• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.3
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• pp.58-64
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• 2021

As the number of registered vehicles increases, traffic congestion will worsen worse, which may act as an inhibitory factor for urban social and economic development. Through accurate traffic flow prediction, various AI techniques have been used to prevent traffic congestion. This paper uses the data from a VDS (Vehicle Detection System) as input variables. This study predicted traffic flow in five levels (free flow, somewhat delayed, delayed, somewhat congested, and congested), rather than predicting traffic flow in two levels (free flow and congested). The Catboost model, which is a machine-learning algorithm, was used in this study. This model predicts traffic flow in five levels and compares and analyzes the accuracy of the prediction with other algorithms. In addition, the preprocessed model that went through RandomizedSerachCv and One-Hot Encoding was compared with the naive one. As a result, the Catboost model without any hyper-parameter showed the highest accuracy of 93%. Overall, the Catboost model analyzes and predicts a large number of categorical traffic data better than any other machine learning and deep learning models, and the initial set parameters are optimized for Catboost.

• Structural Engineering and Mechanics
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• v.78 no.1
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• pp.53-66
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• 2021

The determination of the damage index to reveal the performance level of a structure can constitute the seismic risk generalization approach based on the parametric analysis. This study implemented this concept to one kind of civil engineering structure that is the concrete gravity dam. Different cases of the structure exhibit their individual responses, which constitute different considerations. Therefore, this approach allows the parametric study of concrete as well as soil for evaluating the seismic nature in the generalized case. To ensure that the target algorithm applicable to most of the concrete gravity dams, a very simple procedure has been considered. In order to develop a correlated algorithm (by response surface methodology; RSM) between the ground motion and the structural property, randomized sampling was adopted through a stochastic method called half-fractional central composite design. The responses in the case of fluid-foundation-dam interaction (FFDI) make it more reliable by introducing the foundation as being bounded by infinite elements. To evaluate the seismic generalization of FFDI models, incremental dynamic analysis (IDA) was carried out under the impacts of various earthquake records, which have been selected from the Pacific Earthquake Engineering Research Center data. Here, the displacement-based damage indexed fragility curves have been generated to show the variation in the seismic pattern of the dam. The responses to the sensitivity analysis of the various parameters presented here are the most effective controlling factors for the concrete gravity dam. Finally, to establish the accuracy of the proposed approach, reliable verification was adopted in this study.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.1
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• pp.364-380
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• 2016

The most popular approach in object classification is based on the bag of visual-words model, which has several fundamental problems that restricting the performance of this method, such as low time efficiency, the synonym and polysemy of visual words, and the lack of spatial information between visual words. In view of this, an object classification based on weakly supervised E2LSH and saliency map weighting is proposed. Firstly, E2LSH (Exact Euclidean Locality Sensitive Hashing) is employed to generate a group of weakly randomized visual dictionary by clustering SIFT features of the training dataset, and the selecting process of hash functions is effectively supervised inspired by the random forest ideas to reduce the randomcity of E2LSH. Secondly, graph-based visual saliency (GBVS) algorithm is applied to detect the saliency map of different images and weight the visual words according to the saliency prior. Finally, saliency map weighted visual language model is carried out to accomplish object classification. Experimental results datasets of Pascal 2007 and Caltech-256 indicate that the distinguishability of objects is effectively improved and our method is superior to the state-of-the-art object classification methods.

• Computers and Concrete
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• v.33 no.2
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• pp.137-145
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• 2024

The technique of experimentally determining concrete's compressive strength for a given mix design is time-consuming and difficult. The goal of the current work is to propose a best working predictive model based on different machine learning algorithms such as Gradient Boosting Machine (GBM), Stacked Ensemble (SE), Distributed Random Forest (DRF), Extremely Randomized Trees (XRT), Generalized Linear Model (GLM), and Deep Learning (DL) that can forecast the compressive strength of ternary geopolymer concrete mix without carrying out any experimental procedure. A geopolymer mix uses supplementary cementitious materials obtained as industrial by-products instead of cement. The input variables used for assessing the best machine learning algorithm not only include individual ingredient quantities, but molarity of the alkali activator and age of testing as well. Myriad statistical parameters used to measure the effectiveness of the models in forecasting the compressive strength of ternary geopolymer concrete mix, it has been found that GBM performs better than all other algorithms. A sensitivity analysis carried out towards the end of the study suggests that GBM model predicts results close to the experimental conditions with an accuracy between 95.6 % to 98.2 % for testing and training datasets.

