• Proceedings of the KWS Conference
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• 2009.11a
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• pp.121-121
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• 2009

자동차는 코너 주행 시 In-corner와 Out-corner 의 바퀴 궤적이 달라지므로, 특별한 장치가 없이 좌우 구동 측의 바퀴가 같은 속도로 회전을 하게 되면 정상적인 주행이 불가능하다. 따라서 정상적인 코너 주행이 가능 하려면, 코너 안쪽 바퀴보다 바깥쪽 바퀴가 더 빨리 회전해야 하며 이러한 회전 차를 보상받지 못할 경우 바깥쪽 바퀴가 끌리는 현상이 발생하는데 이를 방지하기 위해 디퍼렌셜 기어가 필요하다. 현재 디퍼렌셜 기어는 디퍼렌셜 케이스와 링기어를 볼트로 체결하는 조립 공법을 통해 생산되고 있다. 하지만 볼트 체결 공법은 조립을 위한 볼트와 볼트 체결을 위한 플랜지와 볼팅을 위한 홀을 가공하는 공정이 필요하기 때문에 재료비 절감 및 생산 효율 향상에 매우 불리하고 볼트체결을 위한 부분 때문에 불필요한 무게가 증가하게 된다. 따라서 본 연구에서는 이러한 기계적 체결 방식을 레이저 용접 방식으로 대체하여 재료비를 절감하고 무게 저감을 통해 주행성능을 향상시키고자 하였다. 링기어의 소재는 침탄처리강(SCM420H)이며 디퍼렌셜 케이스의 소재는 주철(GCD500)을 사용하고 있다. 주철은 용접시 용접부와 열영향부에서 마르텐사이트 조직과 레데브라이트, 시멘타이트 조직이 생성되며 고탄소 모재의 탄소 확산으로 인한 부분 혼합영역에서 탄소 합금이 생성되어 균열이 발생하는 등 용접성이 매우 좋지 않은 것으로 알려져 있다. 이러한 주철의 난용접성을 해결하는 방법으로는 고탄소 모재 용접시 발생하는 탄소의 확산을 억제하거나 예열이나 후열 처리를 통한 냉각 속도의 제어하는 방법과 오스테나이트 안정화 원소를 첨가한 필러와이어를 사용하여 용접시 마르텐사이트와 시멘타이트의 성장을 방해하는 방법 등이 이용되고 있다. 본 연구에서는 예열처리나 후열처리를 통한 주철의 용접법은 대량 생산을 통한 원가절감을 노리는 자동차 업계의 특성에 비추어 볼 때 비용이나 프로세스 구성 면에서 적용하는 것이 어려울 것이라 판단하여 Ni-base filler metal을 통한 주철의 용접법을 선택하였고 그 결과 실차에 적용하기 위한 비틀림 강성 테스트나 내구 테스트는 통과하였으나 NVH 테스트 결과 볼팅 체결 방식에 비하여 소음이 커지는 문제가 발생하고 링기어의 HAZ부가 고경화 되는 문제가 발생하였다. 때문에 용입깊이를 초기 시제품인 5mm에서 4mm로 변경시켜 입열량 감소 및 용접변형을 줄여 소음 문제를 해결하고자 하였으며 링기어의 침탄층을 1mm 절삭하여 링기어 HAZ부의 고경화 문제를 해결하고자 하였다. 이러한 용접 구조 변경이 용접변형 및 강성과 피로에 미치는 영향력을 알아보고자 용접 및 열처리 상용 소프트웨어인 SYSWELD, 구조해석 상용소프트웨어인 NX_NASTRAN, 피로 해석 상용 소프트웨어인 FEMFAT을 이용하여 시뮬레이션 하였고 실제 구조 변경한 용접 시제품과 비교, 분석하였다.

• Korean Journal of Remote Sensing
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• v.37 no.5_2
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• pp.1235-1243
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• 2021

Geostationary Ocean Color Imager 2 (GOCI-II) on Geo-KOMPSAT-2 (GK2B)satellite was developed as a mission successor of GOCI on COMS which had been operated for around 10 years since launch in 2010 to observe and monitor ocean color around Korean peninsula. GOCI-II on GK2B was successfully launched in February of 2020 to continue for detection, monitoring, quantification, and prediction of short/long term changes of coastal ocean environment for marine science research and application purpose. GOCI-II had already finished IAC and IOT including early in-orbit calibration and had been handed over to NOSC (National Ocean Satellite Center) in KHOA (Korea Hydrographic and Oceanographic Agency). Radiometric calibration was periodically conducted using on-board solar calibration system in GOCI-II. The final calibrated gain and offset were applied and validated during IOT. And three video parameter sets for one day and 12 video parameter sets for a year was selected and transferred to NOSC for normal operation. Star measurement-based INR (Image Navigation and Registration) navigation filtering and landmark measurement-based image geometric correction were applied to meet the all INR requirements. The GOCI2 INR software was validated through INR IOT. In this paper, status and results of IOT, radiometric calibration and INR of GOCI-II are analysed and described.

