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In developing Cyber Physical Systems such as smart grid and smart cities, risk management technologies plays an important role on providing safe and secure service. In this paper, focusing on changes of recent threats, we show that an approach by the information security psychology is valuable for the risk management of the system.

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Robust optimization is an approach to find a decision with the best worst-case performance under uncertainty. We introduce the basics of robust optimization, including reduction of simple robust optimization problems to tractable problems. We also show applications of robust optimization techniques to portfolio optimization and machine learning problems.

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This paper proposes a two layers deduplication system for backup process in IT environment. The proposed system implements two chunk sizes in descendent way and activates them simultaneously in a sequence. They eliminate duplicated parts among backup target data in more efficiently than any conventional single layer deduplication system. The system weakens a substantial trade-off between the deduplication rate and the performance, which is defined as the number of chunks reduced. Comparing to a conventional approach the proposed system provides the equivalent deduplication rate with much less performance degradation or more performance with less compensation of deduplication rate. In addition, it achieves better stability of performance when small chunk sizes manipulate heavily duplicated data and densely changed data. The positive effectivenesses of the proposed system are evaluated quantitatively and are compared to a conventional approach by using a simulation. The results are certified with a prototype machine.

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This paper focuses on route optimization for maximization of the number of points visited by an electric vehicle (EV) in a tour beginning and ending at the same point while taking into consideration the battery characteristics and capacity limit. In considering EV range insufficiency as related to regenerative braking and the battery capacity limit, we define the maximum node touring problem as maximizing the number of tour destinations and formalize the problem on this basis. The problem as considered in this paper also includes the changes in route optimization induced by the relationship between the battery capacity limit and the residual charge. An example of this is the situation in which energy regeneration is insufficient because of reaching the fully charged state and more destinations can be visited by changing the tour route in such a way that achieves more efficient energy use. Another example is the situation in which changing the order of certain destinations in the tour induces a change in the cost for every subsequent interval between destinations. We propose a new method to efficiently solve the tour point maximization problem having the abovementioned characteristics.

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In this paper, we studied a multi-objective portfolio selection problem with piecewise linear transaction costs in a fuzzy environment. Transaction costs are expenses incurred when buying or selling securities and they are a burden on investors who frequently make trades to balance their portfolio. To better evaluate portfolio performance with transaction costs, this paper extends a previous study to a new model called fuzzy multi-objective portfolio selection model with piecewise linear transaction costs. A fuzzy simulation-based particle swarm optimization algorithm is designed to solve the model considering investors' individual risk attitude. In addition, several numerical examples are provided to illustrate the effectiveness of this model and algorithm. A conclusion is drawn at the end of this paper.

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A database contains a wealth of hidden knowledge that can be used in decision making to support commerce, business, management, research and other activities. Classification analysis plays a pivotal role in the pattern recognition field, where it is considered as a core method. Algorithms such as support vector machine (SVM) and artificial neural network (ANN) have been proposed to solve the problem of binary classification according to data distributions. But these traditional classification algorithms are unable to provide satisfying results for an imbalanced dataset with special characters. In this paper, we propose a model based on particle swarm optimization (PSO) and support vector machine (SVM) for using in the classification of a large, imbalanced dataset. This model is referred to as the PSO-SVM (particle swarm optimization-based support vector machine) model. PSO was recently proposed as a metaheuristic framework for large, imbalanced dataset classification. The SVM algorithm also exhibits a high level of performance in handling balanced binary classification. Therefore, the novel model proposed here is introduced to improve classification accuracy by combining support vector classification (SVC) with an imbalanced PSO. The G-mean is used to evaluate the final results. In the final section of this paper, the proposed method is compared with some conventional heuristic models. The experimental results demonstrate that the proposed method exhibits a high level of performance for imbalanced dataset classification.

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During the past several years, a large number of studies have been conducted in the area of flexible job shop problems. Intelligent manufacturing planning and scheduling solutions that are based on meta-heuristic methods, such as the simulated annealing and particle swarm optimization, have become common techniques for finding satisfactory solutions within reasonable computational times in real scenarios. However, only a limited number of studies have analyzed the effects of interdependent relationships associated with various decision factors considered for the complex problems. This paper presents a Markov network based estimation of distribution algorithm to address the flexible job shop scheduling problem. The proposal uses a subclass of estimation of distribution algorithms where the effects between decision variables are represented as an undirected graph model. Furthermore, a critical path-based local search method is adopted by the proposed algorithm to achieve better performance. We present an empirical validation for the proposal by applying it to solve various benchmark flexible job shop problems.

