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SSRN 2023年

作者： Tang, Yu Dai, Qi Du, Ye Chen, Lifang Niu, Xuanwen School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China Department of Automation College of Information Science and Engineering China University of Petroleum-Beijing Beijing China Beijing Key Laboratory of Security and Privacy in Intelligent Transportation Beijing Jiaotong University Beijing100044 China College of Science North China University of Science and Technology Hebei Tangshan063210 China Hebei Key Laboratory of Data Science and Application Hebei Tangshan063210 China

Software defect prediction plays a crucial role in ensuring the security and quality of software systems. However, it faces challenges posed by high-dimensional features present in software defect datasets and the limited effectiveness of traditional nonlinear dimensionality reduction methods in extracting essential feature information. To address these issues, we propose a novel approach called learnable three-line hybrid feature fusion (LTHFFA), which incorporates the principle of three-line hybrid breeding into feature fusion for the first time. In this method, three distinct dimensionality reduction techniques are initially employed to obtain three separate sets of features. Subsequently, a learnable weight factor feature fusion method is proposed to facilitate automatic learning and dynamic adjustment of feature weights. By integrating the three feature sets based on the principle of three-line hybrid breeding, we derive learnable three-line hybrid fusion features. These features are then utilized in the context of software defect prediction. Experimental results demonstrate the superior performance of LTHFFA compared to nine other dimensionality reduction methods across seventeen publicly available software defect datasets. LTHFFA exhibits the ability to effectively integrate multiple feature sets, reduce feature redundancy, and enhance predictive accuracy. Moreover, statistical analysis using Friedman ranking and Holm's post-hoc test confirms the significant advantage of LTHFFA over alternative dimensionality reduction methods. © 2023, The Authors. All rights reserved.

关键词： Defects

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Retraction Note: Location algorithm of transfer stations based on density peak and outlier detection

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Applied Intelligence 2024年第2期55卷 1-1页

作者： Shao-Hong, Yan Jia-Yang, Niu Tai-Long, Chen Qiu-Tong, Liu Cen, Yang Jia-Qing, Cheng Zhi-Zhen, Fu Jie, Li North China University of Science and Technology Tangshan China Hebei Key Laboratory of Data Science and Application Tangshan China College of Metallurgy and Energy North China University of Science and Technology Tangshan China

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Retraction Note: Three-way selection random forest algorithm based on decision boundary entropy

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Applied Intelligence 2024年第2期55卷 1-1页

作者： Zhang, Chunying Ren, Jing Liu, Fengchun Li, Xiaoqi Liu, Shouyue College of Science North China University of Science and Technology Tangshan China Key Laboratory of Data Science and Application of Hebei Province Tangshan China College of Qian’an North China University of Science and Technology Tangshan China

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Research on Improved Multiple Users Detection Algorithm in Pattern Division Multiple Access System

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SSRN 2022年

作者： Tang, Wanwei Wang, Lixia Wang, Zedai Han, Guofeng Zhao, Jinlong Yue, Xinwei The Key Laboratory of Intelligent Data Information Processing and Control of Hebei Province Tangshan University Tangshan063000 China The School of Engineering Hong Kong University of Science and Technology Hong Kong999077 Hong Kong The Guofeng Han is with the Tangshan employment service center Tangshan human resources and Social Security Bureau Tangshan063000 China The School of Electronic and Information Engineering Beihang University Beijing100191 China The School of Information and Communication Engineering Beijing Information Science and Technology University Beijing100101 China

Receiver detection algorithm has always been a research hotspot in communication system and the key point to determine the performance of 5G system. Both industry and academia have been committed to finding detection algorithms with lower complexity and higher detection performance. This paper will study how to solve the problems of low detection performance and high complexity of classical algorithms. Taking Pattern Division Multiple Access (PDMA) as an example, this paper improves the existing receiver detection algorithms such as Minimum Mean Square Error-Serial Interference Cancellation(MMSE-SIC), Message Passing Algorithm (MPA) and Expectation Propagation Algorithm (EPA), and puts forward corresponding improved algorithms with specific formulas, i.e. Enhanced-MMSE-SIC, Turbo-like MPA (MPA-Turbo) and Turbo-like EPA with hybrid parallel interference cancellation (EPA-Turbo-PIC). Link-level simulation results and analysis show that the proposed improved algorithms have performance advantages over the corresponding traditional algorithms. © 2022, The Authors. All rights reserved.

