• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.10
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• pp.1435-1440
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• 2021

With the advent of the low interest rate era, many investors are flocking to the stock market. In the past stock market, people invested in stocks labor-intensively through company analysis and their own investment techniques. However, in recent years, stock investment using artificial intelligence and data has been widely used. The success rate of stock prediction through artificial intelligence is currently not high, so various artificial intelligence models are trying to increase the stock prediction rate. In this study, we will look at various artificial intelligence models and examine the pros and cons and prediction rates between each model. This study investigated as stock prediction programs using artificial intelligence artificial neural network (ANN), deep learning or hierarchical learning (DNN), k-nearest neighbor algorithm(k-NN), convolutional neural network (CNN), recurrent neural network (RNN), and LSTMs.

• Journal of the Semiconductor & Display Technology
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• v.21 no.4
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• pp.65-70
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• 2022

In manufacturing and semiconductor industries, transfer robots increase productivity through accurate and continuous work. Due to the nature of the semiconductor process, there are environments where humans cannot intervene to maintain internal temperature and humidity in a clean room. So, transport robots take responsibility over humans. In such an environment where the manpower of the process is cutting down, the lack of maintenance and management technology of the machine may adversely affect the production, and that's why it is necessary to develop a technology for the machine failure diagnosis system. Therefore, this paper tries to identify various causes of failure of transport robots that are widely used in semiconductor automation, and the Prognostics and Health Management (PHM) method is considered for determining and predicting the process of failures. The robot mainly fails in the driving unit due to long-term repetitive motion, and the core components of the driving unit are motors and gear reducer. A simulation drive unit was manufactured and tested around this component and then applied to 6-axis vertical multi-joint robots used in actual industrial sites. Vibration data was collected for each cause of failure of the robot, and then the collected data was processed through signal processing and frequency analysis. The processed data can determine the fault of the robot by utilizing machine learning algorithms such as SVM (Support Vector Machine) and KNN (K-Nearest Neighbor). As a result, the PHM environment was built based on machine learning algorithms using SVM and KNN, confirming that failure prediction was partially possible.

• Journal of Life Science
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• v.33 no.3
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• pp.227-233
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• 2023

The purpose of this paper was to describe a statistical analysis for the spatial distribution of geographical distances of Erythronium japonicum at Mt. Geumjeong in Korea. The spatial pattern of E. japonicum was analyzed according to the nearest neighbor rule, population aggregation under different plot sizes by dispersion indices, and spatial autocorrelation. Most natural plots of E. japonicum were uniformly distributed in the forest community. Disturbed plots were aggregately distributed within 5 m × 5 m of one another. Neighboring patches of E. japonicum were predominantly 7.5~10 m apart on average. If the natural populations of E. japonicum were disturbed by human activities, then the aggregation occurred in a shorter distance than the 7.5~10 m distance scale. The Morisita index (IM) is related to the patchiness index (PAI) that showed the 2.5 m × 5 m plot had an overly steep slope at the west and south areas when the area was smaller than 5 m × 5 m. When the patch size was one 2.5 m × 5 m quadrat at the west distributed area of Mt. Geumjeong, the cluster was determined by both species characteristics and environmental factors. The comparison of Moran's I values to a logistic regression indicated that individuals in E. japonicum populations at Mt. Geumjeong could be explained by isolation by distance.

• Smart Structures and Systems
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• v.21 no.4
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• pp.421-433
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• 2018

Carbon fiber reinforced polymer (CFRP) cable has good mechanical properties and corrosion resistance. However, the anchorage of CFRP cable is a big issue due to the anisotropic property of CFRP material. In this article, a high-efficient bonding anchorage with novel configuration is developed for CFRP cables. The acoustic emission (AE) technique is employed to evaluate the performance of anchorage in the fatigue test and post-fatigue ultimate bearing capacity test. The obtained AE signals are analyzed by using a combination of unsupervised K-means clustering and supervised K-nearest neighbor classification (K-NN) for quantifying the performance of the anchorage and damage evolutions. An AE feature vector (including both frequency and energy characteristics of AE signal) for clustering analysis is proposed and the under-sampling approaches are employed to regress the influence of the imbalanced classes distribution in AE dataset for improving clustering quality. The results indicate that four classes exist in AE dataset, which correspond to the shear deformation of potting compound, matrix cracking, fiber-matrix debonding and fiber fracture in CFRP bars. The AE intensity released by the deformation of potting compound is very slight during the whole loading process and no obvious premature damage observed in CFRP bars aroused by anchorage effect at relative low stress level, indicating the anchorage configuration in this study is reliable.

