• Transactions of the Korean Society of Mechanical Engineers A
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• v.25 no.6
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• pp.1004-1014
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• 2001

The fully automatic algorithm from initial finite element mesh generation to remeshing in two dimensional geometry is introduced using bubble packing method (BPM) for finite element analysis. BPM determines the node placement by force-balancing configuration of bubbles and the triangular meshes are made by Delaunay triangulation with advancing front concept. In BPM, we suggest two node-search algorithms and the adaptive/recursive bubble controls to search the optimal nodal position. To use the automatically generated mesh information in FEA, the new enhanced bandwidth minimization scheme with high efficiency in CPU time is developed. In the remeshing stage, the mesh refinement is incorporated by the control of bubble size using two parameters. And Superconvergent Patch Recovery (SPR) technique is used for error estimation. To verify the capability of this algorithm, we consider two elasticity problems, one is the bending problem of short cantilever beam and the tension problem of infinite plate with hole. The numerical results indicate that the algorithm by BPM is able to refine the mesh based on a posteriori error and control the mesh size easily by two parameters.

• Journal of KIISE:Databases
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• v.28 no.4
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• pp.655-664
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• 2001

The continuous data such as video streams and voice analog signals can be modeled as multidimensional data sequences(MDS's) in the feature space, In this paper, we investigate the clustering technique for multidimensional data sequence, Each sequence is represented by a small number by hyper rectangular clusters for subsequent storage and similarity search processing. We present a linear clustering algorithm that guarantees a predefined level of clustering quality and show its effectiveness via experiments on various video data sets.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.388-388
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• 2019

하도 홍수추적의 방법은 크게 수리학적 방법과 수문학적 방법으로 구분할 수 있다. 수리학적 홍수추적 방법은 정확하지만 대량의 자료가 필요하고 시간이 오래 걸린다. 이와 반대로 수문학적 홍수추적 방법은 정확성은 떨어지지만 소량의 자료만 있으면 되고 시간이 적게 걸린다. 여러 수문학적 홍수추적에 관한 연구들이 있으며 대표적으로 Muskingum 방법이 있다. Muskingum 방법 중 Linear Muskingum Model(LMM)은 방정식의 구조적 한계 때문에 정확한 홍수추적이 어려웠고, 이를 개선하기위하여 Nonlinear Muskingum Model(NLMM), Nonlinear Muskingum Model Incorporation Lateral Flow(NLMM-L) 및 Advanced Nonlinear Muskingum Model Incorporating Lateral Flow(ANLMM-L)이 제안되었다. 본 연구는 수문학적 홍수추적 중 Muskingum 방법의 결과 차이가 어떤 요인으로 인해 발생하는지 검토하였다. 최적화 알고리즘으로 화음탐색법(Harmony Search, HS)을 사용하였으며 LMM, NLMM, NLMM-L 및 ANLMM-L의 매개변수를 산정하였다. 각 방법에 적용 시 HS의 매개변수에 변화를 주어 민감도 분석을 실시하였으며, 분석을 위한 홍수자료는 The Willson Flood data (1947)를 선택하였다. 오차비교방법은 Sum of Squares(SSQ), Root Mean Square Errors(RMSE), Nash-Sutcliffe Efficiency(NSE)를 비교하였다. 비교 결과 알고리즘의 성능에 의한 차이보다 홍수추적 방법의 차이가 더 영향이 큰 것으로 나타났다.

• International Journal of Computer Science & Network Security
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• v.21 no.1
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• pp.40-48
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• 2021

Content-Based Image Retrieval (CBIR) system plays a vital role to retrieve the relevant images as per the user perception from the huge database is a challenging task. Images are represented is to employ a combination of low-level features as per their visual content to form a feature vector. To reduce the search time of a large database while retrieving images, a novel image retrieval technique based on feature dimensionality reduction is being proposed with the exploit of metaheuristic optimization techniques based on Genetic Algorithm (GA), Extended Binary Cuckoo Search (EBCS) and Whale Optimization Algorithm (WOA). Each image in the database is indexed using a feature vector comprising of fuzzified based color histogram descriptor for color and Median binary pattern were derived in the color space from HSI for texture feature variants respectively. Finally, results are being compared in terms of Precision, Recall, F-measure, Accuracy, and error rate with benchmark classification algorithms (Linear discriminant analysis, CatBoost, Extra Trees, Random Forest, Naive Bayes, light gradient boosting, Extreme gradient boosting, k-NN, and Ridge) to validate the efficiency of the proposed approach. Finally, a ranking of the techniques using TOPSIS has been considered choosing the best feature selection technique based on different model parameters.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.2
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• pp.36-42
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• 2021

This paper deals with a vehicle routing problem with resource repositioning (VRPRR) which is a variation of well-known vehicle routing problem with pickup and delivery (VRPPD). VRPRR in which static repositioning of public bikes is a representative case, can be defined as a multi-objective optimization problem aiming at minimizing both transportation cost and the amount of unmet demand. To obtain Pareto sets for the problem, famous multi-objective optimization algorithms such as Strength Pareto Evolutionary Algorithm 2 (SPEA2) can be applied. In addition, a linear combination of two objective functions with weights can be exploited to generate Pareto sets. By varying weight values in the combined single objective function, a set of solutions is created. Experiments accomplished with a standard benchmark problem sets show that Variable Neighborhood Search (VNS) applied to solve a number of single objective function outperforms SPEA2. All generated solutions from SPEA2 are completely dominated by a set of VNS solutions. It seems that local optimization technique inherent in VNS makes it possible to generate near optimal solutions for the single objective function. Also, it shows that trade-off between the number of solutions in Pareto set and the computation time should be considered to obtain good solutions effectively in case of linearly combined single objective function.

