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It has been discussed that electric vehicles should be utilized effectively as movable storage resources. In this research, public electric buses are focused on, which are expected to have controllability with low uncertainty but limitation caused by the rigid bus operation schedule. Recently we reported an analysis of the actual bus operation data and proposed a charging schedule optimization method. The purpose of this research is to create an optimization method that maximizes two indicators related to the benefits of the bus company. Moreover, the sensitivity of the photovoltaic capacity installed at the bus stations is analyzed. An optimization method was created by mixed integer linear programming, and sensitivity analysis simulation was conducted using real city data. This study is based on a realistic simulation with actual bus operation data in a target city. As a result, we confirmed the 107kWh (-15.3%) surplus reduction by the proposed optimization method and the trade-off relationship between the two indicators.

Current power systems on remote islands use diesel generators (DG) that have high operational and maintenance costs, and emit excessive amounts of carbon dioxide. In this respect, power systems based on renewable energy are currently gaining research attention, with the aim of improving the economic performance and reducing greenhouse gas emissions. However, as the output of renewable energy fluctuates and varies depending on weather conditions, a battery energy storage system (BESS) and DG are required to stabilize the renewable energy output. Then the capacity balances of renewable energy, BESS and DG and these operation methods are main issues. In this paper, several output control methods were proposed for DG and the appropriate generators capacity were evaluated. The effectiveness of the methods was then verified by conducting economic and environmental evaluations. This study conducted a numerical simulation using real data, measured in a power system on an existing island in Japan.

A District Heating and Cooling system (DHC) with Combined Heat and Power (CHP) is able to not only satisfy regional heat and cold demand but also generate and control electricity by CHP. Therefore, a DHC with CHP has potential as control reserve for electricity adjustment (EA). In Japan, an EA auction was established to secure control reserve efficiently. In our previous research, potential of EA by a DHC for this auction has already evaluated. Furthermore, it was found that kW and kWh costs of a DHC for EA mainly depend on the heat and cold demand. Operators of DHCs need to comprehend the exact costs for EA when they bid on the auction. Therefore, fluctuations in costs for EA due to changes in heat and cold demand have been a critical issue for operators in decision making for the bid. However, costs for EA of a DHC with comprehensive assumption of various heat and cold demands has not been evaluated. Therefore, in this paper, the kW and kWh costs of an existing DHC are quantified and visualized as a multidimensional function based on the conditions of heat and cold demands. In addition, the factors behind those results are discussed. As a result, this research made it possible to predict the costs for EA of the DHC with heat and cold demand forecasts, and the operator could use it as auxiliary information in decision making for the bid.

Recently the multi-energy network had attracted attentions, which includes electricity, heat and fuel energies. These energies should be traded in market dynamics to maximize total economic benefits. However, energy transmission constraints caused by heat and/or fuel network structure restrict trading with market transaction. So in this paper coordinated EMS based on negotiated transaction mechanism is considered. Then a transaction protocol based on marginal cost of energy accommodation is proposed. Some properties of general multi-energy network of negotiated transaction are also discussed.

In the Smart Grid Technology, Demand Response (DR) scheme had attracted attentions as a new effective electricity supply-demand balance control method. The aim of this paper is to formulate DR aggregation management system with multiple electricity energy users as a control and optimization theory framework. Where, the demand response delay dynamics and stochastic uncertainty as behaviors in each energy user are included as the DR aggregation system model, to represent actual DR aggregation service management process in the electricity market mechanism. For the generalized formulation of the problem, the Model Predictive Optimization & Control scheme is introduced, where control performance, economical value with DR incentive, and also operational risk to fail DR target achievement are formulated. Some practical calculation procedure of dispatching DR command to each energy user as the DR aggregator is also shown.

Summary Energy conservation in building fields is a key issue from an environmental point of view as well as of the industrial, transportation, and residential fields. Half of the total energy consumption in a building is a result of HVAC (heating, ventilation, and air conditioning) systems. In order to conserve energy in an HVAC system, a thermal load prediction model for a building is required. This paper proposes a hybrid modeling approach using a physical and just-in-time (JIT) model for the thermal load predictions for a building. The proposed method has features and benefits such as (1) it is applicable to cases in which past operating data for load prediction model learning are poor; (2) it has a self-checking function, which determines if the data-driven load prediction and the physical-based one are consistent at all times, so that it can determine if something is wrong in the load prediction procedure; (3) it has the ability to adjust the load prediction in real time against a sudden change in the model parameters and environmental conditions. The proposed method is evaluated using real operating data from an existing building, and the improvement in load prediction performance is illustrated.

