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Guided-YNet: Saliency Feature-Guided Interactive Feature Enhancement Lung Tumor Segmentation Network

Computers, Materials & Continua 2024年第9期80卷 4813-4832页

作者： Tao Zhou Yunfeng Pan Huiling Lu Pei Dang Yujie Guo Yaxing Wang School of Computer Science and Engineering North Minzu UniversityYinchuan750021China School of Medical Information&Engineering Ningxia Medical UniversityYinchuan750004China Key Laboratory of Image and Graphics Intelligent Processing of State Ethnic Affairs Commission NorthMinzuUniversityYinchuan750021China

Multimodal lung tumor medical images can provide anatomical and functional information for the same *** as Positron Emission Computed Tomography(PET),Computed Tomography(CT),and *** to utilize the lesion anatomical and functional information effectively and improve the network segmentation performance are key *** solve the problem,the Saliency Feature-Guided Interactive Feature Enhancement Lung Tumor Segmentation Network(Guide-YNet)is proposed in this ***,a double-encoder single-decoder U-Net is used as the backbone in this model,a single-coder single-decoder U-Net is used to generate the saliency guided feature using PET image and transmit it into the skip connection of the backbone,and the high sensitivity of PET images to tumors is used to guide the network to accurately locate ***,a Cross Scale Feature Enhancement Module(CSFEM)is designed to extract multi-scale fusion features after ***,a Cross-Layer Interactive Feature Enhancement Module(CIFEM)is designed in the encoder to enhance the spatial position information and semantic ***,a Cross-Dimension Cross-Layer Feature Enhancement Module(CCFEM)is proposed in the decoder,which effectively extractsmultimodal image features through global attention and multi-dimension local *** proposed method is verified on the lung multimodal medical image datasets,and the results showthat theMean Intersection overUnion(MIoU),Accuracy(Acc),Dice Similarity Coefficient(Dice),Volumetric overlap error(Voe),Relative volume difference(Rvd)of the proposed method on lung lesion segmentation are 87.27%,93.08%,97.77%,95.92%,89.28%,and 88.68%,*** is of great significance for computer-aided diagnosis.

关键词： Medical image segmentation U-Net saliency feature guidance cross-modal feature enhancement cross-dimension feature enhancement

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High Contrast Ratio 4-to-2 Plasmonic Encoder Utilizing Cascaded Ring Resonators 6

High Contrast Ratio 4-to-2 Plasmonic Encoder Utilizing Casca...

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6th International Conference on Electronic Engineering and Informatics, EEI 2024

作者： Chen, Yan Xie, Yiyuan Chai, Junxiong Ye, Yichen College of Electronic and Information Engineering Southwest University Chongqing400715 China Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing Chongqing400715 China Key Laboratory of Networks and Cloud Computing Security of Universities in Chongqing Chongqing400715 China

ISBN: (纸本)9798350353594

In this paper, a novel plasmonic 4-to-2 encoder comprising cascaded ring resonators coated with graphene layers and two metal-insulator-metal (MIM) waveguides. The encoding function operates at 850 nm by modulating the intensity of pump beam focused on the graphene layer to manipulate the state of the plasmonic resonators. The performance of the encoder is analyzed utilizing the finite-difference time-domain method, demonstrating a high contrast ratio of 15.1 dB. Thus, this efficient 4-to-2 optical encoder is well-suited for integration into optical integrated computing devices. © 2024 IEEE.

关键词： Semiconductor insulator boundaries

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Lightweight Monocular 3D Object Detection Based on Attention Mechanism 5

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

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5th Asia-Pacific Conference on Image processing, Electronics and Computers, IPEC 2024

作者： Zhan, Feng Zhang, Zhi Wuhan University of Science and Technology College of Computer Science and Technology Hubei Wuhan430065 China Information Processing and Real-time Industrial System Hubei Province Key Laboratory of Intelligent Hubei Wuhan430065 China Information Processing and Real-time Industrial System Hubei Province Key Laboratory of Intelligent Hubei Wuhan30065 China

ISBN: (纸本)9798350374407

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. © 2024 IEEE.

关键词： Object recognition

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A hybrid wind power prediction model based on seasonal feature decomposition and enhanced feature extraction

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Energy and AI 2024年第4期18卷 368-379页

作者： Weipeng Li Yuting Chong Xin Guo Jun Liu School of Automation and Information Engineering Xi’an University of TechnologyNo.5 Jinhua South RoadXi’an710048ShaanxiChina Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing No.5 Jinhua South RoadXi’an710048ShaanxiChina

Efficient and accurate wind power prediction is crucial for enhancing the reliability and safety of power *** data-driven forecasting methods are regarded as an effective ***,the inherent randomness and nonlinearity of wind power systems,along with the abundance of redundant information in measurement data,present challenges to forecasting *** integration of precise and efficient techniques for data feature decomposition and extraction is essential in conjunction with advanced driven data-forecasting *** on the seasonal variation characteristics of wind energy,a hybrid wind power prediction model based on seasonal feature decomposition and enhanced feature extraction is *** effectiveness and superiority of the proposed method in predictive accuracy are demonstrated through comprehensive multi-model experiment comparisons.

