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arXiv 2024年

作者： Yan, Fei Gao, Jie Feng, Tao Liu, Jianxing School of Information Science and Technology Southwest Jiaotong University Sichuan Chengdu China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Chengdu China Department of Control Science and Engineering Harbin Institute of Technology Harbin150001 China

In this paper, the discrete-time modified algebraic Riccati equation (MARE) is solved when the system model is completely unavailable. To achieve this, firstly a brand new iterative method based on the standard discrete-time algebraic Riccati equation (DARE) and its input weighting matrix is proposed to solve the MARE. For the single-input case, the iteration can be initialized by an arbitrary positive input weighting if and only if the MARE has a stabilizing solution;nevertheless a pre-given input weighting matrix of a sufficiently large magnitude is used to perform the iteration for the multi-input case when the characteristic parameter belongs to a specified subset. Benefit from the developed specific iteration structure, the Q-learning (QL) algorithm can be employed to subtly solve the MARE where only the system input/output data is used thus the system model is not required. Finally, a numerical simulation example is given to verify the effectiveness of the theoretical results and the algorithm. © 2024, CC BY.

关键词： Iterative methods

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Quantitative assessment of interstitial lung disease based on RDNet convolutional network∗

Quantitative assessment of interstitial lung disease based o...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Hou, Fan Zhao, Cai Su, Ningling Wang, Jiening Linning, E. Zheng, Wen Taiyuan University of Technology Institute of Public-Safety and Big Data College of Data Science Taiyuan China Taiyuan University of Technology Center of Information Management and Development Taiyuan China Third Hospital of Shanxi Medical University Shanxi Bethune Hospital Department of Radiology Taiyuan China Shanxi Bethune Hospital Department of Radiology Taiyuan China

ISBN: (纸本)9781665468190

The diagnosis of interstitial lung disease (ILD) mainly relies on physicians' judgement of the results of five signs displayed in computed tomography (HRCT): ground-glass opacity (GGO), reticular opacity (RO) and, as the disease progresses, emphysema (EMPH), consolidation (CONS) and honey combing (HC), but it is difficult to make a quantitative diagnosis of ILD due to physicians' subjective differences, so we build a deep learning method-based interstitial lung disease grade assessment model, RDNet, to achieve lung lesion segmentation, measurement and grade assessment of ILD. First, 3115 HRCTs with five typical signs of ILD patients were collected for training and validation of our method, and the experimental results showed that our method achieved an average segmentation dice coefficient of 81.4% for lesions, which was much better than other models;next, we collected 185 ILD patients with both HRCT and Pulmonary Function Test (PFT), and ILD secondary staging was performed based on PFT, and clinical information was used as a medium to develop a prediction model for the severity of HRCT and ILD staging. After cross-validation, our ILD scoring model can achieve an accuracy of 81.2% (73.6% 88.8%) for predicting disease severity, which can make an ILD staging Accurate prediction can be made to provide clinical assistance to physicians. © 2022 IEEE.

关键词： Forecasting

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application of New Feature Techniques for Multimedia Analysis in Artificial Neural Networks by Using Image Processing

Informatica (Slovenia)

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Informatica (Slovenia) 2024年第11期48卷 113-124页

作者： Liu, Lianqiu Yang, Yongping Chen, Hong Shun School Of Artificial Intelligence and Big Data Chongqing College of Finance and Economics Chongqing402160 China Department of Quality Monitoring and Development Planning Hunan University of Humanities Science and Technology Hunan Loudi417000 China

To evaluate and extract information from multimedia material including photos, videos, and audio, a fusion of image processing and computer vision methods known as kernel principal component analysis (KPCA) is used. The objective is to create a system that can automatically identify relevant aspects of multimedia data and make them available for analysis and decision-making. By merging various processing methods, the fusion of image processing and multimedia analysis may improve analysis efficiency. The CT scans dataset from Kaggle is used to train artificial neural networks (ANN) for use in multimedia analysis. For multimedia analysis by combining image processing, we proposed kernel principal component analysis with artificial neural networks (KPCA-ANN) in this paper. The ability to process, analyze, and understand multimedia data by merging image processing into multimedia analysis has tremendous promise. It may improve decision-making, deepen our comprehension of complicated processes, and provide more fruitful means of information exchange. The experimental findings demonstrate that the proposed strategy has provided an absolute mean error of 7 and a structural similarity index of 86. © 2024 Slovene Society Informatika. All rights reserved.

