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Calibration Signal-Assisted Emitter Localization Under Sensor Position Uncertainty

IEEE Transactions on Aerospace and Electronic Systems 2025年第3期61卷 6913-6927页

作者： You, Ming-Yi Pu, Wenqiang Zhou, Rui Ye, Yun-Xia Xiao, Minrui Wang, Wei Shi, Qingjiang No.36 Research Institute of CETC National Key Laboratory of Electromagnetic Space Security Jiaxing314033 China Shenzhen Research Institute of Big Data Shenzhen Research Institute of Big Data Shenzhen518172 China Shanghai Jiaotong University School of Sensing Science and Engineering Shanghai200240 China Tongji University School of Software Engineering Shanghai200070 China

The uncertainty of sensor position significantly compromises the accuracy of source localization within the direct position determination (DPD) framework. This article explores the use of an external calibrator that emits a calibration signal with a known position to mitigate the impact of sensor position uncertainty on localization accuracy. By utilizing the calibration signal, we develop a refined Gibbs sampling algorithm as the initial step to effectively address the high-dimensional optimization problem associated with sensor position calibration. In the second step, we account for measurement noise and estimation uncertainties in the calibrated sensor positions and propose a revised DPD method, which demonstrates greater resilience to sensor position uncertainty compared to existing methods. We also derive the Cramer–Rao lower bound for emitter position under scenarios both with and without an external calibrator. Numerical simulations are performed to evaluate the performance of our proposed algorithms against several benchmark methods, demonstrating the effectiveness of our approach. © 1965-2011 IEEE.

关键词： Cramer-Rao bounds

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MTSC-Net:A Semi-Supervised Counting Network for Estimating the Number of Slash pine New Shoots

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植物表型组学（英文） 2024年第4期6卷 953-966页

作者： Zhaoxu Zhang Yanjie Li Yue Cao Yu Wang Xuchao Guo Xia Hao College of Information Science and Engineering Shandong Agricultural UniversityTaian271018Shandong ProvinceChina Research Institute of Subtropical Forestry Chinese Academy of ForestryHangzhou 311400Zhejiang ProvinceChina School of Big Data Taishan College of Science and TechnologyTaian271034Shandong ProvinceChina

The new shoot density of slash pine serves as a vital indicator for assessing its growth and photosynthetic capacity,while the number of new shoots offers an intuitive reflection of this *** deep learning methods becoming increasingly popular,automated counting of new shoots has greatly improved in recent years but is still limited by tedious and expensive data collection and *** resolve these issues,this paper proposes a semi-supervised counting network(MTSC-Net)for estimating the number of slash pine new ***,based on the mean-teacher framework,we introduce the improved VGG19 to extract multiscale new shoot ***,to connect local new shoot feature information with global channel features,attention feature fusion module is introduced to achieve effective feature ***,the new shoot density map and density probability distribution are processed in a fine-grained manner through multiscale dilated convolution of the regression head and classification *** addition,a masked image modeling strategy is introduced to encourage the contextual understanding of global new shoot features and improve the counting *** experimental results show that MTSC-Net outperforms other semi-supervised counting models with labeled percentages ranging from 5％to 50％.When the labeled percentage is 5％,the mean absolute error and root mean square error are 17.71 and 25.49,*** findings demonstrate that our work can be used as an efficient semi-supervised counting method to provide automated support for tree breeding and genetic utilization.

