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Development and optimization of PZT suspensions with high solid loading and low viscosity for stereolithography-based additive manufacturing

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CERAMICS INTERNATIONAL 2024年第2期50卷 3325-3333页

作者： Jiao, Yifei Fan, Guifen Chen, Yongan Yu, Wanjun Gong, Feng Liu, Gang Bai, Jiaming Zhou, Peng Zhang, Dongyang Sun, Lining Shenzhen Polytech Univ Inst Intelligent Mfg Technol Shenzhen 518055 Peoples R China Wuhan Inst Technol Hubei Key Lab Plasma Chem & Adv Mat Sch Mat Sci & Engn Wuhan 430205 Peoples R China Huazhong Univ Sci & Technol Sch Opt & Elect Informat Key Lab Funct Mat Elect Informat B MOE Wuhan 430074 Peoples R China Yantai Univ Sch Environm & Mat Engn Yantai 264005 Peoples R China Shenzhen Univ Coll Mechatron & Control Engn Dept Mech & Elect Engn Shenzhen 518055 Peoples R China Southern Univ Sci & Technol Coll Engn Shenzhen 518055 Peoples R China Shenzhen Polytech Univ Inst Crit Mat Integrated Circuits Shenzhen 518055 Peoples R China Soochow Univ Robot & Microsyst Ctr Sch Mech & Elect Engn Suzhou 215021 Peoples R China

Achieving synergy between high solid loading and low viscosity for ceramic suspensions a critical issue in the field of stereolithography. In this work, we successfully obtained a well-dispersed PZT suspension with high solid loading (55 vol%) and low viscosity (3047.5 mPa s at 60 rpm) by adding appropriate amounts of dispersant (1 wt % KOS110) and thickening agent (2 wt% PEG300), providing a foundation for crack-free photosensitive lead zirconate titanate (PZT) samples. This work presented a simple approach for achieving synergy between the high solid loading and low viscosity of ceramic suspensions for stereolithography-based additive manufacturing.

关键词： PZT suspensions Solid loading Viscosity Crack-free

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The diffusion least mean square algorithm with variable q-gradient

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DIGITAL SIGNAL PROCESSING 2022年第0期127卷

作者： Cai, Peng Wang, Shiyuan Qian, Junhui Zhang, Tao Huang, Gangyi Southwest Univ Coll Elect & Informat Engn Chongqing 400715 Peoples R China Chongqing Key Lab Nonlinear Circuits & Intelligen Chongqing 400715 Peoples R China Chongqing Univ Sch Microelect & Commun Engn Chongqing 400044 Peoples R China Chongqing Key Lab Biopercept & Intelligent Inform Chongqing 400044 Peoples R China

To avoid the local minima in optimization for distributed adaptive filters, the q-gradient diffusion least mean square (q-DLMS) algorithm uses the q-gradient vector to estimate parameters of interest in distributed networks. However, the q value in q-DLMS is fixed and required to be determined beforehand. To this end, a novel variable q-DLMS (v-q-DLMS) algorithm is proposed in this paper to improve the performance of q-DLMS and avoid the selection issue of q, simultaneously. The theoretical results of the proposed v-q-DLMS algorithm regarding accuracy and convergence are provided for performance analysis. In addition, the variable q-version of combination rule is derived by minimizing mean square derivation. Simulation results on distributed networks validate the correctness of obtained theoretical results and illustrate the superiorities of the proposed v-q-DLMS algorithm from the aspects of accuracy, convergence rate, and robustness. (C)& nbsp;2022 Elsevier Inc. All rights reserved.

