• Advances in nano research
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• v.15 no.6
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• pp.521-531
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• 2023

Quantum-dot cellular automata (QCA) has shown great potential in the nanoscale regime as a replacement for CMOS technology. This work presents a specific approach to static random-access memory (SRAM) cell based on 2:1 multiplexer, 4-bit SRAM array, and 32-bit SRAM array in QCA. By utilizing the proposed SRAM array, a single-layer 16×32-bit SRAM with the read/write capability is presented using an optimized signal distribution network (SDN) crossover technique. In the present study, an extremely-optimized 2:1 multiplexer is proposed, which is used to implement an extremely-optimized SRAM cell. The results of simulation show the superiority of the proposed 2:1 multiplexer and SRAM cell. This study also provides a more efficient and accurate method for calculating QCA costs. The proposed extremely-optimized SRAM cell and SRAM arrays are advantageous in terms of complexity, delay, area, and QCA cost parameters in comparison with previous designs in QCA, CMOS, and FinFET technologies. Moreover, compared to previous designs in QCA and FinFET technologies, the proposed structure saves total energy consisting of overall energy consumption, switching energy dissipation, and leakage energy dissipation. The energy and structural analyses of the proposed scheme are performed in QCAPro and QCADesigner 2.0.3 tools. According to the simulation results and comparison with previous high-quality studies based on QCA and FinFET design approaches, the proposed SRAM reduces the overall energy consumption by 25%, occupies 33% smaller area, and requires 15% fewer cells. Moreover, the QCA cost is reduced by 35% compared to outstanding designs in the literature.

• Nuclear Engineering and Technology
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• v.56 no.2
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• pp.715-727
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• 2024

During fast neutron imaging, besides the dark current noise and readout noise of the CCD camera, the main noise in fast neutron imaging comes from high-energy gamma rays generated by neutron nuclear reactions in and around the experimental setup. These high-energy gamma rays result in the presence of high-density gamma white spots (GWS) in the fast neutron image. Due to the microscopic quantum characteristics of the neutron beam itself and environmental scattering effects, fast neutron images typically exhibit a mixture of Gaussian noise. Existing denoising methods in neutron images are difficult to handle when dealing with a mixture of GWS and Gaussian noise. Herein we put forward a deep learning approach based on the Swin Transformer UNet (SUNet) model to remove high-density GWS-Gaussian mixture noise from fast neutron images. The improved denoising model utilizes a customized loss function for training, which combines perceptual loss and mean squared error loss to avoid grid-like artifacts caused by using a single perceptual loss. To address the high cost of acquiring real fast neutron images, this study introduces Monte Carlo method to simulate noise data with GWS characteristics by computing the interaction between gamma rays and sensors based on the principle of GWS generation. Ultimately, the experimental scenarios involving simulated neutron noise images and real fast neutron images demonstrate that the proposed method not only improves the quality and signal-to-noise ratio of fast neutron images but also preserves the details of the original images during denoising.

• The Journal of the Convergence on Culture Technology
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• v.10 no.3
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• pp.801-807
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• 2024

Linear motion robots are devices that perform functions such as transferring parts or positioning devices, and require high precision. In companies that develop linear robot application systems, human workers are in charge of quality control and fault diagnosis of linear robots, and the result and accuracy of a fault diagnosis varies depending on the skill level of the person in charge. Recently, there have been many attempts to utilize artificial intelligence to diagnose faults in industrial devices. In this paper, we present a system that automatically diagnoses linear rail and ball screw misalignment of a linear robot using transfer learning. In industrial systems, it is difficult to obtain a lot of learning data, and this causes a data imbalance problem. In this case, a transfer learning model configured by retraining an established model is widely used. The information obtained by using an acceleration sensor and torque sensor was used, and its usefulness was evaluated for each case. After converting the signal obtained from the sensor into a spectrogram image, the type of abnormality was diagnosed using an image recognition artificial intelligence classifier. It is expected that the proposed method can be used not only for linear robots but also for diagnosing other industrial robots.

• Journal of the Korean Society of Radiology
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• v.84 no.1
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• pp.75-91
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• 2023

The technical parameters and imaging interpretation criteria of the Prostate Imaging Reporting and Data System version 2 (PI-RADS v2) using multiparametric MRI (mpMRI) are updated in PI-RADS v2.1. These changes have been an expected improvement for prostate cancer evaluation, although some issues remain unsolved, and new issues have been raised. In this review, a brief overview of PI-RADS v2.1 is and several critical points are discussed as follows: the need for more detailed protocols of mpMRI, lack of validation of the revised transition zone interpretation criteria, the need for clarification for the revised diffusion-weighted imaging and dynamic contrast-enhanced imaging criteria, anterior fibromuscular stroma and central zone assessment, assessment of background signal and tumor aggressiveness, changes in the structured report, the need for the parameters for imaging quality and performance control, and indications for expansion of the system to include other indications.

