Computational Intelligence and Neuroscience The latest articles from Hindawi Publishing Corporation © 2016 , Hindawi Publishing Corporation . All rights reserved. Gender and Age Related Effects While Watching TV Advertisements: An EEG Study Thu, 26 May 2016 06:30:27 +0000 The aim of the present paper is to show how the variation of the EEG frontal cortical asymmetry is related to the general appreciation perceived during the observation of TV advertisements, in particular considering the influence of the gender and age on it. In particular, we investigated the influence of the gender on the perception of a car advertisement (Experiment ) and the influence of the factor age on a chewing gum commercial (Experiment ). Experiment results showed statistically significant higher approach values for the men group throughout the commercial. Results from Experiment showed significant lower values by older adults for the spot, containing scenes not very enjoyed by them. In both studies, there was no statistical significant difference in the scene relative to the product offering between the experimental populations, suggesting the absence in our study of a bias towards the specific product in the evaluated populations. These evidences state the importance of the creativity in advertising, in order to attract the target population. Giulia Cartocci, Patrizia Cherubino, Dario Rossi, Enrica Modica, Anton Giulio Maglione, Gianluca di Flumeri, and Fabio Babiloni Copyright © 2016 Giulia Cartocci et al. All rights reserved. Stratification-Based Outlier Detection over the Deep Web Wed, 25 May 2016 09:32:55 +0000 For many applications, finding rare instances or outliers can be more interesting than finding common patterns. Existing work in outlier detection never considers the context of deep web. In this paper, we argue that, for many scenarios, it is more meaningful to detect outliers over deep web. In the context of deep web, users must submit queries through a query interface to retrieve corresponding data. Therefore, traditional data mining methods cannot be directly applied. The primary contribution of this paper is to develop a new data mining method for outlier detection over deep web. In our approach, the query space of a deep web data source is stratified based on a pilot sample. Neighborhood sampling and uncertainty sampling are developed in this paper with the goal of improving recall and precision based on stratification. Finally, a careful performance evaluation of our algorithm confirms that our approach can effectively detect outliers in deep web. Xuefeng Xian, Pengpeng Zhao, Victor S. Sheng, Ligang Fang, Caidong Gu, Yuanfeng Yang, and Zhiming Cui Copyright © 2016 Xuefeng Xian et al. All rights reserved. An Artificial Intelligence System to Predict Quality of Service in Banking Organizations Sun, 22 May 2016 13:10:18 +0000 Quality of service, that is, the waiting time that customers must endure in order to receive a service, is a critical performance aspect in private and public service organizations. Providing good service quality is particularly important in highly competitive sectors where similar services exist. In this paper, focusing on banking sector, we propose an artificial intelligence system for building a model for the prediction of service quality. While the traditional approach used for building analytical models relies on theories and assumptions about the problem at hand, we propose a novel approach for learning models from actual data. Thus, the proposed approach is not biased by the knowledge that experts may have about the problem, but it is completely based on the available data. The system is based on a recently defined variant of genetic programming that allows practitioners to include the concept of semantics in the search process. This will have beneficial effects on the search process and will produce analytical models that are based only on the data and not on domain-dependent knowledge. Mauro Castelli, Luca Manzoni, and Aleš Popovič Copyright © 2016 Mauro Castelli et al. All rights reserved. A Hybrid Neural Network Model for Sales Forecasting Based on ARIMA and Search Popularity of Article Titles Sun, 22 May 2016 11:24:22 +0000 Enhancing sales and operations planning through forecasting analysis and business intelligence is demanded in many industries and enterprises. Publishing industries usually pick attractive titles and headlines for their stories to increase sales, since popular article titles and headlines can attract readers to buy magazines. In this paper, information retrieval techniques are adopted to extract words from article titles. The popularity measures of article titles are then analyzed by using the search indexes obtained from Google search engine. Backpropagation Neural Networks (BPNNs) have successfully been used to develop prediction models for sales forecasting. In this study, we propose a novel hybrid neural