• Economic and Environmental Geology
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• v.57 no.4
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• pp.353-362
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• 2024

This study examined the spectral characteristics of sediments according to moisture content using an unmanned aerial vehicle (UAV)-based hyperspectral sensor and evaluated the efficiency of moisture content detection at different flight altitudes. For this purpose, hyperspectral images in the 400-1000nm wavelength range were acquired and analyzed at altitudes of 40m and 80m for sediment samples with various moisture contents. The reflectance of the sediments generally showed a decreasing trend as the moisture content increased. Correlation analysis between moisture content and reflectance showed a strong negative correlation (r < -0.8) across the entire 400-900nm range. The moisture content detection model constructed using the Random Forest technique showed detection accuracies of RMSE 2.6%, R² 0.92 at 40m altitude and RMSE 2.2%, R² 0.95 at 80m altitude, confirming that the difference in accuracy between altitudes was minimal. Variable importance analysis revealed that the 600-700nm band played a crucial role in moisture content detection. This study is expected to be utilized in efficient sediment moisture management and natural disaster prediction in the field of environmental monitoring in the future.

• Information Systems Review
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• v.26 no.2
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• pp.137-153
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• 2024

This study aimed to propose early diagnosis and management of dementia, which is increasing in aging societies, and suggest commercial utilization strategies by leveraging digital healthcare technologies, particularly lifelog data collected from wearable devices. By introducing new approaches to dementia prevention and management, this study sought to contribute to the field of dementia prediction and prevention. The research utilized 12,184 pieces of lifelog information (sleep and activity data) and dementia diagnosis data collected from 174 individuals aged between 60 and 80, based on medical pathological diagnoses. During the research process, a multidimensional dataset including sleep and activity data was standardized, and various machine learning algorithms were analyzed, with the random forest model showing the highest ROC-AUC score, indicating superior performance. Furthermore, an ablation test was conducted to evaluate the impact of excluding variables related to sleep and activity on the model's predictive power, confirming that regular sleep and activity have a significant influence on dementia prevention. Lastly, by exploring the potential for commercial utilization strategies of the developed model, the study proposed new directions for the commercial spread of dementia prevention systems.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.23 no.4
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• pp.16-36
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• 2024

This study, developed a dynamic hard shoulder running(HSR) algorithm that includes ending speed and minimum operation time in addition to the starting speed for HSR, and presented an operation plan. The first stage of the algorithm was red, which means vehicles are prohibited from HSR. The second stage is red/amber, in which drivers are notified of HSR, and operators are given time to check whether there is any obstacle to HSR. Stage 3 is green, which vehicles are permitted for HSR. Stage 4 is amber, in which a signal is given to drivers that the end of HSR is imminent. In addition, a minimum time is applied to green and red, but if congestion is severe, red is terminated early to prevent congestion from worsening. The upstream and downstream traffic flow is managed stably through main line ramp metering and lane number matching. The operating standard speed reflects the characteristics of vehicles and drivers, and based on simulation results, 7090 was selected as the optimal operating standard speed considering traffic flow and safety aspects. Therefore it is desirable to apply the travel time divided by the minimum speed of the HSR link as the minimum operating time in order to ensure continuity of traffic flow

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.19 no.4
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• pp.87-101
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• 2024

This study aims to provide insights for the efficient utilization of resources by analyzing the entrepreneurship support efficiency of Korean universities. To identify the factors influencing the number of entrepreneurs, which is the primary goal of university entrepreneurship support, a multiple regression analysis was conducted, identifying five effective independent variables. Using these five identified independent variables as input variables and the number of entrepreneurs as the output variable, the DEA method was used to analyze the efficiency of entrepreneurship support for each university as of 2023. The analysis of 150 four-year universities in Korea showed that nine universities exhibited complete efficiency in both CCR and BCC models. Among the remaining 141 universities that showed inefficiency, the cause was scale for five universities, technology for two universities, and both scale and technology for 134 universities. Regarding the returns to scale, nine universities exhibited CRS, 79 exhibited IRS, and 62 exhibited DRS. Additionally, reference groups that could serve as benchmarks for improving the efficiency of inefficient universities were identified, and target values(projections) for each variable to achieve efficiency were also presented. Despite the limitations of the DEA model, this study helps each university identify the causes of inefficiency in their entrepreneurship support and derive specific improvements to enhance efficiency. This facilitates more efficient resource management and can positively impact the ultimate goals of university entrepreneurship support, such as regional economic development and job creation.

