• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.1
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• pp.175-182
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• 2021

The purpose of this paper is to develop a machine learning-based marine drone to prevent the farming from harmful birds such as ducks. Existing drones have been developed as marine drones to solve the problem of being lost if they collide with birds in the air or are in the sea. We designed a CNN-based learning algorithm to judge harmful birds that appear on the sea by maritime drones operating by autonomous driving. It is designed to transmit video to the control PC by connecting the Raspberry Pi to the camera for location recognition and tracking of harmful birds. After creating a map linked with the location GPS coordinates in advance at the mobile-based control center, the GPS location value for the location of the harmful bird is received and provided, so that a marine drone is dispatched to combat the harmful bird. A bird fighting drone system was designed and implemented.

• Food Science of Animal Resources
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• v.43 no.1
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• pp.73-84
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• 2023

Campylobacteriosis is a common cause of gastrointestinal disease. In this study, we suggest a general strategy of applying gold nanoparticles (AuNPs) in colorimetric biosensors to detect Campylobacter in chicken carcass. Polymerase chain reaction (PCR) was utilized for the amplification of the target genes, and the thiolated PCR products were collected. Following the blending of colloid AuNPs with PCR products, the thiol bound to the surface of AuNPs, forming AuNP-PCR products. The PCR products had a sufficient negative charge, which enabled AuNPs to maintain a dispersed formation under electrostatic repulsion. This platform presented a color change as AuNPs aggregate. It did not need additional time and optimization of pH for PCR amplicons to adhere to the AuNPs. The specificity of AuNPs of modified primer pairs for mapA from Campylobacter jejuni and ceuE from Campylobacter coli was activated perfectly (C. jejuni, p-value: 0.0085; C. coli, p-value: 0.0239) when compared to Salmonella Enteritidis and Escherichia coli as non-Campylobacter species. Likewise, C. jejuni was successfully detected from artificially contaminated chicken carcass samples. According to the sensitivity test, at least 15 ng/μL of Campylobacter PCR products or 1×10³ CFU/mL of cells in the broth was needed for the detection using the optical method.

• Journal of Drive and Control
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• v.20 no.2
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• pp.15-23
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• 2023

In this study, two-dimensional location of crops for auto weeding was detected using deep learning. To construct a dataset for soybean detection, an image-capturing system was developed using a mono camera and single-board computer and the system was mounted on a weeding robot to collect soybean images. A dataset was constructed by extracting RoI (region of interest) from the raw image and each sample was labeled with soybean and the background for classification learning. The deep learning model consisted of four convolutional layers and was trained with a weakly supervised learning method that can provide object localization only using image-level labeling. Localization of the soybean area can be visualized via CAM and the two-dimensional position of the soybean was estimated by clustering the pixels associated with the soybean area and transforming the pixel coordinates to world coordinates. The actual position, which is determined manually as pixel coordinates in the image was evaluated and performances were 6.6(X-axis), 5.1(Y-axis) and 1.2(X-axis), 2.2(Y-axis) for MSE and RMSE about world coordinates, respectively. From the results, we confirmed that the center position of the soybean area derived through deep learning was sufficient for use in automatic weeding systems.

• Journal of Advanced Navigation Technology
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• v.27 no.5
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• pp.495-501
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• 2023

P-3C/K aircraft for maritime patrols that Republic of Korea Navy is using, is equipped with a variety of sensors and communication devices. Collected data from the aircraft is managed as tactical information by flight operators and stored. When the flight mission is completed, this information is transferred to tactical support center on the ground and played back or used for follow-up work through a analysis tool. During a flight mission, there are tens of thousands of detection objects within an hour in KADIZ (Korea air defense identification zone). In contrast, in TSD (tactical situation display), which displays a map when using the analysis tool, all detected objects are expressed as symbols. The increase in display symbols has a significant impact on the TSD image updating and consequently interferes with the smooth operation of operators. In this paper, we propose applying multiple threads and multiple layers to improve the performance of existing TSD. And the performance improvement is proven through the execution results.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.10
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• pp.2627-2642
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• 2023

