• IEMEK Journal of Embedded Systems and Applications
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• v.12 no.5
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• pp.287-293
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• 2017

The naval unmanned surface vehicle (USV) conducts the surveillance operations, based on the mission plan set by the user. For setting the mission planning, the user needs to analyze the suitability of the operation for the mission planning. In this paper, we proposed a simulation program that estimates the probability of detecting targets of the mission planning in the analysis. In the simulation analysis, we design the USV's maneuvering characteristics, radar detection operational performance equipped on the USV, and targets infiltrating into surveillance area in the simulation experiment scenario. Based on the simulation results, we evaluated the mission planning suitability and find a mission planning solution recursively.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.30 no.5
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• pp.389-398
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• 2019

In this paper, we propose a method to detect small unmanned aerial vehicles(UAVs) flying in a real-world environment. Small UAV signals are frequently obscured by clutter signals because UAVs usually fly at low altitudes over urban or mountainous terrain. Therefore, to obtain a desirable detection performance, clutter signals must be considered in addition to noise, and thus, a performance analysis of each clutter removal technique is required. The proposed detection process uses clutter removal and pulse integration methods to suppress clutter and noise signals, and then detects small UAVs by utilizing a constant false alarm rate detector. After applying three clutter removal techniques, we analyzed the performance of each technique in detecting small UAVs. Based on experimental data acquired in a real-world outdoor environment, we found it was possible to derive a clutter removal method suitable for the detection of small UAVs.

• Bulletin of the Korean Chemical Society
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• v.35 no.11
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• pp.3195-3198
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• 2014

An ultra-sensitive detection method for small molecules such as antibiotics was developed using ICP-MS with magnetic and $TiO_2$ nanoparticles. Since most of the antibiotics are too small to employ a sandwich-type extraction through an immunoreaction, a non-specific platform was employed, in which the target was extracted by magnetic separation, followed by tagging with $TiO_2$ nanoparticles of 11.2 nm for ICP-MS measurement. The detection limit for salinomycin obtained from spiked serum samples was $0.4ag\;mL^{-1}$ (${\pm}10.3%$), which was about $1.5{\times}10^6$ times lower than that of LC-MS/MS and about $1.2{\times}10^{11}$ times better than that of ELISA. Such an excellent sensitivity enabled us to study the toxicity of antibiotics exposed to human beings by determining them in serum.

• Current Optics and Photonics
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• v.6 no.4
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• pp.420-429
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• 2022

In this paper, the details and design process of an optical system for space target detection cameras are introduced. The whole system is divided into three structures. The first structure is a short-focus visible light system for rough detection in a large field of view. The field of view is 2°, the effective focal length is 1,125 mm, and the F-number is 3.83. The second structure is a telephoto visible light system for precise detection in a small field of view. The field of view is 1°, the effective focal length is 2,300 mm, and the F-number is 7.67. The third structure is an infrared light detection system. The field of view is 2°, the effective focal length is 390 mm, and the F-number is 1.3. The visible long-focus narrow field of view and visible short-focus wide field of view are switched through a turning mirror. Design results show that the modulation transfer functions of the three structures of the system are close to the diffraction limit. It can further be seen that the short-focus wide-field-of-view distortion is controlled within 0.1%, the long-focus narrow-field-of-view distortion within 0.5%, and the infrared subsystem distortion within 0.2%. The imaging effect is good and the purpose of the design is achieved.

• The Journal of the Acoustical Society of Korea
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• v.36 no.5
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• pp.293-299
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• 2017

Small unmanned platforms maneuvering underwater are the key naval future forces, utilized as the asymmetric power in war. As a method of detecting and identifying such platforms, we introduce a property estimation technique based on an iterative numerical analysis. The property estimation technique can estimate not only the position of a target but also its physical properties. Moreover, it will have a potential in detecting and classifying still target or multiple targets. In this study, we have conducted the property estimation of an small underwater target using the data acquired from the lake experiment. As a result, it shows that the properties of a small platform may be roughly estimated from the in site data even using one channel.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.5
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• pp.1166-1179
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• 2013

In wireless sensor network (WSN) environments, environmental noises are generated by, for example, small passing animals, crickets chirping or foliage blowing and will interfere target detection if the noises are higher than the sensor threshold value. For accurate tracking by acoustic WSNs, these environmental noises should be filtered out before initiating track. This paper presents the effect of environmental noises on target tracking and proposes a new algorithm for the noise mitigation in acoustic WSNs. We find that our noise mitigation algorithm works well even for targets with sensing range shorter than the sensor separation as well as with longer sensing ranges. It is also found that noise duration at each sensor affects the performance of the algorithm. A detection algorithm is also presented to account for the Doppler effect which is an important consideration for tracking higher-speed ground targets. For tracking, we use the weighted sensor position centroid to represent the target position measurement and use the Kalman filter (KF) for tracking.