• The Transactions of the Korea Information Processing Society
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• v.13 no.9
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• pp.404-412
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• 2024

The adoption of cloud environments has revolutionized application deployment and management, with microservices architecture and container technology serving as key enablers of this transformation. However, these advancements have introduced new challenges, particularly the necessity to precisely understand service interactions and conduct detailed analyses of internal processes within complex service environments such as microservices. Traditional monitoring techniques have proven inadequate in effectively analyzing these complex environments, leading to increased interest in eBPF (extended Berkeley Packet Filter) technology as a solution. eBPF is a powerful tool capable of real-time event collection and analysis within the Linux kernel, enabling the monitoring of various events, including file system activities within the kernel space. This paper proposes a container activity analysis system based on eBPF, which monitors events occurring in the kernel space of both containers and host systems in real-time and analyzes the collected data. Furthermore, this paper conducts a comparative analysis of prominent eBPF-based container monitoring systems (Tetragon, Falco, and Tracee), focusing on aspects such as event detection methods, default policy application, event type identification, and system call blocking and alert generation. Through this evaluation, the paper identifies the strengths and weaknesses of each system and determines the necessary features for effective container process monitoring and restriction. In addition, the proposed system is evaluated in terms of container metadata collection, internal activity monitoring, and system metadata integration, and the effectiveness and future potential of eBPF-based monitoring systems.

• The Transactions of the Korea Information Processing Society
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• v.13 no.9
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• pp.395-403
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• 2024

Recently, with the advancement of technology, the automotive industry has seen an increase in network connectivity. CAN (Controller Area Network) bus technology enables fast and efficient data communication between various electronic devices and systems within a vehicle, providing a platform that integrates and manages a wide range of functions, from core systems to auxiliary features. However, this increased connectivity raises concerns about network security, as external attackers could potentially gain access to the automotive network, taking control of the vehicle or stealing personal information. This paper analyzed abnormal messages occurring in CAN and confirmed that message occurrence periodicity, frequency, and data changes are important factors in the detection of abnormal messages. Through DBC decoding, the specific meanings of CAN messages were interpreted. Based on this, a model for classifying abnormalities was proposed using the GRU model to analyze the periodicity and trend of message occurrences by measuring the difference (residual) between the predicted and actual messages occurring within a certain period as an abnormality metric. Additionally, for multi-class classification of attack techniques on abnormal messages, a Random Forest model was introduced as a multi-classifier using message occurrence frequency, periodicity, and residuals, achieving improved performance. This model achieved a high accuracy of over 99% in detecting abnormal messages and demonstrated superior performance compared to other existing models.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.221-241
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• 2018

Deep learning is getting attention recently. The deep learning technique which had been applied in competitions of the International Conference on Image Recognition Technology(ILSVR) and AlphaGo is Convolution Neural Network(CNN). CNN is characterized in that the input image is divided into small sections to recognize the partial features and combine them to recognize as a whole. Deep learning technologies are expected to bring a lot of changes in our lives, but until now, its applications have been limited to image recognition and natural language processing. The use of deep learning techniques for business problems is still an early research stage. If their performance is proved, they can be applied to traditional business problems such as future marketing response prediction, fraud transaction detection, bankruptcy prediction, and so on. So, it is a very meaningful experiment to diagnose the possibility of solving business problems using deep learning technologies based on the case of online shopping companies which have big data, are relatively easy to identify customer behavior and has high utilization values. Especially, in online shopping companies, the competition environment is rapidly changing and becoming more intense. Therefore, analysis of customer behavior for maximizing profit is becoming more and more important for online shopping companies. In this study, we propose 'CNN model of Heterogeneous Information Integration' using CNN as a way to improve the predictive power of customer behavior in online shopping enterprises. In order to propose a model that optimizes the performance, which is a model that learns from the convolution neural network of the multi-layer perceptron structure by combining structured and unstructured information, this model uses 'heterogeneous information integration', 'unstructured information vector conversion', 'multi-layer perceptron design', and evaluate the performance of each architecture, and confirm the proposed model based on the results. In addition, the target variables for predicting customer behavior are defined as six binary classification problems: re-purchaser, churn, frequent shopper, frequent refund shopper, high amount shopper, high discount shopper. In order to verify the usefulness of the proposed model, we conducted experiments using actual data of domestic specific online shopping company. This experiment uses actual transactions, customers, and VOC data of specific online shopping company in Korea. Data extraction criteria are defined for 47,947 customers who registered at least one VOC in January 2011 (1 month). The customer profiles of these customers, as well as a total of 19 months of trading data from September 2010 to March 2012, and VOCs posted for a month are used. The experiment of this study is divided into two stages. In the first step, we evaluate three architectures that affect the performance of the proposed model and select optimal parameters. We evaluate the performance with the proposed model. Experimental results show that the proposed model, which combines both structured and unstructured information, is superior compared to NBC(Naïve Bayes classification), SVM(Support vector machine), and ANN(Artificial neural network). Therefore, it is significant that the use of unstructured information contributes to predict customer behavior, and that CNN can be applied to solve business problems as well as image recognition and natural language processing problems. It can be confirmed through experiments that CNN is more effective in understanding and interpreting the meaning of context in text VOC data. And it is significant that the empirical research based on the actual data of the e-commerce company can extract very meaningful information from the VOC data written in the text format directly by the customer in the prediction of the customer behavior. Finally, through various experiments, it is possible to say that the proposed model provides useful information for the future research related to the parameter selection and its performance.

