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Reliability Measures in Neutrosophic Soft Graphs

Neutrosophic Sets and Systems 2022年 49卷 239-252页

作者： Anirudh, A. Kannan, R. Aravind Sriganesh, R. Sundareswaran, R. Kumar, S. Sampath Shanmugapriya, M. Broumi, Said Dept. of Computer Science Sri Sivasubramaniya Nadar College of Engineering India Dept. of Electrical and Electronics Engineering. Sri Sivasubramaniya Nadar College of Engineering India Dept. of Mathematics Sri Sivasubramaniya Nadar College of Engineering India Laboratory of Information Processing Faculty of Science Ben M'Sik University Hassan II B.P 7955 Sidi Othman Casablanca Morocco

This paper introduces the concept of reliable nodes in neutrosophic soft graphs by evaluating path-based parameters. A reliable node is defined as one which is least susceptible to changes that are quantized by the indeterminacy and falsity values in a neutrosophic tuple. A new path measure called farness and three novel reliability measures which make use of the same are presented. Farness is defined in terms of a novel score function. The first, proximity reliability of a node, computes the farness of a node to its neighbours. The second, intermediate reliability of a node, computes the fraction of paths of minimal farness that pass through it. The third, crisis reliability of a node, is a hybrid of the two previously defined. It considers the farness of a node to its neighbours taking into account the farness of the neighbours to other nodes in the graph. © 2022. All Rights Reserved.

关键词： Reliability

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Hybrid polynomial-based trajectory planning for lower limb exoskeleton robots 18

Hybrid polynomial-based trajectory planning for lower limb e...

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18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023

作者： Li, Shupei Zhang, Chi Yi, Feng Lv, Pingping Yuan, Ting Zhang, Meitong University of Chinese Academy of Sciences Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences College of Materials Science and Opto-Electronic Technology Ningbo315201 China Ningbo Institute of Materials Technology and Engineering Zhejiang Key Laboratory of Robotics and Intelligent Manufacturing Equipment Technology Ningbo315201 China Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo315201 China Hebei University of Science & Technology School of Electrical Engineering Ningbo315201 China

ISBN: (纸本)9798350312201

To ensure smooth, continuous, and precise motion of lower limb exoskeleton robots, this paper proposes a 6-5-6 polynomial trajectory planning method (6-5-6 PTPM) that combines 5th and 6th polynomials. By imposing corresponding kinematic constraints, this method achieves the trajectory plan of seven sub-phases of the gait cycle on the kinematic model of the lower limb exoskeleton robot. The simulation is conducted and compared to the 4-3-4 polynomial trajectory planning method (4-3-4 PTPM). The comparison results indicate that the 6-5-6 PTPM is shown to have a lower error between the planned trajectory and the actual trajectory. Additionally, it avoids spikes of the acceleration curve that existed in the 4-3-4 polynomial method. The proposed method enables the acceleration curve smoother and mechanical impact lessened, which demonstrates the feasibility and stability of the proposed method. © 2023 IEEE.

关键词： Trajectories

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Effect of radiofrequency coil and primary magnetic field on radiolucent composite plates

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Noise and Vibration Worldwide 2023年第1期54卷 44-58页

作者： Gawande, Atul S Mukka Ramachanra, Shreyas Kamyab, Hesam Trung, Nguyen-Thoi Kattimani, Subhaschandra Department of Mechanical Engineering National Institute of Technology Karnataka Surathkal India Malaysia-Japan International Institute of Technology Universiti Teknologi Malaysia Kuala Lumpur Malaysia Laboratory for Applied and Industrial Mathematics Institute of Computational Science and Artificial Intelligence Ho Chi Minh City Viet Nam Faculty of Mechanical-Electrical and Computer Engineering Van Lang University Ho Chi Minh City Viet Nam

Magnetic Resonance Imaging (MRI) systems need a material compatible with the imaging technique with lesser attenuation and provide accurate images without distortion. Carbon fibers are the best-suited materials for x-ray applications because of their radiolucent properties and reduced attenuation characteristics. However, carbon fiber produces images with distortion in the MRI system by absorbing electromagnetic energy because of its conductive nature. In the present study, five different fiber-reinforced radiolucent composite plates are analyzed to predict their suitability for a radio frequency coil and the effect of a primary magnetic field of 1.5 T and 3.0 T on mechanical responses. Simulation models are built to explore the impact of electromagnetic waves and birdcage coil configurations on composite material and quantify the temperature changes caused due to energy absorption. A multiphysics coupling simulation is being used to understand the effect of stacking sequence, ply orientation, and boundary conditions on the response of composite plates under an electro-magneto-mechanical environment. © The Author(s) 2022.

