This paper considers Day-Ahead Market with battery and accumulator to level power generation. First, we model consumer with battery, generator with battery and accumulator to maximize their own profit. Then, the optimal pricing algorithm based on dual decomposition and Steepest Descent Method and proof the stability of the algorithm is proposed. Finally, we show the effectiveness of the algorithm with some numerical simulations which indicate that the proposed algorithm achieves the demand-supply balancing.

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In the conventional electric power system, utilities must supply required electricity to consumers for maintaining power demand and supply. Keeping the demand and supply balance by this way will become very difficult when renewable energy resources are introduced in bulk. To realize more efficient system, consumers must cooperate with suppliers. In this paper, the demand of air-conditioning in house is focused as demand which can be controlled. First, accuracy of CADIEE which we use in this report as thermal load calculation soft is confirmed. Using this simulation soft, simulations are carried out to analyze changes of indoor environment by controlling air-conditioner and benefit to demand and supply balance.

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A large scale diffusion of residential photovoltaic (PV) could result in deterioration of voltage quality of power distribution when generated electricity exceeds electricity demand. Authors evaluated the effect of energy demand management to mitigate this issue while assuming a residential community consisting of 1012 households connected to a feeder of high-voltage distribution line. The energy demand management methods considered in this study is shift of operation schedule of electric appliances and heat-pump water heater. For evaluation, we developed a model combining the following sub-models: 1) a model estimating energy demand and PV generation of households, 2) an operation optimization model for appliances and water heater by using mixed-integer liner programming program, and 3) an model estimating voltage at nodes of the feeder of a high-voltage distribution line. The result revealed that energy demand management is capable of significantly reducing electricity surplus generated by PV during daytime and avoid PV generation suppressed because voltage exceeds the regulated limit. Energy demand management is more effective if it is carried out at nodes belonging to downstream of the distribution line.

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High penetration of variable sources of renewable power generation will lead to various technical issues. In this study, we analyzed the effects of three operation algorithms of home storage battery for preventing increase in voltage in the local distribution networks and for leveling the power system load in the entire power system: Operation [A] using scheduled charged and discharged electricity, Operation [B] using scheduled stored electricity, and Operation [C] by original “purchase-minimum mode” and “grid-charge mode” under the uncertain forecast. Under the constraints of reverse power flow (≤ 1kW or = 0kW), additional restricted PV output and electric bill were reduced by 60% in Operation [C] compared to Operation [A]. These results show that the Operation [C] has high robustness against electricity demand forecast errors.

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Micro grids are expected to be one of the most useful energy systems in terms of efficient use of renewable energy sources with few adverse effects on the main power grids when the micro grids are operated properly from the viewpoint of balancing the electric power supply and demand. However, it is difficult to maintain the supply-and-demand balance because distributed renewable energy generation (DREG), such as photovoltaic generation systems (PVs) and wind turbine generation systems (WTs), account for a large percentage of the generators in the micro grids. Therefore, an operation planning method considering the uncertainty of prediction is needed for ensuring a stable grids operation. This paper presents an optimization method for operating plans of controllable generators in micro grids that copes with the uncertainty of DREG's outputs. In the proposed method, the optimal operating plans are obtained by using an enumeration method or a preconditioned tabu search (TS). Numerical simulations are carried out for a micro grid model in order to verify the usefulness of the proposed method.

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Cultured neurons reconstruct a neuronal network on a culture dish. The network structure is modified by input from outer world in activity dependent manner. Cultured rat hippocampal neurons on a multielectrodes array (MEA) dish are suitable for analyzing such complex network dynamics and developmental processes of network formation. To elucidate network dynamics, we applied shots of electrical stimuli in several types of inter-stimulus intervals. The electrical activity pattern of the living neuronal network evoked by paired stimulation is quite different from one evoked by a single stimulation. In this report, the feature of this type of network hysteresis was investigated and the hysteresis was kept for at least 1 s and not kept for 10 s. In addition, this hysteresis was remarkable only in long cultures with matured complex networks.

