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Elucidating the enhancement of kaolinite flotation by iron content through density functional theory: A study on sodium oleate adsorption efficiency

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International Journal of Mining science and technology 2024年第6期34卷 855-866页

作者： Lingyun Liu Chuilei Kong Hongyu Zhao Fangqin Lu Joint National-Local Engineering Research Centre for Safe and Precise Coal Mining Huainan 232001China Department of Materials Science and Engineering Anhui University of Science and TechnologyHuainan 232001China School of Civil and Resource Engineering University of Science and Technology BeijingBeijing 100083China

This study delves into the intricate relationship between iron(Fe)content in kaolinite and its impact on the adsorption behavior of sodium *** effects of different iron concentrations on adsorption energy,hydrogen bond kinetics and adsorption efficiency were studied through simulation and experimental *** results show that the presence of iron in the kaolinite structure significantly improves the adsorption capacity of sodium *** samples with high iron content have better adsorption properties,lower adsorption energy levels and shorter and stronger hydrogen bonds than pure *** optimal concentration of oleic acid ions for achieving maximum adsorption efficiency was identified as 1.2 mmol/L across different kaolinite *** this concentration,the adsorption rates and capacities reach their peak,with Fe-enriched kaolinite samples exhibiting notably higher flotation recovery *** optimal concentration represents a balance between sufficient oleic acid ion availability for surface interactions and the prevention of self-aggregation phenomena that could hinder *** study offers promising avenues for optimizing the flotation process in mineral processing applications.

关键词： Iron Influence Sodium Oleate Adsorption Kaolinite Surfaces Molecular Interaction Analysis Flotation Efficiency Optimization

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Object Detection Model for Remote Sensing Images Based on YOLOv9

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2025年第3期52卷 840-847页

作者： Hou, Donghao Zhang, Yujun School of Computer and Software Engineering University of Science and Technology Liaoning Anshan114051 China

In the field of object detection for remote sensing images, especially in applications such as environmental monitoring and urban planning, significant progress has been made. This paper addresses the common challenges faced by traditional object detection methods in remote sensing images, such as the large number of targets and complex backgrounds, by proposing a novel network based on YOLOv9. The network innovatively introduces the C3_CD_CGA module, an enhanced module based on Cascaded Group Attention, designed to reduce computational redundancy and increase attention diversity, and enhances the processing capability of multi-scale information through the CD module. The C3 module employs deep asymmetric convolution to mitigate information loss and increase the receptive field. Additionally, the network integrates DSConv with the RepNCSPELAN4 module to adaptively focus on and precisely capture the features of elongated and curved local structures, such as vehicles. The introduction of the CARAFE module further improves the spatial resolution of the feature maps, significantly enhancing performance across various visual tasks. Experimental results show that the improved YOLOv9 achieves a mean average precision (mAP) of 88% on the SIMD dataset, which is an improvement of 1.6% compared to the baseline YOLOv9 model and 1.5% higher than the state-of-the-art YOLO-SE model. This model not only achieves more effective multi-target recognition in complex backgrounds but also strikes a good balance between accuracy and efficiency. © (2025), (International Association of Engineers). All rights reserved.

关键词： Urban planning

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AtomGAN:unsupervised deep learning for fast and accurate defect detection of 2D materials at the atomic scale

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science China(Information sciences) 2023年第6期66卷 102-114页

作者： Danpeng CHENG Wuxin SHA Zuo XU Shide LI Zhigao YIN Yuling LANG Shun TANG Yuan-Cheng CAO State Key Laboratory of Advanced Electromagnetic Engineering and Technology School of Electrical and Electronic EngineeringHuazhong University of Science and Technology School of Computer Science and Technology Huazhong University of Science and Technology CITIC Dicastal Co. Ltd.

The extraction of atomic-level material features from electron microscope images is crucial for studying structure-property relationships and discovering new materials. However, traditional electron microscope analyses rely on time-consuming and complex human operations; thus, they are only applicable to images with a small number of atoms. In addition, the analysis results vary due to observers' individual deviations. Although efforts to introduce automated methods have been performed previously, many of these methods lack sufficient labeled data or require various conditions in the detection process that can only be applied to the target material. Thus, in this study, we developed AtomGAN, which is a robust, unsupervised learning method, that segments defects in classical 2D material systems and the heterostructures of MoS2/WS2automatically. To solve the data scarcity problem, the proposed model is trained on unpaired simulated data that contain point and line defects for MoS2/WS2. The proposed AtomGAN was evaluated on both simulated and real electron microscope images. The results demonstrate that the segmented point defects and line defects are presented perfectly in the resulting figures, with a measurement precision of 96.9%. In addition, the cycled structure of AtomGAN can quickly generate a large number of simulated electron microscope images.

