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42nd Chinese Control Conference, CCC 2023

作者： Yu, Haoyong Sun, Zhe Xu, Yuhua Xu, Lei Gong, Guangfu Xie, Xiangpeng Sun, Zhixin Post Industry Technology Research and Development Center of the State Posts Bureau Nanjing University of Posts and Telecommunications Nanjing210023 China Nanjing University of Posts and Telecommunications Post Big Data Technology and Application Engineering Research Center of Jiangsu Province Nanjing210023 China Nanjing University of Posts and Telecommunications Key Lab of Broadband Wireless Communication and Sensor Network Technology Ministry of Education Nanjing210023 China Anhui Yougu Express Intelligent Technology Co. LTD Anhui Nanling241300 China

ISBN: (纸本)9789887581543

Aiming at the problem that the amplitude of load oscillation is not easy to control in the movement process of overhead crane, this paper designs a switching PID control method based on whale optimization algorithm(WOA) to improve the performance of position control and anti-swing control for overhead crane. WOA is used to better adjust the parameters of PID controllers. The switching control is realized by a switching module, which defaults to the output of the Position PID Controller to provide control force for the trolley. When the absolute value of the load swing angle fed back by the crane system exceeds the warning value, the switching module immediately blocks the output of the Position PID Controller, and uses the Angle PID Controller to determine the control force. In addition, through theoretical analysis and simulation experiments under different conditions, the results verify the effectiveness of the WOA based switching control in position control and anti-swing control for overhead crane. Improve the efficiency and safety of overhead crane in practical industrial applications. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Three term control systems

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A Review of the Evolutionary Algorithm Based VRP Problem 42

A Review of the Evolutionary Algorithm Based VRP Problem

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42nd Chinese Control Conference, CCC 2023

作者： Pan, Lili Hu, Bing Sun, Zhe Xu, Lei Gong, Guangfu Xie, Xiangpeng Sun, Zhixin Post Industry Technology Research and Development Center of the State Posts Bureau Nanjing University of Posts and Telecommunications Nanjing210023 China Nanjing University of Posts and Telecommunications Post Big Data Technology and Application Engineering Research Center of Jiangsu Province Nanjing210023 China Ministry of Education Key Lab of Broadband Wireless Communication and Sensor Network Technology Nanjing University of Posts and Telecommunications Nanjing210023 China Anhui Yougu Express Intelligent Technology Co. LTD Anhui Nanling241300 China

ISBN: (纸本)9789887581543

The problem of vehicle routing planning (VRP) has far-reaching influence, and is widely used in vehicle scheduling, industrial production, transportation, logistics and distribution. The study of VRP and its associated derivations has grown significantly in recent years. So, starting with the fundamental VRP, this paper categorizes the VRP based on its traits and real-world applications. It then in-depth analyses the capacity constrained VRP, time window VRP, batched delivery VRP, dynamic VRP, and stochastic demand VRP, and provides an update on each type of VRP's research. Additionally, a summary of the VRP problem-solving methods is provided. The study of evolutionary algorithms is the main topic of this paper, and the fundamental solution method is provided in accordance with the development of the algorithms. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Vehicle routing

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Learning Structured Twin-Incoherent Twin-Projective Latent Dictionary Pairs for Classification

Learning Structured Twin-Incoherent Twin-Projective Latent D...

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IEEE International Conference on data Mining (ICDM)

作者： Zhao Zhang Yulin Sun Zheng Zhang Yang Wang Guangcan Liu Meng Wang School of Computer Science and Technology Soochow University Suzhou China Key Laboratory of Knowledge Engineering with Big Data (Ministry of Education) Hefei University of Technology School of Computer Science and Information Engineering Hefei University of Technology Hefei China Bio-Computing Research Center Harbin Institute of Technology (Shenzhen) Shenzhen China School of Information and Control Nanjing University of Information Science and Technology Nanjing China

In this paper, we extend the popular dictionary pair learning (DPL) into the scenario of twin-projective latent flexible DPL under a structured twin-incoherence. Technically, a novel framework called Twin-Projective Latent Flexible DPL (TP-DPL) is proposed, which minimizes the twin-incoherence constrained flexibly-relaxed reconstruction error to avoid the possible over-fitting issue and produce accurate reconstruction. In this setting, TP-DPL integrates the twin-incoherence based latent flexible DPL and the joint embedding of codes as well as salient features by twin-projection into a unified model in an adaptive neighborhood-preserving manner. Therefore, TP-DPL can unify the procedures of salient feature representation and classification. The twin-incoherence constraint on coefficients and features can explicitly ensure high intra-class compactness and inter-class separation over them. TP-DPL also integrates the adaptive weighting to preserve local neighborhood of both coefficients and salient features within each class explicitly. For efficiency, TP-DPL selects the Frobenius-norm and abandons the costly l0/l1-norm for group sparse representation. Another byproduct is that TP-DPL can directly apply the class-specific twin-projective reconstruction residual to compute the label of data. Extensive results on public databases show that TP-DPL can deliver the state-of-the-art performance.

