• Smart Media Journal
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• v.7 no.4
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• pp.79-89
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• 2018

In recent years, falls among elderly people have gained serious attention as a major cause of injuries. Falls often lead to fatal consequences due to lack of prompt response and rescue. Therefore, a more accurate fall detection system and an effective feature extraction technique are required to prevent and reduce the risk of such incidents. In this paper, we proposed an efficient feature extraction technique based on multiple sliding windows and validated it through a series of experiments using supervised learning algorithms. The experiments were conducted using the public datasets obtained from tri-axial accelerometers. The results depicted that extraction of the feature from adjacent sliding windows led to high accuracy in supervised machine learning-based fall detection. Also, the experiments conducted in this study suggested that the best accuracy can be achieved by keeping the window size as small as 2 seconds. With the kNN classifier and dataset from wearable sensors, the experiments achieved accuracy rates of 94%.

• Journal of Multimedia Information System
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• v.6 no.1
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• pp.1-6
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• 2019

In the field of industrial applications, the real-time performance of the target detection problem is very important. The most serious time consumption in the pedestrian detection process is the extraction phase of the candidate window. To accelerate the speed, in this paper, a fast extraction of pedestrian candidate window based on the BING (Binarized Normed Gradients) algorithm replaces the traditional sliding window scanning. The BING features are extracted with the positive and negative samples and input into the two-stage SVM (Support Vector Machine) classifier for training. The obtained BING template may include a pedestrian candidate window. The trained template is loaded during detection, and the extracted candidate windows are input into the classifier. The experimental results show that the proposed method can extract fewer candidate window and has a higher recall rate with more rapid speed than the traditional sliding window detection method, so the method improves the detection speed while maintaining the detection accuracy. In addition, the real-time requirement is satisfied.

• Proceedings of the PSK Conference
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• 2003.10b
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• pp.224.1-224.1
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• 2003

A sensitive and selective liquid chromatographic method coupled with tandem mass spectrometry (LC-MS/MS) was developed for the determination of beraprost in human plasma. Plasma samples were transferred into 96-well OASIS HLB extraction plate using an automated sample handling system and the drugs were eluted with methanol. The eluents were then evaporated and reconstituted with water. All sample transfer and solid-phase extraction (SPE) was automated through the application of both the PerkinElmer MultiPROBE II HT and TOMTEC Quadra 96 workstation. (omitted)

• Journal of Multimedia Information System
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• v.9 no.1
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• pp.69-72
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• 2022

Extraction of a salient chromagram is utmost important for cover song identification. Cover song refers to a live performance, a remix, or a new recording of a previously recorded track. This paper utilizes the Savitzky-Golay filters in chromagram extraction for suppressing timber-related components of a music signal, which is not preserved while generating cover songs. By removing the timber-related components, the discriminative tonal components, which are conducive for cover song identification, are emphasized in chromagram. Experiments on cover song identification over two datasets show that the Savitzky-Golay filters are more effective in reducing timber effects in chromagram than other types of filters.

• The Journal of the Acoustical Society of Korea
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• v.17 no.2E
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• pp.31-37
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• 1998

This paper proposes a feature extraction method using wavelet transform for speech recognition. Speech recognition system generally carries out the recognition task based on speech features which are usually obtained via time-frequency representations such as Short-Time Fourier Transform (STFT) and Linear Predictive Coding(LPC). In some respects these methods may not be suitable for representing highly complex speech characteristics. They map the speech features with same may not frequency resolutions at all frequencies. Wavelet transform overcomes some of these limitations. Wavelet transform captures signal with fine time resolutions at high frequencies and fine frequency resolutions at low frequencies, which may present a significant advantage when analyzing highly localized speech events. Based on this motivation, this paper investigates the effectiveness of wavelet transform for feature extraction of wavelet transform for feature extraction focused on enhancing speech recognition. The proposed method is implemented using Sampled Continuous Wavelet Transform (SCWT) and its performance is tested on a speaker-independent isolated word recognizer that discerns 50 Korean words. In particular, the effect of mother wavelet employed and number of voices per octave on the performance of proposed method is investigated. Also the influence on the size of mother wavelet on the performance of proposed method is discussed. Throughout the experiments, the performance of proposed method is discussed. Throughout the experiments, the performance of proposed method is compared with the most prevalent conventional method, MFCC (Mel0frequency Cepstral Coefficient). The experiments show that the recognition performance of the proposed method is better than that of MFCC. But the improvement is marginal while, due to the dimensionality increase, the computational loads of proposed method is substantially greater than that of MFCC.

