• International Journal of Computer Science & Network Security
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• 제22권6호
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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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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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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.

• 대한전기학회논문지
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• 제43권4호
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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.

• 한국콘텐츠학회논문지
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• 제18권2호
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• pp.188-199
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• 2018

본 연구의 목적은 네트워크 이론에 근거하여 기업인 네트워크 활동과 기업역량의 유형을 파악하고 이들 특성과 경영성과 사이의 영향 관계를 비교 분석하는 데 있다. 연구결과 기업인의 네트워크 빈도, 중요도, 신뢰도가 경영성과에 유의한 정(+)의 영향을 미쳤으며, 이때 기업역량(산업정보역량, 기회포착역량, 전략적 유연성, 거래비용절감)의 매개효과가 나타났다. 본 연구는 기업역량을 기업인 네트워크 활동의 직접적인 성과변수로 포함하여 경영성과의 측정을 시도하였으며, 기업인 네트워크 활동의 성과를 높이기 위해 네트워크 범위를 넓히기보다는 가장 중요한 네트워크 원천과의 교류에 집중하는 것이 바람직하며, 네트워크 활동의 질적 특성을 강화하기 위한 노력이 필요하다는 결론을 도출한 데 연구의 시사점이 있다.

• Journal of Information Processing Systems
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• 제17권2호
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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%.

• 한국생산제조학회지
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• 제23권1호
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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.

• 전기학회논문지
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• 제62권9호
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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.

• 한국화재소방학회논문지
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• 제25권5호
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• pp.14-20
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• 2011

건축물이 고층화되면서 피난을 위한 주요 시설인 피난계단에서는 수직적 구조로 인해 연돌효과의 영향이 크게 발생하고 있다. 동절기에 연돌효과로 인한 계단실의 압력상승으로 피난문을 열고 대피하는데 어려움이 있고, 피난계단의 연기안전을 위한 급기가압 제연시스템의 운전성능에 교란이 발생되기 때문에 피난계단에서의 연돌효과를 저감시켜야 더욱 안전한 대피환경을 제공할 수 있다. 본 연구에서는 고층 건물에서의 현장실험을 통해 피난계단에서의 연돌효과를 저감시킬 수 있는 방안을 도출하였고, 네트워크 모델을 사용한 수치해석을 수행하여 정량적이고 상세한 설계방안을 제시하였다. 현장실험 결과 피난계단의 저층부에서 피난계단으로 공기가 급기되고 고층부에서 피난계단의 외부로 공기가 배기되도록 공기의 흐름을 생성시켜 준다면 고층 건물의 피난계단에서의 연돌효과가 저감될 것으로 예측된다.

• Smart Structures and Systems
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• 제29권3호
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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.

• 한국통신학회:학술대회논문집
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• 한국통신학회 2006년도 하계종합학술발표회 논문 초록집
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• pp.277-277
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• 2006