Journal of Intelligent & Fuzzy Systems - Volume 39, issue 3

Authors: Zeng, Wenyi | Ma, Rong | Yin, Qian | Zheng, Xin | Xu, Zeshui

Article Type: Research Article

Abstract: Image segmentation plays an important role in many fields such as computer vision, pattern recognition, machine learning and so on. In recent years, many variants of standard fuzzy C-means (FCM) algorithm have been proposed to explore how to remove noise and reduce uncertainty. In fact, there are uncertainty on the boundary between different patches in images. Considering that hesitant fuzzy set is a useful tool to deal with uncertainty, in this paper, we merge hesitant fuzzy set with fuzzy C-means algorithm, introduce a new kind of method of fuzzification and defuzzification of image and the distance measure between hesitant fuzzy …elements of pixels, present a method to establish hesitant membership degree of hesitant fuzzy element, and propose hesitant fuzzy C-means (HFCM) algorithm. Finally, we compare our proposed HFCM algorithm with some existing fuzzy C-means (FCM) algorithms, and apply HFCM algorithm in natural image, BSDS dataset image, different size images and multi-attribute decision making. These numerical examples illustrate the validity and applicability of our proposed algorithm including its comprehensive performance, reducing running time and almost without loss of accuracy. Show more

Keywords: Hesitant fuzzy set, fuzzy C-means algorithm, hesitant fuzzy C-means algorithm, image segmentation, information fusion

DOI: 10.3233/JIFS-191973

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 3, pp. 3681-3695, 2020

Authors: Liu, Feng-Lang | Chang, Ching-Ter

Article Type: Research Article

Abstract: The operation of small to medium-sized enterprises (SMEs ) is smaller in scope and scale of business that they tend to suffer from financial crises or even bankruptcy when facing an economic downturn [1 ]. Thus, most research for SMEs today focuses on the formulation and practice of strategies, such as exploring the key success factors and innovation research. Taiwan’s forklift industry (TFI ) belongs to SMEs that it is indispensable from construction engineering, but the related research has always been lacking to improve their competitiveness and sustainability. We refer to the relevant literature and expert opinions and use …the fuzzy analytic hierarchy process (FAHP) to verify and rank the 18 factors that affect financial factors in TFI. We summarized these factors into four financial facets, including profits, workforce, holding cost, and stock-out cost. Then, the MCGP-U model is used to find the optimal solution for TFI and Komatsu Forklift Taiwan (KFT ). In addition, KFT is taken as an example to ensure decision-makers (DMs ) remain KFT in a better financial status and competitive advantages under uncertain business environment. As a result, the MCGP-U model optimizes the four financial facets better than by the rule of thumb in KFT, especially in the holding cost section achieving savings as high as 55.9%. Finally, the model runs fast and provides robust results, which is suitable for SMEs, given the characteristics of lacking experts and funds. Show more

Keywords: MCGP-U, SMEs, Forklift, Fuzzy-AHP

DOI: 10.3233/JIFS-191976

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 3, pp. 3697-3712, 2020

Authors: Du, Wen Sheng

Article Type: Research Article

Abstract: Dombi operations which include the Dombi product and Dombi sum are special cases of t -norms and t -conorms besides the algebraic operations. Recently, operations and aggregation operators for q -rung orthopair fuzzy values (q -ROFVs) based on Dombi operations were proposed. In this paper, we further discuss some additional issues relating to Dombi operations and Dombi aggregation operators of q -ROFVs. First, we give a reasonable explanation for the definition of the Dombi scalar multiplication and Dombi exponentiation which are constructed respectively by the Dombi sum and Dombi product over q -ROFVs, and then investigate the fundamental properties of …these operations. Subsequently, the shift-invariance and homogeneity properties of the q -rung orthopair fuzzy Dombi weighted averaging/geometric operators are analyzed. And the boundedness of aforementioned aggregation operators are precisely characterized with respect to the parameter in Dombi operations. Finally, a method for multiattribute decision making is proposed by utilizing the developed operators under the q -rung orthopair fuzzy environment and an example of the selection of investment companies is given to illustrate the detailed decision making process. Show more

Keywords: aggregation operator, Dombi operation, multiattribute decision making, q-rung orthopair fuzzy value

DOI: 10.3233/JIFS-192052

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 3, pp. 3715-3735, 2020

Authors: Joshan Athanesious, J. | Vasuhi, S. | Vaidehi, V. | Shiny Christobel, J. | Jerart Julus, L.

