• 한국전자파학회논문지
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• 제8권1호
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• pp.61-72
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• 1997

대역확산 코드분할 다원 접속(CDMA) 기술을 이용하는 팡대역 코드분할 다원 접속 개인 휴대 통신 ((Wideband CDMA PCS) 기지국용 RF 송수선기를 고찰하였다. 5 MHz의 RF 채널 대역폭을 이용하여 넓은 동작 범위와 고감도에 요구되는 RF 수신기 빛 우수한 스퓨리어스 방사 억제 특성을 가지는 송신기의 설계 및 구 현에 대하여 기술하였다. 상용화된 부품이나 주문 제작된 소자의 규격을 토대로 상용 RF 시율레이션 소프트웨어 를 이용하여 송수신기 구성시 고려되어야 할 사항 및 예상 성능에 대하여 알아보았고, 이에 따라 송수신기를 설계 제작하고 설험하여 성능 규격을 만족하는 좋은 결과를 얻었다

• 한국전자파학회논문지
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• 제9권1호
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• pp.16-24
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• 1998

직접 변조방식을 사용한 장거리 고속 광통신 시스템에서 파이버 색분산은 송신단의 레이저에서 발생된 처핑과 결합하여 심각한 시스템 성능감퇴를 일으킨다. 본 논문에서는 1550[nm] 파장에서 동작하는 2.5[Gbps] 광통신 시스템에서 레이저 처핑이 색분산과 결합하여 시스템에 미치는 영향을 해석하였다. 또한, 레이저 처핑과 파이버 분산을 보상하기 위해서 광 등화기를 사용하였고, 그 결과 수신기 감도가 2~4[dB]정도 개선되었음을 알 수 있었다.

• 한국항해학회지
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• 제3권1호
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• pp.47-77
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• 1979

A microwave Navigational Aid system is suggested suitable for fishing boats too small to be equipped with Radar or Radio-Direction-Finder. The system proposed here is similar to that of Talking-Beacon developed in Japan, but the distinctive modification proposed is that an increase of sixteen times in peak transmitting power, thus an accompanying increase of coverage, is achieved with the same mean transmitting power as that of Japan without sacrificing the clearness of azimuth information, by adopting a pulse repetition modulation instead of pulse width modulation as in Japan system. An experimental land station transmitter of transmitting frequency of 9, 370MHz and of peak power of 35kw with a microwave beam of 1 degree in horizontal width and 7 degrees in vertical width rotating once every three minutes, and also an experimental receiver of 20-dB in sensitivity and of an assumed cost of 100 dollars, operated by a 12 volts battery source are made, and the sail test results are reported showing that a bearing infromation of an accuracy of within two degrees can be obtainable every three minutes at a distance of as far as 24 miles from the transmitter if the transmitter is located as high as 100 meters above sea-level.

• Current Optics and Photonics
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• 제5권3호
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• pp.213-219
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• 2021

We describe a single-channel rubidium (Rb) radio-frequency atomic magnetometer (RFAM) as a receiver that takes magnetic signal resonating with Zeeman splitting of the ground state of Rb. We optimize the performance of the RFAM by recording the response signal and signal-to-noise ratio (SNR) in various parameters and obtain a noise level of 159 $fT{\sqrt{Hz}}$ around 30 kHz. When a resonant radiofrequency magnetic field with a peak amplitude of 8.0 nT is applied, the bandwidth and signal-to-noise ratio are about 650 Hz and 88 dB, respectively. It is a good agreement that RFAM using alkali atoms is suitable for receiving signals in the very low frequency (VLF) carrier band, ranging from 3 kHz to 30 kHz. This study shows the new capabilities of the RFAM in communications applications based on magnetic signals with the VLF carrier band. Such communication can be expected to expand the communication space by overcoming obstacles through the high magnetic sensitive RFAM.

