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Articles 6721 - 6750 of 11289

Full-Text Articles in Artificial Intelligence and Robotics

Design And Implementation Of Cloth Virtual Simulation System For Group Performance, Xiaotian Sun, Boxiang Xiao, Zhengdong Liu Nov 2020

Design And Implementation Of Cloth Virtual Simulation System For Group Performance, Xiaotian Sun, Boxiang Xiao, Zhengdong Liu

Journal of System Simulation

Abstract: Clothing plays an important role in group performance activities and affects directly the overall performance effect. In order to achieve the goal of virtual design and virtual exercise of group performance costume, a virtual simulation system is designed and implemented. The human body model is established by CLO3D, and then individual clothing model is constructed. After the human body and clothing model are imported into Unity, the clothing material is adjusted and the texture map is added. The group model is generated by copying individual clothing models of human body and clothing in Unity. The interactive interface of virtual …

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Optimal Scheduling And Decision Making Method For Dynamic Flexible Job Shop, Wang Yan, Ding Yu Nov 2020

Optimal Scheduling And Decision Making Method For Dynamic Flexible Job Shop, Wang Yan, Ding Yu

Journal of System Simulation

Abstract: For multi-objective dynamic flexible job-shop scheduling, an improved multi-objective differential evolution algorithm is proposed. The adaptive cross-mutation operator is introduced into the differential evolution algorithm to improve its global search capability. The fast non-dominated sorting method based on immunological principles is introduced to improve the quality of the solution set in the selection and sorting. An improved TOPSIS-G1-EVM comprehensive decision-making method is proposed. The comprehensive weight of G1-EVM is calculated by Nash equilibrium theory. The comprehensive weight and TOPSIS evaluation system are combined to evaluate each dispatching scheme. The experimental results show that the optimal scheduling algorithm is superior …

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Fast 3d Medical Image Registration Based On Geometric Feature Invariants, Juping Gu, Tianyu Cheng, Jianping Wang, Hua Liang, Fengshen Zhao, Jiang Ling Nov 2020

Fast 3d Medical Image Registration Based On Geometric Feature Invariants, Juping Gu, Tianyu Cheng, Jianping Wang, Hua Liang, Fengshen Zhao, Jiang Ling

Journal of System Simulation

Abstract: Aiming at the of large amount of computational data and low registration efficiency in 3D cranial medical image registration, a fast registration method based on geometric feature space constraints is proposed. The algorithm extracts three-dimensional contour point clusters, and proposes a feature construction method based on the optimal fitting ring of point clusters. The feature rings and the centroids of each layer are used as feature quantities, and the fast registration is completed by using Iterative Closest Point (ICP) method. The experimental results show that the method has less computation amount, high satisfactory registration accuracy and much faster registration …

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Research On Improvement Of Parameters Calibration Method Of Microscopic Traffic Simulation Model, Chenjing Zhou, Yacong Gao, Rong Jian Nov 2020

Research On Improvement Of Parameters Calibration Method Of Microscopic Traffic Simulation Model, Chenjing Zhou, Yacong Gao, Rong Jian

Journal of System Simulation

Abstract: Model parameter calibration is the precondition of application of micro traffic simulation technology. In view of the lack of refined analysis in the model parameter calibration classification and calibration result determination, the corresponding improvement is proposed. The parameter calibration system divides global parameters and local parameters, provides a micro-simulation model parameter calibration method that combines engineering measurement and intelligent optimization. Based on the information entropy as the analysis index of the parameter calibration results, a method of parameter recursion after clustering is proposed. On the basis of the actual survey data of signalized intersections, A simulation experiments and …

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Design And Simulation On A Novel Sliding Mode Control For Linear Induction Motor, Kaiwei Han, Wang Yan, Zhicheng Ji Nov 2020

Design And Simulation On A Novel Sliding Mode Control For Linear Induction Motor, Kaiwei Han, Wang Yan, Zhicheng Ji

Journal of System Simulation

Abstract: The speed-tracking problem of linear induction motors under the influence of unmatched disturbances is studied. Based on the extended disturbance observers, the novel sliding mode controllers are designed to make the motor have effective response characteristics under unmatched disturbances. The extended disturbance observersis designed for the unmatched disturbances in the linear induction motor model with edge effects. The observations of the disturbance derivatives are filtered. The novel sliding mode controllers are designed based on the observations of the disturbances and their derivatives. The simulations results show that the designed controller has effective response characteristics and robustness. In addition, it …

