• The Journal of the Acoustical Society of Korea
• /
• v.43 no.3
• /
• pp.305-313
• /
• 2024

For effective bi-static sonar operation, detection performance analysis must be performed reflecting the characteristics of sound propagation due to the ocean environment and target information. However, previous studies analyzing bistatic sonar detection performance have either not considered the ocean environment and target characteristics or have been conducted using simplified approaches. Therefore, in this study, we compared and analyzed the bistatic detection performance in Yellow sea and Ulleung basin both with and without considering target characteristics. A numerical analysis model was used to derive an accurate bistatic target strength for the submarine-shaped target, and signal excess was calculated by reflecting the simulated target strength. As a result, significant changes in detection performance were observed depending on the source and receiver locations as well as the target strength.

• The Journal of the Korea Contents Association
• /
• v.12 no.12
• /
• pp.605-615
• /
• 2012

Dance studies inherently have a nature of convergence and integration. Meanwhile, dance studies have extended their realm by investigating dance phenomena from many perspectives which art theories cannot explain. However, the previous and current dance studies are inadequate to explain a confluence society which is characterized as techuim, Interaction, freedom and openness according to the digital revolution. Hence, a result of research trend in domestic dance studies, it is found that dance studies have been studied in four perspectives since the early 2000s: first, a triggering the various studiesa of the convergent and integrative dance; second, an attempt to the convergent and integrative program development research; third, the vitalization of the convergent research on dance digital contents; and fourth, a research on the convergent dance art phenomena. These researches have played an important role in boosting a change in the structure and realm expansion of dance studies that are interdisciplinary research enabling a holistic approach to the integration and convergence between scientific technique, skills of dance art and other studies. However, it should be acknowledged that one problem is the current research development plan or/and research program have very little feasibility and practicality except an interdisciplinary research on the dance digital contents. Therefore, it is suggested for the development of dance studies in the age of convergence as follows: first, a dance convergent study integrated in skills and theories of dance and science that would pave the way for an academic foundation leading to a new humanistic model in the age of convergent; and second, a need for a paradigm shift that theories should be deployed in the scene on a commercial scale in order to produce effectiveness of the interdisciplinary and integrative research on dance studies by turning into a behavioristic research phase. third, it needs to changeover from large scale of convergent performance into small scale of convergent performance based on original idea for accumulation of teachnique research and promotion of dance convergent performance.

• Korean Journal of Optics and Photonics
• /
• v.16 no.3
• /
• pp.182-190
• /
• 2005

The fiber Raman amplifier(FRA) is a distinctly advantageous technology. Due to its wider, flexible gain bandwidth, and intrinsically lower noise characteristics, FRA has become an indispensable technology of today. Various FRA modeling methods, with different levels of convergence speed and accuracy, have been proposed in order to gain valuable insights for the FRA dynamics and optimum design before real implementation. Still, all these approaches share the common platform of coupled ordinary differential equations(ODE) for the Raman equation set that must be solved along the long length of fiber propagation axis. The ODE platform has classically set the bar for achievable convergence speed, resulting exhaustive calculation efforts. In this work, we propose an alternative, highly efficient framework for FRA analysis. In treating the Raman gain as the perturbation factor in an adiabatic process, we achieved implementation of the algorithm by deriving a recursive relation for the integrals of power inside fiber with the effective length and by constructing a matrix formalism for the solution of the given FRA problem. Finally, by adiabatically turning on the Raman process in the fiber as increasing the order of iterations, the FRA solution can be obtained along the iteration axis for the whole length of fiber rather than along the fiber propagation axis, enabling faster convergence speed, at the equivalent accuracy achievable with the methods based on coupled ODEs. Performance comparison in all co-, counter-, bi-directionally pumped multi-channel FRA shows more than 102 times faster with the convergence speed of the Average power method at the same level of accuracy(relative deviation < 0.03dB).

