• The Journal of the Korea Contents Association
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• v.15 no.1
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• pp.31-43
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• 2015

For battery based real-time embedded systems, high performance to meet their real-time constraints and energy efficiency to extend battery life are both essential. Real-Time Dynamic Voltage Scaling (RT-DVS) has been a key technique to satisfy both requirements. In this paper, we present an efficient RT-DVS algorithm called EccEDF that is designed based on ccEDF. The proposed algorithm can precisely calculate the maximum unused utilization with consideration of the elapsed time while keeping the structural simplicity of ccEDF, which overlooked the time needed to run the task in calculating the available slack. The maximum unused utilization can be calculated by dividing remaining execution time($C_i-cc_i$) by remaining time($P_i-E_i$) on completion of the task and it is proved using Fluid scheduling model. We also show that the algorithm outperforms ccEDF in practical applications which is modelled using a PXA250 and a 0.28V-to-1.2V wide-operating-range IA-32 processor model.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.10
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• pp.1845-1852
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• 2008

The key elements of inter prediction are motion estimation(ME) and motion compensation(MC). Motion estimation is to find the optimum motion vectors, not only by using a distance criteria like the SAD, but also by taking into account the resulting number of 비트s in the 비트 stream. Motion compensation is compensate for movement of blocks of current frame. Inter-prediction Encoding is always the main bottleneck in high-quality streaming applications. Therefore, in real-time streaming applications, dedicated hardware for executing Inter-prediction is required. In this paper, we studied a motion estimator(ME) for H.264/AVC. The designed motion estimator is based on 2-D systolic array and it connects processing elements for fast SAD(Sum of Absolute Difference) calculation in parallel. By providing different path for the upper and lower lesion of each reference data and adjusting the input sequence, consecutive calculation for motion estimation is executed without pipeline stall. With data reuse technique, it reduces memory access, and there is no extra delay for finding optimal partitions and motion vectors. The motion estimator supports variable-block size and takes 328 cycles for macro-block calculation. The proposed architecture is local memory-free different from paper [6] using local memory. This motion estimation encoder can be applicable to real-time video processing.

• Asian-Australasian Journal of Animal Sciences
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• v.32 no.9
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• pp.1381-1388
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• 2019

Objective: The aims of this study were to reveal the magnitude of the differences in protein structures at a cellular level as well as protein utilization and availability among soybean meal (SBM), canola meal (CM), and rapeseed meal (RSM) as feedstocks in China. Methods: Experiments were designed to compare the three different types of feedstocks in terms of: i) protein chemical profiles; ii) protein fractions partitioned according to Cornell Net Carbohydrate and Protein System; iii) protein molecular structures and protein second structures; iv) special protein compounds-amino acid (AA); v) total digestible protein and energy values; vi) in situ rumen protein degradability and intestinal digestibility. The protein second structures were measured using FT/IR molecular spectroscopy technique. A summary chemical approach in National Research Council (NRC) model was applied to analyze truly digestible protein. Results: The results showed significant differences in both protein nutritional profiles and protein structure parameters in terms of ${\alpha}-helix$, ${\beta}-sheet$ spectral intensity and their ratio, and amide I, amide II spectral intensity and their ratio among SBM, CM, and RSM. SBM had higher crude protein (CP) and AA content than CM and RSM. For dry matter (DM), SBM, and CM had a higher DM content compared with RSM (p<0.05), whereas no statistical significance was found between SBM and CM (p = 0.28). Effective degradability of CP and DM did not demonstrate significant differences among the three groups (p>0.05). Intestinal digestibility of rumen undegradable protein measured by three-step in vitro method showed that there was significant difference (p = 0.05) among SBM, CM, and RSM, which SBM was the highest and RSM was the lowest with CM in between. NRC modeling results showed that digestible CP content in SBM was significantly higher than that of CM and RSM (p<0.05). Conclusion: This study suggested that SBM and CM contained similar protein value and availability for dairy cattle, while RSM had the lowest protein quality and utilization.

