• 한국도서관정보학회지
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• 제10권
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• pp.95-131
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• 1983

Index is a systematic guide to items contained in, or concepts derived from, a collection, Thus, it is represented as a paired set of index terms (t) and documents (D) : I= {(t,D) vertical bar t .mem. V, D .mem. W), where V is index vocabulary and W is document collection. Indexing is the process of analysing the informational content of records of knowledge and expressing the informational content in the language of the indexing system. It involves: 1) Selecting indexable concepts in a document; and 2) expressing these concepts in the language of the indexing system (as index entries): and an ordered list. Indexing process involves technical, semantic and syntactic problems. Technical problems are related to the accuracy of indexing, which is primarily governed by the indexer's ability of analysing subject, identifying indexable concepts, and coding. The proper levels of indexing exhaustivity, and index language specificity are also significant factors affecting the quality of index. Semantic problems are related to the choice of index terms and the form in which they should be used. Equivalent, hierarchical and affinitive/associative relationships of index terms are involved. Syntactic problems are largely related to the coordination of index terms. This process of coordination arises from the need to be able to search for the intersection of two or more classes defined by terms denoting distinct concepts. Finally, most valuable aspects of alphabetical subject indexing theories and practices are derived from those of Cutter, Kaiser, Ranganathan, Coates, Lynch and Austin, and discussed in details.

• The Journal of Asian Finance, Economics and Business
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• 제7권1호
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• pp.29-36
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• 2020

Stock price multiple is one of the most well-known equity valuation technique used to forecast equity price. It measures by multiplying "the ratio of stock price to a value driver" by a value driver. The value driver can be earning per share (EPS), sales or other financial measurements. The objective of price multiple technique is to evaluate the value of assets and compare how similar assets are priced in the market. Although stock price multiple technique is common in financial filed, studies on the application of the technique in Thailand is still limited. The present study is conducted to serve three major objectives. The first objective is to apply the technique to measure value of firms in banking sector in the Stock Exchange of Thailand. The second objective is to develop composite price multiple index to forecast equity prices. The third objective is to compare valuation accuracy of different value drivers of price multiple (i.e. EPS, Earnings Growth, Earnings Before Interest Taxes Depreciation and Amortization, Sales, Book Value and Composite Index) in forecasting equity prices. Results indicated that EPS is the most accurate value drivers of price multiple used to forecast equity price of firms in baking sector.

• 대한원격탐사학회지
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• 제31권4호
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• pp.337-346
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• 2015

Water body extraction based on backscatter information is an essential process to analyze floodaffected areas from Synthetic Aperture Radar (SAR) image. Water body in SAR image tends to have low backscatter values due to homogeneous surface of water, while non-water body has higher backscatter values than water body. Non-water body, however, may also have low backscatter values in high resolution SAR image such as Kompsat-5 image, depending on surface characteristic of the ground. The objective of this paper is to present a method to increase backscatter contrast between water body and non-water body and also to remove efficiently misclassified pixels beyond true water body area. We create an entropy image using a Gray Level Co-occurrence Matrix (GLCM) and classify the entropy image into water body and non-water body pixels by thresholding of the entropy image. In order to reduce the effect of threshold value, we also propose Water Body Texture Index (WBTI), which measures simultaneously the occurrence of repeated water body pixel pair and the uniformity of water body in the binary entropy image. The proposed method produced high overall accuracy of 99.00% and Kappa coefficient of 90.38% in water body extraction using Kompsat-5 image. The accuracy analysis indicates that the proposed WBTI method is less affected by the choice of threshold value and successfully maintains high overall accuracy and Kappa coefficient in wide threshold range.

• Current Optics and Photonics
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• 제4권3호
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• pp.210-220
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• 2020

Aiming at the problem that the Local Sparse Difference Index algorithm has low accuracy and low efficiency when detecting target anomalies in a hyperspectral image, this paper proposes a Weighted Collaborative Representation and Sparse Difference-Based Hyperspectral Anomaly Detection algorithm, to improve detection accuracy for a hyperspectral image. First, the band subspace is divided according to the band correlation coefficient, which avoids the situation in which there are multiple solutions of the sparse coefficient vector caused by too many bands. Then, the appropriate double-window model is selected, and the background dictionary constructed and weighted according to Euclidean distance, which reduces the influence of mixing anomalous components of the background on the solution of the sparse coefficient vector. Finally, the sparse coefficient vector is solved by the collaborative representation method, and the sparse difference index is calculated to complete the anomaly detection. To prove the effectiveness, the proposed algorithm is compared with the RX, LRX, and LSD algorithms in simulating and analyzing two AVIRIS hyperspectral images. The results show that the proposed algorithm has higher accuracy and a lower false-alarm rate, and yields better results.

• Nuclear Engineering and Technology
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• 제48권1호
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• pp.144-152
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• 2016

Human capabilities, such as technical/nontechnical skills, have begun to be recognized as crucial factors for nuclear safety. One of the most common ways to improve human capabilities in general is training. The nuclear industry has constantly developed and used training as a tool to increase plant efficiency and safety. An integrated training framework was suggested for one of those efforts, especially during simulation training sessions of nuclear power plant operation teams. The developed training evaluation methods are based on measuring the levels of situation awareness of teams in terms of the level of shared confidence and consensus as well as the accuracy of team situation awareness. Verification of the developed methods was conducted by analyzing the training data of real nuclear power plant operation teams. The teams that achieved higher level of shared confidence showed better performance in solving problem situations when coupled with high consensus index values. The accuracy of nuclear power plant operation teams' situation awareness was approximately the same or showed a similar trend as that of senior reactor operators' situation awareness calculated by a situation awareness accuracy index (SAAI). Teams that had higher SAAI values performed better and faster than those that had lower SAAI values.

