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Accurate Replication of Simulations of Governing Equations of Processes in Industry 4.0 Environments with ANNs for Enhanced Monitoring and control

Accurate Replication of Simulations of Governing Equations o...

引用

ieee symposium Series on computational intelligence (SSCI)

作者： Sahil, Kommalapati Bhattacharya, Arya K. Mahindra Ecole Cent Sch Engn Sci Hyderabad India

ISBN: (纸本)9781728124858

Complex governing equations of physical phenomena like the Navier-Stokes' or Maxwell's equations can be numerically solved to yield detailed information on the characteristic variables of a process in the process domain interior, when the values at the boundary are known. This cannot be achieved in real time making it unamenable to achieve true benefits under Industry 4.0 where measured variables are available instantaneously at process boundaries but information in the domain interior is unobtainable for monitoring, control and optimization functions. The Universal Approximation Theorem provides a unique capability to Artificial Neural Networks - the ability to replicate the functionality of arbitrarily complex functions - including those represented by the above governing equations. A trained ANN can in principle replicate this functionality with high accuracy in milliseconds - hence can serve as the method of choice in Industry 4.0 frameworks to acquire characteristic process variables within the domain interior when boundary values are known from sensory inputs. This is however a concept still to be proven. This work intends to demonstrate this principle through numerical experimentation on a physical example that can be easily generalized.

关键词： governing sets of partial differential equations numerical simulations boundary conditions artificial neural networks industry 4.0 real time process characterization

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Distributed Progressive Formation control with One-Way Communication for Multi-Agent Systems

Distributed Progressive Formation Control with One-Way Commu...

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ieee symposium Series on computational intelligence (SSCI)

作者： Queralta, J. Pena Gia, T. N. Tenhunen, H. Westerlund, T. Qingqing, L. Zou, Z. Univ Turku Dept Future Technol Turku Finland Fudan Univ Sch Informat Sci & Technol Shanghai Peoples R China

ISBN: (纸本)9781728124858

The cooperation of multiple robots towards a common goal requires a certain spatial distribution, or formation configuration, of the agents in order to succeed. Centralized controllers that have information about the absolute or relative positions of all agents, or distributed approaches using communication to share system-wide information between agents, are able to calculate optimal individual paths. However, this reserves important computational resources as the number of agents in the system increases. We address the problem of distributed formation control with minimal communication and minimal computational power required. The algorithm introduced in this paper progressively generates a directed path graph to uniquely assign formation positions to all agents. The benefits of the proposed algorithm compared to previous include the need for one-way communication only, low computational complexity, ability to converge without any a priori assignments under certain geometric conditions and need for limited sensing information only. The algorithm can be deployed to computationally constrained devices, enabling its deployment in robots with simpler hardware architectures. The ability to converge and distributively assign positions, or roles, with only one-way communication makes this algorithm robust during deployment, at which time all agents are equivalent and anonymous. Moreover, we account for limited communication and sensing range, and agents only need to have information about other agents in their vicinity in order to make decisions. Communication is simple and allows for scalability without an impact on performance or convergence latency, with a linear dependence on the number of agents. Agents only need to broadcast their status to other neighboring agents and do not reply any message. Finally, this algorithm enables almost-arbitrary configurations. The main limitation for the choice of formation configuration is that all pairs of points forming an edge in

关键词： multi-agent systems formation control distributed control progressive formation control distributed role assignment multi-agent cooperation

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Device Placement Optimization for Deep Neural Networks via One-shot Model and Reinforcement Learning

Device Placement Optimization for Deep Neural Networks via O...

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ieee symposium Series on computational intelligence (SSCI)

作者： Zixiang Ding Yaran Chen Nannan Li Dongbin Zhao State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9781728125473

ISBN: (纸本)9781728125480

With the development of deep learning that employs deep neural networks (DNN) as powerful tool, its computational requirement grows rapid together with the increasing size (e.g. depth and parameter) of DNN. Currently, model and data parallelism are employed for accelerating the training and inference process of DNN. However, the above techniques make placement decision on devices for DNN based on heuristics and intuitions by machine learning experts. In this paper, we propose an novel approach for designing device placement of DNN in an automatic way. For a DNN, we employ a sequence-to-sequence model as controller to sample device placement from a one-shot model, which contains all possible device placements with respect to a specific hardware environment (e.g. CPU and GPU). Then, reinforcement learning treats the execution time of sampled device placement as reward to guide the sequence-to-sequence model for finding better one. The proposed approach is employed to optimize the device placement for both model and data parallelism of Inception-V3 on ImageNet. Experimental results show that the optimal placements discovered by our method can outperform hand-crafted one.

