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Preparation of pressureless-sintered dense cordierite ceramics with high flexural strength by mesoporous powder

Ceramics International 2025年第17期51卷 24202-24208页

作者： Yu, Shuyan Li, Zifan Cui, Jinping Lin, Zhiyu Rao, Pinggen School of Materials Science and Engineering South China University of Technology Guangdong Guangzhou510641 China

Cordierite ceramics are widely utilized due to their excellent thermal shock resistance. However, significant research focuses on synthesizing highly sinterable powders to produce high-purity cordierite ceramics with exceptional mechanical properties and a low thermal expansion coefficient. This study successfully synthesized mesoporous cordierite powders with high sintering activity using a non-hydrolytic sol-gel method incorporating a structure-directing agent. These powders were subsequently employed to fabricate high-performance cordierite ceramics without using sintering aids. The results demonstrated that pure and dense cordierite ceramics were obtained by sintering mesoporous powders calcined at 600 °C under conditions of 1325 °C for 4 h in air, achieving a flexural strength of 168 MPa, fracture toughness of 1.76 MPa m1/2, Vickers hardness of 8.8 GPa, and a thermal expansion coefficient of 2.0 × 10−6°C−1 (room temperature to 800 °C). The method proposed in this work can be applied to prepare high-purity cordierite ceramics, effectively improving the mechanical properties of cordierite ceramics with a very low thermal expansion coefficient, highlighting its considerable potential for practical applications. © 2025 Elsevier Ltd and Techna Group S.r.l.

关键词： Mesopores

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Deep Learning-Powered Camouflaged Object Recognition Model 2

Deep Learning-Powered Camouflaged Object Recognition Model

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Tomar, Riya Chauhan, Naman Chauhan, Chhavi Tomar, Yash Singh, Soumyendra Chauhan, Nishant Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai India University of Petroleum and Energy Studies School of Computer Science Dehradun India Cmr University Department of Computer Science and Engineering Bengaluru India Amrita School of Engineering Amrita Vishwa Vidyapeetham Department of Mathematics Chennai India Amity School of Engineering and Technology Amity University Department of Computer Science and Engineering Noida India

ISBN: (纸本)9798350317558

This research paper delves into the intersection of advanced technology and military strategies, specifically focusing on the imperative task of camouflaged object detection within the Indian Paramilitary forces. Leveraging cutting-edge DL Frameworks, like CNNs, Faster RCNN, and YOLOv8, our methodology aims to enhance the precision and efficiency of identifying concealed weapons and camouflaged objects in real-world scenarios. The collaboration with the Indian Paramilitary has enabled the creation of a dataset tailored to the challenges faced on the ground, reflecting a commitment to addressing contemporary threats. The study explores the global positioning of India in military technology, emphasizing the nation's role as a key player in adopting sophisticated solutions for defense. Beyond technological innovation, this research underscores a noble cause the protection of lives and the fortification of national security. The fusion of advanced technology and a profound sense of duty forms the core of this work, exemplifying a commitment to the greater good. © 2024 IEEE.

关键词： Camouflaged object detection Convolutional Neural Networks (CNNs) Deep Learning Defense Strategies Faster RCNN Indian Paramilitary Military technology Object Recognition Situational Awareness YOLOv8

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Mask Compliance in Public Services: A Comparative Study of Machine Learning and CNN Models for Mask Detection 2

Mask Compliance in Public Services: A Comparative Study of M...

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2nd International Conference on Emerging Trends in Information technology and engineering, ic-ETITE 2024

作者： Abdullah, A. Sheik Altrin Lloyd Hudson, D. Naga Pranava Shashank, V. Aishwarya, S. Shri Varshan, P. School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Chennai India

ISBN: (纸本)9798350328202

The Coronavirus Disease (COVID)-19 pandemic is fast changing our way of life and interfering with international travel and trade. The norm now is to wear a protective facemask. Many public service providers may soon demand users to correctly wear masks in order to access their services. This project offers a condensed method for accomplishing this goal by utilizing CNN models like VGG19, SVM, and Decision Trees as well as machine learning models. With an accuracy of 97%, the first methodology used Resnet50 as a feature extractor and the SVM + Decision tree (combined) - Stacking ensemble method as a classifier. Using the acquired dataset, the second methodology used the VGG19 model, which produced an impressive accuracy of 94.7%. The study will also look into how other elements, such lighting, camera angles, and facial orientations, affect how well the algorithms work. The results of this study can be applied to the development of more accurate and dependable mask detection systems, thereby reducing the transmission of infectious diseases in public areas. © 2024 IEEE.

