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A New Model Using Multiple Feature Clustering and Neural Networks for Forecasting Hourly PM2.5 Article

Hui Liu, Zhihao Long, Zhu Duan, Huipeng Shi

Engineering 2020, Volume 6, Issue 8,   Pages 944-956 doi: 10.1016/j.eng.2020.05.009

Abstract:

Particulate matter with an aerodynamic diameter no greater than 2.5 μm (PM2.5) concentrationdecomposition (MCD)–echo state network (ESN)–particle swarm optimization (PSO), for multi-step PMThe clustered results of the PM2.5 concentration series are decomposed into several sublayersReal PM2.5 concentration data from four cities located in different zones in China are utilizedindicate that the proposed forecasting model is suitable for the multi-step high-precision forecasting of PM

Keywords： PM2.52.5浓度预测     PM2.52.5浓度聚类     经验小波分解     多步预测    

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Application of Fuzzy Pattern Recognition in the Measurement of Slurry Concentration

Li Dejun,Lv Runhua,Wang Runtian

Strategic Study of CAE 2007, Volume 9, Issue 5,   Pages 81-84

Abstract:

  Slurry is widely used in construction projects, and it is important to control the slurry's physical characteristic properly. The acoustic method is used,  which can effectively monitor the physical parameters of slurry,  such as concentration. Data processing affects directly the precision in the measurement of slurry concentration by the sound attenuation and velocity. Based on the fuzzy pattern recognition, data are sorted and further classified, with cooperative clustering algorithm.

Keywords： fuzzy pattern recognition     nearest neighbor(NN)     cooperative clustering algorithm(CCA)     slurry concentration    

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An anchor-based spectral clustering method None

Qin ZHANG, Guo-qiang ZHONG, Jun-yu DONG

Frontiers of Information Technology & Electronic Engineering 2018, Volume 19, Issue 11,   Pages 1385-1396 doi: 10.1631/FITEE.1700262

Abstract:

Spectral clustering is one of the most popular and important clustering methods in pattern recognition, machine learning, and data mining. However, its high computational complexity limits it in applications involving truly large-scale datasets. For a clustering problem with n samples, it needs to compute the eigenvectors of the graph Laplacian with O(n3) time complexity. To address this problem, we propose a novel method called anchor-based spectral clustering (ASC) by employing anchor points of data. Specifically, m (m<<n) anchor points are selected from the dataset, which can basically maintain the intrinsic (manifold) structure of the original data. Then a mapping matrix between the original data and the anchors is constructed. More importantly, it is proved that this data-anchor mapping matrix essentially preserves the clustering structure of the data. Based on this mapping matrix, it is easy to approximate the spectral embedding of the original data. The proposed method scales linearly relative to the size of the data but with low degradation of the clustering performance. The proposed method, ASC, is compared to the classical spectral clustering and two state-of-the-art accelerating methods, i.e., power iteration clustering and landmark-based spectral clustering, on 10 real-world applications under three evaluation metrics. Experimental results show that ASC is consistently faster than the classical spectral clustering with comparable clustering performance, and at least comparable with or better than the state-of-the-art methods on both effectiveness and efficiency.

Keywords： Clustering     Spectral clustering     Graph Laplacian     Anchors    

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The research of grey clustering decision of assembly sequence based on petri net

Mo Qian,Luo Yi

Strategic Study of CAE 2008, Volume 10, Issue 11,   Pages 65-68

Abstract:

This paper establishes assembly model according to the intuitionistic graphics mode characteristics of petri net, and gets feasible assembly sequence according to the principle of petri net. Most of factors influencing assembly sequence are certainly qualitative, fuzzy, non-numerical, assembly sequence is regarded as a gray system, and grey clustering decision method is adopted to evaluate feasible assembly sequence. This paper analyzes the gray classification of the influence factor and studies grey clustering decision method steps. The analysis of example indicates this method can evaluate correctly the feasible assembly sequences according to the principle of petri net and obtain decision vector.

