• Smart Structures and Systems
• /
• v.29 no.4
• /
• pp.625-640
• /
• 2022

Maglev rail joints are vital components serving as connections between the adjacent F-type rail sections in maglev guideway. Damage to maglev rail joints such as bolt looseness may result in rough suspension gap fluctuation, failure of suspension control, and even sudden clash between the electromagnets and F-type rail. The condition monitoring of maglev rail joints is therefore highly desirable to maintain safe operation of maglev. In this connection, an online damage detection approach based on three-dimensional (3D) convolutional neural network (CNN) and time-frequency characterization is developed for simultaneous detection of multiple damage of maglev rail joints in this paper. The training and testing data used for condition evaluation of maglev rail joints consist of two months of acceleration recordings, which were acquired in-situ from different rail joints by an integrated online monitoring system during a maglev train running on a test line. Short-time Fourier transform (STFT) method is applied to transform the raw monitoring data into time-frequency spectrograms (TFS). Three CNN architectures, i.e., small-sized CNN (S-CNN), middle-sized CNN (M-CNN), and large-sized CNN (L-CNN), are configured for trial calculation and the M-CNN model with excellent prediction accuracy and high computational efficiency is finally optioned for multiple damage detection of maglev rail joints. Results show that the rail joints in three different conditions (bolt-looseness-caused rail step, misalignment-caused lateral dislocation, and normal condition) are successfully identified by the proposed approach, even when using data collected from rail joints from which no data were used in the CNN training. The capability of the proposed method is further examined by using the data collected after the loosed bolts have been replaced. In addition, by comparison with the results of CNN using frequency spectrum and traditional neural network using TFS, the proposed TFS-CNN framework is proven more accurate and robust for multiple damage detection of maglev rail joints.

• Bulletin of the Korean Chemical Society
• /
• v.35 no.4
• /
• pp.1011-1015
• /
• 2014

Octadecyl-modified graphene (graphene-C18) was fabricated and used as adsorbent in hollow fiber liquid phase microextraction (HF-LPME) for the first time. The extraction performance of graphene-C18 reinforced HF-LPME was evaluated using chlorophenols as model analytes. The factors affecting the extraction efficiency, such as extraction time, pH of the sample solution, agitation rate, the concentration of graphene-C18 and salt addition were optimized. After the graphene-C18 reinforced HF-LPME of the chlorophenols from honey sample, the analytes were separated and determined by high-performance liquid chromatography. The linearity was observed in the range of 5.0-200.0 ng $g^{-1}$ for 2-chlorophenol and 3-chlorophenol, and 2.0-200.0 ng $g^{-1}$ for 2,3-dichlorophenol and 3,4-dichlorophenol, respectively. The limits of detection (S/N = 3) of the method were lower than 1.5 ng $g^{-1}$. The recoveries of the method were between 88% and 108%. The method is simple, sensitive and has been resoundingly applied to analysis of chlorophenols in honey samples.

• Journal of Microbiology and Biotechnology
• /
• v.31 no.4
• /
• pp.520-528
• /
• 2021

Plesiomonas shigelloides, a member of the family Vibrionaceae, is a gram-negative, rod-shaped, facultative anaerobic bacterium with flagella. P. shigelloides has been isolated from such sources as freshwater, surface water, and many wild and domestic animals. P. shigelloides contains 102 O-antigens and 51 H-antigens. The diversity of O-antigen gene clusters is relatively poorly understood. In addition to O1 and O17 reported by other laboratories, and the 12 O serogroups (O2, O10, O12, O23, O25, O26, O32, O33, O34, O66, O75, and O76) reported previously by us, in the present study, nine new P. shigelloides serogroups (O8, O17, O18, O37, O38, O39, O44, O45, and O61) were sequenced and annotated. The genes for the O-antigens of these nine groups are clustered together in the chromosome between rep and aqpZ. Only O38 possesses the wzm and wzt genes for the synthesis and translocation of O-antigens via the ATP-binding cassette (ABC) transporter pathway; the other eight use the Wzx/Wzy pathway. Phylogenetic analysis using wzx and wzy showed that both genes are diversified. Among the nine new P. shigelloides serogroups, eight use wzx/wzy genes as targets. In addition, we developed an O-antigen-specific PCR assay to detect these nine distinct serogroups with no cross reactions among them.

• Convergence Security Journal
• /
• v.19 no.5
• /
• pp.47-53
• /
• 2019

Network-based sharing of information has evolved into a cloud service environment today, increasing its number of users rapidly, but has become a major target for network-based illegal attackers.. In addition, IP spoofing among attackers' various attack techniques generally involves resource exhaustion attacks. Therefore, fast detection and response techniques are required. The existing detection method for IP spoofing attack performs the final authentication process according to the analysis and matching of traceback information of the client who attempted the connection request. However, the simple comparison method of traceback information may require excessive OTP due to frequent false positives in an environment requiring service transparency. In this paper, symmetric key cryptography based on traceback information is used as mutual authentication information to improve this problem. That is, after generating a traceback-based encryption key, mutual authentication is possible by performing a normal decryption process. In addition, this process could improve the overhead caused by false positives.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.26 no.4
• /
• pp.407-414
• /
• 2008

