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Toward Safe and Efficient Human-Robot Interaction via Behavior-Driven Danger Signaling

IEEE Transactions on Control systems Technology 2024年第1期32卷 214-224页

作者： Hosseinzadeh, Mehdi Sinopoli, Bruno Bobick, Aaron F. Washington State University School of Mechanical and Materials Engineering PullmanWA99164 United States Washington University in St. Louis Department of Electrical and Systems Engineering St. LouisMO63130 United States Washington University in St. Louis Department of Computer Science and Engineering St. LouisMO63130 United States

This article introduces the notion of danger awareness in the context of human-robot interaction, which decodes whether a human is aware of the existence of the robot, and illuminates whether the human is willing to engage in ensuring safety. This article also quantifies the notion as a single binary variable, called danger awareness coefficient, and provides a game-theoretic interpretation for that. Employing an online Bayesian learning method to update the robot's belief about the human's danger awareness by observing their actions, it is shown how the robot can build a predictive human model to anticipate the human's future actions. To enrich robot's observations, and thus to improve safety and efficiency of the robot, the robot is equipped with a danger signaling system to generate awareness in the human. Finally, a planning scheme is proposed to provide an efficient and probabilistically safe plan for the robot. The effectiveness of the proposed scheme is demonstrated through simulation studies on an interaction between a self-driven car and a pedestrian. © 1993-2012 IEEE.

关键词： Human robot interaction

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Classification of Stress using Machine Learning Based on Physiological and Psychological Data from Wearables 15

Classification of Stress using Machine Learning Based on Phy...

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15th IEEE International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management, HNICEM 2023

作者： Pauzi, Tuan Muhamad Affiq Aimullah Tuan Mohd Samah, Asma Abu Dela Cruz, Jennifer C. Ghaffa, Dalilah Nordin, Rosdiadee Abdullah, Nor Fadzilah Universiti Kebangsaan Malaysia Faculty of Engineering and Built Environment Department of Electrical Electronic and Systems Engineering Bangi Malaysia Universiti Kebangsaan Malaysia Faculty of Engineering and Built Environment Wireless Research@UKM Department of Electrical Electronic and Systems Engineering Bangi Malaysia School of Electrical Electronics and Computer Engineering Mapua University Manila Philippines

ISBN: (纸本)9798350381177

Stress is a common psychological issue in today's world, causing individuals to experience physiological and behavioral changes. When someone experiences stress for a prolonged period, it can have an impact on their body and thoughts, leading to severe behaviors. Stress-related problems can be prevented if identified early. Nowadays, researchers' use of machine learning in early disease detection is increasing, offering a solution to stress-related issues. This study focuses on developing a machine learning predictive model for classifying stress levels using physiological data. Physiological data is obtained from an open-source dataset containing biosignal data such as Electrodermal (EDA), Skin Temperature, Heart Rate (HR), Blood Volume Pulse (BVP), and others. The dataset is then used to train a machine learning model using Support Vector Machine (SVM), Random Forest (RF), Adaboost, and Bootstrapping algorithms. Accuracy, precision, recall, and F1-score are used to evaluate the model's performance. As a result, the predictive model using the Bootstrapping algorithm achieves the best performance with an accuracy of 95.82%, a precision of 99.33%, a recall of 98.02%, and F1-score of 94.69% compared to other machine learning classifiers. Creating such models can aid in early stress detection once they are integrated into a wearable device for continuous monitoring purposes. © 2023 IEEE.

关键词： Decision trees

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Water droplets play a role in Internet of Things applications

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Droplet 2024年第3期3卷 27-48页

作者： Feng Wen Chengkuo Lee Department of Electrical and Computer Engineering National University of SingaporeSingapore Center for Intelligent Sensors and MEMS(CISM) National University of SingaporeSingapore i-Lab Key Laboratory of Multifunctional Nanomaterials and Smart SystemsSuzhou Institute of Nano-Tech and Nano-Bionics(SINANO)Chinese Academy of Sciences(CAS)SuzhouChina NUS Graduate School—Integrative Sciences and Engineering Programme(ISEP) National University of SingaporeSingapore

