• The Transactions of the Korean Institute of Power Electronics
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• v.12 no.2
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• pp.115-122
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• 2007

In this paper, a novel fault detection method for fault tolerant control is proposed when the NPC inverter has a open failure in the switching device. The open fault of switching device is detected by checking the variation of a leg-voltage in the neutral-point-clamped inverter and the two phases control method is used for continuously balance the three phases voltage to the load. It can be achieve the fault tolerant control for improving the reliability of the NPC inverter by the fault detection and reconfiguration. This method has fast detection ability and a simple realization for fault detection, compared with a conventional method. Also, this fast detection ability improved the harmful effects such as DC-link voltage unbalance and overstress to other switching devices from a delay of fault detection. The proposed method has been verified by simulation and experiment.

• Imaging Science in Dentistry
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• v.33 no.1
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• pp.27-33
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• 2003

Purpose : The purpose of this experiment was to evaluate the diagnostic ability of a CCD-based digital system (CDX-2000HQ) in the detection of incipient dental caries. Materials and Methods : 93 extracted human teeth with sound proximal surfaces and interproximal artificial cavities were radiographed using 4 imaging methods. Automatically processed No.2 Insight film (Eastman Kodak Co., U.S.A.) was used for conventional radiography, scanned images of conventional radiograms for indirect digital radiography were used. For the direct digital radiography, the CDX-2000HQ CCD system (Biomedisys Co. Korea) was used. The subtraction images were made from two direct digital images by Sunny program in the CDX-2000HQ system. Two radiologists and three endodontists examined the presence of lesions using a five-point confidence scale and compared the diagnostic ability by ROC (Receiver Operating Characteristic) analysis and one way ANOV A test. Results: The mean ROC areas of conventional radiography, indirect digital radiography, direct digital radiography, and digital subtraction radiography were 0.9093, 0.9102, 0.9184, and 0.9056, respectively. The diagnostic ability of direct digital radiography was better than the other imaging modalities, but there were no statistical differences among these imaging modalities (p > 0.05). Coclusion : These results indicate that new CCD-based digital systems (CDX-2000HQ) have the potential to serve as an alternative to conventional radiography in the detection of incipient dental caries.

• 전자공학회논문지 IE
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• v.48 no.2
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• pp.61-70
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• 2011

IEEE 802.11 wireless network supports control frames like RTS/CTS(Request To Send / Clear To Send). Because they is defend to frame collection problems. It helps to solve the frame collection problem but decreases the throughput rate. Also, control frame makes False Node Problem. This problem is makes to other wireless nodes don't work and don't find channels in the same cell and near cells. We proposed a reformed new control frame for efficiency throughput rate and solution of False Node Problem. New control frame is to have added to 4 bytes of channel detection ability at the RTS frames. Channel detection ability supported to check channel at the wireless node start to transmit data frame, We expect that channel detection ability make prevent False Node Problem for increase to access number to channel. We perform comparative analysis in terms of delay(sec) and load(bits/sec) with reform RTS/CTS method which proves the efficiency of the proposed method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.1
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• pp.240-259
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• 2020

Analyzing network traffic is the basis of dealing with network security issues. Most of the network security systems depend on the feature selection of network traffic data and the detection ability of malicious traffic in network can be improved by the correct method of feature selection. An FAFS method, which is short for Fuzzy Association Feature Selection method, is proposed in this paper for network malicious traffic detection. Association rules, which can reflect the relationship among different characteristic attributes of network traffic data, are mined by association analysis. The membership value of association rules are obtained by the calculation of fuzzy reasoning. The data features with the highest correlation intensity in network data sets are calculated by comparing the membership values in association rules. The dimension of data features are reduced and the detection ability of malicious traffic detection algorithm in network is improved by FAFS method. To verify the effect of malicious traffic feature selection by FAFS method, FAFS method is used to select data features of different dataset in this paper. Then, K-Nearest Neighbor algorithm, C4.5 Decision Tree algorithm and Naïve Bayes algorithm are used to test on the dataset above. Moreover, FAFS method is also compared with classical feature selection methods. The analysis of experimental results show that the precision and recall rate of malicious traffic detection in the network can be significantly improved by FAFS method, which provides a valuable reference for the establishment of network security system.

