• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.6
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• pp.1195-1202
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• 2016

Recently, a lot of studies that applying the big data technology to various fields, are progressing actively. In the maritime domain, the big data is the meaningful information which makes and gathers by the navigation and communication equipment from the many ships on the ocean. Also, importance of the maritime safety is emphasized, because maritime accidents are rising with increasing of maritime traffic. To support prevention of maritime accidents, in this paper, we developed a vessel traffic display and statistic system based on AIS messages from the many vessels of maritime. Also, to verify the developed system, we conducted tests for vessel track display function and vessel traffic statistic function based on two test scenarios. Therefore, we verified the effectiveness of the developed system for vessel tracks display, abnormal navigation patterns, checking failure of AIS equipments and maritime traffic statistic analyses.

• Journal of Internet Computing and Services
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• v.21 no.4
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• pp.117-126
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• 2020

A maritime object detection system is an intelligent assistance system to maritime autonomous surface ship(MASS). It detects automatically floating debris, which has a clash risk with objects in the surrounding water and used to be checked by a captain with a naked eye, at a similar level of accuracy to the human check method. It is used to detect objects around a ship. In the past, they were detected with information gathered from radars or sonar devices. With the development of artificial intelligence technology, intelligent CCTV installed in a ship are used to detect various types of floating debris on the course of sailing. If the speed of processing video data slows down due to the various requirements and complexity of MASS, however, there is no guarantee for safety as well as smooth service support. Trying to solve this issue, this study conducted research on the minimization of computation volumes for video data and the increased speed of data processing to detect maritime objects. Unlike previous studies that used the Hough transform algorithm to find the horizon and secure the areas of interest for the concerned objects, the present study proposed a new method of optimizing a binarization algorithm and finding areas whose locations were similar to actual objects in order to improve the speed. A maritime object detection system was materialized based on deep learning CNN to demonstrate the usefulness of the proposed method and assess the performance of the algorithm. The proposed algorithm performed at a speed that was 4 times faster than the old method while keeping the detection accuracy of the old method.

• Journal of the Korea Society for Simulation
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• v.29 no.1
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• pp.47-55
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• 2020

This paper presents a real-time autonomous computation of shot numbers and aiming points against multiple soft targets on grounds by applying an unsupervised learning, k-mean clustering and Monte carlo simulation. For this computation, a 100 × 200 square meters size of virtual battlefield is created where an augmented enemy infantry platoon unit attacks, defences, and is scatted, and a virtual weapon with a lethal range of 15m is modeled. In order to determine damage types of the enemy unit: no damage, light wound, heavy wound and death, Monte carlo simulation is performed to apply the Carlton damage function for the damage effect of the soft targets. In addition, in order to achieve the damage effectiveness of the enemy units in line with the commander's intention, the optimal shot numbers and aiming point locations are calculated in less than 0.4 seconds by applying the k-mean clustering and repetitive Monte carlo simulation. It is hoped that this study will help to develop a system that reduces the decision time for 'detection-decision-shoot' process in battalion-scaled combat units operating Dronebot combat system.

• Smart Media Journal
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• v.6 no.4
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• pp.58-64
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• 2017

Modern society is characterized by rapid increase in world population, aging of the rural population, decrease of cultivation area due to industrialization. The food problem is becoming an important issue with the farmers and becomes rural. Recently, the researches about the field of the smart farm are actively carried out to increase the profit of the rural area. The existing smart farm researches mainly monitor the cultivation environment of the crops in the greenhouse, another way like in the case of poor quality t is being studied that the system to control cultivation environmental factors is automatically activated to keep the cultivation environment of crops in optimum conditions. The researches focus on the crops cultivated indoors, and there are not many studies applied to the cultivation environment of crops grown outside. In this paper, we propose a method to improve the harvestability of poor areas by monitoring the areas with bad harvests by using big data analysis, by precisely predicting the harvest timing of fruit trees growing in orchards. Factors besides for harvesting include fruit color information and fruit weight information We suggest that a harvest correlation factor data collected in real time. It is analyzed using the Apache Spark engine. The Apache Spark engine has excellent performance in real-time data analysis as well as high capacity batch data analysis. User device receiving service supports PC user and smartphone users. A sensing data receiving device purpose Arduino, because it requires only simple processing to receive a sensed data and transmit it to the server. It regulates a harvest time of fruit which produces a good quality fruit, it is needful to determine a poor harvest area or concentrate a bad area. In this paper, we also present an architectural model to determine the bad areas of fruit harvest using strong data analysis.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2017.04a
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• pp.90-90
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• 2017

