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arXiv 2024年

作者： Zhao, Siqi Li, Wangyang Wang, Xiru Foglia, Stevie Tan, Hongzhao Zhang, Bohan Hamoodi, Ameer Nelson, Aimee Gao, Zhen WBooth School of Engineering Practice and Technology McMaster University HamiltonON Canada School of Biomedical Engineering McMaster University HamiltonON Canada Department of Kinesiology McMaster University HamiltonON Canada

Machine learning (ML) and deep learning (DL) techniques have been widely applied to analyze electroencephalography (EEG) signals for disease diagnosis and brain-computer interfaces (BCI). The integration of multimodal data has been shown to enhance the accuracy of ML and DL models. Combining EEG with other modalities can improve clinical decision-making by addressing complex tasks in clinical populations. This systematic literature review explores the use of multimodal EEG data in ML and DL models for clinical applications. A comprehensive search was conducted across PubMed, Web of Science, and Google Scholar, yielding 16 relevant studies after three rounds of filtering. These studies demonstrate the application of multimodal EEG data in addressing clinical challenges, including neuropsychiatric disorders, neurological conditions (e.g., seizure detection), neurodevelopmental disorders (e.g., autism spectrum disorder), and sleep stage classification. Data fusion occurred at three levels: signal, feature, and decision levels. The most commonly used ML models were support vector machines (SVM) and decision trees. Notably, 11 out of the 16 studies reported improvements in model accuracy with multimodal EEG data. This review highlights the potential of multimodal EEG-based ML models in enhancing clinical diagnostics and problem-solving. © 2024, CC BY.

关键词： Electroencephalography

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Construction of Industry-Teaching-Innovation Cases for Robot Training under the Integration of Virtuality and Reality

Construction of Industry-Teaching-Innovation Cases for Robot...

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International Conference on Educational and Information technology (ICEIT)

作者： Jiang Jin-gang Xiong Jiang-long Wang Ya-ping Wang Kai-rui Shen Tao Bao Yudong National Experimental Teaching Demonstration Center for Mechanical Engineering Experiment and Practice Harbin University of Science and Technology Harbin China School of Mechanical and Power Engineering Harbin University of Science and Technology Harbin China School of Measurement and Communication Engineering Harbin University of Science and Technology Harbin China

ISBN: (数字)9798331540883

ISBN: (纸本)9798331540890

Aiming to address the current problems in robot training teaching, this paper focuses on the construction of industry-teaching-innovation cases for robot training under the integration of virtuality and reality, such as the disconnection between training content and industry practice, delays in digital transformation, and the lack of training resources. Relying on the intelligent manufacturing industry academy (IMIA), the teaching model integrates virtual simulation training with practical operation to enhance students' practical skills and innovative abilities. The study introduces complex technical challenges faced by enterprises in production, combining a dual-mentor system and a collaborative resource-sharing mechanism between school and enterprises. This ensures that students can master key skills required by enterprises during training, while innovation and entrepreneurship education is integrated throughout the whole training process to stimulate students' creative thinking and entrepreneurial mindset.

关键词： Training Manufacturing industries Technological innovation Service robots Digital transformation Entrepreneurship Production Delays Manufacturing Problem-solving

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RSSI Indoor Positioning Algorithm Based on Kalman Filtering

RSSI Indoor Positioning Algorithm Based on Kalman Filtering

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Electronic Communication and Artificial Intelligence (IWECAI), International Workshop on

作者： Yinqing Xie Li Jiang School of Electronics and Automation City Institute Dalian University of Technology Dalian China Engineering Practice Center City Institute Dalian University of Technology Dalian China

ISBN: (数字)9798350386943

ISBN: (纸本)9798350386950

In the context of the increasingly deep construction of smart home, indoor localization using wireless network has become a research hotspot. However, in practical applications, the training samples obtained by most indoor localization algorithms are not exactly the same as the detection samples, and the external environmental changes and signal drift also lead to differences in the probability distributions of the two. The indoor positioning technology based on wireless signals has become a research hotspot due to its low cost and easy deployment. Among them, RSSI (Received Signal Strength Indication), as one of the commonly used indoor positioning techniques, its accuracy and stability directly affect the performance of the positioning system. Traditional methods are difficult to ensure accurate positioning in multi-source heterogeneous environments, resulting in decreased positioning accuracy or even unusable. A RSSI indoor positioning algorithm based on Kalman filtering is proposed to address this challenge. Experiments show that the accuracy of this algorithm reaches more than 0.95 under the number of training samples of 300,400,900. The algorithm can effectively suppress the influence of external environment and improve the localization accuracy.

