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This study analyzes introduction state of Green SCM technology in domestic and overseas area and suggests how to reduce greenhouse gases of logistics in the future. Thus, introduction state of Green SCM was reviewed by building and integrating logistic-related informatization systems such as WMS and TMS. And how to apply the system to track toxic substance in supply chain was performed to manage toxic substance. Therefore, it will be necessary to develop a device to display and collect vehicle condition & log through OBD-II, which is widely adopted, and hardware to measure, collect, and monitor carbon dioxide emission and power consumption of fixed facilities.

Supply chain including transportation expend the 21% of domestic energy consumption. It is necessary to diminish the excess energy usage at entire supply chain. This paper deals with the application of MFCA(Material Flow Cost Accounting) for SCM to save energy consumption. We construct the material center corresponding to each logistics function in order to apply the MFCA for GCM(Green Chain Management). We also construct the MFCA framework which consists of MFCA Database Management, MFCA Visualization, GCM Data Integration, GCM Data Tracking, and MFCA Data Predict & Assignment. We expect to help determining the range of logistics function to apply the MFCA for GCM.

본 연구에서는 물류분야에 MFCA를 도입하기 위해 공급업체와 본사간의 MFCA 정 보를 공유할 수 있는 데이터베이스를 구축하여 원활한 정보흐름이 이루어지도록 하였 다. 또한 GCM 프로세스를 분석하여 물류 부문에 MFCA를 적용할 수 있도록 물량센터 를 물류 기능별 프로세스에 맞춰 선정하도록 하였다. 마지막으로 MFCA Framework 를 5가지로 설정(MFCA Database Management, MFCA Visualization, GCM Data Integration, GCM Data Tracking, MFCA Data Predict & Assignment)하여 각 기능 별로 설명하였다. 이를 통해 그동안 환경친화적 물류활동을 구현하는 기업들이 MFCA 를 도입하고자 할 경우 GCM 프로세스 중 MFCA를 적용하는 범위 및 적용대상을 선 정하는데 도움을 줄 수 있을 것으로 기대된다.

This paper presents the first fully parallelized optimization of GCM in a GPU environment. As the era of IoT emerges, a large number of clients communicate with servers, necessitating encrypted communications for security. GCM is a type of AEAD and is currently used in various security protocols, including TLS 1.3 and IPsec. Due to the burden of performing encrypted communication with numerous clients, there has been significant research on utilizing GPUs for high-speed parallel processing in encryption. However, to date, there has been no fully parallelized implementation of GCM on GPUs. This paper proposes a method for parallelizing the challenging GHASH computation in GCM mode, leading to a high-speed parallel implementation of AES-GCM that can exceed 400Gb/s, meeting the requirements of next-generation communication systems. The proposed approach is algorithm-independent and can be applied to any block ciphers. Our implementation on an RTX 4090 demonstrates a performance improvement of *15.38 compared to the maximum processing throughput of a multi-threaded Intel(R) Core(TM) i7-13700K. It also achieves a *17.87 improvement compared to a hybrid CPU?GPU system. Compared to the most researched FPGA implementation for GCM, specifically Xilinx Ultrascale FPGA, our implementation achieves *1.11 better performance. For not only throughput but also power efficiency also better than other implementation, it achieves *3.33 compared to CPU implementation on Intel Xeon E3-1220, also it achieves *21.09 compared to FPGA implementation for AES on Xilinx Virtex 7 series, which is not including full GCM.

GCM(Green Chain Management) is being spread by that companies which apply with eco-friendly concept in entire SCM(Supply Chain Management) perspective is being increased for resolving some recent environmental problems. There are reasons that numerous companies focus on GCM. At first various governments began to directly manage components and material contents about various noxious substances. Second, environment problem management in the entire logistics process from procurement of product materials to distribution of product became to be important according to introduction of certification system that evaluate the environmental impact throughout life-cycle of products. Purposes of this paper are 1) to analyze the GCM process whose important is more increasing in the recent logistics environment change, 2) to comprehend places and sources that generate environmental elements like energy consumption and greenhouse gas emissions in elements of logistics function, and 3) to construct integrated GCM system for observing activity of logistics function throughout life-cycle of products.

