• KIPS Transactions on Computer and Communication Systems
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• v.5 no.9
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• pp.247-260
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• 2016

Sensor networks have been used in many applications such as smart home, smart factory, etc. based on sensor data. Sensor networks can change system requirements and architectures depending on their application areas. Currently, sensor network application cases in ocean environments are very rare because the ocean environments have much difficult accessibility more poor conditions, higher wave heights, more frogs, much heavier salinity, etc., compared with ground environments. In this paper, we propose the requirements, architecture and design of a sensor network system for the littoral sea cage culture monitoring and we also introduce its operation results through the development. The developed system based on our research provides users with functionalities to extract, monitor, and manage underwater environmental conditions suitable to littoral sea cage culturing of fishes.

• Journal of Convergence for Information Technology
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• v.9 no.10
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• pp.101-107
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• 2019

This paper proposes a method for detecting faults in conveyor systems used for transportation of raw materials needed in the thermal power plant and cement industries. A small drone was designed in consideration of the difficulty in accessing the industrial site and the need to use it in wide industrial site. In order to apply the system to the embedded microprocessor, hardware and algorithms considering limited memory and execution time have been proposed. At this time, the failure determination method measures the peak frequency through the measurement, detects the continuity of the high frequency, and performs the failure diagnosis with the high frequency components of noise. The proposed system consists of experimental environment based on the data obtained from the actual thermal power plant, and it is confirmed that the proposed system is useful by conducting virtual environment experiments with the drone designed system. In the future, further research is needed to improve the drone's flight stability and to improve discrimination performance by using more intelligent methods of fault frequency.

• Journal of the Korea Society for Simulation
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• v.25 no.4
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• pp.109-115
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• 2016

This study intended to testify the causality among three variables such as accident, depression and cognitive failure of the train drivers. For this purpose, two research models were suggested. Model 1 hypothesized the causality among three variables as 'depression ${\rightarrow}$ cognitive failure ${\rightarrow}$ accident'. On the other hand, model 2 hypothesized the causality among three variables as 'accident ${\rightarrow}$ depression ${\rightarrow}$ cognitive failure'. Results based on AMOS using 416 train drivers' questionnaire showed that model 2 is more valid than model 1. The statistical result of model 1 showed that depression has a positive effect on cognitive failure, however no significant relationship between depression and accident as well as between cognitive failure and accident. In model 2, the result showed that the accident has a positive effect on cognitive failure mediated by depression. This result suggests the necessity for establishment of countermeasures to mitigate mistake and cognitive failure caused by train drivers in a wider context, considering the causality between accident and depression.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.38 no.4
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• pp.45-55
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• 2015

In this paper, we consider curriculum mining as an application of process mining in the domain of education. The basic objective of the curriculum mining is to construct a registration pattern model by using logs of registration data. However, subject registration patterns of students are very unstructured and complicated, called a spaghetti model, because it has a lot of different cases and high diversity of behaviors. In general, it is typically difficult to develop and analyze registration patterns. In the literature, there was an effort to handle this issue by using clustering based on the features of students and behaviors. However, it is not easy to obtain them in general since they are private and qualitative. Therefore, in this paper, we propose a new framework of curriculum mining applying K-means clustering based on subject attributes to solve the problems caused by unstructured process model obtained. Specifically, we divide subject's attribute data into two parts : categorical and numerical data. Categorical attribute has subject name, class classification, and research field, while numerical attribute has ABEEK goal and semester information. In case of categorical attribute, we suggest a method to quantify them by using binarization. The number of clusters used for K-means clustering, we applied Elbow method using R-squared value representing the variance ratio that can be explained by the number of clusters. The performance of the suggested method was verified by using a log of student registration data from an 'A university' in terms of the simplicity and fitness, which are the typical performance measure of obtained process model in process mining.

• Animal cells and systems
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• v.16 no.3
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• pp.230-236
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• 2012

The Korean hare, Lepus coreanus, is an important mammal in ecosystem food chains, and is distributed across the entire Korean peninsula and northeastern China. Polymorphic microsatellite loci were developed using the biotinenrichment technique for use in population genetics studies. Five trinucleotide and four dinucleotide microsatellite loci were selected and tested on 22 Korean hare specimens collected from Gangwon Province and Gyeongsangbuk Province in South Korea. The number of alleles across the two sampling regions ranged from three to nine with a mean of 6.1. Mean observed and expected heterozygosities and polymorphic information content were 0.540, 0.627 and 0.579, respectively. Only one locus, Lc06, showed departure from Hardy-Weinberg equilibrium after applying the Bonferroni correction. Four microsatellites, Lc01, Lc03, Lc12, and Lc19, satisfied the criteria to serve as a core set of markers recommended for population genetics studies. These new microsatellite markers will be widely applicable to future genetic studies for management and conservation of the Korean hare and related species, including assessment of the genetic diversity and population structure of L. coreanus.

