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Structural, magnetic, and mechanical properties enhancement in Mn-Zn ferrites via controlled lithium ion doping

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Physica B: Condensed Matter 2025年 713卷

作者： Torabi, Naeimeh Savabieh, Hamid Reza Elahi, Ali Atomic and Molecular Division Faculty of Physics Yazd University Yazd Iran Department of Materials Science Tarbiat Modares University Tehran Iran School of Electrical and Computer Engineering University of Tehran Tehran Iran

In this work, lithium-doped manganese-zinc (Mn-Zn) ferrites were synthesized using a conventional solid-state route to investigate the effects of lithium (Li+) incorporation on their structural, magnetic, and mechanical properties. The main objective was to develop phase-pure and mechanically robust magnetic ceramics suitable for high-frequency applications. Ferrite compositions with various Li+ contents (x = 0, 0.03, 0.05, and 0.1) were sintered under controlled atmosphere to promote densification and phase purity. XRD and FTIR analyses confirmed spinel structure formation and the suppression of hematite as a secondary phase with increasing Li+. SEM results revealed enhanced microstructural uniformity, while mechanical tests showed a 28 % increase in flexural strength. Magnetic measurements indicated that saturation magnetization (Ms) increased from 60.8 to 78.4 emu/g with Li+ addition. These results demonstrate that controlled lithium doping is an effective strategy for tailoring both the magnetic and mechanical performance of Mn-Zn ferrite ceramics. © 2025

关键词： Saturation magnetization

CCNETS: A Novel Brain-Inspired Approach for Enhanced Pattern Recognition in Imbalanced Datasets

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arXiv 2024年

作者： Park, Hanbeot Cho, Yunjeong Kim, Hoon-Hee Department of Materials Science and Engineering Pukyong National University Korea Republic of Division of Smart Healthcare Pukyong National University Korea Republic of Department of Computer Engineering and Artificial Intelligence Pukyong National University Korea Republic of

This study introduces CCNETS (Causal Learning with Causal Cooperative Nets), a novel generative model-based classifier designed to tackle the challenge of generating data for imbalanced datasets in pattern recognition. CCNETS is uniquely crafted to emulate brain-like information processing and comprises three main components: Explainer, Producer, and Reasoner. Each component is designed to mimic specific brain functions, which aids in generating high-quality datasets and enhancing classification *** model is particularly focused on addressing the common and significant challenge of handling imbalanced datasets in machine learning. CCNETS's effectiveness is demonstrated through its application to a "fraud dataset," where normal transactions significantly outnumber fraudulent ones (99.83% vs. 0.17%). Traditional methods often struggle with such imbalances, leading to skewed performance metrics. However, CCNETS exhibits superior classification ability, as evidenced by its performance metrics. Specifically, it achieved an F1-score of 0.7992, outperforming traditional models like Autoencoders and Multi-layer Perceptrons (MLP) in the same context. This performance indicates CCNETS's proficiency in more accurately distinguishing between normal and fraudulent patterns. The innovative structure of CCNETS enhances the coherence between generative and classification models, helping to overcome the limitations of pattern recognition that rely solely on generative models. This study emphasizes CCNETS's potential in diverse applications, especially where quality data generation and pattern recognition are key. It proves effective in machine learning, particularly for imbalanced datasets. CCNETS overcomes current challenges in these datasets and advances machine learning with brain-inspired approaches. Copyright © 2024, The Authors. All rights reserved.

关键词： Pattern recognition

Cyber Physical System Role in Stock Market 5

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Cyber Physical System Role in Stock Market

5th International Conference on Devices for Integrated Circuit, DevIC 2023

作者： Dhyani, Aadidevam Bisht, Deepa Kathuria, Samta Gehlot, Anita Chhabra, Gunjan Tiwari, Praveen Uttaranchal Institute of Management Uttaranchal University Dehradun India Uttaranchal University Law College Dehradun Dehradun India Uttaranchal University UIT Division of Research and Innovation Dehradun India Graphic Era Hill University Department of Computer Science and Engineering Dehradun India Swami Rama Himalayan University Uttaranchal India

ISBN: (纸本)9798350347265

The stock market is a system that provides a platform between investors and companies and provides a strong foundation for the proper functioning of the economy. Stock market act as a source of fmance and evaluation tool which is responsible for overall growth and analysis of the economy. The present work focuses on the benefit of technology on the stock market and the effect of technologies on ease and accessibility towards investors. The goal of our study is to analyze the role of the fourth industrial revolution in the financial market and how it can benefit the investors. The articles included in this study are chosen based on the following criteria: journal and review articles, Englishlanguage publications, articles that only addressed ''stock market ''technologies'', ''finance'', ''blockchain ''artificial intelligence and ''economy''. The result of this study confirms that technology is consistently converting the face of the stock market and the stock market and technology are unquestionably essential components in fostering economic growth. Overall based on the study it is examined and concluded that technologies have created an effective impact on the stock market by facilitating smoothness, costeffectiveness, effective stock exchange for traders and investors. This study will be helpful in making an informed investment decision and can help investor for making predictive and less sentimental analysis. © 2023 IEEE.

