• Korean Journal of Environment and Ecology
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• v.21 no.5
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• pp.379-389
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• 2007

This study was carried out to classify forest vegetation structure of Mt. Gaya from April to October in 2006 using phytosociological analysis methodology of Z-M schools. One hundred study sites(quadrat) were surveyed in the area. The forest vegetation was classified into 3 community groups such as Pinus densiflora community group, Cornus controversa community group and artificial forest group. P, densiflora community group was subdivided into 4 communities such as Rhododendron schlippenbachii community. Salix gracilistyla community, Meliosma oldhamii community and P. densiflora typical community. R. schlippendbachii community was subdivided into Potentilla dickinsii group(subdivided into Carpinus coreana subgroup and Melandrynum firmum subgroup) and R. schlippenbachiitypical group. Cornus controversa community group was also subdivided into 4 communities such as Hovenia dulcis community, Quercus aliena community, Ribes maximowicianum community and C. controversa typical community. Artificial forest type indicated 3 communities such as Larix leptolepis community, Pinus rigida community and Castanea crenata community. Accordingly, the vegetation pattern of the surveyed areas were classified into 3 community groups, 11 communities, 2 groups, and 2 subgroups and the forest vegetation was classified into 13 units in total. It is also believed that C. coreana subgroup and M. oldhamii community could be a source for a significant basic data for making vegetation hierarchy and forest distribution zone in the Korean peninsula. H. dulcis community was also considered to be one of the important genetic resources; therefore, those distribution areas are required to be institutionally protected and managed in the near future.

• Journal of the HCI Society of Korea
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• v.10 no.2
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• pp.21-26
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• 2015

NUI (Natural User Interface) system interprets the user's natural movement or the signals from human body to the machine. sEMG (surface electromyogram) can be observed when there is any effort in muscle even without actual movement, which is impossible with camera and accelerometer based NUI system. In sEMG based movement recognition system, the minimal number of electrodes is preferred to minimize the inconvenience. We analyzed the decrease in recognition accuracy as decreasing the number of electrodes. For the four kinds of movement intention without movement, extension (up), flexion (down), abduction (right), and adduction (left), the multilayer perceptron classifier was used with the features of RMS (Root Mean Square) from sEMG. The classification accuracy was 91.9% in four channels, 87.0% in three channels, and 78.9% in two channels. To increase the accuracy in two channels of sEMG, RMSs from previous time epoch (50-200 ms) were used in addition. With the RMSs from 150 ms, the accuracy was increased from 78.9% to 83.6%. The decrease in accuracy with minimal number of electrodes could be compensated partly by utilizing more features in previous RMSs.

• Information Systems Review
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• v.22 no.4
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• pp.21-39
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• 2020

Recently, deep learning has shown high performance in various applications such as pattern analysis and image classification. Especially known as a difficult task in the field of machine learning research, stock market forecasting is an area where the effectiveness of deep learning techniques is being verified by many researchers. This study proposed a deep learning Convolutional Neural Network (CNN) model to predict the direction of stock prices. We then used the feature selection method to improve the performance of the model. We compared the performance of machine learning classifiers against CNN. The classifiers used in this study are as follows: Logistic Regression, Decision Tree, Neural Network, Support Vector Machine, Adaboost, Bagging, and Random Forest. The results of this study confirmed that the CNN showed higher performancecompared with other classifiers in the case of feature selection. The results show that the CNN model effectively predicted the stock price direction by analyzing the embedded values of the financial data

• The Journal of Sustainable Design and Educational Environment Research
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• v.14 no.3
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• pp.9-17
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• 2015

In this paper, there are several analysis on G-SEED, Building Energy Efficiency Rating System, Energy Performance Index, Energy Saving Plan about how they are applied by classification and planning standard. The analysis result found out that G-SEED has low select percentage by having difficulties to managing and additional cost when the each class is selected. And also, Building Energy Efficiency Rating System in school is planed in comparably simple design and similar size and also mostly uses high efficient machines, which was in high lever comparing to the system in facilities in other uses. In the case of EPI, there are differences on acquiring grades by each region. Especially, Gyung-gi region has a low grade on architecture part comparing to other parts, which seems to acquire more grades by strengthen insulation performance. By the result from the three standards, many facilities has only formal plan to pass the required standard without considering specialities of each buildings, which has a tendency to have a pattern to have a minimum criteria. However, School has a symbolic building which has a obligation to be the base of the aim for growing green energy buildings and green education for students. Therefore, planning with understanding of specialities of the facility, having various and rational evaluation standards from the planning of the building is necessary.

