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Efficient Exploration of Mobile Robot Based on DL-RRT and AP-BO

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2024年 73卷 1页

作者： Liu, Yi Zhou, Zhidong Sang, Hong Yu, Shuanghe Yan, Yan Er, Meng Joo Dalian Maritime Univ Coll Marine Elect Engn Dalian 116026 Liaoning Peoples R China

The proliferation in demand for autonomous exploration and map construction of mobile robots has posed an ongoing challenge to the development of an efficient exploration strategy. The unknown map of the path planning environment imposes a challenge for the boundary exploration strategy and boundary point screening mechanism of mobile robots. Motivated by this challenge, an efficient method for boundary exploration using a double-layer rapid-exploration random tree (DL-RRT) is introduced. DL-RRT enables the exploration of boundary points in both global and local manners. Moreover, a boundary point optimization strategy combining affinity propagation and Bayesian optimization (AP-BO) is proposed. The proposed boundary-point exploration strategy is different from traditional target-point exploration. On the one hand, the affinity propagation mechanism is employed in boundary-point optimization, which exhibits superior clustering effects compared with the traditional algorithms. On the other hand, Bayesian optimization can filter the boundary points after clustering. The mobile robot prioritizes these clustered points according to the desired parameters. The comparison results of the simulation are conducted to demonstrate the efficacy of the exploration strategy. The results of the simulation experiment are analyzed through comparison with other methods in terms of running time and path length. It is concluded that the combination of DL-RRT, affinity propagation clustering, and Bayesian optimization enables the robot to efficiently identify boundary points, filter suitable clustering centers, and locate optimal target points. The proposed method exhibits an average reduction in running time by 16.38% compared with using RRT and K-means clustering. In addition, it demonstrates an average decrease in path length by 20.28% compared with using RRT and K-means clustering. At the same time, the standard deviation of the results remains around 1, indicating a stable operatio

关键词： Optimization Mobile robots Robots Bayes methods filtering algorithms Sensors Laser radar Affinity propagation Bayesian optimization boundary exploration double-layer rapid-exploration random tree (DL-RRT) mobile robot

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A Hybrid Approach to Reliable Jamming Identification in UAV Communications Using Combined DNNs and ML algorithms

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IEEE ACCESS 2024年 12卷 178898-178908页

作者： Farkhari, Hamed Viana, Joseanne Kahvazadeh, Sarang Sebastiao, Pedro Jimenez, Victor P. Gil Dinis, Rui Univ Lisbon ISCTE Inst P-1649026 Lisbon Portugal Univ Carlos III Madrid UC3M Dept Teoria Senal & Comunicac Madrid 28903 Spain CTTC CERCA Barcelona 08860 Spain IT P-1049001 Lisbon Portugal Univ Nova Lisboa FCT P-2829516 Caparica Portugal

Deep Neural Networks (DNNs) have gained prominence due to their remarkable accomplishments across various domains, including telecommunications and security. Their integration into decision-making processes within 5G telecommunication systems and UAV security is noteworthy. However, the iterative nature of DNN data processing can introduce uncertainties in classification decisions, impacting their reliability. This paper presents novel combined preprocessing and post-processing techniques designed to enhance the accuracy and reliability of binary classification DNNs by managing uncertainty levels. The study evaluates these methods through calibration error metrics, confidence values, and the Reliability Score (RS), which quantifies the disparity between Mean Accuracy (MA) and Mean Confidence (MC). Additionally, the effectiveness of these methods is demonstrated by applying them to simulated real-world scenarios to improve jamming detection reliability in UAV communications. The proposed algorithms' impact is compared against baseline DNNs and DNNs augmented with the eXtreme Gradient Boosting (XGB) classifier, as well as the latest research to validate our approach. This paper comprehensively overviews the experimental setup, dataset, deep network architecture, preprocessing and post-processing techniques, evaluation metrics, and results. By addressing uncertainty in XGB and DNN outputs, this study improves the trustworthiness of ML-DNN-based decision-making processes in 5G UAV security scenarios.