关键词： network counting estimating mtsc-net number pine semi-supervised shoots slash

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A Surrogate-assisted Multi-objective Grey Wolf Optimizer for Empty-heavy Train Allocation Considering Coordinated Line Utilization Balance

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Journal of Bionic Engineering 2025年第1期22卷 383-397页

作者： Zhigang Du Shaoquan Ni Jeng-Shyang Pan Shuchuan Chu School of Transportation and Logistics Southwest Jiaotong UniversityChengdu610031China School of Artificial Intelligence Nanjing University of Information Science and TechnologyNanjing210044China National and Local Joint Engineering Lab for Integrated Transportation Intelligence Chengdu610031China National Engineering Lab of Comprehensive Transportation Big Data Application Technology Chengdu610031China

This paper introduces the Surrogate-assisted Multi-objective Grey Wolf Optimizer(SMOGWO)as a novel methodology for addressing the complex problem of empty-heavy train allocation,with a focus on line utilization *** integrating surrogate models to approximate the objective functions,SMOGWO significantly improves the efficiency and accuracy of the optimization *** effectiveness of this approach is evaluated using the CEC2009 multi-objective test function suite,where SMOGWO achieves a superiority rate of 76.67%compared to other leading multi-objective ***,the practical applicability of SMOGWO is demonstrated through a case study on empty and heavy train allocation,which validates its ability to balance line capacity,minimize transportation costs,and optimize the technical combination of heavy *** research highlights SMOGWO's potential as a robust solution for optimization challenges in railway transportation,offering valuable contributions toward enhancing operational efficiency and promoting sustainable development in the sector.

关键词： Surrogate-assisted model Grey wolf optimizer Multi-objective optimization Empty-heavy train allocation

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Decomposing and Fusing Intra- and Inter-Sensor Spatio-Temporal Signal for Multi-Sensor Wearable Human Activity Recognition 39

Decomposing and Fusing Intra- and Inter-Sensor Spatio-Tempor...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Xie, Haoyu Li, Haoxuan Zheng, Chunyuan Yuan, Haonan Liao, Guorui Liao, Jun Liu, Li School of Big Data and Software Engineering Chongqing University China Center for Data Science Peking University China School of Mathematical Sciences Peking University China School of Computer Science and Engineering Beihang University China

ISBN: (纸本)157735897X

Wearable Human Activity Recognition (WHAR) is a prominent research area within ubiquitous computing. Multi-sensor synchronous measurement has proven to be more effective for WHAR than using a single sensor. However, existing WHAR methods use shared convolutional kernels for indiscriminate temporal feature extraction across each sensor variable, which fails to effectively capture spatio-temporal relationships of intra-sensor and inter-sensor variables. We propose the DecomposeWHAR model consisting of a decomposition phase and a fusion phase to better model the relationships between modality variables. The decomposition creates high-dimensional representations of each intra-sensor variable through the improved Depth Separable Convolution to capture local temporal features while preserving their unique characteristics. The fusion phase begins by capturing relationships between intra-sensor variables and fusing their features at both the channel and variable levels. Long-range temporal dependencies are modeled using the State Space Model (SSM), and later cross-sensor interactions are dynamically captured through a self-attention mechanism, highlighting inter-sensor spatial correlations. Our model demonstrates superior performance on three widely used WHAR datasets, significantly outperforming state-of-the-art models while maintaining acceptable computational efficiency. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Ambient intelligence

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FCIGAN: A Three-dimensional Ink Rendering Brushwork Based on Generative Adversarial Network

Journal of Network Intelligence

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Journal of Network Intelligence 2024年第3期9卷 1348-1358页

作者： Wang, Qinghua Lin, Wenru Lan, Wei Huang, Jingwei Chen, Yang Long, Xinling Zheng, Lujia Huang, Huilong Zhang, Fuquan School of Computer and Data Science Minjiang University Fuzhou University Town No.200 Xiyuangong Road Fuzhou350108 China Fujian Province University Fuzhou350300 China College of Computer and Big Data Fuzhou University Fuzhou China

