• Journal of Positioning, Navigation, and Timing
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• v.10 no.4
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• pp.315-333
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• 2021

Due to the Global Navigation Satellite System (GNSS) modernization, recently launched GNSS satellites transmit signals at various frequency bands such as L1, L2 and L5. Considering the Korean Positioning System (KPS) signal and other GNSS augmentation signals in the future, there is a high probability of applying more complex communication techniques to the new GNSS signals. For the reason, GNSS receivers based on flexible Software Defined Radio (SDR) concept needs to be developed to evaluate various experimental communication techniques by accessing each signal processing module in detail. This paper proposes a novel SDR-based A-GNSS receiver capable of processing multi-GNSS/RNSS signals at multi-frequency bands. Due to the modular structure, the proposed receiver has high flexibility and expandability. For real-time implementation, A-GNSS server software is designed to provide immediate delivery of satellite ephemeris data on demand. Due to the sampling bandwidth limitation of RF front-ends, multiple SDRs are considered to process the multi-GNSS/RNSS multi-frequency signals simultaneously. To avoid the overflow problem of sampled RF data, an efficient memory buffer management strategy was considered. To collect and process the multi-GNSS/RNSS multi-frequency signals in real-time, the proposed SDR A-GNSS receiver utilizes multiple threads implemented on a CPU and multiple NVIDIA CUDA GPGPUs for parallel processing. To evaluate the performance of the proposed SDR A-GNSS receiver, several experiments were performed with field collected data. By the experiments, it was shown that A-GNSS requirements can be satisfied sufficiently utilizing only milliseconds samples. The continuous signal tracking performance was also confirmed with the hundreds of milliseconds data for multi-GNSS/RNSS multi-frequency signals and with the ten-seconds data for multi-GNSS/RNSS single-frequency signals.

• Animal Bioscience
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• v.36 no.8
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• pp.1156-1166
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• 2023

Objective: Pork belly is a cut of meat with high worldwide demand. However, although the belly is comprised of multiple muscles and fat, unlike the loin muscle, research on their genetic parameters has yet to focus on a representative cut. To use swine breeding, it is necessary to estimate heritability against pork belly traits. Moreover, estimating genetic correlations is needed to identify genetic relationship among the traditional carcass and meat quality traits. This study sought to estimate the heritability of the carcass, belly, and their component traits, as well as the genetic correlations among them, to confirm whether these traits can be improved. Methods: A total of 543 Yorkshire pigs (406 castrated males and 137 females) from 49 sires and 244 dam were used in this study. To estimate genetic parameters, a total of 12 traits such as lean meat production ability, meat quality and pork belly traits were chosen. The heritabilities were estimated by using genome-wide efficient mixed model association software. The statistical model was selected so that farm, carcass weight, sex, and slaughter season were fixed effects. In addition, its genetic parameters were calculated via MTG2 software. Results: The heritability estimates for the 7th belly slice along the whole plate and its components were low to moderate (0.07±0.07 to 0.33±0.07). Moreover, the genetic correlations among the carcass and belly traits were moderate to high (0.28±0.20 to 0.99±0.31). Particularly, the rectus abdominis muscle exhibited a high absolute genetic correlation with the belly and meat quality (0.73±52 to 0.93±0.43). Conclusion: A moderate to high correlation coefficient was obtained based on the genetic parameters. The belly could be genetically improved to contain a larger proportion of muscle regardless of lean meat production ability.

• Journal of IKEEE
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• v.28 no.2
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• pp.123-130
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• 2024

As technology advances swiftly and the lifespan of products becomes increasingly short, there is a demand to fasten the pace of research outcomes, product development, and market introduction. As a result, the researchers and developers need a computational experiment environment that enables rapid verification of the experiment and application of research findings. Such an environment must efficiently harness all available computational resources, manage simulations across diverse test scenarios, and support the experimental data collection. This research introduces the design and implementation of an experimental frame based on a microservice architecture. The experimental frame leverages scripts to utilize computing resources optimally, making it more straightforward for users to conduct simulations. It features an experimental frame capable of automatically deploying scenarios to the computing components. This setup allows for the automatic configuration of both the computing environment and experiments based on user-provided scenarios and experimental software, facilitating effortless execution of simulations.

