Oriental Institute of Technology

Taipei, Taiwan

Oriental Institute of Technology

Taipei, Taiwan
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Patent
Oriental Institute of Technology | Date: 2016-08-09

A waste plastic container recycling system and a method thereof are provided for determining whether the spectrum of a waste container to be recycled belongs to any one of spectrums of plastic materials and for classifying waste containers of different plastic materials since the plastic materials have the different spectral characteristics. Accordingly, the waste plastic container recycling system and the method thereof can accurately recognize the waste containers of different plastic materials regardless of the appearance, or damage, deformation or concealment of the label on the waste containers.


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.


Hong W.-C.,Oriental Institute of Technology
Neurocomputing | Year: 2011

Accurate forecasting of inter-urban traffic flow has been one of the most important issues globally in the research on road traffic congestion. However, the information of inter-urban traffic presents a challenging situation; the traffic flow forecasting involves a rather complex nonlinear data pattern, particularly during daily peak periods, traffic flow data reveals cyclic (seasonal) trend. In the recent years, the support vector regression model (SVR) has been widely used to solve nonlinear regression and time series problems. However, the applications of SVR models to deal with cyclic (seasonal) trend time series have not been widely explored. This investigation presents a traffic flow forecasting model that combines the seasonal support vector regression model with chaotic simulated annealing algorithm (SSVRCSA), to forecast inter-urban traffic flow. Additionally, a numerical example of traffic flow values from northern Taiwan is employed to elucidate the forecasting performance of the proposed SSVRCSA model. The forecasting results indicate that the proposed model yields more accurate forecasting results than the seasonal autoregressive integrated moving average (SARIMA), back-propagation neural network (BPNN) and seasonal Holt-Winters (SHW) models. Therefore, the SSVRCSA model is a promising alternative for forecasting traffic flow. © 2011 Elsevier B.V.


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.


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.


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.


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.


Chen T.-P.,Oriental Institute of Technology
IEEE Transactions on Industrial Electronics | Year: 2012

In this paper, a real-time circulating current reduction method for parallel harmonic-elimination pulsewidth modulation (HEPWM) inverters is proposed. HEPWM techniques are often used in high-capacity inverters. For instance, in a hybrid microgrid, the inverters are employed to transfer power between the dc and ac buses. If the inverters are in parallel operation, the zero-sequence path can be established, and the zero-sequence circulating current will circulate among the inverters. The proposed method installs a cascade null-vector control system behind the conventional three-phase HEPWM modulator. The proposed null-vector control system can be disabled to save switching losses when the zero-sequence circulating current is small, whereas it can be enabled when the zero-sequence circulating current becomes large. The proposed method does not affect the line-to-line voltage waveforms of HEPWM inverters, and it can easily enable/disable the null-vector control system to provide bumpless transfer. Compared with the conventional HEPWM with zero-sequence harmonics elimination, the proposed method can provide an extra 15% modulation index range. Results that were obtained from both simulation and experiments confirmed the performance and effectiveness of the proposed method. © 2011 IEEE.


Pan W.-T.,Oriental Institute of Technology
Knowledge-Based Systems | Year: 2012

The treatment of an optimization problem is a problem that is commonly researched and discussed by scholars from all kinds of fields. If the problem cannot be optimized in dealing with things, usually lots of human power and capital will be wasted, and in the worst case, it could lead to failure and wasted efforts. Therefore, in this article, a much simpler and more robust optimization algorithm compared with the complicated optimization method proposed by past scholars is proposed; the Fruit Fly Optimization Algorithm. In this article, throughout the process of finding the maximal value and minimal value of a function, the function of this algorithm is tested repeatedly, in the mean time, the population size and characteristic is also investigated. Moreover, the financial distress data of Taiwan's enterprise is further collected, and the fruit fly algorithm optimized General Regression Neural Network, General Regression Neural Network and Multiple Regression are adopted to construct a financial distress model. It is found in this article that the RMSE value of the Fruit Fly Optimization Algorithm optimized General Regression Neural Network model has a very good convergence, and the model also has a very good classification and prediction capability. © 2011 Elsevier B.V. All rights reserved.


Patent
Oriental Institute of Technology | Date: 2012-05-25

A chair includes a backrest, and a head and neck support structure arranged at the top side of the backrest for supporting the head and neck of a person resting on the backrest. The head and neck support includes a neck support portion shaped like a convex bar and transversely arranged at the top side of the backrest, and a plurality of radial V-grooves radially upwardly extended from the top side of the neck support portion and inclining upwardly backwardly from the neck support portion in a smooth manner.

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