关键词： Magnetic resonance imaging

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Research on hybrid flow-shop scheduling based on improved genetic algorithm 18

Research on hybrid flow-shop scheduling based on improved ge...

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18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023

作者： Chen, Haopeng Fang, Zaojun Zhong, Caiming Ningbo University Ningbo Institute of Materials & Technology Engineering Chinese & Academy of Sciences Faculty of Electrical Engineering and Computer Science Ningbo China Chinese & Academy of Sciences Zhejiang Key Laboratory of Robotics and Intelligent Manufacturing Equipment Technology Ningbo Institute of Materials & Technology Engineering Ningbo China Ningbo University College of Science & Technology Ningbo China

ISBN: (纸本)9798350312201

Aiming at the scheduling problem in the production process of hybrid flow-shop, a hybrid flow-shop scheduling model with the objective of minimizing the makespan is established, and a neighborhood search adaptive genetic algorithm (NSAGA) based on heuristic decoding strategy is proposed. An encoding strategy based on the first stage processing sequence and a decoding strategy based on a heuristic rule are used to guide the algorithm to find the optimal solution quickly. In order to speed up the convergence of the algorithm, the elitism strategy is introduced into the genetic operation, and adaptive crossover and mutation strategy based on sigmoid function is designed. In order to enhance the local search capability of the algorithm, a neighborhood search strategy is designed. Finally, the effectiveness and superiority of NSAGA for solving the hybrid flow-shop scheduling problem (HFSP) with different scales are verified by simulation experiments. © 2023 IEEE.

关键词： Genetic algorithms

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Cloud and Edge based Smart Agriculture: A Real-World Deployment

Cloud and Edge based Smart Agriculture: A Real-World Deploym...

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2024 International Conference on Circuit, Systems and Communication, ICCSC 2024

作者： Et-Taibi, Bouali Abid, Mohamed Riduan Boufounas, El-Mahjoub Benhaddou, Driss Moulay Ismail University of Meknès Reipt Laboratory Faculty of Sciences and Technology of Errachidia Morocco Al Akhawayn University in Ifrane School of Science and Engineering Ifrane53000 Morocco Columbus State University Tsys School of Computer Science ColumbusGA31907 United States University of Houston Department of Engineering Technology 4800 Calhoun Rd HoustonTX77004 United States Alfaisal University College of Engineering Electrical Engineering Department Riyadh Saudi Arabia

ISBN: (纸本)9798350365306

Smart Agriculture presents significant opportunities for transforming farming methods, although its widespread adoption remains constrained by cost barriers, especially for small and medium-sized farmers. To bridge this divide, we propose a cost-effective solution tailored specifically for soilless cultivation within drip irrigation systems. Our approach harnesses the power of IoT, cloud computing, and edge computing to bolster connectivity and streamline data management. Central to our solution is a three-tiered open-source software architecture, which encompasses local sensors, edge computing, and cloud computing layers. Locally deployed Wireless Sensor Networks facilitate real-time monitoring and data acquisition, while the edge tier orchestrates irrigation tasks in the field using Fuzzy Logic algorithms. The cloud layer serves as the backbone for data analytics, storage, and visualization, ensuring optimal resource management. Through the utilization of IoT protocols, seamless communication is established between sensors, edge devices, and the cloud, enabling efficient data exchange and analysis. Our tangible prototype validates the effectiveness of this platform in optimizing drip irrigation systems. This offering provides an accessible solution for small and medium-scale farmers, empowering them to enhance farming productivity and sustainability. © 2024 IEEE.

关键词： Smart agriculture

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An Enhanced Haar Cascade Face Detection Schema for Gender Recognition

An Enhanced Haar Cascade Face Detection Schema for Gender Re...

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2023 International Conference on Smart Computing and Application, ICSCA 2023

作者： Sridhar, Psvs Chithaluru, Premkumar Kumar, Sunil Cheikhrouhou, Omar Hamam, Habib Computer Science and Engineering Andhra Pradesh Vaddeswaram India Department of Computer Science and Engineering Hyderabad India School of Computer Science University of Petroleum and Energy Studies Dehradun India National School of Engineers of Sfax University of Sfax Ces Laboratory Sfax3038 Tunisia University de Moncton Faculty of Engineering MonctonNBE1A3E9 Canada International Institute of Technology and Management Commune d'Akanda Libreville Gabon School of Electrical Engineering University of Johannesburg Department of Electrical and Electronic Engineering Science Johannesburg2006 South Africa Spectrum of Knowledge Production and Skills Development Sfax Tunisia

