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International Electrical engineering Congress (iEECON)

作者： Somkid Ritnathikul Danupat Duangmalai Khanittha Kaewdang Settasuk Kwuanphet Thanawat Chalermpong Koson Pitaksuttayaprot Department of Computer Engineering Faculty of Agricultural and IndustrialTechnology Phetchabun Rajabhat University Phetchabun Thailand Department of Electrical and Electronic Engineering Faculty of Engineering Ubon Ratchathani University Ubon Ratchathani Thailand Department of Electrical Engineering Faculty of Industrial Technology Nakhon Phanom University Nakhon Phanom Thailand Department of Electronics and Automation Systems Engineering Faculty of Agricultural and Industrial Technology Phetchabun Rajabhat University Phetchabun Thailand

ISBN: (数字)9798331543952

ISBN: (纸本)9798331543969

This paper presents an electronically controlled biquadratic filter designed for precise frequency selection in applications such as audio processing, telecommunications, and cochlear implants. The design utilizes AD830, AD844, and LM13700 integrated circuits, facilitating high-pass, band-pass, and low-pass responses with improved stability and real-time tunability. PSPICE simulations validate the filter's adaptability and performance, demonstrating its effectiveness in dynamic environments and its suitability for high-frequency applications requiring flexible frequency control. The results indicate a calculated pole frequency of 2.23 MHz, while simulations yield a slightly lower value of 2.0 MHz, affirming the design's practical viability and alignment with theoretical expectations.

关键词： Band-pass filters Performance evaluation Adaptation models Low-pass filters SPICE Stability analysis Real-time systems Telecommunications Integrated circuit modeling Frequency control

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Generation of n-dimensional complex chaotic system via parameter matrix configuration

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Chaos, Solitons and Fractals 2025年 197卷

作者： Yao, Jinhui Zhang, Yinxing Bao, Han Hua, Zhongyun School of Computer Science and Technology Harbin Institute of Technology Shenzhen Guangdong518055 China Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China School of Microelectronics and Control Engineering Changzhou University Changzhou213164 China

Complex chaotic systems can exhibit high chaotic complexity due to the presence of complex variables and complex parameters. Most research has focused on real chaotic systems, but complex chaotic systems remain relatively under-explored. To this end, this work presents an n-dimensional complex chaotic system (nD-CCS) generation method utilizing complex parametric Pascal matrices. Initially, these matrices are generated and subsequently utilized as the parameter matrices for constructing the nD chaotic systems. Theoretical analysis demonstrates that the proposed nD-CCS can exhibit chaotic behavior. Extensive experiments show that the nD-CCS possesses more robust chaotic performance than existing nD real chaotic systems. To demonstrate the effectiveness of our method, a four-dimensional complex chaotic map (4D-CCM) is generated and its chaotic behavior is analyzed. Furthermore, we develop a hardware platform to verify the 4D-CCM's implementation on hardware devices. We also design pseudorandom number generators (PRNGs) using the 4D-CCM and test their randomness against the NIST SP800-22 standard. The result indicates excellent randomness in the PRNGs. © 2025

关键词： computer peripheral equipment

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An Embedded System for Eggs Freshness Detection 2nd

An Embedded System for Eggs Freshness Detection

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2nd International Conference on Intelligent Systems and Data Science, ISDS 2024

作者： Pham, Quoc-Hung Nguyen, Thanh-Nhan Vo, Huy-Hoang Nguyen, Duy-Khanh Pham, Tan-Nhat Tran, Nhut-Thanh Faculty of Computer Engineering University of Information Technology Ho Chi Minh City Viet Nam Vietnam National University Ho Chi Minh City Viet Nam Faculty of Automation Engineering Can Tho University Can Tho94000 Viet Nam

ISBN: (纸本)9789819796120

Traditional methods to quantify egg freshness, including laborious sorting and chemical analysis, have been widely used with significant errors and sample destruction. This study introduces a non-destructive system for detecting egg freshness using near-infrared spectroscopy and machine learning models. The research successfully developed a portable embedded system device using a low-cost multi-spectral sensor and Raspberry Pi 4B microprocessor for non-invasive egg freshness detection. Our non-invasive proposed system for detecting egg quality was created with an affordable price of around 250 US dollar and is a portable device. Some regression methods, such as Multiple Linear Regression and Support Vector Regression, were utilized to predict egg freshness with a coefficient of determination of 0.8. It was also compared with invasive measurements. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Near infrared spectroscopy

