Toru Eguchi

A Double-Deck Elevator Group Supervisory Control System Using Genetic Network Programming

Elevator group supervisory control systems (EGSCSs) are designed so that the movement of several elevators in a building is controlled efficiently. The efficient control of EGSCSs using conventional control methods is very difficult due to its complexity, so it is becoming popular to introduce artificial intelligence (AI) technologies into EGSCSs in recent years. As a new approach, a graph-based evolutionary method named genetic network programming (GNP) has been applied to the EGSCSs, and its effectiveness is clarified. The GNP can introduce various a priori knowledge of the EGSCSs in its node functions easily, and can execute an efficient rule-based group supervisory control that is optimized in an evolutionary way. Meanwhile, double-deck elevator systems (DDESs) where two cages are connected in a shaft have been developed for the rising demand of more efficient transport of passengers in high-rise buildings. The DDESs have specific features due to the connection of cages and the need for comfortable riding; so its group supervisory control becomes more complex and requires more efficient group control systems than the conventional single-deck elevator systems (SDESs). In this paper, a new group supervisory control system for DDESs using GNP is proposed, and its optimization and performance evaluation are done through simulations. First, optimization of the GNP for DDSEs is executed. Second, the performance of the proposed method is evaluated by comparison with conventional methods, and the obtained control rules in GNP are studied. Finally, the reduction of space requirements compared with SDESs is confirmed.

Elevator group supervisory control systems using genetic network programming

Genetic network programming (GNP) has been proposed as a new method of evolutionary computation. Until now, GNP has been applied to various problems and its effectiveness was clarified. However, these problems were virtual models, so the applicability and availability of GNP to the real-world applications have not been studied. In this paper, as a first step of applying GNP to the real-world applications, elevator group supervisory control systems (EGSCSs) are considered. Generally, EGSCSs are complex and difficult problems to solve because they are too dynamic and probabilistic. So the design of a useful controller of EGSCSs was very difficult. Recently, the design of such a controller of EGSCSs has been tried actively using artificial intelligence (AI) technologies. In this paper, it is reported that the design of a controller of EGSCSs has been studied using GNP whose characteristic is to use directed graph as its gene instead of bit strings and trees of GA and GP. From simulations, it is clarified that better solutions are obtained by using GNP than other conventional methods and the availability of GNP to real-world applications is confirmed.

Elevator Group Supervisory Control System Using Genetic Network Programming with Functional Localization

Genetic network programming (GNP) whose gene consists of directed graphs has been proposed as a new method of evolutionary computations, and it is recently applied to the elevator group supervisory control system (EGSCS), a real world problem, to confirm its effectiveness. In the previous study, although the flow of traffic in the elevator system is known and fixed, it is changed dynamically with time in real elevator systems. Therefore, the EGSCS with an adaptive control should be studied considering such changes for practical applications. In this paper, the GNP with functional localization is applied to the EGSCS to construct such an adaptive system. In the proposed method, the switching GNP can switch the functionally localized GNPs (assigning GNPs) fitted to several kinds of traffic by detecting the change of the flow of traffic. From the simulations, the adaptability and effectiveness of the proposed method are clarified using the traffic data of a day in an office building

Elevator group supervisory control system using genetic network programming with functional localization

Genetic network programming (GNP) whose gene consists of directed graphs has been proposed as a new method of evolutionary computations, and it is recently applied to the elevator group supervisory control system (EGSCS), a real world problem, to confirm its effectiveness. In the previous study, although the flow of traffic in the elevator system is known and fixed, it is changed dynamically with time in real elevator systems. Therefore, the EGSCS with an adaptive control should be studied considering such changes for practical applications. In this paper, the GNP with functional localization is applied to the EGSCS to construct such an adaptive system. In the proposed method, the switching GNP can switch the functionally localized GNPs (assigning GNPs) fitted to several kinds of traffic by detecting the change of the flow of traffic. From the simulations, the adaptability and effectiveness of the proposed method are clarified using the traffic data of a day in an office building

Symbiotic evolutional models in multiagent systems

Multiagent systems with symbiotic learning and evolution (Masbiole) has been proposed as a new learning and evolutionary method for multiagent systems (MAS) recently, which is based on symbiotic phenomena among creatures. A symbiotic evolutional model of Masbiole is proposed using genetic network programming (GNP), which has been also proposed as one of the evolutionary computations. In the simulations, the proposal Masbiole is applied to the tile-world model and various characteristics of Masbiole have been clarified.