network model for sales forecasting based on the prediction result of time series forecasting and the popularity of article titles. The proposed model uses the historical sales data, popularity of article titles, and the prediction result of a time series, Autoregressive Integrated Moving Average (ARIMA) forecasting method to learn a BPNN-based forecasting model. Our proposed forecasting model is experimentally evaluated by comparing with conventional sales prediction techniques. The experimental result shows that our proposed forecasting method outperforms conventional techniques which do not consider the popularity of title words. Hani Omar, Van Hai Hoang, and Duen-Ren Liu Copyright © 2016 Hani Omar et al. All rights reserved. A General Fuzzy Cerebellar Model Neural Network Multidimensional Classifier Using Intuitionistic Fuzzy Sets for Medical Identification Thu, 19 May 2016 09:00:55 +0000 The diversity of medical factors makes the analysis and judgment of uncertainty one of the challenges of medical diagnosis. A well-designed classification and judgment system for medical uncertainty can increase the rate of correct medical diagnosis. In this paper, a new multidimensional classifier is proposed by using an intelligent algorithm, which is the general fuzzy cerebellar model neural network (GFCMNN). To obtain more information about uncertainty, an intuitionistic fuzzy linguistic term is employed to describe medical features. The solution of classification is obtained by a similarity measurement. The advantages of the novel classifier proposed here are drawn out by comparing the same medical example under the methods of intuitionistic fuzzy sets (IFSs) and intuitionistic fuzzy cross-entropy (IFCE) with different score functions. Cross verification experiments are also taken to further test the classification ability of the GFCMNN multidimensional classifier. All of these experimental results show the effectiveness of the proposed GFCMNN multidimensional classifier and point out that it can assist in supporting for correct medical diagnoses associated with multiple categories. Jing Zhao, Lo-Yi Lin, and Chih-Min Lin Copyright © 2016 Jing Zhao et al. All rights reserved. A Novel Quantum-Behaved Bat Algorithm with Mean Best Position Directed for Numerical Optimization Wed, 18 May 2016 14:16:21 +0000 This paper proposes a novel quantum-behaved bat algorithm with the direction of mean best position (QMBA). In QMBA, the position of each bat is mainly updated by the current optimal solution in the early stage of searching and in the late search it also depends on the mean best position which can enhance the convergence speed of the algorithm. During the process of searching, quantum behavior of bats is introduced which is beneficial to jump out of local optimal solution and make the quantum-behaved bats not easily fall into local optimal solution, and it has better ability to adapt complex environment. Meanwhile, QMBA makes good use of statistical information of best position which bats had experienced to generate better quality solutions. This approach not only inherits the characteristic of quick convergence, simplicity, and easy implementation of original bat algorithm, but also increases the diversity of population and improves the accuracy of solution. Twenty-four benchmark test functions are tested and compared with other variant bat algorithms for numerical optimization the simulation results show that this approach is simple and efficient and can achieve a more accurate solution. Binglian Zhu, Wenyong Zhu, Zijuan Liu, Qingyan Duan, and Long Cao Copyright © 2016 Binglian Zhu et al. All rights reserved. A Guiding Evolutionary Algorithm with Greedy Strategy for Global Optimization Problems Wed, 18 May 2016 11:33:50 +0000 A Guiding Evolutionary Algorithm (GEA) with greedy strategy for global optimization problems is proposed. Inspired by Particle Swarm Optimization, the Genetic Algorithm, and the Bat Algorithm, the GEA was designed to retain some advantages of each method while avoiding some disadvantages. In contrast to the usual Genetic Algorithm, each individual in GEA is crossed with the current global best one instead of a randomly selected individual. The current best individual served as a guide to attract offspring to its region of genotype space. Mutation was added to offspring according to a dynamic mutation probability. To increase the capability of exploitation, a local search mechanism was applied to new individuals according to a dynamic probability of local search. Experimental results show that GEA outperformed the other three typical global optimization algorithms with which it was compared. Leilei Cao, Lihong Xu, and Erik D. Goodman Copyright © 2016 Leilei Cao et al. All rights reserved. Course Control of Underactuated Ship Based on Nonlinear Robust Neural Network Backstepping Method Wed, 18 May 2016 08:27:33 +0000 The problem of course control