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deep learning-Based Optimization of Cloud Enterprise Resource Planning (ERP) Systems for Adaptive Decision Support and Management Effectiveness Analysis

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IEEE ACCESS 2024年 12卷 193402-193415页

作者： Zhang, Li-Sen George Washington Univ Sch Business Washington DC 20052 USA

Modern enterprise resource planning (ERP) systems face the challenge of handling massive amounts of data and supporting real-time decision-making. With the rapid changes in the market environment, traditional ERP systems are limited in their ability to make adaptive decisions. This study aims to address this issue by integrating deep learning techniques to enhance the management effectiveness of ERP systems. The study uses RNNs, CNNs and DRL models for time series prediction, image recognition and resource optimisation, respectively. The experimental results show that RNN achieves 95% accuracy in demand forecasting, CNN 98% accuracy in image recognition, and DRL achieves more than 10% cost savings in resource optimisation. The integrated ERP system achieved a 42.86% reduction in order processing time, a 25% improvement in inventory turnover, an 8% reduction in operating costs, and a 15% improvement in employee satisfaction. This study demonstrates the effectiveness of deep learning to enhance decision support in ERP systems and provides suggestions for future directions of improvement.

关键词： Optimization Accuracy Convolutional neural networks Adaptive systems Decision making Business Recurrent neural networks Predictive models Enterprise resource planning Adaptation models Thesaurus deep learning cloud-based enterprise resource planning system adaptive decision support management effectiveness

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Perceptible Lightweight Zero-Mean Normalized Cross-Correlation for Infrared Template Matching

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IEEE ACCESS 2024年 12卷 164777-164791页

作者： Lee, Seungeon Kim, Donyung Park, Inho Kim, Geonjong Kim, Sungho Yeungnam Univ Dept Elect Engn Gyongsan 38541 Gyeongsangbug D South Korea Hanwha Syst Seoul 04541 South Korea

Infrared template matching is an essential technology that enables reliable and accurate object detection, recognition, and tracking in complex environments. Perceptible Lightweight Zero-mean normalized cross-correlation (ZNCC) Template Matching (PLZ-TM) has been proposed as a tool for matching infrared images obtained from cameras with different fields of view. Aligning such images is challenging because of the involved differences in thermal distributions, focus discrepancies, background elements, and distortions. The first stage of PLZ-TM involves extracting feature maps from the search and template images using a deep learning network. This deep learning network is designed with a Convolutional Neural Network (CNN) architecture that omits pooling layers, thereby minimizing information loss during extraction. The subsequent stage involves matching the feature maps. The matching method utilizes a lightweight ZNCC (ZNCC) module that employs average pooling for training. The deep learning network is trained to optimize the distribution of the output heatmap and the probability at the correct location of the template image. PLZ-TM delivers excellent performance achieving a processing time of only 3.3 ms in matching a $640\times 480$ search image with a $192\times 144$ template image. Moreover, it attains a matching accuracy of 96% on a dataset obtained from infrared cameras with different fields of view.

关键词： Feature extraction deep learning image matching Accuracy Training Cameras real-time systems Brightness Object tracking Convolutional neural networks Infrared imaging Template matching zero-mean normalized cross correlation real-time infrared image matching convolutional neural network

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Flame lift-off detector based on deep learning neural networks

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COMBUSTION AND FLAME 2024年 260卷

作者： Gharib, Mohsen Tischer, Paul Schulze, Olaf Graebner, Martin Richter, Andreas Fraunhofer Inst Ceram Technol & Syst Winterbergstr 28 D-01277 Dresden Germany TU Bergakad Freiberg Inst Energy Proc Engn & Chem Engn Fuechsmuehlenweg 9 D-09599 Freiberg Germany