关键词： Parallel processing Data models computational modeling Optimization Reinforcement learning Hardware Training

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Event-triggering control for Time-varying Formation Tracking of Multi-agent Systems Subjected to Input Saturation

Event-triggering Control for Time-varying Formation Tracking...

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ieee symposium Series on computational intelligence (SSCI)

作者： Liu, Jing Fang, Jian-an Miao, Qingying He, Guang Wang, Lin Donghua Univ Coll Informat Sci & Technol Shanghai 201620 Peoples R China Shanghai Jiao Tong Univ Sch Continuing Educ Shanghai 200240 Peoples R China Anhui Polytech Univ Dept Math Wuhu 241000 Anhui Peoples R China Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai 200240 Peoples R China

ISBN: (纸本)9781728124858

This paper studies event-triggering formation tracking problem of linear multi-agent system (LMAS) with input saturation. First, time-varying formation tracking control protocol are designed based on event-triggering control schemes. Then, by utilizing the low-gain feedback technique, an algorithm is designed to solve the event-triggering formation tracking problem. It is shown that the proposed control strategy guarantees LMAS to accomplishes the specified time-varying formation tracking, and Zeno behaviors is excluded. Finally, simulation example including four follower agents and one leader confirm the effectiveness of the theoretical results.

关键词： time-varying formation tracking event-triggering in-put saturation linear multi-agent system (LMAS)

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State Parameter Prediction of Fire Based on Extreme Learning Machine

State Parameter Prediction of Fire Based on Extreme Learning...

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ieee symposium Series on computational intelligence (SSCI)

作者： Jiang, Chun Fan, Qinqin Shanghai Maritime Univ Logist Res Ctr Shanghai Peoples R China Shanghai Jiao Tong Univ Minist Educ China Key Lab Syst Control & Informat Proc Shanghai Peoples R China

ISBN: (纸本)9781728124858

Fire spread process can be simulated through Fire Dynamics Simulation (FDS) accurately. However, it is hard to assist fire rescue scheduling and real-time guidance of crowd evacuation due to its long computational time. To alleviate this problem, a fire surrogate model using extreme learning machine (ELM) is proposed to predict key state parameters of fire in the current study. The data obtained by FDS are used to train and test the fire surrogate model. The results indicate that the fire surrogate model has good prediction performance and short calculation time. Therefore, the proposed fire surrogate model can be used to help fire rescue and crowd evacuation. Finally, the number of hidden neurons and active function in ELM are analyzed.

关键词： Fire model surrogate model Extreme Learning Machine fire parameters prediction

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The Study and Design of control System of Distribution Network Operation Robot 12

The Study and Design of Control System of Distribution Netwo...

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12th International symposium on computational intelligence and Design (ISCID)

作者： Yan, Yu Zhang, Shenyi Fan, Shaosheng Liu, Youwei State Grid Hunan Elect Power Co Ltd Maintenance Co Changsha Peoples R China Changsha Univ Sci & Technol Coll Elect & Informat Engn Changsha Peoples R China

ISBN: (纸本)9781728146522

Aiming at the high risk degree of manual lapping or replacement of distribution network drainage lines, this paper designs a control system of distribution network operation robot based on STM32 and *** robot adopts the "master-slave" topology that The master controller (STM32F407IGT6) sends commands to the mechanical arm controller (TMS320F28335) through the serial port, and then the mechanical arm controller controls the corresponding motor motion through CAN communication, so as to realize the operation of the mechanical arm. At the same time, the upper computer monitoring interface is written with C# language, which can monitor the motion state of the robot in real time, and modify the motor joint parameters and storage of the robot in real time. The test shows that the robot controlled by this system can realize the task of distribution network better.

