• Journal of the Korean Geotechnical Society
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• v.37 no.9
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• pp.25-36
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• 2021

Recently, the intelligent compaction technology for quality control of earthworks has brought attention as a quality control standard for earthworks. In this study, intelligent compaction technology and earthwork quality control methods were investigated and earthwork quality control procedures using intelligent compaction technology were considered based on field tests. Through the field compaction test of the silty sand (SM) fill material, it was confirmed that CMV and bearing capcaity index from plate load tests increased as the number of compactions increased. Based on the field test data, the average CMV and quality control target CMV were derived. The target CMV (34.2) was calculated through the correlation with the bearing capacity index of the plate load test, and the target CMV (36.6) was calculated through the analysis of the CMV increase rate. In this paper, the on-site compaction quality management procedure and methodology using intelligent compaction technology were discussed, and an intelligent compaction quality management method was proposed to promote the applicability of the technology.

• Journal of Advanced Navigation Technology
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• v.27 no.6
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• pp.770-775
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• 2023

Recently, as the amount of digital data increases exponentially, the importance of data processing systems is being emphasized. In this situation, the selection and construction of data processing systems are becoming more important. In this study, the performance of cloud computing (CC), edge computing (EC), and UAV-based intelligent edge computing (UEC) was compared as a way to solve this problem. The characteristics, strengths, and weaknesses of each method were analyzed. In particular, this study focused on real-time large-capacity data processing situations such as corridor monitoring. When conducting the experiment, a specific scenario was assumed and a penalty was given to the infrastructure. In this way, it was possible to evaluate performance in real situations more accurately. In addition, the effectiveness and limitations of each computing method were more clearly understood, and through this, the help was provided to enable more effective system selection.

• Journal of Korea Spatial Information System Society
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• v.10 no.4
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• pp.89-95
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• 2008

In this paper, we propose an integrated techniques for managing data quality in traffic data warehousing environments. We describe how to collect and construct the traffic data warehouses from the operational databases, such as FTMS and ARTIS. We explain how to configure the traffic data warehouses efficiently. Also, we propose a quality management techniques to provide high quality traffic data for various analytical transactions. Proposed techniques can contribute in providing high quality traffic data to the traffic related users and researcher, thus reducing data preprocessing and evaluation cost.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.3
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• pp.242-247
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• 2015

In this study, we propose the design of Radial Basis Function Neural Network(RBFNN) classifier in order to classify between precipitation and non-precipitation echo. The characteristics of meteorological radar data is analyzed for classifying precipitation and non-precipitation echo. Input variables is selected as DZ, SDZ, VGZ, SPN, DZ_FR, VR by performing pre-processing of UF data based on the characteristics analysis and these are composed of training and test data. Finally, QC data being used in Korea Meteorological Administration is applied to compare with the performance results of proposed classifier.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.1
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• pp.70-85
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• 2021

Deep learning methodology, which has been actively studied in recent years, has improved the performance of artificial intelligence. Accordingly, systems utilizing deep learning have been proposed in various industries. In traffic systems, spatio-temporal graph modeling using GNN was found to be effective in predicting traffic speed. Still, it has a disadvantage that the model is trained inefficiently due to the memory bottleneck. Therefore, in this study, the road network is clustered through the graph clustering algorithm to reduce memory bottlenecks and simultaneously achieve superior performance. In order to verify the proposed method, the similarity of road speed distribution was measured using Jensen-Shannon divergence based on the analysis result of Incheon UTIC data. Then, the road network was clustered by spectrum clustering based on the measured similarity. As a result of the experiments, it was found that when the road network was divided into seven networks, the memory bottleneck was alleviated while recording the best performance compared to the baselines with MAE of 5.52km/h.

• Electronics and Telecommunications Trends
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• v.37 no.1
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• pp.53-62
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• 2022

With increasing size of transformer-based neural networks, a light-weight algorithm and efficient AI accelerator has been developed to train these huge networks in practical design time. In this article, we present a survey of state-of-the-art research on the low-precision computational algorithms especially for floating-point formats and their hardware accelerator. We describe the trends by focusing on the work of two leading research groups-IBM and Seoul National University-which have deep knowledge in both AI algorithm and hardware architecture. For the low-precision algorithm, we summarize two efficient floating-point formats (hybrid FP8 and radix-4 FP4) with accuracy-preserving algorithms for training on the main research stream. Moreover, we describe the AI processor architecture supporting the low-bit mixed precision computing unit including the integer engine.

