• Title/Summary/Keyword: coefficient-based method

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Development of an Acoustic-Based Underwater Image Transmission System

  • Choi, Young-Cheol;Lim, Yong-Kon;Park, Jong-Won;Kim, Sea-Monn;Kim, Seung-Geun;Kim, Sang-Tae
    • Proceedings of the Korea Committee for Ocean Resources and Engineering Conference
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    • 2003.05a
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    • pp.109-114
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    • 2003
  • Wireless communication systems are inevitable for efficient underwater activities. Because of the poor propagation characteristics of light and electromagnetic waves, acoustic waves are generally used for the underwater wireless communication. Although there are many kinds of information type, visual images take an essential role especially for search and identification activities. For this reason, we developed an acoustic-based underwater image transmission system under a dual use technology project supported by MOCIE (Ministry of Commerce, Industry and Energy). For the application to complicated and time-varying underwater environments all-digital transmitter and receiver systems are investigated. Array acoustic transducers are used at the receiver, which have the center frequency of 32kHz and the bandwidth of 4kHz. To improve transmission speed and quality, various algorithms and systems are used. The system design techniques will be discussed in detail including image compression/ decompression system, adaptive beam- forming, fast RLS adaptive equalizer, ${\partial}/4$ QPSK (Quadrilateral Phase Shift Keying) modulator/demodulator, and convolution coding/ Viterbi. Decoding.

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A Study on the Reconstruction of a Frame Based Speech Signal through Dictionary Learning and Adaptive Compressed Sensing (Adaptive Compressed Sensing과 Dictionary Learning을 이용한 프레임 기반 음성신호의 복원에 대한 연구)

  • Jeong, Seongmoon;Lim, Dongmin
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.37A no.12
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    • pp.1122-1132
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    • 2012
  • Compressed sensing has been applied to many fields such as images, speech signals, radars, etc. It has been mainly applied to stationary signals, and reconstruction error could grow as compression ratios are increased by decreasing measurements. To resolve the problem, speech signals are divided into frames and processed in parallel. The frames are made sparse by dictionary learning, and adaptive compressed sensing is applied which designs the compressed sensing reconstruction matrix adaptively by using the difference between the sparse coefficient vector and its reconstruction. Through the proposed method, we could see that fast and accurate reconstruction of non-stationary signals is possible with compressed sensing.

An FPGA-Based Modified Adaptive PID Controller for DC/DC Buck Converters

  • Lv, Ling;Chang, Changyuan;Zhou, Zhiqi;Yuan, Yubo
    • Journal of Power Electronics
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    • v.15 no.2
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    • pp.346-355
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    • 2015
  • On the basis of the conventional PID control algorithm, a modified adaptive PID (MA-PID) control algorithm is presented to improve the steady-state and dynamic performance of closed-loop systems. The proposed method has a straightforward structure without excessively increasing the complexity and cost. It can adaptively adjust the values of the control parameters ($K_p$, $K_i$ and $K_d$) by following a new control law. Simulation results show that the line transient response of the MA-PID is better than that of the adaptive digital PID because the differential coefficient $K_d$ is introduced to changes. In addition, experimental results based on a FPGA indicate that the MA-PID control algorithm reduces the recovery time by 62.5% in response to a 1V line transient, 50% in response to a 500mA load transient, and 23.6% in response to a steady-state deviation, when compared with the conventional PID control algorithm.

Studying the Park-Ang damage index of reinforced concrete structures based on equivalent sinusoidal waves

