• Proceedings of the Korean Society of Computer Information Conference
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• 2015.07a
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• pp.187-190
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• 2015

주행 중 차선 이탈 경고 시스템은 사고 발생 예방 차원에서 매우 높은 효과가 인정되어서 차선이탈 경고 장치(LDWS) 제품들이 출시되고 있다. 본 논문은 블랙박스의 영상을 이용하여 차선 검출에 정확도를 향상하기 위한 알고리즘을 연구한 것으로 특히 차량에 장착되어 있는 블랙박스 영상을 영상 변환 없이, 실시간 소프트웨어 만 으로 처리할 수 있는 알고리즘을 연구한다. 차선인식을 위한 최적의 영상 ROI를 결정하고, 차선 인식 정확도를 향상하기 위한 전 처리 과정을 적용하고, 동영상의 연속성을 잘못된 차선인식에 대한 보정, 인식이 되지 않는 차선에 대한 후보 차선 추천 알고리즘과 시점 변환에 의한 야간, 곡선 도로에 대한 오인식율을 최소화 하는 방법을 제안한다. 도로주행의 다양한 환경에 대한 실험을 진행했으며, 각각의 방법 적용에 의한 오인식율의 감소와 많은 인식 알고리즘 적용에 의한 처리 속도 저하를 개선하기 위한 연구를 진행했으며, 본 논문은 블랙박스 영상을 이용하여 주행 차선 인식을 위한 최적 알고리즘을 제안한다.

• Journal of the Korea Society for Simulation
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• v.19 no.3
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• pp.1-6
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• 2010

Simulation analysis may be the essential means to either evaluate performance of systems or optimize system parameters for new design. Including many variations for design and implementation, MANET (Mobile Ad-hoc NETwork) is one target area of such an analysis. Since every node, however, included in the network has mobility, one MANET could be overlapped or merged with another one which use a different transport protocol. In order to communicate among nodes in this case, the new merged network should configure paths and addresses in advance. Configuring paths and addresses generates much overheads which ultimately cause delay in communicating data. Performance analysis is required to improve the data transport performance by minimizing overheads. This paper proposes a sound address auto-configuration method which is based on an on-demand manner and then presents modeling and performance analysis of the method. NS-2 simulation results verify that the proposed method can not only alleviate overheads, which are inevitably generated for address auto-configuration processes, and but also decentralize them in time.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.925-938
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• 2022

Agricultural reservoirs are an important water resource nationwide and vulnerable to abnormal climate effects such as drought caused by climate change. Therefore, it is required enhanced management for appropriate operation. Although water-level tracking is necessary through continuous monitoring, it is challenging to measure and observe on-site due to practical problems. This study presents an objective comparison between multiple AI models for water-body extraction using radar images that have the advantages of wide coverage, and frequent revisit time. The proposed methods in this study used Sentinel-1 Synthetic Aperture Radar (SAR) images, and unlike common methods of water extraction based on optical images, they are suitable for long-term monitoring because they are less affected by the weather conditions. We built four AI models such as Support Vector Machine (SVM), Random Forest (RF), Artificial Neural Network (ANN), and Automated Machine Learning (AutoML) using drone images, sentinel-1 SAR and DSM data. There are total of 22 reservoirs of less than 1 million tons for the study, including small and medium-sized reservoirs with an effective storage capacity of less than 300,000 tons. 45 images from 22 reservoirs were used for model training and verification, and the results show that the AutoML model was 0.01 to 0.03 better in the water Intersection over Union (IoU) than the other three models, with Accuracy=0.92 and mIoU=0.81 in a test. As the result, AutoML performed as well as the classical machine learning methods and it is expected that the applicability of the water-body extraction technique by AutoML to monitor reservoirs automatically.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.3 no.1 s.4
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• pp.53-65
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• 2004

