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Chinese Automation Congress (CAC)

作者： Xiaoyu Chen Xin Ma Li Shi Teng Li Shandong Xiehe University School of Computer Science Jinan China Beijing University of Chemical Technology School of Information Science and Technology Beijing China Research and Development Department (R&D) Jinan Bodor CNC Machine Co.Ltd Jinan China Shandong University School of Control Science and Engineering Jinan China

Aiming at teaching of specialized courses, this paper constructs a model for addressing both teaching and evaluation methods. Taking the bridge-in, objective, pre-assessment, participatory learning, post-assessment and summary (BOPPPS) model as a teaching objective, the curriculum evaluation system is established by using the analytic hierarchy process (AHP) scheme. As a result, the course model of BOPPPS-AHP is proposed. The AHP evaluation model is established in the form of expert judgment and questionnaire survey, three first-level indicators, and twelve second-level indicators are established. By calculating the evaluation weights of each index and consistency test, it shows that the curriculum quality evaluation system with the proposed model is feasible to the course of Sensor Principle and Application. Through the comparison between the experimental group and the control group, the BOPPPS-AHP model is conducive to enhancing students' learning interest and comprehensive ability.

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Establishing a Certification System for the Field Project of Outdoor Battery Energy Storage System 15

Establishing a Certification System for the Field Project of...

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15th International Conference on Power, Energy, and Electrical Engineering, CPEEE 2025

作者： Lin, Wei-Shan Lin, Liang-Yang Wang, Pei-Jen Wu, Wei-Hung Chen, Chung-Hsien Lin, Ching-Hua National Tsing Hua University Department of Power Mechanical Engineering Hsinchu Taiwan Bureau of Standards Metrology and Inspection MOEA Inspection Technology Division Taipei Taiwan Metal Industries Research & Development Centre Intelligence and System R&D Service Department Taichung Taiwan Metrology Section Hsinchu Branch Bureau of Standards Metrology and Inspection MOEA Hsinchu Taiwan

ISBN: (纸本)9798331520090

Taiwan actively promotes energy transformation and net zero emissions. Generating 15.1% of energy using renewable sources by 2025 and reducing net zero emissions by 2050 is a goal set by Taiwan. Solar, Wind, and other renewable energy sources will be built on a large scale. The intermittent supply features of renewable energy will affect the traditional power grid. An energy storage system is a key technology to reduce the influence of intermittent renewable energy on the power grid, it can solve the problem of integrating renewable energy with the power grid on a large scale. Currently, the technological development of energy storage systems shows a diverse trend. In Taiwan, an energy storage system with a lithium battery is the first choice for most enterprises. Therefore, a certification system for outdoor battery energy storage system field project is proposed in this research for the outdoor grid-integrated energy storage system. This certification system adopts a three-stage certification method, including a design review before construction, field certification after completion, and regular test every two years. It guarantees the safety of the energy storage system and its related equipment. It facilitates the energy storage system industry's development and renewable energy policies. © 2025 IEEE.

关键词： Battery storage

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Establishing a Certification System for the Field Project of Outdoor Battery Energy Storage System

Establishing a Certification System for the Field Project of...

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Power, Energy and Electrical Engineering (CPEEE), International Conference on

作者： Wei-Shan Lin Liang-Yang Lin Pei-Jen Wang Wei-Hung Wu Chung-Hsien Chen Ching-Hua Lin Department of Power Mechanical Engineering National Tsing Hua University Hsinchu Taiwan Inspection Technology Division Bureau of Standards Metrology and Inspection MOEA Taipei Taiwan Intelligence and System R&D Service Department Metal Industries Research & Development Centre Taichung Taiwan Metrology Section Hsinchu Branch Bureau of Standards Metrology and Inspection MOEA Hsinchu Taiwan

ISBN: (数字)9798331520090

ISBN: (纸本)9798331520106

关键词： renewable energy sources reviews Battery energy storage system Net zero Lithium batteries Power grids Safety Certification Standards Energy storage

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Multi-basis continuous wavelet transform feature wavelengths selection and machine learning with hyperspectral imaging for non-destructive prediction of 1,2-propylene glycol content in cased tobacco leaves

