• Journal of Information Technology Applications and Management
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• 제30권1호
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• pp.53-70
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• 2023

Artificial intelligence has become familiar with modern society, not the distant future. As artificial intelligence and machine learning developed more highly and became more complicated, it became difficult for people to grasp its structure and the basis for decision-making. It is because machine learning only shows results, not the whole processes. As artificial intelligence developed and became more common, people wanted the explanation which could provide them the trust on artificial intelligence. This study recognized the necessity and importance of explainable artificial intelligence, XAI, and examined the trends of XAI research by analyzing social networks and analyzing topics with IEEE published from 2004, when the concept of artificial intelligence was defined, to 2022. Through social network analysis, the overall pattern of nodes can be found in a large number of documents and the connection between keywords shows the meaning of the relationship structure, and topic modeling can identify more objective topics by extracting keywords from unstructured data and setting topics. Both analysis methods are suitable for trend analysis. As a result of the analysis, it was found that XAI's application is gradually expanding in various fields as well as machine learning and deep learning.

• ETRI Journal
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• 제46권1호
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• pp.118-126
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• 2024

In human conversations, listeners often utilize brief backchannels such as "uh-huh" or "yeah." Timely backchannels are crucial to understanding and increasing trust among conversational partners. In human-machine conversation systems, users can engage in natural conversations when a conversational agent generates backchannels like a human listener. We propose a method that simultaneously predicts backchannels and recognizes speech in real time. We use a streaming transformer and adopt multitask learning for concurrent backchannel prediction and speech recognition. The experimental results demonstrate the superior performance of our method compared with previous works while maintaining a similar single-task speech recognition performance. Owing to the extremely imbalanced training data distribution, the single-task backchannel prediction model fails to predict any of the backchannel categories, and the proposed multitask approach substantially enhances the backchannel prediction performance. Notably, in the streaming prediction scenario, the performance of backchannel prediction improves by up to 18.7% compared with existing methods.

• 산업경영시스템학회지
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• 제44권4호
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• pp.227-233
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• 2021

Predictive maintenance has been one of important applications of data science technology that creates a predictive model by collecting numerous data related to management targeted equipment. It does not predict equipment failure with just one or two signs, but quantifies and models numerous symptoms and historical data of actual failure. Statistical methods were used a lot in the past as this predictive maintenance method, but recently, many machine learning-based methods have been proposed. Such proposed machine learning-based methods are preferable in that they show more accurate prediction performance. However, with the exception of some learning models such as decision tree-based models, it is very difficult to explicitly know the structure of learning models (Black-Box Model) and to explain to what extent certain attributes (features or variables) of the learning model affected the prediction results. To overcome this problem, a recently proposed study is an explainable artificial intelligence (AI). It is a methodology that makes it easy for users to understand and trust the results of machine learning-based learning models. In this paper, we propose an explainable AI method to further enhance the explanatory power of the existing learning model by targeting the previously proposedpredictive model [5] that learned data from a core facility (Hyper Compressor) of a domestic chemical plant that produces polyethylene. The ensemble prediction model, which is a black box model, wasconverted to a white box model using the Explainable AI. The proposed methodology explains the direction of control for the major features in the failure prediction results through the Explainable AI. Through this methodology, it is possible to flexibly replace the timing of maintenance of the machine and supply and demand of parts, and to improve the efficiency of the facility operation through proper pre-control.

• 산업경영시스템학회지
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• 제40권1호
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• pp.57-64
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• 2017

In order to reduce damages to major railroad components, which have the potential to cause interruptions to railroad services and safety accidents and to generate unnecessary maintenance costs, the development of rolling stock maintenance technology is switching from preventive maintenance based on the inspection period to predictive maintenance technology, led by advanced countries. Furthermore, to enhance trust in accordance with the speedup of system and reduce maintenances cost simultaneously, the demand for fault diagnosis and prognostic health management technology is increasing. The objective of this paper is to propose a highly reliable learning model using various machine learning algorithms that can be applied to critical rolling stock components. This paper presents a model for railway rolling stock component fault diagnosis and conducts a mechanical failure diagnosis of motor components by applying the machine learning technique in order to ensure efficient maintenance support along with a data preprocessing plan for component fault diagnosis. This paper first defines a failure diagnosis model for rolling stock components. Function-based algorithms ANFIS and SMO were used as machine learning techniques for generating the failure diagnosis model. Two tree-based algorithms, RadomForest and CART, were also employed. In order to evaluate the performance of the algorithms to be used for diagnosing failures in motors as a critical railroad component, an experiment was carried out on 2 data sets with different classes (includes 6 classes and 3 class levels). According to the results of the experiment, the random forest algorithm, a tree-based machine learning technique, showed the best performance.

