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GCINet: global convolution interaction network with a pre-trained reversible normalization method for long-term time series forecasting

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Neural computing and applications 2025年第5期37卷 3227-3244页

作者： Fan, Jin Yang, Baoshun Sun, Danfeng Liu, Jie Chen, Qikai Wang, Zhipeng Wu, Jia Hangzhou Dianzi University Hangzhou China Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province Zhejiang Normal University Hangzhou China School of Computing Macquarie University Sydney Australia

Long-term time series forecasting is crucial in various domains, such as weather forecasting and disease forecasting. With the rise of Transformer-based models, we have witnessed a significant improvement in long-term prediction accuracy, as these models use self-attention to capture long-term temporal features in the data during feature extraction. However, this approach has several drawbacks: Firstly, self-attention has quadratic complexity, and reducing its complexity often results in reduced prediction accuracy. Secondly, the distribution of time series data frequently changes over time, a phenomenon known as distribution shift, which is not taken into consideration in most Transformer-based models. This leads to decreased prediction accuracy as the distribution deviates from the one used for model training. Therefore, we have developed a new time series forecasting model, GCINet, which introduces a new mode of interaction based on a convolutional network. This captures both long-term and short-term temporal features while reducing complexity during feature extraction. Additionally, GCINet includes an Inverse Trained Normalization method to address distribution shift, bringing a fresh perspective to the design of time series forecasting models. Our experiments on long-term forecasting tasks demonstrate that GCINet outperforms other commonly used models. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Prediction models

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Multimodal feature fusion based on object relation for video captioning

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CAAI Transactions on Intelligence Technology 2023年第1期8卷 247-259页

作者： Zhiwen Yan Ying Chen Jinlong Song Jia Zhu School of Computing Science South China Normal UniversityGuangzhouChina Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province Zhejiang Normal UniversityJinhuaChina

Video captioning aims at automatically generating a natural language caption to describe the content of a ***,most of the existing methods in the video captioning task ignore the relationship between objects in the video and the correlation between multimodal features,and they also ignore the effect of caption length on the *** study proposes a novel video captioning framework(ORMF)based on the object relation graph and multimodal feature *** uses the similarity and Spatio-temporal relationship of objects in video to construct object relation features graph and introduce graph convolution network(GCN)to encode the object *** the same time,ORMF also constructs a multimodal features fusion network to learn the relationship between different modal *** multimodal feature fusion network is used to fuse the features of different ***,the proposed model calculates the length loss of the caption,making the caption get richer *** experimental results on two public datasets(Microsoft video captioning corpus[MSVD]and Microsoft research-video to text[MSR-VTT])demonstrate the effectiveness of our method.

关键词： approaches deep learning multimodel scene understanding video analysis

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Research on Kiwifruit Detection Algorithm Based on Faster-YOLOv8n 5

Research on Kiwifruit Detection Algorithm Based on Faster-YO...

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5th International Conference on Artificial Intelligence and Computer Engineering, ICAICE 2024

作者： Zhao, Dan Wang, Jiaxing Shan, Wei College of Physics and Electronic Information Huaibei Normal University China Anhui Provincial Key Laboratory of Intelligent Computing and Application Huaibei China

ISBN: (纸本)9798331528911

In view of the complex structure of YOLOv8n model and the large amount of parameters and calculations of the existing kiwifruit detection algorithm, it is difficult to deploy in the terminal. This paper proposes a lightweight kiwifruit detection algorithm model Faster-YOLOv8n based on YOLOv8n. That is, all C2f modules in the YOLOv8n model are merged with the FasterNet lightweight network structure to form a new C2f-Faster module to reduce the consumption of computing resources. The experimental results show that the average detection accuracy mAP of the Faster-YOLOv8n algorithm on the self-made kiwifruit dataset collected in this paper is 96.9 %. Compared with the YOLOv8n algorithm, the model size is reduced by 32 %,the calculation amount is reduced by 22.2 %, the parameter quantity is reduced by 29.6 %, and the detection speed is increased by 26.7 %.Therefore, the detection method proposed in this paper reduces the consumption of computing resources and provides a theoretical reference for the lightweight detection of kiwifruit. © 2024 IEEE.

关键词： Macroinvertebrates

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Research on intelligent traffic pedestrian detection algorithm based on improved YOLOv8 24

Research on intelligent traffic pedestrian detection algorit...

