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Redundancy reduction penalty term of loss function in deep neural network

Knowledge-Based Systems 2025年

作者： Xueheng Hu Shuhuan Wen H.K. Lam Engineering Research Center of the Ministry of Education for Intelligent Control System and Intelligent Equipment Yanshan University Qinhuangdao China Key Laboratory of Industrial Computer Control Engineering of Hebei Province Yanshan University Qinhuangdao China Key Laboratory of Intelligent Control and Neural Information Processing Ministry of Education Yanshan University Qinhuangdao 066004 Hebei Province China The Department of Engineering King’s College London London WC2R 2LS United Kingdom

In this paper, we propose a novel regularization algorithm that is introduced as a penalty term to the loss function. Differing from conventional L1 and L2 regularization methods, our approach does not aim to diminish the weights of individual neurons or enforce sparsity by driving certain neurons to zero. Instead, it functions by increasing the differences between neurons and enhancing the diversity of neurons within each layer. Our method incorporates ensemble learning techniques by treating the layer weight matrix as a collective learning model, where each neuron serving as a weak learner within the layer. The proposed algorithm improves the performance of DCNN by simultaneously considering the distance between multiple filters in the same layer. This algorithm reduces the redundancy of the parameter layer filters in DCNN and enhances its robustness. The penalty term proposed by our algorithm dynamically adjusts its value in a cyclical manner, compelling the neural network to navigate away from its current gradient state. In the parameter space, different weights correspond to different locations. The proposed algorithm quantifies the distance between neurons and iteratively increases the distance between neurons during thereby encouraging greater diversity within the network. Experimental evaluations demonstrate the effectiveness of our algorithm in enhancing neural network performance without requiring adjustments to other hyper-parameters.

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Immune secondary response and clonal selection inspired optimizers

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Progress in Natural Science:Materials International 2009年第2期19卷 237-253页

作者： Maoguo Gong a,Licheng Jiao a,Lining Zhang a,Haifeng Du b a key laboratory of intelligent Perception and image Understanding of Ministry of Education of China,Institute of intelligent Information processing,Xidian University,Xi’an 710071,China b School of Public Policy and Administration,Xi’an Jiaotong University,Xi’an 710049,China [a]Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Institute of Intelligent Information Processing Xidian University Xi’an 710071 China [b]School of Public Policy and Administration Xi’an Jiaotong University Xi’an 710049 China

The immune system’s ability to adapt its B cells to new types of antigen is powered by processes known as clonal selection and affinity maturation. When the body is exposed to the same antigen,immune system usually calls for a more rapid and larger response to the antigen,where B cells have the function of negative adjustment. Based on the clonal selection theory and the dynamic process of immune response,two novel artificial immune system algorithms,secondary response clonal programming algorithm (SRCPA) and secondary response clonal multi-objective algorithm (SRCMOA),are presented for solving single and multi-objective optimization problems,respectively. Clonal selection operator (CSO) and secondary response operator (SRO) are the main operators of SRCPA and SRCMOA. Inspired by the clonal selection theory,CSO reproduces individuals and selects their improved maturated progenies after the affinity mat-uration process. SRO copies certain antibodies to a secondary pool,whose members do not participate in CSO,but these antibodies could be activated by some external stimulations. The update of the secondary pool pays more attention to maintain the population diversity. On the one hand,decimal-string representation makes SRCPA more suitable for solving high-dimensional function optimiza-tion problems. Special mutation and recombination methods are adopted in SRCPA to simulate the somatic mutation and receptor edit-ing process. Compared with some existing evolutionary algorithms,such as OGA/Q,IEA,IMCPA,BGA and AEA,SRCPA is shown to be able to solve complex optimization problems,such as high-dimensional function optimizations,with better performance. On the other hand,SRCMOA combines the Pareto-strength based fitness assignment strategy,CSO and SRO to solve multi-objective optimization problems. The performance comparison between SRCMOA,NSGA-Ⅱ,SPEA,and PAES based on eight well-known test problems shows that SRCMOA has better performance in converging to approximate Pareto

关键词： Clonal selection Secondary response Artificial immune systems Evolutionary algorithms

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Nanocellulose-mediated double-network conductive organohydrogels with self-adhesive, biocompatible and environmentally tolerant properties for wearable biosensors

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Carbohydrate Polymers 2025年 363卷

作者： Zheng, Chunxiao Ma, Jingren Lu, Ya Ye, Mingqiang Zhou, Yihui Yang, Weisheng He, Shuijian Jiang, Shaohua Xiao, Huining Han, Jingquan Co-Innovation Center of Efficient Processing and Utilization of Forest Resources International Innovation Center for Forest Chemicals and Materials Joint International Research Lab of Lignocellulosic Functional Materials College of Materials Science and Engineering Nanjing Forestry University Jiangsu Nanjing210037 China Key Laboratory for Intelligent Nano Materials and Devices of the Ministry of Education State Key Laboratory of Mechanics and Control of Mechanical Structures Nanjing University of Aeronautics and Astronautics Jiangsu Nanjing210016 China Aerospace Kaitian Environmental Technology Co. Ltd Changsha410100 China Hunan Automotive Engineering Vocational University Zhuzhou412001 China Chemical Engineering Department New Brunswick University FrederictonNBE3B 5A3 Canada

