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Explosion smoke and dust detection algorithm based on improved YOLOv8

Explosion smoke and dust detection algorithm based on improv...

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Computer Information Science and Application Technology (CISAT), international conference on

作者： Xuesong Luo Tonghui Qian Xingzhou Chen Wenyu Xiong Institute of Intelligent Manufacturing Jianghan University Wuhan China Wuhan Donglv Technology Co. Ltd Wuhan China

ISBN: (数字)9798350375107

ISBN: (纸本)9798350375114

In response to the low accuracy, slow detection speed, and missed detections of the blasting smoke and dust detection algorithm, this paper proposes the YOLOv8-AIES algorithm. By introducing C2f-AgileBlock to reduce parameters and computational complexity, using iRMB-EMA hybrid attention model to enhance feature extraction, and optimizing feature fusion with SlimNeck, the detection speed of the model is improved, missed detections are reduced, while maintaining lightweight and fast response. the experimental results show that YOLOv8-AIES performs well in mAP@0.5 On the Recall, it reached 94.9% and 93.9%, respectively, with a significant decrease in parameter and computational complexity, making it suitable for blast smoke and dust detection in practical scenarios.

关键词： Information science Accuracy Computational modeling Feature extraction Explosions Detection algorithms Computational complexity

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7th international conference on Emerging Research in Computing, Information, Communication and Applications, ERCICA 2022

7th International Conference on Emerging Research in Computi...

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7th international conference on Emerging Research in Computing, Information, Communication and Applications, ERCICA 2022

ISBN: (纸本)9789811954818

the proceedings contain 85 papers. the special focus in this conference is on Emerging Research in Computing, Information, Communication and Applications. the topics include: Gesture Detection Using Accelerometer and Gyroscope;preface;Teaching Learning-Based Optimization with Learning Enthusiasm Mechanism for Optimal Control of PV Inverters in Utility Grids for Techno-Economic Goals;to Monitor Yoga Posture Without Intervention of Human Expert Using 3D Kinematic Pose Estimation Model—A Bottom-Up Approach;Upshot and Disparity of AI Allied Approaches Over Customary Techniques of Assessment on Chess—An Observation;network Intrusion Detection Using Neural Network Techniques;performing Cryptanalysis on the Secure Way of Communication Using Purple Cipher Machine;artificial Intelligence and Machine Learning for Foundry Industry—A Case Study of Belagavi Foundry Industry;conversion of Sign Language to Text Using Machine Learning;a Hexagonal Sierpinski Fractal Antenna for Multiband Wireless Applications;Multi-level Hierarchical Information-Driven Risk-Sensitive Routing Protocol for Mobile-WSN: MHIR-SRmW;Target Classification Using CNN-LSTM Network with Reduced Sample Size in Surveillance Radar;machine Learning Framework for Prediction of Parkinson’s Disease in Cloud Environment;iron Oxide Nanoparticle Image Analysis Using Machine Learning algorithms;bankruptcy Prediction Using Bi-Level Classification Technique;Infant Brain MRI Segmentation Using Deep Volumetric U-Net with Gamma Transformation;analysis of Deep Learning Architecture-Based Classifier for the Cervical Cancer Classification;covid Vaccine Adverse Side-Effects Prediction with Sequence-to-Sequence Model;Comparison Between ResNet 16 and Inception V4 Network for COVID-19 Prediction;Computational Deep Learning Models for Detection of COVID-19 Using Chest X-Ray Images;an Ensemble Approach for Detecting Malaria Using Classification algorithms;ioT-Enabled Intelligent Home Using Google Assistant.

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N-Path Mixer with Wide Rejection Including the 7thHarmonic for Low Power Multi-standard Receivers 20

N-Path Mixer with Wide Rejection Including the 7thHarmonic f...

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20th IEEE international Interregional NEWCAS conference, NEWCAS 2022

作者： Shakoush, Ali Al Ibrahim, Sana Subias, Serge Podevin, Florence Barragan, Manuel J. Pino-Monroy, Dayana A. Bendjeddou, Imadeddine Lauga-Larroze, Estelle Fesquet, Laurent Taris, thierry Bourdel, Sylvain Université Grenoble Alpes Tima Grenoble Inp Grenoble38000 France Université Grenoble Alpes Tima Cnrs Grenoble38000 France Université de Bordeaux Ims Umr 5818 Cnrs TalenceF-33405 France

ISBN: (数字)9781665401050

ISBN: (纸本)9781665401050

this paper proposes an N-Path mixer rejecting a wide bandwidth 1:8, i.e. including the 7th harmonic, while keeping a low complexity, particularly suitable for low power multistandard receivers. the main principle is based on an accurate choice of the switch control signals and of the gains of the amplifiers stage. the circuit has been manufactured in 28nm FDSOI technology. Measurements prove the theory with an harmonic rejection higher than 45 dB for the 3rd, 5th, and 7th harmonics, an in-band IIP3 of -3.5 dBm, for a power consumption of 22 mW. © 2022 IEEE.

