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Wang M.-W.,Oriental Institute of Technology
Electrophoresis | Year: 2012

To sort and separate erythrocytes contaminated by lead (II) from whole bloodstream flow, the first step is to use a microchannel to transport the blood cells into a microdevice. Within the device, polluted erythrocytes can be separated from the bloodstream by applying local dielectrophoretic (DEP) forces. Exploiting the fact that Pb2+ ions attach to the membranes of the erythrocytes, we utilize the microfluidic DEP device to perform property-based fractionation of the blood samples and to separate the polluted erythrocytes from the continuous bloodstream flow. Atomic absorption spectrometer analysis reveals that, to remove lead-polluted erythrocytes, the most effective driving velocity was less than 0.1 cm/s through our microfluidic DEP device, based on an applied power of 10 Vpeak-peak and a frequency of 15.5 MHz AC field. We were able to remove 80% of the polluted erythrocytes. Using gentle DEP manipulating techniques to efficiently sort unique cells within a complex biological sample may potentially allow biological sorting to be performed outside of hospitals, in facilities without biological analyzing equipment. © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim. Source


Support vector regression (SVR), with hybrid chaotic sequence and evolutionary algorithms to determine suitable values of its three parameters, not only can effectively avoid converging prematurely (i.e., trapping into a local optimum), but also reveals its superior forecasting performance. Electric load sometimes demonstrates a seasonal (cyclic) tendency due to economic activities or climate cyclic nature. The applications of SVR models to deal with seasonal (cyclic) electric load forecasting have not been widely explored. In addition, the concept of recurrent neural networks (RNNs), focused on using past information to capture detailed information, is helpful to be combined into an SVR model. This investigation presents an electric load forecasting model which combines the seasonal recurrent support vector regression model with chaotic artificial bee colony algorithm (namely SRSVRCABC) to improve the forecasting performance. The proposed SRSVRCABC employs the chaotic behavior of honey bees which is with better performance in function optimization to overcome premature local optimum. A numerical example from an existed reference is used to elucidate the forecasting performance of the proposed SRSVRCABC model. The forecasting results indicate that the proposed model yields more accurate forecasting results than ARIMA and TF-ε-SVR-SA models. Therefore, the SRSVRCABC model is a promising alternative for electric load forecasting. © 2011 Elsevier Ltd. Source


Chang J.-L.,Oriental Institute of Technology
International Journal of Robust and Nonlinear Control | Year: 2012

SUMMARY This paper addresses the problem of designing a dynamic output feedback sliding mode control algorithm for linear MIMO systems with mismatched parameter uncertainties along with disturbances and matched nonlinear perturbations. Once the system is in the sliding mode, the proposed output-dependent integral sliding surface can robustly stabilize the closed-loop system and obtain the desired system performance. Two types of mismatched disturbances are considered and their effects on the sliding mode are explored. By introducing an additional dynamics into the controller design, the developed control law can guarantee that the system globally reaches and is maintained on the sliding surface in finite time. Finally, the feasibility of the proposed method is illustrated by numerical examples. © 2011 John Wiley & Sons, Ltd. Source


Hong W.-C.,Oriental Institute of Technology
Energy Policy | Year: 2010

Support vector regression (SVR) had revealed strong potential in accurate electric load forecasting, particularly by employing effective evolutionary algorithms to determine suitable values of its three parameters. Based on previous research results, however, these employed evolutionary algorithms themselves have several drawbacks, such as converging prematurely, reaching slowly the global optimal solution, and trapping into a local optimum. This investigation presents an SVR-based electric load forecasting model that applied a novel algorithm, namely chaotic ant swarm optimization (CAS), to improve the forecasting performance by searching its suitable parameters combination. The proposed CAS combines with the chaotic behavior of single ant and self-organization behavior of ant colony in the foraging process to overcome premature local optimum. The empirical results indicate that the SVR model with CAS (SVRCAS) results in better forecasting performance than the other alternative methods, namely SVRCPSO (SVR with chaotic PSO), SVRCGA (SVR with chaotic GA), regression model, and ANN model. © 2010 Elsevier Ltd. All rights reserved. Source


Ting Y.-L.,Oriental Institute of Technology
Computers and Education | Year: 2013

Numerous studies have proposed and implemented various innovative designs of mobile learning practices, and several pedagogical affordances of mobile technologies in different subject domains have also been suggested. This study proposes a notion for helping instructors design an innovative mobile learning practice in their subject domain. The proposed design notion, interwoven learning interactions, means that the mobile technologies unobtrusively record specific type of social interactions among learners as digital information, and the digital information is synthesized with the rules and principles of subject content to represent the instructional information. When social interaction among peers enables their stimulation and exploration of subject content as well as display of students' thoughts and reasoning, learning is then constructed on the realms of both physical and social experiences linked with the abstract learning content. To illustrate the proposed notion, a sample design is provided and evaluated. The pilot studies and evaluation results illustrate a clearer picture of the design guidelines and offer supporting evidences of the claimed learning benefits. Implications are provided to shed light on this innovative mobile learning design. © 2012 Elsevier Ltd. All rights reserved. Source

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