Browsing by Author "Liu, CS"

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Highly efficient red phosphorescent osmium(II) complexes for OLED applications
(2004-07-19)
Tung, YL
;
Wu, PC
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Liu, CS
;
Chi, Y
;
Yu, JK
The nonionic, red-emitting complexes [Os(fppz)(2)L-2] (L = PPh2Me (1), PPhMe2 (2)) and [Os(bptz)(2)L-2] (L = PPh2Me (3)) were synthesized, showing highly intense red phosphorescent emission in CH2Cl2 solution at lambda(max) 617, 632, and 649 nm, respectively. The electroluminescent properties of these compounds on OLEDs showed promising device efficiencies required for future OLED applications.
Iridium(III) complexes with orthometalated quinoxaline ligands: Subtle tuning of emission to the saturated red color
(2005-03-07)
Hwang, FM
;
Chen, HY
;
Chen, PS
;
Liu, CS
;
Chi, Y
Rational design and syntheses of four iridium complexes (1-4) bearing two substituted quinoxalines and an additional 5-(2-pyridyl) pyrazolate or triazolate as the third coordinating ligand are reported. Single-crystal X-ray diffraction studies of 1 reveal a distorted octahedral geometry, in which two dpqx ligands adopt an eclipse configuration, for which the quinoxaline N atoms and the C atoms of orthometalated phenyl groups are located at the mutual trans-and cis-positions, respectively. The lowest absorption band for all complexes consists of a mixture of heavy-atom Ir(III)-enhanced (MLCT)-M-3 and (3)pipi(*) transitions, and the phosphorescent peak wavelength can be fine-tuned to cover the spectral range of 622-649 nm with high quantum efficiencies. The cyclic voltarnmetry was measured, showing a reversible, metal-centered oxidation with potentials at 0.76-1.03 V, as well as two reversible reduction waves with potentials ranging from -1.61 to -2.06 V, attributed to the sequential addition of two electrons to the more electron-accepting heterocyclic portion of two distinctive cyclometalated CAN ligands. Complex 1 was used as the representative example to fabricate the red-emitting PLEDs by blending it into a PVK-PBD polymer mixture. The devices exhibited the characteristic emission profile of 1 with peak maxima located at 640 nm. The maximum external quantum efficiency was 3.15% ph/el with a brightness of 1751 cd/M-2 at a current density of 67.4 mA/cm(2) and the maximum brightness of 7750 cd/m(2) was achieved at the applied voltage of 21 V and with CIE coordinates of (0.64, 0.31).
Modified Nernst model for on-line control of the chemical oxidation decoloring process
(1996)
Chang, CN
;
Lin, JG
;
Chao, AC
;
Liu, CS
;
環境工程研究所
A modified Nernst equation was used to describe the decoloring reaction using the Oxidation-reduction potential (ORP) as an on-line monitoring and control parameter of the chemical reaction. In the modified equation, an ''S'' term is defined to indicate the oxidative potential (or decoloring potential) of dyes being studied. The laboratory study utilizes sodium hypochlorite (NaOCl) to reduce the color of wastewater spiked with textile dyes. Five dyes were used in preparing the wastewater samples : methyl red (MR), methyl violet (MV), methyl blue (MB), malachite green (MG) and methyl orange (MO). The color was measured by use of an automatic ADMI (American Dye Manufacturer Institute) measurement system in which a visible spectrophotometer was connected to a personal computer and the samples were scanned from 400 nm to 700 nm with a 10 nm interval each step. During the batch decoloring studies, several factors including ADMI, ORP, temperature and pH were continuously monitored with the computer. In this study, the potential of the decoloring compound of the dyes studies are in the following order : MG>MV>MO>MR>MB, with numeric ratios of 2.08 : 1.78 : 1.78 : 1.28 : 1.00, respectively. Copyright (C) 1996 IAWQ. Published by Elsevier Science Ltd.
