• 수산해양교육연구
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• 제27권4호
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• pp.1160-1171
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• 2015

The purpose of this study is to design and develop a computer based learning and test system, which supports not only testing learner's ability but also learning contents with giving feedback and hint. In order to design and develop a computer based learning and test system, Visual Basic dot Net software is used. The system works in three stages: sequential problem solving stage, randomized problem solving stage, and the challenge stage of pass/fail. The results of this study are as follows: (a) We propose the context of design for the computer based learning and test system. (b) We design and develop items display function with sequential and random algorithm in this system. (c) We design and develop pass/fail function by applying SPRT(Sequential Probability Ratio Testing) algorithm in the computer based learning and test system.

• Journal of Platform Technology
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• 제6권4호
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• pp.34-40
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• 2018

현재 인공신경망은 단일 작업에 대해선 뛰어난 성능을 보이나, 다른 종류의 작업을 학습하면 이전 학습 내용을 잊어버리는 단점이 있다. 이를 catastrophic forgetting이라고 한다. 인공신경망의 활용도를 높이긴 위해선 이 현상을 극복해야 한다. catastrophic forgetting을 극복하기 위한 여러 노력이 있다. 하지만 많은 노력이 있었음에도 완벽하게 catastrophic forgetting을 극복하지는 못하였다. 본 논문에서는 여러 노력 중 elastic weight consolidation(EWC)에 사용되는 핵심 개념을 이용하여, 순차적 반복학습을 제시한다. 인공신경망 학습에 많이 쓰이는 MNIST를 확장한 EMNIST 데이터 셋을 이용하여 catastrophic forgetting 현상을 재현하고 이를 순차적 반복학습을 통해 극복하는 실험을 진행하였으며, 그 결과 모든 작업에 대해서 학습이 가능하였다.

• International Journal of Computer Science & Network Security
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• 제23권4호
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• pp.116-122
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• 2023

Sentiment categorization technique be commonly isolated interested in threes significant classifications name Machine Learning Procedure (ML), Lexicon Based Method (LB) also finally, the Hybrid Method. In Machine Learning Methods (ML) utilizes phonetic highlights with apply notable ML algorithm. In this paper, in classification and identification be complete base under in optimizations technique called sequential minimal optimization with Random Forest algorithm (SMORF) for expanding the exhibition and proficiency of sentiment classification framework. The three existing classification algorithms are compared with proposed SMORF algorithm. Imitation result within experiential structure is Precisions (P), recalls (R), F-measures (F) and accuracy metric. The proposed sequential minimal optimization with Random Forest (SMORF) provides the great accuracy.

• Industrial Engineering and Management Systems
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• 제14권3호
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• pp.318-324
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• 2015

This paper proposes a dynamic short-term load forecasting method that utilizes a new sequential learning algorithm based on Relevance Vector Machine (RVM). The method performs general optimization of weights and hyperparameters using the current relevance vectors and newly arriving data. By doing so, the proposed algorithm is trained with the most recent data. Consequently, it extends the RVM algorithm to real-time and nonstationary learning processes. The results of application of the proposed algorithm to prediction of electrical loads indicate that its accuracy is comparable to that of existing nonparametric learning algorithms. Further, the proposed model reduces computational complexity.

• 한국컴퓨터정보학회논문지
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• 제23권11호
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• pp.43-49
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• 2018

In this paper, we propose a displacement measurement method based on deep learning using image data obtained from tensile tests of a material specimen. We focus on the fact that the sequential images during the tension are generated and the displacement of the specimen is represented in the image data. So, we designed sample generation model which makes sequential images of specimen. The behavior of generated images are similar to the real specimen images under tensile force. Using generated images, we trained and validated our model. In the deep neural network, sequential images are assigned to a multi-channel input to train the network. The multi-channel images are composed of sequential images obtained along the time domain. As a result, the neural network learns the temporal information as the images express the correlation with each other along the time domain. In order to verify the proposed method, we conducted experiments by comparing the deformation measuring performance of the neural network changing the displacement range of images.