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Applied Optics 2025年第18期64卷 5087-5098页

作者： Zhongpan Yang Wuyin Jin Jinsong du School of Mechanical and Electronical Engineering Lanzhou University of Technology No. 287 Langongping Road Qilihe District Lanzhou City Gansu Province 730050 China Technology R&D Center Gansu Tobacco Industrial Co. Ltd. No. 1111 Nanbinhe Middle Road Qilihe District Lanzhou City Gansu Province 730050 China Key Laboratory of Tobacco Processing Technology of CNTC Zhengzhou Tobacco Research Institute of CNTC No. 2 Fengyang Street National High-Tech Industrial Development Zone Zhengzhou City Henan Province 450001 China

The process of manufacturing tobacco casings constitutes a critical step in the production of tobacco leaves, exerting a substantial influence on the improvement of their physical and chemical characteristics, and consequently, the quality of the final product. Nevertheless, the prevailing approach to casing accuracy detection is predominantly focused on dosage monitoring, with a paucity of attention being paid to real-time effect evaluation. Current hyperspectral-based detection systems encounter difficulties in extracting trace additive features and managing high-dimensional data under limited sample conditions. A multi-basis continuous wavelet transform (CWT) and machine-learning-integrated framework for non-destructive propylene glycol (PG) content prediction were proposed in this paper, addressing precision limitations in tobacco quality monitoring. The preprocessing of hyperspectral imaging data from six PG concentration levels were undertaken via Savitzky–Golay filtering, followed by multiscale decomposition using three CWT basis functions with morlet, Mexican hat, and Gaussian wavelets. A dual optimization mechanism combining correlation threshold filtering and wavelength frequency statistics was developed to enable efficient feature wavelength selection. Furthermore, a stacking regression model was constructed and compared with standalone algorithms. The results demonstrated that the multiscale combined MMG strategy achieved 79.86% dimensionality reduction by selecting 58 feature wavelengths covering adjacent regions in the near-infrared and short-wave infrared (NIr-SWIr) range, significantly enhancing model generalization compared to full-spectrum inputs. Additionally, the stacking regression model attained optimal performance, with a testing set coefficient of determination of 0.9704, under combined MMG input through synergistic complementarity of heterogeneous base learners, with a root mean square error of 0.3188. It is confirmed in this paper that spec

关键词： deep learning Hyperspectral imaging Machine learning Neural networks Spatial resolution Wavelet transforms

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rapid detection of Four Foodborne Pathogens Based on Terahertz Time domain Spectroscopy

Rapid Detection of Four Foodborne Pathogens Based on Teraher...

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International Conference on Manipulation, Manufacturing and Measurement on the Nanoscale (3M-NANO)

作者： Wenzhi Bian Song Li Tingting Zhang Shuhua Zhang Yujuan Chen Zhiping Li Changchun University of Science and Technology School of Life Science and Technology Changchun China Changchun GeneScience Pharmaceutical Co. Ltd. Research and Development Center for Process and Analysis Methods Changchun China R&D Department Changchun GeneScience Pharmaceutical Co. Ltd. Changchun China Academy of Military Medical Sciences Institute of Military Veterinary Medicine Changchun China

ISBN: (数字)9798350362107

ISBN: (纸本)9798350362114

At present, with the improvement of people's living standards, food borne pathogens, as one of the biggest threats to food safety, are widely valued by people. Therefore, it is urgent to create a fast and efficient method for detecting foodborne pathogens. This article discusses the establishment of a fast and non-destructive detection method for foodborne pathogenic bacteria using terahertz time-domain spectroscopy. In this study, the bacterial dry powder of Cronobacter sakazakii was mixed with polyethylene with different mass ratios (0 %, 1 %, 5 %, 10 %, 20 %, 30 %, 40 %, 50 %, 100 % w/w) to make tablet, the thickness of the tablet is 1~1.4 mm, and the diameter is 1.4 cm, and the mass is 0.18 g. Transmission spectrum was used for detection. By observed and analyzed the absorption spectra, the waveforms of three groups of repeated data showed consistency, and there were obvious characteristic absorption peaks at 1.28 and 1.78 THz. The accuracy of clustering software analysis results is 100 %, which shows that the method has good stability and specificity. The detection limit of Cronobacter sakazakii is 1%. We have successfully established a terahertz time-domain spectral detection method for foodborne pathogens. This method is rapid, stable and sensitive, and has a broad application prospect.

