• Title/Summary/Keyword: Power optimization

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Improved breakdown characteristics of Ga2O3 Schottky barrier diode using floating metal guard ring structure (플로팅 금속 가드링 구조를 이용한 Ga2O3 쇼트키 장벽 다이오드의 항복 특성 개선 연구)

  • Choi, June-Heang;Cha, Ho-Young
    • Journal of IKEEE
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    • v.23 no.1
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    • pp.193-199
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    • 2019
  • In this study, we have proposed a floating metal guard ring structure based on TCAD simulation in order to enhance the breakdown voltage characteristics of gallium oxide ($Ga_2O_3$) vertical high voltage switching Schottky barrier diode. Unlike conventional guard ring structures, the floating metal guard rings do not require an ion implantation process. The locally enhanced high electric field at the anode corner was successfully suppressed by the metal guard rings, resulting in breakdown voltage enhancement. The number of guard rings and their width and spacing were varied for structural optimization during which the current-voltage characteristics and internal electric field and potential distributions were carefully investigated. For an n-type drift layer with a doping concentration of $5{\times}10^{16}cm^{-3}$ and a thickness of $5{\mu}m$, the optimum guard ring structure had 5 guard rings with an individual ring width of $1.5{\mu}m$ and a spacing of $0.2{\mu}m$ between rings. The breakdown voltage was increased from 940 V to 2000 V without degradation of on-resistance by employing the optimum guard ring structure. The proposed floating metal guard ring structure can improve the device performance without requiring an additional fabrication step.

Optimization of Solar Water Battery for Efficient Photoelectrochemical Solar Energy Conversion and Storage (효율적인 광전기화학적 태양에너지 전환과 저장을 위한 Solar Water Battery의 최적화)

  • Go, Hyunju;Park, Yiseul
    • Clean Technology
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    • v.27 no.1
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    • pp.85-92
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    • 2021
  • A solar water battery is a system that generates power using solar energy. It is a combination of photoelectrochemical cells and an energy storage system. It can simultaneously convert and store solar energy without additional external voltage. Solar water batteries consist of photoelectrodes, storage electrodes and counter electrodes, and their properties and combination are important for the performance and the efficiency of the system. In this study, we tried to find the effect that changing the components of solar water batteries has on its system. The effects of the counter electrode during discharge, the kinds of photoelectrode and storage electrode materials, and electrolytes on the solar energy conversion and storage capacitance were studied. The optimized composition (TiO2 : NaFe-PB : Pt foil) exhibited 72.393 mAh g-1 of discharge capacity after 15 h of photocharging. It indicates that the efficiency of solar energy conversion and storage is largely affected by the configuration of the system. Also, the addition of organic pollutants to the chamber of the photoelectrode improved the battery's photo-current and discharge capacity by efficient photoelectron-hole pair separation with simultaneous degradation of organic pollutants. Solar water batteries are a new eco-friendly solar energy conversion and storage system that does not require additional external voltages. It is also expected to be used for water treatment that utilizes solar energy.

A study on the 3-step classification algorithm for the diagnosis and classification of refrigeration system failures and their types (냉동시스템 고장 진단 및 고장유형 분석을 위한 3단계 분류 알고리즘에 관한 연구)

  • Lee, Kangbae;Park, Sungho;Lee, Hui-Won;Lee, Seung-Jae;Lee, Seung-hyun
    • Journal of the Korea Convergence Society
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    • v.12 no.8
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    • pp.31-37
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    • 2021
  • As the size of buildings increases due to urbanization due to the development of industry, the need to purify the air and maintain a comfortable indoor environment is also increasing. With the development of monitoring technology for refrigeration systems, it has become possible to manage the amount of electricity consumed in buildings. In particular, refrigeration systems account for about 40% of power consumption in commercial buildings. Therefore, in order to develop the refrigeration system failure diagnosis algorithm in this study, the purpose of this study was to understand the structure of the refrigeration system, collect and analyze data generated during the operation of the refrigeration system, and quickly detect and classify failure situations with various types and severity . In particular, in order to improve the classification accuracy of failure types that are difficult to classify, a three-step diagnosis and classification algorithm was developed and proposed. A model based on SVM and LGBM was presented as a classification model suitable for each stage after a number of experiments and hyper-parameter optimization process. In this study, the characteristics affecting failure were preserved as much as possible, and all failure types, including refrigerant-related failures, which had been difficult in previous studies, were derived with excellent results.