• Fisheries and Aquatic Sciences
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• v.8 no.3
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• pp.185-187
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• 2005

We tested the in vivo ability of a DNA chip to detect virus-specific genes from virus-infected olive flounder Paralichthys olivaceus and rainbow trout Oncorhynchus mykiss. Target cDNA was obtained from total RNA of virus infected cell lines by reverse transcription (RT) and was labeled with fluorescent dye (Cy5-dUTP). The results show the successful detection of infectious hematopoietic necrosis virus (IHNV) and viral hemorrhagic septicaemia virus (VHSV) genes in the virus-infected fishes.

• Annual Conference of KIPS
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• 2021.05a
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• pp.358-361
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• 2021

In order to improve the accuracy of small target detection more effectively, this paper proposes an improved single shot detector (SSD) target detection and recognition method based on cspdarknet53, which introduces lightweight ECA attention mechanism and Feature Pyramid Network (FPN). First, the original SSD backbone network is replaced with cspdarknet53 to enhance the learning ability of the network. Then, a lightweight ECA attention mechanism is added to the basic convolution block to optimize the network. Finally, FPN is used to gradually fuse the multi-scale feature maps used for detection in the SSD from the deep to the shallow layers of the network to improve the positioning accuracy and classification accuracy of the network. Experiments show that the proposed target detection algorithm has better detection accuracy, and it improves the detection accuracy especially for small targets.

• IEMEK Journal of Embedded Systems and Applications
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• v.18 no.5
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• pp.225-231
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• 2023

With the recent surge in the autonomous driving market, the significance of lane detection technology has escalated. Lane detection plays a pivotal role in autonomous driving systems by identifying lanes to ensure safe vehicle operation. Traditional lane detection models rely on engineers manually extracting lane features from predefined environments. However, real-world road conditions present diverse challenges, hampering the engineers' ability to extract adaptable lane features, resulting in limited performance. Consequently, recent research has focused on developing deep learning based lane detection models to extract lane features directly from data. In this paper, we classify lane detection models into four categories: cluster-based, curve-based, information propagation-based, and anchor-based methods. We conduct an extensive analysis of the strengths and weaknesses of each approach, evaluate the model's performance on an embedded board, and assess their practicality and effectiveness. Based on our findings, we propose future research directions and potential enhancements.

• Journal of Korea Society of Digital Industry and Information Management
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• v.10 no.2
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• pp.75-82
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• 2014

MANET has various types of attacks. In particular, routing attacks using characteristics of movement of nodes and wireless communication is the most threatening because all nodes which configure network perform a function of router which forwards packets. Therefore, mechanisms that detect routing attacks and defense must be applied. In this paper, we proposed hierarchical structure attack detection techniques in order to improve the detection ability against routing attacks. Black hole detection is performed using PIT for monitoring about control packets within cluster and packet information management on the cluster head. Flooding attack prevention is performed using cooperation-based distributed detection technique by member nodes. For this, member node uses NTT for information management of neighbor nodes and threshold whether attack or not receives from cluster head. The performance of attack detection could be further improved by calculating at regular intervals threshold considering the total traffic within cluster in the cluster head.

• Proceedings of the KIEE Conference
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• 2007.04a
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• pp.426-429
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• 2007

When a fire happens in builds or apartments, peoples are tend to be caught in dangerous situations owing to the failure of searching escape route to the outside. In this work, an efficient fire detection and alarm system which makes it possible for the escapers to take adequate actions is proposed. The proposed system consists of two parts. One is fire detection modules which are located at each compartments in a building. The other is fire warning modules equipped with portable flashes having ability of visual/voice warning. Fire detection information is transmitted between each modules wirelessly. In this work, an efficient communication protocol for sensor network-based fire detection system is proposed and its feasibility is verified by practical experiments using MANGO ZDK Development tools

• International Journal of Computer Science & Network Security
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• v.24 no.4
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• pp.179-191
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• 2024

With the advancement of modern technology, cyber-attacks are always rising. Specialized defense systems are needed to protect organizations against these threats. Malicious behavior in the network is discovered using security tools like intrusion detection systems (IDS), firewall, antimalware systems, security information and event management (SIEM). It aids in defending businesses from attacks. Delivering advance threat feeds for precise attack detection in intrusion detection systems is the role of cyber-threat intelligence (CTI) in the study is being presented. In this proposed work CTI feeds are utilized in the detection of assaults accurately in intrusion detection system. The ultimate objective is to identify the attacker behind the attack. Several data sets had been analyzed for attack detection. With the proposed study the ability to identify network attacks has improved by using machine learning algorithms. The proposed model provides 98% accuracy, 97% precision, and 96% recall respectively.