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Welding procedure expert system based on intelligent inference

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Shanghai Jiaotong Daxue Xuebao/Journal of Shanghai Jiaotong University 2014年 48卷 101-104页

作者： Wang, Xue-Wu Li, Yi-Xuan Yang, Shang-Yong Ding, Dong-Yan Gu, Xing-Sheng Key Laboratory of Advanced Control and Optimation for Chemical Processes of Ministry of Education East China University of Science and Technology Shanghai200237 China

Intelligent welding procedure expert system was developed to improve welding procedure selection for welding process in this paper. The expert system was designed based on Client/Server (C/S) mode, and the main functions include system management, knowledge base management, intelligent inference, remote access, etc. BP model and DEPSO based BP model were established to realize parameter estimation. Reasonable parameters can be derived based on the intelligent inference model and input welding condition. Obtained welding procedure can be presented as welding procedure card and multimedia display of welding process. Besides it, the output function makes the data share more convenient. The system is beneficial to welding parameters selection and welding engineer training. ©, 2014, Shanghai Jiao Tong University. All right reserved.

关键词： Neural networks

Sliding Mode control for Interval Type-2 Fuzzy System Under Fading Channels

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Sliding Mode Control for Interval Type-2 Fuzzy System Under ...

International Conference on control and Automation (ICCA)

作者： Yekai Yang Yugang Niu Bei Chen Yuanyuan Zou Key Laboratory of Advanced Control and Optimization for Chemical Process (East China University of Science and Technology) Ministry of Education Shanghai China School of Electric and Electronic Engineering Shanghai University of Engineering Science Shanghai China Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9781728190938

ISBN: (纸本)9781728190945

This paper addresses the problem of sliding mode control for discrete-time interval type-2 fuzzy system subject to fading channels. The multiplicative fading model is established for reflecting the fluctuation of the transmitted signals. Meanwhile, the membership function-dependent sliding function involves the fading coefficient. By using the membership functions depending on the fading signals, the mismatched interval type-2 fuzzy sliding mode controller is designed such that the state trajectories can be driven into the neighborhood of the specified sliding surface. Moreover, the closed-loop fuzzy system is ensured to be mean-square asymptotically stable. Finally, the proposed control scheme is verified by a numerical example.

关键词： Fading channels Fuzzy systems Stability analysis Random variables Numerical models Hidden Markov models Closed loop systems

Sliding mode control for T-S fuzzy stochastic systems with Markovian switching

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Sliding mode control for T-S fuzzy stochastic systems with M...

第三十三届中国控制会议

作者： Bei Chen Yugang Niu Tinggang Jia Central Academe Shanghai Electric Group Co.Ltd. Key Laboratory of Advanced Control and Optimization for Chemical Process(East China University of Science and Technology) Ministry of Education Automation Division Shanghai Electric Group Co.Ltd.

This paper is concerned with the sliding mode control(SMC)problem for a class of T-S fuzzy stochastic systems with Markovian *** purpose of the problem addressed is to integrate the SMC method such that,for all admissible parameter uncertainty,external disturbance and state-dependent noise,the closed-loop system is globally asymptotically ***,it is not required that each nominal local system model shares the same input channel,which is usually assumed in some existing *** is shown that both the reachability of sliding surfaces and the stability of sliding mode dynamics can be ensured.

关键词： Markovian jumping systems,State-dependent noise,T-S fuzzy model,Sliding mode control

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Quantum Go Machine

arXiv 2020年

作者： Qiao, Lu-Feng Gao, Jun Jiao, Zhi-Qiang Zhang, Zhe-Yong Cao, Zhu Ren, Ruo-Jing Zhang, Chao-Ni Hu, Cheng-Qiu Xu, Xiao-Yun Tang, Hao Ma, Zhi-Hao Jin, Xian-Min School of Physics and Astronomy State Key Laboratory of Advanced Optical Communication Systems and Networks Shanghai Jiao Tong University Shanghai200240 China CAS Center for Excellence Synergetic Innovation Center in Quantum Information and Quantum Physics University of Science and Technology of China Hefei Anhui230026 China Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education East China University of Science and Technology Shanghai200237 China School of Mathematical Sciences Shanghai Jiao Tong University Shanghai200240 China