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A Fast Latin Hyper Cube Experiment Design Method Based On Soduku Grouping, Tiantian Zhang, Li Ni, Guanghong Gong, Yuanjie Lu Nov 2020

A Fast Latin Hyper Cube Experiment Design Method Based On Soduku Grouping, Tiantian Zhang, Li Ni, Guanghong Gong, Yuanjie Lu

Journal of System Simulation

Abstract: In order to solve the long optimization time and parameter level combination explosion in the complex experiment design space of Latin Hypercube design, which is one of the most popular method in experiment design, a fast Soduku grouping-based method is proposed. The optimal seed design is expanded and transformed in the grouped spaces. Experiments are conducted to compare the Soduku grouping-based Latin Hypercube design method with other two commonly used Latin Hypercube design methods in the middle and high dimension experiment space. The results show that Soduku grouping method is more efficient in computation and has better space-filling performance.

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Container-Based Automatic Packaging Technology For Complex System Simulation Application, Wang Shuai, Zhu Feng, Yiping Yao, Wenjie Tang, Yuhao Xiao Nov 2020

Container-Based Automatic Packaging Technology For Complex System Simulation Application, Wang Shuai, Zhu Feng, Yiping Yao, Wenjie Tang, Yuhao Xiao

Journal of System Simulation

Abstract: Container-based technology provides a new solution for the rapid and flexible deployment of complex system simulation applications. Container supports service-based packaging of simulation applications, which greatly reduces the difficulty of deploying simulation applications. Current packaging technology mainly relies on manually writing Dockerfile, which results in low packaging efficiency and human errors. A container-based automatic packaging technology for complex system simulation application is proposed, and the reusable library component template is defined. Combined image template is generated by combining simulation application and library component templates. Dockerfile is generated by the combined template after syntax optimization and error detection. The experiments …

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Simulation Of Incentive Mechanism Simulation On Reverse Supply Chain Of Waste Products Based On Dual Drive, Dongshi Sun, Danlan Xie, Guan Feng, Ji Yuan Nov 2020

Simulation Of Incentive Mechanism Simulation On Reverse Supply Chain Of Waste Products Based On Dual Drive, Dongshi Sun, Danlan Xie, Guan Feng, Ji Yuan

Journal of System Simulation

Abstract: In view of the current situation of coordination failure caused by different benefits of different entities in the waste reverse supply chain, based on system dynamics, a three-party evolutionary game model of residents, waste disposal institutions and production enterprises is constructed. Vensim simulation software is used to establish the stock-flow chart with the strategy selection probability as the horizontal variable. In the two cases of exogenous variable in constant and a time-trend, the simulation is carried out separately, and the strategy evolution path of the three parties is obtained without external interference. Through sensitivity analysis, the exogenous variables …

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Effect Of New Titanium Alloy On Biomechanical Behavior Of Dental Implant, Jiwu Zhang, Qiguo Rong Nov 2020

Effect Of New Titanium Alloy On Biomechanical Behavior Of Dental Implant, Jiwu Zhang, Qiguo Rong

Journal of System Simulation

Abstract: Titanium alloy materials have excellent mechanical properties, chemical stability and biocompatibility, and have become the main raw materials for implants. However, the biomechanical compatibility of medical titanium alloys still needs to be improved in order to meet the long-term safety and functionality of patient's clinical treatment requirement. New medical titanium alloy materials with high strength and low modulus play an important role in reducing the loosening and shedding of implants caused by stress shielding. The effects of new medical titanium alloy materials and traditional medical titanium alloy materials on implant structure and stress distribution of jaw are compared and …

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Research On Real-Time Simulation Method Of Bi-Sar Echo In Time-Varying Sea Scene, Guijie Diao, Ni Hong, Zhe Liu, Li Yang Nov 2020

Research On Real-Time Simulation Method Of Bi-Sar Echo In Time-Varying Sea Scene, Guijie Diao, Ni Hong, Zhe Liu, Li Yang

Journal of System Simulation

Abstract: Based on bistatic scattering mechanism and the geometric relationship of bistatic synthetic aperture radar (Bi-SAR), a real-time simulation method of Bi-SAR radio frequency echo in time-varying sea scene is proposed. For the couple scattering in time-varying sea scene, a Bi-SAR echo signal model is established on the basis of multipath scattering model. The Bi-SAR system response function for time-varying sea target is calculated in real-time, using high performance computing technology, high-capacity real-time access technology and full-switching system designing. The simulation results show the effectiveness of the method.

