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arXiv 2023年

作者： Sun, Yuqian Wang, Hanyi Chan, Pok Man Tabibi, Morteza Zhang, Yan Lu, Huan Chen, Yuheng Lee, Chang Hee Asadipour, Ali Computer Science Research Centre Royal College of Art United Kingdom United States Hong Kong Rct.ai United States China Affective Systems and Cognition Lab Industrial Design Department College of Engineering KAIST United Kingdom Royal College of Art United Kingdom

We address the integration of storytelling and Large Language Models (LLMs) to develop engaging and believable Social Chatbots (SCs) in community settings. Motivated by the potential of fictional characters to enhance social interactions, we introduce Storytelling Social Chatbots (SSCs) and the concept of story engineering to transform fictional game characters into "live" social entities within player communities. Our story engineering process includes three steps: (1) Character and story creation, defining the SC's personality and worldview, (2) Presenting Live Stories to the Community, allowing the SC to recount challenges and seek suggestions, and (3) Communication with community members, enabling interaction between the SC and users. We employed the LLM GPT-3 to drive our SSC prototypes, "David" and "Catherine," and evaluated their performance in an online gaming community, "DE (Alias)," on Discord. Our mixed-method analysis, based on questionnaires (N=15) and interviews (N=8) with community members, reveals that storytelling significantly enhances the engagement and believability of SCs in community settings. Copyright © 2023, The Authors. All rights reserved.

关键词： Developing countries

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Stochastic-Robust Planning of Networked Hydrogen-Electrical Microgrids: A Study on Induced Refueling Demand

arXiv

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arXiv 2024年

作者： Sun, Xunhang Cao, Xiaoyu Zeng, Bo Zhai, Qiaozhu Başar, Tamer Guan, Xiaohong School of Automation Science and Engineering The Ministry of Education Key Laboratory for Intelligent Networks and Network Security Xi’an Jiaotong University Shaanxi Xi’an710049 China Coordinated Science Laboratory University of Illinois Urbana-Champaign UrbanaIL61801 United States Smart Integrated Energy Department Sichuan Digital Economy Industry Development Research Institute Sichuan Chengdu610037 China Department of Industrial Engineering The Department of Electrical and Computer Engineering University of Pittsburgh PittsburghPA15106 United States Center for Intelligent and Networked Systems Department of Automation Tsinghua University Beijing100084 China

Hydrogen-electrical microgrids are increasingly assuming an important role on the pathway toward decarbonization of energy and transportation systems. This paper studies networked hydrogen-electrical microgrids planning (NHEMP), considering a critical but often-overlooked issue, i.e., the demand-inducing effect (DIE) associated with infrastructure development decisions. Specifically, higher refueling capacities will attract more refueling demand of hydrogen-powered vehicles (HVs). To capture such interactions between investment decisions and induced refueling demand, we introduce a decision-dependent uncertainty (DDU) set and build a trilevel stochastic-robust formulation. The upper-level determines optimal investment strategies for hydrogen-electrical microgrids, the lower-level optimizes the risk-aware operation schedules across a series of stochastic scenarios, and, for each scenario, the middle-level identifies the "worst" situation of refueling demand within an individual DDU set to ensure economic feasibility. Then, an adaptive and exact decomposition algorithm, based on Parametric Column-and-Constraint Generation (PC&CG), is customized and developed to address the computational challenge and to quantitatively analyze the impact of DIE. Case studies on an IEEE exemplary system validate the effectiveness of the proposed NHEMP model and the PC&CG algorithm. It is worth highlighting that DIE can make an important contribution to the economic benefits of NHEMP, yet its significance will gradually decrease when the main bottleneck transits to other system restrictions. Copyright © 2024, The Authors. All rights reserved.

