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Assessment of Robotic Picking Operations Using a 6 Axis Force/Torque Sensor
No full textThis letter presents a novel architecture for evaluating the success of picking operations that are executed by industrial robots. It is formed by a cascade of machine learning algorithms (kNN and SVM) and uses information obtained by a 6 axis force/torque sensor and, if available, information from the built-in sensors of the robotic gripper. Beyond measuring the success or failure of the entire operation, this architecture makes it possible to detect in real-time when an object is slipping during the picking. Therefore, force and torque signatures are collected during the picking movement of the robot, which is decomposed into five different stages that allows to characterize distinct levels of success over time. Several trials were performed using an industrial robot with two different grippers for picking a long and flexible object. The experiments demonstrate the reliability of the proposed approach under different picking scenarios since, it obtained a testing performance (in terms of accuracy) up to 99.5% of successful identification of the result of the picking operations, considering an universe of 400 attempts. © 2016 IEEE
InGaZnO Thin-Film-Transistor-Based Four-Quadrant High-Gain Analog Multiplier on Glass
Get PDFThis letter presents a novel high-gain four-quadrant analog multiplier using only n-type enhancement indium-gallium-zinc-oxide thin-film-transistors. The proposed circuit improves the gain by using an active load with positive feedback. A Gilbert cell with a diode-connected load is also presented for comparison purposes. Both circuits were fabricated on glass at low temperature (200 degrees C) and were successfully characterized at room temperature under normal ambient conditions, with a power supply of 15 V and 4-pF capacitive load. The novel circuit has shown a gain improvement of 7.2 dB over the Gilbert cell with the diode-connected load. Static linearity response, total harmonic distortion, frequency response, and power consumption are reported. This circuit is an important signal processing building block in large-area sensing and readout systems, specially if data communication is involved
A modified particle swarm optimisation algorithm to solve the part feeding problem at assembly lines
Get PDFThe Assembly Line Part Feeding Problem (ALPFP) is a complex combinatorial optimisation problem concerned with the delivery of the required parts to the assembly workstations in the right quantities at the right time. Solving the ALPFP includes simultaneously solving two sub-problems, namely tour scheduling and tow-train loading. In this article, we first define the problem and formulate it as a multi-objective mixed-integer linear programming model. Then, we carry out a complexity analysis, proving the ALPFP to be NP-complete. A modified particle swarm optimisation (MPSO) algorithm incorporating mutation as part of the position updating scheme is subsequently proposed. The MPSO is capable of finding very good solutions with small time requirements. Computational results are reported, demonstrating the efficiency and effectiveness of the proposed MPSO
Full-Band Quasi-Harmonic Analysis and Synthesis of Musical Instrument Sounds with Adaptive Sinusoids
Get PDFSinusoids are widely used to represent the oscillatory modes of musical instrument sounds in both analysis and synthesis. However, musical instrument sounds feature transients and instrumental noise that are poorly modeled with quasi-stationary sinusoids, requiring spectral decomposition and further dedicated modeling. In this work, we propose a full-band representation that fits sinusoids across the entire spectrum. We use the extended adaptive Quasi-Harmonic Model (eaQHM) to iteratively estimate amplitude- and frequency-modulated (AM-FM) sinusoids able to capture challenging features such as sharp attacks, transients, and instrumental noise. We use the signal-to-reconstruction-error ratio (SRER) as the objective measure for the analysis and synthesis of 89 musical instrument sounds from different instrumental families. We compare against quasi-stationary sinusoids and exponentially damped sinusoids. First, we show that the SRER increases with adaptation in eaQHM. Then, we show that full-band modeling with eaQHM captures partials at the higher frequency end of the spectrum that are neglected by spectral decomposition. Finally, we demonstrate that a frame size equal to three periods of the fundamental frequency results in the highest SRER with AM-FM sinusoids from eaQHM. A listening test confirmed that the musical instrument sounds resynthesized from full-band analysis with eaQHM are virtually perceptually indistinguishable from the original recordings
Curvature and Vibration Sensing Based on Core Diameter Mismatch Structures
Get PDFCore diameter mismatch structures are proposed and experimentally investigated for curvature and vibration sensing. Two configurations are suggested, one approach uses a structure formed by splicing an uncoated short section of multimode fiber between two standard single-mode fibers (SMFs) single-mode-multimode-single-mode (SMS), combined to a fiber optical mirror at its end, and the other approach uses a structure made by splicing a section of SMF between two multimode fibers (SMSMS). In the curvature analysis, the proposed SMS sensor generates the destructive interference patterns when it is bent, varying only the attenuation of the optical signal without wavelength shifts. The SMSMS vibration sensor proved to be suitable to monitor very low frequencies such as 0.1 Hz. The configuration of the proposed sensors presents several interesting features, such as easy fabrication, low cost, high efficiency, and high sensitivity. These advantages make such sensors very useful in a wide range of applications, for instance, structural health monitoring
