• Journal of Korea Multimedia Society
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• v.24 no.3
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• pp.396-405
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• 2021

In surgery to remove pancreatic cancer, it is important to figure out the shape of a patient's pancreas. However, previous studies have a limit to detect a pancreas automatically in abdominal CT images, because the pancreas varies in shape, size and location by patient. Therefore, in this paper, we propose a method of learning various shapes of pancreas according to the patients and adjacent slices using Faster R-CNN based on Inception V2, and automatically detecting the pancreas from abdominal CT images. Model training and testing were performed using the NIH Pancreas-CT Dataset, and intensity normalization was applied to all data to improve pancreatic detection accuracy. Additionally, according to the shape of the pancreas, the test dataset was classified into top, middle, and bottom slices to evaluate the model's performance on each data. The results show that the top data's mAP@.50IoU achieved 91.7% and the bottom data's mAP@.50IoU achieved 95.4%, and the highest performance was the middle data's mAP@.50IoU, 98.5%. Thus, we have confirmed that the model can accurately detect the pancreas in CT images.

• Journal of Digestive Cancer Research
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• v.11 no.1
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• pp.21-29
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• 2023

Despite recent advancements in the diagnosis and treatment of pancreatic cancer, clinical results remain dismal. Furthermore, there are no reliable biomarkers or alternatives beyond carbohydrate antigen 19-9. Circulating tumor cells (CTCs) may be a potential biomarker, but their therapeutic application is constrained by their rarity in peripheral venous blood. Theoretically, the portal vein can be a more appropriate location for the detection of CTCs, because the first venous drainage of pancreatic cancer is portal circulation. According to several studies, the number and detection rate of CTCs may be higher in the portal blood than in the peripheral blood. CTC counts in the portal blood are strongly correlated with several prognostic parameters such as hepatic metastasis, recurrence after surgery, and survival. The phenotypic and genotypic properties analyzed in the captured portal CTCs can assist us to comprehend tumor heterogeneity and predicting the prognosis of pancreatic cancer. The investigations to date are limited by small sample sizes and varied CTC detection techniques. Therefore, a large number of prospective studies are required to confirm portal CTCs as a valid biomarker in pancreatic cancer.

• The Plant Pathology Journal
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• v.39 no.4
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• pp.319-334
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• 2023

Plant disease is an important factor affecting crop yield. With various types and complex conditions, plant diseases cause serious economic losses, as well as modern agriculture constraints. Hence, rapid, accurate, and early identification of crop diseases is of great significance. Recent developments in deep learning, especially convolutional neural network (CNN), have shown impressive performance in plant disease classification. However, most of the existing datasets for plant disease classification are a single background environment rather than a real field environment. In addition, the classification can only obtain the category of a single disease and fail to obtain the location of multiple different diseases, which limits the practical application. Therefore, the object detection method based on CNN can overcome these shortcomings and has broad application prospects. In this study, an annotated apple leaf disease dataset in a real field environment was first constructed to compensate for the lack of existing datasets. Moreover, the Faster R-CNN and YOLOv3 architectures were trained to detect apple leaf diseases in our dataset. Finally, comparative experiments were conducted and a variety of evaluation indicators were analyzed. The experimental results demonstrate that deep learning algorithms represented by YOLOv3 and Faster R-CNN are feasible for plant disease detection and have their own strong points and weaknesses.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2023.05a
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• pp.152-153
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• 2023

AIS has been widely used for maritime traffic assessment for its convenience. However, AIS has problems with position missing due to radio interference and transmission distance limit. On the other hand, satellite radar determines the location of ships over a wide sea regardless of the problems. This study proposes a noble method of stepwise threshold determination to detect ships from Sentinel-1. The proposed method is up to 25 times faster than the existing moving window-based threshold determination method, and the detection accuracy is similar.

• Journal of information and communication convergence engineering
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• v.22 no.2
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• pp.109-120
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• 2024

Trash or garbage is one of the most dangerous health and environmental problems that affect pollution. Pollution affects nature, human life, and wildlife. In this paper, we propose modern solutions for cleaning the environment of trash pollution by enforcing strict action against people who dump trash inappropriately on streets, outside the home, and in unnecessary places. Artificial Intelligence (AI), especially Deep Learning (DL), has been used to automate and solve issues in the world. We availed this as an excellent opportunity to develop a system that identifies trash using a deep convolutional neural network (CNN). This paper proposes a real-time garbage identification system based on a deep CNN architecture with eight distinct classes for the training dataset. After identifying the garbage, the CCTV camera captures a video of the individual placing the trash in the incorrect location and sends an alert notice to the relevant authority.