Apartment Price Prediction Using Deep Learning and Machine Learning (딥러닝과 머신러닝을 이용한 아파트 실거래가 예측)

  • Hakhyun Kim;Hwankyu Yoo;Hayoung Oh
    • KIPS Transactions on Software and Data Engineering
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    • v.12 no.2
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    • pp.59-76
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    • 2023
  • Since the COVID-19 era, the rise in apartment prices has been unconventional. In this uncertain real estate market, price prediction research is very important. In this paper, a model is created to predict the actual transaction price of future apartments after building a vast data set of 870,000 from 2015 to 2020 through data collection and crawling on various real estate sites and collecting as many variables as possible. This study first solved the multicollinearity problem by removing and combining variables. After that, a total of five variable selection algorithms were used to extract meaningful independent variables, such as Forward Selection, Backward Elimination, Stepwise Selection, L1 Regulation, and Principal Component Analysis(PCA). In addition, a total of four machine learning and deep learning algorithms were used for deep neural network(DNN), XGBoost, CatBoost, and Linear Regression to learn the model after hyperparameter optimization and compare predictive power between models. In the additional experiment, the experiment was conducted while changing the number of nodes and layers of the DNN to find the most appropriate number of nodes and layers. In conclusion, as a model with the best performance, the actual transaction price of apartments in 2021 was predicted and compared with the actual data in 2021. Through this, I am confident that machine learning and deep learning will help investors make the right decisions when purchasing homes in various economic situations.

Sensitivity Analysis of Wake Diffusion Patterns in Mountainous Wind Farms according to Wake Model Characteristics on Computational Fluid Dynamics (전산유체역학 후류모델 특성에 따른 산악지형 풍력발전단지 후류확산 형태 민감도 분석)

  • Kim, Seong-Gyun;Ryu, Geon Hwa;Kim, Young-Gon;Moon, Chae-Joo
    • The Journal of the Korea institute of electronic communication sciences
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    • v.17 no.2
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    • pp.265-278
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    • 2022
  • The global energy paradigm is rapidly changing by centering on carbon neutrality, and wind energy is positioning itself as a leader in renewable energy-based power sources. The success of onshore and offshore wind energy projects focuses on securing the economic feasibility of the project, which depends on securing high-quality wind resources and optimal arrangement of wind turbines. In the process of constructing the wind farm, the optimal arrangement method of wind turbines considering the main wind direction is important, and this is related to minimizing the wake effect caused by the fluid passing through the structure located on the windward side. The accuracy of the predictability of the wake effect is determined by the wake model and modeling technique that can properly simulate it. Therefore, in this paper, using WindSim, a commercial CFD model, the wake diffusion pattern is analyzed through the sensitivity study of each wake model of the proposed onshore wind farm located in the mountainous complex terrain in South Korea, and it is intended to be used as basic research data for wind energy projects in complex terrain in the future.

A Redesign of the Military Education Structure of General Universities based on Defense Innovation 4.0 -Focused on Capabilities of Tech-Intensive Junior Officers based on Advanced S&T- (국방혁신4.0 기반의 일반대학의 군사학 교육체계 재설계 방안 -첨단과학기술 기반의 기술집약형 초급 간부 역량 중심으로-)

  • Jung-Ho Eom;Keun-Seog Park;Sang-Pil Chun
    • Convergence Security Journal
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    • v.22 no.4
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    • pp.35-44
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    • 2022
  • Among the five promotion strategies of Defense Innovation 4.0(DI 4.0), the military structure/operation optimization strategy aims to innovate the military structure based on advanced science&technology(S&T), and to integrate advanced S&T in the field of defense operation such as education&training and human resource development. As the future battlefield expands to AI-based unmanned/robot combat systems, space, cyberspace, and electromagnetic fields, it is necessary to train officers with the capabilities required in these battlefields. It is necessary to develop capabilities from junior officers who will lead the future battlefield to operating core advanced power based on the 4th industrial revolution S&T. We review the education system of the military in universities and propose a method of redesigning the education system that is compatible with DI 4.0 and can develop technology-intensive capabilities based on advanced S&T. We propose a operation plan of major and extra-programs that can develop the capabilities of junior officers required for the future battlefield, and also suggest ways to support the army's practical training.

