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Failure Laws of Cyclically Loaded Steel Joints Revealed from a Thermodynamic Perspective

JOURNAL OF EARTHQUAKE ENGINEERING 2025年第3期29卷 610-635页

作者： Shen, Zijie Zhang, Yu Zhou, Guangchun Wu, Zhenyu Harbin Inst Technol Sch Civil Engn 73Huanghe Rd Harbin 150090 Heilongjiang Peoples R China Harbin Inst Technol Sch Civil Engn Key Lab Struct Dynam Behav & Control Minist Educ Harbin Peoples R China Harbin Inst Technol Sch Civil Engn Key Lab Smart Prevent & Mitigat Civil Engn Disaste Minist Ind & Informat Technol Harbin Peoples R China

Using cyclically loaded beam-column joints with cast steel connectors (BCJCC) as a case study (Bao 2012), this work attempts to reveal the failure law from a thermodynamic perspective. By applying a network-free renormalization, modes and characteristic parameters are constructed to characterize stressing state evolution. Due to the attractor effect of phase transition, a clustering algorithm can be applied to reveal the approximate phase transition loads of steel joints. After that, the phase transition loads can be verified using the renormalization perspective, which can also serve as a reference for seismic design.

关键词： Steel joints cyclic loading network-free renormalization clustering algorithm phase transition loads

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Data center multidimensional management strategy based on descending neighborhood DBSCAN algorithm in unsupervised learning

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JOURNAL OF INDUSTRIAL INFORMATION INTEGRATION 2025年 45卷

作者： Liang, Bin Bai, Junqing Xian ShiYou Univ Sch Comp Sci Xian Peoples R China

Cloud users rent virtual machines (VMs) with varying parameters tailored to their unique business requirements. These diverse VM parameters add complexity to data center (DC) management strategies. Among the crucial parameters are CPU and memory, which must be optimized to ensure efficient physical resource utilization and decreased DC energy consumption. This article proposes three algorithms to manage and optimize VMs. Firstly, the density-based spatial clustering of applications with noise (DBSCAN) algorithm is enhanced, leading to the introduction of the descending neighborhood DBSCAN (DNDBSCAN) algorithm. This algorithm facilitates the clustering of physical machines (PMs). Secondly, the cluster center nearest classification algorithm (CCN) is proposed, leveraging VM attributes and the remaining capacity of the cluster center to classify the VMs for deployment. Additionally, the avoid hot spot time correlation algorithm (AHTC) is introduced to handle VM mapping, deploying VMs on the most time-relevant PMs while mitigating hot spots. Lastly, these three algorithms are integrated into a DC multidimensional management strategy based on the DNDBSCAN algorithm within the framework of unsupervised learning (DND). When compared to other algorithms, the DND algorithm demonstrates significant improvement in PM balanced utilization and reduction of DC energy consumption. The average balanced utilization of PM of the DND algorithm is 86 %, which is an average improvement of 11 % compared to the comparative algorithm. The average total energy consumption of the DND algorithm is 124 kW center dot h, which is an average reduction of 41 % compared to the comparative algorithm.

关键词： Data center Unsupervised learning Machine learning clustering algorithm

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An analysis of COVID-19 vaccine hesitancy in the U.S.

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IISE TRANSACTIONS 2025年第3期57卷 246-260页

作者： Bui, Hieu Eksioglu, Sandra Proano, Ruben Pinkley, Sarah Nurre Univ Arkansas Dept Ind Engn Fayetteville AR 72701 USA Rochester Inst Technol Dept Ind & Syst Engn Rochester NY USA

