• Journal of radiological science and technology
• /
• v.45 no.2
• /
• pp.103-109
• /
• 2022

To maintain improved image quality in mammography, the quality control process is performed using the ACR (American college of radiology) phantom. In addition, many studied were performed by fabricating the customized breast phantom to provide more information in mammography. Thus, the purpose of this study was to evaluate the image quality by designing the modified ACR phantoms. The five modified acrlylic ACR phantoms were designed by considering insert position and phantom thickness. The phantoms were consisted of 4.5, 3.0, and 1.5 cm in terms of phantom thickness, and 3.0, 2.0, and 0.5 cm in terms of insert position, respectively. The acquired images were evaluated by PSNR (peak signal to noise ratio), RMSE (root mean square error), CC (correlation coefficient), CNR (contrast to noise ratio), and COV (coefficient of variation). Based on the similarity analysis, the result is suitable between conventional and new designed phantoms. In addition, the CNR and COV results in terms of insert position showed that image quality for 0.5 cm was 2.3 and 27.4% improved compared with 2 and 3 cm, respectively. According to phantom thickness results, the CNR result for 1.5 cm and COV result for 4.5 cm were 50.1 and 62.7% improved compared with that those conditions. In conclusion, we confirmed that the image quality depends on the breast size and thickness through modified ACR phantom study.

• International Journal of Computer Science & Network Security
• /
• v.22 no.7
• /
• pp.91-102
• /
• 2022

With the evolution of wireless sensor network (WSN) technology, the routing policy has foremost importance in the Internet of Things (IoT). A systematic routing policy is one of the primary mechanics to make certain the precise and robust transmission of wireless sensor networks in an energy-efficient manner. In an IoT environment, WSN is utilized for controlling services concerning data like, data gathering, sensing and transmission. With the advantages of IoT potentialities, the traditional routing in a WSN are augmented with decision-making in an energy efficient manner to concur finer optimization. In this paper, we study how to combine IoT-based deep learning classifier with routing called, Kriging Regressive Deep Belief Neural Learning (KR-DBNL) to propose an efficient data packet routing to cope with scalability issues and therefore ensure robust data packet transmission. The KR-DBNL method includes four layers, namely input layer, two hidden layers and one output layer for performing data transmission between source and destination sensor node. Initially, the KR-DBNL method acquires the patient data from different location. Followed by which, the input layer transmits sensor nodes to first hidden layer where analysis of energy consumption, bandwidth consumption and light intensity are made using kriging regression function to perform classification. According to classified results, sensor nodes are classified into higher performance and lower performance sensor nodes. The higher performance sensor nodes are then transmitted to second hidden layer. Here high performance sensor nodes neighbouring sensor with higher signal strength and frequency are selected and sent to the output layer where the actual data packet transmission is performed. Experimental evaluation is carried out on factors such as energy consumption, packet delivery ratio, packet loss rate and end-to-end delay with respect to number of patient data packets and sensor nodes.

• Journal of Ginseng Research
• /
• v.46 no.6
• /
• pp.771-779
• /
• 2022

Background: As a kind of common complication of the surgery of perianal diseases, perianal ulcer is known as a nuisance. This study aims to develop a kind of 20(S)-ginsenoside Rg3 (Rg3)-loaded hydrogel to treat perianal ulcers in a rat model. Methods: The copolymers PLGA₁₆₀₀-PEG₁₀₀₀-PLGA₁₆₀₀ were synthesized by ring-opening polymerization process and Rg3-loaded hydrogel was then developed. The perianal ulcer rat model was established to analyze the treatment efficacy of Rg3-loaded hydrogel for ulceration healing for 15 days. The animals were divided into control group, hydrogel group, free Rg3 group, Rg3-loaded hydrogel group, and Lidocaine Gel® group. The residual wound area rate was calculated and the blood concentrations of interleukin-1 (IL-1), interleukin-6 (IL-6), and vascular endothelial growth factor (VEGF) were recorded. Hematoxylin and eosin (H&E) staining, Masson's Trichrome (MT) staining, and tumor necrosis factor α (TNF-α), Ki-67, CD31, ERK1/2, and NF-κB immunohistochemical staining were performed. Results: The biodegradable and biocompatible hydrogel carries a homogenous interactive porous structure with 10 ㎛ pore size and five weeks in vivo degradation time. The loaded Rg3 can be released sustainably. The in vitro cytotoxicity study showed that the hydrogel had no effect on survival rate of murine skin fibroblasts L929. The Rg3-loaded hydrogel can facilitate perianal ulcer healing by inhibiting local and systematic inflammatory responses, swelling the proliferation of nuclear cells, collagen deposition, and vascularization, and activating ERK signal pathway. Conclusion: The Rg3-loaded hydrogel shows the best treatment efficacy of perianal ulcer and may be a candidate for perianal ulcer treatment.