• Journal of Auto-vehicle Safety Association
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• v.16 no.2
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• pp.20-26
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• 2024

The delivery using drones has been attracting attention because it can innovatively reduce the delivery time from the time of order to completion of delivery compared to the current delivery system, and there have been pilot projects conducted for safe drone delivery. However, the current drone delivery system has the disadvantage of limiting the operational efficiency offered by fully autonomous delivery drones in that drones mainly deliver goods to pre-set landing sites or delivery bases, and the final delivery is still made by humans. In this paper, to overcome these limitations, we propose obstacle detection and landing site selection algorithm based on a vision sensor that enables safe drone landing at the delivery location of the product orderer, and experimentally prove the possibility of station-to-door delivery. The proposed algorithm forms a 3D map of point cloud based on simultaneous localization and mapping (SLAM) technology and presents a grid segmentation technique, allowing drones to stably find a landing site even in places without prior information. We aims to verify the performance of the proposed algorithm through streaming data received from the drone.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.5
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• pp.611-617
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• 2018

The application of health monitoring, including a fault detection technique, is needed to secure the structural safety of large structures. A 2-step crack identification method for detecting the crack location and size of the beam structure is presented. First, a crack occurrence region was estimated using the modified Laplacian operator for the strain mode shape obtained from the distributed local strain data. The crack location and size were then identified based on the natural frequencies obtained from the acceleration data and the neural network technique for the pre-estimated crack occurrence region. The natural frequencies of a cracked beam were calculated based on an equivalent bending stiffness induced by the energy method, and used to generate the training patterns of the neural network. An experimental study was carried out on an aluminum cantilever beam to verify the present method for crack identification. Cracks were produced on the beam, and free vibration tests were performed. A crack occurrence region was estimated using the modified Laplacian operator for the strain mode shape, and the crack location and size were assessed using the natural frequencies and neural network technique. The identified crack occurrence region agrees well with the exact one, and the accuracy of the estimation results for the crack location and size could be enhanced considerably for 3 damage cases. The presented method could be applied effectively to the structural health monitoring of large structures.

• KIPS Transactions on Computer and Communication Systems
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• v.2 no.1
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• pp.7-14
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• 2013

Presently, there are many different technologies used for position detection. However, as signal-receiving devices operating in different locations must detect the precise position of objects located at long distances, it is essential to know the precise time at which an object's or a user's terminal device sends a signal. For this purpose, the existing time of arrival (ToA) technology is not sufficiently reliable, and the existing time difference of arrival (TDoA) technology is more suitable. If a TDoA-based electric surveillance system and other tracking devices fail to achieve precise time-synchronization between devices with separation distance operation, it is impossible to obtain correct TDoA values from the signals sent by the signal-receiving devices; this failure to obtain the correct values directly affects the location estimation error. For this reason, the technology for achieving precise time synchronization between signal-receiving devices in separation distance operation, among the technologies previously mentioned, is a core technology for detecting TDoA-based locations. In this paper, the accuracy of the proposed time synchronization and the measurement error in the TDoA-based location detection technology is evaluated. The TDoA-based location measurement error is significantly improved when using the proposed method for time-synchronization error reduction.

• Journal of the Korea Society of Computer and Information
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• v.20 no.2
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• pp.99-112
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• 2015

Datum point for displaying location and altitude of point has being advantage usefully in various measurement parts. However, datum point has been increasing loss cases owing to weather changes and stratum changes and neglecting meaninglessly. In this paper, we design and implement a multi electronic measurement system monitoring software with functions such as include maximize utilization of existing measurement datum system as well as collected various environment data and detection stratum changes of surround area. Proposed software is implemented to support that reusability and extensibility of software using object oriented modeling method. Our software supports a GUI for electronic measurement datum point administrator as well as for web user and mobile user. Our system can support a graph GUI for various data analysis and reposition in realtime to database that measured location information and various sensing information to prevent loss of electronic measurement datum point and to detected stratum changes. In addition, we include a QR code and RFID recognition function. Finally, we suggest performance evaluation result to confirm stratum changes detection and GPS location error rate.

• Journal of Navigation and Port Research
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• v.48 no.3
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• pp.164-170
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• 2024

The light buoy, a floating structure at sea, is prone to drifting due to external factors such as oceanic weather. This makes it imperative to monitor for any loss or displacement of buoys. In order to address this issue, the Ministry of Oceans and Fisheries aims to issue alerts for buoy displacement by analyzing historical buoy position data to detect patterns. However, periodic lifting inspections, which are conducted every two years, disrupt the buoy's location pattern. As a result, new patterns need to be analyzed after each inspection for location monitoring. In this study, buoy position data from various periods were analyzed using convex hull and distance-based clustering algorithms. In addition, the optimal data collection period was identified in order to accurately recognize buoy location patterns. The findings suggest that a nine-week data collection period established stable location patterns, explaining approximately 89.8% of the variance in location data. These results can improve the management of light buoys based on location patterns and aid in the effective monitoring and early detection of buoy displacement.