• Journal of Korean Society for Geospatial Information Science
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• v.9 no.1 s.17
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• pp.39-47
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• 2001

DTM(Digital Terrain Model) can play a key rule in a great number of the fields of construction Engineering. One of the most important application fields is to determine volume in that the total construction expenses is usually calculated through this. It therefore is necessary to the study on improving the precise of the determination using DTM on account of saving time and cost. On this study, 1:5000 topographic maps issued by NGI in 15 districts involved in Pusan city was digitalized to generate DTM at first. After this step, not only was the determination of the volume as well as readjusted area and height done for the sake of estimating the changable topography caused by cut & fill volume in future but also provided the model to calculate it as results. In addition, comparison among the interpolations, such as Inverse Distance Method and Nearest Neighbor, was respectively done to look over the differences of the volume estimated from each interpolation and also to find the most suitable method. As a result, the former yielded the largest values of area and the volume while the latter gave the smallest ones. Moreover, the values estimated on this study were closely similar to ones obtained by the government of Pusan.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.23 no.3
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• pp.251-260
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• 2005

The Development of CCD has contributed to great advancement in mapping technology with giving benefits to research community of photogrammetry. The purpose of this paper is to find the best selection of interpolation method for creating a terrain model form close range digital photogrammetry. T-test as a kind of statistical analysis was conducted to analyze the similarity of hydraulic model with close range digital photogrammetry and trigonometric leveling. Also, many interpolation methods such as inverse distance, kriging, nearest neighbor and TIN about the hydraulic model interpolation were conducted to compare the results for computer to display actual terrain an optimum interpolation of the digital elevation model form close range digital photogrammetry. The results revealed that kriging and TIN interpolation were efficient methods to judge the hazard interpolation law by analyzing geometric similarity of hydraulic model against hydraulic model application.

• Journal of Convergence for Information Technology
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• v.8 no.1
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• pp.201-206
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• 2018

Recently, as technologies for realizing artificial intelligence have become more common, machine learning is widely used. Machine learning provides insight into collecting large amounts of data, batch processing, and taking final action, but the effects of the work are not immediately integrated into the learning process. In this paper proposed an adaptive learning model to improve the performance of real-time stream analysis as a big business issue. Adaptive learning generates the ensemble by adapting to the complexity of the data set, and the algorithm uses the data needed to determine the optimal data point to sample. In an experiment for six standard data sets, the adaptive learning model outperformed the simple machine learning model for classification at the learning time and accuracy. In particular, the support vector machine showed excellent performance at the end of all ensembles. Adaptive learning is expected to be applicable to a wide range of problems that need to be adaptively updated in the inference of changes in various parameters over time.

• The Journal of Korean Association of Computer Education
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• v.18 no.4
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• pp.55-62
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• 2015

Recommender systems through collaborative filtering has been utilized successfully in various areas by providing with convenience in searching information. Measuring similarity is critical in determining performance of these systems, because it is the criteria for the range of recommenders. This study analyzes distributions of similarity from traditional measures and investigates relations between similarities and the number of co-rated items. With this, this study suggests a method for selecting reliable recommenders by restricting similarities, which compensates for the drawbacks of previous measures. Experimental results showed that restricting similarities of neighbors by upper and lower thresholds yield superior performance than previous methods, especially when consulting fewer nearest neighbors. Maximum improvement of 0.047 for cosine similarity and that of 0.03 for Pearson was achieved. This result tells that a collaborative filtering system using Pearson or cosine similarities should not consult neighbors with very high or low similarities.

• The KIPS Transactions:PartA
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• v.9A no.3
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• pp.295-300
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• 2002

In this paper, we apply the 3-dimension algorithm to the random interleaver and s-random interleaver and analyze the performance of the turbo code system with random interleaver (or s-random interleaver). In general, the performance of interleaver is determined by minimum distance between neighbor data, thus we could improve the performance of interleaver by increasing the distance of the nearest data. The interleaver using 3-dimension algorithm has longer minimum distance and average distance compared to existing random-interleaver (s-random interleaver) because the output data is generated randomly from 3-dimension storage. To verify and compare the performance of our proposed system, the computer simulations have been performed in turbo code system under gaussian noise environment.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.12
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• pp.6069-6091
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• 2017

Augmented Reality (AR) overlays virtual information on real world data, such as displaying useful information on videos/images of a scene. This paper presents an Enhanced AR (EAR) system that displays useful statistical players' information on captured images of a sports game. We focus on the situation where the input image is degraded by strong sunlight. Proposed EAR system consists of an image enhancement technique to improve the accuracy of subsequent player and face detection. The image enhancement is followed by player and face detection, face recognition, and players' statistics display. First, an algorithm based on multi-scale retinex is proposed for image enhancement. Then, to detect players' and faces', we use adaptive boosting and Haar features for feature extraction and classification. The player face recognition algorithm uses boosted linear discriminant analysis to select features and nearest neighbor classifier for classification. The system can be adjusted to work in different types of sports where the input is an image and the desired output is display of information nearby the recognized players. Simulations are carried out on 2096 different images that contain players in diverse conditions. Proposed EAR system demonstrates the great potential of computer vision based approaches to develop AR applications.