ISBN: (纸本)9798350347050

Gender identification software is becoming increasingly popular in the age of machine-to-machine interactions, as the need for person-aligned, effective, and ethical systems become more apparent. In this age of machine-to-machine interactions, automated gender identification software is becoming more popular. The bulk of the systems for gender identification that were examined used either textual or audiovisual data as input to determine their gender. Many different approaches to automatically detect gender based on traits gathered from people's bodies and/or behaviors have been suggested, and there are simply too many to include here. The accuracy of automated gender identification, on the other hand, has always been a topic of worry or a source of criticism. Initial face identification is accomplished by the use of the Haar Cascade face detection method, which is based on the Viola-J ones face recognition algorithm. Facial characteristics such as the eyes, mouth, and nose are then identified. Second, using the Viola-Jones face detection algorithm, the faces and facial characteristics such as the eyes, mouth, and nose are recognized, and the results are shown. In order to increase the accuracy of gender identification, adaptive filters are employed to reduce the amount of noise present before the detection process is carried out. The facial qualities that have been gathered are utilized as input or test data for the neural network, which is subsequently trained on these characteristics. It is intended that the neural network acquires the features of the genders and then functions as a classifier when it comes to detecting them. Instead of saying it another way, a neural network known as Keras is used for feature extraction and gender identification, which is free and open-source software. © 2023 IEEE.

关键词： Feature extraction

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Technology Roadmap for Flexible Sensors

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ACS NANO 2023年第6期17卷 5211-5295页

作者： Luo, Yifei Abidian, Mohammad Reza Ahn, Jong-Hyun Akinwande, Deji Andrews, Anne M. Antonietti, Markus Bao, Zhenan Berggren, Magnus Berkey, Christopher A. Bettinger, Christopher John Chen, Jun Chen, Peng Cheng, Wenlong Cheng, Xu Choi, Seon-Jin Chortos, Alex Dagdeviren, Canan Dauskardt, Reinhold H. Di, Chong-an Dickey, Michael D. Duan, Xiangfeng Facchetti, Antonio Fan, Zhiyong Fang, Yin Feng, Jianyou Feng, Xue Gao, Huajian Gao, Wei Gong, Xiwen Guo, Chuan Fei Guo, Xiaojun Hartel, Martin C. He, Zihan Ho, John S. Hu, Youfan Huang, Qiyao Huang, Yu Huo, Fengwei Hussain, Muhammad M. Javey, Ali Jeong, Unyong Jiang, Chen Jiang, Xingyu Kang, Jiheong Karnaushenko, Daniil Khademhosseini, Ali Kim, Dae-Hyeong Kim, Il-Doo Kireev, Dmitry Kong, Lingxuan Lee, Chengkuo Lee, Nae-Eung Lee, Pooi See Lee, Tae-Woo Li, Fengyu Li, Jinxing Liang, Cuiyuan Lim, Chwee Teck Lin, Yuanjing Lipomi, Darren J. Liu, Jia Liu, Kai Liu, Nan Liu, Ren Liu, Yuxin Liu, Yuxuan Liu, Zhiyuan Liu, Zhuangjian Loh, Xian Jun Lu, Nanshu Lv, Zhisheng Magdassi, Shlomo Malliaras, George G. Matsuhisa, Naoji Nathan, Arokia Niu, Simiao Pan, Jieming Pang, Changhyun Pei, Qibing Peng, Huisheng Qi, Dianpeng Ren, Huaying Rogers, John A. Rowe, Aaron Schmidt, Oliver G. Sekitani, Tsuyoshi Seo, Dae-Gyo Shen, Guozhen Sheng, Xing Shi, Qiongfeng Someya, Takao Song, Yanlin Stavrinidou, Eleni Su, Meng Sun, Xuemei Takei, Kuniharu Tao, Xiao-Ming Tee, Benjamin C. K. Thean, Aaron Voon-Yew Trung, Tran Quang Wan, Changjin Wang, Huiliang Wang, Joseph Wang, Ming Wang, Sihong Wang, Ting Wang, Zhong Lin Weiss, Paul S. Wen, Hanqi Xu, Sheng Xu, Tailin Yan, Hongping Yan, Xuzhou Yang, Hui Yang, Le Yang, Shuaijian Yin, Lan Yu, Cunjiang Yu, Guihua Yu, Jing Yu, Shu-Hong Yu, Xinge Zamburg, Evgeny Zhang, Haixia Zhang, Xiangyu Zhang, Xiaosheng Zhang, Xueji Zhang, Yihui Zhang, Yu Zhao, Siyuan Zhao, Xuanhe Zheng, Yuanjin Zheng, Yu-Qing Zheng, Zijian Zhou, Tao Zhu, Bowen Zhu, Ming Zhu, Rong Zhu, Yangzhi Zhu, Yong Zou, Guijin Chen, Xiaodong Institute of Materials Research and Engineering (IMRE) Agency for Science Technology and Research (A*STAR) 2 Fusionopolis Way #08-03 Innovis Singapore 138634 Republic of Singapore；Innovative Centre for Flexible Devices (iFLEX) School of Materials Science and Engineering Nanyang Technological University Singapore 639798 Singapore Department of Biomedical Engineering University of Houston Houston Texas 77024 United States School of Electrical and Electronic Engineering Yonsei University Seoul 03722 Republic of Korea Department of Electrical and Computer Engineering The University of Texas at Austin Austin Texas 78712 United States；Microelectronics Research Center The University of Texas at Austin Austin Texas 78758 United States Department of Chemistry and Biochemistry California NanoSystems Institute and Department of Psychiatry and Biobehavioral Sciences Semel Institute for Neuroscience and Human Behavior and Hatos Center for Neuropharmacology University of California Los Angeles Los Angeles California 90095 United States Colloid Chemistry Department Max Planck Institute of Colloids and Interfaces 14476 Potsdam Germany Department of Chemical Engineering Stanford University Stanford California 94305 United States Laboratory of Organic Electronics Department of Science and Technology Campus Norrköping Linköping University 83 Linköping Sweden；Wallenberg Initiative Materials Science for Sustainability (WISE) and Wallenberg Wood Science Center (WWSC) SE-100 44 Stockholm Sweden Department of Materials Science and Engineering Stanford University Stanford California 94301 United States Department of Biomedical Engineering and Department of Materials Science and Engineering Carnegie Mellon University Pittsburgh Pennsylvania 15213 United States Department of Bioengineering University of California Los Angeles Los Angeles California 90095 United States School of Chemistry Chemical Engineering and Biotechnology Nanyang Technological University Singapore 637457

