• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.11B
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• pp.1667-1675
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• 2010

This paper presents a high efficiency DC-DC buck converter for mobile device. The circuit employes simplified compensation circuit for its portability and for high efficiency at stand-by mode. This device operates at PFM mode when it enters stand-by mode(light load). In order to place the compensation circuit on chip, the capacitor multiplier method is employed, such that it can minimize the compensation block size of the error amplifier down to 30%. The measurement results show that the buck converter provides a peak efficiency of 93% on PWM mode, and 92.3% on PFM mode. The converter has been fabricated with a $0.35{\mu}m$ CMOS technology. The input voltage of the buck converter ranges from 2.5V to 3.3V and it generates the output of 3.3V.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.11 no.4
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• pp.657-665
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• 2000

In this study, the bit error rate(BER) performance for the speech and data traffic is evaluated by results of the reverse link simulation of CDMA-based IMT-2000. Simulations in the reverse link are achieved for indoor, pedestrian, and vehicular environments, which are provided by ITU-R . Also, in the these simulations, the fast power control of 1.6kHz rate is applied. The amplitude and phase of the fading signal are estimated by using the 5-tap FIR filter, and the soft-decision Viterbi and Reed-Solomon (RS) decoding are applied. Simulation results provide the optimum ratio of pilot power to traffic power, the BER performance according to the number of fingers, and performance comparison between convolutional code and concatenated code at $10^-6$ BER in 5 MHz system.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.7C
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• pp.611-617
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• 2010

In this paper, we investigate the performances of the serial concatenated convolutional codes (SCCC) and low-density parity-check (LDPC) codes on perpendicular magnetic recording (PMR) channels. We discuss the performance of two systems when user bit-densities are 1.7, 2.0, 2.4 and 2.8, respectively. The SCCC system is less complex than LDPC system. The SCCC system consists of recursive systematic convolutional (RSC) codes encoder/decoder, precoder and random interleaver. The decoding algorithm of the SCCC system is the soft message-passing algorithm and the decoding algorithm of the LDPC system is the log domain sum-product algorithm (SPA). When we apply the iterative decoding between channel detector and the error control codes (ECC) decoder, the SCCC system is compatible with the LDPC system even at the high user bit density.

• Composites Research
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• v.28 no.6
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• pp.383-388
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• 2015

Ionic Polymer-Metal Composite (IPMC) consisting of soft membrane plated by platinum electrode layers on both surfaces generates electric energy when subjected to various mechanical stimuli. The paper proposes a circuit model that describes the physical composition of IPMC to predict the voltage generation characteristic corresponding to bending motion. The parameter values in the model are identified to minimize the RMS error between the real and simulated outputs. Following the design of IPMC circuit model, the state observer of the model is designed by using pole placement technique which improves the model accuracy. State observer design technique is also applied to find the inverse model which estimates the input bending angles from the output voltage data. The results show that the inverse model estimates input bending angles fairly well enough for the further applications of IPMC not only as an energy harvester but also as a bending sensor.

• Journal of Korean Tunnelling and Underground Space Association
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• v.19 no.2
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• pp.195-212
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• 2017

When back analysis is used for the assessment of an operating subsea tunnel safety in various measurement information such as stress, water pressure and tunnel lining and ground stiffness degradation, the reliable results within tolerable error rate can be obtained. By utilizing a commercial geotechnical analysis program FLAC3D, back analysis can be performed with a DEA which has already been successfully validated in previous studies. However, relative more time-consumption is the drawback of this approach. For this reason, this study introduced beam-spring model-based on FEM solver which uses less analysis time relatively. Beam-spring program capable of structural analysis of a circular tunnel section was developed by using Python language and combined with the built-DEA. From the measurement datum, expected to estimate the stability of an operation tunnel close to real-time.

• Analytical Science and Technology
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• v.17 no.4
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• pp.308-314
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• 2004

When the polymer was used for the guard raw materials of electronic device, the content of U, Th and their daughter nuclides were known as a factor of soft error. Because emitted alpha ray could be caused of mis-operation. And ionic impurities such as Cl, Fe, Na could shorten the device life-time. For the analysis of trace impurities in the polymer, neutron activation analysis(NAA) and ICP/AES have been studied. To improve the accuracy and sensitivity of the trace and ultratrace metallic impurities in the epoxy and phenol polymer, sample pretreatment method and optimum analytical condition of NAA were developed. Using the above method, U, Th and other 23 trace impurity elements were analyzed.

