• Steel and Composite Structures
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• v.25 no.6
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• pp.663-670
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• 2017

The problem of layup optimization of the composite laminates involves a very complex multidimensional solution space which is usually non-exhaustively explored using different heuristic computational methods such as genetic algorithms (GA). To ensure the convergence to the global optimum of the applied heuristic during the optimization process it is necessary to evaluate a lot of layup configurations. As a consequence the analysis of an individual layup configuration should be fast enough to maintain the convergence time range to an acceptable level. On the other hand the mechanical behavior analysis of composite laminates for any geometry and boundary condition is very convoluted and is performed by computational expensive numerical tools such as finite element analysis (FEA). In this respect some studies propose very fast FEA models used in layup optimization. However, the lower bound of the execution time of FEA models is determined by the global linear system solving which in some complex applications can be unacceptable. Moreover, in some situation it may be highly preferred to decrease the optimization time with the cost of a small reduction in the analysis accuracy. In this paper we explore some machine learning techniques in order to estimate the failure of a layup configuration. The estimated response can be qualitative (the configuration fails or not) or quantitative (the value of the failure factor). The procedure consists of generating a population of random observations (configurations) spread across solution space and evaluating using a FEA model. The machine learning method is then trained using this population and the trained model is then used to estimate failure in the optimization process. The results obtained are very promising as illustrated with an example where the misclassification rate of the qualitative response is smaller than 2%.

• The KIPS Transactions:PartC
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• v.12C no.4 s.100
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• pp.571-580
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• 2005

In fast long-distance networks, TCP's congestion control algorithm has the problem of utilizing bandwidth effectively. Several window-based congestion control protocols for high-speed and large delay networks have been proposed to solve this problem. These protocols deliberate mainly three properties : scalability, TCP-friendliness, and RTT-fairness. These protocols, however, cannot satisfy above three properties at the same time because of the trade-off among them This paper presents a new window-based congestion control algorithm, called EM (Exponential Increase/ Multiplicative Decrease), that simultaneously supports all four properties including fast convergence, which is another important constraint for fast long-distance networks; it can support scalability by increasing congestion window exponentially proportional to the time elapsed since a packet loss; it can support RTT-fairness and TCP-friendliness by considering RTT in its response function; it can support last fair-share convergence by increasing congestion window inversely proportional to the congestion window just before packet loss. We evaluate the performance of EIMD and other algorithms by extensive computer simulations.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.9A
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• pp.755-761
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• 2005

In this paper, we proposed a channel estimation method by impulse signal train in OFDM. In order to estimate the channel response, 4 impulse signals are generated and transmitted during one OFDM (Orthogonal Frequency Division Multiplexing) symbol. The intervals between the impulse signals are all equal in time domain. At the receiver, the impulse response signals are summed and averaged. And then, the averaged impulse response signal is zero padded and fast Fourier transformed to obtain the channel estimation. The BER performance of the proposed method is compared with those of conventional estimation method using the long training sequence in fast fading environments. The simulation results show that the proposed method improves by 3 dB in terms of Eb/No, compared with the conventional method.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2009.04b
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• pp.93-93
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• 2009

In order to overcome the lack of reactivity with hydrogen gas ($H_2$) and utilize unique properties of Carbon Nano Tubes (CNTs) for the application to hydrogen sensors, there have been intensive works on the surface functionalization of CNTs with various types of nanoparticles including Pd. In the present work, we have investigated the effect of dendrimers and Pd nanoparticles to the hydrogen sensing properties of CNTs by comparing three types of samples: Pd/SWNTs (Sample I), Pd/dendrimer/SWNTs (Sample II) and heat-treated Pd/dendrimers/SWNTs (Sample III). As a result of IV measurement under the $H_2$ and air, sample I was found to have a high sensitivity (25%) to $H_2$, but to have a very slow response time (324 s) and recovery rate. On the other hand, Sample II was found to show much faster response time (3 s) and good recovery rate but lower sensitivity (8.6%) than Sample I which is due to induced dipole moments in the dendrimers. Interestingly, Sample III showed both fast response time (7 s) and high sensitivity (25%), indicating that the pyrolysis of the dendrimers during heat treatment which reduce the distance between the surface of the SWNTs and the functionalized Pd nanoparticles plays a key role in improving the sensitivity. The pyrolysis of the dendrimers in Pd nanoparticle-dendrimer-SWNTs was found to enable a significant electrical conductance modulation upon exposure to extremely low concentrations (10 ppm) of $H_2$ in air. Our results demonstrate that the Pd Nanoparticle-Grafted Single-Walled Carbon Nanotubes(SWNTs) with Dendrimers can be used to detect hydrogen, makingoutstanding properties such as fast response, and recovery time, high sensitivity, low detection limit at room temperature compared with other types of hydrogen sensors.

• Transactions on Electrical and Electronic Materials
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• v.17 no.3
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• pp.172-177
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• 2016

Miller feedback compensation is introduced in a low dropout regulator (LDO) in order to obtain a capacitor-free regulator and improve the fast transient response. The conventional LDO has a limited bandwidth because of the large-size output capacitor and parasitic gate capacitance in the power MOSFET. In order to obtain a stable frequency response without the output capacitor, LDO is designed with resistor-capacitor (R-C) compensation and this is achieved with a connection between the gain-stage and the power MOS. An R-C compensator is suggested to provide a pole and zero to improve the stability. The proposed LDO is designed with the 0.35 μm CMOS process. Simulation testing shows that the phase margin in the Bode plot indicates a stable response, which is over 100^o. In the load regulation, the transient time is within 55 μs when the load current changes from 0.1 to 1 mA.

