• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.12
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• pp.3889-3903
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• 2022

To address the problem of low detection accuracy due to training noise caused by mislabeling when Tri-training for network intrusion detection (NID), we propose a Tri-training algorithm based on cross entropy and K-nearest neighbors (TCK) for network intrusion detection. The proposed algorithm uses cross-entropy to replace the classification error rate to better identify the difference between the practical and predicted distributions of the model and reduce the prediction bias of mislabeled data to unlabeled data; K-nearest neighbors are used to remove the mislabeled data and reduce the number of mislabeled data. In order to verify the effectiveness of the algorithm proposed in this paper, experiments were conducted on 12 UCI datasets and NSL-KDD network intrusion datasets, and four indexes including accuracy, recall, F-measure and precision were used for comparison. The experimental results revealed that the TCK has superior performance than the conventional Tri-training algorithms and the Tri-training algorithms using only cross-entropy or K-nearest neighbor strategy.

• Journal of Korean society of Dental Hygiene
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• v.12 no.1
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• pp.225-233
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• 2012

Objectives : This thesis aims to provide basic materials for exploring trends and operational strategy of the cyber training. To achieve study purpose, it analyzed the satisfaction of trainees in regard to the education of field training course of Cyber Oral Health Promotion Program, established and operated between 2007 and 2009. Methods : This study conducted questionnaire surveys after the completion of training to utilize the satisfaction level of 989 respondents among 1,310 employees of public institutions who completed the field training course of Cyber Oral Health Promotion Program. Results : Respondents showed satisfaction on the training course throughout three years in terms of satisfaction of instructional methods (screen characteristics, educational techniques, and so on) according to educational contents, the connection and realization of online education and service satisfaction, necessary for learning in association with educational guidance, offered to learners by a tutor and rapidity in questions and answers. A majority of respondents in all years answered that they participated in training program voluntarily for self-development and improvement in work ability, and they thought that the completion of course would be helpful to their current work and future work. In addition, cyber training program was primarily conducted in the working place by 72.5%, and the most difficult thing in cyber training was to combine work and learning by 60.6%. Conclusions : As shown in the results above, workers in public institutions were satisfied with cyber job training and it was evaluated that cyber job training would be helpful to performing their actual work. Therefore, it is needed to collect and evaluate more diverse requirements of trainees with regard to cyber job training, and the development and operation of job training program that reflects these results sufficiently is required.

• Korean Journal of Remote Sensing
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• v.38 no.4
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• pp.395-409
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• 2022

The purpose of this study is to evaluate the classification performance and applicability when land cover datasets constructed for AI training are cross validation to other areas. For study areas, Gyeongsang-do and Jeolla-do in South Korea were selected as cross validation areas, and training datasets were obtained from AI-Hub. The obtained datasets were applied to the U-Net algorithm, a semantic segmentation algorithm, for each region, and the accuracy was evaluated by applying them to the same and other test areas. There was a difference of about 13-15% in overall classification accuracy between the same and other areas. For rice field, fields and buildings, higher accuracy was shown in the Jeolla-do test areas. For roads, higher accuracy was shown in the Gyeongsang-do test areas. In terms of the difference in accuracy by weight, the result of applying the weights of Gyeongsang-do showed high accuracy for forests, while that of applying the weights of Jeolla-do showed high accuracy for dry fields. The result of land cover classification, it was found that there is a difference in classification performance of existing datasets depending on area. When constructing land cover map for AI training, it is expected that higher quality datasets can be constructed by reflecting the characteristics of various areas. This study is highly scalable from two perspectives. First, it is to apply satellite images to AI study and to the field of land cover. Second, it is expanded based on satellite images and it is possible to use a large scale area and difficult to access.

• The Journal of Information Technology and Database
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• v.2 no.2
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• pp.71-85
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• 1995

Many research results conducted by neural network researchers have claimed that the classification accuracy of neural networks is superior to, or at least equal to that of conventional methods. However, in series of neural network classifications, it was found that the classification accuracy strongly depends on the characteristics of training data set. Even though there are many research reports that the classification accuracy of neural networks can be different, depending on the composition and architecture of the networks, training algorithm, and test data set, very few research addressed the problem of classification accuracy when the basic assumption of data monotonicity is violated, In this research, development project of automated credit evaluation system is described. The finding was that arrangement of training data is critical to successful implementation of neural training to maintain monotonicity of the data set, for enhancing classification accuracy of neural networks.

