• Journal of Electrical Engineering and Technology
• /
• v.3 no.4
• /
• pp.566-570
• /
• 2008

Vertical nanowire SGFETs(Surrounding Gate Field Effect Transistors) provide full gate control over the channel to eliminate short channel effects. This paper presents design and characterization of a differential pair amplifier using NMOS and PMOS SGFETs with a 10nm channel length and a 2nm channel radius. The amplifier dissipates $5{\mu}W$ power and provides 5THz bandwidth with a voltage gain of 16, a linear output voltage swing of 0.5V, and a distortion better than 3% from a 1.8V power supply and a 20aF capacitive load. The 2nd and 3rd order harmonic distortions of the amplifier are -40dBm and -52dBm, respectively, and the 3rd order intermodulation is -24dBm for a two-tone input signal with 10mV amplitude and 10GHz frequency spacing. All these parameters indicate that vertical nanowire surrounding gate transistors are promising candidates for the next generation high speed analog and VLSI technologies.

• Biotechnology and Bioprocess Engineering:BBE
• /
• v.9 no.3
• /
• pp.156-165
• /
• 2004

Concentrations of substrates, glucose, and ammionia in biological processes have been on-line monitored by using glucose-flow injection (FIA) and ammonia-FIA systems. Based on the on-line monitored data the concentrations of substrates have been controlled by an on-off controller, a PID controller, and a neural network (NN) based controller. A simulation program has been developed to test the control quality of each controller and to estimate the control parameters. The on-off controller often produced high oscillations at the set point due to its low robustness. The control quality of a PID controller could have been improved by a high analysis frequency and by a short residence time of sample in a FIA system. A NN-based controller with 3 layers has been developed, and a 3(input)-2(hidden)-1(output) network structure has been found to be optimal for the NN-based controller. The performance of the three controllers has been tested in a simulated process as well as in a cultivation process of Saccharomyces cerevisiae, and the performance has also been compared to simulation results. The NN-based controller with the 3-2-1 network structure was robust and stable against some disturbances, such as a sudden injection of distilled water into a biological process.

• Steel and Composite Structures
• /
• v.18 no.3
• /
• pp.547-563
• /
• 2015

Deflection in a beam of a composite frame is a serviceability design criterion. This paper presents a methodology for rapid prediction of long-term mid-span deflections of beams in composite frames subjected to service load. Neural networks have been developed to predict the inelastic mid-span deflections in beams of frames (typically for 20 years, considering cracking, and time effects, i.e., creep and shrinkage in concrete) from the elastic moments and elastic mid-span deflections (neglecting cracking, and time effects). These models can be used for frames with any number of bays and stories. The training, validating, and testing data sets for the neural networks are generated using a hybrid analytical-numerical procedure of analysis. Multilayered feed-forward networks have been developed using sigmoid function as an activation function and the back propagation-learning algorithm for training. The proposed neural networks are validated for an example frame of different number of spans and stories and the errors are shown to be small. Sensitivity studies are carried out using the developed neural networks. These studies show the influence of variations of input parameters on the output parameter. The neural networks can be used in every day design as they enable rapid prediction of inelastic mid-span deflections with reasonable accuracy for practical purposes and require computational effort which is a fraction of that required for the available methods.

• Journal of Environmental Impact Assessment
• /
• v.13 no.4
• /
• pp.153-164
• /
• 2004

An integration study of time-variable small watershed and stream models (USEPA's SWMM and WASP5) was performed for impact assessment of urbanization on water environment. The study area, the Kyoungan Stream, the tributary of Paldang Lake, was divided into 111 subbasins, based on the topographic condition, land use, and drainage system. RUNOFF block of SWMM was applied to estimate runoff flow and quality. EXTRAN block computed daily and hourly flow according to simulated runoff flow, water supply, and drainage data. SWMM was connected to WASP5 by transforming output file of SWMM into input file of WASP5. The nonpoint source loads and flow data of SWMM were imported to WASP5. The stream was divided into 45 segments based on the watershed delineation. The study included three water quality parameters, BOD, TN, and TP. The validate models were used to examine the impact of urbanization on stream flow and water quality.

• Journal of Power Electronics
• /
• v.19 no.1
• /
• pp.24-33
• /
• 2019

A new modeling method for AC-AC switched capacitor converters (SCCs) is introduced in this study. The proposed analytical method aims to accurately describe the input-output characteristics of AC-AC SCCs and establish a mathematical model for static voltage conversion ratio and equivalent resistance, which are key performance metrics for SCCs. A quantitative analysis of converter regulation capability is addressed on the basis of the modeling method. In this analysis, the effects of the control parameters and individual components on SCCs are illustrated extensively. Component stresses, such as the peak value and transient variation of the voltage/current of the converter, are also presented. The effectiveness of the proposed method is verified by comparing it with the existing modeling method and applying it to an AC-AC SCC with a conversion ratio of three. Two 1 kW prototypes are built in a laboratory, and their experimental results exhibit good agreement with the theoretical analysis.