Humans rely increasingly on sensors to address grand challenges and to improve quality of life in the era of digitalization and big data. For ubiquitous sensing, flexible sensors are developed to overcome the limitations of conventional rigid counterparts. Despite rapid advancement in bench-side research over the last decade, the market adoption of flexible sensors remains limited. To ease and to expedite their deployment, here, we identify bottlenecks hindering the maturation of flexible sensors and propose promising solutions. We first analyze continued...challenges in achieving satisfactory sensing performance for real-world applications and then summarize issues in compatible sensor-biology interfaces, followed by brief discussions on powering and connecting sensor networks. Issues en route to commercialization and for sustainable growth of the sector are also analyzed, highlighting environmental concerns and emphasizing nontechnical issues such as business, regulatory, and ethical considerations. Additionally, we look at future intelligent flexible sensors. In proposing a comprehensive roadmap, we hope to steer research efforts towards common goals and to guide coordinated development strategies from disparate communities. Through such collaborative

关键词： soft materials mechanics engineering flexible electronics conformable sensors bioelectronics human-machine interfaces body area sensor networks technology translation sustainable electronics

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Deep learning-based denoising in projection-domain and reconstruction-domain for low-dose myocardial perfusion SPECT

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Journal of Nuclear Cardiology 2023年第3期30卷 970-985页

作者： Sun, Jingzhang Jiang, Han Du, Yu Li, Chien-Ying Wu, Tung-Hsin Liu, Yi-Hwa Yang, Bang-Hung Mok, Greta S. P. Biomedical Imaging Laboratory (BIG) Department of Electrical and Computer Engineering Faculty of Science and Technology University of Macau Macao Department of Biomedical Imaging and Radiological Sciences National Yang Ming Chiao Tung University Taipei Taiwan Department of Nuclear Medicine Taipei Veterans General Hospital Taipei Taiwan Department of Internal Medicine Yale University School of Medicine New Haven CT United States

