• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.1
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• pp.133-138
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• 2008

In this paper, we propose a novel anchor shot detection system, named to MASD (Multi-phase Anchor Shot Detection), which is a core step of the preprocessing process for the news video analysis. The proposed system is composed of four modules and operates sequentially: 1) skin color detection module for reducing the candidate face regions; 2) face detection module for finding the key-frames with a facial data; 3) vector representation module for the key-frame images using a non-negative matrix factorization; 4) one class SVM module for determining the anchor shots using a support vector data description. Besides the qualitative analysis, our experiments validate that the proposed system shows not only the comparable accuracy to the recently developed methods, but also more faster detection rate than those of others.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.6
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• pp.392-399
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• 2023

This paper proposes a deep learning model to determine the region and depth of cabbage cores for robotic automation of the cabbage core removal process during the kimchi manufacturing process. In addition, rather than predicting the depth of the measured cabbage, a model was presented that simultaneously detects and classifies the area by converting it into a discrete class. For deep learning model learning and verification, RGB images of the harvested cabbage 522 were obtained. The core region and depth labeling and data augmentation techniques from the acquired images was processed. MAP, IoU, acuity, sensitivity, specificity, and F1-score were selected to evaluate the performance of the proposed YOLO-v4 deep learning model-based cabbage core area detection and classification model. As a result, the mAP and IoU values were 0.97 and 0.91, respectively, and the acuity and F1-score values were 96.2% and 95.5% for depth classification, respectively. Through the results of this study, it was confirmed that the depth information of cabbage can be classified, and that it can be used in the development of a robot-automation system for the cabbage core removal process in the future.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.7
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• pp.1321-1323
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• 2007

A new reference point location method based on an edge map is proposed, where an orientation map is defined and used to find the edge map. Experimental results show that the proposed method can effectively detect the core point in poor quality and arch-type fingerprint images and produces better results in terms of the detection rate and accuracy than the sine map-based method.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.11a
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• pp.217-220
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• 2007

In this paper, we present a new anchor shot detection system which is a core step of the preprocessing process for the news video analysis. The proposed system is composed of four modules and operates sequentially: 1) skin color detection module for reducing the candidate face regions; 2) face detection module for finding the key-frames with a facial data; 3) vector representation module for the key-frame images using a non-negative matrix factorization; 4) anchor shot detection module using a support vector data description. According to our computer experiments, the proposed system shows not only the comparable accuracy to the recent other results, but also more faster detection rate than others.

• Proceedings of the Korean Radioactive Waste Society Conference
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• 2009.06a
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• pp.84-85
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• 2009

New approaches for detecting, preventing and remedying environmental damage are important for protection of the environment. Procedures must be developed and implemented to reduce the amount of waste produced in chemical processes, to detect the presence and/or concentration of contaminants and decontaminate fouled environments. Contamination can be classified into three general types: airborne, surface and structural. The most dangerous type is airborne contamination, because of the opportunity for inhalation and ingestion. The second most dangerous type is surface contamination. Surface contamination can be transferred to workers by casual contact and if disturbed can easily be made airborne. The decontamination of the surface in the nuclear facilities has been widely studied with particular emphasis on small and large surfaces. The amount of wastes being produced during decommissioning of nuclear facilities is much higher than the total wastes cumulated during operation. And, the process of decommissioning has a strong possibility of personal's exposure and emission to environment of the radioactive contaminants, requiring through monitoring and estimation of radiation and radioactivity. So, it is important to monitor the radioactive contamination level of the nuclear facilities for the determination of the decontamination method, the establishment of the decommissioning planning, and the worker's safety. But it is very difficult to measure the surface contamination of the floor and wall in the highly contaminated facilities. In this study, the poly(styrene-ethyl acrylate) [poly(St-EA)] core-shell composite polymer for measurement of the radioactive contamination was synthesized by the method of emulsion polymerization. The morphology of the poly(St-EA) composite emulsion particle was core-shell structure, with polystyrene (PS)as the core and poly(ethyl acrylate) (PEA) as the shell. Core-shell polymers of styrene (St)/ethyl acrylate (EA) pair were prepared by sequential emulsion polymerization in the presence of sodium dodecyl sulfate (SOS) as an emulsifier using ammonium persulfate (APS) as an initiator. The polymer was made by impregnating organic scintillators, 2,5-diphenyloxazole (PPO) and 1,4-bis[5-phenyl-2-oxazol]benzene (POPOP). Related tests and analysis confirmed the success in synthesis of composite polymer. The products are characterized by IT-IR spectroscopy, TGA that were used, respectively, to show the structure, the thermal stability of the prepared polymer. Two-phase particles with a core-shell structure were obtained in experiments where the estimated glass transition temperature and the morphologies of emulsion particles. Radiation pollution level the detection about under using examined the beta rays. The morphology of the poly(St-EA) composite polymer synthesized by the method of emulsion polymerization was a core-shell structure, as shown in Fig. 1. Core-shell materials consist of a core structural domain covered by a shell domain. Clearly, the entire surface of PS core was covered by PEA. The inner region was a PS core and the outer region was a PEA shell. The particle size distribution showed similar in the range 350-360 nm.

