• Title/Summary/Keyword: Optimal performances

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Hysteretic performance of SPSWs with trapezoidally horizontal corrugated web-plates

  • Kalali, Hamed;Hajsadeghi, Mohammad;Zirakian, Tadeh;Alaee, Farshid J.
    • Steel and Composite Structures
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    • v.19 no.2
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    • pp.277-292
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    • 2015
  • Previous research has shown that steel plate shear walls (SPSWs) are efficient lateral force-resisting systems against both wind and seismic loads. A properly designed SPSW can have high initial stiffness, strength, and energy absorption capacity as well as superior ductility. SPSWs have been commonly designed with unstiffened and stiffened infill plates based on economical and performance considerations. Recent introduction and application of corrugated plates with advantageous structural features has motivated the researchers to consider the employment of such elements in stiffened SPSWs with the aim of lowering the high construction cost of such high-performing systems. On this basis, this paper presents results from a numerical investigation of the hysteretic performance of SPSWs with trapezoidally corrugated infill plates. Finite element cyclic analyses are conducted on a series of flat- and corrugated-web SPSWs to examine the effects of web-plate thickness, corrugation angle, and number of corrugation half-waves on the hysteretic performance of such structural systems. Results of the parametric studies are indicative of effectiveness of increasing of the three aforementioned web-plate geometrical and corrugation parameters in improving the cyclic response and energy absorption capacity of SPSWs with trapezoidally corrugated infill plates. Increasing of the web-plate thickness and number of corrugation half-waves are found to be the most and the least effective in adjusting the hysteretic performance of such promising lateral force-resisting systems, respectively. Findings of this study also show that optimal selection of the web-plate thickness, corrugation angle, and number of corrugation half-waves along with proper design of the boundary frame members can result in high stiffness, strength, and cyclic performances of such corrugated-web SPSWs.

Study on DPSAM Turbo TCM in Time-Selective Fading Channels (시간 선택적 페이딩 채널 환경에서 DPSAM Turbo TCM에 관한 연구)

  • Kim, Jeong-Su
    • Journal of the Korea Society of Computer and Information
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    • v.18 no.11
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    • pp.107-113
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    • 2013
  • Mobile mobility and data reliability should be guaranteed as well as amounts of data services are essential in the era of smart media. In order to improve the reliability of high-speed data, strong channel coding and modulation techniques are required. In this paper, the structure of Turbo TCM decoder, applying high-order modulation techniques and the DPSAM method which improves performances in time-selective fading channels in the case of burst errors are suggested through the optimal decoding method and iteration decoding so as to improve bandwidth efficiency in Turbo Codes with excellent encoding gain. The proposed method in comparison with the existing method is that 3dB is superior in case that BER is $10^{-2}$ and the number of iterations is 3. In addition, the function is improved at approximately 6dB in case that BER is $10^{-3}$ and the number of iterations is 7. The proposed method requires additional bandwidth; however, the bandwidth loss can be overcome through Turbo TCM technology on the additional bit rate from the bandwidth loss.

Fabrication of PEDOT:PSS/AgNW-based Electrically Conductive Smart Textiles Using the Screen Printing Method and its Application to Signal Transmission Lines (스크린 프린팅을 이용한 PEDOT:PSS/AgNW 기반 전기전도성 스마트 텍스타일의 제조 및 신호전달선으로의 적용)

  • Kang, Heeeun;Lee, Eugene;Cho, Gilsoo
    • Fashion & Textile Research Journal
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    • v.23 no.4
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    • pp.527-535
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    • 2021
  • In this study, electroconductive textiles were developed by screen-printing technology using a complex solution of PEDOT:PSS/AgNW on a polylactic acid nanofiber web. A performance evaluation was then conducted to utilize this electroconductive textile as a signal transmission line. To obtain highly conductive electroconductive textiles, this study sought to determine the optimal mixing ratio of PEDOT:PSS/AgNW. Sheet resistance was measured to evaluate the electrical properties of electroconductive textiles, Finite element-scanning electron microscopy images were then used to examine surface properties, and Fourier transform-infrared analysis was performed to evaluate chemical properties. The signal waveform characteristics of the electroconductive textile were observed using a signal generator and an oscilloscope. Radio-frequency characteristics were then evaluated to confirm frequency range, and bending tests were conducted to evaluate durability. The signal transmission lines produced in this study had a sheet resistance value of 3.30 ?/sq, and signal transmission performance was evaluated to observe that the input value of the voltage was nearly identical to the output value. In addition, S21 analysis confirmed that it was available in the frequency domain up to 35 MHz. The performances of the transmission lines were maintained after 100, 200, 500, and 1,000 repeated bending tests, and sufficient durability was confirmed.