  • Mazloom, Moosa;Pourhaji, Pardis;Shahveisi, Masoud;Jafari, Seyed Hassan
    • Structural Engineering and Mechanics
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    • v.72 no.1
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    • pp.83-97
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    • 2019
  • In this research, the vulnerability of some reinforced concrete frames with different stories are studied based on the Park-Ang Damage Index. The damages of the frames are investigated under various earthquakes with nonlinear dynamic analysis in IDARC software. By examining the most important characteristics of earthquake parameters, the damage index and vulnerability of these frames are investigated in this software. The intensity of Erias, velocity spectral intensity (VSI) and peak ground velocity (PGV) had the highest correlation, and root mean square of displacement ($D_{rms}$) had the lowest correlation coefficient among the parameters. Then, the particle swarm optimization (PSO) algorithm was used, and the sinusoidal waves were equivalent to the used earthquakes according to the most influential parameters above. The damage index equivalent to these waves is estimated using nonlinear dynamics analysis. The comparison between the damages caused by earthquakes and equivalent sinusoidal waves is done too. The generations of sinusoidal waves equivalent to different earthquakes are generalized in some reinforced concrete frames. The equivalent sinusoidal wave method was exact enough because the greatest difference between the results of the main and artificial accelerator damage index was about 5 percent. Also sinusoidal waves were more consistent with the damage indices of the structures compared to the earthquake parameters.

A Study on the Model-Ship Correlation Analysis of Powering Performance (동력추정을 위한 모형선-실선 상관해석에 관한 연구)

  • Yong-Jea Park;Eun-Chan Kim;Chun-Ju Lee;Hyo-Kwan Leem;Ho-Sun Park
    • Journal of the Society of Naval Architects of Korea
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    • v.31 no.1
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    • pp.32-41
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    • 1994
  • This paper presents the model-ship correlations based on model test results of 36 ships. All of model tests were conducted at KRISO towing tank The correlation factors $C_P,\;C_N,\;and\;C_{NP}$ are estimated by the ITTC Standard Method and compared with the results of another towing tank. In the 36 ships, the block coefficients of thirty ships are greater than 0.72. Nevertheless the comparison of factors is in good agreement. The corrections to the scale effect on wake fraction ${\Delta}{\omega}_c$ and roughness allowance $C_{Ac}$ are subject matter in practice. The correction formulae are proposed by functions of ship length and form factor. And the correction formula of resistance coefficient ${\Delta}C_{Fc}$ based on Townsis's hull roughness formula is presented.

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Corporate Investment Behavior and Level of Participation in the Global Value Chain: A Dynamic Panel Data Approach

  • KUANTAN, Dhaha Praviandi;SIREGAR, Hermanto;RATNAWATI, Anny;JUHRO, Solikin M.
    • The Journal of Asian Finance, Economics and Business
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    • v.8 no.12
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    • pp.117-127
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    • 2021
  • This study was conducted to comprehensively identify factors that potentially influence corporate investment behavior, including micro, macro, and sectoral variables. Furthermore, investment behavior was studied across nations based on their participation in the global value chain (GVC), which was evaluated based on commodities, limited manufacturing, advanced manufacturing, and innovative activities. The study uses the dynamic panel data analysis and Generalized Method of Moment (GMM) estimation for a sample of 800 corporations, with data spanning over 2000-2019. The study result shows that in all types of countries, the coefficient lag indicator of capital expenditure statistically has a significant effect on capital expenditure. Sales growth, exchange rate, and GDP have a significant positive effect on corporate investment growth, while DER has a negative effect. In commodity countries, corporate investment is influenced by sales growth, exchange rate, and FCI. The variables that influence corporate investment in manufacturing countries are the FCI, exchange rate, sales growth, GDP, and DER. In innovative countries, variables that significantly affect capital expenditure are DER, GDP, and Tobin Q. In each type of country, the interaction terms between exchange rate and commodity price are positive and statistically significant.

Detection and location of bolt group looseness using ultrasonic guided wave

  • Zhang, Yue;Li, Dongsheng;Zheng, Xutao
    • Smart Structures and Systems
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    • v.24 no.3
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    • pp.293-301
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    • 2019
  • Bolted joints are commonly used in civil infrastructure and mechanical assembly structures. Monitoring and identifying the connection status of bolts is the frontier problem of structural research. The existing research is mainly on the looseness of a single bolt. This article presents a study of assessing the loosening/tightening health state and identifying the loose bolt by using ultrasonic guided wave in a bolt group joint. A bolt-tightening index was proposed for evaluating the looseness of a bolt connection based on correlation coefficient. The tightening/loosening state of the bolt was simulated by changing the bolt torque. More than 180 different measurement tests for total of six bolts were conducted. The results showed that with the bolt torque increases, value of the proposed bolt-tightening index increases. The proposed bolt-tightening index trend was very well reproduced by an analytical expression using a function of the torque applied with an overall percentage error lower than 5%. The developed damage index based on the proposed bolt-tightening index can also be applied to locate the loosest bolt in a bolt group joint. To verify the effectiveness of the proposed method, a bolt group joint experiment with different positions of bolt looseness was performed. Experimental results show that the proposed approach is effective to detect and locate bolt looseness and has a good prospect of finding applications in real-time structural monitoring.