• The Journal of Korea Robotics Society
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• v.17 no.1
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• pp.1-7
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• 2022

Robots are widely used in industries and services. Traditional robots have been used to perform repetitive tasks in a fixed environment, and it is very difficult to solve a problem in which the physical interaction of the surrounding environment or other objects is complicated with the existing control method. Reinforcement learning has been actively studied as a method of machine learning to solve such problems, and provides answers to problems that robots have not solved in the conventional way. Studies on the learning of all physical robots are commonly affected by noise. Complex noises, such as control errors of robots, limitations in performance of measurement equipment, and complexity of physical interactions with surrounding environments and objects, can act as factors that degrade learning. A learning method that works well in a virtual environment may not very effective in a real robot. Therefore, this paper proposes a weighted sum method and a linear regression method as an effective and accurate learning method in a noisy environment. In addition, the bottle flipping was trained on a robot and compared with the existing learning method, the validity of the proposed method was verified.

• Journal of Astronomy and Space Sciences
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• v.40 no.4
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• pp.259-266
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• 2023

The Standard Model (SM) does not provide an information for 26% of dark matter of the universe. In the dark sector, dark matter is supposed to be linked with the hypothetical particles called dark photons that have similar role to photons in electromagnetic interaction in the SM. Besides astronomical observation, there are studies to find dark matter candidates using accelerators. In this paper, we searched for dark photons using future electron-positron colliders, including Circular Electron Positron Collider (CEPC)/CEPC, Future Circular Collider (FCC-ee)/Innovative Detector for Electron-positron Accelerator (IDEA), and International Linear Collider (ILC)/International Large Detector (ILD). Using the parameterized response of the detector simulation of Delphes, we studied the sensitivity of a double dark photon mode at each accelerator/detector. The signal mode is double dark photon decay channel, e⁺e^- → A'A', where A' (dark photon with spin 1) decaying into a muon pair. We used MadGraph5 to generate Monte Carlo (MC) events by means of a Simplified Model. We found the dark photon mass at which the cross-sections were the highest for each accelerator to obtain the maximum number of events. In this paper we show the expected number of dark photon signal events and the detector efficiency of each accelerator. The results of this study can facilitate in the dark photon search by future electron-positron accelerators.

• Journal of Korean society of Dental Hygiene
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• v.24 no.2
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• pp.149-162
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• 2024

Objectives: This study aimed to identify the level of digital oral health literacy among Korean adults in Gangwon-do. Methods: A survey was conducted through face-to-face interviews with adults aged 30-89 residing in Gangwon-do, South Korea. The independent variables were socio-demographic characteristics, including gender, age, education level, income, and occupation. The dependent variable was digital oral health literacy. The statistical analysis included Independent t-test, one-way ANOVA, Pearson correlation analysis, and multiple linear regression analysis, all performed using the SPSS 27.0 software program. Results: The elderly and those with poor socioeconomic status (p<0.001) had lower average scores due to poor digital oral health literacy. The ease of searching for oral health information and the utilization of digital devices to search for information on oral health displayed a significant relationship (r=0.730, p<0.001). To develop the use of digital devices, simplifying the search for oral information (p<0.001) is essential. Conclusions: All socio-demographic characteristics, except gender, displayed a correlation with the level of digital oral health literacy. Both environmental and individual aspects of the respondents' living conditions must be considered to improve digital oral health literacy.

• Journal of the Korean School Mathematics Society
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• v.10 no.2
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• pp.263-288
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• 2007

The purpose of this study is to search the effective teaching-learning program by considering how affect on formation of the process-object perspective of linear function using Excel. In this study we analyzed function units in textbook and examined how Excel affect on the formation of the process-object perspective of linear function. Teaching experiment was based on qualitative case study and performed for five classes with five 8th graders. Data were gathered through observations, audio-taped interviews, video recording of the students 'work, students' worksheets, and detailed field notes. Findings indicate that exploration learning environment using Excel could supplement paper-and-pencil environment. We found that intuitive, dynamic, explorative, feedback skills via Excel can play the role of scaffolding supporting formation of process perspective object perspective of linear function.

• The Transactions of the Korean Institute of Power Electronics
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• v.11 no.3
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• pp.274-283
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• 2006

The Permanent Magnet Synchronous Motor(PMSM) drive systems with ball-screw, gear and timing-belt are widely used in industrial applications such as NC machine, machine tools, robots and factory automation. These systems have torsional vibration in torque transmission from servo motor to mechanical load due to the mechanical couplings. This vibration makes it difficult to achieve quick responses of speed and may result in damage to the mechanical plant. This paper presents adaptive notch filter with auto searching function of vibration frequency to reject the mechanical vibration of linear feeder system with PMSM. The proposed adaptive notch filter can suppress the torque command signal of PMSM in the resonant bandwidth for reject the mechanical torsional vibration. However, the resonant frequency can vary with conditions of mechanical load system and coupling devices, adaptive notch filter can auto search the vibration frequency and suppress the vibration signal bandwidth. Computer simulation and experimental results shows the verification of the proposed adaptive notch filter in linear feeder system with PMSM.