본 연구에서는 강수의 공간적 편차가 큰 산악지역에서 축소기법을 적용하기 위한 방법론을 마련하고 이를 이용하여 미래 강수특성의 변화를 추정하고자 하였다. 이를 위하여 한반도내 산악지역이라고 할 수 있는 남한강유역을 대상유역으로 선 정하였고 일반적인 축소기법 중의 하나인 신경망과 고도자료를 부가자료로 활용하여 유역의 지형적 특성을 반영할 수 있 는 SKlm 기법을 연계하여 신경망-SKlm 모형(ANN-SKlm : Artificial Neural Network - Simple Kriging with varying local means)을 구축하였다. 유역내 6개의 기상관측소 지점의 월강수량을 이용하여 신경망-SKlm 기법과 기존 강수량의 공간분 포 방법인 Thiessen 및 Ordinary Kriging 을 적용하여 비교·평가하였다. 유역내에 보다 밀도있게 구성되어 있는 25개 강우 관측소 지점을 대상으로 각 기법을 평가한 결과 고도자료를 부가자료로 사용하는 SKlm 기법이 가장 우수한 결과를 나타 내었다.

This study established a methodology for the application of downscaling technique in a mountainous area having large spatial variations of rainfall and tried to estimate the change of rainfall characteristics in the future under climate change using the established method. The Namhan river basin, which is in the mountainous area of the Korean peninsula, has been chosen as the study area. Artificial Neural Network - Simple Kriging with varying local means (ANN-SKlm) has been built by combining artificial neural network, which is one of the general downscaling techniques, and SKlm technique, which can reflect the geomorphologic characteristics like elevation of the study area. The evaluation of SKlm technique was done by using the monthly rainfalls at six weather stations which KMA(Korea Meteorological Administration) is managing in the basin. The ANN-SKlm technique was compared with the Thiessen technique and ordinary kriging(OK) technique. According to the evaluation result of each technique the SKlm technique showed the best result.

본 연구는 불교 상담의 한 방편으로 행동주의 코칭의 대표적 모델인 ‘GCM(Grow Coaching Model)’을 어떻게 불교 상담에 적용할 것인지에 관한 융합적인 연구로써 최초로 시도되었다. 그리고 불교 상담과 GCM의 분석을 바탕으로 불교 상담과 코칭 모델의 적용 가능성을 탐색하고 불교적 콘텐츠를 섭렵하여 융합적 코칭 모델을 개발하고자 하였다. GCM은 코치가 피코치의 문제해결과 삶의 치유를 위한 목표 설정(Goal)과 현실 인식(Reality), 선택(Option), 의지 (Will)에 이르는 구조화된 형태의 코칭 방식으로 인간을 성장과 발전의 주체로 인식하고 소통의 기술은 언어적 대화와 질문이다. 반면에 다소 정(靜)적이고 비(非)언어적 성향의 불교 상담은 인간을 불성을 가진 존재로 보았고 소통기술은 주로 위빠사나(vipassana,觀)와 선(seon,禪) 명상을 많이 활용하고 있다. 논자는 불교 상담이라는 커다란 범주(範疇) 안에 하나의 독립된 분야(分野)로서 구조적이고 정형화된 GCM을 기반으로 ‘붓다(Buddha)-GCM’ 즉, B-GCM을 개발하였다. B-GCM은 불자나 비 불자를 대상으로 쉬우면서도 전문적인 상담과 코칭을 지향하며, 불교의 ‘심우도(尋牛圖)’와 신성한 ‘사성제(四聖諦)’의 진리, 단계별 ‘5~15분 명상(冥想)’이라는 콘텐츠를 구축하고 GCM을 접목하여 B-GCM을 초기 중기 후기단계로 분류하였다.

This study was first attempted as a convergence study on how to apply GCM, a representative model of behaviouist coaching, to Buddhist counseling as a method of Buddhist counseling. And based on the analysis of Buddhis counseling and GCM, it was attemped to explore the applicability of the Buddhist counseling and coaching model and to develop a convergence coaching model by immersing in Buddhist content. GCM is a structured coaching method in which the coach can solve the problems of the coach and set goals for the healing of life, perception of reality, choice, and will. and the question and communication skills are verbal conversations and questions. On the other hand, Buddhist counseling, which is rather static and non-verbal, viewed humans as beings with Buddha-nature, and mainly uses vipassana and clear images for communication techniques. The author developed the Buddha-GCM, based on the structured and standardized GCM as an independent field within the large category of Buddhist counseling. B-GCM aims for easy and professional counseling and coaching for Buddhists and non-Buddhists, builds 5-15 minute meditation content for each step, the truth of Buddhism and the divine four holy spirits, and grafts GCM to initiate B-GCM, early stage, mid-stage, and late stage.