Reluctance or refusal to get vaccinated, commonly known as Vaccine Hesitancy (VH), poses a significant challenge to COVID-19 vaccination campaigns. Understanding the factors contributing to VH is essential for shaping effective public health strategies. This study proposes a novel framework for combining machine learning with publicly available data to generate a proxy metric that evaluates the dynamics of VH faster than the currently used survey methods. The metric is input to descriptive classification models that analyze a wide array of data, aiming to identify key factors associated with VH at the county level in the U.S. during the COVID-19 pandemic (i.e., January to October 2021). Both static and dynamic factors are considered. We use a Random Forest classifier that identifies political affiliation and Google search trends as the most significant factors influencing VH behavior. The model categorizes U.S. counties into five distinct clusters based on VH behavior. Cluster 1, with low VH, consists mainly of Democratic-leaning residents who, have the longest life expectancy, have a college degree, have the highest income per capita, and live in metropolitan areas. Cluster 5, with high VH, is predominantly Republican-leaning individuals in non-metropolitan areas. Individuals in Cluster 1 is more responsive to vaccination policies.

关键词： clustering algorithm COVID-19 pandemic vaccine hesitancy classification

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Joint Augmentation and Part Learning for Unsupervised Clothing Change Person Re-Identification

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IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 2025年 20卷 2944-2956页

作者： Pang, Zhiqi Zhao, Lingling Liu, Yang Sharma, Gaurav Wang, Chunyu Harbin Inst Technol Fac Comp Harbin 150001 Peoples R China Univ Rochester Dept Elect & Comp Engn Rochester NY 14627 USA

Clothing change person re-identification (CC-ReID) is a crucial task in intelligent surveillance, aiming to match images of the same person wearing different clothing. Promising performance in existing CC-ReID methods is achieved at the cost of labor-intensive manual annotation of identity labels. While some researchers have explored unsupervised CC-ReID, these methods still depend on additional deep learning models for preprocessing. To eliminate the need for additional models and improve performance, we propose a joint augmentation and part learning (JAPL) framework that obtains clothing change positive pairs in an unsupervised fashion by synergistically combining augmentation-based invariant learning (AugIL) and part-based invariant learning (ParIL). AugIL first constructs clothing change pseudo-positive pairs and then encourages the model to focus on clothing-invariant information by enhancing feature consistency between the pseudo-positive pairs. ParIL beneficially encourages high similarity between inter-cluster clothing change positive pair using part images and a prediction sharpening loss. PartIL also introduces a soft consistency loss that promotes clothing-invariant feature learning by encouraging consistency of class vectors between the real features actually used for CC-ReID and the part features. Experimental results on multiple ReID datasets demonstrate that the proposed JAPL not only surpasses existing unsupervised methods but also achieves competitive performance compared to some supervised CC-ReID methods.

关键词： Clothing Shape Optimization Identification of persons Costs Feature extraction clustering algorithms Cameras Surveillance Deep learning Unsupervised learning contrastive learning clustering algorithm clothing change person re-identification

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Fast White Matter Fiber clustering Using Variational Autoencoder Latent Space 20

Fast White Matter Fiber Clustering Using Variational Autoenc...

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20th International Symposium on Medical Information Processing and Analysis

作者： Navarrete, Sebastian Mangin, Jean-Francois Hernandez, Cecilia Guevara, Pamela Univ Concepcion Fac Engn Concepcion Chile Univ Paris Saclay Baobab Neurospin CEACNRS Gif Sur Yvette France

ISBN: (纸本)9798331528669;9798331528652

Whole brain tractography data contain a large number of streamlines that require algorithms such as clustering to group the data into smaller sets for visualization and analysis. We present a deep-learning clustering algorithm based on the latent space of a variational autoencoder trained on the direct and flipped versions of the streamlines from 10 tractograms (17,294,232 streamlines in total). The model takes advantage of the low-dimensional representation of the data in latent space to apply an HDBSCAN clustering algorithm to perform automatic and fast clustering of the tractography datasets. The proposed method was evaluated in terms of segmentation quality using the Davies-Bouldin index (DB) and execution time against two other state-of-the-art methods, QuickBundles (QB) and FFClust. The results show that the proposed method has the best performance in terms of the DB index, closely followed by QB, and is the second fastest method, only slightly surpassed by FFClust. In addition, the proposed method allows for obtaining meaningful large clusters because it uses metrics based on the density of the groups instead of a distance threshold as the other methods.