关键词： Reliability Classification algorithms Autonomous aerial vehicles Accuracy 5G mobile communication Uncertainty Security Jamming Measurement filtering algorithms Unmanned aerial vehicle deep neural networks machine learning uncertainty reliability jamming identification eXtreme gradient boosting (XGB) classifier 5G 6G

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Optimal Subband Adaptive Filter Over Functional Link Neural Network: algorithms and Applications

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS 2024年

作者： Ye, Jianhong Yu, Yi Chen, Badong Zheng, Zongsheng Chen, Jie Southwest Univ Sci & Technol Sch Informat Engn Robot Technol Used Special Environm Key Lab Sichua Mianyang 621010 Peoples R China Hydrogen Energy & Multienergy Complementary Microg Mianyang 621000 Peoples R China Xi An Jiao Tong Univ Inst Artificial Intelligence & Robot Xian 710049 Peoples R China Sichuan Univ Sch Elect Engn Chengdu 610065 Peoples R China Northwestern Polytech Univ Sch Marine Sci & Technol Xian 710072 Peoples R China

Compared with the functional link neural network (FLNN) algorithm, the delayless multi-sampled multiband-structured subband FLNN (DMSFLNN) algorithm provides fast convergence when encountering highly auto-correlated input signals, but there is a compromise between convergence and steady-state performances. Therefore, in order to overcome this flaw, we develop an optimal DMSFLNN (ODMSFLNN) algorithm by minimizing the mean square deviation of the weight vector with respect to the subband gain vectors. Interestingly, a vectorized version is also proposed for the ODMSFLNN algorithm, which aims at reducing computational complexity. Additionally, this paper also presents a stability analysis of this algorithm. Then, considering the impulsive noise environment, we develop two robust variants of ODMSFLNN that are the R-ODMSFLNN-I and R-ODMSFLNN-II algorithms, which are based on the specified robust function and the energy constraint of the weight update increment, respectively. Finally, to resolve that the DMSFLNN algorithm may not exploit cross-terms of input samples in nonlinear active noise control scenarios, we further propose the subband second-order Volterra filter (SSOVF) framework in an analogy way and apply the R-ODMSFLNN-II learning principle to obtain the robust optimal SSOVF algorithm. Simulations in several nonlinear scenarios have shown that the proposed algorithms perform better than their competitors.

关键词： Adaptive filters Vectors filtering algorithms Convergence Steady-state Stability analysis Nonlinear systems IIR filters Gaussian noise Finite impulse response filters Acoustic echo cancellation active noise control functional link neural network impulsive noises subband adaptive filter

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Learning Non-target Knowledge for Few-shot Semantic Segmentation

Learning Non-target Knowledge for Few-shot Semantic Segmenta...

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IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Liu, Yuanwei Liu, Nian Cao, Qinglong Yao, Xiwen Han, Junwei Shao, Ling Northwestern Polytech Univ Xian Peoples R China Incept Inst Artificial Intelligence Abu Dhabi U Arab Emirates Terminus Grp Beijing Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Existing studies in few-shot semantic segmentation only focus on mining the target object information, however, often are hard to tell ambiguous regions, especially in nontarget regions, which include background (BG) and Distracting Objects (DOs). To alleviate this problem, we propose a novel framework, namely Non-Target Region Eliminating (NTRE) network, to explicitly mine and eliminate BG and DO regions in the query. First, a BG Mining Module (BGMM) is proposed to extract the BG region via learning a general BG prototype. To this end, we design a BG loss to supervise the learning of BGMM only using the known target object segmentation ground truth. Then, a BG Eliminating Module and a DO Eliminating Module are proposed to successively filter out the BG and DO information from the query feature, based on which we can obtain a BG and DO free target object segmentation result. Furthermore, we propose a prototypical contrastive learning algorithm to improve the model ability of distinguishing the target object from DOs. Extensive experiments on both PASCAL-5(i) and COCO-20(i) datasets show that our approach is effective despite its simplicity. Code is available at https://***/LIUYANWEI98/NERTNet

关键词： Computer vision Codes Shape Semantics Prototypes Object segmentation filtering algorithms

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Visibility Informed Bernoulli Filter for Target Tracking in Cluttered Environments 25

Visibility Informed Bernoulli Filter for Target Tracking in ...

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25th International Conference of Information Fusion (FUSION)

作者： Glover, Timothy J. Liu, Cunjia Chen, Wen-Hua Loughborough Univ Dept Aeronaut & Automot Engn Loughborough LE11 3TU Leics England

ISBN: (数字)9781737749721

ISBN: (纸本)9781665489416

Incorporating prior environmental information to the diversely applied field of target tracking is becoming more beneficial in order to leverage maximum sensing performance. Urban scenarios in particular present numerous large obstacles that will block the field of view of most sensors along with many potential sources of measurement clutter. Handling these complications appropriately is imperative to ensure improved target tracking performance. This paper presents a computationally efficient method of integrating visibility information within the Bernoulli particle filter. Through estimation of target visibility with ray casting, the probability of target detection, birth density and spatial target density are modified. Numerical results demonstrate significantly more gradual degradation in target state estimation performance and improved estimation of target existence in the occlusion situation. Faster tracking recovery when emerging from occluded regions is also demonstrated.