Due to the actual demand of remote medical monitoring system, the overall framework of remote medical monitoring system which based on the Internet of things technology and cloud computing technology is designed. Put forward the application solutions of Android mobile terminal equipment in this system, and discusses the development methods of different types of the Android mobile terminal. First, the overall framework of remote medical monitoring system is introduced. Second, the development of data communication method of Android Bluetooth BLE and HTTP is discussed in detail, which is based on the Android framework. The Internet of Things and cloud computing technologies together to build a remote medical monitoring system. Android mobile terminal development on the medical applications of data processing centers and medical data display applications. Finally, the experimental results show that the system not only runs and transfers data stabilize, but also strong real-time, effective and feasible.

This paper defines a new DNP3 link layer frame structure based on the link layer packet characteristics of transmission time requirements and security requirements in substation automation system (SAS). The new frame structure can provide three different work modes: authentication, authorization-encryption, non-authenticated encryption. Then we propose a link-layer security service mechanism in substation automation system based on the GCM-AES, including the session key agreement protocol based on EKE, GMAC-based message authentication protocol, GCM-AES Authentication Encryption-based DNP3 protocol and GCM-AES-based message transform algorithm. Through experimental calculation and analysis, the results show that the new security mechanisms achieve the efficient and safety in substation packets transmission.

본 연구는 기후변화를 고려하여 목표연도 설계강우량을 산정하기 위해서 확률분포함수의 모수와 GCMs(Global Circulation Models)을 통해 모의된 연강수량, 그리고 관측된 연최대강우량 사이의 관계를 이용하여 앙상블 확률강우량을 산정하는 방법을 제안하였다. 목표연도 확률분포함수의 모수는 연강수량을 기후변화의 외부인자로 반영하여 회귀모형에 의해 추정된다. 본 연구에서 제안된 방법은 기후변화 시나리오와 GCM 모형에 따른 불확실성을 고려하여 우리나라 10개 지점에 대해서 설계목표연도인 2020년과 2030년의 확률강우량(지속기간 24시간)을 산정하는데 적용되었다. 그 결과, 강릉, 대전, 서울, 속초 등의 지점은 설계목표연도에서의 확률강우량이 현재(2009년)보다 크게 증가하는 것으로 나타났으며, 광주, 서산, 제주의 경우는 비교적 큰 차이가 나타나지 않았다. 부산, 목포, 대구 등은 목표연도 확률강우량이 현재보다 작게 산정되었다. 이러한 결과는 각 지점에서 관측된 연최대강우시계열의 추세성분이 확률강우량 산정과정에 반영된 것으로 판단된다. 본 연구가 제안하는 설계목표연도 확률강우량 산정법은 기후변화의 영향을 고려하여 10년 후의 목표연도 확률강우량을 산정할 수 있으며, 이러한 확률강우량은 수공구조물 계획 및 설계 시 참고자료로 활용될 수 있을 것이다.

This study proposed a practical approach to estimate design rainfalls that can be applied to design reference year using the relationships among distribution parameters, annual rainfalls estimated from GCMs(Global Circulation Models), and annual maximum rainfalls observed at gauging stations, leading to consider climate change. The highlight of this study is to estimate distribution parameters through regression models with annual rainfalls as a causal factor. The proposed model was applied to estimated ensemble design rainfalls with 24-hr duration in built-up design reference years of 2020 and 2030 at 10 stations considering the uncertainty induced by emission scenarios and GCMs. The overall results indicated that the design rainfalls of Gangneung, Daejeon, Seoul, and Sokcho in 2020 and 2030 are higher than those in 2009(assumed as the current), the design rainfalls of Gwangju, Seosan, and Jeju in 2020 and 2030 are very close to the current, and the design rainfalls of Busan, Mokpo, and Daegu in 2020 and 2030 are lower than the current. It is considered that the trend component of the annual maximum rainfalls is incorporated into the process of estimating design rainfalls. The proposed method can provide design rainfalls in the next 10 more years in which the effect of climate change is reflected, and thus may suggest useful reference or alternatives for the hydrologic plan and management.

우리나라도 세계적인 기후변화 추세에 따라 장래 수자원의 변동이 예상되고 있다. 이러한 문제에 대응하고 피해를 최소화하기 위해서는 미래의 기후를 예측하는 기상모형으로부터 유용한 정보를 추출하여 활용하는 방안을 마련하여야 한다. 본 연구의 목적은 불확실성 분석을 통한 수자원 운영의 중심이 되는 국내 유역들에 대한 GCM모형의 활용성을 평가하는 것이다. 주된 기법은 Kolmogorov-Smirnov test를 이용하여 관측값과 GCM 모의값의 유의성을 평가하는 것이다. GCM의 시공간적 특성을 고려한 기법이 제안되었으며, 유럽의 ECMWF모형과 국내 기상연구소에서 모의한 서로 다른 해상도를 지니는 Metri-GCM의 표면 강수량과 표면온도 자료 등이 추출되어 적용되었다. 관측값으로는 유역대표 강수량, 온도, 유량 등의 자료가 적용되었다. 분석 결과 GCM의 유역별 적용성을 확인할 수 있었는데, 특히 온도 자료의 경우 대부분의 유역에 모든 모형의 모의 결과가 적용 가능함을 확인하였다.

