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20th International Multi-Conference on systems, Signals and Devices, SSD 2023

作者： Khriji, Lazhar Messaoud, Seifeddine Bouaafia, Soulef Ammari, Ahmed Chiheb Machhout, Mohsen Sultan Qaboos University Electrical & Computer Engineering Department Oman Monastir University Laboratory of Electronics & Microelectronics Monastir Tunisia

ISBN: (纸本)9798350332568

Convolutional Neural Networks (CNNs) have grown in popularity for clinical image processing applications like as Covid and cancer detection. A new study, however, shows that hostile attacks with modest, unnoticeable disruptions can damage deep healthcare learning systems. This creates safety issues about using these technologies in healthcare situations. In this study, we will look at the approaches used to fight adversarial attacks on medical imaging. Next, we intend to investigate the resilience of pre-trained CNN architectures, as well as LeNet5 and MobileNetV1 models against Fast Gradient Sign Method (FGSM) attacks in a medical healthcare application-based chest X-ray dataset. We discover that pre-trained CNN models are much more sensitive to antagonistic assaults than other models, due to key feature discrepancies between them and regular models. Finally, we propose to improve the CNN' models security by investigating adversarial training. According to the numerical results, models with lower computational complexity and restricted layers are much more safe against malicious attacks than bigger models which are commonly utilized in medical healthcare systems. © 2023 IEEE.

关键词： Convolutional neural networks

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Automated Surface Classification System Using Vibration Patterns - A Case Study With Wheelchairs

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第4期4卷 884-895页

作者： Yu, Haoxiang Raychoudhury, Vaskar Saha, Snehanshu Edinger, Janick Smith, Roger O. Gani, Md Osman The University of Texas at Austin Department of Electrical and Computer Engineering AustinTX78712 United States Miami University Department of Computer Science & Software Engineering OxfordOH45056 United States Bits Pilani K.K. Birla Goa Campus Department of Computer Science & Information Systems Appcair Zuarinagar403726 India University of Hamburg Department of Informatics & Distributed Operating Systems Hamburg20146 Germany University of Wisconsin-Milwaukee Department of Rehabilitation Sciences & Technology MilwaukeeWI53706 United States University of Maryland Baltimore County Department of Information Systems BaltimoreMD21250 United States

Finding a barrier-free accessible path through the built environment is necessary for wheelchair users. Researchers have identified the effect of surface vibration on the health of wheelchair users and proposed various solutions to identify and annotate mobility barriers. However, the effects of surface-induced vibration on accessibility are yet to be investigated. In order to address this problem, we present the WheelShare system, which uses machine learning to identify surfaces and curbs from vibration captured by accelerometer and gyroscope sensors (both smartphone and wearable device). WheelShare allows users to share their collected data via crowdsourcing. Based on this surface-specific vibration, we recognize 32 different surfaces found across the built environment (in Austria, China, France, Germany, India, and the USA for 32 human subjects) with an accuracy of 97.5%. The recognized surfaces are then grouped into different categories based on their characteristics. Our novel contributions in this article include the following. 1) We have developed a machine-learning-enabled surface recognition system that uses sensor data to capture surface-induced vibration patterns. 2) We have tested the robustness of the system for devices with only accelerometer data (such as low-end smartphones) and achieved an accuracy of 91.6%. 3) We have deployed our prototype WheelShare system for real-time surface classification in new sites that are unseen in training and testing and successfully recognized the surface characteristics. © 2020 IEEE.

关键词： Accelerometers

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Experimental scheme for determining the Berry phase in two-dimensional quantum materials with a flat band

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Physical Review B 2024年第7期110卷 075108-075108页

作者： Li-Li Ye Cheng-Zhen Wang Ying-Cheng Lai School of Electrical Computer and Energy Engineering Wave Transport in Complex Systems Lab Department of Physics Department of Physics

