• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.6
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• pp.2255-2281
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• 2021

Channel characteristic-based physical layer authentication is one potential identity authentication scheme in wireless communication, such as used in a fog computing environment. While existing channel characteristic-based physical layer authentication schemes may be efficient when deployed in the conventional wireless network environment, they may be less efficient and practical for the industrial wireless communication environment due to the varying requirements. We observe that this is a topic that is understudied, and therefore in this paper, we review the constructions and performance of several commonly used test statistics and analyze their performance in typical industrial wireless networks using simulation experiments. The findings from the simulations show a number of limitations in existing channel characteristic-based physical layer authentication schemes. Therefore, we believe that it is a good idea to combine machine learning and multiple test statistics for identity authentication in future industrial wireless network deployment. Four machine learning methods prove that the scheme significantly improves the authentication accuracy and solves the challenge of choosing a threshold.

• International Journal of Computer Science & Network Security
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• v.21 no.4
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• pp.65-74
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• 2021

The rapid change in gold price is an issue of concern in the global economy and financial markets. Gold has been used as a means for trading and transaction around the world for long period of time and it plays an integral role in monetary, business, commercial and financial activities. More importantly, it is used as economic measure for the global economy and will continue to play an important economic vital role - both locally and globally. There has been an explosive growth in demand for efficient and effective scheme to predict gold price due its volatility and fluctuation. Hence, there is need for the development of gold price prediction scheme to assist and support investors, marketers, and financial institutions in making effective economic and monetary decisions. This paper primarily proposed an intelligent based system for predicting and characterizing the gold market trend. The simulation result shows that the proposed intelligent gold price scheme has been able to predict the gold price with high accuracy and precision, and ultimately it has significantly reduced the prediction error when compared to baseline neural network (NN).

• Structural Engineering and Mechanics
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• v.77 no.4
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• pp.495-508
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• 2021

This study devises a novel approach, namely quadruple 1D convolutional neural network, for detecting connection stiffness reduction in steel truss bridge structure using experimental and numerical modal data. The method is developed based on expertise in two domains: firstly, in Structural Health Monitoring, the mode shapes and its high-order derivatives, including second, third, and fourth derivatives, are accurate indicators in assessing damages. Secondly, in the Machine Learning literature, the deep convolutional neural networks are able to extract relevant features from input data, then perform classification tasks with high accuracy and reduced time complexity. The efficacy and effectiveness of the present method are supported through an extensive case study with the railway Nam O bridge. It delivers highly accurate results in assessing damage localization and damage severity for single as well as multiple damage scenarios. In addition, the robustness of this method is tested with the presence of white noise reflecting unavoidable uncertainties in signal processing and modeling in reality. The proposed approach is able to provide stable results with data corrupted by noise up to 10%.

• Geomechanics and Engineering
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• v.20 no.6
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• pp.475-484
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• 2020

The prediction of the ground conditions ahead of a tunnel face is very important, especially for tunnel boring machine (TBM) tunneling, because encountering unexpected anomalies during tunnel excavation can cause a considerable loss of time and money. Several prediction techniques, such as BEAM, TSP, and GPR, have been suggested. However, these methods have various shortcomings, such as low accuracy and low resolution. Most studies on electrical resistivity tomography surveys have been conducted using numerical simulation programs, but laboratory experiments were just a few. Furthermore, most studies of scaled model tests on electrical resistivity tomography were conducted only on the ground surface, which is a different environment as compared to that of mechanized tunneling. This study performed a laboratory experimental test to extend and verify a prediction method proposed by Lee et al., which used electrical resistivity tomography to predict the ground conditions ahead of a tunnel face in TBM tunneling environments. The results showed that the modified dipole-dipole array is better than the other arrays in terms of predicting the location and shape of the anomalies ahead of the tunnel face. Having longer upper and lower borehole lengths led to better accuracy of the survey. However, the number and length of boreholes should be properly controlled according to the field environments in practice. Finally, a modified and verified technique to predict the ground conditions ahead of a tunnel face during TBM tunneling is proposed.

• KIPS Transactions on Computer and Communication Systems
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• v.10 no.9
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• pp.251-260
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• 2021

Gait analysis is one of the research fields for obtaining various information related to gait by analyzing human ambulation. It has been studied for a long time not only in the medical field but also in various academic areas such as mechanical engineering, electronic engineering, and computer engineering. Efforts have been made to determine whether there is a problem with gait through gait analysis. In this paper, as a pre-step to find out gait abnormalities, it is investigated whether it is possible to differentiate whether experiment participants wear elderly simulation suit or not by applying gait data to machine learning models for the same person. For a total of 45 participants, each gait data was collected before and after wearing the simulation suit, and a total of six machine learning models were used to learn the collected data. As a result of using an artificial neural network model to distinguish whether or not the participants wear the suit, it showed 99% accuracy. What this study suggests is that we explored the possibility of judging the presence or absence of abnormality in gait by using machine learning.