• The Journal of Korean Physical Therapy
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• 제26권6호
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• pp.386-390
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• 2014

Purpose: We aimed to investigate whether visuomotor function would be modulated, when healthy subjects performed tracking task after tDCS application over the primary sensorimotor cortex (SM1) in the non-dominant hemisphere. Methods: Thirty four right-handed healthy participants were enrolled, who randomly and evenly divided into two groups, real tDCS group and sham control group. Direct current with intensity of 1 mA was delivered over SM1 for 15 minutes. After tDCS, tracking task was measured, and their performance was calculated by an accuracy index (AI). Results: No significant difference in AI at the baseline between the two groups was observed. The AI of the real tDCS group was significantly increased after electrical stimulation, compared to the sham control group. Two way ANOVA with repeated measurement showed a significant finding in a large main effects of time and group-by-repeated test interaction. Conclusion: This study indicated that application of the anodal tDCS over the SM1 could facilitate higher visuomotor coordination, compared to sham tDCS group. These findings suggest possibility that tDCS can be used as adjuvant brain modulator for improvement of motor accuracy in healthy individuals as well as patients with brain injury.

• The Journal of Asian Finance, Economics and Business
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• 제8권8호
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• pp.399-407
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• 2021

Stock movement is difficult to predict because it has dynamic characteristics and is influenced by many factors. Even so, there are some approaches to predict stock price movements, namely technical analysis, fundamental analysis, and sentiment analysis. Many researches have tried to predict stock price movement by utilizing these analysis techniques. However, the results obtained are varied and inconsistent depending on the variables and object used. This is because stock price movement is influenced by a variety of factors, and it is likely that those studies did not cover all of them. One of which is that no research considers the use of fundamental analysis in terms of currency exchange rates and the use of foreign stock price index movement related to the technical analysis. This research aims to predict stock price movements in Indonesia based on sentiment analysis, technical analysis, and fundamental analysis using Support Vector Machine. The result obtained has a prediction accuracy rate of 65,33% on an average. The inclusion of currency exchange rate and foreign stock price index movement as a predictor in this research which can increase average prediction accuracy rate by 11.78% compared to the prediction without using these two variables which only results in average prediction accuracy rate of 53.55%.

• Smart Structures and Systems
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• 제30권3호
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• pp.287-301
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• 2022

The machine-vision based structural displacement measurement methods are widely used due to its flexible deployment and non-contact measurement characteristics. The accuracy of vision measurement is directly related to the image resolution. In the field of computer vision, super-resolution reconstruction is an emerging method to improve image resolution. Particularly, the deep-learning based image super-resolution methods have shown great potential for improving image resolution and thus the machine-vision based measurement. In this article, we firstly review the latest progress of several deep learning based super-resolution models, together with the public benchmark datasets and the performance evaluation index. Secondly, we construct a binocular visual measurement platform to measure the distances of the adjacent corners on a chessboard that is universally used as a target when measuring the structure displacement via machine-vision based approaches. And then, several typical deep learning based super resolution algorithms are employed to improve the visual measurement performance. Experimental results show that super-resolution reconstruction technology can improve the accuracy of distance measurement of adjacent corners. According to the experimental results, one can find that the measurement accuracy improvement of the super resolution algorithms is not consistent with the existing quantitative performance evaluation index. Lastly, the current challenges and future trends of super resolution algorithms for visual measurement applications are pointed out.

• Journal of Positioning, Navigation, and Timing
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• 제12권4호
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• pp.423-430
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• 2023

One recent notable method for real-time elimination of ionospheric errors in geodetic applications is the Predicted Global Ionosphere Map (PGIM). This study analyzes the level of accuracy achievable when applying the PGIM provided by the Center for Orbit Determination of Europe (CODE) to the Korean Peninsula region. First, an examination of the types and lead times of PGIMs provided by the International GNSS Service (IGS) Analysis Center revealed that CODE's two-day prediction model, C2PG, is available approximately eight hours before midnight. This suggests higher real-time usability compared to the one-day prediction model, C1PG. When evaluating the accuracy of PGIM by assuming the final output of the Global Ionosphere Map (GIM) as a reference, it was found that on days with low solar activity, the error is within ~2 TECU, and on days with high solar activity, the error reaches ~3 TECU. A comparison of the errors introduced when using PGIM and three solar activity indices-Kp index, F10.7, and sunspot number-revealed that F10.7 exhibits a relatively high correlation coefficient compared to Kp-index and sunspot number, confirming the effectiveness of the prediction model.

• Journal of Animal Science and Technology
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• 제65권6호
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• pp.1254-1269
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• 2023

This study aims to predict the change in corn share according to the grazing of 20 gestational sows in a mature corn field by taking images with a camera-equipped unmanned air vehicle (UAV). Deep learning based on convolutional neural networks (CNNs) has been verified for its performance in various areas. It has also demonstrated high recognition accuracy and detection time in agricultural applications such as pest and disease diagnosis and prediction. A large amount of data is required to train CNNs effectively. Still, since UAVs capture only a limited number of images, we propose a data augmentation method that can effectively increase data. And most occupancy prediction predicts occupancy by designing a CNN-based object detector for an image and counting the number of recognized objects or calculating the number of pixels occupied by an object. These methods require complex occupancy rate calculations; the accuracy depends on whether the object features of interest are visible in the image. However, in this study, CNN is not approached as a corn object detection and classification problem but as a function approximation and regression problem so that the occupancy rate of corn objects in an image can be represented as the CNN output. The proposed method effectively estimates occupancy for a limited number of cornfield photos, shows excellent prediction accuracy, and confirms the potential and scalability of deep learning.