• Smart Media Journal
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• v.12 no.11
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• pp.134-144
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• 2023

Nowadays, Deep Learning (DL) technology is being used in several government departments. South Korea imports a lot of seafood. If the demand for fishery products is not accurately predicted, then there will be a shortage of fishery products and the price of the fishery product may rise sharply. So, South Korea's Ministry of Ocean and Fisheries is attempting to accurately predict seafood imports using deep learning. This paper introduces the solution for the fish import prediction in South Korea using the Long Short-Term Memory (LSTM) method. It was found that there was a huge gap between the sum of consumption and export against the sum of production especially in the case of two species that are Hairtail and Pollock. An import prediction is suggested in this research to fill the gap with some advanced Deep Learning methods. This research focuses on import prediction using Machine Learning (ML) and Deep Learning methods to predict the import amount more precisely. For the prediction, two Deep Learning methods were chosen which are Artificial Neural Network (ANN) and Long Short-Term Memory (LSTM). Moreover, the Machine Learning method was also selected for the comparison between the DL and ML. Root Mean Square Error (RMSE) was selected for the error measurement which shows the difference between the predicted and actual values. The results obtained were compared with the average RMSE scores and in terms of percentage. It was found that the LSTM has the lowest RMSE score which showed the prediction with higher accuracy. Meanwhile, ML's RMSE score was higher which shows lower accuracy in prediction. Moreover, Google Trend Search data was used as a new feature to find its impact on prediction outcomes. It was found that it had a positive impact on results as the RMSE values were lowered, increasing the accuracy of the prediction.

• Journal of Information Technology Services
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• v.14 no.2
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• pp.253-273
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• 2015

Cloud computing is provided on demand service via the internet, allowing users to pay for the service they actually use. Categorized as one kind of cloud computing, SaaS is computing resource and software sharing model with can be accessed via the internet. Based on virtualization technology, SaaS is expected to improve the efficiency and quality of the IT service level and performance in company. Therefore this research limited cloud services to SaaS especially focused on collaborative application service, and attempts to identify the factors which impact the performance of collaboration and intention to use. This study adopts technological factors of cloud SaaS services and factors of task characteristics to explore the determinants of collaborative performance and intention to use. An experimental study using student subjects with Google Apps provided empirical validation for our proposed model. Based on 337 data collected from respondents, the major findings are following. First, the characteristics of cloud computing services such as collaboration support, service reliability, and ease of use have positive effects on perceived usefulness of collaborative application while accessability, service reliability, and ease to use have positive effects on intention to use. Second, task interdependence has a positive effects on collaborative performance while task ambiguity factor has not. Third, perceived usefulness of collaborative application have positive effects on intention to use.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.6 no.4
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• pp.315-324
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• 2005

VOD(video on demand) is a video service by users' order, that is, a video service on demand. That means the users can select and watch the video content that has been saved on sewer, out of broadcasting in the usual process like TV. At present the client of VOD system bases on PC. As the PC-based client uses the software MPEG decoder, the main processor specification has an effect on the capacity. Also people, who don't know how to use their PC, cannot be provided the VOD service. The purpose of this paper is to show the process of the development the VOD client system Into the embedded type with hardware MPEG-4 decoder. The main processor is the SC1200 of x86 Family in National Semiconductor with a built-in video processor and the memory is 128Mbyte SDRAM. Also, in order that the VOD service can be provided using the Internet, the Ethernet controller is included. As the hardware MPEG-4 decoder is used in the embedded VOD client system, which is developed, it can make the low capacity of the main processor. Therefore it is able to be developed as a low-price system. The embedded VOD client system is easy for anyone to control easily with the remote control and can be played through TV.

