• 한국의류산업학회지
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• 제18권6호
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• pp.766-775
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• 2016

In the era of globalization and unlimited competition, Korean universities need a breakthrough in their education system according to the changing education landscape, such as lower graduation requirements to cultivate more multi-talented convergence leaders. While each student has different learning capabilities, which results in different performance and achievements in the same class, the uniform education that most universities are currently offering fails to accommodate such differences. Blended learning, synergically combining offline and online classes, enlarges learning space and enriches learning experiences through diversified tools and materials, including multimedia. Recently, universities are increasingly adopting video contents and on-offline convergence learning strategy. Thus, this study suggests a teaching method based on blended learning to more effectively teach existing pattern CAD and virtual CAD in the Apparel Pattern CAD class. To this end, this researcher developed a teaching-learning method and curriculum according to the blended learning phase and video-based contents. The curriculum consisted of 2D CAD (SuperAlpha: Plus) and 3D CAD (CLO) software learning for 15 weeks. Then, it was loaded to the Learning Management System (LMS) and operated for 15 weeks both online and offline. The performance analysis of LMS usage found that class materials, among online postings, were viewed the most. The discussion menu most accurately depicted students' participation, and students who did not participate in discussions were estimated to check postings less than participating students. A survey on the blended learning found that students prefer digital or more digitized classes, while preferring face to face for Q&As.

• Journal of information and communication convergence engineering
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• 제3권3호
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• pp.163-166
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• 2005

The Key exchange protocols are very crucial tools to provide the secure communication in the broadband satellite access network. They should be required to satisfy various requirements such as security, Key confirmation, and Key freshness. In this paper, Two authenticated key exchange protocols TPEKE-E(Two Pass Encrypted Key Exchange-Exchange-Efficient) and TPEKE-S(Two Pass Encrypted Key xchange-Secure) are introduced. A basic idea of the protocols is that a password can be represented by modular addition N, and the number of possible modular addition N representing the password is $2^N$. The TPEKE-E is secure against the attacks including main-in-the-middle attack and off-line dictionary attack, and the performance is excellent so as beyond to comparison with other authenticated key exchange protocols. The TPEKE-S is a slight modification of the TPEKE-E. The TPEKE-S provides computational in feasibility for learning the password without having performed off line dictionary attack while preserving the performance of the TPEKE-E.

• 국제학술발표논문집
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• The 9th International Conference on Construction Engineering and Project Management
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• pp.728-735
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• 2022

Roofers get exposed to increased risk of knee musculoskeletal disorders (MSDs) at different phases of a sloped shingle installation task. As different phases are associated with different risk levels, this study explored the application of machine learning for automated classification of seven phases in a shingle installation task using knee kinematics and roof slope information. An optical motion capture system was used to collect knee kinematics data from nine subjects who mimicked shingle installation on a slope-adjustable wooden platform. Four features were used in building a phase classification model. They were three knee joint rotation angles (i.e., flexion, abduction-adduction, and internal-external rotation) of the subjects, and the roof slope at which they operated. Three ensemble machine learning algorithms (i.e., random forests, decision trees, and k-nearest neighbors) were used for training and prediction. The simulations indicate that the k-nearest neighbor classifier provided the best performance, with an overall accuracy of 92.62%, demonstrating the considerable potential of machine learning methods in detecting shingle installation phases from workers knee joint rotation and roof slope information. This knowledge, with further investigation, may facilitate knee MSD risk identification among roofers and intervention development.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2023년도 학술발표회
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• pp.179-179
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• 2023

