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VARIATIONAL ANALYSIS FOR THE MAXIMAL TIME FUNCTION IN NORMED SPACES

Acta Mathematica Scientia 2024年第5期44卷 1696-1706页

作者： Ziyi ZHOU Yi JIANG School of Mathematical Sciences Visual Computing and Virtual Reality Key LaboratorySichuan Normal UniversityChengdu610066China

For a general normed vector space,a special optimal value function called a maximal time function is *** covers the farthest distance function as a special case,and has a close relationship with the smallest enclosing ball *** properties of the maximal time function are proven,including the convexity,the lower semicontinuity,and the exact characterizations of its subdifferential formulas.

关键词： maximal time function subdifferential normal cone nonsmooth analysis

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New characterizations of partial orders induced by a class of non-divisible t-norms

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Soft computing 2024年第17-18期28卷 9107-9114页

作者： Liu, Zhi-Qiang Wang, Xue-Ping School of Mathematical Sciences Sichuan Normal University Chengdu610066 China Visual Computing and Virtual Reality Key Laboratory of Sichuan Province Sichuan Normal University Chengdu610066 China

In this article, we deal with the algebraic structures of the partial orders induced by non-divisible t-norms. We first give a condition for the partial orders induced by non-divisible t-norms being a meet semi-lattice. Then, we show the condition for the partial orders induced by right-continuous t-norms being a lattice, which is very useful for constructing new lattice structures. Finally, an illustrative example is presented. Our results go a further step towards answering the first open problem raised by Karaçal and Kesicioğlu (Kybernetika 47:300-314, 2011). © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Fuzzy logic

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Approximate Supplement-Based Neighborhood Rough Set Model in Incomplete Hybrid Information Systems 20th

Approximate Supplement-Based Neighborhood Rough Set Model in...

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20th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2023

作者： Meng, Xiong Yang, Jilin Wu, Die Liu, Tang Department of Computer Science Sichuan Normal University Chengdu China Visual Computing and Virtual Reality Key Laboratory of Sichuan Province Sichuan Normal University Chengdu China

ISBN: (纸本)9789819970247

Incomplete hybrid information systems (IHISs) contain hybrid data (e.g., categorical data, numerical data) and incomplete data. With the development of big data, IHISs widely exist in various practical applications. Due to the heterogeneity of hybrid data and the complex semantics of incomplete data, effectively processing the IHIS has become a significant challenge. The established indiscernibility relations of the existing studies for dealing with IHIS over-amplify the uncertainty of missing values, which may achieve unsatisfactory results. In this paper, we propose an approximate supplement-based neighborhood rough set model (AS-NRSM) to deal with the data of IHISs. Specifically, we propose a method to approximate supplement missing values with known values or constructed interval values, and the original IHIS is becoming the constructed IHIS*. Then, we formulate a novel similarity function to construct the improved neighborhood tolerance relation and the corresponding neighborhood tolerance classes. Finally, we design two experiments on 5 UCI data sets by introducing three performance metrics. Experimental results illustrate that the proposed AS-NRSM has higher classification performance than the two representative models. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Semantics

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Optimal Control Policy of M/G/1 Queueing System with Delayed Randomized Multiple Vacations Under the Modified Min(N,D)-Policy Control

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Journal of the Operations Research Society of China 2023年第4期11卷 857-874页

作者： Le Luo Ying-Hui Tang Miao-Miao Yu Wen-Qing Wu School of Mathematical Sciences Sichuan Normal UniversityChengdu610068SichuanChina Nanchong Vocational and Technical College Nanchong637000JiangsuChina Visual Computing and Virtual Reality Key Laboratory of Sichuan Province Sichuan Normal UniversityChengdu610068SichuanChina

Based on the number of customers and the server’s workload,this paper proposes a modified Min(N,D)-policy and discusses an M/G/1 queueing model with delayed randomized multiple vacations under such a *** the well-known stochastic decomposition property of the steady-state queue size,the probability generating function of the steady-state queue length distribution is ***,the explicit expressions of the expected queue length and the additional queue length distribution are derived by some algebraic ***,employing the renewal reward theorem,the explicit expression of the long-run expected cost per unit time is ***,we analyze the optimal policy for economizing the expected cost and compare the optimal Min(N,D)-policy with the optimal N-policy and the optimal D-policy by using numerical examples.

