• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.36 no.3
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• pp.175-185
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• 2000

This paper described on a basic study to organize fishing vessel control system in order to control efficiently fishing vessel in Korean offshore. It was digitalized ARPA image on the fishing processing of a fleet of purse seiner in conducting fishing operation at Cheju offshore in Korea as a digital camera and then simulated by used VTMS. Futhermore, it was investigated on the application of FVTMS which can control efficiently fishing vessels in fishing ground. The results obtained were as follows ; (1) It was taken 16 minutes and 35 minutes to casting and hauling net in fishing processing respectively. The length of rope pulled by scout boat was 200m, tactical diameter in casting net was 340.8m, turning speed was 6kts as well. (2) The processing of casting and hauling net was moved to SW, NE as results of simulation when the current direction and speed set into NE, 2kts and SW, 2kts respectively. Such as these results suggest that can predict to control the fishing vessel previously with information of fishing ground, fishery and ship's maneuvering, etc. (3) The control range of VTMS radar used in simulation was about 16 miles. Although converting from a radar of the control vessel to another one, it was continuously acquired for the vector and the target data. The optimum control position could be determined by measuring and analyzing to distance and direction between the control vessel and the fleet of fishing vessel. (4) The FVTMS(fishing vessel traffic management services) model was suggested that fishing vessels received fishing conditions and safety navigation information can operate safely and efficiently.

• Journal of Life Science
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• v.17 no.12
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• pp.1641-1648
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• 2007

Yippee-like proteins, which have been identified as the homolog of Drosophila yippee protein containing a zinc-finger domain, are known to be highly conserved among eukaryotes. However, their functional roles are still poorly understood. Recently we initiated ordered differential display (ODD)-polymerase chain reaction (PCR) to isolate genes of which expressions are altered following activation of human T cells. On the ODD-PCR image, one PCR-product detected in unstimulated T cells was not detectable at the time when the activated T cells traversed near $G_1/S$ boundary following activation by immobilized anti-CD3. Cloning and nucleotide sequence analysis revealed that the PCR-product was yippee-like 5 (YPEL5) gene, which was known as a human homolog of the Drosophila yippee gene. Northern blot analysis confirmed the amount of ${\sim}2.2$ kb YPEL5 mRNA expression detectable in unstimulated T cells was sustained until 1.5 hr after activation and then rapidly declined to undetectable level by 5 hr. Ectopic expression of YPEL5 gene in human cervix epitheloid carcinoma HeLa cells caused a significant reduction in cell proliferation to the level of 47% of the control. Expression of GFP-YPEL5 fusion protein in HeLa cells showed its nuclear localization. These results demonstrated that the expression level of human YPEL5 mRNA was negatively regulated in the early stage of T cell activation, and suggested that YPEL5 might exert an inhibitory effect on the cell proliferation as a nuclear protein.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.7
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• pp.271-278
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• 2021

As the current cyber attacks become more intelligent, the existing Intrusion Detection System is difficult for detecting intelligent attacks that deviate from the existing stored patterns. In an attempt to solve this, a model of a deep learning-based intrusion detection system that analyzes the pattern of intelligent attacks through data learning has emerged. Intrusion detection systems are divided into host-based and network-based depending on the installation location. Unlike network-based intrusion detection systems, host-based intrusion detection systems have the disadvantage of having to observe the inside and outside of the system as a whole. However, it has the advantage of being able to detect intrusions that cannot be detected by a network-based intrusion detection system. Therefore, in this study, we conducted a study on a host-based intrusion detection system. In order to evaluate and improve the performance of the host-based intrusion detection system model, we used the host-based Leipzig Intrusion Detection-Data Set (LID-DS) published in 2018. In the performance evaluation of the model using that data set, in order to confirm the similarity of each data and reconstructed to identify whether it is normal data or abnormal data, 1D vector data is converted to 3D image data. Also, the deep learning model has the drawback of having to re-learn every time a new cyber attack method is seen. In other words, it is not efficient because it takes a long time to learn a large amount of data. To solve this problem, this paper proposes the Siamese Convolutional Neural Network (Siamese-CNN) to use the Few-Shot Learning method that shows excellent performance by learning the little amount of data. Siamese-CNN determines whether the attacks are of the same type by the similarity score of each sample of cyber attacks converted into images. The accuracy was calculated using Few-Shot Learning technique, and the performance of Vanilla Convolutional Neural Network (Vanilla-CNN) and Siamese-CNN was compared to confirm the performance of Siamese-CNN. As a result of measuring Accuracy, Precision, Recall and F1-Score index, it was confirmed that the recall of the Siamese-CNN model proposed in this study was increased by about 6% from the Vanilla-CNN model.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.89-106
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• 2022

Recently, the amount of video data collected from smartphones, CCTVs, black boxes, and high-definition cameras has increased rapidly. According to the increasing video data, the requirements for analysis and utilization are increasing. Due to the lack of skilled manpower to analyze videos in many industries, machine learning and artificial intelligence are actively used to assist manpower. In this situation, the demand for various computer vision technologies such as object detection and tracking, action detection, emotion detection, and Re-ID also increased rapidly. However, the object detection and tracking technology has many difficulties that degrade performance, such as re-appearance after the object's departure from the video recording location, and occlusion. Accordingly, action and emotion detection models based on object detection and tracking models also have difficulties in extracting data for each object. In addition, deep learning architectures consist of various models suffer from performance degradation due to bottlenects and lack of optimization. In this study, we propose an video analysis system consists of YOLOv5 based DeepSORT object tracking model, SlowFast based action recognition model, Torchreid based Re-ID model, and AWS Rekognition which is emotion recognition service. Proposed model uses single-linkage hierarchical clustering based Re-ID and some processing method which maximize hardware throughput. It has higher accuracy than the performance of the re-identification model using simple metrics, near real-time processing performance, and prevents tracking failure due to object departure and re-emergence, occlusion, etc. By continuously linking the action and facial emotion detection results of each object to the same object, it is possible to efficiently analyze videos. The re-identification model extracts a feature vector from the bounding box of object image detected by the object tracking model for each frame, and applies the single-linkage hierarchical clustering from the past frame using the extracted feature vectors to identify the same object that failed to track. Through the above process, it is possible to re-track the same object that has failed to tracking in the case of re-appearance or occlusion after leaving the video location. As a result, action and facial emotion detection results of the newly recognized object due to the tracking fails can be linked to those of the object that appeared in the past. On the other hand, as a way to improve processing performance, we introduce Bounding Box Queue by Object and Feature Queue method that can reduce RAM memory requirements while maximizing GPU memory throughput. Also we introduce the IoF(Intersection over Face) algorithm that allows facial emotion recognized through AWS Rekognition to be linked with object tracking information. The academic significance of this study is that the two-stage re-identification model can have real-time performance even in a high-cost environment that performs action and facial emotion detection according to processing techniques without reducing the accuracy by using simple metrics to achieve real-time performance. The practical implication of this study is that in various industrial fields that require action and facial emotion detection but have many difficulties due to the fails in object tracking can analyze videos effectively through proposed model. Proposed model which has high accuracy of retrace and processing performance can be used in various fields such as intelligent monitoring, observation services and behavioral or psychological analysis services where the integration of tracking information and extracted metadata creates greate industrial and business value. In the future, in order to measure the object tracking performance more precisely, there is a need to conduct an experiment using the MOT Challenge dataset, which is data used by many international conferences. We will investigate the problem that the IoF algorithm cannot solve to develop an additional complementary algorithm. In addition, we plan to conduct additional research to apply this model to various fields' dataset related to intelligent video analysis.