关键词： Coronavirus

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Wavelength injection locking actively Q-switched random fiber laser based on random phase-shifted fiber Bragg grating and electro-optic modulator

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Optoelectronics Letters 2024年第3期20卷 135-141页

作者： PAN Honggang ZHANG Bo SONG Dianyou CHEN Zhipan CHEN Chunqi LI Rupeng School of Integrated Circuit Science and Engineering Tianjin University of TechnologyTianjin 300384China

In this paper, an actively Q-switched wavelength injection locking random fiber laser(RFL) based on random phase-shifted fiber Bragg grating(RPS-FBG) is proposed, and the performance of the laser is verified by experiments. Within the reflection bandwidth range of RPS-FBG, spanning from 1 549.2 nm to 1 549.9 nm, different laser modes with stable central wavelength and peak power can be selectively chosen by varying the injected light wavelength. The power fluctuation within 1 h is less than 0.1 d Bm, and the central wavelength drift is less than 0.02 nm. When the pump power increases from 90 mW to 300 mW, the pulse width decreases from 3.2 μs to 1.5 μs, and the pulse repetition frequency is 20 kHz. The RFL can reach a stable locking state at the lowest pump power of 100 mW and the lowest injection power of 3 d Bm. When the wavelength is locked, the output pulse is a single pulse. On the contrary, the unlocked output pulse is multi-pulse. The laser has the characteristics of high wavelength tunability in the reflection range of RPS-FBG and it can be an ideal light source in the fields of laser imaging and pulse coding.

关键词： fiber switched modulator

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Enhanced Framework for Authentic and Anonymous Data Sharing Over Cloud Storage Service

Enhanced Framework for Authentic and Anonymous Data Sharing ...

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2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems, IACIS 2024

作者： Naik, Archana Anjali, M. Ushashree, P. Sooda, Kavitha Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Bangalore India Geethanjali College of Engineering and Technology Department of Computer Science and Engineering Hydrabad India B.M.S. College of Engineering Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350360660

This study introduces the concept of anonymous data exchange in banking applications using cloud computing. By intertwining innovative methodologies, the proposed framework bolsters security while introducing novel dimensions to the authentication landscape. Key to the system's architecture is the integration of RSA and SHA algorithms within the framework. In the contemporary digital landscape, the banking sector confronts escalating challenges in establishing robust authentication and security measures to safeguard sensitive customer data and transactions. Color-coded passwords offer a pioneering approach, enhancing traditional authentication methods with added security. Furthermore, the system advocates for integrate multi-factor authentication techniques, thereby reinforcing the authentication process. This integration ensures the confidentiality, integrity, and authenticity of sensitive data transmitted between clients. The performance of this integrated algorithm is compared with other encryption algorithms, and it is observed that the proposed mechanism encryption time is 10% faster. The encrypted message was tested on an online simulator to attempt deciphering and could not be decrypted, confirming its security. © 2024 IEEE.

