Journal of Signal Processing Systems
Springer Science+Business Media
About: Journal of Signal Processing Systems is an academic journal published by Springer Science+Business Media. The journal publishes majorly in the area(s): Computer science & Artificial intelligence. It has an ISSN identifier of 1939-8115. Over the lifetime, 637 publications have been published receiving 3646 citations. The journal is also known as: Journal of signal processing systems (Internet) & Journal of signal processing systems for signal, image, and video technology (Print).
Topics: Computer science, Artificial intelligence, Pattern recognition (psychology), Convolutional neural network, Field-programmable gate array
TL;DR: Effectiveness and feasibility of the 1D CNN based fault diagnosis method is validated by applying it to two commonly used benchmark real vibration data sets and comparing the results with the other competing intelligent fault diagnosis methods.
Abstract: Timely and accurate bearing fault detection and diagnosis is important for reliable and safe operation of industrial systems. In this study, performance of a generic real-time induction bearing fault diagnosis system employing compact adaptive 1D Convolutional Neural Network (CNN) classifier is extensively studied. In the literature, although many studies have developed highly accurate algorithms for detecting bearing faults, their results have generally been limited to relatively small train/test data sets. As opposed to conventional intelligent fault diagnosis systems that usually encapsulate feature extraction, feature selection and classification as distinct blocks, the proposed system takes directly raw time-series sensor data as input and it can efficiently learn optimal features with the proper training. The main advantages of the 1D CNN based approach are 1) its compact architecture configuration (rather than the complex deep architectures) which performs only 1D convolutions making it suitable for real-time fault detection and monitoring, 2) its cost effective and practical real-time hardware implementation, 3) its ability to work without any pre-determined transformation (such as FFT or DWT), hand-crafted feature extraction and feature selection, and 4) its capability to provide efficient training of the classifier with limited size of training data set and limited number of BP iterations. Effectiveness and feasibility of the 1D CNN based fault diagnosis method is validated by applying it to two commonly used benchmark real vibration data sets and comparing the results with the other competing intelligent fault diagnosis methods.
TL;DR: This paper proposes an image retrieval algorithm based on the combination of color and shape features that effectively improves the accuracy of image retrieval.
Abstract: With the development of content-based image retrieval technology, the retrieval efficiency of image retrieval technology is getting higher and higher. For different data images, image retrieval based on color features and shape features can be used to improve retrieval efficiency. However, when a single image feature is retrieved, its retrieval efficiency still cannot meet people’s needs. In this paper, we propose an image retrieval algorithm based on the combination of color and shape features. The cumulative histogram method is used to calculate the color features of the image, and 7 Hu invariant moments are calculated as shape features. The color and shape features are combined with certain weights, and the Euclidean distance is used as the similarity measure. Finally, the image is retrieved, and the related experiments are passed. By comparing with related experiments, the algorithm effectively improves the accuracy of image retrieval.
TL;DR: In this paper, the performance of the D-Wave 2X quantum annealer for finding a maximum clique in a graph is evaluated, and the authors provide formulations of the problem as a quadratic unconstrained binary optimization (QUBO) problem.
Abstract: This paper assesses the performance of the D-Wave 2X (DW) quantum annealer for finding a maximum clique in a graph, one of the most fundamental and important NP-hard problems Because the size of the largest graphs DW can directly solve is quite small (usually around 45 vertices), we also consider decomposition algorithms intended for larger graphs and analyze their performance For smaller graphs that fit DW, we provide formulations of the maximum clique problem as a quadratic unconstrained binary optimization (QUBO) problem, which is one of the two input types (together with the Ising model) acceptable by the machine, and compare several quantum implementations to current classical algorithms such as simulated annealing, Gurobi, and third-party clique finding heuristics We further estimate the contributions of the quantum phase of the quantum annealer and the classical post-processing phase typically used to enhance each solution returned by DW We demonstrate that on random graphs that fit DW, no quantum speedup can be observed compared with the classical algorithms On the other hand, for instances specifically designed to fit well the DW qubit interconnection network, we observe substantial speed-ups in computing time over classical approaches
TL;DR: The experimental results show the delivery ratio about messages is improved significantly and the effectiveness of EEIS is verified, which proves the ability of this equivalent-exchange-based data forwarding incentive scheme to improve network performance.
Abstract: As nodes have limited resources in the socially aware networks, they will have strong selfish behaviors, such as not forwarding messages and losing packets, which will lead to poor network performance. Thus, an equivalent-exchange-based data forwarding incentive scheme (EEIS) will be proposed in this paper. It is main that messages forwarding will be abstracted into a transaction in EEIS. The buyer and seller respectively make a price about the message according to its own resource state and negotiate twice the pricing both side until they agree, then the buyer will send the message and pay a certain virtual currency to the seller. Otherwise, the next message will continue to be traded. Meanwhile, both parties’ resource status, wealth status and the price of messages must be open and transparent to prevent the nodes from making false pricing during the transaction. Ultimately, the experimental results show the delivery ratio about messages is improved significantly and verify the effectiveness of EEIS.
TL;DR: A multiscale learning neural network that contains one-dimension (1D) and two- dimension (2D) convolution channels is proposed that can learn the local correlation of adjacent and nonadjacent intervals in periodic signals, such as vibration data.
Abstract: With the application of intelligent manufacturing becoming more and more widely, the losses caused by mechanical faults of equipment increase. Identifying and troubleshooting faults in an early stage are important. The process of traditional data-driven fault diagnosis method includes data acquisition, fault classification, and feature extraction, in which classification accuracy is directly affected by the result of feature extraction. As a common deep learning method in image recognition, the convolutional neural network (CNN) demonstrates good performance in fault diagnosis. CNN can adaptively extract features from original signals and eliminate the effect of conventional handcrafted features. In this study, a multiscale learning neural network that contains one-dimension (1D) and two-dimension (2D) convolution channels is proposed. The network can learn the local correlation of adjacent and nonadjacent intervals in periodic signals, such as vibration data. The Paderborn data set is came into use to demonstrate the classification accuracy of the method which is brought forward, which includes three conditions of healthy, outer ring (OR) damage and inner ring (IR) damage. The classification accuracy of the method which is put forward is up to 98.58%. The same dataset was applied to test the classification accuracy of support vector machine (SVM) for comparison. And the proposed multiscale learning neural network demonstrates considerable improvements.