• Korean Journal of Remote Sensing
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• v.35 no.1
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• pp.93-115
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• 2019

The availability of high-resolution laser scanning data and advanced machine learning algorithms has enabled an accurate potential rockfall source identification. However, the presence of other mass movements, such as landslides within the same region of interest, poses additional challenges to this task. Thus, this research presents a method based on an integration of Gaussian mixture model (GMM) and ensemble artificial neural network (bagging ANN [BANN]) for automatic detection of potential rockfall sources at Kinta Valley area, Malaysia. The GMM was utilised to determine slope angle thresholds of various geomorphological units. Different algorithms(ANN, support vector machine [SVM] and k nearest neighbour [kNN]) were individually tested with various ensemble models (bagging, voting and boosting). Grid search method was adopted to optimise the hyperparameters of the investigated base models. The proposed model achieves excellent results with success and prediction accuracies at 95% and 94%, respectively. In addition, this technique has achieved excellent accuracies (ROC = 95%) over other methods used. Moreover, the proposed model has achieved the optimal prediction accuracies (92%) on the basis of testing data, thereby indicating that the model can be generalised and replicated in different regions, and the proposed method can be applied to various landslide studies.

• Journal of Korea Multimedia Society
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• v.22 no.6
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• pp.676-683
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• 2019

Wireless Sensor Networks (WSNs) consist of multiple tiny and power constrained sensors which use radio frequencies to carry out sensing in a designated sensor area. To effectively design and implement reliable WSN, it is critical to consider models, protocols, and algorithms that can optimize energy consumption of all the sensor nodes with optimal amount of packet delivery. It has been observed that deploying a single sink node comes with numerous challenges especially in a situation with high node density and congestion. Sensor nodes close to a single sink node receive more transmission traffic load compared to other sensors, thus causing quick depletion of energy which finally leads to an energy hole and sink hole problems. In this paper, we proposed the use of multiple energy efficient sink nodes with brute force technique under optimized parameters to improve on the number of packets delivered within a given time. Simulation results not only depict that, deploying N sink nodes in a sensor area has a maximum limit to offer a justifiable improvement in terms of packet delivery ratio but also offers a reduction in End to End delay and reliability in case of failure of a single sink node, and an improvement in the network lifetime rather than deploying a single static sink node.

• Earthquakes and Structures
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• v.17 no.2
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• pp.205-219
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• 2019

In Peru, construction of dwellings using confined masonry walls (CM) has a high percentage of acceptance within many sectors of the population. It is estimated that only in Lima, 80% of the constructions use CM and at least 70% of these are informal constructions. This mean that they are built without proper technical advice and generally have a high seismic vulnerability. One way to reduce this vulnerability is by reinforcing the walls. However, despite the existence of some reinforcement methods in the market, not all of them can be applied massively because there are other parameters to take into account, as economical, criteria for seismic improvement, reinforcement ratio, etc. Therefore, in this paper the feasibility of using five reinforcement techniques has been studied and compared. These reinforcements are: welded mesh (WM), glass fiber reinforced polymer (GFRP), carbon fiber reinforced polymer (CFRP), steel bar wire mesh (CSM), steel reinforced grout (SRG). The Multi-Criteria Decision Making (MCDM) method can be useful to evaluate the most optimal strengthening technique for a fast, effective and massive use plan in Peru. The results of using MCDM with 10 criteria indicate that the Carbon Fiber Reinforced Polymer (CFRP) and Steel Reinforced Grout (SRG) methods are the most suitable for a massive reinforcement application in Lima.

• Journal of the Society of Naval Architects of Korea
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• v.56 no.2
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• pp.161-167
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• 2019

In general, fatigue analysis is performed by using deterministic model to estimate the optimal parameters. However, the deterministic model is difficult to clearly describe the physical phenomena of fatigue failure that contains many uncertainty factors. With regard to this, efforts have been made in this research to compare with the deterministic model and the stochastic models. Firstly, One deterministic S-N curve was derived from ordinary least squares technique and two P-S-N curves were estimated through Bayesian-linear regression model and Markov-Chain Monte Carlo simulation. Secondly, the distribution of Long-term fatigue damage and fatigue life were predicted by using the parameters obtained from the three methodologies and the long-term stress distribution.

