The idea of greenhouse-gas credit borrowing in global or national context faces severe problems such as international and intergenerational equity issues. Though the merit of cost reduction is obvious, there is minimum possibility for such a scheme to be massively allowed in future international negotiation.
Still, there is a good chance for some players to use the idea of time flexibility. Through numerical model analysis this paper demonstrates the cost reduction merits of using time flexibility on a sector base, taking the Japanese utility sector as an example. Time flexibility is important specifically to the sector because of two characteristics: (1) the sector is capital intensive and has long capital turnover time, and (2) available mitigation option is limited for near future.
A sector like Japanese utility needs some kind of flexibility to virtually realize the merit of using time flexibility. Domestic and international emission trading systems are the most promising candidate to date.

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This paper reports on the coordination assistant system for recycling construction materials. We deal with planning coordination among many people or organizations. It is difficult to design an assistant system for such a job by top-down approach because generally coordination is made asynchronously and relevant members are not fixed. So we applied ‘multi-agent technique’ to design the system. We studied a planning method based on ‘maximum flow and minimum cost algorithm’ for recycling construction materials among many sites, and studied planning data management in asynchronous negotiations. The prototype system was implemented in Java. We expect to increase recycling materials through the planning simulation between many construction sites, and also expect to make this system a good platform on evaluating the recycling policy by changing relevant rules and parameters such as transportation and recycling costs.

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Carbon emissions which would cause global warming were agreed to be constrained at COP3 in Kyoto. In addition, carbon emission permits trading was also approved to be introduced. The emission permits trading is expected to achieve efficient carbon emission reduction, equalizing the marginal costs of the emission reduction for the participating countries. In other words, the permits trading allows participants to reduce emissions where it is least expensive to do so. However, the inadequate introduction of the trading systems may impose unfairly greater burden on some countries, and therefore careful evaluation of the system would be indispensable for its implementation.
In this paper, we attempt to analyze the emission permits trading, using the theory of cooperative games with a global energy model of optimization type. We assumed that seven world regions as players participate the permits trading system under the condition of the emission reduction target presented at COP3 and so on, and show the nucleolus of the grand coalition games, and the computational results of primary energy supplies and CO₂ shadow prices.
The insights of this research indicate that in order to stabilize the grand coalition, a noticeable amount of additional transfer of money would be needed besides the payments associated with the emission permits trading.

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In order to reduce greenhouse gas emissions, we have to introduce measures to suppress economic differences between developed countries (DC) and less developed countries (LDC) as well as accelerate technology improvement. In this sense, Joint Implementation (JI) is a promising measure not only to economically reduce greenhouse gas emissions, but also to facilitate technology transfer from DC to LDC. In this article, we first investigated JI strategies, in which LDC can improve industrial energy efficiency under financial support of DC. Strategies of countries involved in JI was represented with Stackelberg game. Analytical results revealed that JI activities may considerably be diminished in Stackelberg equilibrium, depending on shapes of the marginal cost functions of reducing CO₂. Therefore we proposed measures to prevent the suppression of JI and quantitatively evaluated the potential of this improved JI system. As far as actual data on economy and industry are concerned, we used the ones of Japan and China, since China has been rapidly increasing its economic scale and greenhouse gas emissions. However implications from these analyses are useful also for other countries.

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A method is proposed for developing future global energy scenarios. Among various types of energy models those of optimization type seem the most appropriate to envisage energy scenrios under physical, economic and political constraints, of which the most serious are certainly CO₂ emission reduction targets such as those agreed at COP3 in 1997. One of disadvantages of these models, however, is the possible gap between the criterion adopted in the models and those, although implicitly, used in the real world. Because of this gap the resultant scenarios produced by the models very often differ far from reality. The method proposed in this paper is to derive informations on differences between reality and scenarios produced by the model from shadow prices obtained from application of optimization to the past with the constraints on the regional energy supplies, and then to modify the cost data of the model with these shadow prices so as to reflect reality of the resultant scenarios. Numerical analysis employing DNE21 model developed by the authors demonstrates usefulness of the proposed method.

