• International Journal of Contents
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• 제7권1호
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• pp.14-22
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• 2011

Temporal Sign Language Recognition (TSLR) from hand motion is an active area of gesture recognition research in facilitating efficient communication with deaf people. TSLR systems consist of two stages: a motion sensing step which extracts useful features from signers' motion and a classification process which classifies these features as a performed sign. This work focuses on two of the research problems, namely unknown time varying signal of sign languages in feature extraction stage and computing complexity and time consumption in classification stage due to a very large sign sequences database. In this paper, we propose a combination of Dynamic Time Warping (DTW) and application of the Single hidden Layer Feedforward Neural networks (SLFNs) trained by Extreme Learning Machine (ELM) to cope the limitations. DTW has several advantages over other approaches in that it can align the length of the time series data to a same prior size, while ELM is a useful technique for classifying these warped features. Our experiment demonstrates the efficiency of the proposed method with the recognition accuracy up to 98.67%. The proposed approach can be generalized to more detailed measurements so as to recognize hand gestures, body motion and facial expression.

• Journal of Electrical Engineering and Technology
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• 제11권1호
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• pp.29-37
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• 2016

Fast and accurate fault diagnosis of the position sensor is of great significance to ensure the reliability as well as sensor fault tolerant operation of the Switched Reluctance Wind Generator (SRWG). This paper presents a fault diagnostic scheme for a SRWG based on the residual between the estimated rotor position and the actual output of the position sensor. Extreme Learning Machine (ELM), which could build a nonlinear mapping among flux linkage, current and rotor position, is utilized to design an assembled estimator for the rotor position detection. The data for building the ELM based assembled position estimator is derived from the magnetization curves which are obtained from Finite Element Analysis (FEA) of an SRWG with the structure of 8 stator poles and 6 rotor poles. The effectiveness and accuracy of the proposed fault diagnosis method are verified by simulation at various operating conditions. The results provide a feasible theoretical and technical basis for the effective condition monitoring and predictive maintenance of SRWG.

• Journal of Electrical Engineering and Technology
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• 제9권6호
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• pp.2142-2153
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• 2014

Linearization of transducer characteristic plays a vital role in electronic instrumentation because all transducers have outputs nonlinearly related to the physical variables they sense. If the transducer output is nonlinear, it will produce a whole assortment of problems. Transducers rarely possess a perfectly linear transfer characteristic, but always have some degree of non-linearity over their range of operation. Attempts have been made by many researchers to increase the range of linearity of transducers. This paper presents a method to compensate nonlinearity of Linear Variable Displacement Transducer (LVDT) based on Extreme Learning Machine (ELM) method, Differential Evolution (DE) algorithm and Artificial Neural Network (ANN) trained by Genetic Algorithm (GA). Because of the mechanism structure, LVDT often exhibit inherent nonlinear input-output characteristics. The best approximation capability of optimized ANN technique is beneficial to this. The use of this proposed method is demonstrated through computer simulation with the experimental data of two different LVDTs. The results reveal that the proposed method compensated the presence of nonlinearity in the displacement transducer with very low training time, lowest Mean Square Error (MSE) value and better linearity. This research work involves less computational complexity and it behaves a good performance for nonlinearity compensation for LVDT and has good application prospect.

• Environmental Engineering Research
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• 제22권3호
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• pp.302-311
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• 2017

Nowadays, with the burgeoning development of economy, $CO_2$ emissions increase rapidly in China. It has become a common concern to seek effective methods to forecast $CO_2$ emissions and put forward the targeted reduction measures. This paper proposes a novel hybrid model combined principal component analysis (PCA) with regularized extreme learning machine (RELM) to make $CO_2$ emissions prediction based on the data from 1978 to 2014 in China. First eleven variables are selected on the basis of Pearson coefficient test. Partial autocorrelation function (PACF) is utilized to determine the lag phases of historical $CO_2$ emissions so as to improve the rationality of input selection. Then PCA is employed to reduce the dimensionality of the influential factors. Finally RELM is applied to forecast $CO_2$ emissions. According to the modeling results, the proposed model outperforms a single RELM model, extreme learning machine (ELM), back propagation neural network (BPNN), GM(1,1) and Logistic model in terms of errors. Moreover, it can be clearly seen that ELM-based approaches save more computing time than BPNN. Therefore the developed model is a promising technique in terms of forecasting accuracy and computing efficiency for $CO_2$ emission prediction.

• 지식경영연구
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• 제14권4호
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• pp.1-17
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• 2013

As online communities proliferate, online news sites have received great attention in news media research. Although most of the online news sites provide contents for free, some have adopted the Pay-What-You-Want (PWYW) model by offering a voluntary payment option to the readers. In this study, we investigate the factors which influence subscribers' voluntary payment behavior on an online news site. Drawing upon both the Stimulus-Organism-Response (SOR) framework and the Elaboration Likelihood Model (ELM), we hypothesize that appreciation has a direct effect on the subscribers' voluntary payment behavior, whereas central factors (positive emotional content, cognitive content) and peripheral factors (news sharing, news article length) of the news articles have indirect impacts on voluntary payment behavior through the enhanced appreciation. Based on an empirical analysis of 172 news articles from the Korean online news site that adopted the PWYW pricing model (i.e., Ohmynews.com), we find that appreciation plays a critical role in voluntary payment behavior and that peripheral factors have significant impacts on appreciation. However, the impacts of central factors on appreciation are not found. By identifying influencing factors of subscribers' voluntary payment behavior on online news sites for the first time, this paper suggests a prospective alternative profit model for online news providers faced with fierce competition.

