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48th IEEE International Conference on Acoustics, Speech and Signal processing, ICASSP 2023

作者： Chen, Sixiang Ye, Tian Liu, Yun Liao, Taodong Jiang, Jingxia Chen, Erkang Chen, Peng Southwest University College of Artificial Intelligence Chongqing China Jimei University School of Ocean Information Engineering Xiamen China Fujian Provincial Key Laboratory of Oceanic Information Perception and Intelligent Processing China

ISBN: (纸本)9781728163277

Snow removal causes challenges due to its characteristic of complex degradations. To this end, targeted treatment of multi-scale snow degradations is critical for the network to learn effective snow removal. In order to handle the diverse scenes, we propose a multi-scale projection transformer (MSP-Former), which understands and covers a variety of snow degradation features in a multi-path manner, and integrates comprehensive scene context information for clean reconstruction via self-attention operation. For the local details of various snow degradations, the local capture module is introduced in parallel to assist in the rebuilding of a clean image. Such design achieves the SOTA performance on three desnowing benchmark datasets while costing the low parameters and computational complexity, providing a guarantee of practicality. © 2023 IEEE.

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Nested Linear Array DoA Estimation with Mixed One-bit Quantization

Nested Linear Array DoA Estimation with Mixed One-bit Quanti...

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International Conference on Signal processing Proceedings (ICSP)

作者： Jianjun Huang Hao Huang Li Kang Jianbin Ding Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen China

ISBN: (数字)9798350387384

ISBN: (纸本)9798350387391

Direction of Arrival (DoA) estimation is a key technology in array signal processing. One-bit quantization is a popular method for reducing hardware costs in DoA estimation. However, one-bit quantization introduces significant quantization noise, which can greatly impact the accuracy of DoA estimation. Therefore, we propose a method for DoA estimation based on nested linear array (NLA) and mixed one-bit quantization. This method combines all one-bit quantization and full precision quantization, aiming to leverage the advantages of all one-bit quantization while preserving more signal information, thereby maintaining higher estimation accuracy. Additionally, by employing NLA, the system performance is enhanced and hardware costs are reduced, effectively overcoming the limitations of uniform linear array (ULA). We derived the principles of the covariance matrix for mixed one-bit quantization used in DoA estimation and the calculation method for its CRB. Simulation experiments demonstrate that compared to all one-bit quantization, this method significantly improves DoA estimation performance and achieves a lower Cramer-Rao Bound (CRB). The proposed method can achieve a good trade off between hardware costs and estimation accuracy.

关键词： Quantization (signal) Direction-of-arrival estimation Costs Accuracy Array signal processing System performance Estimation Hardware Covariance matrices Signal to noise ratio

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intelligent Robot for Crop Growth Detection 6

Intelligent Robot for Crop Growth Detection

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6th IEEE Advanced information Management, Communicates, Electronic and Automation control Conference, IMCEC 2024

作者： Li, Meng Tianjin University of Technology and Education College of Automation and Electrical Engineering Tianjin China Tianjin University of Technology and Education Tianjin Key Laboratory of Information Sensing and Intelligent Control Tianjin China

ISBN: (纸本)9798350316520

Tomato is an important vegetable species, also known as tomato. However, it is often jeopardized by invasive diseases, resulting in lower eating quality and yield impacts. The traditional method of detecting pests and diseases mainly relies on manual visual observation, which is more dependent on past experience, and the subjective factors have a greater impact, so the actual effect is not accurate and less efficient. In this paper, we explore a tomato pest and disease detection mobile robot for the detection of pest and disease conditions during tomato cultivation in greenhouse environment. The mobile visual inspection robot uses STM32 microprocessor as the core of the mobile control system, and the FreeRTOS operating system is transplanted to complete the robot's autonomous walking, position detection and other tasks;equipped with the NLE-AI800 artificial intelligence board at the same time using the YOLOV5 modeling framework for the target diagnosis as well as the board end deployment. © 2024 IEEE.

关键词： Greenhouses

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AUCSeg: AUC-oriented Pixel-level Long-tail Semantic Segmentation 38

AUCSeg: AUC-oriented Pixel-level Long-tail Semantic Segmenta...

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38th Conference on Neural information processing Systems, NeurIPS 2024

作者： Han, Boyu Xu, Qianqian Yang, Zhiyong Bao, Shilong Wen, Peisong Jiang, Yangbangyan Huang, Qingming Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS China School of Computer Science and Tech. University of Chinese Academy of Sciences China Peng Cheng Laboratory China Key Laboratory of Big Data Mining and Knowledge Management CAS China

The Area Under the ROC Curve (AUC) is a well-known metric for evaluating instance-level long-tail learning problems. In the past two decades, many AUC optimization methods have been proposed to improve model performance under long-tail distributions. In this paper, we explore AUC optimization methods in the context of pixel-level long-tail semantic segmentation, a much more complicated scenario. This task introduces two major challenges for AUC optimization techniques. On one hand, AUC optimization in a pixel-level task involves complex coupling across loss terms, with structured inner-image and pairwise inter-image dependencies, complicating theoretical analysis. On the other hand, we find that mini-batch estimation of AUC loss in this case requires a larger batch size, resulting in an unaffordable space complexity. To address these issues, we develop a pixel-level AUC loss function and conduct a dependency-graph-based theoretical analysis of the algorithm's generalization ability. Additionally, we design a Tail-Classes Memory Bank (T-Memory Bank) to manage the significant memory demand. Finally, comprehensive experiments across various benchmarks confirm the effectiveness of our proposed AUCSeg method. The code is available at https://***/boyuh/AUCSeg. © 2024 Neural information processing systems foundation. All rights reserved.

