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1st International Conference on Applied Intelligence, ICAI 2023

作者： Li, Hang Yang, Rui Lan, Rushi Luo, Xiaonan Guangxi Key Laboratory of Image Graphics and Intelligent Processing Guilin University of Electronic Science and Technology Guangxi Guilin China Satellite Navigation Positioning and Location Service National and Local Joint Engineering Research Center Guangxi Guilin China

ISBN: (纸本)9789819709021

The anchor-free object detection method avoids the complex hyper-parameter setting problem of the traditional anchor-based method, and has more advantages in accuracy and speed. However, it still has some problems such as low precision of small-scale object detection and poor detection result under complex background. To solve this problem, an improved anchor-free object detection method based on FCOS is proposed in this paper. By adding an efficient channel attention mechanism module to the feature extraction network and using a local cross-channel interaction strategy without dimensionality reduction, the 1D convolution kernel size is adaptively selected to obtain useful dependencies between channels and improve feature extraction capability. A context extraction module is designed in feature fusion network to explore context information from multiple receptive fields and improve classification accuracy. In the training stage, DIoU loss is used to make the regression of the border more stable and accurate, and the training process converges faster. The proposed method is evaluated on COCO2017. The experimental results show that compared with the baseline FCOS method, the average accuracy of the proposed method is improved by 1.3%, which has advantages in comprehensive performance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Object detection

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M3YOLOv5: Feature enhanced YOLOv5 model for mandibular fracture detection

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computers in Biology and Medicine 2024年 173卷 108291-108291页

作者： Zhou, Tao Wang, Hongwei Du, Yuhu Liu, Fengzhen Guo, Yujie Lu, Huiling School of Computer Science and Engineering North Minzu University Yinchuan750021 China School of Medical Information and Engineering Ningxia Medical University Yinchuan750004 China Key Laboratory of Image and Graphics Intelligent Processing of State Ethnic Affairs Commission North Minzu University Yinchuan750021 China

Background: It is very important to detect mandibular fracture region. However, the size of mandibular fracture region is different due to different anatomical positions, different sites and different degrees of force. It is difficult to locate and recognize fracture region accurately. Methods: To solve these problems, M3YOLOv5 model is proposed in this paper. Three feature enhancement strategies are designed, which improve the ability of model to locate and recognize mandibular fracture region. Firstly, Global-Local Feature Extraction Module (GLFEM) is designed. By effectively combining Convolutional Neural Network (CNN) and Transformer, the problem of insufficient global information extraction ability of CNN is complemented, and the positioning ability of the model to the fracture region is improved. Secondly, in order to improve the interaction ability of context information, Deep-Shallow Feature Interaction Module (DSFIM) is designed. In this module, the spatial information in the shallow feature layer is embedded to the deep feature layer by the spatial attention mechanism, and the semantic information in the deep feature layer is embedded to the shallow feature layer by the channel attention mechanism. The fracture region recognition ability of the model is improved. Finally, Multi-scale Multi receptive-field Feature Mixing Module (MMFMM) is designed. Deep separate convolution chains are used in this modal, which is composed by multiple layers of different scales and different dilation coefficients. This method provides richer receptive field for the model, and the ability to detect fracture region of different scales is improved. Results: The precision rate, mAP value, recall rate and F1 value of M3YOLOv5 model on mandibular fracture CT data set are 97.18%, 96.86%, 94.42% and 95.58% respectively. The experimental results show that there is better performance about M3YOLOv5 model than the mainstream detection models. Conclusion: The M3YOLOv5 model can effectiv

关键词： Fracture

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Robust restoration of low-dose cerebral perfusion CT images using NCS-Unet

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Nuclear Science and Techniques 2022年第3期33卷 62-76页

