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Energy, Power and Electrical Engineering (EPEE), International Conference on

作者： Yiqi Cheng Yanru Zhong College of Computer and Information Security Guilin University of Electronic Technology Guilin Guangxi China Guangxi Key Laboratory of Image And Graphic Intelligent Processing Guilin University of Electronic Technology Guilin Guangxi China

ISBN: (数字)9798331518066

ISBN: (纸本)9798331518073

Non-intrusive load monitoring (NILM) estimates the energy usage of individual devices by analyzing the total power meter data from a household. To address the issue of decreased monitoring accuracy with an increasing number of electricity meters, this paper proposes a solution for single appliance load monitoring (Tra-CNN). The solution uses the UK-DALE dataset, which contains high and low frequencies electricity consumption data of multiple households in the UK and the electricity consumption data of each household appliance, and uses the active power of the appliance as the input of the algorithm. The first part of the model consists of a convolutional layer, a normalization layer, and a dropout layer, with the primary task of capturing local dependencies in the data. The second part consists of a Transformer, whose self-attention mechanism associates features at different positions in the sequence to achieve modeling of long-term dependencies. The third part introduces a convolutional neural network, which uses a sliding window for convolution operations to extract local features from different positions and further extract features from sequence data. The final decoder consists of a transposed convolutional layer and a normalization layer, designed to revert the learned features to their original dimensions. Through experimental verification, the effectiveness and superiority of Tra-CNN are shown, which provides important ideas and methods for the further development of NILM technology.

关键词： Meters Home appliances Load monitoring Electricity Computational modeling Transformers Feature extraction Convolutional neural networks Data mining Load modeling

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Corrigendum to "Hyperledger sawtooth based supplychain traceability system for counterfeit drugs" [Comput. Ind. Eng. 190 (2024) 110021] (computers & Industrial Engineering (2024) 190, (S0360835224001426), (10.1016/***.2024.110021))

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computers and Industrial Engineering 2025年 204卷

作者： Nawaz, Anum Wang, Liguan Irfan, Muhammad Westerlund, Tomi Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Shanghai China Turku Intelligent and Embedded Robotic Systems Lab Faculty of Technology University of Turku Finland

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MFAE: Masked frame-level autoencoder with hybrid-supervision for low-resource music transcription

MFAE: Masked frame-level autoencoder with hybrid-supervision...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Yulun Wu Jiahao Zhao Yi Yu Wei Li School of Computer Science and Technology Fudan University Shanghai China Shanghai Key Laboratory of Intelligent Information Processing Fudan University Shanghai China

Automantic Music Transcription (AMT) is an essential topic in music information retrieval (MIR), and it aims to transcribe audio recordings into symbolic representations. Recently, large-scale piano datasets with high-quality notations have been proposed for high-resolution piano transcription, which resulted in domain-specific AMT models achieved state-of- the-art results. However, those methods are hardly generalized to other ’low-resource’ instruments (such as guitar, cello, clarinet, etc.) transcription. In this paper, we propose a hybrid-supervised framework, the masked frame-level autoencoder (MFAE), to solve this issue. The proposed MFAE reconstructs the frame-level features of low-resource data to understand generic representations of low-resource instruments and improves low-resource transcription performance. Experimental results on several low- resource datasets (MAPS, MusicNet, and Guitarset) show that our framework achieves state-of-the-art performance in note-wise scores (Note F1 83.4%\64.1%\86.7%, Note-with-offset F1 59.8%\41.4%\71.6%). Moreover, our framework can be well generalized to various genres of instrument transcription, both in data-plentiful and data-limited scenarios.

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A English Pronunciation Error Detection and Quality Evaluation Model Based on English Text

A English Pronunciation Error Detection and Quality Evaluati...

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computer Engineering and Intelligent Communications (ISCEIC), International Symposium on

作者： Qingkai Guo Guimin Huang Jianxing Lin Ya Zhou School of Computer Science and Information Security Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin China

In English learning, speaking practice is crucial, but traditional classroom teaching can hardly meet the needs of most learners. In this paper, we investigate and improve the two essential techniques of pronunciation error detection and pronunciation quality evaluation in the computer Assisted Language Learning (CALL) system. In terms of pronunciation error detection, We extracted phoneme pairs’ discriminative acoustic and phonological features. We used Support Vector Machine (SVM) for pronunciation error detection, and the accuracy was improved to 81.26%. For pronunciation quality evaluation, we added two new dimensions of pronunciation fluency and intonation, innovatively designed the word duration ratio, and introduced the Support Vector Regression (SVR) algorithm to achieve the effective integration of different dimensional features. The experiments show that the improved pronunciation quality evaluation model has higher performance and practical value.

