• Journal of KIISE
• /
• v.43 no.12
• /
• pp.1334-1341
• /
• 2016

In recent years, the importance of managing software defects in the implementation stage has emerged because of the rapid development and wide-range usage of intelligent smart devices. Even if not a few studies have been conducted on the prediction models for software defects, their outcomes have not been widely shared. This paper proposes an efficient probabilistic management model of software metrics based on the Bayesian network, to overcome limits such as binary defect prediction models. We expect the proposed model to configure the Bayesian network by taking advantage of various software metrics, which can help in identifying improvements for refactoring. Once the source code has improved through code refactoring, the measured related metric values will also change. The proposed model presents probability values reflecting the effects after defect removal, which can be achieved by improving metrics through refactoring. This model could cope with the conclusive binary predictions, and consequently secure flexibilities on decision making, using indeterminate probability values.

• The Journal of the Acoustical Society of Korea
• /
• v.39 no.2
• /
• pp.137-142
• /
• 2020

Many studies based on I-vector have been conducted in a variety of environments, from text-dependent short-utterance to text-independent long-utterance. In this paper, we propose a speaker verification system employing a combination of I-vector with Probabilistic Linear Discriminant Analysis (PLDA) and speaker embedding of Long Short Term Memory (LSTM) with attention mechanism in far-field and noisy environments. The LSTM model's Equal Error Rate (EER) is 15.52 % and the Attention-LSTM model is 8.46 %, improving by 7.06 %. We show that the proposed method solves the problem of the existing extraction process which defines embedding as a heuristic. The EER of the I-vector/PLDA without combining is 6.18 % that shows the best performance. And combined with attention-LSTM based embedding is 2.57 % that is 3.61 % less than the baseline system, and which improves performance by 58.41 %.

• Journal of the Korean Solar Energy Society
• /
• v.34 no.4
• /
• pp.23-29
• /
• 2014

Recently, in power system studies, Multiple Energy Carriers (MECs) such as Energy Hub has been broadly utilized in power system planners and operators. Particularly, Energy Hub performs one of the most important role as the intermediate in implementing the MECs. However, it still needs to be put under examination in both modeling and operating concerns. For instance, a probabilistic optimization model is treated by a robust global optimization technique such as multi-agent genetic algorithm (MAGA) which can support the online economic dispatch of MECs. MAGA also reduces the inevitable uncertainty caused by the integration of selected input energy carriers. However, MAGA only considers current state of the integration of selected input energy carriers in conjunctive with the condition of smart grid environments for decision making in Energy Hub. Thus, in this paper, we propose an immune algorithm based Multiple Energy Carriers System which can adopt the learning process in order to make a self decision making in Energy Hub. In particular, the proposed immune algorithm considers the previous state, the current state, and the future state of the selected input energy carriers in order to predict the next decision making of Energy Hub based on the probabilistic optimization model. The below figure shows the proposed immune algorithm based Multiple Energy Carriers System. Finally, we will compare the online economic dispatch of MECs of two algorithms such as MAGA and immune algorithm based MECs by using Real Time Digital Simulator (RTDS).

• School Mathematics
• /
• v.13 no.3
• /
• pp.363-384
• /
• 2011

This study is based on the recognition that the school mathematics education should reinforce the heuristic and constructional aspects related with discoveries of mathematical rules and understanding of mathematical concepts from real world situations as well as the deductive and formal aspects emphasizing on mathematical contents precisely. The 11th grade students of one class from a city high school with average were chosen. They were given time to learn various functions of Excel in regular classes of "Information Society and Computer" subject. They don't have difficulty using cells, mathematical functions and statistical functions in spreadsheet. Experiment was performed for six weeks and there were two hours of classes in a week. Considering the results of this research, teaching materials using spreadsheets play an important role in helping students to experience probabilistic and statistical reasoning and construct mathematical thinking. This implies that teaching materials using spreadsheet provide students with an opportunity to interact with probabilistic and statistical situations by adopting engineering which can encourage students to observe and experience various aspects of real world in authentic situations.

• Journal of Korea Water Resources Association
• /
• v.41 no.6
• /
• pp.629-641
• /
• 2008

Many studies on sustained droughts have often been limited to the analysis of historic flow series. A major disadvantage in this approach can be described as the lack of long historic flow records needed to obtain a significant number of drought events for the analysis. To overcome this difficulty, one of the present study idea is to use synthetically generated hydrologic series. A methodology is presented to develop flow series based on the probabilistic analysis of the stochastic properties of the observed flows. The method can be utilized to generate a flow series of desired length so as to include many multiyear drought events within the process. In this paper, a concept of creating multiyear drought scenarios is introduced, and its development procedure is illustrated by a case study of the water supply system in Han River Basin. Also, it was found that the generated flow series can be reliably used to predict the long drought duration and sustained drought hydrologic scenarios within a given return period.

