• Geomechanics and Engineering
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• 제21권3호
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• pp.289-300
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• 2020

Estimating the damage induced by an explosion around a blast hole has always been a challenging issue in geotechnical engineering. It is difficult to determine an exact dimension for damage zone since many parameters are involved in the formation of failures, and there are some uncertainties lying in these parameters. Thus, the present study adopted a probabilistic approach towards this problem. First, a reliability model of the problem was established and the failure probability of induced damage was calculated. Then, the corresponding exceedance risk curve was developed indicating the relation between the failure probability and the cracked zone radius. The obtained risk curve indicated that the failure probability drops dramatically by increasing the cracked zone radius so that the probability of exceedance for any crack length greater than 4.5 m is less than 5%. Moreover, the effect of each parameter involved in the probability of failure, including blast hole radius, explosive density, detonation velocity, and tensile strength of the rock, was evaluated by using a sensitivity analysis. Finally, the impact of the decoupling ratio on the reduction of failures was investigated and the location of its maximum influence was demonstrated around the blast point.

• International journal of advanced smart convergence
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• 제9권4호
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• pp.132-138
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• 2020

Worldwide, the number of corona virus disease 2019 (COVID-19) confirmed cases is rapidly increasing. Although vaccines and treatments for COVID-19 are being developed, the disease is unlikely to disappear completely. By attaching a smart sensor to the respirator worn by medical staff, Internet of Things (IoT) technology and artificial intelligence (AI) technology can be used to automatically detect the medical staff's infection symptoms. In the case of medical staff showing symptoms of the disease, appropriate medical treatment can be provided to protect the staff from the greater risk. In this study, we design and develop a system that detects cough, a typical symptom of respiratory infectious diseases, by applying IoT technology and artificial technology to respiratory protection. Because the cough sound is distorted within the respirator, it is difficult to guarantee accuracy in the AI model learned from the general cough sound. Therefore, coughing and non-coughing sounds were recorded using a sensor attached to a respirator, and AI models were trained and performance evaluated with this data. Mel-spectrogram conversion method was used to efficiently classify sound data, and the developed cough recognition system had a sensitivity of 95.12% and a specificity of 100%, and an overall accuracy of 97.94%.

• 대기
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• 제30권4호
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• pp.347-360
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• 2020

The automatic algorithm optimized for the Korean Peninsula was developed to detect and track the center of typhoon based on a geometrical method using high-resolution retrieved WISSDOM (WInd Syntheses System using DOppler Measurements) wind and reflectivity data. This algorithm analyzes the center of typhoon by detecting the geometric circular structure of the typhoon's eye in radar reflectivity and vorticity 2D field data. For optimizing the algorithm, the main factors of the algorithm were selected and the optimal thresholds were determined through sensitivity experiments for each factor. The center of typhoon was detected for 5 typhoon cases that approached or landed on Korean Peninsula. The performance was verified by comparing and analyzing from the best track of Korea Meteorological Administration (KMA). The detection rate for vorticity use was 15% higher on average than that for reflectivity use. The detection rate for vorticity use was up to 90% for DIANMU case in 2010. The difference between the detected locations and best tracks of KMA was 0.2° on average when using reflectivity and vorticity. After the optimization, the detection rate was improved overall, especially the detection rate more increased when using reflectivity than using vorticity. And the difference of location was reduced to 0.18° on average, increasing the accuracy.

• Nuclear Engineering and Technology
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• 제53권3호
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• pp.752-762
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• 2021

The Smoothed Particle Hydrodynamics is one of the most widely used mesh-free numerical method for thermo-fluid dynamics. Due to its Lagrangian nature and simplicity, it is recently gaining popularity in simulating complex physics with large deformations. In this study, the 3D single/two-phase numerical simulations are performed on the Liquid Metal Reactor (LMR) centralized sloshing benchmark experiment using the SPH parallelized using a GPU. In order to capture multi-phase flows with a large density ratio more effectively, the original SPH density and continuity equations are re-formulated in terms of the normalized-density. Based upon this approach, maximum sloshing height and arrival time in various experimental cases are calculated by using both single-phase and multi-phase SPH framework and the results are compared with the benchmark results. Overall, the results of SPH simulations show excellent agreement with all the benchmark experiments both in qualitative and quantitative manners. According to the sensitivity study of the particle-size, the prediction accuracy is gradually increasing with decreasing the particle-size leading to a higher resolution. In addition, it is found that the multi-phase SPH model considering both liquid and air provides a better prediction on the experimental results and the reality.

