• Journal of Computing Science and Engineering
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• v.10 no.2
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• pp.58-67
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• 2016

With the large amount of complex network data that is increasingly available on the Web, link prediction has become a popular data-mining research field. The focus of this paper is on a link-prediction task that can be formulated as a binary classification problem in complex networks. To solve this link-prediction problem, a sparse-classification algorithm called "Truncated Kernel Projection Machine" that is based on empirical-feature selection is proposed. The proposed algorithm is a novel way to achieve a realization of sparse empirical-feature-based learning that is different from those of the regularized kernel-projection machines. The algorithm is more appealing than those of the previous outstanding learning machines since it can be computed efficiently, and it is also implemented easily and stably during the link-prediction task. The algorithm is applied here for link-prediction tasks in different complex networks, and an investigation of several classification algorithms was performed for comparison. The experimental results show that the proposed algorithm outperformed the compared algorithms in several key indices with a smaller number of test errors and greater stability.

• Journal of Construction Engineering and Project Management
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• v.3 no.2
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• pp.1-12
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• 2013

Research indicates that good front-end planning (FEP) results in the achievement of higher levels of project performance. By facilitating collaboration among stakeholders in diverse locations with the use of workflow-enabled processes, such pressures can be reduced, and the overall process and results of FEP can be improved. With these goals, a front-end planning tool (FEPT) has been developed as support for owners and major contractors who are engaged in front-end planning. This paper presents the new FEPT and describes how it has been used for construction megaprojects in the nuclear power, oil and gas, and mining industries. The paper begins with the definitions related to and an explanation of the general process for implementing and applying the FEPT and then describes and analyzes how the FEPT was applied in case study projects in order to test its validity. The results indicate that the FEPT increases the efficiency and effectiveness of front-end planning for the megaprojects studied and that it has the potential to produce similar results for other megaprojects.

• Journal of the Korean Professional Engineers Association
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• v.19 no.4
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• pp.11-21
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• 1986

Mine drainage from coal mines is mostly acidic, polluted and/or contaminated, even if its quantity has increased substantially during recent days. This causes two kinds of problems arising at mining districts; one is the environmental disruption and the other is insufficient water supply for living, employee's bathing and industrial purposes. In order to mitigate the aforementioned problems, a specific equipment of Korea type for mine drainage purification has been developed and its prototype manufactured, followed by its applicability tests implemented at mine site. The results of the tests indicates that the new equipment developed is much lower than and economical compared to, other existing neutralization facilities at home and abroad in capital investment at installation stage, the consumption of neutralizing chemicals at operation stage and the requirements of installation site. Whangji area where the prototype water treatment equipment is installed has been sustaining a short supply of usable water, especially in dry seasons and supplementing about 40㎥ of water brought from a location farther than 4km in distance to meet water requirements. The prototype water treatment equipment is however considered capable of providing compressor cooling water in sufficient amount from winter season In the future.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.12
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• pp.3274-3292
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• 2013

Unsupervised methods for image segmentation are recently drawing attention because most images do not have labels or tags. A topic model is such an unsupervised probabilistic method that captures latent aspects of data, where each latent aspect, or a topic, is associated with one homogeneous region. The results of topic models, however, usually have noises, which decreases the overall segmentation performance. In this paper, to improve the performance of image segmentation using topic models, we propose two topic masks applicable to topic assignments of homogeneous regions obtained from topic models. The topic masks capture the noises among the assigned topic assignments or topic labels, and remove the noises by replacements, just like image masks for pixels. However, as the nature of topic assignments is different from image pixels, the topic masks have properties that are different from the existing image masks for pixels. There are two contributions of this paper. First, the topic masks can be used to reduce the noises of topic assignments obtained from topic models for image segmentation tasks. Second, we test the effectiveness of the topic masks by applying them to segmented images obtained from the Latent Dirichlet Allocation model and the Spatial Latent Dirichlet Allocation model upon the MSRC image dataset. The empirical results show that one of the masks successfully reduces the topic noises.

• Korean Journal of Acupuncture
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• v.33 no.1
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• pp.18-32
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• 2016

Objectives : This study aims to analyze the co-occurrence of pathological symptoms and corresponding acupoints as documented by the comprehensive acupuncture and moxibustion records in the classical texts of Far East traditional medicine as an aid to a more efficient understanding of the tacit treatment principles of ancient physicians. Methods : The Classic of Nourishing Life with Acupuncture and Moxibustion(Zhenjiu Zisheng Jing; hereinafter ZZJ) was selected as the primary reference book for the analysis. The pathology-acupoint co-occurrence analysis was performed by applying 4 values of vector space measures(weighted Euclidean distance, Euclidean distance, $Cram\acute{e}r^{\prime}s$ V and Canberra distance), which measure the distance between the observed and expected co-occurrence counts, and 3 values of probabilistic measures(association strength, Fisher's exact test and Jaccard similarity), which measure the probability of observed co-occurrences. Results : The treatment records contained in ZZJ were preprocessed, which yielded 4162 pathology-acupoint sets. Co-occurrence was performed applying 7 different analysis variables, followed by a prediction simulation. The prediction simulation results revealed the Weighted Euclidean distance had the highest prediction rate with 24.32%, followed by Canberra distance(23.14%) and association strength(21.29%). Conclusions : The weighted Euclidean distance among the vector space measures and the association strength among the probabilistic measures were verified to be the most efficient analysis methods in analyzing the correlation between acupoints and pathologies found in the classical medical texts.