This study evaluates the application of deep learning techniques in real-time flame lift-off detection and lift -off length prediction from flame images. A multi-feed test facility equipped with an optical diagnostic system was used to investigate the flame stability of both liquid and solid fuels. A high-speed camera was used to capture flame images, and image processing techniques were employed to extract the data. In the case of flame detection from images captured by high-speed cameras, the temporal resolution of the images is very high, which is beneficial for detecting rapid changes in the flame dynamics. In the current study, the analysis of 10,000 images by images processing methods takes approximately 60 min. Therefore, traditional methods may not be suitable for real-time monitoring, as they may not be able to capture the detailed variations in the flame appearance and intensity. The objective of this study was to find a fast and computationally efficient way to detect flame lift-off in high-speed online flame monitoring system. Two models were used in this study: a convolutional autoencoder and a regression neural network. The results demonstrate the potential of deep learning techniques in online flame diagnostics. The proposed approach provides a fast and efficient way to process 10,000 images in only 3.5 s, making it suitable for implementation in real-time flame monitoring *** and significancePrevious studies in the field of real-time flame diagnostics have predominantly used low sampling rates to capture flame images. However, when high-speed cameras are used, conventional image processing techniques prove inadequate to provide instant results for real-time monitoring systems. This study presents a novel deep learning approach capable of directly detecting flame lift-off from high-speed images without the need for separate segmentation, thereby significantly enhancing the analysis speed. The results obtained in this paper emphasize the ad

关键词： Flame detection Flame lift-off Neural networks Flame stability Gasification Partial oxidation

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Urban Safety: An image-processing and deep-learning-Based Intelligent Traffic Management and Control System

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SENSORS 2021年第22期21卷 7705-7705页

作者： Reza, Selim Oliveira, Hugo S. Machado, Jose J. M. Tavares, Joao Manuel R. S. Univ Porto Fac Engn Rua Dr Roberto Frias S-N P-4200465 Porto Portugal Univ Porto Fac Engn Dept Engn Mecan Rua Dr Roberto Frias S-N P-4200465 Porto Portugal

With the rapid growth and development of cities, Intelligent Traffic Management and Control (ITMC) is becoming a fundamental component to address the challenges of modern urban traffic management, where a wide range of daily problems need to be addressed in a prompt and expedited manner. Issues such as unpredictable traffic dynamics, resource constraints, and abnormal events pose difficulties to city managers. ITMC aims to increase the efficiency of traffic management by minimizing the odds of traffic problems, by providing real-time traffic state forecasts to better schedule the intersection signal controls. Reliable implementations of ITMC improve the safety of inhabitants and the quality of life, leading to economic growth. In recent years, researchers have proposed different solutions to address specific problems concerning traffic management, ranging from image-processing and deep-learning techniques to forecasting the traffic state and deriving policies to control intersection signals. This review article studies the primary public datasets helpful in developing models to address the identified problems, complemented with a deep analysis of the works related to traffic state forecast and intersection-signal-control models. Our analysis found that deep-learning-based approaches for short-term traffic state forecast and multi-intersection signal control showed reasonable results, but lacked robustness for unusual scenarios, particularly during oversaturated situations, which can be resolved by explicitly addressing these cases, potentially leading to significant improvements of the systems overall. However, there is arguably a long path until these models can be used safely and effectively in real-world scenarios.

关键词： intelligent traffic management traffic forecasting traffic signal control image processing deep learning machine vision

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Two-subnet network for real-world image denoising

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MULtimeDIA TOOLS AND APPLICATIONS 2024年第5期83卷 14757-14773页

作者： Zhou, Lianmin Zhou, Dongming Yang, Hao Yang, Shaoliang Yunnan Univ Sch Informat Sci & Engn Kunming 650504 Peoples R China

Previous researches in synthetic noise image denoising have performed well. However, while these methods remove real-world noise, they result in loss of image detail. To solve the problem, this article proposes a two-subnet network for real-world image denoising (TSIDNet). The proposed TSIDNet consists of two subnets, each subnet is designed with independent purpose. The data processing subnet is used to fit the current training data for denoising. We design a cross fusion module in data processing subnet to fuse the encoder information well and then pass the fusion result to the decoder. To decode the context well, we also design a residual attention block based on polarized self-attention as the decoder. The feature extracting subnet based on transfer learning is used to obtain global robust features of the degraded images. By fusing the information from both subnets, high-quality noise-free images can be obtained. Quantitative and qualitative experimental results on four real-world noisy datasets demonstrate the excellent generalization and denoising performance of our method.

