• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.8
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• pp.2140-2156
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• 2023

The Wireless Sensor Network (WSN), is constructed out of teeny-tiny sensor nodes that are very low-cost, have a low impact on the environment in terms of the amount of power they consume, and are able to successfully transmit data to the base station. The primary challenges that are presented by WSN are those that are posed by the distance between nodes, the amount of energy that is consumed, and the delay in time. The sensor node's source of power supply is a battery, and this particular battery is not capable of being recharged. In this scenario, the amount of energy that is consumed rises in direct proportion to the distance that separates the nodes. Here, we present a Hybrid Firefly Glow-Worm Swarm Optimization (HF-GSO) guided routing strategy for preserving WSNs' low power footprint. An efficient fitness function based on firefly optimization is used to select the Cluster Head (CH) in this procedure. It aids in minimising power consumption and the occurrence of dead sensor nodes. After a cluster head (CH) has been chosen, the Glow-Worm Swarm Optimization (GSO) algorithm is used to figure out the best path for sending data to the sink node. Power consumption, throughput, packet delivery ratio, and network lifetime are just some of the metrics measured and compared between the proposed method and methods that are conceptually similar to those already in use. Simulation results showed that the proposed method significantly reduced energy consumption compared to the state-of-the-art methods, while simultaneously increasing the number of functioning sensor nodes by 2.4%. Proposed method produces superior outcomes compared to alternative optimization-based methods.

• Korean Journal of Soil Science and Fertilizer
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• v.43 no.1
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• pp.96-104
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• 2010

A comparative study was performed in gnotobiotic and greenhouse conditions to evaluate the effect of exogenous application of indole-3-acetic acid (IAA) and inoculation of Methylobacterium spp. possessing 1-aminocyclopropane-1-carboxylate deaminase (ACCD) and IAA activity on red pepperand tomato seedling growth and development. Application of 1.0 ${\mu}g\;mL^{-1}$ IAA positively influenced root growth while high concentrations (>10.0 ${\mu}g\;mL^{-1}$) suppressed root growth of red pepper and tomato under gnotobiotic condition. On the other hand, inoculation of Methylobacterium strains with ACCD activity and IAA or without IAA enhanced root growth in both plants. Similarly, under greenhouse condition the inoculation of Methylobacterium sp. with ACCD activity and IAA enhanced plant fitness recorded as average nodal length and specific leaf weight (SLW) but the effect is comparable with the application of low concentrations of IAA. Seedling length was significantly increased by Methylobacterium strains while total biomass was enhanced by Methylobacterium spp. and exogenous applications of < 10.0 ${\mu}g\;mL^{-1}$ IAA. High concentrations of IAA retard biomass accumulation in red pepper and tomato. These results confirm that bacterial strains with plant growth promoting characters such as IAA and ACCD have characteristic effects on different aspects of growth of red pepper and tomato seedlings which is comparable or better than exogenous applications of synthetic IAA.

• Journal of KIISE:Software and Applications
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• v.28 no.9
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• pp.669-680
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• 2001

Genetic Algorithms are optimization algorithm that mimics biological evolution to solve optimization problems. Genetic algorithms provide an alternative to traditional optimization techniques by using directed random searches to locate optimal solutions in complex fitness landscapes. Hybrid genetic algorithm that is combined with local search called learning can sustain the balance between exploration and exploitation. The genetic traits that each individual in the population learns through evolution are transferred back to the next generation, and when this learning is combined with genetic algorithm we can expect the improvement of the search speed. This paper proposes a genetic algorithm based Cellular Learning with accelerated learning capability for function optimization. Proposed Cellular Learning strategy is based on periodic and convergent behaviors in cellular automata, and on the theory of transmitting to offspring the knowledge and experience that organisms acquire in their lifetime. We compared the search efficiency of Cellular Learning strategy with those of Lamarckian and Baldwin Effect in hybrid genetic algorithm. We showed that the local improvement by cellular learning could enhance the global performance higher by evaluating their performance through the experiment of various test bed functions and also showed that proposed learning strategy could find out the better global optima than conventional method.

• The Korean Journal of Pesticide Science
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• v.5 no.1
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• pp.61-67
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• 2001

Five hundred sixteen isolates of Botrytis elliptica were isolated from infected leaves of Lilium longiflorum from Kangwon alpine areas in Korea during tile seasons from 1998 to 2000 and resistance of these isolates against some fungicides were examined. The isolation frequency of phenotypes resistant to benomyl, procymidone, and diethofencarb were 90.1, 32.4, and 40.9%, respectively. The isolates were divided into six phenotypic groups; RSS, RRS, SSR, SRR, RSR and RRR, representing sensitive (S) or resistant (R) to benzimidazole, dicarboximide, and N-phenylcarbamate fungicides in order. The percentage of six phenotypes were 40.7, 8.5, 7.2, 2.7, 19.8, and 21.1%, respectively. The RSS phenotype was the most frequently isolated, and tile SRR consisted of the extremely minor populations. In comparison studies on tile overwintering ability of each phenotype in relation to the others, the most frequently isolated RSS and SSR had the higher fitness ability than the less frequently isolated RSR, SRR, and RRR. Recently, population increase of tile RSR and RRR phenotypes may have resulted from the increased applications of the mixture of carbendazim and diethofencarb to control benzimidazole-resistant B. elliptica since 1998. The results of this study indicate that careful application of the fungicides is necessary to achieve effective control of leaf blight on lily in Korea.