Energy conservation in building fields is one of the key issues in environmental point of view as well as that of industrial, transportation and residential fields. The half of the total energy consumption in a building is occupied by HVAC (Heating, Ventilating and Air Conditioning) systems. In order to realize energy conservation of HVAC system, a thermal load prediction model for building is required. This paper propose a hybrid modeling approach with physical and Just-in-Time (JIT) model for building thermal load prediction. The proposed method has features and benefits such as, (1) it is applicable to the case in which past operation data for load prediction model learning is poor, (2) it has a self checking function, which always supervises if the data driven load prediction and the physical based one are consistent or not, so it can find if something is wrong in load prediction procedure, (3) it has ability to adjust load prediction in real-time against sudden change of model parameters and environmental conditions. The proposed method is evaluated with real operation data of an existing building, and the improvement of load prediction performance is illustrated.

This paper discusses on a new concept of Event-predictive control, which is an extension of the Event-triggered control. The aim of the research is to apply such asynchronous sampling control strategy to a wireless networked control system to save battery energy consumption in the wireless nodes. The key idea is to maximize the control interval, under the condition of appropriate control performance, to save communication energy cost for battery life of wireless network nodes incorporated in the control system. A heuristic new concept of “predictive event” is introduced which is pre-determined as a cross-point of future predictive response of the controlled system and the stable region trajectory named “admissible set trajectory” to ensure recoverability to stable region. Then the sleep and wake mode of the wireless node is controlled according to the predictive events. Some considerations on stability condition are discussed, which is extendable to a class of nonlinear plants under some assumptions. A numerical example is illustrated to show the effectiveness of the proposed method.

To investigate the choroideremia (CHM) gene of one affected male and one obligate carrier in a Japanese family with choroideremia, and to characterize the related clinical features.We examined one affected man and one carrier woman from a Japanese family. Genomic DNA was extracted from leukocytes of peripheral blood collected from the affected man and his daughter, who is an obligate carrier of choroideremia. Exons 1-15 of the CHM gene were amplified by polymerase chain reaction (PCR) and directly sequenced. We performed ophthalmic examinations including best-corrected visual acuity, slit-lamp examination, fundus examination, electroretinography, and Goldmann perimetry.A novel (967-970+2)delAAAGGT mutation was detected in the CHM gene. The affected man was hemizygous and had night-blindness, chorioretinal atrophy spreading from the posterior pole to the mid-periphery, and bareness of the sclera. His daughter was a heterozygous carrier who had chorioretinal atrophy and mottled appearance of the retinal pigment epithelium.A novel (967-970+2)delAAAGGT mutation existed in the CHM gene of a Japanese family with choroideremia.

A practical model predictive control method is proposed which was applied to a cement raw material mixing process. The method has a hierarchical structure of 3 layers consisted of set point calcula...

This paper is discussed on some new control methodologies for wireless sensor network based control system, minimizing communication energy consumption. Some control problems with communication cost saving are defined. Then, a heuristic control method based on the Model Predictive Control strategy with a receding horizon cost function including control performance and communication cost is proposed. For a state feedback control problem, a sufficient condition to keep stability of the closed loop system is obtained. Some numerical examples are also illustrated.

Notched compact tension specimens of type 304 stainless steel (notch tip radius of 1 mm and 4 mm) were low-cycle-fatigued at stress ratio R = −1 and 0.05 in order to investigate the through-thickness fatigue damage accumulation around the notch. The recrystallization-thermal etching technique was used to observe the accumulated plastic zone APZ with the accumulated plastic strain ∊ac above 0.12(APZ0.12) and APZ with ∊ac above 0.02 (APZ0.02) at midthickness, quarter thickness and surface. Both APZ0.12 and APZ0.02 are formed early during the fatigue (before crack initiation) near midthickness and not at less thickness or at the surface. The reason why the fatigue crack initiates near the midthickness of the notch root surface was found to be due to more fatigue damage near midthickness than at the surface. Magnetic permeability(degree of the martensite formation) at midthickness was also higher than at the surface, supporting the above result.

