Journal of Intelligent & Fuzzy Systems - Volume 33, issue 5

Authors: Chen, Xu | Cheng, Haitao | Wang, Hairong | Li, Weijun

Article Type: Research Article

Abstract: Fuzziness widely exists in most of spatiotemporal applications, but the existing spatiotemporal data models cannot support representation of fuzzy spatiotemporal objects and their semantic relationships. In this paper, after investigating the semantics of fuzzy spatiotemporal data, we propose a novel approach to model fuzzy spatiotemporal object based on UML class diagram. Firstly , we propose a formal definition about fuzzy spatiotemporal Event-Process. On this basis , the UML data model is extended to represent fuzzy spatial and temporal data. Finally , the potential usefulness of our fuzzy spatiotemporal UML object model is demonstrated with an example taken from Fujiwara Effect.

Keywords: Fuzzy spatiotemporal data, UML, event, process, modelling

DOI: 10.3233/JIFS-169322

Citation: Journal of Intelligent & Fuzzy Systems, vol. 33, no. 5, pp. 2727-2736, 2017

Authors: Qi, Chengming | Hu, Lishuan | Yu, Xin

Article Type: Research Article

Abstract: Feature selection aims at selecting a feature subset that has the most discriminative information and preserve most of characteristics from original features in H yperS pectral I mage (HSI) classification. This paper proposes a two-stage feature selection method based on M utual I nformation (MI) and J effries-M atusita (J-M) measure. In first stage, we select a feature subset with minimal redundancy maximal relevance criteria. In second stage, we select further a feature subset from which obtained in first stage by maximizing J-M distance. M ultiple K ernel L earning (MKL) and E nsemble L earning (EL) are promising family …of machine learning algorithms and have been applied extensively in HSI classification. Many MKL methods often formulate the problem as an optimization task. To avoid solving the complicated optimization problem, this paper presents an ensemble learning framework, SMKB (Stochastic Multiple Kernel Boosting), which applies Adaptive Boosting (AdaBoost) and stochastic approach to learning multiple kernel-based classifier for multi-class classification problem. We examine empirical performance of proposed approach on benchmark hyperspectral classification data set in comparison with various state-of-the-art algorithms. Experimental results demonstrate that the proposed method obtains better feature subsets and is more effective and efficient than classical methods. Show more

Keywords: Feature selection, hyperspectral classification, Jeffries-Matusita measure, Multiple Kernel Learning, Mutual Information

DOI: 10.3233/JIFS-169323

Citation: Journal of Intelligent & Fuzzy Systems, vol. 33, no. 5, pp. 2737-2747, 2017

Authors: Qu, Shaojian | Meng, Dehua | Zhou, Yongyi | Dai, Yeming

Article Type: Research Article

Abstract: In this paper, we propose a distributionally robust optimization approach for N -player, nonzero sum finite state/action games with incomplete information where the payoff matrix is stochastic with an imprecise distribution which is assumed to be attached to an a-prior known set. Our model is different from the robust game theory which presents a robust optimization approach to game theory with the uncertain payoff matrix in a compact convex set without probabilistic information which can lead to overly conservative solutions. A distributionally robust approach is used to cope with our setting in the games by combining the stochastic optimization approach …and the robust optimization approach which can be called the distributionally robust games. We show that the existence of the equilibria for the distributionally robust games. The computation method for equilibrium point, with the first- and second information about the uncertain payoff matrix, can be reformulated as semidefinite programming problems which can be tractably realized. A two-echelon supply chain competition with demand uncertainty is analyzed by applying the distributionally robust game theory. Show more

Keywords: Game theory, distributionally robust optimization, semidefinite programming, equilibrium point, supply chain

DOI: 10.3233/JIFS-169324

Citation: Journal of Intelligent & Fuzzy Systems, vol. 33, no. 5, pp. 2749-2762, 2017