关键词： Pathogens Spectroscopy Sensitivity Powders Absorption Stability criteria Software Production facilities Time-domain analysis Standards

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research progress of clay transformation in drilling fluids

Research progress of clay transformation in drilling fluids

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作者： Zhilei Zhang Yichen Zhang Shuting Gong Shouyong Tang rongchao Cheng Zheng Yang Han ren Hong Yang CNPC Engineering Technology R&D Company Limited Zhejiang Oilfield Company Natural gas Exploration & Development Department

As an important slurry material for water-based drilling fluids, montmorillonite has been widely used in oil drilling because of its excellent properties such as expansibility, dispersion and suspension, slurry making, cation exchange and adsorption. However, at a certain temperature and pressure, montmorillonite will transform into illite, chlorite and other minerals, changing the properties of drilling fluids. In order to systematically summarize and sort out the current research status of montmorillonite transformation, this study focused on the research progress of montmorillonite transformation into other minerals under different temperature and pressure conditions, and summarized the transformation mechanism of montmorillonite. The montmorillonite is transformed into illite by adding K+ in the interlayer spacing and removing Si by adding Al to tetrahedron structure. The conversion of montmorillonite to chlorite by adding Fe, Mg ions into the octahedron, and Si in the tetrahedron is replaced by Al. The precipitated brucite layer is connected with the upper and lower layers.

关键词： red research progress of clay transformation in drilling fluids

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Machine Learning with deep Learning Approach for Cyber Security Threats Prevention Model

Machine Learning with Deep Learning Approach for Cyber Secur...

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2023 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2023

作者： Jagan, S. Pokhariyal, rajesh Mahajan, Kirti deepika, Ch L N Sudha, P. deva dutta, Abhradita Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu Chennai India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun248002 India Bharati Vidyapeeth Deemed University Institute of Management & Entrepreneurship Development Department of Information Technology Pune411030 India Department : SOCC Kluniversity 522 502 India St. Martins Engineering College Department of Computer Science and Engineering Telangana Secunderabad India New Horizon College of Engineering Department of Mechanical Engineering Bangalore560103 India

ISBN: (纸本)9798350319200

The need for effective cybersecurity solutions has never been greater than in today's world of constant digitalization and interconnection. This study explores the use of deep learning to the creation of a model for preventing cyber security threats using machine learning. The model is meant to successfully identify and prevent cyber-attacks by analysing network traffic and system behaviour using a convolutional neural network (CNN) linked with variationalautoencoders (VAEs). This study's findings illustrate the model's competency in threat recognition with a high degree of accuracy, precision, and recall. Actively monitoring and evaluating network data, providing automatic threat identification and response, further demonstrates its usefulness when used in the real world. This work sets the path for future developments in quantum-resistant encryption, adversarial machine learning studies, deep learning architectures, autonomous cybersecurity systems, explainable AI, and global threat intelligence cooperation. These new approaches have great potential for paving the way towards a more secure and resilient cyber future. © 2023 IEEE.

关键词： Convolutional Neural Network (CNN) deep Learning Machine Learning Network Traffic Analysis Quantum-resistant Cryptography real-time Monitoring Threat Prevention Variational Autoencoders (VAEs)

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Exploring the Potential of deep Learning in Parkinson's disease drug discovery

Exploring the Potential of Deep Learning in Parkinson's Dise...

