• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.7
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• pp.2131-2153
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• 2022

To achieve accurate detection of tuberculosis (TB) areas in chest radiographs, we design a chest X-ray TB area detection algorithm. The algorithm consists of two stages: the chest X-ray TB classification network (CXTCNet) and the chest X-ray TB area detection network (CXTDNet). CXTCNet is used to judge the presence or absence of TB areas in chest X-ray images, thereby excluding the influence of other lung diseases on the detection of TB areas. It can reduce false positives in the detection network and improve the accuracy of detection results. In CXTCNet, we propose a channel attention mechanism (CAM) module and combine it with DenseNet. This module enables the network to learn more spatial and channel features information about chest X-ray images, thereby improving network performance. CXTDNet is a design based on a sparse object detection algorithm (Sparse R-CNN). A group of fixed learnable proposal boxes and learnable proposal features are using for classification and location. The predictions of the algorithm are output directly without non-maximal suppression post-processing. Furthermore, we use CLAHE to reduce image noise and improve image quality for data preprocessing. Experiments on dataset TBX11K show that the accuracy of the proposed CXTCNet is up to 99.10%, which is better than most current TB classification algorithms. Finally, our proposed chest X-ray TB detection algorithm could achieve AP of 45.35% and AP50 of 74.20%. We also establish a chest X-ray TB dataset with 304 sheets. And experiments on this dataset showed that the accuracy of the diagnosis was comparable to that of radiologists. We hope that our proposed algorithm and established dataset will advance the field of TB detection.

• Journal of the Korea Society for Simulation
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• v.31 no.2
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• pp.11-20
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• 2022

There is a critical need for AI assistance in guard operations of Army base perimeters, which is exacerbated by changes in the national defense and security environment such as force reduction. In addition, the possibility for human error inherent to perimeter guard operations attests to the need for an innovative revamp of current systems. The purpose of this study is to propose a real-time object detection AI tailored to military CCTV surveillance with three unique characteristics. First, training data suitable for situations in which relatively small objects must be recognized is used due to the characteristics of military CCTV. Second, we utilize a data augmentation algorithm suited for military context applied in the data preparation step. Third, a noise reduction algorithm is applied to account for military-specific situations, such as camouflaged targets and unfavorable weather conditions. The proposed system has been field-tested in a real-world setting, and its performance has been verified.

• Journal of the Society of Naval Architects of Korea
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• v.59 no.1
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• pp.46-54
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• 2022

A magnetohydrodynamic (MHD) seawater propulsion thruster has been proposed to reduce propeller noise, propeller pitting, and vessel vibration originated from the propeller cavitation. The MHD thruster was also focused to overcome the limitation of propulsion velocity for the special purpose of marine ships. The research trends and key technologies in the worldwide leading countries are reviewed for the development of MHD propulsion thrusters in Korea. A small performance test device was developed firstly with a conventional solenoid magnet of ≤0.6 Tesla and a helical-type cylindrical duct(inner diameter of 5 cm) of thruster. The artificial seawater was fabricated by a salt solution including a conductivity of 5~6 S/m. The measured flow velocity of artificial seawater in the test device was 0.03~0.42 m/s (0.06~0.84 Knot) with a magnetic field strength of 0.6 Tesla and the applied currents of 10~80 A including the change of anode materials. It was found that the flow direction of seawater was reversed by the directional change of applied current in the solenoid magnet.

