Excess Error Research Articles

Adaptive Weighted Particle Swarm Optimization for Controlling Multiple Switched Reluctance Motors with Enhanced Deviatoric Coupling Control

Switched reluctance motors (SRMs) are widely used in industrial applications due to their advantages. Multi-motor synchronous control systems are crucial in modern industry, as their control strategies significantly impact synchronization performance. Traditional deviation coupling control structures face limitations during the startup phase, leading to excessive tracking errors and exacerbated by uneven load distribution, resulting in desynchronized motor acceleration and increased speed synchronization errors. This study proposes a modified deviation coupling control method based on an adaptive weighted particle swarm optimization (PSO) algorithm to enhance multi-motor synchronization performance. Traditional deviation coupling control applies equal reference torque inputs to each motor’s current loop, failing to address uneven load distribution and causing inconsistent accelerations. To resolve this, a gain equation based on speed deviation is introduced, incorporating self-tracking error and gain coefficients for dynamic synchronization error compensation. The gain coefficients are optimized using the adaptive weighted PSO algorithm to improve system adaptability. A simulation model of a synchronization control system for three SRMs was developed in the Matlab/Simulink R2023b environment. This model compares the synchronization performance of traditional deviation coupling, Fuzzy-PID improved structure, and adaptive PSO improved structure during motor startup, sudden speed increases, and load disturbances. The validated deviation coupling control structure achieved the initial set speed in approximately 0.236 s, demonstrating faster convergence and a 6.35% reduction in settling time. In both the motor startup and sudden speed increase phases, the two optimized methods outperformed the traditional structure in dynamic performance and synchronization accuracy, with the adaptive PSO structure improving synchronization accuracy by 54% and 37.17% over the Fuzzy-PID structure, respectively. Therefore, the PSO-optimized control system demonstrates faster convergence, improved stability, and enhanced synchronization performance.

Adaptive interacting multiple model particle filter based on a dual-pattern bat algorithm for maneuvering target tracking

The interacting multiple model particle filter (IMM-PF) is a filtering method commonly used for nonlinear non-Gaussian radar systems. Its transfer probabilities are usually fixed and dependent on prior information. When tracking maneuvering targets, the IMM-PF may cause excessive tracking errors due to the model switching lag. In addition, the particle impoverishment caused by the resampling of the particle filter seriously affects the filtering accuracy. Therefore, the IMM-PF has difficulty meeting the accuracy and speed requirements of modern high-performance radar target tracking systems. To address these problems, an adaptive interacting multiple model particle filter based on the dual-pattern bat algorithm (ADIMM-DPBA-PF) is proposed for tracking maneuvering targets under nonlinear and non-Gaussian conditions. First, the adaptive model switching mechanism is established to adjust the transition probabilities using the model probability posterior information at consecutive times, improving the model switching efficiency. Second, a particle filter based on the dual-pattern bat algorithm (DPBA-PF) is proposed. The filter exploits global search and local search strategies to optimize the particles and intelligently move the particles to the high likelihood region. Finally, a particle filter based on the dual-pattern bat algorithm is used to form the adaptive interacting multiple model method. The experimental results show that the proposed algorithm has better comprehensive performance than the IMM-PF.

Efficient sensitivity analysis of the thermal profile in powder bed fusion of metals using hypercomplex automatic differentiation finite element method

Rapid cyclic temperature fluctuation occurring in powder bed fusion of metals using a laser beam (PBF-LB/M) influences the formation of flaws in printed parts. Consequently, there is a pressing need to enhance the quality of printed parts by developing innovative methodologies that can predict thermal histories and help uncover the intricate relationships between process parameters and thermal profiles. Sensitivity Analysis (SA) emerges as an essential tool for this, offering the potential for process optimization and enhanced quality control. Nonetheless, conventional SA methodologies often incur in excessive computational costs and potential numerical approximation errors. To address this technical challenge, we present a novel method for SA that integrates the HYPercomplex-based Automatic Differentiation (HYPAD) technique with transient thermal simulations conducted via the finite element method (FEM). Leveraging this methodology, we efficiently and accurately perform SA for PBF-LB/M processes in a post-processing step. Compared to traditional methods like Finite Differences (FD), HYPAD-FEM required 96 % less computational time for obtaining sensitivities for 22 process parameters, under a comparative study conducted within the context of the 2018–02 AM benchmark of the National Institute of Standards and Technology. In summary, HYPAD-FEM offers superior efficiency and accuracy in SA over conventional methods, delivering the best sensitivity of a model without the need for step-size selection and problem or parameter-based implementations.