关键词： Mean square error

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An Improved Lmmse Channel Estimation Algorithm for Mimo-Ofdm System

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SSRN 2022年

作者： Tang, Wanwei Wang, Lixia Wang, Zedai Wei, Mingzhe Zhao, Jinlong Yue, Xinwei The Key Laboratory of Intelligent Data Information Processing and Control of Hebei Province Tangshan University Tangshan063000 China The School of Engineering Hong Kong University of Science and Technology 999077 Hong Kong The School of Electronic and Information Engineering Beihang University Beijing100191 China The School of Information and Communication Engineering Beijing Information Science and Technology University Beijing100101 China

This paper studies channel estimation techniques to recover the received signal more accurately and quickly. The classical Least Squares (LS) and Linear Minimum Mean-squared Error (LMMSE) estimation have the problems of poor estimation performance and high complexity. An Fast-LMMSE channel estimation algorithm is proposed to balance the performance and complexity. In order to apply it to the actual system, the corresponding pilot is also designed. Simulation results are provided and the comparison shows the performance gains achieved by the proposed technique over LS and MMSE estimation in SISO and MIMO communication systems. © 2022, The Authors. All rights reserved.

关键词： Channel estimation

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Explainable seismic damage prediction model based on CELS-WOA-Stacking

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Advanced Engineering Informatics 2025年 66卷

作者： Gu, Yi Yang, Shichao Zhou, Xu Liu, Yongkang College of Civil and Architectural Engineering North China University of Science and Technology Tangshan063210 China College of Artificial Intelligence North China University of Science and Technology Tangshan063210 China College of Science North China University of Science and Technology Tangshan063210 China Hebei Key Laboratory of Data Science and Applications Tangshan063210 China College of Electrical Engineering North China University of Science and Technology Tangshan063210 China

Rapid and accurate assessment of post-earthquake building damage is crucial for effective emergency response and recovery. This study aims to enable knowledge-guided decision support in seismic disaster scenarios and to examine the relationships between influencing factors and seismic damage. Based on the Nepal Earthquake Damage database (NBDP), we developed a hybrid earthquake damage prediction model that combines an integrated stacking framework with an improved whale optimization algorithm (Chaotic-Lévy Elite-enhanced Whale Optimization Algorithm, CELS-WOA). To address model complexity in resource-constrained post-disaster environments, we employed committee voting for feature selection and quantified the contribution of each variable using Shapley Additive Explanations (SHAP). The results demonstrate that our model outperforms single models and traditional ensemble approaches under various optimization strategies, achieving a prediction accuracy of 0.904. Additionally, we analyzed the SHAP values of regional variables in conjunction with seismic intensity information, uncovering latent mechanisms of seismic intensity encoding through geospatial attributes. key indicators affecting seismic damage include pre/post-earthquake building height/floors, geographic location, construction age, and mud-stone material usage. Specifically, building height/floors directly reflect damage, while geographic location influences damage through hidden earthquake intensity. Construction age and mud-stone material influence the damage of the building by reflecting their own ability to resist vibration. This study clarifies the impact of influencing factors on the earthquake loss of buildings based on machine learning, which provides a basis for intelligent loss assessment for earthquake disaster scenarios. © 2025 Elsevier Ltd

关键词： Earthquake effects

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Prediction of software defect using ensemble learning based improved sparrow search algorithm to optimize extreme learning machine

