Moo-Jin Jeong;Joo-Young Oh;Hoon-Hee Park;Joo-Young Lee
Journal of radiological science and technology
/
v.47
no.1
/
pp.29-37
/
2024
This study aims to evaluate the performance of the U-Net based learning model that may vary depending on the histogram equalization algorithm. The subject of the experiment were 17 radiology students of this college, and 1,727 data sets in which the region of interest was set in the thyroid after acquiring ultrasound image data were used. The training set consisted of 1,383 images, the validation set consisted of 172 and the test data set consisted of 172. The equalization algorithm was divided into Histogram Equalization(HE) and Contrast Limited Adaptive Histogram Equalization(CLAHE), and according to the clip limit, it was divided into CLAHE8-1, CLAHE8-2. CLAHE8-3. Deep Learning was learned through size control, histogram equalization, Z-score normalization, and data augmentation. As a result of the experiment, the Attention U-Net showed the highest performance from CLAHE8-2 to 0.8355, and the U-Net and BSU-Net showed the highest performance from CLAHE8-3 to 0.8303 and 0.8277. In the case of mIoU, the Attention U-Net was 0.7175 in CLAHE8-2, the U-Net was 0.7098 and the BSU-Net was 0.7060 in CLAHE8-3. This study attempted to confirm the effects of U-Net, Attention U-Net, and BSU-Net models when histogram equalization is performed on ultrasound images. The increase in Clip Limit can be expected to increase the ROI match with the prediction mask by clarifying the boundaries, which affects the improvement of the contrast of the thyroid area in deep learning model learning, and consequently affects the performance improvement.
Objective: To determine whether volumetric CT texture analysis (CTTA) using fully automatic tumor segmentation can help predict recurrence-free survival (RFS) in patients with intrahepatic mass-forming cholangiocarcinomas (IMCCs) after surgical resection. Materials and Methods: This retrospective study analyzed the preoperative CT scans of 89 patients with IMCCs (64 male; 25 female; mean age, 62.1 years; range, 38-78 years) who underwent surgical resection between January 2005 and December 2016. Volumetric CTTA of IMCCs was performed in late arterial phase images using both fully automatic and semi-automatic liver tumor segmentation techniques. The time spent on segmentation and texture analysis was compared, and the first-order and second-order texture parameters and shape features were extracted. The reliability of CTTA parameters between the techniques was evaluated using intraclass correlation coefficients (ICCs). Intra- and interobserver reproducibility of volumetric CTTAs were also obtained using ICCs. Cox proportional hazard regression were used to predict RFS using CTTA parameters and clinicopathological parameters. Results: The time spent on fully automatic tumor segmentation and CTTA was significantly shorter than that for semi-automatic segmentation: mean ± standard deviation of 1 minutes 37 seconds ± 50 seconds vs. 10 minutes 48 seconds ± 13 minutes 44 seconds (p < 0.001). ICCs of the texture features between the two techniques ranged from 0.215 to 0.980. ICCs for the intraobserver and interobserver reproducibility using fully automatic segmentation were 0.601-0.997 and 0.177-0.984, respectively. Multivariable analysis identified lower first-order mean (hazard ratio [HR], 0.982; p = 0.010), larger pathologic tumor size (HR, 1.171; p < 0.001), and positive lymph node involvement (HR, 2.193; p = 0.014) as significant parameters for shorter RFS using fully automatic segmentation. Conclusion: Volumetric CTTA parameters obtained using fully automatic segmentation could be utilized as prognostic markers in patients with IMCC, with comparable reproducibility in significantly less time compared with semi-automatic segmentation.
