• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.13 no.6
• /
• pp.163-172
• /
• 2003

Proposed in this paper is an authentication mechanism for multimedia streaming data in the Intemet multicast environment. The multicast authentication mechanism is coupled with the packet-level forward error correction code which has been recently applied for a reliable multicast transport transmission. Associated with this, Reed-Solomon erasure code is chosen for tolerating packet loss so that each of the received packets can be authenticated independently of the lost packets.

• East Asian mathematical journal
• /
• v.38 no.2
• /
• pp.257-275
• /
• 2022

Recently, there are studies the argument that arithmetic rules established by the four fundamental arithmetic operations, in other words, commutative laws, associative laws, distributive laws, should be explicitly described in mathematics textbooks and the curriculum. These rules are currently implicitly presented or omitted from textbooks, but they contain important principles that foster mathematical thinking. This study aims to evaluate the current level of understanding of these computation rules and provide implications for the curriculum and textbook writing. To this end, the correct answer ratio of the five arithmetic rules for 1-4 grades 398 in five elementary schools was investigated and the type of error was analyzed and presented, and the subject to learn these rules and the points to be noted in teaching and learning were also presented. These results will help to clarify the achievement criteria and learning contents of the calculation rules, which were implicitly presented in existing national textbooks, in a new 2022 revised curriculum.

• Communications for Statistical Applications and Methods
• /
• v.29 no.1
• /
• pp.41-51
• /
• 2022

We forecast the US oil consumption level taking advantage of google trends. The google trends are the search volumes of the specific search terms that people search on google. We focus on whether proper selection of google trend terms leads to an improvement in forecast performance for oil consumption. As the forecast models, we consider the least absolute shrinkage and selection operator (LASSO) regression and the structured regularization method for large vector autoregressive (VAR-L) model of Nicholson et al. (2017), which select automatically the google trend terms and the lags of the predictors. An out-of-sample forecast comparison reveals that reducing the high dimensional google trend data set to a low-dimensional data set by the LASSO and the VAR-L models produces better forecast performance for oil consumption compared to the frequently-used forecast models such as the autoregressive model, the autoregressive distributed lag model and the vector error correction model.

• SUVANNABHUMI
• /
• v.14 no.1
• /
• pp.193-228
• /
• 2022

This study investigates the production and perception of Vietnamese tones by Japanese, Lao, and Taiwanese second language (L2) learners [n=30], comparing their performance in an Imitation task to that of Identification and Read-Aloud tasks. The results show that the Imitation task is generally easier for L2 speakers than the Identification and Read-Aloud tasks, suggesting that imitation is performed without some of the skills required by the other two tasks. It is also found that Lao and Taiwanese speakers outperform Japanese speakers, suggesting that prior experience with one tone language facilitates the acquisition of tone in another language. The result on speakers' tonal range show that L2 leaners have significantly narrower tonal F0 range than control Vietnamese speakers [n=11]. The results of error pattern analysis and tonal transcription also suggest that non-modal voice (glottal stop and creakiness) and contour tones (bidirectional fall-rise) are more difficult for L2 learners than modal voice tones (e.g., unidirectional contours: rising, falling, and level).

• Journal of information and communication convergence engineering
• /
• v.19 no.4
• /
• pp.241-247
• /
• 2021

Particulate matter has emerged as a serious global problem, necessitating highly reliable information on the matter. Therefore, various algorithms have been used in studies to predict particulate matter. In this study, we compared the prediction performance of neural network models that have been actively studied for particulate matter prediction. Among the neural network algorithms, a deep neural network (DNN), a recurrent neural network, and long short-term memory were used to design the optimal prediction model using a hyper-parameter search. In the comparative analysis of the prediction performance of each model, the DNN model showed a lower root mean square error (RMSE) than the other algorithms in the performance comparison using the RMSE and the level of accuracy as metrics for evaluation. The stability of the recurrent neural network was slightly lower than that of the other algorithms, although the accuracy was higher.

