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检索条件"任意字段=7th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2018"

共 474 条记录，以下是391-400 订阅

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Fault-Tolerant Motion Planning of Redundant Manipulator with Initial Position Error

Fault-Tolerant Motion Planning of Redundant Manipulator with...

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data driven control and learning systems (ddcls)

作者： Kene Li Jin Yang Chengzhi Yuan Jianqin Xu Xisheng Dai Jiawei Luo Department of Mechanical Industrial and Systems Engineering University of Rhode Island Kingston RI USA School of Electrical and Information Engineering Guangxi University of Science and Technology Liuzhou China Engineering Training Center Guangxi University of Science and Technology Liuzhou China Jiangxi Vocational College of Industry and Engineering Pingxiang China

ISBN: (纸本)9781538626191

In the robotic manipulator operation practice, it is necessary to adjust the manipulator initial state to an accurate configuration for executing a given path tracking task. However, it is difficult to achieve a desired accurate configuration, which would lead to an unexpected initial position error of the end-effector. In this paper, based on a new neural-dynamic design method, i.e., Zhang dynamics, a fault-tolerant motion planning scheme is presented to diminish the initial position error arising in the manipulator state adjustment. Such a motion planning scheme of redundant manipulators can rapidly and smoothly diminish the initial position error during the task execution. Computer simulations are presented to illustrate the validity and advantages of the fault-tolerant motion planning scheme with an initial position error based on a four-link manipulator model.

关键词： End effectors Task analysis Planning Fault tolerance Fault tolerant systems

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Identifying Gait Phases from Joint Kinematics during Walking with Switched Linear Dynamical systems 7

Identifying Gait Phases from Joint Kinematics during Walking...

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7th ieee International conference on Biomedical Robotics and Biomechatronics (BIOROB)

作者： Drnach, Luke Essa, Irfan Ting, Lena H. Georgia Inst Technol Atlanta GA 30332 USA Emory Univ Atlanta GA 30322 USA

ISBN: (纸本)9781538681831

Human-robot interaction (HRI) for gait rehabilitation would benefit from data-driven gait models that account for gait phases and gait dynamics. Here we address the current limitation in gait models driven by kinematic data, which do not model interlimb gait dynamics and have not been shown to precisely identify gait events. We used Switched Linear Dynamical systems (SLDS) to model joint angle kinematic data from healthy individuals walking on a treadmill with normal gaits and with gaits perturbed by electrical stimulation. We compared the model-inferred gait phases to gait phases measured externally via a force plate. We found that SLDS models accounted for over 88% of the variation in each joint angle and labeled the joint kinematics with the correct gait phase with 84% precision on average. the transitions between hidden states matched measured gait events, with a median absolute difference of 25ms. To our knowledge, this is the first time that SLDS inferred gait phases have been validated by an external measure of gait, instead of against pre-defined gait phase durations. SLDS provide individual-specific representations of gait that incorporate both gait phases and gait dynamics. SLDS may be useful for developing control policies for HRI aimed at improving gait by allowing for changes in control to be precisely timed to different gait phases.

关键词： Legged locomotion Hidden Markov models Superluminescent diodes Kinematics data models Switches Predictive models

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Anode Effect prediction based on Expectation Maximization and XGBoost model

Anode Effect prediction based on Expectation Maximization an...

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data driven control and learning systems (ddcls)

作者： Zhixin Zhang Gaofeng Xu Hongting Wang Kaibo Zhou School of Automation MOE Key Laboratory of Image Processing and Intelligence Control Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9781538626191

Anode Effect Prediction problem has been drawing great research interest of scientists, due to its significant values in reducing energy consumption and improving the efficiency of aluminum electrolysis. However, a large number of missing values contained in the collected data from the aluminum reduction cell are always neglected in the works, resulting in a decline in prediction accuracy and generalization ability. To solve this problem, a combined model of Expectation Maximization and XGBoost (EM-XGBoost) is proposed. Firstly, the original incomplete samples collected from the aluminum cells are recovered by Expectation Maximization (EM) algorithm. Afterwards, the XGBoost model trains on the recovered data, and then predicts the result for new samples. the more comprehensive metrics accuracy and F1 Score are introduced for evaluation. the results in the experiment show that the proposed model improves the accuracy to 99.7% and the F1 Score can achieve 99.8% under the premise of forecasting 30 minutes in advance. the proposed model not only has a high prediction accuracy, but also owns an excellent generalization ability.

