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作者： Hu, Gang Dou, Wanting Du, Bo Department of Applied Mathematics Xi'an University of Technology Xi'an China School of Computer Science and Engineering Xi'an University of Technology Xi'an China

Degree reduction of ball Said-Ball (BSB) surface is a complex and unsolved problem in computer-aided design (CAD) and computer graphics (CG), which has potential application prospects in many engineering fields of geometric modeling. In this paper, the degree reduction of BSB surface is transformed into the optimization problems of center surface and radius function, and the improved coot optimization algorithm is used to solve it. An effective method to solve the degree reduction of BSB surface by intelligent optimization algorithm is proposed. Firstly, the degree reduction of BSB surface is decomposed into center surface reduction and radius function reduction, and the objective function and constraint conditions are determined according to the optimization idea, then the degree reduction optimization model of center surface and radius function is established. Secondly, an enhanced coot (ECOOT for short) algorithm is proposed, which combines chain strategy, dispersed foraging strategy, and quadratic interpolation strategy. Compared with the original algorithm, ECOOT algorithm has better convergence performance and higher calculation accuracy. Finally, the proposed ECOOT algorithm is used to solve the degree reduction optimization model, and the optimal center surface and minimum distance radius function of the BSB surface after degree reduction are obtained. The experimental results show that the ECOOT algorithm can effectively solve the problem of degree reduction for BSB surface, the degree reduction error is reduced by about 20% compared to the original COOT algorithm, and is superior to other intelligent optimization algorithms in accuracy, robustness, and convergence. © 2023 John Wiley & Sons, Ltd.

关键词： Optimization

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Enhanced capacitive energy storage and dielectric temperature stability of A-site disordered high-entropy perovskite oxides

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Journal of Materials science & technology 2023年第14期145卷 66-73页

作者： Yating Ning Yongping Pu Chunhui Wu Shiyu Zhou Lei Zhang Jinbo Zhang xian Zhang Yangchao Shang School of Materials Science and Engineering Shaanxi University of Science&TechnologyXi’an 710021China School of Electronic Information and Artificial Intelligence Shaanxi University of Science and TechnologyXi’an 710021China

In this work,a novel high entropy perovskite oxide(1-x)(Na_(0.2)Bi_(0.2)Ba_(0.2)Sr_(0.2)Ca_(0.2))TiO_(3-x)NaNbO_(3)(abbrevi-ated as(1-x)NBBSCT-x NN,x=0,0.05,0.1,0.15,and 0.2)was designed to improve temperature dielectric stability and energy storage performance by combining relaxor and antiferroelectric *** optimal composition of x=0.2 exhibits a high energy storage density of 3.51 J/cm^(3),together with wide temperature stable stability(■<15%,-70 to 110℃),excellent frequency stability(W rec andηvary by only±2.1%and±5.2%within the range of 1-600 Hz)and fast discharge rate(t_(0.9)=55.2 ns).This is mainly due to the enhancement of relaxation behavior and increase of E b caused by the decrease of grain *** results offer a new strategy for designing high entropy ceramic materials of high performance in the future.

关键词： Energy storage High-entropy perovskite Temperature stability Relaxor ferroelectrics

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Weakly-supervised instance co-segmentation via tensor-based salient co-peak search

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Frontiers of computer science 2024年第2期18卷 83-92页

作者： Wuxiu QUAN Yu HU Tingting DAN Junyu LI Yue ZHANG Hongmin CAI School of Computer Science Guangdong Polytechnic Normal UniversityGuangzhou 510665China School of Computer Science and Engineering South China University of TechnologyGuangzhou 510006China

Instance co-segmentation aims to segment the co-occurrent instances among two *** task heavily relies on instance-related cues provided by co-peaks,which are generally estimated by exhaustively exploiting all paired candidates in point-to-point ***,such patterns could yield a high number of false-positive co-peaks,resulting in over-segmentation whenever there are mutual *** tackle with this issue,this paper proposes an instance co-segmentation method via tensor-based salient co-peak search(TSCPS-ICS).The proposed method explores high-order correlations via triple-to-triple matching among feature maps to find reliable co-peaks with the help of co-saliency *** proposed method is shown to capture more accurate intra-peaks and inter-peaks among feature maps,reducing the false-positive rate of co-peak *** having accurate co-peaks,one can efficiently infer responses of the targeted *** on four benchmark datasets validate the superior performance of the proposed method.

