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Articles by Hui Lu
Total Records ( 5 ) for Hui Lu
  Hui Lu and Xiao Chen
  This study has presented an enhanced particle swarm optimization approach which is designed to solve constrained optimization problems. The approach incorporates a dynamic inertia weight in order to help the algorithm to find the global and overcome the problem of premature convergence to local optima. The inertia weight of every individual is dynamically controlled by the Euclidean distance between individual and global best individual. The approach was tested with a well-known benchmark. Simulation results show that the suitability of the proposed algorithm in terms of effectiveness and robustness.
  Hui Lu , Ruiyao Niu , Jing Liu and Zheng Zhu
  Task scheduling problem is one of the key technologies for automatic test systems. This study proposes a novel and integrated multicriteria decision mechanism called the chaotic non-dominated sorting genetic algorithm plus analytic hierarchy process (CNSGA-AHP) for the automatic test task scheduling problem (ATSP). This mechanism contains two parts: the multiobjective optimisation algorithm CNSGA and the decision making method AHP. CNSGA hybrids chaotic sequences based on the logistic map and the non-dominated sorting genetic algorithm II (NSGA-II) to avoid becoming trapped in local optima. It is responsible for the search process and obtains a set of compromise solutions. However, getting this set does not completely solve the problem. A best compromise solution still must be chosen out of that set. Thereupon, AHP is used for the final decision making process and chooses a best schedule from the solutions obtained by CNSGA. The applied AHP can handle uncertainty, make the consistency check easily to pass and reduce the workload of decision-makers. A real-world ATSP abstracted from a missile system is applied to verify the effectiveness of CNSGA-AHP. Results show that CNSGA-AHP is very concise and suitable for the ATSP.
  Hui Lu and Ruibo Liu
  To improve precision of the signal conditioning circuit in an automatic test system, an error compensation approach was proposed based on the Loose Type of Wavelet Neural Network (L-WNN), combining wavelet transform with BP neural network. It was applied to get the error curve which stands for the relationship of input voltage and the error of conditioning circuit. To evaluate the performance of the L-WNN model, the error curve was also compared to it got by BP neural network and regression analysis which applied Least Squares Estimate (LSE). The effect of testing on the compensation result shows that significant improvements can be made and that is an efficient method to compensate the error of the signal conditioning circuit.
  Hui Lu , Xiaoteng Wang and Jing Liu
  As the key element of automatic test systems, the Test Task Scheduling Problem (TTSP) is becoming an increasingly important issue. The TTSP often involves two kinds of constraints. The temporal constraints among tasks are often the net ones and the resource constraints are generated due to the lack of enough resources for each task. The current methods to handle the constraints can be classified into optimization and satisfaction. Most of them suffer much difficulty to solve the constraints in the TTSP. However, the optimization methods are abandoned since the infeasible solutions are not allowed in the TTSP to avoid the accidents. The current satisfaction methods are usually for the tree or chine constraints. In addition, there is seldom research on the temporal and resource constraints together. Hence, the Priority-based (PB) encoding is provided to solve the temporal constraints while the Scheme Choice Rule (SCR) is created to find the corresponding schemes to distribute certain resource to these tasks. The compounding of PB and SCR called PBSCR is embedded into a genetic algorithm and applied to two benchmarks. Empirical results suggest that PBSCR outperforms better than not only the optimization methods but also the satisfaction methods. The efficacy of PBSCR is thereby justified.
  Debasis Manna , Nitin Bhardwaj , Mohsin S. Vora , Robert V. Stahelin , Hui Lu and Wonhwa Cho
  Many cytosolic proteins are recruited to the plasma membrane (PM) during cell signaling and other cellular processes. Recent reports have indicated that phosphatidylserine (PS), phosphatidylinositol 4,5-bisphosphate (PtdIns(4,5)P2), and phosphatidylinositol 3,4,5-trisphosphate (PtdIns(3,4,5)P3) that are present in the PM play important roles for their specific PM recruitment. To systematically analyze how these lipids mediate PM targeting of cellular proteins, we performed biophysical, computational, and cell studies of the Ca2+-dependent C2 domain of protein kinase Cα (PKCα) that is known to bind PS and phosphoinositides. In vitro membrane binding measurements by surface plasmon resonance analysis show that PKCα-C2 nonspecifically binds phosphoinositides, including PtdIns(4,5)P2 and PtdIns(3,4,5)P3, but that PS and Ca2+ binding is prerequisite for productive phosphoinositide binding. PtdIns(4,5)P2 or PtdIns(3,4,5)P3 augments the Ca2+- and PS-dependent membrane binding of PKCα-C2 by slowing its membrane dissociation. Molecular dynamics simulations also support that Ca2+-dependent PS binding is essential for membrane interactions of PKCα-C2. PtdIns(4,5)P2 alone cannot drive the membrane attachment of the domain but further stabilizes the Ca2+- and PS-dependent membrane binding. When the fluorescence protein-tagged PKCα-C2 was expressed in NIH-3T3 cells, mutations of phosphoinositide-binding residues or depletion of PtdIns(4,5)P2 and/or PtdIns(3,4,5)P3 from PM did not significantly affect the PM association of the domain but accelerated its dissociation from PM. Also, local synthesis of PtdIns(4,5)P2 or PtdIns(3,4,5)P3 at the PM slowed membrane dissociation of PKCα-C2. Collectively, these studies show that PtdIns(4,5)P2 and PtdIns(3,4,5)P3 augment the Ca2+- and PS-dependent membrane binding of PKCα-C2 by elongating the membrane residence of the domain but cannot drive the PM recruitment of PKCα-C2. These studies also suggest that effective PM recruitment of many cellular proteins may require synergistic actions of PS and phosphoinositides.
 
 
 
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