• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.8
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• pp.3550-3566
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• 2020

As a widely used way to exfiltrate information, wireless covert channel (WCC) brings a serious threat to communication security, which enables the wireless communication process to bypass the authorized access control mechanism to disclose information. Unlike the covert channel on the network layer, wireless covert channels on the physical layer (WCC-P) is a new covert communication mode to implement and improve covert wireless communication. Existing WCC-P scheme modulates the secret message bits into the Gaussian noise, which is also called covert digital communication system based on the joint normal distribution (CJND). Finding the existence of this type of covert channel remains a challenging work due to its high undetectability. In this paper, we exploit the square autocorrelation coefficient (SAC) characteristic of the CJND signal to distinguish the covert communication from legitimate communication. We study the sharp increase of the SAC value when the offset is equal to the symbol length, which is caused by embedding secret information. Then, the SAC value of the measured sample is compared with the threshold value to determine whether the measured sample is CJND sample. When the signal-to-noise ratio reaches 20db, the detection accuracy can reach more than 90%.

• Composites Research
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• v.15 no.5
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• pp.19-26
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• 2002

Real defects in composite structures were detected by using laser measuring system. Four types of real defects, that is, impact-induced delamination in a composite laminate, debond in a honeycomb structure, free-edge delamination in a composite laminate and debond in an adhesive joint, were made by applying several types of loads to the specimens. Laser measuring system such as ESPI and shearography technique were used to detect those defects. Thermal loading method, which can easily induce the surface deformation of specimen, was used to detect the defects. Experimental results show that the defects in composite structures could be easily detected by ESPI and shearography technique. Moreover, it shows that ESPI and shearography technique could be usefully applied to the detection of defects in various kinds of composite structures.

• Korean Journal of Veterinary Service
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• v.40 no.3
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• pp.161-168
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• 2017

Caprine arthritis encephalitis (CAE) virus is a causative agent of caprine arthritis-encephalitis. In our previous study we reported a prevalence of CAE. In this study, we described the further detailed pathological features of CAE and examined the detection of virus by in situ hybridization (ISH). Histopathologically, interstitial pneumonia and bronchopneumonia in lung, focal inflammation in mammary glands, perivascular cuffing in brain, arthritis, and focal necrosis, mild steatosis, inflammatory cell infiltration of liver were noted. CAEV proviral-DNA was identified by nested polymerase chain reaction (PCR) in blood cells, brain, synovial fluid, and lymph node. Confirmation by nested PCR involved amplification of a 296 bp ($1^{st}$ PCR) and 185 bp ($2^{nd}$ PCR) fragments corresponding to a conserved region on the gag gene of CAEV. Positive ISH signals were detected in the brain and liver. In conclusion, significant histopathological findings included parenchymal infection in various organs, including the lung, liver, brain, joint, and mammary gland were noted in the CAEV infected dairy goat. ISH can help confirm the diagnosis of CAE in formalin-fixed samples.

• Korean Journal of Veterinary Research
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• v.60 no.3
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• pp.109-116
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• 2020

This study aimed to develop a semi-nested polymerase chain reaction assay for the direct detection of Mycoplasma synoviae (M. synoviae) from clinical samples using three newly designed oligonucleotide primers specific to the variable lipoprotein haemagglutinin (vlhA) gene and differentiate M. synoviae field strains based on a nucleotide deletion or the insertion of the proline-rich repeat (PRR) region of the vlhA gene. The developed semi-nested polymerase chain reaction (PCR) assay revealed positive results in 12 out of 100 clinical samples collected from chickens showing lameness and joint swelling. Six positive samples were selected randomly for sequencing, and sequence analysis revealed 96.3-100% nucleotide identities compared to the reference sequences. Phylogenetic analysis showed that sequences of the strains in this study were closely related to WVU1853 (Spain), CK.MS.UDL.PK.2014.2 (Pakistan), and F10-2AS (USA) strains, but they were distinct from the M. synoviae-H vaccine strain sequence. M. synoviae obtained from these samples were identified as types A and C with a length of 38 and 32 amino acids, respectively. These results indicated that the specific and sensitive semi-nested PCR could be a useful diagnostic tool for the direct identification of clinical samples, and the sequence analysis of the partial vlhA gene can be useful for typing M. Synoviae.

• Journal of Internet Computing and Services
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• v.20 no.6
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• pp.29-36
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• 2019

When dealing with outdoor images in a variety of computer vision applications, the presence of shadow degrades performance. In order to understand the information occluded by shadow, it is essential to remove the shadow. To solve this problem, in many studies, involves a two-step process of shadow detection and removal. However, the field of shadow detection based on CNN has greatly improved, but the field of shadow removal has been difficult because it needs to be restored after removing the shadow. In this paper, it is assumed that shadow is detected, and shadow-less image is generated by using original image and shadow mask. In previous methods, based on CGAN, the image created by the generator was learned from only the aspect of the image patch in the adversarial learning through the discriminator. In the contrast, we propose a novel method using a discriminator that judges both the whole image and the local patch at the same time. We not only use the residual generator to produce high quality images, but we also use joint loss, which combines reconstruction loss and GAN loss for training stability. To evaluate our approach, we used an ISTD datasets consisting of a single image. The images generated by our approach show sharp and restored detailed information compared to previous methods.

