检索结果-内蒙古大学图书馆

Enhanced Feedback Residual Attention Network with Slanted Scarce Auto-Encoder and IoT Based Industrial Waste Water Treatment System for Effective Waste Management

Enhanced Feedback Residual Attention Network with Slanted Sc...

引用

Multi-Agent Systems for Collaborative Intelligence (ICMSCI), International Conference on

作者： K.V.S. Prasad T. Gavaskar G. Ravivarman P. Chinniah Sophia S Sathyakala S Department of Basic Sciences and Humanities GMR Institute of Technology Vizianagaram Andhra Pradesh India Department of Mechanical Engineering St. Joseph's College of Engineering Chennai Tamil Nadu India Department of Electrical and Electronics Engineering Karpagam Academy of Higher Education Coimbatore Tamil Nadu India Department of Electronics and Communication Engineering ST. Joseph College of Engineering Kanchipuram Tamil Nadu India Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore Tamil Nadu India Department of Management Studies Sona College of Technology Salem Tamil Nadu India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

In the last few decades, rapid industrialization has increased the problem of wastewater treatment and management because of the increasing complexity and toxicity of wastewater that is difficult to treat by any conventional approach. Commonly used waste water treatment systems are unable to treat a variety of pollutants because of deficiency of good classification and optimization processes which results in wastage of resources and harming the environment. Furthermore, existing systems are inflexible, and an ability to change the nature of treatment in real time is impossible, which is crucial for the timely and efficient delivery and adjustment of therapies. To overcome these challenges, there is a need for smart monitoring systems enabled by IoT and effective treatment processes utilizing machine learning for better waste management. This paper comes up with an IoT and Machine Learning based system for the treatment of industrial wastewater that uses an Enhanced Feedback Residual Attention Network with Slanted Scarce Autoencoder to achieve accuracy in pollutant classification and quantification. Fick's Law Optimization is incorporated for optimization which makes the treatment process more efficient by allowing the treatment to be applied only where necessary and in the right amounts. IoT sensors are also part of the system to ensure that the data is collected in real time and the waste is continuously monitored to allow quick reactions to changes in waste. The introduced approach attains higher accuracy as 99%.

关键词： Waste management Accuracy Autoencoders Machine learning Real-time systems Sensor systems Sensors Wastewater Wastewater treatment Optimization

来源：评论

学校读者我要写书评

暂无评论

Medical Protocols and AI-Driven Algorithms for Enhanced Monitoring of Cardiac Implantable Electronic Devices 19thth

Medical Protocols and AI-Driven Algorithms for Enhanced Mo...

引用

19th International Conference on computer Aided Systems Theory, EUROCAST 2024

作者： Kulbacki, Marek Segen, Jakub Chaczko, Zenon Kulbacki, Michal Nikodem, Jan Klempous, Ryszard Bąk, Artur Rozenblit, Jerzy Zyśko, Dorota Hrymniak, Bruno Jagielski, Dariusz Wroclaw University of Technology Wroclaw Poland Wroclaw University of Science and Technology Wroclaw Poland School of Electrical and Data Engineering University of Technology Sydney Ultimo Australia Department of Electrical and Computer Engineering University of Arizona Tucson United States Polish-Japanese Academy of Information Technology Warsaw Poland DIVE IN AI Wroclaw Poland Mathematical Institute University of Wroclaw Wroclaw Poland Center for Heart Diseases 4th Military Hospital Wroclaw Poland

ISBN: (纸本)9783031829598

Implantable cardioverter-defibrillators (ICDs) present several challenges regarding their detection, localization, and continuous monitoring within patients’ bodies. These challenges include maintaining the integrity of electrodes, preventing local tissue irritation, avoiding device displacement, and ensuring compatibility with MRI. Continuous monitoring is critical to ensure patient safety and the functional integrity of the devices. This paper systematically reviews approaches for the comprehensive monitoring of Cardiac Implantable Electronic Devices (CIEDs) over the past ten years, focusing on the CaRDIA-X procedure. We examine advanced protocols and integrated algorithms designed for this purpose. Specifically, we introduce the CaRDIA-X protocol, a manual method for identifying CIEDs based on the analysis of device patterns and the arrangement of elements on X-ray images. We compare this protocol with alternative detection methods, including artificial intelligence algorithms, and emphasize the field’s most significant studies and comprehensive comparative analyses. In conclusion, we propose improvements to the classic CaRDIA-X algorithm, focusing on automating the CaRDIA-X procedure, which holds significant potential for enhancing patient management and therapeutic efficacy. This integrated proposition offers a holistic perspective on CIED performance and potential issues. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Electronic medical equipment

