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IEEE International Symposium on Information (IT) in Medicine and Education, ITME

作者： Guonian Jin Shejie Lu Siping Hu Haihong Xu Jinfeng Gao Fang Liu Haitao Wu School of Computer Science and Technology Institute of Engineering and Technology Hubei University of Science and Technology Xianning PR China International Joint Laboratory of Behavior Optimization Control for Smart Robots College of Computer and Artificial Intelligence Huanghuai University Zhumadian PR China

In IEEE 802.11 wireless LAN, as the number of nodes increases, collisions will increase correspondingly, channel utilization will decline, and the total throughput will decline instead of increasing, leading to system performance degradation. Through in-depth analysis of the root cause of this problem, this paper designs a simple and effective algorithm, which uses ARMA filtering algorithm to measure the conditional collision probability and then calculate the number of nodes in the network. The AP tells all nodes in the network in the form of broadcast when the number of nodes in the network changes, after all nodes receive the broadcast sent by the AP, they dynamically adjust the network parameters accordingly. Finally, NS2 simulation software is used to conduct simulation experiments in a variety of network scenarios, and the experimental results verify that the algorithm in this paper is simple and effective, with less changes to IEEE 802.11, and is suitable for use and promotion in wireless networks. It can optimize the network system performance based on the number of nodes, improve the system throughput, and significantly improve the network performance. According to the number and density of nodes, the algorithm reduces the collision probability by dynamically adjusting the timeslot, accordingly increases the total network throughput, and achieves the purpose of effectively improving the network performance.

关键词： Degradation Wireless LAN Heuristic algorithms Wireless networks System performance Software algorithms Throughput

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Iterative Learning control for Network Systems with Communication Delay or Data Dropout

Iterative Learning Control for Network Systems with Communic...

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Joint 48th IEEE Conference on Decision and control and 28th Chinese control Conference

作者： Chunping Liu Jianxin Xu Jun Wu State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and ControlZhejiang University Department of Mechatronics Engineering Shaoxing College of Arts and Sciences Department of Electrical and Computer Engineering National University of Singapore Hangzhou Qian Jiang expert SUPCON Technology Co.Ltd.SUPCON Park

In this paper,iterative learning control(ILC) is applied to network-based control problems in which communication channels are subject to random transport delay and data *** averaging ILC algorithm is used to overcome the random *** analysis,it is shown that ILC can perform well and achieve asymptotical convergence in ensemble average along the iteration axis,as far as the probability of the transmission delay and data dropout are known a priori.A unique contribution in this work is to illustrate the applicability of ILC to nonlinear systems while both the one-step delay and the data-dropout phenomena are taken into *** analysis and simulations validate the effectiveness of the ILC algorithm for network-based control tasks.

关键词： ILC Iterative Learning control for Network Systems with Communication Delay or Data Dropout dropout

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Design and Analysis of Weather Parameters Using MEMS Technology with Suitable Characteristics for Integration into a Single Chip for Environment Monitoring

Design and Analysis of Weather Parameters Using MEMS Technol...

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Smart Technologies and Systems for Next Generation Computing (ICSTSN), International Conference on

作者： Abdurrashid S. Hassan Vimala A. Juliet G. Pius Agbulu G. Joselin Retna Kumar Raghavendra Reddy N V S Gunasekar Department of Electronics and Control/Instrumentation Engineering SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Mechatronic Engineering School of Engineering Sciences & Technology Chinhoyi University of Technology Chinhoyi Zimbabwe Department of Electronics and Instrumentation Engineering SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Mechanical Engineering RV Institute of Technology and Management Karnataka India Department of Computer Science and Engineering Rajalakshmi Engineering College Chennai India

ISBN: (数字)9798350391565

ISBN: (纸本)9798350391572

This study designs and simulates weather parameters using MEMS technology with suitable characteristics for integration into a single chip for environment monitoring. The sensing and heating filament of the temperature sensor was designed using a thin film platinum resistor, examining its ability to experience stability, increase in resistivity, accuracy, and free-contamination. The temperature sensing and heating element are based on platinum RTD (Resistance Temperature Detector) with meander shape and aluminum as the bonding pad connecting the sensor to the external circuit. A capacitive humidity sensor is implemented in consolidation with a piezoresistive pressure sensor with a square membrane. The capacitive humidity sensor with hole pattern at the upper plate and a polyimide serves as water absorbent sandwiched between the electrodes. The simulation analysis was conducted using finite element analysis in COMSOL Multiphysics version 6.0 . It was observed that the temperature increases from -100oC to - 20oC, and 10oC to 50oC respectively as a result of deviation in relative humidity in the troposphere, and atmosphere. The average changes in capacitances were found at 0.0713/%RH, while the pressure was examined to vary from 1050hPa to 5hPa in a step of 200hPa with a sensitivity of 11.50mv/bar.

