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2022 Chinese Automation Congress, CAC 2022

作者： Shizhao, Wang Shaoguang, Shu Xueshi, Liu Beijing Institute of Control Engineering System Software Engineering Laboratory Beijing China

ISBN: (纸本)9781665465335

The classification of imbalanced and overlapping data is more difficult. When the number of categories is uneven and category areas are overlap, general neural network classifiers can only extract limited features from the data, which makes the model relies too much on existing samples, resulting in a low classification accuracy. Aiming at the above classification problem, this paper proposes a batch-subsample prototype network model. Based on the prototype network, the model achieves the separation of different categories in classification. In the training process, selecting each batch of training data by subsample method, so that the data for training is balanced each time, which effectively reduce the impact of category imbalance and overlapping on model training. This model is tested in the dataset NSL-KDD with the characteristics of imbalance and overlapping. The experimental results show that the classification accuracy of the batch-subsample prototype network (BSPN) model has been effectively improved, and the classification ability of minority categories also be better. To a certain extent, the model solves the problem that the neural network model has poor classification effect on imbalanced and overlapping data. Above all, the batch-subsample prototype network model can improve the classification accuracy in imbalanced and overlapping data, optimize the recognition rate of minority categories, and increase the classification robustness of the model, which is of great significance for imbalanced and overlapping data classification. © 2022 IEEE.

关键词： Classification (of information)

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PAIR: Protein-aptamer interaction prediction based on language models and contrastive learning framework

PAIR: Protein-aptamer interaction prediction based on langua...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Zhang, Jun Yan, Zhiqiang Zeng, Hao Zhu, Zexuan Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen China Shenzhen University College of Computer Science and Software Engineering Shenzhen China

ISBN: (纸本)9798350386226

Aptamers are single-stranded DNA or RNA oligonucleotides that selectively bind to specific targets, making them valuable for drug design and diagnostic applications. Identifying the interactions between aptamers and target proteins is crucial for these applications. The systematic evolution of ligands by exponential enrichment process, traditionally used for this purpose, is challenging and time-consuming. The resulting aptamers often suffer from limitations in stability and diversity. Computational approaches have shown promise in aiding the discovery of high-performance aptamers, but existing methods are usually constrained by insufficient training data and limited generalizability. Recently, advancements in pre-training large language models have offered a new avenue to mitigate the dependency on large datasets. In this study, we propose a novel method to predict aptamer-protein interactions using large language models within a contrastive learning framework. Experimental results demonstrate that our method exhibits superior generalization and outperforms existing approaches. This method holds promise as a powerful tool for predicting aptamer-protein interactions. © 2024 IEEE.

关键词： Contrastive Learning

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Exploring implicit influence for social recommendation based on GNN

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Soft Computing 2024年第19期28卷 11001-11014页

作者： Liu, Zhewei Yang, Peilin Hao, Qingbo Zheng, Wenguang Xiao, Yingyuan Engineering Research Center of Learning-Based Intelligent System Ministry of Education Tianjin University of Technology Tianjin China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin University of Technology Tianjin China

In recent years, the method of using graph neural networks (GNN) to learn users’ social influence has been widely applied to social recommendation and has shown effectiveness, but several important challenges have not been well addressed: (i) Most work fails to consider the user interests (historical user-item interactions) when first building user-user social relationships, which can make it difficult to capture accurate user embedding and thus prevent the model from better exploring the users’ social influence;(ii) Most of the current methods do not build social neighbors (with the same item-user interaction) that belong to the item and do not aggregate information from the perspective of social neighbors, which makes it possible for the item to lose a lot of details when expressing the user’s interest factors. Therefore, to address the above challenges, we propose Exploring Implicit Influence for Social Recommendation Based on GNN (EIIGNN). First, we construct the initial user embedding with user-item interaction information and use the implicit modeling module in user modeling to explore the implicit influence of interest factors on users. In addition, EIIGNN models the social graph structure of item (an item-item graph) so that item can aggregate information from the perspective of their social neighbors, which helps the model learn a more accurate representation of the item. Finally, extensive experimental results on two real-world datasets clearly demonstrate the effectiveness of EIIGNN. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Graph neural networks

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Power Allocation with Non-Orthogonal Multiple Access for 5G Heterogeneous Network 16

Power Allocation with Non-Orthogonal Multiple Access for 5G ...