关键词： clustering algorithm White Matter Tractography Deep-learning

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POSTERIOR BELIEF clustering algorithm FOR ENERGY-EFFICIENT TRACKING IN WIRELESS SENSOR NETWORKS

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INTERNATIONAL JOURNAL ON SMART SENSING AND INTELLIGENT SYSTEMS 2014年第3期7卷 925-941页

作者： Wu, Bo Feng, Yanpeng Zheng, Hongyan Shenzhen Polytech Educ Technol & Informat Ctr Shenzhen 518055 Guangdong Peoples R China

In this paper, we propose a novel posterior belief clustering (PBC) algorithm to solve the tradeoff between target tracking performance and sensors energy consumption in wireless sensor networks. We model the target tracking under dynamic uncertain environment using partially observable Markov decision processes (POMDPs), and transform the optimization of the tradeoff between tracking performance and energy consumption into yielding the optimal value function of POMDPs. We analyze the error of a class of continuous posterior beliefs by Kullback-Leibler (KL) divergence, and cluster these posterior beliefs into one based on the error of KL divergence. So, we calculate the posterior reward value only once for each cluster to eliminate repeated computation. The numerical results show that the proposed algorithm has its effectiveness in optimizing the tradeoff between tracking performance and energy consumption.

关键词： Partially observable Markov decision processes wireless sensor networks target tracking energy consumption posterior belief clustering algorithm

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User Charging Mode Extraction Model Based on Homomorphic Encryption and clustering of Charging Time Data from Multiple Charging Stations 2

User Charging Mode Extraction Model Based on Homomorphic Enc...

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IEEE 2nd International Conference on Power Science and Technology (ICPST)

作者： Hao, Yang Zheng Kaihong Zhang Yiming Liu Sijie Yang Jingxu Yang Ziyang Zeng Lukun Wu Mingqi Yunnan Power Grid Co Ltd Metering Ctr Kunming Yunnan Peoples R China Digital Power Grid Grp Co Ltd China Southern Power Grid Digital Grid Res Inst Metrol Ctr Guangzhou Peoples R China Yunnan Int Co Ltd China Southern Power Grid Kunming Co Kunming Yunnan Peoples R China

ISBN: (纸本)9798350349047;9798350349030

To solve the problem of inaccurate user charging mode extraction using single charging station data, a user charging mode extraction model based on clustering of charging time data from multiple charging stations is proposed. Firstly, the method of using charging time data instead of charging power data for charging mode clustering is proposed to solve the problem that the Euclidean distance or Manhattan distance cannot measure the temporal proximity of charging activities in the case of staggered charging power curves. Secondly, the charging mode fluctuation distance is proposed to reflect the representativeness of the charging mode to the charging data samples, based on which the method of defining the user charging mode is proposed. Next, a user charging mode extraction method based on homomorphic encryption algorithm is proposed. Finally, the arithmetic example verifies the effectiveness of the method of this paper.

关键词： Charging station charging time clustering algorithm homomorphic encryption

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A fast and lightweight train image fault detection model based on convolutional neural networks

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IMAGE AND VISION COMPUTING 2025年 154卷

作者： Zhang, Longxin Zeng, Wenliang Zhou, Peng Deng, Xiaojun Wu, Jiayu Wen, Hong Hunan Univ Technol Sch Comp Sci Zhuzhou 412007 Peoples R China