• International Journal of Computer Science & Network Security
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• 제22권12호
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• pp.98-106
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• 2022

A condition of the retina known as hypertensive retinopathy (HR) is connected to high blood pressure. The severity and persistence of hypertension are directly correlated with the incidence of HR. To avoid blindness, it is essential to recognize and assess HR as soon as possible. Few computer-aided systems are currently available that can diagnose HR issues. On the other hand, those systems focused on gathering characteristics from a variety of retinopathy-related HR lesions and categorizing them using conventional machine-learning algorithms. Consequently, for limited applications, significant and complicated image processing methods are necessary. As seen in recent similar systems, the preciseness of classification is likewise lacking. To address these issues, a new CAD HR-diagnosis system employing the advanced Deep Dense CNN Learning (DD-CNN) technology is being developed to early identify HR. The HR-diagnosis system utilized a convolutional neural network that was previously trained as a feature extractor. The statistical investigation of more than 1400 retinography images is undertaken to assess the accuracy of the implemented system using several performance metrics such as specificity (SP), sensitivity (SE), area under the receiver operating curve (AUC), and accuracy (ACC). On average, we achieved a SE of 97%, ACC of 98%, SP of 99%, and AUC of 0.98. These results indicate that the proposed DD-CNN classifier is used to diagnose hypertensive retinopathy.

• Nuclear Engineering and Technology
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• 제55권1호
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• pp.100-108
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• 2023

In this study, a neural network model inspired by a one-dimensional convolution U-net is developed to automatically accelerate edge localized mode (ELM) detection from big diagnostic data of fusion devices and increase the detection accuracy regardless of the hyperparameter setting. This model recognizes the input signal patterns and overcomes the problems of existing detection algorithms, such as the prominence algorithm and those of differential methods with high sensitivity for the threshold and signal intensity. To train the model, 10 sets of discharge radiation data from the KSTAR are used and sliced into 11091 inputs of length 12 ms, of which 20% are used for validation. According to the receiver operating characteristic curves, our model shows a positive prediction rate and a true prediction rate of approximately 90% each, which is comparable to the best detection performance afforded by other algorithms using their optimized hyperparameters. The accurate and automatic ELM-burst detection methodology used in our model can be beneficial for determining plasma properties, such as the ELM frequency from big data measured in multiple experiments using machines from the KSTAR device and ITER. Additionally, it is applicable to feature detection in the time-series data of other engineering fields.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2021년도 추계학술발표대회
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• pp.805-807
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• 2021

In-hospital cardiac arrest is a significant problem for medical systems. Although the traditional early warning systems have been widely applied, they still contain many drawbacks, such as the high false warning rate and low sensitivity. This paper proposed a strategy that involves a deep learning approach based on a novel interpretable deep tabular data learning architecture, named TabNet, for the Rapid Response System. This study has been processed and validated on a dataset collected from two hospitals of Chonnam National University, Korea, in over 10 years. The learning metrics used for the experiment are the area under the receiver operating characteristic curve score (AUROC) and the area under the precision-recall curve score (AUPRC). The experiment on a large real-time dataset shows that our method improves compared to other machine learning-based approaches.

• Asian Pacific Journal of Cancer Prevention
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• 제16권14호
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• pp.5889-5897
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• 2015

Background: The performance of combined testing visual inspection with acetic acid (VIA) and cervical cytology tests might differ from one setting to another. The average estimate of the testing accuracy across studies is informative, but no meta-analysis has been carried out to assess this combined method. Objective: The objective of this study was to estimate the average sensitivity and specificity of the combined VIA and cervical cytology tests for the detection of cervical precancerous lesions. Materials and Methods: We conducted a systematic review and a meta-analysis, according to the Cochrane Handbook for Systematic Review of Diagnostic Test Accuracy. We considered two cases. In the either-positive result case, a positive result implies positivity in at least one of the tests. A negative result implies negativity in both tests. In the both-positive case, a positive result implies having both tests positive. Eligible studies were identified using Pubmed, Embase, Website of Science, CINHAL and COCRANE databases. True positive, false positive, false negative and true negative values were extracted. Estimates of sensitivity and specificity, positive and negative likelihood (LR) and diagnostic odds ratios (DOR) were pooled using a hierarchical random effect model. Hierarchical summary receiver operating characteristics (HSROC) were generated and heterogeneity was verified through covariates potentially influencing the diagnostic odds ratio. Findings: Nine studies fulfilled inclusion criteria and were included in the analysis. Pooled estimates of the sensitivities of the combined tests in either-positive and both-positive cases were 0.87 (95% CI: 0.83-0.90) and 0.38 (95% CI: 0.29-0.48), respectively. Corresponding specificities were 0.79 (95% CI: 0.63-0.89) and 0.98 (95% CI: 0.96-0.99) respectively. The DORs of the combined tests in either-positive or both-positive result cases were 27.7 (95% CI: 12.5-61.5) and 52 (95% CI: 22.1-122.2), respectively. When including only articles without partial verification bias and also a high-grade cervical intraepithelial neoplasia as a threshold of the disease, DOR of combined test in both-positive result cases remained the highest. However, DORs decreased to 12.1 (95% CI: 6.05-24.1) and 13.8 (95% CI: 7.92-23.9) in studies without partial verification bias for the combined tests in the either-positive and both-positive result cases, respectively. The screener, the place of study and the size of the population significantly influenced the DOR of combined tests in the both-positive result case in restriction analyses that considered only articles with CIN2+ as disease threshold. Conclusions: The combined test in the either-positive result case has a high sensitivity, but a low specificity. These results suggest that the combined test should be considered in developing countries as a primary screening test if facilities exist to confirm, through colposcopy and biopsy, a positive result.