• Journal of Electrical Engineering and Technology
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• v.12 no.6
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• pp.2166-2176
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• 2017

Electricity price prediction plays a crucial part in making the schedule and managing the risk to the competitive electricity market participants. However, it is a difficult and challenging task owing to the characteristics of the nonlinearity, non-stationarity and uncertainty of the price series. This study proposes a hybrid improved strategy which incorporates data preprocessor components and a forecasting engine component to enhance the forecasting accuracy of the electricity price. In the developed forecasting procedure, the Seasonal Adjustment (SA) method and the Ensemble Empirical Mode Decomposition (EEMD) technique are synthesized as the data preprocessing component; the Coupled Simulated Annealing (CSA) optimization method and the Least Square Support Vector Regression (LSSVR) algorithm construct the prediction engine. The proposed hybrid approach is verified with electricity price data sampled from the power market of New South Wales in Australia. The simulation outcome manifests that the proposed hybrid approach obtains the observable improvement in the forecasting accuracy compared with other approaches, which suggests that the proposed combinational approach occupies preferable predication ability and enough precision.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2003.10b
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• pp.26-30
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• 2003

Since the dimension of cold forged part is larger than the cavity size of forging die, the difference results from the various features, such as, the elastic characteristics of die and workpiece, thermal influences, and machine-elasticity. All of these factors should be considered to get more accurate prediction of the dimension of forged part. In this paper, severe FE techniques are proposed to improve the prediction accuracy of dimension for cold forged part. To validate the importance of the above mentioned factors, and the estimated results are compared with the experimental results. The used model is a closed die upsetting of cylindrical billet. The calculated dimensions are well coincided with .the measured values based on the proposed techniques. The proposed techniques have put two simple but important points into Fe simulation. One is the separation of forging stages into 3 steps, from a loading through punch retraction to ejecting stage. The other is the dimensional change, according to the temperature changes due to the deformation. The FE analysis could predict the dimension of cold forged part within the $10{\mu}m$, based on the more realistic consideration.

• Current Optics and Photonics
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• v.1 no.5
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• pp.538-543
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• 2017

Progressive addition lenses (PAL) have very wide application in the modern glasses market. The unique progressive surface can make a lens have progressive refractive power, which can meet the human eye's different needs for distance-vision and near-vision. According to the national glasses fabrication standard, the difference between actual optical power after fabrication and nominal design value should be less than 0.1D over the lens effective area. The optical power distribution of PAL is determined directly by the surface. Consequently, the surface processing accuracy requirement is proposed. Beginning from the surface expressions of progressive addition lenses, the relationship equations between the surface sag and optical power distribution are derived. They are demonstrated through tolerance analysis and test of an example progressive addition lens with addition of 2.09D (5.46D-7.55D). The example addition surface is fabricated under given accuracy by a single-point diamond ultra-precision machine. The optical power of the PAL example is tested with a focal-meter after fabrication. The optical power addition difference between test result and design nominal value is 0.09D, which is less than 0.1D. The derived relationship between the surface error and optical power is verified from the PAL example simulation and test result. It can provide theoretical tolerance analysis proof for the PAL surface fabricating process.

• KEPCO Journal on Electric Power and Energy
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• v.4 no.2
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• pp.89-99
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• 2018

Downtime and malfunction of industrial rotor machines represents a crucial cost burden and productivity loss. Fault diagnosis of this equipment has recently been carried out to detect their fault(s) and cause(s) by using fault classification methods. However, these methods are of limited use in detecting rotor faults because of their hypersensitivity to unexpected and different equipment conditions individually. These limitations tend to affect the accuracy of fault classification since fault-related features calculated from vibration signal are moved to other regions or changed. To improve the limited diagnosis accuracy of existing methods, we propose a new approach for fault diagnosis of rotor machines based on the model generated by supervised learning. Our work is based on feature residual values from vibration signals as fault indices. Our diagnostic model is a robust and flexible process that, once learned from historical data only one time, allows it to apply to different target systems without optimization of algorithms. The performance of the proposed method was evaluated by comparing its results with conventional methods for fault diagnosis of rotor machines. The experimental results show that the proposed method can be used to achieve better fault diagnosis, even when applied to systems with different normal-state signals, scales, and structures, without tuning or the use of a complementary algorithm. The effectiveness of the method was assessed by simulation using various rotor machine models.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.4C
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• pp.425-432
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• 2007

In this paper, we deals with automatic modulation classification capable of classifying incident signals without a priori information. The 7 key features which have good properties of sensitive with modulation types and insensitive with SNR variation are selected. The numerical simulations for classifying 9 modulation types using the these features are performed. The numerical simulations of the 4 types of modulation classifiers are performed the investigation of classification accuracy and execution time to implement the fast modulation classifier in software radio. The simulation result indicated that the execution time of DTC was best and SVC and MDC showed good classification performance. The prototype was implemented with DTC type. With the result of field trials, we confirmed the performance in the prototype was agreed with the numerical simulation result of DTC.