• Journal of Korea Artificial Intelligence Association
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• v.1 no.2
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• pp.1-6
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• 2023

In this paper, we present a new method for classifying malicious URLs to reduce cases of learning difficulties due to unfamiliar and difficult terms related to information protection. This study plans to extract only visually distinguishable features within the URL structure and compare them through map learning algorithms, and to compare the contribution values of the best map learning algorithm methods to extract features that have the most impact on classifying malicious URLs. As research data, Kaggle used data that classified 7,046 malicious URLs and 7.046 normal URLs. As a result of the study, among the three supervised learning algorithms used (Decision Tree, Support Vector Machine, and Logistic Regression), the Decision Tree algorithm showed the best performance with 83% accuracy, 83.1% F1-score and 83.6% Recall values. It was confirmed that the contribution value of https is the highest among whether to use https, sub domain, and prefix and suffix, which can be visually distinguished through the feature contribution of Decision Tree. Although it has been difficult to learn unfamiliar and difficult terms so far, this study will be able to provide an intuitive judgment method without explanation of the terms and prove its usefulness in the field of malicious URL detection.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.177-179
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• 2022

In the Google Deepmind Challenge match, Alphago defeated Korea's Sedol Lee (human) with 4 wins and 1 loss in the Go match. Finally, artificial intelligence is going beyond the use of human intelligence. The Korean government's budget for the Digital New Deal is 9 trillion won in 2022, and an additional 301 types of data construction projects for artificial intelligence learning will be secured. From 2023, the industrial paradigm will change with the use and application of learning of artificial intelligence in all fields of industry. This paper conducts research to utilize artificial intelligence algorithms. Focusing on the analysis and judgment of data in artificial intelligence learning, research on the appropriate target and scope of application of algorithms in artificial intelligence machine learning and deep learning learning is conducted. This study will provide basic data for artificial intelligence in the 4th industrial revolution technology and artificial intelligence robot use in the 5th industrial revolution technology.

• English Language & Literature Teaching
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• v.12 no.4
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• pp.31-68
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• 2006

The present research investigates the acquisition of the English locative alternation by Korean EFL learners, which poses a learnability paradox, taking Pinker's framework of learnability theory as its basis. It addresses two questions (1) how lexical knowledge is represented initially and at different levels of interlanguage development and (2) what kinds of difficulty Korean learners find in the acquisition of English locative verbs and their constructions. Three groups of learners at different proficiency levels with a control group of English native speakers are examined by two instruments: elicited production task and grammaticality judgment task. According to different levels of proficiency, the learners exhibit gradual sensitivity to a change-of-state meaning and obtain complete perception of the meanings of locative verbs (manner-of-motion and change-of-state) and their constructions. Overgeneralization errors are observed in their performance. The errors are due to misinterpretations of particular lexical items in conjunction with the universal linking rules. More fundamental cause of difficulty is accounted for by partial use of learning mechanisms, caused by insufficient L2 input.

• Journal of Korean society of Dental Hygiene
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• v.11 no.2
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• pp.251-261
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• 2011

Objectives : A study was designed to evaluate the level of moral development of dental hygienists in Korea. Methods : Data were collected through self reported questionnaires received from 210 dental hygienists from October 5 to December 29, 2010. The Korean version of the DIT(Defining Issues Test) was adopted to evaluate levels of moral judgment, which was measured by the score of P(%). The data were analyzed by a descriptive analyses and t-test, ANOVA. Results : The mean score of P(%) was $34.59({\pm}14.68)$. The score of P(%) revealed significant differences by religion(p=0.005). Ethics learning was 129(62.6%) in inexperience and participation of ethics education was 128(61.0%) in absence. There were significant differences in ethics learning experience(p=0.004). Conclusions : For enhancing dental hygienists' moral development we encourage them to make the most use of supplemental education.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.839-846
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• 2022

Traditionally, the construction industry has shown low labor productivity and productivity growth. To improve labor productivity, it must first be accurately measured. The existing method uses work-sampling techniques through observation of workers' activities at certain time intervals on site. However, a disadvantage of this method is that the results may differ depending on the observer's judgment and may be inaccurate in the case of a large number of missed scenarios. Therefore, this study proposes a model to automate labor productivity measurement by monitoring workers' actions using a deep learning-based pose estimation method. The results are expected to contribute to productivity improvement on construction sites.

