• Korean Journal of Materials Research
• /
• v.29 no.10
• /
• pp.623-630
• /
• 2019

Because of their excellent stability and highly specific surface area, carbon based materials have received attention as electrode materials of electrical double-layer capacitors(EDLCs). Biomass based carbon materials have been studied for electrode materials of EDLCs; these materials have low capacitance and high-rate performance. We fabricated tofu based porous activated carbon by polymer dissolution reaction and KOH activation. The activated porous carbon(APC-15), which has an optimum condition of 15 wt%, has a high specific surface area($1,296.1m^2\;g^{-1}$), an increased average pore diameter(2.3194 nm), and a high mesopore distribution(32.4 %), as well as increased surface functional groups. In addition, APC has a high specific capacitance($195F\;g^{-1}$) at low current density of $0.1A\;g^{-1}$ and excellent specific capacitance($164F\;g^{-1}$) at high current density of $2.0A\;g^{-1}$. Due to the increased specific surface area, volume ratio of mesopores, and surface functional groups, the specific capacitance and high-rate performance increased. Consequently, the tofu based activated porous carbon can be proposed as an electrode material for high-performance EDLCs.

• Journal of Biomedical Engineering Research
• /
• v.42 no.3
• /
• pp.80-85
• /
• 2021

We developed a model to classify the absence of cervical cancer using deep learning from the cervical image to which the histogram equalization algorithm was applied, and to compare the performance of each model. A total of 4259 images were used for this study, of which 1852 images were normal and 2407 were abnormal. And this paper applied Image Sharpening(IS), Histogram Equalization(HE), and Contrast Limited Adaptive Histogram Equalization(CLAHE) to the original image. Peak Signal-to-Noise Ratio(PSNR) and Structural Similarity index for Measuring image quality(SSIM) were used to assess the quality of images objectively. As a result of assessment, IS showed 81.75dB of PSNR and 0.96 of SSIM, showing the best image quality. CLAHE and HE showed the PSNR of 62.67dB and 62.60dB respectively, while SSIM of CLAHE was shown as 0.86, which is closer to 1 than HE of 0.75. Using ResNet-50 model with transfer learning, digital image-processed images are classified into normal and abnormal each. In conclusion, the classification accuracy of each model is as follows. 90.77% for IS, which shows the highest, 90.26% for CLAHE and 87.60% for HE. As this study shows, applying proper digital image processing which is for cervical images to Computer Aided Diagnosis(CAD) can help both screening and diagnosing.

• Journal of Biomedical Engineering Research
• /
• v.42 no.6
• /
• pp.287-294
• /
• 2021

Cardiotoxicity assessment of all drugs has been performed according to the ICH guidelines since 2005. Non-clinical evaluation S7B has focused on the hERG assay, which has a low specificity problem. The comprehensive in vitro proarrhythmia assay (CiPA) project was initiated to correct this problem, which presented a model for classifying the Torsade de pointes (TdP)-induced risk of drugs as biomarkers calculated through an in silico ventricular model. In this study, we propose a TdP-induced risk group classifier of artificial neural network (ANN)-based. The model was trained with 12 drugs and tested with 16 drugs. The ANN model was performed according to nine features, seven features, five features as an individual ANN model input, and the model with the highest performance was selected and compared with the classification performance of the qNet input logistic regression model. When the five features model was used, the results were AUC 0.93 in the high-risk group, AUC 0.73 in the intermediate-risk group, and 0.92 in the low-risk group. The model's performance using qNet was lower than the ANN model in the high-risk group by 17.6% and in the low-risk group by 29.5%. This study was able to express performance in the three risk groups, and it is a model that solved the problem of low specificity, which is the problem of hERG assay.

• Journal of Biomedical Engineering Research
• /
• v.43 no.6
• /
• pp.434-440
• /
• 2022

Liver transplantation is a critical used treatment method for patients with end-stage liver disease. The number of cases of living donor liver transplantation is increasing due to the imbalance in needs and supplies for brain-dead organ donation. As a result, the importance of the accuracy of the donor's suitability evaluation is also increasing rapidly. To measure the donor's liver volume accurately is the most important, that is absolutely necessary for the recipient's postoperative progress and the donor's safety. Therefore, we propose liver segmentation in abdominal CT images from pre-operation, POD 7, and POD 63 with a two-dimensional U-Net. In addition, we introduce an algorithm to measure the volume of the segmented liver and measure the hepatectomy rate and regeneration rate of pre-operation, POD 7, and POD 63. The performance for the learning model shows the best results in the images from pre-operation. Each dataset from pre-operation, POD 7, and POD 63 has the DSC of 94.55 ± 9.24%, 88.40 ± 18.01%, and 90.64 ± 14.35%. The mean of the measured liver volumes by trained model are 1423.44 ± 270.17 ml in pre-operation, 842.99 ± 190.95 ml in POD 7, and 1048.32 ± 201.02 ml in POD 63. The donor's hepatectomy rate is an average of 39.68 ± 13.06%, and the regeneration rate in POD 63 is an average of 14.78 ± 14.07%.

