• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.7
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• pp.1039-1046
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• 2017

Campus microgrid is designed and built by considering not only power generation but also power consumption management as connected microgrid type because the main goal of the campus microgrid is to save power consumption costs. There are many functions to achieve the goal and they are mainly to use generation-based functions such as islanding operation for peak management and for emergency events. In power distribution operation, Conservation Voltage Reduction (CVR) is applied in order to reduce power consumption. The CVR is defined as a function for load consumption reduction by voltage reduction in order to reduce peak demands and energy consumption. However, application of CVR to microgrid is difficult because the microgrid cannot control a tap of transformer in a substation and the microgrid normally is not designed with phase modifying equipment like a step-voltage-regulator which can control voltage in power distribution system operation. In addition, an impact of the CVR is depended on load characteristics such as a normal load, a rated power, and synchronous motors. Therefore, this paper proposes an application of CVR using linear voltage control based AVR in campus microgrid with power consumption reduction considering characteristics of load and component in the microgrid. The proposed system can be applied to each buildings by a configuration of power distribution cables; and the application results and CVR factor are presented in this paper.

• Smart Structures and Systems
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• v.31 no.4
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• pp.351-363
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• 2023

Visual structural inspections are an inseparable part of post-earthquake damage assessments. With unmanned aerial vehicles (UAVs) establishing a new frontier in visual inspections, there are major computational challenges in processing the collected massive amounts of high-resolution visual data. We propose twin deep learning models that can provide accurate high-resolution structural components and damage segmentation masks efficiently. The traditional approach to cope with high memory computational demands is to either uniformly downsample the raw images at the price of losing fine local details or cropping smaller parts of the images leading to a loss of global contextual information. Therefore, our twin models comprising Trainable Resizing for high-resolution Segmentation Network (TRS-Net) and DmgFormer approaches the global and local semantics from different perspectives. TRS-Net is a compound, high-resolution segmentation architecture equipped with learnable downsampler and upsampler modules to minimize information loss for optimal performance and efficiency. DmgFormer utilizes a transformer backbone and a convolutional decoder head with skip connections on a grid of crops aiming for high precision learning without downsizing. An augmented inference technique is used to boost performance further and reduce the possible loss of context due to grid cropping. Comprehensive experiments have been performed on the 3D physics-based graphics models (PBGMs) synthetic environments in the QuakeCity dataset. The proposed framework is evaluated using several metrics on three segmentation tasks: component type, component damage state, and global damage (crack, rebar, spalling). The models were developed as part of the 2^nd International Competition for Structural Health Monitoring.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2011.10a
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• pp.423-424
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• 2011

This paper has proposed a 920 MHz RF front-end for IEEE 802.15.4g SUN (Smart Utility Network) systems. The proposed 920 MHz RF front-end consists of a driver amplifier, a low noise amplifier, and a RF switch. In the TX mode, the driver amplifier has been designed as a single-ended topology to remove a transformer which causes a loss of the output power from the driver amplifier. In addition, a RF switch is located in the RX path not the TX path. In the RX mode, the proposed low noise amplifier can provide a differential output signal when a single-ended input signal has been applied to. A LC resonant circuit is used as both a load of the drive amplifier and a input matching circuit of the low noise amplifier, reducing the chip area. The proposed 920 MHz RF Front-end has been implemented in a 0.18-um CMOS technology. It consumes 3.6 mA in driver amplifier and 3.1 mA in low noise amplifier from a 1.8 V supply voltage.

• Journal of Internet of Things and Convergence
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• v.10 no.2
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• pp.1-15
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• 2024

The construction of smart communities is a new method and important measure to ensure the security of residential areas. In order to solve the problem of low accuracy in face recognition caused by distorting facial features due to monitoring camera angles and other external factors, this paper proposes the following optimization strategies in designing a face recognition network: firstly, a global graph convolution module is designed to encode facial features as graph nodes, and a multi-scale feature enhancement residual module is designed to extract facial keypoint features in conjunction with the global graph convolution module. Secondly, after obtaining facial keypoints, they are constructed as a directed graph structure, and graph attention mechanisms are used to enhance the representation power of graph features. Finally, tensor computations are performed on the graph features of two faces, and the aggregated features are extracted and discriminated by a fully connected layer to determine whether the individuals' identities are the same. Through various experimental tests, the network designed in this paper achieves an AUC index of 85.65% for facial keypoint localization on the 300W public dataset and 88.92% on a self-built dataset. In terms of face recognition accuracy, the proposed network achieves an accuracy of 83.41% on the IBUG public dataset and 96.74% on a self-built dataset. Experimental results demonstrate that the network designed in this paper exhibits high detection and recognition accuracy for faces in surveillance videos.