In the binaural synthesis environments, wide varieties of the head-related transfer functions (HRTFs) that have measured with a various direction would be desirable to obtain the accurate and various spatial sound images. To reduce the size' of HRTFs, interpolation has been often employed, where the HRTF for any direction is obtained by a limited number of the representative HRTFs. In this paper, we study on the distortion measures for interpolation, which has an important role in interpolation. With lhe various objective distortion metrics, the differences between the interpolated and the measured HRTFs were computed. These were then compared and analyzed with the results from the listening tests. From the results, the objective distortion measures were selected, that reflected the perceptual differences in spatial sound image. This measure was employed in a practical interpolation technique. We applied the proposed method to four kinds of an HRTF set, measured from three human heads and one mannequin. As a result, the Mel-frequency cepstral distortion was shown to be a good predictor for the differences in spatial sound location, when three HRTF measured from human, and the time-domain signal to distortion ratio revealed good prediction results for the entire four HRTF sets.
Fishing boat capsizing accidents account for more than half of all capsize accidents. These can occur for a variety of reasons, including inexperienced operation, bad weather, and poor maintenance. Due to the size and influence of the industry, technological complexity, and regional diversity, fishing ships are relatively under-researched compared to commercial ships. This study aimed to predict the rolling motion time series of fishing boats using an image-based deep learning model. Image-based deep learning can achieve high performance by learning various patterns in a time series. Three image-based deep learning models were used for this purpose: Xception, ResNet50, and CRNN. Xception and ResNet50 are composed of 177 and 184 layers, respectively, while CRNN is composed of 22 relatively thin layers. The experimental results showed that the Xception deep learning model recorded the lowest Symmetric mean absolute percentage error(sMAPE) of 0.04291 and Root Mean Squared Error(RMSE) of 0.0198. ResNet50 and CRNN recorded an RMSE of 0.0217 and 0.022, respectively. This confirms that the models with relatively deeper layers had higher accuracy.
The Journal of the Korea institute of electronic communication sciences
/
v.19
no.3
/
pp.577-582
/
2024
In 2007, the first domestic pet insurance policies were introduced, and by 2023, numerous insurance products had been developed. The pet insurance market has been expanding steadily. However, as of 2022, only 0.8% of all pet owners have subscribed to pet insurance. Pet owners hesitate to enroll in pet insurance due to expensive premiums, unclear coverage details, and strict enrollment criteria. This paper proposes a model capable of detecting pet eye diseases and predicting their health age. Initially, EfficientNet is employed to identify the pet's eye disease, while OpenCV is utilized to locate and measure the size of the disease, enabling the calculation of the pet's healthy age. By leveraging the calculated health age, the aim is to aid insurance companies in determining pet insurance premiums. This model can facilitate the calculation of reasonable pet insurance rates based on the pet's eye condition and health age. Ultimately, the objective is to implement a system capable of detecting pet eye conditions and predicting their health age.
Jung Joo Yoon;Hye Yoom Kim;Ai Lin Tai;Ho Sub Lee;Dae Gill Kang
Herbal Formula Science
/
v.32
no.1
/
pp.11-28
/
2024
Objectives : GunRyeong-Tang(GRT) is a traditional herbal prescription that combines Oryeongsan and Sagunja-tang. This study employed network analysis methods on the components of GRT and target genes related to diabetes complications to predict the improvement effects of GRT on diabetes complications. Methods : The collection of active compounds of GRT and related target genes involved the utilization of public databases and the PubChem database. We selected diabetes complication-related genes using GeneCards and confirmed their correlation through comparative analysis with the target genes of GRT. We constructed a network using Cytoscape 3.9.1 and conducted topological analysis. To predict the mechanism, we performed functional enrichment analysis based on Gene Ontology (GO) biological processes and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Results : Through network analysis, 234 active compounds and 1361 related genes were collected from GRT. A total of 9,136 genes related to diabetes complications were collected, and 1,039 target genes overlapping with the components of GRT were identified. The core genes of this network were TP53, INS, AKT1, ALB, and EGFR. In addition, GRT significantly reduced the H9c2 cell size and the expression of myocardial hypertrophy biomarkers (ANP, BNP), which were increased by high glucose (HG). Conclusions : Through this study, we were able to predict the activity and mechanism of action of GRT on diabetes and diabetic complications, and confirmed the potential of GRT as a treatment for diabetes complications through the effect of GRT on improving myocardial hypertrophy for diabetic cardiomyopathy.
