Optimal configuration of Energy-Efficient Ethernet

The IEEE 802.3az standard provides a new low power mode that Ethernet network interfaces can use to save energy when there is no traffic to transmit. Simultaneously with the final standard approval, several algorithms were proposed to govern the physical interface state transition between the normal active mode and the new low power mode. In fact, the standard leaves this sleeping algorithm unspecified to spur competition among different vendors and achieve the greatest energy savings. In this paper, we try to bring some light to the most well known sleeping algorithms, providing mathematical models for the expected energy savings and the average packet delay inflicted on outgoing traffic. We will then use the models to derive optimum configuration parameters for them under given efficiency constraints.     

Design of a two‐level controller for an active suspension system

The paper focuses on a control design for a vehicle suspension system in which a balance between different performance demands is achieved. The starting point of the control design is a full–car model which contains nonlinear components, i.e. the dynamics of the dampers and springs and nonlinear actuator dynamics. In order to handle the high complexity of the problem this paper proposes the design of a two‐level controller of an active suspension system. The required control force is computed by applying a high‐level controller, which is designed using a linear parameter varying (LPV) method. For the control design the model is augmented with weighting functions specified by the performance demands and the uncertainty assumptions. The actuator generating the necessary control force is modelled as a nonlinear system for which a low‐level force‐tracking controller is designed. To obtain the low‐level controller a backstepping method is proposed. As an alternative solution a feedback linearization method is also presented. The operation of the controller is illustrated through simulation examples. Copyright © 2011 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

Risk in Transport Investments

We discuss how the standard Cost-Benefit Analysis should be modified in order to take risk (and uncertainty) into account. We propose different approaches used in finance (Value at Risk, Conditional Value at Risk, Downside Risk Measures, and Efficiency Ratio) as useful tools to model the impact of risk in project evaluation. After introducing the concepts, we show how they could be used in CBA and provide some simple examples to illustrate how such concepts can be applied to evaluate the desirability of a new project infrastructure.     

Evolutionary Generalized Radial Basis Function neural networks for improving prediction accuracy in gene classification using feature selection

Radial Basis Function Neural Networks (RBFNNs) have been successfully employed in several function approximation and pattern recognition problems. The use of different RBFs in RBFNN has been reported in the literature and here the study centres on the use of the Generalized Radial Basis Function Neural Networks (GRBFNNs). An interesting property of the GRBF is that it can continuously and smoothly reproduce different RBFs by changing a real parameter τ. In addition, the mixed use of different RBF shapes in only one RBFNN is allowed. Generalized Radial Basis Function (GRBF) is based on Generalized Gaussian Distribution (GGD), which adds a shape parameter, τ, to standard Gaussian Distribution. Moreover, this paper describes a hybrid approach, Hybrid Algorithm (HA), which combines evolutionary and gradient-based learning methods to estimate the architecture, weights and node topology of GRBFNN classifiers. The feasibility and benefits of the approach are demonstrated by means of six gene microarray classification problems taken from bioinformatic and biomedical domains. Three filters were applied: Fast Correlation-Based Filter (FCBF), Best Incremental Ranked Subset (BIRS), and Best Agglomerative Ranked Subset (BARS); this was done in order to identify salient expression genes from among the thousands of genes in microarray data that can directly contribute to determining the class membership of each pattern. After different gene subsets were obtained, the proposed methodology was performed using the selected gene subsets as new input variables. The results confirm that the GRBFNN classifier leads to a promising improvement in accuracy.     

Annotation of sentence structure

The focus of this article is on the creation of a collection of sentences manually annotated with respect to their sentence structure. We show that the concept of linear segments—linguistically motivated units, which may be easily detected automatically—serves as a good basis for the identification of clauses in Czech. The segment annotation captures such relationships as subordination, coordination, apposition and parenthesis; based on segmentation charts, individual clauses forming a complex sentence are identified. The annotation of a sentence structure enriches a dependency-based framework with explicit syntactic information on relations among complex units like clauses. We have gathered a collection of 3,444 sentences from the Prague Dependency Treebank, which were annotated with respect to their sentence structure (these sentences comprise 10,746 segments forming 6,341 clauses). The main purpose of the project is to gain a development data—promising results for Czech NLP tools (as a dependency parser or a machine translation system for related languages) that adopt an idea of clause segmentation have been already reported. The collection of sentences with annotated sentence structure provides the possibility of further improvement of such tools.     

