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17th International Congress on Image and Signal Processing, BioMedical engineering and Informatics, CISP-BMEI 2024

作者： Nie, Yutang Yang, Chunming Wei, Mingzhi Jiang, Xianwei Ma, Xin School of Computer Science and Technology Southwest University of Science and Technology China School of Computer Science and Engineering University of Electronic Science and Technology of China China

ISBN: (纸本)9798331507398

To reduce the acquisition cost of aerodynamic heating data for high-speed aircraft by leveraging diverse data sources, this paper proposes a multi-source data fusion method based on sparse sampling and multi-fidelity neural network. First, a large volume of low-fidelity data is generated using low-cost engineering calculation methods along the flight trajectory, and sparse sampling is performed according to the data variation trends. Then, CFD numerical simulation is conducted on the sampled flight parameters to obtain a limited set of high-fidelity data. Finally, high-fidelity data across the flight trajectory is estimated by combining high-fidelity and low-fidelity data through multi-fidelity neural network. The effectiveness of the fusion is verified using heat flux data at the nose tip of a certain aircraft under the simulated flight trajectory. Results indicate that the proposed fusion approach can achieve a prediction error of less than 4% while using only 5% of the CFD data points from the original trajectory, significantly reducing the CFD simulation workload and demonstrating the method's potential for engineering applications. © 2024 IEEE.

关键词： Flight paths

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A comprehensive framework for decoding salty taste information from electroencephalography signals:distinguishing brain reactions to saltiness of comparable intensity

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Food science and Human Wellness 2025年第4期14卷 1538-1545页

作者： Jingjing Liu Yifei Xu Xingwang Lian Tianming Liu Haohao Ning Xi Jiang Shixin Yu Shikun Liu Lu Huang Xiaojun Sun Jiangyong Li Dongfu Xu School of Automation Engineering Northeast Electric Power UniversityJilin 132012China Department of Computer Science the University of GeorgiaAthens 30602USA School of Life Science and Technology University of Electronic Science and Technology of ChinaChengdu 611731China

The perception of salty taste is crucial for individuals to make healthy food ***,the brain electrophysiological signals underlying salty taste perception have been poorly *** this study,electroencephalography(EEG)was used to record brain activity induced by Na Cl solution as a salty taste stimulus.A combination of a custom delivery device and stimulation paradigm was employed to preserve the salty taste signal clearly.A stimulus-response capture method was proposed that could adapt to individual differences in brain responses to salty taste and accurately segment salty taste response *** this method to the EEG processing workflow can form a complete data processing *** results showed that the neural response induced by salty taste reached a high activity level in the initial stage within a short period(0.2 s),and there was a sustained periodic response within 0.75 s after the ***,the salty taste information in the EEG signal was decoded,and discrimination of 2 similar concentrations of salty taste solutions was achieved far above the chance level(average identification rate:89.66%).This study demonstrated experimental paradigms and research methods for understanding salty taste perception,which could provide references for research on other basic tastes.

关键词： Saltiness Salty perception Electroencephalography Stimulus paradigm

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Learning to Communicate Among Agents for Large-Scale Dynamic Path Planning With Genetic Programming Hyperheuristic

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第5期6卷 1269-1283页

作者： Liao, Xiao-Cheng Hu, Xiao-Min Chen, Xiang-Ling Mei, Yi Jia, Ya-Hui Chen, Wei-Neng Victoria University of Wellington Centre for Data Science and Artificial Intelligence School of Engineering and Computer Science Wellington6140 New Zealand Guangdong University of Technology School of Computer Science and Technology Guangzhou510006 China Hanyang University Department of Electrical and Electronic Engineering Ansan15588 Korea Republic of South China University of Technology School of Future Technology Guangzhou510006 China Pazhou Lab Guangzhou510005 China South China University of Technology School of Computer Science and Engineering State Key Laboratory of Subtropical Building and Urban Science Guangzhou510006 China

Genetic programming hyperheuristic (GPHH) has recently become a promising methodology for large-scale dynamic path planning (LDPP) since it can produce reusable heuristics rather than disposable solutions. However, in this methodology, the extracted local and decentralized heuristic for agents that lack a global systemic view sometimes may be problematic. Therefore, a new challenge is to strike a balance between conciseness to improve generalization ability and incorporation of more global information to obtain better performance. In this work, we target the LDPP problem and propose a communication learning mechanism (ComLGP) for GPHH to address the above difficulties. In ComLGP, a communication function is introduced to serve as a communication protocol and exist in the form of an extra terminal in GPHH. Compared to the classic terminals which are fixed in genetic programing, this communication function undergoes optimization along with the evolutionary process of GPHH. In this way, the communication function can be learned which enables agents to communicate without a predefined communication protocol. Then, a caching and lazy updating mechanism for ComLGP is presented to accelerate the calculation of communication content. Last, we verified our method on 22 scenarios including two real world road networks. The experimental results demonstrate that the proposed ComLGP can successfully learn to communicate. Although in the absence of any manually designed communication features, ComLGP is capable of achieving performance competitive to the state-of-the-art method that employs a predefined communication protocol and outperforms the remaining compared methods in most scenarios. © 2020 IEEE.