• Journal of Digital Convergence
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• v.17 no.1
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• pp.9-27
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• 2019

The purpose of this study is to examine consumers' responses to mobile payment services by using a text-mining technique focusing on Samsung Pay as it is used in both online and offline transactions. We conducted text frequency analysis, text clustering analysis, and text network analysis using R programming. The major findings are as follows. First, the most frequently used key words referenced the brand names of the mobile devices, the replacement of traditional wallets and unique functions of Samsung Pay. Second, there was a clear split between positive and negative responses at the macro level. Third, replacement of traditional wallets played a great role in the positive responses and continuous use of mobile payment services. This study provides in-depth understanding of consumer responses toward mobile payment services. It also offers practical implications that may help mobile payment marketers correspond to consumer values and expectations, thus increasing consumer satisfaction.

• Journal of The Korean Society of Agricultural Engineers
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• v.64 no.3
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• pp.9-24
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• 2022

Currently, a vast amount of hydrologic data is accumulated in real-time through automatic water level measuring instruments in agricultural reservoirs. At the same time, false and missing data points are also increasing. The applicability and reliability of quality control of hydrological data must be secured for efficient agricultural water management through calculation of water supply and disaster management. Considering the characteristics of irregularities in hydrological data caused by irrigation water usage and rainfall pattern, the Korea Rural Community Corporation is currently applying the Hampel filter as a water level data quality management method. This method uses window size as a key parameter, and if window size is large, distortion of data may occur and if window size is small, many outliers are not removed which reduces the reliability of the corrected data. Thus, selection of the optimal window size for individual reservoir is required. To ensure reliability, we compared and analyzed the RMSE (Root Mean Square Error) and NSE (Nash-Sutcliffe model efficiency coefficient) of the corrected data and the daily water level of the RIMS (Rural Infrastructure Management System) data, and the automatic outlier detection standards used by the Ministry of Environment. To select the optimal window size, we used the classification performance evaluation index of the error matrix and the rainfall data of the irrigation period, showing the optimal values at 3 h. The efficient reservoir automatic calibration technique can reduce manpower and time required for manual calibration, and is expected to improve the reliability of water level data and the value of water resources.

• Journal of Internet Computing and Services
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• v.22 no.6
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• pp.141-150
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• 2021

Based on Artificial Intelligence technology, AI-enabled warfare is expected to become the main issue in the future warfare. Natural language processing technology is a core technology of AI technology, and it can significantly contribute to reducing the information burden of underrstanidng reports, information objects and intelligences written in natural language by commanders and staff. In this paper, we propose a Language model-based Multi-source Information Integration (LAMII) framework to reduce the information overload of commanders and support rapid decision-making. The proposed LAMII framework consists of the key steps of representation learning based on language models in self-supervsied way and document integration using autoencoders. In the first step, representation learning that can identify the similar relationship between two heterogeneous sentences is performed using the self-supervised learning technique. In the second step, using the learned model, documents that implies similar contents or topics from multiple sources are found and integrated. At this time, the autoencoder is used to measure the information redundancy of the sentences in order to remove the duplicate sentences. In order to prove the superiority of this paper, we conducted comparison experiments using the language models and the benchmark sets used to evaluate their performance. As a result of the experiment, it was demonstrated that the proposed LAMII framework can effectively predict the similar relationship between heterogeneous sentence compared to other language models.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.36 no.4
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• pp.341-350
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• 2023

The templated grain growth (TGG) method has gained significant attention for its ability to produce highly textured piezoelectric ceramics with significantly enhanced performance, making it a promising method for transducer and actuator applications. However, the texturing process using the TGG method requires the optimization of multiple steps, which can be challenging for beginners in this field. Therefore, in this tutorial, we provide an overview of the TGG method mainly based on our previous published works, including its various processing steps such as synthesizing anisotropic-shaped templates with size and size distribution control using the molten salt synthesis technique, tape casting, and identifying key factors for proper alignment of the templates in the target matrix system. Our goal is to provide a resource that can serve as a basic reference for researchers and engineers looking to improve their understanding and utilization of the TGG method for producing textured piezoelectric ceramics.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.6D
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• pp.745-752
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• 2009