关键词： Mixers (machinery)

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Evaluation of User Satisfaction Towards WfMS Implementation in Indonesian Food Company Using Structural Equation Modeling 7

Evaluation of User Satisfaction Towards WfMS Implementation ...

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7th Information Systems international conference, ISICO 2023

作者： Nurkasanah, Ika Savitri, Ni Luh Putu Chandra Mahendrawathi, E.R. Institut Teknologi Sepuluh Nopember Sukolilo Surabaya60111 Indonesia PT. Astra International Tbk. Sunter Jakarta14330 Indonesia

XYZ company implements Workflow Management System (WfMS) to make business processes more efficient. Unfortunately, current WfMS evaluation only inspects operational side, but neglects user satisfaction as critical success factor. this research refers to Poelmans's model to evaluate factors influencing employee satisfaction with WfMS by performing PLS-SEM. Results show that input quality has the most positive relationship with system quality, while perceived usefulness is determined by information and system quality. Perceived use, information quality, and system quality increase user satisfaction. Service quality and WfMS dependency had no significant effect. Since this research is limited to general business processes in manufacturing companies, future research should address deeper analysis by comparing user satisfaction among specific characteristics and different complexity level of business processes. © 2023 the Authors. Published by Elsevier B.V.

关键词： Industrial research

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Fast vehicle detection algorithm based on lightweight YOLO7-tiny 5

Fast vehicle detection algorithm based on lightweight YOLO7-...

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5th international conference on Computer Vision and Data Mining, ICCVDM 2024

作者： Shi, Qiuxiang Zhong, Fei Li, Bo Xu, Ziwei Hong, Junjie Li, Ruoxi China Tobacco Hubei Industrial LLC Wuhan430072 China School of Mechanical Engineering Hubei University of Technology Wuhan430068 China

ISBN: (纸本)9781510682863

the swift and precise detection of vehicles plays a significant role in intelligent transportation systems. Current vehicle detection algorithms encounter challenges of high computational complexity, low detection rate, and limited feasibility on mobile devices. To address these issues, this paper proposes a lightweight vehicle detection algorithm based on YOLOv7-tiny (You Only Look Once version seven) called Ghost-YOLOv7. the width of model is scaled to 0.5 and the standard convolution of the backbone network is replaced with Ghost convolution to achieve a lighter network and improve the detection speed;then a self-designed Ghost bi-directional feature pyramid network (Ghost-BiFPN) is embedded into the neck network to enhance feature extraction capability of the algorithm and enriches semantic information;and a Ghost Decouoled Head (GDH) is employed for accurate prediction of vehicle location and species;finally, a coordinate attention mechanism is introduced into the output layer to suppress environmental interference. the WIoU loss function is employed to further enhance the detection accuracy. Ablation experiments results on the PASCAL VOC dataset demonstrate that Ghost-YOLOv7 outperforms the original YOLOv7-tiny model. It achieving a 29.8% reduction in computation, 37.3% reduction in the number of parameters, 35.1% reduction in model weights, 1.1% higher mean average precision (mAP), the detection speed is higher 27FPS compared with the original algorithm. Ghost-YOLOv7 was also compared on BIT-vehicle datasets as well, and the results show that this algorithm has the overall best performance. © 2024 SPIE.

关键词： Vehicle detection

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Exploring the Computational complexity of SAT Counting and Uniform Sampling with Phase Transitions 24

Exploring the Computational Complexity of SAT Counting and U...

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46th international conference on Software Engineering: Companion, ICSE-Companion 2024

作者： Zeyen, Olivier Cordy, Maxime Perrouin, Gilles Acher, Mathieu University of Luxembourg SnT Luxembourg Université de Namur Belgium Univ Rennes Inria CNRS IRISA France

ISBN: (纸本)9798400705021

PROBLEM: Uniform Random Sampling (URS) is the problem of selecting solutions (models) from a Boolean formula such that each solution gets the same probability of being selected. URS has many applications. In large configurable software systems, one wants an unbiased sample of configurations to look for bugs at an affordable cost [12, 13]. Other applications of URS include deep learning verification (to sample inputs from unknown distributions) [2] and evolutionary algorithms (to initialize the input population) [4]. Model counting (#SAT) - the problem of counting the number of solutions of a Boolean formula - is closely related to URS. these two problems generally rely on the same principles and heuristics to be solved;URS can also be reduced to #SAT [11]. Beyond URS, #SAT has many applications in (configurable) software engineering, such as variability reduction [15], variability evolution and analysis [7, 16], feature prioritization [15] and bug fix prioritization [8]. URS and #SAT are both challenging to solve efficiently: existing solutions hardly scale to real-world formulas [13]. Unlike the traditional problem of satisfiability solving (SAT), the reasons behind the complexity of URS and #SAT have been underexplored. © 2024 IEEE Computer Society. All rights reserved.

