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Short-Term Traffic Forecasting by Mining the Non-Stationarity of Spatiotemporal Patterns

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2021年第10期22卷 6365-6383页

作者： Cheng, Shifen Lu, Feng Peng, Peng Chinese Acad Sci Inst Geog Sci & Nat Resources Res State Key Lab Resources & Environm Informat Syst Beijing 100101 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Fuzhou Univ Acad Digital China Fuzhou 350003 Peoples R China Jiangsu Ctr Collaborat Innovat Geog Informat Reso Nanjing 210023 Peoples R China

Short-term traffic forecasting is important for the development of an intelligent traffic management system. Critical to the performance of the traffic prediction model utilized in such a system is accurate representation of the spatiotemporal traffic characteristics. This can be achieved by integrating spatiotemporal traffic information or the dynamic traffic characteristics in the modeling process. The currently employed spatiotemporal k-nearest neighbor (STKNN) model is based on the spatial heterogeneity and adaptive spatiotemporal parameters of the traffic to improve the prediction accuracy. However, the non-stationary characteristics of the traffic cannot be fully represented by simply modeling the entire time range or all the time partitions based on experience. We therefore developed a dynamic STKNN model (D-STKNN) for short-term traffic forecasting based on the non-stationary spatiotemporal pattern of the road traffic. The different traffic patterns along the road are first automatically determined using an affinity propagation clustering algorithm. The Warped K-Means algorithm is then used to automatically partition the time periods for each traffic pattern. Finally, the D-STKNN model is developed based on the three-dimensional spatiotemporal tensor data models for the different road segments with different traffic patterns during different time periods. The D-STKNN model was verified through extensive experiments performed using actual vehicular speed datasets collected from city roads in Beijing, China, and expressways in California, U.S.A. The proposed model outperforms existing seven baselines in different time periods under different traffic patterns. The results confirmed the imperative of considering the non-stationary spatiotemporal traffic pattern in developing a model for short-term traffic prediction.

关键词： Roads Predictive models Spatiotemporal phenomena Adaptation models Forecasting Vehicle dynamics partitioning algorithms Dynamic spatiotemporal k-nearest neighbor model short-term traffic forecasting spatiotemporal pattern temporal non-stationarity

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Fast algorithms for Semantic Association Search and Pattern Mining

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2021年第4期33卷 1490-1502页

作者： Cheng, Gong Liu, Daxin Qu, Yuzhong Nanjing Univ Natl Key Lab Novel Software Technol Nanjing 210023 Jiangsu Peoples R China Rhein Westfal TH Aachen Knowledge Based Syst Grp D-52056 Aachen Germany

Given a large graph representing relations between entities, searching for complex relationships (called semantic associations, or SAs for short) between a set of entities is a common type of information needs in many domains. Further, numerous SAs are often abstracted into a few frequent high-level conceptual graph patterns (called SA patterns, or SAPs for short), which organize SAs into interpretable subgroups. Whereas the quality and usefulness of SAs and SAPs have been extensively studied in the literature, in this article we aim to develop faster algorithms for SA search and frequent SAP mining. For the former problem, we leverage distances to prune the search space, and implement a distance oracle to balance the time and space for distance calculation. For the latter problem, we exploit both graph structure and labels to induce fine-grained skeleton-based partitions of SAs, which may be pruned to reduce SAP enumeration. Besides, we generate canonical codes for SAs, which not only enable result deduplication but also are reused in SAP mining to improve the overall performance. We extensively evaluate the efficiency of our algorithms on four large graphs, using both random queries and simulated queries which reproduce the extreme case of finding numerous SAs.

