Adaptive Window Research Articles

An adaptive dynamic window approach for UUV obstacle avoidance planning in 3D environments

Unmanned Underwater Vehicles (UUVs) are essential equipment for Marine development, widely used in Marine scientific research, Marine resource survey, and Marine security. The autonomous navigation planning ability in unknown environments is a critical indicator for UUV intelligence. This paper focuses on the particularity of UUV motion and the complexity of the underwater environment and proposes an adaptive Dynamic Window Approach (DWA) for UUV obstacle avoidance planning. The adaptive DWA introduces novel heading angle evaluation and adaptive dynamic strategies to solve the problems of poor adaptability of traditional DWA to complex environments and unreasonable path selection in encountering dynamic obstacles and approaching targets. Simulations verify the effectiveness and superiority of the proposed method.

Cross-scale Vision Transformer for crowd localization

Crowd localization can provide the positions of individuals and the total number of people, which has great application value for security monitoring and public management, meanwhile it meets the challenges of lighting, occlusion and perspective effect. In recent times, Transformer has been applied in crowd localization to overcome these challenges. Yet such kind of methods only consider to integrate the multi-scale information once, which results in incomplete multi-scale information fusion. In this paper, we propose a novel Transformer network named Cross-scale Vision Transformer (CsViT) for crowd localization, which simultaneously fuses multi-scale information during both the encoder and decoder stages and meanwhile building the long-range context dependencies on the combined feature maps. To this end, we design the multi-scale encoder to fuse the feature maps of multiple scales at corresponding positions so as to obtain the combined feature maps, and meanwhile design the multi-scale decoder to integrate the tokens at multiple scales when modeling the long-range context dependencies. Furthermore, we propose Multi-scale SSIM (MsSSIM) loss to adaptively compute head regions and optimize the similarity at multiple scales. Specifically, we set the adaptive windows with different scales for each head and compute the loss values within these windows so as to enhance the accuracy of the predicted distance transform map. We perform comprehensive experiments on five public datasets, and the results obtained validate the effectiveness of our method.

Impact analysis of real and virtual concept drifts on the predictive performance of classifiers

In the domain of machine learning and predictive analytics, classifiers hold significant importance as they are instrumental in extracting valuable patterns and enabling precise predictions from data. However, data distributions in streams are rarely stable in real-world circumstances and may evolve over time. Concept drifts, or dynamic changes in the data, have a major impact on how well classifiers predict outcomes. Furthermore, virtual concept drifts have become equally essential when dealing with the development of virtual environments and synthetic data production tools. This study employs a range of conventional classifiers, and ensemble models to examine the effects of real and virtual concept drifts on the predictive performance of classifiers. In order to ensure the conclusions are applicable to a wide range of domains, the study will be undertaken on a variety of datasets. The generation of incremental data streams from historical records with drifting patterns will replicate real concept drifts. Synthetic data will be incorporated into the training and testing sets to introduce virtual concept drifts. The classifier models are then assessed using the benchmark metrics for performance on these datasets. Comparative evaluations are conducted to evaluate the classifiers’ robustness and adaptability in the face of real and virtual concept drifts. The results of this study will shed light on the performance of various classifiers and models in dynamic data environments while offering helpful insights to businesses for developing machine learning applications that are more resilient. The research examined the impact of real and virtual concept drifts on classifier performance, using three models: Streaming Random Patches (SRP), Adaptive Windowing (ADWIN), and Streaming Random Patches with Adaptive Windowing (SRP-ADWIN). The study found that the SRP-ADWIN ensemble model outperformed SRP and ADWIN individually, effectively adapting to changing data distributions, and showed improved predictive accuracy of 98.63% for virtual drift and 86.46% for real drift.

Design of Large Deformable Lung Image Registration Network with Adaptive Window

Design of Large Deformable Lung Image Registration Network with Adaptive Window

Extraction of Ground Photon for Photon Counting Lidar Over Rugged Mountainous Area Based on Wavelet Transform With Adaptive Window

Extraction of Ground Photon for Photon Counting Lidar Over Rugged Mountainous Area Based on Wavelet Transform With Adaptive Window

Adaptive Local Maximum Synchrosqueezing Transform via Adaptive Window With Time-Varying Function and Time- Varying Searching Region

Adaptive Local Maximum Synchrosqueezing Transform via Adaptive Window With Time-Varying Function and Time- Varying Searching Region

Dynamic Weight Cost Aggregation Algorithm for Stereo Matching Based on Adaptive Window

Dynamic Weight Cost Aggregation Algorithm for Stereo Matching Based on Adaptive Window

Breast Cancer Diagnosis Using Virtualization and Extreme Learning Algorithm Based on Deep Feed Forward Networks

