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신기영(Geeyoung Shin),권동호(Dongho Kwon),김명회(Myunghoe Kim),한승식(Seungsik Han) 한국자동차공학회 2021 한국자동차공학회 부문종합 학술대회 Vol.2021 No.6
As the era of fully autonomous driving approaches, the purpose of the vehicle will change from a vehicle to a living space like a home. For this, it is necessary to change the space in which the passengers stay. For this change, the indoor space is secured larger than before. To do this, the reduction of existing indoor parts is essential. It is thought that the change of the cockpit located in front of the current vehicle will be most prominent. For the change ofcockpit, the representative part that occupies the largest volume among the parts located inside It is a hvac system for indoor cooling/heating. This cooling/heating system has a direct effect on mileage It must be a system capable of responding to heat pump systems, and the interior volume must be reduced to respond to changes. For this, the temperature control door inside the hvac is removed, and the hvac system capable of constructing a heat pump system Development review.
UML을 활용한 미래전 대비 포병 사격지휘 자동화 발전방향 연구
김현식(Hyunsik Kim),홍석준(Seokjun Hong),권동호(Dongho Kwon),김주현(Juhyun Kim),마정목(Jungmok Ma) (사)한국CDE학회 2018 한국CDE학회 논문집 Vol.23 No.4
Fire direction is critical in controlling the overall operation of fire missions. The ROK(Republic of Korea) army is using BTCS A1(Battalion Tactical Command System A1) as the automated fire direction system. However, capability gaps are identified between the current BTCS A1 and the required system in the future. Therefore, we attempt to overcome the gaps and propose measures to improve BTCS A1. First, the concept of fire direction is reviewed and reconstructed using UML(Unified Modeling Language). Based on this, BTCS A1 is compared with other advanced systems and the user survey is conducted. Finally, measures to improve the automated fire direction system for future warfare are suggested in terms of H/W, S/W and communication.
퍼지이론에 기반한 포병 표적 공격우선순위 선정방안 연구
안주한 ( Ahn Joohan ),홍석준 ( Hong Seokjun ),권동호 ( Kwon Dongho ),김주현 ( Kim Juhyun ),마정목 ( Ma Jungmok ) 미래군사학회 2019 한국군사학논총 Vol.8 No.1
The purpose of this study is to present a model for assisting the decision of target priority in artillery. The currently available system considers the intention of the commander, the priority of the request and proximity by separate rankings, which does not reflect all the necessary elements. First, based on field manuals and the discussion with artillery experts, target characters, reliability, commander intention and priority of request are reestablished as critical elements in target priority. Then, the elements are modeled using the fuzzy inference technique in order to make an efficient and automatic decision of attack priority. To verify the proposed model, the target priority of the model is compared with that of 30 artillery officers over 20 artificial targets. The comparative study shows that the proposed model can automatically provide valid attack priorities without a human involvement.
공조 AUTO 사용성 개선을 위한 운전자 맞춤형 학습 제어기 개발
이정훈(Jeonghoon Lee),신기영(Kiyoung Shin),김중재(Joongjae Kim),권동호(Dongho Kwon),이성제(Sungje Lee),황동우(Dongwoo Hwang) 한국자동차공학회 2018 한국자동차공학회 학술대회 및 전시회 Vol.2018 No.11
An ‘auto’ mode on conventional FATC system provides climate control automatically based on the map which is preset. The values preset on the map are setting temperature, ambient temperature, in-car temperature, sun-road, evaporator temperature, coolant temperature, and etc. Those various values are regarded as inputs to calculate Td; thermal index. Calculated Td determines output values for discharging air temperature, Mode selection, AC on/off, blower steps, or compressor capacity to control internal cabin climate environment. The output values; however, does not always guarantee to satisfy every passengers’ climate comfort in cabin. Therefore, customized or passenger climate preference reflection on FATC are needed to provide better climate comfort to passenger in cabin area. To reflect a user climate preference into FATC, machine learning algorithm has been incorporated into FATC logic. Its machine learning algorithm is based on frequency of climate operation setting and lasting time. It is suitable for vehicle climate control because of no need for high performance CPU and heavy memory compared with deep learning and others. This is demonstrated in a bench test and vehicle experiments.