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Automatically labeling Aging Scenarios with a Machine Learning Approach 1

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24th International Conference on Human-Computer Interaction, HCII 2022

作者： An, Ning Xu, Yang Gao, Qinglin Zhu, Wenjie Wu, Aoran Chen, Honglin School of Computer Science and Information Engineering Hefei University of Technology Hefei China Key Laboratory of Knowledge Engineering with Big Data of Ministry of Education Hefei University of Technology Hefei China National Smart Eldercare International S&T Cooperation Base Hefei University of Technology Hefei China Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei China Shanghai Tianyu Senior Living Service Co. Ltd. Shanghai China School of Basic Courses Bengbu Medical College Bengbu China Management and Business Department Skidmore College Saratoga SpringsNY United States Department of Social Sciences University of Eastern Finland Kuopio70150 Finland

ISBN: (数字)9783031179020

ISBN: (纸本)9783031179013

Older adults have diversified needs often associated with particular aging scenarios. People started to use aging scenarios to provide better Smart Eldercare services and develop innovative solutions. The problem is how to manage these scenarios effectively. To our best knowledge, this study is the first to develop a model to provide a structured framework of aging scenarios for research and teaching of eldercare services. labeling aging scenarios can facilitate their collection, classification, and consolidation within this model. This study uses the automatic labeling approach to manage and utilize aging scenarios. Under each class, there are labels with different numbers and smaller granularity to reflect the requirements in each scenario case. Since a scenario case often involves more than one label, properly labeling it becomes a multi-label text classification problem. We extend the multi-label text classification model LSAN for labeling aging scenario cases. For evaluation purposes, we collected 938 scenario cases as the dataset. Experimental results show that our proposed method can achieve an average accuracy of 61.66%, better than other classical methods. © 2022, Springer Nature Switzerland AG.

关键词： Classification (of information)

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Identification of misleading product description in e-commerce website

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Ruan Jian Xue Bao/Journal of Software 2014年 25卷 127-135页

作者： Long, Yin Liu, Hong-Yan He, Jun Hu, He Du, Xiao-Yong Key Laboratory of Data Engineering and Knowledge Engineering of the Ministry of Education Renmin University of China Beijing100872 China School of Information Renmin University of China Beijing100872 China School of Economics and Management Tsinghua University Beijing100084 China

Online shopping has been accepted by more and more consumers. C2C websites provide thousands of offers for consumers as a mainstream e-commerce platform. When customers search products in C2C website, some returned offers have misleading description. Misleading description means that the description does not convey the actual price of products, but usually claiming much lower price for the purpose of attracting more consumers. The misleading offers affect consumers' judgments and bring bad influences on the websites' reputation. This paper proposes an approach that combines statistical model HMM with statistical outlier detection method to detect misleading offers. HMM model is built to determine the product that an offer description really designates, providing an efficient solution to eliminate the ambiguity of the offer description caused by description irregularities. The statistical outlier detection method is effective to deal with limited product offer information. The paper further conducts experiments on real data set of electric business websites and the results demonstrate the effectiveness of the proposed approach. ©2014 ISCAS.

关键词： Websites

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ConsistencyDet: A Robust Object Detector with a Denoising Paradigm of Consistency Model

arXiv

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arXiv 2024年

作者： Jiang, Lifan Wang, Zhihui Wang, Changmiao Li, Ming Leng, Jiaxu Wu, Xindong the College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266510 China Shenzhen Research Institute of Big Data Shenzhen518172 China Key Laboratory of Intelligent Education Technology and Application Zhejiang Normal University Jinhua321004 China the Key Laboratory of Image Cognition Chongqing University of Posts and Telecommunications Chongqing400065 China Jiangsu Key Laboratory of Image and Video Understanding for Social Safety Nanjing University of Science and Technology Nanjing210094 China the Key Laboratory of Knowledge Engineering with Big Data the Ministry of Education of China China School of Computer Science and Information Technology Hefei University of Technology Hefei230009 China

