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Development of the ultra-high linearity flexible sensor using CMC/MXene-regulating conductive polymer strategy through resistance-capacitance hybrid response for pressure/temperature detection

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Journal of Colloid and Interface Science 2025年

作者： Xiaowen Zhu Yunong Zhao Ziyuan Zhou Aijuan Zhang Wuxue Ma Hanqing Liu Zihan Lin Shishun Yang Yuanyuan Fang Xiangjin Kong Yuxiu Zhongling Weiqiang Hong Qi Hong Xiaohui Guo Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Integrated Circuits Anhui University Hefei 230601 China State Key Laboratory of High-Performance Precision Manufacturing Dalian University of Technology Dalian 116024 China Key Laboratory for Micro/Nano Technology and System of Liaoning Province Dalian University of Technology Dalian 116024 China

Multifunctional temperature/pressure sensors have garnered significant research interest due to their broad applicability in wearable electronics and health monitoring. Nevertheless, most pressure sensors often exhibit pronounced nonlinear responses, primarily attributed to the pressure-dependent increase in compressive modulus. In this paper, we report a multifunctional sensor with ultra-high linearity pressure and well- temperature-sensing performance, fabricated via a facile dip-coating process. The hydrogen bonding interaction between carboxymethyl cellulose (CMC) and MXene effectively mitigates the self-stacking tendency of MXene (Ti 3 C 2 T x ) nanosheets. Subsequently, the CMC-modified MXene facilitates the conformational transition of PEDOT (Poly (3,4-ethylenedioxythiophene)) chains from benzoid to quinoid structures. This synergistic effect enables the fabrication of composites with enhanced electromechanical performance. Additionally, an innovative alternating current (AC) measurement is employed to investigate the potential resistance-capacitance hybrid response within the sensors. By optimizing preparation parameters, we achieve an ultra-high linearity (R 2  = 0.9943) across a broad detection range, alongside a well temperature sensitivity (−2.3575 %/°C). Proof-of-concept applications, including quilt slippage detection and pressure ulcer prevention, demonstrate the sensor's multifunctional capabilities. These findings highlight its potential for wearable medical devices and real-time health monitoring.

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Control of space-dependent four-wave mixing in a four-level atomic system

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Physical Review A 2020年第3期102卷 033516-033516页

作者： Jing Qiu Zhiping Wang Dongsheng Ding Zhixiang Huang Benli Yu Key Laboratory of Opto-Electronic Information Acquisition and Manipulation Ministry of Education Anhui University Hefei 230601 China Key Laboratory of Quantum Information University of Science and Technology of China Hefei Anhui 230026 China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei 230039 China

We propose a scheme to demonstrate the manipulation of space-dependent four-wave mixing (FWM) in a four-level atomic system. By adjusting the detuning of the control field, one can effectively control the FWM output field transferred from a pump beam carrying orbital angular momentum. More interestingly, by appropriate choice of the intensity of the control field, the FWM field can be significantly enhanced and phase twist is almost completely suppressed. Furthermore, the superposition modes created by the interference between the FWM field and a same-frequency Gaussian beam are also discussed, showing many interesting properties. Our results may open some possibilities for phase imprinting in Bose-Einstein condensates or atom manipulation with optical tweezers.

关键词： Angular momentum of light Atom optics Coherent control Optical coherence

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Weak Relationship Indicator-Based Evolutionary Algorithm for Multimodal Multi-Objective Optimization

SSRN

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SSRN 2023年

作者： Xiang, Yi Zheng, Jinhua Hu, Yaru Liu, Yuan Zou, Juan Deng, Qi Yang, Shengxiang Key Laboratory of Intelligent Computing and Information Processing Ministry of Education School of Computer Science School of Cyberspace Science of Xiangtan University Hunan Province Xiangtan China Faculty of School of Computer Science School of Cyberspace Science of Xiangtan University Xiangtan411105 China School of Computer Science and Informatics De Montfort University LeicesterLE1 9BH United Kingdom