关键词： M/G/1 queue Modified Min(N,D)-policy Randomized multiple vacations Queue length generating function Optimal joint control policy

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Few-Shot Classification Based on Feature Enhancement Network

Few-Shot Classification Based on Feature Enhancement Network

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Hu, Shasha Su, Han Gao, Ruixuan College of Computer Science Sichuan Normal University Chengdu China Visual Computing and Visual Reality Key Laboratory of Sichuan Province Chengdu China

ISBN: (纸本)9781665410205

Few-shot image classification stands as a pivotal task within the realm of computer vision. However, obtaining accurate class prototypes from limited annotated samples is a challenging problem. In recent years, many methods based on prototype networks have shown excellent performance. Nevertheless, existing methods overlook the discriminative semantic information lost due to sample scarcity and the hidden category information in the query set, failing to address the issue of unreliable prototypes generated from limited annotated samples. In this paper, we propose a feature enhancement network for few-shot classification. To improve the accuracy and robustness of few-shot classification models, we first enhance the support set through learning a weight matrix and then align the enhanced support set prototypes with textual semantics. To avoid being influenced by introduced prior noise, we fuse between semantically aligned prototypes and mean prototypes and ultimately utilize query prototypes for dynamic updating to obtain more accurate class prototypes. Extensive experiments demonstrate that our method achieves competitive performance on miniImageNet and tieredImageNet datasets. Furthermore, it exhibits excellent results in cross-domain few-shot classification. © 2024 IEEE.

关键词： Zero-shot learning

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Perceptual Evaluation Based on WOA-BP Neural Network 2

Perceptual Evaluation Based on WOA-BP Neural Network

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2nd International Conference on Artificial Intelligence and Intelligent Information Processing, AIIIP 2023

作者： Juanni, Jiao Yong, Zheng Wei, Li College of Computer Science Sichuan Normal University Chengdu China Sichuan Normal University Visual Computing and Virtual Reality Key Laboratory of Sichuan Chengdu China

ISBN: (纸本)9798350371451

In modern society, various patterns play an indispensable role. With the help of computer technology, the efficiency of designers in the field of pattern design has been significantly improved. However, in the process of pattern design, we often only rely on the subjective experience of designers, while ignoring the personal preferences of consumers. In order to explore the relationship between different pattern designs and consumers' perceptual evaluation, the Kansei engineering theory is first employed to capture consumers' perceptual evaluation of the patterns. Subsequently, various pattern designs and consumers' perceptual evaluation were utilized as inputs and outputs for the WOA-BP neural network, thereby constructing an intelligent model capable of predicting consumers' perceptual evaluation of different pattern designs. The experimental results demonstrate that the model has a highly accurate prediction level. With this model, designers can quickly adjust the pattern design to better meet the aesthetic needs of today's consumers. © 2023 IEEE.

关键词： Professional aspects

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Enhanced Multi-Style Transfer Method based on VGG Network 2

Enhanced Multi-Style Transfer Method based on VGG Network

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2nd International Conference on Cloud computing, Big Data Application and Software Engineering, CBASE 2023

作者： Zheng, Yong Jiao, Juanni Ye, Fange Li, Wei Sichuan Normal University College of Computer Science Sichuan Chengdu610101 China Sichuan Normal University Visual Computing and Virtual Reality Key Laboratory of Sichuan China

ISBN: (纸本)9798350331448

In the domain of conventional image style transfer, remarkable progress has been achieved;nonetheless, certain issues persist. A large grayscale image with only one channel of brightness information lacks diverse color and texture features. This limits the variety in style transformation, resulting in unsatisfactory diversity of effects. To overcome this challenge, the present study introduces an enhanced multi-style transfer approach founded upon the VGG network. To achieve a greater variety of style transfers for single-channel grayscale images. This is accomplished by introducing a dual VGG network architecture coupled with the incorporation of the Gram matrix concept. Additionally, a multi-style fusion module is introduced to merge various style transfers for single-channel grayscale images. The experimental outcomes unequivocally demonstrate that the proposed approach effectively accomplishes manifold style transfers within single-channel grayscale images. Furthermore, apart from retaining the inherent content attributes, it bestows images with fresh visual allure. In comparison to conventional methods, the approach showcased in this study achieves significant advancements in both the quality and diversity of style transfer. Consequently, this innovative approach not only expands the scope of image style transfer applications but also paves the way for a novel potential in the realm of style transformation for single-channel grayscale images. © 2023 IEEE.