• Journal of Korea Multimedia Society
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• v.21 no.10
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• pp.1182-1194
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• 2018

The Dynamic Adaptive Streaming over HTTP(DASH) is envisioned to evolve to meet an increasing demand on providing seamless video streaming services in the near future. The DASH performance heavily depends on the client's adaptive quality selection algorithm that is not included in the standard. The existing conventional algorithms are basically based on a procedural algorithm that is not easy to capture and reflect all variations of dynamic network and traffic conditions in a variety of network environments. To solve this problem, this paper proposes a novel quality selection mechanism based on the Deep Q-Network(DQN) model, the DQN-based DASH Adaptive Bitrate(ABR) mechanism. The proposed mechanism adopts a new reward calculation method based on five major performance metrics to reflect the current conditions of networks and devices in real time. In addition, the size of the consecutive video segment to be downloaded is also considered as a major learning metric to reflect a variety of video encodings. Experimental results show that the proposed mechanism quickly selects a suitable video quality even in high error rate environments, significantly reducing frequency of quality changes compared to the existing algorithm and simultaneously improving average video quality during video playback.

• Korean System Dynamics Review
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• v.4 no.2
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• pp.95-130
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• 2003

The purpose of this study is to find what causes make public projects hold off, going beyond the planned deadline and budget. Using System Dynamics(SD) and their derivative Urban Dynamics(UD) models, it intends to analyze major feedback loops based on VENSIM and to simulate them with STELLA software, all of which are interrelated with various causes of project delay. To prevent or ameliorate project delay, first of all it advises to focus on endogenous delaying factors not exogenous ones. These factors either reinforce or balance certain loops in complex causal structure, In the case example on the Cremation Building Project in Cheongju, Residents’ participation demand make negotiation put off and delayed negotiation reinforces administrative-expediency planning in order to observe a time limit, on the other hand, once building consensus, it increase both the level of planning performance and public trust. In the meantime, the real planning process used to neglect residents opinions and manage public grievance only through compensation, he a result of simulation, visible fruit of negotiation in the initial phase seems to be not satisfactory owing to funds and time consumed, but after reaching an mutual agreement among stakeholders, planning performance is effective and strategic than administrative-expediency planning viewed in both financial and time angle. It proposes to devise specific tools schematizing project implementation. In order to upgrade the quality of project management, it recommends for planners to adopt key concepts based on SD/UD diagrams and causal loops, which would contribute to enriching Planning abbots.

• Health Policy and Management
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• v.10 no.1
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• pp.126-147
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• 2000

This paper empirically explores the nature of the medical service industry and its various propagation effects on the economy in the input-output model, as revealed by a comparative analysis between Korea and Japan. The main findings of the paper are as follows; First, the growth of medical industry induces above-average effect on employment. Second, the industry is of the characteristics of weak both backward and forward linkage effects implying a 'final demand dependency industry'. When compared with public service sectors, however, the medical services industry shows stronger backward linkage effect than those sectors. Furthermore, it has strong repercussion effects on the goods industries. Third, in order to produce per unit of services, the medical services industry of Korea uses relatively more drugs and medical devices than that of Japan. In general, it has been shown that production structure of medical service industry in Korea is 'hardware-oriented' one; on the other hand, 'software-oriented' in Japan which means that, as intermediate inputs, outsourcing and informatization has been used than those of Korea. From the findings of the paper it could be emphasized that the medical organizations in Korea should put more efforts on shifting the current hardware-oriented production structure to strengthen core competence by enhancing productivity and by outsourcing to improve efficiency of production process. However, the medical organizations in Korea would not have enough incentives for high value-added production structure because they enjoy high operating surplus. Therefore, it would be necessary that government policy should be taken into account of these environments.

• Journal of the Korea Safety Management & Science
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• v.13 no.4
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• pp.9-16
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• 2011

Modern systems become more complex and the demand for systems safety goes up sharply. Thus, the proper handling of the safety requirements in the systems design is getting greatly increased attention these days. Hazard analysis has been one of the active areas of research in connection with systems safety. In this paper, we study a subject on how the hazard analysis results can be incorporated in the systems design. To this end we set up a goal on how to systematically generate safety requirements that should reflect hazard analysis results and be implemented in the systems design and development. To do so, we first review the process for systems design and suggest the associated Model. Then the process and results of hazard analysis are analyzed and Modeled particularly with emphasis on the safety data. The resulting data Model incorporating both the hazard analysis and system life cycle is used in the generation of safety requirements. Based on the developed data Model, the generation of the requirements, the construction of requirements DB, and the change management later on is demonstrated through the use of a computer-aided software tool.