• Journal of the Korean GEO-environmental Society
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• v.16 no.8
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• pp.21-35
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• 2015

Electrical resistivity survey (2D and 3D) were employed for detection of possible weak zone of core zones of three central core earth-rockfill dams in Korea. In the 2D results, the core zones is lower resistivity zone with less than $50{\sim}400ohm{\cdot}m$, and the basement is relatively higher resistivity zone with over $1,000ohm{\cdot}m$. In the 3D results, especially, the weak zone with under $100ohm{\cdot}m$ was detected spatial distribution area in the dam. We also drilled boreholes to collect soil samples of core zones of each dam. Water was not used during boring, because water for rotary wash boring could cause structural damages in earth dams. We found that the soil samples of core zones from all of the boreholes correspond to CL (USCS), but we also found that the fluidized or water-saturated soil samples were found in lower resistivity zones. Therefore, the electrical resistivity survey and drilling method without water are a quick and efficient method for structural-health evaluation which is detection of possible weak zones in earth core rockfill dams.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.10
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• pp.5159-5178
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• 2018

Network Intrusion detection is a rapidly growing field of information security due to its importance for modern IT infrastructure. Many supervised and unsupervised learning techniques have been devised by researchers from discipline of machine learning and data mining to achieve reliable detection of anomalies. In this paper, a deep convolutional neural network (DCNN) based intrusion detection system (IDS) is proposed, implemented and analyzed. Deep CNN core of proposed IDS is fine-tuned using Randomized search over configuration space. Proposed system is trained and tested on NSLKDD training and testing datasets using GPU. Performance comparisons of proposed DCNN model are provided with other classifiers using well-known metrics including Receiver operating characteristics (RoC) curve, Area under RoC curve (AuC), accuracy, precision-recall curve and mean average precision (mAP). The experimental results of proposed DCNN based IDS shows promising results for real world application in anomaly detection systems.

• Journal of Power System Engineering
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• v.16 no.6
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• pp.86-91
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• 2012

A multi purpose environmental monitoring system has been developed as a commercially available standard using the techniques which are FBG(Fiber Bragg Grating), Hetero-core spliced fiber optic sensor and etc, for the purposes of monitoring large scaled structures and preserving natural environments. The monitoring system has been tested and evaluated in a possible outdoor condition in view of the full scaled operation at actual sites to be monitored. Additionally, the developed systems in the previous works conveniently provided us with various options of sensor modules intended for monitoring such physical quantities as displacement, distortion, pressure, binary states, and liquid adhesion. In this paper, we extend the previous results to a water leakage detection problem and develop a sensing system as a result. By the experimental study, it is verified that multi-point leakage detection is possible using single line optical fiber.

• 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.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.31 no.5
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• pp.351-356
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• 2018

Commonly, a live-line alarm can be used to measure the electric field strength of a high-voltage system to calculate its current, but it is hard to detect the electric field of shielded cables or concealed structures, such as underground distribution cables. Current sensors can detect the magnetic field in a single core wire, but they cannot determine the magnetic field about a double-core wire because the currents flow in opposite directions. Therefore, it is very difficult to detect certain current problems, such as a fault current in an extension line comprised of a double line. In this paper, to ultimately develop a sensor that can detect the current regardless of line conditions, we used a simulation to determine the concentration of the magnetic field dependent on the distribution of the external magnetic field and the path of each line's core.