Journal of Intelligent & Fuzzy Systems - Volume 31, issue 2

Authors: Wang, Ting | Xu, Rui | Han, Xianhua | Chen, Yen-Wei | Ishizaki, Yoshitomo | Miyamoto, Masaru | Hattori, Tomohito

Article Type: Research Article

Abstract: The automatic inspection of throw-away tips is very important for quality control in precision cutting. We proposed an image processing based method for automatic inspection of the processing wear of throw-away tips. After image denoising, the proposed method utilized image-patch based principal component analysis method to enhance the cutting worn region while suppress the background region. Then the enhanced worn region was automatically segmented by a simple thresholding method followed by post-processing. The area of the segmented worn region was used as a measure of cutting wear degree. We collected three datasets of time-series images that recorded the processing of …throw-away tips on a product line. One dataset was used to choose optimal parameters of the proposed method, and the other two datasets were used for evaluate its performances. Experimental results showed that the proposed method was able to inspect the cutting wear with high accuracy. Additionally, it was also showed that the proposed method outperformed the conventional thresholding based method. Show more

Keywords: Principal component analysis, segmentation, worn region, throw-away tips, automatic inspection

DOI: 10.3233/JIFS-169020

Citation: Journal of Intelligent & Fuzzy Systems, vol. 31, no. 2, pp. 903-913, 2016

Authors: Cheng, Ching-Hsue | Yang, Jun-He

Article Type: Research Article

Abstract: Rainfall forecasting has been a popular research topic. Precise rainfall prediction can not only assist water management in region of water scarcity, but it can also warn or alleviate the effects of excess or insufficient rainfall. As a result of the advancement in information technology, current prediction methods are more diverse and sophisticated; however they require significant amounts of resources, and time are costly, and the forecast outcomes are still very uncertain. Therefore, this study proposed a novel rainfall forecast model, which combined the proposed integrated non-linear attributes selection method with support vector regression (SVR) to enhance the forecast performance. …First, the proposed integrated non-linear attribute selection method was employed to determine the important attributes that affect rainfall in the mountainous region of Taiwan, and then, the selected attribute data were input into the SVR model to train the rainfall forecast model. To assess the prediction performance of the proposed model, this study collected rainfall data from 2005 to 2014 at monitoring stations in the Taiwanese mountains, and compared the proposed model results with those of the listing models. Experimental results show that the proposed model outperforms the listing models in terms of Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). Show more

Keywords: Component, feature selection, rainfall forecast, SVR, time series

DOI: 10.3233/JIFS-169021

Citation: Journal of Intelligent & Fuzzy Systems, vol. 31, no. 2, pp. 915-925, 2016

Authors: Jiang, Shengyi | Wang, Lianxi

Article Type: Research Article

Abstract: With the extensive increase of the amount of data, such as text categorization, genomic microarray data, bio-informatics and digital images, there are more and more challenges in feature selection. Recently, feature selection has been widely studied in supervised learning, but there is significantly less work in unsupervised learning because of the absence of class information and explicit search criteria. In this work, we introduce a new measure to assess the importance of features in terms of feature separability. A clustering-based feature selection algorithm is then introduced to conduct the feature selection. The proposed algorithm with nearly linear time complexity selects …final feature subset through a ranking procedure based on the separabilities of features and it is applicable to datasets of mixed nature. Experimental results on UCI datasets show that our method, by retaining relevant features, can obtain similar or even better results of classification and clustering for most datasets, and it outperforms other traditional supervised and unsupervised feature selection methods in terms of dimensionality reduction and classification accuracy. Show more

Keywords: Feature selection, feature separability, clustering, unsupervised learning

DOI: 10.3233/JIFS-169022

Citation: Journal of Intelligent & Fuzzy Systems, vol. 31, no. 2, pp. 927-937, 2016