This study suggests a new approach of water level forecasting for extended lead times using original data preprocessing with variational mode decomposition (VMD). Here, two machine learning algorithms including light gradient boosting machine (LGBM) and random forest (RF) were considered to incorporate extended lead times (i.e., 5, 10, 15, 20, 25, 30, 40, and 50 days) forecasting of water levels. At first, the original data at two water level stations (i.e., SW173 and SW269 in Bangladesh) and their decomposed data from VMD were prepared on antecedent lag times to analyze in the datasets of different lead times. Mean absolute error (MAE), root mean squared error (RMSE), and mean squared error (MSE) were used to evaluate the performance of the machine learning models in water level forecasting. As results, it represents that the errors were minimized when the decomposed datasets were considered to predict water levels, rather than the use of original data standalone. It was also noted that LGBM produced lower MAE, RMSE, and MSE values than RF, indicating better performance. For instance, at the SW173 station, LGBM outperformed RF in both decomposed and original data with MAE values of 0.511 and 1.566, compared to RF's MAE values of 0.719 and 1.644, respectively, in a 30-day lead time. The models' performance decreased with increasing lead time, as per the study findings. In summary, preprocessing original data and utilizing machine learning models with decomposed techniques have shown promising results for water level forecasting in higher lead times. It is expected that the approach of this study can assist water management authorities in taking precautionary measures based on forecasted water levels, which is crucial for sustainable water resource utilization.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권4호
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• pp.1424-1440
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• 2015

It is a challenging problem to search the intended images from a large number of candidates. Content based image retrieval (CBIR) is the most promising way to tackle this problem, where the most important topic is to measure the similarity of images so as to cover the variance of shape, color, pose, illumination etc. While previous works made significant progresses, their adaption ability to dataset is not fully explored. In this paper, we propose a similarity learning method on the basis of probabilistic generative model, i.e., probabilistic latent semantic analysis (PLSA). It first derives Fisher kernel, a function over the parameters and variables, based on PLSA. Then, the parameters are determined through simultaneously maximizing the log likelihood function of PLSA and the retrieval performance over the training dataset. The main advantages of this work are twofold: (1) deriving similarity measure based on PLSA which fully exploits the data distribution and Bayes inference; (2) learning model parameters by maximizing the fitting of model to data and the retrieval performance simultaneously. The proposed method (PLSA-FK) is empirically evaluated over three datasets, and the results exhibit promising performance.

• 한국인터넷방송통신학회논문지
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• 제14권2호
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• pp.161-166
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• 2014

스마트 기기 사용의 증가는 많은 사회적 변화를 가져왔다. 특히 교육에서는 보다 높은 학습효과를 위하여 스마트 기기 적용을 연구하여 왔다. 그러나 인문교양은 전통적인 교육 패턴 뿐만 아니라 과목이 가지는 특성 때문에 정보통신에 적용하기가 매우 어려운 분야였다. 본 연구에서는 인문교양 과목을 디지털 방송에 적용하고 그 효과를 증대시키기 위한 방안을 제시하려 한다. 이를 위하여 인문교양의 스마트 콘텐츠 제작 및 연계 방안을 제시하고, 그 실효성을 위하여 디지털 방송에 적용하는 방안에 대한 설문을 통해 적용 가능성을 제시하고자 한다.

• Computers and Concrete
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• 제21권4호
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• pp.407-417
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• 2018

Concrete which is a composite material is one of the most important construction materials. Compressive strength is a commonly used parameter for the assessment of concrete quality. Accurate prediction of concrete compressive strength is an important issue. In this study, we utilized an experimental procedure for the assessment of concrete quality. Firstly, the concrete mix was prepared according to C 20 type concrete, and slump of fresh concrete was about 20 cm. After the placement of fresh concrete to formworks, compaction was achieved using a vibrating screed. After 28 day period, a total of 100 core samples having 75 mm diameter were extracted. On the core samples pulse velocity determination tests and compressive strength tests were performed. Besides, Windsor probe penetration tests and Schmidt hammer tests were also performed. After setting up the data set, twelve artificial intelligence (AI) models compared for predicting the concrete compressive strength. These models can be divided into three categories (i) Functions (i.e., Linear Regression, Simple Linear Regression, Multilayer Perceptron, Support Vector Regression), (ii) Lazy-Learning Algorithms (i.e., IBk Linear NN Search, KStar, Locally Weighted Learning) (iii) Tree-Based Learning Algorithms (i.e., Decision Stump, Model Trees Regression, Random Forest, Random Tree, Reduced Error Pruning Tree). Four evaluation processes, four validation implements (i.e., 10-fold cross validation, 5-fold cross validation, 10% split sample validation & 20% split sample validation) are used to examine the performance of predictive models. This study shows that machine learning regression techniques are promising tools for predicting compressive strength of concrete.

