• Tunnel and Underground Space
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• v.24 no.2
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• pp.155-165
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• 2014

Multiple thermal energy storage (TES) caverns can be used for storing thermal energy on a large scale and for a high-aspect-ratio heat storage design to provide good thermal performance. It may also be necessary to consider the use of multiple caverns with a reduced length when a single, long tunnel-shaped cavern is not suitable for connection to aboveground heat production and injection equipments. When using multiple TES caverns, the separation distance between the caverns is one of the significant factors that should be considered in the design of storage space, and the optimal separation distance should be determined based on a quantitative stability criterion. In this paper, we described a numerical approach for determining the optimal separation distance between multiple caverns for large-scale TES utilization. For reliable stability evaluation of multiple caverns, we employed a probabilistic method which can quantitatively take into account the uncertainty of input parameters by probability distributions, unlike conventional deterministic approaches. The present approach was applied to the design of a conceptual TES model to store hot water for district heating. The probabilistic stability results of this application demonstrated that the approach in our work can be effectively used as a decision-making tool to determine the optimal separation distance between multiple caverns. In addition, the probabilistic results were compared to those obtained through a deterministic analysis, and the comparison results suggested that care should taken in selecting the acceptable level of stability when using deterministic approaches.

• The Journal of the Acoustical Society of Korea
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• v.24 no.1
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• pp.29-37
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• 2005

This paper proposes an automatic genre classification system for Korean traditional music. The Proposed system accepts and classifies queried input music as one of the six musical genres such as Royal Shrine Music, Classcal Chamber Music, Folk Song, Folk Music, Buddhist Music, Shamanist Music based on music contents. In general, content-based music genre classification consists of two stages - music feature vector extraction and Pattern classification. For feature extraction. the system extracts 58 dimensional feature vectors including spectral centroid, spectral rolloff and spectral flux based on STFT and also the coefficient domain features such as LPC, MFCC, and then these features are further optimized using SFS method. For Pattern or genre classification, k-NN, Gaussian, GMM and SVM algorithms are considered. In addition, the proposed system adopts MFC method to settle down the uncertainty problem of the system performance due to the different query Patterns (or portions). From the experimental results. we verify the successful genre classification performance over $97{\%}$ for both the k-NN and SVM classifier, however SVM classifier provides almost three times faster classification performance than the k-NN.

• Journal of Food Hygiene and Safety
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• v.24 no.1
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• pp.63-68
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• 2009

The statistics probability approach for microbial risk assessment (MRA) has been recognized as an efficient method because this probability approach, which can be presented the diversity, variability, and uncertainty for the environmental factors of food processing, provide better realistic results than point estimate. This study was conducted to determine of probability statistics for the environmental factors of the pork-cutting processing i.e. the processing time, the pork meat temperature, and processing room temperature etc. As the input parameters for the MRA, triangular distribution and normal distribution were selected as an efficient probability distribution model, these distributions were analyzed by the simulation. The simulation results showed the processing time estimated 53 min as mean (5% - 22 min and 95% - 98 min), pork meat temperature estimated $4.83^{\circ}C$ as mean (5% - $2.25^{\circ}C$ and 95% - $7.12^{\circ}C$, 48.78% exceed $5^{\circ}C$), and processing room temperature estimated $17^{\circ}C$ as mean (5% - $10.92^{\circ}C$ and 95% - $22.56^{\circ}C$, 71.178% exceed $15^{\circ}C$).