关键词： Stochastic systems

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A Communication-Efficient Decentralized Newton's Method with Provably Faster Convergence

arXiv

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arXiv 2022年

作者： Liu, Huikang Zhang, Jiaojiao So, Anthony Man-Cho Ling, Qing The Research Institute for Interdisciplinary Sciences School of Information Management and Engineering Shanghai University of Finance and Economics China The Division of Decision and Control Systems School of Electrical Engineering and Computer Science Kth Royal Institute of Technology Sweden The Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong Hong Kong The School of Computer Science and Engineering Guangdong Province Key Laboratory of Computational Science Sun Yat-Sen University China The Pazhou Lab China

In this paper, we consider a strongly convex finite-sum minimization problem over a decentralized network and propose a communication-efficient decentralized Newton's method for solving it. The main challenges in designing such an algorithm come from three aspects: (i) mismatch between local gradients/ Hessians and the global ones;(ii) cost of sharing second-order information;(iii) tradeoff among computation and communication. To handle these challenges, we first apply dynamic average consensus (DAC) so that each node is able to use a local gradient approximation and a local Hessian approximation to track the global gradient and Hessian, respectively. Second, since exchanging Hessian approximations is far from communication-efficient, we require the nodes to exchange the compressed ones instead and then apply an error compensation mechanism to correct for the compression noise. Third, we introduce multistep consensus for exchanging local variables and local gradient approximations to balance between computation and communication. With novel analysis, we establish the globally linear (resp., asymptotically super-linear) convergence rate of the proposed method when m is constant (resp., tends to infinity), where m ≥ 1 is the number of consensus inner steps. To the best of our knowledge, this is the first super-linear convergence result for a communication-efficient decentralized Newton's method. Moreover, the rate we establish is provably faster than those of first-order methods. Our numerical results on various applications corroborate the theoretical findings. Copyright © 2022, The Authors. All rights reserved.

关键词： Newton-Raphson method

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Advances in artificial joint bio-friction and wear simulation assessment technology

Advances in artificial joint bio-friction and wear simulatio...

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IEEE International Conference on Robotics and Biomimetics

作者： Yanyan Zhang Yiming Liang Shiqiang Zhu Menglong Ding Yunhe Bai Haoyuan Du Jason Gu Tiefeng Li Research Center for Intelligent Robotics Research Institute of Interdisciplinary Innovation Zhejiang Lab Hangzhou China Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou China Department of Electrical and Computer Engineering Dalhousie University Halifax NS Canada Center for X-mechanics Zhejiang University Hangzhou China Department of Engineering Mechanics Key Laboratory of Soft Machines and Smart Devices of Zhejiang Province Zhejiang University Hangzhou China

ISBN: (纸本)9781665481106

Natural joints have excellent low-friction properties that positively inspire the frictional wear properties of artificial joints. Replacing damaged biological joints with artificial ones can restore bio-function and reduce the pain of arthritic patients. Improvements in the frictional wear properties of artificial joints will effectively increase the life of the prosthesis, especially for young arthritic patients. This paper reviews the lubrication mechanism of natural joints before systematically introducing the development of lubrication and wear simulation techniques for artificial joints, offering references and insights for further studies of artificial joints and the optimization of tribological properties of commercial artificial joint prostheses.

关键词： Pain Friction Biological system modeling Lubrication Surface texture Optimization Surface treatment

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Conceptual Design and Simulation of a BCF Swimming Soft Robotic Fish for Deep-Sea*

Conceptual Design and Simulation of a BCF Swimming Soft Robo...

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IEEE International Conference on Robotics and Biomimetics

作者： Yanyan Zhang Menglong Ding Shiqiang Zhu Yiming Liang Yunhe Bai Keren Xie Minhong Wan Jason Gu Tiefeng Li Research Center for Intelligent Robotics Research Institute of Interdisciplinary Innovation Zhejiang Lab Hangzhou China Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou China Department of Electrical and Computer Engineering Dalhousie University Halifax NS Canada Center for X-mechanics Zhejiang University Hangzhou China Department of Engineering Mechanics Zhejiang University Key Laboratory of Soft Machines and Smart Devices of Zhejiang Province Hangzhou China

ISBN: (纸本)9781665481106

This work provides a design of a deep-sea soft robotic fish propelled by the body and/or caudal fin (BCF) mode. The robotic fish consists of a bionic fish head and a BCF propulsion body. To enhance the swimming performance of the first soft robot fish in the deep sea, the design combines the advantages of high efficiency in BCF propulsion with the large deformation angle of dielectric elastomers (DE) flexible joints. The connection of the bionic fish head and BCF is cast in one piece of silicone to achieve deep-sea pressure self-adaptation of the soft robotic fish. The swimming performance was simulated using the CFD method, and the BCF propulsion behaviors were analyzed to evaluate the design proposed. The design goal was verified through numerical calculation, and a further enhanced strategy for the thrust ability of the fish was presented as well. This work can provide a low-cost bionic soft robot fish solution for deep-sea exploration.