• Electronics and Telecommunications Trends
• /
• v.37 no.4
• /
• pp.28-35
• /
• 2022

To solve the challenge of waste plastics, this study investigated the related technologies and company trends along the plastic life cycle, and primarily describes ICT technologies to improve efficiency in the process of sorting and sorting waste plastics. Waste plastic discharge caused by the explosive increase in parcel traffic because of COVID-19 is also growing exponentially. Hence, waste treatment is emerging as a social challenge. Most of the domestic waste classification depends on the manual process according to the waste pollution level. The plastic material classification approach using the spectroscopy approach reveals a high error in the contaminated waste plastic classification, but if the Artificial Intelligence-based image classification technology is employed together, the classification precision can be enhanced because of the type of waste plastic product and the contaminated part can be differentiated.

• Journal of Advanced Navigation Technology
• /
• v.27 no.4
• /
• pp.391-395
• /
• 2023

Helicopter accidents due to spatial disorientation in low visibility conditions continue to persist as a major issue. These incidents often stem from human error, typically induced by stress, and frequently result in fatal outcomes. This study employs machine learning to analyze flight data and evaluate the efficacy of a flight illusion detection algorithm, laying groundwork for further research. This study collected flight data from approximately 20 pilots using a simulated flight training device to construct a range of flight scenarios. These scenarios included three stages of flight: ascending, level, and descent, and were further categorized into good visibility conditions and 0-mile visibility conditions. The aim was to investigate the occurrence of flight illusions under these conditions. From the extracted data, we obtained a total of 54,000 time-series data points, sampled five times per second. These were then analyzed using a machine learning approach.

• Annual Conference of KIPS
• /
• 2009.11a
• /
• pp.955-956
• /
• 2009

이 Paper는 Server 솔루션에서 사용되는 RAID 컨트롤러가 Disk 상의 에러(Error)를 처리하는 behavior를 분석하고 이를 개선할 수 있는 방법을 제안한다. RAID Level 중에 가장 기본인 RAID 0는 주로 Internet portal site의 Search Engines 이나 Media streaming Servers, 그리고 HD 영상 편집 시스템에서 사용된다. 그러나 Fault tolerance가 지원되지 않기 때문에 Data Volume(logical Disk)의 보존성이 약한 취약점을 가지고 있다. 따라서 이것을 개선하고 Data 보존성을 높일 수 있는 방안을 제시한다.

• Journal of Korea Society of Digital Industry and Information Management
• /
• v.18 no.1
• /
• pp.1-9
• /
• 2022

Galois field arithmetic is important in error correcting codes and public-key cryptography schemes. Hardware realization of these schemes requires an efficient implementation of Galois field arithmetic operations. Multiplication is the main finite field operation and designing efficient multiplier can clearly affect the performance of compute-intensive applications. Diverse algorithms and hardware architectures are presented in the literature for hardware realization of Galois field multiplication to acquire a reduction in time and area. This paper presents a low complexity semi-systolic multiplier to facilitate parallel processing by partitioning Montgomery modular multiplication (MMM) into two independent and identical units and two-level systolic computation scheme. Analytical results indicate that the proposed multiplier achieves lower area-time (AT) complexity compared to related multipliers. Moreover, the proposed method has regularity, concurrency, and modularity, and thus is well suited for VLSI implementation. It can be applied as a core circuit for multiplication and division/exponentiation.

• Journal of Auto-vehicle Safety Association
• /
• v.14 no.1
• /
• pp.39-44
• /
• 2022

This paper presents a development of throttle and brake controller for autonomous vehicle simulation environment. Most of 3D simulator control autonomous vehicle by throttle and brake command. Therefore additional longitudinal controller is required to calculate pedal input from desired acceleration. The controller consists of two parts, feedback controller and feedforward controller. The feedback controller is designed to compensate error between the actual acceleration and desired acceleration calculated from autonomous driving algorithm. The feedforward controller is designed for fast response and the output is determined by the actual vehicle speed and desired acceleration. To verify the performance of the controller, simulations were conducted for various scenarios, and it was confirmed that the controller can successfully follow the target acceleration.