• International Journal of Computer Science & Network Security
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• v.22 no.6
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• pp.230-240
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• 2022

Sharing of online videos via internet is an emerging and important concept in different types of applications like surveillance and video mobile search in different web related applications. So there is need to manage personalized web video retrieval system necessary to explore relevant videos and it helps to peoples who are searching for efficient video relates to specific big data content. To evaluate this process, attributes/features with reduction of dimensionality are computed from videos to explore discriminative aspects of scene in video based on shape, histogram, and texture, annotation of object, co-ordination, color and contour data. Dimensionality reduction is mainly depends on extraction of feature and selection of feature in multi labeled data retrieval from multimedia related data. Many of the researchers are implemented different techniques/approaches to reduce dimensionality based on visual features of video data. But all the techniques have disadvantages and advantages in reduction of dimensionality with advanced features in video retrieval. In this research, we present a Novel Intent based Dimension Reduction Semi-Supervised Learning Approach (NIDRSLA) that examine the reduction of dimensionality with explore exact and fast video retrieval based on different visual features. For dimensionality reduction, NIDRSLA learns the matrix of projection by increasing the dependence between enlarged data and projected space features. Proposed approach also addressed the aforementioned issue (i.e. Segmentation of video with frame selection using low level features and high level features) with efficient object annotation for video representation. Experiments performed on synthetic data set, it demonstrate the efficiency of proposed approach with traditional state-of-the-art video retrieval methodologies.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.147-147
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• 2000

Recently, a considerable amount of attention is being given to the use of wavelets and neural network for modulation and equalization. We proposed a new scheme of equalization for constellation using discrete wavelet transform(DWT) and neural network. The DWT is used for noise reduction and the neural network is used to update the equalizer coefficients adaptively.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.43 no.4
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• pp.687-690
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• 1994

This paper presents a method of obtaining transmission network equivalents from the network's response to the pulse excitation signal. Proposed method is base on Prony signal analysis and jtransfer function identification technique. As a result Thevenin-type of discrete-time filter model can be generated. It can reproduce the driving point impedance characteristic of the network.

• The Journal of the Korea Contents Association
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• v.18 no.2
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• pp.188-199
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• 2018

The purpose of this study is to grasp the type of network activity and corporate competency based on network theory and to compare and analyze the relationship between these characteristics and business performance. The results showed that the frequency, importance, and reliability of CEO's had a positive (+) effect on business performance, and the mediating effect of corporate competency (industrial information competency, opportunity capture competency, strategic flexibility, and transaction cost reduction) appeared. This study tried to measure business performance by including corporate competency as a direct performance variable of CEO's network activity and found that it is desirable to focus on interacting with the most important network sources and to make efforts to strengthen the qualitative characteristics of network activities rather than expanding the scope of the network in order to improve the performance of CEO's Network Activity.

• Journal of Information Processing Systems
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• v.17 no.2
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• pp.411-425
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• 2021

In this paper, a vehicle recognition algorithm based on deep convolutional neural network and compression dictionary is proposed. Firstly, the network structure of fine vehicle recognition based on convolutional neural network is introduced. Then, a vehicle recognition system based on multi-scale pyramid convolutional neural network is constructed. The contribution of different networks to the recognition results is adjusted by the adaptive fusion method that adjusts the network according to the recognition accuracy of a single network. The proportion of output in the network output of the entire multiscale network. Then, the compressed dictionary learning and the data dimension reduction are carried out using the effective block structure method combined with very sparse random projection matrix, which solves the computational complexity caused by high-dimensional features and shortens the dictionary learning time. Finally, the sparse representation classification method is used to realize vehicle type recognition. The experimental results show that the detection effect of the proposed algorithm is stable in sunny, cloudy and rainy weather, and it has strong adaptability to typical application scenarios such as occlusion and blurring, with an average recognition rate of more than 95%.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.23 no.1
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• pp.27-31
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• 2014

This paper describes a tomographic imaging technique for evaluating the thickness reduction of a plate-like structure using a noncontact sensor network based on an electromagnetic acoustic transducer that generates shear horizontal plate waves. Because this technique is based on the effect of mode cutoff and time of flight of guided waves caused by a change in thickness, the tomographic image provides information on the presence of defects in the structure. To verify the performance of the method, artificial defects with various thickness reduction ratios were machined in an aluminum plate, and the tomographic imaging results are reported. The results show that the generated tomographic image displays the thickness reductions and can identify their locations. Therefore, the proposed technique has good potential as a tool for health monitoring of the integrity of plate-like structures.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.9
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• pp.1199-1208
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• 2013

In recent years, the development of application programs for distribution system analysis and control has been essential part for distribution management system (DMS). In this paper, we propose the common database for application programs of distribution management system. The proposed database model has several characteristics as followings. First, the proposed database model is designed for the common use of almost the whole distribution application software. The static equipment model and dynamic type tables are mixed and the parallel table structure is applied. Second, the linked list structure of database are used for the fast processing of applications. The database model includes the hierarchy and non-hierarchy distribution system structure. Third, the reduction method of distribution database is applied. For this, we present the network reduction rules. The basic concept of reduction rules are the electrical unification of successive line section which has not lateral branches and the removal of simple lateral branches which has no devices and other laterals. Proposed database model is tested for the Jeju system of Korea Electric Power Corporation (KEPCO). Through the test, we verified that the proposed database structure can be effectively used to accomplish the distribution system operation.

• Fire Science and Engineering
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• v.25 no.5
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• pp.14-20
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• 2011

As the height of the building increases, the stack effect in stairwell that is main facilities for evacuation becomes stronger. While the pressure rise in stairwell causes difficulties on opening the door for evacuation and has effect on smoke control system, reduction of stack effect will be necessary for providing more safe evacuation environment. The field experiments on pressure field in high-rise building are carried out to present reduction method of stack effect and the numerical analyses using network model are proceeded to design quantitatively the reduction method. As the air flow supplied from outside in lower stair and exhausted to outside in upper stair is formed in stairwell, the stack effect in stairwell is expected to be decreased.

• Smart Structures and Systems
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• v.29 no.3
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• pp.475-484
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• 2022

The loss of cable tension for civil infrastructure reduces structural bearing capacity and causes harmful deformation of structures. Currently, most of the structural health monitoring (SHM) approaches for cables rely on contact transducers. This paper proposes a cable tension identification technology using percussion sound, which provides a fast determination of steel cable tension without physical contact between cables and sensors. Notably, inspired by the concept of tensioning strings for piano tuning, this proposed technology predicts cable tension value by deep learning assisted classification of "percussion" sound from tapping a steel cable. To simulate the non-linear mapping of human ears to sound and to better quantify the minor changes in the high-frequency bands of the sound spectrum generated by percussions, Mel-frequency cepstral coefficients (MFCCs) were extracted as acoustic features to train the deep learning network. A convolutional neural network (CNN) with four convolutional layers and two global pooling layers was employed to identify the cable tension in a certain designed range. Moreover, theoretical and finite element methods (FEM) were conducted to prove the feasibility of the proposed technology. Finally, the identification performance of the proposed technology was experimentally investigated. Overall, results show that the proposed percussion-based technology has great potentials for estimating cable tension for in-situ structural safety assessment.

• Proceedings of the Korean Institute of Communication Sciences Conference
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• 2006.07a
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• pp.277-277
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• 2006