• Journal of the Korean Society for Aviation and Aeronautics
• /
• v.15 no.2
• /
• pp.18-24
• /
• 2007

The high ionospheric spatial gradient during ionospheric storm is the most concern when applying GNSS(Global Navigation Satellite System) augmentation systems for aircraft precision approach. Since the ionospheric gradient level depends on geographical location as well as the storm, understanding the ionospheric gradient statistics over a specific regional area is necessary for operating the augmentation systems. This paper compares three ionosphere gradient computation methods, direct differentiation between two receivers' ionospheric delay signal for a common satellite, derivation from a grid ionosphere map, and derivation from a plate ionosphere map. The plate map method provides a good indication on the gradient variation behavior over a regional area with limited number of GNSS receivers. The residual analysis for the ionosphere storm detection is discussed as well.

• Korean Journal of Plant Tissue Culture
• /
• v.28 no.3
• /
• pp.165-171
• /
• 2001

New visible and fast assay system have been developed for tobacco transformant introduced with adenosine deaminase (ADA) marker gene, which converts cytotoxic adenosine analogues to non-toxic inosine analogues and ammonia. Ammonia was changed to blue color in the solution of phenol-nitoprusside and alkaline-hypochlorite. It was possible to detect activity of ADA visibly on the holes of 96 well plate using tiny explant of transgenic tobacco leaves within 1 hour incubation time. As substrates of ADA enzyme from transgenic plant on the plate, a number of adenosine analogues such as 9-D-arabinofuranosyl adenine, cordycepin, 2'-deoxyadenosine, adenosine and xylofuranosyl adenine were possible for detection of ADA activity. Optimal condition of substrate for ADA enzyme was each 10 mM and pH 7.5 in adenosine solution. Especially, transgenic plant did not convert adenosine to inosine and ammonia in the presence of ADA inhibitor deoxycoformycin, which means that ammonia produced from transgenic plant is due to expression of ADA gene. Now, we show that this detection system can be easily, sensitively, fast and cheaply as well as visibly assayed in vitro as GUS gene system with very small size of transformant explant.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.1 no.3
• /
• pp.105-109
• /
• 2008

In the current study, the authors propose a method for extracting license plate regions by means of a neural network trained to output the plate.s center of gravity. The method is shown to be effective. Since the learning pattern presentation positions are defined by random numbers, a different pattern is submitted to the neural network for learning each time, which enables it to form a neural network with high universality of coverage. The article discusses issues of the optimal learning surface for a license plate covered by the learning pattern, the effect of suppression learning of the number and headlight sections, as well as the effect of learning pattern enlargement/reduction and of concentration value conversion. Results of evaluation tests based on pictures of 595 vehicles taken at an underground parking garage demonstrated detection rates of 98.5%.

• Journal of the Korea Convergence Society
• /
• v.10 no.1
• /
• pp.103-113
• /
• 2019

License plate recognition camera is dedicated device designed for acquiring images of the target vehicle for recognizing letters and numbers in a license plate. Mostly, it is used as a part of the system combined with server and image analysis module rather than as a single use. However, building a system for vehicle license plate recognition is costly because it is required to construct a facility with a server providing the management and analysis of the captured images and an image analysis module providing the extraction of numbers and characters and recognition of the vehicle's plate. In this study, we would like to develop an embedded type convergent camera (Edge Base) which can expand the function of the camera to not only the license plate recognition but also the security CCTV function together and to perform two functions within the camera. This embedded type convergence camera equipped with a high resolution 4K IP camera for clear image acquisition and fast data transmission extracted license plate area by applying YOLO, a deep learning software for multi object recognition based on open source neural network algorithm and detected number and characters of the plate and verified the detection accuracy and recognition accuracy and confirmed that this camera can perform CCTV security function and vehicle number plate recognition function successfully.

• Journal of the Semiconductor & Display Technology
• /
• v.15 no.3
• /
• pp.24-29
• /
• 2016

One of the main applications of digital image processing is the estimation of the number of certain types of objects (cells, seeds, peoples etc.) in an image. Difficulties of these counting problems depends on various factors including shape and size variation, degree of object clustering, contrast between object and background, object texture and its variation, and so on. In this paper, a new automatic colony counting algorithm is proposed. We focused on the two applications: counting the bacteria colonies on the agar plate and estimating the number of seeds from images captured by smartphone camera. To overcome the shape and size variations of the colonies, we adopted the distance transformation and peak detection approach. To estimate the reference size of the colony robustly, we also used k-means clustering algorithm. Experimental results show that our method works well in real world applications.