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Bert Efficacy On Scientific And Medical Datasets: A Systematic Literature Review, Clayton Cohn Nov 2020

Bert Efficacy On Scientific And Medical Datasets: A Systematic Literature Review, Clayton Cohn

College of Computing and Digital Media Dissertations

Bidirectional Encoder Representations from Transformers (BERT) [Devlin et al., 2018] has been shown to be effective at modeling a multitude of datasets across a wide variety of Natural Language Processing (NLP) tasks; however, little research has been done regarding BERT’s effectiveness at modeling domain-specific datasets. Specifically, scientific and medical datasets present a particularly difficult challenge in NLP, as these types of corpora are often rife with technical jargon that is largely absent from the canonical corpora that BERT and other transfer learning models were originally trained on. This thesis is a Systematic Literature Review (SLR) of twenty-seven studies that were …

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A Pmbldc Motor Measurement And Control System Available For Zynq Hardware-In-The-Loop Simulation, Zhiguo Zhou, Jiaen Sun, Jiabao Yu, Xuehua Zhou Nov 2020

A Pmbldc Motor Measurement And Control System Available For Zynq Hardware-In-The-Loop Simulation, Zhiguo Zhou, Jiaen Sun, Jiabao Yu, Xuehua Zhou

Journal of System Simulation

Abstract: In order to solve the modeling of permanent magnet brushless dc(PMBLDC) motor, it is difficult to modify the control algorithm, inconvenient to add and remove the closed loop, and has poor real-time measurement and control capability. Based on hall sensor position detection algorithm and PID control algorithm, combined with piecewise linear method to generate PWM waveform, Simulink graphical modeling platform is used, and a new closed-loop measurement and control method is proposed. A sudden load and sudden speed simulation are carried out to verify the established PMBLDC motor measurement and control system. The simulation results show that the system …

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Integrating Deep Learning And Augmented Reality To Enhance Situational Awareness In Firefighting Environments, Manish Bhattarai Nov 2020

Integrating Deep Learning And Augmented Reality To Enhance Situational Awareness In Firefighting Environments, Manish Bhattarai

Electrical and Computer Engineering ETDs

We present a new four-pronged approach to build firefighter's situational awareness for the first time in the literature. We construct a series of deep learning frameworks built on top of one another to enhance the safety, efficiency, and successful completion of rescue missions conducted by firefighters in emergency first response settings. First, we used a deep Convolutional Neural Network (CNN) system to classify and identify objects of interest from thermal imagery in real-time. Next, we extended this CNN framework for object detection, tracking, segmentation with a Mask RCNN framework, and scene description with a multimodal natural language processing(NLP) framework. Third, …

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Deep Learning With Physics Informed Neural Networks For The Airborne Spread Of Covid-19 In Enclosed Spaces, Udbhav Muthakana, Padmanabhan Seshaiyer, Maziar Raissi, Long Nguyen Nov 2020

Deep Learning With Physics Informed Neural Networks For The Airborne Spread Of Covid-19 In Enclosed Spaces, Udbhav Muthakana, Padmanabhan Seshaiyer, Maziar Raissi, Long Nguyen

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

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Multimodal Data Fusion And Attack Detection In Recommender Systems, Mehmet Aktukmak Nov 2020

Multimodal Data Fusion And Attack Detection In Recommender Systems, Mehmet Aktukmak

USF Tampa Graduate Theses and Dissertations

The commercial platforms that use recommender systems can collect relevant information to produce useful recommendations to the platform users. However, these sources usually contain missing values, imbalanced and heterogeneous data, and noisy observations. Such characteristics render the process of exploiting the information nontrivial, as one should carefully address them during the data fusion process. In addition to the degenerative characteristics, some entries can be fake, i.e., they can be the outcomes of malicious intents to manipulate the system. These entries should be eliminated before incorporation to any recommendation task. Detecting such malicious attacks quickly and accurately and then mitigating them …

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Bist: Bi-Directional Spatio-Temporal Reasoning For Video-Grounded Dialogues, Hung Le, Doyen Sahoo, Nancy F. Chen, Steven C. H. Hoi Nov 2020

Bist: Bi-Directional Spatio-Temporal Reasoning For Video-Grounded Dialogues, Hung Le, Doyen Sahoo, Nancy F. Chen, Steven C. H. Hoi