Efficiency of various structural modeling schemes on evaluating seismic performance and fragility of APR1400 containment building

  • Nguyen, Duy-Duan;Thusa, Bidhek;Park, Hyosang;Azad, Md Samdani;Lee, Tae-Hyung
    • Nuclear Engineering and Technology
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    • v.53 no.8
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    • pp.2696-2707
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    • 2021
  • The purpose of this study is to investigate the efficiency of various structural modeling schemes for evaluating seismic performances and fragility of the reactor containment building (RCB) structure in the advanced power reactor 1400 (APR1400) nuclear power plant (NPP). Four structural modeling schemes, i.e. lumped-mass stick model (LMSM), solid-based finite element model (Solid FEM), multi-layer shell model (MLSM), and beam-truss model (BTM), are developed to simulate the seismic behaviors of the containment structure. A full three-dimensional finite element model (full 3D FEM) is additionally constructed to verify the previous numerical models. A set of input ground motions with response spectra matching to the US NRC 1.60 design spectrum is generated to perform linear and nonlinear time-history analyses. Floor response spectra (FRS) and floor displacements are obtained at the different elevations of the structure since they are critical outputs for evaluating the seismic vulnerability of RCB and secondary components. The results show that the difference in seismic responses between linear and nonlinear analyses gets larger as an earthquake intensity increases. It is observed that the linear analysis underestimates floor displacements while it overestimates floor accelerations. Moreover, a systematic assessment of the capability and efficiency of each structural model is presented thoroughly. MLSM can be an alternative approach to a full 3D FEM, which is complicated in modeling and extremely time-consuming in dynamic analyses. Specifically, BTM is recommended as the optimal model for evaluating the nonlinear seismic performance of NPP structures. Thereafter, linear and nonlinear BTM are employed in a series of time-history analyses to develop fragility curves of RCB for different damage states. It is shown that the linear analysis underestimates the probability of damage of RCB at a given earthquake intensity when compared to the nonlinear analysis. The nonlinear analysis approach is highly suggested for assessing the vulnerability of NPP structures.

Few-Layered MoS2 Nanoparticles Loaded TiO2 Nanosheets with Exposed {001} Facets for Enhanced Photocatalytic Activity

  • Chen, Chujun;Xin, Xia;Zhang, Jinniu;Li, Gang;Zhang, Yafeng;Lu, Hongbing;Gao, Jianzhi;Yang, Zhibo;Wang, Chunlan;He, Ze
    • Nano
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    • v.13 no.11
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    • pp.1850129.1-1850129.10
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    • 2018
  • To improve the high charge carrier recombination rate and low visible light absorption of {001} facets exposed $TiO_2$ [$TiO_2(001)$] nanosheets, few-layered $MoS_2$ nanoparticles were loaded on the surfaces of $TiO_2(001)$ nanosheets by a simple photodeposition method. The photocatalytic activities towards Rhodamine B (RhB) were investigated. The results showed that the $MoS_2-TiO_2(001)$ nanocomposites exhibited much enhanced photocatalytic activities compared with the pure $TiO_2(001)$ nanosheets. At an optimal Mo/Ti molar ratio of 25%, the $MoS_2-TiO_2(001)$ nanocomposites displayed the highest photocatalytic activity, which took only 30 min to degrade 50 mL of RhB (50 mg/L). The active species in the degradation reaction were determined to be $h^+$ and $^{\bullet}OH$ according to the free radical trapping experiments. The reduced charge carrier recombination rate, enhanced visible light utilization and increased surface areas contributed to the enhanced photocatalytic performances of the 25% $MoS_2-TiO_2(001)$ nanocomposites.

Development of Flash Boiling Spray Prediction Model of Multi-hole GDI Injector Using Machine Learning (머신러닝을 이용한 다공형 GDI 인젝터의 플래시 보일링 분무 예측 모델 개발)

  • Chang, Mengzhao;Shin, Dalho;Pham, Quangkhai;Park, Suhan
    • Journal of ILASS-Korea
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    • v.27 no.2
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    • pp.57-65
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    • 2022
  • The purpose of this study is to use machine learning to build a model capable of predicting the flash boiling spray characteristics. In this study, the flash boiling spray was visualized using Shadowgraph visualization technology, and then the spray image was processed with MATLAB to obtain quantitative data of spray characteristics. The experimental conditions were used as input, and the spray characteristics were used as output to train the machine learning model. For the machine learning model, the XGB (extreme gradient boosting) algorithm was used. Finally, the performance of machine learning model was evaluated using R2 and RMSE (root mean square error). In order to have enough data to train the machine learning model, this study used 12 injectors with different design parameters, and set various fuel temperatures and ambient pressures, resulting in about 12,000 data. By comparing the performance of the model with different amounts of training data, it was found that the number of training data must reach at least 7,000 before the model can show optimal performance. The model showed different prediction performances for different spray characteristics. Compared with the upstream spray angle and the downstream spray angle, the model had the best prediction performance for the spray tip penetration. In addition, the prediction performance of the model showed a relatively poor trend in the initial stage of injection and the final stage of injection. The model performance is expired to be further enhanced by optimizing the hyper-parameters input into the model.