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Cultured rat hippocampal network on a multi electrode array (MEA) dish is a useful model for analyzing network electrical dynamics and its developmental changes. Neurons autonomously form a complex network on a MEA dish and spontaneous electrical activity is often observed without any input from the external world. The spontaneous activity is generated by synaptic interactions between neurons and reflects an internal biochemical state of a whole neuronal network. The origin of electrical activity is electrochemical potential generated by active transport of ions, which requires catabolism of ATP. We elucidated the relationship between spontaneous electrical activity and external glucose concentration. The spontaneous activity changed transiently, depending on glucose concentrations. The number of electrical spikes in spontaneous activity increases depending on the concentration of external glucose concentration. Interestingly, this increase is not only suppressed but turn to decrease. In the case of glucose concentration is 17.56 mM, the number of activity is the most, and then it decreases in the case of glucose concentration is more than 20 mM. In addition, the decrease of neuronal activity at a high glucose concentration is not influenced by the blockade of inhibitory synaptic activity. These results suggest that a cultured neuronal network has optimal glucose concentration 17.56 mM, which corresponds to the concentration of glucose in a culture medium.

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To enrich the quality of life of aged human, development of a hearing-aid with which listening is improved comfortably is desired. However, it is peculiarly difficult to fit to sensorineural hearing losses. Therefore, we have studied the development of the appraisal method of voice-hearing power (VHP) by waveform processing. Before now, we proposed the visualizing method for sound waveform to analyze sensibility evaluation with ACE-L (Amplitude Compression/Expansion method - designed with Loudness method) refer to equal-loudness-level contours and non-linear loudness characteristics. In this study, we proposed the method for evaluating VHP of sensorineural hearing losses with ACE-L. To evaluate the effectiveness as hearing-aid fitting, ACE-L was compared with the psychological appraisal. The confusion between /i/ and /u/ was simulated with ACE-L. As the result, ACE-L was effective to objective evaluation of VHP of sensorineural hearing loss was indicated.

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In recent years, the research of soft actuators has been getting increased attention. The soft actuators are expected to apply in medical, biological and welfare fields, because they can ensure high safety for fragile objects from their low mechanical impedance. A miniature pneumatic curling rubber actuator is a kind of them. In this paper, an operator-based sensorless robust nonlinear control by using robust right coprime factorization approach and using support vector regression(SVR) estimation for the miniature pneumatic curling rubber actuator is proposed. Considering practical applications in medical and biological fields, sensorless control is recommended. For realization of sesorless control, SVR estimation is used. That is, first, the nonlinear modeling is obtained by using moment balance. Second, the robust nonlinear control system using robust right coprime factorization approach is designed. Finally, experimental results based on SVR estimation results are given to show the effectiveness of the proposed scheme.

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In this paper, an Extreme Learning Machine (ELM) based parallel compensator is designed by using properties between play and stop hysteresis operators. By using this designed parallel compensator, the effect of hysteretic nonlinearity which precedes perturbed plants can be compensated. Based on the compensated perturbed plants, operator-based feedback controllers and tracking controllers are designed to guarantee the output tracking performance according to the robust right coprime factorization condition. Finally, the numerical simulation comparison results between the proposed method and the previous method are presented to validate the effectiveness of the proposed method.

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In this paper, a method to tune PID controller for unstable plants is proposed. Since the tuning is done in frequency domain, the stability and the bandwidth of the closed-loop system with the tuned controller can be estimated. In order to tune PID controller, the method requires only one-shot transient response data of the closed-loop system stabilized by an arbitrary PID controller and the data is used in frequency domain for classical loop-shaping. The effectiveness and the simplicity of the proposed method are shown through a numerical example study and an experiment.

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In this paper, we propose a template matching algorithm which is applicable for low-textured image like a range image. As for high-speed template matching, the Co-occurrence Probability-based Template Matching (CPTM) is a useful and effective method. This method uses some sets of selected pixel patterns that have relatively low occurrence probability in a template image. By using such a small number of distinctive data, reliable matching has been achieved in addition to high-speed processing. However, this method has a problem that extraction of distinctive pixels will be difficult when distribution of occurrence probability is uniform, for example, it is frequently appeared in range image. We improve the CPTM method for dealing with this problem. A key idea is to optimize geometric pixel relation in the pixel pattern when the proposed method calculates occurrence probability of pixel patterns. Experimental results have confirmed that the proposed method increase the detection rate from 73% to 90% without sacrificing its ability of high-speed. It means that performance of our method is prior to other conventional methods.

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This paper proposes an online type of controller parameter tuning method by modifying the standard fictitious reference iterative tuning method and by utilizing the so-called recursive least-squares (RLS) algorithm, which can cope with variation of plant characteristics adaptively. As used in many applications, the RLS algorithm with a forgetting factor is also applied to give more weight to more recent data, which is appropriate for adaptive controller tuning. Moreover, we extend the proposed method to online tuning of the feed-forward controller of a two-degree-of-freedom control system. Finally, experimental results are provided to demonstrate the effectiveness of the proposed method.