for underactuated surface ship is addressed in this paper. Firstly, neural networks are adopted to determine the parameters of the unknown part of ideal virtual backstepping control, even the weight values of neural network are updated by adaptive technique. Then uniform stability for the convergence of course tracking errors has been proven through Lyapunov stability theory. Finally, simulation experiments are carried out to illustrate the effectiveness of proposed control method. Junjia Yuan, Hao Meng, Qidan Zhu, and Jiajia Zhou Copyright © 2016 Junjia Yuan et al. All rights reserved. Financial Time Series Prediction Using Elman Recurrent Random Neural Networks Wed, 18 May 2016 08:20:12 +0000 In recent years, financial market dynamics forecasting has been a focus of economic research. To predict the price indices of stock markets, we developed an architecture which combined Elman recurrent neural networks with stochastic time effective function. By analyzing the proposed model with the linear regression, complexity invariant distance (CID), and multiscale CID (MCID) analysis methods and taking the model compared with different models such as the backpropagation neural network (BPNN), the stochastic time effective neural network (STNN), and the Elman recurrent neural network (ERNN), the empirical results show that the proposed neural network displays the best performance among these neural networks in financial time series forecasting. Further, the empirical research is performed in testing the predictive effects of SSE, TWSE, KOSPI, and Nikkei225 with the established model, and the corresponding statistical comparisons of the above market indices are also exhibited. The experimental results show that this approach gives good performance in predicting the values from the stock market indices. Jie Wang, Jun Wang, Wen Fang, and Hongli Niu Copyright © 2016 Jie Wang et al. All rights reserved. Biologically Inspired Methods for Imaging, Cognition, Vision, and Intelligence Wed, 18 May 2016 07:32:30 +0000 Yufeng Zheng, Erik Blasch, and Adel S. Elmaghraby Copyright © 2016 Yufeng Zheng et al. All rights reserved. Human Action Recognition Using Improved Salient Dense Trajectories Tue, 17 May 2016 13:22:17 +0000 Human action recognition in videos is a topic of active research in computer vision. Dense trajectory (DT) features were shown to be efficient for representing videos in state-of-the-art approaches. In this paper, we present a more effective approach of video representation using improved salient dense trajectories: first, detecting the motion salient region and extracting the dense trajectories by tracking interest points in each spatial scale separately and then refining the dense trajectories via the analysis of the motion saliency. Then, we compute several descriptors (i.e., trajectory displacement, HOG, HOF, and MBH) in the spatiotemporal volume aligned with the trajectories. Finally, in order to represent the videos better, we optimize the framework of bag-of-words according to the motion salient intensity distribution and the idea of sparse coefficient reconstruction. Our architecture is trained and evaluated on the four standard video actions datasets of KTH, UCF sports, HMDB51, and UCF50, and the experimental results show that our approach performs competitively comparing with the state-of-the-art results. Qingwu Li, Haisu Cheng, Yan Zhou, and Guanying Huo Copyright © 2016 Qingwu Li et al. All rights reserved. A New Modified Artificial Bee Colony Algorithm with Exponential Function Adaptive Steps Tue, 17 May 2016 10:01:22 +0000 As one of the most recent popular swarm intelligence techniques, artificial bee colony algorithm is poor at exploitation and has some defects such as slow search speed, poor population diversity, the stagnation in the working process, and being trapped into the local optimal solution. The purpose of this paper is to develop a new modified artificial bee colony algorithm in view of the initial population structure, subpopulation groups, step updating, and population elimination. Further, depending on opposition-based learning theory and the new modified algorithms, an improved -type grouping method is proposed and the original way of roulette wheel selection is substituted through sensitivity-pheromone way. Then, an adaptive step with exponential functions is designed for replacing the original random step. Finally, based on the new test function versions CEC13, six benchmark functions with the dimensions and are chosen and applied in the experiments for analyzing and comparing the iteration speed and accuracy of the new modified algorithms. The experimental results show that the new modified algorithm has faster and more stable searching and can quickly increase poor population diversity and bring out the global optimal solutions. Wei Mao, Heng-you Lan, and Hao-ru Li Copyright © 2016 Wei Mao et al. All rights reserved. BrainK