• Smart Structures and Systems
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• v.24 no.6
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• pp.803-812
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• 2019

Triplex composite is an epoxy-bonded joint structure, which constitutes the secondary barrier in a liquefied natural gas (LNG) carrier. Defects in the triplex composite weaken its shear strength and may cause leakage of the LNG, thus compromising the structural integrity of the LNG carrier. This paper proposes an autonomous triplex composite inspection (ATCI) system for visualizing and classifying hidden defects in the triplex composite installed inside an LNG carrier. First, heat energy is generated on the surface of the triplex composite using halogen lamps, and the corresponding heat response is measured by an infrared (IR) camera. Next, the region of interest (ROI) is traced and noise components are removed to minimize false indications of defects. After a defect is identified, it is classified as internal void or uncured adhesive and its size and shape are quantified and visualized, respectively. The proposed ATCI system allows the fully automated and contactless detection, classification, and quantification of hidden defects inside the triplex composite. The effectiveness of the proposed ATCI system is validated using the data obtained from actual triplex composite installed in an LNG carrier membrane system.

• Journal of Korea Multimedia Society
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• v.22 no.10
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• pp.1142-1148
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• 2019

Reconstruction of the three-dimensional (3D) environment is a key aspect of augmented reality and augmented virtuality, which utilize and incorporate a user's surroundings. Such reconstruction can be easily realized by employing a Kinect device. However, multiple Kinect devices are required for enhancing the reconstruction density and for spatial expansion. While employing multiple Kinect devices, they must be calibrated with respect to each other in advance, and a marker is often used for this purpose. However, a marker needs to be placed at each calibration, and the result of marker detection significantly affects the calibration accuracy. Therefore, a user-friendly, efficient, accurate, and marker-less method for calibrating multiple Kinect devices is proposed in this study. The proposed method includes a joint tracking algorithm for approximate calibration, and the obtained result is further refined by applying the iterative closest point algorithm. Experimental results indicate that the proposed method is a convenient alternative to conventional marker-based methods for calibrating multiple Kinect devices. Hence, the proposed method can be incorporated in various applications of augmented reality and augmented virtuality that require 3D environment reconstruction by employing multiple Kinect devices.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.1866-1883
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• 2019

As the youngest branch of information hiding, network covert timing channels conceal the existence of secret messages by manipulating the timing information of the overt traffic. The popular model-based framework for constructing covert timing channels always utilizes cumulative distribution function (CDF) of the inter-packet delays (IPDs) to modulate secret messages, whereas discards high-order statistics of the IPDs completely. The consequence is the vulnerability to high-order statistical tests, e.g., entropy test. In this study, a rich security model of covert timing channels is established based on IPD chains, which can be used to measure the distortion of multi-order timing statistics of a covert timing channel. To achieve rich security, we propose two types of covert timing channels based on nested lattices. The CDF of the IPDs is used to construct dot-lattice and interval-lattice for quantization, which can ensure the cell density of the lattice consistent with the joint distribution of the IPDs. Furthermore, compensative quantization and guard band strategy are employed to eliminate the regularity and enhance the robustness, respectively. Experimental results on real traffic show that the proposed schemes are rich-secure, and robust to channel interference, whereas some state-of-the-art covert timing channels cannot evade detection under the rich security model.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.6
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• pp.2908-2924
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• 2019

Multiple input multiple output orthogonal frequency division multiplexing (MIMO-OFDM) is widely applied in wireless communication by virtue of its excellent properties in data transmission rate and transmission accuracy. However, as a major drawback of MIMO-OFDM systems, the high peak-to-average power ratio (PAPR) complicates the design of the power amplifier at the receiver end. Some available PAPR reduction methods such as selective mapping (SLM) suffer from high computational complexity. In this paper, a low-complexity SLM method based on active constellation extension (ACE) and joint space-time selective mapping (AST-SLM) for reducing PAPR in Alamouti STBC MIMO-OFDM systems is proposed. In SLM scheme, two IFFT operations are required for obtaining each transmission sequence pair, and the selected phase vector is transmitted as side information(SI). However, in the proposed AST-SLM method, only a few IFFT operations are required for generating all the transmission sequence pairs. The complexity of AST-SLM is at least 86% less than SLM. In addition, the SI needed in AST-SLM is at least 92.1% less than SLM by using the presented blind detection scheme to estimate SI. We show, analytically and with simulations, that AST-SLM can achieve significant performance of PAPR reduction and close performance of bit error rate (BER) compared to SLM scheme.

• ETRI Journal
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• v.41 no.4
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• pp.494-505
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• 2019

In this paper, we propose a new framework for detecting the unauthorized dumping of garbage in real-world surveillance camera. Although several action/behavior recognition methods have been investigated, these studies are hardly applicable to real-world scenarios because they are mainly focused on well-refined datasets. Because the dumping actions in the real-world take a variety of forms, building a new method to disclose the actions instead of exploiting previous approaches is a better strategy. We detected the dumping action by the change in relation between a person and the object being held by them. To find the person-held object of indefinite form, we used a background subtraction algorithm and human joint estimation. The person-held object was then tracked and the relation model between the joints and objects was built. Finally, the dumping action was detected through the voting-based decision module. In the experiments, we show the effectiveness of the proposed method by testing on real-world videos containing various dumping actions. In addition, the proposed framework is implemented in a real-time monitoring system through a fast online algorithm.

