• Korean Journal of Radiology
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• v.24 no.6
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• pp.574-589
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• 2023

Radiopharmaceuticals targeting prostate-specific membrane antigens (PSMA) are essential for the diagnosis, evaluation, and treatment of prostate cancer (PCa), particularly metastatic castration-resistant PCa, for which conventional treatment is ineffective. These molecular probes include [⁶⁸Ga]PSMA, [¹⁸F]PSMA, [Al¹⁸F]PSMA, [^99mTc]PSMA, and [⁸⁹Zr]PSMA, which are widely used for diagnosis, and [¹⁷⁷Lu]PSMA and [²²⁵Ac]PSMA, which are used for treatment. There are also new types of radiopharmaceuticals. Due to the differentiation and heterogeneity of tumor cells, a subtype of PCa with an extremely poor prognosis, referred to as neuroendocrine prostate cancer (NEPC), has emerged, and its diagnosis and treatment present great challenges. To improve the detection rate of NEPC and prolong patient survival, many researchers have investigated the use of relevant radiopharmaceuticals as targeted molecular probes for the detection and treatment of NEPC lesions, including DOTA-TOC and DOTA-TATE for somatostatin receptors, 4A06 for CUB domain-containing protein 1, and FDG. This review focused on the specific molecular targets and various radionuclides that have been developed for PCa in recent years, including those mentioned above and several others, and aimed to provide valuable up-to-date information and research ideas for future studies.

• The Journal of Korea Robotics Society
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• v.8 no.4
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• pp.238-246
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• 2013

In this paper, a robot vision technique is presented to detect obstacles, particularly approaching humans, in the images acquired by a mobile robot that autonomously navigates in a narrow building corridor. A single low-cost color camera is attached to the robot, and a trapezoidal area is set as a region of interest (ROI) in front of the robot in the camera image. The lower parts of a human such as feet and legs are first detected in the ROI from their appearances in real time as the distance between the robot and the human becomes smaller. Then, the human detection is confirmed by detecting his/her face within a small search region specified above the part detected in the trapezoidal ROI. To increase the credibility of detection, a final decision about human detection is made when a face is detected in two consecutive image frames. We tested the proposed method using images of various people in corridor scenes, and could get promising results. This method can be used for a vision-guided mobile robot to make a detour for avoiding collision with a human during its indoor navigation.

• Asian Oncology Nursing
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• v.12 no.1
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• pp.52-60
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• 2012

Purpose: This study is to determine knowledge about early detection and risk perception of cancer according to taking cancer screening tests in the general population. Methods: The participants were 151 people aged 40 years or older. A questionnaire consisted of knowledge about early detection (warning signs, cancer screening methods, general knowledge for early detection), cancer risk perception and history of cancer screening during past 2 years. Results: The percentages of correct answers were 64.7% in knowledge about warning signs, 73.7% in knowledge of cancer screening tests and 80.1% in general knowledge for early detection. Participants had the highest knowledge about screening methods for stomach cancer and the lowest for liver and colon cancer. The level of risk perception was medium. The participants who participated in cancer screening showed lower risk perception than those who did not. There was no significant relationship between knowledge and performance of cancer screening. The primary reason for not participating in cancer screening was patient's perception of their own health. Conclusion: These results suggest that cancer risk perception can affect the performance of cancer screening and we need to study how to handle this problem. Additionally screening programs should focus on liver cancer and colon cancer.

• ETRI Journal
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• v.44 no.2
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• pp.300-312
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• 2022

Recently, public safety services have attracted significant attention for their ability to protect people from crimes. Rapid detection of dangerous situations (that is, abnormal situations where someone may be harmed or killed) is required in public safety services to reduce the time required to respond to such situations. This study proposes a novel danger detection technology based on multimodal data, which includes data from multiple sensors (for example, accelerometer, gyroscope, heart rate, air pressure, and global positioning system sensors), and multilog data, which includes contextual logs of humans and places (for example, contextual logs of human activities and crime-ridden districts) over time. To recognize human activity (for example, walk, sit, and punch), the proposed technology uses multimodal data analysis with an attitude heading reference system and long short-term memory. The proposed technology also includes multilog data analysis for detecting whether recognized activities of humans are dangerous. The proposed danger detection technology will benefit public safety services by improving danger detection capabilities.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.357-358
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• 2021

Although many people watch the scene with multiple surveillance cameras, it is difficult to ensure that immediate action can be taken in the event of a crime. Therefore, there is a need for a "crime behavior detection system" that can analyze images in real time from multiple surveillance cameras installed in elevators, call immediate crime alerts, and track crime scenes and times effectively. In this paper, a study was conducted to detect violent scenes occurring in elevators using Scene Change Detection. For effective detection, an x2-color histogram combining color histogram and histogram was applied.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.54-57
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• 2022

Korea has a vulnerable environment from the risk of wildfires, which causes great damage every year. To prevent this, a lot of manpower is being used, but the effect is insufficient. If wildfires are detected and extinguished early through artificial intelligence technology, damage to property and people can be prevented. In this paper, we studied the object double detection method with the goal of minimizing the data collection and processing process that occurs in the process of creating an object detection model to minimize the damage of wildfires. In YOLOv5, the original image is primarily detected through a single model trained on a limited image, and the object detected in the original image is cropped through Crop. The possibility of improving the false positive object detection rate was confirmed through the object double detection method that re-detects the cropped image.