关键词： Temperature sensors Temperature Platinum Terrestrial atmosphere Humidity Stability analysis Finite element analysis

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An Initial Research:Towards Accurate Pitch Extraction for Speech Synthesis Based on BLSTM

An Initial Research:Towards Accurate Pitch Extraction for Sp...

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2016 IEEE 13th International Conference on Signal Processing（ICSP2016）

作者： Yibin Zheng Zhengqi Wen Bin Liu Ya Li Jianhua Tao National Laboratory of Pattern Recognition Institute of AutomationChinese Academy Sciences School of Computer and Control Engineering University of Chinese Academy of Sciences CAS Center for Excellence in Brain Science and Intelligence Technology Institute of AutomationChinese Academy Sciences

Accurate pitch extraction from speech is important but challenging problem for speech synthesis. However, the additive nature and long-term suprasegmental property of pitch features have not been fully exploited in most of the existing pitch estimators as they are operated frame by frame. As a result, they would cause some inherent discontinuities, such as double/half F0 errors and unvoiced/voiced(U/V) error. This would adversely affect the quality of synthetic speech as well as the expressiveness of the prosody information. In this paper, we explore the novel use of multi-tasks(Task 1: U/V;Task 2: Pitch) bidirectional long short-term memory recurrent neural network(BLSTM) to model the pitch and voicing decision simultaneously in a unified framework. The features used in this study are extracted from the frequency domain. We compute the log-frequency power spectrogram and then normalize to the long-term speech spectrum to attenuate noises. A filter is then used to enhance the harmonicity. Experiments show that the proposed approach substantially outperforms RAPT, which behaves the best in clean condition. Besides, our proposed approach can even work well with a certain level of background noise.

关键词： pitch extraction voicing decision BLSTM logfrequency power spectrogram speech synthesis

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Shared Situational Awareness with V2X Communication and Set-membership Estimation

arXiv

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arXiv 2023年

作者： Narri, Vandana Alanwar, Amr Mårtensson, Jonas Norén, Christoffer Johansson, Karl Henrik Research and Development Scania CV AB Södertälje151 87 Sweden Division of Decision and Control Systems at School of Electrical Engineering and Computer Science KTH Royal Institute of Technology StockholmSE100 44 Sweden School of Computer Science and Engineering Constructor University Germany

The ability to perceive and comprehend a traffic situation and to estimate the state of the vehicles and road-users in the surrounding of the ego-vehicle is known as situational awareness. Situational awareness for a heavy-duty autonomous vehicle is a critical part of the automation platform and depends on the ego-vehicle’s field-of-view. But when it comes to the urban scenario, the field-of-view of the ego-vehicle is likely to be affected by occlusion and blind spots caused by infrastructure, moving vehicles, and parked vehicles. This paper proposes a framework to improve situational awareness using set-membership estimation and Vehicle-to-Everything (V2X) communication. This framework provides safety guarantees and can adapt to dynamically changing scenarios, and is integrated into an existing complex autonomous platform. A detailed description of the framework implementation and real-time results are illustrated in this paper. Copyright © 2023, The Authors. All rights reserved.

关键词： Vehicle to Everything

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Fusion Learning of Multimodal Neuroimaging with Weighted Graph AutoEncoder

Fusion Learning of Multimodal Neuroimaging with Weighted Gra...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Gen Shi Yifan Zhu Fuquan Zhang Wenjin Liu Yuxiang Yao Xuesong Li School of Computer Science and Technology Beijing Institute of Technology Beijing China Department of Computer Science and Technology Tsinghua University Beijing China College of Computer and Control Engineering Minjiang University Fuzhou China Department of Nephrology Northern Jiangsu People’s Hospital Yangzhou University Yangzhou China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Neuroimaging plays an significant role in diagnosing and pathological study of brain diseases. Considering that both functional and structural abnormalities may lead to brain dis-eases and disorders, single modal neuroimaging approach may not fully characterize brain activities and working modes. Fusion of multimodal neuroimaging data is expected to provide more comprehensive characterization of brain diseases, given that the different modalities contain more complementary information. Recently, Graph Convolutional Networks (GCNs) is shown to have powerful capacity in representation learning for graph-structure data, which is considered to integrate both graph se-mantic structure and node information. Therefore, in this paper, we propose the Weighted Graph AutoEncoder (WGAE), a GCN- driven multimodal fusion model, to learn the combinational latent node representation of fMRI and DTI neuroimaging data, which are used as node features and graph structure respectively in the graph in unsupervised manner. Experimental results on two real-world datasets show the superiority of the proposed model over other existing single-modal or multi-modal methods in learning representations for disease prediction as the downstream task. Furthermore, ablation experiments also show the collaborative contribution of multimodal neuroimaging fusion in the proposed model, and also show the feasibility of assessing the respective importance of the two modalities during the disease prediction.

