A novel self-organizing fuzzy rule-based system for modelling traffic flow behaviour

The study and development of transportation systems have been a focus of attention in recent years, with many research efforts directed in particular at modelling traffic behaviour from both macroscopic and microscopic points of views. Although many statistical regression models of road traffic relationships have been formulated, they have proven to be unsuitable due to multiple and ill-defined traffic characteristics. Alternative methods such as neural networks have thus been sought but, despite some promising results, their design remains problematic and implementation is equally difficult. Another salient issue is that the opaqueness of trained networks prevents understanding the underlying models. Hybrid neuro-fuzzy rule-based systems, which combine the complementary capabilities of both neural networks and fuzzy logic, constitute a more promising technique for modelling traffic flow. This paper describes the application of a specific class of neuro-fuzzy system known as the Pseudo Outer-Product Fuzzy-Neural Network using Truth-Value-Restriction method (POPFNN-TVR) for modelling traffic behaviour. This approach has been shown to perform better on such problems than similar architectures. The results obtained highlight the capability of POPFNN-TVR in fuzzy knowledge extraction for modelling inter-lane relationships in a highway traffic stream, as well as in generalizing from sample data, as compared to traditional feed-forward neural networks using back-propagation learning. The model thus obtained automatically can be understood, analysed, and readily applied for transportation planning.     

A novel associative memory approach to speech enhancement in a vehicular environment

Numerous attempts have been undertaken to apply the spectral subtraction method to cancel noise perturbations but these efforts have yet to produce an algorithm that is able to adapt well to the environmental changes in the perturbations. In addition, the variants of the spectral subtraction method so far proposed in the literature would require a non-voice activity detector (NVAD), for a single microphone system, to store the perturbation. This is used as an estimate for the reference signal. Inaccuracy in the perturbation estimates causes the cleaned speech to be corrupted by musical artifacts, which is unacceptable. Post processing of signals corrupted by the musical artifacts is very costly. This paper provides an alternative approach that employs associative memory for speech enhancement. Extensive comparison is made using the soft computing approaches for noise cancellation based on associative memories. A set of stereo microphones captures the corrupted speech in a vehicle and is used to point to the closest associative memory location. The Wiener filter approach is used to cancel the noise. The paper discusses novel examples of the associative memories using the cerebellum model for noise modeling. Experimental results show the potential of these novel soft computing architectures in generating and adapting the required Weiner filters to cancel perturbation even at signal to noise ratio (SNR) of less than −13 dB.     

Symbolic computation with finite biquandles

A method of computing a basis for the second Yang–Baxter cohomology of a finite biquandle with coefficients in Q$\mathbb{Q}$ and _Zp${\mathbb{Z}}_p$ from a matrix presentation of the finite biquandle is described. We also describe a method for computing the Yang–Baxter cocycle invariants of an oriented knot or link represented as a signed Gauss code. We provide a URL for our Maple implementations of these algorithms.     

Improved MCMAC with momentum, neighborhood, and averagedtrapezoidal output

An improved modified cerebellar articulation controller (MCMAC) neural control algorithm with better learning and recall processes using momentum, neighborhood learning, and averaged trapezoidal output, is proposed in this paper. The learning and recall processes of MCMAC are investigated using the characteristic surface of MCMAC and the control action exerted in controlling a continuously variable transmission (CVT). Extensive experimental results demonstrate a significant improvement with reduced training time and an extended range of trained MCMAC cells. The improvement in recall process using the averaged trapezoidal output (MCMAC-ATO) are contrasted against the original MCMAC using the square of the Pearson product moment correlation coefficient. Experimental results show that the new recall process has significantly reduced the fluctuations in the control action of the MCMAC and addressed partially the problem associated with the resolution of the MCMAC memory array.     

Effects of socio-emotional stressors on ventilation rate and subjective workload during simulated CPR by lay rescuers

Several studies have documented the occurrence of high ventilation rates during cardiopulmonary resuscitation, but to date, there have been no scientific investigation of the causes of hyperventilation. The objective of the current study was to test the effects of socio-emotional stressors on lay rescuers' ventilation rate in a simulated resuscitation setting using a manikin model. A within-subjects experiment with randomized order of conditions tested lay rescuers' ventilation rate on an intubated manikin during exposure to socio-emotional stressors and during a control condition where no external stressors were present. Ventilation rates and subjective workload were significantly higher during exposure to socio-emotional stressors than during the control condition. All but one of the nine participants ventilated at a higher ventilation rate in the experimental condition. All nine participants rated the subjective workload to be higher during exposure to socio-emotional stressors. Hence, exposure to socio-emotional stressors is associated with increased ventilation rates performed by lay rescuers during simulated cardiac arrest using a manikin model. These findings might have implications for the understanding of the type of situations which hyperventilation may occur. Awareness of these situations may have implications for training of lay rescues.     

