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Lifecycle-Aware Online Video Caching

IEEE TRANSACTIONS ON MOBILE COMPUTING 2021年第8期20卷 2624-2636页

作者： Li, Tong Braud, Tristan Li, Yong Hui, Pan Hong Kong Univ Sci & Technol Dept Comp Sci & Engn Syst & Media Lab SyMLab Sai Kung Hong Kong Peoples R China Tsinghua Univ Dept Elect Engn Beijing Natl Res Ctr Informat Sci & Technol Beijing Peoples R China Hong Kong Univ Sci & Technol Dept Comp Sci & Engn Syst & Media Lab SyMLab Hong Kong Peoples R China Univ Helsinki Dept Comp Sci Helsinki 00014 Finland

The current explosion of video traffic compels service providers to deploy caches at edge networks. Nowadays, most caching systems store data with a high programming voltage corresponding to the largest possible 'expiry date', typically on the order of years, which maximizes the cache damage. However, popular videos rarely exhibit lifecycles longer than a couple of months. Consequently, the programming voltage can instead be adapted to fit the lifecycle and mitigate the cache damage accordingly. In this paper, we propose LiA-cache, a Lifecycle-Aware caching policy for online videos. LiA-cache finds both near-optimal caching retention times and cache eviction policies by optimizing traffic delivery cost and cache damage cost conjointly. We first investigate temporal patterns of video access from a real-world dataset covering 10 million online videos collected by one of the largest mobile network operators in the world. We next cluster the videos based on their access lifecycles and integrate the clustering into a general model of the caching system. Specifically, LiA-cache analyzes videos and caches them depending on their cluster label. Compared to other popular policies in real-world scenarios, LiA-cache can reduce cache damage up to 90 perce, while keeping a cache hit ratio close to a policy purely relying on video popularity.

关键词： Shape Programming Mobile computing Optimization Threshold voltage clustering algorithms Video caching video lifecycles caching policy cache damage edge networks

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Real-World Anomaly Detection by Using Digital Twin Systems and Weakly Supervised Learning

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2021年第7期17卷 4733-4742页

作者： Castellani, Andrea Schmitt, Sebastian Squartini, Stefano Honda Res Inst Europe GmbH D-63073 Offenbach Germany Univ Politecn Marche Dept Informat Engn I-60131 Ancona Italy

The continuously growing amount of monitored data in the Industry 4.0 context requires strong and reliable anomaly detection techniques. The advancement of Digital Twin technologies allows for realistic simulations of complex machinery;therefore, it is ideally suited to generate synthetic datasets for the use in anomaly detection approaches when compared to actual measurement data. In this article, we present novel weakly supervised approaches to anomaly detection for industrial settings. The approaches make use of a Digital Twin to generate a training dataset, which simulates the normal operation of the machinery, along with a small set of labeled anomalous measurement from the real machinery. In particular, we introduce a clustering-based approach, called cluster centers (CC), and a neural architecture based on the Siamese Autoencoders (SAE), which are tailored for weakly supervised settings with very few labeled data samples. The performance of the proposed methods is compared against various state-of-the-art anomaly detection algorithms on an application to a real-world dataset from a facility monitoring system, by using a multitude of performance measures. Also, the influence of hyperparameters related to feature extraction and network architecture is investigated. We find that the proposed SAE-based solutions outperform state-of-the-art anomaly detection approaches very robustly for many different hyperparameter settings on all performance measures.

关键词： Anomaly detection Machinery Informatics Training clustering algorithms Monitoring Sensors Anomaly detection Digital Twin (DT) systems Industry 4 0 Siamese neural networks weakly supervised learning

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Research on Local Feature Intelligent Extraction Algorithm of Blurred Image Under Complex Illumination Conditions

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IEEE ACCESS 2021年 9卷 84948-84955页

作者： Wang, Jia Jahng, Surng-Gahb Chung Ang Univ Grad Sch Adv Imaging Sci & Film Seoul 156756 South Korea

