• Proceedings of the Korea Electromagnetic Engineering Society Conference
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• 2003.11a
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• pp.234-238
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• 2003

In this work, we have designed a fully integrated 2.4GHz LC-tuned voltage-controlled oscillator (VCO) with multiple tuning inputs for a $0.25-{\mu}m$ standard CMOS Process. The design of voltage-controlled oscillator is based on an LC-resonator with a spiral inductor of octagonal type and pMOS-varactors. Only two metal layer have been used in the designed inductor. The frequency tuning is achieved by using parallel pMOS transistors as varactors and back-gate tuned pMOS transistors in an active region. Coarse tuning is achieved by using 3-bit pMOS-varactors and fine tuning is performed by using back-gate tuned pMOS transistors in the active region. When 3-bit digital and analog inputs are applied to the designed circuits, voltage-controlled oscillator shows the tuning feature of frequency range between 2.3 GHz and 2.64 GHz. At the power supply voltage of 2.5 V, phase noise is -128dBc/Hz at 3MHz offset from the carrier, Total power dissipation is 7.5 mW.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.12
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• pp.4816-4834
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• 2020

This paper proposes transfer learning and fine-tuning techniques for a deep learning model to detect three distinct brain tumors from Magnetic Resonance Imaging (MRI) scans. In this work, the recent YOLOv4 model trained using a collection of 3064 T1-weighted Contrast-Enhanced (CE)-MRI scans that were pre-processed and labeled for the task. This work trained with the partial 29-layer YOLOv4-Tiny and fine-tuned to work optimally and run efficiently in most platforms with reliable performance. With the help of transfer learning, the model had initial leverage to train faster with pre-trained weights from the COCO dataset, generating a robust set of features required for brain tumor detection. The results yielded the highest mean average precision of 93.14%, a 90.34% precision, 88.58% recall, and 89.45% F1-Score outperforming other previous versions of the YOLO detection models and other studies that used bounding box detections for the same task like Faster R-CNN. As concluded, the YOLOv4-Tiny can work efficiently to detect brain tumors automatically at a rapid phase with the help of proper fine-tuning and transfer learning. This work contributes mainly to assist medical experts in the diagnostic process of brain tumors.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.4
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• pp.811-818
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• 2016

A continuous fine-tuning phase locked loop with an additional negative feedback loop has been proposed. When the phase locked loop is out-of-lock, the phase locked loop has a fast locking characteristic using the continuous band-selection loop. When the phase locked loop is near in-lock, the bandwidth is narrowed with the fine loop. The additional negative feedback loop consists of a voltage controlled oscillator, a frequency voltage converter and its internal loop filter. It serves a negative feedback function to the main phase locked loop, and improves the phase noise characteristics and the stability of the proposed phase locked loop. The additional negative feedback loop makes the continuous fine-tuning loop work stably without any voltage fluctuation in the loop filter. Measurement results of the fabricated phase locked loop in $0.18{\mu}m$ CMOS process show that the phase noise is -109.6dBc/Hz at 2MHz offset from 742.8MHz carrier frequency.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.3
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• pp.570-590
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• 2024

Breast cancer ranks among the most prevalent forms of malignancy and foremost cause of death by cancer worldwide. It is not preventable. Early and precise detection is the only remedy for lowering the rate of mortality and improving the probability of survival for victims. In contrast to present procedures, thermography aids in the early diagnosis of cancer and thereby saves lives. But the accuracy experiences detrimental impact by low sensitivity for small and deep tumours and the subjectivity by physicians in interpreting the images. Employing deep learning approaches for cancer detection can enhance the efficacy. This study explored the utilization of thermography in early identification of breast cancer with the use of a publicly released dataset known as the DMR-IR dataset. For this purpose, we employed a novel approach that entails the utilization of a pre-trained MobileNetV2 model and fine tuning it through transfer learning techniques. We created three models using MobileNetV2: one was a baseline transfer learning model with weights trained from ImageNet dataset, the second was a fine-tuned model with an adaptive learning rate, and the third utilized early stopping with callbacks during fine-tuning. The results showed that the proposed methods achieved average accuracy rates of 85.15%, 95.19%, and 98.69%, respectively, with various performance indicators such as precision, sensitivity and specificity also being investigated.

• Convergence Security Journal
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• v.23 no.5
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• pp.125-133
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• 2023

This paper proposes an AI model that determines parking and non-parking zones based on return authentication photos to address parking issues that may arise in shared electric scooter systems. In this study, we used a pre-trained Segformer_b0 model on ADE20K and fine-tuned it on tactile blocks and electric scooters to extract segmentation maps of objects related to parking and non-parking areas. We also presented a method to perform binary classification of parking and non-parking zones using the Swin model. Finally, after labeling a total of 1,689 images and fine-tuning the SegFomer model, it achieved an mAP of 81.26%, recognizing electric scooters and tactile blocks. The classification model, trained on a total of 2,817 images, achieved an accuracy of 92.11% and an F1-Score of 91.50% for classifying parking and non-parking areas.

