• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.24 no.9
• /
• pp.1193-1201
• /
• 2020

Cell planning, which determines the cell structure for location management of mobile terminals in mobile communications, has been dealt with as an important research task to determine network performance. Among the factors influencing the cell structure planning in mobile communication, the signal cost for location management plays the most important role. In this paper, we propose an optimization algorithm that minimizes the location management cost of all the cells used to plan the cell structure in the network with reporting cell structure in mobile communication. The proposed algorithm uses a Tabu search algorithm, which is a meta-heuristic algorithm, and the proposed algorithm proposes a new neighborhood generation method to obtain a result close to the optimal solution. In order to evaluate the performance of the proposed algorithm, the simulation was performed in terms of location management cost and algorithm execution time. The evaluation results show that the proposed algorithm outperforms the existing genetic algorithm and simulated annealing.

• Journal of The Korean Society of Agricultural Engineers
• /
• v.64 no.1
• /
• pp.79-89
• /
• 2022

Soil moisture is significantly related to crop growth and plays an important role in irrigation management. To predict soil moisture, various process-based model has been developed and used in the world. Various models (Land surface model) may have different performance depending on the model parameters and structures that causes the different model output for the same modeling condition. In this study, the three land surface models (Noah Land Surface Model, Soil Water Atmosphere Plant, Community Land Model) were used to compare the model performance (soil moisture prediction) and develop the multi-model simulation. At first, the genetic algorithm was used to estimate the optimal soil parameters for each model, and the parameters were used to predict soil moisture in the study area. Then, we used the multi-model approach based on Bayesian model averaging (BMA). The results derived from this approach showed a better match to the measurements than the results from the original single land surface model. In addition, identifying the strengths and weaknesses of the single model and utilizing multi-model methods can help to increase the accuracy of soil moisture prediction.

• Land and Housing Review
• /
• v.13 no.1
• /
• pp.113-129
• /
• 2022

A building's eco-friendliness is directly related to various values including the life cycle cost of a building. However, the conventional architectural design method has a limitation in that it cannot create an optimized case according to the surrounding environmental conditions. Therefore, the purpose of this research is to present a design assistance tool that can review planning cases optimized for the environmental conditions of the building site in the planning stage of architectural production. To achieve the purpose of the study, an algorithm for realizing 3D modeling of the region and analysis of the solar environment was produced based on the site contours, building, and road information from the digital topographic map provided by the National Geographic Information Institute. To examine the validity of the developed algorithm, a comparative experiment was conducted targeting the elevation direction of the existing building. As a result, it was found that the optimal elevation direction selected by the algorithm can receive higher insolation compared to the front facade of the main building.

• Smart Structures and Systems
• /
• v.31 no.3
• /
• pp.229-245
• /
• 2023

The absence of excitation measurements may pose a big challenge in the application of structural damage identification owing to the fact that substantial effort is needed to reconstruct or identify unknown input force. To address this issue, in this paper, an iterative strategy, a synergy of Tikhonov regularization method for force identification and modified Jaya algorithm (M-Jaya) for stiffness parameter identification, is developed for damage identification with partial output-only responses. On the one hand, the probabilistic clustering learning technique and nonlinear updating equation are introduced to improve the performance of standard Jaya algorithm. On the other hand, to deal with the difficulty of selection the appropriate regularization parameters in traditional Tikhonov regularization, an improved L-curve method based on B-spline interpolation function is presented. The applicability and effectiveness of the iterative strategy for simultaneous identification of structural damages and unknown input excitation is validated by numerical simulation on a 21-bar truss structure subjected to ambient excitation under noise free and contaminated measurements cases, as well as a series of experimental tests on a five-floor steel frame structure excited by sinusoidal force. The results from these numerical and experimental studies demonstrate that the proposed identification strategy can accurately and effectively identify damage locations and extents without the requirement of force measurements. The proposed M-Jaya algorithm provides more satisfactory performance than genetic algorithm, Gaussian bare-bones artificial bee colony and Jaya algorithm.

• Journal of Drive and Control
• /
• v.20 no.4
• /
• pp.1-8
• /
• 2023

In recent years, robots have been utilized in various industries to reduce workload and enhance work efficiency. The following mobility offers users convenience by autonomously tracking specific locations and targets without the need for additional equipment such as forklifts or carts. In this paper, deep learning techniques were employed to recognize individuals and assign each of them a unique identifier to enable the recognition of a specific person even among multiple individuals. To achieve this, the distance and angle between the robot and the targeted individual are transmitted to respective controllers. Furthermore, this study explored the control methodology for mobility that tracks a specific person, utilizing Simultaneous Localization and Mapping (SLAM) and Proportional-Integral-Derivative (PID) control techniques. In the PID control method, a genetic algorithm is employed to extract the optimal gain value, subsequently evaluating PID performance through simulation. The SLAM method involves generating a map by synchronizing data from a 2D LiDAR and a depth camera using Real-Time Appearance-Based Mapping (RTAB-MAP). Experiments are conducted to compare and analyze the performance of the two control methods, visualizing the paths of both the human and the following mobility.