关键词： Wind power prediction Data-driven Hybrid model Seasonal feature decomposition Gated recurrent networks Attention mechanism

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A Novel Clustering Model TEC for Station Classification 37th

A Novel Clustering Model TEC for Station Classification

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37th International Conference on Advanced information Networking and Applications, AINA 2023

作者： Yuan, Lei Liu, Lijuan Zhu, Shunzhi Chen, Long College of Computer and Information Engineering Xiamen University of Technology Xiamen361024 China Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen361024 China

ISBN: (纸本)9783031290558

Station classification plays an important role in urban intelligent transportation systems. Recently, most of studies usually choose different features to analyze the function of stations. Moreover, they usually choose K-means as the clustering method, which may lead to unstable clustering results. Therefore, a novel clustering model (Transformer Encoder Clustering, TEC) has been proposed for station classification by only using passenger flow data. Firstly, transformer encoder has been applied to deeply extract the features from original passenger flow. Then, an improved Canopy + has been proposed to determine the initial centers for K-means. Finally, a novel evaluation metric (NR) has been introduced to verify the stability and volatility of station classification results. Experiments are conducted on Xiamen bus rapid transit ridership dataset. The results show that the 44 stations have been classified into three categories, and the NRs in all stations are 0.75, which verifies the stability of results for station classification. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： intelligent systems

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A method for generating Dunhuang dance instrumental images based on optimized diffusion model

A method for generating Dunhuang dance instrumental images b...

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2024 International Conference on Algorithms, High Performance Computing, and Artificial Intelligence, AHPCAI 2024

作者： Wang, Jiaxin He, Xiangzhen Suzuki, Kenneth M. Northwest Minzu University Key Laboratory of Linguistic and Cultural Computing Ministry of Education Gansu Lanzhou730030 China Northwest Minzu University Key Laboratory of Minzu Languages and Cultures Intelligent Information Processing Gansu Lanzhou730030 China

ISBN: (纸本)9781510685840

Dunhuang culture is a treasure house of human history and culture. As an integral part of Dunhuang culture, Dunhuang dance is famous for its unique national characteristics and artistic characteristics. In the form of Dunhuang dance, instrumental music and props are used to greatly enrich the stage expression, forming a unique dance form of "holding music while dancing, dancing with instruments". However, the phenomenon of homogenization of instrumental props occurs frequently, which hinders the overall presentation effect and beauty of Dunhuang dance. The development based on the generative diffusion model provides new ideas for the design of Dunhuang dance instrumental props. First, the role played by musical instruments and props in the presentation of Dunhuang dance art was sorted out, and the Dunhuang dance instrumental props were digitally collected and processed, and a special data set was constructed. Secondly, based on the recognition of related instruments, a diffusion model instrumental prop image generation that can be used in the Chinese context is trained. Experimental results show that the instrumental props generated by this method not only conform to realistic logic but also have strong applicability. © 2024 SPIE.

关键词： Photons

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Impact of Crosstalk and Loss on the Training Process of Integrated Photonic Neural Networks 6

Impact of Crosstalk and Loss on the Training Process of Inte...

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6th International Conference on Electronic Engineering and Informatics, EEI 2024

作者： Pang, Jing Su, Ye Xie, Yiyuan College of Electronic and Information Engineering Southwest University Chongqing400715 China Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing Chongqing400715 China Key Laboratory of Networks and Cloud Computing Security of Universities in Chongqing Chongqing400715 China

ISBN: (纸本)9798350353594

Integrated photonic neural networks (IPNNs), with their ability to process large-scale data and parallelize multiple computational tasks, have shown impressive promise for applications in the field of artificial intelligence. However, due to some hard-to-ignore non-ideal characteristics of the underlying silicon photonic MZI devices constituting the IPNN, these non-ideal characteristics can cause the input signal to be affected by crosstalk noise and optical loss during transmission, thus affecting the training and inference accuracy of the IPNN. In order to solve these problems, this paper establishes an analytical model of IPNN affected by crosstalk noise and loss during the training process, covering component level, layer level and system level. Simulations using this model show that the inference accuracy of IPNN decreases by about 65% when the crosstalk noise in the training phase of IPNN reaches -20 dB, while the inference accuracy of IPNN decreases by about 55% when the optical loss reaches -0.5 dB. © 2024 IEEE.