This research aimed to appraise the effectiveness of four optimization approaches - cuckoo optimization algorithm (COA), multi-verse optimization (MVO), particle swarm optimization (PSO), and teaching-learning-based optimization (TLBO) - that were enhanced with an artificial neural network (ANN) in predicting the bearing capacity of shallow foundations located on cohesionless soils. The study utilized a database of 97 laboratory experiments, with 68 experiments for training data sets and 29 for testing data sets. The ANN algorithms were optimized by adjusting various variables, such as population size and number of neurons in each hidden layer, through trial-and-error techniques. Input parameters used for analysis included width, depth, geometry, unit weight, and angle of shearing resistance. After performing sensitivity analysis, it was determined that the optimized architecture for the ANN structure was 5×5×1. The study found that all four models demonstrated exceptional prediction performance: COA-MLP, MVO-MLP, PSO-MLP, and TLBO-MLP. It is worth noting that the MVO-MLP model exhibited superior accuracy in generating network outputs for predicting measured values compared to the other models. The training data sets showed R2 and RMSE values of (0.07184 and 0.9819), (0.04536 and 0.9928), (0.09194 and 0.9702), and (0.04714 and 0.9923) for COA-MLP, MVO-MLP, PSO-MLP, and TLBO-MLP methods respectively. Similarly, the testing data sets produced R2 and RMSE values of (0.08126 and 0.07218), (0.07218 and 0.9814), (0.10827 and 0.95764), and (0.09886 and 0.96481) for COA-MLP, MVO-MLP, PSO-MLP, and TLBO-MLP methods respectively.
The cache size tends to grow in the embedded processor as technology scales to smaller transistors and lower supply voltages. However, larger cache size demands more energy. Accordingly, the ratio of the cache energy consumption to the total processor energy is growing. Many cache energy schemes have been proposed for reducing the cache energy consumption. However, these previous schemes are concerned with one side for reducing the cache energy consumption, dynamic cache energy only, or static cache energy only. In this paper, we propose a hybrid scheme for reducing dynamic and static cache energy, simultaneously. for this hybrid scheme, we adopt two existing techniques to reduce static cache energy consumption, drowsy cache technique, and to reduce dynamic cache energy consumption, way-prediction technique. Additionally, we propose a early wake-up technique based on program counter to reduce penalty caused by applying drowsy cache technique. We focus on level 1 data cache. The hybrid scheme can reduce static and dynamic cache energy consumption simultaneously, furthermore our early wake-up scheme can reduce extra program execution cycles caused by applying the hybrid scheme.
Laser induced breakdown spectroscopy(LIBS) is an simple analysis method for directly quantifying many kinds of soil micro-elements on site using a small size of laser without pre-treatment at any property of materials(solid, liquid and gas). The purpose of this study were to find an optimum condition of the LIBS measurement including wavelengths for quantifying soil elements, to relate spectral properties to the concentration of soil elements using LIBS as a simultaneous un-breakdown quantitative analysis technology, which can be applied for the safety assessment of agricultural products and precision agriculture, and to compare the results with a standardized chemical analysis method. Soil samples classified as fine-silty, mixed, thermic Typic Hapludalf(Memphis series) from grassland and uplands in Tennessee, USA were collected, crushed, and prepared for further analysis or LIBS measurement. The samples were measured using LIBS ranged from 200 to 600 nm(0.03 nm interval) with a Nd:YAG laser at 532 nm, with a beam energy of 25 mJ per pulse, a pulse width of 5 ns, and a repetition rate of 10 Hz. The optimum wavelength(${\lambda}nm$) of LIBS for estimating soil and plant elements were 308.2 nm for Al, 428.3 nm for Ca, 247.8 nm for T-C, 438.3 nm for Fe, 766.5 nm for K, 85.2 nm for Mg, 330.2 nm for Na, 213.6 nm for P, 180.7 nm for S, 288.2 nm for Si, and 351.9 nm for Ti, respectively. Coefficients of determination($r^2$) of calibration curve using standard reference soil samples for each element from LIBS measurement were ranged from 0.863 to 0.977. In comparison with ICP-AES(Inductively coupled plasma atomic emission spectroscopy) measurement, measurement error in terms of relative standard error were calculated. Silicon dioxide(SiO2) concentration estimated from two methods showed good agreement with -3.5% of relative standard error. The relative standard errors for the other elements were high. It implies that the prediction accuracy is low which might be caused by matrix effect such as particle size and constituent of soils. It is necessary to enhance the measurement and prediction accuracy of LIBS by improving pretreatment process, standard reference soil samples, and measurement method for a reliable quantification method.