Keywords： assembly sequence     petri net     grey clustering decision method    

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Pattern Recognition With Fuzzy Central Clustering Algorithms

Zen Huanglin,Yuan Hui,Liu Xiaofang

Strategic Study of CAE 2004, Volume 6, Issue 11,   Pages 33-37

Abstract:

Based on optimization of constrained nonlinear programming, an approach of clustering center and a fuzzy membership function of pattern classification are derived from an objective function of the constrained nonlinear programming. An unsupervised algorithm with recursive expression and a fuzzy central cluster neural network are suggested in this paper. The fuzzy central cluster neural network proposed here can realize crisp decision or fuzzy decision in pattern classification.

Keywords： fuzzy sets     central cluster     pattern recognition     neural network    

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TIE algorithm: a layer over clustering-based taxonomy generation for handling evolving data None

Rabia IRFAN, Sharifullah KHAN, Kashif RAJPOOT, Ali Mustafa QAMAR

Frontiers of Information Technology & Electronic Engineering 2018, Volume 19, Issue 6,   Pages 763-782 doi: 10.1631/FITEE.1700517

Abstract: Taxonomy is generated to effectively organize and access large volume of data. A taxonomy is a way of representing concepts that exist in data. It needs to continuously evolve to reflect changes in data. Existing automatic taxonomy generation techniques do not handle the evolution of data; therefore, the generated taxonomies do not truly represent the data. The evolution of data can be handled by either regenerating taxonomy from scratch, or allowing taxonomy to incrementally evolve whenever changes occur in the data. The former approach is not economical in terms of time and resources. A taxonomy incremental evolution (TIE) algorithm, as proposed, is a novel attempt to handle the data that evolve in time. It serves as a layer over an existing clustering-based taxonomy generation technique and allows an existing taxonomy to incrementally evolve. The algorithm was evaluated in research articles selected from the computing domain. It was found that the taxonomy using the algorithm that evolved with data needed considerably shorter time, and had better quality per unit time as compared to the taxonomy regenerated from scratch.

Keywords： Taxonomy     Clustering algorithms     Information science     Knowledge management     Machine learning    

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Efficient parallel implementation of a density peaks clustering algorithm on graphics processing unit Article

Ke-shi GE, Hua-you SU, Dong-sheng LI, Xi-cheng LU

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 7,   Pages 915-927 doi: 10.1631/FITEE.1601786

Abstract: The density peak (DP) algorithm has been widely used in scientific research due to its novel and effective peak density-based clustering approach. However, the DP algorithm uses each pair of data points several times when determining cluster centers, yielding high computational complexity. In this paper, we focus on accelerating the time-consuming density peaks algorithm with a graphics processing unit (GPU). We analyze the principle of the algorithm to locate its computational bottlenecks, and evaluate its potential for parallelism. In light of our analysis, we propose an efficient parallel DP algorithm targeting on a GPU architecture and implement this parallel method with compute unified device architecture (CUDA), called the ‘CUDA-DP platform’. Specifically, we use shared memory to improve data locality, which reduces the amount of global memory access. To exploit the coalescing accessing mechanism of GPU, we convert the data structure of the CUDA-DP program from array of structures to structure of arrays. In addition, we introduce a binary search-and-sampling method to avoid sorting a large array. The results of the experiment show that CUDA-DP can achieve a 45-fold acceleration when compared to the central processing unit based density peaks implementation.

Keywords： Density peak     Graphics processing unit     Parallel computing     Clustering    

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Robust Maximum Entropy Clustering Algorithm RMEC and Its Outlier Labeling

Deng Zhaohong,Wang Shitong,Wu Xisheng,Hu Dewen

Strategic Study of CAE 2004, Volume 6, Issue 9,   Pages 38-45

Abstract:

In this paper, the novel robust maximum entropy clustering algorithm RMEC, as the improved version of the maximum entropy algorithm MEC, is presented to overcome its drawbacks: very sensitive to outliers and uneasy to label them. With the introduction of Vapnik's ε-insensitive loss function and the new weight factors, the new objective function is re-constructed, and consequently, its new update rules are derived according to the Lagrangian optimization theory. Compared with algorithm MEC, the main contributions of algorithm RMEC exist in its much better robustness for outliers and the fact that it can effectively label outliers in the dataset using the obtained weight factors. The experimental results demonstrate its superior performance in enhancing the robustness and labeling outliers in the dataset.

