPIM) algorithm is proposed to mine erasable itemsets with low profits by analyzing the relationships between products and components. In experiments, a synthetic dataset was used to compare the performance in execution speed and memory usage under various threshold settings. It can also be easily generalized to find erasable itemsets satisfying the other criteria with a quantitative product database. © 2024 ACM.

关键词： Mine safety

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Prescribed-Time Nash Equilibrium Seeking for Pursuit-Evasion Game

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IEEE/CAA Journal of automatica Sinica 2024年第6期11卷 1518-1520页

作者： Lei Xue Jianfeng Ye Yongbao Wu Jian Liu D.C.Wunsch Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of EducationNanjing 210096 School of Automation Southeast UniversityNanjing 210096China Department of Electrical and Computer Engineering Missouri University of Science and TechnologyRollaMO 65409 USA IEEE

Dear Editor,This letter is concerned with prescribed-time Nash equilibrium(PTNE)seeking problem in a pursuit-evasion game(PEG)involving agents with second-order *** order to achieve the prior-given and user-defined convergence time for the PEG,a PTNE seeking algorithm has been developed to facilitate collaboration among multiple pursuers for capturing the evader without the need for any global ***,it is theoretically proved that the prescribedtime convergence of the designed algorithm for achieving Nash equilibrium of ***,the effectiveness of the PTNE method was validated by numerical simulation results.A PEG consists of two groups of agents:evaders and *** pursuers aim to capture the evaders through cooperative efforts,while the evaders strive to evade *** is a classic noncooperative *** has attracted plenty of attention due to its wide application scenarios,such as smart grids[1],formation control[2],[3],and spacecraft rendezvous[4].It is noteworthy that most previous research on seeking the Nash equilibrium of the game,where no agent has an incentive to change its actions,has focused on asymptotic and exponential convergence[5]-[7].

关键词： seeking prescribed convergence

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Estimating Chlorophyll Content from Hyperspectral Data Using Gradient Features 23rd

Estimating Chlorophyll Content from Hyperspectral Data Usin...

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23rd International Conference on Computational science, ICCS 2023

作者： Ruszczak, Bogdan Wijata, Agata M. Nalepa, Jakub Faculty of Electrical Engineering Automatic Control and Informatics Department of Informatics Opole University of Technology Opole Poland Faculty of Biomedical Engineering Silesian University of Technology Zabrze Poland Faculty of Automatic Control Electronics and Computer Science Department of Algorithmics and Software Silesian University of Technology Gliwice Poland KP Labs Gliwice Poland

ISBN: (纸本)9783031360206

Non-invasive estimation of chlorophyll content in plants plays an important role in precision agriculture. This task may be tackled using hyperspectral imaging that acquires numerous narrow bands of the electromagnetic spectrum, which may reflect subtle features of the plant, and inherently offers spatial scalability. Such imagery is, however, high-dimensional, therefore it is challenging to transfer from the imaging device, store and investigate. We propose a machine learning pipeline for estimating chlorophyll content from hyperspectral data. It benefits from the Savitzky-Golay filtering to smooth the (potentially noisy) spectral curves, and from gradient-based features extracted from such a smoothed signal. The experiments revealed that our approach significantly outperforms the state of the art according to the widely-established estimation quality metrics obtained for four chlorophyll-related parameters. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Machine learning

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Exploiting Machine Learning and LSTM for Human Activity Recognition: Using Physiological and Biological Sensor Data from Actigraph 25

Exploiting Machine Learning and LSTM for Human Activity Reco...

