• Journal of KIISE:Software and Applications
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• v.28 no.3
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• pp.211-223
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• 2001

본 논문에서 제안하는 시나리오기반 검증기법의 목적은 UML로 작성된 객체지향 분석모델의 완전성 및 일관성을 진단하는 것이다. 검증기법의 전체 절차는 요구분석을 위한 Use Case 모델링 과정에서 생성되는 Use Case 시나리오와 UML 분석모델로부터 역공학적 방법으로 도출된 객체행위 시나리오와의 상호참조과정 및 시나리오 정보트리 추적과정을 이용하여 단계적으로 수행된다. 본 검증절차를 위하여 우선, UML로 작성된 객체지향 분석모델들은 우선 정형명세언어를 사용하여 Use Case 정형명세로 변환하다. 그 다음에, Use Case 정형명세로부터 해당 Use Case 내의 객체의 정적구조를 표현하는 시나리오 정보트리를 구축하고, Use Case 정형명세 내에 포함되어 있는 객체 동적행위 정보인 메시지 순차에 따라 개별 시나리오흐름을 시나리오 정보트리에 표현한다. 마지막으로 시나리오 정보트리 추적과 시나리오 정보 테이블 참조과정을 중심으로 완전성 및 일관성 검증작업을 수행한다. 즉, 검증하고자 하는 해당 Use Case의 시나리오 정보트리를 이용한 시나리오 추적과정을 통해 생성되는 객체행위 시나리오와 요구분석 과정에서 도출되는 Use Case 시나리오와의 일치여부를 조사하여 분석모델과 사용자 요구사양과의 완전성을 검사한다. 그리고, 시나리오 추적과정을 통해 수집되는 시나리오 관련종보들을 가지고 시나리오 정보 테이블을 작성한 후, 분석과정에서 작성된 클래스 관련정보들의 시나리오 포함 여부를 확인하여 분석모델의 일관성을 검사한다. 한편, 본 논문에서 제안하는 검증기법의 효용성을 증명하기 위해 대학의 수강등록시스템 개발을 위해 UML을 이용해 작성된 분석모델을 특정한 사례로써 적용하여 보았다. 프로세싱 오버헤드 및 메모리와 대역폭 요구량 측면에서 MARS 모델보다 유리함을 알 수 있었다.과는 본 논문에서 제안된 프리페칭 기법이 효율적으로 peak bandwidth를 줄일 수 있다는 것을 나타낸다.ore complicate such a prediction. Although these overestimation sources have been attacked in many existing analysis techniques, we cannot find in the literature any description about questions like which one is most important. Thus, in this paper, we quantitatively analyze the impacts of overestimation sources on the accuracy of the worst case timing analysis. Using the results, we can identify dominant overestimation sources that should be analyzed more accurately to get tighter WCET estimations. To make our method independent of any existing analysis techniques, we use simulation based methodology. We have implemented a MIPS R3000 simulator equipped with several switches, each of which determines the accuracy level of the

• Journal of Advanced Navigation Technology
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• v.22 no.6
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• pp.602-609
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• 2018

Differential GPS (DGPS) is known as a positioning method using pseudo range correction (PRC) which is communicating between a refence receiver and moving receivers. In real world, a moving receiver loses communication with the reference receiver, resulting in loss of PRC real-time communication. In this paper, we assume that the transmission of the pseudo range correction isinterrupted in the middle of real-time positioning situations, in which calibration information is received in the DGPS method. Under the disconnected communication, we propose 'predict DGPS' that real-time virtual PRC model which is modeled by a machine learning algorithm with previously acquired PRC data from a reference receiver. To verify predict DGPS method, we compared and analyzed positioning solutions acquired from real PRC and the virtual PRC. In addition, we show that positioning using the DGPS prediction method on a real road can provide an improved positioning solution assuming a scenario in which PRC communication was cut off.

• Journal of Internet Computing and Services
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• v.23 no.3
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• pp.13-20
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• 2022

IEEE 802.11ay Wi-Fi is the next generation wireless technology and operates in mmWave band. It supports the MU-MIMO (Multiple User Multiple Input Multiple Output) transmission in which an AP (Access Point) can transmit multiple data streams simultaneously to multiple STAs (Stations). To this end, the AP should perform MU-MIMO beamforming training with the STAs. For efficient MU-MIMO beamforming training, it is important for the AP to estimate signal strength measured at each STA at which multiple beams are used simultaneously. Therefore, in the paper, we propose a deep learning-based link quality estimation scheme. Our proposed scheme estimates the signal strength with high accuracy by utilizing a deep learning model pre-trained for a certain indoor or outdoor propagation scenario. Specifically, to estimate the signal strength of the multiple concurrent beams, our scheme uses the signal strengths of the respective single beams, which can be obtained without additional signaling overhead, as the input of the deep learning model. For performance evaluation, we utilized a Q-D (Quasi-Deterministic) Channel Realization open source software and extensive channel measurement campaigns were conducted with NIST (National Institute of Standards and Technology) to implement the millimeter wave (mmWave) channel. Our simulation results demonstrate that our proposed scheme outperforms comparison schemes in terms of the accuracy of the signal strength estimation.