关键词： Genetic programming

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Emotion-Based Music Recommendation System Integrating Facial Expression Recognition and Lyrics Sentiment Analysis

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IEEE Access 2025年 13卷 87740-87752页

作者： Phaneendra Bottu, V.S.G.S. Ragavan, K. Vellore Institute of Technology School of Computer Science and Engineering Tamil Nadu Vellore632007 India

Facial Expression Recognition (FER) has created widespread interest due to its potential uses in personalized technology and mental health, notably in systems that recommend music based on emotion. These systems can improve adaptable user interfaces and support music therapy. While prior research has explored algorithms for FER and their effectiveness in identifying emotions, existing solutions often lack optimal accuracy in pairing emotion recognition with music recommendations, particularly in real-world contexts with diverse user preferences. This study introduces a robust Convolutional Neural Network (CNN)-based model specifically designed for accurate FER, complemented by an innovative music recommendation system. The model was trained and evaluated using the publicly available FER-2013 dataset, which comprises 35,887 labelled facial images, along with a curated music dataset of 1,000 songs annotated with emotions. This approach incorporates a distinctive emotion-based song classification pipeline that utilizes a pre-built text emotion classification model to categorize song lyrics into seven emotional categories: anger, fear, disgust, neutral, surprise, sadness, and happiness. Experimental results demonstrate that our model achieved a FER accuracy of 91.78% and various methodologies discussed for emotion-based music categorization, significantly outperforming baseline methods. These findings offer a scalable framework for personalized and emotionally resonant music experiences, representing a viable solution for applications in tailored entertainment and music therapy. © 2013 IEEE.

关键词： Emotion Recognition

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Fixed-time command filtered output feedback control for twin-roll inclined casting system with prescribed performance

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Mathematical Biosciences and engineering 2024年第2期21卷 2282-2301页

作者： Gao, Dongxiang Zhang, Yujun Wu, Libing Liu, Sihan School of Computer and Software Engineering University of Science and Technology Liaoning Liaoning CO Anshan114051 China School of Science University of Science and Technology Liaoning Liaoning CO Anshan114051 China

The article investigates the issue of fixed-time control with adaptive output feedback for a twin-roll inclined casting system (TRICS) with disturbance. First, by using the mean value theorem, the nonaffine functions are decoupled to simplify the system. Second, radial basis function neural networks (RBFNNs) are introduced to approximate an unknown term, and a nonlinear neural state observer is created to handle the effects of unmeasured states. Then, the backstepping design framework is combined with prescribed performance and command filtering techniques to demonstrate that the scheme proposed in this article guarantees system performance within a fixed-time. The control design parameters determine the upper bound of settling time, regardless of the initial state of the system. Meanwhile, it ensures that all signals in the closed-loop system (CLS) remain bounded, and it can also maintain the tracking error within a predefined range within a fixed time. Finally, simulation results assert the effectiveness of the method. © 2024 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License.

关键词： Closed loop systems

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GP‐FMLNet:A feature matrix learning network enhanced by glyph and phonetic information for Chinese sentiment analysis

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CAAI Transactions on Intelligence technology 2024年第4期9卷 960-972页

作者： Jing Li Dezheng Zhang Yonghong Xie Aziguli Wulamu Yao Zhang School of Computer and Communication Engineering University of Science and Technology BeijingBeijingChina Beijing Key Laboratory of Knowledge Engineering for Materials Science University of Science and Technology BeijingBeijingChina University of Alberta EdmontonAlbertaCanada

Sentiment analysis is a fine‐grained analysis task that aims to identify the sentiment polarity of a specified *** methods in Chinese sentiment analysis tasks only consider sentiment features from a single pole and scale and thus cannot fully exploit and utilise sentiment feature information,making their performance less than *** resolve the problem,the authors propose a new method,GP‐FMLNet,that integrates both glyph and phonetic information and design a novel feature matrix learning process for phonetic features with which to model words that have the same pinyin information but different glyph *** method solves the problem of misspelling words influencing sentiment polarity prediction ***,the authors iteratively mine character,glyph,and pinyin features from the input comments ***,the authors use soft attention and matrix compound modules to model the phonetic features,which empowers their model to keep on zeroing in on the dynamic‐setting words in various positions and to dispense with the impacts of the deceptive‐setting ***-periments on six public datasets prove that the proposed model fully utilises the glyph and phonetic information and improves on the performance of existing Chinese senti-ment analysis algorithms.