Water droplets help life in nature survive,thrive,and *** water droplet serv-ing as one of the indispensable elements in the Internet of Things(IoT),many droplet-oriented technologies,such as microfluidics,droplet manipulation,electrowetting,and energy harvesting,make rapid progress driven by material science,computer science,and ***-based wearable devices are endowed with advantages such as flexibility,sensing ability,and automation for various parameter ***,the continuous exploration of droplet manipulation has led to the emergence of a wide variety of manipulation ***,electrowetting that utilizes exter-nal fields modifying liquid–solid surfaces has found its applications in various areas,including droplet transportation,microfabrication,and *** energy gener-ation from water droplets also presents exciting opportunities for the development of novel electricity *** approaches for droplet utilization underscore the immense potentials and versatilities of droplet-based technologies in the IoT ***,this mini review presents the fundamental droplet-based technologies by summarizing their working mechanisms and methods,device structures,and *** the challenges in materials,fabrication,and system integration,this review shows the overall development roadmap in terms of improved functionality and performance and highlights the opportunities toward multifunctional,self-sustainable,and intelligent systems,which is called for IoT construction.

关键词： IoT utilize fabrication

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A super resolution target separation and reconstruction approach for single channel sar against deceptive jamming

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Defence Technology（防务技术） 2023年第3期21卷 164-175页

作者： Shi-qi Liu Bing Li Bo Zhao Lei Huang Yue-zhou Wu Wei-min Bao Guangdong Key Laboratory of Intelligent Information Processing College of Electronic and Information EngineeringShenzhen UniversityShenzhen518060China School of Electrical Engineering Southwest Jiaotong UniversityChengdu611756China State Key Laboratory of Millimeter Waves Southeast UniversityNanjing210096China School of Computer Science Civil Aviation Flight University of ChinaGuanghan618307China China Aerospace Science and Technology Corporation Beijing100048China

The excellent remote sensing ability of synthetic aperture radar(SAR)will be misled seriously when it encounters deceptive jamming which possesses high fidelity and *** this paper,the dynamic synthetic aperture(DSA)scheme is used to extract the difference between the true and false targets.A simultaneous deceptive jamming suppression and target reconstruction method is proposed for a single channel SAR system to guarantee remote sensing *** system model is formulated as a sparse signal recovery problem with an unknown parametric dictionary to be *** iterative reweighted method is employed to jointly handle the dictionary parameter learning and target reconstruction problem in an majorization-minimization framework,where a surrogate function majorizing the Gaussian entropy in the objective function is introduced to circumvent its *** dictionary parameter learning,the grid mismatching problem in a fixed grid based method is ***,the proposed method can reap a super resolution ***,a simple yet effective DSA section scheme is developed for the SAR data excerpting,in which only two DSAs are *** results about location error and reconstruction power error reveal that the proposed method is able to achieve a good performance in deceptive jamming suppression.

关键词： channel resolution separation

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Advances in Noble Metal-Decorated Metal Oxide Nanomaterials for Chemiresistive Gas Sensors:Overview

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Nano-Micro Letters 2023年第6期15卷 353-427页

作者： Li‑Yuan Zhu Lang‑Xi Ou Li‑Wen Mao Xue‑Yan Wu Yi‑Ping Liu Hong‑Liang Lu State Key Laboratory of ASIC and System Shanghai Institute of Intelligent Electronics and SystemsSchool of MicroelectronicsFudan UniversityShanghai 200433People’s Republic of China School of Opto‑Electronic Information and Computer Engineering University of Shanghai for Science and TechnologyShanghai 200093People’s Republic of China State Key Laboratory of Metal Matrix Composites School of Material Science and EngineeringShanghai Jiao Tong UniversityShanghai 200240People’s Republic of China

Highly sensitive gas sensors with remarkably low detection limits are attractive for diverse practical application fields including real-time environmental monitoring,exhaled breath diagnosis,and food freshness *** various chemiresistive sensing materials,noble metal-decorated semiconducting metal oxides(SMOs)have currently aroused extensive attention by virtue of the unique electronic and catalytic properties of noble *** review highlights the research progress on the designs and applications of different noble metal-decorated SMOs with diverse nanostructures(e.g.,nanoparticles,nanowires,nanorods,nanosheets,nanoflowers,and microspheres)for high-performance gas sensors with higher response,faster response/recovery speed,lower operating temperature,and ultra-low detection *** key topics include Pt,Pd,Au,other noble metals(e.g.,Ag,Ru,and Rh.),and bimetals-decorated SMOs containing ZnO,SnO_(2),WO_(3),other SMOs(e.g.,In_(2)O_(3),Fe_(2)O_(3),and CuO),and heterostructured *** addition to conventional devices,the innovative applications like photo-assisted room temperature gas sensors and mechanically flexible smart wearable devices are also ***,the relevant mechanisms for the sensing performance improvement caused by noble metal decoration,including the electronic sensitization effect and the chemical sensitization effect,have also been summarized in ***,major challenges and future perspectives towards noble metal-decorated SMOs-based chemiresistive gas sensors are proposed.

