• Journal of Applied Reliability
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• v.1 no.2
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• pp.175-181
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• 2001

Recently, the quality of software has become a major issue. The statistical models used in making predictions about the quality of software are termed software reliability growth models (SRGM). However, the existing SRGMs have not been satisfactory in predicting software reliability behavior (Keiller and Miller(1991), Keiller and Littlewood(1984), Musa(1987)). In this paper, we present a fuzzy-based interval estimation of software errors (failures).

• Proceedings of the Korea Air Pollution Research Association Conference
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• 2007.10a
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• pp.117-118
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• 2007

• Magazine of the Korean Society of Agricultural Engineers
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• v.45 no.2
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• pp.116-125
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• 2003

Predictions of stream water quality require both estimation of pollutant loading from different sources and simulation of water quality processes in the stream. Nonpoint source pollution models are often employed for estimating pollutant loading in rural watersheds. In this study, a conjunctive application of SWAT model and WASP model was made and evaluated for its applicability based on the simulation results. Runoff and nutrient loading obtained from the SWAT model were used for generating input data for WASP model. The results showed that the simulated runoff was in good agreement with the observed data and indicated reasonable applicability. Loading for the water quality parameters predicted by WASP model also showed a reasonable agreement with the observed data. It is expected that stream water quality could be predicted by the coupled application of the two models, SWAT and WASP, in rural watersheds.

• International Journal of Computer Science & Network Security
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• v.24 no.3
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• pp.113-124
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• 2024

One of the goals of the Saudi Arabia 2030 vision is to ensure full employment of its citizens. Recruitment of graduates depends on the quality of skills that they may have gained during their study. Hence, the quality of education and ensuring that graduates have sufficient knowledge about the in-demand skills of the market are necessary. However, IT graduates are usually not aware of whether they are suitable for recruitment or not. This study builds a prediction model that can be deployed on the web, where users can input variables to generate predictions. Furthermore, it provides data-driven recommendations of the in-demand skills in the Saudi IT labor market to overcome the unemployment problem. Data were collected from two online job portals: LinkedIn and Bayt.com. Three machine learning algorithms, namely, Support Vector Machine, k-Nearest Neighbor, and Naïve Bayes were used to build the model. Furthermore, descriptive and data analysis methods were employed herein to evaluate the existing gap. Results showed that there existed a gap between labor market employers' expectations of Saudi workers and the skills that the workers were equipped with from their educational institutions. Planned collaboration between industry and education providers is required to narrow down this gap.

• Journal of Environmental Impact Assessment
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• v.9 no.1
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• pp.25-37
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• 2000

This paper presents a study of hydrological and hydraulic model applications in environmental impact statements which were submitted during recent years in Korea. In many cases (almost 70 %), the hydrological and hydraulic changes were neglected from the impact identification processes, even if the proposed actions would cause significant impacts on those environmental items. In most cases where the hydrological and hydraulic impacts were predicted, simple equations were used as an impact prediction tool. Computer models were used in very few cases(5%). Even in these few cases, models were improperly applied and thus the predicted impacts would not be reliable. The improper applications and the impact neglections are attributed to the fact that there are no available model application guidelines as well as no requirements by the review agency. The effects of mitigation measures were not analyzed in most cases. Again, these can be attributed to no formal guidelines available for impact predictions until now. A brief guideline is presented in this paper. This study suggested that the model application should be required and guided in detail by the review agency. It is also suggested that the hydrological and hydraulic items shoud be integrated with the water quality predictions in future, since the non-point source pollution runoff is based on the hydrologic phenomena and the water quality reactions on the hydraulic nature.

• Journal of Construction Engineering and Project Management
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• v.6 no.3
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• pp.14-21
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• 2016

One of the major issues in the material-based or acceptance quality characteristics asphalt pavement Quality Assurance (QA) is that the method does not have rationality to link between the individual materials and the projected performance of the pavement. A new asphalt mix QA method has been recently developed under a national research project using the probabilistic Performance Related Specification (PRS). This advanced PRS QA methodology integrates the AASHTOWare Pavement ME Design$^{(R)}$ technology with the simple performance test concept that bridges the material characteristics with the pavement performance. This paper presents a case study of asphalt pavement performance using the developed PRS QA computer program, named Quality Related Specification Software (QRSS), with an actual pavement project, to demonstrate the developed PRS procedure and to assess the robustness of QRSS in terms of the rationality of the distress predictions. The results of this limited case study show that the new PRS QA method reasonably predicts the pavement performance, properly applied the probabilistic methods, and produced rational pay adjustment.

