Adaptive Window Research Articles

The team orienteering problem with variable time windows

AbstractThe orienteering problem is well‐established within the domain of operational research. This paper introduces the team orienteering problem with variable time windows (TOPVTW) as a new variant of the team orienteering problem with time windows. The distinguishing feature of the TOPVTW is the adaptive time windows, which evolve dynamically as a consequence of the solution. This problem arises within the context of spread processes that must be contained. This propagation dictates the time windows, which should be observed by the teams. In turn, these teams must take actions influencing the spread and, consequently, the time windows. The time windows can affect both the nodes and the paths. Thus, two distinct versions of the problem are delineated, each supported by dedicated mixed integer programming models. An application is illustrated optimizing resources for wildfire suppression. This work serves as an initial stride toward further exploration and analysis within this domain.

A Novel Beam-Domain Direction-of-Arrival Tracking Algorithm for an Underwater Target

Underwater direction-of-arrival (DOA) tracking using a hydrophone array is an important research subject in passive sonar signal processing. In this study, a DOA tracking algorithm based on a novel beam-domain signal processing technique is proposed to ensure robust DOA tracking of an interested underwater target under a low signal-to-noise ratio (SNR) environment. Firstly, the beam-based observation is designed and proposed, which innovatively applies beamforming after array-based observation to achieve specific spatial directivity. Next, the proportional–integral–differential (PID)-optimized Olen–Campton beamforming method (PIDBF) is designed and proposed in the beamforming process to achieve faster and more stable sidelobe control performance to enhance the SNR of the target. The adaptive dynamic beam window is designed and proposed to focusing the observation on more likely observation area. Then, by utilizing the extended Kalman filter (EKF) tracking framework, a novel PIDBF-optimized beam-domain DOA tracking algorithm (PIDBF-EKF) is proposed. Finally, simulations with different SNR scenarios and comprehensive analyses are made to verify the superior performance of the proposed DOA tracking approach.

Q‐Learning Based Adaptive Kalman Filtering With Adaptive Window Length

ABSTRACTIn this article, we propose an adaptive Kalman filtering with adaptive window length based on Q‐learning for dynamic systems with unknown model information. The iteration step length of the Q‐function is quantitatively adjusted through the influence function. The adaptive Kalman filtering algorithm is used to set an appropriate weight matrix for the Q‐function to estimate unknown model parameters. One numerical example and a practice‐oriented case are given to illustrate the effectiveness of the proposed method. It is shown that this filtering can provide state estimates of best accuracy among all the compared methods when the model mismatch and noise statistical characteristics change.

Online Evaluation for the POI-Level Inertial Support to the Grid via Ambient Measurements

As renewable energy sources like wind and solar power increasingly replace traditional energy sources and are integrated into the power grid, the issue of insufficient system inertia is becoming more apparent. This paper presents an online adaptive time window inertia constant identification method based on ambient measurements to identify the equivalent inertia constant of the time-varying inertia at Point of Interface (POI) level. The proposed method takes advantage of the online inertia estimation and the data-driven equivalent inertia constant identification techniques to simultaneously achieve online tracking and accuracy. With this regard, this paper first describes the inertia providers in modern system. Then, based on the frequency and power data measured by the Phasor Measurement Unit (PMU), this paper provides an improved data-driven equivalent inertia constant identification method. Subsequently, the paper proposes an ambient data smoothing method to cope with the numerical errors and provides, as a byproduct, an adaptive time window inertia constant identification. The adaptive time window is designed to enhance the accuracy of the method. Finally, the feasibility and accuracy of the proposed method of tracking synthetic inertia are validated by the simulation tests based on a grid in northwest China with high renewable energy penetration and a Virtual Power Plant (VPP). The experimental results show that the accuracy of this method is within 5%.

An accurate state-of-charge estimation of lithium-ion batteries based on improved particle swarm optimization-adaptive square root cubature kalman filter

The state of charge (SOC) of lithium-ion batteries (LIBs) is regarded as the fundamental parameter of the battery management system (BMS). In this paper, a parameter optimization method for mobile estimation windows based on particle swarm optimization-adaptive square root cubature Kalman filter (PSO-ASRCKF) is established to improve the SOC estimation ability and accuracy of LIBs. The filtering algorithm parameters are optimized to achieve high-precision SOC estimation. An improved ASRCKF with the PSO algorithm to optimize the moving estimation window is constructed to obtain the best adaptive window value. Different temperatures and initial SOC values are used to verify the proposed method under dynamic stress test (DST) and other conditions. The results show that the relative error is mainly distributed within 0.5 % when the SOC is stable. In addition, robustness and adaptability are verified with the root mean square error (RMSE) and the mean absolute error (MAE) values of 0.0019 and 0.0017, respectively, under the DST working condition. The experimental results show that the proposed method can achieve accurate SOC estimation under different temperatures, operating conditions, and initial SOC values.

