A process model for robotic disc grinding

Automation of deburring and cleaning of castings is desirable for many reasons. The major reasons are dangerous working conditions, difficulties in finding workers for cleaning sections, and improved profitability. A suitable robot cell capable of using different tools, such as cup grinders, disc grinders and rotary files, is the solution. This robot should be completed with sensors in order to keep the quality of the cleaned surface at an acceptable level. Although using sensors simplifies both the programming and quality control there are still other problems that need to be solved. These involve selection of machining data, e.g. feeding rate and grinding force in a force controlled operation based on parameters such as tool type, disc grinder and geometry. In order to decrease the programming time, a process model for disc grinders has been developed. This article investigates this process model and pays attention to problems such as wavy or burned surfaces and the effect of a robot's repetition accuracy in the results obtained. Many aspects treated in this article are quite general, and can be applied in other types of grinding operations.     

An entropy-based persistence barcode

In persistent homology, the persistence barcode encodes pairs of simplices meaning birth and death of homology classes. Persistence barcodes depend on the ordering of the simplices (called a filter) of the given simplicial complex. In this paper, we define the notion of “minimal” barcodes in terms of entropy. Starting from a given filtration of a simplicial complex K, an algorithm for computing a “proper” filter (a total ordering of the simplices preserving the partial ordering imposed by the filtration as well as achieving a persistence barcode with small entropy) is detailed, by way of computation, and subsequent modification, of maximum matchings on subgraphs of the Hasse diagram associated to K. Examples demonstrating the utility of computing such a proper ordering on the simplices are given.     

Delay and link utilization aware routing protocol for wireless multimedia sensor networks

Wireless Multimedia Sensor Networks (WMSNs) consist of networks of interconnected devices involved in retrieving multimedia content, such as, video, audio, acoustic, and scalar data, from the environment. The goal of these networks is optimized delivery of multimedia content based on quality of service (QoS) parameters, such as delay, jitter and distortion. In multimedia communications each packet has strict playout deadlines, thus late arriving packets and lost packets are treated equally. It is a challenging task to guarantee soft delay deadlines along with energy minimization, in resource constrained, high data rate WMSNs. Conventional layered approach does not provide optimal solution for guaranteeing soft delay deadlines due to the large amount of overhead involved at each layer. Cross layer approach is fast gaining popularity, due to its ability to exploit the interdependence between different layers, to guarantee QoS constraints like latency, distortion, reliability, throughput and error rate. The paper presents a channel utilization and delay aware routing (CUDAR) protocol for WMSNs. This protocol is based on a cross-layer approach, which provides soft end-to-end delay guarantees along with efficient utilization of resources. Extensive simulation analysis of CUDAR shows that it provides better delay guarantees than existing protocols and consequently reduces jitter and distortion in WMSN communication.     

Short‐Time Linear Quadratic Form Technique for Estimating Fast‐Varying Parameters in Feedback Loops

The precision of a closed‐loop controller system designed for an uncertain plant depends strongly upon the maximum extent to which it is possible to track the trend of time‐varying parameters of the plant. The aim of this study is to describe a new parameter estimation algorithm that is able to follow fast‐varying parameters in closed‐loop systems. The short‐time linear quadratic form (STLQF) estimation algorithm introduced in this paper is a technique for tracking time‐varying parameters based on short‐time analysis of the regressing variables in order to minimize locally a linear quadratic form cost function. The established cost function produces a linear combination of errors with several delays. To meet this objective, mathematical development of the STLQF estimation algorithm is described. To implement the STLQF algorithm, the algorithm is applied to a planar mobile robot with fast‐varying parameters of inertia and viscous and coulomb frictions. Next, performance of the proposed algorithm is assessed against noise effects and variation in the type of parameters.     

Comparative evaluation of performance measures for human driving skills

In this paper, we evaluate the adequacy of several performance measures for the evaluation of driving skills between different drivers. This work was motivated by the need for a training system that captures the driving skills of an expert driver and transfers the skills to novice drivers using a haptic-enabled driving simulator. The performance measures examined include traditional task performance measures, e.g., the mean position error, and a stochastic distance between a pair of hidden Markov models (HMMs), each of which is trained for an individual driver. The emphasis of the latter is on the differences between the stochastic somatosensory processes of human driving skills. For the evaluation, we developed a driving simulator and carried out an experiment that collected the driving data of an expert driver whose data were used as a reference for comparison and of many other subjects. The performance measures were computed from the experimental data, and they were compared to each other. We also collected the subjective judgement scores of the driver’s skills made by a highly-experienced external evaluator, and these subjective scores were compared with the objective performance measures. Analysis results showed that the HMM-based distance metric had a moderately high correlation between the subjective scores and it was also consistent with the other task performance measures, indicating the adequacy of the HMM-based metric as an objective performance measure for driving skill learning. The findings of this work can contribute to developing a driving simulator for training with an objective assessment function of driving skills.     

