• Journal of Distribution Science
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• v.18 no.9
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• pp.31-43
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• 2020

Purpose: This study examined a comprehensive model for assessing the success probability of electric vehicle (EV) commercialization in the Korean market. The study identified three risks associated with successful commercialization which were technology, social, policy, environmental, and consumer risk. Research design, methodology: The assessment of the riskiness was represented by a Bayes belief network, where the probability of success at each stage is conditioned on the outcome of the preceding stage. Probability of success in each stage is either dependent on input (i.e., investment) or external factors (i.e., air quality). Initial input stages were defined as the levels of investment in product R&D, battery technology, production facilities and battery charging facilities. Results: Reasonable levels of investment were obtained by expert opinion from industry experts. Also, a survey was carried out with 78 experts consisting of automaker engineers, managers working at EV parts manufacturers, and automobile industry researchers in government think tanks to obtain the conditional probability distributions. Conclusion: The output of the model was the likelihood of success - expressed as the probability of market acceptance - that depended on the various input values. A model is a useful tool for understanding the EV industry as a whole and explaining the likely ramifications of different investment levels.

• Asia-Pacific Journal of Business
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• v.15 no.1
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• pp.305-318
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• 2024

Purpose - The purpose of this study was to conduct a preliminary study to identify key trends on research articles indexed in KCI in relation to tourism in Jeju and sports tourism. Design/methodology/approach - Information regarding research articles focused on Jeju tourism and sports tourism indexed in KCI (145 and 120 articles respectively) were collected and finally abstract written in Korean of 100 and 91 articles on sports tourism and Jeju tourism respectively were chosen for the further analysis after removing redundant articles. R program was used to analyze keyword frequencies, co-occurring terms, and degree/betweeness centrality measures and visualize the keyword network results. Findings - Event, marketing, content, program, implication, service, stadium, and tourism destination have been identified as keywords with highest frequencies among research on sport tourism, whereas tourism destination, image, brand, content, data, Chinese, satisfaction, eco-tourism service, place of arrival were highly appearing terms among research on Jeju tourism. Research implications or Originality - This study highlighted that Jeju has been interlinked with a range of terms such as programs influencing Jeju tourism, natural environment, tourism-related resources (e.g., museums, dramas, etc.), whereas sports has been closely related to sports event and vaiours types of sports (e.g., bicycle, staking, and scuber), but not to Jeju-do.

• Journal of Electrical Engineering and Technology
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• v.4 no.1
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• pp.28-34
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• 2009

In a Gas Insulated Substation (GIS), Very Fast Transients (VFTs) are generated mainly due to switching operations. These transients may cause internal faults, i.e., layer-to-layer faults in a capacitively graded bushing as it is one of the most important terminal equipment for GIS. The healthiness of the bushing is generally verified by measuring its leakage current. However, the change in current magnitude/pattern is only marginal for different types of fault conditions. Leakage current monitoring (LCM) systems generate large amounts of data and computer aided interpretation of defects may be of great assistance when analyzing this data. In view of the above, ANN techniques have been used in this study for identification of these minor faults. A single layer perceptron network, a two layer feed-forward back propagation network and cascade correlation (CC) network models are used to identify interlayer faults in the bushing. The effectiveness of the CC network over perceptron and back propagation networks in identification of a fault has been analysed as part of the paper.

• International Journal of Computer Science & Network Security
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• v.24 no.5
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• pp.64-72
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• 2024

In recent times cyber attackers can use Artificial Intelligence (AI) to boost the sophistication and scope of attacks. On the defense side, AI is used to enhance defense plans, to boost the robustness, flexibility, and efficiency of defense systems, which means adapting to environmental changes to reduce impacts. With increased developments in the field of information and communication technologies, various exploits occur as a danger sign to cyber security and these exploitations are changing rapidly. Cyber criminals use new, sophisticated tactics to boost their attack speed and size. Consequently, there is a need for more flexible, adaptable and strong cyber defense systems that can identify a wide range of threats in real-time. In recent years, the adoption of AI approaches has increased and maintained a vital role in the detection and prevention of cyber threats. In this paper, an Ensemble Deep Restricted Boltzmann Machine (EDRBM) is developed for the classification of cybersecurity threats in case of a large-scale network environment. The EDRBM acts as a classification model that enables the classification of malicious flowsets from the largescale network. The simulation is conducted to test the efficacy of the proposed EDRBM under various malware attacks. The simulation results show that the proposed method achieves higher classification rate in classifying the malware in the flowsets i.e., malicious flowsets than other methods.

• The Journal of Advanced Prosthodontics
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• v.10 no.1
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• pp.32-42
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• 2018

PURPOSE. The purpose of this study was to derive and compare the inherent color (hue angle, chroma), translucency ($TP_{SCI}$), surface gloss (${\Delta}E^*_{SCE-SCI}$), and surface roughness ($R_a$) amongst selected shades and brands of three hybrid CAD/CAM blocks [GC Cerasmart (CS); Lava Ultimate (LU); Vita Enamic (VE)]. MATERIALS AND METHODS. The specimens (N = 225) were prepared into square-shaped ($12{\times}12mm^2$) with different thicknesses and shades. The measurements of color, translucency, and surface gloss were performed by a reflection spectrophotometer. The surface roughness and surface topography were assessed by white light interferometry. RESULTS. Results revealed that hue and chroma values were influenced by the material type, material shade, and material thickness (P < .001). The order of hue angle amongst the materials was LU > CS > VE, whereas the order of chroma was VE > CS > LU. $TP_{SCI}$ results demonstrated a significant difference in terms of material types and material thicknesses ($P{\leq}.001$). $TP_{SCI}$ values of the tested materials were ordered as LU > CS > VE. ${\Delta}E^*_{SCE-SCI}$ and $R_a$ results were significantly varied amongst the materials (P < .001) and amongst the shades (P < .05). The order of ${\Delta}E^*_{SCE-SCI}$ amongst the materials were as follows $LU>VE{\geq}CS$, whereas the order of $R_a$ was $CS{\geq}VE>LU$. CONCLUSION. Nano-ceramic and polymer-infiltrated-feldspathic ceramic-network CAD/CAM materials exhibited different optical, inherent color and surface parameters.