• Journal of the Korea Society of Computer and Information
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• v.18 no.7
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• pp.183-190
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• 2013

This study aims to analyze the relationships among factors which influence acceptance intention of smart education. Based on literature reviews related with previous technology acceptance models, a potential model and seven hypotheses were suggested. Questionnaire was carried out among 132 students from elementary and secondary schools. They have experiences of utilizing applications of mobile devices for instructional goal. Cronbach alpha of the questionnaire was .78. The collected data were analyzed through path analysis. The results of this research are as follows. Seven hypotheses were adopted: Interaction will affect on perceived usefulness, Interaction will affect on perceived ease of use, Interaction will affect on acceptance intention, Interaction will affect on social influence, Social influence will affect on perceived usefulness, Perceived usefulness will affect on acceptance intention, Perceived ease of use will affect on acceptance intention. The model revised through the results of path analysis had good-fitness. That is, overall fit measures (RMSEA, CFI, NNFI), indexes that show the suitability of the model were quite good.

• Journal of KIISE:Software and Applications
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• v.29 no.5
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• pp.295-310
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• 2002

Software architecture representing a common high-level abstraction of a system can be used as a basis for creating mutual understanding among all stakeholders of the system. In determining a software architecture's fitness with respect to its desired qualities as well as in improving a software architecture, software architecture evaluation is importantly performed. However moat of architecture evaluation methods are not still sufficient in that they do not explicitly consider artifacts discussed during architecture evaluation and their processes are net systematic. As a result, we are hard to follow them. To address these problems, this paper presents the method to evaluate systematically a software architecture with respect to its desired qualities. In this approach, the functional and non-functional requirements are separately handled, and software architecture is represented in the 4+1 view model using UML. Through this initial consideration, the important artifacts such as goals, scope, and target of evaluation are clearly determined. Also, the method provides the well defined process to produce the important evaluation artifacts such as sub-designs, design decisions, rationale, qualities from inputs. In addition, it enables us to determine satisfaction of a architecture with respect its desired qualities or improve a architecture through the structured evaluation results.

• Proceedings of the Optical Society of Korea Conference
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• 2003.07a
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• pp.60-61
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• 2003

A new approach to reduce the computation time of genetic algorithm (GA) for making binary phase holograms is described. Synthesized holograms having diffraction efficiency of 75.8% and uniformity of 5.8% are proven in computer simulation and experimentally demonstrated. Recently, computer-generated holograms (CGHs) having high diffraction efficiency and flexibility of design have been widely developed in many applications such as optical information processing, optical computing, optical interconnection, etc. Among proposed optimization methods, GA has become popular due to its capability of reaching nearly global. However, there exits a drawback to consider when we use the genetic algorithm. It is the large amount of computation time to construct desired holograms. One of the major reasons that the GA' s operation may be time intensive results from the expense of computing the cost function that must Fourier transform the parameters encoded on the hologram into the fitness value. In trying to remedy this drawback, Artificial Neural Network (ANN) has been put forward, allowing CGHs to be created easily and quickly (1), but the quality of reconstructed images is not high enough to use in applications of high preciseness. For that, we are in attempt to find a new approach of combiningthe good properties and performance of both the GA and ANN to make CGHs of high diffraction efficiency in a short time. The optimization of CGH using the genetic algorithm is merely a process of iteration, including selection, crossover, and mutation operators [2]. It is worth noting that the evaluation of the cost function with the aim of selecting better holograms plays an important role in the implementation of the GA. However, this evaluation process wastes much time for Fourier transforming the encoded parameters on the hologram into the value to be solved. Depending on the speed of computer, this process can even last up to ten minutes. It will be more effective if instead of merely generating random holograms in the initial process, a set of approximately desired holograms is employed. By doing so, the initial population will contain less trial holograms equivalent to the reduction of the computation time of GA's. Accordingly, a hybrid algorithm that utilizes a trained neural network to initiate the GA's procedure is proposed. Consequently, the initial population contains less random holograms and is compensated by approximately desired holograms. Figure 1 is the flowchart of the hybrid algorithm in comparison with the classical GA. The procedure of synthesizing a hologram on computer is divided into two steps. First the simulation of holograms based on ANN method [1] to acquire approximately desired holograms is carried. With a teaching data set of 9 characters obtained from the classical GA, the number of layer is 3, the number of hidden node is 100, learning rate is 0.3, and momentum is 0.5, the artificial neural network trained enables us to attain the approximately desired holograms, which are fairly good agreement with what we suggested in the theory. The second step, effect of several parameters on the operation of the hybrid algorithm is investigated. In principle, the operation of the hybrid algorithm and GA are the same except the modification of the initial step. Hence, the verified results in Ref [2] of the parameters such as the probability of crossover and mutation, the tournament size, and the crossover block size are remained unchanged, beside of the reduced population size. The reconstructed image of 76.4% diffraction efficiency and 5.4% uniformity is achieved when the population size is 30, the iteration number is 2000, the probability of crossover is 0.75, and the probability of mutation is 0.001. A comparison between the hybrid algorithm and GA in term of diffraction efficiency and computation time is also evaluated as shown in Fig. 2. With a 66.7% reduction in computation time and a 2% increase in diffraction efficiency compared to the GA method, the hybrid algorithm demonstrates its efficient performance. In the optical experiment, the phase holograms were displayed on a programmable phase modulator (model XGA). Figures 3 are pictures of diffracted patterns of the letter "0" from the holograms generated using the hybrid algorithm. Diffraction efficiency of 75.8% and uniformity of 5.8% are measured. We see that the simulation and experiment results are fairly good agreement with each other. In this paper, Genetic Algorithm and Neural Network have been successfully combined in designing CGHs. This method gives a significant reduction in computation time compared to the GA method while still allowing holograms of high diffraction efficiency and uniformity to be achieved. This work was supported by No.mOl-2001-000-00324-0 (2002)) from the Korea Science & Engineering Foundation.