• Bulletin of the Korean Chemical Society
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• v.33 no.11
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• pp.3696-3700
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• 2012

The colorimetric and fluorescent cyanide recognition properties of 2-[(9-ethyl-9H-carbazol-3-yl)methylene]-propanedinitrile (1) in $CH_3CN-H_2O$ (2/1, v/v, HEPES 10 mM, pH = 7.0) solution were evaluated. The optical recognition process of probe 1 exhibited high sensitivity and selectivity to cyanide ion with the detection limit of $2.04{\times}10^{-6}$ M and barely interfered by other coexisting anions. The sensing mechanism of probe 1 is speculated to undergo a nucleophilic addition of cyanide to dicyanovinyl group present in compound 1. The colorimetric and fluorescent dual-modal response to cyanide makes probe 1 has a potential utility in cyanide detection.

• Smart Structures and Systems
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• v.2 no.2
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• pp.171-188
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• 2006

This paper deals with damage detection using active Lamb waves. The wavelet transform and empirical mode decomposition methods are discussed for measuring the Lamb wave's arrival time of the group velocity. An experimental system to diagnose the damage in the composite plate is developed. A method to optimize this system is also given for practical applications of active Lamb waves, which involve optimal arrangement of the piezoelectric elements to produce single mode Lamb waves. In the paper, the single mode Lamb wave means that there exists no overlapping among different Lamb wave modes and the original Lamb wave signal with the boundary reflection signals. Based on this optimized PZT arrangement method, five damage localizations on different plates are completed and the results using wavelet transform and empirical mode decomposition methods are compared.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.361-369
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• 2022

Unsafe actions and behaviors of workers cause most accidents at construction sites. Nowadays, occupational safety is a top priority at construction sites. However, this problem often requires money and effort from investors or construction owners. Therefore, decreasing the accidents rates of workers and saving monitoring costs for contractors is necessary at construction sites. This study proposes an unsafe behavior detection method based on a skeleton model to classify three common unsafe behaviors on the scaffold: climbing, jumping, and running. First, the OpenPose method is used to obtain the workers' key points. Second, all skeleton datasets are aggregated from the temporary size. Third, the key point dataset becomes the input of the action classification model. The method is effective, with an accuracy rate of 89.6% precision and 90.5% recall of unsafe actions correctly detected in the experiment.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.9
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• pp.4355-4374
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• 2018

Crowd escape event detection has become one of the hottest problems in intelligent surveillance filed. When the 'escape event' occurs, pedestrians will escape in a disordered way with different velocities and directions. Based on these characteristics, this paper proposes a Direction-Collectiveness Model to detect escape event in crowd scenes. First, we extract a set of trajectories from video sequences by using generalized Kanade-Lucas-Tomasi key point tracker (gKLT). Second, a Direction-Collectiveness Model is built based on the randomness of velocity and orientation calculated from the trajectories to express the movement of the crowd. This model can describe the movement of the crowd adequately. To obtain a generalized crowd escape event detector, we adopt an adaptive threshold according to the Direction-Collectiveness index. Experiments conducted on two widely used datasets demonstrate that the proposed model can detect the escape events more effectively from dense crowd.

• Current Optics and Photonics
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• v.4 no.1
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• pp.69-80
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• 2020

A Rayleigh Lidar used for wind detection works by transmitting laser pulses to the atmosphere and receiving backscattering signals from molecules. Because of the weak backscattering signals, a lidar usually uses a high sensitivity photomultiplier as detector and photon counting technology for signal collection. The capturing of returned extremely weak backscattering signals requires the lidar to work on dark background with a long time accumulation to get high signal-to-noise ratio (SNR). Because of the strong solar background during the day, the SNR of lidar during daytime is much lower than that during nighttime, the altitude and accuracy of detection are also restricted greatly. Therefore this article describes an ultra-narrow bandwidth filter (UNBF) that has been developed on 354.7 nm wavelength of laser. The UNBF is used for suppressing the strong solar background that degrades the performance of Rayleigh wind lidar during daytime. The optical structure of UNBF consists of an interference filter (IF), a low resolution Fabry-Perot interferometer (FPI) and a high resolution FPI. The parameters of each optical component of the UNBF are presented in this article. The transmission curve of the aligned UNBF is measured with a tunable laser. Contrasting the result of with-UNBF and with-IF shows that the solar background received by a Licel transient recorder decreases by 50~100 times and that the SNR with-UNBF was improved by 3 times in the altitude range (35 km to 40 km) compared to with-IF at 10:26 to 10:38 on August 29, 2018. By the SNR comparison at four different times of one day, the ratio-values are larger than 1 over the altitude range (25~50 km) in general, the results illustrate that the SNR with-UNBF is better than that with-IF for Rayleigh Lidar during daytime and they demonstrate the effective improvements of solar background restriction of UNBF.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.10
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• pp.4661-4680
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• 2016