• Advanced Industrial SCIence
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• v.1 no.1
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• pp.7-15
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• 2022

To adopt the development in the medical scenario IoT developed towards the advancement with the processing of a large amount of medical data defined as an Internet of Medical Things (IoMT). The vast range of collected medical data is stored in the cloud in the structured manner to process the collected healthcare data. However, it is difficult to handle the huge volume of the healthcare data so it is necessary to develop an appropriate scheme for the healthcare structured data. In this paper, a machine learning mode for processing the structured heath care data collected from the IoMT is suggested. To process the vast range of healthcare data, this paper proposed an MTGPLSTM model for the processing of the medical data. The proposed model integrates the linear regression model for the processing of healthcare information. With the developed model outlier model is implemented based on the FinTech model for the evaluation and prediction of the COVID-19 healthcare dataset collected from the IoMT. The proposed MTGPLSTM model comprises of the regression model to predict and evaluate the planning scheme for the prevention of the infection spreading. The developed model performance is evaluated based on the consideration of the different classifiers such as LR, SVR, RFR, LSTM and the proposed MTGPLSTM model and the different size of data as 1GB, 2GB and 3GB is mainly concerned. The comparative analysis expressed that the proposed MTGPLSTM model achieves ~4% reduced MAPE and RMSE value for the worldwide data; in case of china minimal MAPE value of 0.97 is achieved which is ~ 6% minimal than the existing classifier leads.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.30-30
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• 2023

This study aims to establish the multi-reservoir operation system model in the Upper Mun River Basin which includes 5 main dams namely, Mun Bon (MB), Lamchae (LC), Lam Takhong (LTK), Lam Phraphoeng (LPP), and Lower Lam Chiengkrai (LLCK) Dams. The knowledge and AI technology were applied aiming to develop innovative prototype for SMART dam-reservoir operation in future. Two different sorts of reservoir operation system model namely, Fuzzy Logic (FL) and Constraint Programming (CP) as well as the development of rainfall and reservoir inflow prediction models using Machine Learning (ML) technique were made to help specify the right amount of daily reservoir releases for the Royal Irrigation Department (RID). The model could also provide the essential information particularly for the Office of National Water Resource of Thailand (ONWR) to determine the short-term and long-term water resource management plan and strengthen water security against flood and drought in this region. The simulated results of base case scenario for reservoir operation in the Upper Mun from 2008 to 2021 indicated that in the same circumstances, FL and CP models could specify the new release schemes to increase the reservoir water storages at the beginning of dry season of approximately 125.25 and 142.20 MCM per year. This means that supplying the agricultural water to farmers in dry season could be well managed. In other words, water scarcity problem could substantially be moderated at some extent in case of incapability to control the expansion of cultivated area size properly. Moreover, using AI technology to determine the new reservoir release schemes plays important role in reducing the actual volume of water shortfall in the basin although the drought situation at LTK and LLCK Dams were still existed in some periods of time. Meanwhile, considering the predicted inflow and hydrologic factors downstream of 5 main dams by FL model and minimizing the flood volume by CP model could ensure that flood risk was considerably minimized as a result of new release schemes.

• Annals of Occupational and Environmental Medicine
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• v.35
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• pp.24.1-24.15
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• 2023