关键词： Target tracking Urban areas filtering algorithms Sensor fusion Observers Information filters Particle measurements

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Clustering Plotted Data by Image Segmentation

Clustering Plotted Data by Image Segmentation

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IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Naous, Tarek Sarkar, Srinjay Abid, Abubakar Zou, James Amer Univ Beirut Beirut Lebanon VinAI Res Hanoi Vietnam Hugging Face New York NY USA Stanford Univ Stanford CA 94305 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Clustering is a popular approach to detecting patterns in unlabeled data. Existing clustering methods typically treat samples in a dataset as points in a metric space and compute distances to group together similar points. In this paper, we present a different way of clustering points in 2-dimensional space, inspired by how humans cluster data: by training neural networks to perform instance segmentation on plotted data. Our approach, Visual Clustering, has several advantages over traditional clustering algorithms: it is much faster than most existing clustering algorithms (making it suitable for very large datasets), it agrees strongly with human intuition for clusters, and it is by default hyperparameter free (although additional steps with hyperparameters can be introduced for more control of the algorithm). We describe the method and compare it to ten other clustering methods on synthetic data to illustrate its advantages and disadvantages. We then demonstrate how our approach can be extended to higher-dimensional data and illustrate its performance on real-world data. Our implementation of Visual Clustering is publicly available as a python package that can be installed and used on any dataset in a few lines of code(1). A demo on synthetic datasets is provided(2).

关键词： Training Visualization Image segmentation Road accidents Clustering methods Clustering algorithms filtering algorithms

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Efficient Reinforcement of Bipartite Networks at Billion Scale 38

Efficient Reinforcement of Bipartite Networks at Billion Sca...

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38th IEEE International Conference on Data Engineering (ICDE)

作者： He, Yizhang Wang, Kai Zhang, Wenjie Lin, Xuemin Zhang, Ying Univ New South Wales Sydney NSW Australia Shanghai Jiao Tong Univ Shanghai Peoples R China Univ Technol Sydney Sydney NSW Australia

ISBN: (数字)9781665408837

ISBN: (纸本)9781665408837

Bipartite networks, which model relationships between two different types of entities, are prevalent in many real-world applications. On bipartite networks, the cascading node departure undermines the networks' ability to provide sustainable services, which makes reinforcing bipartite networks a vital problem. Although network reinforcement is extensively studied on unipartite networks, it remains largely unexplored on bipartite graphs. On bipartite networks, (alpha, beta)-core is a stable structure that ensures different minimum engagement levels of the vertices from different layers, and we aim to reinforce bipartite networks by maximizing the (alpha, beta)-core. Specifically, given a bipartite network G, degree constraints alpha and beta, budgets b(1) and b(2), we aim to find b(1) upper layer vertices and b(2) lower layer vertices as anchors and bring them into the (alpha, beta)-core s.t. the number of non-anchor vertices entering in the (alpha, beta)-core is maximized. We prove the problem is NP-hard and propose a heuristic algorithm FILVER to solve the problem. FILVER runs b(1)+b(2) iterations and choose the best anchor in each iteration. Under a filter-verification framework, it reduces the pool of candidate anchors (in the filter stage) and computes the resulting (alpha, beta)-core for each anchor vertex more efficiently (in the verification stage). In addition, filter-stage optimizations are proposed to further reduce "dominated" anchors and allow computation-sharing across iterations. To optimize the verification stage, we explore the cumulative effect of placing multiple anchors, which effectively reduces the number of running iterations. Extensive experiments on 18 real-world datasets and a billion-scale synthetic dataset validate the effectiveness and efficiency of our proposed techniques.