• Journal of the Korean Society of Safety
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• v.31 no.6
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• pp.121-128
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• 2016

Social Media transformed the mass media based information traffic, and it has become a key resource for finding value in enterprises and public institutions. Particularly, in regards to disaster management, the necessity for public participation policy development through the use of social media is emphasized. National Disaster Management Research Institute developed the Social Big Board, which is a system that monitors social Big Data in real time for purposes of implementing social media disaster management. Social Big Board collects a daily average of 36 million tweets in Korean in real time and automatically filters disaster safety related tweets. The filtered tweets are then automatically categorized into 71 disaster safety types. This real time tweet monitoring system provides various information and insights based on the tweets, such as disaster issues, tweet frequency by region, original tweets, etc. The purpose of using this system is to take advantage of the potential benefits of social media in relations to disaster management. It is a first step towards disaster management that communicates with the people that allows us to hear the voice of the people concerning disaster issues and also understand their emotions at the same time. In this paper, Korean language text mining based Social Big Board will be briefly introduced, and disaster issue detection model, which is key algorithms, will be described. Disaster issues are divided into two categories: potential issues, which refers to abnormal signs prior to disaster events, and occurrence issues, which is a notification of disaster events. The detection models of these two categories are defined and the performance of the models are compared and evaluated.

• Journal of Biomedical Engineering Research
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• v.31 no.4
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• pp.321-327
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• 2010

Most of the ear shells of hearing aids are manufactured manually, and it is one of the reasons that the cost of the custom-made hearing aids can be increased. Thus it is required to manufacture the ready-made ear shell for the purpose of easy manufacturing and decrease in cost. In this study, we extract parameters in order to manufacture the ready-made ear shell for CIC type hearing aids and simulate to reconstruct the ear shell using the extracted parameters. To parameter extraction, we set up the eleven parameters for the ready-made ear shell based on anatomical characteristics of the ear canal, and we found values of the parameters from twenty-one impressions in their 20s and twelve impressions in their 60s using aperture detection and feature detection algorithms. Classifying the parameters by size, we also determine the parameters of ready-made ear shell into three types for people in their 20s and two types for people in their 60s. Each ready-made ear shell was simulated to reconstruct using figured parameters, and evaluated the rate of agreement with unused impressions for setting parameters. To evaluate the ready-made ear shell, we calculate the volume ratio and intersection between of the each impression and ready-made ear shell, and the intersection ratio using the intersection volume and ready-made ear shell volume. As a result, the volume ratio was about 70%, and volume match ratio was also up to 70%. It means that the ready-made ear shell we simulated is the significantly matched to impression.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.10a
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• pp.522-525
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• 2015

In this paper, we propose a walking aid system that guides route to visually impaired people in order to recognize uncertain obstacles based on tactile stimulation. The proposed system is composed of the touch-based obstacle detection module, the obstacle height detection module, and the route guidance algorithms. The touch-based obstacle detection module detects each obstacle, which is located at left, right, and front of a visually impaired person by stimulating his thumb with the rotational force of the servomotor. The obstacle height detection module integrates detected data by the linear arrangement of ultrasonic sensors to identify the height of an obstacle about 3 of-phase(i.e., high, medium, low). The proposed route guidance algorithm guides an optimized path to the visually impaired person by updating his current position information based on the signal of the built-in GPS receiver in smartphone. In addition, the route guidance algorithm delivers information with speech to a visually impaired person through Bluetooth commuination in the developed route guidance app. The proposed system can create a path to avoid the obstacles by recognizing the placed situation of the obstacles with exploring the uncertain path.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.45-48
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• 2019

Detection of crosswalk is an important issue for the blind to walk without the help of others. There is a braille block on the sidewalk, which helps the blind to walk. On the other hand, crosswalk is more dangerous due to the moving vehicles. However, there is no appropriate means to induce the blind. In this paper, we propose a method to detect crosswalk in front of a blind and estimate its direction using an image sensor. We adopt multi-ROIs and make their binary versions. In order to determine whether it is a crosswalk, two features are extracted; one is the number of crossing in the binary image and the other is the ratio of white area. We can also estimate the direction of the crosswalk through the slope of the projection data. We evaluated the performance using experimental dataset and the proposed algorithm showed 80% accuracy of detection.