SoHyFIS-Yager: A self-organizing Yager based Hybrid neural Fuzzy Inference System

The Hybrid neural Fuzzy Inference System (HyFIS) is a multilayer adaptive neural fuzzy system for building and optimizing fuzzy models using neural networks. In this paper, the fuzzy Yager inference scheme, which is able to emulate the human deductive reasoning logic, is integrated into the HyFIS model to provide it with a firm and intuitive logical reasoning and decision-making framework. In addition, a self-organizing gaussian Discrete Incremental Clustering (gDIC) technique is implemented in the network to automatically form fuzzy sets in the fuzzification phase. This clustering technique is no longer limited by the need to have prior knowledge about the number of clusters present in each input and output dimensions. The proposed self-organizing Yager based Hybrid neural Fuzzy Inference System (SoHyFIS-Yager) introduces the learning power of neural networks to fuzzy logic systems, while providing linguistic explanations of the fuzzy logic systems to the connectionist networks. Extensive simulations were conducted using the proposed model and its performance demonstrates its superiority as an effective neuro-fuzzy modeling technique.     

EnvironalPlanning: Analytic Network Process Model for Environmentally Conscious Construction Planning

Although the construction pollution index has been put forward and proved to be an efficient approach to reducing or mitigating pollution level during the construction planning stage, the problem of how to select the best construction plan based on distinguishing the degree of its potential adverse environmental impacts is still a research task. This paper first reviews environmental issues and their characteristics in construction, which are critical factors in evaluating potential adverse impacts of a construction plan. These environmental characteristics are then used to structure two decision models for environmental-conscious construction planning by using an analytic network process (ANP), including a complicated model and a simplified model. The two ANP models are combined and called the EnvironalPlanning system, which is applied to evaluate potential adverse environmental impacts of alternative construction plans.     

An integrated model for supplier selection process

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Structurally noise resistant classifier for multi-modal person verification

In this letter we propose a piece-wise linear (PL) classifier for use as the decision stage in a two-modal verification system, comprised of a face and a speech expert. The classifier utilizes a fixed decision boundary that has been specifically designed to account for the effects of noisy audio conditions. Experimental results on the VidTIMIT database show that in clean conditions, the proposed classifier is outperformed by a traditional weighted summation decision stage (using both fixed and adaptive weights). Using white Gaussian noise to corrupt the audio data resulted in the PL classifier obtaining better performance than the fixed approach and similar performance to the adaptive approach. Using a more realistic noise type, namely “operations room” noise from the NOISEX-92 corpus, resulted in the PL classifier obtaining better performance than both the fixed and adaptive approaches. The better results in this case stem from the PL classifier not making a direct assumption about the type of noise that causes the mismatch between training and testing conditions (unlike the adaptive approach). Moreover, the PL classifier has the advantage of having a fixed (non-adaptive, thus simpler) structure.     

Preserving Topology by a Digitization Process

The main task of digital image processing is to recognize properties of real objects based on their digital images. These images are obtained by some sampling device, like a CCD camera, and represented as finite sets of points that are assigned some value in a gray-level or color scale. Based on technical properties of sampling devices, these points are usually assumed to form a square grid and are modeled as finite subsets of Z². Therefore, a fundamental question in digital image processing is which features in the digital image correspond, under certain conditions, to properties of the underlying objects. In practical applications this question is mostly answered by visually judging the obtained digital images. In this paper we present a comprehensive answer to this question with respect to topological properties. In particular, we derive conditions relating properties of real objects to the grid size of the sampling device which guarantee that a real object and its digital image are topologically equivalent. These conditions also imply that two digital images of a given object are topologically equivalent. This means, for example, that shifting or rotating an object or the camera cannot lead to topologically different images, i.e., topological properties of obtained digital images are invariant under shifting and rotation.