• Structural Engineering and Mechanics
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• 제87권5호
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• pp.405-418
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• 2023

This study focused on the effectiveness of seismic isolation technique in case of a reinforced concrete structure with soft story defined as the stiffness irregularity between adjacent stories. In this context, a seismically isolated 3-story reinforced concrete structure was analyzed by gradually increasing the first story height (3.0, 4.5, and 6.0 m). The seismic isolation system of the structure is assumed to be composed of lead rubber bearings (LRB). In the analyses, isolators were modeled by both deteriorating (temperature-dependent analyses) and non-deteriorating (bounding analyses) hysteretic representations. The deterioration in strength of isolator is due to temperature rise in the lead core during cyclic motion. The ground motion pairs used in bi-directional nonlinear dynamic analyses were selected and scaled according to codified procedures. In the analyses, different isolation periods (Tiso) and characteristic strength to weight ratios (Q/W) were considered in order to determine the sensitivity of structural response to the isolator properties. Response quantities under consideration are floor accelerations, and interstory drift ratios. Analyses results are compared for both hysteretic representations of LRBs. Results are also used to assess the significance of the ratio between the horizontal stiffnesses of soft story and isolation system. It is revealed that seismic isolation is a viable method to reduce structural damage in structures with soft story.

• Nuclear Engineering and Technology
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• 제27권6호
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• pp.825-832
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• 1995

SA508 Cl.3 원자로 압력용기강에서 하중변수와 온도가 공기 중에서 피로 균열성장률에 미치는 영향에 대하여 조사하였다. 피로 균열성장률 시험은 12.7mm 두께의 CT(compact tension) 시편을 이용하였으며, 균열길이 추정은 컴플라이언스 방법을 사용하였다. 시험은 0.1, 0.5 하중비와 1, 10Hz의 하중주파수로 상온에서 40$0^{\circ}C$ 까지 온도를 변화시키면서 수행하였다. 12$0^{\circ}C$ 이하의 비교적 낮은 온도에서는 피로균열전파속도는 하중주파수와 온도에 영향을 받지 않았지만, 12$0^{\circ}C$이상의 경우 피로 균열성장률은 온도가 높을수록, 하중주파수가 낮을수록 증가하였다. 이러한 피로균열진전속도의 빨라짐은 균열선단에서의 산화속도의 증가로 인한것으로 생각된다. 또한 하중비의 영향으로 균열닫힘과 산화의 상호작용으로 피로 균열성장률은 상온에서 두드러졌다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권5호
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• pp.1310-1338
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• 2023

As Internet of Things (IoT) applications and devices rapidly grow, cyber-attacks on IoT networks/systems also have an increasing trend, thus increasing the threat to security and privacy. Botnet is one of the threats that dominate the attacks as it can easily compromise devices attached to an IoT networks/systems. The compromised devices will behave like the normal ones, thus it is difficult to recognize them. Several intelligent approaches have been introduced to improve the detection accuracy of this type of cyber-attack, including deep learning and machine learning techniques. Moreover, dimensionality reduction methods are implemented during the preprocessing stage. This research work proposes deep Autoencoder dimensionality reduction method combined with Artificial Neural Network (ANN) classifier as botnet detection system for IoT networks/systems. Experiments were carried out using 3- layer, 4-layer and 5-layer pre-processing data from the MedBIoT dataset. Experimental results show that using a 5-layer Autoencoder has better results, with details of accuracy value of 99.72%, Precision of 99.82%, Sensitivity of 99.82%, Specificity of 99.31%, and F1-score value of 99.82%. On the other hand, the 5-layer Autoencoder model succeeded in reducing the dataset size from 152 MB to 12.6 MB (equivalent to a reduction of 91.2%). Besides that, experiments on the N_BaIoT dataset also have a very high level of accuracy, up to 99.99%.