Cognitive radio (CR) technology is an effective solution to the spectrum scarcity issue. Collaborative spectrum sensing is known as a promising technique to improve the performance of spectrum sensing in cognitive radio networks (CRNs). However, collaborative spectrum sensing is vulnerable to spectrum data falsification (SSDF) attack, where malicious users (MUs) may send false sensing data to mislead other secondary users (SUs) to make an incorrect decision about primary user (PUs) activity, which is one of the key adversaries to the performance of CRNs. In this paper, we propose a coalition based malicious users detection (CMD) algorithm to detect the malicious user in CRNs. The proposed CMD algorithm can efficiently detect MUs base on the Geary'C theory and be modeled as a coalition formation game. Specifically, SSDF attack is one of the key issues to affect the resource allocation process. Focusing on the security issues, in this paper, we analyze the power allocation problem with MUs, and propose MUs detection based power allocation (MPA) algorithm. The MPA algorithm is divided into two steps: the MUs detection step and the optimal power allocation step. Firstly, in the MUs detection step, by the CMD algorithm we can obtain the MUs detection probability and the energy consumption of MUs detection. Secondly, in the optimal power allocation step, we use the Lagrange dual decomposition method to obtain the optimal transmission power of each SU and achieve the maximum utility of the whole CRN. Numerical simulation results show that the proposed CMD and MPA scheme can achieve a considerable performance improvement in MUs detection and power allocation.

• Journal of Microbiology and Biotechnology
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• v.23 no.5
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• pp.630-636
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• 2013

Recently, there has been a growing body of evidence showing that cellular microRNAs (miRNAs) are involved in virus-host interactions. Numerous studies have focused on analyses of the expression profiles of cellular miRNAs, but the expression patterns of Dicer, which is responsible for the generation of miRNAs, have only rarely been explored in Gallus gallus. We developed a duplex real-time reverse transcriptase polymerase chain reaction (RT-PCR) assay for the relative quantification of the mRNAs of Dicer and ${\beta}$-actin in G. gallus. To apply this method, the expression of Dicer in avian cells after infection with avian leukosis virus subgroup J (ALV-J) was detected using our established duplex real-time RT-PCR. The duplex real-time RT-PCR assay is sufficiently sensitive, specific, accurate, reproducible, and cost-effective for the detection of Dicer in G. gallus. Furthermore, this study, for the first time, demonstrated that ALV-J can induce differential expression of Dicer mRNA in the ALV-J-infected cells.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.38 no.5
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• pp.58-64
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• 2001

Recently many informations are transmitted ,md stored as video data, and they are on the rapid increase because of popularization of high performance computer and internet. In this paper, to retrieve video data, shots are found through analysis of video stream and the method of detection of key frame is studied. Finally users can retrieve the video efficiently. This Paper suggests a new feature that is robust to object movement in a shot and is not sensitive to change of color in boundary detection of shots, and proposes the characterizing value that reflects the characteristic of kind of video (movie, drama, news, music video etc,). The key frames are pulled out from many frames by using the local minima and maxima of differential of the value. After original frame(not de image) are reconstructed for key frame, indexing process is performed through computing parameters. Key frames that arc similar to user's query image arc retrieved through computing parameters. It is proved that the proposed methods are better than conventional method from experiments. The retrieval accuracy rate is so high in experiments.

• Journal of Korea Society of Digital Industry and Information Management
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• v.16 no.2
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• pp.27-33
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• 2020

Backward compatibility is one of the key issues for radio equipment supporting IEEE 802.11, the typical wireless local area networks (WLANs) communication protocol. For a successful packet decoding with the backward compatibility, the frame format detection is a core precondition. This paper presents a novel frame format detection method based on a deep learning procedure for WLANs affiliated with IEEE 802.11. Considering that the detection performance of conventional methods is degraded mainly due to the poor performances in the symbol synchronization and/or channel estimation in low signal-to-noise-ratio environments, we propose a novel detection method based on convolutional neural network (CNN) that replaces the entire conventional detection procedures. The proposed deep learning network provides a robust detection directly from the receive data. Through extensive computer simulations performed in the multipath fading channel environments (modeled by Project IEEE 802.11 Task Group ac), the proposed method exhibits superb improvement in the frame format detection compared to the conventional method.

• Nuclear Engineering and Technology
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• v.56 no.4
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• pp.1501-1512
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• 2024

Plasma disruption in tokamak experiments is a challenging issue that causes damage to the device. Reliable prediction methods are needed, but the lack of full understanding of plasma disruption limits the effectiveness of physics-driven methods. Data-driven methods based on supervised learning are commonly used, and they rely on labelled training data. However, manual labelling of disruption precursors is a time-consuming and challenging task, as some precursors are difficult to accurately identify. The mainstream labelling methods assume that the precursor onset occurs at a fixed time before disruption, which leads to mislabeled samples and suboptimal prediction performance. In this paper, we present disruption prediction methods based on anomaly detection to address these issues, demonstrating good prediction performance on J-TEXT and EAST. By evaluating precursor onset times using different anomaly detection algorithms, it is found that labelling methods can be improved since the onset times of different shots are not necessarily the same. The study optimizes precursor labelling using the onset times inferred by the anomaly detection predictor and test the optimized labels on supervised learning disruption predictors. The results on J-TEXT and EAST show that the models trained on the optimized labels outperform those trained on fixed onset time labels.