关键词： image denoising real-world deep learning Fusion mechanism Transfer learning

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An end-to-end deep learning approach for real-time single image dehazing

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JOURNAL OF real-time image processing 2023年第1期20卷 12页

作者： Jeong, Chi Yoon Moon, KyeongDeok Kim, Mooseop Elect & Telecommun Res Inst Artificial Intelligence Res Lab 218 Gajeong Ro Daejeon 34129 South Korea Univ Sci & Technol Artificial Intelligence 217 Gajeong Ro Daejeon 34113 South Korea

image dehazing methods can restore clean images from hazy images and are popularly used as a preprocessing step to improve performance in various image analysis tasks. In recent times, deep learning-based methods have been used to sharply increase the visual quality of restored images, but they require a long computation time. The processing time of image-dehazing methods is one of the important factors to be considered in order not to affect the latency of the main image analysis tasks such as detection and segmentation. We propose an end-to-end network model for real-time image dehazing. We devised a zoomed convolution group that processes computation-intensive operations with low resolution to decrease the processing time of the network model without performance degradation. Additionally, the zoomed convolution group adopts an efficient channel attention module to improve the performance of the network model. Thus, we designed a network model using a zoomed convolution group to progressively recover haze-free images using a coarse-to-fine strategy. By adjusting the sampling ratio and the number of convolution blocks that make up the convolution group, we distributed small and large computational complexities respectively in the early and later operational stages. The experimental results with the proposed method on a public dataset showed a real-time performance comparable to that of another state-of-the-art (SOTA) method. The proposed network's peak-signal-to-noise ratio was 0.8 dB lower than that of the SOTA method, but the processing speed was 10.4 times faster.

关键词： Single-image dehazing Lightweight dehazing networks image restoration End-to-end learning Convolutional neural networks

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The use of intelligent real-time image acquisition system using data mining technology in news acquisition and editing practice

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JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2024年第2期24卷 639-656页

作者： Ma, Xiaowen Shandong Univ Arts Lib Jinan Shandong Peoples R China

Aiming to address the timely dissemination of news information, this work explores the clever utilization of data mining (DM) technology and deep learning (DL) algorithms to construct an intelligent real-time news image acquisition system to meet the urgency of news dissemination needs. First, this work introduces an intelligent real-time news image acquisition system and provides a detailed analysis of its principles and advantages. Throughout this process, the crucial role of DM technology in news image classification and automation is emphasized, especially in dealing with rapidly evolving news events. Next, the work establishes an intelligent real-time news image acquisition model based on DL algorithms, which integrates the essence of DM technology. Through this fusion, the research objective is to enhance the performance of the news image acquisition system to achieve higher real-time and accuracy, which is vital for the swift delivery of news information. Finally, this work investigates the application of the intelligent news image acquisition system in network communication to ensure its adaptability to various network communication scenarios while maintaining accuracy and real-time capabilities. The research results demonstrate that the application of DM technology in combination with DL algorithms can effectively meet the practical needs of the news industry, enhancing the automation of news image processing and enabling faster information delivery to the audience. Notably, the AlexNet model employed performs exceptionally well, achieving recognition rates of up to 99.6% after data augmentation or equalization processing, with an accuracy of 90.9% and a high specificity of 93.38%. This indicates outstanding overall classification accuracy and negative class accuracy, even when distinguishing between news and non-news scenarios. These results clearly underscore the connection between DM technology and news acquisition and editing practices, and emphasize it

关键词： deep learning data mining real-time image acquisition network security AlexNet model

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Enhanced underwater object detection with YOLO-LDFE: a model for improved accuracy with balanced efficiency

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JOURNAL OF real-time image processing 2025年第2期22卷 1-14页

作者： Liu, Jiaxin Zhou, Rigui Li, Yaochong Ren, Pengju Shanghai Maritime Univ Coll Informat Engn Shanghai 201306 Peoples R China

In underwater image analysis, challenges such as complex environments, low model performance, and slow processing efficiency hinder effective object detection, which is crucial for real-time monitoring. To address these issues, we propose a high-precision underwater object detection model named YOLO-LDFE. To overcome the limitations of existing datasets, we have developed a comprehensive multi-species underwater biological dataset, MCUA, containing 9327 labeled images across 14 categories. The primary contributions of this work are as follows: (1) SPPF_SCSA, which integrates multi-semantic information with channel-space attention mechanisms to enhance performance;(2) the substitution of traditional convolutions with GSConv in C2f, reducing model size while maintaining feature extraction;(3) LEDF, which improves performance through multi-level, dense connections. YOLO-LDFE achieves exceptional results, with an average precision of 93% on the URPC2021 dataset, outperforming existing algorithms while maintaining high detection speed, demonstrating its potential for real-time underwater monitoring.