One of the leading causes of death for women worldwide is breast cancer. Early detection and prompt treatment can reduce the risk of breast cancer-related death. Cloud computing and machine learning are crucial for disease diagnosis today, but they are especially important for those who live in distant places with poor access to healthcare. While machine learning-based diagnosis tools act as primary readers and aid radiologists in correctly diagnosing diseases, cloud-based technology can also assist remote diagnostics and telemedicine services. The promise of techniques based on Artificial Neural Networks (ANN) for sickness diagnosis has attracted the attention of several re-searchers. The 4 methods for the proposed research include preprocessing, feature extraction, and classification. A Smart Window Vestige Deletion (SWVD) technique is initially suggested for preprocessing. It consists of Savitzky-Golay (S-G) smoothing, updated 2-stage filtering, and adaptive time window division. This technique separates each channel into multiple time periods by adaptively pre-analyzing its specificity. On each window, an altered 2-stage filtering process is then used to retrieve some tumor information. After applying S-G smoothing and integrating the broken time sequences, the process is complete. In order to deliver effective feature extraction, the Deep Residual based Multiclass for architecture (DRMFA) is used. In histological photos, identify characteristics at the cellular and tissue levels in both tiny and large size patches. Finally, a fresh customized strategy that combines a better crow forage-ELM. Deep learning and the Extreme Learning Machine (ELM) are concepts that have been developed (ACF-ELM). When it comes to diagnosing ailments, the cloud-based ELM performs similarly to certain cutting-edge technology. The cloud-based ELM approach beats alternative solutions, according to the DDSM and INbreast dataset results. Significant experimental results show that the accuracy for data inputs is 0.9845, the precision is 0.96, the recall is 0.94, and the F1 score is 0.95.

Vehicle Heading Enhancement Based on Adaptive Sliding Window Factor Graph Optimization for Gyroscope/Magnetometer

Vehicle Heading Enhancement Based on Adaptive Sliding Window Factor Graph Optimization for Gyroscope/Magnetometer

An Incremental Naive Bayes Learner for Real-time Health Prediction

Healthcare monitoring systems have improved with the Internet of Things and machine learning prediction models. Traditional batch machine-learning approaches cannot generate an effective model since most data will be continuous and real-time. Real-time medical data processing is challenging since the entire data is unavailable during prediction. Here, a continuous model adaptation based on incremental learning is demanded. The development of such a system can significantly improve patient outcomes and reduce healthcare costs. This paper proposes an Incremental Naive Bayes Learner that can handle concept drifts in data. The algorithm keeps a sliding window of data constantly updated as new data is added. The adaptive window size determines how much data is used to train the model at any given time. After processing each chunk of data, the algorithm calculates the model’s accuracy. The system detects a concept drift and dynamically updates the training set if the accuracy drops below a predefined threshold. We conducted a comparative evaluation of state-of-the-art batch and incremental learning algorithms on different medical datasets, demonstrating the impact of incremental learning. The results demonstrate the effectiveness of our approach: the Agrawal dataset achieved the highest accuracy at 66.6%, followed by Dialysis at 61.6%, while Liver and HCC achieved 50% and 58.3% accuracy, respectively. This approach ensures a sustained high level of accuracy in healthcare monitoring systems over time, even amidst concept drift.

Broken Rotor Bars Detection in Inverter-Fed Induction Motors Under Continuous Switching of Different Speed Modes

Broken rotor bars (BRBs) fault detection of inverter-fed induction motors operating in transient regime is difficult, and even more challenging under continuous switching of constant speed, acceleration, and deceleration modes (CADM). In this condition, the BRBs caused amplitude-modulation of fundamental harmonic can be used as a fault indicator. However, the amplitude and frequency of the fundamental harmonic vary widely in continuous switching of CADM, which causes tremendous difficulty in the fault feature extraction. In this paper, a novel approach called Adaptive Window Short-time Esprit (AWSTE) is developed to overcome the described problem. It can track the fundamental frequency and extract the envelope of fundamental harmonic under continuous switching of CADM. Experimental results demonstrate the validity of the proposed approach at different control modes, different load levels, and different noise levels.