It is expected that conditions of water resources will be changed in Korea in accordance with world wide climate change. In order to deal with this problem and find a way of minimizing the effect of future climate change, the usefulness of climate model simulation information is examined in this study. The objective of this study is to assess the applicability of GCM (General Circulation Model) information for Korean water resources management through uncertainty analysis. The methods are based on probabilistic measures of the effectiveness of GCM simulations of an indicator variable for discriminating high versus low regional observations of a target variable. The formulation uses the significance probability of the Kolmogorov-Smirnov test for detecting differences between two variables. An estimator that accounts for climate model simulation and spatial association between the GCM data and observed data is used. Atmospheric general circulation model (AGCM) simulations done by ECMWF (European Centre for Medium-Range Weather Forecasts) with a resolution of $2^{\circ}{\times}2^{\circ}$, and METRI (Meteorological Research Institute, Korea) with resolutions of $2^{\circ}{\times}2^{\circ}$ and $4^{\circ}{\times}5^{\circ}$, were used for indicator variables, while observed mean areal precipitation (MAP) data, discharge data and mean areal temperature data on the seven major river basins in Korea were used for target variables. The results show that GCM simulations are useful in discriminating the high from the low of the observed precipitation, discharge, and temperature values. Temperature especially can be useful regardless of model and season.

The objective of this study is to evaluate hydrologic impacts of climate change according to downscaling methods using the Soil and Water Assessment Tool (SWAT) model at watershed scale. We used the APCC Integrated Modeling Solution (AIMS) for assessing various General Circulation Models (GCMs) and downscaling methods. AIMS provides three downscaling methods: 1) BCSA (Bias-Correction & Stochastic Analogue), 2) Simple Quantile Mapping (SQM), 3) SDQDM (Spatial Disaggregation and Quantile Delta Mapping). To assess future hydrologic responses of climate change, we adopted three GCMs: CESM1-BGC for flood, MIROC-ESM for drought, and HadGEM2-AO for Korea Meteorological Administration (KMA) national standard scenario. Combined nine climate change scenarios were assessed by Expert Team on Climate Change Detection and Indices (ETCCDI). SWAT model was established at the Mankyung watershed and the applicability assessment was completed by performing calibration and validation from 2008 to 2017. Historical reproducibility results from BCSA, SQM, SDQDM of three GCMs show different patterns on annual precipitation, maximum temperature, and four selected ETCCDI. BCSA and SQM showed high historical reproducibility compared with the observed data, however SDQDM was underestimated, possibly due to the uncertainty of future climate data. Future hydrologic responses presented greater variability in SQM and relatively less variability in BCSA and SDQDM. This study implies that reasonable selection of GCMs and downscaling methods considering research objective is important and necessary to minimize uncertainty of climate change scenarios.

정상성 마코프 연쇄 모형은 일강우모의 모형으로 광범위하게 이용되고 있다. 하지만 정상성 마코프 연쇄 모형의 기본가정은 통계학적 특성이 시간에 따라 변화하지 않는 것으로, 일강우모의 시에 평균 또는 분산의 경향적 변화를 효과적으로 반영할 수 없다. 이러한 문제점을 인지하여 본 연구에서는 연주기 및 계절변화에 대하여 우수한 모의 능력을 나타내는 GCM의 모의결과를 입력자료로 이용하여 일강우량을 모의하기 위한 통계학적 상세화(downscaling) 기법인 비정상성 은닉 마코프 모형을 개발하였다. 개발된 모형을 낙동강 유역에 존재하는 영주지점, 문경지점 및 구미지점의 관측강우량에 적용한 결과, 일단위 및 계절단위의 강우량의 통계적 특성을 기존 모형에 비하여 개선된 결과를 도출할 수 있었으며, 또한 개발된 모형은 극치강수량 복원에 있어서도 관측값과 보다 유사한 결과를 보여 주었다. 이러한 점에서 정확성이 확보된 GCM 계절예측자료가 입력자료로 NHMM 모형에 활용된다면 예측기반의 일강수 상세화 모형으로 활용될 수 있을 것으로 판단된다. 이와 더불어, 기후변화 시나리오 입력자료가 사용된다면 기후변화 상세화 모형으로서도 적용될 수 있을 것으로 사료된다.