Three-dimensional ink rendering is a NPR (Non-Photorealistic rendering) art style, widely used in a range of fields, including gaming and ***, CycleGAN is a standard image transformation model, however, it prefers Western painting methods, resulting in less-than-ideal ink rendering effects. As a result, this investigation focuses on the characteristics of ink painting by merging attention processes with three-dimensional depth information. It emphasizes differences in ink color depth to depict the relationship between distance and three-dimensional perception of things in the picture. It also makes use of methods like line thickness and curves to show the contours and textures of things. Additionally, to make the picture as close to the real traditional Chinese ink painting as possible, we use the thickness and bending of lines to show the outline and texture of objects, and highlight the key points or depict some details in the picture by leaving blank. This method is named FCIGAN (Freehand Chinese Ink Generative Adversarial Network). We have also gathered and created a matching dataset called FCI Images to aid in the experiment’s smooth progression. In conclusion, the experimental results show that, the FCIGAN approach suggested in this research has much better performance when compared to conventional techniques and may be used more effectively in the 3D rendering field. © 2024, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Generative adversarial networks

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TriNet: A Synergistic Hybrid Approach for Network Traffic Anomaly Detection

TriNet: A Synergistic Hybrid Approach for Network Traffic An...

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2024 International Conference on Electronics and Devices, Computational science, ICEDCS 2024

作者： Chen, Zirui Zhuhai College of Science and Technology School of Data Science Zhuhai China

ISBN: (纸本)9798331527624

Network traffic anomaly detection is essential for securing digital infrastructures, yet traditional methods often fail to handle the complexity and dynamics of network data effectively. Additionally, this paper presents a hybrid model called TriNet, which consists of convolutional neural networks (CNNNs), long short-term memory (Bi-LSTM) networks, and attention mechanisms integrated into one cohesive system. This allows intricate spatiotemporal patterns in network traffic to be captured in a synergistic way. Our model employs CNNs for extracting spatial features, Bi-LSTMs for modeling temporal sequences, and Attention mechanisms for focusing on critical features, augmented by residual connections to enhance training dynamics. Based on experimental findings conducted on the KDD Cup 1999 dataset, the TriNet model has shown significant improvements in terms of accuracy, precision, recall, as well as the F1 score versus traditional machine learning models. TriNet's innovative architecture and robust performance underscore its potential as a powerful tool for network traffic anomaly detection, offering substantial improvements in network security. © 2024 IEEE.

关键词： Convolutional neural networks

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SymILO: A Symmetry-Aware Learning Framework for Integer Linear Optimization 38

SymILO: A Symmetry-Aware Learning Framework for Integer Line...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Chen, Qian Zhang, Tianjian Yang, Linxin Han, Qingyu Wang, Akang Sun, Ruoyu Luo, Xiaodong Chang, Tsung-Hui School of Science and Engineering The Chinese University of Hong Kong Shenzhen China Shenzhen Research Institute of Big Data China School of Data Science The Chinese University of Hong Kong Shenzhen China Shenzhen International Center for Industrial and Applied Mathematics Shenzhen Research Institute of Big Data China

Integer linear programs (ILPs) are commonly employed to model diverse practical problems such as scheduling and planning. Recently, machine learning techniques have been utilized to solve ILPs. A straightforward idea is to train a model via supervised learning, with an ILP as the input and an optimal solution as the label. An ILP is symmetric if its variables can be permuted without changing the problem structure, resulting in numerous equivalent and optimal solutions. Randomly selecting an optimal solution as the label can introduce variability in the training data, which may hinder the model from learning stable patterns. In this work, we incorporate the intrinsic symmetry of ILPs and propose a novel training framework called SymILO. Specifically, we modify the learning task by introducing solution permutation along with neural network weights as learnable parameters and then design an alternating algorithm to jointly optimize the loss function. We conduct extensive experiments on ILPs involving different symmetries and the computational results demonstrate that our symmetry-aware approach significantly outperforms three existing methods-achieving 50.3%, 66.5%, and 45.4% average improvements, respectively. © 2024 Neural information processing systems foundation. All rights reserved.

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OOD Problem Research in Biochemistry Based on Backdoor Adjustment 5

OOD Problem Research in Biochemistry Based on Backdoor Adjus...

