Adjustment Algorithm Research Articles

Creating adapted environments: enhancing accessibility in virtual reality for upper limb rehabilitation through automated element adjustment

In the last decade, Virtual Reality (VR) has emerged as a promising tool for upper limb rehabilitation, effectively complementing conventional therapies. However, one of the main challenges lies in designing virtual environments that adapt to the specific needs of each patient, considering their unique motor limitations. An inadequately adapted environment can result in overexertion and the inability to perform exercises, negatively affecting both the patient’s motivation and their recovery. This article hypothesizes that automatic calibration and dynamic object adjustment algorithms in virtual reality environments improve accessibility and efficiency in upper limb rehabilitation exercises for patients with SCI. For this purpose, we present an innovative calibration method that individually identifies and maps motor limitations on the left and right sides of the body. As a result, an irregular volume, formed by the interconnection of three elliptical shapes, is generated that envelops the patient and represents their safe range of movements. Furthermore, a second method is introduced that automatically readjusts the location of objects within the virtual environment to the safe space generated, optimizing the patient’s accessibility and interaction with therapy elements. To test the results, an immersive VR environment was designed in which the aforementioned methods were applied for the automatic placement of virtual elements in the peripersonal space (PPS) of the participants. Testing has been carried out at the Hospital Nacional de Parapléjicos in Toledo (HNPT) with patients suffering from spinal cord injuries (SCI) and healthy participants who are SCI specialists. The quantitative results obtained demonstrate that this dynamic adjustment of the environment allows for adaptation that leads to a 100% success rate in task completion after the automatic adjustment, compared to a 62.5% success rate when using a configuration with virtual elements adapted to the motor capabilities of a healthy person (for both healthy participants and patients). This adjustment not only facilitates a greater number of exercise repetitions, but also reduces the time needed to access each object, with an average reduction in time of 47.94% across the entire sample. This reduction is even more significant when considering only the group of SCI patients, with a reduction of 53.78%. Additionally, the qualitative evaluation complements the study with a perception of ease of use for the calibration (mean = 1.29 ± 0.46) and low complexity in accessing the interactive objects after the automatic adjustment (mean = 1.12 ± 0.45). These results demonstrate the effectiveness of the proposed algorithms and the improved user experience.

State of Charge Estimation Based on Improved AILS-CSO Parameter Identification Algorithm

Abstract This paper introduces an adaptive inertial local search-cat-swarm optimization algorithm. The identification of parameters and estimation of charge state for lithium power batteries under diverse operational conditions were undertaken. In this approach, the equivalent circuit model of lithium power batteries is constructed using a second-order RC equivalent circuit model. Then, the cat swarm optimization algorithm is augmented with an adaptive adjustment algorithm for the inertial weight, thereby enhancing the accuracy of the identified battery parameters. Subsequently, the estimated voltage is then compared with the actual voltage in order to validate the feasibility of the algorithm. The root mean square error (RMSE) is less than 0.56%, and the maximum absolute error is 0.116 V. In conclusion, the state of charge of lithium batteries is estimated online under disparate working conditions via the integration of the extended Kalman filter algorithm, and a comparison is conducted. The maximum error is less than 1.74%, and the RMSE is less than 0.5%, indicating that the algorithm exhibits superior stability and estimation accuracy compared to the traditional recursive least squares method.

Gradient Method with Step Adaptation

The paper solves the problem of constructing step adjustment algorithms for a gradient method based on the principle of the steepest descent. The expansion of the step adjustment principle, its formalization and parameterization led the researchers to gradient-type methods with incomplete relaxation or over-relaxation. Such methods require only the gradient of the function to be calculated at the iteration. Optimization of the parameters of the step adaptation algorithms enables us to obtain methods that significantly exceed the steepest descent method in terms of convergence rate. In this paper, we present a universal step adjustment algorithm that does not require selecting optimal parameters. The algorithm is based on orthogonality of successive gradients and replacing complete relaxation with some degree of incomplete relaxation or over-relaxation. Its convergence rate corresponds to algorithms with optimization of the step adaptation algorithm parameters. In our experiments, on average, the proposed algorithm outperforms the steepest descent method by 2.7 times in the number of iterations. The advantage of the proposed methods is their operability under interference conditions. Our paper presents examples of solving test problems in which the interference values are uniformly distributed vectors in a ball with a radius 8 times greater than the gradient norm.