关键词： Diffusion adaptive filter q-gradient Optimization Diffusion LMS Distributed estimation

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Biomimetic breathable nanofiber electronic skins with temperature-controlled self-adhesive and directional moisture-wicking properties for bifunctional pressure and non-contact sensing

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NANO ENERGY 2024年第PartA期128卷

作者： Wang, Guoqing Duan, Jingle Sun, Guifen Wang, Peng Meng, Chuizhou Duan, Derong Zhang, Longxiao Kan, Hao Li, Yang Univ Jinan Sch Informat Sci & Engn Shandong Prov Key Lab Network Based Intelligent Co Jinan 250022 Peoples R China Hebei Univ Technol Hebei Key Lab Smart Sensing & Human Robot Interact State Key Lab Reliabil & Intelligence Elect Equipm Sch Mech EngnEngn Res CtrMinist Educ Intelligent Tianjin 300401 Peoples R China Univ Jinan Sch Mech Engn Jinan 250022 Peoples R China Shandong Univ Sch Integrated Circuits Jinan 250101 Peoples R China

Electronic skin has fast developed in recent years for its promising application in wearable sensing. Multifunction of breathability, self-adhesion, sweat release and diverse sensing is highly required but has not been achieved on a single device. Herein, inspired by natural human skin, we develop a breathable and self-adhesive electronic skin based on an all-nanofiber platform with unique directional moisture-wicking properties and bifunctional pressure and non-contact sensing capability. The multifunctional device is integrated with four functional parts self-adhesive gel, moisture-wicking structure, pressure sensing layer and non-contact detection layer. Benefits from structural design and advanced sensing principles, the sensor not only demonstrates high pressure-sensing sensitivity of 23 kPa-1 and ultrawide non-contact detection range of 2 m but also exhibits excellent breathability of 53.9 mm s-1 and fast moisture-wicking ability. Interestingly, the sensor can be tightly attached to human skin at skin temperature and be easily peeled off at elevated temperatures by using the temperature-controlled reversible phase-transition gel. Towards practical wearable sensing applications, different movements can be identified with 97.3 % accuracy by using the four-layer Multi-Layer Perceptron neural network. This work will boost the development of next-generation multifunctional electronic skins with both wearing comfortability and diverse sensing capability.

关键词： Electrospun nanofiber Non -contact sensor Pressure sensor Directional moisture wicking Electronic skin

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The Existence and Stability Analysis of Periodic Solution of Izhikevich Model

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INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS 2020年第5期18卷 1161-1176页

作者： Li, Yi Li, Chuandong He, Zhilong Shen, Zixiang Southwest Univ Chongqing Key Lab Nonlinear Circuits & Intelligen Chongqing 400715 Peoples R China Southwest Univ Coll Elect & Informat Engn Chongqing 400715 Peoples R China

In this paper, based on the Izhikevich model, a more realistic hybrid impulsive neuron model combining model with state-dependent impulsive effects is proposed. By means of the theory of impulsive semidynamic system, the Poincare section and the ordinary differential equation geometry theory, the properties of the equilibrium points and the sufficient conditions for the existence and stability of different order-1 or order-2 periodic solutions of the system are derived near the equilibrium point or limit cycle. In addition, subthreshold bifurcation behavior which are saddle-node bifurcation, Andronov-Hopf bifurcation, Bogdano-Takens bifurcation and bifurcation behavior of the model with state-dependent impulsive effects are also studied. Final, the main results are illustrated by numerical simulations, and the bifurcation diagram shows that the system becomes more complicated due to the existence of impulses, and there exist bifurcations and chaos phenomena, indicating that the system has rich dynamic behavior.

关键词： Bifurcation impulsive effect Izhikevich model periodic solution Poincare map

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Low power convolutional architectures: Three operator switching systems based on forgetting memristor bridge

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SUSTAINABLE CITIES AND SOCIETY 2021年 69卷 102849-102849页

作者： Chen, Ling Gong, Chencheng Li, Chuandong Huang, Junjian Southwest Univ Chongqing Key Lab Nonlinear Circuits & Intelligen Coll Elect Informat & Engn Chongqing 400715 Peoples R China

