• Journal of Plant Biology
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• v.36 no.3
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• pp.259-266
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• 1993

In Tris-iso-butanol (TIB; Tris buffer pH 8.8 and 1% iso-butanol)-treated chloroplasts, oxygen evolving activity was more inhibited than Tris-treated chloroplasts, but restored highly by 2,6-dichlorophenol-indophenol (DCPIP) and photoreactivation. To understand the mechanism of this results of TIB in photosynthetic electron transport, system, oxygen consumption and evolution of PS I and PS II were measured and protein of the chloroplasts was analysed. In Tris- and TIB-treated chloroplasts, oxygen evolving activity was increased according to the light intensity. Under 48 W·m-2 light intensity, the oxygen evolving activity in both chloroplasts were similar but as the light intensity was increased, TIB-treated chloroplasts showed higher activity. Under 240 W·m-2 light intensity, TIB-treated chloroplasts showed about 25% higher oxygen evolving activity than Tris-treated chloroplasts. Oxygen evolving activity was increased after photoreactivation in both Tris-treated and TIB-treated chloroplasts. Addition of NH4Cl increased the activity in both chloroplasts but in TIB-treated chloroplasts the increase was 30% higher than that in Tris-treated chloroplasts. In PS I, oxygen evolving activity was not inhibited by both treatments whereas in PS II, significant difference was observed between two treatments. Addition of Mn2+ and Ca2+ enhanced oxygen evolution in both Tris- and TIB-treated chloroplasts. Though enhancement was higher in TIB-treated chloroplasts. No difference was observed n protein analysis of the two thylakoid membrane.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.12a
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• pp.255-258
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• 2001

This paper is implementation of cellular automata neural network system which is a living creatures' brain using evolving hardware concept. Cellular automata neural network system is based on the development and the evolution, in other words, it is modeled on the ontogeny and phylogeny of natural living things. The proposed system developes each cell's state in neural network by CA. And it regards code of CA rule as individual of genetic algorithm, and evolved by genetic algorithm. In this paper we implement this system using evolving hardware concept Evolving hardware is reconfigurable hardware whose configuration is under the control of an evolutionary algorithm. We design genetic algorithm process for evolutionary algorithm and cells in cellular automata neural network for the construction of reconfigurable system. The effectiveness of the proposed system is verified by applying it to time-series prediction.

• Communications of the Korean Mathematical Society
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• v.25 no.2
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• pp.291-301
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• 2010

This paper consists of two main parts. Firstly, we introduce an evolving binomial process from a binomial stock model and consider various types of limiting behavior of the logarithm of the evolving binomial process. Among others we find that the logarithm of the binomial process converges weakly to a Gaussian process. Secondly, we provide new approaches for proving the limit theorems for an integral process motivated by the evolving binomial process. We provide a new proof for the uniform strong LLN for the integral process. We also provide a simple proof of the functional CLT by using a restriction of Bernstein inequality and a restricted chaining argument. We apply the functional CLT to derive the LIL for the IID random variables from that for Gaussian.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.9 no.2
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• pp.83-89
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• 2005

In this paper a new algorithm of learning and evolving artificial neural networks using gene expression programming (GEP) is presented. Compared with other traditional algorithms, this new algorithm has more advantages in self-learning and self-organizing, and can find optimal solutions of artificial neural networks more efficiently and elegantly. Simulation experiments show that the algorithm of evolving weights or thresholds can easily find the perfect architecture of artificial neural networks, and obviously improves previous traditional evolving methods of artificial neural networks because the GEP algorithm imitates the evolution of the natural neural system of biology according to genotype schemes of biology to crossover and mutate the genes or chromosomes to generate the next generation, and the optimal architecture of artificial neural networks with evolved weights or thresholds is finally achieved.

• Journal of Advanced Marine Engineering and Technology
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• v.25 no.2
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• pp.383-394
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• 2001

The stabilization control of inverted pendulum (IP) system is difficult because of its nonlinearity and structural unstability. In this paper, an Evolving Neural Network Controller (ENNC) without Error Back Propagation (EBP) is presented. An ENNC is described simply by genetic representation using an encoding strategy for types and slope values of each active functions, biases, weights and so on. By an evolutionary programming which has three genetic operation; selection, crossover and mutation, the predetermine controller is optimally evolved by updating simultaneously the connection patterns and weights of the neural networks. The performances of the proposed ENNC(PENNC)are compared with the one of conventional optimal controller and the conventional evolving neural network controller (CENNC) through the simulation and experimental results. And we showed that the finally optimized PENNC was very useful in the stabilization control of an IP system.

• Proceedings of the Korean Society of Marine Engineers Conference
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• 2001.05a
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• pp.243-248
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• 2001

The stabilization control of Inverted Pendulum(IP) system is difficult because of its nonlinearity and structural unstability. Thus, in this paper, an Evolving Neural Network Controller(ENNC) without Error Back Propagation(EBP) is presented. An ENNC is described simply by genetic representation using an encoding strategy for types and slope values of each active functions, biases, weights and so on. By an evolutionary programming which has three genetic operation; selection, crossover and mutation, the predetermine controller is optimally evolved by updating simultaneously the connection patterns and weights of the neural networks. The performances of the proposed ENNC(PENNC) are compared with the ones of conventional optimal controller and the conventional evolving neural network controller(CENNC) through the simulation and experimental results. And we showed that the finally optimized PENNC was very useful in the stabilization control of an IP system.

