• Journal of rehabilitation welfare engineering & assistive technology
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• v.4 no.1
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• pp.29-34
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• 2010

The fully implantable hearing devices (FIHDs) have been studied to compensate the defect of conventional hearing aids. Typically, a microphone for FIHDs was implanted under the skin of the temporal bone. So, implantable microphone characteristics can be affected by the eating food, chattering teeth and moving artifact. In this paper, we fabricated the physical model that was similar to characteristics of human temporal bone and skin, and we measured implanted microphone sensitivity for effect of bone conducted noise signal. For the measurement of microphone sensitivity, we applied 1 kHz pure sounds that were transmitted to implanted microphone and sine wave vibrations of varied frequency were simultaneously transmitted through the artificial bone. As a result, sensitivity of implanted microphone can be modified by bone conducted signal and this phenomenon was confirmed at varied frequency band.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.5
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• pp.871-879
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• 2020

Perceptual Ad-Blocking is a new advertising blocking technique that detects online advertising by using an artificial intelligence-based advertising image classification model. A recent study has shown that these Perceptual Ad-Blocking models are vulnerable to adversarial attacks using adversarial examples to add noise to images that cause them to be misclassified. In this paper, we prove that existing perceptual Ad-Blocking technique has a weakness for several adversarial example and that Defense-GAN and MagNet who performed well for MNIST dataset and CIFAR-10 dataset are good to advertising dataset. Through this, using Defense-GAN and MagNet techniques, it presents a robust new advertising image classification model for adversarial attacks. According to the results of experiments using various existing adversarial attack techniques, the techniques proposed in this paper were able to secure the accuracy and performance through the robust image classification techniques, and furthermore, they were able to defend a certain level against white-box attacks by attackers who knew the details of defense techniques.

• Journal of Environmental Impact Assessment
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• v.21 no.1
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• pp.1-13
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• 2012

We diagnosed on status and problems of environmental assessment regarding development of offshore wind power, and also on reasonable core assessment items. Most of the coastal wind power are located on the western coastline of Korea and Jeju Island. In the selections of the site for the offshore wind farms, a previous investigations should be conducted with regard to distances from the land, stabilities from external forces (tide, wave, etc.) and topographical changes, and characteristics of the surroundings (distributions of protected area, fishing ground, artificial seagrasses, and shipping traffic). It is needed to assess dispersion of suspended solids, changes of the sea bottom, and impacts on fisheries resources and fishing activities under construction of offshore wind power. Furthermore, the responses of marine organisms to noise and vibration, impacts by electromagnetic fields, impacts on sea birds, hindrances to sea lane routes, and damaged scenery and marine protection areas are thoroughly assessed during operation processes. The consultation criteria in case of development of offshore wind farm is adjusted by focusing marine environmental impact assessment.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.5
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• pp.2233-2252
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• 2018

For the problem of cross-layer joint resource allocation (JRA) in the Long-Term Evolution (LTE)-Advanced standard using carrier aggregation (CA) technology, it is difficult to obtain the optimal resource allocation scheme. This paper proposes a joint resource allocation algorithm based on the weights of user's average quality of experience (JRA-WQOE). In contrast to prevalent algorithms, the proposed method can satisfy the carrier aggregation abilities of different users and consider user fairness. An optimization model is established by considering the user quality of experience (QoE) with the aim of maximizing the total user rate. In this model, user QoE is quantified by the mean opinion score (MOS) model, where the average MOS value of users is defined as the weight factor of the optimization model. The JRA-WQOE algorithm consists of the iteration of two algorithms, a component carrier (CC) and resource block (RB) allocation algorithm called DABC-CCRBA and a subgradient power allocation algorithm called SPA. The former is used to dynamically allocate CC and RB for users with different carrier aggregation capacities, and the latter, which is based on the Lagrangian dual method, is used to optimize the power allocation process. Simulation results showed that the proposed JRA-WQOE algorithm has low computational complexity and fast convergence. Compared with existing algorithms, it affords obvious advantages such as improving the average throughput and fairness to users. With varying numbers of users and signal-to-noise ratios (SNRs), the proposed algorithm achieved higher average QoE values than prevalent algorithms.

• Smart Structures and Systems
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• v.24 no.5
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• pp.567-585
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• 2019

In the past two decades, structural health monitoring (SHM) systems have been widely installed on various civil infrastructures for the tracking of the state of their structural health and the detection of structural damage or abnormality, through long-term monitoring of environmental conditions as well as structural loadings and responses. In an SHM system, there are plenty of sensors to acquire a huge number of monitoring data, which can factually reflect the in-service condition of the target structure. In order to bridge the gap between SHM and structural maintenance and management (SMM), it is necessary to employ advanced data processing methods to convert the original multi-source heterogeneous field monitoring data into different types of specific physical indicators in order to make effective decisions regarding inspection, maintenance and management. Conventional approaches to data analysis are confronted with challenges from environmental noise, the volume of measurement data, the complexity of computation, etc., and they severely constrain the pervasive application of SHM technology. In recent years, with the rapid progress of computing hardware and image acquisition equipment, the deep learning-based data processing approach offers a new channel for excavating the massive data from an SHM system, towards autonomous, accurate and robust processing of the monitoring data. Many researchers from the SHM community have made efforts to explore the applications of deep learning-based approaches for structural damage detection and structural condition assessment. This paper gives a review on the deep learning-based SHM of civil infrastructures with the main content, including a brief summary of the history of the development of deep learning, the applications of deep learning-based data processing approaches in the SHM of many kinds of civil infrastructures, and the key challenges and future trends of the strategy of deep learning-based SHM.

