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2022 International Conference on Algorithms, Microchips and Network Applications

作者： Wang, Ying Wang, Zehao Li, Hong Liu, Peipei Li, Yachao Zuo, Fang Institute of Intelligence Networks System Henan University Henan475001 China Henan Experimental Teaching Demonstration Centre of Modern Network Technology Henan University Henan475001 China Intelligent Data Processing Engineering Research Center of Henan Province Henan University Henan475001 China Institute of Information Engineering Chinese Academy of Sciences Beijing China Henan International Joint Laboratory of Theories and Key Technologies on Intelligence Networks Henan University Henan475001 China

ISBN: (纸本)9781510653290

In order to facilitate government departments to assess security risks and prevent infiltration, it's necessary to recognize the IoT device from open data by the method of Named entity recognition (NER). In this study, a hybrid method of chinese NER which contains neural networks and templates is proposed to extract IoT device information from open web. The model first initiates characters of input through BERT, which can create character embedding while reduce dependency with external datasets. Then, the initiated vectors are fed into a multi-layers BiLSTM model to encode the contextual representation of each character, and a linear CRF layer after BiLSTM is used to assign the scores to every character for entity annotation. In addition, a dictionary and rule base is constructed based on the characteristics of IoT devices to correct the annotation results. We experimented the methods on the dataset of IoT, the results prove the proposed model achieves better recognition effect than other models, and the F-scores value is 88.9%. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Internet of things

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Event-Driven Optimal Regulation with SNAC Framework for Constrained Nonlinear Plants

Event-Driven Optimal Regulation with SNAC Framework for Cons...

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第40届中国控制会议

作者： Lingzhi Hu Ding Wang Mingming Zhao Jin Ren Faculty of Information Technology Beijing University of Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System Bejing University of Technology Beijing Institute of Artificial Intelligence Beijing University of Technology

In order to reduce the computational burden effectively and ensure that the system has good control performance, an event-driven near-optimal control algorithm is developed for discrete-time nonlinear systems with input constraints. A single network adaptive critic algorithm is adopted to address the constrained optimal control problem. First, the optimal cost function and the optimal control strategy are obtained theoretically through the iterative algorithm. Then, an appropriate triggering threshold is established, so that the system satisfies the event-driven mechanism. In addition, the action network of classical adaptive dynamic programming is omitted. The derivative of the approximate cost function is the output of the critic network and then is used to obtain the approximate optimal control law. Furthermore, the convergence analysis of the iterative algorithm is proved. Finally, a simulation example is conducted to verify the effectiveness of the proposed scheme.

关键词： Adaptive Dynamic Programming Control Constraints Discrete-Time systems Event-Driven Control Neural Networks Single Network Adaptive Critic

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Parallel Building Process Management system

Parallel Building Process Management System

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2021 China Automation Congress, CAC 2021

作者： Liu, Sheng Shen, Zhen Dong, Xisong Hu, Bin Xiong, Gang Chen, Xiaoyu State Key Laboratory for Management and Control of Complex Institute of Automation Chinese Academy of Sciences Beijing China Beijing engineering Research Center of intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing China Institution of Intelligent Manufacturing Qingdao Academy of Intelligent Industries Qingdao China Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences Dongguan China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781665426473

With the development of industrialized building and intelligence technology, it becomes necessary and possible to realize building process intelligence management. In recent years, digital twins and parallel management get more and more attentions in academic research and industrial applications. This paper addresses how parallel building system performs intelligence management on industrialized building process. Firstly, actual building model and artificial building model are created according to actual building system. Then business driving engine and simulation engine drive actual building model and artificial building model, respectively. Participants receive and send messages by software interfaces. Business driving engine changes the status of actual building model and sends the changes to simulation engine. Simulation engine updates the status of artificial building model and runs artificial building model with various parameters. Then it sends the parameter with the best effect to business driving engine. Business driving engine sends recommendations to participants according to the best parameter. Thus, building process intelligence management is achieved. © 2021 IEEE

关键词： Engines

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Dynamic Multiobjective Optimal Control with Knowledge-Decision for Wastewater Treatment Process 7

Dynamic Multiobjective Optimal Control with Knowledge-Decisi...

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7th International Conference on Information, Cybernetics, and Computational Social systems, ICCSS 2020

作者： Zhang, Linlin Han, Honggui Qiao, Junfei Beijing Artificial Intelligence Institute Beijing Laboratory for Urban Mass Transit Beijing University of Technology Faculty of Information Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System Engineering Research Center of Digital Community Ministry of Education Beijing China

ISBN: (纸本)9781728162461

Optimal control system plays an important role in the stability and safety of wastewater treatment process (WWTP). However, because of the dynamic complex mechanism of WWTP, it is challenging to decide the suitable set-points of manipulated variables for improving optimal control performance in the dynamic complex environment. Therefore, a dynamic multiobjective optimal control with knowledge-decision (DMOC-KD) is proposed in this paper. First, a dynamic multiobjective optimal control scheme is presented to adapt the dynamic complex environment of WWTP. Second, an adaptive multiobjective particle swarm optimization (AMOPSO), based on distributed knowledge, is presented to determine the suitable optimal set-points of WWTP. Third, a fuzzy neural network (FNN) control method is designed to track the obtained optimal set-points for keeping effluent equality and reducing energy consumption. Finally, this DMOC-KD is compared with other optimal control strategies on benchmark simulation model 1 (BSM1). The results show that this DMOC-KD is superior than most compared strategies. © 2020 IEEE.