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5th International Conference on Artificial Intelligence Technologies and Applications, ICAITA 2023

作者： Gu, Pengfei Yu, Daao School of Data Science University of Science and Technology of China Hefei China

ISBN: (纸本)9781643684840

Due to its ability to deal well with the features of graph structure (social network, molecular structure), graph neural network (GNN) has recently shown amazing capabilities in many fields (biology, chemistry), which has aroused the attention of a large number of researchers on the operation mechanism of GNN model. In order to apply graph neural network to real environment, the problem of out-of-distribution generalization is solved. The difference of data distribution between training environment and real environment is an urgent problem to be solved. In the present study, from the point of view of data, the input graph data is processed to find the core part and filter out the noise part;From the perspective of model method, the parts related to this task (graph classification) in the model are extracted, so as to improve the accuracy and efficiency of the model. However, while these methods are valid from a theoretical point of view, they are using methods that are too simple to actually cut off the effects of non-causal components. However, the current methods of model Angle are to cut the network directly, without considering the information transfer between the model parameters. To solve these problems, the main work of this paper is to propose a distance-based environment selection method, which enables the backdoor adjustment to be implemented to the maximum extent and ensures the robustness of the model. At the same time, it proposes a way for the network to squeeze information during the clipping process, so that the effect of the model pruning algorithm can be optimized. Reduce the complexity of the model and improve the generalization of the model. The method presented in this paper has been validated on data sets in several biochemical fields, and the best performance has been achieved. © 2024 The Authors.

关键词： Graph neural networks

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Research and Implementation of Lung Tumor Segmentation Method Based on Improved U-Net Network

Research and Implementation of Lung Tumor Segmentation Metho...

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2023 International Conference on Image Processing and Computer Vision, IPCV 2023

作者： Peng, Xiaoshu Peng, Junjie Ruan, Peiguang Lv, Wei Zhuhai College of Science and Technology Ali Cloud School of Big Data Application Guangdong Zhuhai China

ISBN: (纸本)9781665473941

In recent years, cancer has posed a serious threat to human health. Computer tomography is the most important auxiliary method for diagnosing cancer, and doctors can study the patient's CT images to determine the location of the lesion and determine treatment plans. But this method not only consumes a lot of doctors' energy, but also may lead to misdiagnosis. With the application of neural network technology in computer-aided diagnosis, it has greatly saved doctors' time reading images and improved the accuracy and accuracy of reading. © 2023 IEEE.

关键词： Diseases

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Adaptive Decision Boundary for Few-Shot Class-Incremental Learning 39

Adaptive Decision Boundary for Few-Shot Class-Incremental Le...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Li, Linhao Tan, Yongzhang Yang, Siyuan Cheng, Hao Dong, Yongfeng Yang, Liang School of Artificial Intelligence and Data Science Hebei University of Technology China College of Computing and Data Science Nanyang Technological University Singapore

ISBN: (纸本)157735897X

Few-Shot Class-Incremental Learning (FSCIL) aims to continuously learn new classes from a limited set of training samples without forgetting knowledge of previously learned classes. Conventional FSCIL methods typically build a robust feature extractor during the base training session with abundant training samples and subsequently freeze this extractor, only fine-tuning the classifier in subsequent incremental phases. However, current strategies primarily focus on preventing catastrophic forgetting, considering only the relationship between novel and base classes, without paying attention to the specific decision spaces of each class. To address this challenge, we propose a plug-and-play Adaptive Decision Boundary Strategy (ADBS), which is compatible with most FSCIL methods. Specifically, we assign a specific decision boundary to each class and adaptively adjust these boundaries during training to optimally refine the decision spaces for the classes in each session. Furthermore, to amplify the distinctiveness between classes, we employ a novel inter-class constraint loss that optimizes the decision boundaries and prototypes for each class. Extensive experiments on three benchmarks, namely CIFAR100, miniImageNet, and CUB200, demonstrate that incorporating our ADBS method with existing FSCIL techniques significantly improves performance, achieving overall state-of-the-art results. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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