Go has long been considered as a testbed for artificial intelligence. By introducing certain quantum features, such as superposition and collapse of wavefunction, we experimentally demonstrate a quantum version of Go by using correlated photon pairs entangled in polarization degree of freedom. The total dimension of Hilbert space of the generated states grows exponentially as two players take turns to place the stones in time series. As nondeterministic and imperfect information games are more difficult to solve using nowadays technology, we excitedly find that the inherent randomness in quantum physics can bring the game nondeterministic trait, which does not exist in the classical counterpart. Some quantum resources, like coherence or entanglement, can also be encoded to represent the state of quantum stones. Adjusting the quantum resource may vary the average imperfect information (as comparison classical Go is a perfect information game) of a single game. We further verify its nondeterministic feature by showing the unpredictability of the time series data obtained from different classes of quantum state. Finally, by comparing quantum Go with a few typical games that are widely studied in artificial intelligence, we find that quantum Go can cover a wide range of game difficulties rather than a single point. Our results establish a paradigm of inventing new games with quantum-enabled difficulties by harnessing inherent quantum features and resources, and provide a versatile platform for the test of new algorithms to both classical and quantum machine learning. Copyright © 2020, The Authors. All rights reserved.

关键词： Degrees of freedom (mechanics)

An online performance monitoring using statistics pattern based kernel independent component analysis for non-Gaussian process

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An online performance monitoring using statistics pattern ba...

Annual Conference of Industrial Electronics Society

作者： Xin Peng Ying Tian Yang Tang Wenli Du Weimin Zhong Feng Qian Key Laboratory of Advanced Control and Optimization for Chemical Process Ministry of Education East China University of Science and Technology Shanghai P. R. China Department of Electrical Engineering University of Shanghai for Science and Technology Shanghai P. R. China

An online monitoring method, which aims to deal with the high order non-Gaussian characteristics in chemical process, is proposed in this paper. In the framework of the proposed method, kernel based independent component analysis is utilized to identify the operation status of the chemical process and statistics pattern analysis is employed to combine with independent component analysis to extract high order information from the process so as to improve the monitoring performance. The modified statistics pattern analysis introduces the Mahalanobis distance into statistics pattern to analyze the inner structure relationship between the samples. Then, the validity and effectiveness of our proposed method is illustrated by applying to a representative non-Gaussian process, Continuous Stirred Tank Reactor (CSTR). The results show that the proposed method has its advantages when compared to other conventional Eigen-decomposition monitoring algorithms.

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Continuous use of ERP-based BCIs with different visual angles in ALS patients

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arXiv 2017年

作者： Jin, Jing Allison, Brendan Z. Zhang, Yu Chen, Yan Zhou, Sijie Dong, Yi Wang, Xingyu Chchocki, Andrzej Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology Shanghai China Department of Neurology Huashan hospital Fudan University Shanghai China Department of Cognitive Science University of California at San Diego San DiegoCA United States Laboratory for Advanced Brain Signal Processing Brain Science Institute RIKEN Wako-shi Japan Systems Research Institute PAS Warsaw Poland Torun Poland

Objective: Amyotrophic lateral sclerosis (ALS) is a rare disease, but is also one of the most common motor neuron diseases, and people of all races and ethnic backgrounds are affected. There is currently no cure. Brain computer interfaces (BCIs) can establish a communication channel directly between the brain and an external device by recognizing brain activities that reflect user intent. Therefore, this technology could help ALS patients in promoting functional independence through BCI-based speller systems and motor assistive devices. Methods: In this paper, two kinds of ERP-based speller systems were tested on 18 ALS patients to: (1) assess performance when they spelled 42 characters online continuously, without a break;and (2) to compare performance between a matrix-based speller paradigm (MS-P, mean visual angle 6◦) and a new speller paradigm that used a larger visual angle called the large visual angle speller paradigm (LS-P, mean visual angle 8◦). Results: Although results showed that there were no significant differences between the two paradigms in accuracy trend over continuous use (p>0.05), the fatigue during the LS-P condition was significantly lower than that of MS-P (p Copyright © 2017, The Authors. All rights reserved.