来源：评论

学校读者我要写书评

暂无评论

Skin-like cryogel electronics from suppressed-freezing tuned polymer amorphization

引用

Nature Communications 2023年第1期14卷 1-10页

作者： Zhang, Xiansheng Yan, Hongwei Xu, Chongzhi Dong, Xia Wang, Yu Fu, Aiping Li, Hao Lee, Jin Yong Zhang, Sheng Ni, Jiahua Gao, Min Wang, Jing Yu, Jinpeng Ge, Shuzhi Sam Jin, Ming Liang Wang, Lili Xia, Yanzhi State Key Laboratory of Bio-Fibers and Eco-Textiles College of Textiles and Clothing Qingdao University Qingdao 266071 China Institute for Future Shandong Key Laboratory of Industrial Control Technology School of Automation Qingdao University Qingdao 266071 China College of Materials Science and Engineering Qingdao University Qingdao 266071 China CAS Key Laboratory of Engineering Plastics Institute of Chemistry Chinese Academy of Sciences Beijing 100190 China Department of Chemistry SungKyunKwan University Suwon 16419 South Korea State Key Laboratory of Fluid Power and Mechatronic Systems College of Mechanical Engineering Zhejiang University Hangzhou 310027 China College of Biological Science and Medical Engineering Donghua University Shanghai 201620 China Institute of Environmental Engineering ETH Zürich Zürich 8093 Switzerland Department of Electrical and Computer Engineering National University of Singapore Singapore 117576 Singapore

The sole situation of semi-crystalline structure induced single performance remarkably limits the green cryogels in the application of soft devices due to uncontrolled freezing field. Here, a facile strategy for achieving multifunctionality of cryogels is proposed using total amorphization of polymer. Through precisely lowering the freezing point of precursor solutions with an antifreezing salt, the suppressed growth of ice is achieved, creating an unusually weak and homogenous aggregation of polymer chains upon freezing, thereby realizing the tunable amorphization of polymer and the coexistence of free and hydrogen bonding hydroxyl groups. Such multi-scale microstructures trigger the integrated properties of tissue-like ultrasoftness (Young’s modulus <10 kPa) yet stretchability, high transparency (~92%), self-adhesion, and instantaneous self-healing (<0.3 s) for cryogels, along with superior ionic-conductivity, antifreezing (−58 °C) and water-retention abilities, pushing the development of skin-like cryogel electronics. These concepts open an attractive branch for cryogels that adopt regulated crystallization behavior for on-demand functionalities. © 2023, Springer Nature Limited.

关键词： Gels and hydrogels Polymers

来源：评论

学校读者我要写书评

暂无评论

A dense graphene monolith with poloxamer prefunctionalization enabling aqueous redispersion to obtain solubilized graphene sheets

引用

Chinese Chemical Letters 2020年第9期31卷 2507-2511页

作者： Chen Ye Fan Zhang Xue Tan Huifang Sun Wen Dai Ke Yang Minghui Yang Shiyu Du Dan Dai Jinhong Yu Nan Jiang Weitao Su Li Fu He Li Jing Kong Cheng-Te Lin Key Laboratory of Marine Materials and Related Technologies Zhejiang Key Laboratory of Marine Materials and Protective TechnologiesNingbo Institute of Materials Technology and EngineeringChinese Academy of SciencesNingbo 315201China Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of SciencesBeijing 100049China Faculty of Materials Science and Engineering Kunming University of Science and TechnologyKunming 650093China Ningbo Institute of Materials Technology and Engineering(NIMTE) Chinese Academy of SciencesNingbo 315201China Division of Functional Materials and Nanodevices Ningbo Institute of Materials Technology and EngineeringChinese Academy of SciencesNingbo 315201China College of Materials and Environmental Engineering Hangzhou Dianzi UniversityHangzhou 310018China Department of Electrical Engineering and Computer Science Massachusetts Institute of TechnologyCambridgeMassachusetts 02139United States