• The Journal of the Acoustical Society of Korea
• /
• v.29 no.7
• /
• pp.411-421
• /
• 2010

A time-reversal mirror (TRM) is useful in diverse areas, such as reverberation ing, target echo enhancement and underwater communication. In underwater communication, the bit error rate has been improved significantly due to the increased signal-to-noise ratio by spatio-temporal focusing. This paper deals with two issues. First, the optimal number of array elements for a given environment was investigated based on the exploitation of spatial diversity. Second, an algorithm was developed to determine the optimal location of the given number of array elements. The formulation is based on a genetic algorithm maximizing the contrast between the foci and area of interest as an objective function. In addition, the developed algorithm was applied to the matched field processing with ocean experimental data for verification. The sea-going data and simulation showed almost 3 dB improvement in the output power at the foci when the array elements were optimally distributed.

• Journal of Animal Science and Technology
• /
• v.52 no.2
• /
• pp.83-90
• /
• 2010

Hanwoo proven bulls have been selected since 1987 and consequently contributed to farmers for the improvement of beef cattle in Korea. The demand for the quality beef production as well as higher production efficiency was erupted after early 2000 as relatively cheap imported beef released. Therefore the pressure on the reformation of selection index for Hanwoo proven bulls have been piled up to furnish with Hanwoo's competitive. A total of 734 progeny test data were analyzed to select traits and their weights in the selection index to meet the beef market requirement. Regression analysis with stepwise selection method was used to select proper trait and its weight for selection index. A series of computer simulation was carried out to compare the currently using selection index with the alternate two selection indices proposed in this study. New selection index using standardized breeding values of Loin eye Muscle Area (LMA), Backfat Thickness (BFT) and Marbling Score (MS) with weight ratio 1:-1:6 was proposed. Results showed higher performance in improving MS and BFT gain by 22% and 31% still holding 86%~89% of genetic gain achieved by current index in Carcass Weight (CW) and LMA when new selection index was fitted. Because, new index has little consideration for production cost, further research should be performed to build selection index including cost and income simultaneously.

• Journal of Animal Science and Technology
• /
• v.44 no.5
• /
• pp.507-518
• /
• 2002

A multiple-trait stochastic computer simulation model was constructed to predict the breeding schemes and selection methods on Hanwoo(Korean cattle). The model could be used four kinds of selection criteria (random, phenotype and true or estimated breeding values). At the test run in various population size for 20 years, all estimated parameters of the each simulated populations were resulted similar to input parameters. The deviations between input and output values of parameter in the large population were smaller than in the small population. The simulated results obtained from ten small populations consisted with one sire and ten dams in each population for 500 years were as follows; Inbreeding coefficients of population were similar to theoretical estimating function. Mean values of each traits selected were randomly drifted by generation, but they were converged into a value when inbreeding coefficients came close to one. Additive genetic variances within each population were reduced by generation, and they were converged into zero when inbreeding coefficients came close to one. These results indicated that the simulated populations hold to statistical properties of input parameters.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.41 no.4
• /
• pp.51-61
• /
• 2004

In the dental field, the 3D tooth model in which each tooth can be manipulated individually is an essential component for the simulation of orthodontic surgery and treatment. To reconstruct such a tooth model from CT slices, we need to define the accurate boundary of each tooth from CT slices. However, the global threshold method, which is commonly used in most existing 3D reconstruction systems, is not effective for the tooth segmentation in the CT image. In tooth CT slices, some teeth touch with other teeth and some are located inside of alveolar bone whose intensity is similar to that of teeth. In this paper, we propose an image segmentation algorithm based on B-spline curve fitting to produce smooth tooth regions from such CT slices. The proposed algorithm prevents the malfitting problem of the B-spline algorithm by providing accurate initial tooth boundary for the fitting process. This paper proposes an optimal threshold scheme using the intensity and shape information passed by previous slice for the initial boundary generation and an efficient B-spline fitting method based on genetic algorithm. The test result shows that the proposed method detects contour of the individual tooth successfully and can produce a smooth and accurate 3D tooth model for the simulation of orthodontic surgery and treatment.

• Journal of the Korea Society for Simulation
• /
• v.19 no.1
• /
• pp.143-151
• /
• 2010

Fingerprint classification is a step to increase the efficiency of an 1:N fingerprint recognition system and plays a role to reduce the matching time of fingerprint and to increase accuracy of recognition. It is difficult to classify fingerprints, because the ridge pattern of each fingerprint class has an overlapping characteristic with more than one class, fingerprint images may include a lot of noise and an input condition is an exceptional case. In this paper, we propose a novel approach to design a stochastic model and to accomplish fingerprint classification using a directional characteristic of fingerprints for an effective classification of various qualities. We compute the directional value by searching a fingerprint ridge pixel by pixel and extract a directional characteristic by merging a computed directional value by fixed pixels unit. The modified Markov model of each fingerprint class is generated using Markov model which is a stochastic information extraction and a recognition method by extracted directional characteristic. The weight list of classification model of each class is decided by analyzing the state transition matrixes of the generated Markov model of each class and the optimized value which improves the performance of fingerprint classification using GA (Genetic Algorithm) is estimated. The performance of the optimized classification model by GA is superior to the model before the optimization by the experiment result of applying the fingerprint database of various qualities to the optimized model by GA. And the proposed method effectively achieved fingerprint classification to exceptional input conditions because this approach is independent of the existence and nonexistence of singular points by the result of analyzing the fingerprint database which is used to the experiments.