• The Journal of Information Systems
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• v.5
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• pp.205-248
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• 1996

Historically, the en-users' acceptance of the expert systems(ES) have generally been used as a proxy for the ES' implementation success by both practitioners and academicians. However, with regard to bank loan decisions, most loan officers approach the acquisition of an ES with apprehension. In order to overcome this skepticism, more research should focus on the behavioral aspects relate to systems acquisition and usage. This research applied Vroom's(1964) expectancy theory in an effort to predict end-users' motivation to use an ES in a bank loan decision context. Because human behaviors and judgements are nonlinear rather than linear functions, accurately predicting human behavior is very difficult. To increase the prediction power for end-users' motivation to use an ES in a bank loan decision context, this research used an artificial neural network (ANN) model. In this research, an attempt was made to evaluate adequacy of the surrogates by analyzing differences between real bank loan officers and student surrogates in applying expectancy theory to estimate bank loan officers' motivation to use ES in a bank loan decision context.

• Proceedings of the Korea Information Processing Society Conference
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• 2009.11a
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• pp.243-244
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• 2009

So far, many researches have been conducted in the area of audio based context recognition. Nevertheless, most of them are based on existing feature extraction techniques derived from linear signal processing such as Fourier transform, wavelet transform, linear prediction... Meanwhile, environmental audio signal may potentially contains non-linear dynamic properties. Therefore, it is a big potential to utilize non-linear dynamic signal processing techniques in audio based context recognition.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.11B
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• pp.1200-1206
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• 2009

In this paper, we propose an energy-efficient MAC(Medium Access Control) protocol for processing context information in ubiquitous sensor network environments. CASMAC(Context Aware Sensor MAC) use context information for energy-efficient operation and its operation principle is as follows. First, we make scenarios with possible prediction for CASMAC. And then we save setted context information in server. When event occur at specific sensor node, and then it send three times sample data to server. According to context information, server process sample data. If server process sample data with event, it receive continuous data from event occur node by a transmission request signal. And then server send data transmission stop signal to event occur node when it do not need to data. If server process sample data with no event, it have not reply. Through we make energy consumption tables and an energy consumption model, we simulate analysis of CASMAC performance. In a result, we gains about 5.7 percents energy reduction compared to SMAC.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.4
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• pp.488-493
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• 2007

In general, recommendation systems quantify real-time context information obtained in the stage of collaborative filtering and use quantified context information in order to recommend services. But the recommendation systems can have problems of recommending inaccurate information because of lack of context information or classifying users into inaccurate groups because of simple classification works in the stage of quantification. In this paper, we solved the problems of lack of context information obtained in real-time by combining users' profile information used in the contents-based filtering and context information obtained in real-time. In addition, we tried collaborative filtering at the quantification stage by improving absolute classification methods to relative ones. As the result of experiments, this method improved prediction preference by 5.8% than real-time recommendation systems using context information in pure P2P environment.

• Journal of Convergence for Information Technology
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• v.10 no.10
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• pp.40-46
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• 2020

With the development of the Internet, users share their experiences and opinions. Since related keywords are used witho0ut considering information such as the general emotion or genre of an unstructured document such as a movie review, the sensitivity accuracy according to the appropriate emotional situation is impaired. Therefore, we propose a system that predicts emotions based on information such as the genre to which the unstructured document created by users belongs or overall emotions. First, representative keyword related to emotion sets such as Joy, Anger, Fear, and Sadness are extracted from the unstructured document, and the normalized weights of the emotional feature words and information of the unstructured document are trained in a system that combines CNN and LSTM as a training set. Finally, by testing the refined words extracted through movie information, morpheme analyzer and n-gram, emoticons, and emojis, it was shown that the accuracy of emotion prediction using emotions and F-measure were improved. The proposed prediction system can predict sentiment appropriately according to the situation by avoiding the error of judging negative due to the use of sad words in sad movies and scary words in horror movies.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.8
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• pp.3295-3311
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• 2020