Electrochemical Switching of Electromagnetism by Hierarchical Disorder Tailored Atomic Scale Polarization.

High-frequency electronic response governs a broad spectrum of electromagnetic applications from radiation protection, and signal compatibility, to energy recovery. Despite various efforts to manage electric conductivity, dynamic control over dielectric polarization for real-time electromagnetic modulation remains a notable challenge. Herein, an electrochemical lithiation-driven hierarchical disordering strategy is demonstrated for actively modulating electromagnetic properties. The controllable formation and diffusion of coherent interfaces and cation vacancies tailor the coupling of atomic electric field and thus the locally polarized domains, which leads to the reversible electromagnetic transparency/absorption switching with a tunable range of -0.8--20.4 dB for the reflection loss and a broad operation bandwidth of 4.6 GHz. Compared to traditional methods of heteroatomic doping, hydrogenation, mechanical deformation, and phase transition, the electrochemical strategy shows a larger regulation scope of dielectric permittivity with the maximum increase ratios of 260% and 1950% for real and imaginary parts, respectively. This enables the construction of various device architectures including the adaptive window and pixelated metasurface. The results offer opportunities to achieve intelligent electromagnetic devices and pave an avenue to rejuvenate various electromagnetic functions of semiconductive oxides.

Tunnel full-space deformation measurement based on improved downsampling and registration approach by point clouds

To fully leverage the volume measurement advantage of three-dimensional laser scanning technology in tunnel deformation monitoring, a full-space deformation analysis is proposed. A Box Improved Moving Average (BIMA) method featured by the adaptive window size downsampling is proposed which aims to achieve varying levels of downsampling in different densities regions. the combination of Maximal Cliques (MAC) and Iterative Closest Point (ICP) is utilized to achieve accurate registration. Point normal information is utilized to determine the correspondence to calculate deformation which shows an average accuracy of 7 % higher than the ICP by a convergence measurement test. The deformation convergence accuracy can go beyond 76 % when the point cloud reserved rate is approximately 10 %. Comparison between the method proposed in this study and the traditional point cloud cross sectional fitting method shows that the discrepancy between these two methods is approximately within 5 mm, with a minimum value of only 0.15 mm.

Comparative Approach to De-Noising TEMPEST Video Frames

Analysis of unintended compromising emissions from Video Display Units (VDUs) is an important topic in research communities. This paper examines the feasibility of recovering the information displayed on the monitor from reconstructed video frames. The study holds particular significance for our understanding of security vulnerabilities associated with the electromagnetic radiation of digital displays. Considering the amount of noise that reconstructed TEMPEST video frames have, the work in this paper focuses on two different approaches to de-noising images for efficient optical character recognition. First, an Adaptive Wiener Filter (AWF) with adaptive window size implemented in the spatial domain was tested, and then a Convolutional Neural Network (CNN) with an encoder–decoder structure that follows both classical auto-encoder model architecture and U-Net architecture (auto-encoder with skip connections). These two techniques resulted in an improvement of more than two times on the Structural Similarity Index Metric (SSIM) for AWF and up to four times for the SSIM for the Deep Learning (DL) approach. In addition, to validate the results, the possibility of text recovery from processed noisy frames was studied using a state-of-the-art Tesseract Optical Character Recognition (OCR) engine. The present work aims to bring to attention the security importance of this topic and the non-negligible character of VDU information leakages.

Dynamic Optimal Obstacle Avoidance Control of AUV Formation Based on MLoTFWA Algorithm

In addressing the optimal formation obstacle avoidance control problem for Autonomous Underwater Vehicles (AUVs) in environments with unknown and moving obstacles, this paper employs the Modified Fireworks Algorithm based on a Loser Elimination Mechanism (MLoTFWA) and constructs a Distributed Model Predictive Control (DMPC) framework to achieve obstacle avoidance for AUV formations. Initially, a prediction model is established, followed by feedback compensation to mitigate the effects of unknown perturbations. An appropriate fitness function is then formulated, and enhancements such as the loser elimination rule are introduced to optimize the fireworks algorithm. Additionally, the concept of an adaptive DMPC prediction window is proposed to conserve resources. The local and global stability of the DMPC formation control framework is theoretically proven. Simulations verify that the control system based on the DMPC framework ensures safe obstacle avoidance for the formation, maintains formation consistency, and achieves the shortest and smoothest path. The improved fireworks algorithm demonstrates superior performance compared with the original fireworks algorithm and other optimization algorithms. In testing, the improved fireworks algorithm exhibits better adaptability, higher average fitness, and best fitness, along with a significantly faster convergence speed. Compared with the ordinary fireworks algorithm, the convergence speed is reduced by 30%.

