• Geomechanics and Engineering
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• 제37권4호
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• pp.395-418
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• 2024

The epicentral region of earthquakes is typically where liquefaction-related damage takes place. To determine the maximum distance, such as maximum epicentral distance (R_e), maximum fault distance (R_f), or maximum hypocentral distance (R_h), at which an earthquake can inflict damage, given its magnitude, this study, using a recently updated global liquefaction database, multiple ML models are built to predict the limiting distances (R_e, R_f, or R_h) required for an earthquake of a given magnitude to cause damage. Four machine learning models LSTM (Long Short-Term Memory), BiLSTM (Bidirectional Long Short-Term Memory), CNN (Convolutional Neural Network), and XGB (Extreme Gradient Boosting) are developed using the Python programming language. All four proposed ML models performed better than empirical models for limiting distance assessment. Among these models, the XGB model outperformed all the models. In order to determine how well the suggested models can predict limiting distances, a number of statistical parameters have been studied. To compare the accuracy of the proposed models, rank analysis, error matrix, and Taylor diagram have been developed. The ML models proposed in this paper are more robust than other current models and may be used to assess the minimal energy of a liquefaction disaster caused by an earthquake or to estimate the maximum distance of a liquefied site provided an earthquake in rapid disaster mapping.

• 한국콘텐츠학회논문지
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• 제13권1호
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• pp.498-511
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• 2013

최근 급격히 팽창한 한국영화산업에도 이제 다시 한 번 대대적인 정비가 필요한 시점이 왔다. 그 중에서도 영화 현장인력 교육의 기반을 마련하여 전문화된 스텝을 양성하는 일은 무엇보다 시급한 과제이다. 이를 위해서는 오랜 시간동안 한국 영화산업의 주된 교육 제도였던 도제 제도를 새롭게 조탁하여, 그 한계를 극복하고 장점을 계승할 수 있는 합리적인 제도를 마련할 필요가 있다. 즉 도제 제도의 근간인 서열적인 직급을 수평적인 전문직 스텝 체계로 전환하고, 현장인력들의 현황 파악, 경력 및 그에 따른 승급과 임금까지도 총괄적으로 관리해주는 산업 정보망을 구축해야 한다. 본 논문은 영화 현장인력 교육의 새로운 체계로서 일터학습과 멘토링 제도의 도입을 제안함으로써, 도제 제도의 장점인 풍부한 현장 경험과 동료 간의 정서적 유대감을 유지하고, 보다 체계적이며 전문적인 현장 학습이 이루어질 수 있는 교육 체제를 마련하고자 한다.

• 수산해양교육연구
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• 제29권3호
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• pp.677-688
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• 2017

The purpose of this study is to analyze the action learning lesson about the improvement process of the job support program of P university students. As a research method, we applied the related classes during the semester to the students who took courses in the course of 'Human Resource Development', which is a subject of P university, and analyzed the learner's reflection journal, interview data. As a result of the research, we went through the problem selection stage, the team construction and the team building stage. And then we searched for the root cause of the problem, clarified the problem, derived the possible solution, determined the priority and created the action plan. There are 10 solutions to the practical problems of poor job camps. Through two interviews with field experts it offered final solutions focused on promoting employment and Camp students participate in the management of post-employment into six camps. According to the first rank, job board integration, vendor selection upon student feedback, reflecting improved late questionnaire, public relations utilizing KakaoTalk, recruiting additional selection criteria, the camp provides recorded images in order. The results of this study suggest that the university's employment support program will strengthen the competitiveness of students' employment and become the basic data for the customized employment support program.