关键词： Biological system modeling Kinematics Soft robotics Propulsion Fish Dielectric elastomers Numerical models

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Enhancing Subject-Independent EEG-Based Auditory Attention Decoding with WGAN and Pearson Correlation Coefficient

Enhancing Subject-Independent EEG-Based Auditory Attention D...

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IEEE International Conference on systems, Man and Cybernetics

作者： Saurav Pahuja Gabriel Ivucic Felix Putze Siqi Cai Haizhou Li Tanja Schultz Machine Listening Lab (MLL) University of Bremen Germany Cognitive Systems Lab (CSL) University of Bremen Germany Department of Electrical and Computer Engineering National University of Singapore Singapore Shenzhen Research Institute of Big Data School of Data Science The Chinese University of Hong Kong Shenzhen China Kriston AI Xiamen China CSL and hosts MLL

Electroencephalography (EEG) related research faces a significant challenge of subject independence due to the variation in brain signals and responses among individuals. While deep learning models hold promise in addressing this challenge, their effectiveness depends on large datasets for training and generalization across participants. To overcome this limitation, we propose a solution to the above limitation by increasing the size and quality of training data for subject-independent auditory attention decoding (AAD) using EEG with deep learning. Specifically, our method employs a Wasserstein Generative Adversarial Network (WGAN) to generate synthetic data, with Pearson correlation filtering the most realistic samples. We evaluated this method on a publicly available dataset of selective auditory attention experiments and showed superior performance in subject-independent AAD performance. The mixed training set, consisting of both real and artificial data generated by the WGAN+Pearson Correlation Coefficient, demonstrated approximately 4% improvement in AAD accuracy for a 1-second window. These results demonstrate that deep learning remains a viable approach to overcoming data scarcity in subject-independent AAD tasks based on EEG. Moreover, the proposed method has the potential to improve the generalization and reliability of EEG classification tasks.

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4-channel DWDM System using Optical Frequency Combs and Active Demultiplexer for Data Center Interconnects

4-channel DWDM System using Optical Frequency Combs and Acti...

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Conference on Lasers and Electro-Optics (CLEO)

作者： Syed Tajammul Ahmad Ankita Jain Prajwal Doddaballapura Lakshmijayasimha Aleksandra Kaszubowska-Anandarajah Prince M. Anandarajah Photonics Systems and Sensing Lab School of Electronics Engineering Dublin City University Dublin Ireland Department of Electrical and Computer Engineering Queen’s University Kingston Canada CONNECT Research Centre Dunlop Oriel House Trinity College Dublin Dublin Ireland

We experimentally demonstrate an OFC and active demultiplexer based 12.5 GHz DWDM system for DCIs. Successful transmission of directly modulated 17.5 Gb/s/λ 16-QAM DMT data is accomplished by using DD-LMS equalizer t... 详细信息

ISBN: (数字)9781957171050

ISBN: (纸本)9781665466660

关键词： Equalizers Filtering Photonic integrated circuits Integrated circuit interconnections Interference Wavelength division multiplexing Optical harmonic generation

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Knowledge discovery of patients reviews on breast cancer drugs: Segmentation of side effects using machine learning techniques

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Heliyon 2024年第19期10卷 e38563页