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District Heating and Cooling (DHC) with Cogeneration System (CGS) is one of the most efficient ways to supply energy to the commercial and residential sectors. This paper discusses introduction feasibility of DHC from the viewpoint of energy saving effect in the whole energy system in Japan.
Although DHC is efficient for the congested area such as an urban business center, such area is limited in capacity and number. In this paper, we evaluated introduction feasibility of DHC with consideration of the profiles of energy demand densities in the commercial and residential sectors in Japan. As a result, we determined the optimum capacity of DHC to be installed in order to meet the energy demand growth.
This paper also discusses operational patterns of CGS and boiler in DHC. In order to improve the efficiency of individual DHC, the CGS should be operated to meet the heat demand, resulting in a low annual load factor. On the other hand, from the viewpoint of efficiency of whole energy system, the improvement of annual load factor of CGS is necessary, which is realized by combined utilization of CGS and boiler. Such operation of CGS improves the introduction feasibility of DHC.

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In this paper, an optical trapping method using optical fiber (Optical Fiber Trapping Method) is proposed. This optical fiber trapping method has many following merits, if the microscopic objects are optically trapped near the fiber end. (i) Optical trapping systems using optical fiber are simple and inexpensive. (ii) Optical sources can be changed easily using optical connectors, (iii) Trapping point is easily noticeable, because a fiber end points out the focal point.
Experimental results are as follows. (i) Optical trapping of a micro-object is easily achieved using an optical fiber whose end is polished to a tapered spherical end. (ii) Optically trapped object can be freely transferred in 2-D plain, synchronized to the trapping fiber. (iii) Optical manipulation characteristics for the optically trapped object depend on the distance between the trapping fiber end and the micro-object.

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This paper describes a telepresense system that provides users background awareness with the following two features. 1) we provide telepresence users with one wide-angle view and a controllable view. To simplify navigation, the two views are seamlessly linked together. Therefore, a user can obtain a detailed view of the specified region by selecting a region of a wide angle view. 2) we provide sensor information from the remote location to notices the user relevant events that may require attention. These tools significantly enhance users' awareness of their remote surroundings.

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In the behavior-based artificial intelligence (AI) approach, there are the following problems that have to be resolved: (1) how do we construct an appropriate arbitration mechanism? and (2) how do we prepare appropriate repertoire of competence modules (i.e. behavior primitives)? To overcome these we have been investigating a new behavior arbitration mechanism by paying close attention to the biological immune system and its adaptation mechanisms. In this paper, we introduce two types of innovation mechanisms in order to construct an appropriate immune network without human intervention.

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The plant model is able to be constructed by the neural networks, i.e., by identifying the plant model using the neural networks. But it is somehow difficult to obtain a control law from the plant model based on this neural network. The iterative inverse method has been proposed for this problem, but for this method it is necessary to determine two parameters (convergence coefficient and iterative number) and calculate the equations iteratively for obtaining a control law. The fact mentioned above is a big drawback for on-line control. This paper is related with improvement of the iterative inverse method using Jacobian of neural networks. It is shown that the proposed method is effective to control the thermal power plant by simulations.

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Demand forecasting is a critical success factor for effective supply chain systems. Many demand forecasting methods based on time-series data have been investigated. However these methods can not consider the on-going market change because of its usage of past time series. In this paper, a new method of estimating the product market share based on real time demand sensing data is proposed. This method consists of the two parts, which are (1) sampling point selection using clustering analysis and (2) estimating product market share using sampled demand data. Numerical experiments show the effectiveness of the proposed method and recent network technology makes it easy to implement this method in real demand forecasting systems.

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The purpose of this paper is to theoretically identify that there exist stationary distributions in the system with transaction lost. In order to examine this type of system, we formulate and analyze the input/output process as the counting process based on point process. Accordingly, we make clear that this stationary distributions are the geometrical distributions as well. We, also analyze the average transit time in the stationary. We presented the simulation results in order to evaluate this system.

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An approach to detect human facial orientation by reconstructing the three-dimensional position of facial points from stereo images is proposed. The purpose is to develop a flexible and accurate facial orientation detection method for human interaction with computers. Four facial points (pupils and mouth corners) are extracted using a global-to-local method and their three-dimensional coordinates are reconstructed from stereo images. Facial orientation is obtained by calculating the normal direction of the plane formed by three facial feature points. The advantages of the proposed method are first, that it is flexible and easy to adapt to different persons and circumstances; and second, that it eliminates the need for prior knowledge of image vs. model correspondence or the physical parameters of facial features. Analytical and experimental results show that the proposed method performs well when the head is not far away from the frontal view. These characteristics are especially useful for constructing a human-computer interaction system.