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Business Process Modeling (BPMod) is paid for attention as a technology that improves quality of information system development. It enables to increase the integrity between business operations and information systems with visualizing specification of business requirement in upper-process. On the other hand, there are problems with development cost which is caused by large variations of business functions, in case of applying BPMod for complicated business process. In this paper, we propose business process design method with hierarchical business variation analysis for improving productivity of model development. It enables to extract the variations of business functions and consolidate the similar business functions. We applied it to an actual project of system development and confirmed its effect.

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The purpose of our research is to develop the continuous anomaly detection method for a web-based system with avoiding false detection by monitoring resource usage. A conventional method detects the anomaly by applying autoregressive model to the difference between the actual resource usage and the estimated resource usage with the design reference value. When the spike that uses much resource momentarily happens on the web-based system, the anomaly is detected falsely in spite of that the anomaly is not continuous and immediately recovered. In order to detect the continuous anomaly, the proposed method checks whether the detected anomaly is continuous or not by judging the resource usage after the observation of a spike. The proposed method judges the trend of increasing resource usage by autoregressive coefficient with the resource usage after the spike. Applying the test of the structural changes to the resource usage before and after the spike, the proposed method detects anomalies in judging statistically whether the trend of the resource usages changes. Experimental results show that the proposed method can decrease the frequency of the false detection to few times and detect the anomaly in 380 seconds, which is practical enough to use for the management of a web-based system.

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Environmental and landscape conservation activities in tourism region are specified as one of the important strategy in the basic plan for promoting a tourism-oriented country showing measures to be token by the whole government. For example eco-friendly tourism style with green mobility is required which enhances to reduce the effects on the environment in tourism by saving of migration energy between sightseeing spots. This paper aims to show a utilization of multifunctional mobile communication device like smartphone for developing tourism destination based on mobility management. To guide tourists to green mobilities, this paper employs a mobile application of tourism routing assistance for Kyoto city which focuses on the determination of migration pathway and transportation. Public transportation of Kyoto city, especially city bus, is sufficiently provided, and there are many sightseeing spots in Kyoto city. The effectiveness of the proposed method is shown by the simulation results which can give various knowledge to develop sustainable tourist destination and to maintain coexistence with local community people.

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Battery-powered mobile routers must control their power consumption to extend their operating time. The mobile routers have both a wide area network (WAN) interface and a local area network (LAN) interface. Of the two interfaces, the LAN interface operated as a wireless LAN (WLAN) access point (AP) does not have a power-saving function in the IEEE 802.11 standard. Therefore, we focus on reducing power consumption of the AP. The conventional method for saving power in APs concerns the MAC queue status. The mobile routers, however, have Layer 3 functionality. That is, IP frames are transferred from the WAN interface to the LAN interface. To save power, the departure process from the Layer 3 queue to MAC queue needs to be modified. In this paper, we propose a departure control from the Layer 3 queue to save power in the WLAN AP. The numerical analysis and simulation results revealed that the proposed method reduces power compared with the conventional method.

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We have been developing a wearable pneumatic total artificial heart (PTAH) system for bridge to transplant, and we evaluated its fundamental performance in in vitro and in vivo experiments. The PTAH system consists of left and right diaphragm-type blood pumps and a compact wearable pneumatic drive (WPD-100) unit. The WPD-100 unit can generate a fixed systole ratio by the non-circular gears. The prototype WPD-100 units with fixed systolic ratio of 35, 40 and 44% were developed. 25 mm and 23 mm Bicarbon valves were mounted in the inlet and outlet ports of the left and right blood pumps. As a result of an overflow-type mock circulation test, more than 8.0 l/min of pump output was obtained at 100 bpm for after load of 80, 100 and 120 mm Hg in all WPD-100 units. An acute animal experiment was also performed using WPD-100 units with fixed systolic ratio of 35 and 44% in a calf weighing 98 kg. The cardiac output ranged between 4.3 and 5.2 l/min at the mean aortic pressures of 108-115 mm Hg at beating rates of 60 to 100 bpm. These results indicated that our PTAH system consisting of two blood pumps and WPD-100 units have sufficient performance for total cardiac replacement.

