• KIPS Transactions on Software and Data Engineering
• /
• v.10 no.11
• /
• pp.449-456
• /
• 2021

An intrusion detection system is a technology that detects abnormal behaviors that violate security, and detects abnormal operations and prevents system attacks. Existing intrusion detection systems have been designed using statistical analysis or anomaly detection techniques for traffic patterns, but modern systems generate a variety of traffic different from existing systems due to rapidly growing technologies, so the existing methods have limitations. In order to overcome this limitation, study on intrusion detection methods applying various machine learning techniques is being actively conducted. In this study, a comparative study was conducted on data preprocessing techniques that can improve the accuracy of anomaly detection using NGIDS-DS (Next Generation IDS Database) generated by simulation equipment for traffic in various network environments. Padding and sliding window were used as data preprocessing, and an oversampling technique with Adversarial Auto-Encoder (AAE) was applied to solve the problem of imbalance between the normal data rate and the abnormal data rate. In addition, the performance improvement of detection accuracy was confirmed by using Skip-gram among the Word2Vec techniques that can extract feature vectors of preprocessed sequence data. PCA-SVM and GRU were used as models for comparative experiments, and the experimental results showed better performance when sliding window, skip-gram, AAE, and GRU were applied.

• Journal of Digital Convergence
• /
• v.19 no.11
• /
• pp.485-498
• /
• 2021

This study confirmed the perceptions of performing arts field workers and related experts under the theme of non-face-to-face performing arts. As a result of the analysis, first, respondents agreed with the practical need for non-face-to-face performing arts, but respondents in the field viewed the lack of "fieldability" and "communication with the audience" of non-face-to-face performances as a problem. Second, respondents who participated in non-face-to-face performance production had negative perceptions of realism, immersion, interaction with the audience, lack of enjoyment outside the performance, and difficulties in securing budgets, filming and editing, and actors' acting commitment. Third, regarding the government's non-face-to-face performing arts-related support policy, they complained that support was only given to specific organizations and a small number of people, and administrative difficulties in support. Through this study, it can be suggested that face-to-face and non-face-to-face performance arts should be treated in a complementary and balanced relationship in terms of government policy.

• Journal of Practical Engineering Education
• /
• v.14 no.2
• /
• pp.267-277
• /
• 2022

Achievement at university is recognized in a comprehensive sense as the level of qualitative change and development that students have embodied as a result of their experience in university education. Therefore, the academic achievement of university students will be given meaning in cooperation with the historical and social demands for diverse human resources such as creativity, leadership, and global ability, but it is practically an indicator of the outcome of university education. Measurement of academic achievement by such credits involves many problems, but in particular, standardization of academic achievement by credits based on evaluation methods, contents, and university rankings is a very difficult problem. In this study, we present a model that uses machine learning techniques to predict whether or not academic achievement is excellent for D-University graduates. The variables used were analyzed using up to 96 personal information and bachelor's information such as graduation year, department number, department name, etc., but when establishing a future education course, only the data after enrollment works effectively. Therefore, the items to be analyzed are limited to the recommended ability to improve the academic achievement of the department/student. In this research, we implemented an academic achievement prediction model through analysis of core abilities that reflect the philosophy, goals, human resources image, and utilized machine learning to affect the impact of the introduction of the prediction model on academic achievement. We plan to apply the results of future research to the establishment of curriculum and student guidance conducted in the department to establish a basis for improving academic achievement.

• Communications of Mathematical Education
• /
• v.36 no.3
• /
• pp.335-353
• /
• 2022