• Journal of Korean Biological Nursing Science
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• v.21 no.1
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• pp.54-61
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• 2019

Purpose: This study aimed to identify the incidence and risks for pressure ulcer among older patients with hip fracture. Methods: The subject were 215 older patients suffering from hip fracture who were admitted for surgical operation from January 1, 2012 to April 30, 2016 in a university-affiliated hospital. The incidence of pressure ulcer was collected retrospectively through medical record review and the risk factors were analyzed using Cox's proportional hazard model. Results: Out of the total, 32 patients (14.9%) developed pressure ulcer with the average occurrence period being 4.72 (${\pm}3.81$) days. Stage II pressure ulcer was the most common at 72.0%. Risk factors included ambulation status before injury (p= .039), spinal anesthesia (p= .029), and stay at intensive care unit after operation (p= .009). Conclusion: Despite pressure ulcer prevention efforts, the incidence remained relatively high. Considering the identified risk factors, more efforts is needed for early detection and prevention of pressure ulcers in such patients.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.4
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• pp.1373-1391
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• 2022

We investigate mobile multiple-input multiple-output (MIMO) molecular communication via diffusion (MCvD) system which is consisted of two source nodes, two destination nodes and one relay node in the mobile three-dimensional channel. First, the combinations of decode-and-forward (DF) relaying protocol and network coding (NC) scheme are implemented at relay node. The adaptive thresholds at relay node and destination nodes can be obtained by maximum a posteriori (MAP) probability detection method. Then the mathematical expressions of the average bit error probability (BEP) of this mobile MIMO MCvD system based on DF and NC scheme are derived. Furthermore, in order to minimize the average BEP, we establish the optimization problem with optimization variables which include the ratio of the number of emitted molecules at two source nodes and the initial position of relay node. We put forward an iterative scheme based on block coordinate descent algorithm which can be used to solve the optimization problem and get optimal values of the optimization variables simultaneously. Finally, the numerical results reveal that the proposed iterative method has good convergence behavior. The average BEP performance of this system can be improved by performing the joint optimizations.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.7
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• pp.2328-2344
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• 2022

2D human pose estimation still faces difficulty in low-resolution images. Most existing top-down approaches scale up the target human bonding box images to the large size and insert the scaled image into the network. Due to up-sampling, artifacts occur in the low-resolution target images, and the degraded images adversely affect the accurate estimation of the joint positions. To address this issue, we propose a multi-resolution input feature fusion network for human pose estimation. Specifically, the bounding box image of the target human is rescaled to multiple input images of various sizes, and the features extracted from the multiple images are fused in the network. Moreover, we introduce a guiding channel which induces the multi-resolution input features to alternatively affect the network according to the resolution of the target image. We conduct experiments on MS COCO dataset which is a representative dataset for 2D human pose estimation, where our method achieves superior performance compared to the strong baseline HRNet and the previous state-of-the-art methods.

• Structural Monitoring and Maintenance
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• v.9 no.3
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• pp.237-258
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• 2022

This paper studies detecting and locating loose bolts using nonlinear guided waves. The 3D Finite Element (FE) simulation is used for the prediction of guided waves' interactions with loose bolted joints. The numerical results are verified by experimentally obtained data. The study considers bolted joints consisting of two bolts. It is shown that the guided waves' interaction with surfaces of a loose bolted joint generates Contact Acoustic Nonlinearity (CAN). The study uses CAN for detecting and locating loose bolts. The processed experimentally obtained data show that the CAN is able to successfully detect and locate loose bolted joints. A 3D FE simulation scheme is developed and validated by experimentally obtained data. It is shown that FE can predict the propagation of guided waves in loose bolts and is also able to detect and locate them. Several numerical case studies with various bolt sizes are created and studied using the validated 3D FE simulation approach. It is shown that the FE simulation modeling approach and the signal processing scheme used in the current study are able to detect and locate the loose bolts in imperfect bolted joints. The outcomes of this research can provide better insights into understanding the interaction of guided waves with loose bolts. The results can also enhance the maintenance and repair of imperfect joints using the nonlinear guided waves technique.

• ETRI Journal
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• v.44 no.2
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• pp.286-299
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• 2022

Human activity recognition in real time is a challenging task. Recently, a plethora of studies has been proposed using deep learning architectures. The implementation of these architectures requires the high computing power of the machine and a massive database. However, handcrafted features-based machine learning models need less computing power and very accurate where features are effectively extracted. In this study, we propose a handcrafted model based on three-dimensional sequential skeleton data. The human body skeleton movement over a frame is computed through joint positions in a frame. The joints of these skeletal frames are projected into two-dimensional space, forming a "movement polygon." These polygons are further transformed into a one-dimensional space by computing amplitudes at different angles from the centroid of polygons. The feature vector is formed by the sampling of these amplitudes at different angles. The performance of the algorithm is evaluated using a support vector machine on four public datasets: MSR Action3D, Berkeley MHAD, TST Fall Detection, and NTU-RGB+D, and the highest accuracies achieved on these datasets are 94.13%, 93.34%, 95.7%, and 86.8%, respectively. These accuracies are compared with similar state-of-the-art and show superior performance.