• 한국철학논집
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• 제56호
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• pp.209-238
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• 2018

본고는 퇴계의 "언행록"에 나타난 사제관계를 통해 퇴계가 추구했던 교육방법 또는 학습법의 특징을 살펴보고, 그 현대 교육적 의미를 탐색한 것이다. 퇴계는 다양한 저술과 제자 교육을 통해 전통 유학 교육에 충실하게 임했다. 특히, 심득(心得)과 궁행(躬行)으로 제자들을 일깨웠는데, 그것은 전통 유학의 교육방식에 기초하였다. 이런 점에서 퇴계는 유학의 교육관과 교육 방식에 따라 스승으로서의 직분에 성실하려고 노력한 것으로 판단된다. 유학에서 스승은 단순한 지식 전수자이기보다는 인간의 전체적 삶의 문제를 고민한 전인적 교사였다. 때문에 스승은 옛것과 새것의 계승과 창조의 측면을 적절히 조화하려는 온고지신(溫故知新)의 자세와 끊임없이 배우며 가르침에 최선을 다하려는 교육자로서의 태도를 견지했다. 퇴계도 제자마다 개인에 대한 가르침에 성실하게 응대하여 학문의 과정에서 공경과 신뢰를 확보했고, 제자들 또한 그런 학문적 인격에 감화받고 스승의 학맥을 이어나갔다. 그것은 현대적 의미의 교육방법이나 학습법의 측면에서 이해하면 인지학습과 성인학습의 측면에서 대비할 수 있다. 퇴계의 사제관계에 드러난 교육방법이나 학습법은 인지학습의 측면에서는 잠재학습과 통찰학습, 그리고 모방학습과 상통하고, 성인학습의 측면에서는 경험학습과 자기주도학습, 그리고 의사소통학습의 측면으로 이해할 수 있다.

• International Journal of Internet, Broadcasting and Communication
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• 제11권2호
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• pp.50-58
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• 2019

The clear and specific objective of this study is to design a false news discriminator algorithm for news articles transmitted on a text-based basis and an architecture that builds it into a system (H/W configuration with Hadoop-based in-memory technology, Deep Learning S/W design for bigdata and SNS linkage). Based on learning data on actual news, the government will submit advanced "fake news" test data as a result and complete theoretical research based on it. The need for research proposed by this study is social cost paid by rumors (including malicious comments) and rumors (written false news) due to the flood of fake news, false reports, rumors and stabbings, among other social challenges. In addition, fake news can distort normal communication channels, undermine human mutual trust, and reduce social capital at the same time. The final purpose of the study is to upgrade the study to a topic that is difficult to distinguish between false and exaggerated, fake and hypocrisy, sincere and false, fraud and error, truth and false.

• 유통과학연구
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• 제20권8호
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• pp.59-67
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• 2022

Purpose: This study identifies how twelve key success factors of distribution strategies for community enterprises in Thailand achieve higher performances. Research design, data, and methodology: The samples in this study were 400 entrepreneurs throughout the country. The instrument for data elicitation was a questionnaire. The descriptive and inferential statistics for data analysis were percentage, mean, standard deviation, T-Test, F-Test, multiple regression, and multiple correlations. Results: The results revealed that, overall, the samples showed high opinions on online distribution strategies in all aspects. In detail, the three highest factors were as follows: 1) electronic satisfaction, 2) product characteristics and electronic trust, and 3) the quality and success in online distribution. In detail, the three highest aspects of online distribution success were customer loyalty, financial performance, and work management, respectively. The online distribution strategies influencing community enterprises' success were electronic trust, electronic loyalty, social information, electronic satisfaction, and online distribution tools, which had a statistical significance of 71. Conclusions: This research has made an essential contribution to community enterprise entrepreneurs should focus on and adopt these 8P+4ODS concepts to increase sales, maintain brand loyalty of existing customers, get new customers, develop learning, and improve the working potentials of community enterprise entrepreneurs.