• Proceedings of the Korean Aquaculture Society Conference
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• 2003.10a
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• pp.119-120
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• 2003

우리나라에 분포하는 모자반류는 모두 28종으로 알려져 있으며 (이와 강 2002) 이 가운데 식용으로 이용되는 것은 모자반 (S. fulvellum)이 대표적이다. 모자반의 양식은 주로 서남해 지역에서 이루어지고 있으며 이들의 종묘생산은 자연에서 생식기탁이 성숙되는 4-5월경에 이루어지는데, 유배의 대량 방출을 위한 성숙 모조의 다량 확보가 어렵고 일시에 유배의 대량 방출을 유도하기 위한 성숙 유도 기법의 연구는 전무한 실정이다. 따라서 이 연구에서는 모조의 실내 배양을 통하여 유배의 대량 방출을 위한 성숙 유도 기법과 배양 조건별 엽체의 성숙 및 난방출율을 구하여 모자반의 조기채묘에 유용한 자료로 사용하고자 하였다. 또한 채묘된 발아체의 초기생장에 필요한 최적 배양 환경을 구명하고자 하였다. 모자반 모조는 전남 진도군 조도 지역의 수심 3-5m에서 채집하였으며, 채집 즉시 실험실로 운반하여 유수식 사육 수조에 수용하였다. 성숙 유도는 20$\ell$ 플라스틱 bottle을 사용하였으며, 성숙률의 정량화를 위하여 암생식기탁을 절단하여 수차례 멸균해수에서 세척후 멸균된 5cm직경의 petri dish에 멸균해수20$m\ell$와 함께 수용하여 Multi-chamber incubator에서 배양하였다. 배양조건은 5개 온도조건 (5, 10, 15, 20, $25^{\circ}C$)과16:8h의 장일 광주기 조건으로하였으며 조도는 80 $\mu$molm$^{-2}$ s$^{-1}$로 하였다. 모든 실험구는 3반복 실험하였으며 2일 간격으로 생식기탁의 생장 및 성숙 그리고 난방출 여부를 현미경하에서 측정하였다. 난이 방출된 모조로부터 유배를 분리하여 3개 조도 구간 (30, 60, 100 $\mu$molm$^{-2}$ s$^{-1}$)과 5개 온도 구간 (5, 10, 15, 20, $25^{\circ}C$)의 조합인 15개 배양 조건하에서 엽체의 길이생장을 측정하였다. 생식기탁으로부터 난의 방출은 15$^{\circ}C$와 2$0^{\circ}C$ 조건에서 배양 2일후부터 방출되기 시작하였으며, 배양 9일후 2$0^{\circ}C$ 조건에서 가장 높은 96.7$\pm$5.8%의 난방출율을 보였다. 또한 15$^{\circ}C$ 조건에서는 배양 9일후 76.7%의 난방출율을 보였다. 1$0^{\circ}C$와 $25^{\circ}C$ 조건에서는 배양 11일까지 36.7%의 난방출율을 나타내어 온도 조건에 따라 난방출 비율에 차이를 보였다. 따라서 이러한 실내 배양 결과를 다량의 모조를 조기에 성숙시키기 위해 모조 수용 수조의 수온을 자연수온보다 2~5$^{\circ}C$ 높은 12~15$^{\circ}C$ 조건으로 유지하여 15일간의 수조 관리 후 모조의 대량 유배 방출을 유도할 수 있었다. 모조 성숙을 위한 사육 수조의 수온을 2$0^{\circ}C$ 이상으로 가온할 경우 엽체의 끝녹음을 유발하였으며 가온에 따르는 가온 비용이 수반되므로 엽체의 난방출율이 70% 이상에 도달하는 15$^{\circ}C$ 조건으로 유지하는 것이 경제적일 뿐만 아니라 엽체의 건전도 유지에도 바람직하였다. 유배의 초기생장은1$0^{\circ}C$와15$^{\circ}C$의 온도조건에서 길이생장이 빠르게 증가하여, 배양 35일 후 15$^{\circ}C$와 60 $\mu$molm$^{-2}$ s$^{-1}$의 조건에서 3.9$\pm$0.2mm로 가장 높은 값을 나타내었다. 엽체의 초기 길이생장은 15$^{\circ}C$, 60 $\mu$molm$^{-2}$ -s$^{-1}$의 조도 조건에서 가장 우세하였으며, 다음으로 30과 100 $\mu$molm$^{-2}$ s$^{-1}$의 조건 순으로 나타났다. 2$0^{\circ}C$와 $25^{\circ}C$의 온도 조건에서는 각각 1.8~2.1mm로 길이생장에 있어 유의한 차이가 없는 것으로 나타났다.

