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Humanoid robots are expected to perform various jobs replacing human beings, but it is difficult for non-expert users to control them. To control humanoid robots easily, various motion imitation systems, in which a humanoid robot does the same motion as a human teacher, have been studied. In the present study, we make two contributions to building easy-to-use imitation systems. The first contribution is a flexible learning system that enables both synchronous real-time imitation, in which the robot imitates human motion almost immediately, and batch imitation, in which the robot’s motion is optimized more accurately after longer measurement of human motion. We use an efficient inference-based optimization method to enable both learning modes. The other contribution is an auditory user interface that accepts voice commands from the user and at the same time identifies the target person to be imitated. This enables the user to easily use the imitation system even when other people are present. Experimental results show that the proposed system can be used in both imitation modes and that it can identify the target person.

Recently, the optimization-by-inference approach has been proposed as a new means for solving high-dimensional optimization problems quickly. Approximate Inference COntrol (AICO) is one of the most successful and promising methods that implement the optimization-by-inference approach. AICO is able to solve stochastic optimal control problems and has already been successfully used in many applications. However, it is known that the iterative inference of AICO sometimes fails to converge to the optimal solution. To make the optimization more robust, in this paper, we propose to take model uncertainty into account. In AICO, the cost function to be minimized is accurate around a particular state of a given stochastic system, but the accuracy is uncertain in regions far from that state. Because using such an uncertain function is harmful to the convergence, we modify AICO, so that it does not use the function in uncertain regions. Our method is easy to implement and does not add much computational time to the original AICO. Experiments using two different scenarios show that our method substantially improves AICO in terms of the rate at which the algorithm produces convergent results.

This paper describes design and development of a system that supports continuous creation of hazard maps by local residents in their daily life. We made an interview survey to design our system in a model traditional town in Saga, Japan. The results show that in spite of continuous efforts, many practical problems remain and residents feel unsafe. Based on these results, we designed and developed a unique ICT-based support system which contributes to community-based disaster prevention/reduction. The continuous resident participation and posting design are core concept for our sustainable community-based approach. Our system continues to support making a hazard map by integrating the community-based hazard information. Local residents register information about the spot (disaster types, a risk level, a photograph, comments, positional information) that can be dangerous in case of disaster. We have evaluated the usefulness and possibilities of our prototype system implemented as an iOS application.

“Hahoe Village” in Andong region is an UNESCO World Heritage Site. It should be protected against various disasters such as fire, flooding, earthquake, etc. Among these disasters, flooding has drastic impact on the lives and properties in a wide area. Since “Hahoe Village” is adjacent to Nakdong River, it is important to monitor the water level near the village. In this paper, we developed a hydrological modelling using multi-layer perceptron (MLP) to predict the water level of Nakdong River near “Hahoe Village”. To develop the prediction model, error back-propagation (EBP) algorithm was used to train the MLP with water level data near the village and rainfall data at the upper reaches of the village. After training with data in 2012 and 2013, we verified the prediction performance of MLP with untrained data in 2014.

The theory of partially observable Markov decision processes (POMDPs) is a useful tool for developing various intelligent agents, and learning hierarchical POMDP models is one of the key approaches for building such agents when the environments of the agents are unknown and large. To learn hierarchical models, bottom-up learning methods in which learning takes place in a layer-by-layer manner from the lowest to the highest layer are already extensively used in some research fields such as hidden Markov models and neural networks. However, little attention has been paid to bottom-up approaches for learning POMDP models. In this paper, we present a novel bottom-up learning algorithm for hierarchical POMDP models and prove that, by using this algorithm, a perfect model (i.e., a model that can perfectly predict future observations) can be learned at least in a class of deterministic POMDP environments

Relative entropy is a divergence measure between two probability density functions of a random variable. Assuming that the random variable has only two alphabets, the relative entropy becomes a cross-entropy error function that can accelerate training convergence of multi-layer perceptron neural networks. Also, the n-th order extension of cross-entropy (nCE) error function exhibits an improved performance in viewpoints of learning convergence and generalization capability. In this paper, we derive a new divergence measure between two probability density functions from the nCE error function. And the new divergence measure is compared with the relative entropy through the use of three-dimensional plots.

When developing a classifier using various objective functions, it is important to compare the performances of the classifiers. Although there are statistical analyses of objective functions for classifiers, simulation results can provide us with direct comparison results and in this case, a comparison criterion is considerably critical. A Receiver Operating Characteristics (ROC) graph is a simulation technique for comparing classifiers and selecting a better one based on a performance. In this paper, we adopt the ROC graph to compare classifiers trained by mean-squared error, cross-entropy error, classification figure of merit, and the n-th order extension of cross-entropy error functions. After the training of feed-forward neural networks using the CEDAR database, the ROC graphs are plotted to help us identify which objective function is better.