• The Plant Pathology Journal
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• v.34 no.5
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• pp.381-392
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• 2018

Clavibacter michiganensis subsp. michiganesis (Cmm) is a quarantine-worthy pest in $M{\acute{e}}xico$. The implementation and validation of new technologies is necessary to reduce the time for bacterial detection in laboratory conditions and Raman spectroscopy is an ambitious technology that has all of the features needed to characterize and identify bacteria. Under controlled conditions a contagion process was induced with Cmm, the disease epidemiology was monitored. Micro-Raman spectroscopy ($532nm\;{\lambda}$ laser) technique was evaluated its performance at assisting on Cmm detection through its characteristic Raman spectrum fingerprint. Our experiment was conducted with tomato plants in a completely randomized block experimental design (13 plants ${\times}$ 4 rows). The Cmm infection was confirmed by 16S rDNA and plants showed symptoms from 48 to 72 h after inoculation, the evolution of the incidence and severity on plant population varied over time and it kept an aggregated spatial pattern. The contagion process reached 79% just 24 days after the epidemic was induced. Micro-Raman spectroscopy proved its speed, efficiency and usefulness as a non-destructive method for the preliminary detection of Cmm. Carotenoid specific bands with wavelengths at 1146 and $1510cm^{-1}$ were the distinguishable markers. Chemometric analyses showed the best performance by the implementation of PCA-LDA supervised classification algorithms applied over Raman spectrum data with 100% of performance in metrics of classifiers (sensitivity, specificity, accuracy, negative and positive predictive value) that allowed us to differentiate Cmm from other endophytic bacteria (Bacillus and Pantoea). The unsupervised KMeans algorithm showed good performance (100, 96, 98, 91 y 100%, respectively).

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.3
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• pp.471-479
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• 2018

Recently, the spread of smart TV based Android iOS Set Top box has become common. This paper propose a new approach to control the TV using gestures away from the era of controlling the TV using remote control. In this paper, the AdaBoost algorithm is applied to gesture recognition by using a mono camera. First, we use Camshift-based Body tracking and estimation algorithm based on Gaussian background removal for body coordinate extraction. Using global and local feature vectors, we recognized gestures with speed change. By tracking the time interval trajectories of hand and wrist, the AdaBoost algorithm with CART algorithm is used to train and classify gestures. The principal component feature vector with high classification success rate is searched using CART algorithm. As a result, 24 optimal feature vectors were found, which showed lower error rate (3.73%) and higher accuracy rate (95.17%) than the existing algorithm.

• Journal of the Korea Convergence Society
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• v.10 no.4
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• pp.1-6
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• 2019

Neural networks have been used to predict the direction of stock index movement from past data. The conventional research that predicts the upward or downward movement of the stock index predicts a rise or fall even with small changes in the index. It is highly likely that losses will occur when trading ETFs by use of the prediction. In this paper, a neural network model that predicts the movement direction of the daily KOrea composite Stock Price Index (KOSPI) to reduce ETF trading losses and earn more than a certain amount per trading is presented. The proposed model has outputs that represent rising (change rate in index ${\geq}{\alpha}$), falling (change rate ${\leq}-{\alpha}$) and neutral ($-{\alpha}$ change rate < ${\alpha}$). If the forecast is rising, buy the Leveraged Exchange Traded Fund (ETF); if it is falling, buy the inverse ETF. The hit ratio (HR) of PNN1 implemented in this paper is 0.720 and 0.616 in the learning and the evaluation respectively. ETF trading yields a yield of 8.386 to 16.324 %. The proposed models show the better ETF trading success rate and yield than the neural network models predicting KOSPI.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.3
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• pp.133-140
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• 2023