关键词： Merging

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A WOA Based Switching PID Control for Overhead Crane 42

A WOA Based Switching PID Control for Overhead Crane

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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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Skill Requirements Analysis for data Analysts Based on Named Entities Recognition 2

Skill Requirements Analysis for Data Analysts Based on Named...

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2nd International Conference on big data and Informatization Education, ICBDIE 2021

作者： Cao, Lina Zhang, Jian Beijing Information Science and Technology University School of Economics and Management Beijing Intl. Sci. and Technology Cooperation Base of Intelligent Decision and Big Data Application Beijing China Beijing Information Science and Technology University School of Economics and Management Laboratory of Big Data Decision Making for Green Development Beijing China

ISBN: (纸本)9781665438704

Currently, with the surge in demand for data analysts, it is beneficial to grasp the social requirement for talents by deeply mining the job requirements of data science. This study used crawlers to acquire the job advertisements from *** and selected data science related positions. Then, the specialty and skill entities in job requirement were annotated manually, and two kinds of Named Entities Recognition models, the Bert-BiLSTM-CRF model and BiLSTM-CRF were applied to extract skill entities and specialty entities from the text data of job requirement. It was found that, the model based on Bert pre-training vector was better. At last, the characteristics of the demand from the perspective of skills and profession were analyzed. © 2021 IEEE.

关键词： Character recognition

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Effects of Optimized Operating Parameters on Combustion Characteristics and NO_(x)Emissions of a Burner based on Orthogonal Analysis

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Journal of Thermal Science 2021年第4期30卷 1212-1223页

作者： XU Qian AKKURT Nevzat YANG Gang ZHU Lidong SHI Kejian WANG Kang ZOU Zhenwei LIU Zhihui WANG Jiulong DU Zhiwei School of Energy and Environmental Engineering University of Science and Technology BeijingBeijing 100083China Beijing Key Laboratory for Energy Conservation and Emission Reduction for Metallurgical Industry Beijing 100083China Department of Mechanical Engineering Munzur UniversityTunceli 62000Turkey Department of Big Data Technology and Application Development Computer Network Information CenterChinese Academy of SciencesBeijing 100190China AVIC Xinxiang Aviation Industry(group)CO. LTDHenan 453002China

To optimize the structure of the burner,improve the combustion performance,and reduce the emission of NO_(x),a self-circulating low NO_(x)combustion technology was used to design a new type of flue gas self-circulating low NO_(x)*** on previous research on the numerical model of combustion and the composition of mixed gas on combustion and NO_(x)emissions,the effect of various factors on the ejection coefficient of the flue gas self-circulating structure was analyzed using the orthogonal test method,and the burner operating parameters,such as preheating temperature and excess air coefficient,were deeply studied through the three-dimensional finite element numerical model in this *** results show that the diameter ratio of the nozzle and the length of the cylindrical section of the flue gas self-circulating structure have great influence on its ejection and mixing *** optimal ejection coefficient was ***,the amount of NO_(x)emissions greatly increased from 6.23×10^(-6)(volume fraction)at the preheating temperature 973 K to 3.5×10^(-3)at preheating temperature 1573 *** the excess air coefficient decreased from 1.2 to 1,the maximum combustion temperature decreased from 2036.3 K to 1954.22 K,and the NO_(x)emissions decreased from 352.29×10^(-6)to 159.73×10^(-6).

关键词： preheating temperature excess air coefficient self-circulating flue gas low oxygen combustion low-NO_(x)emissions

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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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Research on the Loading Method of Logistics Vehicle and Cargo Based on IPSO Algorithm 42

Research on the Loading Method of Logistics Vehicle and Carg...

