• Communications of the Korean Mathematical Society
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• v.35 no.3
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• pp.789-797
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• 2020

By considering a certain univalent function in the open unit disk 𝕌, that maps 𝕌 onto a strip domain, we introduce a new class of analytic and close-to-convex functions by means of a certain non-homogeneous Cauchy-Euler-type differential equation. We determine the coefficient bounds for functions in this new class. Relevant connections of some of the results obtained with those in earlier works are also provided.

• Journal of the Korean Institute of Navigation
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• v.22 no.1
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• pp.15-22
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• 1998

In this paper, discontinuity parts in microstrip line with $\lambda$/4 open stub and one in crank type have been anlayzed by using FDTD(Finite-Difference Time-Domain) analysis method. The noise components, in this case, are occred at the discontinuites of the given microstrip lines, the complex poles were extracted by the analysis using GPOF (Generalized Pencil-of Function) method from electric field of time domain. It has, then, been found that the noise level and the noise frequency components included in signal could be derived.

• Communications of the Korean Mathematical Society
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• v.37 no.2
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• pp.537-549
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• 2022

In this paper, we consider a convex univalent function f_α,β which maps the open unit disc 𝕌 onto the vertical strip domain Ω_α,β = {w ∈ ℂ : α < ℜ < (w) < β} and introduce new subclasses of both close-to-convex and bi-close-to-convex functions with respect to an odd starlike function associated with Ω_α,β. Also, we investigate the Fekete-Szegö type coefficient bounds for functions belonging to these classes.

• Smart Media Journal
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• v.11 no.10
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• pp.89-96
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• 2022

An open domain machine reading comprehension is a model that adds a function to search paragraphs as there are no paragraphs related to a given question. Document searches have an issue of lower performance with a lot of documents despite abundant research with word frequency based TF-IDF. Paragraph selections also have an issue of not extracting paragraph contexts, including sentence characteristics accurately despite a lot of research with word-based embedding. Document reading comprehension has an issue of slow learning due to the growing number of parameters despite a lot of research on BERT. Trying to solve these three issues, this study used BM25 which considered even sentence length and InferSent to get sentence contexts, and proposed an open domain machine reading comprehension with ALBERT to reduce the number of parameters. An experiment was conducted with SQuAD1.1 datasets. BM25 recorded a higher performance of document research than TF-IDF by 3.2%. InferSent showed a higher performance in paragraph selection than Transformer by 0.9%. Finally, as the number of paragraphs increased in document comprehension, ALBERT was 0.4% higher in EM and 0.2% higher in F1.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.8 no.6
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• pp.1218-1227
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• 2004

Multi-domain engineering systems include electrical, mechanical, hydraulic, pneumatic, and thermal components, making it difficult to design a system because of their complexity and inter domain nature. In order to obtain an optimal design, a unified design approach for each domain and an automated search method are required. This paper suggests a method for automatically synthesizing designs for multi-domain systems using the combination of bond graph that is domain independent and genetic programming that is well recognized as a powerful tool for open-ended search. To investigate the effect of proposed approach, an eigenvalue design problem is tested for some sample target sets of eigenvalues with different embryos.

• Phonetics and Speech Sciences
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• v.15 no.3
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• pp.83-88
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• 2023

This study investigates the fine-tuning of large-scale Automatic Speech Recognition (ASR) models, specifically OpenAI's Whisper model, for domain-specific applications using the KsponSpeech dataset. The primary research questions address the effectiveness of targeted lexical item emphasis during fine-tuning, its impact on domain-specific performance, and whether the fine-tuned model can maintain generalization capabilities across different languages and environments. Experiments were conducted using two fine-tuning datasets: Set A, a small subset emphasizing specific lexical items, and Set B, consisting of the entire KsponSpeech dataset. Results showed that fine-tuning with targeted lexical items increased recognition accuracy and improved domain-specific performance, with generalization capabilities maintained when fine-tuned with a smaller dataset. For noisier environments, a trade-off between specificity and generalization capabilities was observed. This study highlights the potential of fine-tuning using minimal domain-specific data to achieve satisfactory results, emphasizing the importance of balancing specialization and generalization for ASR models. Future research could explore different fine-tuning strategies and novel technologies such as prompting to further enhance large-scale ASR models' domain-specific performance.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.39 no.2
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• pp.191-197
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• 2015

