• The Transactions of the Korean Institute of Electrical Engineers A
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• v.50 no.3
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• pp.105-111
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• 2001

The research presented in this paper focuses on a method for the detection of High Impedance Fault(HIF). The method will use the wavelet transform and neural network system. HIF on the multi-grounded three-phase four-wires primary distribution power system cannot be detected effectively by existing over current sensing devices. These paper describes the application of discrete wavelet transform to the various HIF data. These data were measured in actual 22-9kV distribution system. Wavelet transform analysis gives the frequency and time-scale information. The neural network system as a fault detector was trained to discriminate HIF from the normal status by a gradient descent method. The proposed method performed very well by proving the right state when it was applied staged fault data and normal load mimics HIF, such as arc-welder.

• Journal of the Korea Institute of Military Science and Technology
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• v.11 no.4
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• pp.92-98
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• 2008

A beam steering system of X-band 2-D phased array antenna for radar application is developed. The beam steering system consists of real-time command generator, beam steering unit, control PCB of array module and power supply. It plays a role of beam steering and on-line check of phased array antenna. The performance of beam steering system is verified with pulse timing of current control in phase shifters and measurement of far-field of phased array antenna. The developed beam steering system offers basic technology to develop full-scale beam steering system of multi-function radar.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.917-919
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• 2003

This paper demonstrates that RADARSAT ScanSAR data can be an important data source of radar remote sensing for monitoring crop systems and estimation of rice yield for large areas in tropic and sub-tropical regions. Experiments were carried out to show the effectiveness of RADARSAT ScanSAR data for rice yield estimation in whole province of Guangdong, South China. A methodology was developed to deal with a series of issues in extracting rice information from the ScanSAR data, such as topographic influences, levels of agro-management, irregular distribution of paddy fields and different rice cropping systems. A model was provided for rice yield estimation based on the relationship between the backscatter coefficient of multi-temporal SAR data and the biomass of rice.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.611-611
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• 2015

One of the hazard of nature is a drought. Its impact varies from region to region and it is difficult for people to understand and define due to differences in hydrometeorological and social economic aspects across much of the country. In the most general sense, drought originates from a deficiency of precipitation over an extended period of time, usually month, season or more, resulting in a water shortage for some activity, group, or environmental sector. Palmer Drought Severity Index (PDSI) is well known and has been used to study aridity changes in modern and past climates. The PDSI index is estimated over US using USHCN historical data.(e.g. precipitation, temperature, latitude and soil moisture). In this study, low frequency drought variability associated with climate variability such as El-Nino and ENSO is mainly investigated. With respect to the multi-scale analysis, wavelet transform analysis is applied to the PDSI index in order to extract the low frequency band corresponding to 2-8 years. Finally, low frequency patterns associated with drought by comparing global wavelet power, with significance test are explored.

• Wind and Structures
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• v.21 no.4
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• pp.425-442
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• 2015

This paper discusses the processing of the wind loads measured in wind tunnel tests by means of multi-channel pressure scanners, in order to compute the response of 3D structures to atmospheric turbulence in the time domain. Data compression and the resulting computational savings are still a challenge in industrial contexts due to the multiple trial configurations during the construction stages. The advantage and robustness of the bi-orthogonal decomposition (BOD) is demonstrated through an example, a sail glass of the Fondation Louis Vuitton, independently from any tentative physical interpretation of the spatio-temporal decomposition terms. We show however that the energy criterion for the BOD has to be more rigorous than commonly admitted. We find a level of 99.95 % to be necessary in order to recover the extreme values of the loads. Moreover, frequency limitations of wind tunnel experiments are sometimes encountered in passing from the scaled model to the full scale structure. These can be alleviated using a spectral extension of the temporal function terms of the BOD.

• 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%.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.2
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• pp.192-198
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• 2022

Companies are building and utilizing their own data analysis systems according to business characteristics in the distributed cloud. However, as businesses and data types become more complex and diverse, the demand for more efficient analytics has increased. In response to these demands, in this paper, we propose an unsupervised learning-based data analysis agent to which reinforcement learning is applied for effective data analysis. The proposal agent consists of reinforcement learning processing manager and unsupervised learning manager modules. These two modules configure an agent with k-means clustering on multiple nodes and then perform distributed training on multiple data sets. This enables data analysis in a relatively short time compared to conventional systems that perform analysis of large-scale data in one batch.

• Journal of Ceramic Processing Research
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• v.13 no.spc1
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• pp.42-46
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• 2012

The effect of the parameters for few-layered graphene growth by thermal CVD on nickel substrate was investigated. Graphene can be synthesized by using different strategies. Chemical vapor deposition (CVD) has known as one of the most attractive methods to produce graphene due to its good film uniformity, compatibility and large scale production. The control of parameters such as temperature, growth time and pressure in CVD process has been widely recognized as the most important process in graphene growth. Different carbon precursors, methane and acetylene, were introduced in the quartz tube with a variety of growth conditions. Raman spectroscopy was used to confirm the presence of a few- or multi-layered graphene.

• Annual Conference of KIPS
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• 2022.11a
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• pp.539-540
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• 2022

본 논문에서는 정확한 장기 시계열 예측을 위해 시계열 데이터의 다양한 스케일 (시간 규모)에서 표현을 학습하는 트랜스포머 모델을 제안한다. 제안하는 모델은 시계열의 다중 스케일 특징을 추출하고, 이를 트랜스포머에 반영하여 예측 시계열을 생성하는 구조로 되어 있다. 스케일 정규화 과정을 통해 시계열의 전역적 및 지역적인 시간 정보를 효율적으로 융합하여 종속성을 학습한다. 3 가지의 다변량 시계열 데이터를 이용한 실험을 통해 제안하는 방법의 우수성을 보인다.

• Steel and Composite Structures
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• v.50 no.1
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• pp.45-61
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• 2024

This research explores the domain of organic solar cells, a photovoltaic technology employing organic electronics, which encompasses small organic molecules and conductive polymers for efficient light absorption and charge transport, leading to electricity generation from sunlight. A computer simulation is employed to scrutinize resonance and dynamic stability in OSCs, with a focus on size effects introduced by nonlocal strain gradient theory, incorporating additional terms in the governing equations related to displacement and time. Initially, the Navier method serves as an analytical solver to delve into the dynamics of design points. The accuracy of this initial step is verified through a meticulous comparison with high-quality literature. The findings underscore the substantial impact of viscoelastic foundations, size-dependent parameters, and geometric factors on the stability and dynamic deflection of OSCs, with a noteworthy emphasis on the amplified influence of size-dependent parameters in higher values of the different layers' thicknesses.