• Korean Journal of Remote Sensing
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• v.37 no.4
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• pp.777-788
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• 2021

Since aerosol has a relatively short duration and significant spatial variation, satellite observations become more important for the spatially and temporally continuous quantification of aerosol. However, optical remote sensing has the disadvantage that it cannot detect AOD (Aerosol Optical Depth) for the regions covered by clouds or the regions with extremely high concentrations. Such missing values can increase the data uncertainty in the analyses of the Earth's environment. This paper presents a spatial gap-filling framework using a univariate statistical method such as DCT-PLS (Discrete Cosine Transform-based Penalized Least Square Regression) and FMM (Fast Matching Method) inpainting. We conducted a feasibility test for the hourly AOD product from AHI (Advanced Himawari Imager) between January 1 and December 31, 2019, and compared the accuracy statistics of the two spatial gap-filling methods. When the null-pixel area is not very large (null-pixel ratio < 0.6), the validation statistics of DCT-PLS and FMM techniques showed high accuracy of CC=0.988 (MAE=0.020) and CC=0.980 (MAE=0.028), respectively. Together with the AI-based gap-filling method using extra explanatory variables, the DCT-PLS and FMM techniques can be tested for the low-resolution images from the AMI (Advanced Meteorological Imager) of GK2A (Geostationary Korea Multi-purpose Satellite 2A), GEMS (Geostationary Environment Monitoring Spectrometer) and GOCI2 (Geostationary Ocean Color Imager) of GK2B (Geostationary Korea Multi-purpose Satellite 2B) and the high-resolution images from the CAS500 (Compact Advanced Satellite) series soon.

• Geomechanics and Engineering
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• v.33 no.1
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• pp.1-9
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• 2023

The unconfined compression strength (UCS) of soils is commonly used either before or during the construction of geo-structures. In the pre-design stage, UCS as a mechanical property is obtained through a laboratory test that requires cumbersome procedures and high costs from in-situ sampling and sample preparation. As an alternative way, the empirical model established from limited testing cases is used to economically estimate the UCS. However, many parameters affecting the 1D soil compression response hinder employing the traditional statistical analysis. In this study, gene expression programming (GEP) is adopted to develop a prediction model of UCS with common affecting soil properties. A total of 79 undisturbed soil samples are collected, of which 54 samples are utilized for the generation of a predictive model and 25 samples are used to validate the proposed model. Experimental studies are conducted to measure the unconfined compression strength and basic soil index properties. A performance assessment of the prediction model is carried out using statistical checks including the correlation coefficient (R), the root mean square error (RMSE), the mean absolute error (MAE), the relatively squared error (RSE), and external criteria checks. The prediction model has achieved excellent accuracy with values of R, RMSE, MAE, and RSE of 0.98, 10.01, 7.94, and 0.03, respectively for the training data and 0.92, 19.82, 14.56, and 0.15, respectively for the testing data. From the sensitivity analysis and parametric study, the liquid limit and fine content are found to be the most sensitive parameters whereas the sand content is the least critical parameter.

• Genomics & Informatics
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• v.21 no.4
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• pp.46.1-46.10
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• 2023

Colon adenocarcinoma (COAD) is the predominant type of colorectal cancer. Early diagnosis and treatment can significantly improve the prognosis of COAD patients. Anoctamin 7 (ANO7), an anion channel protein, has been implicated in prostate cancer and other types of cancer. In this study, we analyzed the expression of ANO7 and its correlation with clinicopathological characteristics among COAD patients using the Gene Expression Profiling Interactive Analysis 2 (GEPIA2) and the University of Alabama at Birmingham CANcer (UALCAN) databases. The GEPIA2, Kaplan-Meier plotter, and the Survival Genie platform were employed for survival analysis. The co-expression network and potential function of ANO7 in COAD were analyzed using GeneFriends, the Database for Annotation, Visualization and Integrated Discovery (DAVID), GeneMANIA, and Pathway Studio. Our data analysis revealed a significant reduction in ANO7 expression levels within COAD tissues compared to normal tissues. Additionally, ANO7 expression was found to be associated with race and histological subtype. The COAD patients exhibiting low ANO7 expression had lower survival rates compared to those with high ANO7 expression. The genes correlated with ANO7 were significantly enriched in proteolysis and mucin type O-glycan biosynthesis pathway. Furthermore, ANO7 demonstrated a direct interaction and a positive co-expression correlation with mucin 2 (MUC2). In conclusion, our findings suggest that ANO7 might serve as a potential prognostic biomarker and potentially plays a role in proteolysis and mucin biosynthesis in the context of COAD.

