• The Transactions of The Korean Institute of Electrical Engineers
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• 제60권3호
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• pp.639-647
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• 2011

In this paper, we introduce a design methodology of data-centroid Radial Basis Function neural networks with extended polynomial function. The two underlying design mechanisms of such networks involve K-means clustering method and Particle Swarm Optimization(PSO). The proposed algorithm is based on K-means clustering method for efficient processing of data and the optimization of model was carried out using PSO. In this paper, as the connection weight of RBF neural networks, we are able to use four types of polynomials such as simplified, linear, quadratic, and modified quadratic. Using K-means clustering, the center values of Gaussian function as activation function are selected. And the PSO-based RBF neural networks results in a structurally optimized structure and comes with a higher level of flexibility than the one encountered in the conventional RBF neural networks. The PSO-based design procedure being applied at each node of RBF neural networks leads to the selection of preferred parameters with specific local characteristics (such as the number of input variables, a specific set of input variables, and the distribution constant value in activation function) available within the RBF neural networks. To evaluate the performance of the proposed data-centroid RBF neural network with extended polynomial function, the model is experimented with using the nonlinear process data(2-Dimensional synthetic data and Mackey-Glass time series process data) and the Machine Learning dataset(NOx emission process data in gas turbine plant, Automobile Miles per Gallon(MPG) data, and Boston housing data). For the characteristic analysis of the given entire dataset with non-linearity as well as the efficient construction and evaluation of the dynamic network model, the partition of the given entire dataset distinguishes between two cases of Division I(training dataset and testing dataset) and Division II(training dataset, validation dataset, and testing dataset). A comparative analysis shows that the proposed RBF neural networks produces model with higher accuracy as well as more superb predictive capability than other intelligent models presented previously.

• The Korean Journal of Archival Studies
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• 제25호
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• pp.91-129
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• 2010

As it grows more heavily reliant on information system, researchers seek for various ways to manage and utilize of dataset records which is accumulated in public information system. It might be needed to adjust date and enhance the quality of public administrative dataset records during transferring to archive system or sharing server. The purpose of this paper is presenting data adjustment and quality enhancement methods for public administrative dataset records, and it refers to ETT procedure and method of construction of datawarehouses. It suggests seven typical examples and processing method of data adjustment and quality enhancement, which are (1) verification of quantity and data domain (2) code conversion for a consistent code value (3) making component with combinded information (4) making a decision of precision of date data (5) standardization of data (6) comment information about code value (7) capturing of metadata. It should be reviewed during dataset record transfer. This paper made Data adjustment and quality enhancement requirements for dataset record transfer, and it could be used as data quality requirement of administrative information system which produces dataset.

• Architectural research
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• 제13권3호
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• pp.49-56
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• 2011

Competitive bidding in construction is concerned with contractors making strategic decisions in respect of determination of bid price if contractors opt to bid. This study presents a strategy model for deciding optimum tender price with reflecting appropriate profit in competitive bidding using Bayesian regression analysis (BRA). The purpose of the developed model is to help contractors to secure suitable profitability by predicting the actual profit based on key variables. They may affect construction cost at bidding phase, ultimately which help contractors to secure high quality output. The model was tested empirically by application to a bidding dataset collected from a large South Korea contractor. BRA allows contractors to estimate more accurate actual profit by reflecting not only objective information but also subjective experiences and judgments. Consequently, the model can contribute to improvement of decision-making process for setting an optimum tender price.

• Journal of the Korean GEO-environmental Society
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• 제19권1호
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• pp.5-14
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• 2018

All kinds of monitoring data in construction site could have outlier created from diverse cause. In this study generation technique of synthesis value, its regression, final outlier detection and assessment are conducted to distinct outlier data included in extensive time series dataset. Synthesis value having weight factor of correlation between a number of datasets consist of many monitoring data enable to detect outlier by increasing its correlation. Standard artificial dataset in which intentional outliers are inserted has been used for assessment of synthesis value technique. These results showed increase of detection accuracy for outlier and general tendency in case of having different time series models in common. Accuracy of outlier detection increased in case of using more dataset and showing similar time series pattern.

• Journal of Internet Computing and Services
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• 제25권2호
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• pp.11-19
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• 2024

This paper was studied abouta technology for detecting damage to temporary works equipment used in construction sites with explainable artificial intelligence (XAI). Temporary works equipment is mostly composed of steel or aluminum, and it is reused several times due to the characters of the materials in temporary works equipment. However, it sometimes causes accidents at construction sites by using low or decreased quality of temporary works equipment because the regulation and restriction of reuse in them is not strict. Currently, safety rules such as related government laws, standards, and regulations for quality control of temporary works equipment have not been established. Additionally, the inspection results were often different according to the inspector's level of training. To overcome these limitations, a method based with AI and image processing technology was developed. In addition, it was devised by applying explainableartificial intelligence (XAI) technology so that the inspector makes more exact decision with resultsin damage detect with image analysis by the XAI which is a developed AI model for analysis of temporary works equipment. In the experiments, temporary works equipment was photographed with a 4k-quality camera, and the learned artificial intelligence model was trained with 610 labelingdata, and the accuracy was tested by analyzing the image recording data of temporary works equipment. As a result, the accuracy of damage detect by the XAI was 95.0% for the training dataset, 92.0% for the validation dataset, and 90.0% for the test dataset. This was shown aboutthe reliability of the performance of the developed artificial intelligence. It was verified for usability of explainable artificial intelligence to detect damage in temporary works equipment by the experiments. However, to improve the level of commercial software, the XAI need to be trained more by real data set and the ability to detect damage has to be kept or increased when the real data set is applied.