Reconciled estimation of Antarctic ice sheet mass balance and contribution to global sea level change from 1996 to 2021

AbstractThe Antarctic Ice Sheet (AIS) has been losing ice mass and contributing to global sea level rise (GSLR). Given its mass that is enough to cause ∼58 m of GSLR, accurate estimation of mass balance trend is critical for AIS mass loss monitoring and sea level rise forecasting. Here, we present an improved approach to reconciled solutions of mass balance in AIS and its regions from multiple contributing solutions using the input-out, altimetric, and gravimetric methods. In comparison to previous methods, such as IMBIE 2018, this approach utilizes an adaptive data aggregation window to handle the heterogeneity of the contributing solutions, including the number of solutions, temporal distributions, uncertainties, and estimation techniques. We improved the regression-based method by using a two-step procedure that establishes ensembled solutions within each method (input-output, altimetry, or gravimetry) and then estimates the method-independent reconciled solutions. For the first time, 16 contributing solutions from 8 Chinese institutions are used to estimate the reconciled mass balance of AIS and its regions from 1996 to 2021. Our results show that AIS has lost a total ice mass of ∼3213±253 Gt during the period, an equivalent of ∼8.9±0.7 mm of GSLR. There is a sustained mass loss acceleration since 2006, from 88.1±3.6 Gt yr−1 during 1996–2005 to 130.7±8.4 Gt yr−1 during 2006–2013 and further to 157.0±9.0 Gt yr−1 during 2014–2021. The mass loss signal in the West Antarctica and Antarctic Peninsula is dominant and clearly presented in the reconciled estimation and contributing solutions, regardless of estimation methods used and fluctuation of surface mass balance. Uncertainty and challenges remain in mass balance estimation in East Antarctica. This reconciled estimation approach can be extended and applied for improved mass balance estimation in the Greenland Ice Sheet and mountain glacier regions.

Analysis of human ambulation as a chaotic time-series: with nonlinear dynamics tools.

The aim of the present study is to investigate the complexity and stability of human ambulation and the implications on robotic prostheses control systems. Fourteen healthy individuals participate in two experiments, the first group run at three different speeds. The second group ascended and descended stairs of a five-level building block at a self-selected speed. All participants completed the experiment with seven inertial measurement units wrapped around the lower body segments and waist. The data were analyzed to determine the fractal dimension, spectral entropy, and the Lyapunov exponent (LyE). Two methods were used to calculate the long-term LyE, first LyE calculated using the full size of data sets. And the embedding dimensions were calculated using Average Mutual Information (AMI) and the False Nearest Neighbor (FNN) algorithm was used to find the time delay. Besides, a second approach was developed to find long-term LyE where the time delay was based on the average period of the gait cycle using adaptive event-based window. The average values of spectral entropy are 0.538 and 0.575 for stairs ambulation and running, respectively. The degree of uncertainty and complexity increases with the ambulation speed. The short term LyEs for tibia orientation have the minimum range of variation when it comes to stairs ascent and descent. Using two-way analysis of variance we demonstrated the effect of the ambulation speed and type of ambulation on spectral entropy. Moreover, it was shown that the fractal dimension only changed significantly with ambulation speed.

Speed-Aware Audio-Driven Speech Animation using Adaptive Windows

We present a novel method that can generate realistic speech animations of a 3D face from audio using multiple adaptive windows. In contrast to previous studies that use a fixed size audio window, our method accepts an adaptive audio window as input, reflecting the audio speaking rate to use consistent phonemic information. Our system consists of three parts. First, the speaking rate is estimated from the input audio using a neural network trained in a self-supervised manner. Second, the appropriate window size that encloses the audio features is predicted adaptively based on the estimated speaking rate. Another key element lies in the use of multiple audio windows of different sizes as input to the animation generator: a small window to concentrate on detailed information and a large window to consider broad phonemic information near the center frame. Finally, the speech animation is generated from the multiple adaptive audio windows. Our method can generate realistic speech animations from in-the-wild audios at any speaking rate, i.e., fast raps, slow songs, as well as normal speech. We demonstrate via extensive quantitative and qualitative evaluations including a user study that our method outperforms state-of-the-art approaches.