for Structural Image Processing: Creating Electrical Models of the Human Head Mon, 16 May 2016 11:45:34 +0000 BrainK is a set of automated procedures for characterizing the tissues of the human head from MRI, CT, and photogrammetry images. The tissue segmentation and cortical surface extraction support the primary goal of modeling the propagation of electrical currents through head tissues with a finite difference model (FDM) or finite element model (FEM) created from the BrainK geometries. The electrical head model is necessary for accurate source localization of dense array electroencephalographic (dEEG) measures from head surface electrodes. It is also necessary for accurate targeting of cerebral structures with transcranial current injection from those surface electrodes. BrainK must achieve five major tasks: image segmentation, registration of the MRI, CT, and sensor photogrammetry images, cortical surface reconstruction, dipole tessellation of the cortical surface, and Talairach transformation. We describe the approach to each task, and we compare the accuracies for the key tasks of tissue segmentation and cortical surface extraction in relation to existing research tools (FreeSurfer, FSL, SPM, and BrainVisa). BrainK achieves good accuracy with minimal or no user intervention, it deals well with poor quality MR images and tissue abnormalities, and it provides improved computational efficiency over existing research packages. Kai Li, Xenophon Papademetris, and Don M. Tucker Copyright © 2016 Kai Li et al. All rights reserved. Optimization Control of the Color-Coating Production Process for Model Uncertainty Tue, 10 May 2016 16:35:14 +0000 Optimized control of the color-coating production process (CCPP) aims at reducing production costs and improving economic efficiency while meeting quality requirements. However, because optimization control of the CCPP is hampered by model uncertainty, a strategy that considers model uncertainty is proposed. Previous work has introduced a mechanistic model of CCPP based on process analysis to simulate the actual production process and generate process data. The partial least squares method is then applied to develop predictive models of film thickness and economic efficiency. To manage the model uncertainty, the robust optimization approach is introduced to improve the feasibility of the optimized solution. Iterative learning control is then utilized to further refine the model uncertainty. The constrained film thickness is transformed into one of the tracked targets to overcome the drawback that traditional iterative learning control cannot address constraints. The goal setting of economic efficiency is updated continuously according to the film thickness setting until this reaches its desired value. Finally, fuzzy parameter adjustment is adopted to ensure that the economic efficiency and film thickness converge rapidly to their optimized values under the constraint conditions. The effectiveness of the proposed optimization control strategy is validated by simulation results. Dakuo He, Zhengsong Wang, Le Yang, and Zhizhong Mao Copyright © 2016 Dakuo He et al. All rights reserved. Learning to Model Task-Oriented Attention Mon, 09 May 2016 14:32:42 +0000 For many applications in graphics, design, and human computer interaction, it is essential to understand where humans look in a scene with a particular task. Models of saliency can be used to predict fixation locations, but a large body of previous saliency models focused on free-viewing task. They are based on bottom-up computation that does not consider task-oriented image semantics and often does not match actual eye movements. To address this problem, we collected eye tracking data of 11 subjects when they performed some particular search task in 1307 images and annotation data of 2,511 segmented objects with fine contours and 8 semantic attributes. Using this database as training and testing examples, we learn a model of saliency based on bottom-up image features and target position feature. Experimental results demonstrate the importance of the target information in the prediction of task-oriented visual attention. Xiaochun Zou, Xinbo Zhao, Jian Wang, and Yongjia Yang Copyright © 2016 Xiaochun Zou et al. All rights reserved. Regularized Embedded Multiple Kernel Dimensionality Reduction for Mine Signal Processing Mon, 09 May 2016 10:54:05 +0000 Traditional multiple kernel dimensionality reduction models are generally based on graph embedding and manifold assumption. But such assumption might be invalid for some high-dimensional or sparse data due to the curse of dimensionality, which has a negative influence on the performance of multiple kernel learning. In addition, some models might be ill-posed if the rank of matrices in their objective functions was not high enough. To address these issues, we extend the traditional graph embedding framework and propose a