关键词： Wastewater treatment

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PEPT: Expert Finding Meets Personalized Pre-training

arXiv

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arXiv 2023年

作者： Peng, Qiyao Xu, Hongyan Wang, Yinghui Liu, Hongtao Huo, Cuiying Wang, Wenjun School of New Media and Communication Tianjin University Tianjin China College of Intelligence and Computing Tianjin University Tianjin China Key Laboratory of Information System and Technology Beijing Institute of Control and Electronic Technology Beijing China Du Xiaoman Technology Beijing China Yazhou Bay Innovation Institute Hainan Tropical Ocean University Hainan China

Finding experts is essential in Community Question Answering (CQA) platforms as it enables the effective routing of questions to potential users who can provide relevant answers. The key is to personalized learning expert representations based on their historical answered questions, and accurately matching them with target questions. Recently, the application of Pre-trained Language Models (PLMs) have gained significant attraction due to their impressive capability to comprehend textual data, and are widespread used across various domains. There have been some preliminary works exploring the usability of PLMs in expert finding, such as pre-training expert or question representations. However, these models usually learn pure text representations of experts from histories, disregarding personalized and fine-grained expert modeling. For alleviating this, we present a personalized pre-training and fine-tuning paradigm, which could effectively learn expert interest and expertise simultaneously. Specifically, in our pre-training framework, we integrate historical answered questions of one expert with one target question, and regard it as a candidate aware expert-level input unit. Then, we fuse expert IDs into the pre-training for guiding the model to model personalized expert representations, which can help capture the unique characteristics and expertise of each individual expert. Additionally, in our pre-training task, we design: 1) a question-level masked language model task to learn the relatedness between histories, enabling the modeling of question-level expert interest;2) a vote-oriented task to capture question-level expert expertise by predicting the vote score the expert would receive. Through our pre-training framework and tasks, our approach could holistically learn expert representations including interests and expertise. Our method has been extensively evaluated on six real-world CQA datasets, and the experimental results consistently demonstrate the superio

关键词： Question answering

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CRSOT: Cross-Resolution Object Tracking using Unaligned Frame and Event Cameras

arXiv

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arXiv 2024年

作者： Zhu, Yabin Wang, Xiao Li, Chenglong Jiang, Bo Zhu, Lin Huang, Zhixiang Tian, Yonghong Tang, Jin Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Anhui University China School of Computer Science and Technology Anhui University China School of Artificial Intelligence Anhui University China Beijing Institute of Technology Beijing China School of Electronic and Information Engineering Anhui University China Peng Cheng Laboratory Shenzhen China School of Computer Science Peking University Beijing China School of Electronic and Computer Engineering Peking University Shenzhen China

Existing datasets for RGB-DVS tracking are collected with DVS346 camera and their resolution (346 × 260) is low for practical applications. Actually, only visible cameras are deployed in many practical systems, and the newly designed neuromorphic cameras may have different resolutions. The latest neuromorphic sensors can output high-definition event streams, but it is very difficult to achieve strict alignment between events and frames on both spatial and temporal views. Therefore, how to achieve accurate tracking with unaligned neuromorphic and visible sensors is a valuable but unresearched problem. In this work, we formally propose the task of object tracking using unaligned neuromorphic and visible cameras. We build the first unaligned frame-event dataset CRSOT collected with a specially built data acquisition system, which contains 1,030 high-definition RGB-Event video pairs, 304,974 video frames. In addition, we propose a novel unaligned object tracking framework that can realize robust tracking even using the loosely aligned RGB-Event data. Specifically, we extract the template and search regions of RGB and Event data and feed them into a unified ViT backbone for feature embedding. Then, we propose uncertainty perception modules to encode the RGB and Event features, respectively, then, we propose a modality uncertainty fusion module to aggregate the two modalities. These three branches are jointly optimized in the training phase. Extensive experiments demonstrate that our tracker can collaborate the dual modalities for high-performance track- Copyright © 2024, The Authors. All rights reserved.

关键词： Cameras

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An improved RNN modeling algorithm for flotation process of copper-silver ore

An improved RNN modeling algorithm for flotation process of ...