• Korean Security Journal
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• no.48
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• pp.147-176
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• 2016

Korean Peninsula is surrounded by ocean on three sides. Because of this geographical quality over 97% of export and import volumes are exchange by sea. Foreign ship and international passenger vessels carries foreign tourist and globalization and internationalization increases this trends. Leisure population grows with national income increase and interest of ocean. And accidents and incidents rates are also increases. Korea Coast Guard's jurisdiction area is 4.5 times bigger than our country. The length of coastline is 14,963km including islands. One patrol vessel is responsible for 24,068km and one coast guard substation is responsible for 94km. Efficient patrol activities can not be provided. This research focus on this problem. Analyze the status and trends of maritime crime and suggest efficient patrol activities. To deal with increasing maritime crime rate this study suggest to use geographical profile method which developed early 1900s in USA. This geographical profile analyse the spatial characteristic and mapping this result. With this result potential crime zone can be predicted. One of the result is hot spot management which gives data about habitual crime zone. In Korea National Police Agency adopt this method in 2008 and apply on patrol and crime prevention activity by analysis of different criteria. Korea National Police Agency analyse the crime rate with crime type, crime zone and potential crime zone, and hourly, regionally criteria. Korea Coast Guard need to adopt this method and apply on maritime to make maritime crime map, which shows type of crime with regional, periodical result. With this geographical profiling we can set a Criminal Point which shows the place where the crime often occurs. The Criminal Points are set with the data of numerous rates such as homicide, robbery, burglary, missing, collision which happened in ocean. Set this crime as the major crime and manage the data more thoroughly. I expect to enhance the reinforcement of marine crime using this Criminal Points. Because this points will give us efficient way to prevent the maritime crime by placing the patrol vessel where they needed most.

• Journal of Korean Society of Transportation
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• v.20 no.2
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• pp.105-115
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• 2002

For many rears, traffic accident statistics are the most direct measure of safety for a signalized intersection. However it takes more than 2 or 3 yearn to collect certain accident data for adequate sample sizes. And the accident data itself is unreliable because of the difference between accident data recorded and accident that is actually occurred. Therefore, it is rather difficult to evaluate safety for a intersection by using accident data. For these reasons, traffic conflict technique(TCT) was developed as a buick and accurate counter-measure of safety for a intersection. However, the collected conflict data is not always reliable because there is absence of clear criteria for conflict. This study developed objective and accurate conflict criteria, which is shown below based on traffic engineering theory. Frist, the rear-end conflict is regarded, when the following vehicle takes evasive maneuver against the first vehicle within a certain distance, according to car-following theory. Second, lane-change conflict is regarded when the following vehicle takes evasive maneuver against first vehicle which is changing its lane within the minimum stopping distance of the following vehicle. Third, cross and opposing-left turn conflicts are regarded when the vehicle which receives green sign takes evasive maneuver against the vehicle which lost its right-of-way crossing a intersection. As a result of correlation analysis between conflict and accident, it is verified that the suggested conflict criteria in this study ave applicable. And it is proven that estimating safety evaluation for a intersection with conflict data is possible, according to the regression analysis preformed between accident and conflict, EPDO accident and conflict. Adopting the conflict criteria suggested in this study would be both quick and accurate method for diagnosing safety and operational deficiencies and for evaluation improvements at intersections. Further research is required to refine the suggested conflict criteria to extend its application. In addition, it is necessary to develope other types of conflict criteria, not included in this study, in later study.