Time-efficient fault detection and diagnosis system for analog circuits

Time-efficient fault analysis and diagnosis of analog circuits are the most important prerequisites to achieve online health monitoring of electronic equipments, which are involving continuing challenges of ultra-large-scale integration, component tolerance, limited test points but multiple faults. This work reports an FPGA (field programmable gate array)-based analog fault diagnostic system by applying two-dimensional information fusion, two-port network analysis and interval math theory. The proposed system has three advantages over traditional ones. First, it possesses high processing speed and smart circuit size as the embedded algorithms execute parallel on FPGA. Second, the hardware structure has a good compatibility with other diagnostic algorithms. Third, the equipped Ethernet interface enhances its flexibility for remote monitoring and controlling. The experimental results obtained from two realistic example circuits indicate that the proposed methodology had yielded competitive performance in both diagnosis accuracy and time-effectiveness, with about 96% accuracy while within 60 ms computational time.Peer reviewedFinal Published versio

Real-Time Fault Detection and Diagnosis System for Analog and Mixed-Signal Circuits of Acousto-Magnetic EAS Devices

© 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.The paper discusses fault diagnosis of the electronic circuit board, part of acousto-magnetic electronic article surveillance detection devices. The aim is that the end-user can run the fault diagnosis in real time using a portable FPGA-based platform so as to gain insight into the failures that have occurred.Peer reviewe

Optimal Modeled Six-Phase Space Vector Pulse Width Modulation Method for Stator Voltage Harmonic Suppression

Dual Y shift 30 six-phase motors are expected to be extensively applied in high-power yet energy-effective fields, and a harmonic-suppressing control strategy plays a vital role in extending their prominent features of low losses and ultra-quiet operation. Aiming at the suppression of harmonic voltages, this paper proposes a six-phase space vector pulse width modulation method based on an optimization model, namely OM-SVPWM. First, four adjacent large vectors are employed in each of 12 sectors on a fundamental sub-plane. Second, the optimization model is constructed to intelligently determine activation durations of the four vectors, where its objective function aims to minimize the synthesis result on a harmonic sub-plane, and its constraint condition is that the synthesis result on the fundamental sub-plane satisfies a reference vector. Finally, to meet the real-time requirement, optimum solutions are obtained by using general central path following algorithm (GCPFA). Simulation and experiment results prove that, the OM-SVPWM performs around 37% better than a state-of-the-art competitive SVPWM in terms of harmonics suppression, which promise the proposed OM-SVPWM conforms to the energy-effective direction in actual engineering applications.Peer reviewe

Dynamic Virtual Page-based Flash Translation Layer with Novel Hot Data Identification and Adaptive Parallelism Management

Solid-state disks (SSDs) tend to replace traditional motor-driven hard disks in high-end storage devices in past few decades. However, various inherent features, such as out-of-place update [resorting to garbage collection (GC)] and limited endurance (resorting to wear leveling), need to be reduced to a large extent before that day comes. Both the GC and wear leveling fundamentally depend on hot data identification (HDI). In this paper, we propose a hot data-aware flash translation layer architecture based on a dynamic virtual page (DVPFTL) so as to improve the performance and lifetime of NAND flash devices. First, we develop a generalized dual layer HDI (DL-HDI) framework, which is composed of a cold data pre-classifier and a hot data post-identifier. Those can efficiently follow the frequency and recency of information access. Then, we design an adaptive parallelism manager (APM) to assign the clustered data chunks to distinct resident blocks in the SSD so as to prolong its endurance. Finally, the experimental results from our realized SSD prototype indicate that the DVPFTL scheme has reliably improved the parallelizability and endurance of NAND flash devices with improved GC-costs, compared with related works.Peer reviewe

Self-Learning Hot Data Prediction: Where Echo State Network Meets NAND Flash Memories

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Well understanding the access behavior of hot data is significant for NAND flash memory due to its crucial impact on the efficiency of garbage collection (GC) and wear leveling (WL), which respectively dominate the performance and life span of SSD. Generally, both GC and WL rely greatly on the recognition accuracy of hot data identification (HDI). However, in this paper, the first time we propose a novel concept of hot data prediction (HDP), where the conventional HDI becomes unnecessary. First, we develop a hybrid optimized echo state network (HOESN), where sufficiently unbiased and continuously shrunk output weights are learnt by a sparse regression based on L2 and L1/2 regularization. Second, quantum-behaved particle swarm optimization (QPSO) is employed to compute reservoir parameters (i.e., global scaling factor, reservoir size, scaling coefficient and sparsity degree) for further improving prediction accuracy and reliability. Third, in the test on a chaotic benchmark (Rossler), the HOESN performs better than those of six recent state-of-the-art methods. Finally, simulation results about six typical metrics tested on five real disk workloads and on-chip experiment outcomes verified from an actual SSD prototype indicate that our HOESN-based HDP can reliably promote the access performance and endurance of NAND flash memories.Peer reviewe