• Journal of the Korean Society of Propulsion Engineers
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• v.17 no.2
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• pp.71-83
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• 2013

Recently, the advanced condition monitoring methods such as the model-based method and the artificial intelligent method have been applied to maximize the availability as well as to minimize the maintenance cost of the aircraft gas turbines. Among them the non-linear GPA(Gas Path Analysis) method and the GA(Genetic Algorithms) have lots of advantages to diagnose the engines compared to other advanced condition monitoring methods such as the linear GPA, fuzzy logic and neural networks. Therefore this work applies both the non-linear GPA and the GA to diagnose AE3007 turbofan engine for an aircraft, and in case of having sensor noise and bias it is confirmed that the GA is better than the GPA through the comparison of two methods.

• Journal of Convergence for Information Technology
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• v.11 no.11
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• pp.31-37
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• 2021

In this paper, we propose an outlier detection algorithm called C-SCGP to prevent the degradation of localization performance based on RSS (Received Signal Strength) and AOA (Angle of Arrival) in the presence of outliers in wireless sensor networks. Since the accuracy of target estimation can significantly deteriorate due to various cause of outliers such as malfunction of sensor, jamming, and severe noise, it is important to detect and filter out all outliers. The single cluster graph partitioning (SCGP) algorithm has been widely used to remove such outliers. The proposed continuous-SCGP (C-SCGP) algorithm overcomes the weakness of the SCGP that requires the threshold and computing probability of outliers, which are impratical in many applications. The results of numerical simulations show that the performance of C-SCGP without setting threshold and probability computation is the same performance of SCGP.

• Geophysics and Geophysical Exploration
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• v.21 no.4
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• pp.231-243
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• 2018

Streaming potential (SP) arises from fluid flow through effectively connected pores. From this potential, formation water information as well as fluid flow properties can be estimated. As micro particles being located in boundary between subsurface porous media and fluid are charged to form electrical double layer, fluid flow caused by several reasons generates SP, one of electrokinetic phenomena. Occurrence mechanism of SP is complex and signal strength is relatively weak compared to noise. However, application of self potential survey using SP to monitoring of formation fluid is expanding because of its' convenience of exploration without artificial source and repetitiveness of signal. This paper accounts for the occurrence mechanism of SP studied before, including governing equations and analyzes previous various case studies of SP according to the change of physical properties of materials. It helps to increase understanding about SP and also lays the foundations of the application of SP to fields.

• Investigative Magnetic Resonance Imaging
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• v.22 no.4
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• pp.209-217
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• 2018

Purpose: The objective of this study was to obtain improved susceptibility weighted images (SWI) of the cervical spinal cord using respiratory-induced artifact compensation. Materials and Methods: The artifact from $B_0$ fluctuations by respiration could be compensated using a double navigator echo approach. The two navigators were inserted in an SWI sequence before and after the image readouts. The $B_0$ fluctuation was measured by each navigator echoes, and the inverse of the fluctuation was applied to eliminate the artifact from fluctuation. The degree of compensation was quantified using a quality index (QI) term for compensated imaging using each navigator. Also, the effect of compensation was analyzed according to the position of the spinal cord using QI values. Results: Compensation using navigator echo gave the improved visualization of SWI in cervical spinal cord compared to non-compensated images. Before compensation, images were influenced by artificial noise from motion in both the superior (QI = 0.031) and inferior (QI = 0.043) regions. In most parts of the superior regions, the second navigator resulted in better quality (QI = 0.024, P < 0.01) compared to the first navigator, but in the inferior regions the first navigator showed better quality (QI = 0.033, P < 0.01) after correction. Conclusion: Motion compensation using a double navigator method can increase the improvement of the SWI in the cervical spinal cord. The proposed method makes SWI a useful tool for the diagnosis of spinal cord injury by reducing respiratory-induced artifact.

• Structural Engineering and Mechanics
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• v.71 no.4
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• pp.391-405
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• 2019

Compared to the ambient vibration test mainly identifying the structural modal parameters, such as frequency, damping and mode shapes, the impact testing, which benefits from measuring both impacting forces and structural responses, has the merit to identify not only the structural modal parameters but also more detailed structural parameters, in particular flexibility. However, in traditional impact tests, an impacting hammer or artificial excitation device is employed, which restricts the efficiency of tests on various bridge structures. To resolve this problem, we propose a new method whereby a moving vehicle is taken as a continuous exciter and develop a corresponding flexibility identification theory, in which the continuous wheel forces induced by the moving vehicle is considered as structural input and the acceleration response of the bridge as the output, thus a structural flexibility matrix can be identified and then structural deflections of the bridge under arbitrary static loads can be predicted. The proposed method is more convenient, time-saving and cost-effective compared with traditional impact tests. However, because the proposed test produces a spatially continuous force while classical impact forces are spatially discrete, a new flexibility identification theory is required, and a novel structural identification method involving with equivalent load distribution, the enhanced Frequency Response Function (eFRFs) construction and modal scaling factor identification is proposed to make use of the continuous excitation force to identify the basic modal parameters as well as the structural flexibility. Laboratory and numerical examples are given, which validate the effectiveness of the proposed method. Furthermore, parametric analysis including road roughness, vehicle speed, vehicle weight, vehicle's stiffness and damping are conducted and the results obtained demonstrate that the developed method has strong robustness except that the relative error increases with the increase of measurement noise.