• 한국항행학회논문지
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• 제16권2호
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• pp.264-270
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• 2012

e-Training는 체험형, 실습 위주의 교육 훈련으로 기존 e-Learning의 이론형 교육에서 벗어나 학습자에게 실제적 학습, 능동적 학습을 촉진하고 학습적 효과를 향상시킨다. 체험형 훈련 시스템의 대표적인 예로 증강현실을 들 수 있다. 특히 증강현실 시스템이 구축된 환경에서 자동차 정비 훈련자는 자동차 부품의 위치나 정비 순서를 증강 정보를 통해 바로 획득하기 때문에 효과적인 트레이닝을 경험 할 수 있다. 이러한 증강현실 시스템에서 영상 추적(tracking) 기술은 핵심이다. 어떠한 트래킹 기술을 이용하느냐에 따라 증강현실 시스템의 성능이 결정된다. 트래킹 기술은 인식률 및 속도가 중요한데, 이를 위해서는 자동차 정비 e-Training의 특성을 파악하여 그에 적합한 트래킹 기술을 적용해야 한다. 그러므로 본 논문에서는 자동차 정비 e-Training 증강현실 시스템에 맞는 알고리즘 및 트래킹 기술을 고안하여 적용하였다. 실험 결과 실제 자동차 정비에 필요한 엔진 장비를 실시간 인식 및 다각도에서도 인식률을 잃지 않았다.

• ETRI Journal
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• 제46권4호
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• pp.633-647
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• 2024

In e-commerce platforms, sentiment analysis on an enormous number of user reviews efficiently enhances user satisfaction. In this article, an automated product recommendation system is developed based on machine and deep-learning models. In the initial step, the text data are acquired from the Amazon Product Reviews dataset, which includes 60 000 customer reviews with 14 806 neutral reviews, 19 567 negative reviews, and 25 627 positive reviews. Further, the text data denoising is carried out using techniques such as stop word removal, stemming, segregation, lemmatization, and tokenization. Removing stop-words (duplicate and inconsistent text) and other denoising techniques improves the classification performance and decreases the training time of the model. Next, vectorization is accomplished utilizing the term frequency-inverse document frequency technique, which converts denoised text to numerical vectors for faster code execution. The obtained feature vectors are given to the modified convolutional neural network model for sentiment analysis on e-commerce platforms. The empirical result shows that the proposed model obtained a mean accuracy of 97.40% on the APR dataset.

• International Journal of Internet, Broadcasting and Communication
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• 제14권1호
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• pp.53-63
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• 2022

In the paper, we propose a TSCH-based scheduling scheme for IEEE 802.15.4e, which is able to perform the scheduling of its own network by avoiding collision from interference network cluster (INC). Firstly, we model a bipartite graph structure for presenting the slot-frame (channel-slot assignment) of TSCH. Then, based on the bipartite graph edge weight, we utilize the Hungarian assignment algorithm to implement a scheduling scheme. We have employed two features (maximization and minimization) of the Hungarian-based assignment algorithm, which can perform the assignment in terms of minimizing the throughput of INC and maximizing the throughput of own network. Further, in this work, we called the scheme "dual-stage Hungarian-based assignment algorithm". Furthermore, we also propose deep learning (DL) based deep neural network (DNN)scheme, where the data were generated by the dual-stage Hungarian-based assignment algorithm. The performance of the DNN scheme is evaluated by simulations. The simulation results prove that the proposed DNN scheme providessimilar performance to the dual-stage Hungarian-based assignment algorithm while providing a low execution time.