关键词： Search problems Semantics Data mining partitioning algorithms Skeleton Indexes Google Semantic association search entity relationship search frequent tree pattern mining semantic data summarization

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Edge-Based Meta-ICP Algorithm for Reliable Camera Pose Estimation

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IEEE ACCESS 2021年 9卷 89020-89028页

作者： Chen, Chun-Wei Wang, Jonas Shieh, Ming-Der Natl Cheng Kung Univ Dept Elect Engn Tainan 70101 Taiwan Himax Technol Inc Tainan 74148 Taiwan

Camera pose estimation is crucial for 3D surface reconstruction and augmented reality applications. For systems equipped with RGB-D sensors, the corresponding transformation between frames can be effectively estimated using the iterative closest point (ICP) algorithms. Edge points, which cover most of the geometric structures in a frame, are good candidates for control points in ICP. However, the depth of object contour points is hard to accurately measure using commercial RGB-D sensors. Inspired by the model-agnostic meta-learning (MAML) algorithm, this work proposes a meta-ICP algorithm to jointly estimate the optimal transformation for multiple tasks, which are constructed by sampled datapoints. To increase task sampling efficiency, an edge-based task set partition algorithm is introduced for constructing complementary task sets. Moreover, to prevent ICP from being trapped in local minima, a dynamic model adaptation scheme is adopted to disturb the trapped tasks. Experimental results reveal that the probability of unstable estimations can be effectively reduced, indicating a much narrower error distribution of repeated experiments when adopting re-sampled points. With the proposed scheme, the overall absolute trajectory error can be improved by more than 30% as compared to the related edge-based methods using frame-to-frame pose estimation.

关键词： Image edge detection Task analysis partitioning algorithms Cameras Detectors Reliability Pose estimation Camera pose estimation iterative closest point model-agnostic meta-learning

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Fast CU Partition Decision Method Based on Bayes and Improved De-Blocking Filter for H.266/VVC

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IEEE ACCESS 2021年 9卷 70382-70391页

作者： Zhang, Qiuwen Zhao, Yongbo Jiang, Bin Wu, Qinggang Zhengzhou Univ Light Ind Coll Comp & Commun Engn Zhengzhou 450002 Peoples R China

The H.266/Versatile Video Coding (VVC) standard is the latest video coding standards released by the Joint Video Exploration Group (JVET). These new technologies have brought more than 40% increase in compression rate and brought huge coding complexity. In order to reduce the coding complexity of VVC, we propose a fast Coding Unit (CU) partition decision algorithm combining Bayes algorithm and Improved De-blocking Filter (IDBF). The algorithm includes two stages. In the parameter update stage, the original VVC Test Model (VTM) is used to encode every certain video frame, and the Rough Mode Decision evaluation cost (JRMD) generated during the encoding process is used to update the parameters of the Bayes algorithm. In the fast coding stage, the video frame between two parameter update frames is encoded. First, the JRMD of the current CU is calculated and compares it with the split threshold to decide whether the current CU continues to split. If the current CU continues to be split, the current CU is divided into the same 16 simulation blocks, and the IDBF is used to calculate the texture direction and texture complexity of each simulation block. According to the texture features contained in the simulation block, the texture distribution position and texture complexity in the current CU can be judged, so that some bad candidate splitting modes can be discarded. Experimental results show that the proposed method can reduce the complexity of VVC about 56.08%, while Bjontegaard delta bit rate (BDBR) only increases by 1.30%.

关键词： partitioning algorithms Prediction algorithms Encoding Complexity theory Standards Copper Image texture VVC intra prediction improved de-blocking filter Bayes texture characteristics

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A Novel Unsupervised Feature Selection for High-Dimensional Data Based on FCM and k-Nearest Neighbor Rough Sets

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IEEE Transactions on Neural Networks and Learning Systems 2024年第6期36卷 10889-10898页

作者： Weihua Xu Yang Zhang Yuhua Qian College of Artificial Intelligence Southwest University Chongqing China Institute of Big Data Science and Industry Shanxi University Taiyuan China

Large amounts of high-dimensional unlabeled data typically contain only a small portion of truly effective information. Consequently, the issue of unsupervised feature selection methods has gained significant attention in research. However, current unsupervised feature selection approaches face limitations when dealing with datasets that exhibit uneven density, and they also require substantial computational time. To address this problem, this research article proposes a feature extraction technique that combines the Fuzzy C-Means (FCM) and k-nearest neighbor rough sets. FCM is a clustering algorithm grounded in fuzzy theory, which takes into account the inherent data structure and the correlations between different features. Consequently, FCM is particularly well-suited for datasets with uneven density. Our proposed method consists of three steps. First, the FCM algorithm is used to cluster the unlabeled data. Second, a measure that evaluates the importance of features is defined and sorted based on the clustering results. Finally, redundant features are filtered using k-nearest neighbor rough sets while retaining important features, significantly reducing the running time. In addition, we designed the feature selection algorithm (KND-UFS) and conducted experiments on 12 public datasets. We compared KND-UFS with eight existing algorithms in terms of running time, classification accuracy, and the number of selected features. The experimental results provided strong evidence supporting the superior performance of the KND-UFS algorithm.