关键词： deep learning generative adversarial network defect detection atomic resolution 2D materials

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Analysis of recent techniques in marine object detection: a review

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Multimedia Tools and Applications 2024年 1-99页

作者： Bhalla, Shikha Kumar, Ashish Kushwaha, Riti School of Computer Science Engineering and Technology Bennett University Greater Noida India

Marine Object Detection, leveraging computer vision, plays a vital role in detecting objects in marine environments ranging from marine organisms to marine surveillance. However, marine environment presents unique set of challenges that makes the detection process tedious. Marine environment often has low visibility due to color cast, haziness and intricate background. Moreover, marine target objects appear at varying scales and occluded by the surrounding objects. For comprehensive understanding of this domain, we have analyzed the significant studies from the year (2012-2024) in this work. Prisma model is utilized to identify the crucial 111 studies for this review. The review organizes marine object detection techniques into conventional and advance learning approach through the proposed hierarchical tree-like structure. The organized research has been tabulated, offering an in-depth summary of significant work and highlighting the strengths and weakness of the identified studies for the conventional and advance learning approach. Furthermore, a brief examination of 31 benchmark datasets categorized according to the different application domains (marine species, ship and debris) has been conducted and their key attributes are tabulated. In addition, statistical analysis has been performed to understand the growth and future directions in marine object detection domain. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Object detection

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Finite-region H∞ filtering of 2D Markov jump systems with deception attacks 24

Finite-region H∞ filtering of 2D Markov jump systems with d...

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2nd International Conference on Frontiers of Intelligent Manufacturing and Automation, CFIMA 2024

作者： Wang, Guangyu Fang, Jiankang Ren, Chengcheng Wang, Longlong He, Shuping Zhang, Liang School of Computer Science and Technology Anhui University Hefei China School of Electrical Engineering and Automation Anhui University Hefei China

ISBN: (纸本)9798400710681

In this paper, we study the finite-region H∞ filtering problem for two dimensional (2D) Markov jump systems (MJSs) subject to deception attacks. First, the effect of deception attacks are considered and the filtering error is modeled. The sufficient conditions to ensure the stability of the filtering performance are obtained by using the Lyapunov function and linear matrix inequalities. Finally, the effectiveness of the method is verified by a numerical example on Darboux equation. © 2024 Copyright held by the owner/author(s).

关键词： Lyapunov functions

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IensNet: A novel and efficient approach for iris spoof detection via ensemble of deep models

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Multimedia Tools and Applications 2025年 1-30页

作者： Sharma, Deepika Selwal, Arvind School of Computer Science Engineering and Technology Bennett University Greater Noida201310 India Department of Computer Science and Information Technology Central University of Jammu Samba India

Iris biometrics allow contactless authentication, which makes it widely deployed human recognition mechanisms since the couple of years. Susceptibility of iris identification systems remains a challenging task due to diversity in spoof or presentation attacks (PAs) that fails to assure consistency while adopting them in real life scenarios. Hence, iris PAs are the growing concerns that gained significant attention in recent past decade. To alleviate these attacks or recognize presentation attack instruments (PAIs), iris presentation attacks detection (IPAD) algorithms are designed to distinguish a real and fabricated iris trait. Aiming at the efficient iris spoof detection mechanism, in this research work we expound a novel ensemble learning-enabled model (IensNet) that learns three pre-trained and fined-tuned deep models (i.e. DenseNet161, ResNet and VGGNet) for better accuracy and generalized performance. The novel IensNet approach offers several merits (i.e. consolidated strengths of multiple models, improved generalization ability, etc.) as compared to a simple transfer learning strategy where the knowledge is drawn from single pre-trained model. Finally, our approach learns a novel fully-connected dual layer classifier via outcome of three fine-tuned models to yield a final classification result as bonafide or spoof iris trait. Our approach is evaluated on Notre Dame LivDet iris 2017 and Notre Dame contact lenses 2015 anti-spoofing datasets. The experimental analysis of IensNet offers outstanding performance with a lower ACER of 0.2% and 1.4% for Iris-LivDet-2017 and Notre Dame contact lenses 2015 dataset respectively. Besides, IensNet exhibit promising results in cross-dataset environment with an ACA of 91.46%. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Contrastive Learning

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HA-FGOVD: Highlighting Fine-Grained Attributes via Explicit Linear Composition for Open-Vocabulary Object Detection

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IEEE Transactions on Multimedia 2025年 27卷 3171-3183页

作者： Ma, Yuqi Liu, Mengyin Zhu, Chao Yin, Xu-Cheng University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China

Open-vocabulary object detection (OVD) models are considered to be Large Multi-modal Models (LMM), due to their extensive training data and a large number of parameters. Mainstream OVD models prioritize object coarse-grained category rather than focus on their fine-grained attributes, e.g., colors or materials, thus failed to identify objects specified with certain attributes. Despite being pretrained on large-scale image-text pairs with rich attribute information, their latent feature space does not highlight these fine-grained attributes. In this paper, we introduce HA-FGOVD, a universal and explicit method that enhances the attribute-level detection capabilities of frozen OVD models by highlighting fine-grained attributes in explicit linear space. Our approach uses a LLM to extract attribute words in input text as a zero-shot task. Then, token attention masks are adjusted to guide text encoders in extracting both global and attribute-specific features, which are explicitly composited as two vectors in linear space to form a new attribute-highlighted feature for detection tasks. The composition weight scalars can be learned or transferred across different OVD models, showcasing the universality of our method. Experimental results show that HA-FGOVD achieves state-of-the-art performance on the FG-OVD benchmark and demonstrates promising generalization on the OVDEval benchmark, suggesting that our method addresses significant limitations in fine-grained attribute detection and has potential for broader fine-grained detection applications. © 1999-2012 IEEE.

