• EDISON SW 활용 경진대회 논문집
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• 제4회(2015년)
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• pp.105-110
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• 2015

본 연구의 목적은 $SPC/F_2$ water model에 대하여 정해진 여러 온도(250K, 280K, 300K, 350K)에서 경수(light water)와 중수(heavy water)의 일정부피 열용량($C_V$)을 계산하는 것이다. 모의실험은 path integral molecular dynamics (PIMD) 방법을 바탕으로, 계산상에서 물 분자 64개에 대해서 실제 물의 밀도에 맞는 일정한 부피를 설정한 후, 이 때 ring-polymer의 bead수는 양자효과를 보일 수 있는 24개와, 양자효과가 없는1개로 실험을 진행했다. 그 결과로 system의 $C_V$를 얻었고, 수소 동위원소의 변화, 온도의 변화, 양자조건의 여부에 따라 나타나는 차이를 각각 비교했다. 모의실험 결과로 온도가 낮을수록, 양자효과가 클수록, 수소의 질량이 작을수록 열용량이 증가하는 결과를 보였다.

• Journal of Construction Engineering and Project Management
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• 제5권2호
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• pp.35-43
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• 2015

Project Management Consultant (PMC) plays a vital role in the overall performance of any project. Selecting right PMC for right project is the most crucial challenge for any construction owner. Thus, PMC selection is one of the main decisions made by owners at the early phase of construction project. It is not easy for the project owner to select a competent PMC due to the fuzziness, imprecision, vagueness, incomplete and qualitative criteria of the decision. This paper presents a model for selecting PMC contractor using the Fuzzy Analytical Hierarchy Process (FAHP). And a fuzzy number based framework is proposed to be a viable method for PMC contractor selection. A case study to illustrate the application of the model is also presented in this paper.

• Structural Engineering and Mechanics
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• 제42권1호
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• pp.55-72
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• 2012

Precast-cast insitu concrete bridge construction is widely practiced for small to medium span structures. These bridges consist of precast pre-stressed concrete beams of various cross-sections with a cast in-situ reinforced concrete slab. The connection between the beams and the slab is via shear links often included during the manufacturing process of the beams. This form of construction is attractive as it provides for standardisation, reduced formwork and construction time. The assessment of the integrity of shear connectors in existing bridges is a major challenge. A procedure for assessment of shear connectors based on vibration testing and finite element model updating is proposed. The technique is applied successfully to a scaled model bridge model and an existing bridge structure.

• Journal of Magnetics
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• 제22권2호
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• pp.196-202
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• 2017

The occurrence of ferroresonance in electrical systems including nonlinear inductors such as transformers will bring a lot of malicious damages. The intense ferromagnetic saturation of the iron core is the most influential factor in ferroresonance that makes nonsinusoidal current and voltage. So the nonlinear behavior modeling of the magnetic core is the most important challenge in the study of ferroresonance. In this paper, the ferroresonance phenomenon is investigated in a single phase transformer using the finite element method and considering the hysteresis loop. Jiles-Atherton (JA) inverse vector model is used for modeling the hysteresis loop, which provides the accurate nonlinear model of the transformer core. The steady-state analysis of ferroresonance is done while considering different capacitors in series with the no-load transformer. The accurate results from copper losses and iron losses are extracted as the most important specifications of transformers. The validity of the simulation results is confirmed by the corresponding experimental measurements.

• EDISON SW 활용 경진대회 논문집
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• 제5회(2016년)
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• pp.222-227
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• 2016

In this paper, the optimized design for bow structure was investigated by using EDISON software. Considering the mechanism of the bow, non-linear FEM analysis was essential. The factors of the design are height, width, number of holes and taper value. High performance of the internal energy and lowest mass were main issues. The limit of the von-mises stress was yield strength for the material. Material was chosen by considering typical bow material, Aluminum. Using Taguchi method($L_9$), 9 models were selected and contribution rate was calculated for each factors. Following the contribution rate, 3 factors were fixed and optimized model was predicted. After making optimized model for FEM analysis, the value of internal-energy, mass for FEM model were compared with predicted value, calculated the percentage error and figure out the reliability of Taguchi method.