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HWD-YOLO:A New Vision-Based Helmet Wearing Detection Method

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Computers, Materials & Continua 2024年第9期80卷 4543-4560页

作者： Licheng Sun Heping Li Liang Wang College of Information Science and Technology Beijing University of TechnologyBeijing100124China Chinese Institute of Coal Science Beijing100013China State Key Laboratory for Intelligent Coal Mining and Strata Control Beijing100013China Engineering Research Center of Digital Community of Ministry of Education Beijing100124China

It is crucial to ensure workers wear safety helmets when working at a workplace with a high risk of safety accidents,such as construction sites and mine *** existing methods can achieve helmet detection in images,their accuracy and speed still need improvements since complex,cluttered,and large-scale scenes of real workplaces cause server occlusion,illumination change,scale variation,and perspective ***,a new safety helmet-wearing detection method based on deep learning is ***,a new multi-scale contextual aggregation module is proposed to aggregate multi-scale feature information globally and highlight the details of concerned objects in the backbone part of the deep neural ***,a new detection block combining the dilate convolution and attention mechanism is proposed and introduced into the prediction *** block can effectively extract deep featureswhile retaining information on fine-grained details,such as edges and small ***,some newly emerged modules are incorporated into the proposed network to improve safety helmetwearing detection performance *** experiments on open dataset validate the proposed *** reaches better performance on helmet-wearing detection and even outperforms the state-of-the-art *** be more specific,the mAP increases by 3.4%,and the speed increases from17 to 33 fps in comparison with the baseline,You Only Look Once(YOLO)version 5X,and themean average precision increases by 1.0%and the speed increases by 7 fps in comparison with the YOLO version *** generalization ability and portability experiment results show that the proposed improvements could serve as a springboard for deep neural network design to improve object detection performance in complex scenarios.

关键词： Object detection deep learning safety helmet wearing detection feature extraction attention mechanism

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Deep Representation Learning for Electron Ionization Mass Spectra Retrieval

Deep Representation Learning for Electron Ionization Mass Sp...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Liu, Shibo Zhang, Xuan Jiao, Anlei Sun, Shiwei Dian, Longyang Cui, Xuefeng Shandong University School of Computer Science and Technology Qingdao China Chinese Academy of Sciences Key Lab of Intelligent Information Processing Institute of Computing Technology Beijing China Shandong University State Key Laboratory of Microbial Technology Qingdao China

ISBN: (纸本)9798350386226

The task of retrieving and analyzing mass spectra is indispensable for the identification of compounds in mass spectrometry (MS). This methodology is of critical importance as it enables researchers to correlate observed spectra with established databases, thereby precisely determining the chemical composition of samples. The primary challenges to its efficacy lie in optimizing the balance between retrieval accuracy and processing speed. Empirical studies have demonstrated that by converting mass spectra into embeddings via deep learning, it is possible to achieve both high accuracy and speed in retrieval. Nevertheless, there are complex challenges associated with employing deep learning for spectral embedding, particularly within the domain of electron ionization mass spectrometry (EI-MS). In this paper, we introduce a novel representation learning technique termed EI-MS2VEC for EI-MS retrieval. Our spectrum retrieval methodology surpasses current state-of-the-art techniques such as FastEI. For the in-silico library, we attain hit rate@1 and hit rate@10 of 43.6% and 84.5%, respectively, compared to FastEI's 36.7% and 80.4%. Moreover, our retrieval approach operates with an order of magnitude greater speed than FastEI. The source code is available on Github (https://***/xfcui/EI-MS2VEC). © 2024 IEEE.

关键词： Embeddings

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Application of intelligent Medical Fabric Management System based on RFID Technology 41

Application of Intelligent Medical Fabric Management System ...

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第41届中国控制会议

作者： Chongwei Li Genghuang Yang Shuncheng Jin School of Automation and Electrical Engineering Tianjin University of Technology and Education Tianjin Key Laboratory of Information Sensing and Intelligent Control

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

In view of the difficulties and risks existing in the running management of hospital medical fabrics,an intelligent medical fabric management system is designed and implemented to improve and optimize the accurate management of the whole process of sorting,receiving,warehousing and washing of medical *** on the refined and standardized of medical fabrics based on Internet of Things technology such as RFID,the whole process of a complete set for medical fabrics is established to realize the tracing the whole life *** avoid the risk of cross infection during the collection of medical fabrics,the system plays a vital role in the fine management of medical fabrics,and solves the long-term problems such as low sending and receiving efficiency,poor washing quality and losing of medical fabrics.