Event detection from traffic tensors: A hybrid model
Get PDFA traffic tensor or simply origin x destination x time is a new data model for conventional origin/destination (O/D) matrices. Tensor models are traffic data analysis techniques which use this new data model to improve performance. Tensors outperform other models because both temporal and spatial fluctuations of traffic patterns are simultaneously taken into account, obtaining results that follow a more natural pattern. Three major types of fluctuations can occur in traffic tensors: mutations to the overall traffic flows, alterations to the network topology and chaotic behaviors. How can we detect events in a system that is faced with all types of fluctuations during its life cycle? Our initial studies reveal that the current design of tensor models face some difficulties in dealing with such a realistic scenario. We propose a new hybrid tensor model called HTM that enhances the detection ability of tensor models by using a parallel tracking technique on the traffic's topology. However, tensor decomposition techniques such as Tucker, a key step for tensor models, require a complicated parameter that not only is difficult to choose but also affects the model's quality. We address this problem examining a recent technique called adjustable core size Tucker decomposition (ACS-Tucker). Experiments on simulated and real-world data sets from different domains versus several techniques indicate that the proposed model is effective and robust, therefore it constitutes a viable alternative for analysis of the traffic tensors
3D active surfaces for liver segmentation in multisequence MRI images
No full textBiopsies for diagnosis can sometimes be replaced by non-invasive techniques such as CT and MRI. Surgeons require accurate and efficient methods that allow proper segmentation of the organs in order to ensure the most reliable intervention planning. Automated liver segmentation is a difficult and open problem where CT has been more widely explored than MRI. MRI liver segmentation represents a challenge due to the presence of characteristic artifacts, such as partial volumes, noise and low contrast. In this paper, we present a novel method for multichannel MRI automatic liver segmentation. The proposed method consists of the minimization of a 3D active surface by means of the dual approach to the variational formulation of the underlying problem. This active surface evolves over a probability map that is based on a new compact descriptor comprising spatial and multisequence information which is further modeled by means of a liver statistical model. This proposed 3D active surface approach naturally integrates volumetric regularization in the statistical model. The advantages of the compact visual descriptor together with the proposed approach result in a fast and accurate 3D segmentation method. The method was tested on 18 healthy liver studies and results were compared to a gold standard made by expert radiologists. Comparisons with other state-of-the-art approaches are provided by means of nine well established quality metrics. The obtained results improve these methodologies, achieving a Dice Similarity Coefficient of 98.59. © 2016 Elsevier Ireland Ltd
Impacts of Stochastic Wind Power and Storage Participation on Economic Dispatch in Distribution Systems
No full textEvaluating the impact related to stochastic wind generation and generic storage on economic dispatch in distribution system operation is an important issue in power systems. This paper presents the analysis of the impacts of high wind power and storage participation on a distribution system over a period of 24 h using grid reconfiguration for electrical distribution system (EDS) radial operation. In order to meet this objective, a stochastic mixed integer linear programming (SMILP) is proposed, where the balance between load and generation has to be satisfied minimizing the expected cost during the operation period. The model also considers distributed generation (DG) represented by wind scenarios and conventional generation, bus loads represented through a typical demand profile, and generic storage. A case study provides results for a weakly meshed distribution network with 70 buses, describing in a comprehensive manner the effects of stochastic wind scenarios and storage location on distribution network parameters, voltage, substation behavior as well as power losses, and the expected cost of the system
REQAnalytics: A recommender system for requirements maintenance
No full textIn the context of SaaS, where the change requests can be frequent, there is the need for a systematic requirements management process so as to maintain requirements updated and ease the management of changes required to improve the service to provide. Changes to perform need to be prioritized and their impact on the system should be assessed. The extraction and analysis of the use of the servicesprovided through the web and their relationship to the requirements can help identify improvements and help keep the service useful for longer period of time. This paper presents REQAnalytics, a recommender system that collects information on the usage of a web service, relates that information back to the requirements, and generates reports with recommendations and change suggestions that can increase the quality of that service. The proposed approach aims to provide reports of the analysis made in a language closer to the business where, for example, it indicates new workflows and navigation paths, identifies the features that can be removed and presents the relationship between requirements andthe proposed changes helping to maintain the software requirements specification updated and useful. © 2016 SERSC