• Journal of Welding and Joining
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• v.31 no.3
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• pp.44-48
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• 2013

Artificial neural network (ANN) model is applied to predict arc welding process window for automotive steel plate. Target weldment was various automotive steel plate combination with lap fillet joint. The accuracy of prediction was evaluated through comparison experimental result to ANN simulation. The effect of ANN variables on the accuracy is investigated such as number of hidden layers, perceptrons and transfer function type. A static back propagation model is established and tested. The result shows comparatively accurate predictability of the suggested ANN model. However, it restricts to use nonlinear transfer function instead of linear type and suggests only one single hidden layer rather than multiple ones to get better accuracy. In addition to this, obvious fact is affirmed again that the more perceptrons guarantee the better accuracy under the precondition that there are enough experimental database to train the neural network.

• Journal of the Korean Society of Safety
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• v.16 no.3
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• pp.117-124
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• 2001

This paper introduces m analysis framework and procedure for the support of the cognitive error analysis of emergency tasks in nuclear poler plants. The framework provides a new perspective in the utilization of influencing factors into error prediction. The framework can be characterized by two features. First, influencing factors that affect the occurrence of human error me classified into three groups, i.e., task characteristic factors(TCF), situation factors(SF), and performance assisting factors(PAF). This classification aims to support error prediction from the viewpoint of assessing the adequacy of PAF under given TCF and SF. Second, the assessment of influencing factors is made by each cognitive function. Through this, influencing factors assessment and error prediction can be made in an integrative way according to each cognitive function. In addition, it helps analysts identify vulnerable cognitive functions and error factors, and obtain specific nor reduction strategies. The proposed framework was applied to the error analysis of the bleed and feed operation of nuclear emergency tasks.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2022.04a
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• pp.182-183
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• 2022

In this study, compared and analyzed various loss function minimization techniques to present a methodology for developing a natural intelligence-based prediction system. As a result of the analysis, He Initialization was the best with RMSE: 3.78, R2: 0.94, and the error rate was 6%. However, it is considered desirable to construct a prediction system by combining each technique for optimization.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.2
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• pp.784-789
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• 2012

In this paper, a number of anode and reference electrodes that are installed around the target anti-corrosion objects to monitor, appropriate to your situation of a fixed potential and polarization methods can be applied automatically in the system was developed. In particular, this system was configured with a remote electric anti-corrosion automatic control device that have automatically adjust function to uniformal anti-corrosion in all parts of target objects, a corrosion monitoring device to perform the function of corrosion potential detection and a web-based operating program to perform the function of real-time monitoring, control and prediction. Using this system, by preventing oxidative corrosion phenomena can maximize the life of the target anti-corrosion objects.

• Journal of the Korea Institute of Military Science and Technology
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• v.14 no.5
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• pp.768-773
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• 2011

The prediction method of future events using the time histories of velocity or pressure, etc., is a useful way for controlling various air vehicles. For example, the sensors of velocity or pressure can be used to extract the time mode coefficients of eigenmode of flow field, and then the result is applied to suppress wake or drag. The velocity information is mapped to the entire flow field, so this mapping function can be used to predict the future events based on the current information. The mapping function is composed of the huge amount of weight parameters, so the efficient way of finding these parameters is needed. Here, the neural network algorithm is studied to draw a mapping function using the number and location of velocity sensors.

• Journal of Communications and Networks
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• v.6 no.3
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• pp.245-257
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• 2004

Driven by the growing number of the mobile subscribers, efficient channel resource management plays a key role for provisioning multimedia service in the next generation personal communication systems. To reuse limited channel resources, diminishing the coverage areas of cells seems to be the ultimate solution. Thus, however, causes more handover events. To provide seamless connection environment for mobile terminals and applications, this article presents a novel handover scheme called the intelligent channel reservation (ICR) scheme, which exploits the location prediction technologies to accurately reserve channel resources for handover connections. Considering the fact that each mobile terminal has its individual mobility characteristic, the ICR scheme utilizes a channel reserving notification procedure (CRNP) to collect adequate parameters for predicting the future location of individual mobile terminals. These parameters will be utilized by the handover prediction function to estimate the expected handover blocking rate and the expected number of idle channels. Based on the handover prediction estimations, a cost function for calculating the damages from blocking the handover connections and idling channel resources, and a corresponding algorithm for minimizing the cost function are proposed. In addition, a guard channel decision maker (GCDM) determines the appropriate number of guard channels. The experimental results show that the ICR scheme does reduce the handover-blocking rate while keeping the number of idle channels small.