关键词： Anodes Aluminum data models Predictive models Electrochemical processes Measurement Filling

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An Intelligent Car Temperature control System

An Intelligent Car Temperature Control System

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data driven control and learning systems (ddcls)

作者： Xiongnan He Songchen Jiang Qiuye Sun College of Information Science and Engineering Northeastern University Shengyang China

ISBN: (纸本)9781538626191

Nowadays, more and more residential cars apply various of services of energy saving to help themselves improve performances and decrease cost. As for the car air conditioning, some put forward ideas that using neuron-fuzzy method can precisely control the cooling capacity, the other hold the view that power line communication based photovoltaic (PV) system can effectively manage the energy. In this paper, it aims to deal with the shortcomings that aforementioned do not take the realistic environment and the neuron-fuzzy method's disadvantages into consideration. As a result, this paper comes up an intelligent car temperature control system(ICTCS),which, comparing with conventional temperature control systems, has two main advantages--one is using three criterions, namely light intensity outside cars(I),temperature inside cars(T) and sunshine incident angle(α), to judge what kind of environment the car is in on earth and decide car cooling capacity over , the other is applying neuron-fuzzy system to train the comprehensive temperature to try its best to decrease faster. It will refrigerate in different stalls in the standard of difference between temperature inside cars and calculated most suitable temperature. Applying the above system into actual experiments, we can find under the premise that cooling effect stays nearly the same, the energy consumption gets decreased, which is to say, the ICTCS gets good results.

关键词： Automobiles Temperature sensors Batteries Air conditioning

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A Novel Improved Grey Wolf Optimization Algorithm for Numerical Optimization and PID controller Design

A Novel Improved Grey Wolf Optimization Algorithm for Numeri...

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data driven control and learning systems (ddcls)

作者： Tao Zhang Xin Wang Zhenlei Wang Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology Shanghai China Center of Electrical & Electronic Technology Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9781538626191

the grey wolf optimization (GWO) algorithm, one of the recently proposed bio-inspired algorithms, simulates the leadership hierarchy and hunting mechanism of grey wolves in nature. the GWO has a good performance in some optimization tasks, but its search capacity decreases with the increasing search scope and dimension. this paper proposes an improved GWO (IGWO) algorithm, in which Levy flight strategy and a sine cosine operator with adaptive step are incorporated to significantly improve the performance of the algorithm. the Levy flight strategy is used to strengthen the efficiency of global search. the adaptive sine cosine operator is introduced to improve the local search ability. Experimental results based on twenty unconstrained benchmark problems show the superiority of the proposed IGWO. Furthermore, the IGWO is utilized in PID controller design. the comparison results show that the IGWO algorithm is better than, or at least comparable to, other well-established swarm intelligence algorithms.

关键词： Optimization Power system stability Sociology Statistics Prediction algorithms Particle swarm optimization Genetic algorithms

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MEMG: Mobile Encrypted Traffic Classification With Markov Chains and Graph Neural Network 23

MEMG: Mobile Encrypted Traffic Classification With Markov Ch...

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23rd ieee International conference on High Performance Computing and Communications, 7th ieee International conference on data Science and systems, 19th ieee International conference on Smart City and 7th ieee International conference on Dependability in Sensor, Cloud and Big data systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Cai, Wei Gou, Gaopeng Jiang, Minghao Liu, Chang Xiong, Gang Li, Zhen Institute of Information Engineering Chinese Academy of Sciences China School of Cyber Security University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781665494571

In recent years, user privacy and information Security have attracted widespread attention, encryption ratio of mobile traffic has increased tremendously, which has brought considerable challenges to traditional traffic classification meth-ods. Machine learning methods and deep learning methods have become mainstream methods to solve this problem. However, the existing machine learning methods require manual features and cannot adapt to the newly generated traffic patterns. Deep learning methods are capable of learning features from the raw traffic sequences automatically but will increase the calculation costs. To address these challenges, in this paper, we propose a Mobile Encrypted Traffic Classification with Markov Chains and Graph Neural Network (MEMG). We use the Markov chains to mine the hidden topological information of the flow. then we build the flow graph structure on this basis, add the sequence information of traffic in the node feature in the graph. We also design a Graph Neural Network-based classifier to learn the topological and sequential features from the graph. the classifier can map the graph structure to the embedding space, and classify different graph structures by embedding vector differences. We have done comprehensive experiments on both the real-world dataset and the public dataset. the real-world dataset contains the traffic of 29 commonly used mobile encrypted applications collected by us recently, and the total number of traffic exceeds 116,000. Our method outperforms the state-of-the-art methods by 6.1% and 3.5% of the accuracy rate on our dataset and public dataset, respectively. We also lessen the training time overhead and GPU memory usage by 40% and 46%, respectively. © 2021 ieee.

关键词： Training Deep learning Privacy Smart cities Graphics processing units Markov processes Traffic control

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A data-driven Voxel-wise White Matter Fiber Clustering Model Based on Priori Anatomical data

A Data-driven Voxel-wise White Matter Fiber Clustering Model...

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data driven control and learning systems (ddcls)

作者： Zhewen Cao Er Jin Siqi Zhou Ye Wu Yongqiang Li Yuanjing Feng Institute of Information Processing and Automation Zhejiang University of Technology Hangzhou

ISBN: (纸本)9781538626191

Whole-brain fiber imaging allows nondestructive detection of human brain structural connections. the clinical application of this method is often classified as a series of fiber bundle structures of certain significance (function, structure, shape, etc.). Due to the lack of edge structure information of fiber bundles and the high variability of complex white matter structures in individual samples, fiber clustering based on anatomical information is still an open problem. In this paper, a new fiber clustering technique is proposed, which combines spatial features of whole-brain fibers and prior anatomical information as fiber similarity matching and feature extraction. In this work, we focus on the coverage of highly consistent fiber bundles in white matter structures to match anatomic features. the method is based on multiple tests of simulated data and in vivol data. the experimental results show that this method not only improves the highly consistent coverage of fiber bundles and prior anatomical knowledge, but also simplifies the fiber data space to improve the fiber clustering similarity measured population consistency. Finally, we also discuss the application of this method in clinical research.