关键词： Network architecture

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Product and Complex Structures on 3-Bihom-Lie Algebras

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Algebra Colloquium 2023年第4期30卷 685-700页

作者： Juan Li Ying Hou Liangyun Chen School of Mathematical Sciences Visual Computing and Virtual Reality Key Laboratory of Sichuan ProvinceSichuan Normal UniversityChengdu 610068China College of Science Northeast Forestry UniversityHarbin 150040China School of Mathematics and Statistics Northeast Normal UniversityChangchun 130024China

In this paper,we introduce the notion of a product structure on a 3-Bihom-Lie algebra,which is a Nijenhuis operator with some *** prove that a 3-Bihom-Lie algebra has a product structure if and only if it is the direct sum of two vector spaces which are also *** we give four special conditions under each of which a 3-Bihom-Lie algebra has a special ***,we introduce a complex structure on a 3-Bihom-Lie algebra and there are also four types of special complex ***,we establish the relation between a complex structure and a product structure.

关键词： 3-Bihom-Lie algebras product structures complex structures complex product structures

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Predicting Robustness Performance with Noises in Network Representation

Predicting Robustness Performance with Noises in Network Rep...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Wu, Chengpei Xu, Siyi Li, Junli School of Computer Science Sichuan Normal University Chengdu610101 China Sichuan Normal University Visual Computing and Virtual Reality Key Laboratory of Sichuan Sichuan Chengdu610068 China

ISBN: (纸本)9798350337020

The connectivity and controllability of complex networks play an important role in ensuring the proper functioning of network systems. Robustness of connectivity and controllability is the ability of a network to maintain its basic functions against various malicious attacks. Convolutional neural network (CNN)-based approaches provide an efficient framework to approximate the network robustness, which significantly reduces computation time compared to attack simulations. In this paper, the performance of CNN-based prediction for connectivity and controllability robustness is investigated, when there are noises in the network input representation. Two CNN-based predictors are compared and investigated, 1) convolutional neural network-based robustness predictor (CNN-RP), and 2) spatial pyramid pooling-based convolutional neural network (CNN-SPP). Two aspects of network information noises are considered and investigated, 1) the random node information noises (RNIN), and 2) the random edge information noises (REIN). The following main conclusions are obtained from extensive experimental studies on synthetic networks: 1) CNN-RP is more tolerant than CNN-SPP to network noises, 2) The characteristics of small-world and scale-free networks make them have a favorable anti-noise ability, and 3) RNIN has less impact on CNN-based prediction performance than REIN, RNIN and REIN show opposite effects on prediction performance when the size of the predicted network is out of the training network size range. © 2023 IEEE.

关键词： Complex network connectivity controllability noise robustness

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A Nested Edge Addition Strategy for Network Controllability Robustness Enhancement

A Nested Edge Addition Strategy for Network Controllability ...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Wu, Chengpei Xu, Siyi Yu, Zhuoran Li, Junli School of Computer Science Sichuan Normal University Chengdu610101 China Sichuan Normal University Visual Computing and Virtual Reality Key Laboratory of Sichuan Sichuan Chengdu610068 China

ISBN: (纸本)9798350337020

Edge rectification is a widely used method to enhance network robustness. However, in some networked systems, edge rectification may be challenging or even infeasible to implement. An edge addition strategy is proposed as an alternative optimization method in this paper. Nested Ring Structure (NRS), whereby each node's edges connect its nearest neighbors along the backbone direction, have exhibited robust controllability against random attacks. Therefore, The Nested Edge Addition (NEA) strategy is proposed, which enhances network controllability by building NRS through edge addition to a given initial network. With a small number of added edges, NEA can rapidly enhance network controllability, allowing the network to be controlled using just one driver node. The more nested edges are added, the stronger the NRS in a network, thus exhibiting better controllability robustness. The effectiveness of NEA is verified by simulations on both synthetic and real-world networks. Extensive experimental results demonstrate that NEA is an efficient strategy for designing network topology and optimizing real-world networks. © 2023 IEEE.

关键词： complex network controllability robustness edge addition network controllability network optimization

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