IJARAI Volume 2 Issue 7

Paper 1: Comparative study between the proposed shape independent clustering method and the conventional methods (K-means and the other)

Abstract: Cluster analysis aims at identifying groups of similar objects and, therefore helps to discover distribution of patterns and interesting correlations in the data sets. In this paper, we propose to provide a consistent partitioning of a dataset which allows identifying any shape of cluster patterns in case of numerical clustering, convex or non-convex. The method is based on layered structure representation that be obtained from measurement distance and angle of numerical data to the centroid data and based on the iterative clustering construction utilizing a nearest neighbor distance between clusters to merge. Encourage result show the effectiveness of the proposed technique.

Author 1: Kohei Arai

Author 2: Cahya Rahmad

Keywords: clustering algorithms; mlccd; shape independence clustering;

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Paper 2: Effect of Driver Scope Awareness in the Lane Changing Maneuvers Using Cellular Automaton Model

Abstract: This paper investigated the effect of drivers’ visibility and their perception (e.g., to estimate the speed and arrival time of another vehicle) on the lane changing maneuver. The term of scope awareness was used to describe the visibility required by the driver to make a perception about road condition and the speed of vehicle that exist in that road. A computer simulation model was conducted to show this driver awareness behavior. This studying attempt to precisely catching the lane changing behavior and illustrate the scope awareness parameter that reflects driver behavior. This paper proposes a simple cellular automata model for studying driver visibility effects of lane changing maneuver and driver perception of estimated speed. Different values of scope awareness were examined to capture its effect on the traffic flow. Simulation results show the ability of this model to capture the important features of lane changing maneuver and revealed the appearance of the short-thin solid line jam and the wide solid line jam in the traffic flow as the consequences of lane changing maneuver.

Author 1: Kohei Arai

Author 2: Steven Ray Sentinuwo

Keywords: scope awareness; lane changing meneuver; speed estimation; spontaneous braking.

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Paper 3: Validity of Spontaneous Braking and Lane Changing with Scope of Awareness by Using Measured Traffic Flow

Abstract: This paper presents the validation method and its evaluation of the spontaneous braking and lane changing with scope awareness parameter. By using the real traffic flow data, the traffic cellular automaton model that accommodate these two driver behaviors, e.g., spontaneous braking and driver scope awareness has been compared and evaluated. The real traffic flow data have been observed via video-recording captured from real traffic situation. The validation results shown that by accommodate spontaneous braking and scope awareness parameters, the model can produced traffic flow’s accuracy value 83.9% compared to the real traffic flow data.

Author 1: Kohei Arai

Author 2: Steven Ray Sentinuwo

Keywords: traffic model validation; spontaneous braking; scope awareness; traffic cellular automaton.

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Paper 4: Recovering Method of Missing Data Based on Proposed Modified Kalman Filter When Time Series of Mean Data is Known

Abstract: Recovering method of missing data based on the proposed modified Kalman filter for the case that the time series of mean data is know is proposed. There are some cases of which although a portion of data is missing, mean value of the time series of data is known. For instance, although coarse resolution of imagery data are acquired every day, fine resolution of imagery data are missing sometimes. In other words, coarse resolution of imaging sensor has wide swath width while fine resolution of imaging sensor has narrow swath, in general. Therefore, coarse resolution of sensor data can be acquired every day while fine resolution of sensor data can be acquired not so frequently. It would be nice to become able to create frequently acquired fine resolution of sensor data (every day) using the previously acquired fine resolution of sensor data together with the coarse resolution of sensor data. The proposed method allows creation of fine resolution sensor data with the aforementioned method based on a modified Kalman filter. As an example of the proposed method, prediction of missing ASTER/VNIR data based on Kalman filter using simultaneously acquired MODIS data as a mean value of time series data in revision of filter status is attempted together with a comparative study of prediction errors for both conventional Kalman filter and the proposed modified Kalman filter which utilizes mean value of time series data derived from the other sources. Experimental data shows that 4 to 111% of prediction error reduction can be achieved by the proposed modified Kalman filter in comparison to the conventional Kalman filter. It is found that the reduction rate depends on the mean value accuracy of time series data derived from the other data sources. The experimental results with remote sensing satellite imagery data show a validity of the proposed method

