• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.4
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• pp.358-365
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• 2017

In this paper, we propose an object recognition method using image information to improve the efficiency of visual servoingfor industrial manipulators in industry. This is an image-processing method for real-time responses to an abnormal situation or to external environment change in a work object by utilizing camera-image information of an industrial manipulator. The object recognition method proposed in this paper uses the Otsu method, a thresholding technique based on separation of the V channel containing color information and the S channel, in which it is easy to separate the background from the HSV channel in order to improve the recognition rate of the existing Harris Corner algorithm. Through this study, when the work object is not placed in the correct position due to external factors or from being twisted,the position is calculated and provided to the industrial manipulator.

• Journal of Korea Multimedia Society
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• v.9 no.5
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• pp.595-605
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• 2006

In this thesis, GPS and the electronic mapping were used to realize such a system by recognizing license plate numbers and identifying the location of objects that move at synchronous times with simulated movement in the electronic map. As well, throughout the study, a camera attached to a PDA, one of the mobile devices, automatically recognized and confirmed acquired license plate numbers from the front and back of each car. Using this mobile technique in a wireless network, searches for specific plate numbers and information about the location of the car is transmitted to a remote server. The use of such a GPS-based system allows for the measurement of topography and the effective acquisition of a car's location. The information is then transmitted to a central controlling center and stored as text to be reproduced later in the form of diagrams. Getting positional information through GPS and using image-processing with a PDA makes it possible to estimate the correct information of a car's location and to transmit the specific information of the car to a control center simultaneously, so that the center will get information such as type of the car, possibility of the defects that a car might have, and possibly to offer help with those functions. Such information can establish a mobile system that can recognize and accurately trace the location of cars.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.3
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• pp.422-427
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• 2010

Korean Game industry, especially MMORPG(Massively Multiplayer Online Game) has been rapidly expanding in these days. But As game industry is growing, lots of online game security incidents have also been increasing and getting prevailing. One of the most critical security incidents is 'Game Bots', which are programs to play MMORPG instead of human players. If player let the game bots play for them, they can get a lot of benefic game elements (experience points, items, etc.) without any effort, and it is considered unfair to other players. Plenty of game companies try to prevent bots, but it does not work well. In this paper, we propose a behavior pattern model for detecting bots. We analyzed behaviors of human players as well as bots and identified six game features to build the model to differentiate game bots from human players. Based on these features, we made a Naive Bayesian classifier to reasoning the game bot or not. To evaluated our method, we used 10 game bot data and 6 human Player data. As a result, we classify Game bot and human player with 88% accuracy.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.4
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• pp.503-508
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• 2010

Recently, the advance of mobile devices has made various services possible beyond simple communication. One of services is the predicting the future path of users and providing the most suitable location based service based on the prediction results. Almost of these prediction methods are based on previous path data. Thus, calculating similarities between current location information and the previous trajectories for path prediction is an important operation. The collected trajectory data have a huge amount of location information generally. These information needs the high computational cost for calculating similarities. For reducing computational cost, the meaningful location based trajectory model approaches are proposed. However, most of the previous researches are considering only the physical information such as stay time and the distance for extracting the meaningful locations. Thus, they will probably ignore the characteristics of users for meaningful location extraction. In this paper, we suggest a meaningful location extracting and trajectory simplification approach considering the stay time, distance, and additionally interaction information of user. The method collects the location information using GPS device and interaction information between the user and the others. Using these data, the proposed method defines the proximity of the people who are related with the user. The system extracts the meaningful locations based on the calculated proximities, stay time and distance. Using the selected meaningful locations the trajectories are simplified. For verifying the usability of the proposed method, we collect the behavioral data of smart phone users. Using these data, we measure the suitability of meaningful location extraction method, and the accuracy of prediction approach based on simplified trajectories. Following these result, we confirmed the usability of proposed method.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.6
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• pp.716-720
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• 2006

Fuzzy sets are good for abstracting and unifying information using natural language like terms. However, fuzzy sets embody vagueness and users may have different attitude to the vagueness, each user may choose difference one as the best among several fuzzy values. In this paper, we develop a method teaming a user's, preference on fuzzy values and select one which fits to his preference. Users' preferences are modeled with artificial neural networks. We gather learning data from users by asking to choose the best from two fuzzy values in several representative cases of comparing two fuzzy sets. In order to establish tile representative comparing cases, we enumerate more than 600 cases and cluster them into several groups. Neural networks ate trained with the users' answer and the given two fuzzy values in each case. Experiments show that the proposed method produces outputs closet to users' preference than other methods.