Keywords： entropy     clustering     robustness     outliers     ε-insensitive loss function     weight factors    

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A Hierarchical-Based Initialization Method for K-Means Algorithm

Tang Jiubin,Lu Jianfeng,Tang Zhenmin, Yang Jingyu

Strategic Study of CAE 2007, Volume 9, Issue 11,   Pages 74-79

Abstract:

K-means algorithm is one of common clustering algorithms,  but the cluster center initialization is a hard problem.  In this paper,  a hierarchical-based initialization approach is proposed for K-Means algorithm.  The general clustering problem is treated as weighted clustering problem,  the original data is sampled level by level to reduce the data amount.  Then clustering is carried out at each level by top-down.  The initial center of each level is mapped from the clustering center of upper level and this procedure is repeated until the original data level is reached.  As a result,  the initial center for the original data is obtained.  Both the experimental results on simulated data and real data show that the proposed method has high converging speed,  high quality of clustering and is insensitive to noise,  which is superior to some existing clustering algorithms.

Keywords： hierarchical technique     initial cluster centers     weighted data     K-means clustering    

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A Comprehensive Approach for the Clustering of Similar-Performance Cells for the Design of a Lithium-Ion Battery Module for Electric Vehicles Article

Wei Li, Siqi Chen, Xiongbin Peng, Mi Xiao, Liang Gao, Akhil Garg, Nengsheng Bao

Engineering 2019, Volume 5, Issue 4,   Pages 795-802 doi: 10.1016/j.eng.2019.07.005

Abstract:

An energy-storage system comprised of lithium-ion battery modules is considered to be a core component of new energy vehicles, as it provides the main power source for the transmission system. However, manufacturing defects in battery modules lead to variations in performance among the cells used in series or parallel configuration. This variation results in incomplete charge and discharge of batteries and non-uniform temperature distribution, which further lead to reduction of cycle life and battery capacity over time. To solve this problem, this work uses experimental and numerical methods to conduct a comprehensive investigation on the clustering of battery cells with similar performance in order to produce a battery module with improved electrochemical performance. Experiments were first performed by dismantling battery modules for the measurement of performance parameters. The k-means clustering and support vector clustering (SVC) algorithms were then employed to produce battery modules composed of 12 cells each. Experimental verification of the results obtained from the clustering analysis was performed by measuring the temperature rise in the cells over a certain period, while air cooling was provided. It was found that the SVC-clustered battery module in Category 3 exhibited the best performance, with a maximum observed temperature of 32 ℃. By contrast, the maximum observed temperatures of the other battery modules were higher, at 40 ℃ for Category 1 (manufacturer), 36 ℃ for Category 2 (manufacturer), and 35 ℃ for Category 4 (k-means-clustered battery module).

Keywords： Clustering algorithm     Battery module     Equalization     Electric vehicle    

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Visual interactive image clustering: a target-independent approach for configuration optimization in machine vision measurement Research Article

Lvhan PAN, Guodao SUN, Baofeng CHANG, Wang XIA, Qi JIANG, Jingwei TANG, Ronghua LIANG

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 3,   Pages 355-372 doi: 10.1631/FITEE.2200547

Abstract: (MVM) is an essential approach that measures the area or length of a target efficiently and non-destructively for product quality control. The result of MVM is determined by its configuration, especially the in image acquisition and the algorithmic in image processing. In a traditional workflow, engineers constantly adjust and verify the configuration for an acceptable result, which is time-consuming and significantly depends on expertise. To address these challenges, we propose a target-independent approach, , which facilitates configuration optimization by grouping images into different clusters to suggest lighting schemes with common parameters. Our approach has four steps: data preparation, data sampling, data processing, and visual analysis with our visualization system. During preparation, engineers design several candidate lighting schemes to acquire images and develop an algorithm to process images. Our approach samples engineer-defined parameters for each image and obtains results by executing the algorithm. The core of data processing is the explainable measurement of the relationships among images using the algorithmic parameters. Based on the image relationships, we develop VMExplorer, a visual analytics system that assists engineers in grouping images into clusters and exploring parameters. Finally, engineers can determine an appropriate lighting scheme with robust parameter combinations. To demonstrate the effectiveness and usability of our approach, we conduct a case study with engineers and obtain feedback from expert interviews.