• The Journal of Information Technology and Database
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• v.8 no.1
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• pp.95-104
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• 2001

In the text classification domain, labeling the training documents is an expensive process because it requires human expertise and is a tedious, time-consuming task. Therefore, it is important to reduce the manual labeling of training documents while improving the text classifier. Selective sampling, a form of active learning, reduces the number of training documents that needs to be labeled by examining the unlabeled documents and selecting the most informative ones for manual labeling. We apply this methodology to Naive Bayes, a text classifier renowned as a successful method in text classification. One of the most important issues in selective sampling is to determine the criterion when selecting the training documents from the large pool of unlabeled documents. In this paper, we propose two measures that would determine this criterion : the Mean Absolute Deviation (MAD) and the entropy measure. The experimental results, using Renters 21578 corpus, show that this proposed learning method improves Naive Bayes text classifier more than the existing ones.

• Nuclear Engineering and Technology
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• v.55 no.10
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• pp.3515-3524
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• 2023

A reinforcement learning framework is proposed for the control problem of outlet steam pressure of the once-through steam generator(OTSG) in this paper. The double-layer controller using Proximal Policy Optimization(PPO) algorithm is applied in the control structure of the OTSG. The PPO algorithm can train the neural networks continuously according to the process of interaction with the environment and then the trained controller can realize better control for the OTSG. Meanwhile, reinforcement learning has the characteristic of difficult application in real-world objects, this paper proposes an innovative pretraining method to solve this problem. The difficulty in the application of reinforcement learning lies in training. The optimal strategy of each step is summed up through trial and error, and the training cost is very high. In this paper, the LSTM model is adopted as the training environment for pretraining, which saves training time and improves efficiency. The experimental results show that this method can realize the self-adjustment of control parameters under various working conditions, and the control effect has the advantages of small overshoot, fast stabilization speed, and strong adaptive ability.

• Journal of the Korean Society of Safety
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• v.10 no.3
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• pp.115-119
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• 1995

The effectiveness of an ensuring the facility safety depends on the ability to train the worker efficiently and strategy of facility control. This requires the instructor's awareness of the worker's current knowledge, in the specific areas of the worker's lacks of knowledge, and preferred methods of training. This paper presents a development of knowledge based on CBT system which will reduce the role of instructor from the training loop and be used the high technological method such as computer animation technique.

• International Journal of Computer Science & Network Security
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• v.21 no.3
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• pp.21-30
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• 2021

Economic transformations have led to an increase in the role of creative assets and their central role in public life. Changes in creative activity have led to a change in the organization of the work of institutes engaged in the training of specialists, in particular teachers of labor education. Methods and approaches to training determine the development of creative industries, being the basis for models of professional training of future teachers of labor training. The purpose of an article was to develop a modern model of professional training of future teachers of labor training based on the concept of creative economy. The methodology is based on the concepts of holistic craft and creative economy. Based on the integration of pedagogical learning models "Craft as design and problem-solving", "Craft as skill and knowledge building", "Craft as product-making" and "Craft as self-expression" developed and experimentally confirmed the conceptual model of professional training of future teachers of labor training. The proposed model forms a practitioner with professional, technical, digital and creative skills who is able to transfer the experience to students. The training course "Creativity and creative thinking" has been developed. The model provided for the development of a course based on the strategy of developing professional creativity, flexibility, improvisation, openness, student activity, joint practice, student-oriented approach. The practical value implies the adaptation of the developed model of professional training of future teachers of labor education during the training of teachers in higher education, which is confirmed in the experiment.

• Journal of Information Technology Services
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• v.20 no.1
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• pp.67-79
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• 2021

The advent of the 4th Industrial Revolution is also causing many changes in defense operations. Defense reform and the fourth industrial revolution promoted smart defense innovation, and attempts are being made to incorporate cutting-edge science and technology into various fields such as weapons systems and defense operations. Education and training is one of the areas in which information and intelligence are urgently needed in the spirit of defense operations. Due to the nature of defense education and training, which aims to fight against the enemy, there is no emphasis on psychological training in the field rather than informatization, but in developed countries with various experiences of modern warfare, investment and vitalization of education and training are vital. Through this, efforts are being made to foster soldiers with problem-solving skills in uncertain battlefields. The informatization and intelligence of defense education and training is no longer a matter that can be delayed, and the innovation of education and training using cutting-edge science and technology can be said to be an age-old task to improve the results of education and training in the fourth industrial revolution. The purpose of this is because the application of related technologies is not the goal itself as the 4th Industrial Revolution arrives, but it has been made possible through the rapid advancement of science and technology that has made it difficult to realize education and training, even though it has long been desired. Ultimately, education and training data will be integrated and artificial intelligence-based intelligent learning systems will maximize the performance of education and training, thereby improving the combat readiness.

• International Journal of Quality Innovation
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• v.5 no.2
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• pp.122-131
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• 2004

Because of three reasons: rare natural resources, high dependent ratio and rapid wage increases, Korea must take the non-price competitive strategy. The developing process of quality management system in Korea can be categorized into five stages according to changes in economic policy. In order to develop the Korean quality management system effectively, we should try to connect total quality management with management system and emphasize the training and education.