The purpose of this study is to identify the characteristics and types of errors in the conceptual image of Korean language learners according to the types of terms in mathematics that are the basis for solving mathematical word problems, and to prepare basic data for effective teaching and learning methods in solving the word problems of Korean language learners. To do this, a case study was conducted targeting four Korean language learners to analyze the specific conceptual images of terms registered in curriculum and terms that were not registered in curriculum but used in textbooks. As a result of this study, first, it is necessary to guide Korean language learners by using sufficient visualization material so that they can form appropriate conceptual definitions for terms in school mathematics. Second, it is necessary to understand the specific relationship between the language used in the home of Korean language learners and the conceptual image of terms in school mathematics. Third, it is necessary to pay attention to the passive term, which has difficulty in understanding the meaning rather than the active term. Fourth, even for Korean language learners who do not have difficulties in daily communication, it is necessary to instruct them on everyday language that are not registered in the curriculum but used in math textbooks. Fifth, terms in school mathematics should be taught in consideration of the types of errors that reflect the linguistic characteristics of Korean language learners shown in the explanation of terms. This recognition is expected to be helpful in teaching word problem solving for Korean language learners with different linguistic backgrounds.

• Korean Journal of Poultry Science
• /
• v.49 no.3
• /
• pp.157-165
• /
• 2022

Chickens are a species of vertebrate with varying colors. Various colors of chickens must be classified to find color-related genes. In the past, color scoring was performed based on human visual observation. Therefore, chicken colors have not been measured with precise standards. In order to solve this problem, a computer vision approach was used in this study. Image quantization based on k-means clustering for all pixels of RGB values can objectively distinguish inherited colors that are expressed in various ways. This study was also conducted to determine whether plumage color differences exist in the reciprocal cross lines between two breeds: black Yeonsan Ogye (YO) and White Leghorn (WL). Line B is a crossbred line between YO males and WL females while Line L is a reciprocal crossbred line between WL males and YO females. One male and ten females were selected for each F₁ line, and full-sib mating was conducted to generate 883 F₂ birds. The results indicate that the distribution of light and dark colors of k-means clustering converged to 7:3. Additionally, the color of Line B was lighter than that of Line L (P<0.01). This study suggests that the genes underlying plumage colors can be identified using quantification values from the computer vision approach described in this study.

• The Journal of the Acoustical Society of Korea
• /
• v.42 no.1
• /
• pp.25-31
• /
• 2023

In order to estimate the radial speed of an underwater object so-called target with active sonar, Continuous Wave (CW) pulse is generally used, but if a target is slow and at near distance, it is not easy to estimate the radial velocity of the target due to acoustic reverberation in the ocean. In 2017, Wang et al. utilized broadband signal of two Hyperbolic Frequency Modulation (HFM) pulses, which is known as a doppler-invariant pulse, with equal frequency band and in opposite sweep directions to overcome this problem and successfully estimate the radial speed of slow-moving nearby target. They demonstrated the estimation of the radial velocity with computer simulation using the parameters of two HFM starting time differences and receiving times. However, for it uses two HFM pulses with equal frequency, cross-correlation between the two pulses negatively affect the detection performance. To mitigate this cross-correlation effect, we suggest using two different band HFM with the opposite sweep directions. In this paper, a method of radial velocity estimation is derived and simulated using two HFM pulses with the pulse length of 1 second and bandwidth of 400 Hz. Applying the suggested method, the radial velocity was estimated with approximately 6 % of relative error in the simulation.

• Journal of Industrial Convergence
• /
• v.21 no.1
• /
• pp.175-185
• /
• 2023

Recently, novel viral infections such as COVID-19 have spread and pose a serious public health problem. In particular, these diseases have a fatal effect on the elderly, threatening life and causing serious social and economic losses. Accordingly, applications such as telemedicine, healthcare, and disease prevention using the Internet of Things (IoT) and artificial intelligence (AI) have been introduced in many industries to improve disease detection, monitoring, and quarantine performance. However, since existing technologies are not applied quickly and comprehensively to the sudden emergence of infectious diseases, they have not been able to prevent large-scale infection and the nationwide spread of infectious diseases in society. Therefore, in this paper, we try to predict the spread of infection by collecting various infection information with regional limitations through a virus disease information collector and performing AI analysis and severity matching through an AI broker. Finally, through the Korea Centers for Disease Control and Prevention, danger alerts are issued to the elderly, messages are sent to block the spread, and information on evacuation from infected areas is quickly provided. A realistic elderly support system compares the location information of the elderly with the information of the infected area and provides an intuitive danger area (infected area) avoidance function with an augmented reality-based smartphone application. When the elderly visit an infected area is confirmed, quarantine management services are provided automatically. In the future, the proposed system can be used as a method of preventing a crushing accident due to sudden crowd concentration in advance by identifying the location-based user density.