• KIPS Transactions on Computer and Communication Systems
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• v.10 no.10
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• pp.269-276
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• 2021

Recently, smart sensors are used in various environments, and the implementation of ad-hoc sensor networks (ASNs) is a hot research topic. Unfortunately, traditional sensor network routing algorithms focus on specific control issues, and they can't be directly applied to the ASN operation. In this paper, we propose a new routing protocol by using the Q-learning technology, Main challenge of proposed approach is to extend the life of ASNs through efficient energy allocation while obtaining the balanced system performance. The proposed method enhances the Q-learning effect by considering various environmental factors. When a transmission fails, node penalty is accumulated to increase the successful communication probability. Especially, each node stores the Q value of the adjacent node in its own Q table. Every time a data transfer is executed, the Q values are updated and accumulated to learn to select the optimal routing route. Simulation results confirm that the proposed method can choose an energy-efficient routing path, and gets an excellent network performance compared with the existing ASN routing protocols.

• Journal of Convergence for Information Technology
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• v.11 no.10
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• pp.36-44
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• 2021

Estimation on SW Sizing applied to fintech is very difficult, a task requiring long time, it is difficult for client companies and developer companies to accurately calculate the size of software development. The size is generally estimated based on the experience of project managers and the general functional scoring method. In this paper, propose a model that can be applied to fintech development projects by quantitatively calculating the required functions from the user's point of view, measuring the scale, and calculating the scale in real time. Through the proposed model, the amount of work can be estimated prior to development and the size can be measured, and the M/M and the estimated quotation amount can be calculated based on the program list by each layer. In future studies, by securing size computation data on existing the Fintech Project in mass, research on accurate size computation would be required.

• Convergence Security Journal
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• v.21 no.3
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• pp.13-23
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• 2021

The attacker's techniques and tools are becoming intelligent and sophisticated. Existing Anti-Virus cannot prevent security accident. So the security threats on the endpoint should also be considered. Recently, EDR security solutions to protect endpoints have emerged, but they focus on visibility. There is still a lack of detection and responsiveness. In this paper, we use real-world EDR event logs to aggregate knowledge-based MITRE ATT&CK and autoencoder-based anomaly detection techniques to detect anomalies in order to screen effective analysis and analysis targets from a security manager perspective. After that, detected anomaly attack signs show the security manager an alarm along with log information and can be connected to legacy systems. The experiment detected EDR event logs for 5 days, and verified them with hybrid analysis search. Therefore, it is expected to produce results on when, which IPs and processes is suspected based on the EDR event log and create a secure endpoint environment through measures on the suspicious IP/Process.

• The Journal of Society for e-Business Studies
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• v.23 no.4
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• pp.19-39
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• 2018

Recently, fake news has attracted worldwide attentions regardless of the fields. The Hyundai Research Institute estimated that the amount of fake news damage reached about 30.9 trillion won per year. The government is making efforts to develop artificial intelligence source technology to detect fake news such as holding "artificial intelligence R&D challenge" competition on the title of "searching for fake news." Fact checking services are also being provided in various private sector fields. Nevertheless, in academic fields, there are also many attempts have been conducted in detecting the fake news. Typically, there are different attempts in detecting fake news such as expert-based, collective intelligence-based, artificial intelligence-based, and semantic-based. However, the more accurate the fake news manipulation is, the more difficult it is to identify the authenticity of the news by analyzing the news itself. Furthermore, the accuracy of most fake news detection models tends to be overestimated. Therefore, in this study, we first propose a method to secure the fairness of false news detection model accuracy. Secondly, we propose a method to identify the authenticity of the news using the social data broadly generated by the reaction to the news as well as the contents of the news.

• Journal of Engineering Education Research
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• v.21 no.3
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• pp.56-65
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• 2018

With rapid advance of technologies including information and communication technologies, jobs are evolving faster than ever. Architectural engineering is no exception in this regard, and the green architectural engineering is emerging fast as a promising new field. In this study, a Delphi study of expert architectural engineers are conducted to find out (1) near future prospects of the field, (2) near future emerging jobs, (3) competencies needed for these jobs, and (4) educational content necessary to build these competencies with regards to the green architectural engineering. Initial Delphi survey consisting of open-ended questions in the above four areas were conducted and came out with 65 items after duplicate removal and semantic refinements. Further refinements via second and third wave of Delphi results into 40 items that the 13 architectural engineering experts may largely agree upon as future prospects with regards to the green architectural engineering. Findings indicate that it is expected that the demand for green architectural engineering and needs for automatic energy control system increase. Also, collaborations with other fields is becoming more and more important in green architectural engineering. The professional work management skills such as knowledge convergence, problem solving, collaboration skills, and creativity linking components from various related areas seem to also be on the increasing need. Near future ready critical skills are found to be the building environment control techniques (thermal, light, sound, and air), the data processing techniques like data mining, energy monitoring, and the control and utilization of environmental analysis software. Experts also agree on new curriculum for green building architecture to be developed with more of converging subjects across disciplines for future ready professional skills and experiences. Major topics to be covered in the near future includes building environment studies, building energy management, energy reduction systems, indoor air quality, global environment and natural phenomena, and machinery and electrical facility. Architectural engineering community should be concerned with building up the competencies identified in this Delphi preparing for fast advancing future.