As the mobile service, the cloud service and M2M (Machine to Machine) service are becoming more and more popular, network traffic increases drastically and the requirements for network services are diversifying. In this paper we propose the transport network architecture using packet transport technology, which attains the integration of expanding traffic fairly, and effectively. We propose the implementation of network traffic control functionalities, which can attain the variety of requirements for traffic control from many kinds of network services.

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With the growth of wireless LANs, the number of wireless LAN stations (STAs) associated with an access point (AP) is increasing. For STAs in the power-saving mode (PS mode), an AP advertises the buffering status of data frames for each STA by means of a beacon, then each STA requests the transmission of buffered frames. If two or more STAs request data frame transmission, they may not receive data frames immediately because there might be a waiting time due to other STAs' transmissions. Power is consumed by this waiting time because the STAs are in the awake state. To reduce this power consumption, we propose a traffic distribution method, utilizing a multiple basic service set identification (BSSID). The primary problem with the conventional method is that traffic is congested only after beacon transmission, thus, traffic must be distributed. Usually, the multiple BSSID is used for virtual networks, i.e., virtual access points (APs). Using virtual APs built by multiple BSSID in the proposed method, the AP controls the association of virtual APs and STAs. By distributing the number of STAs associated with virtual APs, the waiting time due to other STAs' transmissions is reduced. Simulation results reveal that the proposed method reduces power consumed by STAs by means of multiple BSSID.

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The activity-induced manganese-enhanced MRI (AIM-MRI) is one of the candidate methods for measuring the neuronal activity noninvasively in vivo. This method has the potential to measure the history of the neuronal activity, since the Mn²⁺ can enter the active neuron through voltage-dependent Ca²⁺ channels. However, it has not been clear that the relation between the neuronal activity and the accumulation of Mn²⁺ in the cell.   At first, we confirmed that the longitudinal relaxation time of H⁺ (T₁) measured by means of MRI apparatus was related to the [Mn²⁺] using the pseudo intracellular phantoms with various concentration of Mn²⁺. The inverse of T₁ (1/T₁) was proportional to [Mn²⁺], thus [Mn²⁺] can be measured by MRI quantitatively. Next, we investigated the relation between the neuronal activity and the accumulation of Mn²⁺ in the striatal GABAergic neurons using the method of the Mn²⁺ quenching of Fura-2 LR, a family of fura fluorescent Ca²⁺ indicators. The amount of the Ca²⁺ influx was correlated to the neuronal activity evoked by tetanic stimulations with various frequencies. In the same slice preparation, the amounts of the fluorescence quench induced by the tetanic stimulations under the condition of 50 µM MnCl₂ administration were recorded. As a result, the amount of the quench was proportional to the amount of the Ca²⁺ influx. These results suggested that a cell, which has higher activity, accumulated larger amount of Mn²⁺ in the neuron. Thus, our results supported that AIM-MRI can measure the history of the neuronal activities in the whole brain noninvasively in vivo.

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In recent years, short-range radar such as automotive radar has been attracting attention. In the radar, transmission method and signal processing achieve a high range resolution and long range detection performance is required. In this paper, we propose the Stepped Multiple Frequency CPC Radar that is expected to achieve a high range resolution and long range detection performance. In the proposed method, the transmission sequence and the phase correction process expected to suppress the inter symbol interference and reduce the effects of Doppler frequency. A simulation result on the side-lobe level of CPC pulse compression is shown to verify the effectiveness of the method on the effect of Doppler shift. The other simulation result is shown to verify the improvement of S/N ratio by the signal processing. The results of the experiments also indicated that the radar could achieve high range resolution with narrowband receiver.

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The electricity market in Japan is being deregulated and the retail market in Distribution Network (DN) will be fully liberalized in near future. In the liberalized electricity market, the retailer procures and supplies the power for the consumers in DN to maximize the profit under market price uncertainty. On the other hand, the consumers in DN can efficiently control their loads with developing Demand Response (DR) technology and hence power flow in DN can be complicated depending on the DR. However, different kind of the consumers such as residential, commercial and industrial have different DR characteristics. Furthermore, the variation of distribution line loss caused by DR can affect the wheeling rate in DN and the retailer's profit. From this point of view, the decision making of the market players in DN under uncertainty should be modeled considering the kind of consumers and physical state in DN. This paper proposes a novel electricity market model with DR, and a bi-level programming problem based on the Stackelberg game model is formulated to analyze the behavior of players in the market.

