• Journal of the Korea Institute of Information Security & Cryptology
• /
• 제31권3호
• /
• pp.401-410
• /
• 2021

Recently, as cyber attacks targeting industrial control systems increase, various studies are being conducted on the detection of abnormalities in industrial processes. Considering that the industrial process is deterministic and regular, It is appropriate to determine abnormality by comparing the predicted value of the detection model from which normal data is trained and the actual value. In this paper, HAI Datasets 20.07 and 21.03 are used. In addition, an ensemble model is created by combining models that have applied different time steps to Gated Recurrent Units. Then, the detection performance of the single model and the ensemble recurrent neural networks model were compared through various performance evaluation analysis, and It was confirmed that the proposed model is more suitable for abnormal detection in industrial control systems.

• Journal of the Korea Society of Computer and Information
• /
• 제28권4호
• /
• pp.13-20
• /
• 2023

In this paper, the actual data of VOC reduction facilities was analyzed through a model that detects and predicts data anomalies. Using the USAD model, which shows stable performance in the field of anomaly detection, anomalies in real-time data are detected and sensors that cause anomalies are searched. In addition, we propose a method of predicting and warning, when abnormalities that time will occur by predicting future outliers with an auto-regressive model. The experiment was conducted with the actual data of the VOC reduction facility, and the anomaly detection test results showed high detection rates with precision, recall, and F1-score of 98.54%, 89.08%, and 93.57%, respectively. As a result, averaging of the precision, recall, and F1-score for 8 sensors of detection rates were 99.64%, 99.37%, and 99.63%. In addition, the Hamming loss obtained to confirm the validity of the detection experiment for each sensor was 0.0058, showing stable performance. And the abnormal prediction test result showed stable performance with an average absolute error of 0.0902.

• Imaging Science in Dentistry
• /
• 제40권1호
• /
• pp.25-32
• /
• 2010

Purpose : This study was designed to evaluate the efficacy and utility of panoramic radiograph for the improvement of the periodic oral examinations. Materials and Methods : Clinical examinations and panoramic examinations were done for the 242 subjects of oral examinations. The results of panoramic radiograph interpretation were compared with the clinical findings. Two questionnaires were created. One was carried out before the panoramic examination and the other done afterwards, to find out the subjects' cognition and satisfaction for the clinical and panoramic examinations. Results : 1. Panoramic findings showed a higher detection rate of 31.9% for periodontal diseases, and 23.1% for dental caries than clinical findings. 2. The additional abnormalities detected through panoramic examinations were impacted tooth in 81 subjects (33.6%), maxillary sinus abnormalities in 28 subjects (11.6%), condylar abnormalities in 5 subjects (2.1%), congenital and acquired dental anormalies in 59 subjects (24.5%), and other miscellaneous abnormalities in 34 subjects (14.1%). 3. 164 subjects (67.8%) were satisfied with the current periodic oral examination, and 75 subjects (31.1%) hoped for better accuracy. 4. In the first and second questionnaire, 154 subjects (67.0%) and 163 subjects (70.6%) responded respectively that panoramic examination was necessary, and 193 subjects (83.2%) responded that it actually helped. Conclusion : The panoramic examination was revealed to improve the effectiveness of the periodic oral examination and to increase the satisfaction of the subjects of examination.

• Asian Pacific Journal of Cancer Prevention
• /
• 제16권18호
• /
• pp.8495-8501
• /
• 2016

Purpose: This study was conducted to assess the agreement and differences between cervical self-sampling with a Kato device (KSSD) and gynecologist sampling for Pap cytology and human papillomavirus DNA (HPV DNA) detection. Materials and Methods: Women underwent self-sampling followed by gynecologist sampling during screening at two primary health clinics. Pap cytology of cervical specimens was evaluated for specimen adequacy, presence of endocervical cells or transformation zone cells and cytological interpretation for cells abnormalities. Cervical specimens were also extracted and tested for HPV DNA detection. Positive HPV smears underwent gene sequencing and HPV genotyping by referring to the online NCBI gene bank. Results were compared between samplings by Kappa agreement and McNemar test. Results: For Pap specimen adequacy, KSSD showed 100% agreement with gynecologist sampling but had only 32.3% agreement for presence of endocervical cells. Both sampling showed 100% agreement with only 1 case detected HSIL favouring CIN2 for cytology result. HPV DNA detection showed 86.2%agreement (K=0.64, 95% CI 0.524-0.756, p=0.001) between samplings. KSSD and gynaecologist sampling identified high risk HPV in 17.3% and 23.9% respectively (p=0.014). Conclusion: The self-sampling using Kato device can serve as a tool in Pap cytology and HPV DNA detection in low resource settings in Malaysia. Self-sampling devices such as KSSD can be used as an alternative technique to gynaecologist sampling for cervical cancer screening among rural populations in Malaysia.

• Journal of the Korea Convergence Society
• /
• 제12권2호
• /
• pp.1-6
• /
• 2021

In the modern society, traffic problems are occurring as vehicle ownership increases. In particular, the incidence of highway traffic accidents is low, but the fatality rate is high. Therefore, a technology for detecting an abnormality in a vehicle is being studied. Among them, there is a vehicle anomaly detection technology using deep learning. This detects vehicle abnormalities such as a stopped vehicle due to an accident or engine failure. However, if an abnormality occurs on the road, it is possible to quickly respond to the driver's location. In this study, we propose a deep learning-based vehicle anomaly detection using road CCTV data. The proposed method preprocesses the road CCTV data. The pre-processing uses the background extraction algorithm MOG2 to separate the background and the foreground. The foreground refers to a vehicle with displacement, and a vehicle with an abnormality on the road is judged as a background because there is no displacement. The image that the background is extracted detects an object using YOLOv4. It is determined that the vehicle is abnormal.