A Study on the Optimization of α-Al2O3 Powder Manufacturing for the Application of Separators for Lithium-Ion Secondary Batteries (리튬이차전지용 분리막 적용을 위한 α-알루미나 분말 제조 최적화 연구)

  • Dong-Myeong Moon;Da-Eun Hyun;Ji-Hui Oh;Jwa-Bin Jeon;Yong-Nam Kim;Kyoung-Hoon Jeong;Jong-Kun Lee;Sang-Mo Koo;Dong-Won Lee;Jong-Min Oh
    • Journal of the Korean Institute of Electrical and Electronic Material Engineers
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    • v.36 no.6
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    • pp.638-646
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    • 2023
  • Recently, active research has been conducted to enhance the power characteristics and thermal stability of lithium-ion batteries (LiBs) by modifying separators using a ceramic coating method. However, since the thermal properties and surface features of the separator vary depending on the characteristics of the ceramic powders applied to the separator, it is crucial to manufacture ceramic powders optimized for the separator's performance. In this study, we evaluated the characteristics of three types of α-alumina (A-1, A-2, and A-3) produced with varying dispersant contents and milling times, in addition to commercial α-alumina (AES-11). Subsequently, the optimized powders (A-3) were coated onto the separator using an aqueous binder for comparison with the characteristics of an AES-11 coated separator and an uncoated PE separator. The A-3 coated separator improved electrolyte wettability with a low contact angle (44.69°) and increased puncture strength (538 gf). Furthermore, it exhibited excellent thermal stability, with a shrinkage value of 5.64% when exposed to 140℃ for 1 hour, compared to the AES11 coated separator (6.09%) and the bare PE separator (69.64%).

Improvement of Face Recognition Algorithm for Residential Area Surveillance System Based on Graph Convolution Network (그래프 컨벌루션 네트워크 기반 주거지역 감시시스템의 얼굴인식 알고리즘 개선)

  • Tan Heyi;Byung-Won Min
    • 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.

Cost Analysis of the Recent Projects for Overseas Vanadium Metallurgical Processing Plants (해외 바나듐 제련 플랜트 관련 사업 비용 분석)

  • Gyuri Kim;Sang-hun Lee
    • Resources Recycling
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    • v.33 no.3
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    • pp.3-11
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    • 2024
  • This study addressed the cost structure of metallurgical plants for vanadium recovery or production, which were previously planned or implemented. Vanadium metallurgy consists of several sub-processes such as such as pretreatment, roasting, leaching, precipitation, and filtration, in order to finally produce vanadium pentoxide. Here, lots of costs should be spent for such plants, in which these costs are largely divided into CAPEX (Capital Expenditure) and OPEX (Operational Expenditure). As a result, the capacities (feed input rates) and vanadium contents are various along the target projects for this study. However, final production rates and grades of vanadium pentoxide showed relatively small differences. In addition, a noticeable correlation is found between capacities and specific operating costs, in that a steadily decreasing trend is described with a non-linear curve with around -0.3 power. Therefore, for the plant capacity below 100,000 tons per year, the specific operating cost rapidly decreases as the capacity increases, whereas the cost remains relatively stable in the range of 0.6 to 1.2 million tons per year of the capacity. From a technical perspective, effective optimization of the metallurgical process plant can be achieved by improving vanadium recovery rate in the pre-treatment and/or roasting-leaching processes. Finally, the results of this study should be updated through future research with on-going field verification and further detailed cost analysis.

Bottom electrode optimization for the applications of ferroelectric memory device (강유전체 기억소자 응용을 위한 하부전극 최적화 연구)

  • Jung, S.M.;Choi, Y.S.;Lim, D.G.;Park, Y.;Song, J.T.;Yi, J.
    • Journal of the Korean Crystal Growth and Crystal Technology
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    • v.8 no.4
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    • pp.599-604
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    • 1998
  • We have investigated Pt and $RuO_2$ as a bottom electrode for ferroelectric capacitor applications. The bottom electrodes were prepared by using an RF magnetron sputtering method. Some of the investigated parameters were a substrate temperature, gas flow rate, RF power for the film growth, and post annealing effect. The substrate temperature strongly influenced the surface morphology and resistivity of the bottom electrodes as well as the film crystallographic structure. XRD results on Pt films showed a mixed phase of (111) and (200) peak for the substrate temperature ranged from RT to $200^{\circ}C$, and a preferred (111) orientation for $300^{\circ}C$. From the XRD and AFM results, we recommend the substrate temperature of $300^{\circ}C$ and RF power 80W for the Pt bottom electrode growth. With the variation of an oxygen partial pressure from 0 to 50%, we learned that only Ru metal was grown with 0~5% of $O_2$ gas, mixed phase of Ru and $RuO_2$ for $O_ 2$ partial pressure between 10~40%, and a pure $RuO_2$ phase with $O_2$ partial pressure of 50%. This result indicates that a double layer of $RuO_2/Ru$ can be grown in a process with the modulation of gas flow rate. Double layer structure is expected to reduce the fatigue problem while keeping a low electrical resistivity. As post anneal temperature was increased from RT to $700^{\circ}C$, the resistivity of Pt and $RuO_2$ was decreased linearly. This paper presents the optimized process conditions of the bottom electrodes for memory device applications.

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