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Petri net is a well-known bipartite graph theory to model and analyze discrete event systems. The properties of Petri net can be classified into two types, i.e., behavioral property and structural property. Many behavioral properties are investigated in association with the markings of Petri nets. On the other hand, the structural properties are just considered based on the Petri net structure without markings. In this meaning, Petri net has been classified to normal, cycle and parallel structures according to its homogenous state matrix equation. As Petri net is a bipartite graph, its structure can be transformed into a directed graph and the Mason's theorem can be applied to know the properties of the original net. In this paper, we discuss the relationship between Petri net structure and directed graph, and describe some results for the normal and cycle structures of Petri nets. Furthermore, several useful concepts, for example, transitive graph and transitive matrix are also defined here, and its characteristic polynomial and characteristic equation are used to find out the cycle structures.

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In this paper, we propose a new network element model named a“weighted-sum-based sine element”for neural networks. We also derive a learning algorithm based on the back-propagation algorithms for multilayer networks. The weighted-sum-based sine element receives an inner product between an input pattern vector and its weight vector as its input value, and uses an mine transformation of sine function as its output function. The proposed“weighted-sum-based sine network”is capable of improving the learning speed as well as the convergence rate because its output function does not have any saturated regions which cause slow learning speed of the back-propagation learning using standard sigmoid elements. We demonstrate the advantages of the proposed network by solving N-bits parity problems, Fisher's Iris classification problem, a function approximation problem and the two-spirals problem. Experimental results indicate that our weighted-sum-based sine network consistently obtains better results than the conventional sigmoid network in terms of both the learning speed and the convergence rate.

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Many studies have been done with higher order neural networks. They include many studies from theory to application. With them, studies which have shown the superiority of higher order neural networks in combinatorial optimization problems, pattern classification, associative memory, and have given construction methods of effective networks in order to control the growth of the number of weights in using multi-layered higher order neural networks have been made. Although there are many applied studies as these, little is known about theoretical studies like macroscopic properties.
In the previous paper, we have shown transformation of distance between two patterns for higher order random neural networks (HORNNs) with the digital state {0, 1} models using statistical method. This paper describes transformation of distance between two patterns for HORNNs with the analog [0, 1] model using the same method and comparison between analog and digital higher order neural networks is made. Further, one example is given in order to show the superiority over the ability of them.

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To ensure the safety of large-scale civil engineering structures, a detailed geological survey is required, particularly to identify the borders between different strata because the stratal border may become a landslide surface. The current borehole TV system technique allows stratal borders to be traced visually; however, this system is both time and labor intensive.
The present paper introduces a new technique, the Sampled Active Contour Model (Sampled-ACM), which can identify a stratal border more quickly and in more detail. The Sampled-ACM has the following characteristics: (1) By extending the Sampled-ACM into a virtual closed curve, the characteristics of a borehole image can be used. (2) Discontinuous stratal borders can be extracted as a smooth continuous curve. (3) The dip and strike of a stratal border can be easily calculated. These advantages enable highly detailed, expert evaluation of a stratal border to be performed quickly, efficiently, and almost automatically.

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It is well known that ABR (Auditory Brainstem Responses) waveforms are used when the judge of the brain death or the check of some hearing troubles and so on are needed. The physiological side of this responses is nearly made clear, but the systematic mechanism of them is not investigated sufficiently.
Therefore the quantitative estimation of those systematic mechanism is not yet obtained sufficiently. In this paper, firstly the polynomial approximation results of the entire ABR waveform are shown by using the Chebyshev's q-function.
Secondarily the tendency of the latency (a parameter of ABR) is considered and compared with the experimental formula which was independently derived from the another data and analysis. Through these discussion it was shown that the indices of the quantitative analysis of characteristics of ABR parameters were given as the polynomial approximate functions of the use of the approximate method applying the Chebyshev's q-function

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