• Journal of Engineering Education Research
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• v.23 no.5
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• pp.61-67
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• 2020

In the associate degree curriculum of the department of mechanical engineering, the results of the study are presented on the structure and content of a subject based on the case-based learning method. As an case, equipment called a laminator that is actually used in the manufacturing site was selected. Class deals with specific engineering issues at each stage of laminator manufacturing (design-machining-assembly-measurement-maintenance) in connection with general engineering topics in prerequisites in the curriculum. Topics include tolerance fit, length measurement, assembly practice, measurement design and statistics of machine maintenance, etc. Courses that apply the case-based learning method may be included in the curriculum as complementary roles to those that apply other student-centered learning method.

• Journal of IKEEE
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• v.22 no.3
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• pp.816-821
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• 2018

Recently, machine learning algorithms based on artificial neural networks started to be used widely as classifiers in the field of EEG research for emotion analysis and disease diagnosis. When a machine learning model is used to classify EEG data, if training data is composed of only data having similar characteristics, classification performance may be deteriorated when applied to data of another group. In this paper, we propose a method to construct training data set by selecting several groups of data using semi-supervised learning algorithm to improve these problems. We then compared the performance of the two models by training the model with a training data set consisting of data with similar characteristics to the training data set constructed using the proposed method.

• International Journal of Computer Science & Network Security
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• v.23 no.11
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• pp.190-194
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• 2023

By looking the importance of communication, data delivery and access in various sectors including governmental, business and individual for any kind of data, it becomes mandatory to identify faults and flaws during cyber communication. To protect personal, governmental and business data from being misused from numerous advanced attacks, there is the need of cyber security. The information security provides massive protection to both the host machine as well as network. The learning methods are used for analyzing as well as preventing various attacks. Machine learning is one of the branch of Artificial Intelligence that plays a potential learning techniques to detect the cyber-attacks. In the proposed methodology, the Decision Tree (DT) which is also a kind of supervised learning model, is combined with the different cross-validation method to determine the accuracy and the execution time to identify the cyber-attacks from a very recent dataset of different network attack activities of network traffic in the UNSW-NB15 dataset. It is a hybrid method in which different types of attributes including Gini Index and Entropy of DT model has been implemented separately to identify the most accurate procedure to detect intrusion with respect to the execution time. The different DT methodologies including DT using Gini Index, DT using train-split method and DT using information entropy along with their respective subdivision such as using K-Fold validation, using Stratified K-Fold validation are implemented.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.8 no.2
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• pp.351-358
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• 2018

Reinforcement learning is a machine learning method which aims to determine a policy to get optimal actions in dynamic and stochastic environments. But reinforcement learning has high computational complexity and needs a lot of time to get solution, so it is not easily applicable to uncertain and continuous environments. To tackle the complexity problem, AC (actor-critic) method is used and it separates an action-value function into a value function and an action decision policy. Also, in transfer learning method, the knowledge constructed in one environment is adapted to another environment, so it reduces the time to learn in a reinforcement learning method. In this paper, we present AC method and transfer learning method to solve the problem of a reinforcement learning method. Finally, we analyze the case study which a transfer learning method is used to solve BS(base station) switching problem in wireless access networks.

• Journal of KIISE:Software and Applications
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• v.29 no.9
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• pp.654-668
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• 2002

In this paper, we present an empirical study for improving the Korean text chunking based on machine learning and feature set selection approaches. We focus on two issues: the problem of selecting feature set for Korean chunking, and the problem of alleviating the data sparseness. To select a proper feature set, we use a heuristic method of searching through the space of feature sets using the estimated performance from a machine learning algorithm as a measure of "incremental usefulness" of a particular feature set. Besides, for smoothing the data sparseness, we suggest a method of using a general part-of-speech tag set and selective lexical information under the consideration of Korean language characteristics. Experimental results showed that chunk tags and lexical information within a given context window are important features and spacing unit information is less important than others, which are independent on the machine teaming techniques. Furthermore, using the selective lexical information gives not only a smoothing effect but also the reduction of the feature space than using all of lexical information. Korean text chunking based on the memory-based learning and the decision tree learning with the selected feature space showed the performance of precision/recall of 90.99%/92.52%, and 93.39%/93.41% respectively.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.11
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• pp.479-488
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• 2022