• Journal of the Society of Disaster Information
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• v.17 no.3
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• pp.589-599
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• 2021

Purpose: A reliable indicator of congested traffic speed is essential in providing the information of traffic flow states about motorway sections. The aim of this study is to propose an adaptive indicator of congested speed which is employed for deciding the traffic flow states for individual motorway sections using disaggregated section-based speed data. Method: Typically, the state of traffic flow is categorized into the three: uncongested, mixed, congested states. A method, presented in this study, was developed for identifying boundary speed values of road sections through categorizing the three traffic flow states with individual vehicular speed values. The boundary speed state of each road segment is determined using the speed distributions of mixed and congested traffic states. Result: Analysis results revealed that boundary speed values between mixed and congested states for road sections were similar to those of US and EU criteria (i.e., 48.28~66.0 kph). This indicates that boundary speed values could be different according to road sections. Conclusion: It is expected that the method and indicator, proposed in this study, could be efficaciously used for providing ad-hoc real-time traffic states and computing traffic congestion costs for motorway sections in the era of big data.

• Journal of Korea Port Economic Association
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• v.37 no.1
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• pp.71-85
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• 2021

A variety of methods have been proposed through a number of studies on sophisticated demand forecasting models that can reduce logistics costs. These studies mainly determine the applicable demand forecasting model based on the pattern of demand quantity and try to judge the accuracy of the model through statistical verification. Demand patterns can be broadly divided into regularity and irregularity. A regular pattern means that the order is regular and the order quantity is constant. In this case, predicting demand mainly through regression model or time series model was used. However, this demand is called "intermittent demand" when irregular and fluctuating amount of order quantity is large, and there is a high possibility of error in demand prediction with existing regression model or time series model. For items that show intermittent demand, predicting demand is mainly done using Croston or HOLTS. In this study, we analyze the demand patterns of various items of air cargo with intermittent patterns and apply the most appropriate model to predict and verify the demand. In this process, intermittent optimal demand forecasting model of air cargo is proposed by analyzing the fit of various models of air cargo by item and region.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.05a
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• pp.665-667
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• 2014

Wellness is IT fused with the user manage and maintain the health of a service can help you. If you are using an existing Fitness Center to yourself by choosing appliances that fit with the risk of injury in order to learn how the efficient movement had existed for a long time was needed. To resolve, use the personal training but more expensive cost of people's problems, and shown again in the habit of exercising alone will have difficulty. This paper provides a variety of smart phones based on a hybrid app with compatibility with the platform and personalized training market system. Users of the Fitness Center is built into smart phones in the history of their movement sensors or transmits to the Web by typing directly. This is based on exercise programs tailored to users via the training market. Personal training marketplace has a variety of users, check the history of this movement he can recommend an exercise program for themselves can be applied by selecting the. This provides users with the right exercise program can do long-term exercise habits can be proactive and goal setting.

• Explosives and Blasting
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• v.36 no.4
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• pp.1-8
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• 2018

This numerical study was intended to evaluate the applicability of the half charge blasting to mining and tunnelling. The half charge blasting is a method that two separate rounds are sequentially blasted for the rock burdens in which long blast holes have already been drilled at one operation. The aim of the method is to decrease the construction cost and period in mining and tunnelling projects as well as to increase the blasting efficiency. Several numerical analyses were conducted by using the Euler-Lagrange solver on ANSYS AUTODYN to identify the effects of the suggested method on the blasting results in underground excavations. The overall performance of the suggested method was also compared to an ordinary blasting method. The analysis model was comprised of the Eulerian parts (explosive, air, and stemming materials) and the Lagrangian parts (rock material). As a result, it was found that, owing to the air decks formed in the bottom parts of the long blast holes, the first round of the suggested method presented a higher shock pressure and particle velocities in the vicinity of the blast holes compared to the ordinary blasting method.