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16th IEEE Malaysia International Conference on Communication, MICC 2023

作者： Johari, Muhammad Amirul Aiman Apandi, Nur Ilyana Anwar Muhammad, Nor Aishah Faculty of Electical Technology and Engineering Melaka Malaysia Universiti Teknologi Malaysia Telecommunication Software and System Faculty of Electrical Engineering Johor Malaysia

ISBN: (纸本)9798350304343

The fifth generation of mobile network technology (5G) through heterogeneous networks (HetNet) demands increasing the capacity of cellular wireless communications networks. In addition to the HetNet, a Non-Orthogonal Multiple Access (NOMA) can enhance its performance techniques to support a large number of users. Coordinated multi-point communication (CoMP) has also been identified as a desirable technology for reducing interference in HetNet. However, existing techniques cannot fulfil the high demands of the next 5G, which require direct interference between users in such an orthogonal architecture and good system-level performance. This study aimed to determine the optimal resource allocation for the downlink HetNet 5G NOMA system. The system models were derived to analyze the power performance based on the power allocation factor. An algorithm is constructed based on the deep learning coordinated multi-point to simulate the performance of the downlink 5G. Deep learning can be implemented to improve resource allocation in the context of CoMP by instructing a neural network to predict the optimal power allocation for a specific set of users to resemble the NOMA with HetNet system. Simulation validates the proposed algorithm in terms of the power allocation factor and the impact of the SNR. The results indicate that the power allocation factor ratio for various user signal strengths is capable of allocating dynamic resources to meet the need for efficient resource management. © 2023 IEEE.

关键词： Heterogeneous networks

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Active Deduction Heuristic Algorithm of Standard Contradiction Separation Rule Based on Reinforcement Learning 18

Active Deduction Heuristic Algorithm of Standard Contradicti...

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18th International Conference on Intelligent systems and Knowledge engineering, ISKE 2023

作者： Zhong, Jian Liu, Peiyao Chen, Shuwei School of Computer and Software Engineering National-Local Joint Engineering Laboratory of System Credibility Automatic Verification Xihua University Chengdu China

ISBN: (纸本)9798350318401

Machine learning has been a prominent research focus in recent years, and integrating machine learning methods into automated theorem provers has become a current trend in theorem proving research. Standard contradiction separation rule is a newly proposed multi-clause, dynamic, and synergized deductive inference rule. The key aspect of standard contradiction separation rule is how to plan deduction paths. In order to better plan the deduction path, this paper explores the dynamic learning within the process of contradiction separation deduction and introduces a novel active deductive heuristic algorithm based on reinforcement learning. Experimental results demonstrate that this new algorithm enhances the reasoning capability of the theorem prover Prover9. © 2023 IEEE.

关键词： Reinforcement learning

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Development of Mathematical Methods and Algorithms for Filtering Images Obtained from Unmanned Aerial Vehicle Camera

Development of Mathematical Methods and Algorithms for Filte...

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2023 International Conference on Industrial engineering, Applications and Manufacturing, ICIEAM 2023

作者： Zaw, Hein Htet Hein, Zaw Mikhailovich, Portnov E. National Research University of Electronic Technology Institute of System and Software Engineering and Information Technologies Zelenograd Moscow Russia

ISBN: (纸本)9781665475952

Recently, the use of unmanned aerial vehicles (UAVs) has become an interesting and active research topic in the field of aerial photography. Such an increase in popularity makes the task of finding optimal hardware and software configurations for UAVs, developing systems for compensating adverse environmental effects, and navigation systems in space, etc. The key factors behind this trend are the relatively low cost of implementing such projects and the speed of obtaining data. The main problems preventing full automation of data processing of UAV imagery are motion blur with a still camera, full image blur with camera movement, and camera focus blur. When shooting from a UAV in bad weather conditions, blurry video frames often occur that require image filter. This blurring can interfere with visual analysis and interpretation of data, cause errors, and reduce the accuracy of automatic photogrammetric processing algorithms. This article describes the proposed algorithm that performs element-by-element image filtering based on the use of neural-like structures. © 2023 IEEE.

关键词： Support vector machines

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Multi-Stage Transfer Learning Evolutionary Algorithm for Dynamic Multiobjective Optimization 13

Multi-Stage Transfer Learning Evolutionary Algorithm for Dyn...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Wang, Qianhui Zhu, Qingling Ji, Junkai College of Computer Science and Software Engineering Shenzhen University Shenzhen China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen518060 China

ISBN: (纸本)9798350308365

Recently, the application of transfer learning within dynamic multiobjective evolutionary algorithms (DMOEAs) has shown significant potential to solve dynamic multiobjective optimization problems (DMOPs). This approach utilizes the transfer learning technique which reuses information from previous environments to accelerate the search process in the new environment. However, the risk of negative transfer can lead to an erroneous search direction and inefficient use of computational resources. To address this issue, this paper proposes a novel multistage transfer learning DMOEA, named MSTL. The algorithm is designed to enhance the convergence and diversity of the population through the combination of the pre-transfer learning stage and the transfer learning and validation stage when environmental changes occur. In the pre-transfer learning stage, a suitable target domain, source domain, and validation classifier sample sets are generated guided by historical information. In the transfer learning and validation stage, the TrAdaboost technique to transfer knowledge from the target to the source domain, with the results verified by two validation classifiers to mitigate negative transfer. Experimental results demonstrate that our proposed algorithm outperforms four competing algorithms in terms of diversity and convergence on a widely recognized benchmark suite. © 2024 IEEE.