• Journal of Communications and Networks
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• v.8 no.1
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• pp.93-105
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• 2006

The domination of the Internet by TCP-based services has spawned many efforts to provide high network utilization with low loss and delay in a simple and scalable manner. Active queue management (AQM) algorithms attempt to achieve these goals by regulating queues at bottleneck links to provide useful feedback to TCP sources. While many AQM algorithms have been proposed, most suffer from instability, require careful configuration of nonintuitive control parameters, or are not practical because of slow response to dynamic traffic changes. In this paper, we propose a new AQM algorithm, hybrid random early detection (HRED), that combines the more effective elements of recent algorithms with a random early detection (RED) core. HRED maps instantaneous queue length to a drop probability, automatically adjusting the slope and intercept of the mapping function to account for changes in traffic load and to keep queue length within the desired operating range. We demonstrate that straightforward selection of HRED parameters results in stable operation under steady load and rapid adaptation to changes in load. Simulation and implementation tests confirm this stability, and indicate that overall performances of HRED are substantially better than those of earlier AQM algorithms. Finally, HRED control parameters provide several intuitive approaches to trading between required memory, queue stability, and response time.

• Bulletin of the Korean Chemical Society
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• v.26 no.8
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• pp.1219-1224
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• 2005

We prepared lead ion-selective PVC membranes that were based on N,N'-bis-thiophene-2-ylmethylene-ethane-1,2-diamine as a membrane carrier. The membrane electrode has a linear dynamic range between 1.0 ${\times}$ $10^{-5}$ and 1.0 ${\times}$ $10^{-1}$ M with a Nernstian slope of 29.79 mV per decade, and its detection limit was 2.04 ${\times}$ $10^{-6}$ M at room temperature. The potentiometric response is independent of the pH of the solution in the pH range of 5-7. The proposed electrode revealed good selectivity and response for $Pb^{2+}$ over a wide variety of other metal ions in pH 5.0 buffer solutions, and there was good reproducibility of the base line on the subsequent measurements. The membrane electrode has a relatively fast response time, satisfactory reproducibility and a relatively long life time.

• Bulletin of the Korean Chemical Society
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• v.32 no.10
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• pp.3592-3596
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• 2011

A novel poly(vinylchloride)-based membrane sensor using $N^1$,$N^3$,$N^5$-tris(2-(2,3-dihydroxybenzylamino)-ethyl)cyclohexane-1,3,5-tricarboxamide (CYCOENCAT, L) as ionophore has been prepared and explored as $Fe^{3+}$ selective electrode. The membrane electrode composed of ionophore, poly(vinylchloride) and o-nitropheyloctyl ether in the optimum ratio 4:33:63 gave excellent potentiometric response characteristics, and displayed a linear log[$Fe^{3+}$] versus EMF response over a wide concentration range of $1.0{\times}10^{-5}-1.0{\times}10^{-1}$ M with super nernstian slope of 28.0 mV/decade and the detection limit of $8.0{\times}10^{-6}$ M. The proposed ion selective electrode showed fast response time (< 15 s), wide pH range (3.0-7.0), high non-aqueous tolerance (up to 20%) and adequate long life time (120 days). It also exhibited very good selectivity for $Fe^{3+}$ relative to a wide variety of alkali, alkaline earth, transition and heavy metal ions. Further, the analytical applicability of the sensor was tested as an indicator electrode in the potentiometric titration of $Fe^{3+}$ with EDTA.

• Journal of Power Electronics
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• v.15 no.4
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• pp.1093-1104
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• 2015

A standard repetitive controller (RC) is theoretically able to replace a bank of resonant controllers in harmonic signals tracking applications. However, the traditional RC has some drawbacks such as a poor dynamic response and a complex structure to compensate grid frequency deviations for an effective unified power quality conditioner (UPQC) control scheme. In order to solve these problems, an improved RC with an outstanding dynamic response and a simplified grid frequency adaptive scheme is proposed for UPQC control systems in this paper. The control strategy developed for the UPQC has delay time, i.e., one-sixth of a fundamental period (T_p/6), repetitive controllers. As a result, the UPQC system can provide a fast dynamic response along with good compensation performance under both nonlinear and unbalanced loads. Furthermore, to guarantee the excellent performance of the UPQC under grid frequency deviations, a grid frequency adaptive scheme was developed for the RC using a simple first order Padé's approximation. When compared with other approaches, the proposed control method is simpler in structure and requires little computing time. Moreover, the entire control strategy can be easily implemented with a low-cost DSP. The effectiveness of the proposed control method is verified through various experimental tests.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.40 no.3
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• pp.171-180
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• 2003

A content-based image retrieval(CBIR) system builds the image database using low-level features such as color, shape and texture and provides similar images that user wants to retrieve when the retrieval request occurs. What the user is interest in is a response time in consideration of the building time to build the index database and the response time to obtain the retrieval results from the query image. In a content-based image retrieval system, the similarity computing time comparing a query with images in database takes the most time in whole response time. In this paper, we propose the two-phase search method with the clustering technique of feature vector in order to minimize the similarity computing time. Experimental results show that this two-phase search method is 2-times faster than the conventional full-search method using original features of ail images in image database, while maintaining the same retrieval relevance as the conventional full-search method. And the proposed method is more effective as the number of images increases.