作者： Kai Chen Li-Bo Zhang Jia-Shun Liu Yuan Gao Zhan Wu Hai-Chen Zhu Chang-Ping Du Xiao-Li Mai Chun-Feng Yang Yang Chen Southeast University Nanjing 210096China School of Cyber Science and Engineering Southeast UniversityNanjing 210096China Key Laboratory of Computer Network and Information Integration(Southeast University) Ministry of EducationNanjing 210096China Department of Radiology General Hospital of the Northern Theater of the Chinese People’s Liberation ArmyShenyang 110016China Laboratory of Image Science and Technology The School of Computer Science and EngineeringSoutheast UniversityNanjing 210096China Department of Radiology Nanjing Drum Tower HospitalThe Affiliated Hospital of Nanjing University Medical SchoolNanjing 210008China Jiangsu Key Laboratory of Molecular and Functional Imaging Department of RadiologyZhongda HospitalSoutheast UniversityNanjing 210009China Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing School of Computer Science and EngineeringSoutheast UniversityNanjing 210096China

Cerebral perfusion computed tomography(PCT)is an important imaging modality for evaluating cerebrovascular diseases and stroke *** widespread public concern about the potential cancer risks and health hazards associated with cumulative radiation exposure in PCT imaging,considerable research has been conducted to reduce the radiation dose in X-ray-based brain perfusion *** the dose of X-rays causes severe noise and artifacts in PCT *** solve this problem,we propose a deep learning method called *** exceptional characteristics of non-subsampled contourlet transform(NSCT)and the Sobel filter are introduced into *** decomposes the convolved features into high-and low-frequency *** decomposed high-frequency component retains image edges,contrast imaging traces,and noise,whereas the low-frequency component retains the main image *** Sobel filter extracts the contours of the original image and the imaging traces caused by the contrast agent *** extracted information is added to NCS-Unet to improve its performance in noise reduction and artifact *** and quantitative analyses demonstrated that the proposed NCS-Unet can improve the quality of low-dose cone-beam CT perfusion reconstruction images and the accuracy of perfusion parameter calculations.

关键词： Cerebral perfusion CT Low-dose image denoising Perfusion parameters

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Efficient Fault Detection in Nonlinear Industrial Process: A Reduced Kernel PCA-Based Spectral Clustering Approach

Efficient Fault Detection in Nonlinear Industrial Process: A...

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Artificial Intelligence, Applications, Innovation and Ethics (AI2E), International Conference for

作者： Khadija Attouri Majdi Mansouri Mansour Hajji Kais Bouzrara Laboratory of Advanced Materials and Nanotechnologies Higher Institute of Applied Sciences and Technology of Kasserine Kairouan University Tunisia Department of Electrical and Computer Engineering College of Engineering Sultan Qaboos University Muscat Oman Research Laboratory of Automation Signal Processing and Image National Engineering School of Monastir Tunisia

ISBN: (数字)9798331513733

ISBN: (纸本)9798331513740

The challenges posed by nonlinearities in industrial systems necessitate innovative techniques that outperform the limitations of traditional methods such as principal component analysis (PCA). While Kernel Principal Component Analysis (KPCA) offers a robust solution to handle nonlinear data, its computational requirements are a significant issue, especially for large-sized datasets. In this work, we propose a novel technique, namely, reduced kernel principal component analysis-based spectral clustering (RKPCA SpC ), to monitor and detect faults in the benchmark Tennessee Eastman process. The suggested approach addresses the complexity associated with KPCA by reducing data size during the model training phase. This reduction involves retaining only the principal components, preserving informative features, and selecting pertinent samples without compromising the original data's content. The efficacy of the proposed method is evaluated through key performance metrics, including false alarm rate (FAR), missed detection rate (MDR), detection time delay (DTD), and computation time (CT). Additionally, gained execution time (GET), gained storage space (GSP), and loss function (LF) are considered, providing a comprehensive assessment of the developed paradigms' effectiveness. The results demonstrate the promising capabilities of our proposed scheme.