关键词： Feature extraction Clustering algorithms Rough sets Nearest neighbor methods Classification algorithms partitioning algorithms Approximation algorithms Information systems Data mining Accuracy

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QoS-Based Data Aggregation and Resource Allocation Algorithm for Machine Type Communication Devices in Next-Generation Networks

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IEEE ACCESS 2021年 9卷 119735-119754页

作者： Ahmed, Nasser Rikli, Nasser-Eddine King Saud Univ Coll Comp Sci & Informat Syst CCIS Dept Comp Engn Riyadh 11451 Saudi Arabia

Machine Type Communication (MTC) becomes one of enablers of the internet of things, it faces many challenges in its integration with human-to-human (H2H) communication methods. To this aim, the Long Term Evolution (LTE) needs some adaptation in the scheduling algorithms that assign resources efficiently to both MTC devices (MTCDs) and H2H users. The minimum amount of LTE resources that can be assigned to one user is much larger than the requirements of a single MTCD. In this paper, a QoS-enabled algorithm is proposed to aggregate MTCD traffic coming from many sources at the Relay Node (RN) that classifies and aggregates the MTCD traffic based on the source type and delay requirements. In this study, three types of MTCD and one H2H sources will be considered. Each type of MTCD traffic will be grouped into a separate queue, and will be served with the appropriate priority. Resources are then assigned to the aggregated MTC traffic instead of an individual assignment for each MTCD, while the H2H users will be directly connected to the LTE. Two schemes of resource partitioning and sharing between the MTCDs and the H2H users will be considered: one proportional and the other moving-boundary. Simulation models will be built to evaluate the proposed algorithms. While the obtained results for the first scheme showed a clear improvement in LTE resource utilization for the MTCDs, a negative effect was noticed in the performance of the H2H users. The second scheme achieved a positive improvement for both MTCDs and H2H users.

关键词： Resource management Long Term Evolution Quality of service Delays Data aggregation Multiplexing partitioning algorithms 5G networks data aggregation Internet of Things (IoT) MTC resource allocation quality of service (QoS)

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Comparative Analysis Review of Pioneering DBSCAN and Successive Density-Based Clustering algorithms

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IEEE ACCESS 2021年 9卷 87918-87935页

作者： Bushra, Adil Abdu Yi, Gangman Dongguk Univ Dept Multimedia Engn Seoul 04620 South Korea

The density-based spatial clustering of applications with noise (DBSCAN) is regarded as a pioneering algorithm of the density-based clustering technique. It provides the ability to handle outlier objects, detect clusters of different shapes, and disregard the need for prior knowledge about existing clusters in a dataset. These features along with its simplistic approach helped it become widely applicable in many areas of science. However, for all its accolades, the DBSCAN still has limitations in terms of performance, its ability to detect clusters of varying densities, and its dependence on user input parameters. Multiple DBSCAN-inspired algorithms have been subsequently proposed to alleviate these and more problems of the algorithm. In this paper, the implementation, features, strengths, and drawbacks of the DBSCAN are thoroughly examined. The successive algorithms proposed to provide improvement on the original DBSCAN are classified based on their motivations and are discussed. Experimental tests were conducted to understand and compare the changes presented by a C++ implementation of these algorithms along with the original DBSCAN algorithm. Finally, the analytical evaluation is presented based on the results found.