Research Square

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Research Square 2021年

作者： Tang, Yu Dai, Qi Yang, Mengyuan Chen, Lifang College of Science North China University of Science and Technology Hebei Tangshan063210 China Department of Automation College of Information Science and Engineering China University of Petroleum-Beijing Beijing China Hebei Key Laboratory of Data Science and Application Hebei Tangshan063210 China

For the traditional ensemble learning algorithm of software defect prediction, the base predictor exists the problem that too many parameters are difficult to optimize, resulting in the optimized performance of the model unable to be obtained. An ensemble learning algorithm for software defect prediction that is proposed by using the improved sparrow search algorithm to optimize the extreme learning machine, which divided into three parts. Firstly, the improved sparrow search algorithm (ISSA) is proposed to improve the optimization ability and convergence speed, and the performance of the improved sparrow search algorithm is tested by using eight benchmark test functions. Secondly, ISSA is used to optimize extreme learning machine (ISSA-ELM) to improve the prediction ability. Finally, the optimized ensemble learning algorithm (ISSA-ELM-Bagging) is presented in the Bagging algorithm which improve the prediction performance of ELM in software defect datasets. Experiments are carried out in six groups of software defect datasets. The experimental results show that ISSA-ELM-Bagging ensemble learning algorithm is significantly better than the other four comparison algorithms under the six evaluation indexes of Precision, Recall, F-measure, MCC, Accuracy and G-mean, which has better stability and generalization ability. © 2021, CC BY.

关键词： Forecasting

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Subjective cognitive decline domain improves accuracy of plasma Aβ_(42)/Aβ_(40)for preclinical Alzheimer’s disease diagnosis:The SILCODE study

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Chinese Medical Journal 2024年第9期137卷 1127-1129页

作者： Mingkai Zhang Xiaoni Wang Weina Zhao Yuxia Li Chao Ying Jiehui Jiang Yanning Cai Jie Lu Ying Han Department of Neurology Xuanwu Hospital of Capital Medical UniversityBeijing 100053China Department of Neurology Sir Run Run Shaw HospitalAffiliated with School of MedicineZhejiang UniversityHangzhouZhejiang 310016China Department of Neurology Hongqi Hospital Affiliated to Mudanjiang Medical UniversityMudanjiangHeilongjiang 157011China Heilongjiang Key Laboratory of Ischemic Stroke Prevention and Treatment MudanjiangHeilongjiang 157011China Department of Neurology Tangshan Central HospitalTangshanHebei 063000China Department of Neurobiology Xuanwu Hospital of Capital Medical UniversityKey Laboratory for Neurodegenerative Diseases of the Ministry of EducationBeijing 100053China Institute for Advanced Communication and Data Science Shanghai UniversityShanghai 200444China Department of Radiology and Nuclear Medicine Xuanwu HospitalCapital Medical UniversityBeijing 100053China Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics Beijing 100053China Key Laboratory of Neurodegenerative Diseases Ministry of EducationBeijing 100053China School of Biomedical Engineering Hainan UniversityHaikouHainan 570228China Center of Alzheimer’s Disease Beijing Institute for Brain DisordersBeijing 100053China National Clinical Research Center for Geriatric Disorders Beijing 100053China Institute of Biomedical Engineering Shenzhen Bay LaboratoryShenzhenGuangdong 518132China

To the Editor:Accumulating evidence has shown that the preclinical stage of Alzheimer’s disease(AD)(i.e.,asymptomatic amyloidosis)lasts for decades before the onset of cognitive symptoms,providing a large window for early *** pathology,the earliest pathological change associated with AD,can be detected in vivo with cerebrospinal fluid(CSF)analysis or positron emission tomography(PET),and its presence is necessary for the diagnosis of preclinical AD(pre-AD).However,both PET scans and CSF analyses are expensive,hampering their use in large-scale ***,blood-based biomarkers are desirable alternatives,as they are cost-effective and not invasive.