关键词： Training Measurement Technological innovation Fault detection Delay effects Computational modeling Complexity theory Kernel Monitoring Principal component analysis

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A Speech Cryptosystem Using the New Chaotic System with a Capsule-Shaped Equilibrium Curve

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computers, Materials & Continua 2023年第6期75卷 5987-6006页

作者： Mohamad Afendee Mohamed Talal Bonny Aceng Sambas Sundarapandian Vaidyanathan Wafaa Al Nassan Sen Zhang Khaled Obaideen Mustafa Mamat Mohd Kamal Mohd Nawawi Faculty of Informatics and Computing Universiti Sultan Zainal AbidinMalaysia Department of Computer Engineering University of SharjahSharjah26666UAE Department of Mechanical Engineering Universitas Muhammadiyah TasikmalayaIndonesia Research and Development Centre Vel Tech UniversityAvadiChennaiIndia School of Artificial Intelligence and Automationand Key Laboratory of Image Processingand Intelligent Control of Education Ministry of ChinaHuazhong University of Scienceand TechnologyWuhan430074China Institute of Strategic Industrial Decision Modeling School of Quantitative SciencesUniversiti Utara Malaysia06010UUM SintokKedahMalaysia

In recent years,there are numerous studies on chaotic systems with special equilibrium curves having various shapes such as circle,butterfly,heart and *** paper describes a new 3-D chaotic dynamical system with a capsule-shaped equilibrium *** proposed chaotic system has two quadratic,two cubic and two quartic nonlinear *** is noted that the proposed chaotic system has a hidden attractor since it has an infinite number of equilibrium *** is also established that the proposed chaotic system exhibits multi-stability with two coexisting chaotic attractors for the same parameter values but differential initial states.A detailed bifurcation analysis with respect to variations in the system parameters is portrayed for the new chaotic system with capsule equilibrium *** have shown MATLAB plots to illustrate the capsule equilibrium curve,phase orbits of the new chaotic system,bifurcation diagrams and *** an engineering application,we have proposed a speech cryptosystem with a numerical algorithm,which is based on our novel 3-D chaotic system with a capsule-shaped equilibrium *** proposed speech cryptosystem follows its security evolution and implementation on Field Programmable Gate Array(FPGA)*** results show that the proposed encryption system utilizes 33%of the FPGA,while the maximum clock frequency is 178.28 MHz.

关键词： Chaos chaotic systems hidden attractors multi-stability bifurcation speech cryptosystem FPGA design

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Pagml: Precise Alignment Guided Metric Learning for Sketch-Based 3d Shape Retrieval

SSRN

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SSRN 2023年

作者： Bai, Shaojin Bai, Jing Xu, Hao Tuo, Jiwen Liu, Min School of Computer Science and Engineering North Minzu University Yinchuan750021 China National Ethnic Affairs Commission Image graphics intelligent processing laboratory Yinchuan750021 China School of Mechanical Engineering Purdue University West Lafayette47907 United States

Sketch-based 3D shape retrieval has always been a hot research topic in the computer vision community. The main challenge is to alleviate the cross-modality discrepancies such that the retrieval accuracy can be improved. In this paper, we propose a novel Precise Alignment Guided Metric Learning (PAGML) method based on master-auxiliary cross-modality retrieval framework. An auxiliary learning network is developed to indirectly guide the master learning model to extract features of rich semantic information, so as to achieve a semantic alignment between the cross-modality data. Furthermore, considering that the unbalanced data distributions led to the poor uniformity in the common embedding space, a loss function dedicated for the imbalanced cross-modality data is designed to achieve a rigid alignment between sketches and 3D shapes of the same category by pulling their rich semantic representations to the rigid center of the category. As a result, a more precise alignment between the cross-modality embedding features of same category is approached gradually, which further alleviates the cross-modality discrepancies and improves the cross-modality retrieval accuracies. Extensive experiments on two public benchmark datasets demonstrate that the proposed PAGML surpasses the state-of-the-art methods in retrieval accuracy and has excellent generalization abilities to unseen classes. © 2023, The Authors. All rights reserved.

