• Journal of the Korea Institute of Information Security & Cryptology
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• v.27 no.3
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• pp.563-577
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• 2017

Due to the Internet and computing capability, new and variant malware are discovered around 1 Million per day. Companies use dynamic analysis such as behavior analysis on virtual machines for unknown malware detection because attackers use unknown malware which is not detected by signature based AV effectively. But growing number of malware types are not only PE(Portable Executable) but also non-PE such as MS word or PDF therefore dynamic analysis must need more resources and computing powers to improve detection effectiveness. This study elicits the pre-filtering system evaluation factor to improve effective dynamic malware analysis system and presents and verifies the decision making model and the formula for solution selection using AHP(Analytics Hierarchy Process)

• Journal of the Korea institute for structural maintenance and inspection
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• v.3 no.3
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• pp.223-236
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• 1999

The need to monitor and undertake remidial works on large structures has greatly increased in recent years due to the appearance of widespread faults in large structures such as bridges and buildings, etc, of 20 or more years of age. The health condition of structures must be monitored continuously to maintenance the structures. In order to do in-situ monitoring, the sensor is necessary to be embedded in the structures. Fiber optic sensors can be embedded in the structures to get the health information in the structures. The fiber sensor was constructed with $3{\times}3$ fiber couplers to sense the multi-point strains and failure instants. The 4 RC (reinforced concrete) beams were made to 2 of A type, 2 of B type beams. These beams were reinforced by the reinforcing bars, and were tested under the flexural loading. The behavior of the beams was simultaneously measured by the fiber optic sensors, electrical strain gages, and LVDT. The states of the beams were interpreted by these all signals. By these experiments, There were verified that the fiber optic sensors could measure the structural strains and failure instants of the RC beams, The fiber sensors were well operated until the failure of the beams. It was shown that the strains of the reinforcing steel bar can be used to monitor the health condition of the beams through the flexural test of RC beams. On the other words, the results were arrived that the two strains in the reinforcing bar measured at the same point can give the information of the structural health status. Also, the failure instants of beams were well detected from the fiber optic filtered signals.

• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.6
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• pp.1309-1316
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• 2018

Cattle estrus detection system based on Internet of Things is designed and implemented by using Arduino pro-mini, gyroscope, acceleration sensor, bluetooth master and slave module. The implemented system measures cattle's moving and the measured data are transmitted to the computer connected to RX module by bluetooth TX module. They are plotted in 2-dimensional graph on the computer monitor and the number of transition at each sensor axis are calculated from the graph. The detected and gathered data from the system are analyzed by the proposed algorithm to decide which cows are in the estrus or not. The method to apply bluetooth scatternet is shown and the proposed system can be used to increase the success rate of artificial insemination in normal estrus by detecting the cow's behaviors such as the number of jumping. In this paper, the implemented cattle behavior detecting the system(TX module) are strapped on cattle's leg and it measures the cattle behaviors for determining where that a cattle is estrus or not by the proposed algorithm.

• KNOM Review
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• v.21 no.2
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• pp.18-25
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• 2018

Since the development of block-chain technology by Satoshi Nakamoto and Bitcoin pioneered a new cryptocurrency market, a number of scale of cryptocurrency have emerged. There are crimes taking place using the anonymity and vulnerabilities of block-chain technology, and many studies are underway to improve vulnerability and prevent crime. However, they are not enough to detect users who commit crimes. Therefore, it is very important to detect abnormal behavior such as money laundering and stealing cryptocurrency from the network. In this paper, the characteristics of the transactions and user graphs in the Bitcoin network are collected and statistical information is extracted from them and presented as plots on the log scale. Finally, we analyze visualized plots according to the Densification Power Law and Power Law Degree, as a result, present features appropriate for detection of anomalies involving abnormal transactions and abnormal users in the Bitcoin network.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.4
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• pp.373-379
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• 2019

In this study, image-based flock monitoring and density evaluation were conducted for broiler chicks welfare. Image data were captured by using a mono camera and region of broiler chicks in the image was detected using converting to HSV color model, thresholding, and clustering with filtering. The results show that region detection was performed with 5% relative error and 0.81 IoU on average. The detected region was corrected to the actual region by projection into ground using coordinate transformation between camera and real-world. The flock density of broiler chicks was estimated using the corrected actual region, and it was observed with an average of 80%. The developed algorithm can be applied to the broiler chicks house through enhancing accuracy of region detection and low-cost system configuration.