With the development of technology and society, artificial intelligence has entered the era of deep learning neural networks. Convolution operation and image processing are fundamental technologies for deep neural networks and artificial intelligence, so reducing the energy consumption of convolution operation and increasing the speed of image processing will promote the development of a sustainable intelligent society. Traditional image processing technologies are based on the Von Neumann architecture, which is slow and not convenient for the hardware implementation of neural networks. Therefore, this paper breaks through the Von Neumann architecture, and uses the forgetting memristor bridge to realize the parallel image processing on the neumorphic chips. We use the forgetting characteristics of the memristor bridge to switch operators, and design singleoperator switching, double-operator switching and K-operator switching three system architectures. The image operator switching systems designed in this paper not only have the similar processing effect of the traditional way, but also have the advantages of easy control, fast running speed (the processing speed is reduced from ms to ?s), and low power consumption (the power consumption is reduced by nearly half), which will bring immeasurable benefits to the sustainable intelligent society.

关键词： Long-and short-term memory Forgetting memristor bridge Operator switching system Image processing

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Active learning with extreme learning machine for online imbalanced multiclass classification

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KNOWLEDGE-BASED SYSTEMS 2021年 231卷 107385-107385页

作者： Qin, Jiongming Wang, Cong Zou, Qinhong Sun, Yubin Chen, Bin Southwest Univ Coll Elect & Informat Engn Chongqing Key Lab Nonlinear Circuits & Intelligen Chongqing 400715 Peoples R China

Active learning (AL) can significantly reduce the cost of labeling instances. Extreme learning machine (ELM) has low computational cost, extremely fast training speed and strong generalization ability. Previous studies have shown that the combination of them can generate efficient learning models. Nevertheless, these researches did not focus on multiclass imbalanced data. Cold start may occur and the performance of classifier is also reduced due to the imbalanced distribution of categories. Moreover, there is no framework for processing stream-based data. To address these problems, an improved framework called AL for class incremental and weighted sequential ELM (AI-WSELM) is proposed in this paper, and its advantages are as follows: (1) similarity query and margin sampling were used to alleviate cold start and select uncertain instances, respectively, (2) an improved weighting strategy was used to tackle stream-based multiclass imbalanced distribution, (3) a class incremental mechanism was added to deal with new categories appeared in the subsequent batches, and (4) AI-WSELM greatly reduced the cost of labeling samples when ensuring classification performance. The simulation results show that the proposed model has satisfactory performance compared to the existing ELMs and the other related algorithms, which indicates the feasibility and effectiveness of AI-WSELM. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Active learning Extreme learning machine Multiclass imbalanced classification Query strategy Class incremental

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EEG-based Emotion Recognition on Unstable Patterns over Time

EEG-based Emotion Recognition on Unstable Patterns over Time

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International Conference on Digital Home (ICDH)

作者： Guopei Wu Sheng Qin Yuling Luo Qiang Fu Jin Zeng Su Yang Junxiu Liu Guangxi Key Lab of Brain-inspired Computing and Intelligent Chips School of Electronic and Information Engineering Guangxi Normal University Guilin China Education Department of Guangxi Zhuang Autonomous Region Key Laboratory of Nonlinear Circuits and Optical Communications (Guangxi Normal University) Guilin China Department of Computer Science Swansea University UK

ISBN: (数字)9798331509873

ISBN: (纸本)9798331509880

In brain-computer interface (BCI) systems, users' emotion can be recognized by using electroencephalography (EEG) data. Recent researches proposed different methods for feature extraction and EEG-based emotion classification. However, EEG data collected over time is not always stable and accuracies of emotion recognition are found not robust. In this paper, a novel EEG emotion recognition system is proposed by combining ReliefF for extracting features, and Long Short-Term Memory Network coupled with Support Vector Machine for classification. A public EEG emotion dataset of SEED which contains three different recordings of EEG data is used in the experiments. It is shown that the proposed network is effective for mitigating the phenomenon of unstable accuracies, and achieves higher emotion classification accuracies compared to other approaches using the same dataset.