Research Collection School Of Computing and Information Systems

Video-grounded dialogues are very challenging due to (i) the complexity of videos which contain both spatial and temporal variations, and (ii) the complexity of user utterances which query different segments and/or different objects in videos over multiple dialogue turns. However, existing approaches to video-grounded dialogues often focus on superficial temporal-level visual cues, but neglect more fine-grained spatial signals from videos. To address this drawback, we propose Bi-directional Spatio-Temporal Learning (BiST), a vision-language neural framework for high-resolution queries in videos based on textual cues. Specifically, our approach not only exploits both spatial and temporal-level information, but also learns dynamic information diffusion …

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Machine Learning Integrated Design For Additive Manufacturing, Jingchao Jiang, Yi Xiong, Zhiyuan Zhang, David W. Rosen Nov 2020

Machine Learning Integrated Design For Additive Manufacturing, Jingchao Jiang, Yi Xiong, Zhiyuan Zhang, David W. Rosen

Research Collection School Of Computing and Information Systems

For improving manufacturing efficiency and minimizing costs, design for additive manufacturing (AM) has been accordingly proposed. The existing design for AM methods are mainly surrogate model based. Due to the increasingly available data nowadays, machine learning (ML) has been applied to medical diagnosis, image processing, prediction, classification, learning association, etc. A variety of studies have also been carried out to use machine learning for optimizing the process parameters of AM with corresponding objectives. In this paper, a ML integrated design for AM framework is proposed, which takes advantage of ML that can learn the complex relationships between the design and …

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Global Context Aware Convolutions For 3d Point Cloud Understanding, Zhiyuan Zhang, Binh-Son Hua, Wei Chen, Yibin Tian, Sai-Kit Yeung Nov 2020

Global Context Aware Convolutions For 3d Point Cloud Understanding, Zhiyuan Zhang, Binh-Son Hua, Wei Chen, Yibin Tian, Sai-Kit Yeung

Research Collection School Of Computing and Information Systems

Recent advances in deep learning for 3D point clouds have shown great promises in scene understanding tasks thanks to the introduction of convolution operators to consume 3D point clouds directly in a neural network. Point cloud data, however, could have arbitrary rotations, especially those acquired from 3D scanning. Recent works show that it is possible to design point cloud convolutions with rotation invariance property, but such methods generally do not perform as well as translation-invariant only convolution. We found that a key reason is that compared to point coordinates, rotation-invariant features consumed by point cloud convolution are not as distinctive. …

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Uniconv: A Unified Conversational Neural Architecture For Multi-Domain Task-Oriented Dialogues, Hung Le, Doyen Sahoo, Chenghao Liu, Nancy F. Chen, Steven C. H. Hoi Nov 2020

Uniconv: A Unified Conversational Neural Architecture For Multi-Domain Task-Oriented Dialogues, Hung Le, Doyen Sahoo, Chenghao Liu, Nancy F. Chen, Steven C. H. Hoi

Research Collection School Of Computing and Information Systems

Building an end-to-end conversational agent for multi-domain task-oriented dialogues has been an open challenge for two main reasons. First, tracking dialogue states of multiple domains is non-trivial as the dialogue agent must obtain complete states from all relevant domains, some of which might have shared slots among domains as well as unique slots specifically for one domain only. Second, the dialogue agent must also process various types of information across domains, including dialogue context, dialogue states, and database, to generate natural responses to users. Unlike the existing approaches that are often designed to train each module separately, we propose “UniConv" …

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Extending The Functional Subnetwork Approach To A Generalized Linear Integrate-And-Fire Neuron Model, Nicholas Szczecinski, Roger Quinn, Alexander J. Hunt Nov 2020

Extending The Functional Subnetwork Approach To A Generalized Linear Integrate-And-Fire Neuron Model, Nicholas Szczecinski, Roger Quinn, Alexander J. Hunt

Mechanical and Materials Engineering Faculty Publications and Presentations

Engineering neural networks to perform specific tasks often represents a monumental challenge in determining network architecture and parameter values. In this work, we extend our previously-developed method for tuning networks of non-spiking neurons, the “Functional subnetwork approach” (FSA), to the tuning of networks composed of spiking neurons. This extension enables the direct assembly and tuning of networks of spiking neurons and synapses based on the network’s intended function, without the use of global optimization ormachine learning. To extend the FSA, we show that the dynamics of a generalized linear integrate and fire (GLIF) neuronmodel have fundamental similarities to those of …

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Law Library Blog (November 2020): Legal Beagle's Blog Archive, Roger Williams University School Of Law Nov 2020

Law Library Blog (November 2020): Legal Beagle's Blog Archive, Roger Williams University School Of Law

Law Library Newsletters/Blog

No abstract provided.