Multimodal multi-objective problems (MMOPs) refer to multiple equivalent Pareto sets (PSs) that can be mapped to the same Pareto optimal front (PF) and have widespread applications in real life. Traditional multimodal multiobjective algorithms (MMEAs) use strong relationships (such as global domination relationships and global indicators) to guide population convergence. This may lead to two problems: first, when there are PSs with different search difficulties, the population will tend to explore the easier-to-search PSs and lose the more difficult-to-search PSs. Second, it cannot retain local PSs well (slightly worse in the objective space but far apart in the decision space). To address these issues, we propose a weak relationship indicator-based MMEA that includes weak convergence indicators and density evaluation indicators. The weak convergence indicator considers the relationship between an individual and a certain number of its neighbors. The density evaluation indicator comprehensively considers the density information of the individual and its neighbors. This allows the population to retain solutions from different PSs during exploration, even local PSs. In addition, an archive based on weak convergence indicators also retains excellent solutions generated during the evolution of the population. Experimental results show that our algo-rithm has higher competitive performance than seven other state-of-the-art MMEAs on chosen benchmark problems. © 2023, The Authors. All rights reserved.

关键词： Multiobjective optimization

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Dynamic Constrained Multi-Objective Optimization Algorithm Based on Co-Evolution and Diversity Enhancement

SSRN

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SSRN 2023年

作者： Che, Wang Zheng, Jinhua Hu, Yaru Zou, Juan Yang, Shengxiang Key Laboratory of Intelligent Computing and Information Processing Ministry of Education School of Computer Science School of Cyberspace Science of Xiangtan University Hunan Province Xiangtan China Faculty of School of Computer Science School of Cyberspace Science of Xiangtan University Xiangtan411105 China School of Computer Science and Informatics De Montfort University LeicesterLE1 9BH United Kingdom

Dynamic constrained multi-objective optimization problems (DCMOPs) involve objectives, constraints, and parameters that change over time. This kind of problem presents a greater challenge for evolutionary algorithms because it requires the population to quickly track the changing pareto-optimal set (PS) while being subject to constrained limitations and maintaining a good distribution of solutions. This paper proposes a dynamic constrained multi-objective optimization algorithm based on co-evolution and diversity enhancement (CEDE). The static optimization part involves the co-evolution of three populations. We evolve the first population to prioritize the preservation of feasible individuals. The second population is evolved to assist the first population in finding better feasible regions, while the third population is evolved to aid the first population in exploring feasible regions comprehensively. At different stages, the co-evolution of these three populations will also be adjusted. Additionally, to prevent the elimination of potentially valuable infeasible individuals due to pareto domination, we employ an archive set to store and update these individuals. When the environment changes, we propose a diversity enhancement strategy, which includes a diversity maintenance strategy and a center point-based exploration strategy. The experimental results demonstrate that CEDE is highly competitive in handling DCMOPs compared to several state-of-the-art algorithms. © 2023, The Authors. All rights reserved.

关键词： Evolutionary algorithms

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Simulating Maxwell-Schrodinger Equations by High-Order Symplectic FDTD Algorithm

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IEEE JOURNAL ON MULTISCALE AND MULTIPHYSICS COMPUTATIONAL TECHNIQUES 2019年 4卷 143-151页

作者： Xie, Guoda Huang, Zhixiang Fang, Ming Sha, Wei E., I Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei China Key Laboratory of Micro-Nano Electronic Devices and Smart Systems of Zhejiang Province College of Information Science and Electronic Engineering Zhejiang University Hangzhou China

A novel symplectic algorithm is proposed to solve the Maxwell Schrodinger (M-S) system for investigating light-matter interaction. Using the fourth-order symplectic integration and fourth-order collocated differences, M-S equations are discretized in temporal and spatial domains, respectively. The symplectic finite-difference time-domain (SFDTD) algorithm is developed for accurate and efficient study of coherent interaction between electromagnetic fields and artificial atoms. Particularly, the Dirichlet boundary condition is adopted for modeling the Rabi oscillation problems under the semiclassical framework. To implement the Dirichlet boundary condition, image theory is introduced, tailored to the high-order collocated differences. For validating the proposed SFDTD algorithm, three-dimensional numerical studies of the population inversion in the Rabi oscillation are presented. Numerical results show that the proposed high-order SFDTD(4, 4) algorithm exhibits better numerical performance than the conventional FDTD(2, 2) approach at the aspects of accuracy and efficiency for the long-term simulation. The proposed algorithm opens up a promising way toward a high-accurate energy-conservation modeling and simulation of complex dynamics in nanoscale light-matter interaction.