• Journal of the Institute of Convergence Signal Processing
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• v.21 no.1
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• pp.29-37
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• 2020

This paper implements a method of extracting an object about a dog through real-time image analysis and classifying dog behaviors from the extracted images. The Darknet YOLO was used to detect dog objects, and the Teachable Machine provided by Google was used to classify behavior patterns from the extracted images. The trained Teachable Machine is saved in Google Drive and can be used by ml5.js implemented on a node.js server. By implementing an interactive web server using a socket.io module on the node.js server, the classified results are transmitted to the user's smart phone or PC in real time so that it can be checked anytime, anywhere.

• Smart Media Journal
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• v.11 no.4
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• pp.62-68
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• 2022

To prevent the spread of disease in aquaculture, it is a need for a system to predict fish diseases while monitoring the water quality environment and the status of growing fish in real time. The existing research in predicting fish disease were image processing techniques. Recently, there have been more studies on disease prediction methods through deep learning techniques. This paper introduces the research results on how to predict diseases of Paralichthys Olivaceus with deep learning technology in aquaculture. The method enhances the performance of disease detection rates by including data augmentation and pre-processing in camera images collected from aquaculture. In this method, it is expected that early detection of disease fish will prevent fishery disasters such as mass closure of fish in aquaculture and reduce the damage of the spread of diseases to local aquaculture to prevent the decline in sales.

• Journal of the Society of Disaster Information
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• v.18 no.1
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• pp.154-162
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• 2022

Purpose: The government intends to come up with measures to revitalize safety management at construction sites to shift safety management at construction sites from human capabilities to system-oriented management systems using advanced technologies AIoT and Mobile technologies. Method: The construction site safety management monitoring system using AIoT and Mobile technology conducted an experiment on the effectiveness of the construction site by applying three algorithms: virtual fence, fire monitoring, and recognition of not wearing a safety helmet. Result: The number of workers in the experiment was 215 and 7.61 virtual fence intrusion was 3.5% compared to the number of subjects and 0.16 fire detection were 0.07% compared to the subjects, and the average monthly rate of not wearing a safety helmet was 8.79, 4.05% compared to the subjects. Conclusion: It was found that the construction site safety management monitoring system using AIoT and Mobile technology has a valid effect on the construction site.

• Proceedings of the National Institute of Ecology of the Republic of Korea
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• v.3 no.2
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• pp.67-72
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• 2022

Ecological research relies on the interpretation of large amounts of visual data obtained from extensive wildlife surveys, but such large-scale image interpretation is costly and time-consuming. Using an artificial intelligence (AI) machine learning model, especially convolution neural networks (CNN), it is possible to streamline these manual tasks on image information and to protect wildlife and record and predict behavior. Ecological research using deep-learning-based object recognition technology includes various research purposes such as identifying, detecting, and identifying species of wild animals, and identification of the location of poachers in real-time. These advances in the application of AI technology can enable efficient management of endangered wildlife, animal detection in various environments, and real-time analysis of image information collected by unmanned aerial vehicles. Furthermore, the need for school education and social use on biodiversity and environmental issues using AI is raised. School education and citizen science related to ecological activities using AI technology can enhance environmental awareness, and strengthen more knowledge and problem-solving skills in science and research processes. Under these prospects, in this paper, we compare the results of our early 2013 study, which automatically identified African cichlid fish species using photographic data of them, with the results of reanalysis by CNN deep learning method. By using PyTorch and PyTorch Lightning frameworks, we achieve an accuracy of 82.54% and an F1-score of 0.77 with minimal programming and data preprocessing effort. This is a significant improvement over the previous our machine learning methods, which required heavy feature engineering costs and had 78% accuracy.

• Journal of the Korea Society of Computer and Information
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• v.27 no.12
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• pp.85-92
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• 2022

In this study, we aim to design an action game that increases the user experience. In order to increase the immersion of the game, the characteristics of the game used by the user were analyzed, and the systemic and visual characteristics of the game were designed with reference to each characteristic. The proposed method uses Unity 3D to implement an interaction system between objects in the game and is designed in a way that allows users to immerse themselves in the game. To induce immersion through the visual elements of the game, 2D objects and players are placed in a 3D space, and a 2D dynamic light shader is added. It is composed of inter-combat rules and monster behavior pattern collision detection and event detection. The proposed method contained the user experience with the implementation thesis, and showed the game's possibility of leading the user's affordance.