novel regularized embedded multiple kernel dimensionality reduction method. Different from the conventional convex relaxation technique, the proposed algorithm directly takes advantage of a binary search and an alternative optimization scheme to obtain optimal solutions efficiently. The experimental results demonstrate the effectiveness of the proposed method for supervised, unsupervised, and semisupervised scenarios. Shuang Li, Bing Liu, and Chen Zhang Copyright © 2016 Shuang Li et al. All rights reserved. An Improved Genetic Fuzzy Logic Control Method to Reduce the Enlargement of Coal Floor Deformation in Shearer Memory Cutting Process Wed, 27 Apr 2016 11:19:30 +0000 In order to reduce the enlargement of coal floor deformation and the manual adjustment frequency of rocker arms, an improved approach through integration of improved genetic algorithm and fuzzy logic control (GFLC) method is proposed. The enlargement of coal floor deformation is analyzed and a model is built. Then, the framework of proposed approach is built. Moreover, the constituents of GA such as tangent function roulette wheel selection (Tan-RWS) selection, uniform crossover, and nonuniform mutation are employed to enhance the performance of GFLC. Finally, two simulation examples and an industrial application example are carried out and the results indicate that the proposed method is feasible and efficient. Chao Tan, Rongxin Xu, Zhongbin Wang, Lei Si, and Xinhua Liu Copyright © 2016 Chao Tan et al. All rights reserved. Spike Code Flow in Cultured Neuronal Networks Wed, 27 Apr 2016 11:11:23 +0000 We observed spike trains produced by one-shot electrical stimulation with 8 × 8 multielectrodes in cultured neuronal networks. Each electrode accepted spikes from several neurons. We extracted the short codes from spike trains and obtained a code spectrum with a nominal time accuracy of 1%. We then constructed code flow maps as movies of the electrode array to observe the code flow of “1101” and “1011,” which are typical pseudorandom sequence such as that we often encountered in a literature and our experiments. They seemed to flow from one electrode to the neighboring one and maintained their shape to some extent. To quantify the flow, we calculated the “maximum cross-correlations” among neighboring electrodes, to find the direction of maximum flow of the codes with lengths less than 8. Normalized maximum cross-correlations were almost constant irrespective of code. Furthermore, if the spike trains were shuffled in interval orders or in electrodes, they became significantly small. Thus, the analysis suggested that local codes of approximately constant shape propagated and conveyed information across the network. Hence, the codes can serve as visible and trackable marks of propagating spike waves as well as evaluating information flow in the neuronal network. Shinichi Tamura, Yoshi Nishitani, Chie Hosokawa, Tomomitsu Miyoshi, Hajime Sawai, Takuya Kamimura, Yasushi Yagi, Yuko Mizuno-Matsumoto, and Yen-Wei Chen Copyright © 2016 Shinichi Tamura et al. All rights reserved. Simulation of Code Spectrum and Code Flow of Cultured Neuronal Networks Wed, 27 Apr 2016 11:01:59 +0000 It has been shown that, in cultured neuronal networks on a multielectrode, pseudorandom-like sequences (codes) are detected, and they flow with some spatial decay constant. Each cultured neuronal network is characterized by a specific spectrum curve. That is, we may consider the spectrum curve as a “signature” of its associated neuronal network that is dependent on the characteristics of neurons and network configuration, including the weight distribution. In the present study, we used an integrate-and-fire model of neurons with intrinsic and instantaneous fluctuations of characteristics for performing a simulation of a code spectrum from multielectrodes on a 2D mesh neural network. We showed that it is possible to estimate the characteristics of neurons such as the distribution of number of neurons around each electrode and their refractory periods. Although this process is a reverse problem and theoretically the solutions are not sufficiently guaranteed, the parameters seem to be consistent with those of neurons. That is, the proposed neural network model may adequately reflect the behavior of a cultured neuronal network. Furthermore, such prospect is discussed that code analysis will provide a base of communication within a neural network that will also create a base of natural intelligence. Shinichi Tamura, Yoshi Nishitani, Chie Hosokawa, Tomomitsu Miyoshi, and Hajime Sawai Copyright © 2016 Shinichi Tamura et al. All rights reserved. Parallelizing Backpropagation Neural Network Using MapReduce and Cascading Model Wed, 27 Apr 2016 09:12:36 +0000 Artificial Neural Network (ANN) is a widely used algorithm in pattern recognition, classification, and prediction fields. Among a number of neural networks, backpropagation neural