• Proceedings of the Korea Inteligent Information System Society Conference
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• 2005.11a
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• pp.241-250
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• 2005

New product development (NPD) is defined as the transformation of a market opportunity and a set of assumptions about product technology into a product available for sale. Managers charged with project selection decisions in the NPD process, such as go/no-go choices and specific resource allocation decisions, are faced with a complicated problem. Therefore, the ability to develop new successful products has identifies as a major determinant in sustaining a firm's competitive advantage. The purpose of this study is to develop a new evaluation model for NPD project selection in the high -tech industry using support vector machines (SYM). The evaluation model is developed through two phases. In the first phase, binary (go/no-go) classification prediction model, i.e. SVM for high-tech NPD project selection is developed. In the second phase. using the predicted output value of SVM, feasibility grade is calculated for the final NPD project decision making. In this study, the feasibility grades are also divided as three level grades. We assume that the frequency of NPD project cases is symmetrically determined according to the feasibility grades and misclassification errors are partially minimized by the multiple grades. However, the horizon of grade level can be changed by firms' NPD strategy. Our proposed feasibility grade method is more reasonable in NPD decision problems by considering particularly risk factor of NPD in viewpoints of future NPD success probability. In our empirical study using Korean NPD cases, the SVM significantly outperformed ANN and logistic regression as benchmark models in hit ratio. And the feasibility grades generated from the predicted output value of SVM showed that they can offer a useful guideline for NPD project selection.

• The KIPS Transactions:PartD
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• v.13D no.5 s.108
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• pp.725-730
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• 2006

Recently Web services have become a key technology which is indispensable for e-business. Due to its ability to provide the desired information or service regardless of time and place, integrating current application systems within a single business or between multiple businesses with standardized technologies are realized using the open network and Internet. However, the current Web Services Retrieval Systems, based on text oriented search are incapable of providing reliable search results by perceiving the similarity or interrelation between the various terms. Currently there are no web services retrieval models containing such semantic web functions. This research work is purported for solving such problems by designing and implementing an extended Semantic Web Services Retrieval Model that is capable of searching for general web documents, UDDI and semantic web documents. Execution result is proposed in this paper and its efficiency and accuracy are verified through it.

• Food Science of Animal Resources
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• v.32 no.4
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• pp.448-457
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• 2012

The predictive ability for off-flavor development and quality change of ground beef was evaluated using a microbial time temperature indicator (TTI). Quality indices such as off-flavor detection (OFD) time, color, pH, volatile basic nitrogen (VBN), aerobic mesophilic bacteria (AMB) counts, and lactic acid bacteria (LAB) counts were measured during storage at 5, 10, 15, and $25^{\circ}C$, respectively. Arrhenius activation energies (Ea) were estimated for temperature dependence. The Ea values for TTI response (changes in titratable acidity (TA)), VBN, AMB counts, LAB counts, and freshness, which is defined based on OFD time for quality indices of ground beef, were 106.22 kJ/mol, 58.98 kJ/mol, 110.35 kJ/mol, 116.65 kJ/mol, and 92.73 kJ/mol, respectively. The Ea of microbial TTI was found to be closer to those of the AMB counts, LAB counts, and freshness. Therefore, AMB counts, LAB counts, and freshness could be predicted accurately by the microbial TTI response due to their Ea similarity. The microbial TTI exhibited consistent relationships between its TA change and corresponding quality indices, such as AMB counts, LAB counts, and freshness, regardless of storage temperature. Conclusively, the results established that the developed microbial TTI can be used in intelligent packaging technology for representing some selected quality indices of ground beef.

• Journal of Internet Computing and Services
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• v.22 no.4
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• pp.13-28
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• 2021

Reinforcement Learning (RL) is a machine learning algorithm that repeat the closed-loop process that agents perform actions specified by the policy, the action is evaluated with a reward function, and the policy gets updated accordingly. The key benefit of RL is the ability to optimze the policy with action evaluation. Hence, it can effectively be applied to developing advanced intelligent systems and autonomous systems. Conventional RL incoporates a single policy, a reward function, and relatively simple policy update, and hence its utilization was limited. In this paper, we propose an extended RL model that considers multiple instances of RL elements. We define a formal model of the key elements and their computing model of the extended RL. Then, we propose design methods for applying to system development. As a case stud of applying the proposed formal model and the design methods, we present the design and implementation of an advanced car navigator system that guides multiple cars to reaching their destinations efficiently.

