• KIPS Transactions on Software and Data Engineering
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• v.6 no.12
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• pp.565-572
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• 2017

Traditional method for time series analysis, autoregressive integrated moving average (ARIMA) allows to mine significant patterns from the past observations using autocorrelation and to forecast future sequences. However, Korean baseball games do not have regular intervals to analyze relationship among the past attendance observations. To address this issue, we propose artificial neural network (ANN) based attendance prediction model using various measures including performance, team characteristics and social influences. We optimized ANNs using grid search to construct optimal model for regression problem. The evaluation shows that the optimal and ensemble model outperform the baseline model, linear regression model.

• Science of Emotion and Sensibility
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• v.17 no.2
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• pp.13-24
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• 2014

Mobile phone makers compete for market shares through domination in media advertisements. These include domination of advertisements (Ads) in TV and the internet. However, the abundance and complexity of the competitions of Ads in TV does not guarantee advertising success which can influence consumers' emotion and the purchase intention towards the brand. This research analyses the case of a directional model on Attitude-towards-the-Ad model as a baseline into a new proposed correlation models (MacKenzie, Scott, &Lutz, 1989). The survey targets the involvements of Asian smartphone owners' attitude on advertisements, brands and purchase intentions. CFA (Confirmatory factor Analysis) was used in the research experiments, including hypothesis testing, the outcome of model fit which revealed significant levels and were successful. The study revealed that all three paths have consistently high coefficient paths (Attitude to Ads - Attitude to Brands - Purchase Intention), showing significant value of (${\beta}$=>.80), which supported each correlation factors. Therefore, this structural model, could set standards for creative managers and advertising teams to improve the brands visibility and build strong influences on attitudes in advertisements and improve purchase intentions.

• Culinary science and hospitality research
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• v.13 no.4
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• pp.45-56
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• 2007

The purpose of this study was to measure the causal relationships among affective belief, ambivalence, subjective norm, attitude, intention to consume and meat consumption. A total of 318 questionnaires were completed. The structural equation model was used to measure the causal effects among constructs. The results demonstrated that the confirmatory factor analysis model provided excellent model fit. The proposed model yielded a significantly better fit to the data than the baseline model. The effects of affective belief, ambivalence and subjective norm on attitude were statistically significant. The effect of subjective norm on intention was statistically significant. As expected, subjective norm and attitude had significant effects on meat consumption. Moreover, affective belief, ambivalence and subjective norm had indirect influences on meat consumption. Subjective norm also had an indirect influence on intention. The overall findings offered strong empirical support for the intuitive notion that improving the level of attitude toward eating meat can increase favorable intentions and decrease unfavorable intentions to reduce future meat consumption.

• Structural Monitoring and Maintenance
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• v.2 no.4
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• pp.399-418
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• 2015

Automated damage detection through Structural Health Monitoring (SHM) techniques has become an active area of research in the bridge engineering community but widespread implementation on in-service infrastructure still presents some challenges. In the meantime, visual inspection remains as the most common method for condition assessment even though collected information is highly subjective and certain types of damage can be overlooked by the inspector. In this article, a Frequency Response Functions-based model updating algorithm is evaluated using experimentally collected data from the University of Central Florida (UCF)-Benchmark Structure. A protocol for measurement selection and a regularization technique are presented in this work in order to provide the most well-conditioned model updating scenario for the target structure. The proposed technique is composed of two main stages. First, the initial finite element model (FEM) is calibrated through model updating so that it captures the dynamic signature of the UCF Benchmark Structure in its healthy condition. Second, based upon collected data from the damaged condition, the updating process is repeated on the baseline (healthy) FEM. The difference between the updated parameters from subsequent stages revealed both location and extent of damage in a "blind" scenario, without any previous information about type and location of damage.

• Preventive Nutrition and Food Science
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• v.3 no.1
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• pp.22-26
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• 1998

Model food containing common salt(NaCl) was subjected to the thermoluminescene(TL) detection whether it is irradiated or not. Salt irradiated with $^60Co$-gamma ray and electron beam exhibited a characteristic TL gowcurve depending on the irradiation dose, showing major peaks at $206^{\circ}C$ and $326^{\circ}C$. The intensity of TL glowcurves was directly proportional to the irradiated doses regardless of irradiation sources at each concentration of salt. A high correlation coefficient was observed for irradiated salt between the irradiation doses and the corresponding TL responses. At the same dose, the intensity of TL glowcurve increased as the concentration of salt increased in the test sample. TL glowcurves of nonirradiated salt and irradiated model food without salt were negligible and similar to a baseline . However, irradiated model food containing salt gave rise to a characteristic TL glowcurve with two major peaks at about $240^{\circ}C$ and $300^{\circ}C$, respectively. The results showed that salt played a role as an internla as well as external indicator in TL measurements, indicating that TL will be applicable to other condiments and spices with salt for their detection whether they are irradiated or not.