Background: The construction workers are vulnerable to fatigue due to high physical workload. This study aimed to investigate the relationship between overwork and heart rate in construction workers and propose a scheme to prevent overwork in advance. Methods: We measured the heart rates of construction workers at a construction site of a residential and commercial complex in Seoul from August to October 2021 and develop an index that monitors overwork in real-time. A total of 66 Korean workers participated in the study, wearing real-time heart rate monitoring equipment. The relative heart rate (RHR) was calculated using the minimum and maximum heart rates, and the maximum acceptable working time (MAWT) was estimated using RHR to calculate the workload. The overwork index (OI) was defined as the cumulative workload evaluated with the MAWT. An appropriate scenario line (PSL) was set as an index that can be compared to the OI to evaluate the degree of overwork in real-time. The excess overwork index (EOI) was evaluated in real-time during work performance using the difference between the OI and the PSL. The EOI value was used to perform receiver operating characteristic (ROC) curve analysis to find the optimal cut-off value for classification of overwork state. Results: Of the 60 participants analyzed, 28 (46.7%) were classified as the overwork group based on their RHR. ROC curve analysis showed that the EOI was a good predictor of overwork, with an area under the curve of 0.824. The optimal cut-off values ranged from 21.8% to 24.0% depending on the method used to determine the cut-off point. Conclusion: The EOI showed promising results as a predictive tool to assess overwork in real-time using heart rate monitoring and calculation through MAWT. Further research is needed to assess physical workload accurately and determine cut-off values across industries.

• Korean Journal of Agricultural and Forest Meteorology
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• v.8 no.4
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• pp.229-241
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• 2006

Global air temperature has risen by $0.6^{\circ}C$ over the last one hundred years due to increased atmospheric greenhouse gases. Moreover, this global warming trend is projected to continue in the future. This study was carried out to evaluate spatial variations in rice production areas by simulating rice-growth and development with projected high resolution climate data in Korea far 2011-2100, which was geospatially interpolated from the 25 km gridded data based on the IPCC SRES A2 emission scenario. Satellite remote sensing data were used to pinpoint the rice-growing areas, and corresponding climate data were aggregated to represent the official 'crop reporting county'. For the simulation experiment, we used a CERES-Rice model modified by introducing two equations to calculate the leaf appearance rate based on the effective temperature and existing leaf number and the final number of leaves based on day-length in the photoperiod sensitive phase of rice. We tested the performance of this model using data-sets obtained from transplanting dates and nitrogen fertilization rates experiments over three years (2002 to 2004). The simulation results showed a good performance of this model in heading date prediction [$R^2$=0.9586 for early (Odaebyeo), $R^2$=0.9681 for medium (Hwasungbyeo), and $R^2$=0.9477 for late (Dongjinbyeo) maturity cultivars]. A modified version of CERES-Rice was used to simulate the growth and development of three Japonica varieties, representing early, medium, and late maturity classes, to project crop status for climatological normal years between 2011 and 2100. In order to compare the temporal changes, three sets of data representing 3 climatological years (2011-2040, 2041-2070, and 2071-2100) were successively used to run the model. Simulated growth and yield data of the three Japonica cultivars under the observed climate for 1971-2000 was set as a reference. Compared with the current normal, heading date was accelerated by 7 days for 2011-2040 and 20 days for 2071-2100. Physiological maturity was accelerated by 15 days for 2011-2040 and 30 days for 2071-2100. Rice yield was in general reduced by 6-25%, 3-26%, and 3-25% per 10a in early, medium, and late maturity classes, respectively. However, mid to late maturing varieties showed an increased yield in northern Gyeonggi Province and in most of Kwangwon Province in 2071-2100.

• Korean Journal of Remote Sensing
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• v.35 no.3
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• pp.415-431
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• 2019

The satellite-viewed cloudiness, a ratio of cloudy pixels to total pixels ($C_{sat,\;prev}$), inevitably differs from the "ground-viewed" cloudiness ($C_{grd}$) due to different viewpoints. Here we develop an algorithm to retrieve the satellite-viewed, but adjusted cloudiness to $C_{grd} (C_{sat,\;adj})$. The key process of the algorithm is to convert the cloudiness projected on the plane surface into the cloudiness on the celestial hemisphere from the observer. For this conversion, the supplementary satellite retrievals such as cloud detection and cloud top pressure are used as they provide locations of cloudy pixels and cloud base height information, respectively. The algorithm is tested for Himawari-8 level 1B data. The $C_{sat,\;adj}$ and $C_{sat,\;prev}$ are retrieved and validated with $C_{grd}$ of SYNOP station over Korea (22 stations) and China (724 stations) during only daytime for the first seven days of every month from July 2016 to June 2017. As results, the mean error of $C_{sat,\;adj}$ (0.61) is less that than that of $C_{sat,\;prev}$ (1.01). The percent of detection for 'Cloudy' scenario of $C_{sat,\;adj}$ (73%) is higher than that of $C_{sat,\;prev}$ (60%) The percent of correction, the accuracy, of $C_{sat,\;adj}$ is 61%, while that of $C_{sat,\;prev}$ is 55% for all seasons. For the December-January-February period when cloudy pixels are readily overestimated, the proportion of correction of $C_{sat,\;adj$ is 60%, while that of $C_{sat,\;prev}$ is 56%. Therefore, we conclude that the present algorithm can effectively get the satellite cloudiness near to the ground-viewed cloudiness.

