• Korean Journal of Bronchoesophagology
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• v.2 no.2
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• pp.222-226
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• 1996

The increasing incidence of multiple primary carcinomas occuring in the upper aerodigestive tract is well documented, with the accepted incidence being as high as 20-30%. The fiberoptic endoscopy has also enabled visualization of areas previously inaccessible without general anesthesia. A prospective panendoscopic examination of the upper aerodigestive tract was peformed on 104 patients with squamous cell carcinoma of head & neck in our hospital between 1989 and 1994. Five second primary cancers (4.8% ：2 stomach, 2 esophagus, 1 lung cancers) were detected endoscopically. These finding should reinforce the belief that head & neck cancer is a panmucosal disease of the aerodigestive tract that silent second primary cancers are not uncommon. So every effort should be done to detect second primary cancers in head & neck squamous cell carcinomas. Panendoscopy has proved valuable in achieving that.

• KSTLE International Journal
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• v.5 no.1
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• pp.28-31
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• 2004

The object of the present work is the natural frequency analysis of subambient pressure tri-pad and pico sliders. Head/disk interaction during start/stop and constant speed were detected by using the acoustic emission (AE) test system. The frequency spectrum analysis is performed using the AE signal obtained during the head/disk interaction. The FFT (Fast Fourier Transform) analysis of the AE signals is used to understand the interaction between the AE signal and the state of contact. Natural frequency analysis was performed using the Ansys program. The results indicate acceptable accordance of finite element calculation results with the experimental results.

• Journal of the Korean Association of Oral and Maxillofacial Surgeons
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• v.44 no.1
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• pp.18-24
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• 2018

Objectives: Foreign bodies (FBs) account for 3.8% of all pathologies of the head and neck region, and approximately one third of them are missed on initial examination. Thus, FBs represent diagnostic challenges to maxillofacial surgeons, rendering it necessary to employ an appropriate imaging modality in suspected cases. Materials and Methods: In this cross-sectional study, five different materials, including wood, metal, glass, tooth and stone, were prepared in three sizes (0.5, 1, and 2 mm) and placed in three locations (soft tissue, air-filled space and bone surface) within a sheep's head (one day after death) and scanned by panoramic radiography, cone-beam computed tomography (CBCT), and ultrasonography (US) devices. The images were reviewed, and accuracy of the detection modalities was recorded. The data were analyzed statistically using the Kruskal-Wallis, Mann-Whitney U-test, Friedman, Wilcoxon signed-rank and kappa tests (P<0.05). Results: CBCT was more accurate in detection of FBs than panoramic radiography and US (P<0.001). Metal was the most visible FB in all of modalities. US was the most accurate technique for detecting wooden materials, and CBCT was the best modality for detecting all other materials, regardless of size or location (P<0.05). The detection accuracy of US was greater in soft tissue, while both CBCT and panoramic radiography had minimal accuracy in detection of FBs in soft tissue. Conclusion: CBCT was the most accurate detection modality for all the sizes, locations and compositions of FBs, except for the wooden materials. Therefore, we recommend CBCT as the gold standard of imaging for detecting FBs in the maxillofacial region.

• Journal of Animal Science and Technology
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• v.63 no.6
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• pp.1453-1463
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• 2021

Feeding is the most important behavior that represents the health and welfare of weanling pigs. The early detection of feed refusal is crucial for the control of disease in the initial stages and the detection of empty feeders for adding feed in a timely manner. This paper proposes a real-time technique for the detection and recognition of small pigs using a deep-leaning-based method. The proposed model focuses on detecting pigs on a feeder in a feeding position. Conventional methods detect pigs and then classify them into different behavior gestures. In contrast, in the proposed method, these two tasks are combined into a single process to detect only feeding behavior to increase the speed of detection. Considering the significant differences between pig behaviors at different sizes, adaptive adjustments are introduced into a you-only-look-once (YOLO) model, including an angle optimization strategy between the head and body for detecting a head in a feeder. According to experimental results, this method can detect the feeding behavior of pigs and screen non-feeding positions with 95.66%, 94.22%, and 96.56% average precision (AP) at an intersection over union (IoU) threshold of 0.5 for YOLOv3, YOLOv4, and an additional layer and with the proposed activation function, respectively. Drinking behavior was detected with 86.86%, 89.16%, and 86.41% AP at a 0.5 IoU threshold for YOLOv3, YOLOv4, and the proposed activation function, respectively. In terms of detection and classification, the results of our study demonstrate that the proposed method yields higher precision and recall compared to conventional methods.

• Journal of Korea Multimedia Society
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• v.11 no.6
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• pp.787-795
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• 2008

Human recognition on camera is an interesting topic in computer vision. While fingerprint and face recognition have been become common, gait is considered as a new biometric feature for distance recognition. In this paper, we propose a gait recognition algorithm based on the knee angle, 2 feet distance, walking velocity and head direction of a person who appear in camera view on one gait cycle. The background subtraction method firstly use for binary moving object extraction and then base on it we continue detect the leg region, head region and get gait features (leg angle, leg swing amplitude). Another feature, walking speed, also can be detected after a gait cycle finished. And then, we compute the errors between calculated features and stored features for recognition. This method gives good results when we performed testing using indoor and outdoor landscape in both lateral, oblique view.