• Journal of Digital Convergence
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• v.17 no.10
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• pp.251-258
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• 2019

In the future, various products are created in various fields using artificial intelligence. In this age, it is a very important problem to know the operation principle of artificial intelligence learning method and to use it correctly. This paper introduces artificial intelligence learning methods that have been known so far. Learning of artificial intelligence is based on the fixed point iteration method of mathematics. The GD(Gradient Descent) method, which adjusts the convergence speed based on the fixed point iteration method, the Momentum method to summate the amount of gradient, and finally, the Adam method that mixed these methods. This paper describes the advantages and disadvantages of each method. In particularly, the Adam method having adaptivity controls learning ability of machine learning. And we analyze how these methods affect digital signals. The changes in the learning process of digital signals are the basis of accurate application and accurate judgment in the future work and research using artificial intelligence.

• Proceedings of the KIEE Conference
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• 2004.05a
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• pp.144-146
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• 2004

It is known that Global Positioning System(GPS) is the most efficient navigation system because it provides precise position information on the all areas of Earth regardless of metrology. Until now, the size of GPS receivers has become smaller and the performance of receivers has become higher. So receivers provide the position information of not only static system but also dynamic system. Usually, users make similar movement trajectory according to their life pattern and it is possible to build up efficient database by collecting only the repeated users' position. Because position information calculated by the receiver is erroneous about 10-30m within 5% error tolerance, the position information is oscillated even on the same area. In this paper, we propose the system that can estimate whether users are out of trajectory or in dangerous situation by soft-computing method.

• Convergence Security Journal
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• v.23 no.5
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• pp.117-123
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• 2023

Recently, as the demand for Generative Artificial Intelligence (AI) and artificial intelligence has increased, the seriousness of misuse and abuse has emerged. However, intelligent CCTV, which maximizes detection of abnormal behavior, is of great help to prevent crime in the military and police. AI performs learning as taught by humans and then proceeds with self-learning. Since AI makes judgments according to the learned results, it is necessary to clearly understand the characteristics of learning. However, it is often difficult to visually judge strange and abnormal behaviors that are ambiguous even for humans to judge. It is very difficult to learn this with the eyes of artificial intelligence, and the result of learning is very many False Positive, False Negative, and True Negative. In response, this paper presented standards and methods for clarifying the learning of AI's strange and abnormal behaviors, and presented learning measures to maximize the judgment ability of intelligent CCTV's False Positive, False Negative, and True Negative. Through this paper, it is expected that the artificial intelligence engine performance of intelligent CCTV currently in use can be maximized, and the ratio of False Positive and False Negative can be minimized..

• 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.

• Earthquakes and Structures
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• v.18 no.6
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• pp.719-730
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• 2020

Recently, population based optimization algorithms are developed to deal with a variety of optimization problems. In this paper, the salp swarm algorithm (SSA) is dramatically enhanced and a new algorithm is named Enhanced Salp Swarm Algorithm (ESSA) which is effectively utilized in optimization problems. To generate the ESSA, an opposition-based learning and merit function methods are added to standard SSA to enhance both exploration and exploitation abilities. To have a clear judgment about the performance of the ESSA, firstly, it is employed to solve some mathematical benchmark test functions. Next, it is exploited to deal with engineering problems such as optimally designing the benchmark buildings equipped with multiple tuned mass damper (MTMD) under earthquake excitation. By comparing the obtained results with those obtained from other algorithms, it can be concluded that the proposed new ESSA algorithm not only provides very competitive results, but also it can be successfully applied to the optimal design of the MTMD.