U-shaped energy loss curves utilization for distributed generation optimization in distribution networks

We propose novel techniques to find the optimal achieve the maximum loss reduction for distribution networks location, size, and power factor of distributed generation （DG） to Determining the optimal DG location and size is achieved simultaneously using the energy loss curves technique for a pre-selected power factor that gives the best DG operation. Based on the network＇s total load demand, four DG sizes are selected. They are used to form energy loss curves for each bus and then for determining the optimal DG options. The study shows that by defining the energy loss minimization as the objective function, the time-varying load demand significantly affects the sizing of DG resources in distribution networks, whereas consideration of power loss as the objective function leads to inconsistent interpretation of loss reduction and other calculations. The devised technique was tested on two test distribution systems of varying size and complexity and validated by comparison with the exhaustive iterative method （EIM） and recently published results. Results showed that the proposed technique can provide an optimal solution with less computation.     

A review of interoperability assessment models

Interoperability is the ability of systems to provide services to and accept services from other systems, and to use the services exchanged so as to operate together in a more effective manner. The fact that interoperability can be improved means that the metrics for measuring interoperability can be defined. For the purpose of measuring the interoperability between systems, an interoperability assessment model is required. This paper deals with the existing interoperability assessment models. A compara- tive analysis among these models is provided to evaluate the similarities and differences in their philosophy and implementation. The analysis yields a set of recommendations for any party that is open to the idea of creating or improving an interoperability assessment model.     

The axes response and resonance identification for a machine tool

The X-Y axes, distributed-lumped model, for a machine tool system are derived. Model validations using measured results are presented. Adaptation of the model accommodating thereby the Z axis transmission is provided. The combined X-Y-Z axes response, for a cylindrical cutting operation, enabling an assessment of machining integrity, is detailed. Resonance conditions and the identification of adverse cutting frequencies are investigated. Dynamic amplification and the time domain performance of the system is determined.     

Objective evaluation of speech dysfluencies using wavelet packet transform with sample entropy

Dysfluency and stuttering are a break or interruption of normal speech such as repetition, prolongation, interjection of syllables, sounds, words or phrases and involuntary silent pauses or blocks in communication. Stuttering assessment through manual classification of speech dysfluencies is subjective, inconsistent, time consuming and prone to error. This paper proposes an objective evaluation of speech dysfluencies based on the wavelet packet transform with sample entropy features. Dysfluent speech signals are decomposed into six levels by using wavelet packet transform. Sample entropy (SampEn) features are extracted at every level of decomposition and they are used as features to characterize the speech dysfluencies (stuttered events). Three different classifiers such as k-nearest neighbor (kNN), linear discriminant analysis (LDA) based classifier and support vector machine (SVM) are used to investigate the performance of the sample entropy features for the classification of speech dysfluencies. 10-fold cross validation method is used for testing the reliability of the classifier results. The effect of different wavelet families on the classification performance is also performed. Experimental results demonstrate that the proposed features and classification algorithms give very promising classification accuracy of 96.67% with the standard deviation of 0.37 and also that the proposed method can be used to help speech language pathologist in classifying speech dysfluencies.     

Sound source localization using compressive sensing-based feature extraction and spatial sparsity

In this paper, we propose a source localization algorithm based on a sparse Fast Fourier Transform (FFT)-based feature extraction method and spatial sparsity. We represent the sound source positions as a sparse vector by discretely segmenting the space with a circular grid. The location vector is related to microphone measurements through a linear equation, which can be estimated at each microphone. For this linear dimensionality reduction, we have utilized a Compressive Sensing (CS) and two-level FFT-based feature extraction method which combines two sets of audio signal features and covers both short-time and long-time properties of the signal. The proposed feature extraction method leads to a sparse representation of audio signals. As a result, a significant reduction in the dimensionality of the signals is achieved. In comparison to the state-of-the-art methods, the proposed method improves the accuracy while the complexity is reduced in some cases.