关键词： Crosstalk

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Source Localization for Extremely Large-Scale Antenna Arrays with Spatial Non-Stationarity 48

Source Localization for Extremely Large-Scale Antenna Arrays...

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

作者： Wu, Xiaohuan Sun, Ji Jia, Xiaoyuan Wang, Shuxin Nanjing University of Posts and Telecommunications School of Telecommunication and Information Engineering Nanjing210003 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen Key Laboratory of Media Security Shenzhen518060 China

ISBN: (纸本)9781728163277

Extremely large-scale antenna array (ELAA) is a promising technique in 6G and autonomous driving thanks to its high spatial resolution. However, due to the extremely large array aperture, the sources may only "see"a portion of the array, called visibility region (VR). Since the information of VR is usually unknown a priori, traditional methods may encounter performance degradation. In this paper, we show that under the exact steering vector model of ELAA, the eigenvectors of the signal subspace and the steering vectors are approximately collinear in most scenarios. Thus, the angle and range can be easily estimated by using the eigenvectors of the signal subspace. Moreover, the VRs of each source can be also estimated from the eigenvectors of signal subspace. Numerical results show that the estimation performance of our method is comparable to MUSIC with known VR information. © 2023 IEEE.

关键词： collinear exact steering vector extremely largescale antenna array (ELAA) Source localization spatial non-stationarity

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ANALYSIS OF TRIPARTITE GAME IN POWER SYSTEMS BASED ON UNCERTAIN NASH EQUILIBRIUM THEORY

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UPB Scientific Bulletin, Series A: Applied Mathematics and Physics 2025年第1期87卷 117-128页

作者： Wu, Liang Yan, Xiaolin Yao, Zhangsong Zhao, Teng School of Management and Ningxia Key Laboratory of intelligent information and Big Data Processing North Minzu University Ningxia Yinchuan750021 China School of Mathematical Sciences Tiangong University Tianjin300387 China School of Information Engineering Nanjing Xiaozhuang University Nanjing211171 China School of Mathematics and Information Science North Minzu University Ningxia Yinchuan750021 China

Power systems serve as the fundamental infrastructure for the socioeconomic development of modern societies. Researching power systems can stimulate the growth of the power industry and contribute to the sustainable development of society. This paper aims to address uncertainties prevalent in power systems, including extreme weather events, disruptions in renewable energy supply, and adjustments in economic policies. To achieve this objective, an uncertain Nash equilibrium model is established. Subsequently, a game theoretic analysis is conducted to maximize the interests of users, grid companies, and proxy purchasers. Under the condition of not necessarily continuous differentiability, the Riemann-Stieltjes discrete approximation method has been proposed. The difficulty of calculating the expectation of the optimal revenue function has been resolved. Weak first-order equilibrium condition based on Clarke’s generalized gradient, the convergence and convergence rate of uncertain Nash equilibrium solutions are proved. © 2025, Politechnica university of Bucharest. All rights reserved.

关键词： power system tripartite game uncertain Nash equilibrium uncertainty theory

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Non-negative Matrix Factorization Method Based on Mixed Gaussian Kernels 1

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3rd International Conference on Artificial Intelligence Logic and Applications, AILA 2023

作者： Yang, Zhenting Pei, Jihong Ye, Sicong College of Electronics and Information Engineering Shenzhen University Guangdong Shenzhen China Guangdong Key Laboratory of Intelligent Information Processing Guangdong Shenzhen China

ISBN: (数字)9789819978694

ISBN: (纸本)9789819978687

Non-negative Matrix Factorization (NMF) is a data dimensionality reduction method, which can process large scale and high dimensional data more efficiently. Among them, kernel-based non-negative matrix factorization maps data to high-dimensional space, which is a nonlinear NMF method and is mainly used to extract high-order features of image data. The feature information obtained by different kernel functions has different characteristics and makes different contributions to image classification. However, most of the researches only consider single-kernel NMF, and some multi-kernel NMF methods also have some problems such as uneven weight distribution, which loses the feature information of some kernel functions. Therefore, it is necessary to study the NMF method of multi-kernel fusion with adaptive weights. In this paper, a non-negative matrix factorization method based on mixed Gaussian kernels is proposed. By fusing multiple Gaussian kernels with different weights and updating weights adaptively, the feature information of different Gaussian kernels can complement each other, which has good performance on public datasets FERET, ORL and Yale Face, indicating that the algorithm can effectively improve the classification performance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Non-negative matrix factorization

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