• International Journal of Internet, Broadcasting and Communication
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• v.13 no.2
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• pp.187-194
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• 2021

This paper presents a margin-based face liveness detection method with behavioral confirmation to prevent spoofing attacks using deep learning techniques. The proposed method provides a possibility to prevent biometric person authentication systems from replay and printed spoofing attacks. For this work, a set of real face images and fake face images was collected and a face liveness detection model is trained on the constructed dataset. Traditional face liveness detection methods exploit the face image covering only the face regions of the human head image. However, outside of this region of interest (ROI) might include useful features such as phone edges and fingers. The proposed face liveness detection method was experimentally tested on the author's own dataset. Collected databases are trained and experimental results show that the trained model distinguishes real face images and fake images correctly.

• The Journal of Korea Robotics Society
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• v.12 no.3
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• pp.313-321
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• 2017

One of the most frequently performed tasks in human-robot interaction (HRI), intelligent vehicles, and security systems is face related applications such as face recognition, facial expression recognition, driver state monitoring, and gaze estimation. In these applications, accurate head pose estimation is an important issue. However, conventional methods have been lacking in accuracy, robustness or processing speed in practical use. In this paper, we propose a novel method for estimating head pose with a monocular camera. The proposed algorithm is based on a deep neural network for multi-task learning using a small grayscale image. This network jointly detects multi-view faces and estimates head pose in hard environmental conditions such as illumination change and large pose change. The proposed framework quantitatively and qualitatively outperforms the state-of-the-art method with an average head pose mean error of less than $4.5^{\circ}$ in real-time.

• Journal of the Korean Association of Oral and Maxillofacial Surgeons
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• v.44 no.5
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• pp.220-224
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• 2018

Objectives: The aim of this study was to determine the incidence and characteristics of second primary malignancy (SPM) in patients with head and neck squamous cell carcinoma treated at a tertiary care hospital. Materials and Methods: We retrospectively reviewed the medical records of 221 patients who underwent surgery with or without adjuvant treatment for head and neck cancer from 2000 to 2002. Data of age, sex, risk factors, sites of primary and SPM, TNM stage of primary tumor, incidence of SPM, and survival were collected from medical charts. Results: Eighteen patients developed SPM during a median follow-up of 67 months, with an overall incidence of 8.14%. In addition, 77.7% of SPMs occurred in the oral cavity, followed by 11% in the lungs. The 5-year overall survival after the diagnosis of SPM in the head or neck was 70%, compared to 30% for SPM in other body regions. Conclusion: Considering a high incidence of SPM, i.e., 8.14%, in a mean follow-up period of 67 months suggests the need for long-term follow-up. Since treatment of SPM has shown an acceptable survival rate, early detection and curative therapy should be emphasized.

• Journal of Medicine and Life Science
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• v.20 no.2
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• pp.103-106
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• 2023

Post-acute coronavirus disease (COVID-19) syndrome is defined as persistent symptoms or delayed complications after COVID-19. Several cases of cranial nerve invasion related to COVID-19 have been reported. However, to our knowledge, no cases of solitary unilateral hypoglossal nerve paralysis after mild COVID-19 without intubation have been reported to date. Herein, we report the case of a 64-year-old man with unilateral hypoglossal nerve palsy as a complication of COVID-19. He complained of dysarthria and tongue discomfort 2 weeks after COVID-19 onset. Brain and neck computed tomography, magnetic resonance imaging, ultrasonography, and blood tests ruled out other possible causes. The patient's nerve palsy was rapidly diagnosed and improved with early rehabilitation. Understanding of the pathology of COVID-19 is still limited. Physicians should focus on patients' symptoms and their relationship to COVID-19, and investigate complications immediately. This case highlights the importance of early detection and rehabilitation of post-acute COVID-19 syndrome.

• Journal of International Society for Simulation Surgery
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• v.2 no.1
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• pp.33-39
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• 2015

Purpose The head of infants under 24 months old who has Craniosynostosis grows extraordinarily that makes head shape unusual. To diagnose the Craniosynostosis, surgeon has to inspect computed tomography(CT) images of the patient in person. It's very time consuming process. Moreover, without a surgeon, it's difficult to diagnose the Craniosynostosis. Therefore, we developed technique which detects Craniosynostosis automatically from the CT volume. Materials and Methods At first, rotation correction is performed to the 3D CT volume for detection of the Craniosynostosis. Then, cranial area is extracted using the iterative thresholding method we proposed. Lastly, we diagnose Craniosynostosis by analyzing centroid relationships of clusters of cranial bone which was divided by cranial suture. Results Using this automatical cranial detection technique, we can diagnose Craniosynostosis correctly. The proposed method resulted in 100% sensitivity and 90% specificity. The method perfectly diagnosed abnormal patients. Conclusion By plugging-in the software on CT machine, it will be able to warn the possibility of Craniosynostosis. It is expected that early treatment of Craniosynostosis would be possible with our proposed algorithm.