• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.11
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• pp.3950-3969
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• 2021

Radio Frequency (RF)-based indoor localization technologies play significant roles in various Internet of Things (IoT) services (e.g., location-based service). Most such technologies require that all the devices comply with a specified technology (e.g., WiFi, ZigBee, and Bluetooth). However, this requirement limits its application scenarios in today's IoT context where multiple devices complied with different standards coexist in a shared environment. To bridge the gap, in this paper, we propose a cross-technology localization approach, which is able to localize target nodes using a different type of devices. Specifically, the proposed framework reuses the existing WiFi infrastructure without introducing additional cost to localize Non-WiFi device (i.e., ZigBee). The key idea is to leverage the interference between devices that share the same operating frequency (e.g., 2.4GHz). Such interference exhibits unique patterns that depend on the target device's location, thus it can be leveraged for cross-technology localization. The proposed framework uses Principal Components Analysis (PCA) to extract salient features of the received WiFi signals, and leverages Dynamic Time Warping (DTW), Gradient Boosting Regression Tree (GBRT) to improve the robustness of our system. We conduct experiments in real scenario and investigate the impact of different factors. Experimental results show that the average localization accuracy of our prototype can reach 1.54m, which demonstrates a promising direction of building cross-technology technologies to fulfill the needs of modern IoT context.

• Journal of the Korea Institute of Military Science and Technology
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• v.25 no.6
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• pp.637-647
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• 2022

Since the concept of Manned-UnManned Teaming(MUM-T) and Unmanned Aircraft System(UAS) can efficiently respond to rapidly changing battle space, many studies are being conducted as key components of the mosaic warfare environment. In this paper, we propose a rule-based AI engagement model based on Basic Fighter Maneuver(BFM) capable of Within-Visual-Range(WVR) air-to-air combat and a simulation environment in which human pilots can participate. In order to develop a rule-based AI engagement model that can pilot a fighter with a 6-DOF dynamics model, tactical manuals and human pilot experience were configured as knowledge specifications and modeled as a behavior tree structure. Based on this, we improved the shortcomings of existing air combat models. The proposed model not only showed a 100 % winning rate in engagement with human pilots, but also visualized decision-making processes such as tactical situations and maneuvering behaviors in real time. We expect that the results of this research will serve as a basis for development of various AI-based engagement models and simulators for human pilot training and embedded software test platform for fighter.

• Journal of the Korea Convergence Society
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• v.12 no.11
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• pp.91-97
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• 2021

This paper uses a data extension technique originally designed for the rule refinement problem to handling incomplete data. This technique is characterized in that each event can have a weight indicating importance, and each variable can be expressed as a probability value. Since the key problem in this paper is to find the probability that is closest to the missing value and replace the missing value with the probability, three different algorithms are used to find the probability for the missing value and then store it in this data structure format. And, after learning to classify each information area with the SVM classification algorithm for evaluation of each probability structure, it compares with the original information and measures how much they match each other. The three algorithms for the imputation probability of the missing value use the same data structure, but have different characteristics in the approach method, so it is expected that it can be used for various purposes depending on the application field.

• Journal of the Korean Crystal Growth and Crystal Technology
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• v.32 no.1
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• pp.25-32
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• 2022

Investment casting is a production method commonly used to manufacture precision equipment, medical fields, and accessories, and has continued to develop through the modernization of equipment and high quality of materials, and its scope of use has been expanded. The purpose of this study is to minimize the defect rate by deriving structural improvement and standardization of mold temperature, which are key elements of the investment casting process, to minimize the defect rate. The scope of the study is limited to jewelry manufacturing casting processes suitable for understanding the structure and principles of small gate, and an experimental research is to be conducted by using soft Wax, gypsum powder, and 14 K gold as research materials. According to the results, the most appropriate casting standard temperature for the casting pattern of Alloy 14 k was the lowest turbulence at 980℃ flask temperature of 550℃, so good products could be produced. As a future task of this study, detailed studies are needed to data the structure and system temperature of small gate, reduce production defects in the field, and provide data for excellent investment casting competitiveness.

• 한국정보교육학회:학술대회논문집
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• 2021.08a
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• pp.149-157
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• 2021

Ahead of the introduction of artificial intelligence education into the revised curriculum in 2022, various class cases based on artificial intelligence should be developed. In this study, we designed an artificial intelligence education program based on explainable artificial intelligence using design-based research. Artificial intelligence, which covers three areas of basic, utilization, and ethics of artificial intelligence and can be easily connected to real-life cases, is set as a key topic. In general design-based studies, more than three repetitive processes are performed, but the results of this study are based on the results of the primary design, application, and evaluation. We plan to design a program on artificial intelligence that is more complete based on the third modification and supplementation by applying it to the school later. This research will help the development of artificial intelligence education introduced at school.