关键词： diagnosis clinical Alzheimer

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Optimal mixed control of networked reaction-diffusion systems

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Physical Review Research 2025年第1期7卷 013262-013262页

作者： Xiaofeng Luo Runzi He Lifeng Hou Shupeng Gao Zhen Jin Gui-Quan Sun Lili Chang Ludovico Minati Stefano Boccaletti School of Mathematics North University of China Shanxi Taiyuan 030051 Peoples's Republic of China Sino-Europe Complex Science Center North University of China Shanxi Taiyuan 030051 Peoples's Republic of China School of Computer Science and Technology North University of China Shanxi Taiyuan 030051 Peoples's Republic of China Complex Systems Research Center Shanxi University Shanxi Taiyuan 030006 Peoples's Republic of China Tangshan Research Institute Beijing Institute of Technology Hebei Tangshan 063000 Peoples's Republic of China School of Mechanical Engineering North China University of Science and Technology Hebei Tangshan 063000 Peoples's Republic of China Key Laboratory of Complex Systems and Data Science of Ministry of Education Shanxi Taiyuan 030006 Peoples's Republic of China School of Life Science and Technology University of Electronic Science and Technology of China Chengdu 611731 Peoples's Republic of China Center for Mind/Brain Sciences (CIMeC) University of Trento 38123 Trento Italy CNR-Institute of Complex Systems Via Madonna del Piano 10 50019 Sesto Fiorentino Italy Institute of Interdisciplinary Intelligent Science Ningbo University of Technology Ningbo 315211 Peoples's Republic of China

We introduce a control scheme able to steer the evolution of networked reaction-diffusion systems toward any intended dynamics. Specifically, we consider simplicial complexes of reaction-diffusion systems that can be collapsed into weighted complex networks by leveraging the linear relationship between the Laplacian matrix of the simplicial complex and the Laplacian matrix of each order simplex. The method integrates reaction and network-based diffusion controls and, as far as effectiveness and cost are concerned, features a significantly improved performance over traditional single-variable control methods. Numerical simulations using the FitzHugh-Nagumo and susceptible-infected-removed models demonstrate the resilience of the proposed mixed control across diverse systems, highlighting the potential of the method for complex system management and intervention strategies.

关键词： Laplace transforms

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From local to national metabolism:a review and a scale-up framework

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Ecosystem Health and Sustainability 2020年第1期6卷 334-352,333页

作者： Ningyu Yan Gengyuan Liu Maddalena Ripa Ning Wang Hongmei Zheng Francesco Gonella State Key Joint Laboratory of Environment Simulation and Pollution Control School of EnvironmentBeijing Normal UniversityBeijingChina Beijing Engineering Research Center for Watershed Environmental Restoration&Integrated Ecological Regulation BeijingChina Institut De Ciencia I Tecnologia Ambientals Universitat Autonoma De BarcelonaBellaterraSpain Beijing Key Lab of Green Development Decision Based on Big Data Beijing Information Science and Technology UniversityChina Department of Resource Management Tangshan Normal UniversityTangshanChina Department of Molecular Sciences and Nanosystems UniversitàCa’Foscari VeneziaVeniceItaly

Research background:Countries are likely the most important subjects involved in the environmental control and response to global environmental issues,while the majority of the related metabolic analyses are focused on the metabolism at the city ***:Analyzing why and how to scale up the metabolic study from city to ***:Combining quantitative analysis with a literature review socio-metabolic research,since it is an effective method to study resource and environmental issues and has been applied at different ***:1)A single city or a smaller area is hardly self-sufficient,and its sustainability and resilience needs the support of the surrounding environment;2)At the country scale,systems exhibit a higher level of self-organization and a corresponding higher level of complexity,addressing the need for applying the metabolic theory at the national scale;3)The emergy analysis methods show its advantages in study metabolic processes for national metabolism;4)Input-output analysis plays an important role in and region ***:The interactions among and within the scales are nested,as well as the goals and ***,socio-metabolic research on scales differ in their priority goals,and the methods adopted must be targeted.

关键词： National metabolism socio-metabolic research sustainability scale-up

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