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NEAT-Net: An Unsupervised Cross-View Prior Inpainting Network for CBCT Metal Artifact Reduction

NEAT-Net: An Unsupervised Cross-View Prior Inpainting Networ...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Yang Yang Zhan Tong Xinyun Zhong Yijie Shi Yan Xi Wenxue Yu Zhan Wu Yang Chen Laboratory of Image Science and Technology the School of Computer Science and Engineering Southeast University Nanjing China First-Imaging Tech Shanghai China Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing the Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Southeast University Nanjing China

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Intraoperative Cone-Beam Computed Tomography (CBCT) facilitates intraoperative navigation for Minimally Invasive Spine Surgery (MISS). However, high-attenuation metal implants used in MISS often cause metal artifacts in the reconstructed CBCT images. Current algorithms do not consider the cross-view information in the projection-domain for metal artifact reduction (MAR). Inaccurate projection-domain inpainting results in CBCT MAR lead to tissue blurring and secondary artifacts, significantly compromising the accuracy of CBCT-guided MISS and increasing surgical risks. To address the above challenge, in this paper, we propose a novel unsupervised cross-view prior inpainting network for CBCT Metal Artifact Reduction named NEAT-Net. Firstly, a cross-view prior multi-scale inpainting module is constructed to learn the inter-view complementary information. Secondly, a hybrid feature attention module is proposed to adaptively fuse cross-view features. In addition, an unsupervised training approach is proposed to directly learn from metal-affected data. Extensive experiments are conducted to verify the effectiveness of our algorithm on a real clinical dataset.

关键词： Training Minimally invasive surgery Accuracy Navigation Fuses Computed tomography Metals Implants Engineering in medicine and biology Image reconstruction

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ANAS: Sentence Similarity Calculation Based on Automatic Neural Architecture Search 4th

ANAS: Sentence Similarity Calculation Based on Automatic Neu...

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4th IFIP TC 12 International Conference on Intelligence Science, ICIS 2020

作者： Wang, Dong-Sheng Zhao, Cui-Ping Wang, Qi Ofori, Kwame Dwomoh Han, Bin Liu, Ga-Qiong Wang, Shi Wang, Hua-Yu Zhang, Jing School of Computer Science Jiangsu University of Science of Technology Zhenjiang Jiangsu212001 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China School of Army Engineering University Nanjing Jiangsu210007 China

ISBN: (纸本)9783030748258

Sentence similarity calculation is one of several research topics in natural language processing. It has been widely used in information retrieval, intelligent question answering and other fields. Traditional machine learning methods generally extract sentence features through manually defined feature templates and then perform similarity calculations. This type of method usually requires more human intervention and constantly has to deal with domain migration problems. Automatically extracting sentence features using certain deep learning algorithms helps to solve domain migration problems. The training data is of the domain is required to complete the extraction process. However, the neural network structure design in the deep learning model usually requires experienced experts to carry out multiple rounds of the tuning design phase. Using an automatic neural architecture search (ANAS) technology deals with all the network structure design problems. This paper proposes a sentence similarity calculation method based on neural architecture search. This method uses a combination of grid search and random search. The method in this paper is tested on the Quora "Question-Pairs" data set and has an accuracy of 81.8%. The experimental results show that the method proposed in this paper can efficiently and automatically learn the network structure of the deep learning model to achieve high accuracy. © 2021, IFIP International Federation for information processing.

关键词： Learning algorithms

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Lightweight Monocular 3D Object Detection Based on Attention Mechanism

Lightweight Monocular 3D Object Detection Based on Attention...

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Image processing, Electronics and computers (IPEC), Asia-Pacific Conference on

作者： Feng Zhan Zhi Zhang College of Computer Science and Technology Wuhan University of Science and Technology Wuhan Hubei China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan Hubei China

ISBN: (数字)9798350374407

ISBN: (纸本)9798350374414

In the field of autonomous driving, 3D target detection is an important technology. In view of the shortcomings of existing monocular 3D detection algorithms in terms of accuracy and real-time performance, we propose a lightweight 3D target detection algorithm based on the attention mechanism. In order to ensure the real-time performance of the model, we use depthwise separable convolutions in the backbone network. The feature maps of each scale are then connected to an EMA module, which can fuse spatial attention and cross-dimensional Coordinate attention is used to enhance the feature extraction capability of the network, and BiFPN is subsequently used for multi-scale feature fusion. Finally, experiments were conducted on the KITTI dataset, and the results showed that our algorithm was superior to other methods in both accuracy and speed.