network (BPNN) has become the most famous one due to its remarkable function approximation ability. However, a standard BPNN frequently employs a large number of sum and sigmoid calculations, which may result in low efficiency in dealing with large volume of data. Therefore to parallelize BPNN using distributed computing technologies is an effective way to improve the algorithm performance in terms of efficiency. However, traditional parallelization may lead to accuracy loss. Although several complements have been done, it is still difficult to find out a compromise between efficiency and precision. This paper presents a parallelized BPNN based on MapReduce computing model which supplies advanced features including fault tolerance, data replication, and load balancing. And also to improve the algorithm performance in terms of precision, this paper creates a cascading model based classification approach, which helps to refine the classification results. The experimental results indicate that the presented parallelized BPNN is able to offer high efficiency whilst maintaining excellent precision in enabling large-scale machine learning. Yang Liu, Weizhe Jing, and Lixiong Xu Copyright © 2016 Yang Liu et al. All rights reserved. A Framework for the Comparative Assessment of Neuronal Spike Sorting Algorithms towards More Accurate Off-Line and On-Line Microelectrode Arrays Data Analysis Wed, 27 Apr 2016 07:40:22 +0000 Neuronal spike sorting algorithms are designed to retrieve neuronal network activity on a single-cell level from extracellular multiunit recordings with Microelectrode Arrays (MEAs). In typical analysis of MEA data, one spike sorting algorithm is applied indiscriminately to all electrode signals. However, this approach neglects the dependency of algorithms’ performances on the neuronal signals properties at each channel, which require data-centric methods. Moreover, sorting is commonly performed off-line, which is time and memory consuming and prevents researchers from having an immediate glance at ongoing experiments. The aim of this work is to provide a versatile framework to support the evaluation and comparison of different spike classification algorithms suitable for both off-line and on-line analysis. We incorporated different spike sorting “building blocks” into a Matlab-based software, including 4 feature extraction methods, 3 feature clustering methods, and 1 template matching classifier. The framework was validated by applying different algorithms on simulated and real signals from neuronal cultures coupled to MEAs. Moreover, the system has been proven effective in running on-line analysis on a standard desktop computer, after the selection of the most suitable sorting methods. This work provides a useful and versatile instrument for a supported comparison of different options for spike sorting towards more accurate off-line and on-line MEA data analysis. Giulia Regalia, Stefania Coelli, Emilia Biffi, Giancarlo Ferrigno, and Alessandra Pedrocchi Copyright © 2016 Giulia Regalia et al. All rights reserved. Inversion for Refractivity Parameters Using a Dynamic Adaptive Cuckoo Search with Crossover Operator Algorithm Tue, 26 Apr 2016 13:25:28 +0000 Using the RFC technique to estimate refractivity parameters is a complex nonlinear optimization problem. In this paper, an improved cuckoo search (CS) algorithm is proposed to deal with this problem. To enhance the performance of the CS algorithm, a parameter dynamic adaptive operation and crossover operation were integrated into the standard CS (DACS-CO). Rechenberg’s 1/5 criteria combined with learning factor were used to control the parameter dynamic adaptive adjusting process. The crossover operation of genetic algorithm was utilized to guarantee the population diversity. The new hybrid algorithm has better local search ability and contributes to superior performance. To verify the ability of the DACS-CO algorithm to estimate atmospheric refractivity parameters, the simulation data and real radar clutter data are both implemented. The numerical experiments demonstrate that the DACS-CO algorithm can provide an effective method for near-real-time estimation of the atmospheric refractivity profile from radar clutter. Zhihua Zhang, Zheng Sheng, Hanqing Shi, and Zhiqiang Fan Copyright © 2016 Zhihua Zhang et al. All rights reserved. Semisupervised Clustering by Iterative Partition and Regression with Neuroscience Applications Tue, 26 Apr 2016 12:30:15 +0000 Regression clustering is a mixture of unsupervised and supervised statistical learning and data mining method which is found in a wide range of applications including artificial intelligence and neuroscience. It performs unsupervised learning when it clusters the data according to their respective unobserved regression hyperplanes. The method also performs supervised learning when it fits regression hyperplanes to the corresponding data clusters. Applying