Transformer and Adaptive Threshold Sliding Window for Improving Violence Detection in Videos.

This paper presents a comprehensive approach to detect violent events in videos by combining CrimeNet, a Vision Transformer (ViT) model with structured neural learning and adversarial regularization, with an adaptive threshold sliding window model based on the Transformer architecture. CrimeNet demonstrates exceptional performance on all datasets (XD-Violence, UCF-Crime, NTU-CCTV Fights, UBI-Fights, Real Life Violence Situations, MediEval, RWF-2000, Hockey Fights, Violent Flows, Surveillance Camera Fights, and Movies Fight), achieving high AUC ROC and AUC PR values (up to 99% and 100%, respectively). However, the generalization of CrimeNet to cross-dataset experiments posed some problems, resulting in a 20-30% decrease in performance, for instance, training in UCF-Crime and testing in XD-Violence resulted in 70.20% in AUC ROC. The sliding window model with adaptive thresholding effectively solves these problems by automatically adjusting the violence detection threshold, resulting in a substantial improvement in detection accuracy. By applying the sliding window model as post-processing to CrimeNet results, we were able to improve detection accuracy by 10% to 15% in cross-dataset experiments. Future lines of research include improving generalization, addressing data imbalance, exploring multimodal representations, testing in real-world applications, and extending the approach to complex human interactions.

A study on an improved laser speckle contrast blood flow imaging methodology

The laser speckle contrast imaging technique based on the dynamic light scattering theory presents a non-scanning and wide-field method for blood flow imaging. However, its accuracy in biological tissues is limited to the decreased contrast and reduced image clarity as conventional single-exposure approaches are susceptible to static scattering. In this paper, based on the adaptive window space direction contrast (awsdK) imaging method proposed by the laboratory, combined with the optimized single exposure technology, the effect of static scattering under a single exposure is reduced. The experimental results show that the method can effectively correct static scattering and eliminate the effect of system noise on speckle contrast. This method not only improves the imaging quality, but also realizes the rapid monitoring of blood flow changes by using the speckle contrast ratio measured in a single exposure, which provides an effective solution for the further development of laser speckle contrast imaging technology.

A Modified EMD Technique for Broken Rotor Bar Fault Detection in Induction Machines.

Induction machines (IMs) are commonly used in various industrial sectors. It is essential to recognize IM defects at their earliest stage so as to prevent machine performance degradation and improve production quality and safety. This work will focus on IM broken rotor bar (BRB) fault detection, as BRB fault could generate extra heating, vibration, acoustic noise, or even sparks in IMs. In this paper, a modified empirical mode decomposition (EMD) technique, or MEMD, is proposed for BRB fault detection using motor current signature analysis. A smart sensor-based data acquisition (DAQ) system is developed by our research team and is used to collect current signals wirelessly. The MEMD takes several processing steps. Firstly, correlation-based EMD analysis is undertaken to select the most representative intrinsic mode function (IMF). Secondly, an adaptive window function is suggested for spectral operation and analysis to detect the BRB fault. Thirdly, a new reference function is proposed to generate the fault index for fault severity diagnosis analytically. The effectiveness of the proposed MEMD technique is verified experimentally.

CoWPE: Adaptive Context Window Adjustment in LLMs for Complex Input Queries

CoWPE: Adaptive Context Window Adjustment in LLMs for Complex Input Queries

Stereo Matching Method with Cost Volume Collaborative Filtering

Aiming at the problem of matching ambiguity and low disparity accuracy at the object boundary in stereo matching, a novel stereo matching algorithm with cost volume collaborative filtering is proposed. Firstly, for each pixel, two support windows are built, namely a local cross- support window as well as a global support window for the whole image. Secondly, a new adaptive weighted guide filter with a cross-support window as a kernel window is derived, and it is used to locally filter the cost volume. In addition, a minimum spanning tree is constructed in the whole image window, and then the minimum spanning tree filter is used to globally filter the cost volume. The collaborative filtering of cost volume is realized by fusing the filtering results of the local filter and global filter, so that each pixel can not only receive the support of the neighboring pixels in the local adaptive window, but can also receive the effective support of other pixels in the whole image, thus effectively eliminating the matching ambiguity in different texture regions while maintaining the disparity edges. The experimental results show that the average matching error rate of our method on the Middlebury stereo images is 3.17%. Compared with the other state-of-the-art methods, our method has higher robustness and matching accuracy, the generated disparity maps are smoother, and the disparity edges are better preserved.