关键词： Authentication Protocol

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An ensembled-deep-learning paradigm trained with a self-improved coyote optimization algorithm (SI-COA) for crop disease detection

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Multimedia Tools and Applications 2025年第4期84卷 1697-1724页

作者： Shukla, Preeti Chandanan, Amit Kumar Department of Computer Science and Information Technology Guru Ghasidas Vishwavidyalaya CG Bilaspur India Department of Computer Science & Engineering Guru Ghasidas Vishwavidyalaya CG Bilaspur India

In recent times, drastic climate changes have caused a substantial increase in the growth of crop diseases. This causes large-scale demolition of crops, decreases cultivation, and eventually leads to the financial loss of farmers. Due to the rapid growth in a variety of diseases and adequate knowledge of farmers, identification, and treatment of the disease have become a major challenge. The leaves have texture and visual similarities which are attributes for the identification of disease type. Hence, computer vision employed with deep learning provides a way to solve this problem. In this research work, a novel voting-based ensembled-deep-learning paradigm is developed for crop disease detection. Initially, the collected raw images are pre-processed via Laplacian Filter and morphological operations. From, the pre-processed image, the ROI region is identified using K-means Clustering. Then, from the ROI regions, the features like texture feature (Chi-squared Local Binary Pattern (CLBP)), Scale Invariant Feature Transform (SIFT), Gray-Level Co-occurrence Matrix (GLCM)), color features, and shape features are extracted. Then, the optimal features are selected by using Self-Improved Coyote Optimization Algorithm (SI-COA) is an extended version of the standard Coyote Optimization Algorithm (COA) and is a metaheuristic for optimization that is inspired by the canis latrans species. For crop disease detection, the voting-based ensembled-deep-learning paradigm was used which includes the Attention-based Bi-LSTM, Recurrent Neural Networks (RNNs), and Optimized Deep Neural Networks (O-DNN). The weight of DNN is optimized via Self-Improved Coyote Optimization Algorithm (SI-COA). The overall outcome of this process is evaluated by using various performance metrics such as Precision, Recall, F1-Score, Accuracy, Sensitivity and Specificity, NPV, FNR, FPR, and MCC, as well. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2

关键词： Long short-term memory

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A deep graph convolution network with attention for clustering scRNA-seq data

A deep graph convolution network with attention for clusteri...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Chen, Xiang Yu, Junnan Peng, Li Li, Min Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan China Central South University School of Computer Science and Engineering Changsha China

ISBN: (纸本)9798350337488

Identifying cell types is a primary objective in single-cell RNA sequencing (scRNA-seq) analysis, and clustering is a widely used method for achieving this goal. However, the abundance of data and the presence of noise pose significant challenges for single-cell clustering. We propose a novel method called scGCNClustering, which leverages a deep graph convolution network (GCN) with attention. scGCNClustering comprises two core models. The first model is a deep GCN encoder that effectively preserves both the topological structure and feature information in scRNA-seq data, enabling accurate cell segregation. Additionally, we incorporate an attention module into the GCN encoder to capture precise global similarities between cells. The second model is a deep zero-inflated negative binomial (ZINB) decoder, which approximates the true distribution of scRNA-seq data. Extensive analysis conducted on six real scRNA-seq datasets demonstrates that scGCNClustering achieves promising performance in scRNA-seq clustering. © 2023 IEEE.

关键词： Clustering Graph convolution network Single cell RNA-seq

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Predictive Analysis of Liver Disease Using Random Forest Machine Learning Algorithm 1

Predictive Analysis of Liver Disease Using Random Forest Mac...

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1st International Conference on Innovations in Communications, Electrical and computer engineering, ICICEC 2024

作者： Vetrithangam, D. Kumar, Nallabariki Praveen Arya, Neha Tiwari, Amrita Raju, G. Jaya Srinivas, Gollapalli Veera Satya Chandigarh University Department of Computer Science & Engineering Mohali India Vnr Vignana Jyothi Institute of Engineering and Technology Department of Computer Science and Engineering Hyderabad India Ajay Kumar Garg Engineering College Department of Computer Science and Engineering Ghaziabad India Aditya College of Engineering and Technology Department of Computer Science and Engineering Surampalem India Aditya College of Engineering and Technology Department of Mechanical Engineering Surampalem India