Traditionally, multi-label feature extraction algorithm is used to extract local features of blurred images under complex illumination conditions. The computational complexity is extremely high and the extraction efficiency is low. Therefore, in this paper, the local feature intelligent extraction algorithm for blurred images under complex illumination conditions is proposed. The improved image segmentation algorithm based on local fuzzy C-means clustering is used to cluster and segment the blurred images. The image feature recognition algorithm based on wavelet transform and LBP log domain feature extraction is adopted. The blurred image is transformed from the spatial domain to the logarithmic domain to make two-stage discrete wavelet decomposition, and the high-frequency component is used to reconstruct the original image to perform high-pass filtering on the blurred image. By filtering the low-frequency illumination component to compensate the complex illumination, the block LBP is used to extract the local texture features of the blurred image after illumination compensation. The experimental results on Yale-B face database show that the proposed algorithm can effectively extract the local features of blurred images under complex illumination conditions, and the maximum robustness of the algorithm is 0.44, the maximum value of feature extraction error rate is 0.25, and the maximum value of feature extraction speed growth rate is 96%, with high robustness, accuracy and extraction efficiency.

关键词： Feature extraction Lighting clustering algorithms Image segmentation Wavelet domain Histograms Discrete wavelet transforms Complex illumination local feature intelligent extraction clustering segmentation algorithm wavelet decomposition

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Shadow-Cuts Minimization/Maximization and Complex Hopfield Neural Networks

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2021年第3期32卷 1096-1109页

作者： Uykan, Zekeriya Amer Univ Middle East Coll Engn & Technol Egaila Kuwait

In this article, we continue our very recent work by extending it to the complex case. Having been inspired by the real Hopfield neural network (HNN) results, our investigations here yield various novel results, some of which are as follows. First, extending the "biased pseudo-cut" concept to the complex HNN (CHNN) case, we introduce a "shadow-cut" that is defined as the sum of intercluster phased edges. Second, while the discrete-time real HNN strictly minimizes the "biased pseudo-cut" in each neuron state change, the CHNN "tends" to minimize the shadow-cut (as the CHNN energy function is minimized). Third, these definitions pose a novel L-phased graph clustering (partitioning) problem in which the sum of the shadow-cuts is minimized (or maximized) for the Hermitian complex and the directed graphs whose edges are (possibly arbitrary positive/negative) complex numbers. Finally, combining the CHNN and the pioneering algorithm GADIA of Babadi and Tarokh and their modified versions, we propose simple indirect algorithms to solve the defined shadow-cuts minimization/maximization problem. The proposed algorithms naturally include the CHNN as well as the GADIA as its special cases. The computer simulations confirm the findings.

关键词： Neural networks Image edge detection Optimization Neurons Wireless communication Matrix converters clustering algorithms Associative memory clustering complex Hopfield neural networks (CHNNs) GADIA graphs with (positive and negative) complex edges L-phased partitioning problem

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Moving Object Detection by 3D Flow Field Analysis

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2021年第4期22卷 1950-1963页

作者： Jiang, Cansen Paudel, Danda Pani Fofi, David Fougerolle, Yohan Demonceaux, Cedric Univ Bourgogne Franche Comte ImViA VIBOT EMR CNRS 6000 F-71200 Le Creusot France Swiss Fed Inst Technol Comp Vis Lab CH-8092 Zurich Switzerland

Map-based localization and sensing are one of the key components in autonomous driving technologies, where high quality 3D map reconstruction is fundamentally utmost important. However, due to the highly dynamic and uncontrollable properties of real world environment, building a high quality 3D map is not straightforward and requires several strong assumptions. To address this challenge, we present a complete framework, which detects and extracts the moving objects from a sequence of unordered and texture-less point clouds, to build high quality static maps. To accurately detect the moving objects from data acquired with a possibly fast moving platform, we propose a novel 3D Flow Field Analysis approach in which we inspect the motion behaviour of the registered point sets. The proposed algorithm elegantly models the temporal and spatial displacement of the moving objects. Thus, both small moving objects (e.g. walking pedestrians) and large moving objects (e.g. moving trucks) can be detected effectively. Further, by incorporating the Sparse Subspace clustering framework, we propose a Sparse Flow clustering algorithm to group the 3D motion flows under both the constraints of motion similarity and spatial closeness. To this end, the static scene parts and the moving objects can be independently processed to achieve photo-realistic 3D reconstructions. Finally, we show that the proposed 3D Flow Field Analysis algorithm and the Sparse Flow clustering approach are highly effective for motion detection and segmentation, as exemplified on the KITTI benchmark, and yield high quality reconstructed static-maps as well as rigidly moving objects.