Management of social infrastructure has been advanced from facility management (FM) to asset management (AM), which adopts the aggressive and proactive methods in predicting the deterioration of infrastructure, prevents failures, and eventually saves maintenance cost. Infrastructure asset management is not a simple engineering technique, but it is a new paradigm evolved from facility management practices. To implement the infrastructure asset management successfully, organizational reform is very important. This paper suggests critical success factors and key performance indicators to implement the infrastructure asset management for facility managers of government owned social infrastructures such as roads and bridges. Reorganizing the facility management group requires new vision, objectives, strategies for the paradigm-changing asset management. This paper uses Balanced Score Card (BSC) which is a proven method in measuring and setting new objectives for an organization. Once the performance indicators are reviewed repeatedly by facility managers through experts workshops, developed BSC can be used in practice. This paper discusses the development of robust BSC scoring method through in depth literature reviews and investigation of asset management practices of domestic and international cases.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.2B
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• pp.177-185
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• 2008

Accurate monitoring of snow cover is a key component for studying climate and global as well as for daily weather forecasting and snowmelt runoff modelling. The few observed data related to snowmelt was the major cause of difficulty in extracting snowmelt factors such as snow cover area, snow depth and depletion curve. Remote sensing technology is very effective to observe a wide area. Although many researchers have used remote sensing for snow observation, there were a few discussions on the characteristics of spatial and temporal variation. Snow cover maps were derived from NOAA AVHRR images for the winter seasons from 1997 to 2006. Distributed snow depth was mapped by overlapping between snow cover maps and interpolated snowfall maps from 69 meteorological observation stations. Model parameters (Snow Cover Area: SCA, snow depth, Snow cover Depletion Curve: SDC) were built for 7 major watersheds in South Korea. The decrease pattern of SCA for time (day) was expressed as exponentially decay function, and the determination coefficient was ranged from 0.46 to 0.88. The SCA decreased 70% to 100% from the maximum SCA when 10 days passed.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.26 no.6
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• pp.67-84
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• 2023

In some areas of the Republic of Korea, the designation and management of conservation areas do not adequately reflect regional characteristics and often impose behavioral regulations without considering the local context. One prominent example is the Busan EDC area. As a result, conflicts may arise, including large-scale civil complaints, regarding the conservation and utilization of these areas. Therefore, for the efficient designation and management of protected areas, it is necessary to consider various ecosystem factors, changes in land use, and regional characteristics. In this study, we specifically focused on the Busan EDC area and applied machine learning techniques to analyze the habitat of regional species. Additionally, we employed Explainable Artificial Intelligence techniques to interpret the results of our analysis. To analyze the regional characteristics of the waterfront area in the Busan EDC district and the habitat of migratory birds, we used bird observations as dependent variables, distinguishing between presence and absence. The independent variables were constructed using land cover, elevation, slope, bridges, and river depth data. We utilized the XGBoost (eXtreme Gradient Boosting) model, known for its excellent performance in various fields, to predict the habitat probabilities of 11 bird species. Furthermore, we employed the SHapley Additive exPlanations technique, one of the representative methodologies of XAI, to analyze the relative importance and impact of the variables used in the model. The analysis results showed that in the EDC business district, as one moves closer to the river from the waterfront, the likelihood of bird habitat increases based on the overlapping habitat probabilities of the analyzed bird species. By synthesizing the major variables influencing the habitat of each species, key variables such as rivers, rice fields, fields, pastures, inland wetlands, tidal flats, orchards, cultivated lands, cliffs & rocks, elevation, lakes, and deciduous forests were identified as areas that can serve as habitats, shelters, resting places, and feeding grounds for birds. On the other hand, artificial structures such as bridges, railways, and other public facilities were found to have a negative impact on bird habitat. The development of a management plan for conservation areas based on the objective analysis presented in this study is expected to be extensively utilized in the future. It will provide diverse evidential materials for establishing effective conservation area management strategies.