The Effective Use by Primary Care Clinicians of a Comprehensive Computerized Insulin Dose Adjustment Algorithm.

Primary care clinicians (PCCs) manage 90% of patients with diabetes, 30% of whom require insulin with a substantial number poorly controlled because of the challenges that PCCs face (time constraints and lack of experience). The author has developed Federal Drug Administration cleared and Conformite Europeenne mark registered comprehensive computerized insulin dose adjustment algorithms (CIDAAs) to enable PCCs to significantly lower HbA1c levels in insulin-requiring patients. Reports sent to PCCs contain scatter plots of glucose readings, their organization into pre- and postprandial and before bedtime values, their analyses, and recommendations for insulin dose adjustments (if indicated) that the PCC can accept or modify. The glucose readings are provided to the CIDAAs for analysis at either in-person visits or remotely. The new doses accepted by PCCs serve as the basis for the subsequent report. Published studies evaluating this comprehensive CIDAA involved 104 poorly controlled patients taking insulin for greater than or equal to six months who were independently managed by PCCs. Over four to six months, initial HbA1c levels of 9.7% fell by 1.7%. Combining these results with 138 other better controlled patients in real-world situations, initial measured and estimated HbA1c levels of 8.3% fell by 0.7% in 6.4 months enabling PCCs to significantly improve glycemic control. Other advantages of PCCs utilizing these comprehensive CIDAAs are saving time for PCCs so that they can address non-diabetes issues and/or see other patients and ongoing PCC education in adjusting insulin doses by matching glucose patterns and dose-change recommendations with subsequent glycemic responses.

Reconstruction of High-Resolution Solar Spectral Irradiance Based on Residual Channel Attention Networks

The accurate measurement of high-resolution solar spectral irradiance (SSI) and its variations at the top of the atmosphere is crucial for solar physics, the Earth’s climate, and the in-orbit calibration of optical satellites. However, existing space-based solar spectral irradiance instruments achieve high-precision SSI measurements at the cost of spectral resolution, which falls short of meeting the requirements for identifying fine solar spectral features. Therefore, this paper proposes a new method for reconstructing high-resolution solar spectral irradiance based on a residual channel attention network. This method considers the stability of SSI spectral features and employs residual channel attention blocks to enhance the expression ability of key features, achieving the high-accuracy reconstruction of spectral features. Additionally, to address the issue of excessively large output features from the residual channel attention blocks, a scaling coefficient adjustment network block is introduced to achieve the high-accuracy reconstruction of spectral absolute values. Finally, the proposed method is validated using the measured SSI dataset from SCIAMACHY on Envisat-1 and the simulated dataset from TSIS-1 SIM. The validation results show that, compared to existing scaling coefficient adjustment algorithms, the proposed method achieves single-spectrum super-resolution reconstruction without relying on external data, with a Mean Absolute Percentage Error (MAPE) of 0.0302% for the reconstructed spectra based on the dataset. The proposed method achieves higher-resolution reconstruction results while ensuring the accuracy of SSI.

REDUCE—A Tool Supporting Inconsistencies Reduction in the Decision-Making Process