Dental arch expansion is one of the method used to solve the dental crowding problem by non-extraction. Many formulae using tooth size have been suggested to predict ideal inter-premolar and inter-molar width. The purpose of this study was to evaluate the adequacy of some upper dental arch width prediction methods, namely Pont's method, Schmuth's method and Cha's method. The sample consisted of the casts of 119 Korean young adults who had no muscular abnormality, no skeletal discrepancy, and Angle's Class I molar relationships. Measurements were obtained directly from plaster casts; they Included mesiodistal crown diameters of the four maxillary incisors, as well as maxillary inter-first-premolar and inter-first-molar arch widths as specified by Pont. The correlation coefficients between the sum of incisors(SI) and upper dental arch width were calculated. The differences between predicted width and actual width were classified as overestimated, properestimated, and underestimated. The data obtained from each group were analyzed for statistical differences. The results were as follows : 1. Upper dental arch width indices were calculated from SI in normal occlusion (81.96 : premolar index, 62.55 : molar index). 2. Low correlations between SI and arch width were noted in normal occlusion (0.50 in the inter-premolar width, 0.39 in the inter-molar width). 3. Pont's formula and Schmuth's formula tended to overestimate the inter-premolar width. A more even distribution of estimates was noted in Cha's fomula. 4. Cases within $\pm$1 mm range of observed inter-premolar width were $45\%$ in the Cha's formula, $40\%$ in the Pont's formula, and $39\%$ in the Schmuth's formula. 5. All formulae had a tendency to underestimate the inter-molar width, but Cha's formula had better predictability than others. 6. Cases within $\pm$1 mm range of observed inter-molar width were $40\%$ in the Cha's formula, $29\%$ in the Pont's formula, and $13\%$ of Schmuth's formula. The data presented in this study does not support the clinical usefulness of ideal arch width prediction methods using the mesiodistal width of maxillary incisors.
본 웹사이트에 게시된 이메일 주소가 전자우편 수집 프로그램이나
그 밖의 기술적 장치를 이용하여 무단으로 수집되는 것을 거부하며,
이를 위반시 정보통신망법에 의해 형사 처벌됨을 유념하시기 바랍니다.
[게시일 2004년 10월 1일]
이용약관
제 1 장 총칙
제 1 조 (목적)
이 이용약관은 KoreaScience 홈페이지(이하 “당 사이트”)에서 제공하는 인터넷 서비스(이하 '서비스')의 가입조건 및 이용에 관한 제반 사항과 기타 필요한 사항을 구체적으로 규정함을 목적으로 합니다.
제 2 조 (용어의 정의)
① "이용자"라 함은 당 사이트에 접속하여 이 약관에 따라 당 사이트가 제공하는 서비스를 받는 회원 및 비회원을
말합니다.
② "회원"이라 함은 서비스를 이용하기 위하여 당 사이트에 개인정보를 제공하여 아이디(ID)와 비밀번호를 부여
받은 자를 말합니다.
③ "회원 아이디(ID)"라 함은 회원의 식별 및 서비스 이용을 위하여 자신이 선정한 문자 및 숫자의 조합을
말합니다.
④ "비밀번호(패스워드)"라 함은 회원이 자신의 비밀보호를 위하여 선정한 문자 및 숫자의 조합을 말합니다.
제 3 조 (이용약관의 효력 및 변경)
① 이 약관은 당 사이트에 게시하거나 기타의 방법으로 회원에게 공지함으로써 효력이 발생합니다.