A Novel Adaptive Random Access Window (RAW) Slot Adjustment Model for IoT Network Using an Integrated Scheme

The RAW (Restricted Access Window) component of the IoT network is deployed to reduce traffic and channel contention in dense and heterogeneous sensor network environment. It divides sensor nodes into groups and slots, allowing channel access only to one RAW slot at a time. Several algorithms and improved channel utilization optimization models have been proposed to optimize the RAW parameters, to ensure a contention free network or at least, minimally reduce it. These techniques often rely on previous traffic demands schedules, collision analysis and send/receive matrices to accurately predict the future of stations’ interactions in an IoT environment. Thus systematically adjusting its operations to reduce contention among the stations and the Access Point(AP), thereby ensuring a flexible transmission even in a dense network environment. This paper critically investigated, analysed and proposed a novel approach to RAW size adjustment. The new approach will invoke data mining instruments and optimisation algorithms to improve performance of stations in an IoT environment and thereafter simulated (with respect to some specified QoS parameters) to ascertain it’s (output) performance index with the already existing models

DEEP NEURAL MULTI WAVELET SEGMENTATION AND RESNET-50 IMAGE CLASSIFICATION (DNMWS-RESNET-50IC) BASED EARLY GLAUCOMA DETECTION

Glaucoma, a leading cause of irreversible blindness worldwide, results from progressive damage to the optic nerve, often linked to elevated Intra Ocular Pressure (IOP). Early detection is critical for preventing vision loss, yet traditional diagnostic methods can be limited in accessibility and effectiveness, particularly in the early stages of the disease. Addressing the need for early detection, we propose the Deep Neural Multi-Wavelet Segmentation and ResNet-50 Image Classification (DNMWS-ResNet-50IC) method, specifically designed to detect glaucoma at an early stage using retinal fundus images. In this work, we apply Anisotropic Gaussian Filtering for the preprocessing of retinal fundus images, effectively reducing image noise while preserving the original quality of the image pixels by creating an adaptive window size and scale space. We then utilize multi- wavelet-based image segmentation, leveraging wavelet transforms to analyze and decompose the image into its various frequency components. This technique is particularly advantageous for managing images with complex structures and textures. Subsequently, the segmented features are classified using the ResNet-50 model, which categorizes the images as normal, abnormal, or indicative of early- stage glaucoma. The effectiveness of the proposed method is assessed by measuring three key performance indicators sensitivity, specificity, and accuracy on digital retinal images from the HRF image database. Additionally, the model's performance is further evaluated on a separate test set, considering metrics such as accuracy, precision, recall, and prediction time.

A fast adaptive spatio-temporal fusion method to enhanced Fit-FC.

Space-time fusion is an economical and efficient way to solve "space-time contradiction". Among all kinds of space-time fusion methods, Fit-FC space-time fusion method based on weight Function is widely used. However, this method is based on the linear model to depict the phase change, but the phase change in the real scene is complicated, and the linear model is difficult to accurately capture the phase change, resulting in the spectral distortion of the fusion image. In addition, pixel-by-pixel scanning with moving Windows leads to inefficiency issues, limiting its use in large-scale and long-term tasks. To overcome these limitations, this paper developed a simple and fast adaptive remote sensing image Spatio-Temporal fusion method based on Fit-FC, called Adapt Lasso-Fit-FC (AL-FF). Firstly, the sparse characteristics of time phase change between images are explored, and a time phase change estimation model based on sparse regression is constructed, which overcomes the fuzzy problem of fusion image caused by the failure of linear regression to capture complex nonlinear time phase transition in the weighted Function method, making the algorithm better at capturing details. Secondly, an adaptive window selection Function is established to overcome the problem of manually setting parameters on different data sets, improve the convenience of the algorithm and robustness of the application on different data sets, and make the algorithm simpler and more efficient. Finally, the improved AL-FF algorithm is compared with other algorithms to verify the performance improvement. Compared with the current advanced Spatio-Temporal fusion methods, AL-FF algorithm has stronger detail capture ability and can generate more accurate fusion results. In addition, the computational efficiency is significantly improved, and the efficiency is increased by more than 20 times compared with the current mainstream method.