• Journal of the Korean Society for Nondestructive Testing
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• v.18 no.5
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• pp.389-397
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• 1998

For an effective and efficient management of large amounts of preservice/inservice inspection(PSI/ISI) data in nuclear power plants, an intellegent Windows 95-based data management program was developed. This program enables the prompt extraction of previously conducted PSI/ISI conditions and results so that the time-consuming data management, painstaking data processing and analysis in the past are avoided. The program extracts, and the associated remedies. Furthermore, additional inspection data and comments can be easily added or deleted for subsequent PSI/ISI operation. Although the initial version of the program was applied to Kori nuclear power plant, this program can be equally applied to other nuclear power plant. And also this program can be used to offer the fundamental data for application of evaluation data related to fracture mechanics analysis(FMA), probabilistic reliability assessment(PRA) of PSI/ISI results, performance demonstration initiative(PDI) and risk-informed ISI based on probability of detection(POD) information of ultrasonic examination. Besides, the program can be further developed as a unique PSI/ISI data management expert system that can be apart of PSI/ISI data management expert system that can be a part of PSI/ISI Total Support System(TSS) for Korean nuclear power plants.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.3
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• pp.91-98
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• 2021

Today's information and communication technology is rapidly developing, the security of IT infrastructure is becoming more important, and at the same time, cyber attacks of various forms are becoming more advanced and sophisticated like intelligent persistent attacks (Advanced Persistent Threat). Early defense or prediction of increasingly sophisticated cyber attacks is extremely important, and in many cases, the analysis of network-based intrusion detection systems (NIDS) related data alone cannot prevent rapidly changing cyber attacks. Therefore, we are currently using data generated by intrusion detection systems to protect against cyber attacks described above through Host-based Intrusion Detection System (HIDS) data analysis. In this paper, we conducted a comparative study on machine learning algorithms using LID-DS (Leipzig Intrusion Detection-Data Set) host-based intrusion detection data including thread information, metadata, and buffer data missing from previously used data sets. The algorithms used were Decision Tree, Naive Bayes, MLP (Multi-Layer Perceptron), Logistic Regression, LSTM (Long Short-Term Memory model), and RNN (Recurrent Neural Network). Accuracy, accuracy, recall, F1-Score indicators and error rates were measured for evaluation. As a result, the LSTM algorithm had the highest accuracy.

• Journal of Intelligence and Information Systems
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• v.9 no.3
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• pp.193-205
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• 2003

In this paper, we suggest user authentication and authorization system for internet banking by face recognition. The system is one of Biometrics technology to verify and authorize personnel identification and is more unobtrusive than the other technologies, because they use physiological characteristics such as fingerprint, hand geometry, iris to their system that people have to touch it. Also, the face recognition system requires only a few devices such as a camera and keypad, so it is easy to apply it to the real world. The face recognition algorithms open to the public are separated by their analysis method differ from what characteristic of the human face use. There are PCA (principal Component Analysis), ICA (Independent Component Analysis), FDA (Fisher Discriminant Analysis). Among these, physiological data of encrypted form is translated utilizing PCA which is the most fundamental algorithm that analyze face feature, and we suggests design method of user authentication system that can do send-receive fast and exactly.

• Journal of the Korean Society of Safety
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• v.36 no.5
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• pp.1-9
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• 2021

With the convergence of the information and communication technologies, a new age of technological civilization has arrived. This is the age of intelligent revolution, known as the 4th industrial revolution. The 4th industrial revolution is based on technological innovations, such as robots, big data analysis, artificial intelligence, and unmanned transportation facilities. This revolution would interconnect all the people, things, and economy, and hence will lead to the expansion of the industry. A high-density, high-capacity energy technology is required to maintain this interconnection. As a next-generation energy source, lithium-ion batteries are in the spotlight today. However, lithium-ion batteries can cause thermal runaway and fire because of electrical, thermal, and mechanical abuse. In this study, thermal runaway was induced in 72.5 Ah NCM pouch-type lithium-ion batteries because of thermal abuse. The surface of the pouch-type lithium-ion batteries was heated by the hot plate heating method, and the effect of the rate of increase in the surface temperature on the thermal runaway trigger time was analyzed using Minitab 19, a statistical analysis program. The correlation analysis results confirmed that there existed a strong negative relationship between each variable, while the regression analysis demonstrated that the thermal runaway trigger time of lithium-ion batteries can be predicted from the rate of increase in their surface temperature.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.69-94
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• 2017