② 당 사이트는 이 약관을 개정할 경우에 적용일자 및 개정사유를 명시하여 현행 약관과 함께 당 사이트의
초기화면에 그 적용일자 7일 이전부터 적용일자 전일까지 공지합니다. 다만, 회원에게 불리하게 약관내용을
변경하는 경우에는 최소한 30일 이상의 사전 유예기간을 두고 공지합니다. 이 경우 당 사이트는 개정 전
내용과 개정 후 내용을 명확하게 비교하여 이용자가 알기 쉽도록 표시합니다.
제 4 조(약관 외 준칙)
① 이 약관은 당 사이트가 제공하는 서비스에 관한 이용안내와 함께 적용됩니다.
② 이 약관에 명시되지 아니한 사항은 관계법령의 규정이 적용됩니다.
제 2 장 이용계약의 체결
제 5 조 (이용계약의 성립 등)
① 이용계약은 이용고객이 당 사이트가 정한 약관에 「동의합니다」를 선택하고, 당 사이트가 정한
온라인신청양식을 작성하여 서비스 이용을 신청한 후, 당 사이트가 이를 승낙함으로써 성립합니다.
② 제1항의 승낙은 당 사이트가 제공하는 과학기술정보검색, 맞춤정보, 서지정보 등 다른 서비스의 이용승낙을
포함합니다.
제 6 조 (회원가입)
서비스를 이용하고자 하는 고객은 당 사이트에서 정한 회원가입양식에 개인정보를 기재하여 가입을 하여야 합니다.
제 7 조 (개인정보의 보호 및 사용)
당 사이트는 관계법령이 정하는 바에 따라 회원 등록정보를 포함한 회원의 개인정보를 보호하기 위해 노력합니다. 회원 개인정보의 보호 및 사용에 대해서는 관련법령 및 당 사이트의 개인정보 보호정책이 적용됩니다.
제 8 조 (이용 신청의 승낙과 제한)
① 당 사이트는 제6조의 규정에 의한 이용신청고객에 대하여 서비스 이용을 승낙합니다.
② 당 사이트는 아래사항에 해당하는 경우에 대해서 승낙하지 아니 합니다.
- 이용계약 신청서의 내용을 허위로 기재한 경우
- 기타 규정한 제반사항을 위반하며 신청하는 경우
제 9 조 (회원 ID 부여 및 변경 등)
① 당 사이트는 이용고객에 대하여 약관에 정하는 바에 따라 자신이 선정한 회원 ID를 부여합니다.
② 회원 ID는 원칙적으로 변경이 불가하며 부득이한 사유로 인하여 변경 하고자 하는 경우에는 해당 ID를
해지하고 재가입해야 합니다.
③ 기타 회원 개인정보 관리 및 변경 등에 관한 사항은 서비스별 안내에 정하는 바에 의합니다.
제 3 장 계약 당사자의 의무
제 10 조 (KISTI의 의무)
① 당 사이트는 이용고객이 희망한 서비스 제공 개시일에 특별한 사정이 없는 한 서비스를 이용할 수 있도록
하여야 합니다.
② 당 사이트는 개인정보 보호를 위해 보안시스템을 구축하며 개인정보 보호정책을 공시하고 준수합니다.
③ 당 사이트는 회원으로부터 제기되는 의견이나 불만이 정당하다고 객관적으로 인정될 경우에는 적절한 절차를
거쳐 즉시 처리하여야 합니다. 다만, 즉시 처리가 곤란한 경우는 회원에게 그 사유와 처리일정을 통보하여야
합니다.
제 11 조 (회원의 의무)
① 이용자는 회원가입 신청 또는 회원정보 변경 시 실명으로 모든 사항을 사실에 근거하여 작성하여야 하며,
허위 또는 타인의 정보를 등록할 경우 일체의 권리를 주장할 수 없습니다.
② 당 사이트가 관계법령 및 개인정보 보호정책에 의거하여 그 책임을 지는 경우를 제외하고 회원에게 부여된
ID의 비밀번호 관리소홀, 부정사용에 의하여 발생하는 모든 결과에 대한 책임은 회원에게 있습니다.