ISBN: (纸本)9798350376517

The proposed system's objective is to improve the performance of diagnosing liver diseases through machine learning by using the Random Forest algorithm. Such systems accommodate a detailed database that comprises of several parameters of the patient's health status, through which important factors that cause liver disease may be determined. The analysis utilizes Random Forest, a feature selection technique that boosts the efficacy of the model's results in determining the outcomes of liver diseases with accuracy and recall. The training is conducted to reduce the component's error rate as well as to determine the ability of the system to perform complex tasks. Likewise, the proposed solution also focuses on the easy-to-use front-end for the target groups - the healthcare organizations - so the solution perfectly fits into their day-to-day work. In sum, this system is designed to become an innovative, cutting edge, and evidence-based tool for the notification and prevention of the liver disease in its early stage so as to enhance patients' outcomes and make more efficient use of the health care facilities. © 2024 IEEE.

关键词： Contrastive Learning

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Sensorless control of permanent magnet synchronous motor based on two stage low pass filtering sliding mode observer

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Soft Computing 2024年 1-14页

作者： Zou, Xinhong Yang, Kai Luo, Yixiao School of Electrical and Electronic Engineering Huazhong University of Science and Technology Wuhan430074 China

This paper proposes a sensorless control strategy of permanent magnet synchronous motor (PMSM) based on two stage low pass filtering sliding mode observer (SMO). Due to the switching characteristics of sliding mode control, traditional SMO has inherent chattering problem, and traditional SMO has disadvantages such as poor observer accuracy and poor adaptive ability. To suppress the system chattering problem caused by SMO, a new continuous function is used to replace the discontinuous sign function as the switching function. To improve the adaptive ability of the system, the boundary layer thickness of the proposed switching function is designed as a variable. The boundary layer thickness varies with the distance from the sliding surface to the system state. This paper also proposes a two stage low pass filter (LPF) to filter the noise and harmonics in the estimated back EMF more effectively, so as to improve the observation accuracy. The cut-off frequency of the two stage LPF can vary adaptively with the motor speed. Therefore, the proposed two stage LPF can achieve effective filtering when the motor speed changes. The back EMF signal will produce phase delay after being filtered by two stage LPF. To offset the phase delay, a two stage variable phase delay compensation is designed for the two stage variable cut-off frequency LPF. The effectiveness and feasibility of the proposed method are verified by simulation and experiment. The results of the traditional SMO and the proposed SMO on the speed and rotor position observation under different working conditions are compared. The simulation and experimental results show that compared with the traditional SMO, the proposed SMO effectively improves the observation performance and robustness. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Permanent magnets

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Multiphysics numerical investigation of photothermal self-driving characteristics of SiO_(2)@Au Janus microparticles for drug delivery

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Particuology 2023年第5期76卷 165-175页

作者： Ying Liu Xinzhi Wang Yurong He School of Energy Science and Engineering Harbin Institute of TechnologyHarbin150001China Heilongjiang Key Laboratory of New Energy Storage Materials and Processes School of Energy Science and EngineeringHarbin Institute of TechnologyHarbin50001China

The photothermal self-driving process of Janus microparticles has wide application prospects in the fields of *** silica and gold have good biocompatibility and high photothermal conversion efficiency,the SiO_(2)@Au Janus microparticles are widely used as drug *** on the multiphysics coupling method,the photothermal self-driving process of SiO_(2)@Au Janus microparticles was investi-gated,wherein the substrate was SiO_(2)particles and one side of the particles was coated with gold *** a continuous wave laser with irradiation of 20 W/cm^(2),the distance covered by the Janus particles was increased by increasing the thickness of the gold film and reducing the size of the SiO_(2)particles;the self-driving characteristics of the Janus particles were controlled substantially by increasing the intensity of the incident *** on the simulation results,the thermophoretic motion and Brownian motion of particles can be measured by comparing the absolute values of the thermophoretic force impulse,Brownian force impulse,and drag force *** Brownian force acting on Janus microparticles under low laser power cannot be ***,the minimum laser power required for Janus particles to overcome Brownian motion was *** results can effectively guide the design of Janus particles in biomedicine and systematically analyze the mechanism of particle thermophoretic motion during drug delivery.

关键词： Janus microparticles Photothermal self-driving Multiphysics coupling method Brownian

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