The stationary Markov chain model has been widely used as a daily rainfall simulation model. A main assumption of the stationary Markov model is that statistical characteristics do not change over time and do not have any trends. In other words, the stationary Markov chain model for daily rainfall simulation essentially can not incorporate any changes in mean or variance into the model. Here we develop a Non-stationary hidden Markov chain model (NHMM) based stochastic downscaling scheme for simulating the daily rainfall sequences, using general circulation models (GCMs) as inputs. It has been acknowledged that GCMs perform well with respect to annual and seasonal variation at large spatial scale and they stand as one of the primary sources for obtaining forecasts. The proposed model is applied to daily rainfall series at three stations in Nakdong watershed. The model showed a better performance in reproducing most of the statistics associated with daily and seasonal rainfall. In particular, the proposed model provided a significant improvement in reproducing the extremes. It was confirmed that the proposed model could be used as a downscaling model for the purpose of generating plausible daily rainfall scenarios if elaborate GCM forecasts can used as a predictor. Also, the proposed NHMM model can be applied to climate change studies if GCM based climate change scenarios are used as inputs.

일반적으로 기후변화영향에 관한 연구수행을 위해 전지구기후모형(GCM; Global Climate Model)이 사용되고 있다. 하지만 GCM은 공간해상도(Spatial resolution)가 거칠기 때문에 수문학 분야에서 주로 사용되는 유역규모의 지역적인 스케일특성과 물리적 특징을 표현하는데 한계가 있다. 또한 GCM 기후변수들 중 강수량의 경우 한반도 지역의 6월과 10월 사이에 연강수량의 67% 이상이 집중되는 계절성을 반영하지 못하고 있으며, 높은 불확실성을 보이고 있다. 본 연구에서는 GCM 기반의 다지점 인공신경망기법을 적용한 상세화(Downscaling)를 실시하였다. GCM의 24개 2D변수에 대한 주성분분석을 실시하여 신경망의 학습인자로 사용하였으며, 학습, 검증 및 예측기간은 각각 1981~1995년, 1996~2000년, 2011~2100년으로 A1B 시나리오를 대상으로 상세화를 실시하였다. 또한, 여름철 태풍사상을 모의하기 위한 Stochastic Typhoon Simulation기법과 Baseline과 Projection 사이의 강수량 보정을 위한 Dynamic Quantile Mapping 기법을 적용하여, 강수량의 불확실성을 최소화 하고자 하였다.

IDF 곡선은 수리구조물의 설계에 이용되며 본 연구에서는 기후변화를 고려한 GCM의 시간적 공간적 축소화기법을 통하여 미래의 IDF 곡선을 생성하였다. GCM자료로는 HadCM3과 CGCM3의 지역주의와 경제발전을 지향하는 A2시나리오를 이용하였다. GCM자료에 대한 공간적인 축소화기법으로 다중회귀 모형인 SDSM(Statistical DownScaling Model)을 이용하여 2030년, 2050년, 2080년의 미래의 일강우 자료를 생성하였다. 이를 다시 시간적 축소화기법인 GEV분포를 이용한 Scaling-Invariance기법을 적용하여 시단위의 강우자료를 생성하였다. 이를 통해 최종적으로 HadCM3과 CGCM3에 대한 각각 미래의 IDF곡선을 생성하였다. CGCM3의 경우 지속적인 강우강도의 증가를 보였지만 HadCM3의 경우 2050년대 감소하다 2080년대 다시 증가하는 양상을 보였다. 또한 CGCM3의 경우 HadCM3의 경우보다 좀 더 높은 강우 강도를 보였다. 본 연구의 대상지역은 서울지역이며 생성된 자료의 신뢰성을 확보하기위하여 서울기상관측소의 1961년부터~2000년까지의 일단위 강우자료를 이용하여 검 보정을 수행하였다.

A study on the correlation between four GCM types and Dam and Bangkwang types of Hyungsang medicine comes to the following conclusions : GCM I type is closely related to Bangkwang type in Hyungsang medicine. GCM II type corresponds to Dam type. In terms of obesity and emaciation, GCM I type is related to obesity and GCM II type to emaciation. In terms of Right and Left, GCM I type is more active in Left, GCM II type in Right. In terms of movement, GCM I and II types are kinetic and GCM III and IV types are static. In terms of Front and Back, the disease of GCM I type usually appears on Back but the treatment starts on Front. In GCM II type, disease mainly occurs on Front but the treatment starts on Back. GCM I type is susceptible to an alimentary disease and constitutionally predisposed to damp-phlegm in Hyungsang medicine. GCM II type is easily affected by a circulatory illness and very vulnerable to heat with blood deficiency and depression and stagnation of Ki.