• Journal of the Korean Society of Marine Environment & Safety
• /
• v.28 no.6
• /
• pp.1054-1062
• /
• 2022

The population of Korea's solar salt-producing regions is rapidly aging, resulting in a decrease in the number of productive workers. In solar salt production, salt collection is the most labor-intensive operation because existing salt collection vehicles require human operators. Therefore, we intend to develop an unmanned solar salt collection vehicle to reduce manpower requirements. The unmanned solar salt collection vehicle is designed to identify the salt collection status and location in the salt plate via color detection, the color detection performance is a crucial consideration. Therefore, an image processing algorithm was developed to improve color detection performance. The algorithm generates an around-view image by using resizing, rotation, and perspective transformation of the input image, set the RoI to transform only the corresponding area to the HSV color model, and detects the color area through an AND operation. The detected color area was expanded and noise removed using morphological operations, and the area of the detection region was calculated using contour and image moment. The calculated area is compared with the set area to determine the location case of the collection vehicle within the salt plate. The performance was evaluated by comparing the calculated area of the final detected color to which the algorithm was applied and the area of the detected color in each step of the algorithm. It was confirmed that the color detection performance is improved by at least 25-99% for salt detection, at least 44-68% for red color, and an average of 7% for blue and an average of 15% for green. The proposed approach is well-suited to the operation of unmanned solar salt collection vehicles.

• Journal of Navigation and Port Research
• /
• v.28 no.1
• /
• pp.43-49
• /
• 2004

Defects inspection of commodities are very important with those design and manufacturing process and essential to strengthen the competitiveness of those. If on-line automatic defects detection is performed without damaging to products, the production cost shall be curtailed through the reducing man-power, economical management of Q.C(Quality Control). In this paper, it is suggested three spatial filtering methods which can extract the necessary information in case of defects being on the surface of object like iron plate. In addition, the dependence of filtering characteristics on parameters such as the pitch and width of slits is analyzed and the surface defect detection system is constructed. Several experiments were carried out for determining the adequate spatial filtering method through comparing and analyzing effects of parameters like defect's size and shape, intensity of light, noise of coherent source and slit number.

• Journal of the Korea institute for structural maintenance and inspection
• /
• v.24 no.6
• /
• pp.206-216
• /
• 2020

As the number of aging railway bridges in Korea increases, maintenance costs due to aging are increasing and continuous management is becoming more important. However, while the number of old facilities to be managed increases, there is a shortage of professional personnel capable of inspecting and diagnosing these old facilities. To solve these problems, this study presents an improved model that can detect Local damage to structures using machine learning techniques of AI technology. To construct a damage detection machine learning model, an analysis model of the bridge was set by referring to the design drawing of a non-ballasted plate-girder railroad bridge. Static strain data according to the damage scenario was extracted with the analysis model, and the Local damage index based on the reliability of the bridge was presented using statistical techniques. Damage was performed in a three-step process of identifying the damage existence, the damage location, and the damage severity. In the estimation of the damage severity, a linear regression model was additionally considered to detect random damage. Finally, the random damage location was estimated and verified using a machine learning-based damage detection classification learning model and a regression model.

• Journal of Korea Multimedia Society
• /
• v.8 no.11
• /
• pp.1449-1461
• /
• 2005

In this paper, we propose a vehicle recognition system based on the classification of vehicle identification mark and recognition of vehicle license plate. In the proposed algorithm, From the input vehicle image, we first simulate preprocessing procedures such as noise reduction, thinning etc., and detect vehicle identification mark and license plate region using the frequency distribution of intensity variation. And then, we classify extracted vehicle candidate region into identification mark, character and number of vehicle by using structural feature informations of vehicle. Lastly, we recognize vehicle informations with recognition of identification mark, character and number of vehicle using hybrid and vertical/horizontal pattern vector method. In the proposed algorithm, we used three properties of vehicle informations such as Independency property, discriminance property and frequency distribution of intensity variation property. In the vehicle images, identification mark is generally independent of the types of vehicle and vehicle identification mark. And also, the license plate region between character and background as well as horizontal/vertical intensity variations are more noticeable than other regions. To show the efficiency of the propofed algorithm, we tested it on 350 vehicle images and found that the propofed method shows good Performance regardless of irregular environment conditions as well as noise, size, and location of vehicles.

• Journal of Microbiology and Biotechnology
• /
• v.18 no.8
• /
• pp.1453-1458
• /
• 2008

For quantitative PCR assay of Salmonella enterica serovar Typhimurium in food samples, a real-time PCR method was developed, based on DNA genome equivalent. Specific primers and probe designed based on the STM4497 gene of S. Typhimurium LT2 showed the specificity to S. Typhimurium. Threshold cycle (Ct) values of real-time PCR were obtained from a quantitative standard curve with genomic DNA of Salmonella Typhimurium. In addition, the recovery of S. Typhimurium inoculated artificially to chicken samples with $4.5{\times}10^5$ to 4.5 CFU/ml was evaluated by using real-time PCR and plate-count methods. Result showed that the number of cells calculated from the real-time PCR method had good correlation with that of the plate-count method. This real-time PCR method could be applicable to the detection and quantification of S. Typhimurium in food samples.