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Short-Term Load Forecasting Based on Graph Convolution and Dendritic Deep Learning

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IEEE Transactions on Network Science and engineering 2025年

作者： Zhang, Chunyang Yu, Yang Zhang, Tengfei Song, Keyu Wang, Yirui Gao, Shangce Nanjing University of Posts and Telecommunications College of Automation & College of Artificial Intelligence Nanjing210023 China Ningbo University Faculty of Electrical Engineering and Computer Science Zhejiang Key Laboratory of Mobile Network Application Technology Zhejiang315211 China University of Toyama Faculty of Engineering Toyama-shi930-8555 Japan

Short-term load forecasting (STLF) is a significant task to the planning, operation and control of future power systems. The increasing number of devices connected to the system has led to more complex characteristics and forms of load, which has brought considerable difficulties to the relevant methods in achieving higher load prediction accuracy and reliability. In this regard, this study proposes a deep learning model that combines graph convolutional network (GCN), gated recurrent unit (GRU), and dendritic neural model (DNM) to forecast electric load more accurately. Firstly, the sample load data is constructed into graph data with individual time steps as nodes. A GCN is used to extract the hidden features while allowing a full communication between the time steps feature data. A GRU is then used to capture the time-dependent relationship of the data. Finally, a dendritic layer instead of a fully connected layer is used as the output to integrate data features in depth. Experiments are conducted to verify the validity of the proposed model and compared it with several effective deep learning models, including CNN_LSTM, Transformer and Kolmogorov-Arnold Networks (KAN). The results show a significant improvement in prediction compared to the baseline models, with mean absolute percentage error(MAPE) of 1.62% and 3.98%, coefficient of determination(R2) of 0.983 and 0.928 respectively on two load datasets at different levels of aggregation, nationally and regionally. © 2013 IEEE.

关键词： Convolutional neural networks

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Comparison of Unsupervised Learning Algorithms for K-Means Clustering Efficiency in IoE Sensor Detection Systems for Smart Agriculture

Comparison of Unsupervised Learning Algorithms for K-Means C...

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International Electrical engineering Congress (iEECON)

作者： Wallop Tipwong Adisorn Sirikham Jessada Konpang Putthiphong Kirdpipat Yutana Chongjarearn Oradee Preutisrunyanont Department of Electrical and Telecommunication Engineering Rajamangala University of Technology Krungthep Bangkok Thailand Department of Electrical and Computer Engineering Faculty of Engineering Thammasat School of Engineering Thammasat University Pathum Thani Thailand Department of Robotic Engineering and Automation System Dhurakij Pundit University Bangkok Thailand

ISBN: (数字)9798331543952

ISBN: (纸本)9798331543969

This study investigates the application of clustering algorithms to data collected from color sensors providing frequency signals during the cultivation process. Continuous sampling allowed for real-time data collection, and variations in temperature and humidity were observed to influence signal reception. The dataset's large volume and varying sample sizes necessitated the evaluation of several clustering models for efficient data processing. The experiment tested K-Means, K-Means++, Mini-Batch K-Means, and Bisecting K-Means algorithms. The results reveal that the computation time for each model increased with the number of groups. Specifically, the K-Means, K-Means++, and Mini-Batch K-Means models exhibited shorter computation times, ranging from 0.02 to 0.03 seconds for two-group scenarios, while the Bisecting K-Means algorithm took significantly longer, approximately 0.10 seconds. Despite the variation in processing times, all models performed similarly in terms of clustering quality, with an average Silhouette Score of 0.82. This score indicates that the algorithms effectively separated data points into well-defined groups while maintaining high accuracy in clustering. Additionally, the analysis highlighted the importance of balancing computation time and clustering quality, particularly as the number of clusters increases. These findings suggest that while computational efficiency may vary across models, the clustering quality remains robust, providing valuable insights for data analysis in agricultural sensor networks. The study's results demonstrate that each clustering model is capable of handling large datasets with high accuracy in grouping, making them applicable to real-world agricultural data processing scenarios. In conclusion, the experiment confirms that all tested models, despite differing in computation time, successfully partitioned the data with high-quality results, offering promising applications for data analysis and optimization in agricult

关键词： Temperature sensors Analytical models Image color analysis Computational modeling Clustering algorithms Approximation algorithms Data models Sensor systems Computational efficiency Optical sensors

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Advanced AI techniques for root disease classification in dental X-rays using deep learning and metaheuristic approach