• Korean Journal of Construction Engineering and Management
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• v.21 no.5
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• pp.55-63
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• 2020

Earthwork automation has emerged as a promising technology in the construction industry, and the application of earthwork automation technology is starting from the acquisition and processing of point cloud data of the site. Point cloud data has more than a million data due to vast extent of the construction site, and the processing time of the original point cloud data is critical because it takes tens or hundreds of hours to generate a Digital Terrain Model (DTM), and enhancement of the processing time can largely impact on the efficiency of the modeling. Currently, a benchmark program (BP) is actively used for the purpose of both point cloud data processing and BIM design as an integrated program in Korea, however, there are some aspects to be modified and refined. This study modified the BP, and developed an updated program by adopting a compile-based development environment, newly designed UI/UX, and OpenGL while maintaining existing PCD processing functions, and expended compatibility of the PCD file formats. We conducted a comparative test in terms of loading speed with different number of point cloud data, and the results showed that 92 to 99% performance increase was found in the developed program. This program can be used as a foundation for the development of a program that reduces the gap between design and construction by integrating PCD and earthwork BIM functions in the future.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.14 no.1
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• pp.77-84
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• 2015

In 2011, greenhouse gas emissions of transport sector were 85.04 million $tonCO_2eq$ and road emissions accounted for 95% of total emissions in the transport sector. There are few innovative technologies to reduce greenhouse gas emissions aside from eco-driving education and public relation program. Therefore, this paper focused on analyzing optimal acceleration by certain road grades and suggested fuel-efficient driving method for various uphill sections. Scenarios were established by driving modes. Speed profiles were generated by scenarios and speed variations. Each speed profile applied to Comprehensive Modal Emission Model and then each fuel consumption was estimated. Driving mode and speed variation that minimized fuel consumption were driven according to grade percent and uphill distance. When driving in the eco-friendly mode of the driving and speed variation, reduction rate of fuel consumption was evaluated by comparison between eco-driving and cruise control mode. When a vehicle drove under eco-driving mode at 100kph, 90kph and 80kph on uphill road, fuel consumptions were reduced by 33.9%, 30.8% and 5.3%, respectively.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.4
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• pp.724-729
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• 2008

In the case of 'MAEMI', the Typhoon which formed in September, 2003, the largest-scale damage of tidal wave was caused by the co-occurrence of Typhoon surge and full tide. Until now Korea has been focusing on the calculating the amount of damage and its restoration to cope with these sea and harbor disasters. It is essential to establish some systematic counterplans to diminish such damages of large-scale tidal invasion on coastal lowlands considering the recent weather conditions of growing scale of typhoons. Therefore, the purpose of this research is to make the counterplans for prevention against disasters fulfilled effectively based on the data conducted by comparing and analyzing the accuracy between observation values and the results of estimating the greatest overflow area according to abnormal tidal levels centered on Masan area where there was the severest damage from tidal wave at that time. It's necessary utilize data like high-resolution satellite image and LiDAR(etc.) for correct analysis data considering geographical characteristics of dangerous area from the storm surge. And we must make a solution to minimize the damage by making data of dangerous section of flood into GIS Database using those data (as stated above) and drawing correcter damage function.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.96-97
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• 2019