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Communicating Uncertain Information From Deep Learning Models In Human Machine Teams, Harishankar V. Subramanian, Casey I. Canfield, Daniel Burton Shank, Luke Andrews, Cihan H. Dagli Oct 2020

Communicating Uncertain Information From Deep Learning Models In Human Machine Teams, Harishankar V. Subramanian, Casey I. Canfield, Daniel Burton Shank, Luke Andrews, Cihan H. Dagli

Engineering Management and Systems Engineering Faculty Research & Creative Works

The role of human-machine teams in society is increasing, as big data and computing power explode. One popular approach to AI is deep learning, which is useful for classification, feature identification, and predictive modeling. However, deep learning models often suffer from inadequate transparency and poor explainability. One aspect of human systems integration is the design of interfaces that support human decision-making. AI models have multiple types of uncertainty embedded, which may be difficult for users to understand. Humans that use these tools need to understand how much they should trust the AI. This study evaluates one simple approach for communicating …

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Finite-Time State Estimation For An Inverted Pendulum Under Input-Multiplicative Uncertainty, Sergey V. Drakunov, William Mackunis, Anu Kossery Jayaprakash, Krishna Bhavithavya Kidambi, Mahmut Reyhanoglu Oct 2020

Finite-Time State Estimation For An Inverted Pendulum Under Input-Multiplicative Uncertainty, Sergey V. Drakunov, William Mackunis, Anu Kossery Jayaprakash, Krishna Bhavithavya Kidambi, Mahmut Reyhanoglu

Publications

A sliding mode observer is presented, which is rigorously proven to achieve finite-time state estimation of a dual-parallel underactuated (i.e., single-input multi-output) cart inverted pendulum system in the presence of parametric uncertainty. A salient feature of the proposed sliding mode observer design is that a rigorous analysis is provided, which proves finite-time estimation of the complete system state in the presence of input-multiplicative parametric uncertainty. The performance of the proposed observer design is demonstrated through numerical case studies using both sliding mode control (SMC)- and linear quadratic regulator (LQR)-based closed-loop control systems. The main contribution presented here is the rigorous …

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Espade: An Efficient And Semantically Secure Shortest Path Discovery For Outsourced Location-Based Services, Bharath K. Samanthula, Divyadharshini Karthikeyan, Boxiang Dong, K. Anitha Kumari Oct 2020

Espade: An Efficient And Semantically Secure Shortest Path Discovery For Outsourced Location-Based Services, Bharath K. Samanthula, Divyadharshini Karthikeyan, Boxiang Dong, K. Anitha Kumari

Department of Computer Science Faculty Scholarship and Creative Works

With the rapid growth of smart devices and technological advancements in tracking geospatial data, the demand for Location-Based Services (LBS) is facing a constant rise in several domains, including military, healthcare and transportation. It is a natural step to migrate LBS to a cloud environment to achieve on-demand scalability and increased resiliency. Nonetheless, outsourcing sensitive location data to a third-party cloud provider raises a host of privacy concerns as the data owners have reduced visibility and control over the outsourced data. In this paper, we consider outsourced LBS where users want to retrieve map directions without disclosing their location information. …

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Research On Optimal Configuration Design Method Of Stewart Platform, Xuwei Fan, Lili Yang, Cheng Yu, Xiaoning Zhou, Yexin Zhang Oct 2020

Research On Optimal Configuration Design Method Of Stewart Platform, Xuwei Fan, Lili Yang, Cheng Yu, Xiaoning Zhou, Yexin Zhang

Journal of System Simulation

Abstract: Based on the structural singularity and configuration singularity of parallel mechanism, a safety mechanism design scheme of the Stewart platform is proposed to improve the workspace efficiency and engineering practicability. Taking the Stewart platform without any particularity as the research object, and considering the singular constraints of the structure, a dexterity index is proposed to achieve the optimization of the structural parameters. Analyzing and constructing the kinematics model of the Stewart platform, analyzing the singularities of the configuration bifurcations of 16 typical extreme poses, a secure workspace verification algorithm is proposed to make the whole workspace free of singularity. …