Object detection, a quintessential task in the realm of perceptual computing, can be tackled using a generative methodology. In the present study, we introduce a novel framework designed to articulate object detection as a denoising diffusion process, which operates on the perturbed bounding boxes of annotated entities. This framework, termed ConsistencyDet, leverages an innovative denoising concept known as the Consistency Model. The hallmark of this model is its self-consistency feature, which empowers the model to map distorted information from any temporal stage back to its pristine state, thereby realizing a "one-step denoising" mechanism. Such an attribute markedly elevates the operational efficiency of the model, setting it apart from the conventional Diffusion Model. Throughout the training phase, ConsistencyDet initiates the diffusion sequence with noise-infused boxes derived from the ground-truth annotations and conditions the model to perform the denoising task. Subsequently, in the inference stage, the model employs a denoising sampling strategy that commences with bounding boxes randomly sampled from a normal distribution. Through iterative refinement, the model transforms an assortment of arbitrarily generated boxes into definitive detections. Comprehensive evaluations employing standard benchmarks, such as MS-COCO and LVIS, corroborate that ConsistencyDet surpasses other leading-edge detectors in performance metrics. Our code is available at https://***/Tankowa/ConsistencyDet. Copyright © 2024, The Authors. All rights reserved.

关键词： Object detection

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Change detection of hyperspectral remote sensing images based on deep belief network

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International Journal of Earth Sciences and engineering 2016年第5期9卷 2096-2105页

作者： Huang, Fenghua Xu, Chao Zhu, Zhangzhi Fuzhou University Fuzhou350116 China Yango College Fuzhou350015 China Key Lab of Spatial Data Mining & Information Sharing of Ministry of Education Fuzhou University Fuzhou350002 China National Engineering Research Centre of Geospatial Information Technology Fuzhou University Fuzhou350002 China Spatial Information Engineering Research Centre of Fujian Province Fuzhou University Fuzhou350002 China

To address the performance bottlenecks of existing methods for change detection of hyperspectral remote sensing (HSRS) images, a new scheme for change detection of HSRS based on deep belief network (CDHSRS-DBN) is proposed. First, the HSRS images collected at two different phases (dual-temporal HSRS images) are pre-processed and registered, and then the spectral-difference images of the dual-temporal images are computed. Next, the endmember spectrums and the abundance-difference images are extracted using the pixel unmixing method based on independent component analysis (ICA). The low-layer feature vector of deep learning in CDHSRS-DBN adopts the integrated feature vector that consists of the pixel spectral-difference vector, endmember abundance-difference vector and the pixel spectral feature angle vector. Finally, a deep belief network (DBN) model that contains multi-layer restricted Boltzmann machines (RBM) and a support vector machine (SVM) is devised, and the weights of connections between visible and hidden layers are adjusted through pre-training. The accuracy of change detection is further improved by fine-tuning all the weights via the SVM classifier. In order to evaluate the performance of the proposed CDHSRS-DBN method, four pairs of EO-1 Hyperion test images at different phases, which collected in four different experimental zones, are used as the test data and CDHSRS-DBN is compared with six other typical HSRS change detection algorithms (CDHSRS-SCD, CDHSRS-MPD, CDHSRS-ICA, IR-MAD, CD-PCA and PCCD). The experiments focus on the detection of land-use changes. The average recall and precision reach 90.39% and 87.10%, respectively. The average value of F-Score and time consumption is 0.8871 and 242.5, respectively. Experimental results demonstrate the better performance of CDHSRS-DBN to detect changes of multi-temporal HSRS images accurately and efficiently. © 2016 CAFET-INNOVA TECHNICAL SOCIETY. All rights reserved.

关键词： Change detection

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Sub-graph matching-based building changes detection using high-resolution remote sensing images

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International Journal of Earth Sciences and engineering 2016年第5期9卷 2172-2181页

作者： Huang, Feng-Hua Mao, Zheng-Yuan Shi, Wen-Zao Fuzhou University Fuzhou350116 China Yango College Fuzhou350015 China Key Lab of Spatial Data Mining & Information Sharing of Ministry of Education Fuzhou University Fuzhou350002 China National Engineering Research Centre of Geospatial Information Technology Fuzhou University Fuzhou350002 China Spatial Information Engineering Research Centre of Fujian Province Fuzhou University Fuzhou350002 China

