• Journal of the Korean Society of Systems Engineering
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• v.16 no.2
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• pp.97-109
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• 2020

On March 11, 2011, an earthquake followed by a tsunami caused an extended station blackout (SBO) at the Fukushima Dai-ichi NPP Units. The accident was initiated by a total loss of both onsite and offsite electrical power resulting in the loss of the ultimate heat sink for several days, and a consequent core melt in some units where proper mitigation strategies could not be implemented in a timely fashion. To enhance the plant's coping capability, the Diverse and Flexible Strategies (FLEX) were proposed to append the Emergency Operation Procedures (EOPs) by relying on portable equipment as an additional line of defense. To assess the success window of FLEX strategies, all sources of uncertainties need to be considered, using a physics-based model or system code. This necessitates conducting a large number of simulations to reflect all potential variations in initial, boundary, and design conditions as well as thermophysical properties, empirical models, and scenario uncertainties. Alternatively, data-driven models may provide a fast tool to predict the success window of FLEX strategies given the underlying uncertainties. This paper explores the applicability of Artificial Intelligence (AI) to identify the success window of FLEX strategy for extended SBO. The developed model can be trained and validated using data produced by the lumped parameter thermal-hydraulic code, MARS-KS, as best estimate system code loosely coupled with Dakota for uncertainty quantification. A Systems Engineering (SE) approach is used to plan and manage the process of using AI to predict the success window of FLEX strategies under extended SBO conditions.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.6
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• pp.69-74
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• 2022

Even after the drunk driving law was revised through the Yoon Chang-ho Act in 2019, the proportion of habitual offenders among all drunk drivers in 2021 was 4.7%, up 0.5% from 2018. In addition, drunk driving is not easily stopped due to the addiction of alcohol, and there is a high probability of recidivism in accidents as it is often driven again. Therefore, in this paper, to prevent this, when alcohol is measured using its own sensor rather than a manual police measure, the vehicle stops and related data such as the current location and time are automatically saved. Since it is not possible to develop directly on the car, this system was developed by converging various technologies and sensors such as Arduino board, Firebase, and GPS based on the IoT environment in consideration of the simulation environment.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.9 no.4
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• pp.1-12
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• 2010

This paper deals with the transmission interval of vehicle data in smart highway where WAVE (Wireless Access for Vehicular Environments) systems have been installed for advanced road infrastructure. The vehicle data could be collected at every second, which is containing location information of the vehicle as well the vehicle speed, RPM, fuel consuming and safety data. The safety data such as DTC code, can be collected through OBD-II. These vehicle data can be used for valuable contents for processing and providing traffic information. In this paper, we propose a model to decide the collection interval of vehicle information in real time environment. This model can change the transmission interval along with special and time-variant traffic condition based on the 32 scenarios using microscopic traffic simulator, VISSIM. We have reviewed the transmission interval, communication transmission quantity and communication interval, tried to confirm about communication possibility and BPS, etc for each scenario. As results, in 2-lane from 1km highway segment, most appropriate transmission interval is 2 times over spatial basic segment considering to communication specification. In the future, if a variety of wireless technologies on the road is introduced, this paper considering not only traffic condition but also wireless network specification will be utilized the high value.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.3D
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• pp.423-429
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• 2008

The object of the present study is to estimate the potential effects of climate change and land use on soil erosion in the mid-east Korea. Simulated precipitation by CCCma climate model during 2030-2050 is used to model predicted soil erosion, and results are compared to observation. Simulation results allow relative comparison of the impact of climate change on soil erosion between current and predicted future condition. Expected land use changes driven by socio-economic change and plant growth driven by the increase of temperature and are taken into accounts in a comprehensive way. Mean precipitation increases by 17.7% (24.5%) for A2 (B2) during 2030-2050 compared to the observation period (1966-1998). In general predicted soil erosion for the B2 scenario is larger than that for the A2 scenario. Predicted soil erosion increases by 48%~90% under climate change except the scenario 1 and 2. Predicted soil erosion under the influence of temperature-induced fast plant growth, higher evapotranspiration rate, and fertilization effect (scenario 5 and 6) is approximately 25% less than that in the scenario 3 and 4. On the basis of the results it is said that precipitation and the corresponding soil erosion is likely to increase in the future and care needs to be taken in the study area.

• Journal of KIISE:Computer Systems and Theory
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• v.32 no.7
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• pp.349-358
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• 2005

False sharing is a result of co-location of unrelated data in the same unit of memory coherency, and is one source of unnecessary overhead being of no help to keep the memory coherency in multiprocessor systems. Moreover. the damage caused by false sharing becomes large in proportion to the granularity of memory coherency. To reduce false sharing in a page-based DSM system, it is necessary to allocate unrelated data objects that have different access patterns into the separate shared pages. In this paper we propose call-site tracing-based shared memory allocator. shortly CSTallocator. CSTallocator expects that the data objects requested from the different call-sites may have different access patterns in the future. So CSTailocator places each data object requested from the different call-sites into the separate shared pages, and consequently data objects that have the same call-site are likely to get together into the same shared pages. We use execution-driven simulation of real parallel applications to evaluate the effectiveness of our CSTallocator. Our observations show that by using CSTallocator a considerable amount of false sharing misses can be additionally reduced in comparison with the existing techniques.

