• Journal of Distribution Science
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• v.12 no.6
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• pp.67-79
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• 2014

Purpose - This study aims to accomplish three major research goals. First, it strives to change consumers' focus from peripheral routes to a central route of public service advertising related to the good health policy, without problematic effects, by influencing consumers' knowledge or involvement. Second, this study examines the elaboration likelihood model (ELM) and construal level theory (CLT). Specifically, we consider that the central route of ELM might correspond with the focal goal of CLT. Third, this study analyzes ELM through CLT. That is, ELM predicted that low involvement would take the peripheral route, and high involvement would take the central route. Research design, data, and methodology - This study consisted of three experiments. The first experiment had a 2×2 between-subject design. The subjects were university students and the research period was approximately one year. The first independent variable was the involvement of the overweight issue; this variable was measured and split by the median. The second independent variable was the temporal distance (near vs. distant future); this variable was manipulated. The second experiment also had a 2×2 between-subject design. The first variable was the involvement of cervical adenocarcinoma prevention, and was considered already manipulated by sex. Specifically, males had a low involvement of the disease, but females had high involvement. The second independent variable was priming (power vs. submissive). Power priming would induce abstract thinking, but submissive priming would take concrete processing. The third experiment had a 2×2×2 between-subject design. The first variable was cognitive depletion, and was manipulated by memorizing 9-digit numbers. The second and third independent variables were involvement and abstract thinking induction, such as prior experiments. Data were collected through questionnaires, and were analyzed by an SPSS program. Major hypotheses were tested by examining the interaction effects through ANOVA. Results - Major findings are as follows. First, even for low-involved consumers in the overweight category, distant future manipulation induced them to focus not on the peripheral route but on the central route of the public service advertisement. This result does not correspond to the typical ELM prediction. Second, under power priming, low-involved males of the cervical adenocarcinoma category focused on the peripheral route because of the induction to abstract thinking. This result replicated the first experiment, and confirmed the theoretical robustness. Third, high-involved females focused not on the central but on the peripheral route under the mixed condition of cognitive depletion and near future manipulation. Depletion consumed cognitive resources, and the processing mode of consumers changed from systematic to heuristic. Conclusions - ELM needs to be complemented through CLT in context of public service good health advertising. Specifically, the involvement of ELM may impact consumers' thinking mode (abstract vs. concrete), and the interaction effects may influence consumers' focus on advertising (central vs. peripheral route). This study's limitations were bounded subjects, limited stimuli, and somewhat weak external validity.

• IEIE Transactions on Smart Processing and Computing
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• v.2 no.6
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• pp.323-331
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• 2013

An extreme learning machine (ELM) is an efficient learning algorithm that is based on the generalized single, hidden-layer feed-forward networks (SLFNs), which perform well in classification applications. Many studies have demonstrated its superiority over the existing classical algorithms: support vector machine (SVM) and BP neural network. This paper presents a novel face recognition approach based on a multi-class project extreme learning machine (MPELM) classifier and 2D Gabor transform. First, all face image features were extracted using 2D Gabor filters, and the MPELM classifier was used to determine the final face classification. Two well-known face databases (CMU-PIE and ORL) were used to evaluate the performance. The experimental results showed that the MPELM-based method outperformed the ELM-based method as well as other methods.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.66 no.4
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• pp.194-199
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• 2017

Power transformers are an important factor for power transmission and cause fatal losses if faults occur. Various diagnostic methods have been applied to predict the failure and to identify the cause of the failure. Typical diagnostic methods include the IEC diagnostic method, the Duval diagnostic method, the Rogers diagnostic method, and the Doernenburg diagnostic method using the ratio of the main gas. However, each diagnostic method has a disadvantage in that it can't diagnose the state of the power transformer unless the gas ratio is within the defined range. In order to solve these problems, we propose a diagnosis method using ELM based intelligent algorithm and fuzzy membership function. The final diagnosis is performed by multiplying the result of diagnosis in the four diagnostic methods (IEC, Duval, Rogers, and Doernenburg) by the fuzzy membership values. To show its effectiveness, the proposed fault diagnostic system has been intensively tested with the dissolved gases acquired from various power transformers.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.7
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• pp.3076-3092
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• 2020

An IKPCA-ELM-based intrusion detection method is developed to address the problem of the low accuracy and slow speed of intrusion detection caused by redundancies and high dimensions of data in the network. First, in order to reduce the effects of uneven sample distribution and sample attribute differences on the extraction of KPCA features, the sample attribute mean and mean square error are introduced into the Gaussian radial basis function and polynomial kernel function respectively, and the two improved kernel functions are combined to construct a hybrid kernel function. Second, an improved particle swarm optimization (IPSO) algorithm is proposed to determine the optimal hybrid kernel function for improved kernel principal component analysis (IKPCA). Finally, IKPCA is conducted to complete feature extraction, and an extreme learning machine (ELM) is applied to classify common attack type detection. The experimental results demonstrate the effectiveness of the constructed hybrid kernel function. Compared with other intrusion detection methods, IKPCA-ELM not only ensures high accuracy rates, but also reduces the detection time and false alarm rate, especially reducing the false alarm rate of small sample attacks.