• Journal of IKEEE
• /
• v.26 no.3
• /
• pp.364-372
• /
• 2022

This paper proposes the design and implementation results for human and object classification systems utilizing frequency modulated continuous wave (FMCW) radar sensor. Such a system requires the process of radar sensor signal processing for multi-target detection and the process of deep learning for the classification of human and object. Since deep learning requires such a great amount of computation and data processing, the lightweight process is utmost essential. Therefore, binary neural network (BNN) structure was adopted, operating convolution neural network (CNN) computation in a binary condition. In addition, for the real-time operation, a hardware accelerator was implemented and verified via FPGA platform. Based on performance evaluation and verified results, it is confirmed that the accuracy for multi-target classification of 90.5%, reduced memory usage by 96.87% compared to CNN and the run time of 5ms are achieved.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.17 no.5
• /
• pp.767-774
• /
• 2022

In this paper raw spectrum data were analyzed to suggest the moving forward of performance evaluation and quality control of wind profilers of four manufacturers operating in South Korea. For the analysis, the profile of the spectrum averaged by season and the profile of four statistical values (minimum, average, median, and maximum) calculated by Power Spectrum Density (PSD) were used. The quality of spectrum data was the best for LAP-3000, followed by YKJ3, PCL-1300, and CLC-11-H. In Cheorwon and Chupungnyeong, where PCL-1300 was installed, the variability of the spectrum due to ground clutter and non-meteorological signals was large, so ground clutter removal and signal processing such as moving average and multi-peak were required. In Gunsan and Paju, where CLC-11-H was installed, DC (Direct Current) bias and propagation folding were found, so it is necessary to remove the DC bias and limit the effective altitude for observation.

• Journal of the Korean Society of Marine Environment & Safety
• /
• v.27 no.7
• /
• pp.1088-1097
• /
• 2021

Vibration data of mechanical equipment inevitably have noise. This noise adversely af ects the maintenance of mechanical equipment. Accordingly, the performance of a learning model depends on how effectively the noise of the data is removed. In this study, the noise of the data was removed using the Denoising Auto Encoder (DAE) technique which does not include the characteristic extraction process in preprocessing time series data. In addition, the performance was compared with that of the Wavelet Transform, which is widely used for machine signal processing. The performance comparison was conducted by calculating the failure detection rate. For a more accurate comparison, a classification performance evaluation criterion, the F-1 Score, was calculated. Failure data were detected using the One-Class SVM technique. The performance comparison, revealed that the DAE technique performed better than the Wavelet Transform technique in terms of failure diagnosis and error rate.

• The Journal of the Convergence on Culture Technology
• /
• v.8 no.6
• /
• pp.741-747
• /
• 2022

Fatigue lowers an individual's ability and makes it difficult to perform work. As fatigue accumulates, concentration decreases and thus the possibility of causing a safety accident increases. Awareness of fatigue is subjective, but it is necessary to quantitatively measure the level of fatigue in the actual field. In previous studies, it was proposed to measure the level of fatigue by expert judgment by adding objective indicators such as bio-signal analysis to subjective evaluations such as multidisciplinary fatigue scales. However this method is difficult to evaluate fatigue in real time in daily life. This paper is a study on the fatigue classification model that determines the fatigue level of workers in real time using speech data recorded in the field. Machine learning models such as logistic classification, support vector machine, and random forest are trained using speech data collected in the field. The performance evaluation showed good performance with accuracy of 0.677 to 0.758, of which logistic classification showed the best performance. From the experimental results, it can be seen that it is possible to classify the fatigue level using speech signals.