• Phonetics and Speech Sciences
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• v.15 no.3
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• pp.75-82
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• 2023

Automatic speech recognition (ASR) has been revolutionized with deep learning-based approaches, among which self-supervised learning methods have proven to be particularly effective. In this study, we aim to enhance the performance of OpenAI's Whisper model, a multilingual ASR system on the Korean language. Whisper was pretrained on a large corpus (around 680,000 hours) of web speech data and has demonstrated strong recognition performance for major languages. However, it faces challenges in recognizing languages such as Korean, which is not major language while training. We address this issue by fine-tuning the Whisper model with an additional dataset comprising about 1,000 hours of Korean speech. We also compare its performance against a Transformer model that was trained from scratch using the same dataset. Our results indicate that fine-tuning the Whisper model significantly improved its Korean speech recognition capabilities in terms of character error rate (CER). Specifically, the performance improved with increasing model size. However, the Whisper model's performance on English deteriorated post fine-tuning, emphasizing the need for further research to develop robust multilingual models. Our study demonstrates the potential of utilizing a fine-tuned Whisper model for Korean ASR applications. Future work will focus on multilingual recognition and optimization for real-time inference.

• Proceedings of the KIEE Conference
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• 1992.07a
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• pp.476-479
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• 1992

This paper presents a neural network method for tuning PlD controller of a time-varying process. Three gains of PlD controller are tuned for a certain desirable response pattern by back-propagation neural network. The neural network is trained using changes of output features vs. changes of PlD gains. But sometimes it needs longer training time and larger structure to train the correlation between the process and controller on entire region of the process. The difficulty in system identification is that the inverse function of the system can not be clearly stated. To cope with the problem, we do not train the neural network to respond correctly for the entire regions but train for only local region where the system is heading toward by training the neural network and tuning of the PlD controller. It may be trained for fine-tuning itself. Simulation results show that the adaptive PID controller using neural network trained in the local area performs remarkably for time-varying second order process.

• Journal of Power Electronics
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• v.17 no.6
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• pp.1577-1586
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• 2017

The Proportional-Integral-Derivative (PID) controller is still the most widespread control strategy in the industry. PID controllers have gained popularity due to their simplicity, better control performance and excellent robustness to uncertainties. This paper presents the optimal tuning of a PID controller for domestic induction heating systems with a series resonant inverter for controlling the induction heating power. The objective is to design a stable and superior control system by tuning the PID controller with a derivative filter (PIDF) through Fuzzy logic. The paper also compares the performance of the Fuzzy PIDF controller with that of a Ziegler-Nichols PID controller and a fine-tuned PID controller with a derivative filter. The system modeling and controllers are simulated in MATLAB/SIMULINK. The results obtained show the effectiveness and superiority of the proposed Fuzzy PID controller with a derivative filter.

• Proceedings of the IEEK Conference
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• 2003.11c
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• pp.32-36
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• 2003

In this work, we have designed a fully integrated 2.4GHz LC-tuned voltage-controlled oscillator (VCO) with multiple tuning inputs for a 0.25-$\mu\textrm{m}$ standard CMOS process. The design of voltage-controlled oscillator is based on an LC-resonator with a spiral inductor of octagonal type and pMOS-varactors. Only two metal layer have been used in the designed inductor. The frequency tuning is achieved by using parallel pMOS transistors as varactors and back-gate tuned pMOS transistors in an active region. Coarse tuning is achieved by using 3-bit pMOS-varactors and fine tuning is performed by using back-gate tuned pMOS transistors in the active region. When 3-bit digital and analog inputs are applied to the designed circuits, voltage-controlled oscillator shows the tuning feature of frequency range between 2.3 GHz and 2.64 GHz. At the power supply voltage of 2.5 V, phase noise is -128dBc/Hz at 3MHz offset from the carrier. Total power dissipation is 7.5 mW.

• Journal of Korean Institute of Industrial Engineers
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• v.28 no.1
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• pp.76-86
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• 2002

In this paper, we suggest a hybrid genetic algorithm reinforced by a fuzzy logic controller (flc-HGA) to overcome weaknesses of conventional genetic algorithms: the problem of parameter fine-tuning, the lack of local search ability, and the convergence speed in searching process. In the proposed flc-HGA, a fuzzy logic controller is used to adaptively regulate the fine-tuning structure of genetic algorithm (GA) parameters and a local search technique is applied to find a better solution in GA loop. In numerical examples, we apply the proposed algorithm to a simple test problem and two complex combinatorial optimization problems. Experiment results show that the proposed algorithm outperforms conventional GAs and heuristics.