Trains playa vital role in the life of residents. Fault detection of trains is essential to ensuring their safe operation. Aiming at the problems of many parameters, slow detection speed, and low detection accuracy of the current train image fault detection model, a fast and lightweight train image fault detection model using convolutional neural network (FL-TINet) is proposed in this study. First, the joint depthwise separable convolution and divided-channel convolution strategy are applied to the feature extraction network in FL-TINet to reduce the number of parameters and computation amount in the backbone network, thereby increasing the detection speed. Second, a mixed attention mechanism is designed to make FL-TINet focus on key features. Finally, an improved discrete K-means clustering algorithm is designed to set the anchor boxes so that the anchor box can cover the object better, thereby improving the detection accuracy. Experimental results on PASCAL 2012 and train datasets show that FL-TINet can detect faults at 119 frames per second. Compared with the state-of-the-art CenterNet, RetinaNet, SSD, Faster R-CNN, MobileNet, YOLOv3, YOLOv4, YOLOv7Tiny, YOLOv8_n and YOLOX-Tiny models, FL-TINet's detection speed is increased by 96.37% on average, and it has higher detection accuracy and fewer parameters. The robustness test shows that FL-TINet can resist noise and illumination changes well.

关键词： Attention mechanism clustering algorithm Convolutional neural network Depthwise separable convolution Fault detection

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A data-driven exploration of the complexity of the phosphorus-centered pnictogen bonds

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MOLECULAR PHYSICS 2025年第3期123卷

作者： Liu, Fang Wang, Haiyan Du, Likai Bohai Univ Coll Informat Sci & Technol Jinzhou Liaoning Peoples R China Shandong Univ Aeronaut Sch Informat Engn Jinan Shandong Peoples R China Jinzhou Med Univ Sch Biol Informat Engn Jinzhou Liaoning Peoples R China

Phosphorus-centered pnictogen bonds, similar to well-studied halogen bonds, play a vital role in molecular recognition and assembly. This study aims to explain how pnictogen bonds interact with other noncovalent interactions. A model system, phosphines (PH3), which is also present in phosphorus cycles and reduces planetary atmospheres, is examined. The investigation of the phosphorus trifluoride (PF3) molecule explores its substituent effects. To generate a broad spectrum of molecular configurations, a specially tailored protocol for sampling and optimisation was implemented. The configurations were refined to uncover primary interaction patterns, and a clustering algorithm revealed unique interaction patterns. This report presents the energy stability and distribution of all the clusters. Our findings verify the prevalent presence of pnictogen bonds, identified by their geometric characteristics and co-occurrence with hydrogen bonds, which show an almost linear correlation. Another significant discovery is the correlation between various energy decomposition elements, especially regarding the electrostatic energy and overall binding energy. These results are anticipated to significantly contribute towards our comprehension of non-covalent interactions among phosphorus-containing molecules and the formulation of empirical models having physical interpretations.

关键词： Data-driven phosphorus-centered pnictogen bonds clustering algorithm linear correlation energy decomposition

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Load Balanced clustering Based on Imperialist Competitive algorithm in Wireless Sensor Networks

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WIRELESS PERSONAL COMMUNICATIONS 2020年第1期112卷 371-385页

作者： Dehestani, Fahimeh Jabraeil Jamali, Mohammad Ali Islamic Azad Univ Shabestar Branch Dept Comp Engn Shabestar Iran

Energy consumption is one of the most serious issues in designing Wireless Sensor Networks (WSNs) for maximizing its lifetime and stability. clustering is considered as one of the topology control methods for maintaining the stability of WSNs which can significantly reduce energy consumption in WSNs. However, using different methods for the selection of cluster head is an important challenge in this domain of research. Load balanced clustering is known as an NP-hard problem for a WSN along with unequal load for sensor nodes. The Imperialist Competitive algorithm (ICA) is regarded as an evolutionary method which can be used for finding a quick and efficient solution to such problems. In this paper, a clustering method with an evolutionary approach is introduced which investigates the issues of load balance and energy consumption of WSNs in the equal and unequal load modes so as to select optimal cluster heads. Simulation of the proposed method, carried out via NS2, indicated that it improves the criteria of energy consumption, the number of active sensor nodes and execution time.

关键词： Wireless sensor network clustering algorithm Imperialist competitive algorithm Load balancing

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