• 한국산학기술학회논문지
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• 제15권11호
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• pp.6877-6883
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• 2014

지속적인 사회발전으로 인하여 호소의 수질오염이 악화되고 있다. 호소의 특성 및 유역의 전반적인 측면에서 수질오염에 따른 호소의 취약성을 평가하기 위해, 본 논문에서는 기후변화 취약성 평가를 이용하여 호소 취약성-탄력성지수(LVRI)를 산정하였다. 금강 수계의 6개 호소에 대하여 취약성평가로 분류되는 3개의 주요 인자인 노출, 민감도, 적응능력을 대표하는 총 11개 세부평가항목들은 선정하였다. 또한, 엔트로피(Entropy) 방법에 의한 가중치를 산정하여 호소 취약성-탄력지수를 산정하였다. 제안된 호소의 취약성 평가방안의 활용성 검토를 위해 가중치 적용 유 무에 따른 취약성 우선순위를 비교 검토하였다. 본 논문에서 선정된 호소의 취약성 우선순위는 장지적인 수질분석 및 수질관리에 도움이 될 것으로 판단된다.

• 한국지능시스템학회논문지
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• 제15권2호
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• pp.245-250
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• 2005

인터넷의 급격한 발전으로 교육환경$\cdot$방법에 대한 새로운 패러다임 창출요구가 증가하고 있으며 전통적인 교육산업도 교육의 전 분야에서 이론 활용한 e-teaming이 많은 분야에서 도입되었고, 빠른 속도로 그 영역이 확장되고 있다. 이러한 e-learning 확산 노력에 힘입어 그동안 e-learning의 학습자 만족도에 대한 연구도 많이 진행되어 왔지만 기업체를 대상으로 한 연구가 거의 대부분이었고 고등학교를 대상으로 한 연구는 거의 없는 실정이다. 따라서, 본 연구에서는 이러한 배경을 바탕으로 고등학생을 대상으로 한 e-learning 교육만족도 평가를 위한 모델을 제안하고, 제안한 모델을 대상으로 퍼지구조 모델링법을 이용하여 고등학생의 e-learning 교육 만족도에 관한 의식구조를 분석하였다. 또한, 의식구조분석의 결과가 고려된 평가모델을 구축하여 e-learning 교육 만족도를 평가하고, 민감도분석을 통하여 e-learning 교육만족도 향상 방안을 제시 하였다.

• 한국콘텐츠학회논문지
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• 제7권11호
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• pp.140-149
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• 2007

주얼리(Jewelry) 관련 산업 사회인식전환은 교육이론과 실무자의 책임의식 변화가 선행 되어야 한다. 이 논문은 주얼리 산업 중 주얼리 디자인에 관한 관심과 증가가 이어져 문화경영으로서 비젼(Vision)과 가능성을 가져 올 것인가에 대한 논의로 주얼리 디자인의 논리적 체계화와 주얼리 디자이너의 윤리적 교육과정이 차세대 주얼리 문화산업에 미치는 영향력에 대하여 조사하였다. 세계의 주얼리 산업 중 이탈리아의 주얼리 교육과 산업을 통해 주얼리 디자인의 존재방식과 최근 트렌드, 소비 형태에 따른 디자인 경영의 새로운 콘텐츠와 사례들을 들어 분석함으로서 국가적 경쟁력을 넘어 선 미래의 문화 산업으로의 발전 가능성과 그에 동반하는 주얼리 교육의 윤리적 철학의 기초를 제공하였다. 디자이너의 지각과 행동이 디자인의 존재방식과 경영에 공감과 감성이라는 주얼리 디자인의 새로운 전통의 기반을 제공하는 것에 의의를 찾고자 한다.