• Journal of Korea Water Resources Association
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• v.57 no.2
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• pp.127-137
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• 2024

Physical models, which are often used for water resource management, are difficult to build and operate with input data and may involve the subjective views of users. In recent years, research using data-driven models such as machine learning has been actively conducted to compensate for these problems in the field of water resources, and in this study, an artificial neural network was used to simulate long-term rainfall runoff in the Osipcheon watershed in Samcheok-si, Gangwon-do. For this purpose, three input data groups (meteorological observations, daily precipitation and potential evapotranspiration, and daily precipitation - potential evapotranspiration) were constructed from meteorological data, and the results of training the LSTM (Long Short-term Memory) artificial neural network model were compared and analyzed. As a result, the performance of LSTM-Model 1 using only meteorological observations was the highest, and six LSTM-MLP ensemble models with MLP artificial neural networks were built to simulate long-term runoff in the Fifty Thousand Watershed. The comparison between the LSTM and LSTM-MLP models showed that both models had generally similar results, but the MAE, MSE, and RMSE of LSTM-MLP were reduced compared to LSTM, especially in the low-flow part. As the results of LSTM-MLP show an improvement in the low-flow part, it is judged that in the future, in addition to the LSTM-MLP model, various ensemble models such as CNN can be used to build physical models and create sulfur curves in large basins that take a long time to run and unmeasured basins that lack input data.

• Asian Journal of Atmospheric Environment
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• v.8 no.1
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• pp.25-34
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• 2014

In winter 2013, extreme air pollution by fine particulate matter ($PM_{2.5}$) in China attracted much public attention. In order to simulate the $PM_{2.5}$ pollution, the Community Multiscale Air Quality model driven by the Weather Research and Forecasting model was applied to East Asia in a period from 1 January 2013 to 5 February 2013. The model generally reproduced $PM_{2.5}$ concentration in China with emission data in the year 2006. Therefore, the extreme $PM_{2.5}$ pollution seems to be mainly attributed to meteorological (weak wind and stable) conditions rather than emission increases in the past several years. The model well simulated temporal and spatial variations in $PM_{2.5}$ concentrations in Japan as well as China, indicating that the model well captured characteristics of the $PM_{2.5}$ pollutions in both areas on the windward and leeward sides in East Asia in the study period. In addition, contribution rates of four anthropogenic emission sectors (power generation, industrial, residential and transportation) in China to $PM_{2.5}$ concentration were estimated by conducting zero-out emission sensitivity runs. Among the four sectors, the residential sector had the highest contribution to $PM_{2.5}$ concentration. Therefore, the extreme $PM_{2.5}$ pollution may be also attributed to large emissions from combustion for heating in cold regions in China.

• Transactions of the Korean hydrogen and new energy society
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• v.24 no.4
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• pp.295-301
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• 2013

Long-term global energy-demand growth is expected to increase driven by strong energy-demand growth from developing countries. Fusion power offers the prospect of an almost inexhaustible source of energy for future generations, even though it also presents so far insurmountable scientific and engineering challenges. One of the challenges is safe handling of hydrogen isotopes. Metal hydrides such as depleted uranium hydride or ZrCo hydride are used as a storage medium for hydrogen isotopes reversibly. The metal hydrides bind with hydrogen very strongly. In this paper, we carried out a modeling and simulation work for absorption/desorption of hydrogen by ZrCo in a horizontal annulus cylinder bed. A comprehensive mathematical description of a metal hydride hydrogen storage vessel was developed. This model was calibrated against experimental data obtained from our experimental system containing ZrCo metal hydride. The model was capable of predicting the performance of the bed for not only both the storage and delivery processes but also heat transfer operations. This model should thus be very useful for the design and development of the next generation of metal hydride hydrogen isotope storage systems.

• Journal of Korean Society for Geospatial Information Science
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• v.25 no.1
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• pp.3-8
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• 2017

In this study future wind resource maps have been produced under climate change scenario using ensemble regional climate model weather research and forecasting(WRF) for the period from 2045 to 2054(mid 21st century). Then various spatiotemporal analysis has been conducted in terms of monthly and diurnal. As a result, monthly variation(monsoon circulation) was larger than diurnal variation(land-sea circulation) throughout the South Korea. Strong wind area with high wind power energy was varied on months and regions. During whole years, strong wind with high wind resource was pronounced at cold(warm) months in particular Gangwon mountainous and coastal areas(southwestern coastal area) driven by strong northwesterly(southwesterly). Projected strong and weak wind were presented in January and September, respectively. Diurnal variation were large over inland and mountainous area while coastal area were small. This new monthly and diurnal variation would be useful to high resource area analysis and long-term operation of wind power according to wind variability in future.

• Journal of KIISE:Computer Systems and Theory
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• v.27 no.4
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• pp.383-393
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• 2000

In shared memory multiprocessor systems, false sharing occurs when several independent data objects, not shared but accessed by different processors, are allocated to the same coherency unit of memory. False sharing is one of the major factors that may degrade the performance of memory coherency protocols. This paper presents a new shared memory allocation scheme to reduce false sharing of parallel applications where master processor controls allocation of all the shared objects. Our scheme allocates the objects to temporary address space for the moment, and actually places each object in the address space of processor that first accesses the object later. Its goal is to allocate independent objects that may have different access patterns to different pages. We use execution-driven simulation of real parallel applications to evaluate the effectiveness of our scheme. Experimental results show that by using our scheme a considerable amount of false sharing faults can be reduced with low overhead.