This study introduces an automatic image matching algorithm that can be applied for the scale different image pairs consisting of the satellite pushbroom images and the aerial frame images. The proposed method is based on several image processing techniques such as pre-processing, filtering, edge thinning, interest point extraction, and key-descriptor matching, in order to enhance the matching accuracy and the processing speed. The proposed method utilizes various characteristics, such as the different geometry of image acquisition and the different radiometric characteristics, of the multi-sensor images. In addition, the suggested method uses the sensor model to minimize search area and eliminate false-matching points automatically.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.34 no.5
• /
• pp.479-493
• /
• 2016

LiDAR(Light Detection and Ranging) data acquired from ALS(Airborne Laser Scanner) has been intensively utilized to reconstruct object models. Especially, researches for 3D modeling from LiDAR data have been performed to establish high quality spatial information such as precise 3D city models and true orthoimages efficiently. To reconstruct object models from irregularly distributed LiDAR point clouds, sensor calibration, noise removal, filtering to separate objects from ground surfaces are required as pre-processing. Classification and segmentation based on geometric homogeneity of the features, grouping and representation of the segmented surfaces, topological analysis of the surface patches for modeling, and accuracy assessment are accompanied by modeling procedure. While many modeling methods are based on the segmentation process, this paper proposed to extract key points directly for building modeling without segmentation. The method was applied to simulated and real data sets with various roof shapes. The results demonstrate feasibility of the proposed method through the accuracy analysis.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.23 no.4
• /
• pp.477-485
• /
• 2019

As the drones industry evolved, the security of the drone network has been important. In this paper, MTD (Moving Target Detection) technique is applied to the drone network for improving security. The existing MTD scheme has a risk that the hash value is exposed during the wireless communication process, and it is restricted to apply the one-to-many network. Therefore, we proposed PHT (Preposition Hash Table) scheme to prevent exposure of hash values during wireless communication. By reducing the risk of cryptographic key exposure, the use time of the cryptographic key can be extended and the security of the drone network will be improved. In addition, the cryptographic key exchange is not performed during flight, it is advantageous to apply PHT for a swarm drone network. Through simulation, we confirmed that the proposed scheme can contribute to the security of the drone network.

• Korean Chemical Engineering Research
• /
• v.58 no.2
• /
• pp.224-229
• /
• 2020

We propose a machine learning-based method for proactively detecting faults in LDPE processes and predicting equipment lifespan. It is important to detect and prevent unexpected faults in chemical processes in order to maximize safety and productivity. Since LDPE process is a high-pressure process up to 3,000 kg/㎠g or more, once ESD occurs, it can result in productivity loss due to increased maintenance periods. By collecting key variables operation data of the process and using unsupervised machine leaning methods, we developed a fault detection model which detected 4 ESDs 2.4 days prior to the occurrence. In addition, it was confirmed that the life expectancy of a hyper compressor can be predicted by using the physically significant key variables.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.14 no.10
• /
• pp.3989-4006
• /
• 2020

The intelligent agriculture monitoring is based on the perception and analysis of environmental data, which enables the monitoring of the production environment and the control of environmental regulation equipment. As the scale of the application continues to expand, a large amount of data will be generated from the perception layer and uploaded to the cloud service, which will bring challenges of insufficient bandwidth and processing capacity. A fog-based offline and real-time hybrid data analysis architecture was proposed in this paper, which combines offline and real-time analysis to enable real-time data processing on resource-constrained IoT devices. Furthermore, we propose a data process-ing algorithm based on the incremental principal component analysis, which can achieve data dimensionality reduction and update of principal components. We also introduce the concept of Squared Prediction Error (SPE) value and realize the abnormal detection of data through the combination of SPE value and data fusion algorithm. To ensure the accuracy and effectiveness of the algorithm, we design a regular-SPE hybrid model update strategy, which enables the principal component to be updated on demand when data anomalies are found. In addition, this strategy can significantly reduce resource consumption growth due to the data analysis architectures. Practical datasets-based simulations have confirmed that the proposed algorithm can perform data fusion and exception processing in real-time on resource-constrained devices; Our model update strategy can reduce the overall system resource consumption while ensuring the accuracy of the algorithm.

• Journal of the Korea Society of Computer and Information
• /
• v.11 no.4 s.42
• /
• pp.147-154
• /
• 2006

Effective and efficient representation of color features of multiple video frames is an important vet challenging task for visual information management systems. This paper Proposes a Video Browsing Service(VBS) that provides both the video content retrieval and the video browsing by the real-time user interface on Web. For the scene segmentation and key frame extraction of video sequence, we proposes an efficient scene change detection method that combine the RGB color histogram with the X2 (Chi Square) histogram. Resulting key frames are linked by both physical and logical indexing. This system involves the video editing and retrieval function of a VCR's. Three elements that are the date, the need and the subject are used for video browsing. A Video Browsing Service is implemented with MySQL, PHP and JMF under Apache Web Server.