Authors: Ekker, Knut

Article Type: Research Article

Abstract: An emergency management tool developed for training police, fire and ambulance teams in Norway and Sweden provided the data for this paper. The teams communicated with representatives of the local power companies and county and municipality officials in responding to various emergency scenarios in a web-based training tool. The project generated rich textual data of the content of the communications as well as a range of quantitative data on who communicated with whom, how often and with what type of information. The author analyzed the qualitative data using the NVivo software package and the quantitative analysis used the R statistics …package and the social network analysis (SNA) module. The textual analysis shows distinct patterns of concepts and terms used by the various emergency response agencies. The quantitative analysis illustrates the flow of communication among the participants of the emergency management training (EMT). Visual representation of both the qualitative and quantitative data from the project provides a thorough insight into processes of communication among emergency response personnel in role-playing training sessions. The data visualization enhances the debriefing session following emergency response training. The research group at Mid Sweden University and NORD University recently received funding for a three-year continuation of the project. The new project will emphasize the demand side (community stakeholders) in addition to the supply side (the emergency personnel). Show more

Keywords: Emergency management, crisis communication, training software, qualitative data, content analysis, quantitative data, R statistics, social network analysis, SNA

DOI: 10.3233/JIFS-169023

Citation: Journal of Intelligent & Fuzzy Systems, vol. 31, no. 2, pp. 939-948, 2016

Authors: Zhang, Jieqiong | Yang, Kongyu

Article Type: Research Article

Abstract: Clustering analysis as one of the key components of data mining has been widely applied. This paper aimed to apply a clustering algorithm to classify and evaluate securities investment funds. It established a fund evaluation index system by researching the indexes that are influenced by the performance of funds. It drew upon domestic and foreign mature funds evaluation theory and used the data mining function of Excel to establish a clustering analysis model. Finally, this paper used 40 equity funds as sample data to conduct an empirical research. The cluster results would be beneficial in evaluating funds’ performance and guiding …the decision making on rational investment. Show more

Keywords: Clustering analysis, evaluation, securities investment funds

DOI: 10.3233/JIFS-169024

Citation: Journal of Intelligent & Fuzzy Systems, vol. 31, no. 2, pp. 949-956, 2016

Authors: Zheng, Weihua | Xiao, Shenping | Li, Kenli | Li, Keqin | Jiang, Weijin

Article Type: Research Article

Abstract: Discrete Fourier transform (DFT) finds various applications in signal processing, image processing, artificial intelligent, and fuzzy logic etc. DFT is often computed efficiently with Fast Fourier transform (FFT). The modified split radix FFT (MSRFFT) algorithm implements a length-N =2m DFT achieving a reduction of arithmetic complexity compared to split-radix FFT (SRFFT). In this paper, a simplified algorithm is proposed for the MSRFFT algorithm, reducing the number of real coefficients evaluated from 5/8N  - 2 to 15/32N  - 2 and the number of groups of decomposition from 4 to 3. A implementation approach is also presented. The approach makes data-path of …the MSRFFT regular similar to that of the radix-2 FFT algorithm. The experimental results show that (1) MSRFFT consumes less time on central processing units (CPUs) with sufficient cache than existing algorithms; (2) the proposed implementation method can save execution time on CPUs and general processing units (GPUs). Show more

Keywords: Fast Fourier transform (FFT), general processing unit (GPU) parallelism, modified split-radix (MSR), split-radix (SR)

DOI: 10.3233/JIFS-169025

Citation: Journal of Intelligent & Fuzzy Systems, vol. 31, no. 2, pp. 957-965, 2016

Authors: Luo, Jiawei | Lin, Dingyu | Cao, Buwen

Article Type: Research Article

Abstract: With the increasing of available protein-protein interaction (PPI) data, many computational methods have been explored to identify protein complexes from PPI networks. Majority of algorithms employ the feature of local neighbors to detect local dense subgraphs which correspond to protein complexes. Those approaches neglect the inherent core-attachment structure of protein complexes, which to an extent affect the protein complexes of prediction accuracy. In this paper, we propose a new algorithm for predicting protein complexes, deriving from the framework of the core-attachment. The proposed method first obtains the triangular structures of the core of protein complexes, name as cells, in which …the edge-clustering coefficient is used. And then the cells are expanded to protein complex cores based on the closeness. Finally, the attachments are added to their corresponding cores to form the final protein complexes. The experimental results on two yeast PPI data show our method outperform the existing algorithms in terms of matched protein complexes and biological significance using two benchmark data sets. Show more