The realization of good aqueous dispersibility of commercial graphene products composed of exfoliated graphene sheets is of significance for downstream ***,the tap density of commercial graphene powder is quite low(0.03-0.1 kg/m3),meaning that 1 kg graphene powder occupies about 10-30 m3 in volume during ***,the available content of commercial graphene dispersion/slurry in aqueous medium cannot exceed 5 wt%,although the density is high(≈1050 kg/m3).In this work,a graphene monolith was prepared by oven-drying of graphene sheets prefunctionalized with poloxamer *** graphene monoliths not only have a high density(1500 kg/m^3) and high graphene content(≈10 wt%),but also a full capability to be completely redispersed(≈100%) in water by bath sonication to obtain solubilized graphene sheets,whose lateral size and thickness are unchanged compared to as-exfoliated ***,a simple empirical method was proposed to predict the redispersion capability of graphene monoliths using different poloxamers by contact angle *** results provide a universal approach to make exfoliated graphene-based products with better downstream availability and lower transportation cost.

关键词： Dense monolith Graphene sheets Poloxamer Aqueous redispersion Surface functionalization

来源：评论

学校读者我要写书评

暂无评论

A Hybrid Approach to Optimal Power Allocation in MIMO-NOMA: Integrating DGNN and Tyrannosaurus Optimization

A Hybrid Approach to Optimal Power Allocation in MIMO-NOMA: ...

引用

Sustainable Expert Systems (ICSES), International Conference on

作者： Anurag Vijay Agrawal A. Anuradha Kranti D Patil Vilaskumar Patil K. Karthikumar S V V Satyanarayana Tallapragada Department of Electronics and Communication Engineering Bhagwant Institute of Technology Muzaffarnagar Uttar Pradesh India Department of Computer Science Sri Aditya Degree College Bhimavaram Andhra Pradesh India Department of E & TC Trinity Academy of Engineering GHRCEM Pune Maharashtra India Department of Electronics and Communication Engineering Faculty of Engineering and Technology Sharnbasva University Kalaburagi Karnataka India Department of Electrical and Electronic Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai Tamil Nadu India Department of Electronics and Communication Engineering School of Engineering Mohan Babu University Tirupati Andhra Pradesh India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

One of the primary technologies necessary for the upcoming sixth generation of communication networks (6G) is massive multi-input multi-output, or MIMO. However, enormous MIMO presents a challenge in reducing energy use and the sum rate demand due to the power use of the several radio frequencies (RF) links in 6G technology. Therefore, this research presented a novel Deformable Graph Neural Network (DGNN) with Tyrannosaurus optimization for the optimal power distribution that maximized the sum rate and energy efficiency of the MIMO NOMA system. The adaptive mechanism of DGNN enables more accurate and efficient power allocation decisions. The integrated Tyrannosaurus optimization ensures the model converging on optimal power allocation strategies that maximize both sum rate and energy efficiency. The efficiency of the recommended model is compared in regarding sum rate and Energy Efficiency (EE) with the some of the recent approaches. The proposed method gained the greater sum rate and EE outperforming all other compared models.

关键词： 6G mobile communication Radio frequency NOMA Adaptation models Simulation Energy efficiency Hybrid power systems Resource management Optimization Signal to noise ratio

来源：评论

学校读者我要写书评

暂无评论

GETAE: Graph Information Enhanced Deep Neural Network Ensemble ArchitecturE for Fake News Detection

arXiv

引用

arXiv 2024年

作者： Truica, Ciprian-Octavian Apostol, Elena-Simona Marogel, Marius Paschke, Adrian Computer Science and Engineering Department Faculty of Automatic Control and Computers National University of Science and Technology Politehnica Bucharest Splaiul Independentei 313 Bucharest060042 Romania Academy of Romanian Scientists Ilfov 3 Bucharest050044 Romania Department of Mathematics and Computer Science Freie Universität Berlin Arnimallee 14 Berlin14195 Germany Fraunhofer Institute for Open Communication Systems Kaiserin-Augusta-Allee 31 Berlin10589 Germany