Background: Low-dose (LD) myocardial perfusion (MP) SPECT suffers from high noise level, leading to compromised diagnostic accuracy. Here we investigated the denoising performance for MP-SPECT using a conditional generative adversarial network (cGAN) in projection-domain (cGAN-prj) and reconstruction-domain (cGAN-recon). Methods: Sixty-four noisy SPECT projections were simulated for a population of 100 XCAT phantoms with different anatomical variations and ^99mTc-sestamibi distributions. Series of LD projections were obtained by scaling the full dose (FD) count rate to be 1/20 to 1/2 of the original. Twenty patients with ^99mTc-sestamibi stress SPECT/CT scans were retrospectively analyzed. For each patient, LD SPECT images (7/10 to 1/10 of FD) were generated from the FD list mode data. All projections were reconstructed by the quantitative OS-EM method. A 3D cGAN was implemented to predict FD images from their corresponding LD images in the projection- and reconstruction-domain. The denoised projections were reconstructed for analysis in various quantitative indices along with cGAN-recon, Gaussian, and Butterworth-filtered images. Results: cGAN denoising improves image quality as compared to LD and conventional post-reconstruction filtering. cGAN-prj can further reduce the dose level as compared to cGAN-recon without compromising the image quality. Conclusions: Denoising based on cGAN-prj is superior to cGAN-recon for MP-SPECT. © 2022, The Author(s) under exclusive licence to American Society of Nuclear Cardiology.

关键词： deep learning low dose myocardial perfusion SPECT projection reconstruction

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Towards the Improvement of the Cognitive Process of the Synthesized Speech of Mathematical Expression in MathML: An Eye-Tracking 2

Towards the Improvement of the Cognitive Process of the Synt...

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2nd International Conference on Interactive Media, Smart Systems and Emerging Technologies, IMET 2022

作者： Souza, Adriana Freitas, Diamantino Bahia Federal Institute of Education Science and Technology Bahia Brazil Doctoral Program in Digital Media Faculty of Engineering of the University of Porto Porto Portugal Faculty of Engineering of the University of Porto Speech Processing Laboratory Electrical and Computer Engineering Department Porto Portugal

ISBN: (数字)9781665470162

ISBN: (纸本)9781665470162

Being one essential part of the solutions we are developing to provide accessibility for blind persons, synthesized speech of mathematical content, although having evolved in naturalness in recent years, still keeps a characteristic of generating very long outputs, which makes it difficult for a person who uses a screen reader to memorize. Eye-gaze tracking has been useful for studying cognitive processes and student's effort in performing tasks therefore it seems attractive as a means to understand how people analyze, process, and break down extensive math expressions into smaller ones. This understanding can help to build a similar model of auditory rendering of MathML capable of avoiding cognitive overload. Therefore, we carried out an experiment to do a gaze-based analysis of reading and memorizing mathematical expressions. Our sample consisted of fourteen participants, and seven mathematical expressions were used in the experiment. The results showed that the computed complexity of the expressions and the average duration of eye-gaze fixations have a strong and significant relationship. Therefore, we conclude that the complexity variable must always be considered in the speech synthesis process of mathematical expressions. However, when considering why participants requested the repetition of an expression with not larger complexity during the memorization process, a tentative interpretation based on the number of MathML tree nodes and the computed complexity of the expressions was not enough to explain it, indicating that other factors need to be investigated. Although some answers to the research questions were not yet conclusive, we consider this research relevant because it points to directions for the work in order to minimize the cognitive processing and mental load of the synthesized speech rendering of mathematical expressions. © 2022 IEEE.

关键词： Eye tracking

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An Attention-Based Model for Clinical Time Series Prediction: Enhancing ICU Readmission Prediction

An Attention-Based Model for Clinical Time Series Prediction...

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International eConference on computer and Knowledge engineering (ICCKE)

作者： Hananeh Sadat Madinei Mohammad Reza Keyvanpour Seyed Vahab Shojaedini Department of Computer Engineering Data Mining Laboratory Faculty of Engineering Alzahra University Tehran Iran Department of Electrical Engineering Iranian Research Organization for Science and Technology Tehran Iran

ISBN: (数字)9798331511272

ISBN: (纸本)9798331511289

In recent years, time series prediction has become a highly interesting topic in various applied areas, including clinical time series analysis. Hospitals and other clinical healthcare systems collect Electronic Health Records. These records contain important information that needs to be mined. This information provides a solid basis for predicting hospital readmission, helping to improve healthcare, enhance patient outcomes, and optimize hospital resources. This study introduces a novel deep learning attention-based approach for predicting 30-day ICU readmissions using clinical time series information extracted from the MIMIC III dataset. This approach incorporates time-aware positional encoding into the attention mechanism to enhance predictive performance by capturing the temporal representation of data and considering the temporal order of clinical time series data. This model is compared with cutting-edge models. The significant enhancements in the model performance achieved by the proposed method highlight the effectiveness of incorporating time-aware positional encoding in capturing temporal dependencies and improving prediction outcomes. These results underscore the potential of advanced attention mechanisms in clinical time series analysis.

关键词： Deep learning Analytical models Attention mechanisms Hospitals Computational modeling Time series analysis Predictive models Encoding Data models Electronic medical records

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