Recently, increase of demand for big data analysis has been driving the vigorous development of related technologies and tools. In addition, development of IT and increased penetration rate of smart devices are producing a large amount of data. According to this phenomenon, data analysis technology is rapidly becoming popular. Also, attempts to acquire insights through data analysis have been continuously increasing. It means that the big data analysis will be more important in various industries for the foreseeable future. Big data analysis is generally performed by a small number of experts and delivered to each demander of analysis. However, increase of interest about big data analysis arouses activation of computer programming education and development of many programs for data analysis. Accordingly, the entry barriers of big data analysis are gradually lowering and data analysis technology being spread out. As the result, big data analysis is expected to be performed by demanders of analysis themselves. Along with this, interest about various unstructured data is continually increasing. Especially, a lot of attention is focused on using text data. Emergence of new platforms and techniques using the web bring about mass production of text data and active attempt to analyze text data. Furthermore, result of text analysis has been utilized in various fields. Text mining is a concept that embraces various theories and techniques for text analysis. Many text mining techniques are utilized in this field for various research purposes, topic modeling is one of the most widely used and studied. Topic modeling is a technique that extracts the major issues from a lot of documents, identifies the documents that correspond to each issue and provides identified documents as a cluster. It is evaluated as a very useful technique in that reflect the semantic elements of the document. Traditional topic modeling is based on the distribution of key terms across the entire document. Thus, it is essential to analyze the entire document at once to identify topic of each document. This condition causes a long time in analysis process when topic modeling is applied to a lot of documents. In addition, it has a scalability problem that is an exponential increase in the processing time with the increase of analysis objects. This problem is particularly noticeable when the documents are distributed across multiple systems or regions. To overcome these problems, divide and conquer approach can be applied to topic modeling. It means dividing a large number of documents into sub-units and deriving topics through repetition of topic modeling to each unit. This method can be used for topic modeling on a large number of documents with limited system resources, and can improve processing speed of topic modeling. It also can significantly reduce analysis time and cost through ability to analyze documents in each location or place without combining analysis object documents. However, despite many advantages, this method has two major problems. First, the relationship between local topics derived from each unit and global topics derived from entire document is unclear. It means that in each document, local topics can be identified, but global topics cannot be identified. Second, a method for measuring the accuracy of the proposed methodology should be established. That is to say, assuming that global topic is ideal answer, the difference in a local topic on a global topic needs to be measured. By those difficulties, the study in this method is not performed sufficiently, compare with other studies dealing with topic modeling. In this paper, we propose a topic modeling approach to solve the above two problems. First of all, we divide the entire document cluster(Global set) into sub-clusters(Local set), and generate the reduced entire document cluster(RGS, Reduced global set) that consist of delegated documents extracted from each local set. We try to solve the first problem by mapping RGS topics and local topics. Along with this, we verify the accuracy of the proposed methodology by detecting documents, whether to be discerned as the same topic at result of global and local set. Using 24,000 news articles, we conduct experiments to evaluate practical applicability of the proposed methodology. In addition, through additional experiment, we confirmed that the proposed methodology can provide similar results to the entire topic modeling. We also proposed a reasonable method for comparing the result of both methods.

