• International Journal of Reliability and Applications
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• v.3 no.1
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• pp.37-50
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• 2002

Simultaneous estimation of accuracy and probability corresponding to a prediction interval is considered in this study. Traditional application of confidence interval forecasting consists in evaluation of interval limits for a given significance level. The wider is this interval, the higher is probability and the lower is the forecast precision. In this paper a measure of stochastic forecast accuracy is introduced, and a procedure for balanced estimation of both the predicting accuracy and confidence probability is elaborated. Solution can be obtained in an optimizing approach. Suggested method is applied to constructing confidence intervals for parameters estimated by normal and t distributions

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.3
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• pp.1091-1110
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• 2016

Hybrid deployment under current cloud data centers is a combination of online and offline services, which improves the utilization of the cluster resources. However, the performance of the cluster is often affected by the online services in the hybrid deployment environment. To improve the response time of online service (e.g. search engine), an effective scheduling algorithm based on Storm is proposed. At the component level, the algorithm dispatches the component with more influence to the optimal performance node. Inside the component, a reasonable resource allocation strategy is used. By searching the compressed index first and then filtering the complete index, the execution speed of the component is improved with similar accuracy. Experiments show that our algorithm can guarantee search accuracy of 95.94%, while increasing the response speed by 68.03%.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.22 no.49
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• pp.77-87
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• 1999

A simple transformation of the $C^{*}$$_{pm}$, $C_{pp}$ can be regard as a process incapability index, provides an uncontaminated separation between information concerning the process accuracy and precision while this kind of information separation is not available with the $C^{*}$$_{pm}$. Recently, Kyung-seok Shin et. al. introduced an improved process incapability index $C^{*}$$_{pmk}$ by the transformation of the $C_{pmk}$. Accordingly, in this article process incapability index $C^{*}$$_{psk}$ will be proposed by the transformation of the $C_{psk}$. The motivation behind introduction of $C^{*}$$_{psk}$ is that $C_{psk}$ has failed to overcome is that it cannot distinguish process of high conforming output proportions from those of low conforming output proportions. Process incapability index $C^{*}$$_{psk}$ can identify a difference between two things by comparison on $C_{psk}$ and $C^{*}$$_{psk}$.EX>$_{psk}$.EX>$_{psk}$.

• Journal of Korean Library and Information Science Society
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• v.10
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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.

• Nuclear Engineering and Technology
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• v.48 no.1
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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.

• Journal of Animal Science and Technology
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• v.65 no.6
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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.

• Current Optics and Photonics
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• v.4 no.1
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• pp.44-49
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• 2020

In this paper, a new method is presented to measure the refractive index of single plain glass or multilayered materials, based on a laser frequency-shifted confocal feedback microscope. Combining the laser frequency-shifted feedback technique and the confocal effect, the method can attain high axial-positioning accuracy, stability and sensitivity. Measurements of different samples are given, including N-BK7 glass, Silica plain glass, and a microfluidic chip with four layers. The results for N-BK7 glass and Silica plain glass show that the measurement uncertainty in the refractive index is better than 0.001. Meanwhile, the feasibility of this method for multilayered materials is tested. Compared to conventional methods, this system is more compact and has less difficulty in sample processing, and thus is promising for applications in the area of refractive-index measurement.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.1
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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.

• Journal of Environmental Impact Assessment
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• v.14 no.3
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• pp.127-134
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• 2005

Recently, aquaculture farm sites have been increased with demand of the expensive fish species and sea food like as seaweed, laver and oyster. Therefore coastal water quality have been deteriorated by organic contamination from marine aquaculture farm sites. For protecting of coastal environment, we need to control the location of aquaculture sites. The purpose of this study is to detect the laver aquaculture sites using multispectral remotely sensed data with autodetection algorithm. In order to detect the aquaculture sites, density slice and contour and vegetation index methods were applied with SPOT and IKONOS data of Shinan area. The marine aquaculture farm sites were extracted by density slice and contour methods with one band digital number(DN) carrying 65% accuracy. However, vegetation index algorithm carried out 75% accuracy using near-infra red and red bands. Extraction of the laver aquaculture site using remotely sensed data will provide the efficient digital map for coastal water management strategies and red tide GIS management system.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.8
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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%.