• Current Optics and Photonics
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• v.7 no.6
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• pp.708-713
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• 2023

A robust all-fiber nonlinear amplifying loop-mirror-based mode-locked femtosecond laser is demonstrated. Power-dependent nonlinear phase shift in a Sagnac loop enables stable and power-efficient mode-locking working as an artificial saturable absorber. The pump power is adjusted to achieve the lowest intensity noise for stable long-term operation. The minimum pump power for mode-locking is 180 mW, and the optimal pump power is 300 mW. The lowest integrated root-mean-square relative intensity noise of a free-running mode-locked laser is 0.009% [integration bandwidth: 1 Hz-10 MHz]. The long-term repetition-rate instability of a free-running mode-locked laser is 10^-7 over 1,000 s averaging time. The repetition-rate phase noise scaled at 10-GHz carrier is -122 dBc/Hz at 10 kHz Fourier frequency. The demonstrated method can be applied as a seed source in high-precision real-time mid-infrared molecular spectroscopy.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.2
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• pp.907-923
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• 2020

For the severe haze situation in the Beijing-Tianjin-Hebei region, conventional fine particulate matter (PM2.5) concentration prediction methods based on pollutant data face problems such as incomplete data, which may lead to poor prediction performance. Therefore, this paper proposes a method of predicting the PM2.5 concentration based on image analysis technology that combines image data, which can reflect the original weather conditions, with currently popular machine learning methods. First, based on local parameter estimation, autoregressive (AR) model analysis and local estimation of the increase in image blur, we extract features from the weather images using an approach inspired by free energy and a no-reference robust metric model. Next, we compare the coefficient energy and contrast difference of each pixel in the AR model and then use the percentages to calculate the image sharpness to derive the overall mass fraction. Furthermore, the results are compared. The relationship between residual value and PM2.5 concentration is fitted by generalized Gauss distribution (GGD) model. Finally, nonlinear mapping is performed via the wavelet neural network (WNN) method to obtain the PM2.5 concentration. Experimental results obtained on real data show that the proposed method offers an improved prediction accuracy and lower root mean square error (RMSE).

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.9
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• pp.791-796
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• 2013

This paper proposes a fuzzy logic gain scheduling method for depth controller of the AUV (Autonomous Underwater Vehicle). Gains of depth controller are calculated by using multi-loop root locus technique. Fuzzy logic based gain scheduling approach is used to modify multi-loop gains as control condition. It is illustrated by simulations that the proposed fuzzy logic gain scheduling method yields smaller rising time and overshoot compared to the fixed-gain controller. Finally, being implemented on real hardwares, all the proposed algorithms are validated with integrations of hardware and software altogether by HILS.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.8
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• pp.3779-3784
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• 2012

Real-time process monitoring and diagnosis of industrial processes is one of important operational tasks for quality and safety reasons. The objective of fault diagnosis or identification is to find process variables responsible for causing a specific fault in the process. This helps process operators to investigate root causes more effectively. This work assesses the applicability of combining a nonlinear statistical technique of kernel Fisher discriminant analysis with a preprocessing method as a tool of on-line fault identification. To compare its performance to existing linear principal component analysis (PCA) identification scheme, a case study on a benchmark process was performed to show that the fault identification scheme produced more reliable diagnosis results than linear method.

• Journal of Internet Computing and Services
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• v.25 no.2
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• pp.57-67
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• 2024

This paper explores the potential of electromyography (EMG) as a means of gesture recognition for user input in gesture-based interaction. EMG utilizes small electrodes within muscles to detect and interpret user movements, presenting a viable input method. To classify user gestures based on EMG data, machine learning techniques are employed, necessitating the preprocessing of raw EMG data to extract relevant features. EMG characteristics can be expressed through formulas such as Integrated EMG (IEMG), Mean Absolute Value (MAV), Simple Square Integral (SSI), Variance (VAR), and Root Mean Square (RMS). Additionally, determining the suitable time for gesture classification is crucial, considering the perceptual, cognitive, and response times required for user input. To address this, segment sizes ranging from a minimum of 100ms to a maximum of 1,000ms are varied, and feature extraction is performed to identify the optimal segment size for gesture classification. Notably, data learning employs overlapped segmentation to reduce the interval between data points, thereby increasing the quantity of training data. Using this approach, the paper employs four machine learning models (KNN, SVC, RF, XGBoost) to train and evaluate the system, achieving accuracy rates exceeding 96% for all models in real-time gesture input scenarios with a maximum segment size of 200ms.