• Journal of the Korea Society of Computer and Information
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• v.25 no.4
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• pp.165-172
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• 2020

In this paper we propose an uses on-device-based edge computing technology and big data analysis methods through the use of on-device-based edge computing technology and analysis of big data, which are distributed computing paradigms that introduce computations and storage devices where necessary to solve problems such as transmission delays that occur when data is transmitted to central centers and processed in current general smart factories. However, even if edge computing-based technology is applied in practice, the increase in devices on the network edge will result in large amounts of data being transferred to the data center, resulting in the network band reaching its limits, which, despite the improvement of network technology, does not guarantee acceptable transfer speeds and response times, which are critical requirements for many applications. It provides the basis for developing into an AI-based facility prediction conservation analysis tool that can apply deep learning suitable for big data in the future by supporting intelligent facility management that can support productivity growth through research that can be applied to the field of facility preservation and smart factory industry with integrated hardware technology that can accommodate these requirements and factory management and control technology.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.16 no.6
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• pp.182-194
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• 2017

Most of the drone (Unmanned Aerial Vehicle) research in terms of traffic management involves detecting and tracking roads or vehicles. The purpose of analyzing image footage in the transportation sector is to overcome the limitations of the existing traffic data collection system (vehicle detectors, DSRC, etc.). With regards to this, drones are the good alternatives. However, due to limitation in their maximum flight time, they are appropriate to use as a complementary rather than replacing the existing collection system. Therefore, further research is needed for utilizing drones for transportation analysis purpose. Traffic problems often arise from one particular section or a point that expands to the whole road network and drones can be fully utilized to analyze these particular sections. Based on the study on the uses of traffic survey analysis, this study is conducted by extracting traffic flow parameters from video images(range 800~1000m) of highway unit segments that were taken by drones. In addition, video images were taken at a high altitude with the development of imaging technologies.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.6
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• pp.163-179
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• 2020

With the expansion of platform-based taxi service, mobility and convenience of users are getting better. However, due to profitability problem, marginalized areas in the supply of the service are expected to appear. As such, this study analyzed spatial marginalization of taxi service caused by imbalance in supply and demand during the night-time when public transportation service is suspended. According to hot-spot analysis of taxi, outskirt of a city and residential areas showed high vacancy and greater number of drop-offs compared to the number of pick-ups. On the contrary, they were confirmed low in the center and sub-centers of a city. Centrality analysis also showed a similar pattern with hot-spot analysis. Due to this, drivers may refuse to pick up a customer bound for an area with lower out-degree centrality compared to in-degree centrality as it might be difficult for the drivers to pick up another customer after dropping off the current customer. Thus, customers may need to wait for a taxi for a longer time. For this reason, improvement in spatial marginalization caused by mismatch of supply and demand is required. Also, the outcome of this study is expected to be utilized as a basic data.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.3
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• pp.53-61
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• 2022

Despite the increasing importance of pedestrians and their walking individualities, walking activity time characteristics are yet to be studied. This study analyzes the walking activity time characteristics by group using the Time Use Survey data. In order to analyze the characteristics of each pedestrian group, cluster analysis and correspondence analysis were performed by dividing the walking styles into utilitarian and leisure-purpose walking. Those who did not undertake utilitarian walking were mainly the worker group, whereas subjects who walked could be classified into homemaker and student groups. The peak of the student group appeared clearly in the morning, with a dispersed peak obtained during the afternoon. Although the peak of the homemaker group was not precise, it was confirmed that they mainly walked in the afternoon. The worker group also did not participate in leisure-purpose walking, while the elderly group mostly undertook walking for leisure. These walking activity time characteristics of pedestrians are expected to be applied when establishing related pedestrian policies.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.5
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• pp.103-116
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• 2022

The South Korean Ministry of Land, Infrastructure, and Transport designated seven automated driving test beds required to evaluate vehicle performance every year for the expansion of mobility services based on automated driving. As a fundamental study, we suggested a necessary example of evaluating the performance with a satisfaction survey for the services before the evaluation. First, we surveyed the perception of automated driving services of users and the public in Sejong-si, South Korea. The survey showed that the users had a higher level of awareness of automated driving technology and intention to use it than the public. Second, the satisfaction survey was conducted on demand-responsive public transportation and automated driving service users. Notably, using the Wilcoxon Rank Sum Test, among the non-parametric statistical analysis methods, we found that safety-related factors affected the overall satisfaction of users of automated driving services. On the other hand, in the case of the demand-responsive public transportation service users, factors related to service convenience affected overall satisfaction. Hence, the results of these surveys are expected to be used as basic data and guidelines to improve the quality of automated driving services and policy establishment.