• Journal of Navigation and Port Research
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• v.43 no.1
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• pp.72-78
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• 2019

The second-hand ship market provides immediate access to the freight market for shipping investors. When introducing second-hand vessels, the precise estimate of the price is crucial to the decision-making process because it directly affects the burden of capital cost to investors in the future. Previous studies on the second-hand market have mainly focused on the market efficiency. The number of papers on the estimation of second-hand vessel values is very limited. This study proposes an artificial neural network model that has not been attempted in previous studies. Six factors, freight, new-building price, orderbook, scrap price, age and vessel size, that affect the second-hand ship price were identified through literature review. The employed data is 366 real trading records of Panamax second-hand vessels reported to Clarkson between January 2016 and December 2018. Statistical filtering was carried out through correlation analysis and stepwise regression analysis, and three parameters, which are freight, age and size, were selected. Ten-fold cross validation was used to estimate the hyper-parameters of the artificial neural network model. The result of this study confirmed that the performance of the artificial neural network model is better than that of simple stepwise regression analysis. The application of the statistical verification process and artificial neural network model differentiates this paper from others. In addition, it is expected that a scientific model that satisfies both statistical rationality and accuracy of the results will make a contribution to real-life practices.

• Membrane Journal
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• v.31 no.1
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• pp.35-51
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• 2021

Advancements in thin-film nanocomposite (TFN) membrane technology for nanofiltration is crucial for removing pollutants from natural resources. In recent years, various metal-organic framework (MOF) modifications have been tested to overcome the drawbacks that are inevitable with conventional thin-film composite (TFC) and TFN membranes. In general, MIL-101(Cr), UiO-66, ZIF-8, and HKUST-1 [Cu3(BCT2)] are MOFs that were proven to exhibit excellent membrane performance in terms of solvent permeability and solute rejection; their respective studies are reviewed in this article. Other novelties, such as the simultaneous use of different MOFs and unique MOF layering techniques (e.g., dip-coating, spray pre-disposition, Langmuir-Schaefer film, etc.) are also discussed as they present alternate solutions for membrane enhancement and/or preparation convenience. Not only are these MOF-modified TFN membranes frequently shown to improve separation performance from their respective TFC and TFN membranes, but many reports also explain their potential for a cost-effective and environmentally friendly process. In this review the thin film nanocomposite nanofiltration membrane is discussed.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.3
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• pp.133-139
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• 2021

Currently, the process of constructing a high-definition road map has a high proportion of manual labor, so there are limitations in construction time and cost. Research to automate map production with high-definition road maps using artificial intelligence is being actively conducted, but since the construction of training data for the map construction is also done manually, there is a need to automatically build training data. Therefore, in this study, after converting to images using point clouds acquired by a mobile mapping system, the road marking areas were extracted through image reclassification and overlap analysis using thresholds. Then, a methodology was proposed to automatically construct training data for deep learning data for the high-definition road map through the classification of the polygon types in the extracted regions. As a result of training 2,764 lane data constructed through the proposed methodology on a deep learning-based PointNet model, the training accuracy was 99.977%, and as a result of predicting the lanes of three color types using the trained model, the accuracy was 99.566%. Therefore, it was found that the methodology proposed in this study can efficiently produce training data for high-definition road maps, and it is believed that the map production process of road markings can also be automated.

• Journal of the Korea institute for structural maintenance and inspection
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• v.26 no.5
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• pp.30-42
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• 2022

Due to the increase in construction of tunnels, the burdens of maintenance works for tunnel structures have been increasing in Korea. In addition, the increase of traffic volume and aging of materials also threatens the safety of tunnel facilities, therefore, maintenance costs are expected to increase significantly in the future. Accordingly, automated condition assessment technologies like image-based tunnel scanning system for inspection and diagnosis of tunnel facilities have been proposed. For image-based tunnel scanning system, it is key to create a planar image through stitching of multiple images captured by tunnel scanning system. In this study, performance of feature-based stitching algorithms suitable for stitching tunnel scanning images was evaluated. In order to find a suitable algorithm SIFT, ORB, and BRISK are compared. The performance of the proposed algorithm was determined by the number of feature extraction, calculation speed, accuracy of feature matching, and image stitching result. As for stitching performance, SIFT algorithm was the best in all parts of tunnel image. ORB and BRISK also showed satisfactory performance and short calculation time. SIFT can be used to generate precise planar images. ORB and BRISK also showed satisfactory stitching results, confirming the possibility of being used when real-time stitching is required.