• Structural Engineering and Mechanics
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• v.29 no.3
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• pp.311-325
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• 2008

Noting that damage occurrence of offshore jacket platforms is concentrated in two structural regions that are in the vicinity of still water surface and close to the seabed, a damage detection method by using only partial measurement of vibration in a suspect region was presented in this paper, which can not only locate damaged members but also evaluate damage severities. Then employing an experiment platform model under white-noise ground excitation by shaking table and using modal parameters of the first three modes identified by a scalar-type ARMA method on undamaged and damaged structures, the feasibility of the damage detection method was discussed. Modal parameters from eigenvalue analysis on the structural FEM model were also used to help the discussions. It is demonstrated that the damage detection algorithm is feasible on damage location and severity evaluation for broken slanted braces and it is robust against the errors of baseline FEM model to real structure when the principal errors is formed by difference of modal frequencies. It is also found that Z-value changes of modal shapes also play a role in the precise detection of damage.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.11
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• pp.3935-3949
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• 2021

With the rapid development of Internet and big data technology, various online social network platforms have been established, producing massive information every day. Hot topic discovery aims to dig out meaningful content that users commonly concern about from the massive information on the Internet. Most of the existing hot topic discovery methods focus on a single network data source, and can hardly grasp hot spots as a whole, nor meet the challenges of text sparsity and topic hotness evaluation in cross-network scenarios. This paper proposes a novel hot topic discovery method across social network based on an im-proved LDA model, which first integrates the text information from multiple social network platforms into a unified data set, then obtains the potential topic distribution in the text through the improved LDA model. Finally, it adopts a heat evaluation method based on the word frequency of topic label words to take the latent topic with the highest heat value as a hot topic. This paper obtains data from the online social networks and constructs a cross-network topic discovery data set. The experimental results demonstrate the superiority of the proposed method compared to baseline methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.1
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• pp.147-165
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• 2021

In this work, we empirically evaluated the efficiency of the recent EfficientNetB0 model to identify and diagnose malaria parasite infections in blood smears. The dataset used was collected and classified by relevant experts from the Lister Hill National Centre for Biomedical Communications (LHNCBC). We prepared our samples with minimal image transformations as opposed to others, as we focused more on the feature extraction capability of the EfficientNetB0 baseline model. We applied transfer learning to increase the initial feature sets and reduced the training time to train our model. We then fine-tuned it to work with our proposed layers and re-trained the entire model to learn from our prepared dataset. The highest overall accuracy attained from our evaluated results was 94.70% from fifty epochs and followed by 94.68% within just ten. Additional visualization and analysis using the Gradient-weighted Class Activation Mapping (Grad-CAM) algorithm visualized how effectively our fine-tuned EfficientNetB0 detected infections better than other recent state-of-the-art DCNN models. This study, therefore, concludes that when fine-tuned, the recent EfficientNetB0 will generate highly accurate deep learning solutions for the identification of malaria parasites in blood smears without the need for stringent pre-processing, optimization, or data augmentation of images.

• Nuclear Engineering and Technology
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• v.53 no.12
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• pp.4022-4032
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• 2021

In this study, machine learning (ML) techniques were used to model surveillance test data of nuclear power plants from an international database of the ASTM E10.02 committee. Regression modeling was conducted using various techniques, including Cubist, XGBoost, and a support vector machine. The root mean square deviation of each ML model for the baseline dataset was less than that of the ASTM E900-15 nonlinear regression model. With respect to the interpolation, the ML methods provided excellent predictions with relatively few computations when applied to the given data range. The effect of the explanatory variables on the transition temperature shift (TTS) for the ML methods was analyzed, and the trends were slightly different from those for the ASTM E900-15 model. ML methods showed some weakness in the extrapolation of the fluence in comparison to the ASTM E900-15, while the Cubist method achieved an extrapolation to a certain extent. To achieve a more reliable prediction of the TTS, it was confirmed that advanced techniques should be considered for extrapolation when applying ML modeling.

• Journal of Ocean Engineering and Technology
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• v.36 no.4
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• pp.251-269
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• 2022

The process-based XBeach model has numerous empirical parameters because of insufficient understanding of hydrodynamics and sediment transport on the nearshore; hence, it is necessary to calibrate parameters to apply to various study areas and wave conditions. Therefore, the calibration process of parameters is essential for the improvement of model performance. Generally, the trial-and-error method is widely used; however, this method is passive and limited to various and comprehensive parameter ranges. In this study, the Generalized Likelihood Uncertainty Estimation (GLUE) method was used to estimate the optimal range of three parameters (gamma, facua, and gamma2) using morphological field data collected in Maengbang beach during the four typhoons that struck from September to October 2019. The model performance and optimal range of empirical parameters were evaluated using Brier Skill Score (BSS) along with the baseline profiles, sensitivity, and likelihood density analysis of BSS in the GLUE tools. Accordingly, the optimal parameter combinations were derived when facua was less than 0.15 and simulated well the shifting shape, from crescentic sand bar to alongshore uniform sand bars in the surf zone of Maengbang beach after storm impact. However, the erosion and accretion patterns nearby in the surf zone and shoreline remain challenges in the XBeach model.