• Korean Journal of Agricultural and Forest Meteorology
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• v.22 no.1
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• pp.1-12
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• 2020

As one of the essential resources in the agricultural process, soil moisture has been carefully managed by predicting future changes and deficits. In recent years, statistics and machine learning based approach to predict soil moisture has been preferred in academia for its generalizability and ease of use in the field. However, little is known that machine learning based soil moisture prediction is applicable in the situation of South Korea. In this sense, this paper aims to examine 1) whether publicly available weather data generated in South Korea has sufficient quality to predict soil moisture, 2) which machine learning algorithm would perform best in the situation of South Korea, and 3) whether a single machine learning model could be generally applicable in various regions. We used various machine learning methods such as Support Vector Machines (SVM), Random Forest (RF), Extremely Randomized Trees (ET), Gradient Boosting Machines (GBM), and Deep Feedforward Network (DFN) to predict future soil moisture in Andong, Boseong, Cheolwon, Suncheon region with open source weather data. As a result, GBM model showed the lowest prediction error in every data set we used (R squared: 0.96, RMSE: 1.8). Furthermore, GBM showed the lowest variance of prediction error between regions which indicates it has the highest generalizability.

• Journal of Korean Academy of Fundamentals of Nursing
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• v.15 no.1
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• pp.34-44
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• 2008

Purpose: The purpose of this study was to develop an eye care protocol for intensive care unit (ICU) patients. Method: A systematic review was conducted to develop an eye care protocol for ICU patients. Searches were performed using computerized databases (CINAHL, MEDLINE, EBM Review) and citation search from 1996 to January 2007. For the keywords, "eye care", and "randomized controlled trial" were used to identify experimental studies regarding eye care for ICU patients. After reviewing the collected studies, a preliminary eye care protocol algorithm was created. Then, content validity was examined with ophthalmologists and ICU nurses. Results: Six studies were included to serve as a basis for framing of the preliminary algorithm. The final eye care protocol was completed after verifying the preliminary algorithm's content validity. The final eye care protocol was organized in the following manner: 3 items in the assessment stage, 7 items in the no-risk stage, 4 items in the low-risk stage, and 5 items in the high-risk stage. Conclusion: The results indicate that, for ICU patients, nurses can broaden their knowledge regarding ocular diseases, as well as improve their practice-based eye care nursing performance.

• Korean Journal of Remote Sensing
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• v.31 no.2
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• pp.111-125
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• 2015

In this study, the target detection using both high-resolution satellite SAR and Elecro-Optical (EO) images such as TerraSAR-X and WorldView-2 is performed, considering the characteristics of targets. The targets of our interest are featured by being stationary and appearing as cluster targets. After the target detection of SAR image by using Constant False Alarm Rate (CFAR) algorithm, a series of processes is performed in order to reduce false alarms, including pixel clustering, network clustering and coherence analysis. We extend further our algorithm by adopting the fast and effective ellipse detection in EO image using randomized hough transform, which is significantly reducing the number of false alarms. The performance of proposed algorithm has been tested and analyzed on TerraSAR-X SAR and WordView-2 EO images. As a result, the average false alarm for group of targets is 1.8 groups/$64km^2$ and the false alarms of single target range from 0.03 to 0.3 targets/$km^2$. The results show that groups of targets are successfully identified with very low false alarms.