• 컴퓨터교육학회논문지
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• 제9권2호
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• pp.69-78
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• 2006

교육평가란 학습자들을 서열별로 등급화 하는 과정이 아니라 적절한 평가를 통해 학습자의 문제를 해결하고 교육 과정을 개선하여 교육적 효과를 높이는 과정이다. 기존의 웹 기반 평가 시스템은 학습자의 인지 수준을 정 오답 이분 변수로만 측정하였다. 또한 수준별 평가를 지원하나 평가 시 학습동기 부여와 흥미를 이끌어 내는데 미흡하였으며, 평가 후 틀린 문제에 대한 피드백만을 제공하여 추후 학습 방향 설정과 재학습이 효율적으로 이루어지지 못한 문제점이 있다. 본 논문에서는 문제의 답지에 따라 학습자의 인지 능력을 좀 더 세밀하게 추정하고자 부분점수를 고려하여 문제를 출제하고 학습자의 수준을 평가하였으며 평가가 끝나면 학습 진단을 제공하여 피드백 학습이 효과적으로 이루어질 수 있도록 하였다.

• Nuclear Engineering and Technology
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• 제54권1호
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• pp.193-206
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• 2022

Several algorithms for nuclear power plant (NPP) break event detection, isolation, localization, and size estimation are proposed. A break event can be promptly detected and isolated after its occurrence by simultaneously monitoring changes in the sensing readings and by employing an interquartile range-based isolation scheme. By considering the multi-sensor data block of a break to be rank-one, it can be located as the position whose lead field vector is most orthogonal to the noise subspace of that data block using the Multiple Signal Classification (MUSIC) algorithm. Owing to the flexibility of deep neural networks in selecting the best regression model for the available data, we can estimate the break size using multiple-sensor recordings of the break regardless of the sensor types. The efficacy of the proposed algorithms was evaluated using the data generated by Maanshan NPP simulator. The experimental results demonstrated that the MUSIC method could distinguish two near breaks. However, if the two breaks were close and of small sizes, the MUSIC method might wrongly locate them. The break sizes estimated by the proposed deep learning model were close to their actual values, but relative errors of more than 8% were seen while estimating small breaks' sizes.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권12호
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• pp.4456-4475
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• 2021

The system for re-identifying persons is used to find and verify the persons crossing through different spots using various cameras. Much research has been done to re-identify the person by utilising features with deep-learned or hand-crafted information. Deep learning techniques segregate and analyse the features of their layers in various forms, and the output is complex feature vectors. This paper proposes a distinctive framework called Integrated Level Feature Pattern (ILFP) framework, which integrates local and global features. A new deep learning architecture named modified XceptionNet (m-XceptionNet) is also proposed in this work, which extracts the global features effectively with lesser complexity. The proposed framework gives better performance in Rank1 metric for Market1501 (96.15%), CUHK03 (82.29%) and the newly created NEC01 (96.66%) datasets than the existing works. The mean Average Precision (mAP) calculated using the proposed framework gives 92%, 85% and 98%, respectively, for the same datasets.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제15권3호
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• pp.159-165
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• 2015

The need for the recognition of music emotion has become apparent in many music information retrieval applications. In addition to the large pool of techniques that have already been developed in machine learning and data mining, various emerging applications have led to a wealth of newly proposed techniques. In the music information retrieval community, many studies and applications have concentrated on tag-based music recommendation. The limitation of music emotion tags is the ambiguity caused by a single music tag covering too many subcategories. To overcome this, multiple tags can be used simultaneously to specify music clips more precisely. In this paper, we propose a novel technique to rank the proper tag combinations based on the acoustic similarity of music clips.