• ETRI Journal
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• v.29 no.3
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• pp.353-362
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• 2007

In this paper, we propose a novel unsupervised video object extraction algorithm for individual images or image sequences with low depth of field (DOF). Low DOF is a popular photographic technique which enables the representation of the photographer's intention by giving a clear focus only on an object of interest (OOI). We first describe a fast and efficient scheme for extracting OOIs from individual low-DOF images and then extend it to deal with image sequences with low DOF in the next part. The basic algorithm unfolds into three modules. In the first module, a higher-order statistics map, which represents the spatial distribution of the high-frequency components, is obtained from an input low-DOF image. The second module locates the block-based OOI for further processing. Using the block-based OOI, the final OOI is obtained with pixel-level accuracy. We also present an algorithm to extend the extraction scheme to image sequences with low DOF. The proposed system does not require any user assistance to determine the initial OOI. This is possible due to the use of low-DOF images. The experimental results indicate that the proposed algorithm can serve as an effective tool for applications, such as 2D to 3D and photo-realistic video scene generation.

• Journal of Ocean Engineering and Technology
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• v.28 no.5
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• pp.474-483
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• 2014

The majority of existing WECs (wave energy converters) are designed to achieve maximum power at a resonance condition. In the case of a single WEC, its size must be large enough for tuning, and it has high efficiency only within a limited frequency band. Recently, wave power extraction by deploying many small buoys in a compact array has been studied under the assumption that the buoy's size and separation distance are much smaller than the water depth, wave length, and size of the array. A boundary value problem involving the macro-scale boundary condition on the mean surface covered by an infinite strip of buoys is solved using the eigenfunction expansion method. The energy extraction efficiency (${\varepsilon}=1-R^2_f-T^2_r$), where $R_f$ and $T_r$ are the reflection and transmission coefficients for a strip array of buoys, is assessed for various combinations of packing ratio, strip width, and PTO damping coefficient.

• KSBB Journal
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• v.23 no.5
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• pp.362-368
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• 2008

High Hydrostatic Pressure assisted (HHP) process enhancement for food and allied industries are reported in this paper review. Recently, considerable research has been devoted to the improvement of mild thermal processing techniques and to the development of alternative mild processing technologies. HHP assisted can enhance existing extraction, processes and enable new commercial extraction opportunities and processes. New HHP processing approaches have been proposed, including, the potential for modification of plant cell material to provide improved bioavailability of micro nutrients while retaining the natural-like quality, simultaneous extraction. Therefore, High Hydrostatic Pressure assisted (HHP) technologies could have a strong presence in the future of the biotechnology industry.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.653-655
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• 2005

More than 70 percent of terrestrial territory of Korea is mountainous areas where degradation becomes serious year by year due to illegal tombs, expanding golf courses and stone mine development. We elaborate the potential usage of high resolution image for the monitoring of the phenomena. We made the classification of tombs and the statistical radiometric characteristics of graves were identified from this project. The graves could be classified to 4 groups from the field survey. As compared with grouping data after clustering and discriminant analysis, the two results coincided with each other. Object-oriented classification algorithm for feature extraction was theoretically researched in this project. And we did a pilot project, which was performed with mixed methods. That is, the conventional methods such as unsupervised and supervised classification were mixed up with the new method for feature extraction, object-oriented classification method. This methodology showed about $60\%$ classification accuracy for extracting tombs from satellite imagery. The extraction of tombs' geographical coordinates and graves themselves from satellite image was performed in this project. The stone mines and golf courses are extracted by NDVI and GVI. The accuracy of classification was around 89 percent. The location accuracy showed extraction of tombs from one-meter resolution image is cheaper and quicker way than GPS method. Finally we interviewed local government officers and made analyses on the current situation of mountainous area management and potential usage of KOMPSAT-II images. Based on the requirement analysis, we developed software, which is to management and monitoring system for mountainous area for local government.

• Journal of the Korean Society for information Management
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• v.23 no.2
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• pp.167-183
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• 2006

This paper describes a metadata extraction technique based on natural language processing (NLP) which extracts personalized information from email communications between financial analysts and their clients. Personalized means connecting users with content in a personally meaningful way to create, grow, and retain online relationships. Personalization often results in the creation of user profiles that store individuals' preferences regarding goods or services offered by various e-commerce merchants. We developed an automatic metadata extraction system designed to process textual data such as emails, discussion group postings, or chat group transcriptions. The focus of this paper is the recognition of emotional contents such as mood and urgency, which are embedded in the business communications, as metadata.