关键词： Maxwell Schrodinger (M-S) equations nanoscale light-matter interaction Rabi oscillation symplectic finite-difference time-domain (SFDTD)

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An Efficient Software Faults Localization Method based on Program Spectrum

An Efficient Software Faults Localization Method based on Pr...

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Information Technology,Big Data and Artificial Intelligence (ICIBA), International Conference on IEEE International Conference on

作者： Xiaoran Yu Huanrong Tang Juan Zou Fan Yu Key Laboratory of Intelligent Computing and Information Processing Xiangtan University Ministry of Education Xiangtan China

ISBN: (数字)9781728152240

ISBN: (纸本)9781728152257

The Spectrum-based Software Fault Localization (SBFL) method has been proved to be effective. The SBFL mainly analyzes the execution information of the test case set on each program entity and counts the suspiciousness value of each program entity through the suspiciousness formula. Different measurement methods of program entities have a significant influence on the accuracy of software faults localization because the effectiveness of the SBFL method depends on the suspiciousness calculation formula used. This paper proposes a new suspiciousness formula, fully considered the coverage information of different dimensions of the program spectrum. The suspiciousness formula can be widely used in a variety of faults localization methods extended on the SBFL method. The experimental results show that this method has advantages in effectiveness and efficiency of faults localization.

关键词： Software Software debugging Maintenance engineering Debugging Task analysis Software quality Software measurement

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LasHeR: A large-scale high-diversity benchmark for RGBT tracking

arXiv

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arXiv 2021年

作者： Li, Chenglong Xue, Wanlin Jia, Yaqing Qu, Zhichen Luo, Bin Tang, Jin Sun, Dengdi Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Artificial Intelligence Anhui University Hefei230601 China Information Materials and Intelligent Sensing Laboratory of Anhui Province Key Lab of Intelligent Computing and Signal Processing of Ministry of Education Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei230601 China

RGBT tracking receives a surge of interest in the computer vision community, but this research field lacks a large-scale and high-diversity benchmark dataset, which is essential for both the training of deep RGBT trackers and the comprehensive evaluation of RGBT tracking methods. To this end, we present a Large-scale High-diversity benchmark for short-term RGBT tracking (LasHeR) in this work. LasHeR consists of 1224 visible and thermal infrared video pairs with more than 730K frame pairs in total. Each frame pair is spatially aligned and manually annotated with a bounding box, making the dataset well and densely annotated. LasHeR is highly diverse capturing from a broad range of object categories, camera viewpoints, scene complexities and environmental factors across seasons, weathers, day and night. We conduct a comprehensive performance evaluation of 12 RGBT tracking algorithms on the LasHeR dataset and present detailed analysis. In addition, we release the unaligned version of LasHeR to attract the research interest for alignment-free RGBT tracking, which is a more practical task in real-world applications. The datasets and evaluation protocols are available at: https://***/mmic-lcl/Datasets-and-benchmark-code. Copyright © 2021, The Authors. All rights reserved.

关键词： Large dataset

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IF2Net: Innately Forgetting-Free Networks for Continual Learning

arXiv

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arXiv 2023年

作者： Li, Depeng Wang, Tianqi Xu, Bingrong Kawaguchi, Kenji Zeng, Zhigang Suganthan, Ponnuthurai Nagaratnam The School of Artificial Intelligence and Automation The Institute of Artificial Intelligence Huazhong University of Science and Technology The Key Laboratory of Image Processing and Intelligent Control Education Ministry of China Wuhan430074 China The School of Automation Wuhan University of Technology Wuhan430070 China The School of Computing National University of Singapore 117417 Singapore The School of Electrical and Electronic Engineering Nanyang Technological University Singapore639798 Singapore The KINDI Center for Computing Research College of Engineering Qatar University Doha Qatar