• Korean Journal of Audiology
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• v.24 no.3
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• pp.119-126
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• 2020

Background and Objectives: In distracting listening conditions, individuals need to pay extra attention to selectively listen to the target sounds. To investigate the amount of listening effort required in reverberating and noisy backgrounds, a semantic mismatch was examined. Subjects and Methods: Electroencephalography was performed in 18 voluntary healthy participants using a 64-channel system to obtain N400 latencies. They were asked to listen to sounds and see letters in 2 reverberated×2 noisy paradigms (i.e., Q-0 ms, Q-2000 ms, 3 dB-0 ms, and 3 dB-2000 ms). With auditory-visual pairings, the participants were required to answer whether the auditory primes and letter targets did or did not match. Results: Q-0 ms revealed the shortest N400 latency, whereas the latency was significantly increased at 3 dB-2000 ms. Further, Q-2000 ms showed approximately a 47 ms delayed latency compared to 3 dB-0 ms. Interestingly, the presence of reverberation significantly increased N400 latencies. Under the distracting conditions, both noise and reverberation involved stronger frontal activation. Conclusions: The current distracting listening conditions could interrupt the semantic mismatch processing in the brain. The presence of reverberation, specifically a 2000 ms delay, necessitates additional mental effort, as evidenced in the delayed N400 latency and the involvement of the frontal sources in this study.

• Journal of Audiology & Otology
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• v.24 no.3
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• pp.119-126
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• 2020

Background and Objectives: In distracting listening conditions, individuals need to pay extra attention to selectively listen to the target sounds. To investigate the amount of listening effort required in reverberating and noisy backgrounds, a semantic mismatch was examined. Subjects and Methods: Electroencephalography was performed in 18 voluntary healthy participants using a 64-channel system to obtain N400 latencies. They were asked to listen to sounds and see letters in 2 reverberated×2 noisy paradigms (i.e., Q-0 ms, Q-2000 ms, 3 dB-0 ms, and 3 dB-2000 ms). With auditory-visual pairings, the participants were required to answer whether the auditory primes and letter targets did or did not match. Results: Q-0 ms revealed the shortest N400 latency, whereas the latency was significantly increased at 3 dB-2000 ms. Further, Q-2000 ms showed approximately a 47 ms delayed latency compared to 3 dB-0 ms. Interestingly, the presence of reverberation significantly increased N400 latencies. Under the distracting conditions, both noise and reverberation involved stronger frontal activation. Conclusions: The current distracting listening conditions could interrupt the semantic mismatch processing in the brain. The presence of reverberation, specifically a 2000 ms delay, necessitates additional mental effort, as evidenced in the delayed N400 latency and the involvement of the frontal sources in this study.

• Current Optics and Photonics
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• v.6 no.2
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• pp.171-182
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• 2022

A single-photon laser and mid-wave infrared (MWIR) common aperture optical system was designed and developed to detect and range a long-distance civil aviation aircraft. The secondary mirror of the Ritchey-Chretien (R-C) optical system was chosen as a dichroic lens to realize the design of a common aperture system for the laser and MWIR. Point spread function (PSF) ellipticity was introduced to evaluate the coupling efficiency of the laser receiving system. A small aperture stop and narrow filter were set in the secondary image plane and an afocal light path of the laser system, respectively, and the stray light suppression ability of the small aperture stop was verified by modeling and simulation. With high-precision manufacturing technology by single point diamond turning (SPDT) and a high-efficiency dichroic coating, the laser/MWIR common aperture optical system with a 𝜑300 mm aluminum alloy mirror obtained images of buildings at a distance of 5 km with great quality. A civil aviation aircraft detection experiment was conducted. The results show that the common aperture system could detect and track long-distance civil aviation aircraft effectively, and the coverage was more than 450 km (signal-to-noise ratio = 6.3). It satisfied the application requirements for earlier warning and ranging of long-range targets in the area of aviation, aerospace and ground detection systems.