关键词： Noble metal Bimetal Semiconducting metal oxide Chemiresistive gas sensor Electronic sensitization Chemical sensitization

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Real-Time Implementation of Quadrotor UAV Control System Based on a Deep Reinforcement Learning Approach

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computers, Materials & Continua 2024年第12期81卷 4757-4786页

作者： Taha Yacine Trad Kheireddine Choutri Mohand Lagha Souham Meshoul Fouad Khenfri Raouf Fareh Hadil Shaiba Aeronautical Sciences Laboratory Aeronautical and Spatial Studies InstituteBlida 1 UniversityBlida0900Algeria Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Energy and Embedded Systems for Transportation Research Department ESTACA-LABMontigny-Le-Bretonneux78066France Department of Electrical Engineering University of SharjahSharjah27272United Arab Emirates Department of Computer Science College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

The popularity of quadrotor Unmanned Aerial Vehicles(UAVs)stems from their simple propulsion systems and structural ***,their complex and nonlinear dynamic behavior presents a significant challenge for control,necessitating sophisticated algorithms to ensure stability and accuracy in *** strategies have been explored by researchers and control engineers,with learning-based methods like reinforcement learning,deep learning,and neural networks showing promise in enhancing the robustness and adaptability of quadrotor control *** paper investigates a Reinforcement Learning(RL)approach for both high and low-level quadrotor control systems,focusing on attitude stabilization and position tracking tasks.A novel reward function and actor-critic network structures are designed to stimulate high-order observable states,improving the agent’s understanding of the quadrotor’s dynamics and environmental *** address the challenge of RL hyper-parameter tuning,a new framework is introduced that combines Simulated Annealing(SA)with a reinforcement learning algorithm,specifically Simulated Annealing-Twin Delayed Deep Deterministic Policy Gradient(SA-TD3).This approach is evaluated for path-following and stabilization tasks through comparative assessments with two commonly used control methods:Backstepping and Sliding Mode Control(SMC).While the implementation of the well-trained agents exhibited unexpected behavior during real-world testing,a reduced neural network used for altitude control was successfully implemented on a Parrot Mambo mini *** results showcase the potential of the proposed SA-TD3 framework for real-world applications,demonstrating improved stability and precision across various test scenarios and highlighting its feasibility for practical deployment.

关键词： Deep reinforcement learning hyper-parameters optimization path following quadrotor twin delayed deep deterministic policy gradient and simulated annealing

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LIMIT THEOREMS FOR β-LAGUERRE AND β-JACOBI ENSEMBLES

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Acta Mathematica Scientia 2022年第5期42卷 2025-2039页

作者： Naqi HUANG Yutao MA Faculty of Electrical Engineering Mathematics and Computer ScienceDelft University of TechnologyMekelweg 42628 CD DelftThe Netherlands School of Mathematical Sciences&Laboratory of Mathematics and Complea Systems of Ministry of Education Beijing Normal UniversityBeijing 100875China

We use tridiagonal models to study the limiting behavior of β-Laguerre and β-Jacobi ensembles,focusing on the limiting behavior of the extremal eigenvalues and the central limit theorem for the two *** the central limit theorem of β-Laguerre ensembles,we follow the idea in[1]while giving a modified version for the generalized *** we use the total variation distance between the two sorts of ensembles to obtain the limiting behavior of β-Jacobi ensembles.

关键词： beta-ensembles largest and smallest eigenvalues central limit theorem total variationdistance

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Beyond Linear Binning: Logarithmic Insights for Calibrated Machine Learning in Wireless systems 100

Beyond Linear Binning: Logarithmic Insights for Calibrated M...