Continual learning can incrementally absorb new concepts without interfering with previously learned knowledge. Motivated by the characteristics of neural networks, in which information is stored in weights on connections, we investigated how to design an Innately Forgetting-Free Network (IF2Net) for continual learning context. This study proposed a straightforward yet effective learning paradigm by ingeniously keeping the weights relative to each seen task untouched before and after learning a new task. We first presented the novel representation-level learning on task sequences with random weights. This technique refers to tweaking the drifted representations caused by randomization back to their separate task-optimal working states, but the involved weights are frozen and reused (opposite to well-known layer-wise updates of weights). Then, sequential decision-making without forgetting can be achieved by projecting the output weight updates into the parsimonious orthogonal space, making the adaptations not disturb old knowledge while maintaining model plasticity. IF2Net allows a single network to inherently learn unlimited mapping rules without telling task identities at test time by integrating the respective strengths of randomization and orthogonalization. We validated the effectiveness of our approach in the extensive theoretical analysis and empirical study. Copyright © 2023, The Authors. All rights reserved.

关键词： Decision making

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On Error Performance and Concatenated Coding of Polar Codes in AWGN Channels

On Error Performance and Concatenated Coding of Polar Codes ...

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第四届遥感与无线通信国际会议

作者： Jing Jin Rui Deng Tiansheng Liu Liping Li Key Laboratory of Intelligent Computing and Signal Processing (Ministry of Education) Anhui University Department of Electronic Engineering and Information Science University of Science and Technology

In additive white Gaussian noise（AWGN） channels, construction of polar codes is needed for every operating signal-to-noise ratio（SNR）. Recently, the proposal of the design-SNR reduces the computation effort in constructing polar codes. In this paper, we prove that although the BER performance of the design-SNR construction is not affected, the packet-error-rate（PER） performance is degraded compared with the point-by-point construction. Therefore, a concatenation scheme is proposed to improve the degraded PER performance. Results show the validity of the proposed concatenation scheme when employing the design-SNR construction.

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MFCLIP: Multi-modal Fine-grained CLIP for Generalizable Diffusion Face Forgery Detection

arXiv

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arXiv 2024年

作者： Zhang, Yaning Wang, Tianyi Yu, Zitong Gao, Zan Shen, Linlin Chen, Shengyong Jinan250353 China Nanyang Technological University 50 Nanyang Ave Block N 4 639798 Singapore School of Computing and Information Technology Great Bay University Dongguan523000 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China Jinan250014 China The Key Laboratory of Computer Vision and System Ministry of Education Tianjin University of Technology Tianjin300384 China Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518129 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen518060 China

The rapid development of photo-realistic face generation methods has raised significant concerns in society and academia, highlighting the urgent need for robust and generalizable face forgery detection (FFD) techniques. Although existing approaches mainly capture face forgery patterns using image modality, other modalities like fine-grained noises and texts are not fully explored, which limits the generalization capability of the model. In addition, most FFD methods tend to identify facial images generated by GAN, but struggle to detect unseen diffusion-synthesized ones. To address the limitations, we aim to leverage the cutting-edge foundation model, contrastive language-image pre-training (CLIP), to achieve generalizable diffusion face forgery detection (DFFD). In this paper, we propose a novel multi-modal fine-grained CLIP (MFCLIP) model, which mines comprehensive and fine-grained forgery traces across image-noise modalities via language-guided face forgery representation learning, to facilitate the advancement of DFFD. Specifically, we devise a fine-grained language encoder (FLE) that extracts fine global language features from hierarchical text prompts. We design a multi-modal vision encoder (MVE) to capture global image forgery embeddings as well as fine-grained noise forgery patterns extracted from the richest patch, and integrate them to mine general visual forgery traces. Moreover, we build an innovative plug-and-play sample pair attention (SPA) method to emphasize relevant negative pairs and suppress irrelevant ones, allowing cross-modality sample pairs to conduct more flexible alignment. Extensive experiments and visualizations show that our model outperforms the state of the arts on different settings like cross-generator, cross-forgery, and cross-dataset evaluations. Copyright © 2024, The Authors. All rights reserved.

关键词： Alignment

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