With the wide application of hyperspectral images, it becomes more and more important to compress hyperspectral images. Conventional recursive least squares (CRLS) algorithm has great potentiality in lossless compression for hyperspectral images. The prediction accuracy of CRLS is closely related to the correlations between the reference bands and the current band, and the similarity between pixels in prediction context. According to this characteristic, we present an improved CRLS with adaptive band selection and adaptive predictor selection (CRLS-ABS-APS). Firstly, a spectral vector correlation coefficient-based k-means clustering algorithm is employed to generate clustering map. Afterwards, an adaptive band selection strategy based on inter-spectral correlation coefficient is adopted to select the reference bands for each band. Then, an adaptive predictor selection strategy based on clustering map is adopted to select the optimal CRLS predictor for each pixel. In addition, a double snake scan mode is used to further improve the similarity of prediction context, and a recursive average estimation method is used to accelerate the local average calculation. Finally, the prediction residuals are entropy encoded by arithmetic encoder. Experiments on the Airborne Visible Infrared Imaging Spectrometer (AVIRIS) 2006 data set show that the CRLS-ABS-APS achieves average bit rates of 3.28 bpp, 5.55 bpp and 2.39 bpp on the three subsets, respectively. The results indicate that the CRLS-ABS-APS effectively improves the compression effect with lower computation complexity, and outperforms to the current state-of-the-art methods.

• Journal of Broadcast Engineering
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• v.12 no.6
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• pp.611-623
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• 2007

The H.264|AVC standard incorporates an intra prediction tool into inter frame coding. However, this leads to excessive amount of increase in encoding time, thus resulting in the difficulty in real-time implementation of software encoders. In this paper, we first propose an early decision on intra prediction coding and a fast intra prediction method using the characteristics of block contents and the context of neighboring block modes for the intra prediction in the inter frame coding of H.264/AVC. Basically, the proposed methods determine a skip condition on whether the $4{\times}4$ intra prediction is to be used in the inter frame coding by considering the content characteristics of each block to be encoded and the context of its neighboring blocks. The performance of our proposed methods is compared with the Joint Model reference software version 11.0 of H.264|AVC. The experimental results show that our proposed methods allow for 41.63% reduction in the total encoding time with negligible amounts of PSNR drops and bitrate increases, compared to the original Joint Model reference software version 11.0.

• Journal of Korea Multimedia Society
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• v.9 no.12
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• pp.1709-1718
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• 2006

Users are able to use the information and service more free than previous wire network due to development of wireless network and device. For this reason, various studies on ubiquitous networks have been conducted. Various contexts brought in this ubiquitous environment, have recognized user's action through sensors. This results in the provision of better services. Because services exist in various places in ubiquitous networks, the application has the time of services searching. In addition, user's context is very dynamic, so a method needs to be found to recommend services to user by context. Therefore, techniques for reducing the time of service and increasing accuracy of recommendation are being studied. But it is difficult to quickly and appropriately provide large numbers of services, because only basic context information is stored. For this reason, we suggest DUPS(Dimension User Profile System), which stores location, time, and frequency information of often used services. Because previous technique used to simple information for recommending service without predicting services which is going to use on future, we can provide better service, and improve accuracy over previous techniques.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.10a
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• pp.100-103
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• 2008

In this paper, we propose H.264 Encoder integrating Intra Prediction, Deblocking filter, Context-Based Adaptive Variable Length Coding, and Motion Estimation encoder module. This designed module can be operated in 440 cycle for one-macroblock. To verify the Encoder architecture, we developed the reference C from JM 9.4 and verified the our developed hardware using test vector generated by reference C. The designed circuit can be operated in 166MHz clock system, and has 1800k gate counts using Charterd 0.18um process including SRAM memory. Manufactured chip has the size of $6{\times}6mm$ and 208 pins package.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1996.10a
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• pp.19-22
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• 1996

A traditional approach to the prediction of economic and financial variables takes the form of statistical models to summarize past observations and to project them into the envisioned future. Over the past decade, an increasing number of organizations has turned to the use of neural networks. To date, however, many spheres of interest still lack a systematic evaluation of the statistical and neural approaches. One of these lies in the prediction of corporate bond yields for Korea. This paper reports on a comparative evaluation of ARIMA models and neural networks in the context of interest rate prediction. An additional experiment relates to an integration of the two methods. More specifically, the statistical model serves as a filter by providing estimtes which are then used as input into the neural network models.