This paper presents REDUCE, a free online tool designed to support decision-making processes by addressing inconsistency in multiplicative pairwise comparison (PC) matrices, a key element of many multi-criteria decision-making (MCDM) methods, including the analytic hierarchy process (AHP). AHP relies on pairwise comparisons to assign weights to decision criteria or alternatives, but human-generated PC matrices often exhibit inconsistencies. Consistency is evaluated using Saaty’s consistency ratio (CR), where a value below 0.10 is considered acceptable. Higher inconsistency levels necessitate matrix corrections, which are challenging if the original expert is unavailable or revision constraints exist. REDUCE autonomously reduces inconsistency in PC matrices using two different algorithms that require no expert intervention. The tool accommodates different PC matrices, enabling users to specify the desired CR threshold (e.g., CR≤0.10) and select the algorithm for adjustment. It ensures the resulting matrix is consistent while preserving the original preference structure to the greatest extent possible. Additionally, REDUCE calculates weights for the compared entities, making it a valuable tool for applications of AHP and related methodologies. Quantitative evaluations demonstrate that REDUCE can improve matrices with high inconsistency (e.g., CR=0.25) to acceptable levels (e.g., CR=0.08) while retaining up to 95% of the original preference integrity, depending on the chosen algorithm. By addressing the accessibility gap for small and medium enterprises (SMEs) that lack resources for costly decision-making software or expert consultants, REDUCE facilitates broader adoption of MCDM tools. This work highlights the potential of REDUCE to enhance decision-making reliability and accessibility in resource-constrained environments.

Research on Omnidirectional Gait Switching and Attitude Control in Hexapod Robots.

To tackle the challenges of poor stability during real-time random gait switching and precise trajectory control for hexapod robots under limited stride and steering conditions, a novel real-time replanning gait switching control strategy based on an omnidirectional gait and fuzzy inference is proposed, along with an attitude control method based on the single-neuron adaptive proportional-integral-derivative (PID). To start, a kinematic model of a hexapod robot was developed through the Denavit-Hartenberg (D-H) kinematics analysis, linking joint movement parameters to the end foot's endpoint pose, which formed the foundation for designing various gaits, including omnidirectional and compound gaits. Incorporating an omnidirectional gait could effectively resolve the challenge of precise trajectory control for the hexapod robot under limited stride and steering conditions. Next, a real-time replanning gait switching strategy based on an omnidirectional gait and fuzzy inference was introduced to tackle the issue of significant impacts and low stability encountered during gait transitions. Finally, in view of further enhancing the stability of the hexapod robot, an attitude adjustment algorithm based on the single-neuron adaptive PID was presented. Extensive experiments confirmed the effectiveness of this approach. The results show that our approach enabled the robot to switch gaits seamlessly in real time, effectively addressing the challenge of precise trajectory control under limited stride and steering conditions; moreover, it significantly improved the hexapod robot's dynamic stability during its motion, enabling it to adapt to complex and changing environments.

Multi-branch convolutional neural network in building polygonization using remote sensing images

AbstractBuilding extraction and polygonization is important for urban studies, such as urbanization monitoring, urban planning. Remote sensing images, especially in RGB bands, provide sufficient semantic information which is useful for the task of building extraction and polygonization. Deep learning using Convolutional Neural Networks (CNNs) is proven to be successful in many fields, including building extraction from remote sensing images. In this paper, we propose a two-stage method to solve the task of building polygonization from remote sensing images based on deep learning. Firstly, we decompose a 2‑D building footprint model into three basic geometry primitives. Leveraging stacked Multi-Branch Modules (MBMs), we separate the task of building extraction into tasks of predicting the three geometry primitives using our proposed CNN. At the second stage, we propose an efficient enhanced building polygonization and adjustment algorithm to generate the final building polygons. This algorithm is able to handle both building blocks and individual buildings. We evaluate our model on three open datasets. For building blocks, our model achieved average precision of 62.7% and average recall of 73.6% on the CrowdAI mapping challenge dataset, and 13.9% and 24.4% respectively on the Urban Building Classification (UBC) dataset which contains mainly individual buildings. On the Inria aerial image dataset, the proposed method achieved Intersection over Union (IoU) over 71%.

Multi-output discrete grey model tailored for electricity consumption forecast

To address the limitation of many conventional grey forecasting methods that prioritize temporal dynamics analysis while often overlooking the essential spatial characteristics, we developed a multi-output discrete grey model tailored for forecasting electricity consumption in China's Yangtze River Delta, incorporating spatial effects. Specifically, we design a dynamic spatial interaction matrix to precisely capture the spatial correlations among neighboring areas, and integrate multiple sets of fixed-effect parameters to adeptly address spatial heterogeneity. Additionally, we implement regularization technique to prevent overfitting and utilize the Bayesian Optimization algorithm for hyper-parameter adjustment, considerably enhancing the model's stability of parameter estimation and its resilience to noise. Finally, a comprehensive case study, benchmarked against seven other models, highlights our model's outstanding multi-step predictive accuracy and robustness against input data uncertainty, exceeding the performance of existing methods.