• Advances in nano research
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• v.13 no.4
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• pp.325-339
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• 2022

Endophytes ascertain a symbiotic relationship with plants as promoters of growth, defense mechanism etc. This study is a first report to screen the endophytic population in Waltheria indica, a tropical medicinal plant. 5 bacterial and 3 fungal strains in leaves, 3 bacterial and 1 yeast species in stems were differentiated morphologically and identified by biochemical and molecular methods. The phylogenetic tree of the isolated endophytes was constructed using MEGA X. Silver nanoparticles were biosynthesized from a rare endophytic bacterium Cupriavidus metallidurans isolated from the leaf of W. indica. The formation of silver nanoparticles was confirmed by UV-Visible spectrophotometer that evidenced a strong absorption band at 408.5 nm of UV-Visible range with crystalline nature and average particle size of 16.4 nm by Particle size analyzer. The Fourier Transform Infra-Red spectrum displayed the presence of various functional groups that stabilized the nanoparticles. X-ray diffraction peaks were conferred to face centered cubic structure. Transmission Electron Microscope and Scanning Electron Microscope revealed the spherical-shaped, polycrystalline nature with the presence of elemental silver analyzed by Energy Dispersive of X-Ray spectrum. Selected area electron diffraction also confirmed the orientation of AgNPs at 111, 200, 220, 311 planes similar to X-ray diffraction analysis. The synthesized nanoparticles are evaluated for antimicrobial activity against 7 bacterial and 3 fungal pathogens. A good zone of inhibition was observed against pathogenic bacteria than fungal pathogens. Thus the study could hold a key aspect in drug discovery research and other pharmacological conducts of human clinical conditions.

• Journal of The Korean Society of Integrative Medicine
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• v.12 no.2
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• pp.155-166
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• 2024

Purpose : This study aims to identify key factors for predicting dropout risk at the university level and to provide a foundation for policy development aimed at dropout prevention. This study explores the optimal machine learning algorithm by comparing the performance of various algorithms using data on college students' dropout risks. Methods : We collected data on factors influencing dropout risk and propensity were collected from N University. The collected data were applied to several machine learning algorithms, including random forest, decision tree, artificial neural network, logistic regression, support vector machine (SVM), k-nearest neighbor (k-NN) classification, and Naive Bayes. The performance of these models was compared and evaluated, with a focus on predictive validity and the identification of significant dropout factors through the information gain index of machine learning. Results : The binary logistic regression analysis showed that the year of the program, department, grades, and year of entry had a statistically significant effect on the dropout risk. The performance of each machine learning algorithm showed that random forest performed the best. The results showed that the relative importance of the predictor variables was highest for department, age, grade, and residence, in the order of whether or not they matched the school location. Conclusion : Machine learning-based prediction of dropout risk focuses on the early identification of students at risk. The types and causes of dropout crises vary significantly among students. It is important to identify the types and causes of dropout crises so that appropriate actions and support can be taken to remove risk factors and increase protective factors. The relative importance of the factors affecting dropout risk found in this study will help guide educational prescriptions for preventing college student dropout.

• International Journal of Computer Science & Network Security
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• v.24 no.5
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• pp.111-118
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• 2024

In general network-based intrusion detection system is designed to detect malicious behavior directed at a network or its resources. The key goal of this paper is to look at network data and identify whether it is normal traffic data or anomaly traffic data specifically for accounting information systems. In today's world, there are a variety of principles for detecting various forms of network-based intrusion. In this paper, we are using supervised machine learning techniques. Classification models are used to train and validate data. Using these algorithms we are training the system using a training dataset then we use this trained system to detect intrusion from the testing dataset. In our proposed method, we will detect whether the network data is normal or an anomaly. Using this method we can avoid unauthorized activity on the network and systems under that network. The Decision Tree and K-Nearest Neighbor are applied to the proposed model to classify abnormal to normal behaviors of network traffic data. In addition to that, Logistic Regression Classifier and Support Vector Classification algorithms are used in our model to support proposed concepts. Furthermore, a feature selection method is used to collect valuable information from the dataset to enhance the efficiency of the proposed approach. Random Forest machine learning algorithm is used, which assists the system to identify crucial aspects and focus on them rather than all the features them. The experimental findings revealed that the suggested method for network intrusion detection has a neglected false alarm rate, with the accuracy of the result expected to be between 95% and 100%. As a result of the high precision rate, this concept can be used to detect network data intrusion and prevent vulnerabilities on the network.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.161-177
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• 2015