Authors: Fei, Mei | Yi, Pan | Kedong, Zhu | Jianyong, Zheng

Article Type: Research Article

Abstract: As an important content of smart grid’s intelligentialize, high voltage circuit breaker (HVCB) can enhance security and stability of power system observably. Therefore, on-line monitoring and fault diagnosis technologies develop significantly. In order to promote diagnosis accuracy and learning ability, an on-line hybrid method is proposed for fault diagnosis of HVCB in this paper, and characteristics of control coil current are used as modeling data. The diagnosis model built by this method includes three modules: fault detection, fault learning and fault recognition. Fault detection is used to inspect monitoring data and find the fault samples using kernel principal component analysis …(KPCA). Fault learning is used to judge whether there is new knowledge which is built by kernel fuzzy C-means (KFCM), and the new knowledge can be brought into this diagnosis model to update the fault recognition module subsequently. Fault recognition is used to realize automatic categorizing of fault data to identify the fault type using multi-classification support vector machine (SVM). The diagnosis conclusion will be obtained finally. Perfect results in diagnosing typical failure of HVCB using the proposed method have been proved by data experiment. Show more

Keywords: Fault diagnosis, high voltage circuit breaker, kernel fuzzy C-means, kernel principal component analysis, support vector machine

DOI: 10.3233/JIFS-169325

Citation: Journal of Intelligent & Fuzzy Systems, vol. 33, no. 5, pp. 2763-2774, 2017

Authors: sun, Shunyuan | sun, Li | chen, Shu

Article Type: Research Article

Abstract: Energy-Efficient Uneven Clustering (EEUC) protocol is applied to low-redundancy Wireless Sensor Network (WSN), the uneven consumption of sensor node energy leads to nodes’ early death which will seriously affect the network quality. This paper proposes the Chinese Remainder Theorem (CRT) division algorithm for EEUC Energy-Efficient Uneven Clustering routing protocol optimization. The nodes split the packets before forwarding and balance the energy consumption of the nodes forward packets which results in alleviating hot problems of wireless sensor networks. We also add energy and distance parameters to the threshold function in the cluster heads selection mechanism, and optimize the cluster semidiameter to …make the cluster head election distribution more reasonable which will help to optimize the network energy consumption. The result of simulation indicates that the algorithm we propose can make energy consumption of lower redundant sensor network nodes more balanced and delay the first death time of node as well as prolong the network life time effectively. Show more

Keywords: LEACH protocol, Uneven Cluster-Based, energy balance, competitive cluster radius, low redundancy

DOI: 10.3233/JIFS-169326

Citation: Journal of Intelligent & Fuzzy Systems, vol. 33, no. 5, pp. 2775-2782, 2017

Authors: Tong, Qiang | Cheng, Haitao

Article Type: Research Article

Abstract: RDF (Resource Description Framework) and RDF Schema (RDF(S)) are the normative languages to represent the Web information. Extracting RDF(S) from the existing data sources is becoming an important research issue. However, information imprecision and uncertainty exist in many real-world applications and hence fuzzy data modeling has been extensively investigated in various data models. In particular, fuzzy Entity-Relationship (ER) model has been widely applied to fuzzy data modeling in many application domains. Therefore, how to extract RDF(S) from the existing fuzzy ER models becomes an important issue to be solved. In this paper, we propose a formal approach for extracting RDF(S) …from fuzzy ER models and implement a prototype tool. We first give the formal definitions of fuzzy ER models and RDF(S). After that , a formal approach for extracting RDF(S) from fuzzy ER models is proposed. Also , an extraction example is provided. Finally , based on the proposed approach, a prototype extraction tool is implemented, and the experiments show that the approach and the tool are feasible. Show more

Keywords: Fuzzy ER models, RDF, RDF schema, extraction

DOI: 10.3233/JIFS-169327

Citation: Journal of Intelligent & Fuzzy Systems, vol. 33, no. 5, pp. 2783-2792, 2017

Authors: Zhang, Xiaonan | Yuan, Jianhu | An, Lizhou | Shen, Xinmin | Song, Jiaxing | Bai, Panfeng | Tian, Aoke

Article Type: Research Article

Abstract: Based on POC set theory and SOC unit, a 1T3R parallel stable platform is proposed. Topology structure design of low-DOF parallel stable platform is studied. Both inverse and direct kinematics and Jacobean matrix of this mechanism are analyzed. Based on the structure of the whole platform, a three-dimensional solid model is established with Pro/Engineer software, and kinematics simulation analysis is performed in ADAMS. It establishes base for the application of this kind of parallel stable platform and the follow-up research for analysis of kinematics and workspace. The results of this study on the stability of the institutional platform have an …important significance. Show more

Keywords: Position analysis, inverse and direct kinematics, Jacobean matrix, parallel stable platform, three-dimensional solid model