• Journal of Powder Materials
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• v.28 no.4
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• pp.325-330
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• 2021

In this study, a ZnS film of 8-mm thickness was prepared on graphite using a hot-wall-type CVD technique. The ZnS thick film was then hot isostatically pressed under different pressures (125-205 MPa) in an argon atmosphere. The effects of pressure were systematically studied in terms of crystallographic orientation, grain size, density, and transmittance during the HIP process. X-ray diffraction pattern analysis revealed that the preferred (111) orientation was well developed after a pressure of 80 MPa was applied during the HIP process. A high transmittance of 61.8% in HIP-ZnS was obtained under the optimal conditions (1010℃, 205 MPa, 6 h) as compared with a range of approximately 10% for the CVD-ZnS thick film under a 550-nm wavelength. In addition, the main cause of the improvement in transmittance was determined to be the disappearance of the scattering factor due to grain growth and the increase in density.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.33 no.6
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• pp.351-358
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• 2020

This paper presents a tuned mass damper (TMD) utilizing a parametric design technique to reduce the dynamic responses to seismic loads of retractable large spatial structures. An artificial intelligence algorithm was developed to automatically search for the installation position of the damping device. This enables confirming the dynamic response of the structure in real time while finding the optimum position for the damping device. Further, the optimum mass of the damping device is determined from among several alternatives, and a design that can be effectively applied to both open and closed conditions of the roof is obtained.

• Journal of IKEEE
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• v.25 no.1
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• pp.83-87
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• 2021

In harsh environments such as fog or fine dust, the cameras' detection ability for object recognition may significantly decrease. In order to accurately obtain important information even in bad weather, fog removal algorithms are necessarily required. Research has been conducted in various ways, such as computer vision/data-based fog removal technology. In those techniques, estimating the amount of fog through the input image's depth information is an important procedure. In this paper, a linear model is presented under the assumption that the image dark channel dictionary, saturation ∗ value, and sharpness characteristics are linearly related to depth information. The proposed method of haze removal through a linear model shows the superiority of algorithm performance in quantitative numerical evaluation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.6
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• pp.2204-2224
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• 2021

This paper studies secure wireless transmission from a multi-antenna transmitter to a single-antenna intended receiver overheard by multiple eavesdroppers with considering the imperfect channel state information (CSI) of wiretap channel. To enhance security of communication link, the artificial noise (AN) is generated at transmitter. We first design the robust joint optimal beamforming of secret signal and AN to minimize transmit power with constraints of security quality of service (QoS), i.e., minimum allowable signal-to-interference-and-noise ratio (SINR) at receiver and maximum tolerable SINR at eavesdroppers. The formulated design problem is shown to be nonconvex and we transfer it into linear matrix inequalities (LMIs). The semidefinite relaxation (SDR) technique is used and the approximated method is proved to solve the original problem exactly. To verify the robustness and tightness of proposed beamforming, we also provide a method to calculate the worst-case SINR at eavesdroppers for a designed transmit scheme using semidefinite programming (SDP). Additionally, the secrecy rate maximization is explored for fixed total transmit power. To tackle the nonconvexity of original formulation, we develop an iterative approach employing sequential parametric convex approximation (SPCA). The simulation results illustrate that the proposed robust transmit schemes can effectively improve the transmit performance.

• Asia-Pacific Journal of Atmospheric Sciences
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• v.54 no.4
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• pp.527-544
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• 2018

This paper presents a nighttime sea fog detection algorithm incorporating unsupervised learning technique. The algorithm is based on data sets that combine brightness temperatures from the $3.7{\mu}m$ and $10.8{\mu}m$ channels of the meteorological imager (MI) onboard the Communication, Ocean and Meteorological Satellite (COMS), with sea surface temperature from the Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA). Previous algorithms generally employed threshold values including the brightness temperature difference between the near infrared and infrared. The threshold values were previously determined from climatological analysis or model simulation. Although this method using predetermined thresholds is very simple and effective in detecting low cloud, it has difficulty in distinguishing fog from stratus because they share similar characteristics of particle size and altitude. In order to improve this, the unsupervised learning approach, which allows a more effective interpretation from the insufficient information, has been utilized. The unsupervised learning method employed in this paper is the expectation-maximization (EM) algorithm that is widely used in incomplete data problems. It identifies distinguishing features of the data by organizing and optimizing the data. This allows for the application of optimal threshold values for fog detection by considering the characteristics of a specific domain. The algorithm has been evaluated using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) vertical profile products, which showed promising results within a local domain with probability of detection (POD) of 0.753 and critical success index (CSI) of 0.477, respectively.

• Korean Journal of Metals and Materials
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• v.46 no.7
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• pp.464-470
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• 2008

Metallic nanostructures have great potential for bio-chemical sensor applications due to the excitation of localized surface plasmon and its sensitive response to environmental change. Unlike the commonly explored absorption-based sensing, the optical scattering provides single particle detection scheme. For the localized surface plasmon resonance spectroscopy, the metallic nanostructures with controlled shape and size have been usually fabricated on adhesion-layer pre-coated transparent glass substrates. In this study, we calculated the optical scattering properties of plasmonic Au nanodisc using a discrete dipole approximation method and analyzed the effect of adhesion layer on them. Our result also indicates that there is a trade-off between the surface plasmon damping and the capability of supporting nanostructures in determining the optimal thickness of adhesion layer. Marginal thickness of Ti adhesion layer for supporting Au nanostructures fabricated on a silica glass substrate was experimentally analyzed by an adhesion strength test using a nano-indentation technique.