关键词： Three-dimensional displays Trajectory clustering algorithms Motion segmentation Image reconstruction Dynamics Tracking Motion flow detection motion segmentation dynamic scene analysis 3D map reconstruction

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Personalized Control Strategy of Electronic Brake Booster With Driving Behaviors Identification

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2021年第12期70卷 12593-12603页

作者： Zhu, Bing Zhang, Yihan Zhao, Jian Tian, Haijian Wu, Jian Jilin Univ State Key Lab Automot Simulat & Control Changchun 130012 Peoples R China Jilin Univ Coll Automot Engn Changchun 130012 Peoples R China

To improve the comfort and user acceptance of the intelligent vehicle braking system, this paper proposes a control strategy for the electronic brake booster (Ebooster) that considers differences in driving behaviors. First, a real-vehicle driving data collection platform is built to collect the driving behavior data of 84 drivers under typical vehicle-following braking conditions. Subsequently, the k-means clustering algorithm optimized by particle swarm optimization (PSO) is used to realize the cluster analysis of driving behaviors, and the support vector machine (SVM) algorithm optimized by the genetic algorithm (GA) is used to complete the identification of driving behaviors. Next, through the analysis of the boost characteristics of the self-designed Ebooster, driving behaviors are matched with boost characteristics, and a personalized basic boosting control strategy based on the driver behavior is designed. Finally, a hardware-in-the-loop (HiL) bench and a real-vehicle test platform are designed and built to verify the effectiveness of the personalized control strategy of the Ebooster. The results show that this method can effectively identify the type of driver and make personalized adjustments to the basic boosting strategy of the Ebooster, which effectively improves the personalization of the braking system, resulting in a higher pedal response and user acceptance.

关键词： Vehicles Brakes Hidden Markov models Support vector machines Genetic algorithms Data acquisition clustering algorithms Intelligent vehicle personalized electronic brake booster driving behaviors identification support vector machine genetic algorithm

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Multiple Kernel k-Means With Low-Rank Neighborhood Kernel

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IEEE ACCESS 2021年 9卷 3291-3300页

作者： Ou, Qiyuan Gao, Long Zhu, En Natl Univ Def Technol Sch Comp Changsha 410073 Peoples R China

Multiple kernel clustering algorithms achieve promising performances by exploring the complementary information from kernel matrices corresponding to each data view. Most of the existing methods aim to construct a consensus kernel for the afterward clustering. However, they ignore that the desired kernel is supposed to reveal the cluster structure among samples and thus to be low rank. As a consequence, the corresponding clustering performance could decrease. To address this issue, we propose a low-rank kernel learning approach for multiple kernel clustering. Specifically, instead of regularizing the consensus kernel with low-rank constraints, we use a re-parameterize scheme for the kernel matrix. Meanwhile, the consensus kernel is located in the neighborhood area of the linear combination of base kernels. An alternate optimization strategy is designed to solve the resulting optimization problem. We evaluate the proposed method on 13 benchmark datasets with 9 state-of-the-art algorithms. As is demonstrated by experimental results, our proposed algorithm achieves superior clustering scores against the compared algorithms on the reported popular multiple kernel datasets.

关键词： Kernel clustering algorithms Optimization Task analysis Sparse matrices Matrices Licenses clustering algorithms multiple kernel clustering multiple view clustering

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Improved Analysis of Online Balanced clustering 19th

Improved Analysis of Online Balanced Clustering

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19th International Workshop on Approximation and Online algorithms, WAOA 2021

作者： Bienkowski, Marcin Böhm, Martin Koutecký, Martin Rothvoß, Thomas Sgall, Jiří Veselý, Pavel Institute of Computer Science University of Wroclaw Wroclaw Poland Computer Science Institute of Charles University Faculty of Mathematics and Physics Prague Czech Republic University of Washington Seattle United States

ISBN: (纸本)9783030927011

In the online balanced graph repartitioning problem, one has to maintain a clustering of n nodes into clusters, each having k= n/ nodes. During runtime, an online algorithm is given a stream of communication requests between pairs of nodes: an inter-cluster communication costs one unit, while the intra-cluster communication is free. An algorithm can change the clustering, paying unit cost for each moved node. This natural problem admits a simple O(2· k2) -competitive algorithm Comp, whose performance is far apart from the best known lower bound of Ω(· k). One of open questions is whether the dependency on can be made linear;this question is of practical importance as in the typical datacenter application where virtual machines are clustered on physical servers, is of several orders of magnitude larger than k. We answer this question affirmatively, proving that a simple modification of Comp is (· 2O(k)) -competitive. On the technical level, we achieve our bound by translating the problem to a system of linear integer equations and using Graver bases to show the existence of a "small" solution. © 2021, Springer Nature Switzerland AG.