• Korean Journal of Radiology
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• 제22권3호
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• pp.344-353
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• 2021

Objective: The mitotic count of gastrointestinal stromal tumors (GIST) is closely associated with the risk of planting and metastasis. The purpose of this study was to develop a predictive model for the mitotic index of local primary GIST, based on deep learning algorithm. Materials and Methods: Abdominal contrast-enhanced CT images of 148 pathologically confirmed GIST cases were retrospectively collected for the development of a deep learning classification algorithm. The areas of GIST masses on the CT images were retrospectively labelled by an experienced radiologist. The postoperative pathological mitotic count was considered as the gold standard (high mitotic count, > 5/50 high-power fields [HPFs]; low mitotic count, ≤ 5/50 HPFs). A binary classification model was trained on the basis of the VGG16 convolutional neural network, using the CT images with the training set (n = 108), validation set (n = 20), and the test set (n = 20). The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated at both, the image level and the patient level. The receiver operating characteristic curves were generated on the basis of the model prediction results and the area under curves (AUCs) were calculated. The risk categories of the tumors were predicted according to the Armed Forces Institute of Pathology criteria. Results: At the image level, the classification prediction results of the mitotic counts in the test cohort were as follows: sensitivity 85.7% (95% confidence interval [CI]: 0.834-0.877), specificity 67.5% (95% CI: 0.636-0.712), PPV 82.1% (95% CI: 0.797-0.843), NPV 73.0% (95% CI: 0.691-0.766), and AUC 0.771 (95% CI: 0.750-0.791). At the patient level, the classification prediction results in the test cohort were as follows: sensitivity 90.0% (95% CI: 0.541-0.995), specificity 70.0% (95% CI: 0.354-0.919), PPV 75.0% (95% CI: 0.428-0.933), NPV 87.5% (95% CI: 0.467-0.993), and AUC 0.800 (95% CI: 0.563-0.943). Conclusion: We developed and preliminarily verified the GIST mitotic count binary prediction model, based on the VGG convolutional neural network. The model displayed a good predictive performance.

• ETRI Journal
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• 제26권3호
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• pp.229-240
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• 2004

This paper reports on our development of a dual-mode transceiver for a CMOS high-rate Bluetooth system-onchip solution. The transceiver includes most of the radio building blocks such as an active complex filter, a Gaussian frequency shift keying (GFSK) demodulator, a variable gain amplifier (VGA), a dc offset cancellation circuit, a quadrature local oscillator (LO) generator, and an RF front-end. It is designed for both the normal-rate Bluetooth with an instantaneous bit rate of 1 Mb/s and the high-rate Bluetooth of up to 12 Mb/s. The receiver employs a dualconversion combined with a baseband dual-path architecture for resolving many problems such as flicker noise, dc offset, and power consumption of the dual-mode system. The transceiver requires none of the external image-rejection and intermediate frequency (IF) channel filters by using an LO of 1.6 GHz and the fifth order onchip filters. The chip is fabricated on a $6.5-mm^{2}$ die using a standard $0.25-{\mu}m$ CMOS technology. Experimental results show an in-band image-rejection ratio of 40 dB, an IIP3 of -5 dBm, and a sensitivity of -77 dBm for the Bluetooth mode when the losses from the external components are compensated. It consumes 42 mA in receive ${\pi}/4-diffrential$ quadrature phase-shift keying $({\pi}/4-DQPSK)$ mode of 8 Mb/s, 35 mA in receive GFSK mode of 1 Mb/s, and 32 mA in transmit mode from a 2.5-V supply. These results indicate that the architecture and circuits are adaptable to the implementation of a low-cost, multi-mode, high-speed wireless personal area network.