• Journal of Christian Education in Korea
• /
• v.74
• /
• pp.107-134
• /
• 2023

The purpose of this study is to provide a foundation for establishing the correct direction of education that utilizes artificial intelligence, a key technology of the Fourth Industrial Revolution, in the context of Christian education. To achieve this, theoretical and literature research was conducted. First, the research analyzed the historical development of artificial intelligence to understand its characteristics. Second, the research analyzed the use of artificial intelligence in convergence education from an educational perspective and examined the current policy direction in South Korea. Through this analysis, the research examined the direction of Christian education in the era of artificial intelligence. In particular, the research critically examined the perspectives of continuity and change in the context of Christian education in the era of artificial intelligence. The research reflected upon the fundamental educational purposes of Christian education that should remain unchanged despite the changing times. Furthermore, the research deliberated on the educational curriculum and teaching methods that should adapt to the changing dynamics of the era. In conclusion, this research emphasizes that even in the era of artificial intelligence, the fundamental objectives of Christian education should not be compromised. The utilization of artificial intelligence in education should serve as a tool that fulfills the mission permitted by God. Therefore, Christian education should remain centered around God, rooted in the principles of the Bible. Moreover, Christian education should aim to foster creative and convergent Christian nurturing. To achieve this, it is crucial to provide learners with an educational environment that actively utilizes AI-based hybrid learning environments and metaverse educational platforms, combining online and offline learning spaces. Moreover, to enhance learners' engagement and effectiveness in education, it is essential to actively utilize AI-based edutech that reflects the aforementioned educational environments. Lastly, in order to cultivate Christian learners with dynamic knowledge, it is crucial to employ a variety of teaching and learning methods grounded in constructivist theories, which emphasize active learner participation, collaboration, inquiry, and reflection. These approaches seek to align knowledge with life experiences, promoting a holistic convergence of faith and learning.

• Journal of Intelligence and Information Systems
• /
• v.19 no.4
• /
• pp.55-79
• /
• 2013

In this paper, we propose a hybrid genetic algorithm (HGA) approach to effectively solve the reverse logistics network with centralized centers (RLNCC). For the proposed HGA approach, genetic algorithm (GA) is used as a main algorithm. For implementing GA, a new bit-string representation scheme using 0 and 1 values is suggested, which can easily make initial population of GA. As genetic operators, the elitist strategy in enlarged sampling space developed by Gen and Chang (1997), a new two-point crossover operator, and a new random mutation operator are used for selection, crossover and mutation, respectively. For hybrid concept of GA, an iterative hill climbing method (IHCM) developed by Michalewicz (1994) is inserted into HGA search loop. The IHCM is one of local search techniques and precisely explores the space converged by GA search. The RLNCC is composed of collection centers, remanufacturing centers, redistribution centers, and secondary markets in reverse logistics networks. Of the centers and secondary markets, only one collection center, remanufacturing center, redistribution center, and secondary market should be opened in reverse logistics networks. Some assumptions are considered for effectively implementing the RLNCC The RLNCC is represented by a mixed integer programming (MIP) model using indexes, parameters and decision variables. The objective function of the MIP model is to minimize the total cost which is consisted of transportation cost, fixed cost, and handling cost. The transportation cost is obtained by transporting the returned products between each centers and secondary markets. The fixed cost is calculated by opening or closing decision at each center and secondary markets. That is, if there are three collection centers (the opening costs of collection center 1 2, and 3 are 10.5, 12.1, 8.9, respectively), and the collection center 1 is opened and the remainders are all closed, then the fixed cost is 10.5. The handling cost means the cost of treating the products returned from customers at each center and secondary markets which are opened at each RLNCC stage. The RLNCC is solved by the proposed HGA approach. In numerical experiment, the proposed HGA and a conventional competing approach is compared with each other using various measures of performance. For the conventional competing approach, the GA approach by Yun (2013) is used. The GA approach has not any local search technique such as the IHCM proposed the HGA approach. As measures of performance, CPU time, optimal solution, and optimal setting are used. Two types of the RLNCC with different numbers of customers, collection centers, remanufacturing centers, redistribution centers and secondary markets are presented for comparing the performances of the HGA and GA approaches. The MIP models using the two types of the RLNCC are programmed by Visual Basic Version 6.0, and the computer implementing environment is the IBM compatible PC with 3.06Ghz CPU speed and 1GB RAM on Windows XP. The parameters used in the HGA and GA approaches are that the total number of generations is 10,000, population size 20, crossover rate 0.5, mutation rate 0.1, and the search range for the IHCM is 2.0. Total 20 iterations are made for eliminating the randomness of the searches of the HGA and GA approaches. With performance comparisons, network representations by opening/closing decision, and convergence processes using two types of the RLNCCs, the experimental result shows that the HGA has significantly better performance in terms of the optimal solution than the GA, though the GA is slightly quicker than the HGA in terms of the CPU time. Finally, it has been proved that the proposed HGA approach is more efficient than conventional GA approach in two types of the RLNCC since the former has a GA search process as well as a local search process for additional search scheme, while the latter has a GA search process alone. For a future study, much more large-sized RLNCCs will be tested for robustness of our approach.