• Journal of Biomedical Engineering Research
• /
• v.43 no.6
• /
• pp.390-398
• /
• 2022

Ultrasound examination using ultrasound equipment is an ultrasound device that images human organs using sound waves and is used in various areas such as diagnosis, follow-up, and treatment of diseases. However, if the quality of ultrasound equipment is not guaranteed, the possibility of misdiagnosis increases, and the diagnosis rate decreases. Accordingly, The Korean Society of Radiology and Korea society of Ultrasound in Medicine presented guidelines for quality management of ultrasound equipment using ATS-539 phantom. The DenseNet201 classification algorithm shows 99.25% accuracy and 5.17% loss in the Dead Zone, 97.52% loss in Axial/Lateral Resolution, 96.98% accuracy and 20.64% loss in Sensitivity, 93.44% accuracy and 22.07% loss in the Gray scale and Dynamic Range. As a result, it is the best and is judged to be an algorithm that can be used for quantitative evaluation. Through this study, it can be seen that if quantitative evaluation using artificial intelligence is conducted in the qualitative evaluation item of ultrasonic equipment, the reliability of ultrasonic equipment can be increased with high accuracy.

• Journal of Biomedical Engineering Research
• /
• v.43 no.4
• /
• pp.280-289
• /
• 2022

In this paper, we present a QRS-complex detection algorithm to calculate an accurate heartbeat and clearly recognize irregular rhythm from ECG signals. The conventional Pan-Tompkins algorithm brings false QRS detection in the derivative when QRS and noise signals have similar instant variation. The proposed algorithm uses amplitude differences in 7 adjacent samples to detect QRS-complex which has the highest amplitude variation. The calculated amplitude is cubed to dominate QRS-complex and the moving average method is applied to diminish the noise signal's amplitude. Finally, a decision rule with a threshold value is applied to detect accurate QRS-complex. The calculated signals with Pan-Tompkins and proposed algorithms were compared by signal-to-noise ratio to evaluate the noise reduction degree. QRS-complex detection performance was confirmed by sensitivity and the positive predictive value(PPV). Normal ECG, muscle noise ECG, PVC, and atrial fibrillation signals were achieved which were measured from an ECG simulator. The signal-to-noise ratio difference between Pan-Tompkins and the proposed algorithm were 8.1, 8.5, 9.6, and 4.7, respectively. All ratio of the proposed algorithm is higher than the Pan-Tompkins values. It indicates that the proposed algorithm is more robust to noise than the Pan-Tompkins algorithm. The Pan-Tompkins algorithm and the proposed algorithm showed similar sensitivity and PPV at most waveforms. However, with a noisy atrial fibrillation signal, the PPV for QRS-complex has different values, 42% for the Pan-Tompkins algorithm and 100% for the proposed algorithm. It means that the proposed algorithm has superiority for QRS-complex detection in a noisy environment.

• Journal of The Korea Institute of Healthcare Architecture
• /
• v.29 no.1
• /
• pp.7-19
• /
• 2023

Purpose: Mobility impairment persons are all people who experience mobility difficulties in their daily lives, which makes up about 30% of the population in Seoul Metropolitan City; this number is expected to increase with population aging. As the number of mobility impairment persons in need of vision correction increases, it is necessary to create the Universal Design guidelines and to provide the foundation to access convenience facilities at the entrance of optical shops, a health and medical institution. Methods: Of the 2,282 optical shops located in Seoul, 252 optical shops were chosen for data collection of actual photos, from April 10, 2022 to September 4, 2022. Based on the photographs, the height difference between the entrance and the sidewalk, safety handles, and opening and closing methods of entrances were investigated, as these factors correspond to the accessibility and the mobility of the mobility impairment persons. Results: Of the 252 optical shops surveyed, 114 (45.2%) have resolved the problems of height difference through improving horizontal accessibility (61) or using ramps (53). 36 (14.3%) optical shops chose automatic doors for opening and closing methods of the entrance. Implications: The rate of installation of access convenience facilities for the entrance of optical shops is slightly lower than the rate of installation of ramps, surveyed by the Ministry of Health and Welfare. It is necessary to apply the Universal Design to access convenience facilities for the entrance of optical shops for not only the mobility impairment persons but all people, regardless of age or ability, to conveniently access healthcare services.