Keywords： Machine vision measurement     Lighting scheme design     Parameter optimization     Visual interactive image clustering    

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A social tag clustering method based on common co-occurrence group similarity

Hui-zong LI,Xue-gang HU,Yao-jin LIN,Wei HE,Jian-han PAN

Frontiers of Information Technology & Electronic Engineering 2016, Volume 17, Issue 2,   Pages 122-134 doi: 10.1631/FITEE.1500187

Abstract:

Social tagging systems are widely applied in Web 2.0. Many users use these systems to create, organize,manage, and share Internet resources freely. However, many ambiguous and uncontrolled tags produced by social tagging systems not only worsen users’ experience, but also restrict resources’ retrieval efficiency. Tag clustering can aggregate tags with similar semantics together, and help mitigate the above problems. In this paper, we first present a common co-occurrence group similarity based approach, which employs the ternary relation among users,resources, and tags to measure the semantic relevance between tags. Then we propose a spectral clustering method to address the high dimensionality and sparsity of the annotating data. Finally, experimental results show that the proposed method is useful and efficient.

Keywords： Social tagging systems     Tag co-occurrence     Spectral clustering     Group similarity http://dx.doi.org/10.1631/FITEE.1500187    

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A Decision-making Method Based on Fuzzy Sets and Rough Sets Theory

Luo Dang

Strategic Study of CAE 2004, Volume 6, Issue 12,   Pages 32-36

Abstract:

In this paper, a combined decision-making model is presented for dealing with uncertain and imprecise problems, based on the difference between fuzzy sets and rough sets theories. The model is firstly to classify the uncertain examples given using fuzzy clustering analysis, to make a decision table specified,and then to simplify the decision table by means of the rough sets theory. It gives all possible minimal decision algorithms associated with the decision table. These decision algorithms have found application in larger field.

Keywords： fuzzy clustering     rough sets     decision table     decision rule    

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Embedding expert demonstrations into clustering buffer for effective deep reinforcement learning Research Article

Shihmin WANG, Binqi ZHAO, Zhengfeng ZHANG, Junping ZHANG, Jian PU

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 11,   Pages 1541-1556 doi: 10.1631/FITEE.2300084

Abstract: As one of the most fundamental topics in (RL), is essential to the deployment of deep RL algorithms. Unlike most existing exploration methods that sample an action from different types of posterior distributions, we focus on the policy and propose an efficient selective sampling approach to improve by modeling the internal hierarchy of the environment. Specifically, we first employ in the policy to generate an action candidate set. Then we introduce a clustering buffer for modeling the internal hierarchy, which consists of on-policy data, off-policy data, and expert data to evaluate actions from the clusters in the action candidate set in the exploration stage. In this way, our approach is able to take advantage of the supervision information in the expert demonstration data. Experiments on six different continuous locomotion environments demonstrate superior performance and faster convergence of selective sampling. In particular, on the LGSVL task, our method can reduce the number of convergence steps by 46.7% and the convergence time by 28.5%. Furthermore, our code is open-source for reproducibility. The code is available at https://github.com/Shihwin/SelectiveSampling.