关键词： Clustering algorithms partitioning algorithms Data models Object recognition Classification algorithms Task analysis Spatial databases Unsupervised learning clustering DBSCAN spatial database

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Large-Scale Network Community Detection Using Similarity-Guided Merge and Refinement

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IEEE ACCESS 2021年 9卷 78538-78552页

作者： Tunali, Volkan Maltepe Univ Fac Engn & Nat Sci Dept Software Engn TR-34857 Istanbul Turkey

It is possible to extract valuable insights about the functional properties of a system by identifying and inspecting the community structure in the network that models the system. Community detection aims to extract these community structures from networks. Many community detection methods have been proposed that consider the problem from different perspectives. However, with the emergence of very large and complex networks from variety of domains, there has been a growing need for community detection methods that can operate at scale effectively and efficiently. Considering this, we propose a novel algorithm for large-scale community detection, based on two novel similarity indices we propose as well. In the first stage of our proposed algorithm, we generate candidate communities using a mechanism similar to information propagation very rapidly. Then, we merge small candidates that have fewer nodes than a calculated threshold with the larger ones using similarity between nodes and communities. Next, we engage a refinement operation on the candidates by moving all nodes to the candidates to which they are most similar using the same similarity index again. After that, we merge small communities with larger ones by using the similarity between communities until no gain in the modularity is obtained. Finally, in the last stage, we employ the same refinement operation as in the third stage. With an extensive experimentation on real-world and artificially-generated benchmark networks, we demonstrate and verify the performance and effectiveness of the proposed algorithm comparing it with the state-of-the-art methods. Experimental results indicate that our algorithm scales very well with growing size and complexity of networks. Besides, our algorithm outperforms most state-of-the-art community detection methods both in detection performance and computation time.

关键词： Heuristic algorithms Benchmark testing Clustering algorithms Complex networks Tools Social networking (online) partitioning algorithms Network community detection network clustering network science network analysis complex networks

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Clustering Hypergraphs via the MapEquation

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IEEE ACCESS 2021年 9卷 72377-72386页

作者： Swan, Matthew Zhan, Justin Univ Nevada Dept Math Las Vegas NV 89154 USA Univ Arkansas Dept Comp Sci & Comp Engn Fayetteville AR 72701 USA

A hypergraph is a generalization of a graph in that the restriction of pairwise affinity scores is lifted in favor of affinity scores that can be evaluated between an arbitrary number of inputs. Hypergraphs clustering is the process of finding groups in which members of a given hypergraph exhibit a high similarity and dissimilarity with members outside their group. In this paper, we generalize the well-known MapEquation, an optimization equation used in the clustering of nonhypergraphs, for hypergraphs. We develop an agglomerative algorithm, Hypergraph Random Walks (HRW), to find an approximate solution to the generalized MapEquation. Our algorithm requires neither hyperparameter setting nor any restriction on the underlying hypergraph. We show that our algorithm has a strong theoretical performance on the newly defined ring of hyper cliques and demonstrate that our algorithm scales to hypergraphs with large edge sets.

关键词： Clustering algorithms Approximation algorithms partitioning algorithms Linear programming Entropy Tensors Optimization Hypergraph clustering random walks MapEquation hyper cliques

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A Community Structure Enhancement-Based Community Detection Algorithm for Complex Networks

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2021年第5期51卷 2833-2846页

作者： Su, Yansen Liu, Chunlong Niu, Yunyun Cheng, Fan Zhang, Xingyi Anhui Univ Sch Comp Sci & Technol Key Lab Intelligent Comp & Signal Proc Minist Educ Hefei 230601 Peoples R China China Univ Geosci Sch Informat Engn Beijing 100083 Peoples R China

Community detection has been recognized as one of the most important tools to discover useful information hidden in complex networks which is usually hard to be obtained by simple observations. Existing community detection algorithms have demonstrated their effectiveness on a variety of complex networks, most of them, however, suffer from the scalability issue on complex networks without a clear community structure due to the challenge in the detection of ambiguous community structure. To address this issue, in this paper, we propose a community structure enhancement method, termed CSE, for community detection in complex networks. In the proposed CSE, the community structure of a network is enhanced by adding links between the nodes possibly belonging to the same community and reducing links between those belonging to different communities, thereby converting an ambiguous community structure into a structure much clearer than the original one. The experimental results show the superior performance of the proposed CSE over five state-of-the-art community detection algorithms on both synthetic benchmark networks and real-world networks, especially for those without a clear community structure.

关键词： Detection algorithms Complex networks partitioning algorithms Clustering algorithms Tools Signal processing algorithms Community detection community structure enhancement complex network

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