关键词： Brain computer interface

Multi-task GANs for semantic segmentation and depth completion with cycle consistency

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arXiv 2020年

作者： Zhang, Chongzhen Tang, Yang Zhao, Chaoqiang Sun, Qiyu Ye, Zhencheng Kurths, Jürgen Key Laboratory of Advanced Control and Optimization for Chemical Process Ministry of Education East China University of Science and Technology Shanghai200237 China Potsdam Institute for Climate Impact Research Potsdam14473 Germany Institute of Physics Humboldt University of Berlin Berlin12489 Germany

Semantic segmentation and depth completion are two challenging tasks in scene understanding, and they are widely used in robotics and autonomous driving. Although several studies have been proposed to jointly train these two tasks using some small modifications, like changing the last layer, the result of one task is not utilized to improve the performance of the other one despite that there are some similarities between these two tasks. In this paper, we propose multi-task generative adversarial networks (Multi-task GANs), which are not only competent in semantic segmentation and depth completion, but also improve the accuracy of depth completion through generated semantic images. In addition, we improve the details of generated semantic images based on CycleGAN by introducing multi-scale spatial pooling blocks and the structural similarity reconstruction loss. Furthermore, considering the inner consistency between semantic and geometric structures, we develop a semantic-guided smoothness loss to improve depth completion results. Extensive experiments on Cityscapes dataset and KITTI depth completion benchmark show that the Multi-task GANs are capable of achieving competitive performance for both semantic segmentation and depth completion tasks. © 2020, CC BY.

关键词： Semantic Segmentation

Tiny Adversarial Multi-Objective Oneshot Neural Architecture Search

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arXiv 2021年

作者： Xie, Guoyang Wang, Jinbao Yu, Guo Zheng, Feng Jin, Yaochu Department of Computer Science University of Surrey Guildford SurreyGU2 7XH United Kingdom Department of Computer Science Southern University of Science and Technology Shenzhen518055 China Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology Shanghai 200237 China Faculty of Technology Bielefeld University Bielefleld D-33615 Germany

Due to limited computational cost and energy consumption, most neural network models deployed in mobile devices are tiny. However, tiny neural networks are commonly very vulnerable to attacks. Current research has proved that larger model size can improve robustness, but little research focuses on how to enhance the robustness of tiny neural networks. Our work focuses on how to improve the robustness of tiny neural networks without seriously deteriorating of clean accuracy under mobile-level resources. To this end, we propose a multiobjective oneshot network architecture search (NAS) algorithm to obtain the best trade-off networks in terms of the adversarial accuracy, the clean accuracy and the model size. Specifically, we design a novel search space based on new tiny blocks and channels to balance model size and adversarial performance. Moreover, since the supernet significantly affects the performance of subnets in our NAS algorithm, we reveal the insights into how the supernet helps to obtain the best subnet under white-box adversarial attacks. Concretely, we explore a new adversarial training paradigm by analyzing the adversarial transferability and the difference between training the subnets from scratch and fine-tuning. Finally, we make a statistical analysis for the layer-wise combination of certain blocks and channels on the first non-dominated front, which can serve as a guideline to design tiny neural network architectures for the resilience of adversarial perturbations. Copyright © 2021, The Authors. All rights reserved.

关键词： Multiobjective optimization

Deep adaptive fuzzy clustering for evolutionary unsupervised representation learning

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arXiv 2021年

作者： Tan, Dayu Huang, Zheng Peng, Xin Zhong, Weimin Mahalec, Vladimir Key Laboratory of Advanced Control and Optimization for Chemical Processes Ministry of Education East China University of Science and Technology Shanghai200237 China School of Engineering Practice and Technology McMaster University HamiltonONL8S 4L7 Canada Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai200092 China Department of Chemical Engineering McMaster University HamiltonONL8S 4L7 Canada

Cluster assignment of large and complex images is a crucial but challenging task in pattern recognition and computer vision. In this study, we explore the possibility of employing fuzzy clustering in a deep neural network framework. Thus, we present a novel evolutionary unsupervised learning representation model with iterative optimization. It implements the deep adaptive fuzzy clustering (DAFC) strategy that learns a convolutional neural network classifier from given only unlabeled data samples. DAFC consists of a deep feature quality-verifying model and a fuzzy clustering model, where deep feature representation learning loss function and embedded fuzzy clustering with the weighted adaptive entropy is implemented. We joint fuzzy clustering to the deep reconstruction model, in which fuzzy membership is utilized to represent a clear structure of deep cluster assignments and jointly optimize for the deep representation learning and clustering. Also, the joint model evaluates current clustering performance by inspecting whether the re-sampled data from estimated bottleneck space have consistent clustering properties to progressively improve the deep clustering model. Comprehensive experiments on a variety of datasets show that the proposed method obtains a substantially better performance for both reconstruction and clustering quality when compared to the other state-of-the-art deep clustering methods, as demonstrated with the in-depth analysis in the extensive experiments. Copyright © 2021, The Authors. All rights reserved.

关键词： Fuzzy clustering