Intelligent Systems with Applications

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Intelligent Systems with Applications 2025年 26卷

作者： Enkvetchakul, Prem Khonjun, Surajet Pitakaso, Rapeepan Srichok, Thanatkij Luesak, Peerawat Kaewta, Chutchai Gonwirat, Sarayut Boonmee, Chawis Noowattana, Matus Srisuwandee, Thitinon Department of Information Technology Faculty of Science Buriram Rajabhat University Buriram31000 Thailand Artificial Intelligence Optimization SMART Laboratory Industrial Engineering Department Faculty of Engineering Ubon Ratchathani University Ubon Ratchathani34190 Thailand Department of Industrial Engineering Faculty of Engineering Rajamangala University of Technology Lanna Chiang Rai57120 Thailand Digital Innovation Faculty of Computer Science Ubon Ratchathani Rajabhat University Mueang Ubon Ratchathani34000 Thailand Department of Computer Engineering and Automation Kalasin University Kalasin46000 Thailand Department of Industrial Engineering Faculty of Engineering Chiang Mai University Chiang Mai Thailand Dental Department Samrong Hospital Samrong District Ubon Ratchathani34360 Thailand

Root dental diseases remain among the most diagnostically challenging conditions in oral healthcare, often leading to treatment delays and suboptimal outcomes. This study is motivated by the limitations of existing automated diagnostic systems, which tend to focus on superficial abnormalities and overlook complex root pathologies such as pulpal infections, periapical lesions, and progressive periodontitis. To bridge this critical gap, we propose an advanced AI-based classification model that integrates ensemble deep learning architectures with a hybrid metaheuristic optimization strategy-namely, the non-population-based Artificial Multiple Intelligence System (np-AMIS) for image augmentation and the population-based AMIS (pop-AMIS) for adaptive decision fusion. This dual-phase approach enhances feature diversity, classification robustness, and computational efficiency. The model was trained and validated on two proprietary datasets, TD-1 and TD-2, achieving classification accuracies of 98.87 % and 98.41 %, respectively. It was further implemented in a real-world application via the Automated Teeth Disease and Abnormality Classification System (A-TD-A-CS), demonstrating 98.95 % accuracy, a rapid response time of 1.5 s, and a System Usability Scale (SUS) score of 94.5 from dental professionals. The system's ability to accurately identify multiple root disease categories highlights its clinical viability and transformative potential. In addition to its current performance, this study lays the groundwork for future extensions to multi-center datasets and cross-modality diagnostics using cone-beam CT or intraoral scans, further advancing intelligent dental care. © 2025

关键词： Deep learning

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State of Health Estimation for Lithium-Ion Batteries Using Electrochemical Impedance Spectroscopy and a Multi-Scale Kernel Extreme Learning Machine

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World Electric Vehicle Journal 2025年第4期16卷 224-224页

作者： Peng, Jichang Gao, Ya Cai, Lei Zhang, Ming Sun, Chenghao Liu, Haitao School of Electric Power Engineering Nanjing Institute of Technology Nanjing211167 China Faculty of Computer Science and Engineering Xi’an University of Technology Xi’an710048 China School of Information Engineering Ordos Institute of Technology Ordos017000 China School of Electrical and Automation Engineering Nanjing Normal University Nanjing210023 China

An accurate state of health (SOH) estimation for lithium-ion batteries (LIBs) is crucial for reliable operations and extending service life. While electrochemical impedance spectroscopy (EIS) effectively characterizes LIBs degradation patterns, the high dimensionality of EIS data poses challenges for an efficient analysis. This study proposes a novel method that combines EIS with an equivalent circuit model (ECM) and distribution of relaxation time (DRT) analysis to extract low-dimensional health features from high-dimensional EIS data. A multi-scale kernel extreme learning machine (MS-KELM), optimized by the Sparrow Search Algorithm (SSA), estimates battery SOH with an average mean absolute error (MAE) of 1.37% and a root mean square error (RMSE) of 1.76%. In addition, compared with support vector regression (SVR) and Gaussian process regression (GPR), the proposed method reduces computational time by factors of 4 to 30 and lowers memory usage by approximately 18%. © 2025 by the authors.

关键词： Lithium-ion batteries

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Natural Language Processing for Subjective Exam Grading in Programming Explanations in Thai Language

Natural Language Processing for Subjective Exam Grading in P...