• Journal of Korean Neurosurgical Society
• /
• 제30권12호
• /
• pp.1417-1421
• /
• 2001

Objective : The purposes of this study are to compare imaging features with operative findings and to determine significance of imaging studies for early detection of craniosynostosis(CS). Methods : Plain radiograph of skull and three-dimensional(3D) CT reconstruction were analyzed in 10 consecutive patients with CS to assess the presence and the extent of synostosis. The radiological findings were investigated and compared with operative findings. Results : The locations of lesion were coronal suture in 6, sagittal suture in 3 and multiple sutures in one patient, and the age ranged 1 to 53 months(mean age : 17.4 months). Reconstructive procedures with or without advancement of supraorbital rim were performed in coronal CS patients and ${\pi}$-procedures or synostectomy were done in sagittal CS patients. Radi-ological abnormalities such as sutural indistinctness or sclerosis, bony ridge, bossing and other bony deformities were nearly consistent with surgical findings. Conclusion : The interpretation of imaging study are very important for early detection of craniosynostosis, especially, the plain radiographs of skull. Also 3D CT imaging is helpful in diagnosis and surgical planing of craniosynostosis. There are no significant differences between imaging features and operative findings in CS patients.

• International Journal of Computer Science & Network Security
• /
• 제21권5호
• /
• pp.1-8
• /
• 2021

Prostate cancer is one of the most diagnosed malignancies found across the world today. American cancer society in recent research predicted that over 174,600 new prostate cancer cases found and nearly 31,620 death cases recorded. Researchers are developing modest and accurate methodologies to detect and diagnose prostate cancer. Recent work has been done in radiology to detect prostate tumors using ultrasound imaging and resonance imaging techniques. Transrectal ultrasound and Magnetic resonance images of the prostate gland help in the detection of cancer in the prostate gland. The proposed paper is based on comparison and analysis between two novel image segmentation approaches. Seed region growing and cluster based image segmentation is used to extract the region from trans-rectal ultrasound prostate and MR prostate images. The region of extraction represents the abnormality area that presents in men's prostate gland. Detection of such abnormalities in the prostate gland helps in the identification and treatment of prostate cancer

• Journal of Medicine and Life Science
• /
• 제20권1호
• /
• pp.48-52
• /
• 2023

High-grade serous carcinoma (HGSC) is the most common type of pelvic cancer among women. Serous tubal intraepithelial carcinoma (STIC) is a precursor lesion of HGSC. Herein, we report a rare occurrence of STIC in patients undergoing surgery for benign indications without a family history of ovarian cancer. A 77-year-old woman underwent total laparoscopic hysterectomy and bilateral salpingo-oophorectomy for uterine prolapse. Pathological examination revealed bilateral STIC without ovarian abnormalities, and no other abnormal findings were noted. Another patient, a 49-year-old woman, underwent laparoscopic total hysterectomy and bilateral salpingectomy for uterine fibroids. STIC lesions were observed in both fallopian tubes. Subsequently, a staging was performed. No additional lesions were found, and the patient was followedup through imaging and blood tests. As reports of STIC lesions are rare, data on their clinical outcomes and management strategies are limited. In this report, we present cases of incidental STIC in benign surgery and discuss its proper interpretation and management. Through the early detection of STIC lesions, patients with risk factors can be identified in advance, which will allow prevention and early detection of ovarian cancer. Opportunistic salpingectomy was also actively discussed in this regard.

• IEMEK Journal of Embedded Systems and Applications
• /
• 제18권2호
• /
• pp.41-50
• /
• 2023

Gait analysis is an important tool in the clinical management of cerebral palsy, allowing for the assessment of condition severity, identification of potential gait abnormalities, planning and evaluation of interventions, and providing a baseline for future comparisons. However, traditional methods of gait analysis are costly and time-consuming, leading to a need for a more convenient and continuous method. This paper proposes a method for analyzing the posture of cerebral palsy patients using only smartphone videos and deep learning models, including a ResNet-based image tilt correction, AlphaPose for human pose estimation, and SmoothNet for temporal smoothing. The indicators employed in medical practice, such as the imbalance angles of shoulder and pelvis and the joint angles of spine-thighs, knees and ankles, were precisely examined. The proposed system surpassed pose estimation alone, reducing the mean absolute error for imbalance angles in frontal videos from 4.196° to 2.971° and for joint angles in sagittal videos from 5.889° to 5.442°.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• 제46권4호
• /
• pp.173-180
• /
• 2023

Smart factory companies are installing various sensors in production facilities and collecting field data. However, there are relatively few companies that actively utilize collected data, academic research using field data is actively underway. This study seeks to develop a model that detects anomalies in the process by analyzing spindle power data from a company that processes shafts used in automobile throttle valves. Since the data collected during machining processing is time series data, the model was developed through unsupervised learning by applying the Holt Winters technique and various deep learning algorithms such as RNN, LSTM, GRU, BiRNN, BiLSTM, and BiGRU. To evaluate each model, the difference between predicted and actual values was compared using MSE and RMSE. The BiLSTM model showed the optimal results based on RMSE. In order to diagnose abnormalities in the developed model, the critical point was set using statistical techniques in consultation with experts in the field and verified. By collecting and preprocessing real-world data and developing a model, this study serves as a case study of utilizing time-series data in small and medium-sized enterprises.