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Microfluidic devices using droplets are possible to perform high throughput screening of single-cell samples through encapsulating cells into droplets. In conventional encapsulation methods, devices have T-junction and flow-focusing geometry for droplet generation. Those techniques are difficult to achieve uniform distribution of cells, or to place a single-cell in droplets. Moreover, during screening samples, it is necessary to have a mechanism to hold droplets at one place for long time. It is, however, slightly hard to add such a function in conventional devices. Thus, we propose a new encapsulation method of single-cell using triangular microwells. In this approach, each single-cell is isolated, encapsulated, and held at one place using triangular microwells. This approach has benefits that the droplets size can be regulated by the size of microwells. In addition, the optimization of encapsulation is easier than conventional methods because the flow rate of oil phase does not affect to the encapsulation.

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We examined underlying mechanisms of apparent motion illusion of beta movement by obtaining neuromagnetic responses of event-related fields (ERFs). A simple setting for visual stimulation of two circles, presented 10 degrees apart each other horizontally, was used in experiment 1. The first circle of a duration of 16.7 msec was followed by the second with three conditions of stimulus-onset-asynchrony: (a) 16.7 msec almost the two circles seen simultaneously, (b) 83.3 msec perceived optimally as beta movement, and (c) 549.8 msec occurred isolatedly. In experiment 2, three conditions of real motion for visual stimulation were set as: (a) six circles presented successively for one animation, (b) fourteen circles for one animation, and (c) thirty-four circles for one animation. We applied minimum current estimates (MCEs) to obtain the source activity of the ERFs for beta movement, and then calculated an average amplitude for each five 100 msec epoch after second stimulus onset in experiment 1, and after first stimulus onset in experiment 2. As the results, the optimal condition showed maximum activities at the first 100 msec epoch at parietal region in experiment 1, suggesting that the motion components of MCEs were emerging from this epoch. In experiment 2, three conditions of real motion evoked prolonged MCE activities for the temporal and central areas, suggesting these components being changed with the “speed” of motion perception. The neuromagnetic activities of beta movement and motion perception may originate from parietal areas within the 100 msec after the percept of the movement.

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Minimum Variance Control(MVC) is a control system of discrete time often used on the scene. But by discretization, unstable zeros may occur. And a plant becomes a non-minimum phase system, even if it is minimum phase system in continuous time. MVC can not treat a non-minimum phase system. Then, unstable zero is eliminated by introducing the delta operator which approximates discrete time to continuous time and can treat it. This technique can be extended to Multi-Inputs Multi-Outputs(MIMO) systems. This paper uses the decoupling control method called skeletal matrix, because mutual interference may have big influence in control by the MIMO system. A stable MIMO-MVC system is attained by the proposed approach which combines the delta operator and the decoupling method.

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In this study, we introduce a system for tracking multiple people using multiple active cameras. Our main objective is to capture as many targets as possible at any time, using a limited number of active cameras. In our context, an active camera is a statically located pan-tilt-zoom camera.  The use of active cameras for tracking has not been thoroughly researched, because it is relatively easier to set up and use static cameras. However, there are many properties of active cameras that we can exploit. Our results show that an approximately two-fold increase in relative accuracy can be achieved without any significant increases in computational costs.  Our main contributions include removing the necessity for the individual detection of each tracked target, estimating the future states of the system using a simplified fluid simulation, and finally unifying the active camera tracking method using a minimum risk formulation. We also improved the accuracy by developing an efficient method for attracting cameras towards targets located far away from the present camera configuration.

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Many conventional methods for distinguishing road surface condition using car-mounted cameras have already been proposed. However, most of these methods are only effective for daytime and bright conditions. We need to expand the application of distinguishing methods at night-time, which is a much more dangerous environment. In this paper, we propose a new distinction method for road surface conditions at night-time. The method uses only video information acquired by an inexpensive car-mounted video camera and analyzes the difference in road surface features for each condition. The image features of the road surface depend on the illumination conditions such as street lamps, signal lights, reflections and other light sources. We analyze the image features based on color information and the presence of other light sources. As a result, the distinction of road surface conditions has been achieved with high accuracy, including the areas illuminated by street lamps and other light sources.

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This paper describes a new scanning method based on deflected flying ink jet of Electro-statically extracted ink jet of di-electric ink. Our proposed scanning scheme is a method in which recording is made by causing ink to fly jet in a deflected pattern with the nozzle at the tip. This method of scanning line has less conspicuous compared with other scanning methods in available, and of achieving favorable tone graduation. For these features, the present method appears to be effective as a smooth tone reproduction of filtered images.

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＊投稿手引の改正に伴い，2013年10月1日以降に新規に投稿され，その後掲載決定となった論文および資料には，Extended Summaryがありません。

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