Experimentally feasible methods to determine the Berry phase, a fundamental quantity characterizing a quantum material, are often needed in applications. We develop an approach to detecting the Berry phase by using a class of two-dimensional (2D) Dirac materials with a flat band, the α−T3 lattices. The properties of this class of quantum materials are controlled by a single parameter 0≤α≤1, where the left and right end points correspond to graphene with pseudospin-12 and the dice lattice with pseudospin-1 Dirac-Weyl quasiparticles, respectively, and each specific value of α represents a material with a unique Berry phase. Applying a constant electric field to the α−T3 lattice, we calculate the resulting electric current and find a one-to-one correspondence between the current and the Berry phase in both the linear and nonlinear response regimes. In the linear (Kubo) regime, the main physics is the Zitterbewegung effect. In the nonlinear regime, the Schwinger mechanism dominates. Beyond the nonlinear regime, Bloch-Zener oscillations can arise. Measuring the current thus provides an effective and experimentally feasible way to determine the Berry phase for this spectrum of 2D quantum materials.

关键词： Dirac fermions Electronic materials 2-dimensional systems

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Evaluation of Leakage Currents in Memristor Crossbar Arrays

Evaluation of Leakage Currents in Memristor Crossbar Arrays

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2023 IEEE National Aerospace and Electronics Conference, NAECON 2023

作者： Kasongo, Muana Ytterdal, Trond Lee, John Rizkalla, Maher Kumar, Mukesh Iupui Electrical and Computer Engineering Department Indianapolis United States Norwegian Institute of Technology Electronic Systems Department Trondheim Norway Indian Institute of Technology Indore Electrical Engineering Department Indore India

ISBN: (纸本)9798350338782

Memristors are novel electronic devices. Memristor-based crossbar arrays have gained a lot of attention for potential applications in nonvolatile memory, in-memory computing, logic design, neuromorphic computing systems, and neural networks. In this paper, we demonstrate the sneaky path current issue within a 3×3 crossbar array. 1T1M scheme has been used to reduce the leakage current. Integrating nMOS and pMOS devices in series with memristors in the crossbar array has optimized the leakage currents. The results show that nMOS devices reduce the leakage currents by 97.35%, whereas pMOS devices reduce the leakage currents by 92.20%. Thus, nMOS outperform pMOS devices in leakage currents optimization. © 2023 IEEE.

关键词： Memristors

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Cooperative Multi-Agent Deep Reinforcement Learning for Adaptive Decentralized Emergency Voltage Control

Cooperative Multi-Agent Deep Reinforcement Learning for Adap...

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2024 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2024

作者： Zhang, Ying Yue, Meng Montana State University Department of Electrical and Computer Engineering BozemanMT United States Brookhaven National Laboratory Interdisciplinary Science Department UptonNY United States

ISBN: (纸本)9798350313604

Under voltage load shedding (UVLS) for power grid emergency control builds the last defensive perimeter to prevent cascade outages and blackouts in case of contingencies. This letter proposes a novel cooperative multi-Agent deep reinforcement learning (MADRL)-based UVLS algorithm in an adaptive decentralized way. With well-designed input signals reflecting the voltage deviation, newly structured neural networks are developed as intelligent agents to obtain control actions and their probabilities to accommodate high uncertainties in volatile power system operations. Moreover, the interaction among the agents for coordinated control is implemented and refined by a state-of-The-Art attention mechanism, which helps agents concentratively learn effective interacted information. The proposed method realizes decentralized coordinated control, adapting to extremely high uncertainties. Case studies on an IEEE benchmark system indicate the superior performance of the proposed algorithm. © 2024 IEEE.

关键词： Reinforcement learning

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A Reverse Path Planning Approach for Enhanced Performance of Multi-Degree-of-Freedom Industrial Manipulators

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computer Modeling in engineering & Sciences 2024年第5期139卷 1357-1379页

作者： Zhiwei Lin Hui Wang Tianding Chen Yingtao Jiang Jianmei Jiang Yingpin Chen Key Laboratory of Light Field Manipulation and System Integration Applications in Fujian Province School of Physics and Information EngineeringMinnan Normal UniversityZhangzhou363000China Department of Electrical and Computer Engineering University of NevadaLas Vegas89154USA College of Material Engineering Fujian Agriculture and Forestry UniversityFuzhou350000China