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42nd Chinese Control Conference, CCC 2023

作者： Zhao, Zewei Wang, Huqing 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 Post Big Data Technology and Application Engineering Research Center of Jiangsu Province Nanjing University of Posts and Telecommunications 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

Logistics vehicle and cargo matching problem is a typical combination optimization problem, which has important theoretical and application value in the field of logistics distribution. The proved particle swarm optimization (PSO) algorithm is one of the most extensive optimization methods studied in recent years, with excellent global search ability and convergence speed. Based on the IPSO algorithm, this paper proposes an effective IPSO algorithm model for the matching problem of logistics vehicles and goods, and conducts the corresponding comparative experiment. The final experimental results show that the algorithm has good results in practical application and can provide a feasible optimization method for solving the problem of logistics vehicles and goods. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Particle swarm optimization (PSO)

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Salix-Leaf: Find Main Veins of Signal Clusters for Practical Parallel Decoding

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IEEE Transactions on Mobile Computing 2025年

作者： Li, Yajun Zhao, Jumin Li, Dengao Wu, Hejun Xu, Shuang Bai, Ruiqin Taiyuan University of Technology College of Electronic Information and Optical Engineering Taiyuan China Key Laboratory of Big Data Fusion Analysis and Application of Shanxi Province Taiyuan China Intelligent Perception Engineering Technology Center of Shanxi Taiyuan China Taiyuan University of Technology College of Computer Science and Technology College of Data Science Taiyuan China Sun Yat-sen University Department of Computer Science Guangzhou China

Parallel decoding of backscatter improves communication throughput by enabling concurrent transmission of backscatter tags. In practical applications of parallel decoding, it is extremely difficult to distinguish collided signals in superclusters where multiple signal clusters overlap. Existing methods are usually effective for superclusters with uniformly distributed signals. Nevertheless, there are many more scenarios in which signals in superclusters tend to gather unevenly, and existing methods cannot work. Such uneven clustering of signals occurs due to the following two possible causes: (1) signal-strengthdifferences (SSDs) among tags;or (2) cluster drifting (CD) driven by interferences from other objects within communication environments. This paper proposes a novel scheme called SalixLeaf, which aims to identify the main veins of signal clusters to address this problem of superclusters with unevenly distributed signals. Salix-Leaf identifies the main vein of each signal cluster for fine-grained clustering so that the direction of the main veins can be used to verify the accuracy of clustering. In addition, SalixLeaf employs a supercluster decomposer that divides signals into different segments for clustering analysis, enhancing robustness and practicability. Experimental results show that Salix-Leaf achieves a 1.2-fold increase in throughput and a 25% reduction in bit error rate (BER) compared to the state-of-the-art. © 2002-2012 IEEE.

关键词： Backscattering

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Optimal Model Selection Methodology for Machine Learning-Based Diabetes Risk Prediction

Optimal Model Selection Methodology for Machine Learning-Bas...

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Global Conference for Advancement in technology (GCAT)

作者： Sanjay Kumar Palit Abhiroop Nandi Ray Judhajit Sanyal Information Technology Department Calcutta Institute of Engineering and Management Kolkata India Android Application Development Mirafra Technologies Bengaluru India Data Strategy & AI Mestastop Solutions Bengaluru India

ISBN: (数字)9798350376685

ISBN: (纸本)9798350376692

Machine learning has been shown to have widespread application in the accurate and consistent determination of disease risk for multiple diverse disease types. Among such types of machine learning models, tree-based algorithms are popular for their simplicity and effectiveness. Meta-estimation models have also demonstrated effectiveness for multiple such scenarios. The present work consequently examines and compares the performances of multiple tree-based models with significant difference in model complexity, in accurately predicting diabetes in prospective patients. Evaluation metrics such as accuracy, precision, recall and F1-score are employed to determine the optimal model for a given dataset among a handful of such models. For determination of model optimality in the context of a specific dataset, the variation in performance measures is also observed with change in factors such as the ratio between training and testing dataset sizes. Based on such assessments, optimal model performance can be isolated and such optimal models identified for diverse use cases.

关键词： Training Adaptation models Accuracy Predictive models Boosting Stability analysis Diabetes Random forests Diseases Testing

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