Parkinson's disease (PD) is a typical, chronic neurodegenerative disease involving the concentration of dopamine, which can disrupt motor activity and cause different degrees of gait disturbance relevant to PD severity in patients. As current clinical PD diagnosis is a complex, time-consuming, and challenging task that relays on physicians' subjective evaluation of visual observations, gait disturbance has been extensively explored to make automatic detection of PD diagnosis and severity rating and provides auxiliary information for physicians' decisions using gait data from various acquisition devices. Among them, wearable sensors have the advantage of flexibility since they do not limit the wearers' activity sphere in this application scenario. In this paper, an attention-based temporal network (ATN) is designed for the time series structure of gait data (vertical ground reaction force signals) from foot sensor systems, to learn the discriminative differences related to PD severity levels hidden in sequential data. The structure of the proposed method is illuminated by Transformer Network for its success in excavating temporal information, containing three modules: a preprocessing module to map intra-moment features, a feature extractor computing complicated gait characteristic of the whole signal sequence in the temporal dimension, and a classifier for the final decision-making about PD severity assessment. The experiment is conducted on the public dataset PDgait of VGRF signals to verify the proposed model's validity and show promising classification performance compared with several existing methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.2
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• pp.456-477
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• 2024

With information technology's rapid development, the Internet faces serious security problems. Studies have shown that malware has become a primary means of attacking the Internet. Therefore, adversarial samples have become a vital breakthrough point for studying malware. By studying adversarial samples, we can gain insights into the behavior and characteristics of malware, evaluate the performance of existing detectors in the face of deceptive samples, and help to discover vulnerabilities and improve detection methods for better performance. However, existing adversarial sample generation methods still need help regarding escape effectiveness and mobility. For instance, researchers have attempted to incorporate perturbation methods like Fast Gradient Sign Method (FGSM), Projected Gradient Descent (PGD), and others into adversarial samples to obfuscate detectors. However, these methods are only effective in specific environments and yield limited evasion effectiveness. To solve the above problems, this paper proposes a malware adversarial sample generation method (PixGAN) based on the pixel attention mechanism, which aims to improve adversarial samples' escape effect and mobility. The method transforms malware into grey-scale images and introduces the pixel attention mechanism in the Deep Convolution Generative Adversarial Networks (DCGAN) model to weigh the critical pixels in the grey-scale map, which improves the modeling ability of the generator and discriminator, thus enhancing the escape effect and mobility of the adversarial samples. The escape rate (ASR) is used as an evaluation index of the quality of the adversarial samples. The experimental results show that the adversarial samples generated by PixGAN achieve escape rates of 97%, 94%, 35%, 39%, and 43% on the Random Forest (RF), Support Vector Machine (SVM), Convolutional Neural Network (CNN), Convolutional Neural Network and Recurrent Neural Network (CNN_RNN), and Convolutional Neural Network and Long Short Term Memory (CNN_LSTM) algorithmic detectors, respectively.

• Geomechanics and Engineering
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• v.36 no.3
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• pp.205-215
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• 2024

Manually mapping fractures in construction stone mines is challenging, time-consuming, and hazardous. In this method, there is no physical access to all points. In contrast, digital image processing offers a safe, cost-effective, and fast alternative, with the capability to map all joints. In this study, two methods of detecting the trace of discontinuities using image processing in construction stone mines are presented. To achieve this, we employ two modified Hough transform algorithms and the degree of neighborhood technique. Initially, we introduced a method for selecting the best edge detector and smoothing algorithms. Subsequently, the Canny detector and median smoother were identified as the most efficient tools. To trace discontinuities using the mentioned methods, common preprocessing steps were initially applied to the image. Following this, each of the two algorithms followed a distinct approach. The Hough transform algorithm was first applied to the image, and the traces were represented through line drawings. Subsequently, the Hough transform results were refined using fuzzy clustering and reduced clustering algorithms, along with a novel algorithm known as the farthest points' algorithm. Additionally, we developed another algorithm, the degree of neighborhood, tailored for detecting discontinuity traces in construction stones. After completing the common preprocessing steps, the thinning operation was performed on the target image, and the degree of neighborhood for lineament pixels was determined. Subsequently, short lines were removed, and the discontinuities were determined based on the degree of neighborhood. In the final step, we connected lines that were previously separated using the method to be described. The comparison of results demonstrates that image processing is a suitable tool for identifying rock mass discontinuity traces. Finally, a comparison of two images from different construction stone mines presented at the end of this study reveals that in images with fewer traces of discontinuities and a softer texture, both algorithms effectively detect the discontinuity traces.