• Journal of Microbiology and Biotechnology
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• v.31 no.9
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• pp.1311-1322
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• 2021

Microbially induced calcium carbonate precipitation (MICP) has recently become an intelligent and environmentally friendly method for repairing cracks in concrete. To improve on this ability of microbial materials concrete repair, we applied random mutagenesis and optimization of mineralization conditions to improve the quantity and crystal form of microbially precipitated calcium carbonate. Sporosarcina pasteurii ATCC 11859 was used as the starting strain to obtain the mutant with high urease activity by atmospheric and room temperature plasma (ARTP) mutagenesis. Next, we investigated the optimal biomineralization conditions and precipitation crystal form using Plackett-Burman experimental design and response surface methodology (RSM). Biomineralization with 0.73 mol/l calcium chloride, 45 g/l urea, reaction temperature of 45℃, and reaction time of 22 h, significantly increased the amount of precipitated calcium carbonate, which was deposited in the form of calcite crystals. Finally, the repair of concrete using the optimized biomineralization process was evaluated. A comparison of water absorption and adhesion of concrete specimens before and after repairs showed that concrete cracks and surface defects could be efficiently repaired. This study provides a new method to engineer biocementing material for concrete repair.

• Smart Structures and Systems
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• v.23 no.1
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• pp.1-14
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• 2019

Control algorithms are the most important aspects in successful control of structures against earthquakes. In recent years, intelligent control methods rather than classical control methods have been more considered by researchers, due to some specific capabilities such as handling nonlinear and complex systems, adaptability, and robustness to errors and uncertainties. However, due to lack of learning ability of fuzzy controller, it is used in combination with a genetic algorithm, which in turn suffers from some problems like premature convergence around an incorrect target. Therefore in this research, the introduction and design of the Fuzzy Cooperative Coevolution (Fuzzy CoCo) controller and Adaptive Neural-Fuzzy Inference System (ANFIS) have been innovatively presented for semi-active seismic control. In this research, in order to improve the seismic behavior of structures, a semi-active control of building using Magneto Rheological (MR) damper is proposed to determine input voltage of Magneto Rheological (MR) dampers using ANFIS and Fuzzy CoCo. Genetic Algorithm (GA) is used to optimize the performance of controllers. In this paper, the design of controllers is based on the reduction of the Park-Ang damage index. In order to assess the effectiveness of the designed control system, its function is numerically studied on a 9-story benchmark building, and is compared to those of a Wavelet Neural Network (WNN), fuzzy logic controller optimized by genetic algorithm (GAFLC), Linear Quadratic Gaussian (LQG) and Clipped Optimal Control (COC) systems in terms of seismic performance. The results showed desirable performance of the ANFIS and Fuzzy CoCo controllers in considerably reducing the structure responses under different earthquakes; for instance ANFIS and Fuzzy CoCo controllers showed respectively 38 and 46% reductions in peak inter-story drift ($J_1$) compared to the LQG controller; 30 and 39% reductions in $J_1$ compared to the COC controller and 3 and 16% reductions in $J_1$ compared to the GAFLC controller. When compared to other controllers, one can conclude that Fuzzy CoCo controller performs better.

• Korean Journal of Audiology
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• v.25 no.3
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• pp.119-123
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• 2021