• Journal of the Korea Safety Management & Science
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• v.4 no.2
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• pp.11-22
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• 2002

In this study, an asymmetric lifting posture prediction model was developed, which was a three-dimensional model with 12 links and 23 degrees of freedom open kinematic chains. Although previous researchers have proposed biomechanical, psychophysical, or physiological measures as cost functions, for solving redundancy, they lack in accuracy in predicting actual lifting postures and most of them are confined to the two-dimensional model. To develop an asymmetric lifting posture prediction model, we used the resolved motion method for accurately simulating the lifting motion in a reasonable time. Furthermore, in solving the redundant problem of the human posture prediction, a moment weighted Joint Range Availability (JRA) was used as a cost function in order to consider dynamic lifting. However, it is known that the moment weighted JRA as a cost function predicted the lower extremity and L5/S1 joint motions better than the upper extremities, while the constant weighted JRA as a cost function predicted the latter better than the former. To compensate for this, we proposed a hybrid moment weighted JRA as a new cost function with moment weighted for only the lower extremity. In order to validate the proposed cost function, the predicted and real lifting postures for various lifting conditions were compared by using the root mean square(RMS) error. This hybrid JRA reduced RMS more than the previous cost functions. Therefore, it is concluded that the cost function of a hybrid moment weighted JRA can be used to predict three-dimensional lifting postures. To compare with the predicted trajectories and the real lifting movements, graphical validations were performed. The results also showed that the hybrid moment weighted cost function model was found to have generated the postures more similar to the real movements.

• The Plant Pathology Journal
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• v.39 no.4
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• pp.309-318
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• 2023

Huanglongbing (HLB) is one of the most destructive diseases in citrus, which imperils the sustainability of citriculture worldwide. The presumed causal agent of HLB, 'Candidatus Liberibacter asiaticus' (CLas) is a non-culturable phloem-limited α-proteobacterium transmitted by Asian citrus psyllids (ACP, Diaphorina citri Kuwayama). A widely adopted method for HLB diagnosis is based on quantitative real-time polymerase chain reaction (qPCR). Although HLB diagnostic qPCR provides high sensitivity and good reproducibility, it is limited by time-consuming DNA preparation from plant tissue or ACP and the requirement of proper lab instruments including a thermal cycler to conduct qPCR. In an attempt to develop a quick assay that can be deployed in the field for CLas detection, we developed a real-time loop-mediated isothermal amplification (rt-LAMP) assay by targeting the CLas five copy nrdB gene. The rt-LAMP assay using various plant sample types and psyllids successfully detected the nrdB target as low as ~2.6 Log10 copies. Although the rt-LAMP assay was less sensitive than laboratory-based qPCR (detection limit ~10 copies), the data obtained with citrus leaf and bark and ACP showed that the rt-LAMP assay has >96% CLas detection rate, compared to that of laboratory-based qPCR. However, the CLas detection rate in fibrous roots was significantly decreased compared to qPCR due to low CLas titer in some root DNA sample. We also demonstrated that the rt-LAMP assay can be used with a crude leaf DNA extract which is fully deployable in the field for quick and reliable HLB screening.

• Water for future
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• v.28 no.5
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• pp.219-233
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• 1995

A non-stationary multivariate model is selected in which the mean and variance of rainfall are not temporally or spatially constant. And the rainfall prediction system is constructed which uses the recursive estimation algorithm, Kalman filter, to estimate system states and parameters of rainfall model simulataneously. The on-line, real-time, multivariate short-term, rainfall prediction for multi-stations and lead-times is carried out through the estimation of non-stationary mean and variance by the storm counter method, the normalized residual covariance and rainfall speed. The results of rainfall prediction system model agree with those generated by non-stationary multivariate model. The longer the lead time is, the larger the root mean square error becomes and the further the model efficiency decreases form 1. Thus, the accuracy of the rainfall prediction decreases as the lead time gets longer. Also it shows that the mean obtained by storm counter method constitutes the most significant part of the rainfall structure.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.3
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• pp.34-41
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• 2014

Through applying hostile radar signals that use stagger PRI to PRI transform in real time, we can analyze stagger PRI and calculate the future TOA for active cancellation by using measured TOA and estimated PRI. We shows the effect of the errors that are contained in PRI and measured TOA. Then, it will suggest the technique to improve the accuracy of estimated PRI and the TOA averaging method for reducing the effect of measured TOA error. Finally, we will show that accuracy of estimated future TOA that is calculated by proposed scheme is higher than that of future TOA that is simply calculated with TDOA and newest TOA through comparing RMSE performance.

• Speech Sciences
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• v.10 no.4
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• pp.7-21
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• 2003

Sums-of-Products models were built for segment duration prediction of spoken Korean. An experiment for the modelling was carried out to apply the results to Korean text-to-speech synthesis systems. 670 read sentences were analyzed. trained and tested for the construction of the duration models. Traditional sequential rule systems were extended to simple additive, multiplicative and additive-multiplicative models based on Sums-of-Products modelling. The parameters used in the modelling include the properties of the target segment and its neighbors and the target segment's position in the prosodic structure. Two optimisation strategies were used: the downhill simplex method and the simulated annealing method. The performance of the models was measured by the correlation coefficient and the root mean squared prediction error (RMSE) between actual and predicted duration in the test data. The best performance was obtained when the data was trained and tested by ' additive-multiplicative models. ' The correlation for the vowel duration prediction was 0.69 and the RMSE. 31.80 ms. while the correlation for the consonant duration prediction was 0.54 and the RMSE. 29.02 ms. The results were not good enough to be applied to the real-time text-to-speech systems. Further investigation of feature interactions is required for the better performance of the Sums-of-Products models.