Metaheuristic Algorithm and Laser Projection for Adjusting the Model of the Last Lower Surface to a Footprint.

Nowadays, metaheuristic algorithms have been applied to optimize last lower-surface models. Also, the last lower-surface model has been adjusted through the computational algorithms to perform custom shoe lasts. Therefore, it is necessary to implement nature-inspired metaheuristic algorithms to perform the adjustment of last lower-surface model to the footprint topography. In this study, a metaheuristic genetic algorithm is implemented to adjust the last lower surface model to the footprint topography. The genetic algorithm is constructed through an objective function, which is defined through the last lower Bezier model and footprint topography, where a mean error function moves the last lower surface toward the footprint topography through the initial population. Also, the search space is deduced from the last lower surface and footprint topography. In this way, the genetic algorithm performs explorations and exploitations to optimize a Bezier surface model, which generates the adjusted last lower surface, where the surface is recovered via laser line scanning. Thus, the metaheuristic algorithm enhances the last lower-surface adjustment to improve the custom last manufacture. This contribution is elucidated by a discussion based on the proposed metaheuristic algorithm for surface model adjustment and the optimization methods implemented in recent years.

Adjustment algorithm of damper openings of tangentially fired boilers based on secondary air distribution

In the design of tangentially fired boilers, because of the limitations of the occupied area and on-site space, the structural relationship between the windbox of the secondary air pipe and the burner nozzle is the main pipe and the parallel branch pipe. The distribution of secondary air flow in each layer of the burner nozzle is not uniform when the boiler is running at peak-shaving operation. Therefore, after the distribution method of secondary air flow is determined, the adjust of air flow through the precise adjustment of secondary air damper opening is necessary. An adjustment model of secondary air damper opening under a hot condition is established through the effective coupling of the Newton–Raphson descent iteration method and a three-dimensional numerical simulation method. For different secondary air distribution objectives, precise adjustment of secondary air damper opening can be achieved through a few times of numerical simulation calculation using this model. Given the secondary air pipe of a 350 MWe supercritical tangentially fired boiler as the research object and two secondary air distribution objectives as examples, the feasibility and accuracy of the model are verified by realizing precise adjustment of damper openings.

Improving Real-Time Trend Estimates Using Local Parametrization of Polynomial Regression Filters

This paper examines and compares real-time estimates of the trend-cycle component using moving averages constructed with local polynomial regression. It enables the reproduction of Henderson’s symmetric and Musgrave’s asymmetric filters used in the X-13ARIMA-SEATS seasonal adjustment algorithm. This paper proposes two extensions of local polynomial filters for real-time trend-cycle estimates: first including a timeliness criterion to minimize the phase shift; second with procedure for parametrizing asymmetric filters locally while they are generally parametrized globally, which can be suboptimal around turning points. An empirical comparison, based on simulated and real data, shows that modeling polynomial trends that are too complex introduces more revisions without reducing the phase shift, and that local parametrization reduces the delay in detecting turning points and reduces revisions. The results are reproducible and all the methods can be easily applied using the R package rjd3filters.

Speed-Adaptive PI Collection and PP Adjustment in Indoor VLC Network

In this paper, a novel adjustment algorithm of the position-predicted period (PP) in an indoor visible light communication (VLC) network is proposed. The algorithm is adaptive to the movement speed. It contains two key parts: speed-adaptive position information (PI) collection and speed-adaptive PP adjustment. By the user’s mobile characteristics, speed-adaptive PI collection is realized to lift prediction accuracy. By distribution characteristics of received power, speed-adaptive PP adjustment is achieved to avoid unnecessary predictions. By the access point (AP) selection, based on position prediction and the PP adjustment algorithm adaptive to movement speed, the user’s transmission quality under different movement speeds can be improved. Finally, by simulation, the effectiveness of this algorithm is demonstrated.