关键词： weakly-supervised co-segmentation co-peak tensormatching deep network instance segmentation

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Surface Damage Detection Algorithm of Elevator Wire Rope based on YOLOv5s

Surface Damage Detection Algorithm of Elevator Wire Rope bas...

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2024 International Conference on Intelligent Computing and Robotics, ICICR 2024

作者： Hanif, Muhammad Wahab Li, Zhanli Bashir, Rehmat Farooq, Sardar Annes Khan, Jamshaid Sadiq, Imran Sohail Computer Science Department Xi'An University of Science and Technology Xi'an China School of Mechanical Engineering Xi'An University of Science and Technology Xi'an China School of Safety and Management Xi'An University of Science and Technology Xi'an China

ISBN: (纸本)9798350372274

Elevators, as essential equipment in modern urban high-rise buildings and in mining industry, rely heavily on traction steel ropes, which represent the elevator's most crucial components. During prolonged usage, issues like wire breakage and strand breakage may occur. If not promptly addressed, these issues can lead to various safety incidents resulting in injuries and property damage. Existing methods for steel rope detection, such as visual inspection and electromagnetic testing, are susceptible to external environmental influences, leading to insufficient stability in detection results. Therefore, this study proposes the use of artificial intelligence algorithms (YOLOv5s) for surface damage detection in steel ropes. Firstly, a dataset for surface damage on steel ropes has been created. Subsequently, the YOLOv5s network model has been trained using the custom dataset. The trained YOLOv5s network has been employed to extract features from images of different types of steel ropes. Experimental results shows that the steel rope surface damage detection model based on the YOLOv5s network exhibits high accuracy, excellent robustness, and fast computation speed, achieving an average detection speed of 6.7 FPS and a testing accuracy of 95.3%. This model provides an effective reference for early warning and alarm systems in elevator incidents, carrying notable practical significance. ©2024 IEEE.

关键词： Tall buildings

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Minimal Context-Switching Data Race Detection with Dataflow Tracking

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Journal of computer science & technology 2024年第1期39卷 211-226页

作者：郑龙李洋辛杰刘海峰郑然廖小飞金海 National Engineering Research Center for Big Data Technology and System School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China Services Computing Technology and System Laboratory School of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China Cluster and Grid Computing Laboratory School of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China

Data race is one of the most important concurrent anomalies in multi-threaded *** con-straint-based techniques are leveraged into race detection,which is able to find all the races that can be found by any oth-er sound race ***,this constraint-based approach has serious limitations on helping programmers analyze and understand data ***,it may report a large number of false positives due to the unrecognized dataflow propa-gation of the ***,it recommends a wide range of thread context switches to schedule the reported race(in-cluding the false one)whenever this race is exposed during the constraint-solving *** ad hoc recommendation imposes too many context switches,which complicates the data race *** address these two limitations in the state-of-the-art constraint-based race detection,this paper proposes DFTracker,an improved constraint-based race detec-tor to recommend each data race with minimal thread context ***,we reduce the false positives by ana-lyzing and tracking the dataflow in the *** this means,DFTracker thus reduces the unnecessary analysis of false race *** further propose a novel algorithm to recommend an effective race schedule with minimal thread con-text switches for each data *** experimental results on the real applications demonstrate that 1)without removing any true data race,DFTracker effectively prunes false positives by 68%in comparison with the state-of-the-art constraint-based race detector;2)DFTracker recommends as low as 2.6-8.3(4.7 on average)thread context switches per data race in the real world,which is 81.6%fewer context switches per data race than the state-of-the-art constraint based race ***,DFTracker can be used as an effective tool to understand the data race for programmers.

关键词： data race satisfiability modulo theory multi-threaded program dynamic detection

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A Prediction-Based Multi-Objective VM Consolidation Approach for Cloud Data Centers

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computers, Materials & Continua 2024年第7期80卷 1601-1631页

作者： xialin Liu Junsheng Wu Lijun Chen Jiyuan Hu School of Computer Science Northwestern Polytechnical UniversityXi’an710005China School of Computer Science and Technology Xi’an University of Posts and TelecommunicationsXi’an710021China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi’an University of Posts and TelecommunicationsXi’an710021China School of Software and Microelectronics Northwestern Polytechnical UniversityXi’an710005China School of Automation Xi’an University of Posts and TelecommunicationsXi’an710021China