• Journal of Information Processing Systems
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• 제16권1호
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• pp.6-29
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• 2020

Biometrics identification using multiple modalities has attracted the attention of many researchers as it produces more robust and trustworthy results than single modality biometrics. In this paper, we present a novel multimodal recognition system that trains a deep learning network to automatically learn features after extracting multiple biometric modalities from a single data source, i.e., facial video clips. Utilizing different modalities, i.e., left ear, left profile face, frontal face, right profile face, and right ear, present in the facial video clips, we train supervised denoising auto-encoders to automatically extract robust and non-redundant features. The automatically learned features are then used to train modality specific sparse classifiers to perform the multimodal recognition. Moreover, the proposed technique has proven robust when some of the above modalities were missing during the testing. The proposed system has three main components that are responsible for detection, which consists of modality specific detectors to automatically detect images of different modalities present in facial video clips; feature selection, which uses supervised denoising sparse auto-encoders network to capture discriminative representations that are robust to the illumination and pose variations; and classification, which consists of a set of modality specific sparse representation classifiers for unimodal recognition, followed by score level fusion of the recognition results of the available modalities. Experiments conducted on the constrained facial video dataset (WVU) and the unconstrained facial video dataset (HONDA/UCSD), resulted in a 99.17% and 97.14% Rank-1 recognition rates, respectively. The multimodal recognition accuracy demonstrates the superiority and robustness of the proposed approach irrespective of the illumination, non-planar movement, and pose variations present in the video clips even in the situation of missing modalities.

• Safety and Health at Work
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• 제9권4호
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• pp.381-387
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• 2018

Background: Physician behaviors that undermine a culture of safety have gained increasing attention as health-care organizations strive to create a culture of safety and reduce medical errors. We developed, implemented, and assessed a course to teach physicians skills regarding effective coping and interpersonal communication skills and present our results regarding outcomes. Methods: We examined a professional development program specifically designed to address unprofessional or distressed behaviors of physicians, and we evaluated the impact on burnout, quality of life, and emotional flooding scores of the physicians. Assessments of burnout, quality of life, and emotional flooding were assessed preintervention and postintervention. Results: Results demonstrated statistically significant reductions over time in physicians' emotional flooding and emotional exhaustion (EE). Specifically, using a Wilcoxon Signed-Rank test, results revealed that flooding scores at follow-up were statistically significantly lower than at baseline, V = 590, p < 0.05, and EE and personal accomplishment distributions were found to significantly deviate from normal as indicated by Shapiroe-Wilks tests (p < 0.05). A Wilcoxon signed-rank test indicated that EE scores were significantly higher at baseline compared to follow-up 1, V = 285, p < 0.05. Conclusion: We conclude that the physician participants who enrolled in the educational skills training program improved scores on emotional flooding and EE and that this may be indicative of improved skills related to their experiences and learning in the program. These improved skills in physicians may have a positive impact on the overall culture of safety in the health system setting.

• 한국정보과학회논문지:컴퓨팅의 실제 및 레터
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• 제10권6호
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• pp.529-536
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• 2004

본 논문에서는 질의응답시스템의 성능을 개선하기 위해 문장의 위치정보와 질의형태분류기를 사용하여 질의에 대한 대답순위를 조정하는 새로운 질의-문서 유사도 계산을 제안한다. 이를 위해 첫째로 문서내용을 표현하고 문서의 위치정보를 반영하기 위해 개념그래프를 사용한다. 이 방법은 문서비교에 대표적으로 사용되는 Dice-Coefficient에 기반하고 문장에서 단어의 위치정보론 반영한 유사도 계산이다. 두번째로 질의응답시스템의 대답순위를 개선하기 위하여 질의형태를 고려한 기계학습을 통한 질문에 대한 분류를 하였으며 이를 위해서 뉴스그룹의 FAQ 문서 30,000개를 가지고 기계학습 방법인 나이브 베이지안을 사용한 분류기를 구현하였다. 이에 대한 평가를 위해 세계적인 정보검색대회인 TREC-9의 질의응답시스템분야에 제출된 데이타를 가지고 실험하였으며 기존의 방법에 비해 자동학습기법을 사용하였음에도 평균상호순위가 0.29, 상위 5위에 정답을 포함시킨 경우가 55.1%의 성능을 보였다. 이 방법은 다른 시스템과 달리 질의형태분류를 기계학습 방법을 사용하여 자동으로 학습하는 것에 의의를 갖는다.