Browsing by Author "Shen, YC"

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Automated information. mining on multimedia TV news archives
(2005)
Lai, PS
;
Cheng, SS
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Sun, SY
;
Huang, TY
;
Su, JM
This paper addresses an integrated information mining techniques for multimedia TV-news archive. The utilizes techniques from the fields of acoustic, image, and video analysis, for information retrieval on news story title, newsman and scene identification. The goal is to construct a compact yet meaningful abstraction of broadcast news video, allowing users to browse through large amounts of data in a non-linear fashion with flexibility and efficiency. By using acoustic analysis, the system can classify video into news versus commercials, with 90% accuracy on a data set of 400 hours TV-news recorded off the air from July 2003 to August of 2004. By applying speaker identification and/or image detection techniques, each news stories can be segmented with an accuracy of 96%. On screen captions or subtitles are recognized by OCR techniques to produce the text title of each news stories. The extracted title words can be used to link or to navigate more related News contents on the WWW. In cooperation with facial and scene analysis and recognition techniques, OCR results can provide users with multimodality query for specific news stories. Some experimental results are presented and discussed for the system reliability and performance evaluation and comparison.
Forecasting electricity market prices: A neural network based approach
(2004)
Xu, YY
;
Hsieh, R
;
Lu, Y
;
Shen, YC
;
Chuang, SC
This paper presents a neural network approach to forecast the Phelix Base (PB) electricity market prices for European Energy Exchange (EEX). Up to now there has been little scientific work on forecasting the price development on the electricity markets. In this study, the Phelix Base moving average (PBMA), the moving difference (PBMD), and multilayer feedforward neural networks (MLNN) are used to predict various period for 7, 14, 21, 28, 63, 91, 182, and 273 days ahead of electric prices. The experimental results of forecasting by MLNNs and linear methods (autoregressive error model) are compared and discussed. The MLNNs outperform from 11.4% to 64.6% superior to the traditional linear regression method. It seems that the proposed MLNN can be very useful in predicting the electricity market prices of EEX.
Rendering spatial sound on headsets for five-channel audio
(2003)
Shen, YC
;
You, SCD
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資訊工程學系
;
Department of Computer Science
Rendering spatial sound using a headset for five-channel signals requires convolving the incoming signals with the head-related impulse responses representing the sound directions of the five channels. Although the idea is simple, the implementation requires a large amount of computation. In this paper, we propose a simple approach to reduce the computational burden to about one-fifth of that of direct implementation. Compared with the approach based on the CAPZ (Common-Acoustical-Pole and Zero) model, the proposed approach has a better error performance with almost the same computation.