Nonlinear self-induced vibration and operability envelope analysis of production strings in marine natural gas development

  • Liu, Kang;Chen, Guoming;Zhu, Gaogeng;Zhu, Jingyu
    • International Journal of Naval Architecture and Ocean Engineering
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    • v.11 no.1
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    • pp.344-352
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    • 2019
  • Marine production strings are continuously affected by unstable internal fluid during operation. In this paper, the structural governing equation for marine production string self-induced vibration is constructed. A finite element analysis model is established based on Euler-Bernoulli theory and solved by the Newmark method. Furthermore, based on reliability theory, a self-design procedure is developed to determine the operability envelope for marine production string self-induced vibration. Case studies show: the response frequency of the production strings is consistent with the excitation frequency under harmonic fluctuation and mainly determined by the first-order natural frequency under stochastic fluctuation. The operability envelope for marine production string self-induced vibration is a near symmetrical trapezium. With the increasing of natural gas output, the permissible fluctuation coefficient dramatically decreases. A reasonable centralizer spacing, increasing top tension, and controlling natural gas output are of great significance to the risk control in marine production string operation.

An Integrated Artificial Neural Network-based Precipitation Revision Model

  • Li, Tao;Xu, Wenduo;Wang, Li Na;Li, Ningpeng;Ren, Yongjun;Xia, Jinyue
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.15 no.5
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    • pp.1690-1707
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    • 2021
  • Precipitation prediction during flood season has been a key task of climate prediction for a long time. This type of prediction is linked with the national economy and people's livelihood, and is also one of the difficult problems in climatology. At present, there are some precipitation forecast models for the flood season, but there are also some deviations from these models, which makes it difficult to forecast accurately. In this paper, based on the measured precipitation data from the flood season from 1993 to 2019 and the precipitation return data of CWRF, ANN cycle modeling and a weighted integration method is used to correct the CWRF used in today's operational systems. The MAE and TCC of the precipitation forecast in the flood season are used to check the prediction performance of the proposed algorithm model. The results demonstrate a good correction effect for the proposed algorithm. In particular, the MAE error of the new algorithm is reduced by about 50%, while the time correlation TCC is improved by about 40%. Therefore, both the generalization of the correction results and the prediction performance are improved.

Prediction of long-term compressive strength of concrete with admixtures using hybrid swarm-based algorithms

  • Huang, Lihua;Jiang, Wei;Wang, Yuling;Zhu, Yirong;Afzal, Mansour
    • Smart Structures and Systems
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    • v.29 no.3
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    • pp.433-444
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    • 2022
  • Concrete is a most utilized material in the construction industry that have main components. The strength of concrete can be improved by adding some admixtures. Evaluating the impact of fly ash (FA) and silica fume (SF) on the long-term compressive strength (CS) of concrete provokes to find the significant parameters in predicting the CS, which could be useful in the practical works and would be extensible in the future analysis. In this study, to evaluate the effective parameters in predicting the CS of concrete containing admixtures in the long-term and present a fitted equation, the multivariate adaptive regression splines (MARS) method has been used, which could find a relationship between independent and dependent variables. Next, for optimizing the output equation, biogeography-based optimization (BBO), particle swarm optimization (PSO), and hybrid PSOBBO methods have been utilized to find the most optimal conclusions. It could be concluded that for CS predictions in the long-term, all proposed models have the coefficient of determination (R2) larger than 0.9243. Furthermore, MARS-PSOBBO could be offered as the best model to predict CS between three hybrid algorithms accurately.