关键词： Embeddings

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Pansharpening Methods Based on a Redundant Contourlet Transform

Pansharpening Methods Based on a Redundant Contourlet Transf...

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Earth Observation and Geo-Spatial Information (ICEOGI), International Conference on

作者： Soumeya Ourabia Nadia Baaziz Youcef Smara Departement of Electronics University Center of Tipaza -Morsli Abdelah-Institut of Sciences Laboratory of Image Processing and Radiation -USTHB Tipaza Algeria Department of Computer Science and Engineering Research Laboratory in Multimedia Information University of Quebec in Outaouais - UQO Gatineau Québec Canada Laboratory of Image Processing and Radiation Faculty of electrical engineering University of Sciences and Technology Houari Boumediene -USTHB Algiers Algeria

The pansharpening of high-resolution panchromatic (Pan) image and low-resolution multispectral (MS) images represents an important task in the remote sensing field. It allows the joint exploitation of the information derived from both types of images. However, this may cause a distortion of spectral information or a lack in details and structures. The multiresolution analysis using wavelet transforms has proved its efficiency in the pansharpening domain, due to the representation of the image content over different resolution levels. In image fusion applications, we always need more detail information to be incorporated in the pansharpening procedure in order to produce enhanced results. However, the limitation of the wavelet transform to three types of oriented detail coefficients prevents this need from being met. To overcome this limitation, we propose to use the redundant contourlet transform (RCT) which extracts a richer multiscale directional information from the image. For this purpose, two RCT-based pansharpening methods are introduced and suitable data fusion procedures are described. We conducted several experiments on two different datasets acquired respectively by ALSAT-2A and IKONOS satellites. The visual results as well as quantitative results from evaluation metrics demonstrate the performance of the proposed methods.

关键词：

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Teeth Yolact: A Model Fusing Deep-Shallow, Local-Global And Edge Features

SSRN

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SSRN 2024年

作者： Zhou, Tao Wang, Yaxing Chai, Wenwen Pan, Yunfeng Zhang, Zhe Lu, Huiling Xia, Yong School of Computer Science and Engineering North Minzu University Yinchuan750021 China School of Medical Information and Engineering Ningxia Medical University Yin Chuan750004 China Key Laboratory of Image and Graphics Intelligent Processing State Ethnic Affairs Commission North Minzu University Yinchuan750021 China School of Computer Northwestern Polytechnical University Xi’an710072 China

Background and ObjectiveThe instance segmentation of impacted tooth in the oral panoramic X-ray images is research hot. However, impacted tooth in panoramic X-Ray images lead to teeth deformities, low contrast between the tooth and periodontal tissue. In this paper, a Teeth YOLACT instance segmentation model is *** main contributions of this paper are as follows: Firstly, a Multi-scale Res-Transformer Module (MRTM) is designed, in the module, the depth-wise separable convolution with different perceptive fields are used to improve the model sensitivity to the lesion size, the Vision Transformer are used to improve the model perception ability about global features. Secondly, the Context Interaction-awareness Module (CIaM) is designed to fuse deep and shallow features. The shallow spatial features are guided by the deep semantic features. Then the shallow spatial features are embedded into the deep semantic features, and the cross-weighted attention mechanism is used to efficiently aggregate the deep and shallow features, richer context information are obtained. Thirdly, the Edge-preserving perception Module(E2PM) is designed to enhance the teeth edge features. The first order differential operator is used to get the tooth edge weight, perception ability of tooth edge features is improved. The shallow spatial feature is fused by linear mapping, weight concatenation and matrix multiplication operations to preserve the tooth edge information. Finally, comparison experiments and ablation experiments are conducted on the oral panoramic X-ray image *** results show that the APdet, APseg, ARdet, ARseg, mAPdet and mAPseg indicators of the proposed model are 89.9%, 91.9%, 77.4%, 77.6%, 72.8% and 73.5%, *** are some positive significances about the dental computer-aided diagnosis based on panoramic oral X-ray images. © 2024, The Authors. All rights reserved.