• Mycobiology
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• v.35 no.1
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• pp.6-10
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• 2007

Present experiments were conducted to determine the possibility of artificial culture with various sawdust of P. gilvus. The pH value was 6.0 of oak sawdust, 6.5 of mulberry sawdust, 6.6 of elm sawdust, 6.3 of acacia sawdust and 6.1 of apple tree sawdust. Mycelial density on elm sawdust and acacia sawdust were lower than those of oak sawdust, and apple sawdust. Weight of fresh fruiting body showed that 179 g on oak tree, 227 g on oak sawdust, 21 g on elm tree, 76 g on elm sawdust, 106 g on apple tree, and 170 g on apple sawdust. Among them, the yield of oak substrates was the highest whereas acacia sawdust was the lowest, and it is concluded that the yields of sawdust substrates were higher than log substrates. P. gilvus grown on various sawdusts and logs used in this study have shown similar in anti-tumor activity against P388.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.64 no.2
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• pp.74-78
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• 2015

With the improvement of living standards and economic development, electricity consumption continues to grow. The electricity is a special energy which is hard to store, so its supply must be consistent with the demand. The objective of electricity demand forecasting is to make best use of electricity energy and provide balance between supply and demand. Hence, it is very important work to forecast electricity demand with higher precision. So, various forecasting methods have been developed. They can be divided into five broad categories such as time series models, regression based model, artificial intelligence techniques and fuzzy logic method without considering special-day effects. Electricity demand patterns on holidays can be often idiosyncratic and cause significant forecasting errors. Such effects are known as special-day effects and are recognized as an important issue in determining electricity demand data. In this research, we developed the power demand forecasting method using ELM(Extreme Learning Machine) for special day, particularly, lunar new year and Chuseok holiday.

• Environmental Engineering Research
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• v.24 no.3
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• pp.404-411
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• 2019

In this study, we designed a data-driven model to predict chlorophyll-a using M5P model tree and extreme learning machine (ELM). The Juksan weir in the Youngsan River has high chlorophyll-a, which is the primary indicator of algal bloom every year. Short-term algal bloom prediction is important for environmental management and ecological assessment. Two models were developed and evaluated for short-term algal bloom prediction. M5P is a classification and regression-analysis-based method, and ELM is a feed-forward neural network with fast learning using the least square estimate for regression. The dataset used in this study includes water temperature, rainfall, solar radiation, total nitrogen, total phosphorus, N/P ratio, and chlorophyll-a, which were collected on a daily basis from January 2013 to December 2016. The M5P model showed that the prediction model after one day had the highest performance power and dropped off rapidly starting with predictions after three days. Comparing the performance power of the ELM model with the M5P model, it was found that the performance power of the 1-7 d chlorophyll-a prediction model was higher. Moreover, in a period of rapidly increasing algal blooms, the ELM model showed higher accuracy than the M5P model.

• Fashion & Textile Research Journal
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• v.23 no.2
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• pp.151-158
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• 2021

The purpose of this study was to develop high-quality naturally dyed leisurewear with images of traditional Korean clothing that keeps a psychologically comfortable and physically pleasant environment at home and in vacation spots. The root bark of elm trees, the atopic skin, is also known to be effective for the relief of rhinitis and atopic diseases as well as stress and insomnia. However, there is insufficient color in the bark for the dyeing of fashion products, so to compensate for the lack of color, for dyeing purposes it was combined with a composite extract called Rubia akane Nakai resulting in a relatively bright red color. Except for the light fastness, all the fastnesses were rated 4 to 5, showing excellent results. Through complex dyeing using elm bark and pods extract the author produced four high-quality vests, one-piece, a gown, and jeogori-pantsuits of silk materials with Korean images that are suitable wear for relaxing comfortably at home and during breaks and which provide a comfortable and physically pleasant experience. The vest was made with the formal style of Bae-ja and Dang-eu, the dress is made of Cheok-lik, and the gown is made of Wonsam. It will be meaningful at a time when the importance of rest is increasing due to the healing clothes worn by busy modern people.

• Nuclear Engineering and Technology
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• v.55 no.1
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• pp.100-108
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• 2023

In this study, a neural network model inspired by a one-dimensional convolution U-net is developed to automatically accelerate edge localized mode (ELM) detection from big diagnostic data of fusion devices and increase the detection accuracy regardless of the hyperparameter setting. This model recognizes the input signal patterns and overcomes the problems of existing detection algorithms, such as the prominence algorithm and those of differential methods with high sensitivity for the threshold and signal intensity. To train the model, 10 sets of discharge radiation data from the KSTAR are used and sliced into 11091 inputs of length 12 ms, of which 20% are used for validation. According to the receiver operating characteristic curves, our model shows a positive prediction rate and a true prediction rate of approximately 90% each, which is comparable to the best detection performance afforded by other algorithms using their optimized hyperparameters. The accurate and automatic ELM-burst detection methodology used in our model can be beneficial for determining plasma properties, such as the ELM frequency from big data measured in multiple experiments using machines from the KSTAR device and ITER. Additionally, it is applicable to feature detection in the time-series data of other engineering fields.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.25 no.5
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• pp.1225-1233
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• 2015

This study investigates the effects of information security awareness arising from E-Commerce in terms of the Elaboration Likelihood Model(ELM) and analyzes the moderating effect of the trust's involvement and experience. Consumers are using E-Commerce Web sites, depending on the level of involvement and experience in E-Commerce. This study is based on the ELM, the information security awareness of consumer confidence in E-Commerce form, according to the degree of experience and involvement suggested a theoretical model to describe the effect that the scaling and, through empirical studies validation of model. Consumer confidence is formed the attitude of the E-Commerce company through different paths, depending on the type of awareness in the E-Commerce web site, this moderate has the effect of consumer involvement and experience. Studying the information security awareness of consumer in the on E-Commerce is considered to present a new perspective on trust.