Background and Objectives: The influence of music training on different areas of the brain has been extensively researched, but the underlying neurobehavioral mechanisms remain unknown. In the present study, the effects of training for more than three years in Carnatic music (an Indian form of music) on the discrimination ability of different areas of the brain were tested using P300 analysis at three electrode placement sites. Subjects and Methods: A total of 27 individuals, including 13 singers aged 16-30 years (mean±standard deviation, 23±3.2 years) and 14 non-singers aged 16-30 years (mean age, 24±2.9 years), participated in this study. The singers had 3-5 years of formal training experience in Carnatic music. Cortical activities in areas corresponding to attention, discrimination, and memory were tested using P300 analysis, and the tests were performed using the Intelligent Hearing System. Results: The mean P300 amplitude of the singers at the Fz electrode placement site (5.64±1.81) was significantly higher than that of the non-singers (3.85±1.60; t(25)=3.3, p<0.05). The amplitude at the Cz electrode placement site in singers (5.90±2.18) was significantly higher than that in non-singers (3.46±1.40; t(25)=3.3, p<0.05). The amplitude at the Pz electrode placement site in singers (4.94±1.89) was significantly higher than that in non-singers (3.57±1.50; t(25)=3.3, p<0.05). Among singers, the mean P300 amplitude was significantly higher in the Cz site than the other placement sites, and among non-singers, the mean P300 amplitude was significantly higher in the Fz site than the other placement sites, i.e., music training facilitated enhancement of the P300 amplitude at the Cz site. Conclusions: The findings of this study suggest that more than three years of training in Carnatic singing can enhance neural coding to discriminate subtle differences, leading to enhanced discrimination abilities of the brain, mainly in the generation site corresponding to Cz electrode placement.

• Journal of Convergence for Information Technology
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• v.11 no.5
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• pp.23-29
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• 2021

Recently, a situation in which a specific content service is impossible worldwide has occurred due to a failure of the platform service and a significant social and economic problem has been caused in the global service market. In order to secure the stability of platform services, intelligent platform operation management is required. In this study, big data flow management(BDFM) and implementation method were proposed to quickly detect to abnormal service status in the platform operation environment. As a result of analyzing, BDFM technique improved the characteristics of abnormal failure detection by more than 30% compared to the traditional NMS. The big data flow management method has the advantage of being able to quickly detect platform system failures and abnormal service conditions, and it is expected that when connected with AI-based technology, platform management is performed intelligently and the ability to prevent and preserve failures can be greatly improved.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.6
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• pp.1207-1212
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• 2022

Realistic media, the next-generation media technology in the era of the 4th industrial revolution, is becoming an issue as a technology to experience through an environment that optimizes user experience, especially! It is rapidly developing into the health and healthcare convergence and complex fields. Realistic media technologies and services are being adopted to solve the problems of the increase in chronic diseases due to the increase in the elderly population and the lack of infrastructure and professional manpower in the fields of cognitive training and rehabilitation. Therefore, in this study, a cognitive training system was designed and implemented for the purpose of improving cognitive ability and daily life activity in subjects with mild cognitive impairment (MCI) who require cognitive rehabilitation. In the future, an integrated service platform with interactive communication and immediate feedback as an intelligent cognitive rehabilitation integrated platform based on AI and BigData is left as a research project.

• Journal of Audiology & Otology
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• v.25 no.3
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• pp.119-123
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• 2021

Background and Objectives: The influence of music training on different areas of the brain has been extensively researched, but the underlying neurobehavioral mechanisms remain unknown. In the present study, the effects of training for more than three years in Carnatic music (an Indian form of music) on the discrimination ability of different areas of the brain were tested using P300 analysis at three electrode placement sites. Subjects and Methods: A total of 27 individuals, including 13 singers aged 16-30 years (mean±standard deviation, 23±3.2 years) and 14 non-singers aged 16-30 years (mean age, 24±2.9 years), participated in this study. The singers had 3-5 years of formal training experience in Carnatic music. Cortical activities in areas corresponding to attention, discrimination, and memory were tested using P300 analysis, and the tests were performed using the Intelligent Hearing System. Results: The mean P300 amplitude of the singers at the Fz electrode placement site (5.64±1.81) was significantly higher than that of the non-singers (3.85±1.60; t(25)=3.3, p<0.05). The amplitude at the Cz electrode placement site in singers (5.90±2.18) was significantly higher than that in non-singers (3.46±1.40; t(25)=3.3, p<0.05). The amplitude at the Pz electrode placement site in singers (4.94±1.89) was significantly higher than that in non-singers (3.57±1.50; t(25)=3.3, p<0.05). Among singers, the mean P300 amplitude was significantly higher in the Cz site than the other placement sites, and among non-singers, the mean P300 amplitude was significantly higher in the Fz site than the other placement sites, i.e., music training facilitated enhancement of the P300 amplitude at the Cz site. Conclusions: The findings of this study suggest that more than three years of training in Carnatic singing can enhance neural coding to discriminate subtle differences, leading to enhanced discrimination abilities of the brain, mainly in the generation site corresponding to Cz electrode placement.