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Humanoid Control Technology for Lower Limb Rehabilitation Robots Based on Human Gait Data

Humanoid Control Technology for Lower Limb Rehabilitation Ro...

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2024 international conference on Advanced Mechatronic Systems, ICAMechS 2024

作者： Gao, Shengda Wang, Aihui Duan, Huichao Yue, Xuebing Li, Hengyi Dong, Jinkang Zhongyuan University of Technology School of Automation and Electrical Engineering Zhengzhou450007 China

ISBN: (纸本)9798350366204

The lower limb rehabilitation robot is a wearable exoskeleton bionic device that integrates robotic features with human walking characteristics. This paper explores the control strategies, gait data acquisition and processing methods, as well as the design and experimental validation of a humanoid control system for lower limb rehabilitation robots. By collecting gait data from healthy individuals using the NOKOV 3D infrared passive optical motion capture system, a gait dataset was established, and methods such as spline interpolation and Gaussian regression were employed to integrate the gait data. A humanoid control system based on real gait data was designed, utilizing a high-performance computer, R4SE controller, and joint motors to simulate gait patterns. Experimental results demonstrate that the system effectively follows target gait trajectories, achieving high trajectory tracking accuracy and smoothness, ensuring the safety and effectiveness of rehabilitation training. Future research will incorporate a more diverse group of subjects and intelligent control algorithms to enhance the system's adaptability and intelligence. © 2024 IEEE.

关键词： Exoskeleton (Robotics)

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Enhanced Botnet Attack Detection using Machine Learning and Neural Networks 7

Enhanced Botnet Attack Detection using Machine Learning and ...

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7th international conference on Inventive Computation Technologies, ICICT 2024

作者： Dharaneish, V.C. Rajakrishnan, Sandeep Sudhay, S. Senthil Kumar, T. Siddhesh, T.S. Vajipayajula, Sulakshan Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science And Engineering Coimbatore India Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science And Engineering Bengaluru India Ibm India Pvt Ltd Ibm Security Bengaluru India

ISBN: (纸本)9798350359299

In an era characterized by pervasive interconnectivity, Internet of Things (IoT) devices have revolutionized automation, facilitating seamless data exchange. However, this technological leap has triggered heightened concerns about data security amid proliferating cyber-attacks. Given the omnipresence of IoT systems, the need for fortified networks to thwart potential intrusions is imperative. This work focuses on crafting an Intrusion Detection System (IDS) for IoT network security based on Machine learning (ML) and Deep Learning (DL) techniques, utilizing the Bot-IoT dataset from the University of New South Wales. Using a small dataset after preprocessing and feature selection, a comparative analysis of multiclass classification models, including Multinomial Logistic Regression, Artificial Neural Networks, Decision Tree Classifier, and Random Forest Classifier, is conducted. The results highlight the Decision Tree Classifier as the optimal model, with an exceptional accuracy of 99.98%, closely followed by the Random Forest Classifier with an accuracy of 99.91%. Automated hyperparameter tuning using GridSearchCV significantly contributes to enhancing model performance. A thorough evaluation, incorporating multi-classification metrics like Accuracy, Precision, Recall, and F1-score, is done to ensure a comprehensive assessment of the models. This research offers valuable insights into fortifying IoT network security, with implications for safeguarding the integrity of interconnected systems. © 2024 IEEE.

关键词： Internet of things

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Investigating the Effects of AI-Driven Systems on Data Science automation

Investigating the Effects of AI-Driven Systems on Data Scien...