• Journal of the korean academy of Pediatric Dentistry
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• v.29 no.2
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• pp.243-254
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• 2002

For the purpose of evaluating the biocompatability of 3 kinds of metallic materials frequently used in pediatric dentistry (stainless steel crown, orthodontic band, orthodontic wire), cellular and molecular studies, including cell growth and proliferation, screening of cell death with determination of types whether necrosis or apoptosis and changes in expressions of related signaling molecules were examined, using cultured human gingival fibroblasts (HGF-1), HGF-1 was cultured in Dulbecco's modified Eagle's medium. among which the 3rd to 6th generations of HGF-1 were used. The specimen were divided into stainless steel crown (R), band (B) and wire (W). The immunocytochemical study was done for the detection of anti-PCNA (proliferating cell nuclear antigen) labeling. With extracted protein, western blot was done for the detection of ERK1/2, JNK, and p38, using individual antibodies. Cultured cells proliferated, remarkably till 7 day and slightly at 11 day. There was no statistical significance in the counts of proliferating HGF-1 between control and experimental groups (p>0.05). Relative growth rates were no statistically significant difference between control and experimental groups (p>0.05). PCNA labeling indexes showing similar patterns in control and experimental groups. The expressions of ERK1 and ERK2, p38 were similar in control and experimental groups. The expression of JNK increased at 1st day, slightly decreased at 4th day and markedly increased at 7th and 11 day. Although the patterns of control and experimental groups were similar, the increased expressions of JNK at late period suggest a possible stress due to inhibited cell growth and proliferation, and worse culture condition. Conclusively, the 3 kinds of metal specimens used in this study did not induce cellular and molecular hazards during short term culture of HGF-1. But, for the better clinical stability, the establishment of long period culture and animal experiment was thought necessary.

• Geophysics and Geophysical Exploration
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• v.9 no.4
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• pp.271-278
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• 2006

Investigations of underground cavities are required to provide useful information for the reinforcement design and monitoring of the ground subsidence areas. It is, therefore, necessary to develop integrated geophysical techniques incorporating different geophysical methods in order to accurately image and to map underground cavities in the ground subsidence areas. In this study, we conducted geophysical investigations for development of integrated geophysical techniques to detect underground cavities at the field test site in the ground subsidence area, located at Yongweol-ri, Muan-eup, Muan-gun, Jeollanam-do. We examined the applicability of geophysical methods such as electrical resistivity, electromagnetic, and microgravity to cavity detection with the aid of borehole survey results. The underground cavities are widely present within the limestone bedrock overlain by the alluvial deposits in the test site where the ground subsidences have occurred in the past. The limestone cavities are mostly filled with groundwater or clays saturated with water in the site. The cavities, thus, have low electrical resistivity and density compared to the surrounding host bedrock. The results of the study have shown that the zones of low resistivity and density correspond to the zones of the cavities identified in the boreholes at the site, and that the geophysical methods used are very effective to detect the underground cavities. Furthermore, we could map the distribution of cavities more precisely with the study results incorporated from the various geophysical methods. It is also important to notice that the microgravity method, which has rarely used in Korea, is a very promising tool to detect underground cavities.

• Journal of Plant Biotechnology
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• v.39 no.1
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• pp.33-48
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• 2012

As a scientific curiosity to understand the structure and the function of crops and experimental efforts to apply it to plant breeding, genetic maps have been constructed in various crops. Especially, in the case of Brassica crop, genetic mapping has been accelerated since genetic information of model plant $Arabidopsis$ was available. As a result, the whole $B.$ $rapa$ genome (A genome) sequencing has recently been done. The genome sequences offer opportunities to develop molecular markers for genetic analysis in $Brassica$ crops. RFLP markers are widely used as the basis for genetic map construction, but detection system is inefficiency. The technical efficiency and analysis speed of the PCR-based markers become more preferable for many form of $Brassica$ genome study. The massive sequence informative markers such as SSR, SNP and InDels are also available to increase the density of markers for high-resolution genetic analysis. The high density maps are invaluable resources for QTLs analysis, marker assisted selection (MAS), map-based cloning and comparative analysis within $Brassica$ as well as related crop species. Additionally, the advents of new technology, next-generation technique, have served as a momentum for molecular breeding. Here we summarize genetic and genomic resources and suggest their applications for the molecular breeding in $Brassica$ crop.