关键词： Artificial intelligence

Differentially Private ℓ1-norm Linear Regression with Heavy-tailed Data

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Differentially Private ℓ1-norm Linear Regression with Heavy...

IEEE International Symposium on Information Theory

作者： Di Wang Jinhui Xu Division of Computer Electrical and Mathematical Sciences and Engineering King Abdullah University of Science and Technology Thuwal Saudi Arabia Department of Computer Science and Engineering State University of New York at Buffalo Buffalo NY

ISBN: (数字)9781665421591

ISBN: (纸本)9781665421607

We study the problem of Differentially Private Stochastic Convex Optimization (DP-SCO) with heavy-tailed data. Specifically, we focus on the ℓ 1 -norm linear regression in the ϵ-DP model. While most of the previous work focuses on the case where the loss function is Lipschitz, here we only need to assume the variates has bounded moments. Firstly, we study the case where the ℓ 2 norm of data has bounded second order moment. We propose an algorithm which is based on the exponential mechanism and show that it is possible to achieve an upper bound of $\tilde O\left( {\sqrt {\frac{d}{{n\varepsilon }}} } \right)$ (with high probability). Next, we relax the assumption to bounded θ-th order moment with some θ ∈ (1,2) and show that it is possible to achieve an upper bound of $\tilde O\left( {{{\left( {\sqrt {\frac{d}{{n\varepsilon }}} } \right)}^{\frac{{\theta - 1}}{\theta }}}} \right)$. Our algorithms can also be extended to more relaxed cases where only each coordinate of the data has bounded moments, and we can get an upper bound of $\tilde O\left( {\sqrt {\frac{d}{{n\varepsilon }}} } \right)$ and $\tilde O\left( {\frac{d}{{{{\left( {n\varepsilon } \right)}^{\frac{{\theta - 1}}{\theta }}}}}} \right)$ in the second and θ-th moment case respectively.

关键词： Upper bound Linear regression Stochastic processes Convex functions Information theory

FOCUSED ACTIVE LEARNING FOR HISTOPATHOLOGICAL IMAGE CLASSIFICATION

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arXiv 2024年

作者： Schmidt, Arne Morales-Álvarez, Pablo Cooper, Lee A.D. Newberg, Lee A. Enquobahrie, Andinet Katsaggelos, Aggelos K. Molina, Rafael Dep. of Computer Science and AI University of Granada Granada Spain Dep. of Statistics and Operations Research University of Granada Granada Spain Dep. of Pathology Northwestern University Chicago United States Kitware Inc Carrboro United States Dep. of Electrical Computer Engineering Northwestern University Evanston United States

Active Learning (AL) has the potential to solve a major problem of digital pathology: the efficient acquisition of labeled data for machine learning algorithms. However, existing AL methods often struggle in realistic settings with artifacts, ambiguities, and class imbalances, as commonly seen in the medical field. The lack of precise uncertainty estimations leads to the acquisition of images with a low informative value. To address these challenges, we propose Focused Active Learning (FocAL), which combines a Bayesian Neural Network with Out-of-Distribution detection to estimate different uncertainties for the acquisition function. Specifically, the weighted epistemic uncertainty accounts for the class imbalance, aleatoric uncertainty for ambiguous images, and an OoD score for artifacts. We perform extensive experiments to validate our method on MNIST and the real-world Panda dataset for the classification of prostate cancer. The results confirm that other AL methods are’distracted’ by ambiguities and artifacts which harm the performance. FocAL effectively focuses on the most informative images, avoiding ambiguities and artifacts during acquisition. For both experiments, FocAL outperforms existing AL approaches, reaching a Cohen’s kappa of 0.764 with only 0.69% of the labeled Panda data. © 2024, CC BY.

关键词： Image classification

FRONT-END ADAPTER: ADAPTING FRONT-END INPUT OF SPEECH BASED SELF-SUPERVISED LEARNING FOR SPEECH RECOGNITION

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arXiv 2023年

作者： Chen, Xie Ma, Ziyang Tang, Changli Wang, Yujin Zheng, Zhisheng MoE Key Lab of Artificial Intelligence AI Institute X-LANCE Lab Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Department of Electronic Engineering Tsinghua University Beijing China

Recent years have witnessed a boom in self-supervised learning (SSL) in various areas including speech processing. Speech based SSL models present promising performance in a range of speech related tasks. However, the training of SSL models is computationally expensive and a common practice is to fine-tune a released SSL model on the specific task. It is essential to use consistent front-end input during pre-training and fine-tuning. This consistency may introduce potential issues when the optimal front-end is not the same as that used in pre-training. In this paper, we propose a simple but effective front-end adapter to address this front-end discrepancy. By minimizing the distance between the outputs of different front-ends, the filterbank feature (Fbank) can be compatible with SSL models which are pre-trained with waveform. The experiment results demonstrate the effectiveness of our proposed front-end adapter on several popular SSL models for the speech recognition task. Copyright © 2023, The Authors. All rights reserved.

