• KIEE International Transactions on Power Engineering
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• v.4A no.2
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• pp.100-105
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• 2004

Installed capacity markets in the northeast of the United States ensure that adequate generation exists to satisfy regional loss of load probability (LOLP) criterion. LOLP studies are conducted to determine the amount of capacity that is needed, but they do not consider several factors that substantially affect the calculated distribution of available capacity. These studies do not account for the fact that generation availability increases during periods of high demand and therefore prices, common-cause failures that result in multiple generation units being unavailable at the same time, and the negative correlation between load and available capacity due to temperature and humidity. A categorization of incidents in an existing bulk power reliability database is proposed to analyze the existence and frequency of independent failures and those associated with resource dynamics. Findings are augmented with other empirical findings. Monte Carlo methods are proposed to model these resource dynamics. Using the IEEE Reliability Test System as a single-bus case study, the LOLP results change substantially when these factors are considered. Better data collection is necessary to support the more comprehensive modeling of resource adequacy that is proposed. In addition, a parallel processing method is used to offset the increase in computational times required to model these dynamics.

• Journal of the Korean Institute of Landscape Architecture
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• v.26 no.3
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• pp.288-296
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• 1998

Natural or human disturbances cause landscape changes, which may be measured by the degree of heterogeneity. In a 16km$\times$19km area, divided into 100m$\times$100m cells, of Seoul city and its suburb, land covers are classified into 6 groups in aerial photos and land use maps. The degree of heterogeneity is defined as the number of cells that surround a central cell but have different land cover from the central cell divided by 8. The value of the degree of heterogeneity is between 0 and 1. Major findings are 1) Both urban and natural areas have low degree of heterogeneity, about 0.15~0.17. 2) Suburban area under heavy pressure of development and urbanization has highest degree of heterogeneity, about 0.25. 3) The peak of the degree of heterogeneity moved about 4.5km outward in 22 years. 4) Outer suburban area has lower degree of heterogeneity as the area is a greenbelt or forest. 5) The results show the areas with higher degree of heterogeneity which may need landscape management plans, and natural areas with lower degree of heterogeneity which may need landscape conservation plans. A landscape change model may be built for a specific city when this technique is applied to multiple sectors of the city, and the model may predict future landscape changes of the city.

• Earthquakes and Structures
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• v.17 no.1
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• pp.49-61
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• 2019

A novel asymmetrical resistance friction damper (ARFD) was proposed in this study to be applied on a rocking column base. The damper comprises multiple steel plates and was fastened using high-strength bolts. The sliding surfaces can be switched into one another and can cause strength to be higher in the loading direction than in the unloading direction. By combining the asymmetrical resistance with the restoring resistance that is generated due to an axial load on the column, the rocking column base can develop a self-centering behavior and achieve high connection strength. Cyclic tests on the ARFD proved that the damper performs a stable asymmetrical hysteretic loop. The desired hysteretic behavior was achieved by tuning the bolt pretension force and the diameter of the round bolt hole. In this study, full-scale, flexural tests were conducted to evaluate the performance of the column base and to verify the analytical model. The results indicated that the column base exhibits a stable self-centering behavior up to a drift angle of 4%. The decompression moment and maximum strength reached 42% and 88% of the full plastic moment of the section, respectively, under a column axial force ratio of approximately 0.2. The strengths and self-centering capacity can be obtained by determining the bolt pretension force. The analytical model results revealed good agreement with the experimental results.

