• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.4B
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• pp.288-299
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• 2012

To ensure data confidentiality and fine-grained access control in business environment, system model using KP-ABE(Key Policy-Attribute Based Encryption) and PRE(Proxy Re-Encryption) has been proposed recently. However, in previous study, data confidentiality has been effected by decryption right concentrated on cloud server. Also, Yu's work does not consider a access privilege management, so existing work become dangerous to collusion attack between malicious user and cloud server. To resolve this problem, we propose secure system model against collusion attack through dividing data file into header which is sent to privilege manager group and body which is sent to cloud server and prevent modification attack for proxy re-encryption key using d Secret Sharing, We construct protocol model in medical environment.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.8
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• pp.2473-2489
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• 2022

Urban development has brought about the increasing saturation of urban traffic demand, and traffic congestion has become the primary problem in transportation. Roads are in a state of waiting in line or even congestion, which seriously affects people's enthusiasm and efficiency of travel. This paper mainly studies the discrete domain path planning method based on the flow data. Taking the traffic flow data based on the highway network structure as the research object, this paper uses the deep learning theory technology to complete the path weight determination process, optimizes the path planning algorithm, realizes the vehicle path planning application for the expressway, and carries on the deployment operation in the highway company. The path topology is constructed to transform the actual road information into abstract space that the machine can understand. An appropriate data structure is used for storage, and a path topology based on the modeling background of expressway is constructed to realize the mutual mapping between the two. Experiments show that the proposed method can further reduce the interpolation error, and the interpolation error in the case of random missing is smaller than that in the other two missing modes. In order to improve the real-time performance of vehicle path planning, the association features are selected, the path weights are calculated comprehensively, and the traditional path planning algorithm structure is optimized. It is of great significance for the sustainable development of cities.

• Asia pacific journal of information systems
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• v.32 no.2
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• pp.226-248
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• 2022

The existing model for sentiment analysis of product reviews learned from past data and new data was labeled based on training. But new data was never used by the existing system for making a decision. The proposed Aspect-based multi-agent Deep Reinforcement learning Sentiment Analysis (ADRSA) model learned from its very first data without the help of any training dataset and labeled a sentence with aspect category and sentiment polarity. It keeps on learning from the new data and updates its knowledge for improving its intelligence. The decision of the proposed system changed over time based on the new data. So, the accuracy of the sentiment analysis using deep reinforcement learning was improved over supervised learning and unsupervised learning methods. Hence, the sentiments of premium customers on a particular site can be explored to other customers effectively. A dynamic environment with a strong knowledge base can help the system to remember the sentences and usage State Action Reward State Action (SARSA) algorithm with Bidirectional Encoder Representations from Transformers (BERT) model improved the performance of the proposed system in terms of accuracy when compared to the state of art methods.

• Structural Engineering and Mechanics
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• v.68 no.6
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• pp.691-700
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• 2018

Ground granulated blast furnace slag (GGBS) is a by product obtained from iron and steel industries, useful in the design and development of high quality cement paste/mortar and concrete. This paper investigates the applicability of relevance vector machine (RVM) based regression model to predict the compressive strength of various GGBS based concrete mixes. Compressive strength data for various GGBS based concrete mixes has been obtained by considering the effect of water binder ratio and steel fibres. RVM is a machine learning technique which employs Bayesian inference to obtain parsimonious solutions for regression and classification. The RVM is an extension of support vector machine which couples probabilistic classification and regression. RVM is established based on a Bayesian formulation of a linear model with an appropriate prior that results in a sparse representation. Compressive strength model has been developed by using MATLAB software for training and prediction. About 70% of the data has been used for development of RVM model and 30% of the data is used for validation. The predicted compressive strength for GGBS based concrete mixes is found to be in very good agreement with those of the corresponding experimental observations.

• Journal of the Korean Statistical Society
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• v.28 no.3
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• pp.311-324
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• 1999

The problem of 'outliers', observations which look suspicious in some way, has long been one of the most concern in the statistical structure to experimenters and data analysts. We propose a model for an outlier problem and also analyze it in linear regression model using a Bayesian approach. Then we use the mean-shift model and SSVS(George and McCulloch, 1993)'s idea which is based on the data augmentation method. The advantage of proposed method is to find a subset of data which is most suspicious in the given model by the posterior probability. The MCMC method(Gibbs sampler) can be used to overcome the complicated Bayesian computation. Finally, a proposed method is applied to a simulated data and a real data.