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Research On Adaptive Routing Algorithm For Wireless Weak-Connection Network, Hua Xiang, Hongjuan Yao, Wang Hai, Wang Zhao, Jietao Zhang, Lili Shu Oct 2020

Research On Adaptive Routing Algorithm For Wireless Weak-Connection Network, Hua Xiang, Hongjuan Yao, Wang Hai, Wang Zhao, Jietao Zhang, Lili Shu

Journal of System Simulation

Abstract: The wireless weak-connected network has the characteristics of long delay, high dynamic topology, and unstable links. With the lack of continuity from the source end to the destination end of network connection, in order to solve the problem of communication difficulty, the intelligence and adaptability of Physarum polycephalum are introduced, and the adaptive wireless weak-connected network routing algorithm is proposed. A wireless weak-connected network model is build and the mathematical relationships of link capacity is deduced. The next-hop selection strategy and optimal routing strategy is designed to achieve the best-effort delivery of data in wireless weak-connected network environmrnt. Simulation …

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Visual Feedback Fuzzy Control For A Robot Manipulator Based On Svr Learning, Xianxia Zhang, Jinqiang Zhang, Zhiyuan Li, Shiwei Ma, Banghua Yang Oct 2020

Visual Feedback Fuzzy Control For A Robot Manipulator Based On Svr Learning, Xianxia Zhang, Jinqiang Zhang, Zhiyuan Li, Shiwei Ma, Banghua Yang

Journal of System Simulation

Abstract: A fuzzy controller based on SVR learning is proposed for uncalibrated robot visual servoing. In this paper, a fuzzy controller is used to directly construct the nonlinear mapping between image features and robot joint motion. The fuzzy basis function of the fuzzy controller is taken as the kernel function of an SVR and the equivalent relationship between the SVR and the fuzzy controller is established. The learned support vector from the SVR is used as the rule of the fuzzy controller. Since all rules are learned from the data, there is no need to manually design the rules. …

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Gru-Based Car-Following Behavior Simulation Model, Fei Rong, Liu Fang, Xie Guo, Hei Xinhong, Shasha Li, Hu Bo Oct 2020

Gru-Based Car-Following Behavior Simulation Model, Fei Rong, Liu Fang, Xie Guo, Hei Xinhong, Shasha Li, Hu Bo

Journal of System Simulation

Abstract: The accuracy of acceleration prediction can be effectively improved by the driver's memory in car-following behavior. A new car-following model based on the General Motors (GM) and the gate control unit (GRU) is proposed. The car-following data between small vehicles with similar driving behavior are obtained by data preprocessing. The established model is calibrated by the car-following data, and the optimal parameters and structure of the model are determined. According to car-following characteristics, the effectiveness of model is verified by simulation. It is confirmed that the model has high robustness and improved simulation accuracy comparing with the traditional models.

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Estimation Of Space-Time Of Urban Building Population Based On Mobile Phone Big Data, Hu Yang, Xiaoyong Zhang, Xiao Di Oct 2020

Estimation Of Space-Time Of Urban Building Population Based On Mobile Phone Big Data, Hu Yang, Xiaoyong Zhang, Xiao Di

Journal of System Simulation

Abstract: With the acceleration of the urbanization process, mastering the population distribution on a fine scale is of great significance for urban disaster assessment, emergency response management and public resource allocation. The rapid development of the Internet and the popularity of smartphones have prompted mobile phones to become the sensors of human activity. A method of urban building population estimation based on mobile phone big data is proposed. The method analyzes the crowd activity law of buildings with different functions based on mobile phone positioning big data in typical areas, calculates the population capacity of different functional buildings, and …

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Simulation Research On Optimization Of Agv Charging Strategy For Automated Terminal, Danlan Xie, Guo Di, Ji Yuan, Zhipeng Gao Oct 2020

Simulation Research On Optimization Of Agv Charging Strategy For Automated Terminal, Danlan Xie, Guo Di, Ji Yuan, Zhipeng Gao

Journal of System Simulation

Abstract: As the automated container terminal is the development trend of terminal, AGV (automated guided vehicle) becomes the most widely used horizontal transportation tool and it is important to make its reasonable charging strategy. Aiming at the shortcomings of the current AGV, a charging strategy of offline charging being primary and online charging being auxiliary is proposed. In order to solve the problem of location selection of online charging station, a quick method of selecting effective stations by using heat zone map is proposed. Through a large number of simulation experiments using this strategy, the fact that the number …

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