关键词： Support vector machines Emotion recognition Accuracy Feature extraction Electroencephalography Brain-computer interfaces Recording Classification algorithms Long short term memory Biological neural networks

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Decentralized stochastic optimization algorithms using uncoordinated step-sizes over unbalanced directed networks

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SIGNAL PROCESSING 2021年 180卷 107894-107894页

作者： Hu, Jinhui Ran, Liang Du, Zhenyuan Li, Huaqing Southwest Univ Chongqing Key Lab Nonlinear Circuits & Intelligen Coll Elect & Informat Engn Chongqing 400715 Peoples R China

Decentralized stochastic gradient methods play significant roles in large-scale optimization that finds many practical applications in signal processing, machine learning, and coordinated control. In this short communication, we focus on studying large-scale convex optimization problems over multi-agent systems, where the mutual goal of agents in the network is to optimize a global objective function expressed as a sum of local objective functions. Each agent in the system uses only local computation and communication in the overall process without leaking their private information. We first introduce both the local stochastic averaging gradient method and the local stochastic variance-reduced gradient method into decentralized convex optimization (DCO) over unbalanced directed networks. Then, in order to increase the autonomy and flexibility of the agents, uncoordinated step-sizes are considered. Based on the wellknown variance-reduced stochastic gradient methods and uncoordinated step-sizes, we propose two fully decentralized algorithms named SAGA-UDN and SVRG-UDN for DCO over unbalanced directed networks, respectively. Finally, both SAGA-UDN and SVRG-UDN are numerically studied under various network parameters and objective functions. Some real-world data sets are used in simulations to demonstrate the improved performance of SAGA-UDN and SVRG-UDN. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Stochastic gradients Decentralized convex optimization Unbalanced directed networks Multi-agent systems Large-scale problems

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Mixed neurodynamic optimization for the operation of multiple energy systems considering economic and environmental aspects

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ENERGY 2021年 232卷 120965-120965页

作者： Feng, Peiling He, Xing Southwest Univ Coll Elect & Informat Engn Chongqing Key Lab Nonlinear Circuits & Intelligen Chongqing 400715 Peoples R China

ABSTR A C T In addition to economic goals, environmental constraints play an increasingly important role in the operation of multiple energy systems. A optimization model combining the minimization of energy cost and the carbon emissions is established to evaluate environmental impact, which is significant in the policy making policy making of the energy saving and emission reduction. In this paper, a bi-level model of multiple energy systems is proposed, which considers the constraints of operation and emission. The upper-level model studies the optimal allocation of electric power and natural gas in multiple energy systems. Based on the energy hub modeling method, the low-level model investigates the optimal en-ergy supply allocation of multiple energy carriers. The mixed neurodynamic algorithm combining neurodynamic algorithm and intelligent algorithm is used to get the optimization results. Simulation results verify the effectiveness of the model and algorithm. (c) 2021 Elsevier Ltd. All rights reserved.

关键词： Multiple energy systems Carbon emission Mixed neurodynamic algorithm Energy hub

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Univariate kernel sums correntropy for adaptive filtering

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APPLIED ACOUSTICS 2021年 184卷 108316-108316页

作者： Nan, Shanghan Qian, Guobing Southwest Univ Coll Elect & Informat Engn Chongqing Key Lab Nonlinear Circuits & Intelligen Chongqing 400715 Peoples R China

In this paper, we focus on a special form of univariate kernel and, by specifying the relationship between its parameters, make its first derivative close to a neat form. By using this kind of univariate kernel sums instead of Gaussian kernel function, we put forward the sum of univariate kernels maximum correntropy criterion (SKMCC) on the basis of maximum entropy criterion (MCC) and apply it to adaptive filtering. We study the characteristics of the performance surface of the new algorithm and confirm the theoretical results in the simulation. Its superior performance is proved by comparing with other latest adaptive algorithms. (C) 2021 Elsevier Ltd. All rights reserved.

关键词： Univariate kernel sums Adaptive filtering Correntropy SKMCC algorithm

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