• 정보교육학회논문지
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• 제16권3호
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• pp.283-289
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• 2012

인터넷을 이용한 원격교육이 매우 빠르게 확산됨에 따라 원격교육을 수강하는 학습자에 대한 본인 확인과 실제 학습 여부를 확인하는 것에 대한 어려움도 점점 더 커지고 있다. 원격교육에서는 교수자와 학습자가 상호 대면하지 않아도 수업이 이루어지므로 본인 출석 여부를 시스템적으로 확인해 주어야 한다. 종래의 확인 방법으로는 로그인 체크, SMS 본인 인증, 돌발퀴즈 등의 방법이 있다. 그러나 학습 시간 전반에 걸쳐 학습자 본인이 잘 참여하고 있는지를 확인하기에는 기술적인 한계가 적지 않다. 이에 본 연구에서는 실제 학습자가 학습시간 전반에 걸쳐 잘 참여하고 있는지를 PC 카메라의 이미지 촬영 기반으로 확인할 수 있는 시스템을 설계 및 구현하고자 한다. 이 시스템은 원격교육, 원격평가 등에 있어 학습자의 실제 참여 여부를 판단할 수 있게 함으로써 원격교육 신뢰성을 더욱 높일 수 있을 것으로 기대한다.

• International Journal of Computer Science & Network Security
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• 제23권9호
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• pp.47-54
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• 2023

For Bengali music emotion classification, deep learning models, particularly CNN and RNN are frequently used. But previous researches had the flaws of low accuracy and overfitting problem. In this research, attention-based Conv1D and BiGRU model is designed for music emotion classification and comparative experimentation shows that the proposed model is classifying emotions more accurate. We have proposed a Conv1D and Bi-GRU with the attention-based model for emotion classification of our Bengali music dataset. The model integrates attention-based. Wav preprocessing makes use of MFCCs. To reduce the dimensionality of the feature space, contextual features were extracted from two Conv1D layers. In order to solve the overfitting problems, dropouts are utilized. Two bidirectional GRUs networks are used to update previous and future emotion representation of the output from the Conv1D layers. Two BiGRU layers are conntected to an attention mechanism to give various MFCC feature vectors more attention. Moreover, the attention mechanism has increased the accuracy of the proposed classification model. The vector is finally classified into four emotion classes: Angry, Happy, Relax, Sad; using a dense, fully connected layer with softmax activation. The proposed Conv1D+BiGRU+Attention model is efficient at classifying emotions in the Bengali music dataset than baseline methods. For our Bengali music dataset, the performance of our proposed model is 95%.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제10권5호
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• pp.179-186
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• 2021

로봇이 사람과 같이 다양하고 복잡한 사물 조작을 하기 위해서는 휴먼형 로봇 손의 사물 파지 작업이 필수적이다. 자유도 (Degree of Freedom, DoF)가 높은 휴먼형(anthropomorphic) 로봇 손을 학습시키기 위하여 사람 데모(human demonstration)가 결합한 강화학습 최적화 방법이 제안되었다. 본 연구에서는 강화학습 최적화 방법에 사람 데모가 결합한 Demonstration Augmented Natural Policy Gradient (DA-NPG)와 NPG의 성능 비교를 통하여 행동 복제의 효율성을 확인하고, DA-NPG, DA-Trust Region Policy Optimization (DA-TRPO), DA-Proximal Policy Optimization (DA-PPO)의 최적화 방법의 성능 평가를 위하여 6 종의 물체에 대한 휴먼형 로봇 손의 사물 조작 작업을 수행한다. 학습 후 DA-NPG와 NPG를 비교한 결과, NPG의 물체 파지 성공률은 평균 60%, DA-NPG는 평균 99.33%로, 휴먼형 로봇 손의 사물 조작 강화학습에 행동 복제가 효율적임을 증명하였다. 또한, DA-NPG는 DA-TRPO와 유사한 성능을 보이면서 모든 물체에 대한 사물 파지에 성공하였고 가장 안정적이었다. 반면, DA-TRPO와 DA-PPO는 사물 조작에 실패한 물체가 존재하여 불안정한 성능을 보였다. 본 연구에서 제안하는 방법은 향후 실제 휴먼형 로봇에 적용하여 휴먼형 로봇 손의 사물 조작 지능 개발에 유용할 것으로 전망된다.