Virtual machine(VM)consolidation aims to run VMs on the least number of physical machines(PMs).The optimal consolidation significantly reduces energy consumption(EC),quality of service(QoS)in applications,and resource *** paper proposes a prediction-basedmulti-objective VMconsolidation approach to search for the best mapping between VMs and PMs with good timeliness and practical *** use a hybrid model based on Auto-Regressive Integrated Moving Average(ARIMA)and Support Vector Regression(SVR)(HPAS)as a prediction model and consolidate VMs to PMs based on prediction results by HPAS,aiming at minimizing the total EC,performance degradation(PD),migration cost(MC)and resource wastage(RW)*** results usingMicrosoft Azure trace show the proposed approach has better prediction accuracy and overcomes the multi-objective consolidation approach without prediction(i.e.,Non-dominated sorting genetic algorithm 2,Nsga2)and the renowned Overload Host Detection(OHD)approaches without prediction,such as Linear Regression(LR),Median Absolute Deviation(MAD)and Inter-Quartile Range(IQR).

关键词： VM consolidation prediction multi-objective optimization machine learning

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Coarse-to-fine lightweight meta-embedding for ID-based recommendations

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science China(Information sciences) 2025年第4期68卷 82-97页

作者： Yang WANG Haipeng LIU Zeqian YI Biao QIAN Meng WANG School of Computer Science and Information Engineering Hefei University of Technology College of Information and Intelligence Hunan Agricultural University

State-of-the-art recommender systems are increasingly focused on optimizing implementation efficiency, such as enabling on-device recommendations under memory constraints. Current methods commonly use lightweight embeddings for users and items or employ compact embeddings to enhance reusability and reduce memory usage. However, these approaches consider only the coarse-grained aspects of embeddings, overlooking subtle semantic nuances. This limitation results in an adversarial degradation of meta-embedding performance, impeding the system's ability to capture intricate relationships between users and items, leading to suboptimal recommendations. To address this, we propose a novel approach to efficiently learn meta-embeddings with varying grained and apply fine-grained meta-embeddings to strengthen the representation of their coarse-grained counterparts. Specifically, we introduce a recommender system based on a graph neural network, where each user and item is represented as a node. These nodes are directly connected to coarse-grained virtual nodes and indirectly linked to fine-grained virtual nodes, facilitating learning of multi-grained semantics. Fine-grained semantics are captured through sparse meta-embeddings, which dynamically balance embedding uniqueness and memory constraints. To ensure their sparseness, we rely on initialization methods such as sparse principal component analysis combined with a soft thresholding activation function. Moreover, we propose a weight-bridging update strategy that aligns coarse-grained meta-embedding with several fine-grained meta-embeddings based on the underlying semantic properties of users and items. Comprehensive experiments demonstrate that our method outperforms existing baselines. The code of our proposal is available at https://***/htyjers/C2F-MetaEmbed.

关键词： lightweight meta-embedding coarse-to-fine learning ID-based recommendations

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Dual-Task Contrastive Meta-Learning for Few-Shot Cross-Domain Emotion Recognition

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computers, Materials & Continua 2025年第2期82卷 2331-2352页

作者： Yujiao Tang Yadong Wu Yuanmei He Jilin Liu Weihan Zhang School of Computer Science and Engineering Sichuan University of Science and EngineeringYibin644002China School of Mechanical and Power Engineering Chongqing University of Science and TechnologyChongqing401331China

Emotion recognition plays a crucial role in various fields and is a key task in natural language processing (NLP). The objective is to identify and interpret emotional expressions in text. However, traditional emotion recognition approaches often struggle in few-shot cross-domain scenarios due to their limited capacity to generalize semantic features across different domains. Additionally, these methods face challenges in accurately capturing complex emotional states, particularly those that are subtle or implicit. To overcome these limitations, we introduce a novel approach called Dual-Task Contrastive Meta-Learning (DTCML). This method combines meta-learning and contrastive learning to improve emotion recognition. Meta-learning enhances the model’s ability to generalize to new emotional tasks, while instance contrastive learning further refines the model by distinguishing unique features within each category, enabling it to better differentiate complex emotional expressions. Prototype contrastive learning, in turn, helps the model address the semantic complexity of emotions across different domains, enabling the model to learn fine-grained emotions expression. By leveraging dual tasks, DTCML learns from two domains simultaneously, the model is encouraged to learn more diverse and generalizable emotions features, thereby improving its cross-domain adaptability and robustness, and enhancing its generalization ability. We evaluated the performance of DTCML across four cross-domain settings, and the results show that our method outperforms the best baseline by 5.88%, 12.04%, 8.49%, and 8.40% in terms of accuracy.

