Reliability-Based Method for Assessing Liquefaction Potential of Soils

This paper describes a probabilistic method for assessing the liquefaction potential of sandy soils. The proposed probabilistic method is formulated based on the results of reliability analyses of 225 field records, observations of soil performance against liquefaction. The results of the present study show that a meaningful mapping between notional probability and an actual relative frequency measure of the occurrence of liquefaction can be obtained with the proposed method. Twenty case records from the 1989 Loma Prieta earthquake are further analyzed to demonstrate the proposed reliability-based method. The developed method has the potential of becoming a practical tool for engineers involved in the assessment of liquefaction potential.     

CORDIC algorithm for vectoring mode without constant scalingfactors

A new CORDIC algorithm is presented that can be used for the vectoring mode without requiring constant scaling factors. The algorithm can also be used to carry out complete transformation from rectangular co-ordinates (x,y) to polar co-ordinates (ρ&thetas;) in each iteration. The exponent difference of x and y is computed so as to speed up convergence. This new CORDIC algorithm has an average of 0.75 n iterations for n-bit input data and can achieve>94.78% 23 bit accuracy. It is also suitable for VLSI chip implementation due to the regular architecture required     

Tribological impact of touchdown detection on bit patterned media robustness

Touchdown detection by thermal-flying height control (TFC) has been implemented to calibrate flying height (FH) of magnetic sliders for all hard disk drives. For bit patterned media (BPM) to be successful as a revolutionary technology to further increase recording areal density, it must experience the touchdown process with similar robustness as conventional continuous media. Here we numerically study the tribological impact of TFC touchdown detection on the continuous media and unplanarized BPM by three-dimensional (3D) transient finite-element models with the frictional heating and thermal-elastic-plastic materials included. Our results demonstrate that the continuous media exhibits no plastic deformation due to the TFC touchdown with an over-push as large as 2 nm, whereas the plastic strain of the BPM may reach 3 % at higher sliding velocities, and it exists over a wide range of bulge radius and disk velocity. Such plastic deformation can lead to permanent media damage and data loss. Besides, the temperature rise of the BPM (~27 K) is approximately 1.3 times of that of the continuous media (~21 K), and may have to be considered when designing a robust head-disk interface for BPM. Although planarization may improve slider’s flyability performance, our analysis shows that planarizing BPM with SiO₂ deteriorates the tribological robustness of the media in particular at a high disk velocity probably due to the inhomogeneous composition and mismatch of material properties between the filling material and recording material. Hence extreme caution must be exercised when choosing a filling material.     

A hybrid of genetic algorithm and particle swarm optimization for recurrent network design

An evolutionary recurrent network which automates the design of recurrent neural/fuzzy networks using a new evolutionary learning algorithm is proposed in this paper. This new evolutionary learning algorithm is based on a hybrid of genetic algorithm (GA) and particle swarm optimization (PSO), and is thus called HGAPSO. In HGAPSO, individuals in a new generation are created, not only by crossover and mutation operation as in GA, but also by PSO. The concept of elite strategy is adopted in HGAPSO, where the upper-half of the best-performing individuals in a population are regarded as elites. However, instead of being reproduced directly to the next generation, these elites are first enhanced. The group constituted by the elites is regarded as a swarm, and each elite corresponds to a particle within it. In this regard, the elites are enhanced by PSO, an operation which mimics the maturing phenomenon in nature. These enhanced elites constitute half of the population in the new generation, whereas the other half is generated by performing crossover and mutation operation on these enhanced elites. HGAPSO is applied to recurrent neural/fuzzy network design as follows. For recurrent neural network, a fully connected recurrent neural network is designed and applied to a temporal sequence production problem. For recurrent fuzzy network design, a Takagi-Sugeno-Kang-type recurrent fuzzy network is designed and applied to dynamic plant control. The performance of HGAPSO is compared to both GA and PSO in these recurrent networks design problems, demonstrating its superiority.     

Two efficient two-factor authenticated key exchange protocols in public wireless LANs

Recently, Parks et al. proposed an authentication and key agreement protocol for low-power PDAs in public wireless LANs using two factors including a password and a token, e.g. a smart card. They claimed that their proposed scheme provided mutual authentication, identity privacy, half-forward secrecy and low computation cost for a client including just one symmetric key encryption and five hash operations. In this paper, we point out that Park et al.’s protocol is vulnerable to the dictionary attack upon identity privacy. We propose two schemes with mutual authentication, half-forward secrecy, lower computation cost and less exchanged messages than Park et al.’s protocol. In additional to these properties, identity privacy, which is not satisfied by Park et al.’s protocol, is also provided by our second scheme.     