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The purpose of this research is to reduce the computational complexity of the Peculiarity Factor (PF). Recently, PF has been adopted as the index for anomaly data detection, and it is widely used in various mining scenes. The fact that PF has become a powerful mining tool is positive because its calculation method is extremely simple and the results of the calculation are easy to understand visually. One of the most important problems for using PF for large-scale data is the rapidly increasing computational complexity required when the data volume increases. The computational complexity of PF is in the polynomial order because the PF of each data is calculated distantly over all the data. In this study, we propose an approximation methodology for PF for computational reduction and for enhanced robustness using the vector quantization model. Approximate values of PF are calculated by replacing the actual data with the nodes of vector quantization model. By calculating PF based on the vector quantization node vectors, we achieve restraint in the increasing computational complexity.

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The purpose of this paper is to improve the efficiency of the single-phase PWM DC-AC inverter based on the optimization of the switching-phase of the PWM. The improvement of the efficiency means to improve the quality of the output waveform, the efficiency of the conversion, and the reduction of the switching-loss. We propose an evaluation function which corresponds to the minimum value of the evaluation function by using the particle swarm optimization algorithm. We improve the efficiency by setting a criterion of each index. As the result, the proposed algorithm can reduce high harmonic components in the switching times smaller than that of the conventional triangle wave comparison method. Also, we confirm that the algorithm can improve the quality of the output waveform and the efficiency of the conversion.

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Modern heuristic optimization algorithms developed in '90s have been a particular focus of attention because of their simplicity, easy software implementation, and moreover, the interesting phenomena that their performance emerged from the interactions among the particles.  In this paper, we see that we can get emergent performance as an optimization algorithm by increasing the number of particles on Evolution Strategy. Considering that, we try to increase the interactions among the particles in order to get better performance. We define parameter tuning rule designing as an optimization problem, and use Genetic Programming to find those for Evolution Strategy. In addition, we evaluate the generated tuning rules using statistical tests and several benchmarks to verify that the proposed methods and the generated rules are effective ones.

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This paper proposes a new approach to solving tsumego and playing games in computer go. The approach is based on the Monte-Carlo tree search (MCTS) algorithm. To be more specific, the proposed method improves how to execute a single playout on a leaf node. Our method brings diversity to playout so that only a particular part of the Monte-Carlo tree will not grow in the depth direction. The diversity of playout can be brought to realization by embedding tabu lists into all leaf nodes. Here, the tabu list means a short memory which is very popular in the tabu search algorithm. Tabu lists impose restriction on several moves from the first move during the playout so as to control the points of prohibited moves. If a candidate move corresponds with an element contained in the tabu list, then the move is treated as the prohibited move. It is possible that the diversity of playout could induce the behavior of MCTS to the best-first search on a more uniformly-broadened tree. This works well for solving tsumego which has the only path without branching. Furthermore, the variable-length tabu lists embedded into the MCTS would have an advantage in playing 9×9 go.

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In recent years, the number of articles that describe protein structures and functions is rapidly increasing. Retrieval of desired articles from a large collection of articles is highly needed. This paper presents an article-based retrieval method for articles on protein structure analysis. In this method, an article is considered as a set of structural and functional concepts of protein using Gene Ontology and other databases, and the similarity between articles can be evaluated by using a concept graph generated by integrating Gene Ontology and InterPro database. An article includes various types of information, which makes it difficult to decide the viewpoint from which the users intend to retrieve articles in the article-based retrieval. To solve this problem, our proposed method use more than one article as a single query to clarify users' intention in the retrieval request,which improves the retrieval accuracy. The effectiveness of our proposed method was confirmed by evaluating its accuracy through retrieval experiments, especially for retrieving more recent articles.  

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In order to manage river bank, we propose a method to classify vegetation in river band by Support Vector Machine (SVM) on the basis of texture information. The method comprises three steps: detection of vegetation, initial classification by SVM, and reclassification to reduce noises. The results for 38 images obtained by the Akita Office of River and National Highway show that the proposed method using both texture information and RGB components can accurately classify grasses and harmful vegetation in comparison with a case using the RGB original components.

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