• Journal of Intelligence and Information Systems
• /
• v.21 no.1
• /
• pp.1-13
• /
• 2015

As opinion mining in big data applications has been highlighted, a lot of research on unstructured data has made. Lots of social media on the Internet generate unstructured or semi-structured data every second and they are often made by natural or human languages we use in daily life. Many words in human languages have multiple meanings or senses. In this result, it is very difficult for computers to extract useful information from these datasets. Traditional web search engines are usually based on keyword search, resulting in incorrect search results which are far from users' intentions. Even though a lot of progress in enhancing the performance of search engines has made over the last years in order to provide users with appropriate results, there is still so much to improve it. Word sense disambiguation can play a very important role in dealing with natural language processing and is considered as one of the most difficult problems in this area. Major approaches to word sense disambiguation can be classified as knowledge-base, supervised corpus-based, and unsupervised corpus-based approaches. This paper presents a method which automatically generates a corpus for word sense disambiguation by taking advantage of examples in existing dictionaries and avoids expensive sense tagging processes. It experiments the effectiveness of the method based on Naïve Bayes Model, which is one of supervised learning algorithms, by using Korean standard unabridged dictionary and Sejong Corpus. Korean standard unabridged dictionary has approximately 57,000 sentences. Sejong Corpus has about 790,000 sentences tagged with part-of-speech and senses all together. For the experiment of this study, Korean standard unabridged dictionary and Sejong Corpus were experimented as a combination and separate entities using cross validation. Only nouns, target subjects in word sense disambiguation, were selected. 93,522 word senses among 265,655 nouns and 56,914 sentences from related proverbs and examples were additionally combined in the corpus. Sejong Corpus was easily merged with Korean standard unabridged dictionary because Sejong Corpus was tagged based on sense indices defined by Korean standard unabridged dictionary. Sense vectors were formed after the merged corpus was created. Terms used in creating sense vectors were added in the named entity dictionary of Korean morphological analyzer. By using the extended named entity dictionary, term vectors were extracted from the input sentences and then term vectors for the sentences were created. Given the extracted term vector and the sense vector model made during the pre-processing stage, the sense-tagged terms were determined by the vector space model based word sense disambiguation. In addition, this study shows the effectiveness of merged corpus from examples in Korean standard unabridged dictionary and Sejong Corpus. The experiment shows the better results in precision and recall are found with the merged corpus. This study suggests it can practically enhance the performance of internet search engines and help us to understand more accurate meaning of a sentence in natural language processing pertinent to search engines, opinion mining, and text mining. Naïve Bayes classifier used in this study represents a supervised learning algorithm and uses Bayes theorem. Naïve Bayes classifier has an assumption that all senses are independent. Even though the assumption of Naïve Bayes classifier is not realistic and ignores the correlation between attributes, Naïve Bayes classifier is widely used because of its simplicity and in practice it is known to be very effective in many applications such as text classification and medical diagnosis. However, further research need to be carried out to consider all possible combinations and/or partial combinations of all senses in a sentence. Also, the effectiveness of word sense disambiguation may be improved if rhetorical structures or morphological dependencies between words are analyzed through syntactic analysis.