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International Electrical engineering Congress (iEECON)

作者： Waris Rattanimit Thanyarat Nomponkrang Electrical Technology and Automation System Faculty of Engineering and Technology Siam Technology College Bangkok Thailand Department of Computer Education King Mongkut’s University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9798331543952

ISBN: (纸本)9798331543969

In the digital era, learning programming has become an essential skill, and assessing students' understanding in this area plays a crucial role in enhancing educational quality. However, evaluating subjective programming exam responses, especially those involving conceptual explanations or procedural descriptions, remains a challenging task due to the diversity and complexity of answers. Manual grading can lead to delays and inconsistencies in scoring. This research aims to develop an automated system for evaluating Thai subjective programming exams using Natural Language Processing (NLP) techniques to assess the correctness of responses. The system is implemented in Python, utilizing the ThaiNLP library for word tokenization, alongside Machine Learning algorithms such as Naive Bayes, Decision Tree, and Support Vector Machine (SVM) for classification and prediction of answers. The experimental dataset includes responses from 120 students who answered subjective questions about Python commands. Expert testing indicated high user satisfaction with an average score of 4.05. The findings show that SVM achieved the highest accuracy at 85.33%. This system can enhance efficiency and consistency in exam evaluation, reducing grading time and contributing to standardized scoring.

关键词： Support vector machines Electrical engineering Machine learning algorithms Manuals Machine learning Tokenization Libraries Programming profession Python Testing

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Using Multi-Feature Weak Consensus Model to Discover Essential Proteins

IEEE Transactions on Computational Biology and Bioinformatic...

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IEEE Transactions on Computational Biology and Bioinformatics 2025年第1期22卷 322-332页

作者： Zhipeng Hu Gaoshi Li Xinlong Luo Jiafei Liu Jingli Wu Wei Peng Xiaoshu Zhu Key Lab of Education Blockchain and Intelligent Technology Ministry of Education The Guangxi Key Lab of Multi-source Information Mining & Security The College of Computer Science and Engineering Guangxi Normal University Guilin China Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China School of Computer and Information Security & School of Software Engineering Guilin University of Electronic Science and Technology Guilin China

Essential proteins play an essential role in cell survival and replication. Currently, more and more computational methods are developed to identify essential proteins, which overcome the time-consuming, costly and inefficient shortcomings with biological experimental methods. In order to improve the recognition rate, some new methods by fusing multiple features are developed, but they seldom consider the connection among features. After analyzing a large number of methods based on multi-feature fusion, a phenomenon among features is found, called weak consensus, then a weak consensus model to fuse these features is proposed in this paper. After analyzing the relationship between a protein and its neighbors in protein-protein interaction networks, a new centrality, namely neighborhood aggregation centrality(NAC) is developed in this paper. Then, a Max-Min strategy is used to integrate NAC with Pearson correlation coefficient and Jaccard similarity coefficient based on gene expression data to obtain local importance score. In addition, orthologous feature score is used to measure proteins conservation. Finally, by using the weak consensus model to fuse orthologous feature score with local importance score, a new method WOL is proposed in this paper. Then experiments are performed on *** data. The results show that compared with WDC, PeC, ION, JDC, NCCO and E_POC, WOL has a higher recognition rate.

关键词： Proteins Computational biology Bioinformatics Feature extraction Biological system modeling Fuses Computational modeling Shape Reliability Minimax techniques

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Multi-Domain Feature Analysis and Application Research of GPR Aliased Signals

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Sensors 2025年第9期25卷 2741-2741页

作者： Li, Chuan Wang, Yawei Ma, Qibing Wan, Xiaorong Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650051 China Yunnan Key Laboratory of Computer Technology Applications Kunming University of Science and Technology Kunming650051 China

In radar detection of concrete structures, the significant differences in electromagnetic properties between rebar and concrete result in strong reflections at rebar interfaces. The electromagnetic waves reflected by the dual-layer rebar interfere with and superimpose, severely obscuring their characteristic signals, making accurate identification challenging. This study investigates the aliasing effect in ground-penetrating radar (GPR) signals through simulation analysis of aliased signals at different rebar spacings and examines their characteristics across the time, frequency, and time–frequency domains. Experimental results indicate amplitude increases in the time domain. The Hilbert transform effectively extracts instantaneous phase inversions, and STFT provides an intuitive time–frequency distribution, facilitating the extraction and analysis of signal features. Additionally, this study includes the design and implementation of an aliasing peak point extraction algorithm with a relative error of less than 10% in practical applications. © 2025 by the authors.

关键词： Anti-aliasing

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