• Computers and Concrete
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• v.27 no.4
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• pp.319-332
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• 2021

The performance of gene expression programming (GEP) in predicting the compressive strength of bacteria-incorporated geopolymer concrete (GPC) was examined in this study. Ground-granulated blast-furnace slag (GGBS), new bacterial strains, fly ash (FA), silica fume (SF), metakaolin (MK), and manufactured sand were used as ingredients in the concrete mixture. For the geopolymer preparation, an 8 M sodium hydroxide (NaOH) solution was used, and the ambient curing temperature (28℃) was maintained for all mixtures. The ratio of sodium silicate (Na2SiO3) to NaOH was 2.33, and the ratio of alkaline liquid to binder was 0.35. Based on experimental data collected from the literature, an evolutionary-based algorithm (GEP) was proposed to develop new predictive models for estimating the compressive strength of GPC containing bacteria. Data were classified into training and testing sets to obtain a closed-form solution using GEP. Independent variables for the model were the constituent materials of GPC, such as FA, MK, SF, and Bacillus bacteria. A total of six GEP formulations were developed for predicting the compressive strength of bacteria-incorporated GPC obtained at 1, 3, 7, 28, 56, and 90 days of curing. 80% and 20% of the data were used for training and testing the models, respectively. R2 values in the range of 0.9747 and 0.9950 (including train and test dataset) were obtained for the concrete samples, which showed that GEP can be used to predict the compressive strength of GPC containing bacteria with minimal error. Moreover, the GEP models were in good agreement with the experimental datasets and were robust and reliable. The models developed could serve as a tool for concrete constructors using geopolymers within the framework of this research.

• Advances in nano research
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• v.12 no.2
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• pp.185-195
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• 2022

Deep learning is another field of artificial intelligence (AI) utilized for computer aided diagnosis (CAD) and image processing in scientific research. Considering numerous mechanical repetitive tasks, reading image slices need time and improper with geographical limits, so the counting of image information is hard due to its strong subjectivity that raise the error ratio in misdiagnosis. Regarding the highest mortality rate of Lung cancer, there is a need for biopsy for determining its class for additional treatment. Deep learning has recently given strong tools in diagnose of lung cancer and making therapeutic regimen. However, identifying the pathological lung cancer's class by CT images in beginning phase because of the absence of powerful AI models and public training data set is difficult. Convolutional Neural Network (CNN) was proposed with its essential function in recognizing the pathological CT images. 472 patients subjected to staging FDG-PET/CT were selected in 2 months prior to surgery or biopsy. CNN was developed and showed the accuracy of 87%, 69%, and 69% in training, validation, and test sets, respectively, for T1-T2 and T3-T4 lung cancer classification. Subsequently, CNN (or deep learning) could improve the CT images' data set, indicating that the application of classifiers is adequate to accomplish better exactness in distinguishing pathological CT images that performs better than few deep learning models, such as ResNet-34, Alex Net, and Dense Net with or without Soft max weights.

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

• Geomechanics and Engineering
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• v.31 no.6
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• pp.545-556
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• 2022

Evaluation and optimization of tunnel wall convergence (TWC) plays a vital role in preventing potential problems during tunnel construction and utilization stage. When convergence occurs at a high rate, it can lead to significant problems such as reducing the advance rate and safety, which in turn increases operating costs. In order to design an effective solution, it is important to accurately predict the degree of TWC; this can reduce the level of concern and have a positive effect on the design. With the development of soft computing methods, the use of deep learning algorithms and neural networks in tunnel construction has expanded in recent years. The current study aims to employ the long-short-term memory (LSTM) deep neural network predictor model to predict the TWC, based on 550 data points of observed parameters developed by collecting required data from different tunnelling projects. Among the data collected during the pre-construction and construction phases of the project, 80% is randomly used to train the model and the rest is used to test the model. Several loss functions including root mean square error (RMSE) and coefficient of determination (R²) were used to assess the performance and precision of the applied method. The results of the proposed models indicate an acceptable and reliable accuracy. In fact, the results show that the predicted values are in good agreement with the observed actual data. The proposed model can be considered for use in similar ground and tunneling conditions. It is important to note that this work has the potential to reduce the tunneling uncertainties significantly and make deep learning a valuable tool for planning tunnels.