• Korean Journal of Fisheries and Aquatic Sciences
• /
• v.56 no.1
• /
• pp.21-27
• /
• 2023

Lotus Nelumbo nucifera seed protein (LSP) was isolated by alkaline solubilization after removing fat and phenolics by hexane and ethanol treatment. Antioxidant peptides from LSP were produced with Alcalase^® and pepsin and hydroxyl radical scavenging activities were determined. LSP-Alcalase^® hydrolysates showed higher hydroxyl radical scavenging activity than LSP-pepsin hydrolysates. To purify antioxidant peptides, LSP-Alcalase^® hydrolysates were subjected to high performance liquid chromatography (HPLC) separation on the C18 column and the active fraction was further purified using a Superdex^TM peptide 10/300 GL column. Finally, the active fraction (F8-2) was evaluated for antioxidant activities by 2,2-diphenyl-1-picrylhydrazyl (DPPH), hydroxyl radical scavenging, and oxygen radical absorbance capacity (ORAC) assays. The EC₅₀ values of the F8-2 were 105.81±0.02 ㎍/mL for DPPH and 32.26±0.02 ㎍/mL for hydroxyl radical and the F8-2 exhibited 7.22 μM trolox equivalent (TE)/100 ㎍ F8-2. Glutathione (GSH), which is a positive control, showed EC₅₀ values of 19.87±0.01 ㎍/mL for DPPH and 15.95±0.03 ㎍/mL for hydroxyl radical and an ORAC value of 14.17±0.03 μM TE/100 ㎍ GSH. Finally, sixteen peptides were identified by liquid chromatography-tandem mass spectrometry (LC-MS/MS). Among them, Ile-Tyr and Leu-Tyr showed higher antioxidant scores.

• Journal of Biomedical Engineering Research
• /
• v.44 no.3
• /
• pp.167-175
• /
• 2023

Chest PA is the basic examination of radiographic imaging. Moreover, Chest PA's demands are constantly increasing because of the Increase in respiratory diseases. However, it is not meeting the demand due to problems such as a shortage of radiological technologist, sexual shame caused by patient contact, and the spread of infectious diseases. There have been many cases of using artificial intelligence to solve this problem. Therefore, the purpose of this research is to build an artificial intelligence dataset of Chest PA and to find a posture evaluation method. To construct the posture dataset, the posture image is acquired during actual and simulated examination and classified correct and incorrect posture of the patient. And to evaluate the artificial intelligence posture method, a posture estimation algorithm is used to preprocess the dataset and an artificial intelligence classification algorithm is applied. As a result, Chest PA posture dataset is validated with in over 95% accuracy in all artificial intelligence classification and the accuracy is improved through the Top-Down posture estimation algorithm AlphaPose and the classification InceptionV3 algorithm. Based on this, it will be possible to build a non-face-to-face automatic Chest PA examination system using artificial intelligence.

• Journal of Biomedical Engineering Research
• /
• v.44 no.3
• /
• pp.182-189
• /
• 2023

CT is a medical device that acquires medical images based on Attenuation coefficient of human organs related to X-rays. In addition, using this theory, it can acquire sagittal and coronal planes and 3D images of the human body. Then, CT is essential device for universal diagnostic test. But Exposure of CT scan is so high that it is regulated and managed with special medical equipment. As the special medical equipment, CT must implement quality control. In detail of quality control, Spatial resolution of existing phantom imaging tests, Contrast resolution and clinical image evaluation are qualitative tests. These tests are not objective, so the reliability of the CT undermine trust. Therefore, by applying an artificial intelligence classification model, we wanted to confirm the possibility of quantitative evaluation of the qualitative evaluation part of the phantom test. We used intelligence classification models (VGG19, DenseNet201, EfficientNet B2, inception_resnet_v2, ResNet50V2, and Xception). And the fine-tuning process used for learning was additionally performed. As a result, in all classification models, the accuracy of spatial resolution was 0.9562 or higher, the precision was 0.9535, the recall was 1, the loss value was 0.1774, and the learning time was from a maximum of 14 minutes to a minimum of 8 minutes and 10 seconds. Through the experimental results, it was concluded that the artificial intelligence model can be applied to CT implements quality control in spatial resolution and contrast resolution.