In the preceding studies, an analysis of Saga Prefectural sightseeing information by a Self-Organizing Map (SOM) has been tried. And recent development on Information and Communication Technology (ICT) will help us to access any results via the mobile devices easily. This is why the mobile devices, e.g., smartphones and tablet computers, have an operating system installed, and we can improve their functions by downloading any applications on the Web. Then, in order to realize this basic idea, development of an application for the mobile devices is investigated through some computer simulations on the standard desktop PC in this paper. As a result, it is found that i) a developed feature map is useful to identify some candidate topics, ii) a touchscreen is suitable to show the feature map, and iii) arrangement of the feature map can be modified based on our interests. Then, it is concluded that the proposed idea seems to be applicable, even though further consideration is required to brush it up.

近年提案された仮想磁場漸弱法とは，ホップフィールドネットワークとスピングラス理論の類似性に着目した組み合わせ最適化問題の新しい解探索法である．これは，外部磁場を徐々に弱めることで安定なスピン配列を実現するという物理学世界の思考実験をホップフィールドネットワーク世界に導入したもので，対応するニューロン閾値を仮想的な磁場パラメータとみなして制御し，最適解を求めようとする非常に簡便な手法である．先行研究によれば，仮想磁場パラメータの導入で正答率が改善するとの報告があるものの，その極性反転（負値→正値あるいは正値→負値）が重要であるとの指摘もあり，これまでのところ，その詳細については不明なところが多い．そこで本論文では，これまでの経緯を含めて問題点を整理したうえで，Nクイーン問題やナンバー・プレイス問題を取り上げ，さらに複数の仮想磁場パラメータ設定法を用意して，その検証を行った．その結果，いずれの課題においても，仮想磁場パラメータの極性反転が正答率改善に結び付いていることを確認し，その有効性を明らかにした．

Numerous studies on time series prediction have been undertaken by a variety of researchers. Most of them typically used unidirectional computation flow, that is, present signals are applied to the model as an input and predicted future signals are derived from the model as an output. On the contrary, bidirectional computation style has been proposed recently and applied to prediction tasks. A bidirectional neural network model consists of two mutually connected subnetworks and performs direct and inverse transformations bidirectionally. To apply this model to time series prediction tasks, one subnetwork is trained a conventional future prediction task and the other is trained an additional task for past prediction. Since the coupling effects between the future and past prediction subsystems promote the model's signal processing ability, bidirectionalization of the computing architecture makes it possible to improve its performance. Furthermore, in order to investigate the acquired signal transformation, two kinds of chaotic time series—the Mackey–Glass time series and “Data Set A”—are adopted in this paper. As a result of computer simulations, it has been found experimentally that the direct and inverse transformations developed independently and their information integration give the bidirectional model an advantage over the unidirectional one. © 2003 Wiley Periodicals, Inc. Electr Eng Jpn, 145(3): 50–60, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/eej.10232

In general, a material discrimination task is achieved by following two steps: One is an analytical step for estimating constructive elements of the object, and the other is a computational step for identifying its material. The first step is simple because only measuring plural parameters is required to reach the correct answer. But the second step is complex and various kinds of techniques have been proposed. Then, one of the neural network models called a self-organizing map (SOM), a good tool for topology-preserving projection, is adopted in this paper. Firstly, each material is examined with a micro multi-functional tactile sensor. Secondly, in the training stage, the average data of several measurement trials are applied to the SOM. Finally, in the test stage, the actual measurement raw data, which are completely new to the trained SOM, are applied and the measurement object is estimated from the location of the emerging “winner” neuron. As a result of computer simulations, it is found xperimentally that measured information space is divided into several regions representing each material successfully. Furthermore, an acquired feature map for material discrimination is useful against several untraining data.

This paper presents a novel multi-functional tactile sensor. It has a mono-structure, but has three kinds of sensing functions for discriminating material properties and a touch force sensing function which is a basic function in a tactile sensor. The proposed sensor is used to discriminate typical materials such as iron, aluminium, copper, brass, rubber, acrylic resin, styrene foam and so on.The sensor consists of two comb shaped metallic lines on both faces of a thin insulated substrate with eight terminals. From different combinations of these terminals, the four sensing functions discribed above can be realized.The experiments show good results to discriminate each sample of materials. The proposed sensor is expected to serve as a step toward the development of an artificial skin tactile sensing system in the future.