In the domain of autonomous industrial manipulators,precise positioning and appropriate posture selection in path planning are pivotal for tasks involving obstacle avoidance,such as handling,heat sealing,and *** Multi-Degree-of-Freedom(MDOF)manipulators offer kinematic redundancy,aiding in the derivation of optimal inverse kinematic solutions to meet position and posture requisites,their path planning entails intricate multiobjective optimization,encompassing path,posture,and joint motion *** satisfactory results in practical scenarios remains *** response,this study introduces a novel Reverse Path Planning(RPP)methodology tailored for industrial *** approach commences by conceptualizing the manipulator’s end-effector as an agent within a reinforcement learning(RL)framework,wherein the state space,action set,and reward function are precisely defined to expedite the search for an initial collision-free *** enhance convergence speed,the Q-learning algorithm in RL is augmented with ***,we formulate the cylindrical bounding box of the manipulator based on its Denavit-Hartenberg(DH)parameters and propose a swift collision detection ***,the motion performance of the end-effector is refined through a bidirectional search,and joint weighting coefficients are introduced to mitigate motion in high-power *** efficacy of the proposed RPP methodology is rigorously examined through extensive simulations conducted on a six-degree-of-freedom(6-DOF)manipulator encountering two distinct obstacle configurations and target *** results substantiate that the RPP method adeptly orchestrates the computation of the shortest collision-free path while adhering to specific posture constraints at the target ***,itminimizes both posture angle deviations and joint motion,showcasing its prowess in enhancing the operational performance of MDOF industrial manipulators.

关键词： Reverse path planning Dyna-Q bidirectional search posture angle joint motion

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Using Monte Carlo simulated PPGs signals to train a Deep Learning model to predict Hemoglobin levels

Using Monte Carlo simulated PPGs signals to train a Deep Lea...

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2023 Emerging Topics in Artificial Intelligence, ETAI 2023

作者： Alvarez, Michael Arzuaga, Emmanuel Sierra, Heidy Laboratory for Applied Remote Sensing Imaging and Photonics United States Department of Computer science & Engineering United States Department of Electrical and Computer Engineering United States University of Puerto Rico Mayaguez Puerto Rico

ISBN: (纸本)9781510665248

Measuring Hemoglobin (Hb) levels is required for the assessment of different health conditions, such as anemia, a condition where there are insufficient healthy red blood cells to carry enough oxygen to the body’s tissues. Measuring Hb levels requires the extraction of a blood sample, which is then sent to a laboratory for analysis. This is an invasive procedure that may add challenges to the continuous monitoring of Hb levels. Noninvasive techniques, including imaging and photoplethysmography (PPG) signals combined with machine learning techniques, are being investigated for continuous measurements of Hb. However, the availability of real data to train the algorithms is limited to establishing a generalization and implementation of such techniques in healthcare settings. In this work, we present a computational model based on Monte Carlo simulations that can generate multispectral PPG signals that cover a broad range of Hb levels. These signals are then used to train a Deep Learning (DL) model to estimate hemoglobin levels. Through this approach, valuable insights about the relationships between PPG signals, oxygen saturation, and Hb levels are learned by the DL model. The signals were generated by propagating a source in a volume that contains the skin tissue properties and the target physiological parameters. The source consisted of plane waves using the 660nm and 890 nm wavelengths. A range of 6 g/dL to 18dL Hb values was used to generate 468 PPGs to train a Convolutional Neural Network (CNN). The initial results show high accuracy in detecting low levels of Hb. To the best of our knowledge, the complexity of biological interactions involved in measuring hemoglobin levels has yet to be fully modeled. The presented model offers an alternative approach to studying the effects of changes in Hb levels on the PPGs signal morphology and its interaction with other physiological parameters that are present in the optical path of the measured signals. © 2023 SPIE.