• The Korean Journal of Physiology and Pharmacology
• /
• v.26 no.2
• /
• pp.87-94
• /
• 2022

Myocardial infarction promotes cardiac remodeling and myocardial fibrosis, thus leading to cardiac dysfunction or heart failure. Peiminine has been regarded as a traditional anti-fibrotic Chinese medicine in pulmonary fibrosis. However, the role of peiminine in myocardial infarction-induced myocardial injury and fibrosis remained elusive. Firstly, rat model of myocardial infarction was established using ligation of the left coronary artery, which were then intraperitoneally injected with 2 or 5 mg/kg peiminine once a day for 4 weeks. Echocardiography and haemodynamic evaluation results showed that peiminine treatment reduced left ventricular end-diastolic pressure, and enhanced maximum rate of increase/decrease of left ventricle pressure (± dP/dt max) and left ventricular systolic pressure, which ameliorate the cardiac function. Secondly, myocardial infarction-induced myocardial injury and infarct size were also attenuated by peiminine. Moreover, peiminine inhibited myocardial infarction-induced increase of interleukin (IL)-1β, IL-6 and tumor necrosis factor-α production, as well as the myocardial cell apoptosis, in the rats. Thirdly, peiminine also decreased the myocardial fibrosis related protein expression including collagen I and collagen III. Lastly, peiminine reduced the expression of p38 and phosphorylation of extracellular signal-regulated kinase 1/2 in rat model of myocardial infarction. In conclusion, peiminine has a cardioprotective effect against myocardial infarction-induced myocardial injury and fibrosis, which can be attributed to the inactivation of mitogen-activated protein kinase pathway.

• Investigative Magnetic Resonance Imaging
• /
• v.25 no.4
• /
• pp.323-331
• /
• 2021

Purpose: To investigate the diagnostic criteria of T1-weighted imaging (T1W) and time-of-flight (TOF) imaging for detecting intraplaque hemorrhage (IPH) of a vertebrobasilar artery (VBA) compared with simultaneous non-contrast angiography and intraplaque hemorrhage (SNAP) imaging. Materials and Methods: Eighty-seven patients with VBA atherosclerosis who underwent high resolution MR imaging for evaluation of VBA plaque were reviewed. The presence and location of VBA plaque and IPH on SNAP were determined. The signal intensity (SI) of the VBA plaque on T1W and TOF imaging was manually measured and the SI ratio against adjacent muscles was calculated. The receiver-operating characteristic (ROC) curve was used to compare the diagnostic accuracy for detecting VBA IPH. Results: Of 87 patients, 67 had IPH and 20 had no IPH on SNAP. The SI ratio between VBA IPH and temporalis muscle on T1W was significantly higher than that in the no-IPH group (235.9 ± 16.8 vs. 120.0 ± 5.1, P < 0.001). The SI ratio between IPH and temporalis muscle on TOF was also significantly higher than that in the no-IPH group (236.8 ± 13.3 vs. 112.8 ± 7.4, P < 0.001). Diagnostic efficacies of SI ratios on TOF and TIW were excellent (AUC: 0.976 on TOF and 0.964 on T1W; cutoff value: 136.7% for TOF imaging and 135.1% for T1W imaging). Conclusion: Compared with SNAP, cutoff levels of the SI ratio between VBA plaque and temporalis muscle on T1W and TOF imaging for detecting IPH were approximately 1.35 times.

• Journal of Biomedical Engineering Research
• /
• v.44 no.2
• /
• pp.118-124
• /
• 2023

Many recent studies have reported that the quality of input learning data was vital to the detection of regions of interest. However, due to a lack of research on the quality of learning data on lesion detetcting using gastroscopy, we aimed to quantify the impact of quality difference in endoscopic images to lesion detection models using Image Quality Assessment (IQA) algorithms. Through IQA methods such as BRISQUE (Blind/Referenceless Image Spatial Quality Evaluation), Laplacian Score, and PSNR (Peak Signal-To-Noise) algorithm on 430 sheets of high quality data (HQD) and 430 sheets of low quality data (PQD), we showed that there were significant differences between high and low quality images in lesion detecting through BRISQUE and Laplacian scores (p<0.05). The PSNR value showed 10.62±1.76 dB on average, illustrating the lower lesion detection performance of PQD than HQD. In addition, F1-Score of HQD showed higher detection performance at 77.42±3.36% while F1-Score of PQD showed 66.82±9.07%. Through this study, we hope to contribute to future gastroscopy lesion detection assistance systems that involve IQA algorithms by emphasizing the importance of using high quality data over lower quality data.