关键词： Location awareness Training Accuracy Wireless networks Smart homes Filtering algorithms Indoor environment

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Artificial Intelligence based Health Monitoring System on IoTH platform 2

Artificial Intelligence based Health Monitoring System on Io...

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2nd International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2023

作者： Dankan Gowda, V. Sathya, B. Kavitha, R. Kumar, Avinash Kalpana, V. Hariram, Venkatesan Bms Institute of Technology and Management Department of Electronics and Communication Engineering Karnataka Bangalore India Velammal College of Engineering and Technology Department of Information Technology Tamilnadu Madurai India Maya Industries Corp Haryana Gurgaon India Mohan Babu University School of Engineering & Technology Department of Ece Tirupati India Vinayaka Mission's Homoeopathic Medical College and Hospital A Constituent College of Vinayaka Mission's Research Foundation Deemed to Be University Department of Practice of Medicine Tamilnadu Salem India

ISBN: (纸本)9798350325799

In order to fight the pandemic situations like COVID-19, researchers are working towards find an early vaccination and are developing a multidisciplinary strategy that explores the most sophisticated Internet of Things (IoT) technologies. By utilizing IoT-based industrial manufacturing of breathing units, masks, and other medical equipment, patients might either be observed in hospitals or separated at home in a secured manner by utilising these items;alternatively, new methods of passive ventilation could be devised. It is a non-contact, alcohol-based hand sanitizer dispenser that is used in a number of environments including hospitals, work places, companies, schools, and other educational institutions. An automated hand sanitizer distributing machine is a system that distributes hand sanitizer automatically. When compared to liquid soap or solid soap, alcohol is primarily a solvent that also serves as a highly effective disinfectant. Additionally, since alcohol is volatile and vaporises immediately after application to hands, it does not need water to be washed away. It has additionally been demonstrated that a concentration of alcohol in the hands that is more than 70% may be able to destroy the Corona virus. An infrared sensor is used to detect when a hand is placed in close proximity to it. This sensor is also used as a microcontroller to estimate the distance between the hand and the sensor, which ultimately results in the pump being activated to dispense the hand sanitizer. In order to keep track of the distance between the two objects, ultrasonic sensors are used;when the distance is becoming too near, a buzzer will ring to warn the user. The internal body temperature is measured using an infrared temperature sensor, and a patient's health will be evaluated with the assistance of a temperature sensor as well as a pulse sensor. Finally, a few data points will be sent to the cloud when the evaluation is complete. © 2023 IEEE.

关键词： COVID-19

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Ideological and Political Construction of “Innovation and Entrepreneurship Fundamentals” Course Based on Four Orientations

Ideological and Political Construction of “Innovation and E...

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International Conference on engineering Education (ICEED)

作者： Jin-gang Jiang Yu-jian Tan Yu-dong Bao Kai-rui Wang Tao Shen Ye Dai National Experimental Teaching Demonstration Center for Mechanical Engineering Experiment and Practice Harbin University of Science and Technology Harbin China School of Mechanical and Power Engineering Harbin University of Science and Technology Harbin China School of Measurement and Communication Engineering Harbin University of Science and Technology Harbin China

ISBN: (数字)9798350367416

ISBN: (纸本)9798350367423

Innovation and Entrepreneurship Fundamentals, as a crucial mandatory component of social practice, plays a significant role in fostering students' innovative consciousness and entrepreneurial capabilities. Addressing the deficiency in integrating ideological and political elements within traditional curricula, a novel pedagogical approach has been proposed, one that is grounded in the “Four Orientations” and characterized by an immersive, distinctive ideological and political curriculum model. This model ingeniously weaves distinctive ideological and political components into the foundational course of innovation and entrepreneurship. An eclectic course evaluation framework has been established, which integrates the assessment of ideological and political content with the curriculum's subject matter, thereby harmonizing the evaluation criteria across various assessment phases. The construction of ideological and political courses more effectively integrates ideological and political education into professional instruction, achieving dual objectives of knowledge dissemination and value shaping. This approach aims to cultivate a new generation of youth endowed with an innovative spirit and a sense of social responsibility for the benefit of the nation and society.