Parallel synchronous and asynchronous space-decomposition algorithms for large-scale minimization problems
(2000-10-01)
Liu, CS
;
Tseng, CH
;
機械工程學系
;
Department of Mechanical Engineering
Three parallel space-decomposition minimization (PSDM) algorithms, based on the parallel variable transformation (PVT) and the parallel gradient distribution (PGD) algorithms (O.L. Mangasarian, SIMA Journal on Control and Optimization, vol. 33, no. 6, pp. 1916-1925.), are presented for solving convex or nonconvex unconstrained minimization problems. The PSDM algorithms decompose the variable space into subspaces and distribute these decomposed subproblems among parallel processors. It is shown that if all decomposed subproblems are uncoupled of each other, they can be solved independently. Otherwise, the parallel algorithms presented in this paper can be used. Numerical experiments show that these parallel algorithms can save processor time, particularly for medium and large-scale problems. Up to six parallel processors are connected by Ethernet networks to solve four large-scale minimization problems. The results are compared with those obtained by using sequential algorithms run on a single processor. An application of the PSDM algorithms to the training of multilayer Adaptive Linear Neurons (Madaline) and a new parallel architecture for such parallel training are also presented.
Polygon building block route to sp(2)-carbon-based materials
(2001-07-18)
Lee, CY
;
Chiu, HT
;
Peng, CW
;
Yen, MY
;
Chang, YH
Pentagonal- and hexogonal-shaped building block precursors provide a novel, high-yield method for the preparation of carbon onions and nanosized graphite particles. A reaction of hexachlorocyclopentadiene (C5Cl6) with sodium (see Figure) offers carbon onions with 8-50 spherical and elliptical shells. The same reaction with hexachlorobenzene (C6Cl6) results in a mixture of graphite crystallites. [GRAPHICS]
Quadratic optimization method for multilayer neural networks with local error-backpropagation
(1999-08-01)
Liu, CS
;
Tseng, CH
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機械工程學系
;
Department of Mechanical Engineering
A fast new local error-backpropagation (LBP) algorithm is presented for the training of multilayer neural networks. This algorithm is based on the definition of a new local mean-squared error function. If the local desired outputs have been estimated, the multilayer neural networks can be decomposed into a set of adaptive linear elements (Adaline) that can be trained by quadratic optimization methods. Among a lot of quadratic optimization methods, the conjugate gradient (CG) method is one of the most famous methods that can find the global optimal solution of quadratic problems within finite steps. The iteration number and the computation time are significantly reduced because the stepsize is computed without line-search. Experimental results on the pattern recognition and memorizing of spatiotemporal patterns are provided.
Space-decomposition minimization method for large-scale minimization problems
(1999-04-01)
Liu, CS
;
Tseng, CH
;
機械工程學系
;
Department of Mechanical Engineering
This paper introduces a set of new algorithms, called the Space-Decomposition Minimization (SDM) algorithms, that decomposes the minimization problem into subproblems. If the decomposed-space subproblems are not coupled to each other, they can be solved independently with any convergent algorithm; otherwise, iterative algorithms presented in this paper can be used. Furthermore, if the design space is further decomposed into one-dimensional decomposed spaces, the solution can be found directly using one-dimensional search methods. A hybrid algorithm that yields the benefits of the SDM algorithm and the conjugate gradient method is also given. An example that demonstrates application of SDM algorithm to the learning of a single-layer perceptron neural network is presented, and five large-scale numerical problems are used to test the SDM algorithms. The results obtained are compared with results from the conjugate gradient method. (C) 1999 Elsevier Science Ltd. All rights reserved.
Space-decomposition multiplier method for constrained minimization problems
(2001-01-01)
Liu, CS
;
Tseng, CH
;
機械工程學系
;
Department of Mechanical Engineering
In this paper, a new multiplier method that decomposes variable space into decomposed spaces is introduced. This method allows constrained minimization problems to be decomposed into subproblems. A potential constraint strategy that uses only part of the constraint set in the decomposed-space subproblems is also presented to increase the efficiency of this new space-decomposition multiplier method. Three examples are given to demonstrate this method and the potential constraint strategy. (C) 2001 Elsevier Science Ltd. All rights reserved.
Two-level learning algorithm for multilayer neural networks
(1998)
Liu, CS
;
Tseng, CH
;
機械工程學系
;
Department of Mechanical Engineering
A two-level learning algorithm that decomposes multilayer neural networks into a set of sub-networks is presented. A lot of popular optimization methods, such as conjugate-gradient and quasi-Neurton methods, cart be utilized to train these sub-networks. In addition, if the activation functions are hard-limiting functions, the multilayer neural networks can be trained by the perceptron learning rule in this two-level learning algorithm. Two experimental problems are given as examples for this algorithm.