Generalized Completed Local Binary Patterns for Time-Efficient Steel Surface Defect Classification

© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted ncomponent of this work in other works.Efficient defect classification is one of the most important preconditions to achieve online quality inspection for hot-rolled strip steels. It is extremely challenging owing to various defect appearances, large intraclass variation, ambiguous interclass distance, and unstable gray values. In this paper, a generalized completed local binary patterns (GCLBP) framework is proposed. Two variants of improved completed local binary patterns (ICLBP) and improved completed noise-invariant local-structure patterns (ICNLP) under the GCLBP framework are developed for steel surface defect classification. Different from conventional local binary patterns variants, descriptive information hidden in nonuniform patterns is innovatively excavated for the better defect representation. This paper focuses on the following aspects. First, a lightweight searching algorithm is established for exploiting the dominant nonuniform patterns (DNUPs). Second, a hybrid pattern code mapping mechanism is proposed to encode all the uniform patterns and DNUPs. Third, feature extraction is carried out under the GCLBP framework. Finally, histogram matching is efficiently accomplished by simple nearest-neighbor classifier. The classification accuracy and time efficiency are verified on a widely recognized texture database (Outex) and a real-world steel surface defect database [Northeastern University (NEU)]. The experimental results promise that the proposed method can be widely applied in online automatic optical inspection instruments for hot-rolled strip steel.Peer reviewe

Surface Defect Classification for Hot-Rolled Steel Strips by Selectively Dominant Local Binary Patterns

Developments in defect descriptors and computer vision-based algorithms for automatic optical inspection (AOI) allows for further development in image-based measurements. Defect classification is a vital part of an optical-imaging-based surface quality measuring instrument. The high-speed production rhythm of hot continuous rolling requires an ultra-rapid response to every component as well as algorithms in AOI instrument. In this paper, a simple, fast, yet robust texture descriptor, namely selectively dominant local binary patterns (SDLBPs), is proposed for defect classification. First, an intelligent searching algorithm with a quantitative thresholding mechanism is built to excavate the dominant non-uniform patterns (DNUPs). Second, two convertible schemes of pattern code mapping are developed for binary encoding of all uniform patterns and DNUPs. Third, feature extraction is carried out under SDLBP framework. Finally, an adaptive region weighting method is built for further strengthening the original nearest neighbor classifier in the feature matching stage. The extensive experiments carried out on an open texture database (Outex) and an actual surface defect database (Dragon) indicates that our proposed SDLBP yields promising performance on both classification accuracy and time efficiencyPeer reviewe

Preparation and ageing-resistant properties of polyester composites modified with functional nanoscale additives

This study investigated ageing-resistant properties of carboxyl-terminated polyester (polyethylene glycol terephthalate) composites modified with nanoscale titanium dioxide particles (nano-TiO(2)). The nano-TiO(2) was pretreated by a dry coating method, with aluminate coupling agent as a functional grafting additive. The agglomeration resistance was evaluated, which exhibited significant improvement for the modified nanoparticles. Then, the effects of the modified nano-TiO(2) on the crosslinking and ageing-resistant properties of the composites were studied. With a real-time Fourier transform infrared (FT-IR) measurement, the nano-TiO(2) displayed promoting effect on the crosslinking of polyester resin with triglycidyl isocyanurate (TGIC) as crosslinking agent. Moreover, the gloss retention, colour aberration and the surface morphologies of the composites during accelerated UV ageing (1500 hours) were investigated. The results demonstrated much less degree of ageing degradation for the nanocomposites, indicating an important role of the nano-TiO(2) in improving the ageing-resistant properties of synthetic polymer composites

Jointly optimized echo state network for short-term channel state information prediction of fading channel

© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Accurately obtaining channel state information (CSI) in wireless systems is significant but challenging. This paper focuses the technique of machine-learning-based channel estimation. In particular, a jointly optimized echo state network (JOESN) is proposed to form a concept of the CSI prediction which is made up of two interacting aspects of output weight regularization and initial parameter optimization. First, in order to enhance noise robustness, a sparse regression based on L2 regularization is employed to finely learn the output weights of ESN. Second, vital reservoir parameters (i.e., global scaling factor, reservoir size, scaling coefficient and sparsity degree) are learned by a linear-weighted particle swarm optimization (LWPSO) for further improve the prediction accuracy and reliability. The experiments about computational complexity and three evaluating metrics are carried out on two chaotic benchmarks and one real-world dataset. The analyzed results indicate that the JOESN performs promisingly on multivariate chaotic time series prediction.Final Accepted Versio