关键词： Technological innovation Entrepreneurship Solids engineering education

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Optimization of Teaching Evaluation Methods for Comprehensive practice Courses in Mechatronic engineering Based on GMM Algorithm

Optimization of Teaching Evaluation Methods for Comprehensiv...

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International Conference on engineering Education (ICEED)

作者： Jin-Gang Jiang Zhong-Hao Xue Ye Dai Yu-Dong Bao De-Dong Tang Hai-Yan Du School of Mechanical and Power Engineering Harbin University of Science and Technology Harbin China National Experimental Teaching Demonstration Center for Mechanical Engineering Experiment and Practice Harbin University of Science and Technology Harbin China

ISBN: (数字)9798350367416

ISBN: (纸本)9798350367423

Aiming at the problems existing in the teaching evaluation method of the comprehensive practice course of Mechatronic engineering based on the concept of OBE engineering education, using the GMM algorithm for clustering, relying on the provincial higher education teaching reform project, optimizing the teaching evaluation method of the course, so as to improve the OBE teaching mode of the “comprehensive practice” course with a multilevel and diversified teaching evaluation system, to cultivate the students' ability to apply knowledge comprehensively and their ability to engage in engineering practice and their sense of innovation, and to satisfy the new requirements of “New engineering” and “Made in China 2025” for the electromechanical majors and to promote the enhancement of the quality of the personnel training for the Mechatronic engineering majors.

关键词： Training Visualization Technological innovation Mechatronics Education Clustering algorithms Solids Personnel engineering education Optimization

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Optimization and Design of Intelligent Sorting Line System Based on Digital Twins 5

Optimization and Design of Intelligent Sorting Line System B...

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5th International Conference on Internet of Things, Automation and Artificial Intelligence, IoTAAI 2023

作者： Zhu, Yingzhuang Bing, Zhigang Zhao, Di Li, Ying Lai, Zhanhang School of Automation and Electrical Engineering Tianjin University of Technology and Education Tianjin China Key Laboratory of Information Sensing & Intelligent Control Tianjin China GongJiang Carbon Peaking and Carbon Neutrality Research Institute Tianjin China Engineering Teaching Practice Training Center of Tiangong University Tianjin China

ISBN: (纸本)9798400716485

Traditional manufacturing industries are currently facing limitations in terms of time, space, cost, and security. The traditional manufacturing system has problems such as higher operating costs, untimely response speed, lower degree of intelligence, and difficulty in system optimization. To meet the needs of the development of intelligent manufacturing, with the help of the new generation of digital twin technology, we explore the overall framework and design ideas for the combination of digital twin and intelligent manufacturing. Through digital twin technology, the intelligent manufacturing production line is mapped into the virtual space. The energy consumption of traditional manufacturing systems based on actual physical equipment in the testing phase is effectively reduced. Testing and running the manufacturing production line in the virtual space solves the problem of time and space limitations of the physical production line. By constructing an intelligent manufacturing production system that combines virtual and natural, the control accuracy and response speed of the manufacturing system are significantly improved. © 2023 ACM.

关键词： Energy utilization

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Enhancing Sign Language Recognition for Hearing-Impaired Individuals Using Deep Learning 2

Enhancing Sign Language Recognition for Hearing-Impaired Ind...

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2nd IEEE International Conference on Data Science and Network Security, ICDSNS 2024

作者： Arularasan, R. Balaji, D. Garugu, Srisudha Jallepalli, Venkateswara Rao Nithyanandh, S. Singaram, Gokulakannan Agni College of Technology Department of Mechatronics Engineering Chennai India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Chennai India Medchal India Mb School of Pharmaceutical Sciences Mohan Babu University Department of Pharmacy Practice Tirupati India Psg College of Arts and Science Department of Mca Coimbatore India Department of Community Medicine Salem India

ISBN: (纸本)9798350373110

Communicating with people who have developmental disabilities and hearing difficulties requires the use of sign language. It presents the SSODL-ASLR model, which is customized for people with speech and hearing impairments, by using advances in CNNs and HCI. The model works in two stages: first, it uses the Mask RCNN model for sign language detection, and then it uses an SSO approach and the SM-SVM model for sign language classification. Prolonged simulations demonstrated significant improvements with the suggested SSODL-ASLR model: 15% better accuracy, reaching 95%;92% precision;94% recall;and 93.5% F1 score. The model also achieved a reduction in memory footprint to 250 MB, a processing speed of 120 FPS, a training time of 12 hours, and an inference time of 20 ms. These results demonstrate the revolutionary effect of the SSODL-ASLR paradigm in promoting automated sign language recognition and enhancing the deaf and mute community's communication accessibility. © 2024 IEEE.