A Recurrent Self-Evolving Interval Type-2 Fuzzy Neural Network for Dynamic System Processing

This paper proposes a recurrent self-evolving interval type-2 fuzzy neural network (RSEIT2FNN) for dynamic system processing. An RSEIT2FNN incorporates type-2 fuzzy sets in a recurrent neural fuzzy system in order to increase the noise resistance of a system. The antecedent parts in each recurrent fuzzy rule in the RSEIT2FNN are interval type-2 fuzzy sets, and the consequent part is of the Takagi-Sugeno-Kang (TSK) type with interval weights. The antecedent part of RSEIT2FNN forms a local internal feedback loop by feeding the rule firing strength of each rule back to itself. The TSK-type consequent part is a linear model of exogenous inputs. The RSEIT2FNN initially contains no rules; all rules are learned online via structure and parameter learning. The structure learning uses online type-2 fuzzy clustering. For the parameter learning, the consequent part parameters are tuned by a rule-ordered Kalman filter algorithm to improve learning performance. The antecedent type-2 fuzzy sets and internal feedback loop weights are learned by a gradient descent algorithm. The RSEIT2FNN is applied to simulations of dynamic system identifications and chaotic signal prediction under both noise-free and noisy conditions. Comparisons with type-1 recurrent fuzzy neural networks validate the performance of the RSEIT2FNN.     

Characterization and comparison of diblock and triblock amphiphilic copolymers of poly(δ‐valerolactone)

Three types of pegylated amphiphilic copolymers of poly(δ‐valerolactone) (PVL) were copolymerized with methoxy poly(ethylene glycol) (MePEG) and poly(ethylene glycol) (PEG₄₀₀₀ and PEG_10,000), respectively. Pegylation of PVL allowed copolymers possessing amphiphilic property and efficiently self‐assembled to form micelles with a low critical micelle concentration (CMC) in the range of 10^?7–10^?8M. The average molecular weight of copolymers was in the range of 10,000–20,000 Da, and the polydispersity of copolymers was about 1.7–1.8. Higher mobility of low molecular weight PEG (i.e., MePEG and PEG₄₀₀₀) than high molecular weight PEG_10,000 allowed valerolactone ring opening more efficient in terms of PVL/MePEG and PVL/PEG₄₀₀₀ copolymers possessing longer chain length in hydrophobic domain. Pegylated PVL with low CMC and triblock structure was preferred to encapsulate drug during micelle formation. Although all of these amphiphilic copolymers exhibited controlled release character, the micelles formed by triblock copolymer possessed a more stable core‐shell conformation than that by diblock copolymer, and resulted in the release of drug from triblock micelles slower than that from diblock micelles. © 2006 Wiley Periodicals, Inc. J Appl Polym Sci 100: 1836–1841, 2006     

Optimization of site investigation program for improved statistical characterization of geotechnical property based on random field theory

This paper presents a framework for optimization of site investigation program, within which the robustness of the site investigation program and the investigation effort are optimized. A site investigation program is judged robust if the derived statistics of the geotechnical property of interest are robust against the uncertainties caused by limited data availability and test error. In this study, a Markov chain Monte Carlo simulation-based Bayesian inference approach was used to characterize the statistics of the intended geotechnical property. The robustness of the site investigation program was formulated as a byproduct of the Bayesian inference of the geotechnical property statistics. The proposed framework for optimization of the site investigation program was implemented as a bi-objective optimization problem that considers both robustness and investigation effort. The concepts of Pareto Front and knee point were employed to aid in making an informed decision regarding selection of site investigation program. The effectiveness and significance of the proposed framework were demonstrated through a simulation study.     

Gender Recognition Based on Computer Vision System

Detecting human gender from complex background, illumination variations and objects under computer vision system is very difficult but important for an adaptive information service. In this paper, a preliminary design and some experimental results of gender recognition will be presented from the walking movement that utilizes the gait-energy image (GEI) with denoised energy image (DEI) pre-processing as a machine learning support vector machine (SVM) classifier to train and extract its characteristics. The results show that the proposed method can adopt some characteristic values and the accuracy can reach up to 100% gender recognition rate under combining the horizontal added vertical feature and using a normal image size and test data when people are walking at a fixed angle. Meanwhile, it will be able to achieve over 80% rate within some allowed fault-tolerant angle range.