关键词： Dynamic multiobjective optimization evolutionary algorithm transfer learning

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Research on Adversarial Sample Attack Defense Methods for Speech Recognition 7

Research on Adversarial Sample Attack Defense Methods for Sp...

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7th International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2024

作者： He, Chunlan Bi, Yujian Liu, Xingwei Wang, Qi Sun, Hong School of Computer and Software Engineering Xihua University Chengdu China Chengdu Jiuzhou Electronic Information System Co. Ltd Chengdu China

ISBN: (纸本)9798350350890

Recently, deep learning has achieved significant success in various fields such as image classification, speech recognition, and natural language understanding. However, the security issues associated with deep learning are becoming increasingly apparent, particularly the challenges posed byad-versarial sample attacks. These attacks, which are both covert and potentially harmful, pose substantial security threats to deep learning models. In response to this, we propose a novel defense method against adversarial sample attacks tailored for speech recognition. This method involves constructing an audio reconstruction model that restores adversarial speech samples back to their normal state. The model incorporates an im-proved Wavenet residual network structure based on dilated convolutions, and utilizes Mel Frequency Cepstral Coefficients to enhance audio reconstruction. Experimental results indicate that the proposed audio reconstruction model exhibits superior reconstruction capabilities, effectively restoring and defending against various adversarial sample attacks. © 2024 IEEE.

关键词： Adversarial Defense Adversarial Speech Samples Audio Reconstruction Deep Learning Mel Frequency Cepstral Coefficients

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3DGCformer: 3-Dimensional Graph Convolutional transformer for multi-step origin–destination matrix forecasting

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Applied Intelligence 2025年第6期55卷 1-18页

作者： Huang, Yiou Deng, Hao Zhao, Shengjie School of Computer Science and Technology Tongji University Shanghai China Key Laboratory of Embedded System and Service Computing Shanghai China Engineering Research Center of Key Software Technologies for Smart City Perception and Planning Ministry of Education Shanghai China

Forecasting Human mobility is of great significance in the simulation and control of infectious diseases like COVID-19. To get a clear picture of potential future outbreaks, it is necessary to forecast multi-step Origin–Destination (OD) matrices for a relatively long period in the future. However, multi-step Origin–Destination Matrix Forecasting (ODMF) is a non-trivial problem. First, previous ODMF models only forecast the OD matrix for the next time-step, and they cannot perform well on long-term multi-step forecasts due to error accumulation. Second, many ODMF methods capture spatial and temporal dependencies with separate modules, which is insufficient to model spatio-temporal correlations in the time-varying OD matrix sequence. To address the challenges in multi-step ODMF, we propose 3-Dimensional Graph Convolutional Transformer (3DGCformer). As an enhancement of the original 3DGCN, we propose a novel Origin–Destination Feature Propagation (ODFP) rule between 3DGCN layers and integrate 2 3DGCNs with different spatio-temporal graphs and corresponding feature propagation rules to model the formation of OD flows in a more comprehensive way. For multi-step forecasts, 3DGCformer uses Transformer to capture long-term global temporal dependency, and adapt its decoder using labeled tokens to avoid error accumulation and improve time efficiency. To avoid information loss as the number of regions increases, we propose a patch embedding approach to convert data from 3DGCNs to the Transformer module. We perform extensive experiments on 4 real-world human mobility datasets, and the results show that our proposed model outperforms the state-of-the-art methods. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Convolution

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New Generation Recyclable MV Cables for Futre Grids - A Case Study

New Generation Recyclable MV Cables for Futre Grids - A Case...

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2023 IEEE International Conference on Energy Technologies for Future Grids, ETFG 2023

作者： Chahal, J.S. Wong, K.L. Gill, Navjot Rmit University School of Engineering Melbourne Australia DIgSILENT Pacific Pty. Ltd. Power System Software & Engineering Melbourne Australia

ISBN: (纸本)9781665471640

The demand for clean energy is increasing rapidly all over the world. The renewable energy sources (e.g., wind, solar) have a greater share among other sources in the new capacity addition due to shorter turnaround time. Generally, large-scale solar and wind farms are connected to electricity grids using medium voltage (MV) cross-linked polyethylene (XLPE) cables. Recently, polypropylene (PP) based cables have been found to possess equally better or superior electrical and thermal properties as compared to XLPE cables and these are completely recyclable unlike XLPE cables. The problem with XLPE cables is non-recyclability due to cross-linking. The energy consumption in production of PP based cables is approximately 50% less as compared to XLPE cables and shorter lead times. This paper presents an analysis of XLPE and PP cables for a typical large- scale solar and wind farm. The result shows that the use of PP based cables contributes less to greenhouse gas emissions and is, therefore, environment friendly and contributes to the circular economy. © 2023 IEEE.

关键词： Future grids greenhouse emissions MV Cables Polypropylene XLPE

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