• Atmosphere
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• v.24 no.3
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• pp.303-315
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• 2014

Terrestrial ecosystem plays the important role as carbon sink in the global carbon cycle. Understanding of interactions of terrestrial carbon cycle with climate is important for better prediction of future climate change. In this paper, terrestrial carbon cycle is investigated by Hadley Centre Global Environmental Model, version 2, Carbon Cycle (HadGEM2-CC) that considers vegetation dynamics and an interactive carbon cycle with climate. The simulation for future projection is based on the three (8.5/4.5/2.6) representative concentration pathways (RCPs) from 2006 to 2100 and compared with historical land carbon uptake from 1979 to 2005. Projected changes in ecological features such as production, respiration, net ecosystem exchange and climate condition show similar pattern in three RCPs, while the response amplitude in each RCPs are different. For all RCP scenarios, temperature and precipitation increase with rising of the atmospheric $CO_2$. Such climate conditions are favorable for vegetation growth and extension, causing future increase of terrestrial carbon uptakes in all RCPs. At the end of 21st century, the global average of gross and net primary productions and respiration increase in all RCPs and terrestrial ecosystem remains as carbon sink. This enhancement of land $CO_2$ uptake is attributed by the vegetated area expansion, increasing LAI, and early onset of growing season. After mid-21st century, temperature rising leads to excessive increase of soil respiration than net primary production and thus the terrestrial carbon uptake begins to fall since that time. Regionally the NEE average value of East-Asia ($90^{\circ}E-140^{\circ}E$, $20^{\circ}N{\sim}60^{\circ}N$) area is bigger than that of the same latitude band. In the end-$21^{st}$ the NEE mean values in East-Asia area are $-2.09PgC\;yr^{-1}$, $-1.12PgC\;yr^{-1}$, $-0.47PgC\;yr^{-1}$ and zonal mean NEEs of the same latitude region are $-1.12PgC\;yr^{-1}$, $-0.55PgC\;yr^{-1}$, $-0.17PgC\;yr^{-1}$ for RCP 8.5, 4.5, 2.6.

• Journal of the Korean Society for Marine Environment & Energy
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• v.12 no.4
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• pp.307-319
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• 2009

Carbon dioxide capture and storage (CCS) technology has been regarded as one of the most possible and practical option to reduce the emission of carbon dioxide ($CO_2$) and consequently to mitigate the climate change. Korean government also have started a 10-year R&D project on $CO_2$ storage in sea-bed geological structure including gas field and deep saline aquifer since 2005. Various relevant researches are carried out to cover the initial survey of suitable geological structure storage site, monitoring of the stored $CO_2$ behavior, basic design of $CO_2$ transport and storage process and the risk assessment and management related to $CO_2$ leakage from engineered and geological processes. Leakage of $CO_2$ to the marine environment can change the chemistry of seawater including the pH and carbonate composition and also influence adversely on the diverse living organisms in ecosystems. Recently, IMO (International Maritime Organization) have developed the risk assessment and management framework for the $CO_2$ sequestration in sub-seabed geological structures (CS-SSGS) and considered the sequestration as a waste management option to mitigate greenhouse gas emissions. This framework for CS-SSGS aims to provide generic guidance to the Contracting Parties to the London Convention and Protocol, in order to characterize the risks to the marine environment from CS-SSGS on a site-specific basis and also to collect the necessary information to develop a management strategy to address uncertainties and any residual risks. The environmental risk assessment (ERA) plan for $CO_2$ storage work should include site selection and characterization, exposure assessment with probable leak scenario, risk assessment from direct and in-direct impact to the living organisms and risk management strategy. Domestic trial of the $CO_2$ capture and sequestration in to the marine geologic formation also should be accomplished through risk management with specified ERA approaches based on the IMO framework. The risk assessment procedure for $CO_2$ marine storage should contain the following components; 1) prediction of leakage probabilities with the reliable leakage scenarios from both engineered and geological part, 2) understanding on physio-chemical fate of $CO_2$ in marine environment especially for the candidate sites, 3) exposure assessment methods for various receptors in marine environments, 4) database production on the toxic effect of $CO_2$ to the ecologically and economically important species, and finally 5) development of surveillance procedures on the environmental changes with adequate monitoring techniques.