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2024 2nd International Conference on Internet of Things and Cloud computing Technology, IoTCCT 2024

作者： Yue, Yurong Pan, Lihui Shan, Wei School of Physics and Electronic Information Huaibei Normal University Huaibei China Anhui Provincial Key Laboratory of Intelligent Computing and Application Huaibei China

ISBN: (纸本)9798400710148

Aiming at the problem that the pedestrian detection effect of illumination change, occlusion and different postures is not good in intelligent traffic scene, an optimized YOLOv8 is proposed to enhance its pedestrian detection ability. Based on YOLOv8, Coordinate Attention (CoordAtt) is introduced into the backbone network to enhance the sensitivity of the model to pedestrian location information and improve the detection accuracy. On the public test benchmark of Caltech Pedestrian Dataset, compared with the baseline algorithm, the proposed algorithm improves mAP0.5 by 1.3%, mAP0.5: 0.95 by 0.3%, and LAMR by 1.8%. Compared with the popular algorithm, mAP0.5 and mAP0.5: 0.95 are as high as 0.875 and 0.543 respectively, and LAMR is as low as 0.286, which is better than the popular algorithm. The algorithm has been tested to show that the accuracy of excellent performance detection has been significantly improved, and the phenomenon of missed detection has been greatly reduced, which can serve the intelligent transportation more effectively and reduce the potential safety hazards caused by missed detection. © 2024 Copyright held by the owner/author(s).

关键词： CoordAtt

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Research on intelligent Traffic Pedestrian Detection Algorithm Based on Improved YOLOv8 5

Research on Intelligent Traffic Pedestrian Detection Algorit...

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5th International Conference on Artificial Intelligence and Computer Engineering, ICAICE 2024

作者： Yue, Yurong Kong, Xiaoyang Pan, Rui Shan, Wei College of Physics and Electronic Information Huaibei Normal University Huaibei China Anhui Provincial Key Laboratory of Intelligent Computing and Application Huaibei China

ISBN: (纸本)9798331528911

Aiming at the problem of poor detection of long-distance pedestrians in intelligent traffic scenarios, an improved YOLOv8 pedestrian detection algorithm is proposed. Using YOLOv8 as the basic network, SPD-Conv is embedded before the C2f layer of backbone and after the Conv layer of neck as the key preprocessing step. Experimental results on the public dataset Caltech Pedestrian Dataset show that compared with the baseline algorithm, mAP0.5 is increased by 3%, mAP0.5: 0.95 is increased by 5.8%, and LAMR is reduced by 5.5%. Compared with the popular algorithm, mAP0.5 and mAP0.5: 0.95 are as high as 0.892 and 0.598, respectively, and LAMR is as low as 0.249, which is better than the popular algorithm. The results show that the algorithm achieves a significant improvement in accuracy in pedestrian detection tasks, and effectively reduces the occurrence of missed detection. It can serve intelligent transportation more effectively and reduce the potential safety hazards caused by missed detection. © 2024 IEEE.

关键词： intelligent transportation pedestrian detection spatial to deep convolution

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Lightweight Small Target Detection Algorithm for UAV Infrared Aerial Photography Based on YOLOv8 5

Lightweight Small Target Detection Algorithm for UAV Infrare...

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5th International Conference on Artificial Intelligence and Computer Engineering, ICAICE 2024

作者： Li, Jinfeng Zhu, Xiaodong Shan, Wei College of Physics and Electronic Information Huaibei Normal University Huaibei China Anhui Provincial Key Laboratory of Intelligent Computing and Application Huaibei China

ISBN: (纸本)9798331528911

Aiming at the complexity of UAV infrared air target detection model,a lightweight target detection algorithm based on YOLOv8 n is proposed. By introducing a lightweight Depthwise Convolution module, the model size is reduced to facilitate deployment on mobile devices. The experimental results on the HIT-UAV dataset show that compared with the benchmark model YOLOv8n, the improved algorithm reduces the number of parameters and GFLOPs by 0.6M and 1.2G, respectively. In summary, the effectiveness of the algorithm is verified, which provides the possibility for the UAV to perform real-time target detection tasks. © 2024 IEEE.

关键词： Aerial photography

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Multi Strategy Improvement of Newton-Raphson-Based Optimizer and Engineering application 6

Multi Strategy Improvement of Newton-Raphson-Based Optimizer...

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6th International Conference on Machine Learning, Big Data and Business Intelligence, MLBDBI 2024

作者： Cheng, Shaogang Yin, Jun Liu, Tingting College of Computing Qinghai Normal University Xining China Qinghai Normal University The State Key Laboratory of Tibetan Intelligent Information Processing and Application Xining China

ISBN: (纸本)9798331541798

A multi strategy improved Newton- Raphson-Based Optimizer (NRBO) is proposed to address the problem of slow convergence speed in the later stage of algorithm iteration and difficulty in finding the global optimal solution when facing complex functions. Firstly, using a set of best points instead of randomly generated initial populations to evenly distribute individuals in the optimization space improves the efficiency of the algorithms global and local search. Secondly, based on the idea of differential evolution, the initial population is mutated and crossed to accelerate the iteration speed. Then, in the optimal update stage, the Levi flight strategy is introduced to enhance the algorithms ability to jump out of local optima. The effectiveness of the proposed algorithm was tested on the CEC2005 test set, and compared with other algorithms on two test sets. The results showed that the convergence speed and accuracy of the algorithm were significantly improved. Finally, the proposed algorithm was applied to two practical engineering optimization problems, and simulation results showed that the algorithm proposed in this paper has better practicality and robustness compared to other algorithms. © 2024 IEEE.