Article Type: Research Article

Abstract: Detection of abnormal events in a traffic scene is a highly challenging task due to vast field of view, continuous stream of video data, various object interactions and complex events in Video Surveillance. Hence, this research proposes novel schemes using machine learning approach to detect abnormal events such as illegal U-turn, presence of pedestrian in driving region, wrong side driving and frequent lane change. Recently, Density Based Spatial Clustering of Applications with Noise (DBSCAN) is a popular method that has been used for clustering the trajectory datasets. The existing Density Based Clustering approach used for Abnormal detection in traffic scene …uses random selection of cluster radius (Eps) and minimum points (minpts) needed to form a cluster. This random selection is time consuming and inefficient clustering results in accuracy reduction in abnormal detection. So, Adaptive Density based Spatial Clustering of Applications with Noise (ADBSCAN) is proposed for the detection of abnormal events based on spatial temporal information relating to individual objects which determines the optimal values for the cluster radius (Eps) using the slope calculation of the K-d plot. Gaussion Mixture Model (GMM) is used for obtaining the moving foreground regions and region-based tracking is used for the identification of the objects in successive frames. The centroid of the region is calculated using image moments. If there is an occlusion between the vehicles then vehicle identification number (Id no) is used to differentiate them. The main advantage in this technique is clustering/labelling the normal pattern without the help of manual intervention. The effectiveness of ADBSCAN is experimentally evaluated using a real time benchmark video traffic dataset and it found that it gives better accuracy in detecting anomalies than the state-of-the-art techniques. Show more

Keywords: Abnormal detection, adaptive density, Eps, K-dist, minpts, slope

DOI: 10.3233/JIFS-192062

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 3, pp. 3737-3747, 2020

Authors: Ke, Ting | Li, Min | Zhang, Lidong | Lv, Hui | Ge, Xuechun

Article Type: Research Article

Abstract: In some real applications, only limited labeled positive examples and many unlabeled examples are available, but there are no negative examples. Such learning is termed as positive and unlabeled (PU) learning. PU learning algorithm has been studied extensively in recent years. However, the classical ones based on the Support Vector Machines (SVMs) are assumed that labeled positive data is independent and identically distributed (i.i.d) and the sample size is large enough. It leads to two obvious shortcomings. On the one hand, the performance is not satisfactory, especially when the number of the labeled positive examples is small. On the other …hand, classification results are not optimistic when datasets are Non-i.i.d. For this reason, this paper proposes a novel SVM classifier using Chebyshev distance to measure the empirical risk and designs an efficient iterative algorithm, named L ∞  - BSVM in short. L ∞  - BSVM includes the following merits: (1) it allows all sample points to participate in learning to prompt classification performance, especially in the case where the size of labeled data is small; (2) it minimizes the distance of the sample points that are (outliers in Non-i.i.d) farthest from the hyper-plane, where outliers are sufficiently taken into consideration (3) our iterative algorithm can solve large scale optimization problem with low time complexity and ensure the convergence of the optimum solution. Finally, extensive experiments on three types of datasets: artificial Non-i.i.d datasets, fault diagnosis of railway turnout with few labeled data (abnormal turnout) and six benchmark real-world datasets verify above opinions again and demonstrate that our classifier is much better than state-of-the-art competitors, such as B-SVM , LUHC , Pulce , B-LSSVM , NB and so on. Show more

Keywords: Optimization, SVMs, Chebyshev distance, structural risk, empirical risk

DOI: 10.3233/JIFS-192064

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 3, pp. 3749-3767, 2020

Authors: Han, Zhisong | Liang, Yaling | Chen, Zengqun | Zhou, Zhiheng

Article Type: Research Article

Abstract: Video-based person re-identification aims to match videos of pedestrians captured by non-overlapping cameras. Video provides spatial information and temporal information. However, most existing methods do not combine these two types of information well and ignore that they are of different importance in most cases. To address the above issues, we propose a two-stream network with a joint distance metric for measuring the similarity of two videos. The proposed two-stream network has several appealing properties. First, the spatial stream focuses on multiple parts of a person and outputs robust local spatial features. Second, a lightweight and effective temporal information extraction block …is introduced in video-based person re-identification. In the inference stage, the distance of two videos is measured by the weighted sum of spatial distance and temporal distance. We conduct extensive experiments on four public datasets, i.e., MARS, PRID2011, iLIDS-VID and DukeMTMC-VideoReID to show that our proposed approach outperforms existing methods in video-based person re-ID. Show more