关键词： Measurement White matter Feature extraction Grey matter Streaming media Image reconstruction Shape

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Preliminary data Processing by Means of Onboard Computer systems of Spacecraft 7

Preliminary data Processing by Means of Onboard Computer Sys...

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7th Mediterranean conference on Embedded Computing (MECO)

作者： Konkin, Yuriy, V Kolesenkov, Aleksandr N. Sablina, Victoria A. Ashapkina, Maria S. RSREU Dept Space Technol Ryazan Russia RSREU Dept Elect Comp Ryazan Russia RSREU Dept Micro & Nanoelect Ryazan Russia

ISBN: (纸本)9781538656839

the article is devoted to the solution of scientific problems of developing stand-alone storage technologies as well as data processing methods oriented to the usage of small spacecraft in onboard computer systems while solving the tasks of aggregating information from technical vision system sensors, machine learning and intellectual data processing. the relevance of the project is caused by the necessity to solve the tasks requiring large amount of calculations on board, in the conditions of limited computing resources concerning memory volume and performance. the work solves the task of developing and unifying effective built-in onboard control systems and database control systems with low requirements to computer power on board the small spacecraft. Scientific significance of such problem solution is to receive new efficient technologies of control onboard systems design on the basis of open source operating systems and software. the implementation of the project will allow receiving scientific and technological results as well as creating basic technologies for innovation development of local and international markets of goods and services in the sphere of intellectual built-in systems.

关键词： processing storage onboard system ERS data format data database DBMS ultrasmall spacecraft satellite

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Industrial 5.0 Aquaponics System Using Machine learning Techniques 7

Industrial 5.0 Aquaponics System Using Machine Learning Tech...

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7th International conference on Electronics, Communication and Aerospace Technology, ICECA 2023

作者： Venkatraman, M. Surendran, R. Saveetha School of Engineering Department of Computer Science and Engineering Saveetha Institute of Medical and Technical Science Chennai India

ISBN: (纸本)9798350340600

Due to the advancements in digital technology, the fifth industrial revolution, also known as aquaponics, has brought changes in the traditional manufacturing and industrial processes. the primary objective of the industry 5.0 initiative is to encourage customization and innovation while simultaneously increasing the productivity, efficiency, and adaptability of manufacturing processes. the industry's coming to terms with the 5.0 vision is underpinned by machine learning. It makes it possible for machines and systems to see patterns in data, make decisions based on that data, and learn without having to be explicitly programmed. Algorithms that learn from machines are able to analyze vast amounts of data collected from sensors, machines, and other sources in order to make predictions, improve processes, and acquire insights. this capacity has applications in a variety of domains, including quality control, predictive maintenance, supply chain optimization, process optimization, and autonomous robots. Machine learning-based predictive maintenance reduces downtime and optimizes maintenance schedules by detecting industrial equipment faults. Machine learning-powered quality control systems monitor manufacturing, identify errors, and ensure product quality. U sing machine learning algorithms on supply chain data improves inventory management, logistics, and efficiency. Process optimization uses machine learning to find trends in production data to improve efficiency and energy use. Machine learning-enabled autonomous robots and intelligent decision- making systems help humans complete complicated tasks safely and efficiently. Aquaponics is a major step toward a more connected, intelligent, and data-driven industrial landscape. © 2023 ieee.

关键词： Decision making

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Anomaly Detection of Satellite Telemetry in Orbit Based on Sequence and Point Feature Combination

Anomaly Detection of Satellite Telemetry in Orbit Based on S...

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ieee data driven control and learning systems conference

作者： Ying Du Xin Liang Fei Wang Chao Sun XiaoFei Hua 63758 Unit of PLA Xi'an Satellite Control Center

ISBN: (纸本)9781538626191

Aiming at the detection problems of low accuracy, high false alarm rate in the detection of satellite telemetry timing data, a satellite telemetry anomaly detection model is presented based on combination of sequence and point features. the model firstly makes steady separation in telemetry data to obtain data trends and steady residual;as the data trends contain most of telemetry sequence information, the non-stationary telemetry sequence feature is reconstructed on each separation layer by performing inverse data trends transform on these coefficients. And stable residual is mainly composed of telemetry random point, the telemetry point feature is reconstructed on the final separation layer by performing inverse steady residual transform on these coefficients. Finally, the integrated anomaly detection model, that is double autoregressive combined model, is constructed by fuse the two features. the results of anomaly detection experiment in the telemetry data of a certain type of satellite power system show that the model can adapt to the complex changes of satellite telemetry data in orbit, reduce decision interference from noise and outliers effectively, reduce the false alarm rate, improve detection precision, achieve effective detection in the anomaly threshold of satellite telemetry data on orbit.

关键词： Telemetry Sequence feature Point feature Anomaly detection Feature combination Integrated detection model

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