• Journal of the Earthquake Engineering Society of Korea
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• v.28 no.4
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• pp.183-191
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• 2024

Accurate seismic vulnerability assessment requires high quality and large amounts of ground motion data. Ground motion data generated from time series contains not only the seismic waves but also the background noise. Therefore, it is crucial to determine the high-pass cut-off frequency to reduce the background noise. Traditional methods for determining the high-pass filter frequency are based on human inspection, such as comparing the noise and the signal Fourier Amplitude Spectrum (FAS), f² trend line fitting, and inspection of the displacement curve after filtering. However, these methods are subject to human error and unsuitable for automating the process. This study used a deep learning approach to determine the high-pass filter frequency. We used the Mel-spectrogram for feature extraction and mixup technique to overcome the lack of data. We selected convolutional neural network (CNN) models such as ResNet, DenseNet, and EfficientNet for transfer learning. Additionally, we chose ViT and DeiT for transformer-based models. The results showed that ResNet had the highest performance with R² (the coefficient of determination) at 0.977 and the lowest mean absolute error (MAE) and RMSE (root mean square error) at 0.006 and 0.074, respectively. When applied to a seismic event and compared to the traditional methods, the determination of the high-pass filter frequency through the deep learning method showed a difference of 0.1 Hz, which demonstrates that it can be used as a replacement for traditional methods. We anticipate that this study will pave the way for automating ground motion processing, which could be applied to the system to handle large amounts of data efficiently.

• Asia Marketing Journal
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• v.10 no.1
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• pp.25-64
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• 2008

Third-Party awards are growing in popularity. They are the hit product of the year chosen by The Korea Economic Daily, the best 10 products of the year chosen by Sports paper, the best hit product chosen by consulting firm and the best venture company of the year chosen by Information and Communication Ministry. Then these questions may be followed. Why industry likes this type of advertisement? Does this type of advertisement influences consumers' purchase intention? And if it does, how? Many researchers have been interested in external cue of product quality by focusing research effort on brand, price, producer, warranty etc. However, important but under-explored area is the role of third-party reference for signaling product quality. This paper comes from the idea that the third-party reference may signal consumers like manufacturer brand, product brand, product price, and shop brand. We develop a related theories to address research questions and drive some research hypotheses based on the previous studies probing source credibility, attribution, and signal theory. We put more emphasis on source credibility. We conducted the research based on 3x2x2x2 between group factorial design to explore causal relationship between the third party award winning advertising(real, fictional, no) and the purchase intention of consumers exposed to other information simultaneously such as product type(experience, search), distribution channel(direct, indirect) and perceived price(high, low). Since subjects are divided into 2 groups based on the means of response without extra experimental stimulus in case of perceived price. 12 different advertisements are used for conducting this study. The results are followings. First, the source credibility of the third party goes up, consumers' purchase intention would go up. It seems that consumers think the credibility of the third-party most when they are exposed to the third party award winning advertisement. Second, the product type does moderate the relationship between the third-party award winning advertisement and purchase intention. And the type of the distribution channel also moderates this relationship. The consumers' purchase intention goes up higher when they buy experience good and there is significant difference of purchase intention when consumers are exposed to direct channel treatment condition. But, perceived price has nothing to do with the third-party winning advertisement context for raising consumer intention to buy advertised product.

• Journal of the Korean Society of Radiology
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• v.83 no.3
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• pp.669-679
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• 2022

Purpose To evaluate the feasibility of pediatric low-dose facial CT reconstructed with filtered back projection (FBP) using adequate kernels. Materials and Methods We retrospectively reviewed the clinical and imaging data of children aged < 10 years who underwent facial CT at our emergency department. The patients were divided into two groups: low-dose CT (LDCT; Group A, n = 73) with a fixed 80-kVp tube potential and automatic tube current modulation (ATCM) and standard-dose CT (SDCT; Group B, n = 40) with a fixed 120-kVp tube potential and ATCM. All images were reconstructed with FBP using bone and soft tissue kernels in Group A and only bone kernel in Group B. The groups were compared in terms of image noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR). Two radiologists subjectively scored the overall image quality of bony and soft tissue structures. The CT dose index volume and dose-length product were recorded. Results Image noise was higher in Group A than in Group B in bone kernel images (p < 0.001). Group A using a soft tissue kernel showed the highest SNR and CNR for all soft tissue structures (all p < 0.001). In the qualitative analysis of bony structures, Group A scores were found to be similar to or higher than Group B scores on comparing bone kernel images. In the qualitative analysis of soft tissue structures, there was no significant difference between Group A using a soft tissue kernel and Group B using a bone kernel with a soft tissue window setting (p > 0.05). Group A showed a 76.9% reduction in radiation dose compared to Group B (3.2 ± 0.2 mGy vs. 13.9 ± 1.5 mGy; p < 0.001). Conclusion The addition of a soft tissue kernel image to conventional CT reconstructed with FBP enables the use of pediatric low-dose facial CT protocol while maintaining image quality.