• Smart Structures and Systems
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• v.30 no.6
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• pp.613-626
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• 2022

Guaranteeing the quality and integrity of structural health monitoring (SHM) data is very important for an effective assessment of structural condition. However, sensory system may malfunction due to sensor fault or harsh operational environment, resulting in multiple types of data anomaly existing in the measured data. Efficiently and automatically identifying anomalies from the vast amounts of measured data is significant for assessing the structural conditions and early warning for structural failure in SHM. The major challenges of current automated data anomaly detection methods are the imbalance of dataset categories. In terms of the feature of actual anomalous data, this paper proposes a data anomaly detection method based on data-level and deep learning technique for SHM of civil engineering structures. The proposed method consists of a data balancing phase to prepare a comprehensive training dataset based on data-level technique, and an anomaly detection phase based on a sophisticatedly designed network. The advanced densely connected convolutional network (DenseNet) and Transformer encoder are embedded in the specific network to facilitate extraction of both detail and global features of response data, and to establish the mapping between the highest level of abstractive features and data anomaly class. Numerical studies on a steel frame model are conducted to evaluate the performance and noise immunity of using the proposed network for data anomaly detection. The applicability of the proposed method for data anomaly classification is validated with the measured data of a practical supertall structure. The proposed method presents a remarkable performance on data anomaly detection, which reaches a 95.7% overall accuracy with practical engineering structural monitoring data, which demonstrates the effectiveness of data balancing and the robust classification capability of the proposed network.

• Tunnel and Underground Space
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• v.32 no.6
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• pp.345-352
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• 2022

The existing NATM (New Austrian Tunneling Method) has induced civil compliants due to blasting vibration and noise. Machanized excavation methods such as TBM (Tunnel Boring Machine) are being adopted in the planning and construction of tunneling projects. Shield TBM method is composed of repetition processes of TBM excavation and segment installation, the machine has to be stopped during the later process. Consecutive excavation technology using helical segment is under developing to minimize the stoppage time. The modification of thrust jacks and module are planned to ensure the advance force acting on the inclined surface of helical segment. Also, the integrated system design of hydraulic circuit will be remodeled. This means that the system deactivate the jacks on the installing segment while the others automatically act the thrusting forces on the existing segments. This report briefly introduces the mechanical research part of the current consecutive excavation technological development project of TBM.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.12
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• pp.4292-4307
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• 2021

For the research field of night infrared surveillance video, the target imaging in the video is easily affected by the light due to the characteristics of the active infrared camera and the classical ViBe algorithm has some problems for moving target extraction because of background misjudgment, noise interference, ghost shadow and so on. Therefore, an improved ViBe algorithm (I-ViBe) for moving target extraction in night infrared surveillance video is proposed in this paper. Firstly, the video frames are sampled and judged by the degree of light influence, and the video frame is divided into three situations: no light change, small light change, and severe light change. Secondly, the ViBe algorithm is extracted the moving target when there is no light change. The segmentation factor of the ViBe algorithm is adaptively changed to reduce the impact of the light on the ViBe algorithm when the light change is small. The moving target is extracted using the region growing algorithm improved by the image entropy in the differential image of the current frame and the background model when the illumination changes drastically. Based on the results of the simulation, the I-ViBe algorithm proposed has better robustness to the influence of illumination. When extracting moving targets at night the I-ViBe algorithm can make target extraction more accurate and provide more effective data for further night behavior recognition and target tracking.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.1
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• pp.45-50
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• 2022

This paper compares the equalization performance of the DSE-MMA (Dithered Signed Error-MMA) and DQE-MMA (Dithered Quantized Error-MMA) adaptive equalization algorithm based on the dither signal in order to reduce the intersymbol interference occurs at communication channel. These algorithm was emerged in ordr to reduction of arithmetic operation than current MMA, it makes the independent and identical distribute the quantized error component by performing the 1 or N bit quautizer after adding the dither singal in obtaining the error signal for adapting process. It is possible to improve the robustness performance of adaptive algorithm, but degrade the MSE performance in steady state by dither signal. The paper directly compare the DSE-MMA and DQE-MMA adaptive equalization performance of the same concept of dithering in the same communication channel and signal to noise ratio by computer simulation. As a result of simulation, the DQE-MMA has more better in the every performance index, equalizer output constellation, residual isi, MSE and SER performance, but not in convergence speed.