A Novel Fractional High-Order Sliding Mode Control for Enhanced Bioreactor Performance

This research introduces a fractional high-order sliding mode control (FHOSMC) method that utilises an inverse integral fractional order, 0<β<1, as the high order on the FHOSMC reaching law, exhibiting a novel contribution in the related field of study. The application of the proposed approach into a bioreactor system via diffeomorphism operations demonstrates a notable improvement in the management of the bioreactor dynamics versus classic controllers. The numerical findings highlight an improved precision in tracking reference signals and an enhanced plant stability compared to proportional–integral–derivative (PID) controller implementations within challenging disturbance scenarios. The FHOSMC effectively maintains the biomass concentration at desired levels, reducing the wear of the system as well as implementation expenses. Furthermore, the theoretical analysis of the convergence within time indicates substantial potential for further enhancements. Subsequent studies might focus on extending this control approach to bioreactor systems that integrate sensor technologies and the formulation of adaptive algorithms for real-time adjustments of β-type fractional-orders.

Nonlinear terminal sliding mode trajectory tracking control algorithm for industrial robots based on dung beetle optimizer

Abstract To enhance the precision of trajectory tracking control in industrial robots, a nonlinear end-to-end sliding mode trajectory tracking control algorithm for industrial robots based on the dung beetle algorithm is proposed. By employing nonlinear terminal sliding mode control to resolve the issue of inadequate trajectory tracking control performance in industrial robots, and integrating the dung beetle optimizer (DBO) algorithm for control parameter adjustment, significant enhancements are achieved in trajectory tracking accuracy.

Addressing Constraint Coupling and Autonomous Decision-Making Challenges: An Analysis of Large-Scale UAV Trajectory-Planning Techniques

With the increase in UAV scale and mission diversity, trajectory planning systems faces more and more complex constraints, which are often conflicting and strongly coupled, placing higher demands on the real-time and response capabilities of the system. At the same time, conflicts and strong coupling pose challenges the autonomous decision-making capability of the system, affecting the accuracy and efficiency of the planning system in complex environments. However, recent research advances addressing these issues have not been fully summarized. An in-depth exploration of constraint handling techniques and autonomous decision-making issues will be of great significance to the development of large-scale UAV systems. Therefore, this paper aims to provide a comprehensive overview of this topic. Firstly, the functions and application scenarios of large-scale UAV trajectory planning are introduced and classified in detail according to the planning method, realization function and the presence or absence of constraints. Then, the constraint handling techniques are described in detail, focusing on the priority ranking of constraints and the principles of their fusion and transformation methods. Then, the importance of autonomous decision-making in large-scale UAV trajectory planning is described in depth, and related dynamic adjustment algorithms are introduced. Finally, the future research directions and challenges of large-scale UAV trajectory planning are outlooked, providing directions and references for future research in the fields of UAV clustering and UAV cooperative flight.

Graph-Based Bidirectional Transformer Decision Threshold Adjustment Algorithm for Class-Imbalanced Molecular Data

Data sets with imbalanced class sizes, where one class size is much smaller than that of others, occur exceedingly often in many applications, including those with biological foundations, such as disease diagnosis and drug discovery. Therefore, it is extremely important to be able to identify data elements of classes of various sizes, as a failure to do so can result in heavy costs. Nonetheless, many data classification procedures do not perform well on imbalanced data sets as they often fail to detect elements belonging to underrepresented classes. In this work, we propose the BTDT-MBO algorithm, incorporating Merriman–Bence–Osher (MBO) approaches and a bidirectional transformer, as well as distance correlation and decision threshold adjustments, for data classification tasks on highly imbalanced molecular data sets, where the sizes of the classes vary greatly. The proposed technique not only integrates adjustments in the classification threshold for the MBO algorithm in order to help deal with the class imbalance, but also uses a bidirectional transformer procedure based on an attention mechanism for self-supervised learning. In addition, the model implements distance correlation as a weight function for the similarity graph-based framework on which the adjusted MBO algorithm operates. The proposed method is validated using six molecular data sets and compared to other related techniques. The computational experiments show that the proposed technique is superior to competing approaches even in the case of a high class imbalance ratio.