Keywords: Core-attachment, protein complex, protein-protein interaction, triangular structure

DOI: 10.3233/JIFS-169026

Citation: Journal of Intelligent & Fuzzy Systems, vol. 31, no. 2, pp. 967-978, 2016

Authors: Deng, Xiaoheng | Pan, Yan | Shen, Hailan | Gui, Jingsong

Article Type: Research Article

Abstract: Influence maximization is a problem of identifying a small set of highly influential individuals such that obtaining the maximum value of influence spread in social networks. How to evaluate the influence is essential to solve the influence maximization problem. Meanwhile, finding out influence propagation paths is one of key factors in the assessment of influence spread. However, since nodes’ degrees are utilized by most of existent models and algorithms to estimate the activation probabilities on edges, node features are always ignored in the evaluation of influence ability for different users. In this paper, besides the node features, the Credit Distribution …(CD) model is extended to incorporate the time-critical aspect of influence in online social networks. After assigning credit along with the action propagation paths, we pick up the node which has maximal marginal gain in each iteration to form the seed set. The experiments we performed on real datasets demonstrate that our approach is efficient and reasonable for identifying seed nodes, and the influence spread prediction by our approach is more accurate than that of original method which disregards node features in the influence evaluation and diffusion process. Show more

Keywords: Online social networks, influence evaluation, influence maximization, credit distribution, greedy algorithm

DOI: 10.3233/JIFS-169027

Citation: Journal of Intelligent & Fuzzy Systems, vol. 31, no. 2, pp. 979-990, 2016

Authors: Liu, Chang | Luo, Juan | Song, Yanchao

Article Type: Research Article

Abstract: Environment monitoring is one of the typical application scenarios of the wireless sensor networks. As an energy limited system, most of the energy consumption is for the data transmission. As a well-known principle, the difference among the physical parameters of adjacent nodes is approximate a constant. Eliminating these data to be transmitted will lead to remarkable energy saving. A correlative pattern based data aggregation mechanism following this principle is proposed in this paper, which is named the Correlative Pattern based Data Aggregation (CPDA). CPDA mines the correlations of every adjacent nodes pair, and generates a correlation graph of the network, …then builds an aggregation routing tree for each connected component of correlation graph based on the shortest path methodology. Following the CPDA algorithm, a node’s sensed data will be suppressed when the data and the children’s match the restriction that is defined by CPDA. When the aggregated data arrive at the Sink node, all the data can be recovered. The recovery error will be limited within a specified small error threshold based on the reversed mechanism. The simulations based on the data set of Berkeley lab show that CPDA has excellent performance in aggregation degree and average error. Further more, a real established temperature sensing experiment also gives the same conclusion. Show more

Keywords: Constant correlation, data aggregation, shortest path, wireless sensor networks

DOI: 10.3233/JIFS-169028

Citation: Journal of Intelligent & Fuzzy Systems, vol. 31, no. 2, pp. 991-999, 2016

Authors: Park, Min-Kee

Article Type: Research Article

Abstract: When a train is delayed because of a disturbance, the time interval between successive trains increases, and high-frequency metro lines can become unstable. Time interval control is therefore necessary in preventing such instabilities. In this paper, we propose a traffic regulation algorithm that is easy to implement and guarantees system stability. In the proposed method, controlled trains are determined from time interval deviations between successive trains, and the control algorithm for both staying time and running time is designed using a discrete traffic model to ensure an optimal time interval between successive trains. The results of a computer simulation are …also given to demonstrate the validity of the proposed algorithm. Show more

Keywords: Traffic regulation, traffic control, traffic model, time interval, stability

DOI: 10.3233/JIFS-169029

Citation: Journal of Intelligent & Fuzzy Systems, vol. 31, no. 2, pp. 1001-1008, 2016