Author 1: Kohei Arai

Keywords: Kalman filter; nremote sensing satellite image; time series analysis

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Paper 5: 3D Skeleton model derived from Kinect Depth Sensor Camera and its application to walking style quality evaluations

Abstract: Feature extraction for gait recognition has been created widely. The ancestor for this task is divided into two parts, model based and free-model based. Model-based approaches obtain a set of static or dynamic skeleton parameters via modeling or tracking body components such as limbs, legs, arms and thighs. Model-free approaches focus on shapes of silhouettes or the entire movement of physical bodies. Model-free approaches are insensitive to the quality of silhouettes. Its advantage is a low computational costs comparing to model-based approaches. However, they are usually not robust to viewpoints and scale. Imaging technology also developed quickly this decades. Motion capture (mocap) device integrated with motion sensor has an expensive price and can only be owned by big animation studio. Fortunately now already existed Kinect camera equipped with depth sensor image in the market with very low price compare to any mocap device. Of course the accuracy not as good as the expensive one, but using some preprocessing we can remove the jittery and noisy in the 3D skeleton points. Our proposed method is part of model based feature extraction and we call it 3D Skeleton model. 3D skeleton model for extracting gait itself is a new model style considering all the previous model is using 2D skeleton model. The advantages itself is getting accurate coordinate of 3D point for each skeleton model rather than only 2D point. We use Kinect to get the depth data. We use Ipisoft mocap software to extract 3d skeleton model from Kinect video. From the experimental results shows 86.36% correctly classified instances using SVM.

Author 1: Kohei Arai

Author 2: Rosa Andrie Asmara

Keywords: Disable gait classification; 3D Skeleton Model; SVM; Biometrics

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Paper 6: Contradiction Resolution between Self and Outer Evaluation for Supervised Multi-Layered Neural Networks

Abstract: In this paper, we propose a new type of informationtheoretic method. We suppose that a neuron should be evaluated from different points of view to precisely discern its properties. In this paper, we restrict ourselves to two types of evaluation methods for neurons, namely, self and outer-evaluation. A neuron fires only as a result of evaluating itself, while the neuron can fire as a result of evaluation by all surrounding neurons. Selfand outer-evaluation should be equivalent to each other. When contradiction between two types of evaluation exists, the contradiction should be as small as possible. Contradiction between self- and outer-evaluations is realized in terms of the Kullback- Leibler divergence between two types of neurons. Contradiction between self- and outer-evaluation can be resolved by decreasing the contradiction ratio between the two types of evaluation in terms of KL divergence. This method is expected to extract the main features in input patterns, if those are shared by two types of evaluation. We applied the method to two data sets, namely, the logistic and dollar-yen exchange rate data. In both problems, experimental results showed that visualization performance could be improved, leading to clearer class structure for both problems. In addition, when visualization was improved, generalization performance did not necessarily degrade, showing the possibility of networks with better visualization and prediction performance.

Author 1: Ryotaro Kamimura

Keywords: contradiction resolution; self- and outer-evaluation; visualization; self-organizing maps

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Paper 7: Weapon Target Assignment with Combinatorial Optimization Techniques

Abstract: Weapon Target Assignment (WTA) is the assignment of friendly weapons to the hostile targets in order to protect friendly assets or destroy the hostile targets and considered as a NP-complete problem. Thus, it is very hard to solve it for real time or near-real time operational needs. In this study, genetic algorithm (GA), tabu search (TS), simulated annealing (SA) and Variable Neighborhood Search (VNS) combinatorial optimization techniques are applied to the WTA problem and their results are compared with each other and also with the optimized GAMS solutions. Algorithms are tested on the large scale problem instances. It is found that all the algorithms effectively converge to the near global optimum point(s) (a good quality) and the efficiency of the solutions (speed of solution) might be improved according to the operational needs. VNS and SA solution qualities are better than both GA and TS.

Author 1: Asim Tokgöz

Author 2: Serol Bulkan

Keywords: Weapon Target Assignment; Combinatorial Optimization; Genetic Algorithm; Tabu Search; Simulated Annealing; Variable Neighborhood Search; WTA; WASA; TEWASA

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