• Journal of Sensor Science and Technology
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• v.33 no.5
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• pp.298-304
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• 2024

In Korea, fires are the second most common type of disaster, causing large-scale damages. The installation of fire detectors is legislated to prevent fires and minimize damage. Conventional fire detectors have limitations in initial suppression of failures because they detect fires when large amounts of smoke and heat are generated. Additionally, frequent malfunctions in fire detectors may cause users to turn them off. To address these issues, recent studies focus on accurately detecting even small-scale fires using multi-sensor and deep-learning technologies. They also aim at quick fire detection and thermal decomposition using gas. However, these studies are not practical because they overlook the heavy computations involved. Therefore, we propose a fast and accurate fire detection system based on multi-sensor and deep-learning technologies. In addition, we propose a computation-reduction method for selecting sensors suitable for detection using the Pearson correlation coefficient. Specifically, we use a moving average to handle outliers and two-stage labeling to reduce false detections during preprocessing. Subsequently, a deep-learning model is selected as LSTM for analyzing the temporal sequence. Then, we analyze the data using a correlation analysis. Consequently, the model using a small data group with low correlation achieves an accuracy of 99.88% and a false detection rate of 0.12%.

• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.1
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• pp.92-111
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• 2021

As fine dust negatively affects disease, industry and economy, the people are sensitive to fine dust. Therefore, if the occurrence of fine dust can be predicted, countermeasures can be prepared in advance, which can be helpful for life and economy. Fine dust is affected by the weather and the degree of concentration of fine dust emission sources. The industrial sector has the largest amount of fine dust emissions, and in industrial complexes, factories emit a lot of fine dust as fine dust emission sources. This study targets regions with old industrial complexes in local cities. The purpose of this study is to explore the factors that cause fine dust and develop a predictive model that can predict the occurrence of fine dust. weather data and fine dust data were used, and variables that influence the generation of fine dust were extracted through multiple regression analysis. Based on the results of multiple regression analysis, a model with high predictive power was extracted by learning with a machine learning regression learner model. The performance of the model was confirmed using test data. As a result, the models with high predictive power were linear regression model, Gaussian process regression model, and support vector machine. The proportion of training data and predictive power were not proportional. In addition, the average value of the difference between the predicted value and the measured value was not large, but when the measured value was high, the predictive power was decreased. The results of this study can be developed as a more systematic and precise fine dust prediction service by combining meteorological data and urban big data through local government data hubs. Lastly, it will be an opportunity to promote the development of smart industrial complexes.

• Ocean Systems Engineering
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• v.12 no.2
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• pp.225-245
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• 2022

More and more shipping containers are falling into the sea due to bad weather. Containers lost at sea negatively affect the shipping line, the trader and the consumer, and the environment. The question of locating and recovering dropped containers is a challenging engineering problem. Model-testing of small-scaled container models is proposed as an efficient way to investigate their falling trajectories to salvage them. In this study, we first build a standard 20-ft container model in SOLIDWORKS. Then, a three-dimensional (3D) geometric model in the STL (Standard Tessellation Language) format is exported to a Stratasys F170 Fused Deposition Modeling (FDM) printer. In total, six models were made of acrylonitrile styrene acrylate (ASA) and printed for the purpose of testing. They represent three different loading conditions with different densities and center of gravity (COG). Two samples for each condition were tested. The physical models were dropped into the towing tank of University of New Orleans (UNO). From the experimental tests, it is found that the impact of the initial position after sinking can cause a certain initial rolling velocity, which may have a great impact on the lateral displacement, and subsequently affect the final landing position. This series of model tests not only provide experimental data for the study of the trajectory of box-shape objects but also provide a valuable reference for maritime salvage operations and for the pipeline layout design.

• Geophysics and Geophysical Exploration
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• v.23 no.3
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• pp.168-177
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• 2020

Seabed multiple reflections (seabed multiples) are the main cause of misinterpretations of primary reflections in both shot gathers and stack sections. Accordingly, seabed multiples need to be suppressed throughout data processing. Conventional model-driven methods, such as prediction-error deconvolution, Radon filtering, and data-driven methods, such as the surface-related multiple elimination technique, have been used to attenuate multiple reflections. However, the vast majority of processing workflows require time-consuming steps when testing and selecting the processing parameters in addition to computational power and skilled data-processing techniques. To attenuate seabed multiples in seismic reflection data, input gathers with seabed multiples and label gathers without seabed multiples were generated via numerical modeling using the Marmousi2 velocity structure. The training data consisted of normal-moveout-corrected common midpoint gathers fed into a U-Net neural network. The well-trained model was found to effectively attenuate the seabed multiples according to the image similarity between the prediction result and the target data, and demonstrated good applicability to field data.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.1
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• pp.46-63
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• 2024