• Journal of The Korean Society of Agricultural Engineers
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• v.53 no.4
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• pp.11-19
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• 2011

PADDIMOD2 was deloped to estimate nonpoint source pollution from paddy rice fields. The PADDIMOD2 was enhanced to estimate runoff and pollutant load during non-growing as well as growing season and to be easily used for public by development of Excel based system. Nutrient concentration and hydrology were based on Dirac delta function and continuous source function, and tank model for growing season and Event Mean Concentrations (EMCs) and SCS-Curve Number method for non-growing season. The PADDIMOD2 consists of three main component (input data, parameters data, and output data) by including eight Excel spread sheets. As a result of model application, total precipitation and irrigation were 1,051.7 mm and 439.2 mm, respectivley and surface runoff and water loss including infiltration and evapotranspiration were 463.0 mm and 947.9 mm, respectively. Annual nutrient loadings of T-N and T-P from study area were 6.7 kg/$km^2$/day and 0.5 kg/$km^2$/day, respectively. Development of PADDIMOD2 was focused on minimizing input data and maximizing user friendly system and is expected to be useful tool to evaluate various non-structure BMPs and estimate unit load from paddy rice fields for application at Korean TMDL.

• IEMEK Journal of Embedded Systems and Applications
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• v.17 no.5
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• pp.289-296
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• 2022

In this paper, we developed a deep learning-based recyclable object detection model. The model is developed based on YOLOv5 that is a one-stage detector. The deep learning model detects and classifies the recyclable object into 7 categories: paper, carton, can, glass, pet, plastic, and vinyl. We propose two methods for recyclable object detection models to solve problems during training. Bounding Box CutMix solved the no-objects training images problem of Mosaic, a data augmentation used in YOLOv5. Standardized Distance-based IoU replaced DIoU using a normalization factor that is not affected by the center point distance of the bounding boxes. The recyclable object detection model showed a final mAP performance of 0.91978 with Bounding Box CutMix and 0.91149 with Standardized Distance-based IoU.

• Asia pacific journal of information systems
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• v.2 no.1
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• pp.3-15
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• 1992

This paper proposes a new referential model for data retrieval as an alternative to exact matching. While exact matching is an effective data retrieval model, it is based on fairly strict assumptions and limits our capabilities in data retrieval. This study redefines data retrieval to include non-binary data retrieval in addition to binary data retrieval, proposes Double Anchor Preference Model (DAPM), and analyzes its logical charateristics. DAPM supports non-binary data retrieval. Further, it produces the same result as exact matching for the conventional binary data retrieval. These findings show that, at the logical level, the proposed DAPM retains all the desirable features for data retrieval.

• The Bulletin of The Korean Astronomical Society
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• v.37 no.2
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• pp.123.1-123.1
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• 2012

We have compared near-real time Kp forecast models based on neural network (NN) and support vector machine (SVM) algorithms. We consider four models as follows: (1) a NN model using ACE solar wind data; (2) a SVM model using ACE solar wind data; (3) a NN model using ACE solar wind data and preliminary kp values from US ground-based magnetometers; (4) a SVM model using the same input data as model 3. For the comparison of these models, we estimate correlation coefficients and RMS errors between the observed Kp and the predicted Kp. As a result, we found that the model 3 is better than the other models. The values of correlation coefficients and RMS error of the model 3 are 0.93 and 0.48, respectively. For the forecast evaluation of models for geomagnetic storms ($Kp{\geq}6$), we present contingency tables and estimate statistical parameters such as probability of detection yes (PODy), false alarm ratio (FAR), bias, and critical success index (CSI). From a comparison of these statistical parameters, we found that the SVM models (model 2 and model 4) are better than the NN models (model 1 and model 3). The values of PODy and CSI of the model 4 are the highest among these models (PODy: 0.57 and CSI: 0.48). From these results, we suggest that the NN models are better than the SVM models for predicting Kp and the SVM models are better than the NN models for forecasting geomagnetic storms.

• Communications for Statistical Applications and Methods
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• v.14 no.1
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• pp.205-213
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• 2007

In this paper, the normal mixture model subjected to general linear restriction for component-means based on linear regression is proposed, and its fitting method by EM algorithm and Lagrange multiplier is provided. This model is applied to gene clustering of microarray expression data, which demonstrates it has very good performances for real data set. This model also allows to obtain the clusters that an analyst wants to find out in the fashion that the hypothesis for component-means is represented by the design matrices and the linear restriction matrices.