作者： Nilashi, Mehrbakhsh Ahmadi, Hossein Abumalloh, Rabab Ali Alrizq, Mesfer Alghamdi, Abdullah Alyami, Sultan UCSI Graduate Business School UCSI University Cheras Kuala Lumpur 56000 Malaysia Centre for Health Technology Faculty of Health University of Plymouth Plymouth PL4 8AA United Kingdom Department of Computer Science and Engineering Qatar University Doha 2713 Qatar Information Systems Dept. College of Computer Science and Information Systems Najran University Najran Saudi Arabia Computer Science Dept. College of Computer Science and Information Systems Najran University Najran Saudi Arabia AI Lab Scientific and Engineering Research Center (SERC) Najran University Najran Saudi Arabia Centre for Business Informatics and Industrial Management UCSI Graduate Business School UCSI University Malaysia

Breast cancer stands as the most frequently diagnosed life-threatening cancer among women worldwide. Understanding patients' drug experiences is essential to improving treatment strategies and outcomes. In this research, we conduct knowledge discovery on breast cancer drugs using patients' reviews. A new machine learning approach is developed by employing clustering, text mining and regression techniques. We first use Latent Dirichlet Allocation (LDA) technique to discover the main aspects of patients' experiences from the patients' reviews on breast cancer drugs. We also use Expectation-Maximization (EM) algorithm to segment the data based on patients' overall satisfaction. We then use the Forward Entry Regression technique to find the relationship between aspects of patients' experiences and drug's effectiveness in each segment. The textual reviews analysis on breast cancer drugs found 8 main side effects: Musculoskeletal Effects, Menopausal Effects, Dermatological Effects, Metabolic Effects, Gastrointestinal Effects, Neurological and Cognitive Effects, Respiratory Effects and Cardiovascular. The results are provided and discussed. The findings of this study are expected to offer valuable insights and practical guidance for prospective patients, aiding them in making informed decisions regarding breast cancer drug consumption. © 2024 The Authors

关键词： Breast cancer Drugs Knowledge discovery Machine learning Online reviews Public health Text mining

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EARTHQUAKE PREDICTION EVALUATION BASED on VLF DATA USING A NOVEL INTERSECTION-UNION METHOD 24

EARTHQUAKE PREDICTION EVALUATION BASED on VLF DATA USING A N...

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2021 24th ISPRS Congress on Imaging Today, Foreseeing Tomorrow, Commission IV

作者： Barghi, W. Delavar, M.R. Shahabadi, M. Zare, M. Eslaminezhad, S.A. Bayat, H. Department of GIS School of Surveying and Geospatial Eng. College of Engineering University of Tehran Tehran Iran Center of Excellence in Geomatic Eng. in Disaster Management School of Surveying and Geospatial Engineering College of Engineering University of Tehran Tehran Iran Photonics Research Lab Center of Excellence on Applied Electromagnetic Systems School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran Engineering Seismology Department International Institute of Earthquake Engineering and Seismology Tehran Iran

Electromagnetic phenomena, especially those in the Very Low Frequency/Low Frequency (VLF/LF) bands are promising for short-term earthquake prediction. Seismo-ionospheric perturbations cause a variety of changes in different receiver-transmitter VLF/LF signal paths. Therefore, independent and simultaneous observations at different points thus in different VLF/LF signal propagation paths are necessary to better predict the earthquake. Most of the previous research on VLF data have been based on one path or limited number of paths which examined perturbations in the time domain and less attention has been paid to estimate the location of the earthquake. In the present research, using wavelet analysis, the temporal variations of seismo-ionospheric perturbations and the approximate time of earthquake are predicted. Clear disturbances are observed two weeks before the Kumamoto earthquake happened in Japan in 2016. The novelty of this study is to present an approach called Intersection-Union method to predict earthquake location. Based on the geometry of a VLF/LF network, the Intersection-Union method was introduced to estimate the earthquake epicenter. This method is based on the overlay of earthquake occurrence probable areas. With simultaneous use of different propagation paths by the Intersection-Union method, an area with a radius of about 300 km was determined as the probable location of the earthquake epicenter. The accuracy of the proposed method is 300 km compared with 1000 km accuracy of other earthquake location prediction scenarios. © Copyright: Author(s) 2021.

关键词： Earthquakes

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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 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 Singapore Nanobionics Group Department of Chemical and Biological Engineering Monash University Clayton Australia 3800 Monash

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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