• 한국정밀공학회지
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• 제33권9호
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• pp.731-737
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• 2016

In the quest for improved capacity and accelerated dehydration speed of drum type washing machines, an increase in vibration emerges as a major challenge. In an attempt to derive a new design with reduced vibration, a full finite element model of washing machine has been developed, with experimental verification. After modal analyses of several design variants, a new design of the cabinet has been proposed. Forced vibration analysis of the new model suggests that 19% reduction in cabinet vibration amplitude can be achieved with this design.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권3호
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• pp.1464-1480
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• 2019

Robust tracking of infrared (IR) pedestrian targets with various backgrounds, e.g. appearance changes, illumination variations, and background disturbances, is a great challenge in the infrared image processing field. In the paper, we address a new tracking method for IR pedestrian targets via multi-feature local sparse representation (SR), which consists of three important modules. In the first module, a multi-feature local SR model is constructed. Considering the characterization of infrared pedestrian targets, the gray and edge features are first extracted from all target templates, and then fused into the model learning process. In the second module, an effective tracker is proposed via the learned model. To improve the computational efficiency, a sliding window mechanism with multiple scales is first used to scan the current frame to sample the target candidates. Then, the candidates are recognized via sparse reconstruction residual analysis. In the third module, an adaptive dictionary update approach is designed to further improve the tracking performance. The results demonstrate that our method outperforms several classical methods for infrared pedestrian tracking.

• International Journal of Computer Science & Network Security
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• 제22권6호
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• pp.139-144
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• 2022

Technological advancements taken the health care industry by a storm by embedding sensors in human body to measure their vitals. These smart solutions provide better and flexible health care to patients, and also easy monitoring for the medical practitioners. However, these innovative solutions provide their own set of challenges. The major challenge faced by embedding sensors in body is the issue of lack of infinite energy source. This work presents a meta-heuristic based routing model using modified PSO, and adopts an energy harvesting scheme to improve the network lifetime. The routing process is governed by modifying the fitness function of PSO to include charge, temperature and other vital factors required for node selection. A reactive routing model is adopted to ensure reliable packet delivery. Experiments have been performed and comparisons indicate that the proposed Energy Harvesting and Modified PSO (EHMP) model demonstrates low overhead, higher network lifetime and better network stability.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권12호
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• pp.3855-3867
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• 2022

Name separation (splitting full names into surnames and given names) is not a tedious task in a multiethnic country because the procedure for splitting surnames and given names is ethnicity-specific. Malaysia has multiple main ethnic groups; therefore, separating Malaysian full names into surnames and given names proves a challenge. In this study, we develop a two-phase framework for Malaysian name separation using deep learning. In the initial phase, we predict the ethnicity of full names. We propose a recurrent neural network with long short-term memory network-based model with character embeddings for prediction. Based on the predicted ethnicity, we use a rule-based algorithm for splitting full names into surnames and given names in the second phase. We evaluate the performance of the proposed model against various machine learning models and demonstrate that it outperforms them by an average of 9%. Moreover, transfer learning and fine-tuning of the proposed model with an additional dataset results in an improvement of up to 7% on average.

• 시스템엔지니어링학술지
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• 제17권2호
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• pp.91-97
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• 2021

User interface for cancer classification system is a software application with clinician's friendly tools and functions to diagnose cancer from pathology images. Pathology evolved from manual diagnosis to computer-aided diagnosis with the help of Artificial Intelligence tools and algorithms. In this paper, we explained each block of the project life cycle for the implementation of automated breast cancer classification software using AI and machine learning algorithms to classify normal and invasive breast histology images. The system was designed to help the pathologists in an automatic and efficient diagnosis of breast cancer. To design the classification model, Hematoxylin and Eosin (H&E) stained breast histology images were obtained from the ICIAR Breast Cancer challenge. These images are stain normalized to minimize the error that can occur during model training due to pathological stains. The normalized dataset was fed into the ResNet-34 for the classification of normal and invasive breast cancer images. ResNet-34 gave 94% accuracy, 93% F Score, 95% of model Recall, and 91% precision.