A series of accidents, which are non-recurrent and non-anticipated, are called incidents. These incidents make standard traffic flows interrupt, which result in the decrease of road capacity and a number of social and economic costs, such as the traffic congestion and air pollution. In order to prevent the hazard of incidents, domestic and foreign traffic management center are likely to opt auto-sense system with algorithms of auto-incident sense. However, it is evaluated that the algorithms have a low function with frequent wrong alarms, even if they accurately ry to speculate the incidents. In the case of bottleneck which has lack of road capacity, compared with other roads, due to inefficient road structured over-capacity of the demand of on-off ramp, the incidents regularly take place. Nonetheless, it can be more difficult to speculate the auto-incidents sense owing to similar incidents, such as the queue of in-out flows of cars and the change of road line. Throughout this research, the function of the model has improved excluding near road line in the module of the incidents which is based on the auto-incidents algorithms during the sense of the congestion of ramp areas.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.7
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• pp.85-91
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• 2016

Typically, radar systems change the pulse repetition interval of their modulated signal in order to avoid detection. On the other hand the radar-signal detection system tries to detect the modulation pattern. The histogram or auto-correlation methods are usually used to detect the PRI pattern of the radar signal. However these methods tend to lost the sequence information of the PRI pulses. This paper proposes a PRI-sequence detection algorithm based on the finite-state machine that could detect not only the PRI pattern but also their sequence.

• The Journal of the Korea Contents Association
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• v.7 no.7
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• pp.60-67
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• 2007

The spectrum sensing is important for decision of using frequency band. It checks the frequency band for cognitive radio system. In this paper, we apply autocorrelation function to the energy detection method. We use the autocorrelation function to improve the performance of spectrum sensing method based on the energy detection method. This method is different from cyclostationary process method where parameters such as the mean or the autocorrelation function are time-varying periodically. And we propose improved method that is robust in poor radio environment. If the proposed method applies for sensing in the cognitive radio system, it will have the structural simplicity and the fast computation of spectrum sensing.

• Journal of Korea Game Society
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• v.11 no.1
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• pp.121-129
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• 2011

In an online-game, the various game service victimized cases are generated by the bots program or auto program. Particularly, the abnormal collection of the game money and item loses the inherent fun of a game. It reaches ultimately the definite bad effect to the game life cycle. This paper collects and analyzes the pattern of game behavior change for the bots detection method. By using the game activity changing information of the human and game activity changing information of the bots, the degree of resemblance was measured. It utilized in the bots detection method. In an experiment, by using the served online-game, the model of a user and bots were generated and similarity was distinguished. And the reasonable result was confirmed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.9
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• pp.2927-2941
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• 2022

The primary task of machine fault diagnosis is to judge whether the current state is normal or damaged, so it is a typical binary classification problem with mutual exclusion. Mutually exclusive events and out-of-domain detection have one thing in common: there are two types of data and no intersection. We proposed a fusion model method to improve the accuracy of machine fault diagnosis, which is based on the mutual exclusivity of events and the commonality of out-of-distribution detection, and finally generalized to all binary classification problems. It is reported that the performance of a convolutional neural network (CNN) will decrease as the recognition type increases, so the variational auto-encoder (VAE) is used as the primary model. Two VAE models are used to train the machine's normal and fault sound data. Two reconstruction probabilities will be obtained during the test. The smaller value is transformed into a correction value of another value according to the mutually exclusive characteristics. Finally, the classification result is obtained according to the fusion algorithm. Filtering normal data features from fault data features is proposed, which shields the interference and makes the fault features more prominent. We confirm that good performance improvements have been achieved in the machine fault detection data set, and the results are better than most mainstream models.

• Proceedings of the KAIS Fall Conference
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• 2002.11a
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• pp.212-215
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• 2002

유성음에서의 피치 측정 알고리즘은 보편적이고 단순하여 구현하기 용이하다. 피치는 간단한 전환을 통해 음정을 구할 수 있다. 이렇게 구한 음정 정보를 이용하여 미디를 이용한 자동반주기의 음정 조절을 가능하게 할 수 있다. 본 연구에서는 쉽게 피치를 구하기 위해 저가의 방식인 AMDF(Average Magnitude Difference Function) 알고리즘을 이용하여 피치를 구하였고 이를 미디 음정 정보로 전환하기 위한 방법을 제안하였다. 이를 이용하면 가수의 음정에 맞게 자동반주기가 음정을 보정하여 음악을 연주하여 줄 수 있다.

• Proceedings of the KIEE Conference
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• 1987.07b
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• pp.1065-1068
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• 1987

This study is to show the method of corresponding points-detection by sampling and normalizing. And it explains the procedures of the animation package which generate animation through the collation of image codes.