• Journal of Navigation and Port Research
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• v.44 no.2
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• pp.110-118
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• 2020

With the expansion of the shipping and port logistics industry in the 21st century, the traffic density is continuously increased because of the increase in volumes of world sea freight and fleets, as well as the increase in the causes of potential marine accidents, such as ship collisions and stranding. Accordingly, the International Maritime Organization (IMO) has requested that the installation and operation of VTS should be applied in areas with high risk of marine traffic, and the request should be included as one of the Safety of Life at Sea (SOLAS) regulations. In this paper, the fundamental requirements of the radar system for vessel traffic services were analyzed and the analyzing factors were based on the IALA guideline.s This paper also includes results for the requirement and recommendation analysis on detection distance, target separation, and the target position accuracy of X-band radar. Also, to check if it satisfies the requirement of detection distance, range and azimuth separation of small point targets, and target position accuracy from the IALA guidelines, the test was conducted through the radar image acquired at the VTS center, and hence, the validity of the technical performance requirements was confirmed.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.1243-1244
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• 2022

In recent years, the growing interest in off-site construction has led to factories scaling up their manufacturing and production processes in the construction sector. Consequently, continuous large-scale site monitoring in low-variability environments, such as prefabricated components production plants (precast concrete production), has gained increasing importance. Although many studies on computer vision-based site monitoring have been conducted, challenges for deploying this technology for large-scale field applications still remain. One of the issues is collecting and transmitting vast amounts of video data. Continuous site monitoring systems are based on real-time video data collection and analysis, which requires excessive computational resources and network traffic. In addition, it is difficult to integrate various object information with different sizes and scales into a single scene. Various sizes and types of objects (e.g., workers, heavy equipment, and materials) exist in a plant production environment, and these objects should be detected simultaneously for effective site monitoring. However, with the existing object detection algorithms, it is difficult to simultaneously detect objects with significant differences in size because collecting and training massive amounts of object image data with various scales is necessary. This study thus developed a large-scale site monitoring system using edge computing and a small-object detection system to solve these problems. Edge computing is a distributed information technology architecture wherein the image or video data is processed near the originating source, not on a centralized server or cloud. By inferring information from the AI computing module equipped with CCTVs and communicating only the processed information with the server, it is possible to reduce excessive network traffic. Small-object detection is an innovative method to detect different-sized objects by cropping the raw image and setting the appropriate number of rows and columns for image splitting based on the target object size. This enables the detection of small objects from cropped and magnified images. The detected small objects can then be expressed in the original image. In the inference process, this study used the YOLO-v5 algorithm, known for its fast processing speed and widely used for real-time object detection. This method could effectively detect large and even small objects that were difficult to detect with the existing object detection algorithms. When the large-scale site monitoring system was tested, it performed well in detecting small objects, such as workers in a large-scale view of construction sites, which were inaccurately detected by the existing algorithms. Our next goal is to incorporate various safety monitoring and risk analysis algorithms into this system, such as collision risk estimation, based on the time-to-collision concept, enabling the optimization of safety routes by accumulating workers' paths and inferring the risky areas based on workers' trajectory patterns. Through such developments, this continuous large-scale site monitoring system can guide a construction plant's safety management system more effectively.

• The Journal of Korea Robotics Society
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• v.14 no.1
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• pp.1-7
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• 2019

Recently, smart factories have attracted much attention as a result of the 4th Industrial Revolution. Existing factory automation technologies are generally designed for simple repetition without using vision sensors. Even small object assemblies are still dependent on manual work. To satisfy the needs for replacing the existing system with new technology such as bin picking and visual servoing, precision and real-time application should be core. Therefore in our work we focused on the core elements by using deep learning algorithm to detect and classify the target object for real-time and analyzing the object features. We chose YOLO CNN which is capable of real-time working and combining the two tasks as mentioned above though there are lots of good deep learning algorithms such as Mask R-CNN and Fast R-CNN. Then through the line and inside features extracted from target object, we can obtain final outline and estimate object posture.

• The Journal of the Acoustical Society of Korea
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• v.43 no.2
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• pp.225-233
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• 2024

Active sonar transmits sound waves to detect covertly maneuvering underwater objects and detects the signals reflected back from the target. However, in addition to the target's echo, the active sonar's received signal is mixed with seafloor, sea surface reverberation, biological noise, and other noise, making target recognition difficult. Conventional techniques for detecting signals above a threshold not only cause false detections or miss targets depending on the set threshold, but also have the problem of having to set an appropriate threshold for various underwater environments. To overcome this, research has been conducted on automatic calculation of threshold values through techniques such as Constant False Alarm Rate (CFAR) and application of advanced tracking filters and association techniques, but there are limitations in environments where a significant number of detections occur. As deep learning technology has recently developed, efforts have been made to apply it in the field of underwater target detection, but it is very difficult to acquire active sonar data for discriminator learning, so not only is the data rare, but there are only a very small number of targets and a relatively large number of non-targets. There are difficulties due to the imbalance of data. In this paper, the image of the energy distribution of the detection signal is used, and a classifier is learned in a way that takes into account the imbalance of the data to distinguish between targets and non-targets and added to the existing technique. Through the proposed technique, target misclassification was minimized and non-targets were eliminated, making target recognition easier for active sonar operators. And the effectiveness of the proposed technique was verified through sea experiment data obtained in the East Sea.