1,721,001 research outputs found
Defect detection of printed circuit board surface based on an improved YOLOv8 with FasterNet backbone algorithms
No full textPrinted circuit board constitutes a crucial element of electronic equipment, and its surface defects can seriously affect the performance and reliability of the product. To promptly and accurately detect and identify these surface defects, this paper proposes a method for defect detection of printed circuit board surface based on an improved YOLOv8 with FasterNet backbone algorithms. Firstly, FasterNet is employed as the backbone network structure to minimize unnecessary computational overhead and memory accesses, enabling a more streamlined and effective extraction of spatial characteristics. Then, in the Neck layer, the C2f module is exchanged for the C2f_Normalization-based Attention Module. This allows for a more precise focus on important weights, thereby reducing unnecessary computations and parameters. Finally, the loss function of YOLOv8 is substituted with Wise Intersection over Union, which comprehensively considers the surrounding area information and flexibly adjusts the weights. The effectiveness of the method is demonstrated by experimental results on two publicly available PCB datasets. The mAP50 reaches 91.1% with a P of 88.6%, and the mAP50-95 stands at 46.9% on the PCB-AoI dataset. On the HRIPCB dataset, the method attains a mAP50 of 94.4%, a P of 96.1%, and a mAP50-95 of 58.3%. Compared to existing methods, this method shows a comprehensive performance advantage
Resilient machine learning for steel surface defect detection based on lightweight convolution
No full textSteel, as a crucial material extensively used in various fields, has a critical impact on the determination of the stability and reliability of engineering structures. Nevertheless, because of inevitable factors in manufacturing, transportation, and other processes, steel may exhibit various surface defects during production and handling. To address these defects, the investigation puts forward a resilient machine-learning method for steel surface defect detection based on lightweight convolution. First, to reduce redundant features, complexity, and computational cost, the Spatial and Channel Reconstruction Convolution (ScConv) module is added before the Spatial Pyramid Pooling-Fast (SPPF) within the YOLOv8n's backbone network. Second, in the Neck layer, lightweight convolution GSConv is used to replace the convolutional modules, and the efficient cross-stage partial network (CSP) module, VoV-GSCSP is substituted for the C2f module to alleviate the model burden while maintaining accuracy. Then, to focus on important information related to the current task, the Coordinate Attention module is added to the Neck layer. Finally, the activation function of YOLOv8n has been swapped for the Leaky Rectified Linear Unit (LeakyReLU) to effectively address issues such as gradient vanishing and overfitting. The method achieved a mean Average Precision (mAP) of 77.7% on the NEU-DET dataset, which is an improvement of 4.7% over the original YOLOv8n. Additionally, the frames per second (FPS) reached 17.36 f/s, representing a 5.79 f/s increase compared to the original YOLOv8n. On the GC10-DET dataset, mAP improves by 5.5%, with a FPS of 15.63 f/s. A plethora of experimentation on both datasets illustrates the method's robustness, meeting the precision criteria for detecting metal defects
Intelligent emergency traffic signal control system with pedestrian access
No full textWith the integration of artificial intelligence and traffic systems, intelligent traffic systems are utilizing enhanced perception coverage and computational capabilities to provide data -intensive solutions, achieving higher levels of performance than traditional systems. This paper combines the D3QN algorithm from deep reinforcement learning with practical issues and proposes an intelligent emergency traffic signal control system based on Deep Reinforcement Learning (DRL). The system takes into account pedestrian movement and utilizes real -time traffic data and environmental information to model traffic flow and road conditions within a novel state space. It employs the Dueling Double Deep Q-Network (D3QN) to optimize signal control strategies. The system dynamically adjusts signal timings to enhance operational efficiency at intersections. By using the Weibull distribution to simulate realistic traffic congestion and actual traffic data from Shanyin Road in Hangzhou for validation, the results demonstrate that this method converges faster and is more stable compared to other methods, significantly reducing traffic congestion. Furthermore, by incorporating pedestrian movement, this method reduces pedestrian waiting times by 44.736% during peak periods and 22.95% during off-peak periods, while maintaining comparable vehicle queue lengths, delay times, and carbon dioxide emissions. This approach shows the potential improvement of smart urban mobility and resolving intersection congestion challenges
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Stability analysis and stabilization of discrete-time switched nonlinear systems with mode-dependent average dwell time under nested actuator saturation
No full textIn this paper, the stability analysis and stabilization of a class of mode-dependent mean residence time-based discrete-time switching nonlinear systems are addressed. More specifically, under the mode-dependent average dwell time (MDADT) switching mode, combined with the practical problem, the nonlinear factor of nested actuator saturation (NAS) is introduced. Firstly, in order to ensure the system stability, based on the parameter-dependent discontinuous switching Lyapunov function and several basic lemmas, a state feedback controller is designed that makes the closed-loop system (CLS) achieve local exponential stability (LES) by solving the optimal problem in terms of linear matrix inequalities (LMIs). Secondly, the system considers the maximum attraction domain that can be achieved by the NAS system under the conditions. The simulation results show the effectiveness of the proposed design method. At the same time, the efficiency of the proposed method is verified via a water tank example
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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