The performance of lithium ion batteries depends on the usage environment and the combination ratio of cathode materials. In order to develop a high-performance lithium-ion battery, it is necessary to manufacture the battery and measure its performance while varying the cathode material ratio. However, it takes a lot of time and money to directly develop batteries and measure their performance for all combinations of variables. Therefore, research to predict the performance of a battery using an artificial intelligence model has been actively conducted. However, since measurement experiments were conducted with the same battery in the existing published battery data, the cathode material combination ratio was fixed and was not included as a data attribute. In this paper, we define a training data model required to develop an artificial intelligence model that can predict battery performance according to the combination ratio of cathode materials. We analyzed the factors that can affect the performance of lithium-ion batteries and defined the mass of each cathode material and battery usage environment (cycle, current, temperature, time) as input data and the battery power and capacity as target data. In the battery data in different experimental environments, each battery data maintained a unique pattern, and the battery classification model showed that each battery was classified with an error of about 2%.

• Journal of the Korean Arthroscopy Society
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• v.10 no.1
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• pp.53-60
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• 2006

Purpose: This study is to analyze the clinical and radiological results after arthroscopic reduction and internal fixation of intra-articular fractures of lateral tibial plateau. Materials and Methods: The subject of the study are the 13 cases of the patients visited orthopedics surgery during March year 2000 to August year 2004 because of intra-articular fractures of lateral tibial plateau and were treated with arthroscopic reduction and internal fixation. X-rays and CT or MRI were both carried out to identify the precise pattern of fracture and the degree of depression which showed out to be all type 2 by Schatzker fracture classification. And in 9 of the cases, autogenous and allogenous bone grafts were given as bone loss were severe. The average age was 48, age group between 31 and 66, and average follow up period of about 38 months ($13{\sim}65months$). Radiological ratings were given by comparing the X-rays of degree of joint congruency before and after the operation, functional ratings by analyzing IKDC score and Lysholm score. Combined injuries observed after arthroscopy were posterior cruciate ligament injury in 1 case, meniscus injury in 4 cases and medial collateral ligament in 2 cases. Results: During follow up, X-rays showed well-maintained reduction of articular surface in all cases and no complications such as joint depression, fracture reduction loss, angular deformity or malunion were found. Average Lysholm score at last follow up was 87 points ranging from 65 to 97, in 8 of the cases excellent, 3 good, 1 fair and 1 poor according to Lynsholm classification. Average IKDC score was 92 (from 82 to 99). Conclusion: Not only does arthroscopic reduction of lateral tibial plateau fracture bring exact reduction of articular surface, but also, is considered to be a good way of operation to diagnose and treat combined injuries of knee joint using arthroscopy.

• Journal of Korea Water Resources Association
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• v.55 no.6
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• pp.447-459
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• 2022

This study estimated the grid-type precipitation quantile for the Korean Peninsula using PERSIANN-CCS-CDR (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System-Climate Data Record), a satellite based re-analysis precipitation data. The period considered is a total of 38 years from 1983 to 2020. The spatial resolution of the data is 0.04° and the temporal resolution is 3 hours. For the probability distribution, the Gumbel distribution which is generally used for frequency analysis was used, and the probability weighted moment method was applied to estimate parameters. The duration ranged from 3 hours to 144 hours, and the return period from 2 years to 500 years was considered. The results were compared and reviewed with the estimated precipitation quantile using precipitation data from the Automated Synoptic Observing System (ASOS) weather station. As a result, the parameter estimates of the Gumbel distribution from the PERSIANN-CCS-CDR showed a similar pattern to the results of the ASOS as the duration increased, and the estimates of precipitation quantiles showed a rather large difference when the duration was short. However, when the duration was 18 h or longer, the difference decreased to less than about 20%. In addition, the difference between results of the South and North Korea was examined, it was confirmed that the location parameters among parameters of the Gumbel distribution was markedly different. As the duration increased, the precipitation quantile in North Korea was relatively smaller than those in South Korea, and it was 84% of that of South Korea for a duration of 3 h, and 70-75% of that of South Korea for a duration of 144 h.