• Asia pacific journal of information systems
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• 제24권1호
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• pp.65-91
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• 2014

Our study adopted ELM (Elaboration Likelihood Model) to measure the impact of central and peripheral cues on e-healthcare website behavior and its consequence on perceived loyalty of users. While most of ELM studies did not elaborate the antecedent of both central and peripheral cues, we measured the antecedents of those information processing routes to clarify how technical and quality factors (i.e. information organization, security concern, and website attractiveness) develop the nature of either central or peripheral route. We found that information organization was the main antecedent of information quality presented on the website. Second, the results revealed that website security has a positive effect on website credibility. Third, we also found that website attractiveness was positively associated with website credibility. Fourth, consistent with elaboration likelihood model, the empirical findings suggested that information quality (central cue) and website credibility (peripheral cue) were strong predictors of behavior intention to use health website. Our findings also suggested that behavior intention to use health website significantly influenced perceived loyalty.

• Membrane and Water Treatment
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• 제4권1호
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• pp.11-25
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• 2013

Emulsion liquid membrane (ELM) process suffers from emulsion instability problem. So far, emulsion produced by mechanical methods such as stirrer and homogenizer has big size and high emulsion breakage. This paper discussed the application of emulsion produced by sonicator to extract cadmium in a batch ELM system. The emulsions consist of N,N-Dioctyl-1-octanamine (trioctylamine/TOA), nitrogen trihydride (ammonia/NH4OH), sorbitan monooleate (Span 80), and kerosene as carrier, stripping solution, emulsifying agent, and organic diluent, respectively. Effects of comprehensive parameters on extraction efficiency of Cd(II) such as emulsification time, extraction time, stirring speed, surfactant concentration, initial feed phase concentration, carrier concentration, volume ratio of the emulsion to feed phase, and pH of initial feed phase were evaluated. The results showed that extraction efficiencies of Cd(II) greater than 98% could be obtained under the following conditions: 15 minutes of emulsification time, 4 wt.% of Span 80 concentration, 4 wt.% of TOA concentration, 15 minutes of extraction time, 250 rpm of stirring speed, 100 ppm of initial feed concentration, volume ratio of emulsion to feed phase of 1:5, and initial feed pH of 1.53.

• 미생물학회지
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• 제19권4호
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• pp.163-173
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• 1981

The distribution of methanol-assimilating yeasts on three different sources (elm bark, soil and fresh-water mud) and the growth conditions of a new strain of Candidaboidinii (SIO) wereexamines. From 150 samples, 91 methanol yeasts were isolated through enrichment culture ; they were identified as 77 strains of Candida boidinii including four new strains, 5 isolates of Torulopsis pinus, 3 strains of Hansenula polymorpha and one sstrain of Pichia pastoris respectively. The comparison of these yeasts with three sources indicated that decaying bark of elm tree other two, and that Gandida boidinii was most frequently distributed in all three sources. Four new strains of Candida boidinii were freshly isolated and their taxonomical properties were discussed. Of them, SIO strain was selected and characterized for its growth on methanol. This yeast could grow well on less than 1%(v/v) methanol. However, its growth was inhibited at 10% methanol. The cell yield was 3.1g (dry weight) per 1000ml of mineral mediurr, containing 1%(v/v) methanol as well as 01.% yeast extract as additive. The concentration of 0.1% yeast extract appears to be effective for the biomass production. Optimum conditions for growth on methanol was found to be : $28^{\circ}C,\;NH_4^+$ as nitrogen sources, thiamine as vitamin, and pH 4.5 to 6.0. The cell composition was as follows : crude protein and nucleic acids were 54% and 7% respectively. The amino acids were also described.

• Nuclear Engineering and Technology
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• 제51권7호
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• pp.1860-1865
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• 2019

The confinement degradation of the energetic particles during RMP would be a key issue in success of realizing the successful energy production using fusion plasma, because a 3.5 MeV energetic alpha particle should be able to sustain the burning plasma after the ignition. As KSTAR recent results indicate the generation of high-performance plasma(${\beta}_p{\sim}3$), the confinement of the energetic particles is also an important key aspect in neutral beam driven plasma. In general, the measured absolute value of the neutron intensity is generally used for to estimating the confinement time of energetic particles by comparing it with the theoretical value based on transport calculations. However, the availability of, but for its calculation process, many accurate diagnostic data of plasma parameters such as thermal and incident fast ion density, are essential to the calculation process. In this paper, the time evolution of the neutron signal from an He3 counter during the beam blank has permitted to facilitate the estimation of the slowing down time of energetic particles and the method is applied to investigate the fast ion effect on ELM suppressed KSTAR plasma which is heated by high energy deuterium neutral beams.

• Structural Engineering and Mechanics
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• 제70권5호
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• pp.639-647
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• 2019

The performance of precast concrete structures is greatly influenced by the behaviour of beam-to-column connections. A single connection may be required to transfer several loads simultaneously so each one of those loads must be considered in the design. A good connection combines practicality and economy, which requires an understanding of several factors; including strength, serviceability, erection and economics. This research work focuses on the performance aspect of a specific type of beam-to-column connection using partly hidden corbel in precast concrete structures. In this study, the results of experimental assessment of the proposed beam-to-column connection in precast concrete frames was used. The purpose of this research is to develop and apply the Extreme Learning Machine (ELM) for moment-rotation prediction of precast beam-to-column connections. The ELM results are compared with genetic programming (GP) and artificial neural network (ANN). The reliability of the computational models was accessed based on simulation results and using several statistical indicators.