최근 홍수파 또는 댐 및 보 등의 하천구조물과, 본류와 지류의 합류로 인해 발생하는 배수영향 등으로 하천에 고리형 수위-유량관계가 나타나 수위-유량관계식의 신뢰도가 저하된다고 판단되는 대하천 본류와 합류부 인근 지류에서 초음파유속계 측정결과를 지표로 활용하는 자동유량장치가 현재 58개소에 도입되어 운용되고 있다. 그러나, 최근 4대강 사업으로 다기능보 설치 등 하천의 수리적 특성이 변동하였고, 지류에서 주로 운용되는 수위-유량관계 기반 유량관측소에 본류-지류합류로 인한 배수영향으로 고리형 수위-유량관계가 형성될 경우, 지류 수위관측소를 자동유량관측소로 대체할 필요성이 제기되고 있다. 또한, 기존 자동유량관측소의 경우 홍수량이 기준이하로 나타날 경우 수위-유량 관계식으로 대체하는 경우도 있는데 적용기준이 명확하지 않다. 본 연구에서는 신규 자동유량장치 설치, 기존 수위관측소의 자동유량관측소대체, 자동유량관측소에서 수위-유량관계곡선의 활용 기준 마련 등을 명확하게 판단하기 위해 고리형 수위-유량관계 패턴을 분석하고 배수영향구간을 산정하여 제시하고 지류 수위관측소의 존속여부, 유량자료의 적절성을 판단할 수 있는 방법론을 제시 하고자한다. 이를 위해, 다기능보 설치 및 지류-본류로 인한 지류하천의 배수영향과 홍수파에 의한 고리형 수위-유량관계의 양상을 다양한 수문사상에 대해 분석하였다. 분석방법으로는 대상유역을 선정하고, 대상유역에 대한 부정류 모형을 구축하기 위해 HEC-RAS 모형을 사용하고, 대상유역에 대한 HEC-RAS 지형자료와 유량입력자료를 구축한다. HEC-RAS 부정류 모형 구축 후 다양한 본류와 지류의 유량을 가정하여 고리형 수위-유량 발생 패턴을 분석한다. 지류-본류 합류 영향의 경우, 본류영향의 유무를 고려하여 상승된 수위의 비율로부터 일정 유의수준을 두어 배수영향구간을 산정한다. 다기능보의 영향은 다기능보 설치 전후를 가정하여 영향을 분석한다. 본 연구에서는 시범구역으로 하류에 창녕함안보가 위치한 낙동강과 합류하는 남강과 하류에 달성보가 위치한 낙동강과 합류하는 금호강을 대상하천으로 하여 위 분석방법을 적용하였다. 분석결과 남강의 경우 지류 유량이 작은 경우에도 본류 유량이 상대적으로 큰 경우 배수영향에 의한 고리형이 나타났고, 다기능보에 의한 배수영향은 매우 적은 것으로 나타났다. 또한, 지류의 유량이 큰 경우 홍수파에 의한 고리형 관계도 발생함을 알 수 있었다. 금호강의 경우 또한 마찬가지로 본류의 유량에 의해 발생하는 고려형 관계가 지배적임을 알 수 있었다. 따라서 이러한 결과를 바탕으로 95%의 유의 수준을 기반으로 고리형 발생 여부를 확일할 수 있는 모노그래프를 남강, 금호강에 대해 작성하여 수위-유량관계 기반 유량 산정의 적절성을 판단하는 기준으로 제안하였다.

• Korean Journal of Construction Engineering and Management
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• v.20 no.6
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• pp.126-131
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• 2019

The estimation of available solar energy at particular locations is critical to find and assess suitable locations of PV sites. The amount of PV power generation is however affected by various geographical factors (e.g., weather), which may make it difficult to identify the complex relationship between affecting factors and power outputs and to apply findings from one study to another in different locations. This study thus undertakes a regression analysis using data collected from 172 PV plants spatially distributed in Korea to identify critical weather conditions and estimate the potential power generation of PV systems. Such data also include solar radiation, precipitation, fine dust, humidity, temperature, cloud amount, sunshine duration, and wind speed. The estimated PV power generation is then compared to the actual PV power generation to evaluate prediction performance. As a result, the proposed model achieves a MAPE of 11.696(%) and an R-squred of 0.979. It is also found that the variables, excluding humidity, are all statistically significant in predicting the efficiency of PV power generation. According, this study may facilitate the understanding of what weather conditions can be considered and the estimation of PV power generation for evaluating and determining suitable locations of PV facilities.