Keywords: Person re-identification, two-stream network, local information, temporal information, similarity measurement

DOI: 10.3233/JIFS-192067

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 3, pp. 3769-3781, 2020

Authors: Yun, Yong Sik

Article Type: Research Article

Abstract: We generalized triangular fuzzy numbers from ℝ to ℝ 2 . By defining parametric operations between two α -cuts, which are regions, we obtained parametric operations for two triangular fuzzy numbers defined on ℝ 2 . We also generalized triangular fuzzy numbers from ℝ 2 to ℝ 3 . By defining parametric operations between two α -cuts, which are subsets of ℝ 3 …, we derived parametric operations for two triangular fuzzy numbers defined on ℝ 3 . For the calculation of Zadeh’s principle operators, the definition of parametric operations between two α -cuts, which are subsets of ℝ 3 , is critical. Show more

Keywords: Zadeh’s max-min composition operator, 3-dimensional triangular fuzzy number, 47N99

DOI: 10.3233/JIFS-192095

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 3, pp. 3783-3793, 2020

Authors: Li, Zhiming | Ai, Mingyao | Sun, Shuman

Article Type: Research Article

Abstract: This paper proposes three methods to estimate the parameters in uncertain differential equations (UDEs) based on discrete observation data. The first method is designed for a class of UDEs in which their solutions have the explicit expressions of uncertainty distribution. The second method is given to solve the estimation problem through the inverse uncertainty distribution. In the third method, the unknown parameters of UDEs are estimated by the solution of the corresponding α -path. These methods are interpreted to be efficient and practical by using a popular UDE with exponential solutions and obtaining the detailed estimators of the parameters.

Keywords: Uncertain differential equation, uncertainty distribution, inverse uncertainty distribution, α-path

DOI: 10.3233/JIFS-192119

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 3, pp. 3795-3804, 2020

Authors: Luo, Zhiming | Wang, Pei

Article Type: Research Article

Abstract: In this paper, limit theory of set-valued functions defined on an interval (for short, isv -functions) is preliminarily established. Firstly, the concept of isv -functions is introduced. Secondly, limits of isv -functions are proposed and their properties are obtained. Thirdly, point-wise continuity of isv -functions and continuous isv -functions are discussed. Finally, an application of this theory for rough sets is given.

Keywords: isv-function, limit, continuity, rough set

DOI: 10.3233/JIFS-192142

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 3, pp. 3805-3823, 2020

Authors: Chen, Yibin | Nie, Guohao | Zhang, Huanlong | Feng, Yuxing | Yang, Guanglu

Article Type: Research Article

Abstract: Kernel Correlation Filter (KCF) tracker has shown great potential on precision, robustness and efficiency. However, the candidate region used to train the correlation filter is fixed, so tracking is difficult when the target escapes from the search window due to fast motion. In this paper, an improved KCF is put forward for long-term tracking. At first, the moth-flame optimization (MFO) algorithm is introduced into tracking to search for lost target. Then, the candidate sample strategy of KCF tracking method is adjusted by MFO algorithm to make it has the capability of fast motion tracking. Finally, we use the conservative learning …correlation filter to judge the moving state of the target, and combine the improved KCF tracker to form a unified tracking framework. The proposed algorithm is tested on a self-made dataset benchmark. Moreover, our method obtains scores for both the distance precision plot (0.891 and 0.842) and overlap success plots (0.631 and 0.601) on the OTB-2013 and OTB-2015 data sets, respectively. The results demonstrate the feasibility and effectiveness compared with the state-of-the-art methods, especially in dealing with fast or uncertain motion. Show more

Keywords: Kernel correlation filter, moth-flame optimization, fast motion, visual tracking

DOI: 10.3233/JIFS-192172

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 3, pp. 3825-3837, 2020