• Animal Bioscience
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• v.37 no.4
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• pp.555-566
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• 2024

Objective: This study aimed to assess the genetic parameters and accuracy of genomic predictions for twenty-four linear body conformation traits and overall conformation scores in Korean Holstein dairy cows. Methods: A dataset of 2,206 Korean Holsteins was collected, and genotyping was performed using the Illumina Bovine 50K single nucleotide polymorphism (SNP) chip. The traits investigated included body traits (stature, height at front end, chest width, body depth, angularity, body condition score, and locomotion), rump traits (rump angle, rump width, and loin strength), feet and leg traits (rear leg set, rear leg rear view, foot angle, heel depth, and bone quality), udder traits (udder depth, udder texture, udder support, fore udder attachment, front teat placement, front teat length, rear udder height, rear udder width, and rear teat placement), and overall conformation score. Accuracy of genomic predictions was assessed using the single-trait animal model genomic best linear unbiased prediction method implemented in the ASReml-SA v4.2 software. Results: Heritability estimates ranged from 0.10 to 0.50 for body traits, 0.21 to 0.35 for rump traits, 0.13 to 0.29 for feet and leg traits, and 0.05 to 0.46 for udder traits. Rump traits exhibited the highest average heritability (0.29), while feet and leg traits had the lowest estimates (0.21). Accuracy of genomic predictions varied among the twenty-four linear body conformation traits, ranging from 0.26 to 0.49. The heritability and prediction accuracy of genomic estimated breeding value (GEBV) for the overall conformation score were 0.45 and 0.46, respectively. The GEBVs for body conformation traits in Korean Holstein cows had low accuracy, falling below the 50% threshold. Conclusion: The limited response to selection for body conformation traits in Korean Holsteins may be attributed to both the low heritability of these traits and the lower accuracy estimates for GEBVs. Further research is needed to enhance the accuracy of GEBVs and improve the selection response for these traits.

• Proceedings of the Korea Concrete Institute Conference
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• 2006.11a
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• pp.1-12
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• 2006

For advanced civil engineering structures a service life of hundred up to hundred fifty and even two hundred years is sometimes required. The prediction of the performance of concrete structures over such a long period requires accurate and reliable predictive models. Most of the presently used, mostly experience based models don't have the quality and reliability that is required for reliable long-term predictions. The models designers are searching for should be based on an accurate description of the relevant degradation mechanisms. The starting point of such models is a realistic description of the microstructure of the concrete. In this presentation the need and the role of fundamental microstructural models for predicting the performance of concrete structures will be presented. An example will be given of a microstructural model with a proven potential for long-term predictions. Besides this also the role of models in general, i.e. in the whole design and execution process of concrete structures, will be dealt with. Finally recent trends in concrete research will be presented, like the research on self-healing cement-bases systems.

• 한국신재생에너지학회:학술대회논문집
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• 2008.10a
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• pp.278-281
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• 2008

In these days the installation of wind turbines or wind parks includes a high financial risk. So for the planning and the constructing of wind farms, long-term data of wind speed and wind direction is required. However, in most cases only few data are available at the designated places. Traditional Measure-Correlate-Predict (MCP) can extend this data by using data of nearby meteorological stations. But also Neural Networks can create such long-term predictions. The key issue of this paper is to demonstrate the possibility and the quality of predictions using Neural Networks. Thereto this paper compares the results of different MCP Models and Neural Networks for creating long-term data with various indexes.

• Journal of Power System Engineering
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• v.13 no.2
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• pp.63-70
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• 2009

This paper discusses the influence on long-tenn predictions of the ship response in ocean by using the Global Wave Statistics data, GWS, and wave information from the remote sensing satellites. GWS's standard scatter diagrams of significant wave height and zero-crossing wave period are suggested to be corrected to a round number of 0.01/1000 fitted with a statistical analytic model of the conditional lognormal distribution for zero-crossing wave period. The GEOSAT satellite data are utilized which presented by I. R. Young and G. J. Holland (1996, named as GEOSAT data). At first, qualities of this data are investigated, and statistical characteristic trends are studied by means of applying known probability distribution functions. The wave height data of GEOSAT are compared to the data observed onboard merchant ships, the data observed by measure instrument installed on the ocean-going container ship and so on. To execute a long-tenn prediction of ship response, joint probability functions between wave height and wave period are introduced, therefore long-term statistical predictions are executed by using the functions.