关键词： Medical Fabrics RFID Internet of Things technology intelligent Textile Cabinet

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Real-time arbitrarily-shaped scene text spotting with weakly supervised points

Real-time arbitrarily-shaped scene text spotting with weakly...

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2023 International Conference on Computer Graphics, Artificial Intelligence, and Data processing, ICCAID 2023

作者： Zhao, Longyang Zhang, Shuhao Xu, Zhi School of Computer Science and Information Security Guilin University of Electronics Technology No.1 Jinji road Guilin541004 China Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin541004 China

ISBN: (纸本)9781510677685

Recently, there has been widespread interest in arbitrary-shaped scene text spotting, showing notable progress in both speed and accuracy. However, common issues such as model complexity, slow inference speed, and challenges in deployment persist. For this reason, this paper proposes Weakly Supervised Point-collection Network (WSPNet) for arbitrarily shaped scene text, which adopts deformable convolutional single-shot network architecture to realize multi-branch parallel reading of text, and combines weakly supervised text line point sampling to realize sampling recognition and transcription of arbitrarily shaped text instances. To enhance the robustness of multi-scale fusion, we utilize the Text Feature Fusion (TFF) module to dynamically acquire key features at different scales during the fusion process. This approach aims to reduce discrepancies in scale fusion. Extensive experiments on arbitrarily shaped benchmarks show that WSPNet achieves competitive accuracy and speed. For example, the proposed method achieves an end-to-end text recognition F-measure of 63.4 on the Total-Text dataset at 35.5 FPS, which is competitive with the best results in terms of speed and accuracy balance. © 2024 SPIE.

关键词： Network architecture

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Short-Term Smart Grid Load Forecasting Based on CNN-BiLSTM with Attention Mechanism 6

Short-Term Smart Grid Load Forecasting Based on CNN-BiLSTM w...

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6th International Conference on Energy, Power and Grid, ICEPG 2024

作者： Sun, Feihu Zhong, Yanru Guilin University of Electronic Technology School of Computer Science and Information Security Guangxi Guilin China Guilin University of Electronic Technology Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guangxi Guilin China

ISBN: (纸本)9798350377798

The existing power load data exhibits complex multidimensionality, while existing techniques often focus on learning and forecasting from single load data. To fully exploit the complex multidimensionality of the existing power load data and enhance the accuracy of short-term load forecasting, a smart grid short-term load forecasting model based on attention mechanism and CNN-BiLSTM is proposed. Firstly, the one-dimensional convolutional network (1D-CNN) slides along the temporal dimension to extract multidimensional features such as temperature and humidity from the raw power load data. Secondly, the Bidirectional Long Short-Term Memory network (BiLSTM) captures both historical and future information of the data through gate mechanisms and the bidirectional network structure. Lastly, the attention mechanism allocates weights to focus on important parts of the load sequence data, enhancing the model's ability to identify key information and further improving the accuracy of short-term power load forecasting. The method is experimented on a real-world multivariate load dataset and compared with existing learning methods. The proposed model achieved lower RMSE in 24-hour load forecasting compared to CNN-LSTM, CNN-BiLSTM, LSTM-Attention, BiLSTM-Attention and CNN-LSTM-Attention models by 13.2%, 10.6%, 6.7%, 5.7% and 5.9% respectively. © 2024 IEEE.

关键词： Long short-term memory

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Attention information Fusion Emotion Analysis Model Based on BiLSTM-BERT 4

Attention Information Fusion Emotion Analysis Model Based on...

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4th International Conference on Consumer Electronics and Computer Engineering, ICCECE 2024

作者： Cheng, Zhi-Wei Li, Shun-Xin Wuhan University of Science and Technology Department of Computer Science and Technology Wuhan China Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial Wuhan China

ISBN: (纸本)9798350384062

Aiming at the problem that most sentiment analysis methods do not focus on the weight fusion of local context and global context information and the important information in fusion features is not prominent, AIFM-BB (Attention information fusion emotion analysis model based on BiLSTM-BERT) is proposed. The model uses a pre-trained BERT model to process text vectors and a collaborative speech analysis algorithm to process audio vectors, and introduces BiLSTM and Attention mechanisms. The weight of the feature vector is dynamically adjusted by combining the global information and local information of text and audio and the attention information of text and audio modes. The emotion analysis results are obtained through the Softmax layer after the fusion features are self-focused. The experimental results show that the accuracy of the proposed AIFM-BB model on the CMU-MOSI dataset is 85.3%, which is 2.8 percentage points higher than the baseline. In the multi-category emotion classification task, the accuracy of the model is 44.7%, an improvement of 1.1 percentage points compared with baseline. The accuracy is 83.6% on the CMU-MOSEI dataset, an improvement of 1.4 percentage points compared to baseline. © 2024 IEEE.

关键词： Sentiment analysis

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