Keywords： Reinforcement learning     Sample efficiency     Sampling process     Clustering methods     Autonomous driving    

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Controlling the contact levels of details for fast and precise haptic collision detection Article

A Ram CHOI, Sung Min KIM, Mee Young SUNG

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 8,   Pages 1117-1130 doi: 10.1631/FITEE.1500498

Abstract: For accurate and stable haptic rendering, collision detection for interactive haptic applications has to be done by filling in or covering target objects as tightly as possible with bounding volumes (spheres, axis-aligned bounding boxes, oriented bounding boxes, or polytopes). In this paper, we propose a method for creating bounding spheres with respect to the contact levels of details (CLOD), which can fit objects while maintaining the balance between high speed and precision of collision detection. Our method is composed mainly of two parts: bounding sphere formation and two-level collision detection. To specify further, bounding sphere formation can be divided into two steps: creating spheres and clustering spheres. Two-level collision detection has two stages as well: fast detection of spheres and precise detection in spheres. First, bounding spheres are created for initial fast probing to detect collisions of spheres. Once a collision is probed, a more precise detection is executed by examining the distance between a haptic pointer and each mesh inside the colliding boundaries. To achieve this refined level of detection, a special data structure of a bounding volume needs to be defined to include all mesh information in the sphere. After performing a number of experiments to examine the usefulness and performance of our method, we have concluded that our algorithm is fast and precise enough for haptic simulations. The high speed detection is achieved through the clustering of spheres, while detection precision is realized by voxel-based direct collision detection. Our method retains its originality through the CLOD by distance-based clustering.

Keywords： Collision detection     Haptic rendering     Bounding sphere     Clustering     Contact levels of details (CLOD)    

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Title Author Date Type Operation

A New Model Using Multiple Feature Clustering and Neural Networks for Forecasting Hourly PM2.5

Hui Liu, Zhihao Long, Zhu Duan, Huipeng Shi

Journal Article

Application of Fuzzy Pattern Recognition in the Measurement of Slurry Concentration

Li Dejun,Lv Runhua,Wang Runtian

Journal Article

An anchor-based spectral clustering method

Qin ZHANG, Guo-qiang ZHONG, Jun-yu DONG

Journal Article

The research of grey clustering decision of assembly sequence based on petri net

Mo Qian,Luo Yi

Journal Article

Pattern Recognition With Fuzzy Central Clustering Algorithms

Zen Huanglin,Yuan Hui,Liu Xiaofang

Journal Article

TIE algorithm: a layer over clustering-based taxonomy generation for handling evolving data

Rabia IRFAN, Sharifullah KHAN, Kashif RAJPOOT, Ali Mustafa QAMAR

Journal Article

Efficient parallel implementation of a density peaks clustering algorithm on graphics processing unit

Ke-shi GE, Hua-you SU, Dong-sheng LI, Xi-cheng LU

Journal Article

Robust Maximum Entropy Clustering Algorithm RMEC and Its Outlier Labeling

Deng Zhaohong,Wang Shitong,Wu Xisheng,Hu Dewen

Journal Article

A Hierarchical-Based Initialization Method for K-Means Algorithm

Tang Jiubin,Lu Jianfeng,Tang Zhenmin, Yang Jingyu

Journal Article

A Comprehensive Approach for the Clustering of Similar-Performance Cells for the Design of a Lithium-Ion Battery Module for Electric Vehicles

Wei Li, Siqi Chen, Xiongbin Peng, Mi Xiao, Liang Gao, Akhil Garg, Nengsheng Bao

Journal Article

Visual interactive image clustering: a target-independent approach for configuration optimization in machine vision measurement

Lvhan PAN, Guodao SUN, Baofeng CHANG, Wang XIA, Qi JIANG, Jingwei TANG, Ronghua LIANG

Journal Article

A social tag clustering method based on common co-occurrence group similarity

Hui-zong LI,Xue-gang HU,Yao-jin LIN,Wei HE,Jian-han PAN

Journal Article

A Decision-making Method Based on Fuzzy Sets and Rough Sets Theory

Luo Dang

Journal Article

Embedding expert demonstrations into clustering buffer for effective deep reinforcement learning

Shihmin WANG, Binqi ZHAO, Zhengfeng ZHANG, Junping ZHANG, Jian PU

Journal Article

Controlling the contact levels of details for fast and precise haptic collision detection

A Ram CHOI, Sung Min KIM, Mee Young SUNG

Journal Article