③ 회원은 당 사이트 및 제 3자의 지적 재산권을 침해해서는 안 됩니다.
제 4 장 서비스의 이용
제 12 조 (서비스 이용 시간)
① 서비스 이용은 당 사이트의 업무상 또는 기술상 특별한 지장이 없는 한 연중무휴, 1일 24시간 운영을
원칙으로 합니다. 단, 당 사이트는 시스템 정기점검, 증설 및 교체를 위해 당 사이트가 정한 날이나 시간에
서비스를 일시 중단할 수 있으며, 예정되어 있는 작업으로 인한 서비스 일시중단은 당 사이트 홈페이지를
통해 사전에 공지합니다.
② 당 사이트는 서비스를 특정범위로 분할하여 각 범위별로 이용가능시간을 별도로 지정할 수 있습니다. 다만
이 경우 그 내용을 공지합니다.
제 13 조 (홈페이지 저작권)
① NDSL에서 제공하는 모든 저작물의 저작권은 원저작자에게 있으며, KISTI는 복제/배포/전송권을 확보하고
있습니다.
② NDSL에서 제공하는 콘텐츠를 상업적 및 기타 영리목적으로 복제/배포/전송할 경우 사전에 KISTI의 허락을
받아야 합니다.
③ NDSL에서 제공하는 콘텐츠를 보도, 비평, 교육, 연구 등을 위하여 정당한 범위 안에서 공정한 관행에
합치되게 인용할 수 있습니다.
④ NDSL에서 제공하는 콘텐츠를 무단 복제, 전송, 배포 기타 저작권법에 위반되는 방법으로 이용할 경우
저작권법 제136조에 따라 5년 이하의 징역 또는 5천만 원 이하의 벌금에 처해질 수 있습니다.
제 14 조 (유료서비스)
① 당 사이트 및 협력기관이 정한 유료서비스(원문복사 등)는 별도로 정해진 바에 따르며, 변경사항은 시행 전에
당 사이트 홈페이지를 통하여 회원에게 공지합니다.
② 유료서비스를 이용하려는 회원은 정해진 요금체계에 따라 요금을 납부해야 합니다.
제 5 장 계약 해지 및 이용 제한
제 15 조 (계약 해지)
회원이 이용계약을 해지하고자 하는 때에는 [가입해지] 메뉴를 이용해 직접 해지해야 합니다.
제 16 조 (서비스 이용제한)
① 당 사이트는 회원이 서비스 이용내용에 있어서 본 약관 제 11조 내용을 위반하거나, 다음 각 호에 해당하는
경우 서비스 이용을 제한할 수 있습니다.
- 2년 이상 서비스를 이용한 적이 없는 경우
- 기타 정상적인 서비스 운영에 방해가 될 경우
② 상기 이용제한 규정에 따라 서비스를 이용하는 회원에게 서비스 이용에 대하여 별도 공지 없이 서비스 이용의
일시정지, 이용계약 해지 할 수 있습니다.
제 17 조 (전자우편주소 수집 금지)
회원은 전자우편주소 추출기 등을 이용하여 전자우편주소를 수집 또는 제3자에게 제공할 수 없습니다.
제 6 장 손해배상 및 기타사항
제 18 조 (손해배상)
당 사이트는 무료로 제공되는 서비스와 관련하여 회원에게 어떠한 손해가 발생하더라도 당 사이트가 고의 또는 과실로 인한 손해발생을 제외하고는 이에 대하여 책임을 부담하지 아니합니다.
제 19 조 (관할 법원)
서비스 이용으로 발생한 분쟁에 대해 소송이 제기되는 경우 민사 소송법상의 관할 법원에 제기합니다.
[부 칙]
1. (시행일) 이 약관은 2016년 9월 5일부터 적용되며, 종전 약관은 본 약관으로 대체되며, 개정된 약관의 적용일 이전 가입자도 개정된 약관의 적용을 받습니다.