关键词： Underwater object detection deep learn Feature fusion Lightweight

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deep learning-based borescope image processing for aero-engine blade in-situ damage detection

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AEROSPACE SCIENCE AND TECHNOLOGY 2022年第0期123卷

作者： Shang, Hongbing Sun, Chuang Liu, Jinxin Chen, Xuefeng Yan, Ruqiang Xi An Jiao Tong Univ Sch Mech Engn Xian 710049 Shaanxi Peoples R China

As an important part of aero-engines, blades are used to compress the air entering the engine and produce a lot of thrusts. Catastrophic and sudden accidents caused by blade failure seriously threaten aero-engine operation safety, hence it is necessary to check blades regularly to ensure their health and reliability. As a common non-destructive testing technology, borescope inspection is widely used in health monitoring and maintenance of aero-engine blades. However, traditional borescope inspection mainly relies on artificial vision and it is a time-consuming and experience-dependent process. In this paper, a deep learning-based blade damage detection method is proposed to endow borescope inspection with intelligence. The proposed method pays more attention to texture information, which reflects the types of damages. It is applicable to the situation requiring higher localization accuracy due to the balance between coarse-grained and fine-grained localization. In this method, the enhanced Mask R CNN network with three functions of damage mode classification, damage localization, and damage area segmentation is constructed. Moreover, a texture-focus multi-scale feature fusion network is used to give more attention to the shallow texture information which reflects the shape of damage. Balanced L1 loss is introduced to balance coarse-grained and fine-grained localization by adjusting the gradient and loss of easy samples. We also propose practical evaluation metrics for blade damage detection and make detailed evaluations and discussions. Extensive experiments are conducted on simulated and real aeroengine damaged blade datasets to verify the effectiveness and progressiveness of our method, and the results show the method has great potential for intelligent detection of aero-engine in-situ blade damage.(c) 2022 Elsevier Masson SAS. All rights reserved.

关键词： Aero-engine blade Intelligent borescope inspection image processing Damage detection deep learning

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Enhancing manufacturing process accuracy: A multidisciplinary approach integrating computer vision, machine learning, and control systems

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JOURNAL OF MANUFACTURING PROCESSES 2025年 142卷 453-467页

作者： Ramesh, Kaki Deshmukh, Sandip Ray, Tathagata Parimi, Chandu Birla Inst Sci & Technol Pilani Dept Mech Engn Hyderabad Campus Hyderabad 500078 Telangana India Birla Inst Sci & Technol Pilani Dept Comp Sci & Informat Syst Hyderabad Campus Hyderabad 500078 Telangana India Birla Inst Sci & Technol Pilani Dept Civil Engn Hyderabad Campus Hyderabad 500078 Telangana India

Manufacturing industries face significant challenges in producing high-quality, faultless products within limited timeframes. Conventional human-based inspection methods are still prone to errors and cannot guarantee precise component placement, potentially leading to product failures, user hazards, and substantial financial and reputational losses. This research presents a workflow to automate an inspection system that integrates computer vision, machine learning, image processing, and control systems to address these challenges. The proposed system employs a microcontroller and stepper motors to control a highly calibrated camera, enabling precise and efficient product inspection. At its core, the system utilizes the YOLOv5 model for object detection, specifically identifying hole marks and holes on products pre-assembly. This deep learning model was chosen for its real-time detection capabilities and high accuracy, achieving a mean Average Precision (mAP) of 0.95, which surpasses many current industry standards. Following object detection, advanced image processing techniques are applied to determine the precise position of detected features. Our approach achieves a notable error rate of 0.2 %, offering improvements over traditional inspection methods. Our system offers the potential to reduce inspection processing time and improve fault identification accuracy in real-time applications. Our research contributes to the field of industrial automation by introducing a seamless integration of state-of-the-art computer vision techniques with practical control systems. The system's modular design allows for easy adaptation to various manufacturing environments, benefiting industries with complex assembly processes, such as electronics, automotive manufacturing, etc. While the current implementation focuses on hole detection, future work will explore expanding the system's capabilities to identify a broader range of defects and adapt to different product types. This re

关键词： deep learning image processing Assembly Manufacturing industries Quality assurance

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