DOI: 10.3233/JIFS-169328

Citation: Journal of Intelligent & Fuzzy Systems, vol. 33, no. 5, pp. 2793-2799, 2017

Authors: Kaijun, Leng | Wen, Shi | Xuejun, Hu | Lin, Pan

Article Type: Research Article

Abstract: Firms are being increasingly pressured under the current marketing environment, the uncertainty and risks has greatly influenced the Supply Chain Management project implementation. In this paper, we present a new buffer sizing method that takes into consideration the activity duration risk as well as the multi-resource constraint risk under uncertainty. First of all, the key risk factors are identified and the SCM activity duration risk is effectively assessed by using the Bayesian Network. Secondly, the resource constraint risk is appropriately determined by using the resource flow network. The, a reasonable buffer sizing method is developed for the construction of a …stable critical chain scheduling plan. A numerical example and comparative study validate the effectiveness and practicability of the proposed method, which is capable of maintaining the schedule stability with a short project makespan, while ensuring a high on-time completion rate. Show more

Keywords: Supply chain management, critical chain management, theory of constraints, buffer management

DOI: 10.3233/JIFS-169329

Citation: Journal of Intelligent & Fuzzy Systems, vol. 33, no. 5, pp. 2801-2809, 2017

Authors: Lin, Wenliang | Deng, Zhongliang | Li, Xueming | Fang, Qin | Li, Ning | Wang, Ke

Article Type: Research Article

Abstract: LBS (Location Based Services) have been a type of “killer application” for ongoing and upcoming internet services. ILBD (Indoor Location Big data) are extremely big multimedia data indeed. However, indoor location data are more complicated than outdoor. Lack of unified representation model and data redundancies make ILBD hard to cluster and mine location based values. Therefore, this paper proposes a new multi-dimensional features model and compacted clustering for ILBD. Unified ILBD model combines spatial and time features of different scales and states, which employs normalized data frames to pre-process original data. Scalable Euclidean extending distance is designed to characterize relationships between heterogeneous …data and represent connection of different dimensions. In order to reduce ILBD redundancies and flaws, compacted clustering method are proposed, which construct location ontology and sensations parameters to determine ILBD main affecting elements, the sluggish elements would be filtered and shrink to decrease the amount of ILBD. The new multi-dimensional features model would be applied in LBS framework. The tests and simulations verify proposed model have enhanced 36.7% convergence estimation RMSE and 12.3% regional flow estimation accuracy performance, which improve accuracy of ILBD mining and reduce ILBD redundancies and flaws. Show more

Keywords: LBS, ILBD, data process, clustering and mining, data models

DOI: 10.3233/JIFS-169330

Citation: Journal of Intelligent & Fuzzy Systems, vol. 33, no. 5, pp. 2811-2822, 2017

Authors: Lu, Zhang | Wenbi, Rao | Daxing, Xu | Hailun, Wang

Article Type: Research Article

Abstract:  In the system with unknown random bias, it is common to treat the bias as part of the system state, that is augmented state Kalman filter (ASKF) which will lead to overflow and can not work in computer system. To avoid use the ASKF, two-stage Kalman filter was proposed. It is generally known that the computational complexity of Kalman Filter is highly when all sensor measurements are processed centrally. In order to overcome this disadvantage, High-degree Cubature Information Filter (HCIF) is introduced. Based on HCIF, two-stage High-degree Cubature Information Filter (TSHCIF) is proposed for the nonlinear system with the unknown …random bias in order to estimate the high-dimensional state and centralized process the measurement values. The key idea for TSHCIF is using the matrix transformation technique which is called Two-stage transformation in the framework of HCIF and make the covariance matrices block diagonal. The estimate of TSHCIF can be expressed as the output by the bias free filter and bias filter. Simulation results that validate the predicted efficiency improvements and it is shown that the proposed TSHCIF is mathematically equivalent to the augment HCIF (AHCIF). Show more

Keywords: High-degree Cubature Information Filter (HCIF), Two-stage HCIF (TSHCIF), nonlinear two-stage transformation (T transformation), nonlinear system, unknown random bias

DOI: 10.3233/JIFS-169331

Citation: Journal of Intelligent & Fuzzy Systems, vol. 33, no. 5, pp. 2823-2835, 2017