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Advances in Computing research on Science Engineering and technology (ACrOSET), International Conference on

作者： ravula Arun Kumar M. Ponnrajakumari M ravi Kumar A. Nirisha raja Chandrasekaran Tejashree Tejpal Moharekar Department of CSE Vardhaman College of Engineering Telanagana India Department of ECE Velammal Engineering College Chennai India Department of CSE KL University Vijayawada India Department. of CSIT MLR Institute of Technology Telangana India Department of ECE Sagunthala R&D Institute of Science and Technology Chennai India Yashwantrao Chavan School of Rural Development Shivaji University Kolhapur India

ISBN: (数字)9798350388800

ISBN: (纸本)9798350388817

Complex and complex neurological illnesses like Parkinson's disease (Pd) are difficult to investigate. This publication introduces “Neural Pathway Profiling for Parkinson's disease drug discovery.” It leverages cutting-edge deep learning. GCNs explain molecular interactions, LSTMs examine temporal sequences, and VAEs learn by forming new patterns. Our research uses genes, proteins, and clinical data to explain how Parkinson's disease's various components interact in complex ways. LSTMs illustrate how clinical data is connected to time, GCNs reveal how molecules interact in biological networks, and VAEs discover hidden chemical structures for generative learning. The blended design reveals key brain connections and Parkinson's disease therapeutic targets.

关键词： drugs deep learning Heating systems Ethics Accuracy Sensitivity Parkinson's disease Computational modeling Neural pathways drug discovery

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Initial Study on Applicability of Thermal Imaging for Microplastics Identification in Aquatic Medium

SSRN

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SSrN 2023年

作者： Manikandan, E. Pravin, Sheena Christabel Kiruthika, V. Gopikashree, S. Chandan, Sai Gokul H.T. Karthigeyan, K.A. Shoba, S. Centre for Innovation and Product Development Vellore Institute of Technology Chennai Campus Chennai India School of Electronics Engineering Vellore Institute of Technology Chennai Campus Chennai India Department of ECE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Centre for Advanced Data Science Vellore Institute of Technology Chennai Campus Chennai India

Owing to plastic's durability, plastic waste management is challenging. The current issue is that millions of tonnes of plastic end up in the ocean, possibly resulting in microplastic pollution and becoming the most prevalent underwater contaminant. Efficient identification of microplastic pollution is a scientific challenge because as the size of the particles decreases, it becomes more difficult to recognize and detect them. In this work, we demonstrate a deep learning-based approach for microplastics identification. The dataset is acquired using the thermal imaging technique. data collection is followed by a data pre-processing step and finally, a classification step. The reported best accuracy, 97.42%, is achieved using the CNN model. The proposed approach allows for the deployment of increasingly diversified models as deep-learning models are still progressing. © 2023, The Authors. All rights reserved.

关键词： data handling

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A Sample-driven Selection Framework: Towards Graph Contrastive Networks with reinforcement Learning 24

A Sample-driven Selection Framework: Towards Graph Contrasti...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Zheng, Xiangping Hao, Xiuxin Wu, Bo Bao, Xigang Zhang, Xuan Li, Wei Liang, Xun College of Computer Science and Technology Harbin Engineering University Heilongjiang Harbin China Modeling and Emulation in E-Government National Engineering Laboratory Heilongjiang Harbin China Qingdao Innovation and Development Base of Harbin Engineering University Shandong Qindao China R&d and External Relations Department Xiangjiang Laboratory Hunan Changsha China School of Artificial Intelligence and Advanced Computing Hunan University of Technology and Business Hunan Changsha China Changsha Artificial Intelligence Social Laboratory Hunan Changsha China School of Information Renmin University of China Beijing China Guanghua School of Management Peking University Beijing China Harvest Fund Management Co. Ltd. Beijing China

ISBN: (纸本)9798400706868

Graph Contrastive Learning (GCL) aims to address the issue of label scarcity by leveraging graph structures to propagate labels from a limited set of labeled data to a broader range of unlabeled data. However, recent GCL methods often rely on uniform negative sample selection schemes, such as random sampling, which results in suboptimal performance. To tackle this challenge, we present GraphSaSe, a tailored approach specifically designed for graph contrastive learning. Our method introduces an innovative reinforcement learning strategy that translates the divergence between positive pairs into a reinforcement reward mechanism. This mechanism generates selection probabilities to dynamically guide the selection of negative samples during training. We explore the impact of negative sample selection at different stages in graph contrastive learning and analyze how the discount factor affects the reward mechanism in reinforcement learning. These studies enhance the overall performance of the model. Comprehensive experimentation across diverse real-world datasets validates the effectiveness of our algorithm, positioning it favorably against contemporary state-of-the-art methodologies. © 2024 ACM.

关键词： Contrastive Learning

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