• Journal of the Korea Institute of Information and Communication Engineering
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• v.8 no.3
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• pp.535-540
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• 2004

In this paper, We proposed a rectangular slot antenna with CPW feeder. Slot antennas fed by CPW are attractive due to the simple fabrication simplicity and ease of integration with active devices. This antenna consists of two parts, inner patch and outer patch to realize wide-band antenna by multi-frequency. Also, We fabricated a proposed rectangular slot antenna, confirm characteristics of multi-frequency by tuning antenna parameters, inner antenna's location and size. The experimental results show that each resonant frequency of a fabricated antenna is measured at almost 1, 9GHz, 2.8GHz, 3, 5GHz, 4, 9GHz. In radiation patterns each resonant frequency, radiation pattern 4-th resonant frequency is the same ad that of TM11 in patch antenna. Therefore, the experimental and theoretical results shows that a proposal rectangular slot antenna have triple resonant frequencies.

• Proceedings of the Korean Vacuum Society Conference
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• 2013.08a
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• pp.90.2-90.2
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• 2013

Vertically-aligned silicon nanostructure arrays (SNAs) have been drawing much attention due to their useful electrical properties, large surface area, and quantum confinement effect. SNAs are typically fabricated by chemical vapor deposition, reactive ion etching, or wet chemical etching. Recently, metal-assisted chemical etching process, which is relatively simple and cost-effective, in combination with nanosphere lithography was recently demonstrated for vertical SNA fabrication with controlled SNA diameters, lengths, and densities. However, this method exhibits limitations in terms of large-area preparation of unperiodic nanostructures and SNA geometry tuning independent of inter-structure separation. In this work, we introduced the layerby- layer deposition of polyelectrolytes for holding uniformly dispersed polystyrene beads as mask and demonstrated the fabrication of well-dispersed vertical SNAs with controlled geometric parameters on large substrates. Additionally, we present a new means of building in vitro neuronal networks using vertical nanowire arrays. Primary culture of rat hippocampal neurons were deposited on the bare and conducting polymer-coated SNAs and maintained for several weeks while their viability remains for several weeks. Combined with the recently-developed transfection method via nanowire internalization, the patterned vertical nanostructures will contribute to understanding how synaptic connectivity and site-specific perturbation will affect global neuronal network function in an extant in vitro neuronal circuit.

• International Journal of Computer Science & Network Security
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• v.22 no.10
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• pp.171-176
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• 2022

A dramatic rise in the number of people dying from heart disease has prompted efforts to find a way to identify it sooner using efficient approaches. A variety of variables contribute to the condition and even hereditary factors. The current estimate approaches use an automated diagnostic system that fails to attain a high level of accuracy because it includes irrelevant dataset information. This paper presents an effective neural network with convolutional layers for classifying clinical data that is highly class-imbalanced. Traditional approaches rely on massive amounts of data rather than precise predictions. Data must be picked carefully in order to achieve an earlier prediction process. It's a setback for analysis if the data obtained is just partially complete. However, feature extraction is a major challenge in classification and prediction since increased data increases the training time of traditional machine learning classifiers. The work integrates the CNN-MDRP classifier (convolutional neural network (CNN)-based efficient multimodal disease risk prediction with TANFIS (tuned adaptive neuro-fuzzy inference system) for earlier accurate prediction. Perform data cleaning by transforming partial data to informative data from the dataset in this project. The recommended TANFIS tuning parameters are then improved using a Laplace Gaussian mutation-based grasshopper and moth flame optimization approach (LGM²G). The proposed approach yields a prediction accuracy of 98.40 percent when compared to current algorithms.

• International Journal of Computer Science & Network Security
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• v.21 no.11
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• pp.73-80
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• 2021

Nowadays microblogs have become the most popular platforms to obtain and spread information. Twitter is one of the most used platforms to share everyday life event. However, rumors and misinformation on Arabic social media platforms has become pervasive which can create inestimable harm to society. Therefore, it is imperative to tackle and study this issue to distinguish the verified information from the unverified ones. There is an increasing interest in rumor detection on microblogs recently, however, it is mostly applied on English language while the work on Arabic language is still ongoing research topic and need more efforts. In this paper, we propose a combined Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) to detect rumors on Twitter dataset. Various experiments were conducted to choose the best hyper-parameters tuning to achieve the best results. Moreover, different neural network models are used to evaluate performance and compare results. Experiments show that the CNN-LSTM model achieved the best accuracy 0.95 and an F1-score of 0.94 which outperform the state-of-the-art methods.

