• Journal of Engineering Education Research
• /
• v.17 no.4
• /
• pp.15-20
• /
• 2014

The non-contact easy detecting system of nursing person's body vital information and their behaviors monitoring system are developed, which consist of "Kinect" sensor and thermography camera. The "Kinect" sensor can catch the body contour and the body moving behavior, and output their imaging data realtime. The thermography camera can detect respiration state and body temperature, etc. In this study, the practicability of this system was verified.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 2004.10a
• /
• pp.809-813
• /
• 2004

In this paper, we developed a network camera for CMOS camera module inspection. The design, implementation details including embedded linux porting and CPLD logics, and performance of network camera are described. The network camera consists of SoC(S3C4530A), CPLD and CMOS image sensor. In order to image data of CMOS image sensor we designed capture logics on CPLD by using VHDL program. Embedded Linux such as uClinux is performed on the network camera to utilize development environment and TCP/IP protocol specification. The application is based on socket communication between GUI on PC and Embedded Linux based network camera. When JPEG compression is applied, the transmission speed was improved enough for this system to be used for an alternative of expensive CCTV or remote monitoring system in a power plant and uninhabited places.

• Journal of the Korean Society of Environmental Restoration Technology
• /
• v.26 no.4
• /
• pp.17-34
• /
• 2023

Camera trapping has been used as a non-invasive survey method that minimizes anthropogenic disturbance to ecosystems. Nevertheless, it is labor-intensive and time-consuming, requiring researchers to quantify species and populations. In this study, we aimed to improve the preprocessing of camera trapping data by utilizing an object detection algorithm. Wildlife monitoring using unmanned sensor cameras was conducted in a forested urban forest and a green space on a university campus in Cheonan City, Chungcheongnam-do, Korea. The collected camera trapping data were classified by a researcher to identify the occurrence of species. The data was then used to test the performance of the YOLO-X object detection algorithm for wildlife detection. The camera trapping resulted in 10,500 images of the urban forest and 51,974 images of green spaces on campus. Out of the total 62,474 images, 52,993 images (84.82%) were found to be false positives, while 9,481 images (15.18%) were found to contain wildlife. As a result of wildlife monitoring, 19 species of birds, 5 species of mammals, and 1 species of reptile were observed within the study area. In addition, there were statistically significant differences in the frequency of occurrence of the following species according to the type of urban greenery: Parus varius(t = -3.035, p < 0.01), Parus major(t = 2.112, p < 0.05), Passer montanus(t = 2.112, p < 0.05), Paradoxornis webbianus(t = 2.112, p < 0.05), Turdus hortulorum(t = -4.026, p < 0.001), and Sitta europaea(t = -2.189, p < 0.05). The detection performance of the YOLO-X model for wildlife occurrence was analyzed, and it successfully classified 94.2% of the camera trapping data. In particular, the number of true positive predictions was 7,809 images and the number of false negative predictions was 51,044 images. In this study, the object detection algorithm YOLO-X model was used to detect the presence of wildlife in the camera trapping data. In this study, the YOLO-X model was used with a filter activated to detect 10 specific animal taxa out of the 80 classes trained on the COCO dataset, without any additional training. In future studies, it is necessary to create and apply training data for key occurrence species to make the model suitable for wildlife monitoring.

• Journal of information and communication convergence engineering
• /
• v.4 no.2
• /
• pp.92-96
• /
• 2006

Hardware for monitoring the water quality using water fleas is developed. Water flea is a frequently used biological sensor for monitoring the water quality. Water fleas quickly respond to the incoming toxic water by changing their activity when they are exposed. By measuring the activity of water fleas, the incoming toxic water is instantly detected. So far the measurement of activity of water fleas has been done with a system equipped with both a light source of LED and a light detector of photo transistor. Water flea itself is, however, sensitive to light resulting in incorrect response and the system has two inconvenient separate parts of the light source and the detector. This paper suggests a system using a CCD camera instead of a light source and a detector. The suggested system processes the image data from the CCD camera in real time without any delay. The developed system becomes a part of the remote water monitoring embedded system.

• Journal of Drive and Control
• /
• v.17 no.4
• /
• pp.15-22
• /
• 2020

With the need for efficient maintenance technology to reduce maintenance costs for steel bridges, repainting robots are being developed to automate the work in narrow and poor bridge spaces. The repainting robot is equipped with a blasting module to remove paint layers and contaminants. This study developed a prototype monitoring module to be mounted on the repainting robot. The monitoring module analyzes the condition of the painting surface through a camera installed in the front, guides the direction of movement of the robot, and provides the operator with a video to check the working status after blasting through a camera installed in the back. Various image visibility enhancement technologies were applied to the monitoring module to overcome worksite challenges where incomplete lighting and dust occurs.