One of the biggest dangers in the globe is water contamination. Water is a necessity for human survival. In most cities, the digging of borewells is restricted. In some cities, the borewell is allowed for only drinking water. Hence, the scarcity of drinking water is a vital issue for industries and villas. Most of the water sources in and around the cities are also polluted, and it will cause significant health issues. Real-time quality observation is necessary to guarantee a secure supply of drinking water. We offer a model of a low-cost system of monitoring real-time water quality using IoT to address this issue. The potential for supporting the real world has expanded with the introduction of IoT and other sensors. Multiple sensors make up the suggested system, which is utilized to identify the physical and chemical features of the water. Various sensors can measure the parameters such as temperature, pH, and turbidity. The core controller can process the values measured by sensors. An Arduino model is implemented in the core controller. The sensor data is forwarded to the cloud database using a WI-FI setup. The observed data will be transferred and stored in a cloud-based database for further processing. It wasn't easy to analyze the water quality every time. Hence, an Optimized Neural Network-based automation system identifies water quality from remote locations. The performance of the feed-forward neural network classifier is further enhanced with a hybrid GA- PSO algorithm. The optimized neural network outperforms water quality prediction applications and yields 91% accuracy. The accuracy of the developed model is increased by 20% because of optimizing network parameters compared to the traditional feed-forward neural network. Significant improvement in precision and recall is also evidenced in the proposed work.

• Journal of the Korea Institute of Building Construction
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• v.16 no.4
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• pp.349-357
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• 2016

Recently, the quantity of risk in construction project has been inflated due to the fact that current construction projects have been large and complicated. Therefore, a study on the risk management methods is necessary that can predict and respond to the need in complicated modern construction projects. In this study, the objective is to analyze the cause of accident in actual construction sites and develop a risk assessment model based on insurance claims records. To reach the goal of this study, first, the frequency and severity of accidents are analyzed the causes of accidents based on the classification; progress rate, season, and total construction costs. Second, a risk assessment model is developed by utilizing a multiple regression analysis. The dependent variable is loss ratio of material damage and three categories; natural hazards, geographic information, and construction method & ability, are used as the independent variables. The model's adjusted R-square is 0.455. The contributions of this study will be used as a material for a quantitative risk analysis model development and review of the construction risk factors for future study.

• Fire Science and Engineering
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• v.17 no.4
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• pp.76-85
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• 2003

In this study, we write down the definitions, their causes and the techniques of analysis as a theoretical consideration of common cause failures, and investigate the limitation and the importance of the common cause failures by applying to the analysis on the fire protection as a representative safety facility. As you can know in the reliability analysis, most impressive cause is the malfunctions of pumping operations; especially the common cause failure of two pumps is dominant. In other words, it is possible to assess system-reliability as twice as actual without CCF From these, CCF is extraordinarily important and the results are highly dependent on the CCF factor. And although it would increase with multiple installations, the reliability are not defined as linear with those multiplications. In addition, the differences in results due to the models for analysis are not significant, whereas the various sources of data produce highly different results. Therefore, we conclude that the reliabilities are dependent on the quality of the usable data much better than the variety of models. As a result, the basic and engineering device for the preventions of CCF of the multiple facilities is to design it as reliably as to design the fire-water pump. That is to say, we must assess those reliabilities using PFD whether they are appropriate to SIL (Safety Integrity Level) which is required for the reliability in SIS (Safety Instrumented System). The result of the analysis on the reliability of the fire-water supply with CCF shows that PFD is 3.80E-3, so that it cannot be said to be designed as safely as in the level of SIL5. However, without CCF, PFD is 1.82E-3 which means that they are designed as unsafely as before.