关键词： computer aided diagnosis

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PFMRC: A Unified MRC Framework for Chinese Named Entity Recognition via Semantic Feature Fusion

PFMRC: A Unified MRC Framework for Chinese Named Entity Reco...

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Artificial Intelligence, Robotics, and Communication (ICAIRC), IEEE International Conference on

作者： Zhenrong Deng Jinglin Zhang Yannan Xiao Shiwei Wei Guangxi Key Laboratory of Images and Graphics Intelligent Processing Guilin University of Electronic Technology Guilin China Nanning Research Institute Guilin University of Electronic Technology Guilin China School of Computer Science and Engineering Guilin University of Aerospace Technology Guilin China

ISBN: (数字)9798331531225

ISBN: (纸本)9798331531232

The landscape of natural language processing (NLP) has been revolutionized by advancements that enable the upstream training of knowledge-rich models for downstream tasks, particularly named entity recognition (NER). Traditional NER approaches, treating it as a sequence labeling problem, often fail to deeply understand the whole text and to capture syntactic nuances of text. To address these issues, we introduce PFMRC, a novel machine reading comprehension (MRC) framework that leverages the strengths of both contextual semantics and syntactic information for enhanced NER performance. PFMRC employs a dual-pronged feature extraction strategy, utilizing RoBERTa and BiGRU to encode text features for contextual semantic information and representing them as node features of the graph structure, and a weighted embedding representation to translate syntactic dependencies into graph-structured edge features. Our model further incorporates Graph Attention Networks (GAT) to harmonize node and edge features, facilitating a nuanced understanding of text. The integration of prior knowledge and the modeling of intricate syntactic relationships through Graph Attention Networks (GAT) not only improves entity recognition accuracy but also enriches the feature representation, leading to more robust NER outcomes. Extensive experiments on three Chinese datasets demonstrate PFMRC's superior performance over state-of-the-art models.

关键词： Training Accuracy Translation Semantics Refining Named entity recognition Syntactics Feature extraction Solids Robots

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Short-Term Photovoltaic Power Forecasting-Based Kernel Ridge Regression

Short-Term Photovoltaic Power Forecasting-Based Kernel Ridge...

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Artificial Intelligence, Applications, Innovation and Ethics (AI2E), International Conference for

作者： Manel Marweni Majdi Mansouri Mansour Hajji Mohamed Faouzi Mimouni Research Laboratory of Automation Signal Processing and Image National Engineering School of Monastir Tunisia Department of Electrical and Computer Engineering College of Engineering Sultan Qaboos University Muscat Oman Laboratory of Advanced Materials and Nanotechnologies Higher Institute of Applied Sciences and Technology of Kasserine Kairouan University Kasserine Tunisia

ISBN: (数字)9798331513733

ISBN: (纸本)9798331513740

The modern energy landscape is undergoing a seismic change from traditional, finite energy sources and toward cleaner, renewable alternatives. The restrictions faced by traditional sources, which are not only finite but increasingly shrink in the face of burgeoning global energy demands driven by population increase and industrial expansion, are driving this change. Although promising, renewable energy poses complications, particularly the reliance on climatic conditions. An important aspect of addressing these difficulties is effective energy management within distribution systems, which includes forecasting and optimization phases. This research focuses on forecasting using an advanced machine learning (ML) approach. Accurately forecasting renewable energy generation over time is critical for improving energy management. This technique is evaluated using a variety of performance indicators, including Mean Error (ME), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and the Coefficient of Determination (R 2 ). Empirical studies support the method's usefulness, demonstrating noteworthy performance with low error rates.

关键词： Photovoltaic systems Renewable energy sources Technological innovation Accuracy Machine learning Predictive models Forecasting Kernel Energy management Optimization

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