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international conference on Computing and Networking Technology (ICCNT)

作者： K K Senthilkumar Pradnya S. Mehta M V Panduranga Rao Megha Pandey Baldev Singh Saniya Khurana Department of Electronics and Communication Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Computer Engineering Vishwakarma Institute of Information Technology Pune INDIA Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Bangalore Karnataka India Department of Computer Science & Engineering Vivekananda Global University Jaipur India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and technology Chitkara University Punjab India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

This article investigates the effect of AIdriven structures on the automation of information science. AI-pushed structures and automation of records technology have ended up an increasing number of essential because of the growing complexity of information and the numerous and ever-increasing data resources in today’s digital international. This text explores the approaches in which AI-driven structures have converted how records technology is being computerized and the way it has impacted the method. Moreover, the article looks at the benefits and shortcomings of AI-driven structures in terms of automation of data science as well as the various regions which are still open for research that allows you to, in addition, improve the automation of information technology. Finally, the article proposes future study guidelines in this field, allowing the continued development of AI-driven records science automation.

关键词： Information science automation Data science Complexity theory Reliability Information technology Artificial intelligence Guidelines

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Research on positionless control of mining high force energy density permanent magnet direct drive integrated machine based on improved sliding mode observer 3

Research on positionless control of mining high force energy...

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3rd international conference on Consumer Electronics and computer engineering, ICCECE 2023

作者： Wang, Sen Chen, Nan Jia, Zijian Liang, Daili Xiao, Yanhui Yu, Wanhong Wang, Wenshuo Wang, Lei Zhu, Yuqin Zhang, Li Liu, Hao Yu, Zhanyang Du, Jianmin Shenyang Institute of Engineering Department of College of Automation Liaoning Shenyang China China Coal Technology and Engineering Group Shenyang Resreach Institute State Key Laboratory of Coal Mine Safety Technology 113122 China Shenyang University of Technology Liaoning Shenyang China

ISBN: (纸本)9798350331578

In order to realize sensorless control of mine high energy density permanent magnet direct drive integrated machine, improved sliding mode control is used to estimate rotor position and rotational speed. Due to the frequent switching of working conditions in the mine, the control system of the integrated machine is affected to a certain extent. In order to reduce the chattering effect caused by traditional sliding mode control, a sensorless vector control system with double closed-loop speed and current was built by adding phase-locked loop to improve the stability and observation accuracy of the control system. The simulation results show that the system has high state estimation accuracy, stable operation and fast response speed, which can cope with complex working conditions in mine, and is suitable for the mine with high energy density permanent magnet direct drive all-in-one. © 2023 IEEE.

关键词： Position control

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Deep Learning for Accurate Bone Age Estimation: A Reliable VGG19-Based Methodology

Deep Learning for Accurate Bone Age Estimation: A Reliable V...

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2024 international conference on Decision Aid Sciences and Applications, DASA 2024

作者： Kanishka Gupta, Varun Bhattacherjee, Abhishek Dogra, Ayush Jain, Eshika Kaushik, Pratham CSE-APEX Chandigarh University Department of Computer Science Chandigarh India Mullana India Lovely Professional University Department of Computer Science Engineering Punjab Phagwara India Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura140401 India

ISBN: (纸本)9798350369106

Bone age assessment plays a critical role in diagnosing growth disorders and identifying developmental milestones, providing guidance for the timing of various clinical interventions. Traditional approaches are primarily subjective and heavily rely on visual evaluations. This study introduces a robust, accurate, and automated predictive model utilizing deep learning, built on a Kaggle dataset containing 12,611 annotated medical images. Stratified sampling was followed by splitting the data into balanced training (75%) and validation (25%) subsets. It had a pre-trained VGG19 model over ImageNet for feature extraction, after which a custom-defined set of dense layers was attached to it that would ultimately perform the detailed classification. The model was trained using Adam optimizer and tested by using the accuracy, precision, recall, F1 score and confusion matrices. The system also yielded validation accuracy of 94.77% with high precision and recall and these results were across the age groups. These outcomes support the model and attest to its viability for use in clinical setting. Thus, data augmentation and preprocessing methods were also used successfully to reduce overfitting and improve generality. The study has pointed out the transforming impact of AI in medical imagery, a strong tool for the automation of bone age assessment with excellent reliability. As a result, the applications of this AI-driven solution extend beyond clinical diagnostics to include forensic analysis and growth monitoring, showcasing its versatility across various healthcare domains. This thus helps develop efficiency and accuracy in evaluating skeletal maturity by addressing the limitations of traditional methods. © 2024 IEEE.