• The Journal of Advanced Prosthodontics
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• v.10 no.1
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• pp.8-17
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• 2018

PURPOSE. The study aimed to identify the accuracy and reproducibility of preparations made by gypsum materials of various colors using quantitative and semi-quantitative three-dimensional (3D) approach. MATERIALS AND METHODS. A titanium maxillary first molar preparation was created as reference dataset (REF). Silicone impressions were duplicated from REF and randomized into 6 groups (n=8). Gypsum preparations were formed and grouped according to the color of gypsum materials, and light-scanned to obtain prepared datasets (PRE). Then, in terms of accuracy, PRE were superimposed on REF using the best-fit-algorithm and PRE underwent intragroup pairwise best-fit alignment for assessing reproducibility. Root mean square deviation (RMSD) and degrees of similarity (DS) were computed and analyzed with SPSS 20.0 statistical software (${\alpha}=.05$). RESULTS. In terms of accuracy, PREs in 3D directions were increased in the 6 color groups (from 19.38 to $20.88{\mu}m$), of which the marginal and internal variations ranged $51.36-58.26{\mu}m$ and $18.33-20.04{\mu}m$, respectively. On the other hand, RMSD value and DS-scores did not show significant differences among groups. Regarding reproducibility, both RMSD and DS-scores showed statistically significant differences among groups, while RMSD values of the 6 color groups were less than $5{\mu}m$, of which blue color group was the smallest ($3.27{\pm}0.24{\mu}m$) and white color group was the largest ($4.24{\pm}0.36{\mu}m$). These results were consistent with the DS data. CONCLUSION. The 3D volume of the PREs was predisposed towards an increase during digitalization, which was unaffected by gypsum color. Furthermore, the reproducibility of digitalizing scanning differed negligibly among different gypsum colors, especially in comparison to clinically observed discrepancies.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.4B
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• pp.165-173
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• 2008

In this paper, we propose a new reliable broadcast protocol referred to as timer-based reliable broadcast (TRB) for wireless sensor networks (WSNs). The proposed TRB scheme exploits (1) bitmap based explicit ACK to effectively reduce the unnecessary error control messages and (2) randomized timer for ACK transmission to substantially reduce the possibility of contentions. Although it has been argued that 100% reliability is not necessary in WSNs, there should be messages (such as mission-critical message, task assignment, software updates, etc.) that need to be reliably delivered to the entire sensor field. We propose to use the TRB algorithm for such cases. Performance evaluation shows that the TRB scheme achieves 100 % reliability significantly better than other schemes with expense of slightly increased energy consumption.

• The Journal of Korean Obstetrics and Gynecology
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• v.21 no.4
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• pp.193-206
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• 2008

Purpose: In spite of many arguments on the result of WHI (Women's Health Initiative) study, no one can deny the necessity for researches on the alternative treatment to HRT (hormone replacement therapy). In this study, the author wanted to investigate the method of precedent acupuncture RCTs (randomized controlled trials) to make out the appropriate acupuncture study design on postmenopausal and perimenopausal hot flashes in Korea. Methods: Precedent studies were investigated using Pubmed search and key-words "acupuncture and hot flash", "acupuncture and menopause", "acupuncture and vasomotor", limited to RCT, from 2000 to 2008 April. Results: As a result. 10 studies were searched. In the hereafter studies, multi-center clinical trials which consist of population group of postmenopausal and perimenopausal women that would be pre-stratified and more than 50 patients per treatment arm seem adequate. Sham control study can make out the proper consequence because many people are get used to acupuncture in Korea. Flexible choice of acupoints addressed an individual's symptoms using standardized algorithm is recommended. Treatment consist of 4 weeks' observation, 11 acupuncture sessions during 7 weeks, follow-up of 3 months or more after treatment and hot flash score as a primary outcome measure seem appropriate. After all, higher level of description according to global standard must be obtained in the study report and publishing. Conclusion: The researchers should develope the methodology of acupuncture clinical trial on the postmenopausal and perimenopausal hot flashes.