• Investigative Magnetic Resonance Imaging
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• v.1 no.1
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• pp.109-113
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• 1997

Purpose: To evaluate the differences of functional imaging patterns between conventional spoiled gradient echo (SPGR) and echo planar imaging (EPI) methods in cerebral motor cortex activation. Materials and Methods: Functional MR imaging of cerebral motor cortex activation was examined on a 1.5T MR unit with SPGR (TRfrE/flip angle=50ms/4Oms/$30^{\circ}$, FOV=300mm, matrix $size=256{\times}256$, slice thickness=5mm) and an interleaved single shot gradient echo EPI (TRfrE/flip angle = 3000ms/40ms/$90^{\circ}$, FOV=300mm, matrix $size=128{\times}128$, slice thickness=5mm) techniques in five male healthy volunteers. A total of 160 images in one slice and 960 images in 6 slices were obtained with SPGR and EPI, respectively. A right finger movement was accomplished with a paradigm of an 8 activation/ 8 rest periods. The cross-correlation was used for a statistical mapping algorithm. We evaluated any differences of the time series and the signal intensity changes between the rest and activation periods obtained with two techniques. Also, the locations and areas of the activation sites were compared between two techniques. Results: The activation sites in the motor cortex were accurately localized with both methods. In the signal intensity changes between the rest and activation periods at the activation regions, no significant differences were found between EPI and SPGR. Signal to noise ratio (SNR) of the time series data was higher in EPI than in SPGR by two folds. Also, larger pixels were distributed over small p-values at the activation sites in EPI. Conclusions: Good quality functional MR imaging of the cerebral motor cortex activation could be obtained with both SPGR and EPI. However, EPI is preferable because it provides more precise information on hemodynamics related to neural activities than SPGR due to high sensitivity.

• Journal of radiological science and technology
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• v.31 no.3
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• pp.223-228
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• 2008

The most critical point in the medical use of radiation is to minimize the patient's entrance dose while maintaining the diagnostic function. Low-energy photons (long wave X-ray) among diagnostic X-rays are unnecessary because they are mostly absorbed and contribute the increase of patient's entrance dose. The most effective method to eliminate the low-energy photons is to use the filtering plate. The experiments were performed by observing the image quality. The skin entrance dose was 0.3 mmCu (copper) filter. A total of 80 images were prepared as two sets of 40 cuts. In the first set (of 40 cuts), 20 cuts were prepared for the non-filter set and another 20 cuts for the Cu filter of signal + noise image set. In the second set of 40 cuts, 20 cuts were prepared for the non-filter set and another 20 cuts for the Cu filter of non-signal image (noisy image) with random location of diameter 4 mm and 3 mm thickness of acryl disc for ROC signal at the chest phantom. P(S/s) and P(S/n) were calculated and the ROC curve was described in terms of sensitivity and specificity. Accuracy were evaluated after reading by five radiologists. The number of optically observable lesions was counted through ANSI chest phantom and contrast-detail phantom by recommendation of AAPM when non-filter or Cu filter was used, and the skin entrance dose was also measured for both conditions. As the result of the study, when the Cu filter was applied, favorable outcomes were observed on, the ROC Curve was located on the upper left area, sensitivity, accuracy and the number of CD phantom lesions were reasonable. Furthermore, if skin entrance dose was reduced, the use of additional filtration may be required to be considered in many other cases.

• Journal of the Korean Society of Radiology
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• v.12 no.2
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• pp.201-208
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• 2018

The purpose of this study was to investigate the optimal flip angle by measuring the SNR and CNR according to the angle of changes of the MRI technique using the Image J program. A total of 30 normal volunteers were assessed by using a 1.5T magnetic resonance imaging system (Philips, Medical System, Achieva). For the MRI angiography, we set the region of interest in four regions and evaluated the SNR and CNR. The statistical significance of SNR and CNR was calculated by one-way ANOVA using quantitative analysis at five different positions. The Bonferroni method was used for post-hoc analyzes. Statistical significance was determined by using ANOVA analysis at p<0.05 and Bonferroni method was used as a post-hoc analysis. The results of this study, the measurement values of ACA(SNR:$876.59{\pm}14.22$, CNR:$1999.7{\pm}12.5$), PCA(SNR:$863.48{\pm}13.29$, CNR:$1870.18{\pm}12.56$), ICA(SNR:$1116.87{\pm}08.34$, CNR:$2979.37{\pm}14.69$) and MCA(SNR:$848.66{\pm}15.25$, CNR:$2199.25{\pm}13.48$) were obtained with the high signal intensity at $25^{\circ}$(p<0.05). The values of a1, a2, a3, p1, p2, p3, m1, m2 and m3 were also the same (p<0.05). Post-hoc analysis results, There was a statistically significant difference (p=0.000) between $10^{\circ}$, $15^{\circ}$, $20^{\circ}$ on the $25^{\circ}$ reference for the flip angle, but no significant results were obtained with $30^{\circ}$(p<0.05). In concision, because the signal intensity decreased at $30^{\circ}$, this study revealed that the optimal flip angles were $25^{\circ}$ in cerebrovascular MR angiography.