생력화를 위한 구기자의 수확 기계화는 열악한 수확작업환경을 쾌적한 작업환경으로 개선하고 노동력 감소, 생산비 절감을 할 수 있다. 관행 손 수확과 진동 고리형 수확기 방법보다 높은 작업 능률 향상으로 영농규모의 확대 촉진 및 안정적인 영농 구조를 구축하여 재배농가의 생산비를 절감하여 경쟁력을 높일 수 있으며, 기존 인력에 의존하였던 수확작업을 기계화함으로서 전업농 및 대단위 경작이 가능하게 함으로서 국내에서 생산한 양질의 구기자를 국민에게 안정적으로 제공할 수 있다. 따라서, 본 연구는 구기자 수확작업의 생력화를 위하여 개발 보급된 수목형의 재배법 특성을 분석하고 이를 토대로 타격장치를 적용한 보행형 구기자 수확기를 개발하는데 목적이 있다. 수목형 구기자나무의 분지에 착과되어 있는 숙과를 주행하면서 탈과 할 수 있는 탈과 장치를 제작하기 위하여 타격형 탈과 장치를 3D 모델링 작업(Inventor V.11, Autodesk, USA) 후 시작기를 제작, 구기자 수확 시작기는 주행부, 타격장치, 집과부, 분지유인부로 구성하였다. 구기자 수확 시작기의 최대 높이는 형태학적 특성을 토대로 타격봉의 높이를 900 mm 이하로 제한하였으며, 조향장치의 높이는 800 mm로 하였다. 주행부는 구기자 재식 조사결과를 이용하여 고랑 폭 1,500 mm 이하에서 자유롭게 전 후진 이동이 가능하고 경사로 등을 주행 시에도 안전성을 높이기 위해 자동브레이크 기능이 있으며 타격장치의 타격 봉은 알루미늄 재질로 지름 100 mm, 길이 400 mm로 설계 제작하였으며, 구기자 분지 타격 시 분지와 타격 봉이 수직 상태로 타격이 가능하도록 제작, 집과장치는 포장의 두둑, 고랑은 일괄 표준화가 되어 있지않아 청양구기자시험장에서 측정한 재배법을 바탕으로 설계된 수집부 프레임의 적용범위는 폭 450 mm, 길이 720 mm, 높이 1,500 mm를 집과 범위로 하여 설계 제작하였다. 타격 방식을 적용한 구기자 수확기 성능평가 결과 조숙기에 30초 이상의 탈과 시 87.5 % 이상 탈과는 어려울 것으로 판단되었으며, 성숙기에는 타격시간에 관계없이 92 %의 매우 우수한 탈과율이 나타났다. 성숙기의 주행속도 48 m/h 일 때 탈과율과 집과율은 89 %, 92 %로 나타났다.단위작업시간당 최대 수확 능력은 관행작업 2.9 kg/hr, 진동고리형 수확기 5.2 kg/hr, 타격방식을 적용한 구기자 수확기는 최소 7.6 kg/hr, 최대 24.1 kg/hr로 관행작업과 비교하여 주행속도와 시기별 최소 2.6배, 최대 8.3배의 작업 성능 차이가 나타났다. 재배양식에서는 기계화 수목형이 적합한 것으로 나타났고, 타격방식을 적용한 보행형 구기자 수확기를 이용하여 수형별 시간대별 수확성능을 시험한 결과 우수한 결과가 나타났다. 이에 따라 구기자 재배 농가에 기계화수목형 재배법을 보급하고 타격방식을 적용한 구기자 수확기를 이용하면 작업환경 개선과 노동력, 인건비 절감을 통한 영농규모의 확대 촉진 및 안정적인 영농 구조로 구기자 경쟁력 제고를 할 수 있을 것으로 판단되어진다.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.15 no.1
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• pp.66-76
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• 2016

Determination of type of a vehicle is being used in various areas such as collecting tolls, collecting statistical traffic data and traffic prognosis. Because most of the vehicle type classification systems depend on vehicle length indirectly or directly, highly reliable automatic vehicle length measurement system is crucial for them. This study makes use of a pencil beam laser rangemeter and devises a mechanical device which rotates the laser rangemeter. The implemented system measures the range between a point and the laser rangemeter then indicates it as a spherical coordinate. We obtain several silhouettes of cross section of the vehicle, the rate of change of the silhouettes, signs of the rates then squares the rates to apply cell averaging constant false alarm rate (CA-CFAR) technique to find out where the border is between the vehicle and the background. Using the border and trigonometry, we calculated the length of the vehicle and confirmed that the calculated vehicle length is about 94% of actual length.