High-resolution remote sensing (HRRS) images of urban regions have large viewing angle variations, significant noise jamming, and obvious building shadows. Hence, deviation and distortion usually occur to the buildings in HRRS images collected at different phases (multi-temporal HRRS images). The traditional detection methodology is ineffective for accurate and efficient building changes detection in multi-temporal HRRS images. In order to address these problems, this paper proposes a sub-graph matching-based building changes detection (SGMBCD) scheme. First, this paper presents a Graphcut-based buildings extracting (GCBE) method from multi-temporal HRRS images. Next, a sub-graph matching-based registration (SGMR) method is devised to register the previously extracted buildings from multi-temporal HRRS images and to obtain matched ASIFT feature point pairs and singular points. Finally, singular points and overlay analysis-based method (SPOA) is developed to detect building changes in multi-temporal HRRS images. The types of building changes included in this paper are changes (e.g., erection, dismantling, repairing, and reconstruction) and non-changes. In order to demonstrate the effectiveness of the proposed SGMBCD scheme, it is compared with five typical algorithms (i.e., BCDBPM, SDBBCD, BCDBICO, NCUTBCD, and RSMBCD) on three sets of multi-temporal WorldView2 test images. Experimental results show that in comparison with the other methods, SGMBCD can effectively address the challenging problem of building changes detection in multi-temporal HRRS images. The average recall ratio, precision ratio and F value is 91.47%, 86.49% and 88.91% respectively, and the average time consumption is 60.3 s. This demonstrates SGMBCD can detect building changes in multi-temporal HRRS images accurately and efficiently. © 2016 CAFET-INNOVA TECHNICAL SOCIETY. All rights reserved.

关键词： Buildings

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An Empirical Study on Information Extraction using Large Language Models

arXiv

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arXiv 2023年

作者： Han, Ridong Yang, Chaohao Peng, Tao Tiwari, Prayag Wan, Xiang Liu, Lu Wang, Benyou College of Computer Science and Technology Jilin University China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University China College of Software Jilin University China Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen China School of Data Science The Chinese University of Hong Kong Shenzhen China School of Information Technology Halmstad University Sweden

Human-like large language models (LLMs), especially the most powerful and popular ones in OpenAI’s GPT family, have proven to be very helpful for many natural language processing (NLP) related tasks. Therefore, various attempts have been made to apply LLMs to information extraction (IE), which is a fundamental NLP task that involves extracting information from unstructured plain text. To demonstrate the latest representative progress in LLMs’ information extraction ability, we assess the information extraction ability of GPT-4 (the latest version of GPT at the time of writing this paper) from four perspectives: Performance, Evaluation Criteria, Robustness, and Error Types. Our results suggest a visible performance gap between GPT-4 and state-of-the-art (SOTA) IE methods. To alleviate this problem, considering the LLMs’ human-like characteristics, we propose and analyze the effects of a series of simple prompt-based methods, which can be generalized to other LLMs and NLP tasks. Rich experiments show our methods’ effectiveness and some of their remaining issues in improving GPT-4’s information extraction ability. Copyright © 2023, The Authors. All rights reserved.

关键词： Information retrieval

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Online Attentive Kernel-Based Temporal Difference Learning

arXiv

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arXiv 2022年

作者： Yang, Guang Chen, Xingguo Yang, Shangdong Wang, Huihui Dong, Shaokang Gao, Yang The the Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing University of Posts and Telecommunications National Engineering Laboratory for Agri-Product Quality Traceability Beijing Technology and Business University China The State Key Laboratory for Novel Software Technology Nanjing University China The PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information Ministry of Education Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology China

With rising uncertainty in the real world, online Reinforcement Learning (RL) has been receiving increasing attention due to its fast learning capability and improving data efficiency. However, online RL often suffers from complex Value Function Approximation (VFA) and catastrophic interference, creating difficulty for the deep neural network to be applied to an online RL algorithm in a fully online setting. Therefore, a simpler and more adaptive approach is introduced to evaluate value function with the kernel-based model. Sparse representations are superior at handling interference, indicating that competitive sparse representations should be learnable, non-prior, non-truncated and explicit when compared with current sparse representation methods. Moreover, in learning sparse representations, attention mechanisms are utilized to represent the degree of sparsification, and a smooth attentive function is introduced into the kernel-based VFA. In this paper, we propose an Online Attentive Kernel-Based Temporal Difference (OAKTD) algorithm using two-timescale optimization and provide convergence analysis of our proposed algorithm. Experimental evaluations showed that OAKTD outperformed several Online Kernel-based Temporal Difference (OKTD) learning algorithms in addition to the Temporal Difference (TD) learning algorithm with Tile Coding on public Mountain Car, Acrobot, CartPole and Puddle World tasks. Copyright © 2022, The Authors. All rights reserved.

关键词： Learning algorithms

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