• Journal of Drive and Control
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• 제18권4호
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• pp.9-18
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• 2021

This study proposes a method for creating learning datasets to recognize obstacles using deep learning algorithms in automated construction machinery or an autonomous vehicle. Recently, many researchers and engineers have developed various recognition algorithms based on deep learning following an increase in computing power. In particular, the image classification technology and image segmentation technology represent deep learning recognition algorithms. They are used to identify obstacles that interfere with the driving situation of an autonomous vehicle. Therefore, various organizations and companies have started distributing open datasets, but there is a remote possibility that they will perfectly match the user's desired environment. In this study, we created an interface of the virtual simulator such that users can easily create their desired training dataset. In addition, the customized dataset was further advanced by using the RDBMS system, and the recognition rate was improved.

• International conference on construction engineering and project management
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• The 9th International Conference on Construction Engineering and Project Management
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• pp.912-919
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• 2022

The study is an image-based assessment that uses image processing techniques to determine the condition of concrete with surface cracks. The preparations of the dataset include resizing and image filtering to ensure statistical homogeneity and noise reduction. The image dataset is then segmented, making it more suited for extracting important features and easier to evaluate. The image is transformed into grayscale which removes the hue and saturation but retains the luminance. To create a clean edge map, the edge detection process is utilized to extract the major edge features of the image. The Otsu method is used to minimize intraclass variation between black and white pixels. Additionally, the median filter was employed to reduce noise while keeping the borders of the image. Image processing techniques are used to enhance the significant features of the concrete image, especially the defects. In this study, the tonal zones of the histogram and its properties are used to analyze the condition of the concrete. By examining the histogram, the viewer will be able to determine the information on the image through the number of pixels associated and each tonal characteristic on a graph. The features of the five tonal zones of the histogram which implies the qualities of the concrete image may be evaluated based on the quality of the contrast, brightness, highlights, shadow spikes, or the condition of the shadow region that corresponds to the foreground.

• Safety and Health at Work
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• 제14권3호
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• pp.259-266
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• 2023

Background: Recent research on occupational injuries in companies has faced difficulties in obtaining representative data, leading to studies relying on surveys or case studies. Moreover, it is difficult to find studies on how a company's industry characteristics affect occupational injuries. This study aims to address these limitations. Methods: We collected 11 years of disclosure data from 1,247 listed companies in the Korean stock market and combined it with their occupational injury histories collected by the Republic of Korea Occupational Safety and Health Agency (KOSHA) to build a dataset. We attempted to analyze a linear panel model by dividing the dataset into manufacturing, construction, and other industries. Results: The higher proportion of full-time employees and better job skills correlate with lower occupational injuries in other industries. The wage increase reduces occupational injuries in manufacturing and other industries, but the substitution effect produces the opposite outcome in construction. Also, foreign ownership and credit ratings increase effectively reduce occupational injuries mainly in the manufacturing industry. Conclusion: Our results suggest that in explaining the relationship between corporate characteristics and occupational injuries, it is necessary to consider the nature of the industry more closely, and in particular, employment and labor policies for preventing occupational injuries need to be selectively applied according to industry. In addition, to improve the limitations and increase the usability of the research results, further detailed studies are needed in the future.

• Geomechanics and Engineering
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• 제30권2호
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• pp.107-121
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• 2022

Coal pillar assessment is of broad importance to underground engineering structure, as the pillar failure can lead to enormous disasters. Because of the highly non-linear correlation between the pillar failure and its influential attributes, conventional forecasting techniques cannot generate accurate outcomes. To approximate the complex behavior of coal pillar, this paper elucidates a new idea to forecast the underground coal pillar stability using combined unsupervised-supervised learning. In order to build a database of the study, a total of 90 patterns of pillar cases were collected from authentic engineering structures. A state-of-the art feature depletion method, t-distribution symmetric neighbor embedding (t-SNE) has been employed to reduce significance of actual data features. Consequently, an unsupervised machine learning technique K-mean clustering was followed to reassign the t-SNE dimensionality reduced data in order to compute the relative class of coal pillar cases. Following that, the reassign dataset was divided into two parts: 70 percent for training dataset and 30 percent for testing dataset, respectively. The accuracy of the predicted data was then examined using support vector classifier (SVC) model performance measures such as precision, recall, and f₁-score. As a result, the proposed model can be employed for properly predicting the pillar failure class in a variety of underground rock engineering projects.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 한국정보통신학회 2021년도 춘계학술대회
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• pp.31-33
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• 2021

In this paper, a dataset was designed and built to improve the accuracy of finger direction detection using an object detection algorithm based on You Only Look Once (YOLO). In order to improve the object detection performance, about 200 finger image data sets were trained, and to confirm that the detection accuracy differs from each other according to the angle of the palm, 50 comparison groups of different angles were configured and tested. As a result of the experiment, it was confirmed that the detection accuracy of palm located in a direction close to 90° is higher than that of other angles.