In today’s digital age, fake news has become a major problem that has serious consequences, ranging from social unrest to political upheaval. To address this issue, new methods for detecting and mitigating fake news are required. In this work, we propose to incorporate contextual and network-aware features into the detection process. This involves analyzing not only the content of a news article but also the context in which it was shared and the network of users who shared it, i.e., the information diffusion. Thus, we propose GETAE, Graph Information Enhanced Deep Neural Network Ensemble ArchitecturE for Fake News Detection, a novel ensemble architecture that uses textual content together with the social interactions to improve fake news detection. GETAE contains two Branches: the Text Branch and the Propagation Branch. The Text Branch uses Word and Transformer Embeddings and a Deep Neural Network based on feed-forward and bidirectional Recurrent Neural Networks ([Bi]RNN) for learning novel contextual features and creating a novel Text Content Embedding. The Propagation Branch considers the information propagation within the graph network and proposes a Deep Learning architecture that employs Node Embeddings to create novel Propagation Embedding. GETAE Ensemble combines the two novel embeddings, i.e., Text Content Embedding and Propagation Embedding, to create a novel Propagation-Enhanced Content Embedding which is afterward used for classification. The experimental results obtained on two real-world publicly available datasets, i.e., Twitter15 and Twitter16, prove that using this approach improves fake news detection and outperforms state-of-the-art models. © 2024, CC BY-NC-ND.

关键词： Graph embeddings

来源：评论

学校读者我要写书评

暂无评论

Generalization-Enhanced Few-Shot Object Detection in Remote Sensing

arXiv

引用

arXiv 2025年

作者： Lin, Hui Li, Nan Yao, Pengjuan Dong, Kexin Guo, Yuhan Hong, Danfeng Zhang, Ying Wen, Congcong China Academy of Electronics and Information Technology Beijing100846 China National Satellite Meteorological Center Innovation Center for FengYun Meteorological Satellite China Meteorological Administration Beijing100081 China State Key Laboratory of Hydroscience and Engineering Department of Hydraulic Engineering Tsinghua University Beijing100084 China Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing100083 China Department of Electrical and Computer Engineering New York University Abu Dhabi Abu Dhabi United Arab Emirates

Object detection is a fundamental task in computer vision that involves accurately locating and classifying objects within images or video frames. In remote sensing, this task is particularly challenging due to the high resolution, multi-scale features, and diverse ground object characteristics inherent in satellite and UAV imagery. These challenges necessitate more advanced approaches for effective object detection in such environments. While deep learning methods have achieved remarkable success in remote sensing object detection, they typically rely on large amounts of labeled data. Acquiring sufficient labeled data, particularly for novel or rare objects, is both challenging and time-consuming in remote sensing scenarios, limiting the generalization capabilities of existing models. To address these challenges, few-shot learning (FSL) has emerged as a promising approach, aiming to enable models to learn new classes from limited labeled examples. Building on this concept, few-shot object detection (FSOD) specifically targets object detection challenges in data-limited conditions. However, the generalization capability of FSOD models, particularly in remote sensing, is often constrained by the complex and diverse characteristics of the objects present in such environments. In this paper, we propose the Generalization-Enhanced Few-Shot Object Detection (GE-FSOD) model to improve the generalization capability in remote sensing FSOD tasks. Our model introduces three key innovations: the Cross-Level Fusion Pyramid Attention Network (CFPAN) for enhanced multi-scale feature representation, the Multi-Stage Refinement Region Proposal Network (MRRPN) for more accurate region proposals, and the Generalized Classification Loss (GCL) for improved classification performance in few-shot scenarios. GE-FSOD demonstrates superior robustness and accuracy in remote sensing FSOD tasks through these enhancements. Extensive experiments on the DIOR and NWPU VHR-10 datasets show that our

关键词： Labeled data

来源：评论

学校读者我要写书评

暂无评论

Accelerating Surrogate Assisted Evolutionary Algorithms Via Explainable Machine Learning

SSRN

引用

SSRN 2024年

作者： Li, Bingdong Yang, Yanting Liu, Dacheng Zhang, Yan Zhou, Aimin Yao, Xin Shanghai Institute of AI for Education East China Normal University Shanghai200062 China School of Computer Science and Technology East China Normal University Shanghai200062 China Department of Space Microwave Remote Sensing System Aerospace Information Research Institute Chinese Academy of Sciences Beijing100090 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100039 China Department of Statistics and Data Science Southern University of Science and Technology Shenzhen518055 China