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25th IEEE International Conference on Industrial Technology, ICIT 2024

作者： Oyeleye, Matthew Chen, Tianhua Su, Pan Antoniou, Grigoris School Of Computing And Engineering University Of Huddersfield Department Of Computer Science United Kingdom School Of Control And Computer Engineering North China Electric Power University China

ISBN: (纸本)9798350340266

Human activity recognition involves identifying the daily living activities of an individual through the utilization of sensor attributes and intelligent learning algorithms. The identification of intricate human activities proves to be a labo-rious task, given the inherent difficulty of capturing long-term dependencies and extracting efficient features from unprocessed sensor data. For this purpose, this study aims at recognizing and classifying human activities using physiological and biological sensor data generated by Actigraph, as they can accurately measure moderate-to-vigorous intensity physical which is mostly affected by body composition and also better suited for self-monitoring. We examined the effectiveness of these features by applying prevalent machine learning classifiers and long short-term memory (LSTM) networks on recently publicly available data, which includes accelerometer and heart rate recordings. The results from our experiments showed that LSTM models performed better than conventional ML classifiers with the best result achieving an accuracy of 86.5%. The findings also confirms the significance of the heart rate in accurately classifying and identification of human activity more. © 2024 IEEE.

关键词： Long short-term memory

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Energy Efficient Hardware Acceleration of Neural Networks with Power-of-Two Quantisation

Energy Efficient Hardware Acceleration of Neural Networks wi...

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International Conference on computer Vision and Graphics, ICCVG 2022

作者： Przewlocka-Rus, Dominika Kryjak, Tomasz Embedded Vision Systems Group Computer Vision Laboratory Department of Automatic Control and Robotics AGH University of Science and Technology Krakow Poland

ISBN: (纸本)9783031220241

Deep neural networks virtually dominate the domain of most modern vision systems, providing high performance at a cost of increased computational complexity. Since for those systems it is often required to operate both in real-time and with minimal energy consumption (e.g., for wearable devices or autonomous vehicles, edge Internet of Things (IoT), sensor networks), various network optimisation techniques are used, e.g., quantisation, pruning, or dedicated lightweight architectures. Due to the logarithmic distribution of weights in neural network layers, a method providing high performance with significant reduction in computational precision (for 4-bit weights and less) is the Power-of-Two (PoT) quantisation (and therefore also with a logarithmic distribution). This method introduces additional possibilities of replacing the typical for neural networks Multiply and ACcumulate (MAC—performing, e.g., convolution operations) units, with more energy-efficient Bitshift and ACcumulate (BAC). In this paper, we show that a hardware neural network accelerator with PoT weights implemented on the Zynq UltraScale + MPSoC ZCU104 SoC FPGA can be at least 1.4x more energy efficient than the uniform quantisation version. To further reduce the actual power requirement by omitting part of the computation for zero weights, we also propose a new pruning method adapted to logarithmic quantisation. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： System-on-chip

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YOLOv8n-CGW: A novel approach to multi-oriented vehicle detection in intelligent transportation systems

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Multimedia Tools and Applications 2025年第7期84卷 3809-3840页

作者： Berwo, Michael Abebe Fang, Yong Sarwar, Nadeem Mahmood, Jabar Aljohani, Mansourah Elhosseini, Mostafa School of Information and Engineering Chang’an University Shaanxi Xi’an710064 China Department of Computer Science Bahria University Lahore Campus Punjab Lahore54600 Pakistan Department of Information Technology Faculty of Computing and Information Technology University of Sialkot Punjab Sialkot51040 Pakistan College of Computer Science and Engineering Taibah University Yanbu46421 Saudi Arabia Computers and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura35516 Egypt

In the context of Intelligent Transportation Systems (ITS), the role of vehicle detection and classification is indispensable for streamlining transportation management, refining traffic control, and conducting in-depth accident analyses. However, the intricate task of accurately detecting multi-oriented vehicles in diverse scenarios remains a challenge, even with the advancements in ITS. Factors such as vehicle morphology, design, road conditions, structural details, and climatic variables provide a significant part in the detection. To address this, our study introduces an advanced YOLOv8n model, specifically the YOLOv8n-CGW, which is built upon pre-trained deep learning frameworks. This model is meticulously tailored to enhance the detection capabilities for multi-oriented vehicles. Comprehensive evaluations on esteemed datasets like MtV, MERGED, Custom dataset, DAWN, WEDGE, and UA-DETRAC underscore the superiority of our approach over existing models. Notably, our YOLOv8n-CGW model achieves a remarkable mAP of 83.3% and boasts a swift inference time of 0.0019s, setting a new benchmark in the domain. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Deep neural networks

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