A new wavelet analysis method is proposed, that is a combination of the wavelet transform, a nonlinear transformation and a correlation analysis. In this paper, it is called ‘Extended Wavelet Analysis (EWA)’ procedure. Furthermore, the ‘Model Based EWA’ method is proposed in which the nonlinear transformation is based on a model of the diagnosed object. It can analyze a short transient signal and it is effective for the correlation analysis between the wavelet spectrum and arbitrary variables in the object. It was applied to the diagnosis of an elevator system. Some simulation results and experimental results reveal the effectiveness of the EWA procedure.

Almost all control problem are essentially multi-objective optimization problems which include such items as closed loop stabilization and economic cost optimization As a dynamic optimizer for the process operation, a new Model Predictive Control (MPC) algorithm based on the multi-objective optimization problem is formulated and analyzed. It uses an extended cost function including both conventional penalty terms for control errors or fluctuations, and newly introduced linear cost terms for output and/or input variables. The effectiveness of the proposed method, from the viewpoint of economic cost, is confirmed with some simulation studies, especially in the case of rapid change of the process operating conditions.

The newly developed building energy management system (BEMS) is presented. The main feature of the BEMS is the following 2 technologies. (1) The new air-conditioning control technology for the energy saving of buildings. The method is mainly focused on the compatibility of energy savings and comfort. The energy saving is achieved through the twin coil air handling unit that controls room humidity and temperature independently without energy loss and by the optimal operation of HVAC (Heating, Ventilating and air-conditioning) system, manipulating the supplying airflow temperature to the rooms, room temperature and the humidity. The comfort is kept by the index (PMV: Predicted Mean Vote) that is calculated with room temperature, humidity, radiation temperature, wind velocity and so on. In order to avoid large-scale nonlinear programming for optimal conditions, the driving function is introduced. The effectiveness of the HVAC control technology is proved through a building HVAC data and the simulations using the data. (2) Automated demand responding mechanism. The mechanism has function that predicts how much electricity demand is trimmed for a given reward. It also has ability to communicate with community energy management server for finding appropriate balances between electricity demand and generations.

In this paper, wireless sensor network (WSN) topology optimization for dependability is considered, where two metrics “reliability” and “integrity” for WSN are defined. First, static topology optimization problem for reliability, integrity and joint optimization of them are investigated and some trade-off relationships are clarified. Then, dynamic topology optimization problem is proposed, where some node connections to their parents' nodes change dynamically with a stochastic distribution rate. Then a heuristic optimization method and some functions for each node to realize a decentralized optimization are proposed. Finally some numerical examples are illustrated to demonstrate the effectiveness of these optimization methods and the optimal WSN topology in various optimization problems.

Model predictive control (MPC) enables to handle multi-objective optimal operations and control in various kinds of process controls. In fact, simultaneous optimization of stability, quality constrains and economical optimality, sometimes it fails into ldquoinfeasiblerdquo solution. Then a supervisory control function in the hierarchical MPC configuration is proposed. It evaluates sensitivity of optimization and controllability in sense of ldquocontrol degrees of freedomrdquo and adjusts the control system structure as multi-input and multi-output closed loop system to be feasible. It realizes a kind of automatic ldquoPlug and Playrdquo function for control input. Some numerical simulations for general blending control in material process are evaluated to show the effectiveness of the proposed method.

This paper is discussed on some new control methodologies for wireless sensor network based closed loop control systems. Such a system is required to save battery energy consumption, which will be achieved by saving communication speed. Then some new control strategies considering communication interval, which corresponds to sampling period of discrete control system, are considered here. The communication saved control problems is defined. Then, some heuristic control methods are proposed. One of them is based on the optimization of a cost function including control performance and communication penally. Two types of control optimization problems are defined, which correspond to state observer problem and state feedback control problem. These wireless sensor network conscious control methods will meet industrial requirements.

Recently two degrees of freedom PID control systems have attracted attention, because of their better process control quality for both set point variable tracking and disturbance resistivity than that of conventional PID control systems. This paper describes a practical auto-tuning method for the two degrees of freedom PID control system based on frequency region methods; the well known recursive least square parameter estimation method, a frequency characteristics conversion method and a model matching method in frequency region. By combining frequency region method, the auto-tuning method can use for both minimum phase process and non-minimum phase process. This method was introduced into the two degrees of freedom PID auto-tuning controller. The superior features of the controller and the results of field tests are presented.