关键词： Three-dimensional displays Attention mechanisms Accuracy Head Fuses Object detection Feature extraction Real-time systems Data mining Visual perception

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LSU-NET: LIGHTWEIGHT AUTOMATIC ORGANS SEGMENTATION NETWORK FOR MEDICAL IMAGES

arXiv

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arXiv 2025年

作者： Ding, Yujie Teng, Shenghua Li, Zuoyong Chen, Xiao College of Electronic and Information Engineering Shandong University of Science and Technology Qingdao266590 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control School of Computer and Big Data Minjiang University Fuzhou350121 China

UNet and its variants have widespread applications in medical image segmentation. However, the substantial number of parameters and computational complexity of these models make them less suitable for use in clinical settings with limited computational resources. To address this limitation, we propose a novel Lightweight Shift U-Net (LSU-Net). We integrate the Light Conv Block and the Tokenized Shift Block in a lightweight manner, combining them with a dynamic weight multi-loss design for efficient dynamic weight allocation. The Light Conv Block effectively captures features with a low parameter count by combining standard convolutions with depthwise separable convolutions. The Tokenized Shift Block optimizes feature representation by shifting and capturing deep features through a combination of the Spatial Shift Block and depthwise separable convolutions. Dynamic adjustment of the loss weights at each layer approaches the optimal solution and enhances training stability. We validated LSU-Net on the UWMGI and MSD Colon datasets, and experimental results demonstrate that LSU-Net outperforms most state-of-the-art segmentation architectures. Copyright © 2025, The Authors. All rights reserved.

关键词： Convolution

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Thunderstorm Prediction Method Based on CNN-BiLSTM Using BEADS∗ 15

Thunderstorm Prediction Method Based on CNN-BiLSTM Using BEA...

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15th IEEE International Conference on Electronic Measurement and Instruments, ICEMI 2021

作者： Yang, Xu Xing, Hongyan Ji, Xinyuan Collaborative Innovation Center for Meteorological Disaster Prediction and Evaluation Nanjing University of Information Science and Technology Jiangsu Key Laboratory of Meteorological Detection and Information Processing Nanjing China

ISBN: (纸本)9781665444910

Atmospheric electric field signal (AEFS) usually superimposes low-frequency noise, which has a negative effect on thunderstorm monitoring. A thunderstorm prediction method based on Convolutional Neural Network (CNN) and Bidirectional Long Short Term Memory (BiLSTM) is proposed. Firstly, AEFS is divided into useful, baseline and noise components by BEADS. After getting the estimated value of the useful signal, the denoised signal is obtained. Then, the AEFS prediction model is built based on BiLSTM. After inputting the AEFS spatial features extracted by CNN into the model, a CNN-BiLSTM hybrid model for thunderstorm prediction is formed. After analyzing the performance of the method, we carried out the experiment in thunderstorm weather. Results show that the SNR of AEFS processed by BEADS is improved effectively. It's worth noting that the determining coefficients before and after BEADS are all above 94.12%, showing a good effect. Finally, the effectiveness of the method is proved again by the coincidence between the predicted results and the radar chart. © 2021 IEEE.

关键词： Thunderstorms

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TransERR: Translation-based Knowledge Graph Embedding via Efficient Relation Rotation

arXiv

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arXiv 2023年

作者： Li, Jiang Su, Xiangdong Zhang, Fujun Gao, Guanglai College of Computer Science Inner Mongolia University Hohhot China National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian China Inner Mongolia Key Laboratory of Mongolian Information Processing Technology China

This paper presents a translation-based knowledge geraph embedding method via efficient relation rotation (TransERR), a straightforward yet effective alternative to traditional translation-based knowledge graph embedding models. Different from the previous translation-based models, TransERR encodes knowledge graphs in the hypercomplex-valued space, thus enabling it to possess a higher degree of translation freedom in mining latent information between the head and tail entities. To further minimize the translation distance, TransERR adaptively rotates the head entity and the tail entity with their corresponding unit quaternions, which are learnable in model training. We also provide mathematical proofs to demonstrate the ability of TransERR in modeling various relation patterns, including symmetry, antisymmetry, inversion, composition, and subrelation patterns. The experiments on 10 benchmark datasets validate the effectiveness and the generalization of TransERR. The results also indicate that TransERR can better encode large-scale datasets with fewer parameters than the previous translation-based models. Our code and datasets are available at https://***/dellixx/TransERR. © 2023, CC BY-NC-ND.

关键词： Knowledge graph

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