关键词： Coarse-grained modeling

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UCD_Net: dilated convolution-enhanced upsampling fusion for advanced lung disease classification

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Multimedia Tools and Applications 2025年 1-19页

作者： Sharma, Suchit Shelke, Nitin Arvind School of Computer Science Engineering and Technology Bennett University Greater Noida201310 India

Deep Learning has recently been in trend when it comes to medical image analysis as it uses Convolution Neural Network (CNN), which utilizes multi-layer processing to extract intricate and complex features from the data. Similar to other research work, we used Chest X-ray (CXR) images as our dataset and classified them into five categories of lung disease, namely bacterial pneumonia, COVID-19, normal, pneumothorax, and tuberculosis. This study proposes a novel CNN, UCD_Net, which uses a convolutional upsampling layer featuring bicubic interpolation along with a stack of convolution layers before inputting the images into the fine-tuned Xception model and resorts to multiple dilated convolution layers before predicting output. This 2D CNN model is used to achieve optimal feature extraction within minimal time and space constraints. The dataset was divided into a ratio of 0.66 and 0.33 for the training set and testing set, respectively. The best performance is recorded by our proposed model, UCD_Net, with a training accuracy equal to 99.23%, a validation accuracy equal to 98.05%, a recall of 97.8%, a precision of 97.2%, and a fi-score equal to 97.6%. The model performance on the same dataset is also compared to four state-of-the-art architectures, namely ResNet 50, VGG16, Xception, and ResNet152. After our UCD_Net, the best performance was seen using ResNet 50, with accuracy, recall, and precision of 95.45%, 94.5%, and 95.2%, respectively. Training the model on a larger dataset and subsequent fine-tuning can lead to more research and model development. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Lung cancer

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A privacy and compliance in regulated anonymous payment system based on blockchain

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Journal of Ambient Intelligence and Humanized Computing 2024年第8期15卷 3141-3157页

作者： Elfadul, Issameldeen Wu, Lijun Elhabob, Rashad Elkhalil, Ahmed School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu610054 China School of Computer and Information Engineering Qilu Institute of Technology Shandong Jinan250200 China Faculty of Computer Science and Information Technology Karary University Khartoum Sudan

Decentralized Anonymous Payment Systems (DAP), often known as cryptocurrencies, stand out as some of the most innovative and successful applications on the blockchain. These systems have garnered significant attention in the financial industry due to their highly secure and reliable features. Regrettably, the DAP system can be exploited to fund illegal activities such as drug dealing and terrorism. Therefore, governments are increasingly worried about the illicit use of DAP systems, which poses a critical threat to their security. This paper proposes Privacy and Compliance in Regulated Anonymous Payment System Based on Blockchain (PCRAP), which provides government supervision and enforces regulations over transactions without sacrificing the essential idea of the blockchain, that is, without surrendering transaction privacy or anonymity of the participants. The key characteristic of the proposed scheme is using a ring signature and stealth address to ensure the anonymity of both the sender and receiver of the transaction. Moreover, a Merkle Tree is used to guarantee government supervision and enforce regulations. Our proposed scheme satisfies most of the stringent security requirements and complies with the standards of secure payment systems. Additionally, while our work supports government regulations and supervision, it guarantees unconditional anonymity for users. Furthermore, the performance analysis demonstrates that our suggested scheme still remains applicable and effective even when achieving complete anonymity. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Blockchain

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Event-triggered tracking control for a class of nonholonomic systems in chained form

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science China(Information sciences) 2023年第7期66卷 147-161页

作者： Liang XU Youfeng SU He CAI Center for Discrete Mathematics and Theoretical Computer Science Fuzhou University College of Computer and Data Science Fuzhou University School of Automation Science and Engineering South China University of Technology

In this study, the event-triggered asymptotic tracking control problem is considered for a class of nonholonomic systems in chained form for the time-varying reference input. First, to eliminate the ripple phenomenon caused by the imprecise compensation of the time-varying reference input, a novel time-varying event-triggered piecewise continuous control law and a triggering mechanism with a time-varying triggering function are developed. Second, an explicit integral input-to-state stable Lyapunov function is constructed for the time-varying closed-loop system regarding the sampling error as the external input. The origin of the closed-loop system is shown to be uniformly globally asymptotically stable for any global exponential decaying threshold signals, which in turn rules out the Zeno behavior. Moreover, infinitely fast sampling can be avoided by appropriately tuning the exponential convergence rate of the threshold signal. A numerical simulation example is provided to illustrate the proposed control approach.

关键词： event-triggered nonholonomic systems strict Lyapunov function tracking integral input-to-state stable

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