Sample Average Approximation Method for Task Assignment with Uncertainty (불확실성을 갖는 작업 할당 문제를 위한 표본 평균 근사법)

  • Gwang, Kim
    • Journal of Korea Society of Industrial Information Systems
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    • v.28 no.1
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    • pp.27-34
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    • 2023
  • The optimal assignment problem between agents and tasks is known as one of the representative problems of combinatorial optimization and an NP-hard problem. This paper covers multi agent-multi task assignment problems with uncertain completion probability. The completion probabilities are generally uncertain due to endogenous (agent or task) or exogenous factors in the system. Assignment decisions without considering uncertainty can be ineffective in a real situation that has volatility. To consider uncertain completion probability mathematically, a mathematical formulation with stochastic programming is illustrated. We also present an algorithm by using the sample average approximation method to solve the problem efficiently. The algorithm can obtain an assignment decision and the upper and lower bounds of the assignment problem. Through numerical experiments, we present the optimality gap and the variance of the gap to confirm the performances of the results. This shows the excellence and robustness of the assignment decisions obtained by the algorithm in the problem with uncertainty.

Implementation of Efficient Distributed Crawler through Stepwise Crawling Node Allocation

  • Kim, Hyuntae;Byun, Junhyung;Na, Yoseph;Jung, Yuchul
    • Journal of Advanced Information Technology and Convergence
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    • v.10 no.2
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    • pp.15-31
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    • 2020
  • Various websites have been created due to the increased use of the Internet, and the number of documents distributed through these websites has increased proportionally. However, it is not easy to collect newly updated documents rapidly. Web crawling methods have been used to continuously collect and manage new documents, whereas existing crawling systems applying a single node demonstrate limited performances. Furthermore, crawlers applying distribution methods exhibit a problem related to effective node management for crawling. This study proposes an efficient distributed crawler through stepwise crawling node allocation, which identifies websites' properties and establishes crawling policies based on the properties identified to collect a large number of documents from multiple websites. The proposed crawler can calculate the number of documents included in a website, compare data collection time and the amount of data collected based on the number of nodes allocated to a specific website by repeatedly visiting the website, and automatically allocate the optimal number of nodes to each website for crawling. An experiment is conducted where the proposed and single-node methods are applied to 12 different websites; the experimental result indicates that the proposed crawler's data collection time decreased significantly compared with that of a single node crawler. This result is obtained because the proposed crawler applied data collection policies according to websites. Besides, it is confirmed that the work rate of the proposed model increased.

Development of IPM(Intelligent Power Module) IGBT switch performance evaluation system for the driving of the A.C. motor (교류 전동기 구동을 위한 IPM(Intelligent Power Module) IGBT 스위치 성능 분석 방법 개발)

  • Choi, Jung-Keyng
    • The Journal of Korea Institute of Information, Electronics, and Communication Technology
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    • v.15 no.4
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    • pp.291-297
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    • 2022
  • This Paper is about the study that design performance and reliability measurement circuits of the IPM which is an intelligent switching switch module included at an inverter circuits for driving of A.C. Servo motors in home appliance. IPM is a core device of motor driver and it's switching characteristics should be retained uniformly during the driving of a servo system. All of it's specification, the collector emitter switch on voltage Vce(on) spec. is very important. As the IPM are core part of inverters and producing from several brands and versions, for optimal performances of application systems a method and measurement & evaluation system to measure Vce(on) value, collector emitter switch on voltage, of the IPM IGBT switches with various brands are required. Especially, the proposed method can measure and evaluate Vce(on) values of IPM with load at mounting state on the motor driving circuits and proposed measurement & evaluation system can be important instrument systems for IPM user companies.

A Comparison of Meta-learning and Transfer-learning for Few-shot Jamming Signal Classification

  • Jin, Mi-Hyun;Koo, Ddeo-Ol-Ra;Kim, Kang-Suk
    • Journal of Positioning, Navigation, and Timing
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    • v.11 no.3
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    • pp.163-172
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    • 2022
  • Typical anti-jamming technologies based on array antennas, Space Time Adaptive Process (STAP) & Space Frequency Adaptive Process (SFAP), are very effective algorithms to perform nulling and beamforming. However, it does not perform equally well for all types of jamming signals. If the anti-jamming algorithm is not optimized for each signal type, anti-jamming performance deteriorates and the operation stability of the system become worse by unnecessary computation. Therefore, jamming classification technique is required to obtain optimal anti-jamming performance. Machine learning, which has recently been in the spotlight, can be considered to classify jamming signal. In general, performing supervised learning for classification requires a huge amount of data and new learning for unfamiliar signal. In the case of jamming signal classification, it is difficult to obtain large amount of data because outdoor jamming signal reception environment is difficult to configure and the signal type of attacker is unknown. Therefore, this paper proposes few-shot jamming signal classification technique using meta-learning and transfer-learning to train the model using a small amount of data. A training dataset is constructed by anti-jamming algorithm input data within the GNSS receiver when jamming signals are applied. For meta-learning, Model-Agnostic Meta-Learning (MAML) algorithm with a general Convolution Neural Networks (CNN) model is used, and the same CNN model is used for transfer-learning. They are trained through episodic training using training datasets on developed our Python-based simulator. The results show both algorithms can be trained with less data and immediately respond to new signal types. Also, the performances of two algorithms are compared to determine which algorithm is more suitable for classifying jamming signals.