Multi-Robot Collaborative Mapping with Integrated Point-Line Features for Visual SLAM.

Simultaneous Localization and Mapping (SLAM) enables mobile robots to autonomously perform localization and mapping tasks in unknown environments. Despite significant progress achieved by visual SLAM systems in ideal conditions, relying solely on a single robot and point features for mapping in large-scale indoor environments with weak-texture structures can affect mapping efficiency and accuracy. Therefore, this paper proposes a multi-robot collaborative mapping method based on point-line fusion to address this issue. This method is designed for indoor environments with weak-texture structures for localization and mapping. The feature-extraction algorithm, which combines point and line features, supplements the existing environment point feature-extraction method by introducing a line feature-extraction step. This integration ensures the accuracy of visual odometry estimation in scenes with pronounced weak-texture structure features. For relatively large indoor scenes, a scene-recognition-based map-fusion method is proposed in this paper to enhance mapping efficiency. This method relies on visual bag of words to determine overlapping areas in the scene, while also proposing a keyframe-extraction method based on photogrammetry to improve the algorithm's robustness. By combining the Perspective-3-Point (P3P) algorithm and Bundle Adjustment (BA) algorithm, the relative pose-transformation relationships of multi-robots in overlapping scenes are resolved, and map fusion is performed based on these relative pose relationships. We evaluated our algorithm on public datasets and a mobile robot platform. The experimental results demonstrate that the proposed algorithm exhibits higher robustness and mapping accuracy. It shows significant effectiveness in handling mapping in scenarios with weak texture and structure, as well as in small-scale map fusion.

Hybrid deep learning with pelican optimization algorithm for M2M communication on UAV image classification

<p>Machine-to-machine (M2M) communication for unmanned aerial vehicle (UAVs) and image classification is essential to current remote sensing and data processing. UAVs and ground stations or other linked devices exchange information seamlessly using M2M communication. M2M connectivity helps UAVs with cameras and sensors communicate aerial pictures in real time or post-mission for image categorization and analysis. During flight, UAVs acquire massive volumes of picture data. Image classification, commonly using deep learning (DL) methods like convolutional neural network (CNN), automatically categorizes and annotates photos based on predetermined classes or attributes. This work uses UAV photos to produce hybrid deep learning with pelican optimization algorithm for M2M communication (HDLPOA-M2MC). HDLPOA-M2MC automates UAV picture class identification. GhostNet model is used to derive features in HDLPOA-M2MC. The HDLPOA-M2MC approach leverages pelican optimization algorithm (POA) for hyperparameter adjustment in this investigation. Finally, autoencoder-deep belief network (AE-DBN) model can classify. The HDLPOA-M2MC method’s enhanced outcomes were shown by several studies. The complete results showed that HDLPOA M2MC performed better across measures.</p>

Anti-Spectral Interference Waveform Design Based on High-Order Norm Optimized Autocorrelation Sidelobes Properties

This paper introduces a robust waveform design method aimed at reducing the impact of electromagnetic interference in radar systems, thereby enhancing target detection accuracy. We propose utilizing a high-order p-norm to characterize the peak sidelobe level (PSL) of the waveform. Additionally, the method incorporates spectral zero-trapping within known interfering frequency bands to mitigate interference effects. A unified optimization objective function is developed to ensure optimal correlation properties of waveforms for dual-use in radar and communication systems. By employing the AdamW algorithm for dynamic adjustment of the iteration factor, combined with a gradient descent search, this method refines both the autocorrelation of the waveform and its resilience to known disturbances. Experimental results demonstrate that our approach significantly improves autocorrelation performance over randomly generated initial waveforms. Moreover, the introduction of spectral zero-trapping notably enhances interference suppression in targeted frequency bands, thereby boosting overall signal performance. Our method effectively balances interference rejection with the minimization of sidelobe levels, offering a pragmatic waveform solution for complex radar environments.