• Journal of the Korea institute for structural maintenance and inspection
• /
• v.17 no.6
• /
• pp.1-10
• /
• 2013

Probabilistic nonlinear first ply failure loads of flat composite panels and nonlinear buckling loads of curved composite panels with cutouts are estimated to provide the more reliable main load carrying structure in the renewable energy industry and offshore structures. The response surface method approximates limit state surface to a second order polynomial form of random variables with the results of deterministic finite element analyses at given sampling design points. Furthermore, the iterative linear interpolation scheme is used to obtain a more accurate approximation of the limit state surface near the most probable failure point (MPFP). The advanced first order second moment method and the Monte Carlo method are performed on an approximated limit state surface to evaluate the probability of failure. Finally, the sensitivity of the reliability index with respect to transformed random variables is investigated to figure out the main random variables that have an effect on failures.

• Journal of the Korean Society for Railway
• /
• v.12 no.2
• /
• pp.205-211
• /
• 2009

There is need to reform the road-based logistic transportation system into the railway-based logistics transportation system in order to decrease the total social cost related with logistics transportation. And new transportation modes such as dual mode trailer (DMT) are under consideration, which are expected to decrease current market share of road. But, most of current studies about estimating economical efficiency are focused on developing the probabilistic choice model and then estimating the market share of each mode. We present an approach to compute the optimal market share of each mode in terms of total social cost. To do so, we suggest an optimization model capturing both user choice to maximize his utility and subsidy policy intended to minimize total social cost, simultaneously. Using this model, we present the optimal modal split of container freight.

• International conference on construction engineering and project management
• /
• 2022.06a
• /
• pp.328-335
• /
• 2022

With the advance of robot capabilities and functionalities, construction robots assisting construction workers have been increasingly deployed on construction sites to improve safety, efficiency and productivity. For close-proximity human-robot collaboration in construction sites, robots need to be aware of the context, especially construction worker's behavior, in real-time to avoid collision with workers. To recognize human behavior, most previous studies obtained 3D human poses using a single camera or an RGB-depth (RGB-D) camera. However, single-camera detection has limitations such as occlusions, detection failure, and sensor malfunction, and an RGB-D camera may suffer from interference from lighting conditions and surface material. To address these issues, this study proposes a novel method of 3D human pose estimation by extracting 2D location of each joint from multiple images captured at the same time from different viewpoints, fusing each joint's 2D locations, and estimating the 3D joint location. For higher accuracy, the probabilistic representation is used to extract the 2D location of the joints, considering each joint location extracted from images as a noisy partial observation. Then, this study estimates the 3D human pose by fusing the probabilistic 2D joint locations to maximize the likelihood. The proposed method was evaluated in both simulation and laboratory settings, and the results demonstrated the accuracy of estimation and the feasibility in practice. This study contributes to ensuring human safety in close-proximity human-robot collaboration by providing a novel method of 3D human pose estimation.

• The Journal of Engineering Geology
• /
• v.26 no.4
• /
• pp.485-495
• /
• 2016

To reinforce and improve the soft ground under a breakwater while using materials efficiently, the replacement ratio and leaving periods of surcharge load are optimized probabilistically. The results of Bayesian updating of the random variables using prior information decrease uncertainty by up to 39.8%, and using prior information with more samples results in a sharp decrease in uncertainty. Replacement ratios of 15%-40% are analyzed using First Order Reliability Method and Monte Carlo simulation to optimize the replacement ratio. The results show that replacement ratios of 20% and 25% are acceptable at the column jet grouting area and the granular compaction pile area, respectively. Life cycle costs are also compared to optimize the replacement ratios within allowable ranges. The results show that a range of 20%-30% is the most economical during the total life cycle. This means that initial construction cost, maintenance cost and failure loss cost are minimized during total life cycle. Probabilistic analysis for leaving periods of shows that three months acceptable. Design optimization with respect to life cycle cost is important to minimize maintenance costs and retain the performance of the structures for the required period. Therefore, more case studies that consider the maintenance costs of soil structures are necessary to establish relevant design codes.

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
• /
• v.14 no.4
• /
• pp.423-434
• /
• 2016

The importance of severe nuclear accidents and probabilistic safety assessment (PSA) were brought to international attention with the occurrence of severe nuclear accidents caused by the extreme natural disaster at Fukushima Daiichi nuclear power plant in Japan. In Korea, studies on level 3 PSA had made little progress until recently. The code systems of level 3 PSA, MACCS2 (MELCORE Accident Consequence Code System 2, US), COSYMA (COde SYstem from MAria, EU) and OSCAAR (Off-Site Consequence Analysis code for Atmospheric Releases in reactor accidents, JAPAN), were reviewed in this study, and the disadvantages and limitations of MACCS2 were also analyzed. Experts from Korea and abroad pointed out that the limitations of MACCS2 include the following: MACCS2 cannot simulate multi-unit accidents/release from spent fuel pools, and its atmospheric dispersion is based on a simple Gaussian plume model. Some of these limitations have been improved in the updated versions of MACCS2. The absence of a marine and aquatic dispersion model and the limited simulating range of food-chain and economic models are also important aspects that need to be improved. This paper is expected to be utilized as basic research material for developing a Korean code system for assessing off-site consequences of severe nuclear accidents.