regression clustering in practice requires means of determining the underlying number of clusters in the data, finding the cluster label of each data point, and estimating the regression coefficients of the model. In this paper, we review the estimation and selection issues in regression clustering with regard to the least squares and robust statistical methods. We also provide a model selection based technique to determine the number of regression clusters underlying the data. We further develop a computing procedure for regression clustering estimation and selection. Finally, simulation studies are presented for assessing the procedure, together with analyzing a real data set on RGB cell marking in neuroscience to illustrate and interpret the method. Guoqi Qian, Yuehua Wu, Davide Ferrari, Puxue Qiao, and Frédéric Hollande Copyright © 2016 Guoqi Qian et al. All rights reserved. Evaluation of a Home Biomonitoring Autonomous Mobile Robot Sun, 24 Apr 2016 13:22:46 +0000 Increasing population age demands more services in healthcare domain. It has been shown that mobile robots could be a potential solution to home biomonitoring for the elderly. Through our previous studies, a mobile robot system that is able to track a subject and identify his daily living activities has been developed. However, the system has not been tested in any home living scenarios. In this study we did a series of experiments to investigate the accuracy of activity recognition of the mobile robot in a home living scenario. The daily activities tested in the evaluation experiment include watching TV and sleeping. A dataset recorded by a distributed distance-measuring sensor network was used as a reference to the activity recognition results. It was shown that the accuracy is not consistent for all the activities; that is, mobile robot could achieve a high success rate in some activities but a poor success rate in others. It was found that the observation position of the mobile robot and subject surroundings have high impact on the accuracy of the activity recognition, due to the variability of the home living daily activities and their transitional process. The possibility of improvement of recognition accuracy has been shown too. Enrique Dorronzoro Zubiete, Keigo Nakahata, Nevrez Imamoglu, Masashi Sekine, Guanghao Sun, Isabel Gomez, and Wenwei Yu Copyright © 2016 Enrique Dorronzoro Zubiete et al. All rights reserved. Planning the City Logistics Terminal Location by Applying the Green -Median Model and Type-2 Neurofuzzy Network Tue, 19 Apr 2016 13:13:57 +0000 The paper herein presents green -median problem (GMP) which uses the adaptive type-2 neural network for the processing of environmental and sociological parameters including costs of logistics operators and demonstrates the influence of these parameters on planning the location for the city logistics terminal (CLT) within the discrete network. CLT shows direct effects on increment of traffic volume especially in urban areas, which further results in negative environmental effects such as air pollution and noise as well as increased number of urban populations suffering from bronchitis, asthma, and similar respiratory infections. By applying the green -median model (GMM), negative effects on environment and health in urban areas caused by delivery vehicles may be reduced to minimum. This model creates real possibilities for making the proper investment decisions so as profitable investments may be realized in the field of transport infrastructure. The paper herein also includes testing of GMM in real conditions on four CLT locations in Belgrade City zone. Dragan Pamučar, Ljubislav Vasin, Predrag Atanasković, and Milica Miličić Copyright © 2016 Dragan Pamučar et al. All rights reserved. Online Knowledge-Based Model for Big Data Topic Extraction Tue, 19 Apr 2016 11:16:02 +0000 Lifelong machine learning (LML) models learn with experience maintaining a knowledge-base, without user intervention. Unlike traditional single-domain models they can easily scale up to explore big data. The existing LML models have high data dependency, consume more resources, and do not support streaming data. This paper proposes online LML model (OAMC) to support streaming data with reduced data dependency. With engineering the knowledge-base and introducing new knowledge features the learning pattern of the model is improved for data arriving in pieces. OAMC improves accuracy as topic coherence by 7% for streaming data while reducing the processing cost to half. Muhammad Taimoor Khan, Mehr Durrani, Shehzad Khalid, and Furqan Aziz Copyright © 2016 Muhammad Taimoor Khan et al. All rights reserved. A Driving Behaviour Model of Electrical Wheelchair Users Mon, 11 Apr 2016 12:49:43 +0000 In spite of the presence of powered wheelchairs, some of the users still experience steering challenges and manoeuvring difficulties that limit their capacity of navigating