Exploring Stakeholders’ Perspectives on the Barriers to the Application of Cost-Reduction Techniques in Public Higher Educational Building Delivery

The assessment of barriers to the application of cost-reduction techniques in delivering educational buildings in Nigeria is essential in addressing the infrastructural shortage, building performance, delay, cost, time overrun, and abandonment in the delivery of higher educational buildings (HEBs). This study examines barriers to applying cost-reduction techniques in educational buildings in southwestern Nigeria. Using a survey design, the questionnaire was distributed to stakeholders who participated in delivering the government intervention, private donors, and internally generated revenue educational buildings in public tertiary institutions in southwestern Nigeria from 2012 to 2022. A total of 150 copies of the questionnaire were administered, while 133 responses were obtained and analyzed. To begin with, data reliability and validity were examined using Bartlett’s sphericity, Cronbach’s alpha, and Kaiser–Meyer–Olkin (KMO) tests, accordingly, followed by descriptive, Kruskal–Wallis H test, and exploratory factor analysis. The six components obtained from exploratory factor analysis for explaining the barriers to applying cost-reduction techniques in educational buildings were as follows: ambiguity in HEB contracts awards and project executions, lack of control from the HEIs management over HEB project delivery, perceived political influence in HEB procurement, unrealistic contract requirements and change orders, non-prioritization of automation integration in HEB delivery, and deficiencies in contract documents and costing. This study recommends establishing a project-monitoring team involving independent consultants from project inception to reduce excessive errors, practices of assigning contracts to the lowest bidder, and excessive claims for variation orders that escalate the project’s final sum.

Investigating the impact of asymmetric macular sensitivity on visual acuity chart reading in choroideremia.

Degeneration in choroideremia, unlike typical centripetal photoreceptor degenerations, is centred temporal to the fovea. Once the fovea is affected, the nasal visual field (temporal retina) is relatively spared, and the preferred retinal locus shifts temporally. Therefore, when reading left to right, only the right eye reads into a scotoma. We investigate how this unique property affects the ability to read an eye chart. Standard- and low-luminance visual acuity (VA) for right and left eyes were measured with the Early Treatment of Diabetic Retinopathy Study (ETDRS) chart. Letters in each line were labelled by column position. The numbers of letter errors for each position across the whole chart were summed to produce total column error scores for each participant. Macular sensitivity was assessed using microperimetry. Central sensitivity asymmetry was determined by the temporal-versus-nasal central macular difference and subsequently correlated to a weighted ETDRS column error score. Healthy volunteers and participants with X-linked retinitis pigmentosa GTPase regulator associated retinitis pigmentosa (RPGR-RP) were used as controls. Thirty-nine choroideremia participants (median age 44.9 years [IQR 35.7-53.5]), 23 RPGR-RP participants (median age 30.8 years [IQR 26.5-40.5]) and 35 healthy controls (median age 23.8 years [IQR 20.3-29.0]) were examined. In choroideremia, standard VA in the right eye showed significantly greater ETDRS column errors on the temporal side compared with the nasal side (p = 0.002). This significantly correlated with greater asymmetry in temporal-versus-nasal central macular sensitivity (p = 0.04). No significant patterns in ETDRS column errors or central macular sensitivity were seen in the choroideremia left eyes, nor in RPGR-RP and control eyes. Difficulty in tracking across lines during ETDRS VA testing may cause excess errors independent of true VA. VA assessment with single-letter optotype systems may be more suitable, particularly for patients with choroideremia, and potentially other retinal diseases with asymmetric central macular sensitivity or large central scotomas including geographic atrophy.