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“Twenty-first century COE (center of excellence) committee” officially announces judging requirements for selecting (thirty) excellent research fund programs. Such a selection algorithm is a voting system. This paper proposes a voting algorithm for electing research fund programs, e. g., thirty programs of 21st century COE. The proposed algorithm distributes the total budget of fund programs after electing fund programs. In this developed algorithm, competitive a) voting and b) distributing are as below.
a) After many members in the committee are elected from various fields, programs are evaluated by the elected members from different viewpoints. That is, programs are voted and decided by majority.
b) Total budget is distributed among the elected programs, each of which suggests a special research project plan, as follows: First, the total budget is divided among all the members, and then the divided budget of each member is moreover distributed among the elected programs. This procedure is carried out by tendering “points” instead of money. Thus the total money given to each elected program is determined by the sum of points tendered by all the members.

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This paper describes the voluntary eye blink detection method using electrooculogram (EOG) for controlling a powered wheelchair. This study aims to apply double blink, left and right wink to control commands for a powered wheelchair such as go/stop, left and right turn. Our previous study showed that these eye blinks can be detected by using EOG and classification models specialized for individuals. Therefore, the persons who are not registered cannot use this system because the amplitude of EOG has an individual difference. In this study, we proposed the voluntary eye blink detection method that has robustness for individual difference of EOG amplitude by using correlation coefficient with template signal. As the result of simulations, an averaged accuracy of 98.05% was obtained, and the efficacy of powered wheelchair applied the proposed method was confirmed.

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In recent years a study of the evolvable hardware (EHW) which can adapt to new and unknown environments attracts much attention among hardware designers. EHW is reconfigurable hardware and can be implemented combining reconfigurable devices such as FPGA (Field Programmable Gate Array) and evolutionary computation such as Genetic Algorithms (GAs). As such research of EHW, Block-Based Neural Networks (BBNNs) have been proposed. BBNNs have simplified network structures and their weights and network structure can be optimized at the same time using GAs. The learning of BBNNs using GAs are, however, not efficient because genetic operators, such as crossover and mutation, often alter the network structure drastically. In this paper, we propose a new update method of the network structure in order to solve this problem. The proposed method is based on locally update of the network structure and is able to maintain the structural similarity. In addition, we introduce an evaluation method which determines the convergence of weight learning in order to improve the efficiency of learning. In the proposed method, the network structure is updated after the learning of weights converges. In order to evaluate the proposed method, we implement BBNNs on FPGA. As a result, the proposed method is able to learn the weights and structure simultaneously, and we confirmed it is effective compared with the conventional method.

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In recent years, access log analysis for marketing is great demand, since access log data have information about users' behaviors. If you get users' behaviors, you can recommend efficient information or the optimization of Web sites. In this paper, the main object is discovering knowledge on customers' behaviors in their purchase processes from access log data on an EC(Electric Commerce) site. We construct a network from the access log data and measure the importance of Web pages analyzing the network with PageRank algorithm. Since the network represents customers' transition information, we can evaluate the Web pages according to customers' behaviors. We confirmed that the proposed method found rival products and customers' behavior differences among product categories. We also confirmed that a rank generated with our proposed method was different from ones generated with comparative approaches; PV and Staying time.

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We propose a new method for tiered storage and evaluate the characteristics of the method. In this method, a fast tier is divided into two areas, and the data in each area is managed on the basis of two I/O (Input/Output) measurement periods. When I/Os to these areas increase, the proposed method allocates frequently accessed areas to a fast tier in advance, therefore, improving the storage I/O performance. We evaluate our proposed method by simulating storage I/Os and data migration between tiers. When I/O locality is high or frequently accessed areas rarely move, the proposed method is the most effective and improves the storage I/O performance by up to 44.9%.

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An anomaly detection method based on multi-dimensional time-series sensing data has been developed on the purpose of enabling condition based maintenance. The proposed method generates normal state models using the learning data selected by the plant operation information and detects anomaly based on the distance between the model and the data. Local sub-space classifier is applied for normal state model and adequate threshold is calculated using learning data. The proposed method was evaluated using 4 datasets of time-series sensing data obtained from real equipments. It was confirmed that anomaly signs several days before equipment faults was detected properly while false detection hardly occurred.

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