关键词： aspect‐level sentiment analysis deep learning feature extraction glyph and phonetic feature matrix compound learning

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A Generative Image Steganography Based on Disentangled Attribute Feature Transformation and Invertible Mapping Rule

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computers, Materials & Continua 2025年第4期83卷 1149-1171页

作者： Xiang Zhang Shenyan Han Wenbin Huang Daoyong Fu School of Computer Science Nanjing University of Information Science and TechnologyNanjing210044China Engineering Research Center of Digital Forensics Nanjing University of Information Science and TechnologyMinistry of EducationNanjing210044China

Generative image steganography is a technique that directly generates stego images from secret *** traditional methods,it theoretically resists steganalysis because there is no cover ***,the existing generative image steganography methods generally have good steganography performance,but there is still potential room for enhancing both the quality of stego images and the accuracy of secret information ***,this paper proposes a generative image steganography algorithm based on attribute feature transformation and invertible mapping ***,the reference image is disentangled by a content and an attribute encoder to obtain content features and attribute features,***,a mean mapping rule is introduced to map the binary secret information into a noise vector,conforming to the distribution of attribute *** noise vector is input into the generator to produce the attribute transformed stego image with the content feature of the reference ***,we design an adversarial loss,a reconstruction loss,and an image diversity loss to train the proposed *** results demonstrate that the stego images generated by the proposed method are of high quality,with an average extraction accuracy of 99.4%for the hidden ***,since the stego image has a uniform distribution similar to the attribute-transformed image without secret information,it effectively resists both subjective and objective steganalysis.

关键词： Image information hiding generative information hiding disentangled attribute feature transformation invertible mapping rule steganalysis resistance

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A Survey of the Application of Neural Networks to Event Extraction

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Tsinghua science and technology 2025年第2期30卷 748-768页

作者： Jianye Xie Yulan Zhang Huaizhen Kou Xiaoran Zhao Zhikang Feng Lekang Song Weiyi Zhong College of Computer Science and Technology China University of Petroleum(East China)Qingdao 266000China Shandong Provincial University Laboratory for Protected Horticulture Weifang University of Science and TechnologyWeifang 261000China School of Computer Science and Engineering Nanjing University of Science and TechnologyNanjing 210000China School of Computer Science Qufu Normal UniversityRizhao 276800China

Event extraction is an important part of natural language information extraction,and it’s widely employed in other natural language processing tasks including question answering and machine reading ***,there is a lack of recent comprehensive survey papers on event *** the past few years,numerous high-quality and innovative event extraction methods have been proposed,making it necessary to consolidate these new developments with previous work in order to provide a clear overview for researchers and serve as a reference for future *** addition,event detection is a fundamental sub-task in event extraction,previous survey papers have often overlooked the related work on event ***,this paper aims to bridge these gaps by presenting a comprehensive survey of event extraction,including recent advancements and an analysis of previous research on event *** resources for event extraction are first introduced in this research,and then the numerous neural network models currently employed in event extraction tasks are divided into four types:word sequence-based methods,graph-based neural network methods,external knowledge-based approaches,and prompt-based *** compare and contrast them in depth,pointing out the flaws and difficulties with existing ***,we discuss the future of event extraction development.

关键词： event extraction natural language processing event extraction methods graph neural network prompt-based learning

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Leveraging Network Slicing in SDNs for Handling Application Failures

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2024年第4期51卷 409-421页

作者： Lokesh, Bommareddy Rajagopalan, Narendran School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravati522237 India Department of Computer Science and Engineering National Institute of Technology Puducherry Karaikal609609 India

Unlike traditional networks, Software-defined networks (SDNs) provide an overall view and centralized control of all the devices in the network. SDNs enable the network administrator to implement the network policy by programming applications on top of the SDN controller using generic APIs. One or more controller instances can be deployed to administer the data flow by maintaining unified control of the entire network. The controller is expected to respond quickly to queries from forwarding devices. Presuming quick responses from the controller while enforcing a complex security mechanism is unreasonable. In this paper, the authors propose a unique, adaptive, lightweight, yet efficient technique called ISOLATOR to mitigate the effect of insider attacks and malfunctioning of distributed applications in an SDN-enabled cloud. The proposed security application upon detecting any suspicious activity by a virtual machine, isolates it by removing the interface to its respective shared network and reconnects it through a restricted network operating in a highly selective mode. By subjecting the data traffic to deep packet inspection, the restricted network searches for a match with a known worm pattern. The application is programmed for the OpenDayLight controller and the results show a significant improvement in identifying malicious activities with minimal latency and computational cost. © (2024), (International Association of Engineers). All Rights Reserved.

关键词： Controllers

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Automated object recognition in high-resolution optical remote sensing imagery

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National science Review 2023年第6期10卷 38-41页

作者： Yazhou Yao Tao Chen Hanbo Bi Xinhao Cai Gensheng Pei Guoye Yang Zhiyuan Yan Xian Sun Xing Xu Hai Zhang School of Computer Science and Engineering Nanjing University of Science and Technology Aerospace Information Research Institute Chinese Academy of Sciences School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Key Laboratory of Network Information System Technology (NIST) Aerospace Information Research Institute Chinese Academy of Sciences Department of Computer Science and Technology Tsinghua University School of Computer Science and Engineering University of Electronic Science and Technology of China Pazhou Laboratory (Huangpu) School of Mathematics Northwest University

INTRODUCTION With the rapid development of remote sensing technology,high-quality remote sensing images have become widely *** automated object detection and recognition of these images,which aims to automatically locate objects of interest in remote sensing images and distinguish their specific categories,is an important fundamental task in the *** provides an effective means for geospatial object monitoring in many social applications,such as intelligent transportation,urban planning,environmental monitoring and homeland security.

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