关键词： Deep learning

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Deep Surrogate Network Enhancement of Non-dominated Sorting Genetic algorithms for Improved Multi-objective Optimization 7th

Deep Surrogate Network Enhancement of Non-dominated Sorting ...

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7th IFToMM Asian conference on Mechanism and Machine Science, Asian MMS 2024

作者： Ibarayev, Sayat Omarov, Batyrkhan Amanov, Bekzat Ibrayeva, Arman Momynkulov, Zeinel Joldasbekov Institute of Mechanics and Engineering Almaty Kazakhstan International Information Technology University Almaty Kazakhstan

ISBN: (纸本)9783031675683

this research paper introduces an innovative approach to multi-objective optimization by integrating a Deep Surrogate Network (DSN) with the Non-Dominated Sorting Genetic Algorithm (NSGA), resulting in the novel NSGA-DSN model. Aimed at addressing the computational challenges associated with traditional NSGA models, the NSGA-DSN model leverages the advanced predictive capabilities of deep learning to approximate the fitness landscape, thereby significantly reducing the number of fitness evaluations required during the optimization process. Our comparative analyses reveal that the NSGA-DSN model not only achieves faster convergence rates but also produces solutions of higher accuracy and reduced mean squared errors compared to conventional NSGA approaches. these advancements underscore the model’s potential to enhance the efficiency and effectiveness of solving complex multi-objective optimization problems. Despite its reliance on high-quality training data and the computational demands of deep learning, the NSGA-DSN model represents a significant step forward in evolutionary algorithms, offering a promising solution for real-world applications across various domains. this paper discusses the development, implementation, and comparative performance of the NSGA-DSN model, highlighting its contributions to the field of computational optimization. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Genetic algorithms

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Proceedings of 2022 7th international conference on Machine Learning Technologies, ICMLT 2022

Proceedings of 2022 7th International Conference on Machine ...

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7th international conference on Machine Learning Technologies, ICMLT 2022

ISBN: (纸本)9781450395748

the proceedings contain 45 papers. the topics discussed include: machine learning in textual criticism — an examination of the performance of supervised machine learning algorithms in reconstructing the text of the Greek new testament;monitoring and control of environmental parameters to predict growth in citrus crops using machine learning;exploratory study of battery aging analysis with machine learning models to complete multi-physical ones for more adaptable systems;predictive modeling of HR dynamics using machine learning;the application of lasso in stock selection;emotion recognition of Chinese traditional folk music using an assembling machine learning method;a machine learning based experimental analysis for rugby sevens in China national games;and application of weight-based feature selection in artificial neural network with transition layer.

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A Study on the Effect of Antenna Array Parameters in Optimizing the Radiation Pattern

A Study on the Effect of Antenna Array Parameters in Optimiz...

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international conference on Computing Communication Control and Automation (ICCUBEA)

作者： Archisman Banerjee Sankarshan Pradhan Ayan Dutta Bitan Misra School of Electronics Engineering KIIT-Deemed University Bhubaneswar India HCL Technologies Kolkata India Department of Computer Science and Engineering Techno International New Town Kolkata India

In this work, aconcentric elliptical array of antennas is optimized with the help of three relatively new and efficient meta-heuristic algorithms; namely, Teaching Learning Based Optimization (TLBO), Multi-Verse Optimization (MVO), and Grey Wolf Optimization(GWO). this work demonstrates a detailed study of the effect of various antenna array contro lvariables for the improvemen to normalized power patterns generated by elliptical array *** objective of this study is to reduce Side Lobe Level (SLL) value to the possible amount and simultaneously reach the desired First Null BeamWidth (FNBW) value. A number of cases are studied and the outcomes are exhibited in a desegregated way with the best optimal values of eccentricities, semi-major axis, and inter-element spacing. An investigation is performed to explore theperformance of the algorithms based on the common antenna parameters such as the number of elements, obtained SLL, and obtained FNBW. Additionally, the statistical specifications of all the algorithms are examined in terms of mean, standard deviation, best objective function, and time complexity. T-test iscarried out at the end of this study, separately on each acquired data set to authenticate the result.

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7th international conference on Information, Communication and Computing Technology, ICICCT 2022

7th International Conference on Information, Communication a...

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7th international conference on Information, Communication and Computing Technology, ICICCT 2022

ISBN: (纸本)9783031209765

the proceedings contain 11 papers. the special focus in this conference is on Information, Communication and Computing Technology. the topics include: A Systematic Study on LoRa Communication in IoT: Implementation Challenges and Research Solutions;A Low complexity CFO Reduction Technique for LFDMA Systems;DICCh-D: Detecting IPv6-Based Covert Channels Using DNN;image Caption Generation for Low Light Images;a Study on Feature Selection for Gender Detection in Speech Processing for Assamese Language;Bengali Fake News Detection: Transfer Learning Based Technique with Masked LM Process by BERT;crowd Counting Using Federated Learning and Domain Adaptation;news Headline Generation Using Abstractive Text Summarization;preface.

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