AWV-MOS-LIO: Adaptive Window Visibility based Moving Object Segmentation with LiDAR Inertial Odometry

AWV-MOS-LIO: Adaptive Window Visibility based Moving Object Segmentation with LiDAR Inertial Odometry

Machine Learning‐Based Distraction‐Free Method for Measuring the Optical Displacement of Long‐Span Bridge Structures

A photogrammetric displacement measurement method based on machine learning was proposed to improve the robustness to environmental disturbances. (1) To reduce the target positioning error caused by environmental vibration especially atmospheric turbulence, a machine learning‐based weighted location algorithm combined with an adaptive window selection strategy was developed. In an outdoor displacement table experiment, the proposed method’s root mean squared error (RMSE) is 0.04 mm when the distance is 50 m, showing better accuracy and stability. (2) To complement/correct the missing or anomalous data caused by adverse external conditions, such as severe occlusion or camera shaking, a fast data self‐diagnosis using a correlation vector machine was performed to make full use of the full‐field measurement results obtained from the images. The applicability of the proposed method in extreme cases was demonstrated in designed experiments and an actual displacement measurement task of a long‐span bridge subjected to vortex‐induced vibration.

On Hierarchical Multi-UAV Dubins Traveling Salesman Problem Paths in a Complex Obstacle Environment.

This article aims to solve a hierarchical multi-UAV Dubins traveling salesman problem (HMDTSP). Optimal hierarchical coverage and multi-UAV collaboration are achieved by the proposed approaches in a 3-D complex obstacle environment. A multi-UAV multilayer projection clustering (MMPC) algorithm is presented to reduce the cumulative distance from multilayer targets to corresponding cluster centers. A straight-line flight judgment (SFJ) was developed to reduce the calculation of obstacle avoidance. An improved adaptive window probabilistic roadmap (AWPRM) algorithm is addressed to plan obstacle-avoidance paths. The AWPRM improves the feasibility of finding the optimal sequence based on the proposed SFJ compared with a traditional probabilistic roadmap. To solve the solution to TSP with obstacles constraints, the proposed sequencing-bundling-bridging (SBB) framework combines the bundling ant colony system (BACS) and homotopic AWPRM. An obstacle-avoidance optimal curved path is constructed with a turning radius constraint based on the Dubins method and followed up by solving the TSP sequence. The results of simulation experiments indicated that the proposed strategies can provide a set of feasible solutions for HMDTSPs in a complex obstacle environment.

Enhancing Autofocus in Non-Mydriatic Fundus Photography: A Fast and Robust Approach with Adaptive Window and Path-Optimized Search

Non-mydriatic fundus photography (NMFP) plays a vital role in diagnosing eye diseases, with its performance primarily dependent on the autofocus process. However, even minor maloperations or eye micro-movements can compromise fundus imaging quality, leading to autofocus inaccuracy and a heightened risk of misdiagnosis. To enhance the autofocus performance in NMFP, a fast and robust fundus autofocus method with adaptive window and path-optimized search is proposed. In this method, the adaptive focus window is used to suppress irrelevant image contents and correct the sharpness curve, and the path-optimized search is constructed to overcome the curve’s local extrema, in order to achieve rapid focus position convergence. This method was simulated and clinically studied with the self-developed autofocus system for NMFP. The results of 80 cases of human eye imaging show that, compared with similar autofocus methods, this method achieves a focus success rate of 90% with the least axial scanning, and can adapt to non-ideal imaging conditions such as pupil misalignment, eyelash occlusion, and nystagmus.

Stereo matching algorithm based on improved census transform and minimum spanning tree cost aggregation

Existing stereo matching algorithms suffer from issues such as susceptibility to distortion, weak noise resistance, and a high rate of mismatches in regions with weak textures and discontinuous disparities. To address these challenges, this paper proposes a stereo matching algorithm based on an improved census transform and minimum spanning tree (MST) cost aggregation. In the cost calculation phase, we employ a Gaussian-weighted transformation window and incorporate gradient and edge information to perform weighted fusion of the results. In the cost aggregation process, we introduce a collaborative adaptive window. Each pixel acquires information from the support window of the guided filter (GF) and other pixels within the MST. Furthermore, we integrate the SLIC superpixel segmentation algorithm into MST construction. These two components work synergistically to assign appropriate adaptive weights to pixels, facilitating coordinated cost volume aggregation. Different optimization methods are applied to address mismatched points of various types in post-disparity processing.Performance evaluation using the Middlebury dataset and KITTI dataset demonstrates that our proposed algorithm not only enhances matching accuracy in regions with discontinuous disparities and weak textures but also exhibits significantly improved robustness to interference. Additionally, the resulting disparity map displays smoother edges.