关键词： Contrastive learning emotion recognition cross-domain learning dual-task meta-learning

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MSIsensor-RNA:Microsatellite Instability Detection for Bulk and Single-cell Gene Expression Data

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Genomics, Proteomics & Bioinformatics 2024年第3期22卷 85-90页

作者： Peng Jia Xuanhao Yang xiaofei Yang Tingjie Wang Yu Xu Kai Ye Department of Gynecology and Obstetrics Center for Mathematical MedicalThe First Affiliated Hospital of Xi’an Jiaotong UniversityXi’an 710061China MOE Key Lab for Intelligent Networks&Networks Security Faculty of Electronic and Information EngineeringXi’an Jiaotong UniversityXi’an 710049China School of Automation Science and Engineering Faculty of Electronic and Information EngineeringXi’an Jiaotong UniversityXi’an 710049China School of Computer Science and Technology Faculty of Electronic and Information EngineeringXi’an Jiaotong UniversityXi’an 710049China Genome Institute The First Affiliated Hospital of Xi’an Jiaotong UniversityXi’an 710061China School of Life Science and Technology Xi’an Jiaotong UniversityXi’an 710049China

Microsatellite instability(MSI)is an indispensable biomarker in cancer ***,MSI scoring methods by high-throughput omics methods have gained popularity and demonstrated better performance than the gold standard method for MSI ***,the MSI detection method on expression data,especially single-cell expression data,is still lacking,limiting the scope of clinical application and prohibiting the investigation of MSI at a single-cell ***,we developed MSIsensor-RNA,an accurate,robust,adaptable,and standalone software to detect MSI status based on expression values of MSI-associated *** demonstrated the favorable performance and promise of MSIsensor-RNA in both bulk and single-cell gene expression data in multiplatform technologies including RNA sequencing(RNA-seq),microarray,and single-cell ***-RNA is a versatile,efficient,and robust method for MSI status detection from both bulk and single-cell gene expression data in clinical studies and ***-RNA is available at https://***/xjtu-omics/msisensor-rna.

关键词： Microsatellite instability Gene expression Single-cell RNA-seq RNA-seq Microarray

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Multi-Scale Time Series Segmentation Network Based on Eddy Current Testing for Detecting Surface Metal Defects

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IEEE/CAA Journal of Automatica Sinica 2025年第3期12卷 528-538页

作者： xiaorui Li xiaojuan Ban Haoran Qiao Zhaolin Yuan Hong-Ning Dai Chao Yao Yu Guo Mohammad S.Obaidat George Q.Huang the School of Intelligence Science and Technology University of Science and Technology Beijing the Beijing Advanced Innovation Center for Materials Genome Engineering the Key Laboratory of Intelligent Bionic Unmanned Systems and the Institute of Materials Intelligent Technology Liaoning Academy of Materials IEEE the Department of Computer Science Hong Kong Baptist University the School of Computer and Communication Engineering Key Laboratory of Advanced Materials and Devices for Post-Moore Chips Ministry of Education University of Science and Technology Beijing the Beijing Advanced Innovation Center for Materials Genome Engineering University of Science and Technology Beijing the School of Computer and Communication Engineering University of Science and Technology Beijing the King Abdullah Ⅱ School of Information Technology The University of Jordan the Department of Computational Intelligence the School of Computing SRM University the School of Engineering The Amity University The Hong Kong Polytechnic University

In high-risk industrial environments like nuclear power plants, precise defect identification and localization are essential for maintaining production stability and safety. However, the complexity of such a harsh environment leads to significant variations in the shape and size of the defects. To address this challenge, we propose the multivariate time series segmentation network(MSSN), which adopts a multiscale convolutional network with multi-stage and depth-separable convolutions for efficient feature extraction through variable-length templates. To tackle the classification difficulty caused by structural signal variance, MSSN employs logarithmic normalization to adjust instance distributions. Furthermore, it integrates classification with smoothing loss functions to accurately identify defect segments amid similar structural and defect signal subsequences. Our algorithm evaluated on both the Mackey-Glass dataset and industrial dataset achieves over 95% localization and demonstrates the capture capability on the synthetic dataset. In a nuclear plant's heat transfer tube dataset, it captures 90% of defect instances with75% middle localization F1 score.

关键词： Eddy current testing nondestructive testing semantic segmentation time series analysis

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