• The KIPS Transactions:PartB
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• v.12B no.2 s.98
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• pp.209-214
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• 2005

The use of protein interaction data is highly reliable for predicting functions to proteins without function in proteomics study. The computational studies on protein function prediction are mostly based on the concept of guilt-by-association and utilize large-scale interaction map from revealed protein-protein interaction data. This study compares graph-based approaches such as neighbor-counting and $\chi^2-statistics$ methods using protein-protein interaction data and proposes an approach that is effective in analyzing large-scale protein interaction data. The proposed approach is also based protein interaction map but sequence similarity and heuristic knowledge to make prediction results more reliable. The test result of the proposed approach is given for KDD Cup 2001 competition data along with those of neighbor-counting and $\chi^2-statistics$ methods.

• Geomechanics and Engineering
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• v.33 no.6
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• pp.567-581
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• 2023

The prediction and achievement of a proper rock fragmentation size is the main challenge of blasting operations in surface mines. This is because an optimum size distribution can optimize the overall mine/plant economics. To this end, this study attempts to develop four improved artificial intelligence models to predict rock fragmentation through cascaded forward neural network (CFNN) and radial basis function neural network (RBFNN) models. In this regards, the CFNN was trained by the Levenberg-Marquardt algorithm (LMA) and Conjugate gradient backpropagation (CGP). Further, the RBFNN was optimized by the Dragonfly Algorithm (DA) and teaching-learning-based optimization (TLBO). For developing the models, the database required was collected from the Midouk copper mine, Iran. After modeling, the statistical functions were computed to check the accuracy of the models, and the root mean square errors (RMSEs) of CFNN-LMA, CFNN-CGP, RBFNN-DA, and RBFNN-TLBO were obtained as 1.0656, 1.9698, 2.2235, and 1.6216, respectively. Accordingly, CFNN-LMA, with the lowest RMSE, was determined as the model with the best prediction results among the four examined in this study.

• The Journal of the Korea Contents Association
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• v.9 no.5
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• pp.332-336
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• 2009

Functional prediction of unannotated proteins is one of the most important tasks in yeast genomics. Analysis of a protein-protein interaction network leads to a better understanding of the functions of unannotated proteins. A number of researches have been performed for the functional prediction of unannotated proteins from a protein-protein interaction network. A chi-square method is one of the existing methods for the functional prediction of unannotated proteins from a protein-protein interaction network. But, the method does not consider the topology of network. In this paper, we propose a novel method that is able to predict specific molecular functions for unannotated proteins from a protein-protein interaction network. To do this, we investigated all protein interaction DBs of yeast in the public sites such as MIPS, DIP, and SGD. For the prediction of unannotated proteins, we employed a modified chi-square measure based on neighborhood counting and we assess the prediction accuracy of protein function from a protein-protein interaction network.

• KIEAE Journal
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• v.4 no.4
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• pp.11-18
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• 2004

In this study, the measurement system was developed for the measurement of pollutants from building materials, and specimens were made with concrete, gypsum board, mortar and wall paper. Characteristics of VOCs and TVOC concentration and Emission Factor as a function of time were assessed, and the conclusion was drawn as follows. (1) From predicting TVOC concentration decrease of specimen 7 with the wall paper attached to the concrete, the graph may become linear by converting the value of y-axis into the log function, and the prediction equation can be expressed as $y=34906{\ast}e^{-0.0093{\ast}time}$. Moreover, chi-square value was 0.83 which is relatively high value, indicating that TVOC concentration can be properly predicted if the same materials are used indoors. (2) From predicting VOCs Emission Factor decrease of specimen 7, the prediction equation can be expressed as $EF=15111{\ast}e^{-0.0093{\ast}time}$, and chi-square value was 0.83. (3) From predicting TVOC concentration decrease of specimen 7, prediction equation can be considered to be $y=254323{\ast}(1-e^{-0.1046{\ast}time})$, and chi-square was 0.994 which is significantly high value, indicating that indoor TVOC concentration can be properly predicted if the same materials are used indoors. Furthermore, the prediction of concentration decrease using cumulative value of hourly measured concentration is considered to be more accurate than that using just hourly measured value directly. (4) From predicting Emission Factor decrease with cumulative hourly data of Emission Factor, chi-square appeared to be higher than that by just using hourly data of Emission Factor directly. Therefore, the prediction of Emission Factor with cumulative hourly data can provide more reliable prediction equation than the case by using just hourly concentration directly.