Conductive hydrogels attracted increasing attention due to their excellent stretchability, self-adhesion and biocompatibility. However, conventional conductive hydrogels face the critical problems in extreme environments, such as freezing at low temperatures and evaporating at high temperatures. Introducing traditional cryoprotectant can improve environmental tolerance but reduce the flexibility and conductivity. How to simultaneously achieve high electrical conductivity, mechanical toughness, strong adhesion and extreme environmental-resistance remains a challenge. Herein, a highly stretchable, self-adhesive, biocompatible, extremely temperature-tolerant, double-networked conductive TEMPO-oxidized cellulose nanofibers-graphene/ polyacrylamide-polyacrylic acid-polydopamine-glycerol (TG/P-LP-Gl) organohydrogel is synthesized. The strong hydrogen bonding interactions between glycerol and water inhibit ice formation and prevent water evaporation, providing long-term stability in extreme environments. With 50 % glycerol content, the organohydrogel exhibits promising tensile strength (110.3 kPa), compressive strength (2.64 MPa) and adhesive strength (29.7 kPa), retaining its properties at −20 °C and 70 °C. In addition, the organohydrogel is also used for real-time monitoring of human movements, with the gauge factor (GF) reaching around 15. Therefore, the TG/P-LP-Gl organohydrogel offers a promising solution for sensors in extreme environments with great potential for high performance, low cost and environmentally friendly wearable biosensors. © 2025 Elsevier Ltd

关键词： Compressive strength

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Geometric Algebra Based Kalman Filter Trajectory Prediction 3

Geometric Algebra Based Kalman Filter Trajectory Prediction

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3rd International Symposium on Aerospace Engineering and Systems, ISAES 2024

作者： Li, Taiyu Jiao, Weidong Civil Aviation University of China Tianjin Key Laboratory of Intelligent Signal and Image Processing Tianjin China

ISBN: (纸本)9798350350418

To improve the safety and efficiency of aviation operation, a Kalman filtered trajectory prediction algorithm based on geometric algebra is proposed in this space. Firstly, the Kalman filter equations for trajectory prediction in geometric algebraic space are given by taking position, velocity and acceleration as state variables and position, velocity and acceleration in a typical trajectory as observations. Secondly, the matrix operation in the geometric algebraic space is simplified to the matrix operation by establishing a composite geometric algebraic space. Then, the versor operator in the conformal geometric algebraic space is introduced into the Kalman filter, and the steps of geometric algebra-based Kalman filter trajectory prediction are given to improve the prediction rate and accuracy. The experimental results show that the proposed trajectory prediction algorithm has the highest accuracy and the operation efficiency is improved by 27.66% compared with the Kalman filter algorithm in Euclidean space and the DBSCAN clustering algorithm. © 2024 IEEE.

关键词： Trajectories

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An English Pronunciation Evaluation Model Based on Acoustic Phonetics Features 4

An English Pronunciation Evaluation Model Based on Acoustic ...

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4th International Conference on Computer Science and Blockchain, CCSB 2024

作者： Wang, Hongkang Guo, Qingkai Huang, Guimin Zhou, Ya Guilin University of Electronic Technology Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin China

ISBN: (纸本)9798350356502

Oral English practice constitutes a crucial component of English language acquisition for non-native speakers;however, traditional classroom teaching methods are increasingly insufficient to address the diverse needs of learners. This paper enhances the evaluation of pronunciation quality within Computer-Assisted Language Learning (CALL) system. To assess the fluency of English learners' pronunciation, this paper introduces the innovative feature known as wordDurationRatio. Additionally, an intonation evaluation method utilizing dynamic time warping (DTW) algorithms is explored. Finally, this study incorporates support vector regression (SVR) algorithms to effectively fuse features, enhancing the assessment result of pronunciation quality. © 2024 IEEE.

关键词： Support vector regression

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image retrieval using the modified intersecting cortical model

Image retrieval using the modified intersecting cortical mod...

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2011 IEEE International conference on intelligent Computation and Bio-Medical Instrumentation, ICBMI 2011

作者： Yuan, Wenjin Zhang, Xuming School of Life Science and Technology Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9780769546230

The intersecting cortical model(ICM) is driven from the pulse-coupled neural networks (PCNN), and a lot of works have been done on it. In this paper, we have proposed a method, which is modified from the PCNN, to retrieval the image. This method uses the iteration times of the ICM as the feature, and we do it repeatedly. So we will get the feature vectors and utilize the features to retrieval the image. And the experimental results have proved the effectiveness of the proposed method. While the proposed method shows some advantages like the feature curves are linear and steady. © 2011 IEEE.