A novel time-frequency analysis method for fault diagnosis based on generalized S-transform and synchroextracting transform

Rotating machinery usually operates under variable-speed conditions, and how to effectively handle nonstationary signal in fault diagnosis is a critical task. The time-frequency analysis (TFA) method is widely used in the processing of nonstationary signal. To improve the time-frequency resolution and clearly identify instantaneous frequency (IF) characteristics, the adaptive generalized S-synchroextracting transform (AGSSET), which is a novel TFA method proposed in this paper. Firstly, a new transform named adaptive generalized S-transform (AGST) is put forward by optimizing the window function of generalized S transform. In this paper, an adaptive window function optimization method based on the frequency spectrum of the vibration signal is introduced, and the energy concentration measure is used to determine the window function’s parameters in AGST. Simultaneously, the synchrony extraction idea is incorporated into the AGST, then the AGSSET is derived. To address more complex IF characteristics, the synchronous extraction operator (SEO) is reconstructed. In the simulation experiment, the GMLC signal model is selected to represent nonstationary signal and to verify the effectiveness of the proposed method. In addition, bearing fault data is also used for fault diagnosis experiments. The results of both numerical simulation and experimental analysis indicate that AGSSET performs well in identifying the time-varying IF characteristic in nonstationary signals. It can also efficiently detect faults with high accuracy and strong stability.

Decision-Change Informed Rejection Improves Robustness in Pattern Recognition-Based Myoelectric Control.

Post-processing techniques have been shown to improve the quality of the decision stream generated by classifiers used in pattern-recognition-based myoelectric control. However, these techniques have largely been tested individually and on well-behaved, stationary data, failing to fully evaluate their trade-offs between smoothing and latency during dynamic use. Correspondingly, in this work, we survey and compare 8 different post-processing and decision stream improvement schemes in the context of continuous and dynamic class transitions: majority vote, Bayesian fusion, onset locking, outlier detection, confidence-based rejection, confidence scaling, prior adjustment, and adaptive windowing. We then propose two new temporally aware post-processing schemes that use changes in the decision and confidence streams to better reject uncertain decisions. Our decision-change informed rejection (DCIR) approach outperforms existing schemes during both steady-state and transitions based on error rates and decision stream volatility whether using conventional or deep classifiers. These results suggest that added robustness can be gained by appropriately leveraging temporal context in myoelectric control.

Enhancement based convolutional dictionary network with adaptive window for low-dose CT denoising.

Recently, one promising approach to suppress noise/artifacts in low-dose CT (LDCT) images is the CNN-based approach, which learns the mapping function from LDCT to normal-dose CT (NDCT). However, most CNN-based methods are purely data-driven, thus lacking sufficient interpretability and often losing details. To solve this problem, we propose a deep convolutional dictionary learning method for LDCT denoising, in which a novel convolutional dictionary learning model with adaptive window (CDL-AW) is designed, and a corresponding enhancement-based convolutional dictionary learning network (called ECDAW-Net) is constructed to unfold the CDL-AW model iteratively using the proximal gradient descent technique. In detail, the adaptive window-constrained convolutional dictionary atom is proposed to alleviate spectrum leakage caused by data truncation during convolution. Furthermore, in the ECDAW-Net, a multi-scale edge extraction module that consists of LoG and Sobel convolution layers is proposed in the unfolding iteration, to supplement lost textures and details. Additionally, to further improve the detail retention ability, the ECDAW-Net is trained by the compound loss function of the pixel-level MSE loss and the proposed patch-level loss, which can assist to retain richer structural information. Applying ECDAW-Net to the Mayo dataset, we obtained the highest peak signal-to-noise ratio (33.94) and sub-optimal structural similarity (0.92). Compared with some state-of-art methods, the interpretable ECDAW-Net performs well in suppressing noise/artifacts and preserving textures of tissue.

Abnormal Data Detection Based on Adaptive Sliding Window and Weighted Multiscale Local Outlier Factor for Machinery Health Monitoring

Identifying abnormal data to improve data quality is of great importance for machinery health monitoring (MHM). Existing abnormal data detection methods generally depend on appropriate parameter settings and prior knowledge of data distribution, which result in relatively low adaptability to MHM data. To obtain more reliable MHM results, this paper proposed a novel method to detect abnormal data. First, the concept of adaptive sliding window (ASW) is defined and the advantages of ASW in avoiding data leakage and data redundancy are derived. The ASW can optimally divide the overall data into several segments. Next, statistical factors in time- and frequency-domain are extracted from these segments to generate corresponding research objects. Then, an improved weighted multiscale local outlier factor (WMLOF) algorithm is proposed herein to evaluate the data anomaly degree of each object. The WMLOF has the ability to assess and fuse the local outlier factor characteristics at multiple scales, and eventually yields a more comprehensive WMLOF value to evaluate the anomaly degree of the MHM data. Finally, the effectiveness and superiority of the ASW and WMLOF are validated by a synthetic simulation of a faulty rolling bearing and two engineering projects pertaining to a railway vehicle gearbox, and bench test data. Comparative analysis shows that the proposed abnormal data detection method based on the ASW and WMLOF strategies outperforms five reported algorithms in outlier detection.