关键词： Deep learning

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Micro-expression Recognition Based on Apex Frame Selection Strategy and Optical Flow Features 8th

Micro-expression Recognition Based on Apex Frame Selection S...

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8th international conference on Life System Modeling and Simulation, LSMS 2024 and 8th international conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2024

作者： Zhang, Yukun Fei, Zixaing Zhou, Wenju Fei, Minrui Zhou, Huiyu The School of Mechatronic Engineering and Automation Shanghai University Shanghai200444 China The School of Computer Engineering and Science Shanghai University Shanghai200444 China The School of Computing and Mathematical Sciences University of Leicester LeicesterLE1 7RH United Kingdom

ISBN: (纸本)9789819601875

Micro-expressions are generated by real emotions and are difficult to fake. The movements of micro-expressions are localized and the intensity of movements is weak. It is hard for current algorithms to accurately and quickly recognize micro-expressions in video clips. Therefore, we propose a micro-expression recognition method based on an apex frame selection strategy and optical flow features to address this issue. The method utilizes an apex frame extraction strategy based on changes in image optical flow information to select the start frame and the apex frame that can characterize the entire video. Simultaneously, the optical flow information features that characterize the start frame and the apex frame are preserved. The optical flow features extracted are then used as input information for the ResNet18 network for classification. The approach we propose is able to better capture the spatial features of facial micro-expressions and improve the accuracy of micro-expression recognition. The method suggested in this paper holds significant potential for various applications in fields such as psychotherapy and disease recognition. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Apex frame Micro-expression Optical flow method ResNet18 Spatial characteristics

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A Software Design Method for Creation and Utilization of Experiential Knowledge

A Software Design Method for Creation and Utilization of Exp...

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2022 IEEE international conference on Electrical, computer, and Energy Technologies, ICECET 2022

作者： Hoshino, Takayuki Yoshioka, Rentaro University of Aizu BIPROGY Inc. Technology Research & Innovation The Graduate School of Computer Science and Engineering Tokyo Japan University of Aizu The Graduate School of Computer Science and Engineering Aizu-Wakamatsu Japan

ISBN: (数字)9781665470872

ISBN: (纸本)9781665470872

Until now, knowledge-related efforts in engineering, including software engineering, have aimed to automate human activities. But, unfortunately, the use of knowledge for automation leads to the black-boxing of the system, making it difficult to respond flexibly to changes and shaking the reliability, safety, and trustworthiness of computer processing results. When it is necessary to respond to change and diversity and realize a sustainable society, knowledge must be used for change, diversity, and creation for sustainability, not for automating existing mechanisms. To realize such knowledge creation and utilization, it is necessary to design a software support environment and make it available as a platform for knowledge creation. This paper will show the design and effect of a knowledge creation platform for human beings to use knowledge in their activities and evolve, deepen, continue, and maintain their activities. © 2022 IEEE.

关键词： Software design

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Management Strategies for Monocrystalline SiC Substrate Production Lines under Downtime Disturbances

Management Strategies for Monocrystalline SiC Substrate Prod...

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2022 international conference on automation, Robotics and computer engineering, ICARCE 2022

作者： Zhu, Xisheng Zhang, Dan Ai, Wenhao Fu, Weidong Zuo, Dunwen Nanjing University of Aeronautics and Astronautics College of Mechanical and Electrical Engineering Nanjing China

ISBN: (纸本)9781665475488

In order to reduce the system performance loss and restore the normal operation for the downtime disturbance event on the monocrystalline SiC substrate production line, a prediction model of the performance of the SiC wafer substrate production line is established based on the G/G/m/b queuing model. A production line simulation model is built in the Witness simulation software to verify the feasibility of the prediction model. A production line performance optimization model under downtime disturbance with the buffer capacity and processing preparation time as optimization parameters is established combining with the concept of time window. And the management strategy is obtained by the optimization solution of the model using genetic algorithm. The feasibility of the proposed method is verified based on the improved coupled map lattices production network propagation model. The results of the simulation validation in Witness software show that the proposed method improves the production performance of the production line under equipment downtime. © 2022 IEEE.