A series of surrogate-assisted evolutionary algorithms (SAEAs) have been proposed to handle expensive multi-objective optimization problems (EMOPs). However, the surrogate of these SAEAs is underutilized to a large extent, which limits the search efficiency of these algorithms. To be specific, existing algorithms do not sufficiently exploit the estimated solution quality information from the surrogate models during offspring generation. To address this issue, this paper proposes an SAEA framework named EXO-SAEA (EXplanation Operator based Surrogate-Assisted Evolutionary Algorithm). First, it divides the current population into two populations according to the a priori knowledge from the surrogate model. Then, for each solution in the first population, EXO-SAEA employs the SHapley Additive exPlanations (SHAP) model to estimate the contribution of each decision variable to the quality of the solution. After that, the Shapley values are then normalized for the offspring generation of the first population, while the second population uses generic GA operators. Two representative surrogate-assisted evolutionary algorithms are used to instantiate the proposed framework. Experimental results on the DTLZ test suite and three real-world optimization problems involving six state-of-the-art algorithms demonstrate the effectiveness of the proposed framework. © 2024, The Authors. All rights reserved.

关键词： Multiobjective optimization

来源：评论

学校读者我要写书评

暂无评论

Skin Lesion Classification using Deep Feature Fusion and Selection Using XGBoost Classifier

Skin Lesion Classification using Deep Feature Fusion and Sel...

引用

IEEE Students' Conference on Electrical, Electronics and computer science (SCEECS)

作者： Ritesh Maurya Anant Krisn Bais T Gopalakrishnan Malay Kishore Dutta Nageshwar Nath Pandey Srinivasa Murthy Y V Amity Centre for Artificial Intelligence Amity University Noida India Amity Institute of Biotechnology Amity University Noida India Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Department of Computer Science and Engineering ITER Siksha ‘O’ Anusandhan Bhubaneswar Odisha India

Skin cancer is a potentially fatal condition that needs to be detected as soon as possible in order to be treated effectively. Deep convolutional neural networks (DCNNs) have shown promising results in the prediction of skin cancer in recent years. This study presents a novel approach for skin cancer identification using deep feature fusion and selection based on the significance score obtained with the XGBoost classifier. The proposed method combines features from the state-of-the-art pre-trained DCNNs, such as EfficientNetB3, ResNet50, VGG16, ConvNeXtTiny, and DenseNet121, to extract high-level features from dermoscopic images. These features capture the intricate patterns and textures associated with malignant and benign skin cancers. Based on the relevance score that the XGBoost classifier awarded to each feature, the K-Best (K=1000) features were chosen. Using the XGBoost classifier, the suggested technique has successfully classified dermoscopic pictures of benign and malignant melanoma, yielding an area under the curve value of 0.95. In comparison to stand-alone DCNN-based techniques, the experimental findings show that the suggested feature fusion and selection strategy has obtained greater accuracy. Additionally, an analysis has been done on how the performance of the suggested classifier is affected by the quantity of characteristics that are chosen.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Multi-authentication system with face and finger print detection using image processing

Multi-authentication system with face and finger print detec...

引用

New Frontiers in Communication, Automation, Management and Security (ICCAMS), International Conference on

作者： Shaleen Bhatnagar S.Pravinth Raja S Uma Department of Computer Science and Engineering Manipal Academy of Higher Education Manipal Institute of Technology Bengaluru Manipal India School of CSE & IS Presidency University Bengaluru Karnataka India Department of BCA Nagarjuna Degree College Bangalore India

ISBN: (数字)9798350317060

ISBN: (纸本)9798350317077

Detecting faces and fingerprints within images serves as a pivotal component in multiple computer vision applications. These applications encompass but are not limited to face recognition, facial expression analysis, and biometric systems designed for fingerprint identification. Owing to the considerable variation within the same class of images due to environmental influences on facial characteristics, the automated detection of faces and fingerprints poses a challenging binary classification task. In our research, we introduce an approach that combines a cascade face detection method with fingerprint data. This method leverages various types of features and classifiers for step-by-step elimination of non-face elements in the authentication process. The candidate feature set is constructed using Haar cascade and Minutiae points features of various granularities. Experimental results show enhanced performance in comparison to cutting-edge techniques.

关键词： Image sensors Image processing Fingers Fingerprint recognition Real-time systems Face detection Security

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：