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Chinese Control and Decision Conference, CCDC

作者： Kang Wang Liwei Pang Xiaoli Li Xin Zhao Faculty of Information Technology Beijing University of Technology Beijing Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing Engineering Research Center of Digital Community of Ministry of Education Beijing China Railway 19th Bureau Group Mining Investment Co. Beijing

ISBN: (纸本)9781665478977

Concentrate grade is an important evaluation index in the flotation process, which reflects the rich degree of specific recovery components in the concentrate. In this paper, the copper and silver ore grades in LinXi Mining Co., Ltd are taken as the research object. Given the difficulty in identifying the flotation process through mechanism modeling, the recurrent neural network model of the flotation process is established by using the idea of data-driven. Considering that the learning parameters are randomly selected, an improved RNN model is proposed by introducing the index function which optimizes the model parameters to obtain better modeling performance. The simulation results show that the improved recurrent neural network model can effectively identify the flotation process compared with the traditional RNN model and ARMAX model. key Words: Flotation process, Recurrent neural network, Concentrate grade, Data driven, Model optimization

关键词： Silver Recurrent neural networks Simulation Ores Process control Production Predictive models

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Wafer Map Defect Pattern Recognition Based on Self-Organizing Incremental Neural Network

Wafer Map Defect Pattern Recognition Based on Self-Organizin...

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Chinese Automation Congress (CAC)

作者： Naigong Yu Qiao Xu Mohammad Mehedi Hasan Kai Jiang Faculty of Information Technology Beijing University of Technology Beijing China Beijing Key Laboratory of Computing Intelligence and Intelligent System Beijing China Green Energy andArchitechture Research Center China Energy Investment Corporation Beijing China

ISBN: (纸本)9781665426480

Wafer map defect pattern (WMDP) recognition is critical to evaluate the semiconductor manufacturing process. Traditional WMDP recognition algorithms, such as deep learning-based methods, only focus on improving the accuracies of the seen patterns in the training set but ignore the recognition and incremental generalization of new emerging patterns. The model can only be retrained by using the overall dataset composed of new and old patterns to gain the ability to recognize the new patterns. To address the above problems, this paper proposes a WMDP recognition method based on self-organizing incremental neural network. The proposed method detects abnormal patterns according to the relationship between input response and adaptive threshold of winner neurons, updates the model only with new pattern samples by dynamically adjusting the number or connection of neurons. Experiments show that the proposed method has the ability to classify known patterns and discover unknown abnormal patterns. Its unique competitive learning mechanism makes it possible to update the model only with new pattern samples without catastrophic forgetting.

关键词： Training Semiconductor device modeling Learning systems Neurons Production Semiconductor device manufacture Hardware

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TOWARDS GENERALIZABLE GRAPH CONTRASTIVE LEARNING: AN INFORMATION THEORY PERSPECTIVE

arXiv

引用

arXiv 2022年

作者： Yuan, Yige Xu, Bingbing Shen, Huawei Cao, Qi Cen, Keting Zheng, Wen Cheng, Xueqi Data Intelligence System Research Center Institute of Computing Technology Chinese Academy of Sciences Beijing China CAS Key Laboratory of Network Data Science and Technology Institute of Com- puting Technology Chinese Academy of Sciences Beijing China

Graph contrastive learning (GCL) emerges as the most representative approach for graph representation learning, which leverages the principle of maximizing mutual information (InfoMax) to learn node representations applied in downstream tasks. To explore better generalization from GCL to downstream tasks, previous methods heuristically define data augmentation or pretext tasks. However, the generalization ability of GCL and its theoretical principle are still less reported. In this paper, we first propose a metric named GCL-GE for GCL generalization ability. Considering the intractability of the metric due to the agnostic downstream task, we theoretically prove a mutual information upper bound for it from an information-theoretic perspective. Guided by the bound, we design a GCL framework named InfoAdv with enhanced generalization ability, which jointly optimizes the generalization metric and InfoMax to strike the right balance between pretext task fitting and the generalization ability on downstream tasks. We empirically validate our theoretical findings on a number of representative benchmarks, and experimental results demonstrate that our model achieves state-of-the-art performance. Copyright © 2022, The Authors. All rights reserved.

关键词： Information theory

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A deep learning system for predicting time to progression of diabetic retinopathy

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NATURE MEDICINE 2024年第2期30卷 358-359页

作者： [Anonymous] Shanghai Belt and Road International Joint Laboratory for Intelligent Prevention and Treatment of Metabolic Disorders Department of Computer Science and Engineering School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Department of Endocrinology and Metabolism Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai Diabetes Institute Shanghai Clinical Center for Diabetes Shanghai China MOE Key Laboratory of AI School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Department of Ophthalmology Huadong Sanatorium Wuxi China Department of Ophthalmology Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai China Department of Ophthalmology and Visual Sciences The Chinese University of Hong Kong Hong Kong China Singapore Eye Research Institute Singapore National Eye Centre Singapore Singapore Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong China Department of Chemical and Biological Engineering The Hong Kong University of Science and Technology Hong Kong China State Key Laboratory of Ophthalmology Zhongshan Ophthalmic Center Sun Yat-sen University Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science Guangzhou China Department of Ophthalmology Peking Union Medical College Hospital Peking Union Medical College Chinese Academy of Medical Sciences Beijing China Medical Records and Statistics Office Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai China Department of Geriatrics Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Tech

We developed and validated a deep learning system (termed DeepDR Plus) in a diverse, multiethnic, multi-country dataset to predict personalized risk and time to progression of diabetic retinopathy. We show that DeepDR Plus can be integrated into the clinical workflow to promote individualized intervention strategies for the management of diabetic retinopathy.

关键词： Diabetes complications Machine learning Predictive markers

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