关键词： Convolutional neural networks

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Glucose Prediction Using Population-based Models and Genetic Data in Type 1 Diabetes Patients 46

Glucose Prediction Using Population-based Models and Genetic...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Katsarou, Daphne N. Georga, Eleni I. Sakaloglou, Prodromos Christou, Maria A. Georgiou, Ioannis Tigas, Stelios Papaloukas, Costas Fotiadis, Dimitrios I. University of Ioannina Dept. of Materials Science and Engineering Dept. of Biological Applications and Technology Ioannina Greece University of Ioannina Dept. of Materials Science and Engineering Ioannina Greece Laboratory of Medical Genetics in Clinical Practice Faculty of Medicine School of Health Sciences Ioannina Greece University Hospital of Ioannina Dept. of Endocrinology Ioannina Greece University of Ioannina Biomedical Research Institute Foundation for Research and Technology-Hellas Dept. of Biological Applications and Technology Ioannina Greece University of Ioannina Biomedical Research Institute Foundation for Research and Technology-Hellas Dept. of Materials Science and Engineering Ioannina Greece

ISBN: (纸本)9798350371499

Type 1 Diabetes (T1D) is a chronic autoimmune disorder causing dys-glycaemia. Technological advancements, such as continuous glucose sensors and closed-loop insulin delivery systems, are potential solutions to more efficient disease management. Hyperglycaemia contributes to the development of long-term micro-or macro-vascular complications;on the other hand, hypoglycaemia remains an acute and potentially life-threatening challenge impacting the quality of life for individuals with T1D. This study is a prodromal analysis of how specific genetic variants related to hypoglycaemia can be introduced to short-term predictive modelling methods of subcutaneous glucose concentration prediction in people with T1D, aiming at improving prediction errors, especially, in hypoglycaemic regions. We employed a K-mode-based clustering of study participants based on their genetic data and we developed separate population-based univariate long short-term memory (LSTM) models per cluster. Cluster-wise population models, developed and tested on a dataset of 47 T1D patients, exhibited a MAPE of 11.6%, 12.8% and 12.0% on the 3 identified clusters, respectively, for a prediction horizon of 30 min. Cluster-wise predictions outperformed global population models for the case of Group 0 which possess an increased percentage of very low (250 mg/dL) glucose values. This finding shows that incorporating genetic data into the pipeline of population-based glucose predictive modelling in T1D can improve the prediction error. © 2024 IEEE.

关键词： Prediction models

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Research on supply chain intelligent bid evaluation management system based on AHP-TOPSIS 24

Research on supply chain intelligent bid evaluation manageme...

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5th International Conference on Computer Science and Management technology, ICCSMT 2024

作者： Yang, Zheng Jin, Hao Chen, Shuai Sun, Jinzhou Tang, Fei Chen, Mo E-commerce Co. Ltd. Fujian Xiamen China Wuhan Hangke Logistics Co.Ltd. Hubei Wuhan China School of Information Management Wuhan University Hubei Wuhan China School of Electrical and Automation Wuhan University Hubei Wuhan China Hubei Engineering and Technology Research Center for AC/DC Intelligent Distribution Network Hubei Wuhan China College Students Engineering Training and Innovation Practice Center Hubei Wuhan China

ISBN: (纸本)9798400709999

The intelligent transformation of the bid evaluation process in supply chains has become a prominent area of *** paper analyzes the functional framework of the intelligent bid evaluation management system for supply chains and ranks the importance of functions involved in the evaluation ***, the paper examines the challenges faced by existing supply chain platforms during their intelligent *** then proposes an evaluation system for the functional importance of bid evaluation, utilizing the Analytic Hierarchy Process (AHP) and TOPSIS *** converting subjective judgments into objective data, this study comprehensively analyzes three key factors in the bid evaluation process: bid integrity, risk level, and evaluation *** weights are determined based on expert assessments, leading to the optimization and ranking of intelligent bid evaluation *** results reveal that tender integrity and supplier risk are critical in the evaluation process, with functions such as core term extraction, integrity detection, and format error identification playing essential *** research not only offers scientific decision support for the development of intelligent bid evaluation management systems in the supply chain but also provides a theoretical foundation for advancing supply chain intelligence. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： AHP

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