• Journal of The Korean Association of Information Education
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• v.26 no.4
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• pp.249-263
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• 2022

The purpose of this study is to investigate the effects of assessment by adopting adaptive learning in higher education that are rarely examined in previous studies. In particular, this study applied research questions: 1) How does technical perception, perceived contents and features, and perceived integration of the AI-based adaptive system with lecture affect overall satisfaction, overall effectiveness, overall usefulness, overall motivation for the study, and intention to use it with other classes? 2) How do overall satisfaction, overall effectiveness, overall usefulness, motivation for the class, and intention to use affect loyalty on the AI-based adaptive system? This study conducted online surveys after the completion of the classes adopted AI-based adaptive learning system, ALEKS. This study applied ANOVA, regression, and factor analyses. The results of this study found that perceived integration of the AI-based adaptive learning system with the lectures on overall satisfaction, effectiveness, motivation, and intention to use for other classes showed significant with higher effect size. The results of this study provides implication that the AI-based learning system help improve learning outcomes in graduate level studies. The results provide policy and managerial implications that the AI-based adaptive learning system should improve better customer relationships in higher education.

• Information Systems Review
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• v.19 no.4
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• pp.135-156
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• 2017

An intelligent personal assistant (IPA) is a software agent that assists people to perform basic tasks or services for an individual by commonly providing information via natural language. In spite of the versatile capabilities of the IPA to answer a user's simple information-based queries, such as the weather and driving directions, the actual usage rates for IPA services are limited to date. In this research, to evaluate the factors affecting the intention to use IPA, we develop an empirical model based on technology acceptance model, innovation diffusion theory, and IS success model. Afterward, we collect 203 questionnaires from actual users of IPAs. Finally, the structural equation model validates the causal relationship between the constructs of the model. Consequently, the innovation characteristics of IPA drawn from innovation diffusion theory, namely, relative advantage, compatibility, observability, all exerted a positive influence on perceived usefulness. Furthermore, information quality, a quality characteristic of IPA obtained from DeLone and McLean's IS success model, presented a positive effect on perceived usefulness and perceived ease of use. Finally, the perceived intelligence of IPA displayed a positive influence on perceived usefulness and ease of use. This characteristic was also a major factor that can increase the intention to use the IPA. Given these research findings, this study is significant for identifying factors that may influence the intention to use the IPA by providing strategic guidelines to relevant business operators and establishing an integrated model.

• Journal of the Korea Convergence Society
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• v.13 no.3
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• pp.11-22
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• 2022

Recently, research and development using various Artificial Intelligence (AI) technologies are being conducted in the field of education. Among the AI in Education (AIEd), conversational agents are not limited by time and space, and can learn more effectively by combining them with various AI technologies such as voice recognition and translation. This paper conducted a trend analysis on platforms that have a large number of users and used conversational agents for English learning among commercialized application. Currently commercialized educational platforms using conversational agent through trend analysis has several limitations and problems. To analyze specific problems and limitations, a comparative experiment was conducted with the latest pre-trained large-capacity dialogue model. Sensibleness and Specificity Average (SSA) human evaluation was conducted to evaluate conversational human-likeness. Based on the experiment, this paper propose the need for trained with large-capacity parameters dialogue models, educational data, and information retrieval functions for effective English conversation learning.

• TTA Journal
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• s.105
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• pp.19-23
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• 2006

USN은 무선통신 기술을 기반으로 센서들끼리 자율적인 무선 네트워크를 구성하고 사물 및 환경에 관련된 대용량 센싱 데이터들을 전송, 취합, 저장, 분석하여 사용자의 현재 상황에 맞는 정보를 적절히 제공해 줄 수 있는 기술 및 첨단 지능형 서비스이다. 홈의 모든 사물에 부착된 센서와 USN이 연계될 경우 모든 사물이 언제 어디서나 스스로 네트워킹하는 지능화된 홈의 구현은 더욱 앞당겨질 것이다. 이번호는 '홈네트워크 + RFID/USN'컨버전스 표준화 관련하여 특집 원고를 집필한 산 · 학 · 연의 관련 전문가들로부터 표준화 및 서비스 동향과 전망에 대해 들어본다.