• Magazine of the Korean Society of Agricultural Engineers
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• v.28 no.1
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• pp.51-59
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• 1986

Through the hydraulic model test, a more convenient and accurate method of deter- mining discharge coefficients in the sluice type of tide gates can be derived by the use of aubmergence ratio as a parameter. The results obtained are summarized as follows; 1. Discharge coefficients under submerged flow conditions can be obtained by the application of sutmergerice ratio (S) to the free flow equation of the broad-erested we r. 2. The critical submergence ratios (Scr) for the flat basin and the broad-crested types of sill have the same value of 0.8. 3. Under free flow conditions, the discharge coefficient (m) are 0.37 and 0. 35 for the flat basin and the broad-crested types of sill respectively. However, when submerged flow condition exists, the discharge coefficients for both types of sill is given by a regression equation of discharge coefficients (IL) on submergence ratios (8) expressed as; m 1.3- 1. 17S. 4. The relationships between S and Froude number (Fr), for the flat basin and the broad-crested types of sill are Fr=2. 79-2.495 and Fr2.5=5. 7-6.16S respectively. From the above relationships, it can be concluded that m can also be expressed in terms of the Froude number which is a very relevant hydraulic parameter of the open channel hydraulics.

• Management Science and Financial Engineering
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• v.19 no.1
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• pp.29-36
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• 2013

This paper considers common due-date assignment and scheduling on parallel machines. The main decisions are: (a) deter-mining the common due-date; (b) allocating jobs to machines; and (c) sequencing the jobs assigned to each machine. The objective is to minimize the sum of the penalties associated with common due-date assignment, earliness and tardiness. As an extension of the existing studies on the problem, we consider sequence-dependent setup times that depend on the type of job just completed and on the job to be processed. The sequence-dependent setups, commonly found in various manufacturing systems, make the problem much more complicated. To represent the problem more clearly, a mixed integer programming model is suggested, and due to the complexity of the problem, two heuristics, one with individual sequence-dependent setup times and the other with aggregated sequence-dependent setup times, are suggested after analyzing the characteristics of the problem. Computational experiments were done on a number of test instances and the results are reported.

• International Journal of Internet, Broadcasting and Communication
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• v.10 no.2
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• pp.25-30
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• 2018

This paper is about wine quality classification with multilayer perceptron using the deep neural network. Wine complexity is an issue when predicting the quality. And the deep neural network is considered when using complex dataset. Wine Producers always aim high to get the highest possible quality. They are working on how to achieve the best results with minimum cost and efforts. Deep learning is the possible solution for them. It can help them to understand the pattern and predictions. Although there have been past researchers, which shows how artificial neural network or data mining can be used with different techniques, in this paper, rather not focusing on various techniques, we evaluate how a deep learning model predicts for the quality using two different activation functions. It will help wine producers to decide, how to lead their business with deep learning. Prediction performance could change tremendously with different models and techniques used. There are many factors, which, impact the quality of the wine. Therefore, it is a good idea to use best features for prediction. However, it could also be a good idea to test this dataset without separating these features. It means we use all features so that the system can consider all the feature. In the experiment, due to the limited data set and limited features provided, it was not possible for a system to choose the effective features.

• Smart Structures and Systems
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• v.22 no.4
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• pp.459-468
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• 2018

In this paper, the effect of non-persistent joints was determined on the behavior of concrete specimens subjected to biaxial loading through numerical modeling using particle flow code in two dimensions (PFC2D). Firstly, a numerical model was calibrated by uniaxial, Brazilian and triaxial experimental results to ensure the conformity of the simulated numerical model's response. Secondly, sixteen rectangular models with dimension of 100 mm by 100 mm were developed. Each model contains two non-persistent joints with lengths of 40 mm and 20 mm, respectively. The angularity of the larger joint changes from $30^{\circ}$ to $90^{\circ}$. In each configuration, the small joint angularity changes from $0^{\circ}$ to $90^{\circ}$ in $30^{\circ}$ increments. All of the models were under confining stress of 1 MPa. By using of the biaxial test configuration, the failure process was visually observed. Discrete element simulations demonstrated that macro shear fractures in models are because of microscopic tensile breakage of a large number of bonded discs. The failure pattern in Rock Bridge is mostly affected by joint overlapping whereas the biaxial strength is closely related to the failure pattern.

• Geophysics and Geophysical Exploration
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• v.3 no.3
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• pp.83-87
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• 2000

The noise prediction filter using a local/remote reference was developed to obtain a high quality data from seismic surveys over the area where seismic transmission power is limited. The method used in the noise prediction filter is a 3-layer neural network whose algorithm is backpropagation. A NRF (Noise Reduction Factor) value of about 3.0 was obtained with appling training and test data to the trained noise prediction filter. However, the scaling technique generally used for minimizing EM noise from electric and electromagnetic data cannot reduce seismic noise, since the technique can allow only amplitude difference between two time series measured at the primary and reference sites.