• Asian-Australasian Journal of Animal Sciences
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• v.25 no.7
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• pp.903-912
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• 2012

In Erhualian and Yorkshire reciprocal cross $F_1$ pig populations, we examined the mRNA expression characteristic of liver-derived IGF-1, IGF-1R, IGF-2, IGF-2R and IGFBP-3 during the embryonic and postnatal developmental periods (E50, E70, E90, D1, D20, D70, D120 and D180). Our results demonstrated that the IGF-system genes mRNA levels exhibited an ontogenetic expression pattern, which was potentially associated with the porcine embryonic development, postnatal growth, organogenesis and even the initiation and acceleration of puberty. The expression pattern of IGF-system genes showed variation in the reciprocal cross ($F_1$ YE and EY pigs). This study also involved the expression features of imprinted genes IGF-2 and IGF-2R. The parent-of-origin effect of imprinted genes was reflected by their differential expression between the reciprocal crosses populations. The correlation analysis also indicated that the regulatory network and mechanisms involved in the IGF system were a complex issue that needs to be more fully explored. A better understanding of IGF system components and their interactive mechanisms will enable researchers to gain insights not only into animal organogenesis but also into somatic growth development and even reproduction.

• Computers and Concrete
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• v.32 no.2
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• pp.149-163
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• 2023

One of the main design parameters traditionally utilized in projects of geotechnical engineering is the uniaxial compressive strength. The present paper employed three artificial intelligence methods, i.e., the stochastic fractal search (SFS), the multi-verse optimization (MVO), and the vortex search algorithm (VSA), in order to determine the compressive strength of concrete (CSC). For the same reason, 1030 concrete specimens were subjected to compressive strength tests. According to the obtained laboratory results, the fly ash, cement, water, slag, coarse aggregates, fine aggregates, and SP were subjected to tests as the input parameters of the model in order to decide the optimum input configuration for the estimation of the compressive strength. The performance was evaluated by employing three criteria, i.e., the root mean square error (RMSE), mean absolute error (MAE), and the determination coefficient (R²). The evaluation of the error criteria and the determination coefficient obtained from the above three techniques indicates that the SFS-MLP technique outperformed the MVO-MLP and VSA-MLP methods. The developed artificial neural network models exhibit higher amounts of errors and lower correlation coefficients in comparison with other models. Nonetheless, the use of the stochastic fractal search algorithm has resulted in considerable enhancement in precision and accuracy of the evaluations conducted through the artificial neural network and has enhanced its performance. According to the results, the utilized SFS-MLP technique showed a better performance in the estimation of the compressive strength of concrete (R²=0.99932 and 0.99942, and RMSE=0.32611 and 0.24922). The novelty of our study is the use of a large dataset composed of 1030 entries and optimization of the learning scheme of the neural prediction model via a data distribution of a 20:80 testing-to-training ratio.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.01a
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• pp.431-434
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• 2001

Knowledge discovery in databases(KDD) is the process for extracting valid, novel, potentially useful and understandable knowledge form real data. There are many academic and industrial activities with new technologies and application areas. Particularly, data mining is the core step in the KDD process, consisting of many algorithms to perform clustering, pattern recognition and rule induction functions. The main goal of these algorithms is prediction and description. Prediction means the assessment of unknown variables. Description is concerned with providing understandable results in a compatible format to human users. We introduce an efficient data mining algorithm considering predictive and descriptive capability. Reasonable pattern is derived from real world data by a revised neural network model and a proposed fuzzy rule extraction technique is applied to obtain understandable knowledge. The proposed neural network model is a hierarchical self-organizing system. The rule base is compatible to decision makers perception because the generated fuzzy rule set reflects the human information process. Results from real world application are analyzed to evaluate the system\`s performance.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.30 no.1
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• pp.38-43
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• 2016

Recent social needs for promoting traffic safety increased and the demand social security in economic, increasing the demand for environmentally friendly rail transport. In particular, when train express such as to secure reliability KTX(Korea Train eXpress) from potential disaster(fire) in the train operation caused by the train express running has been very important. Railroad fire extinguishing system is operated to fire exploding before reaching the flashing point more important than early to quickly detect because of CAN(Controller Area Network) communication to fire suppression and fire receiver, interface, fire fighting equipment from HMI((Human Machine Interface) and fire high-performance to research and development for intelligent composite fire receiver is required.

• Journal of the Korean Society of Visualization
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• v.19 no.3
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• pp.31-38
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• 2021

The energy harvesting system using an inverted flag is analyzed by using an immersed boundary method to consider the fluid and solid interaction. The inverted flag flutters at a lower critical velocity than a conventional flag. A fluttering motion is classified into straight, symmetric, asymmetric, biased, and over flapping modes. The optimal energy harvesting efficiency is observed at the biased flapping mode. Using the three different machine learning algorithms, i.e., artificial neural network, random forest, support vector regression, the energy harvesting efficiency is predicted by taking bending rigidity, inclination angle, and flapping frequency as input variables. The R² value of the artificial neural network and random forest algorithms is observed to be more than 0.9.