The message passing interface (MPI) and hybrid programming models for the parallel computation of a pressure equation were compared in a distributed memory system. Both models were based on domain decomposition, and two numbers of the sub-domain were selected by considering the efficiency of the hybrid model. The parallel performances for various problem sizes were measured using up to 96 threads. It was found that in addition to the cache-memory size, the overhead of the MPI communication/OpenMP directives affected the parallel performance. For small problems, the parallel performance was low because the percentage of the overhead of the MPI communication/OpenMP directives increased as the number of threads increased, and MPI was better than the hybrid model because it had a smaller communication overhead. For large problems, the parallel performance was high because, in addition to the cache effect, the percentage of the communication overhead was relatively low compared to that for small problems, and the hybrid model was better than MPI because the communication overhead of MPI was more dominant than that of the OpenMP directives in the hybrid model.

• 한국전산유체공학회:학술대회논문집
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• 2011.05a
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• pp.327-330
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• 2011

CFD simulation is widely used in various industries, universities and research centers. In Korea most of the researchers use foreign commercial S/W packages especially in industries. But commercial CFD packages have some problems as limit to source code and very high license foe. So from several years ago open source CFD code has been widely spread as an alternative. But in Korea there are a few users of open source code. Insufficiency of performance validation as for accuracy, robustness, convenience and parallel speed-up is important obstacles of open source code. So we tested some validation cases as to incompressible external aerodynamics and internal flaws and now are doing compressible flaws. As the first stage of compressible flow validation, we simulated Korea next generation high speed train(HEMU). It's running condition is 400km/hr and maximum Mach number reaches up to 0.4. With the high speed train we tested accuracy, robustness and parallel performance of open source CFD code OpenFOAM Because there isn't experimental data we compared results with widely used commercial code. When use $1^{st}$ order upwind scheme aerodynamic forces are very similar to commercial code. But using $2^{nd}$ order upwind scheme there was some discrepancy. The reason of the difference is not clear yet. Mesh manipulation, domain decomposition, post-processing and robustness are satisfactory. Paralle lperformance is similar to commercial code.

• International Journal of Computer Science & Network Security
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• v.21 no.3
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• pp.145-152
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• 2021

In the Recent times, various technological enhancements in the field of artificial intelligence and big data has been noticed. This advancement coupled with the evolution of the 5G communication and Internet of Things technologies, has helped in the development in the domain of smart mine construction. The development of unmanned vehicles with enhanced and smart scheduling system for open-pit mine transportation is one such much needed application. Traditional open-pit mining systems, which often cause vehicle delays and congestion, are controlled by human authority. The number of sensors has been used to operate unmanned cars in an open-pit mine. The sensors haves been used to prove the real-time data in large quantity. Using this data, we analyses and create an improved transportation scheduling mechanism so as to optimize the paths for the vehicles. Considering the huge amount the data received and aggregated through various sensors or sources like, the GPS data of the unmanned vehicle, the equipment information, an intelligent, and multi-target, open-pit mine unmanned vehicle schedules model was developed. It is also matched with real open-pit mine product to reduce transport costs, overall unmanned vehicle wait times and fluctuation in ore quality. To resolve the issue of scheduling the transportation, we prefer to use algorithms based on artificial intelligence. To improve the convergence, distribution, and diversity of the classic, rapidly non-dominated genetic trial algorithm, to solve limited high-dimensional multi-objective problems, we propose a decomposition-based restricted genetic algorithm for dominance (DBCDP-NSGA-II).

• International Journal of Computer Science & Network Security
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• v.21 no.11
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• pp.97-104
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• 2021

The authors provide characteristics of the open educational platforms, classification and quantitative analysis regarding the availability of IT courses, teaching language, thematic directions on the following platforms: Coursera, EdX, Udemy, MIT Open Course Ware, OpenLearn, Intuit, Prometheus, UoPeople, Open Learning Initiative, Open University of Maidan (OUM). The quantitative analysis results are structured and visualized by tables and diagrams. The authors propose to use open educational resources (teaching, learning or research materials that are in the public domain or released with an intellectual property license that allows free use, adaptation, and distribution) for organization of independent work; for organization of distance or correspondence training; for professional development of teachers; for possibility and expediency of author's methods dissemination in the development of their own courses and promoting them on open platforms. Post-project activities are considered in comparing the courses content of one thematic direction, as well as studying the experience of their attending on different platforms.