关键词： Hemoglobin

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Improved Dijkstra Algorithm for Mobile Robot Path Planning and Obstacle Avoidance

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computers, Materials & Continua 2022年第9期72卷 5939-5954页

作者： Shaher Alshammrei Sahbi Boubaker Lioua Kolsi Department of Mechanical Engineering College of EngineeringUniversity of Ha’ilHa’il2440Saudi Arabia Department of Computer and Network Engineering College of Computer Science and EngineeringUniversity of JeddahJeddah21959Saudi Arabia Research Laboratory of Metrology and Energy Systems National Engineering SchoolEnergy Engineering DepartmentUniversity of MonastirMonastir5000Tunisia

Optimal path planning avoiding obstacles is among the most attractive applications of mobile robots(MRs)in both research and *** this paper,an optimal collision-free algorithm is designed and implemented practically based on an improved Dijkstra *** achieve this research objectives,first,the MR obstacle-free environment is modeled as a diagraph including nodes,edges and ***,Dijkstra algorithm is used offline to generate the shortest path driving the MR from a starting point to a target *** its movement,the robot should follow the previously obtained path and stop at each node to test if there is an obstacle between the current node and the immediately following *** this aim,the MR was equipped with an ultrasonic sensor used as obstacle *** an obstacle is found,the MR updates its diagraph by excluding the corresponding ***,Dijkstra algorithm runs on the modified *** procedure is repeated until reaching the target *** verify the efficiency of the proposed approach,a simulation was carried out on a hand-made MR and an environment including 9 nodes,19 edges and 2 *** obtained optimal path avoiding obstacles has been transferred into motion control and implemented practically using line tracking *** study has shown that the improved Dijkstra algorithm can efficiently solve optimal path planning in environments including obstacles and that STEAM-based MRs are efficient cost-effective tools to practically implement the designed algorithm.

关键词： Mobile robot(MR) STEAM path planning obstacle avoidance improved dijkstra algorithm

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Min-Max Optimization Under Delays

Min-Max Optimization Under Delays

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American Control Conference (ACC)

作者： Arman Adibi Aritra Mitra Hamed Hassani Department of Electrical and Computer Engineering Princeton University Department of Electrical and Computer Engineering North Carolina State University Department of Electrical and Systems Engineering University of Pennsylvania

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

Delays and asynchrony are inevitable in large-scale machine-learning problems where communication plays a key role. As such, several works have extensively analyzed stochastic optimization with delayed gradients. However, as far as we are aware, no analogous theory is available for min- max optimization, a topic that has gained recent popularity due to applications in adversarial robustness, game theory, and reinforcement learning. Motivated by this gap, we examine the performance of standard min-max optimization algorithms with delayed gradient updates. First, we show (empirically) that even small delays can cause prominent algorithms like Extra-gradient (EG) to diverge on simple instances for which EG guarantees convergence in the absence of delays. Our empirical study thus suggests the need for a careful analysis of delayed versions of min-max optimization algorithms. Accordingly, under suitable technical assumptions, we prove that Gradient Descent-Ascent (GDA) and EG with delayed updates continue to guarantee convergence to saddle points for convex-concave and strongly convex-strongly concave settings. Our complexity bounds reveal, in a transparent manner, the slow-down in convergence caused by delays.

关键词： Reinforcement learning Robustness Delays Complexity theory Game theory Optimization Standards

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Computational Fluid Dynamics Modeling Analysis of a Martian Rotorcraft With Individual Blade Control1

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ASME Letters in Dynamic systems and Control 2023年第2期3卷 021005页

作者： Schulman, Paul Berndt, Samuel-Hunter Roman, Christiahn Tan, Xiaobo Department of Electrical and Computer Engineering Michigan State University East LansingMI48824 United States Jet Propulsion Laboratory California Institute of Technology PasadenaCA91109 United States

Computational fluid dynamics (CFD) analysis was conducted on a proposed blade root-actuated individual blade control (IBC) system for future Martian rotorcraft. IBC offers many potential benefits to rotary-winged exploration of Mars, including precision control of rotor blade forces. This study seeks to provide an estimate of rotor blade force and system power as a basis for concept feasibility analysis and experimental prototyping. ANSYS FLUENT was used to compute blade pitching moment, lift, and drag under various feathering waveforms, amplitudes, biases, and frequencies. It is determined that the rapid feathering characteristic of IBC has a non-negligible impact on blade forces. It is also found that actuators with power ratings on the order of 101 W are likely sufficient for blade actuation on Martian rotorcraft. © 2024 American Society of Mechanical Engineers (ASME). All rights reserved.

关键词： Vibration analysis

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