关键词： image retrieval

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Research on YOLOv8-Based Household Item Detection for Indoor Scenes 1

Research on YOLOv8-Based Household Item Detection for Indoor...

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1st IEEE International Conference on Data Intelligence and Innovative Application, DIIA 2024

作者： Wu, Jingwen Luo, Xiaonan Guilin University of Electronic Technology Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin China

ISBN: (纸本)9798331504434

This research is driven by the growing need for efficient, scalable object detection in smart home applications with real-time performance and resource constraints. In this study, we utilize the YOLOv8 deep learning model for household item detection, focusing on its performance in real-time scenarios. We compare different versions of YOLOv8 - specifically, YOLOv8n, YOLOv8s, and YOLOv8m - using a preprocessed custom dataset. The results indicate that YOLOv8n stands out due to its lower parameter count and faster inference speed, making it ideal for real-time applications, even though it has slightly lower precision and recall. In contrast, while YOLOv8s and YOLOv8m show improvements in certain metrics, their higher computational demands limit their effectiveness in resource-constrained settings. Overall, YOLOv8n effectively balances speed and accuracy, offering a viable solution for household item detection. © 2024 IEEE.

关键词： Smart homes

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Application of intelligent Schedule Management in Volunteer Service System 1

Application of Intelligent Schedule Management in Volunteer ...

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1st IEEE International Conference on Data Intelligence and Innovative Application, DIIA 2024

作者： Li, Yongxin Luo, Xiaonan Guilin University of Electronic Technology Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin China

ISBN: (纸本)9798331504434

As the demand for volunteer services grows, enhancing the efficiency and management of these services has become a key issue. This paper explores methods of integrating intelligent scheduling management functions with volunteer service systems to optimize volunteer time management and task allocation. By analyzing existing volunteer service systems, we propose a novel system framework that integrates intelligent scheduling management. This framework updates volunteer schedules in real- time, automatically matches tasks with volunteers' time windows, and optimizes task distribution using smart algorithms. Experimental results show that volunteer service systems with integrated intelligent scheduling management significantly improve task allocation accuracy and volunteer satisfaction. This research provides new ideas and practical basis for the intelligent development of volunteer services. © 2024 IEEE.

关键词： Digital family digital platform volunteer service

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Liver Tumor image Segmentation Method Based on U-Net Network 1

Liver Tumor Image Segmentation Method Based on U-Net Network

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1st IEEE International Conference on Data Intelligence and Innovative Application, DIIA 2024

作者： Lu, Dinghong Luo, Xiaonan Guilin University of Electronic Technology Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin China

ISBN: (纸本)9798331504434

Liver tumors are the most common and fatal ones globally, bringing great harm to human life. In order to ascertain the precise location, size, and volume of tumors, the conventional method of manual segmentation necessitates that physicians delineate the tumor boundaries within the patients' abdominal CT scans, a process that demands considerable expenditure of time and effort on the part of the medical staff. A method harnessing the U-Net neural network, predicated on deep learning, has been introduced in this study to augment the accuracy and efficiency of liver tumor segmentation in imaging. The U-Net network has been enhanced with the incorporation of Adam optimization and callback mechanisms to bolster the training efficacy and preserve the most effective model. Furthermore, the implementation of the binary crossentropy loss function has been pivotal for optimizing the neural network's convergence and predictive accuracy, which in turn has led to enhanced segmentation results. The experimental findings suggest that the proposed method is capable of automating the segmentation of liver tumor images, including the precise delineation of tumors and those with indistinct margins, achieving a segmentation accuracy of 99.64%. © 2024 IEEE.

关键词： Computerized tomography

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Design of Smart Home System Based on ZigBee Technology 1

Design of Smart Home System Based on ZigBee Technology

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1st IEEE International Conference on Data Intelligence and Innovative Application, DIIA 2024

作者： Liang, Yinfan Luo, Xiaonan Guilin University of Electronic Technology Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin China

ISBN: (纸本)9798331504434

With the rise of the Internet of Things (loT) and digital homes, smart home technology has also advanced. Smart homes can proactively sense people's needs, providing users with a more comfortable and secure living environment, while also saving energy and enhancing safety, thereby greatly improving the quality of life. Thanks to loT technology, smart home devices can connect to the home network, enabling data sharing and centralized control. This project applies ZigBee and MQTT protocols to the field of smart homes, achieving intelligent control and remote control of devices such as lighting, appliances, and security. The ZigBee protocol is used for networking, connecting terminal nodes in the facility layer with the coordinator node, while the MQTT protocol is used to connect the coordinator node to the internet. This project has realized remote control, intelligent control, and smart security of the home system, providing users with a more convenient and comfortable home experience. © 2024 IEEE.

关键词： Smart homes

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