• Geomechanics and Engineering
• /
• v.28 no.6
• /
• pp.599-611
• /
• 2022

Tendon reinforced cemented soil is applied extensively in foundation stabilisation and improvement, especially in areas with soft clay. To solve the deterioration problem led by steel corrosion, the glass fiber-reinforced polymer (GFRP) tendon is introduced to substitute the traditional steel tendon. The interface bond strength between the cemented soil matrix and GFRP tendon demonstrates the outstanding mechanical property of this composite. However, the lack of research between the influence factors and bond strength hinders the application. To evaluate these factors, back propagation neural network (BPNN) is applied to predict the relationship between them and bond strength. Since adjusting BPNN parameters is time-consuming and laborious, the particle swarm optimisation (PSO) algorithm is proposed. This study evaluated the influence of water content, cement content, curing time, and slip distance on the bond performance of GFRP tendon-reinforced cemented soils (GTRCS). The results showed that the ultimate and residual bond strengths were both in positive proportion to cement content and negative to water content. The sample cured for 28 days with 30% water content and 50% cement content had the largest ultimate strength (3879.40 kPa). The PSO-BPNN model was tuned with 3 neurons in the input layer, 10 in the hidden layer, and 1 in the output layer. It showed outstanding performance on a large database comprising 405 testing results. Its higher correlation coefficient (0.908) and lower root-mean-square error (239.11 kPa) were obtained compared to multiple linear regression (MLR) and logistic regression (LR). In addition, a sensitivity analysis was applied to acquire the ranking of the input variables. The results illustrated that the cement content performed the strongest influence on bond strength, followed by the water content and slip displacement.

• Proceedings of the Korean Society for Bioinformatics Conference
• /
• 2005.09a
• /
• pp.202-205
• /
• 2005

Cell phenotypes are determined by the concerted activity of thousands of genes and their products. This activity is coordinated by a complex network that regulates the expression of genes. Understanding this organization is crucial to elucidate cellular activities, and many researches have tried to construct gene regulatory networks from mRNA expression data which are nowadays the most available and have a lot of information for cellular processes. Several computational tools, such as Boolean network, Qualitative network, Bayesian network, and so on, have been applied to infer these networks. Among them, Bayesian networks that we chose as the inference tool have been often used in this field recently due to their well-established theoretical foundation and statistical robustness. However, the relative insufficiency of experiments with respect to the number of genes leads to many false positive inferences. To alleviate this problem, we had developed the algorithm of MONET(MOdularized NETwork learning), which is a new method for inferring modularized gene networks by utilizing two complementary sources of information: biological annotations and gene expression. Afterward, we have packaged and improved MONET by combining dispersed functional blocks, extending species which can be inputted in this system, reducing the time complexities by improving algorithms, and simplifying input/output formats and parameters so that it can be utilized in actual fields. In this paper, we present the architecture of MONET system that we have improved.

• Structural Engineering and Mechanics
• /
• v.54 no.2
• /
• pp.337-362
• /
• 2015

This paper presents a feasibility study on structural damage alarming and localization of long-span cable-supported bridges using multi-novelty indices formulated by monitoring-derived modal parameters. The proposed method which requires neither structural model nor damage model is applicable to structures of arbitrary complexity. With the intention to enhance the tolerance to measurement noise/uncertainty and the sensitivity to structural damage, an improved novelty index is formulated in terms of auto-associative neural networks (ANNs) where the output vector is designated to differ from the input vector while the training of the ANNs needs only the measured modal properties of the intact structure under in-service conditions. After validating the enhanced capability of the improved novelty index for structural damage alarming over the commonly configured novelty index, the performance of the improved novelty index for damage occurrence detection of large-scale bridges is examined through numerical simulation studies of the suspension Tsing Ma Bridge (TMB) and the cable-stayed Ting Kau Bridge (TKB) incurred with different types of structural damage. Then the improved novelty index is extended to formulate multi-novelty indices in terms of the measured modal frequencies and incomplete modeshape components for damage region identification. The capability of the formulated multi-novelty indices for damage region identification is also examined through numerical simulations of the TMB and TKB.

• International Journal of Internet, Broadcasting and Communication
• /
• v.11 no.4
• /
• pp.26-30
• /
• 2019

This paper proposes a new voice activity detection (VAD) method which is based on SNR and non-intrusive speech intelligibility estimation. In the conventional SNR-based VAD methods, voice activity probability is obtained by estimating frame-wise SNR at each spectral component. However these methods lack performance in various noisy environments. We devise a hybrid VAD method that uses non-intrusive speech intelligibility estimation as well as SNR estimation, where the speech intelligibility score is estimated based on deep neural network. In order to train model parameters of deep neural network, we use MFCC vector and the intrusive speech intelligibility score, STOI (Short-Time Objective Intelligent Measure), as input and output, respectively. We developed speech presence measure to classify each noisy frame as voice or non-voice by calculating the weighted average of the estimated STOI value and the conventional SNR-based VAD value at each frame. Experimental results show that the proposed method has better performance than the conventional VAD method in various noisy environments, especially when the SNR is very low.

• Nuclear Engineering and Technology
• /
• v.54 no.4
• /
• pp.1516-1525
• /
• 2022

Interfacing the output of severe accident analysis with the input of radiological consequence analysis is an important and mandatory procedure at the beginning of Level 3 PSA. Such interfacing between the severe accident analysis code MELCOR and MACCS, one of the most commonly used consequence analysis codes, is relatively tractable since they share the same chemical groups, and the related interfacing software, MelMACCS, has already been developed. However, the linking between MAAP, another frequently used code for severe accident analyses, and MACCS has difficulties because MAAP employs a different chemical grouping method than MACCS historically did. More specifically, MAAP groups by chemical compound, while MACCS groups by chemical element. An appropriate interfacing method between MAAP and MACCS has therefore long been requested by users. This study suggests a way of extracting relevant information from MAAP results and providing proper source term information to MACCS by an appropriate treatment. Various parameters are covered in terms of magnitude and manner of release in this study, and special treatment is made for a bypass scenario. It is expected that the suggested approach will provide an important contribution as a guide to interface MAAP and MACCS when performing radiological consequence analyses.