• Journal of Digital Convergence
• /
• v.10 no.9
• /
• pp.27-37
• /
• 2012

Internet privacy has become a significant issue in recent years in light of the sharp increase in internet-based social and economic activities. The technology which collects, processes and disseminates personal information is improving significantly and the demand for personal information is rising given its inherent value in regard to targeted marketing and customized services. The high value placed on personal information has turned it into a commodity with economic worth which can be transacted in the marketplace. Therefore, it is strongly required to approach the issue of privacy from economic perspective in addition to the prevailing approaches. This article analyzes the behaviors of consumers and firms in gathering personal information, and shielding it from unauthorized access, using a game theory framework in which players strive to do their best under the given conditions. The analysis shows that there exist no market forces which require all firms to respect consumer privacy, and that government intervention in the form of a nudging incentive for information sharing and/or strict regulation is necessary.

• Journal of Digital Convergence
• /
• v.12 no.1
• /
• pp.445-455
• /
• 2014

This study has targeted to comparatively analyze smoking cessation success rates and success factors among new enrollees and re-enrollees in Smoking Cessation Clinics for its efficient operation. A total of 319,908 smokers who were enrolled in the Smoking Cessation Clinics in one of 253 public health centers across the nation for more than 6 months from July 16, 2009 to July 15, 2010 were examined. According to the comparative analysis, the following results have been obtained. According to the results, it has been confirmed that it is necessary to determine why smoking cessation success rates are low and take additional efforts to increase the rates for the effective operation of smoking cessation clinics. In addition, smoking cessation success rates were higher when only BT(Behavior Therapy) was given than when both BT and NRT(Nicotine Replacement Therapy) were provided to new enrollees while they were lower when only BT was provided than when both BT and NRT were given to re-enrollees. Therefore, it is necessary to provide differentiated service types depending on the type of enrollment. Hence, it is also required for the government to take various approaches in terms of a direction for a smoking cessation policy.

• Journal of KIISE:Software and Applications
• /
• v.31 no.3
• /
• pp.293-307
• /
• 2004

We consider the problem of scheduling tasks of a precedence constrained task graph, where each task has its execution time and deadline, onto a set of identical processors in a way that simultaneously minimizes the number of processors required and the total tardiness of tasks. Most existing approaches tend to focus on the minimization of the total tardiness of tasks. In another methods, solutions to this problem are usually computed by combining the two objectives into a simple criterion to be optimized. In this paper, the minimization is carried out using a multiobjective genetic algorithm (GA) that independently considers both criteria by using a vector-valued cost function. We present various GA components that are well suited to the problem of task scheduling, such as a non-trivial encoding strategy. a domination-based selection operator, and a heuristic crossover operator We also provide three local improvement heuristics that facilitate the fast convergence of GA's. The experimental results showed that when compared to five methods used previously, such as list-scheduling algorithms and a specific genetic algorithm, the Performance of our algorithm was comparable or better for 178 out of 180 randomly generated task graphs.

• Journal of Intelligence and Information Systems
• /
• v.9 no.1
• /
• pp.227-249
• /
• 2003

Prediction of corporate failure using past financial data is a well-documented topic. Early studies of bankruptcy prediction used statistical techniques such as multiple discriminant analysis, logit and probit. Recently, however, numerous studies have demonstrated that artificial intelligence such as neural networks can be an alternative methodology for classification problems to which traditional statistical methods have long been applied. In building neural network model, the selection of independent and dependent variables should be approached with great care and should be treated as model construction process. Irrespective of the efficiency of a teaming procedure in terms of convergence, generalization and stability, the ultimate performance of the estimator will depend on the relevance of the selected input variables and the quality of the data used. Approaches developed in statistical methods such as correlation analysis and stepwise selection method are often very useful. These methods, however, may not be the optimal ones for the development of neural network model. In this paper, we propose a genetic algorithms approach to find an optimal or near optimal input variables fur neural network modeling. The proposed approach is demonstrated by applications to bankruptcy prediction modeling. Our experimental results show that this approach increases overall classification accuracy rate significantly.