关键词： Neuroimaging Representation learning Pathology Predictive models Functional magnetic resonance imaging Brain modeling Data models

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Reduced Network Cumulative Constraint Violation for Distributed Bandit Convex Optimization under Slater’s Condition

arXiv

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arXiv 2024年

作者： Zhang, Kunpeng Yi, Xinlei Ding, Jinliang Cao, Ming Johansson, Karl H. Yang, Tao State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China Department of Control Science and Engineering College of Electronics and Information Engineering Tongji University Shanghai201800 China Engineering and Technology Institute Groningen Faculty of Science and Engineering University of Groningen GroningenAG 9747 Netherlands Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden Digital Futures Stockholm10044 Sweden

This paper studies the distributed bandit convex optimization problem with time-varying inequality constraints, where the goal is to minimize network regret and cumulative constraint violation. To calculate network cumulative constraint violation, existing distributed bandit online algorithms solving this problem directly use the clipped constraint function to replace its original constraint function. However, the use of the clipping operation renders Slater’s condition (i.e, there exists a point that strictly satisfies the inequality constraints at all iterations) ineffective to achieve reduced network cumulative constraint violation. To tackle this challenge, we propose a new distributed bandit online primal–dual algorithm. If local loss functions are convex, we show that the proposed algorithm establishes an O(T max{c,1−c})network regret bound and an O(T 1−c/2) network cumulative constraint violation bound, where T is the total number of iterations and c ∈ (0, 1) is a user-defined trade-off parameter. When Slater’s condition holds, the network cumulative constraint violation bound is reduced to O(T 1−c). In addition, if local loss functions are strongly convex, for the case where strongly convex parameters are unknown, the network regret bound is reduced to O(T1−c), and the network cumulative constraint violation bound is O(T1−c/2) and O(T1−c) without and with Slater’s condition, respectively. For the case where strongly convex parameters are known, the network regret bound is further reduced to O)(log(T)), and the network cumulative constraint violation bound is reduced to O(log(T)T) and O(log(T)) without and with Slater’s condition, respectively. To the best of our knowledge, this paper is among the first to establish reduced (network) cumulative constraint violation bounds for (distributed) bandit convex optimization with time-varying constraints under Slater’s condition. Finally, a numerical example is provided to verify the theoretical results. Copyright © 2

关键词： Convex optimization

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Identification of nonlinear systems by nonparametric approach with application to control of flexible robot manipulator *

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IFAC Proceedings Volumes 2009年第13期42卷 611-616页

作者： Adam Krzyżak Jerzy Z. Sαsiadek Anthony Green Department of Computer Science and Software Engineering Concordia University Montreal Quebec Canada H3G 1M8 Institute of Control Engineering Szczecin University of Technology Szczecin Poland Department of Aerospace Engineering Carleton University Ottawa Ontario Canada K1S 5B6

Abstract We apply nonparametric techniques to identify nonlinear dynamic block-oriented systems of Hammerstein type. Hammerstein system consists of a memoryless nonlinear system followed by a dynamic, linear system. We introduce identification algorithms based on input-output observations for both systems and study their convergence and the rates. The performance of identification algorithms is validated in simulation studies. We apply Hammerstein system identification algorithms to identification of nonlinearities in a flexible robot manipulator.

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Two short proofs regarding the logarithmic least squares optimality in Chen, K., Kou, G., Tarn, J. M., Song, Y. (2015): Bridging the gap between missing and inconsistent values in eliciting preference from pairwise comparison matrices, Annals of Operations Research 235(1):155–175

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Annals of Operations Research 2017年第1期253卷 707-708页

作者： Sándor Bozóki Laboratory on Engineering and Management Intelligence Research Group of Operations Research and Decision Systems Institute for Computer Science and Control Hungarian Academy of Sciences Budapest Hungary Department of Operations Research and Actuarial Sciences Corvinus University of Budapest Budapest Hungary

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Interactive on-line evaluation of robot motion control

Interactive on-line evaluation of robot motion control

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IEEE Conference on control Technology and Applications (CCTA)

作者： A. Valera A. Robertsson K. Nilsson R. Johansson Department of System Engineering & Automation Valencia Technical University Valencia Spain Department of Automatic Control Lund Institute of Technology Lund University Lund Sweden Lund Institute of Technology Department of Computer Science Lund University Lund Sweden

This paper presents a new real-time architecture for motion control of industrial robots. The new control system obtained has two main advantages: first it provides a total open control architecture and the second advantage is the simplicity and the interactivity of the platform developed. Experimental evaluation of a passivity-based control scheme shows the benefits of the architecture which is unique in the sense that open and advanced control can be combined with built-in safety logic as required in industrial applications.

关键词： Robot motion Motion control Robot control Industrial control control systems Electrical equipment industry Service robots Robot sensing systems Safety Logic

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