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100th IEEE Vehicular Technology Conference, VTC 2024-Fall

作者： Raina, Rashika Simmons, Nidhi Simmons, David E. Yacoub, Michel Daoud Queen's University Centre for Wireless Innovation School of Electronics Electrical Engineering and Computer Science BelfastBT3 9DT United Kingdom Dhali Holdings Ltd. BelfastBT5 7HW United Kingdom University of Campinas Wireless Technology Laboratory School of Electrical and Computer Engineering Campinas13083-970 Brazil

ISBN: (纸本)9798331517786

In this paper, we explore the calibration of a machine learning (ML)-based outage predictor aimed at optimizing resource allocation to minimize outages in communication systems. We model the wireless channel using an auto-correlated time series with samples distributed in accordance with a Rayleigh fading process. Our novel contribution concerns proposing histogram-based reliability plots that employ logarithmic binning to assess its impact on the model calibration compared to the traditional linear binning technique. We train our ML model using an outage loss function (OLF) tailored to this system and the well-known binary cross entropy (BCE). Additionally, we analyze the effect of different model parameters on the calibration performance of this outage predictor. Our findings demonstrate that logarithmic binning reveals nuanced calibration traits ignored by linear binning, particularly at lower confidence levels. This finding is crucial for wireless systems for which understanding behavior at very low probabilities is essential. Additionally, we observe that our ML model trained with OLF becomes more overconfident as the classification threshold increases and in scenarios characterized with rare events, namely outages. This observation serves as a tool for improving the calibration properties of OLF. © 2024 IEEE.

关键词： Resource allocation

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Resource Allocation as a Market: A Case Study on Multi-Server Multi-Model Federated Learning

Resource Allocation as a Market: A Case Study on Multi-Serve...

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Annual Conference on Wireless On Demand Network systems and Services (WONS)

作者： Maria Diamanti Aisha B Rahman Panagiotis Charatsaris Eirini Eleni Tsiropoulou Symeon Papavassiliou Institute of Communication and Computer Systems School of Electrical and Computer Engineering National Technical University of Athens Athens Greece Dept. of Electrical and Computer Engineering University of New Mexico Albuquerque NM USA

ISBN: (数字)9783903176713

ISBN: (纸本)9798331522025

6G networks envision a seamless integration of data and AI into their core operations, marking a new era where distributed client devices actively engage in intelligent processes, supported by a diverse ecosystem of AI service providers. This shift necessitates approaching traditional resource allocation problems from a market perspective, transparently reflecting the gains and losses of clients and providers within the ecosystem. In this paper, we use a multi-server, multi-model Federated Learning (FL) network as a case study to propose a market-based resource allocation framework. The framework models the joint problem of client resource allocation and pricing as a Fisher market, where servers compete to attract clients with private data and local computing resources for distributed model training. The joint problem is formulated as a convex program, aiming to maximize the total server utility related to their model’s accuracy, while incorporating clients’ total computing resource constraints and servers’ budget constraints. An analytical solution to the convex program is derived concluding the so-called Market Equilibrium (ME) point, where market clearance is achieved. The effectiveness of the proposed market-based resource allocation framework is validated through comparisons with established resource allocation and sharing schemes from the literature.

关键词： Federated learning Computational modeling Biological system modeling Ecosystems Distributed databases Pricing Data models Resource management Servers Artificial intelligence

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Performance Analysis of Multipath TCP over FSO/mmWave Vehicular Networks 16

Performance Analysis of Multipath TCP over FSO/mmWave Vehicu...

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16th International Conference on Advanced Technologies for Communications, ATC 2023

作者： Vu, Ngoc T. T. Le, Hoang D. Dang, Khanh D. Nguyen, Chuyen T. Pham, Anh T. Hanoi University of Science and Technology School of Electrical and Electronic Engineering Viet Nam The University of Aizu Computer Communications Laboratory Aizuwakamatsu965-8580 Japan

ISBN: (纸本)9798350301328

Multipath TCP (MPTCP) is a crucial protocol in 5G/6G wireless mobile networks, offering enhanced end-to-end throughput performance by capitalizing on multiple-path connectivity. In the context of vehicular networks, the increasing demand for high MPTCP throughput and ubiquitous connectivity necessitates the simultaneous utilization of terrestrial and space infrastructures. In this respect, we introduce a comprehensive analytical framework of MPTCP for vehicular networks exploiting the last-mile access provided by the terrestrial base station (BS)-based millimeter wave (mmWave) and high-altitude platforms (HAP)-based free-space optical (FSO) links. Notably, the analytical framework enables the assessment of MPTCP throughput/goodput performance under the impact of transmission errors and buffer overflow/reordering at the last-mile mmWave/FSO access. Numerical results provide insights into the MPTCP performance over FSO/mmWave vehicular networks and highlight a substantial throughput gain over the state-of-the-art MPTCP networks. © 2023 IEEE.

关键词： Millimeter waves

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