• Journal of Service Research and Studies
• /
• v.10 no.4
• /
• pp.43-55
• /
• 2020

Recently, as the importance of experience data increases, there are many attempts to deal with experience data from a data science perspective. In the case of approaching as a collection method of a quantitative survey method that seeks to quantify numerically such as big data, it is difficult to interpret the value of experience in a wide range, and it is relatively expensive and time consuming, and personal information infringement There is a limit to the analysis due to the risk of However, since ethnography, a procedure for collecting experience data based on qualitative research, is mainly carried out in the natural real environment of future customers from the perspective of users, it is possible to confirm the nature that customers face with a small sample. In addition, it is also easy to interpret the relational dimension of the empirical data. Although the ethnography method of collecting experiential data is economical and efficient, it is important to reduce errors in the collection process because the lack of scientific procedures for the data collection process can be a problem. It is important to secure the validity of whether the correct measurement tool is used for ethnography-based experiential data collection and to secure the reliability of the use of a valid measurement tool and method by accurately selecting the measurement target. From this point of view, it is necessary to verify the reliability of the research method that clearly selects the measurement target and secures the validity for the development of the correct measurement method and tool for the collection of ethnography experience data. Therefore, in this study, a verification study was conducted on the data and methodology cases of the'I know you_AI' service that analyzes the customer experience of self-employed based on the ethnography method of collecting experience data..

• Journal of the Korean Society of Marine Environment & Safety
• /
• v.28 no.7
• /
• pp.1259-1266
• /
• 2022

Recently, due to the specialization of structural design standards and evaluation methods, the classification rules are being integrated. A good example is the common international rules (CSR). However, detailed regulations are presented only for the cargo hold area where the longitudinal load is greatly applied, and no specific evaluation guidelines exist for the bow and stern structures. Structural design of the mentioned area is carried out depending on the design experience of the shipbuilder, and because no clear standard exists even in the classification, determining the root cause is difficult even if a structural damage problem occurs. In this study, an engineering-based solution was presented to identify the root cause of representative cases of buckling damage that occurs mainly in the stern. Buckling may occur at the panel wall owing to hull girder bending moment acting on the stern structure, and the plate thickness must be increased or vertical stiffeners must be added to increase the buckling rigidity. For structural strength verification based on finite element analysis modeling, reasonable solutions for load conditions, boundary conditions, modeling methods, and evaluation criteria were presented. This result is expected to be helpful in examining the structural strength of the stern part of similar carriers in the future.

• KIPS Transactions on Software and Data Engineering
• /
• v.12 no.2
• /
• pp.59-76
• /
• 2023

Since the COVID-19 era, the rise in apartment prices has been unconventional. In this uncertain real estate market, price prediction research is very important. In this paper, a model is created to predict the actual transaction price of future apartments after building a vast data set of 870,000 from 2015 to 2020 through data collection and crawling on various real estate sites and collecting as many variables as possible. This study first solved the multicollinearity problem by removing and combining variables. After that, a total of five variable selection algorithms were used to extract meaningful independent variables, such as Forward Selection, Backward Elimination, Stepwise Selection, L1 Regulation, and Principal Component Analysis(PCA). In addition, a total of four machine learning and deep learning algorithms were used for deep neural network(DNN), XGBoost, CatBoost, and Linear Regression to learn the model after hyperparameter optimization and compare predictive power between models. In the additional experiment, the experiment was conducted while changing the number of nodes and layers of the DNN to find the most appropriate number of nodes and layers. In conclusion, as a model with the best performance, the actual transaction price of apartments in 2021 was predicted and compared with the actual data in 2021. Through this, I am confident that machine learning and deep learning will help investors make the right decisions when purchasing homes in various economic situations.