In recent years, with the rapid development of machine learning technology, research utilizing machine learning has been actively conducted in fields such as cognition, reasoning and judgment, and action among various technologies constituting intelligent systems. In order to utilize this machine learning, it is indispensable to collect data for learning. However, the types of data generated vary according to the environment in which the data is generated, and the types and forms of data required are different depending on the learning model to be used for machine learning. Due to this, there is a problem that the existing data collection method cannot be reused in a new environment, and a specialized data collection module must be developed each time. In this paper, we propose a specification-based methology for data collection in artificial intelligence system to solve the above problems, ensure the reusability of the data collection method according to the data collection environment, and automate the implementation of the data collection function.

• Journal of Digital Convergence
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• v.18 no.2
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• pp.261-268
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• 2020

Machine learning is achieved by making a cost function from a given digital signal data and optimizing the cost function. The cost function here has local minimums in the cost function depending on the amount of digital signal data and the structure of the neural network. These local minimums make a problem that prevents learning. Among the many ways of solving these methods, our proposed method is to change the learning step-size. Unlike existed methods using the learning rate (step-size) as a fixed constant, the use of multivariate function as the cost function prevent unnecessary machine learning and find the best way to the minimum value. Numerical experiments show that the results of the proposed method improve about 3%(88.8%→91.5%) performance using the proposed method rather than the existed methods.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.7
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• pp.633-640
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• 2001

In this paper we examine the machine learning issues raised by the domain of the Intrusion Detection Systems(IDS), which have difficulty successfully classifying intruders. There systems also require a significant amount of computational overhead making it difficult to create robust real-time IDS. Machine learning techniques can reduce the human effort required to build these systems and can improve their performance. Genetic algorithms are used to improve the performance of search problems, while data mining has been used for data analysis. Data Mining is the exploration and analysis of large quantities of data to discover meaningful patterns and rules. Among the tasks for data mining, we concentrate the classification task. Since classification is the basic element of human way of thinking, it is a well-studied problem in a wide variety of application. In this paper, we propose a classifier system based on genetic algorithm, and the proposed system is evaluated by applying it to IDS problem related to classification task in data mining. We report our experiments in using these method on KDD audit data.

• Korean Journal of Computational Design and Engineering
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• v.2 no.2
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• pp.111-121
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• 1997

This study intends to develop a fixture planning module as a part of the planning system for cutting. The fixture module uses machine learning method to reuse previous failure results so that the system can reduce the repeated failures. Machine learning is one of efforts to incorporate human reasoning ability into a computerized system. A human expert designs better than a novice does because he has a wide experience in a specific area. This study implements the machine learning algorithm to have a wide experience in the fixture planning area as a human expert does. When the fixture planner finds a setup failure for the suggested operations by a process planner, it makes the process planner store its attributes and other information for the failed setup. Then the process planner applies the learned knowledge when it meets a similar case so that the planner can reduce possibility of setup failure. Also the system can teach a novice user by showing a failed setup with a modified setup.

• Journal of Information Processing Systems
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• v.8 no.2
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• pp.241-262
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• 2012

An understanding of quality attributes is relevant for the software organization to deliver high software reliability. An empirical assessment of metrics to predict the quality attributes is essential in order to gain insight about the quality of software in the early phases of software development and to ensure corrective actions. In this paper, we predict a model to estimate fault proneness using Object Oriented CK metrics and QMOOD metrics. We apply one statistical method and six machine learning methods to predict the models. The proposed models are validated using dataset collected from Open Source software. The results are analyzed using Area Under the Curve (AUC) obtained from Receiver Operating Characteristics (ROC) analysis. The results show that the model predicted using the random forest and bagging methods outperformed all the other models. Hence, based on these results it is reasonable to claim that quality models have a significant relevance with Object Oriented metrics and that machine learning methods have a comparable performance with statistical methods.