• Journal of Intelligence and Information Systems
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• v.9 no.2
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• pp.19-38
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• 2003

In this research, we propose the mechanism to develop self-evolving expert systems (SEES) based on data mining (DM), fuzzy neural networks (FNN), and relational database (RDB)-driven forward/backward inference engine. Most researchers had tried to develop a text-oriented knowledge base (KB) and inference engine (IE). However, this approach had some limitations such as 1) automatic rule extraction, 2) manipulation of ambiguousness in knowledge, 3) expandability of knowledge base, and 4) speed of inference. To overcome these limitations, knowledge engineers had tried to develop an automatic knowledge extraction mechanism. As a result, the adaptability of the expert systems was improved. Nonetheless, they didn't suggest a hybrid and generalized solution to develop self-evolving expert systems. To this purpose, we propose an automatic knowledge acquisition and composite inference mechanism based on DM, FNN, and RDB-driven inference engine. Our proposed mechanism has five advantages. First, it can extract and reduce the specific domain knowledge from incomplete database by using data mining technology. Second, our proposed mechanism can manipulate the ambiguousness in knowledge by using fuzzy membership functions. Third, it can construct the relational knowledge base and expand the knowledge base unlimitedly with RDBMS (relational database management systems) module. Fourth, our proposed hybrid data mining mechanism can reflect both association rule-based logical inference and complicate fuzzy relationships. Fifth, RDB-driven forward and backward inference time is shorter than the traditional text-oriented inference time.

• Proceedings of the KAIS Fall Conference
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• 2003.11a
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• pp.99-104
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• 2003

In this research, we proposed the mechanism to develop self evolving expert systems (SEES) based on data mining (DM), fuzzy neural networks (FNN), and relational database (RDB)-driven forward/backward inference engine. Most former researchers tried to develop a text-oriented knowledge base (KB) and inference engine (IE). However, thy have some limitations such as 1) automatic rule extraction, 2) manipulation of ambiguousness in knowledge, 3) expandability of knowledge base, and 4) speed of inference. To overcome these limitations, many of researchers had tried to develop an automatic knowledge extraction and refining mechanisms. As a result, the adaptability of the expert systems was improved. Nonetheless, they didn't suggest a hybrid and generalized solution to develop self-evolving expert systems. To this purpose, in this study, we propose an automatic knowledge acquisition and composite inference mechanism based on DM, FNN, and RDB-driven inference. Our proposed mechanism has five advantages empirically. First, it could extract and reduce the specific domain knowledge from incomplete database by using data mining algorithm. Second, our proposed mechanism could manipulate the ambiguousness in knowledge by using fuzzy membership functions. Third, it could construct the relational knowledge base and expand the knowledge base unlimitedly with RDBMS (relational database management systems). Fourth, our proposed hybrid data mining mechanism can reflect both association rule-based logical inference and complicate fuzzy logic. Fifth, RDB-driven forward and backward inference is faster than the traditional text-oriented inference.

• Proceedings of the Korean Vacuum Society Conference
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• 2013.08a
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• pp.251.2-251.2
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• 2013

Natural photosynthesis utilizes two proteins, photosystem I and photosystem II, to efficiently oxidize water and reduce NADP+ to NADPH. Artificial photosynthesis which mimics this process achieve water splitting through a two-step Z-schematic water splitting process using man-made synthetic materials for hydrogen fuel production. In this study, Z-scheme system was achieved from the hybrid materials which composed of hydrogen production part as photosystem I protein and water oxidizing part as semiconductor BiVO4. Utilizing photosystem I as the hydrogen evolving part overcomes the problems of existing hydrogen evolving p-type semiconductors such as water instability, expensive cost, few available choices and poor red light (>600 nm) absorbance. Some problems of photosystem II, oxygen evolving part of natural photosynthesis, such as demanding isolation process and D1 photo-damage can also be solved by utilizing BiVO4 as the oxygen evolving part. Preceding research has not suggested any protein-inorganic-hybrid Z-scheme composed of both materials from natural photosynthesis and artificial photosynthesis. In this study, to realize this Z-schematic electron transfer, diffusion step of electron carrier, which usually degrades natural photosynthesis efficiency, was eliminated. Instead, BiVO4 and Pt-photosystem I were all linked together by the mediator gold. Synthesized all-solid-state hybrid materials show enhanced hydrogen evolution ability directly from water when illuminated with visible light.

• Asia-Pacific Journal of Business
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• v.11 no.2
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• pp.15-25
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• 2020

Purpose - The purpose of this study is to examine whether the change of settlement procedures have an impact on the distribution of day of the week effect in the UK and French markets or not. U.K and France changed their systems from fixed settlement date systems to fixed settlement lag systems Design/methodology/approach - This study adopted the data of the specific stock market indices such as FTSE 100 in the U.K market and FRCAC 40 in the French market, This study constructs a test of the differences in mean returns across the days of the week by computing the regression equations for each country index. Findings - First, this study found that the evolving settlement procedures in stock exchanges have an effect on stock return of day of the week. Second, long-run improvements in market efficiency may have diminished the effects of certain anomalies in recent periods. Improvements in market efficiency and evolving settlement systems may cause the disappearance of the weekend effect. Research implications or Originality - The Implication of this study is that recent settlement systems contributed to the disappearance of the weekend effect and explains improvements in market efficiency and diminishments of market anomaly. This study may be the first study which examines whether evolving settlement systems have an effect on the disappearance of the weekend effect in the market or not.