关键词： clustering algorithms

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A Dynamic Adaptive Subgroup-to-Subgroup Compatibility-Based Conflict Detection and Resolution Model for Multicriteria Large-Scale Group Decision Making

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IEEE TRANSACTIONS ON CYBERNETICS 2021年第10期51卷 4784-4795页

作者： Tang, Ming Liao, Huchang Herrera-Viedma, Enrique Chen, C. L. Philip Pedrycz, Witold Sichuan Univ Business Sch Chengdu 610064 Peoples R China King Abdulaziz Univ Fac Comp & Informat Technol Jeddah Saudi Arabia Univ Granada Andalusian Res Inst Data Sci & Computat Intellige Granada 18071 Spain King Abdulaziz Univ Fac Engn Dept Elect & Comp Engn Jeddah 21589 Saudi Arabia Univ Macau Fac Sci & Technol Dept Comp & Informat Sci Macau Peoples R China Dalian Maritime Univ Nav Coll Dalian 116026 Peoples R China Northwestern Polytech Univ Unmanned Syst Res Inst Xian 710072 Peoples R China Univ Alberta Dept Elect & Comp Engn Edmonton AB T6G 2R3 Canada

The current societal demands and technological developments have resulted in the participation of a large number of experts in making decisions as a group. Conflicts are imminent in groups and conflict management is complex and necessary especially in a large group. However, there are few studies that quantitatively research the conflict detection and resolution in the large-group context, especially in the multicriteria large-group decision making (GDM) context. This article proposes a dynamic adaptive subgroup-to-subgroup conflict model to solve multicriteria large-scale GDM problems. A compatibility index is proposed based on two kinds of conflicts among experts: 1) cognitive conflict and 2) interest conflict. Then, the fuzzy c-means clustering algorithm is used to classify experts into several subgroups. A subgroup-to-subgroup conflict detection method and a weight-determination approach are developed based on the clustering results. Afterward, a conflict resolution model, which can dynamically generate feedback suggestion, is introduced. Finally, an illustrative example is provided to demonstrate the effectiveness and applicability of the proposed model.

关键词： Decision making Indexes Adaptation models Cybernetics Classification algorithms clustering algorithms Compatibility index conflict fuzzy c-means clustering (FCM) multicriteria large-scale group decision making (LSGDM) subgroup-to-subgroup

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Iterative Local-Search Heuristic for Weighted Vehicle Routing Problem

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2021年第6期22卷 3444-3454页

作者： Wang, Xinyu Shao, Shuai Tang, Jiafu Dongbei Univ Finance & Econ Sch Management Sci & Engn Dalian 116025 Peoples R China

Solutions to the weighted vehicle routing problem (WVRP) find numerous applications, such as home-to-work bus service, toll-by-weight cargo transportation, perishable-food delivery, and hazardous waste collection. This paper presents mathematical WVRP models corresponding to both collection and delivery cases. This study demonstrates WVRPs to be NP-hard, and proposes an efficient heuristic method (RI-ILS) to solve WVRPs. RI-ILS is based on principles of regret-insertion and iterative local-search. Several computational experiments were performed in this study to demonstrate the utility of the RI-ILS approach. As observed, when solving traditional vehicle routing problems (VRPs), RI-ILS yields good results under all testing conditions. In particular, it provides new best-known solutions under 12 testing conditions. When solving WVRPs, the RI-ILS method outperforms two recently published state-of-the-art algorithms in terms of solution quality, computational time, and stability. Case studies have also been performed in this research based on real-world data obtained from two Chinese companies that provide home-to-work bus service to their employees.

关键词： Linear programming Routing Transportation Vehicle routing clustering algorithms Mathematical model Weighted vehicle routing problem (WVRP) home-to-work bus service collection and delivery cases regret-insertion iterative local-search

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