关键词： Heuristic algorithms Conferences filtering algorithms Data engineering Computational efficiency Bipartite graph Optimization

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A Single Default Discrimination Model Based on the Selection of Multiple Single Models

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IEEE ACCESS 2024年 12卷 166870-166884页

作者： Mandour, Mohamed Abdelaziz Chi, Guotai Amran, Gehad Abdullah Alsalman, Hussain Mansoura Univ Fac Commerce Dept Business Adm Mansoura 35516 Egypt Dalian Univ Technol Sch Econ & Management Dept Business Adm Dalian 116024 Peoples R China Dalian Univ Technol Sch Econ & Management Dept Management Sci & Engn Dalian 116024 Peoples R China King Saud Univ Coll Comp & Informat Sci Dept Comp Sci Riyadh 11543 Saudi Arabia

The banking industry and other financial institutions face the economic problem of deciding whether it is appropriate to provide a client with credit who later demonstrates to be a good risk. Credit risk assessment is more crucial than ever in light of the recent global economic collapse and the terrible circumstances associated with COVID-19. Banks must utilize their resources, which include knowledge about their customers, to decide who may borrow money and is likely to pay it back. Feature selection critically choosing the optimal features for credit default discrimination. Removing outliers or noisy data from training sets is an alternative approach to improving discrimination model performance. This paper using optimal features through chi square (CS)- recursive feature elimination cross validation (RFECV) and select the optimal companies through Local outlier factor (LOF) as preprocessing combination to build single Default Discrimination Model for Chinese listed companies' dataset. Our model effectiveness has been demonstrated through in-depth comparisons with the baseline models across two datasets. The findings are based on a combination of data from Chinese listed companies and robustness cross German credit dataset. Experimental results verify the proposed model ability to generate multiple high-performance for credit default discrimination.

关键词： Feature extraction Training Machine learning Nearest neighbor methods Information filters filtering algorithms Accuracy Predictive models Prediction algorithms Decision trees Credit default discrimination optimal features optimal companies single classifiers classification task

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Biological Eagle-Eye-Based Correlation Filter Learning for Fast UAV Tracking

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2024年 73卷 1页

作者： Wu, Tongyan Duan, Haibin Zeng, Zhigang Beihang Univ Sch Automat Sci & Elect Engn State Key Lab Virtual Real Technol & Syst Beijing 100083 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China

As a cutting-edge tracking algorithm for unmanned aerial vehicles (UAVs), discriminative correlation filter (DCF)-based trackers have evolved by embracing diverse feature extraction and computational methods. However, many DCF-based trackers struggle to adapt to background variations encountered during UAV flights due to the lack of image feature enhancement and velocity-dependent scale adjustment. The high-speed movement of UAVs requires trackers that can leverage fast computational capabilities to keep pace. Taking inspiration from the impressive hunting skills of eagles, this article introduces a DCF-based tracker called eagle-eye-based correlation filter (EECF). The EECF tracker utilizes a "dual fovea" mechanism for image preprocessing, enhancing the utilization of target information in candidate regions. By integrating color opponent and contrast adaptation into the feature extraction process, the EECF tracker emulates the perceptual characteristics of eagle vision, enabling robust target recognition in complex backgrounds. We introduce masking and regularization techniques based on the "lateral inhibition" mechanism observed in eagle visual pathways. The EECF tracker showcases exceptional computational speed and surpasses other trackers in tracking success rates. Extensive experiments carried out on four publicly available UAV tracking benchmarks and custom drone datasets confirm the superiority of our tracker over other DCF-based options. Our algorithm achieves a maximum tracking speed of 86 FPS while maintaining higher accuracy than most comparative algorithms.

关键词： Target tracking Image color analysis Feature extraction Autonomous aerial vehicles filtering algorithms Tracking Correlation Bionic perception correlation filter dynamic tracking eagle eye unmanned aerial vehicle (UAV)

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A Frequency-Domain Recursive Least-Squares Adaptive filtering Algorithm Based On A Kronecker Product Decomposition

A Frequency-Domain Recursive Least-Squares Adaptive Filterin...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Hongsen He Jingdong Chen Jacob Benesty Yi Yu School of Information Engineering Southwest University of Science and Technology Mianyang China CIAIC and Shaanxi Provincial Key Laboratory of Artificial Intelligence Northwestern Polytechnical University Xi’an China INRS-EMT University of Quebec Montreal Canada

This paper proposes a frequency-domain recursive least-squares (RLS) adaptive filtering algorithm for identifying time-varying acoustic systems in noisy environments. The Kronecker product (KP) is employed to decompose the model filter of the acoustic channel impulse response into two sets of short sub-filters, based on which a generalized frequency-domain signal model and the associated cost function are established. A KP based RLS algorithm is subsequently deduced. In comparison with the conventional frequency-domain RLS adaptive filter, the presented algorithm is not only computationally more efficient, but also has a faster convergence rate for the identification of acoustic systems regardless of whether the excitation is a white sequence or a speech signal.

关键词： Adaptation models Adaptive systems Frequency-domain analysis Signal processing algorithms Adaptive filters filtering algorithms Cost function

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