关键词： Silicon carbide

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Recent Trends in Brain Tumor Prediction Using Machine Learning and Deep Learning 16

Recent Trends in Brain Tumor Prediction Using Machine Learni...

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16th IEEE international conference on Computational Intelligence and Communication Networks, CICN 2024

作者： Hariyani, Gulshan Mantri, Shamla Dr. Vishwanath Karad MIT-World Peace University Dept. of Computer Engineering and Technology Pune India

ISBN: (纸本)9798331505264

A spontaneous growth of abnormal cells in brain tissues that increases the pressure inside the skull is called a brain tumor. This tumor can be non-cancerous or cancerous based on characteristics, but at most can cause life-threatening if not treated in the early stages. Machine learning and deep learning approaches have become a potent tool in the field of medical imaging in recent years, providing brain tumor identification and classification with increased automation and accuracy. This review explores the recent advancements in machine learning and deep learning models, which have shown better results in brain tumor prediction. Mainly the type of images that are considered for brain tumor capturing are Magnetic Resonance Imaging (MRI), as these images provide great insights into brain tissues, so in this paper, we are referring to MRI-type images only. This paper also covers various segmentation techniques mentioned in multiple studies, as it is a time-consuming job for radiologists to deeply understand MRI images and highlight the tumor present. In this paper, we have also mentioned what metrics are used to analyze these models, like accuracy, sensitivity, specificity, and more, to know if the models are robust. © 2024 IEEE.

关键词： Tumors

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Intelligent Systems for Early Malaria Disease Detection in Patient Cells Using Transfer Learning Approaches 4

Intelligent Systems for Early Malaria Disease Detection in P...

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4th international conference on Computing, Mathematics and engineering Technologies, iCoMET 2023

作者： Zaman Khan, Gul Ali Shah, Ibrar Farhatullah Hassan, Muhammad Abul Junaid, Hazrat Sardar, Fouzia Uet Department of Computer Software Engineering Mardan Pakistan School of Automation China University of Geosciences Wuhan430074 China University of Trento Department of Information Engineering and Computer Science Italy University of Malakand Department of Computer Science and Information Technology Pakistan University F Malakand Department of Zoology Dir Lower Pakistan

ISBN: (纸本)9798350335316

Malaria is an infectious disease spread by mosquitoes that effect humans and other animals. It is a massive threat to humanity, with instances growing each year. It is essential to prevent and diagnose malaria immediately and efficiently. For the time being, conventional methods are used for diagnosing malaria in which the patient's blood sample is examined by microscope or by using malaria RTD kits. This approach has several limitations because it requires medical expertise, is expensive, takes a long time, and the results are unsatisfactory. Artificial intelligence-based systems can prevent and help in diagnose of this infectious disease. Because of these limitations, the proposed work has proposed an AI-based diagnosis system that can detect malaria parasites immediately and efficiently. In the proposed experiment, we have applied four different pre-trained deep learning models on the image dataset with some preprocessing and optimization techniques for malaria parasite detection. After investigations, evaluation matrices such as precision, Recall, F1-score, sensitivity, and specificity are used to measure the performance of the proposed models. The Inception-Resnet outperformed by achieving 95% accuracy, VGG16 achieved 92% accuracy, inception achieved 93% accuracy, and VGG19 achieved 91% accuracy. The positive outcomes of this study show that this approach performs much better than the approaches currently used. Furthermore, the proposed method is relevant to health experts for screening purposes. © 2023 IEEE.

关键词： Diseases

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