With the rapid evolution of technology, the size, number, and the type of databases has increased concomitantly, so data mining approaches face many challenging applications from databases. One such application is discovery of fraud patterns from agricultural product wholesale transaction instances. The agricultural product wholesale market in Korea is huge, and vast numbers of transactions have been made every day. The demand for agricultural products continues to grow, and the use of electronic auction systems raises the efficiency of operations of wholesale market. Certainly, the number of unusual transactions is also assumed to be increased in proportion to the trading amount, where an unusual transaction is often the first sign of fraud. However, it is very difficult to identify and detect these transactions and the corresponding fraud occurred in agricultural product wholesale market because the types of fraud are more intelligent than ever before. The fraud can be detected by verifying the overall transaction records manually, but it requires significant amount of human resources, and ultimately is not a practical approach. Frauds also can be revealed by victim's report or complaint. But there are usually no victims in the agricultural product wholesale frauds because they are committed by collusion of an auction company and an intermediary wholesaler. Nevertheless, it is required to monitor transaction records continuously and to make an effort to prevent any fraud, because the fraud not only disturbs the fair trade order of the market but also reduces the credibility of the market rapidly. Applying data mining to such an environment is very useful since it can discover unknown fraud patterns or features from a large volume of transaction data properly. The objective of this research is to empirically investigate the factors necessary to detect fraud transactions in an agricultural product wholesale market by developing a data mining based fraud detection model. One of major frauds is the phantom transaction, which is a colluding transaction by the seller(auction company or forwarder) and buyer(intermediary wholesaler) to commit the fraud transaction. They pretend to fulfill the transaction by recording false data in the online transaction processing system without actually selling products, and the seller receives money from the buyer. This leads to the overstatement of sales performance and illegal money transfers, which reduces the credibility of market. This paper reviews the environment of wholesale market such as types of transactions, roles of participants of the market, and various types and characteristics of frauds, and introduces the whole process of developing the phantom transaction detection model. The process consists of the following 4 modules: (1) Data cleaning and standardization (2) Statistical data analysis such as distribution and correlation analysis, (3) Construction of classification model using decision-tree induction approach, (4) Verification of the model in terms of hit ratio. We collected real data from 6 associations of agricultural producers in metropolitan markets. Final model with a decision-tree induction approach revealed that monthly average trading price of item offered by forwarders is a key variable in detecting the phantom transaction. The verification procedure also confirmed the suitability of the results. However, even though the performance of the results of this research is satisfactory, sensitive issues are still remained for improving classification accuracy and conciseness of rules. One such issue is the robustness of data mining model. Data mining is very much data-oriented, so data mining models tend to be very sensitive to changes of data or situations. Thus, it is evident that this non-robustness of data mining model requires continuous remodeling as data or situation changes. We hope that this paper suggest valuable guideline to organizations and companies that consider introducing or constructing a fraud detection model in the future.

• Journal of the Korean Institute of Landscape Architecture
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• v.40 no.6
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• pp.23-34
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• 2012

This Study is about the "Comprehensive Landscape Improvement Plan for Gu-ryoung Seaside Village that was one of most exhibited projects for developing sea villages." The formulations of the plan were supervised by the Ministry of Land, Transport, and Maritime Affairs and were executed by the Goheung Country. Rather than proposing renovations for the landscape, this study maintains the existing order and attempts to examine the plan by scrutinizing the vernacular design language of the landscape. In the study, community members had the opportunity to express their opinions and ideas about the community through workshops composed of community participation programs, and participated in the decision-making process through consultation meetings. The conclusion of this study was relevant to the activities of the committee on landscape improvement. The Comprehensive Landscape Improvement Plan has three objectives: (1) resorting and modifying the natural landscape, (2) restructuring the roadways, and (3) modifying key spaces. In the end, the role of Gu-ryong Mountain as a background of the landscape was focused on tree planting drives that were undertaken, and accessibility to the sea front was improved. Second, in restructuring the roadways, rough roads were restored and unconnected roads were connected to ensure a network of roads along the sea front, inner roads in the village, roads at the Fringes Mountains, and stone roads on the mud flat. In addition, roads were named according to the character of the landscape and signs were installed. Finally, the existing key spaces, in which community members came together, were restored and new key spaces were created for the outdoor activities of the inhabitants and the diverse experience of visitors. A guideline was also created to regulate private areas such as roofs, walls, fences of residential buildings, and private container boxes and fishing gear along the sea front. The strength of this study is that it is seeking to determine the greatest potential of the landscape and set the plan by examining the lives of community members. Some problems were found during the development of this study. Further, there were problems in the community's understanding as elaborated below. First is the gap between community members' awareness and practice. Even though they were aware of the problems with the village landscape, they hesitated to implement improvements. Second, community members have misunderstandings about the landscape the improvement plan. The local government and the residents have understood this plan as a development project; for example, new building construction or the extension of roads. Third, residents are not aware that continuous attention and improvements are required for the upkeep of the landscape in the sea village. The plan to improve the landscape should promote a balance between making the area as a tourist attraction and maintaining the lives and cultural activities, because the sea village system incorporates settlements, economy, and culture.