A detection problem: Sensitivity and uncertainty analysis of a land surface temperature approach to detecting dynamics of water use by groundwater-dependent vegetation

Sustainable management of groundwater-dependent vegetation (GDV) requires the accurate identification of GDVs, characterisation of their water use dynamics and an understanding of associated errors. This paper presents sensitivity and uncertainty analyses of one GDV mapping method which uses temperature differences between time-series of modelled and observed land surface temperature (LST) to detect groundwater use by vegetation in a subtropical woodland. Uncertainty in modelled LST was quantified using the Jacobian method with error variances obtained from literature. Groundwater use was inferred where modelled and observed LST were significantly different using a Student's t-test. Modelled LST was most sensitive to low-range wind speeds (<1.5 m s⁻¹), low-range vegetation height (<=0.5 m), and low-range leaf area index (<=0.5 m² m⁻²), limiting the detectability of groundwater use by vegetation under such conditions. The model-data approach was well-suited to detection of GDV because model-data errors were lowest for climatic conditions conducive to groundwater use.     

A novel elevator group control algorithm based on binocular-cameras corridor passenger detection and tracking

Multimedia Tools and Applications - The conventional elevator arrives at the floor in response to the user request and where is to dispatch an elevator car. There is one big problem that when...     

Virtual-Point-Based asymptotic tracking control of 4WS vehicles

This paper studies the lateral and longitudinal path tracking control of four-wheel steering vehicles. By the introduction of virtual points, a robust and adaptive path tracking control strategy is proposed to simultaneously counteract modeling uncertainties, unexpected disturbances, and coupling effects. An adaptive model-based feedforward adaptive term and the robust integral of the sign of the error (RISE) feedback term can be used to yield an asymptotic tracking result, which improve the tracking performance and reduce the control effort. The stability of closed-loop system is analyzed using a Lyapunov-based method. Simulation results are provided to demonstrate the performance of the proposed controller under different driving conditions.     

Online hybrid traffic classifier for Peer-to-Peer systems based on network processors

It is estimated that 70% or more of broadband bandwidth is consumed by transmitting music, games, video and other content through Peer-to-Peer (P2P) clients. In order to detect, identify, and manage P2P traffic, some port, payload and transport layer feature based methods were proposed. Most of them were applied to offline traffic classification mainly due to the performance reason. In this paper, a network processors (NPs) based online hybrid traffic classifier is proposed. The designed hardware classifier is able to classify P2P traffic based on the static characteristic namely on line speed, and the Flexible Neural Tree(FNT) based software classifier helps learning and selecting P2P traffic attributes from the statistical characteristics of the P2P traffic. Experiment results illustrate that the hybrid classifier performs well for online classification of P2P traffic from gigabit network. The proposed framework also depicts good expansion capabilities to add new P2P features and to adapt to new P2P applications online.     

Discovery and representation of the preferences of automatically detected groups: Exploiting the link between group modeling and clustering

There are types of information systems, like those that produce group recommendations or a market segmentation, in which it is necessary to aggregate big amounts of data about a group of users in order to filter the data. Group modeling is the process that combines multiple user models into a single model that represents the knowledge available about the preferences of the users in a group. In group recommendation, group modeling allows a system to derive a group preference for each item. Different strategies lead to completely different group models, so the strategy used to model a group has to be evaluated in the domain in which the group recommender system operates. This paper evaluates group modeling strategies in a group recommendation scenario in which groups are detected by clustering the users. Once users are clustered and groups are formed, different strategies are tested, in order to find the one that allows a group recommender system to get the best accuracy. Experimental results show that the strategy used to build the group models strongly affects the performance of a group recommender system. An interesting property derived by our study is that clustering and group modeling are strongly connected. Indeed, the modeling strategy takes the same role that the centroid has when users are clustered, by producing group preferences that are equally distant from the preferences of every user. This “continuity” among the two tasks is essential in order to build accurate group recommendations.     