effectively. For such users, steering support and assistive systems may be very necessary. To appreciate the assistance, there is need that the assistive control is adaptable to the user’s steering behaviour. This paper contributes to wheelchair steering improvement by modelling the steering behaviour of powered wheelchair users, for integration into the control system. More precisely, the modelling is based on the improved Directed Potential Field (DPF) method for trajectory planning. The method has facilitated the formulation of a simple behaviour model that is also linear in parameters. To obtain the steering data for parameter identification, seven individuals participated in driving the wheelchair in different virtual worlds on the augmented platform. The obtained data facilitated the estimation of user parameters, using the ordinary least square method, with satisfactory regression analysis results. S. O. Onyango, Y. Hamam, K. Djouani, B. Daachi, and N. Steyn Copyright © 2016 S. O. Onyango et al. All rights reserved. An Interactive Astronaut-Robot System with Gesture Control Mon, 11 Apr 2016 08:46:33 +0000 Human-robot interaction (HRI) plays an important role in future planetary exploration mission, where astronauts with extravehicular activities (EVA) have to communicate with robot assistants by speech-type or gesture-type user interfaces embedded in their space suits. This paper presents an interactive astronaut-robot system integrating a data-glove with a space suit for the astronaut to use hand gestures to control a snake-like robot. Support vector machine (SVM) is employed to recognize hand gestures and particle swarm optimization (PSO) algorithm is used to optimize the parameters of SVM to further improve its recognition accuracy. Various hand gestures from American Sign Language (ASL) have been selected and used to test and validate the performance of the proposed system. Jinguo Liu, Yifan Luo, and Zhaojie Ju Copyright © 2016 Jinguo Liu et al. All rights reserved. Gait Planning and Stability Control of a Quadruped Robot Sun, 10 Apr 2016 12:31:24 +0000 In order to realize smooth gait planning and stability control of a quadruped robot, a new controller algorithm based on CPG-ZMP (central pattern generator-zero moment point) is put forward in this paper. To generate smooth gait and shorten the adjusting time of the model oscillation system, a new CPG model controller and its gait switching strategy based on Wilson-Cowan model are presented in the paper. The control signals of knee-hip joints are obtained by the improved multi-DOF reduced order control theory. To realize stability control, the adaptive speed adjustment and gait switch are completed by the real-time computing of ZMP. Experiment results show that the quadruped robot’s gaits are efficiently generated and the gait switch is smooth in the CPG control algorithm. Meanwhile, the stability of robot’s movement is improved greatly with the CPG-ZMP algorithm. The algorithm in this paper has good practicability, which lays a foundation for the production of the robot prototype. Junmin Li, Jinge Wang, Simon X. Yang, Kedong Zhou, and Huijuan Tang Copyright © 2016 Junmin Li et al. All rights reserved. Characterization of Visual Scanning Patterns in Air Traffic Control Thu, 07 Apr 2016 12:06:36 +0000 Characterization of air traffic controllers’ (ATCs’) visual scanning strategies is a challenging issue due to the dynamic movement of multiple aircraft and increasing complexity of scanpaths (order of eye fixations and saccades) over time. Additionally, terminologies and methods are lacking to accurately characterize the eye tracking data into simplified visual scanning strategies linguistically expressed by ATCs. As an intermediate step to automate the characterization classification process, we (1) defined and developed new concepts to systematically filter complex visual scanpaths into simpler and more manageable forms and (2) developed procedures to map visual scanpaths with linguistic inputs to reduce the human judgement bias during interrater agreement. The developed concepts and procedures were applied to investigating the visual scanpaths of expert ATCs using scenarios with different aircraft congestion levels. Furthermore, oculomotor trends were analyzed to identify the influence of aircraft congestion on scan time and number of comparisons among aircraft. The findings show that (1) the scanpaths filtered at the highest intensity led to more consistent mapping with the ATCs’ linguistic inputs, (2) the pattern classification occurrences differed between scenarios, and (3) increasing aircraft congestion caused increased scan times and aircraft pairwise comparisons. The results provide a foundation for better characterizing complex scanpaths in a dynamic task and automating the analysis process. Sarah N. McClung and Ziho Kang Copyright © 2016 Sarah N. McClung and Ziho Kang. All rights reserved.