• Transactions of Materials Processing
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• v.32 no.2
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• pp.74-80
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• 2023

A deformation behavior of commercially pure titanium (CP-Ti) is highly dependent on material and processing parameters, such as deformation temperature, deformation direction, and strain rate. This study aims to predict the multivariable and nonlinear tensile behavior of CP-Ti using machine learning based on three algorithms: artificial neural network (ANN), light gradient boosting machine (LGBM), and long short-term memory (LSTM). The predictivity for tensile behaviors at the cryogenic temperature was lower than those in the room temperature due to the larger data scattering in the train dataset used in the machine learning. Although LGBM showed the lowest value of root mean squared error, it was not the best strategy owing to the overfitting and step-function morphology different from the actual data. LSTM performed the best as it effectively learned the continuous characteristics of a flow curve as well as it spent the reduced time for machine learning, even without sufficient database and hyperparameter tuning.

• Proceedings of the KIEE Conference
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• 2008.10b
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• pp.187-192
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• 2008

A saturation and deadzone compensator is designed for robot systems using fuzzy logic (FL) and neural network (NN). The classification property of FL system and the function approximation ability of the NN make them the natural candidate for the rejection of errors induced by the saturation and deadzone. The tuning algorithms are given for the fuzzy logic parameters and the NN weights, so that the saturation and deadzone compensation scheme becomes adaptive, guaranteeing small tracking errors and bounded parameter estimates. Formal nonlinear stability proofs are given to show that the tracking error is small. The NN saturation and FL deadzone compensator is simulated on a robot system to show its efficacy.

• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.794-796
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• 1999

본 논문에서는 3상 유도전동기의 속도를 제어하는데 기존 제어기의 문제점을 해결하고 최적화하기 위해서 유전자 알고리즘을 이용한 하이브리드 퍼지 -PID(HFPID) 제어기를 고안하고, 이에 대한 파라미터 설정 방법을 제안한다. 유도전동기의 제어는 지연시간이 길고, 비선형성이 강하며, 부하변동이 잦은 프로세스이기 때문에, 기존의 제어방식으로는 만족할만한 결과를 얻을 수 없다. 제안한 하이브리드 퍼지-PID 제어기는 PID 제어기의 장점인 과도기의 우수성과 퍼지 제어기의 장점인 정상기의 우수성을 퍼지 변수로 결합시켜 설계한다. 이 제어기에 유전자 알고리즘을 적용하여 최적의 퍼지 및 PID 파라미터를 설정하다. 그리고 이 제어기를 3상 유도전동기의 속도 제어에 응용한다. 또한 속도오차에 대한 룩업 표를 만들어 온라인 실시간 제어를 가능하게 한다. 이상의 과정을 3상 유도전동기에서 컴퓨터 시뮬레이션 하였다. 시뮬레이션 결과를 비교해 볼 때, 하이브리드 퍼지-PID 제어기는 기존의 제어기 보다 전동기의 속도 및 토크성분 전류 둥의 특성에서 우수한 성능을 보였다.

• Journal of KIISE
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• v.42 no.11
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• pp.1404-1409
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• 2015

In this paper, we present a novel approach to analyzing large-scale JavaScript applications statically by tuning the analysis scalability, possibly sacrificing soundness. For a given sound static baseline analysis of JavaScript programs, our framework allows users to define a sound approximation of selected executions that they wish to analyze, and it derives a tuned static analysis that can analyze the selected executions practically. The selected executions serve as parameters of the framework by taking a trade-off between the scalability and the soundness of the derived analyses. We formally describe our framework in the abstract interpretation setting and present two instances of the framework.

• The KIPS Transactions:PartB
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• v.9B no.2
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• pp.231-236
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• 2002

The classification of the speech-sound block comes from the multi-resolution analysis property of the discrete wavelet transform, which is used to reduce the computational time for the pre-processing of speech recognition. The merging algorithm is proposed to extract vapid speech-sounds in terms of position and frequency range. It performs unvoiced/voiced classification and denoising. Since the merging algorithm can decide the processing parameters relating to voices only and is independent of system noises, it is useful for extracting valid speech-sounds. The merging algorithm has an adaptive feature for arbitrary system noises and an excellent denoising signal-to-noise ratio and a useful system tuning for the system implementation.

• Korean Chemical Engineering Research
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• v.45 no.1
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• pp.12-16
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• 2007

Temperature control of an autothermal methanol reforming reactor which uses the copper-zinc oxide catalyst was studied. Temperature at 1cm below the hot-spot point in the reactor was used for the controlled variable, and the air flow rate was used for the manipulated variable. A first order plus time delay model was identified and controller parameters were obtained by applying the IMC-PI tuning rule to the identified model. With this controller, we could control the reforming reactor temperature within ${\pm}5^{\circ}C$ over 100 hours. Change of the hot-spot point due to the catalyst degradation was investigated and it could be used to design an adaptive controller.