关键词： Supervised learning

Resilient distributed integral control for multimachine power systems with inherent input constraint satisfaction

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Resilient distributed integral control for multimachine powe...

Mediterranean Conference on Control and Automation (MED)

作者： Theodoros E. Kavvathas George C. Konstantopoulos Charalambos Konstantinou Department of Electrical and Computer Engineering University of Patras Rion Greece CEMSE Division King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia

In this paper, a novel distributed controller for multimachine power systems is proposed to guarantee grid frequency restoration and accurate real and reactive power sharing among the generator units, while maintaining the generator inputs (mechanical torque and field excitation voltage) within given bounds. The boundedness of the controller outputs (generator inputs) is rigorously proven using vector field theory. It is additionally shown that even if one generator input reaches its upper/lower limit, the remaining units can still accomplish the desired control tasks without modifying the controller structure or dynamics; hence introducing enhanced system resilience using the proposed approach. This has been accomplished in a unified control structure while using neighbour-to-neighbour communication, thus maintaining the distributed nature of the controller. An example of a 10-bus, 4-machine power system is simulated to verify the proposed controller performance under sudden changes of the load demand

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A3lign-DFER: Pioneering Comprehensive Dynamic Affective Alignment for Dynamic Facial Expression Recognition with CLIP

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arXiv 2024年

作者： Tao, Zeng Wang, Yan Lin, Junxiong Wang, Haoran Mai, Xinji Yu, Jiawen Tong, Xuan Zhou, Ziheng Yan, Shaoqi Zhao, Qing Han, Liyuan Zhang, Wenqiang Shanghai Engineering Research Center of AI & Robotics Academy for Engineering & Technology Fudan University Shanghai China School of Information Science and Technology Fudan University Shanghai China Institute of Automation Chinese Academy of Sciences Beijing China Engineering Research Center of AI & Robotics Ministry of Education Academy for Engineering & Technology Fudan University Shanghai China Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University Shanghai China

The performance of CLIP in dynamic facial expression recognition (DFER) task doesn’t yield exceptional results as observed in other CLIP-based classification tasks. While CLIP’s primary objective is to achieve alignment between images and text in the feature space, DFER poses challenges due to the abstract nature of text and the dynamic nature of video, making label representation limited and perfect alignment difficult. To address this issue, we have designed A3lign-DFER, which introduces a new DFER labeling paradigm to comprehensively achieve alignment, thus enhancing CLIP’s suitability for the DFER task. Specifically, our A3lign-DFER method is designed with multiple modules that work together to obtain the most suitable expanded-dimensional embeddings for classification and to achieve alignment in three key aspects: affective, dynamic, and bidirectional. We replace the input label text with a learnable Multi-Dimensional Alignment Token (MAT), enabling alignment of text to facial expression video samples in both affective and dynamic dimensions. After CLIP feature extraction, we introduce the Joint Dynamic Alignment Synchronizer (JAS), further facilitating synchronization and alignment in the temporal dimension. Additionally, we implement a Bidirectional Alignment Training Paradigm (BAP) to ensure gradual and steady training of parameters for both modalities. Our insightful and concise A3lign-DFER method achieves state-of-the-art results on multiple DFER datasets, including DFEW, FERV39k, and MAFW. Extensive ablation experiments and visualization studies demonstrate the effectiveness of A3lign-DFER. The code will be available in the future. © 2024, CC BY.

关键词： Alignment

Accelerated Modelling of Interfaces for Electronic Devices using Graph Neural Networks

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arXiv 2023年

作者： Brahma, Pratik Bhattaram, Krishnakumar Salahuddin, Sayeef Department of Electrical Engineering and Computer Sciences University of California Berkeley United States Materials Science Division Lawrence Berkeley National Laboratory United States

Modern microelectronic devices are composed of interfaces between a large number of materials, many of which are in amorphous or polycrystalline phases. Modeling such non-crystalline materials using first-principles methods such as density functional theory is often numerically intractable. Recently, graph neural networks (GNNs) have shown potential to achieve linear complexity with accuracies comparable to ab-initio methods. Here, we demonstrate the applicability of GNNs to accelerate the atomistic computational pipeline for predicting macroscopic transistor transport characteristics via learning microscopic physical properties. We generate amorphous heterostructures, specifically the HfO2-SiO2-Si semiconductor-dielectric transistor gate stack, via GNN predicted atomic forces, and show excellent accuracy in predicting transport characteristics including injection velocity for nanoslab silicon channels. This work paves the way for faster and more scalable methods to model modern advanced electronic devices via GNNs. © 2023, CC BY.

关键词： Silica