Design of data feature-driven 1D/2D convolutional neural networks classifier for recycling black plastic wastes through laser spectroscopy

In this study, two types of convolutional neural network (CNN) classifiers are designed to handle the problem of classifying black plastic wastes. In particular, the black plastic wastes have the property of absorbing laser light coming from spectrometer. Therefore, the classification of black plastic wastes remains still a challenging problem compared to classifying other colored plastic wastes using existing spectroscopy (i.e., NIR). When it comes the classification problem of black plastic wastes, effective classification techniques by the laser spectroscopy of Fourier Transform-Infrared Radiation (FT-IR) with Attenuated Total Reflectance (ATR) and Raman to analyze the classification problem of black plastic wastes are introduced. Due to the strong ability of extracting spatial features and remarkable performance in image classification, 1D and 2D CNN through data features are designed as classifiers. The technique of chemical peak points selection is considered to reduce data redundancy. Furthermore, through the selection of data features based on the extracted 1D data with peak points is introduced. Experimental results demonstrate that 2DCNN classifier designed with the help of 2D data feature selection as well as 1DCNN classifier shows the best performance compared with other reported methods for classifying black plastic wastes.     

A multipopulation genetic algorithm aimed at multimodal optimization

This paper considers a new method that enables a genetic algorithm (GA) to identify and maintain multiple optima of a multimodal function, by creating subpopulations within the niches defined by the multiple optima, thus warranting a good ‘diversity’. The algorithm is based on a splitting of the traditional GA into a sequence of two processes. Since the GA behavior is determined by the exploration/exploitation balance, during the first step (Exploration), the multipopulation GA coupled with a speciation method detects the potential niches by classifying ‘similar’ individuals in the same population. Once the niches are detected, the algorithm achieves an intensification (Exploitation), by allocating a separate portion of the search space to each population. These two steps are alternately performed at a given frequency. Empirical results obtained with F6 Schaffer‘s function are then presented to show the reliability of the algorithm.     

A spectroelectrochemical study on electrochemically synthesized poly(thienyl biphenyl) film

The electrochemically synthesized conducting copolymer consisting of 3-octylthiophene and biphenyl units has been characterized by in situ resonance Raman spectroscopy, UV–visible spectroscopy, in situ FTIR attenuated total reflectance spectroscopy and cyclic voltammetry. The structure of the formed material is compared with the two homopolymers: poly(3-octylthiophene) or poly(paraphenylene). The results show that the thiophene segments are longer than the phenylene segments in the poly(thienyl biphenyl) film formed. During electrochemical doping the Raman spectra indicate changes in the polymer backbone that can be assigned to transformation of the neutral form into the charged form.     

Particle Swarm Optimization inspired by starling flock behavior

Swarm intelligence is a meta-heuristic algorithm which is widely used nowadays for efficient solution of optimization problems. Particle Swarm Optimization (PSO) is one of the most popular types of swarm intelligence algorithm. This paper proposes a new Particle Swarm Optimization algorithm called Starling PSO based on the collective response of starlings. Although PSO performs well in many problems, algorithms in this category lack mechanisms which add diversity to exploration in the search process. Our proposed algorithm introduces a new mechanism into PSO to add diversity, a mechanism which is inspired by the collective response behavior of starlings. This mechanism consists of three major steps: initialization, which prepares alternative populations for the next steps; identifying seven nearest neighbors; and orientation change which adjusts velocity and position of particles based on those neighbors and selects the best alternative. Because of this collective response mechanism, the Starling PSO explores a wider area of the search space and thus avoids suboptimal solutions. We tested the algorithm with commonly used numerical benchmarking functions as well as applying it to a real world application involving data clustering. In these evaluations, we compared Starling PSO with a variety of state of the art algorithms. The results show that Starling PSO improves the performance of the original PSO and yields the optimal solution in many numerical benchmarking experiments. It also gives the best results in almost all clustering experiments.