关键词： Swarm intelligence

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Research on Infrared Pedestrian and Vehicle Detection Algorithm from the Perspective of UAV - Small target detection based on YOLOv8 24

Research on Infrared Pedestrian and Vehicle Detection Algori...

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3rd International Conference on Computer, Artificial Intelligence and Control Engineering, CAICE 2024

作者： Li, Jinfeng Ma, Long Zhu, Jing Yue, Yurong Zhao, Dan Shan, Wei Dong, Xuan School of Physics and Electronic Information Huaibei Normal University Anhui Provincial Key Laboratory of Intelligent Computing and Application 100 Dongshan Road Huaibei235000 China

ISBN: (纸本)9798400716942

In the infrared pedestrian and vehicle target detection task from the perspective of UAV, there are problems such as small target size and ambiguous scale features, which can easily lead to underdetection and false detection. An improved small target detection algorithm YOLOv8 was proposed. On the basis of YOLOv8, a small target detection layer is added as the fourth output layer, which can detect smaller targets. Experimental results indicate that in the HIT-UAV dataset experiment, the accuracy P, recall R and average accuracy (mAP) of the improved algorithm are 84.9%, 72.3%, 77.7% and 51.7%, respectively, which are improved by 5.7%, 6.4%, 7% and 6.1% compared with the original YOLOv8 network, respectively. The detection accuracy of small targets is improved, and the effectiveness verification of the network is improved. © 2024 ACM.

关键词： Target tracking

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Gradient-Based Differential kWTA Network With application to Competitive Coordination of Multiple Robots

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IEEE/CAA Journal of Automatica Sinica 2022年第8期9卷 1452-1463页

作者： Mei Liu Xiaoyan Zhang Mingsheng Shang Long Jin School of Information Science and Engineering Lanzhou UniversityLanzhou 730000 State Key Laboratory of Tibetan Intelligent Information Processing and Application Xining 810016China Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent TechnologyChinese Academy of SciencesChongqing 400714China Shenzhen Research Institute Lanzhou UniversityShenzhen 518063China

Aiming at the k-winners-take-all(kWTA)operation,this paper proposes a gradient-based differential kWTA(GDk WTA)*** obtaining the network,theorems and related proofs are provided to guarantee the exponential convergence and noise resistance of the proposed GD-kWTA ***,numerical simulations are conducted to substantiate the preferable performance of the proposed network as compared with the traditional ***,the GD-k WTA network,backed with a consensus filter,is utilized as a robust control scheme for modeling the competition behavior in the multi-robot coordination,thereby further demonstrating its effectiveness and feasibility.

关键词： Consensus filter k-winners-take-all(kWTA) multirobot coordination noise resistance

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High-Performance Thin Film Lithium Niobate Electro-optic Modulator By Embedding Structure

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IEEE Photonics Technology Letters 2025年第13期37卷 701-704页

作者： Song, Shuxiang Li, Haisheng Yang, Jiayu Yin, Yihui Yang, Wanli Li, Yue Li, Hangyu Cen, Mingcan Guangxi Normal University Guangxi Key Laboratory of Brain-inspired Computing and Intelligent Chips School of Electronic and Information Engineering Guilin China Guilin China Guangxi Key Laboratory of Precision Navigation Technology and Application Guilin University of Electronic Technology 541004 China 34th Research Institute of China Electronics Technology Group Corporation Guilin541004 China

Electro-optic modulators with low driving voltage and wide bandwidth are critical for advanced analog and digital communication systems. Improving the modulation efficiency of thin-film lithium niobate (TFLN) modulators is essential to reducing drive voltage and device length, thereby enabling compact chip integration and enhanced modulation bandwidth. This work introduces a novel TFLN modulator featuring an embedded dual capacitance-loaded traveling-wave electrode (EDC-TWE) to achieve superior electro-optic modulation efficiency and minimized optical loss. The fabricated modulator, with a 10-mm-long modulation region, achieves a half-wave voltage of 1.45 V and an electro-optic modulation bandwidth exceeding 40 GHz, demonstrating its potential for high-performance optical communication applications. © 1989-2012 IEEE.

关键词： Digital communication systems

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