关键词： distribution network STM32 TMS320 master-slave mechanical arm

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Design of digital control High-voltage DC Power Supply 12

Design of digital Control High-voltage DC Power Supply

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12th International symposium on computational intelligence and Design (ISCID)

作者： Li, Hui Tang, Jie Lin, Li Shaoyang Univ Hunan Prov Key Lab Grids Operat & Control Multipo Shaoyang 422000 Peoples R China

ISBN: (纸本)9781728146522

In order to detect electro-osmosis flow conveniently, a digital small-power high-voltage DC (HDC) power supply based on DSP is introduced which is fully controllable and fast response. The structure of the main circuit is Buck converter which is controlled by the voltage PWM control technology. A voltage feedback closed loop is used in the circuit, in which the digital optical fiber is used for isolation and transmission. Addition to data collection, the DSP controller calculates the duty cycle, which is based on the feedback signal, so that the output voltage can be real-timely traced and monitored. Consequently, the principle of whole system including control circuit and main circuit is described. Finally, the feasibility of the proposed approach is verified on a simulink model with 220V input and 4000V/1mA output. The results show that the HDC system meets the need of automatically detect electro-osmosis experiment and gives full play to the convenience of digital power and control.

关键词： Voltage Pulse Width Modulation (PWM) digital PID regulator TMS320F2812 Matlab/simulink

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A Zoning Search Strategy for Differential Evolution Variants

A Zoning Search Strategy for Differential Evolution Variants

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ieee symposium Series on computational intelligence (SSCI)

作者： Fan, Qinqin Cao, Bin Shanghai Maritime Univ Logist Res Ctr Shanghai Peoples R China Shanghai Jiao Tong Univ Key Lab Syst Control & Informat Proc Minist Educ China Shanghai Peoples R China Hebei Univ Technol Sch Artificial Intelligence Tianjin Peoples R China

ISBN: (纸本)9781728124858

computational time and solution precision arc two major concerns in the evolutionary computation (EC). To save the computational time, various high-performance computing techniques have been successfully employed to help meta-heuristic algorithms. Moreover, most of previous studies have focused on the performance improvement of meta-heuristic algorithms for improving the solution precision. However, the use of highperformance computing techniques and the improvement of algorithm performance do not mean that meta-heuristic algorithms can locate a promising region in some cases due to their search capability limitation. Therefore, converting improved computation power indirectly into algorithm search capability is a vital task. To achieve the above goal, a zoning search (ZS) strategy is proposed in the current work. In ZS, some variables of an optimization problem are selected randomly, and then they are divided into some segments. i.e., the entire search space of the optimization problem is divided into some subspaces. Finally, a selected meta-heuristic algorithm is used to find a satisfactory solution in each subspace. The effectiveness of ZS is demonstrated on ieee CEC2014. Results suggest that ZS is a highly competitive approach to solve complex optimization problems.

关键词： differential evolution evolutionary computation high-performance computing paranseter estimation

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Modeling of mixed data for Poisson prediction

Modeling of mixed data for Poisson prediction

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International symposium on Applied computational intelligence and Informatics ( SACI)

作者： Matej Petrouš Evženie Uglickich Faculty of Transportation Sciences Czech Technical University Prague Department of Signal Processing Institute of Information Theory and Automation Prague Czech Republic

ISBN: (数字)9781728173771

ISBN: (纸本)9781728173788

The paper deals with the task of modeling mixed continuous Gaussian and discrete Poisson data observed on a multimodal system. The proposed solution is based on recursive algorithms of Bayesian mixture estimation. The main contributions of the approach are: (i) the use of the discretized information of normal variables in the form of their clusters in order to keep the one-pass recursive estimation methodology and (ii) the prediction of the multimodal Poisson variable. Experiments with simulated and real data are presented.

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Inferring Human Brain Structural Connectivity Based on Neural Networks

Inferring Human Brain Structural Connectivity Based on Neura...

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ieee symposium Series on computational intelligence (SSCI)

作者： Yuan, Yue Wang, Yanjiang Chen, Xue Wei, Fu China Univ Petr East China Coll Control Sci & Engn Qingdao Peoples R China

ISBN: (纸本)9781728124858

A central and fundamental issue in cognitive neuroscience is to comprehend the relationship between human brain functional and structural connectivity. Previous studies normally focus on the relationship by predicting functional connectivity from structural connectivity and show there is a cohesive correlation between the two types of networks. In this paper, we investigate the relation by revealing the true anatomical connections from the functional correlations using multi-layer neural networks, which is trained to learn the intrinsic mapping mechanism and recover some missed connections with diffusion magnetic resonance imaging (dMRI) tractography, particularly the cross-hemispheric homotopic connections. We execute the method to a dataset with 246 brain areas acquired from 147 subjects. The results show that around 65% of the average intrahemispheric structural connections are correctly inferred.

关键词： brain connectivity functional MRI (fMRI) dMRI Neural Networks

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