Elevator group supervisory control system using genetic network programming with reinforcement learning

Since genetic network programming (GNP) has been proposed as a new method of evolutionary computation, many studies have been done on its applications which cover not only virtual world problems but also real world systems like elevator group supervisory control system (EGSCS) which is a very large scale stochastic dynamic optimization problem. From those researches, most of the significant features of GNP have been verified comparing to genetic algorithm (GA) and genetic programming (GP). Especially, the improvement of the performances on EGSCS using GNP showed an interesting and promising prospect in this field. On the other hand, some studies based on GNP with reinforcement learning (RL) revealed a better performance over conventional GNP on some problems such as tile-world models. As a basic study, reinforcement learning is introduced in this paper expecting to enhance EGSCS controller using GNP

SIMULTANEOUS DETERMINATION OF KETO AND NON-KETO BILE ACIDS IN HUMAN SERUM BY GAS CHROMATOGRAPHY WITH SELECTED ION MONITORING

A reliable method for the simultaneous determination of keto and non-keto bile acids in human serum was developed. Carbonyl substituents of bile acid ethyl esters were converted into methyloxime and hydroxyl substituents into dimethylethylsilyl ethers and the products were analysed directly by capillary gas chromatography with selected ion monitoring using [2H4]chenodeoxycholic and [2H4]3 alpha-hydroxy-7-oxo-5 beta-cholanoic acids as internal standards. The bile acid peaks on the selected ion chromatogram were separated without interference from endogenous substances present in serum. Recoveries of individual keto bile acids added to serum range from 74.4 to 94.7% with a mean of 87.1%. Eight kinds of keto bile acids not previously found in sera of normal subjects, namely 3-oxo-, 3-oxo-7 alpha-hydroxy-, 3-oxo-12 alpha-hydroxy-, 3 alpha-hydroxy-7-oxo, 3 alpha-hydroxy-12-oxo-, 3-oxo-7 alpha,12 alpha-dihydroxy-, 3 alpha,7 alpha-dihydroxy-12-oxo- and 3 alpha,12 alpha-dihydroxy-7-oxo-5 beta-cholanoic acids were identified and quantified. The total concentration of keto bile acids was found to be 0.16 +/- 0.08 nmol/ml and constituted 2.9 +/- 1.5% of that of the usual non-keto bile acids in peripheral venous serum.

A Robust and Flexible Control System to Reduce Environmental Effects of Thermal Power Plants

Regulations on environmental effects due to such issues as nitrogen oxide (NOx) and carbon monoxide (CO) emissions from thermal power plants have become stricter[1]; hence the need for compliance with these regulations has been increasing. To meet this need, several technologies with respect to fuel combustion, exhaust gas treatment and operational control have been developed[2-4]. The technologies for the fuel combustion and the exhaust gas treatment include a low NOx burner and an air quality control system, and they are capable of reducing impact on the environment as physical and chemical implementation methods. The operational control technology for the thermal power plants is constantly required to receive changes in operational conditions. It is difficult to realize operational control which responds to combustion properties. To overcome this issue, the operational control must be able to reduce NOx and CO emissions flexibly in accordance with such changes. Robustness is also required in such control because the measured NOx and CO data often include noise. Therefore, a robust and flexible plant control system is strongly desired to reduce environmental effects from thermal power plants efficiently. Several studies have proposed plant control technologies to reduce the environmental effects[4-10]. These technologies are classified into two types of methods: model based and non-model based methods. The former methods include an optimization algorithm and a numerical model to estimate plant properties using neural networks (NNs)[11,12] and multivariable model predictive control[13]. The optimization algorithm searches for optimal control signals to reduce NOx and CO emissions using the numerical model. The latter methods have no models and they generates the optimal control signals by fuzzy logic[14]. A fuzzy logic controller outputs the optimal control signals for multivariable inputs using fuzzy rule bases. The fuzzy rule bases are based on a priori knowledge of plant control, and they can be tuned by parameters. These technologies require the measured plant data for initial tuning of the model properties and the parameters of rules when the technologies are installed in plants. It usually takes some time to collect enough plant data. In addition, the search for control