• 정보처리학회논문지B
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• 제11B권4호
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• pp.477-484
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• 2004

데이터 마이닝에서 연속패턴(sequential pattern) 생성기술은 시차를 두고 발생한 사건들에 대하여 잠재해있는 패턴을 발견하는 기술을 의미한다. 본 연구는 정보이론을 이용하여 데이터베이스로부터 연속패턴을 자동으로 발견하는 방법에 관한 내용이다. 기존의 방법들이 한 속성내에서의 연속패턴만을 탐지하는 일차원 연속패턴을 생성하는데 비하여 본 연구에서 제시하는 방법은 데이터베이스내의 모든 속성간의 연속패턴 관계를 탐지할 수 있는 다차원 연속패턴을 생성할 수 있다. 본 연구에서는 연속패턴 생성을 위하여 헬링거(Hellinger) 변량을 사용하였으며 이를 이용하여 발견된 연속패턴들의 중요도를 측정할 수 있었다. 또한 헬링거 변량의 함수적인 특성을 분석하여 연속패턴 추출의 복잡도를 줄이기 위한 두 가지의 법칙이 제안되었고 다수의 실험 데이터를 통하여 다차원의 연속패턴을 생성할 수 있음을 보였다.

• 인터넷정보학회논문지
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• 제11권3호
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• pp.25-32
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• 2010

게임은 여러 캐릭터와 상태공간을 갖고 있다. 그러므로 학습을 하는데 많은 시간이 걸린다. 본 논문에서는 강화학습 알고리즘을 이용하였다. 보상 값을 받아 캐릭터가 학습하게 하여 지능적으로 움직이게 하였다. 학습초기에는 학습속도가 느려진다. 순차예측 알고리즘을 개선하여 학습에 적용하였다. 기존 강화학습으로 구현된 게임과 비교 실험하였다. 실험결과 개선 구현된 게임의 성능이 학습속도 측면에서 30% 까지 향상됨을 알 수 있었다.

• 한국멀티미디어학회논문지
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• 제13권12호
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• pp.1820-1831
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• 2010

When interacting with unknown environments, an autonomous agent needs to decide which action or action order can result in a good state and determine the transition probability based on the current state and the action taken. The traditional multiple sequential learning model requires predefined probability of the states' transition. This paper proposes a multiple sequential learning and prediction system with definition of autonomous states to enhance the automatic performance of existing AI algorithms. In sequence learning process, the sensed states are classified into several group by a set of proposed motivation filters to reduce the learning computation. In prediction process, the learning agent makes a decision based on the estimation of each state's cost to get a high payoff from the given environment. The proposed learning and prediction algorithms heightens the automatic planning of the autonomous agent for interacting with the dynamic unknown environment. This model was tested in a virtual library.

• 한국수학교육학회지시리즈D:수학교육연구
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• 제25권1호
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• pp.1-20
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• 2022

Collaborative learning has been highlighted as an effective method of teachers' professional development in various studies. To disclose teachers' discourse threads in the process of collaborative learning for developing their knowledge, this paper adopted two methods including "content analysis" and "time-sequential analysis" of learning analytics. Such analyses were implemented for mining teachers' updated knowledge and the discourse threads in the discussion during collaborative learning. The materials for analysis involved two aspects: one was from the video-taped lesson observation reports written by teachers before and after discussing, and the other was from their discourses during the discussion process. The results proved that teachers' knowledge for teaching the centroid of a triangle was updated in the collaborative learning period, and also revealed the discourse threads of teachers' collaboration contained "requesting information or opinions", "building on ideas", and "providing evidence or reasoning", with the emphasis on "challenging ideas or re-focusing talk"

• Journal of information and communication convergence engineering
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• 제22권1호
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• pp.44-55
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• 2024

High-utility itemset mining (HUIM) is a dominant technology that enables enterprises to make real-time decisions, including supply chain management, customer segmentation, and business analytics. However, classical support value-driven Apriori solutions are confined and unable to meet real-time enterprise demands, especially for large amounts of input data. This study introduces a groundbreaking model for top-N high utility itemset mining in real-time enterprise applications. Unlike traditional Apriori-based solutions, the proposed convolutional sequential embedding metrics-driven cosine-similarity-based multilayer perception learning model leverages global and contextual features, including semantic attributes, for enhanced top-N recommendations over sequential transactions. The MATLAB-based simulations of the model on diverse datasets, demonstrated an impressive precision (0.5632), mean absolute error (MAE) (0.7610), hit rate (HR)@K (0.5720), and normalized discounted cumulative gain (NDCG)@K (0.4268). The average MAE across different datasets and latent dimensions was 0.608. Additionally, the model achieved remarkable cumulative accuracy and precision of 97.94% and 97.04% in performance, respectively, surpassing existing state-of-the-art models. This affirms the robustness and effectiveness of the proposed model in real-time enterprise scenarios.