• Smart Structures and Systems
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• v.12 no.2
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• pp.181-207
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• 2013

In this paper, a statistical reference-free real-time damage detection methodology is proposed for detecting joint and member damage of truss bridge structures. For the statistical damage sensitive index (DSI), wavelet packet decomposition (WPD) in conjunction with the log likelihood ratio was suggested. A sensitivity test for selecting a wavelet packet that is most sensitive to damage level was conducted and determination of the level of decomposition was also described. Advantages of the proposed method for applications to real-time health monitoring systems were demonstrated by using the log likelihood ratios instead of likelihood ratios. A laboratory truss bridge structure instrumented with accelerometers and a shaker was used for experimental verification tests of the proposed methodology. The statistical reference-free real-time damage detection algorithm was successfully implemented and verified by detecting three damage types frequently observed in truss bridge structures - such as loss of bolts, loosening of bolts at multiple locations, sectional loss of members - without reference signals from pristine structure. The DSI based on WPD and the log likelihood ratio showed consistent and reliable results under different damage scenarios.

• Journal of Korean Institute of Industrial Engineers
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• v.39 no.4
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• pp.290-297
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• 2013

The increasing level of operation in high-tech industry is likely to require ever more complex structure in reliability problem. Furthermore, system failures are more significant on society as a whole than ever before. Reliability redundancy optimization problem (RROP) plays a important role in the designing and analyzing the complex system. RROP involves selection of components with multiple choices and redundancy levels for maximizing system reliability with constraints such as cost, weight, etc. Meanwhile, previous works on RROP dealt with system with perfect failure detection, which gave at most a good solution. However, we studied RROP with imperfect failure detection and switching. Using absorbing Markov Chain, we present not a good solution but the optimal one. In this study, the optimal system configuration is designed with warm and cold-standby redundancy for k-out-of-n system in terms of MTTF that is one of the performance measures of reliability.

• Journal of the Korea Institute of Military Science and Technology
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• v.18 no.4
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• pp.451-458
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• 2015

This paper handles self-collision avoidance for a rescue robot with redundant manipulators. In order to detect all available self-collisions in advance, minimum distances between arbitrary robot parts should be monitored in real-time. For the minimum distance estimation, we suggest a modified method from a previous skeleton algorithm which has less computation burden and realize collision avoidance based on a potential function using the proposed algorithm. The resultant command by collision avoidance should not disturb a given primary task, so null-space of joint solution from a CLIK is utilized for collision avoidance by a gradient projection method.

• Journal of Communications and Networks
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• v.14 no.5
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• pp.471-480
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• 2012

Reliable detection of primary user activity increases the opportunity to access temporarily unused bands and prevents harmful interference to the primary system. By extracting a global decision from local sensing results, cooperative sensing achieves high reliability against multipath fading. For the effective combining of sensing results, which is generalized by a likelihood ratio test, the fusion center should learn some parameters, such as the probabilities of primary transmission, false alarm, and detection at the local sensors. During the training period in supervised learning, the on/off log of primary transmission serves as the output label of decision statistics from the local sensor. In this paper, we extend unsupervised learning techniques with an expectation maximization algorithm for cooperative spectrum sensing, which does not require an external primary transmission log. Local sensors report binary hard decisions to the fusion center and adjust their operating points to enhance learning performance. Increasing the number of sensors, the joint-expectation step makes a confident classification on the primary transmission as in the supervised learning. Thereby, the proposed scheme provides accurate parameter estimates and a fast convergence rate even in low signal-to-noise ratio regimes, where the primary signal is dominated by the noise at the local sensors.

• Journal of Sensor Science and Technology
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• v.32 no.4
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• pp.219-222
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• 2023

Partial discharges (PD) have long been recognized as a major contributing factor to catastrophic failures in high-power equipment. As the demand for high voltage direct current (HVDC) facilities continues to rise, the significance of on-line and real-time monitoring of PD becomes increasingly prominent. In this study, we have designed, fabricated, and characterized a highly sensitive and cost-effective PD sensor comprising a pair of copper electrodes with different arc lengths. The key advantage of our sensor is its non-invasive nature, as it can be installed at any location along the entire power cable without requiring structural modifications. In contrast, conventional PD sensors are typically limited to installation at cable terminals or insulation joint boxes, often necessitating invasive alterations. Our PD sensor demonstrates exceptional accuracy in estimating PD location, with a success rate exceeding 95% in the straight sections of the power cable and surpassing 89% in curved sections. These remarkable characteristics indicate its high potential for realtime and on-line detection of PD.