• Journal of Ecology and Environment
• /
• v.47 no.4
• /
• pp.211-218
• /
• 2023

Background: Traditional wildlife monitoring has often relied on invasive techniques posing risks to species and demanding substantial resources. To address this, camera traps emerged as non-invasive alternatives, albeit primarily tailored for larger mammals, posing limitations for small mammal research. Thus, the Mostela, an innovative tool designed to overcome these challenges, was introduced to monitor small mammals in South Korea. Results: The Mostela was deployed at two study sites in South Korea, yielding compelling evidence of its efficiency in capturing small mammal species. By analyzing the collected data, we calculated the relative abundance of each species and elucidated their activity patterns. Conclusions: In summary, the Mostela system demonstrates substantial potential for advancing small mammal monitoring, offering valuable insights into diversity, community dynamics, activity patterns, and habitat preferences. Its application extends to the detection of endangered and rare species, further contributing to wildlife conservation efforts in South Korea. Consequently, the Mostela system stands as a valuable addition to the toolkit of conservationists and researchers, fostering ethical and non-invasive research practices while advancing our understanding of small mammal populations and ecosystems.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 2008.11a
• /
• pp.697-698
• /
• 2008

• Journal of the Korean Society of Environmental Restoration Technology
• /
• v.17 no.3
• /
• pp.65-74
• /
• 2014

The aim of this study was to evaluate the monitoring efficiency of camera trapping in wild animals and to determine ways to increase its utilization. Nineteen sensor cameras were installed in Sobaeksan National Park from October 2012 to September 2013. During the study period, a total of 1045 terrestrial mammal photos were secured and 15 species habitats were identified. Shooting frequency was higher for medium and large mammals, especially full images of carnivores accounted for approximately 83%. A comparison of track surveys revealed that camera trapping was highly efficient and helped in capturing real image of species. The supply of lure and bait stimulates the sense of smell in carnivores, which further enhances the capturing of images by camera trapping. The results of this study provide data on the ecological characteristics of mammals, which can aid in determining habitat use by these animals, and thereby facilitate prevention of crop damage by wildlife.

• Journal of Sensor Science and Technology
• /
• v.27 no.1
• /
• pp.6-12
• /
• 2018

In this paper, a 32-variable pulse position modulation (32-VPPM) scheme is proposed to support a red-green-blue light-emitting-diode (RGB-LED)-based optical camera communication (OCC) system. Our proposed modulation scheme is designed to enhance the OCC data transmission rate, which is targeted for the wearable biomedical data monitoring system. The OCC technology has been utilized as an alternative solution to the radio frequency (RF) wireless system for long-term self-healthcare monitoring. Different biomedical signals, such as electrocardiograms, photoplethysmograms, and respiration signals are being monitored and transmitted wirelessly from the wearable biomedical device to the smartphone receiver. A common 30 frames per second (fps) smartphone camera with a CMOS image sensor is used to record a transmitted optical signal. Moreover, the overall proposed system architecture, modulation scheme, and data demodulation are discussed in this paper. The experimental result shows that the proposed system is able to achieve > 9 kbps using only a common smartphone camera receiver.

• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 1998.10a
• /
• pp.413-414
• /
• 1998

This paper presents a real-time Spatial Decision Support System(SDSS) for security camera image monitoring. Other SDSSs are not real-time systems, i.e., they show the images that are already transformed into data format such as virtual reality. In our system, the image is broadcasted in real-time since the purpose of the security camera needs to do it in real-time. With these real-time images, other systems do not add up anything more; the screen just shows the images from the camera. However in our system, we created a motion detection system so that the supervisor(Judge) of a sec.urity monitoring system does not have to pay attention to it constantly. In other words, we created a judge advising system for the supervisor of the security monitoring system. Most of small objects do not need the supervisor's attention since they could be birds, cats, dogs, etc. if they show up in the screen image. In this new system the system only report the unusual change to the supervisor by calculating the motion and size of objects in the screen. Thus the supervisor can be liberated from the 24-hour concentration duty; instead he/she can be only alerted when the real security threat such as a big moving object like an human intruder appears. Thus this system can be called a real-time Spatial DSS. The utility of this system is proved mathematically by using the concept of entropy. In other words, big objects like human intruders increase the entropy of the screen images significantly therefore the supervisor must be alerted. Thus by proving its utility of the system theoretically, we can claim that our new real-time SDSS is superior to others which do not use our technique.hnique.