An efficient initialization approach of Q-learning for mobile robots

This article demonstrates that Q-learning can be accelerated by appropriately specifying initial Q-values using dynamic wave expansion neural network. In our method, the neural network has the same topography as robot work space. Each neuron corresponds to a certain discrete state. Every neuron of the network will reach an equilibrium state according to the initial environment information. The activity of the special neuron denotes the maximum cumulative reward by following the optimal policy from the corresponding state when the network is stable. Then the initial Q-values are defined as the immediate reward plus the maximum cumulative reward by following the optimal policy beginning at the succeeding state. In this way, we create a mapping between the known environment information and the initial values of Q-table based on neural network. The prior knowledge can be incorporated into the learning system, and give robots a better learning foundation. Results of experiments in a grid world problem show that neural network-based Q-learning enables a robot to acquire an optimal policy with better learning performance compared to conventional Q-learning and potential field-based Qlearning.     

Passive internet-based crane teleoperation with haptic aids

Crane operation is a challenging task, due to the combined problem of obstacle avoidance and load swing suppression in underactuated conditions. This paper presents a human-machine interface that increases the operator’s perception of a gantry crane’s workspace. With this aim, a virtual environment resembling the workspace is connected with a haptic device. This allows the user to receive not only visual but also tactile feedback, thus increasing maneuvering safety. Additionally, this capability is integrated in a teleoperation setup, adopting a passivity-based control approach that guarantees overall stability. This includes also the design of controllers by means of the IDA-PBC method. Experimental results carried out with a laboratory crane show its feasibility for internet-based teleoperation and demonstrate the improvements on the system performance.     

Path tracking control of Lagrange systems with obstacle avoidance

This paper addresses a path tracking problem with obstacle avoidance for Lagrange systems. The proposed method is based on field potential methods in combination with navigation functions for obstacle avoidance. First, it is shown that a simple combination of the navigation function with the conventional path tracking controller does not work. Therefore, in order to cope with this problem, a new feedback law is proposed for a path parameter which characterizes the reference path. It is proved that the proposed controller achieves both path following and collision avoidance. Moreover, since the method adopts bounded navigation functions, the proposed controllers generate bounded input signals even when target systems approach obstacles. Finally, an experimental evaluation is performed with a two-link manipulator to illustrate the effectiveness of the proposed method.     

Electrokinetically driven concentration of particles and cells by dielectrophoresis with DC-offset AC electric field

Insulator-based dielectrophoresis (iDEP) has been successfully used for on-chip manipulations of biological samples. Despite its effectiveness, iDEP typically requires high DC voltages to achieve sufficient electric field; this is mainly due to the coupled phenomena among linear electrokinetics: electroosmosis (EO) and electrophoresis (EP) and nonlinear electrokinetics: dielectrophoresis (DEP). This paper presents a microfluidic technique using DC-offset AC electric field for electrokinetic concentration of particles and cells by repulsive iDEP. This technique introduces AC electric field for producing iDEP which is decoupled from electroosmosis (EO) and electrophoresis (EP). The repulsive iDEP is generated in a PDMS tapered contraction channel that induces non-uniform electric field. The benefits of introducing AC electric field component are threefold: (i) it contributes to DEP force acting on particles, (ii) it suppresses EO flow and (iii) it does not cause any EP motion. As a result, the required DC field component that is mainly used to transport particles on the basis of EO and EP can be significantly reduced. Experimental results supported by numerical simulations showed that the total DC-offset AC electric field strength required to concentrate 15-μm particles is significantly reduced up to 85.9% as compared to using sole DC electric field. Parametric experimental studies showed that the higher buffer concentration, larger particle size and higher ratio of AC-to-DC electric field are favorable for particle concentration. In addition, the proposed technique was demonstrated for concentration of yeast cells.     

A power-free deposited microbead plug-based microfluidic chip for whole-blood immunoassay

We present a deposited microbead plug (DMBP)-based microfluidic chip capable of performing plasma extraction and on-chip immunoassay. The DMBP used as a porous blood filter provides pure blood plasma without the contamination of blood cells or beads. Capillary-driven flow eliminates the requirement of external pumps. The human IgG and goat anti-human IgG sample-to-answer assay was performed in this chip within 600 s using only a 10 μl whole-blood sample. This easy-to-use, rapid, inexpensive, and disposable DMBP-based chip holds a great promise for point-of-care application.