• Journal of Korean Society of Transportation
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• v.28 no.3
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• pp.169-183
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• 2010

Image-based traffic information collection systems have entered widespread adoption and use in many countries since these systems are not only capable of replacing existing loop-based detectors which have limitations in management and administration, but are also capable of providing and managing a wide variety of traffic related information. In addition, these systems are expanding rapidly in terms of purpose and scope of use. Currently, the utilization of image processing technology in the field of traffic accident management is limited to installing surveillance cameras on locations where traffic accidents are expected to occur and digitalizing of recorded data. Accurately recording the sequence of situations around a traffic accident in a freeway and then objectively and clearly analyzing how such accident occurred is more urgent and important than anything else in resolving a traffic accident. Therefore, in this research, existing technologies, this freeway attribute, velocity changes, volume changes, occupancy changes reflect judge the primary. Furthermore, We pointed out by many past researches while presenting and implementing an active and environmentally adaptive methodology capable of effectively reducing false detection situations which frequently occur even with the Gaussian Mixture model analytical method which has been considered the best among well-known environmental obstacle reduction methods. Therefore, in this way, the accident was the final decision. Also, environmental factors occur frequently, and with the index finger situations, effectively reducing that can actively and environmentally adaptive techniques through accident final judgment. This implementation of the evaluate performance of the experiment road of 12 incidents in simulated and the jang-hang IC's real-time accident experiment. As a result, the do well detection 93.33%, false alarm 6.7% as showed high reliability.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.28 no.2
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• pp.385-395
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• 2018

With the development of Internet, cyber attack has become a major threat. To detect cyber attacks, intrusion detection system(IDS) has been widely deployed. But IDS has a critical weakness which is that it generates a large number of false alarms. One of the promising techniques that reduce the false alarms in real time is machine learning. However, there are problems that must be solved to use machine learning. So, many machine learning approaches have been applied to this field. But so far, researchers have not focused on features. Despite the features of IDS alerts are important for performance of model, the approach to feature is ignored. In this paper, we propose new feature set which can improve the performance of model and can be extracted from a single alarm. New features are motivated from security analyst's know-how. We trained and tested the proposed model applied new feature set with real IDS alerts. Experimental results indicate the proposed model can achieve better accuracy and false positive rate than SVM model with ordinary features.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.8 no.8
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• pp.1620-1625
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• 2004

To advance fisheries, we set developmental directions of fishery information by grasping present situations and analyzing maritime & fisheries issues. We promote various policies through effective systematical information data bases, based on both control and utilization of oceanic resources. For these puposes, it is imperative that we set up fisheries communication networks. There are satellite assisted informational networks to assist fishing vessels with their marine based movements. However, there's no hope for poorly equipped fishermen to adopt this network because of extravagant network call charges. So we think that using existing SSB communication system is the best plan. We organize fishery communication network by HF SSB communication which doesn't have operational costs. We build wireless transmitting and receiving stations that are basic systems of informnation, and equip wireless data communication systems by the use of wireless communication network protocols in coastal stations. It is necessary that a fish boat has a terminal device for wireless data communication. In this research we can conclude that if we transmit the location of a fishing boat in-real time through GPS channels then we propose that some methods be formulated to able terminal devices on fishing boats to collect various types of information, such as meteorological and oceanic conditions.

• The Journal of the Korea Contents Association
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• v.14 no.11
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• pp.467-475
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• 2014

With the development of genomics, wearable device and IT/NT, a vast amount of bio-medical data are generated recently. Also, healthcare industries based on big-data are booming and big-data technology based on bio-medical data is rising rapidly as a core technology for improving the national health and aged society. A pathway is the biological deep knowledge that represents the relations of dynamics and interaction among proteins, genes and cells by a network. A pathway is wildly being used as an important part of a bio-medical big-data analysis. However, a pathway analysis requires a lot of time and effort because a pathway is very diverse and high volume. Also, multidimensional analysis systems for various pathways are nonexistent even now. In this paper, we proposed a pathway analysis system that collects user interest pathways from KEGG pathway database that supports the most widely used pathways, constructs a network based on a hierarchy structure of pathways and analyzes the relations of dynamics and interaction among pathways by clustering and selecting core pathways from the network. Finally, to verify the superiority of our pathway analysis system, we evaluate the performance of our system in various experiments.