• 한국IT서비스학회:학술대회논문집
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• 2008.05a
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• pp.511-515
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• 2008

이웃기반 협력 필터링을 이용한 추천시스템은 적은 평가 자료로 인해 추천 성능에 문제가 생긴다. 이는 다른 고객의 정보도 추천에 사용하는 협력 필터링에서 이웃고객 선정에 문제가 생겨 추천시스템의 신뢰가 떨어진다. 본 논문은 추천시스템의 신뢰를 높이기 위한 방법으로 선호도 평가치가 적은 상품을 임계값을 이용하여 선별하고 이에 따라 고객의 표준편차를 조사하였다. 그리고 표준편차가 낮은 고객에 대한 MAE를 분석하여 예측의 정확도가 높아짐을 알 수 있었다.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.05a
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• pp.338-339
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• 2021

본 논문에서는 제주시에서 오픈데이터로 공개한 버스탑승 기록을 기반으로 이용도가 높은 버스노선에 대해 특정정류장에서의 도착시간 예측모델을 구축한다. 버스들의 평균주행 속도, 운행시간대, 교통량 등을 입력으로 한 모델을 Sklearn을 이용하여 생성하고 MAE와 손실율 등의 성능을 분석한다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.2B
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• pp.138-143
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• 2003

One of the important design issues for IP routers responsible for packet forwarding in computer networks is the route-lookup mechanism. For each incoming packet, IP routing requires that a router performs a longest-prefix-match address lookup in order to determine the next hop that the incoming packet should be forwarded to. In this paper, we present a new scheme which applies the hashing function for IP address lookup. In the proposed scheme, the forwarding table is composed of multiple SRAMs, and each SRAM represents an address lookup table in each prefix. Hashing function is applied in order to find out the matching entries from the address lookup tables in parallel, and the entry with the longest prefix match among them is selected. Simulation using the MAE-WEST router example shows that a large routing table with 37000 entries can be compacted to a forwarding table of 300 Kbytes in the proposed scheme. It is also shown that the proposed scheme achieves one route lookup every 1.93 memory accesses in average.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.1B
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• pp.18-28
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• 2004

Efficient hardware implementation of address lookup is one of the most important design issues of internet routers. Address lookup significantly impacts router performance since routers need to process tens-to-hundred millions of packets per second in real time. In this paper, we propose a practical IP address lookup structure based on the binary tree of prefixes of different lengths. The proposed structure produces multiple balanced trees, and hence it solve the issues due to the unbalanced binary prefix tree of the existing scheme. The proposed structure is implemented using pipelined binary search combined with a small size TCAM. Performance evaluation results show that the proposed architecture requires a 2000-entry TCAM and total 245 kbyte SRAMs to store about 30,000 prefix samples from MAE-WEST router, and an address lookup is achieved by a single memory access. The proposed scheme scales very well with both of large databases and longer addresses as in IPv6.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.2B
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• pp.158-166
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• 2004

Address lookup is one of the most essential functions of the Internet routers and a very important feature in evaluating router performance. Due to the facts that the Internet traffic keeps growing and the number of routing table entries is continuously growing, efficient address-lookup mechanism is indispensable. In recent years, various fast address-lookup schemes have been proposed, but most of those schemes are not practical in terms of the memory size required for routing table and the complexity required in table update In this paper, we have proposed a parallel IP address lookup architecture based on multiple hashing. The proposed scheme has advantages in required memory size, the number of memory accesses, and table update. We have evaluated the performance of the proposed scheme through simulation using data from MAE-WEST router. The simulation result shows that the proposed scheme requires a single memory access for the address lookup of each route when 203kbytes of memory and a few-hundred-entry TCAM are used.

• The KIPS Transactions:PartD
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• v.9D no.6
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• pp.1009-1016
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• 2002

We design and implement a new case-based recommender system using implicit rating information for a digital content site. Our system consists of the User Profile Generation module, the Similarity Evaluation and Recommendation module, and the Personalized Mailing module. In the User Profile Generation Module, we define intra-attribute and inter-attribute weight deriver from own's past interests of a user stored in the access logs to extract individual preferences for a content. A new similarity function is presented in the Similarity Evaluation and Recommendation Module to estimate similarities between new items set and the user profile. The Personalized Mailing Module sends individual recommended mails that are transformed into platform-independent XML document format to users. To verify the efficiency of our system, we have performed experimental comparisons between the proposed model and the collaborative filtering technique by mean absolute error (MAE) and receiver operating characteristic (ROC) values. The results show that the proposed model is more efficient than the traditional collaborative filtering technique.

• Journal of Digital Convergence
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• v.16 no.12
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• pp.293-299
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• 2018

The weather has a lot of influence on the cultivation of crops. Weather information on agricultural crop cultivation areas is indispensable for efficient cultivation and management of agricultural crops. Despite the high demand for agricultural weather, research on this is in short supply. In this research, we deal with the production method of agricultural weather in Jeollanam-do, which is the main production area of onions through GloSea5 and deep learning. A deep neural network model using the sliding window method was used and utilized to train daily weather prediction for predicting the agricultural weather. RMSE and MAE are used for evaluating the accuracy of the model. The accuracy improves as the learning period increases, so we compare the prediction performance according to the learning period and the prediction period. As a result of the analysis, although the learning period and the prediction period are similar, there was a limit to reflect the trend according to the seasonal change. a modified deep layer neural network model was presented, that applying the difference between the predicted value and the observed value to the next day predicted value.