Application of drone technology in power grid engineering

Abstract With the dynamic adjustments in eco-control zones, spatial layouts can be further optimized, but this has brought some challenges to the planning and construction of power grid projects. Utilizing drones and related technologies, it is possible to conduct surveys of eco-control zones with clear boundaries and unclear boundaries, which is beneficial for early risk avoidance in power grid projects. The results indicate that the use of orthophoto and oblique photography can effectively measure the boundaries of control areas and classify the ecological types within the research area. Maximizing the use of drone surveying technology can effectively identify control area boundaries, but attention must be paid to the factors affecting drone operations to prevent excessive measurement errors and reduce the accuracy of control area identification.

THE IMPLEMENTATION OF PATIENT SAFETY GOALS FOR PATIENTS' SATISFACTION IN THE HEMODIALYSIS UNIT

Background: Surveys on patient safety in dialysis units uncover a range of significant patient safety issues. Hemodialysis centers are particularly vulnerable to adverse events due to a number of risk factors, such as machine malfunctions, excessive blood loss, patient falls, prescription errors, and inadequate infection control procedures. Aim: Analyze the problem of implementing patient safety goals and describe the patients' satisfaction with the implementation of patient safety goals. Methods: This study employs a concurrent embedded methodology with a mixed-methods design, utilizing quantitative data to complement the qualitative data. Applying the focus group discussion (FGD) technique, questionnaires and observations of hemodialysis patients' satisfaction with implementing patient safety goals were utilized to complete the data collection. Results: According to the patient satisfaction survey, two patients were worried that their dialyzer tubes had been mixed up, earning a negative score of 5.13%. 23.07% of patients had negative results on the infection prevention risk questionnaire; 3 patients (7.69%) only seldom cleaned their hands before starting dialysis, and 6 patients (15.38%) did not. Conclusion: The implementation of patients' identification and the reduction of infection risk through hand hygiene have not been carried out consistently, concerning patient safety goals in the hemodialysis unit. Keywords: hand hygiene, hemodialysis, patient safety goals, patients' satisfaction, patients' identification

Kinetic Langevin MCMC sampling without gradient Lipschitz continuity - the strongly convex case

In this article we consider sampling from log concave distributions in Hamiltonian setting, without assuming that the objective gradient is globally Lipschitz. We propose two algorithms based on monotone polygonal (tamed) Euler schemes, to sample from a target measure, and provide non-asymptotic 2-Wasserstein distance bounds between the law of the process of each algorithm and the target measure. Finally, we apply these results to bound the excess risk optimization error of the associated optimization problem.

Research on dynamic target steady and quasi-grasping method of coal gangue sorting robot based on global planning and local vision

ABSTRACT Using robots instead of manual sorting of coal gangue can significantly improve the sorting rate. However, the stable grasping of the target gangue by the manipulator is the key to the efficient sorting of robot. The coal gangue on the belt conveyor has the characteristics of high speed and high quality. In the process of grasping, the velocity difference between the end of the manipulator and the coal gangue will cause impact load. In addition, the belt speed fluctuation and deviation also lead to the inaccurate location of the gangue, resulting in excessive position error of the manipulator during grasping, leading to grasping failure. For this reason, a synchronous tracking trajectory planning method of manipulator based on global planning and local visual servo is proposed in this paper. In this method, the manipulator is controlled by the BPNG method to reach the top of the target gangue quickly, and the accurate location of the target gangue is completed by the extended FDSST tracking algorithm. The manipulator uses the “position-velocity-acceleration” three-loop PID square method to realize the synchronous tracking trajectory planning of the target gangue. The experimental results show that this method can achieve stable and accurate grasping of target gangue by manipulator.

Validation of MyFORTA: An Automated Tool to Improve Medications in Older People Based on the FORTA List.

Listing tools have been developed to improve medications in older patients, including the Fit fOR The Aged (FORTA) list, a clinically validated, positive-negative list of medication appropriateness. Here, we aim to validate MyFORTA, an automated tool for individualized application of the FORTA list. 331 participants of a multi-center cohort study (AgeCoDe) for whom the FORTA score (sum of overtreatment and undertreatment errors) had been determined manually (gold standard [GS]) were reassessed using the automated MyFORTA (MF) tool. This tool determines the score from ATC and ICD codes combined with clinical parameters. The FORTA scores were 9.01±2.91 (mean±SD, MF) versus 6.02±2.52 (GS) (p<0.00001). Removing undertreatment errors for calcium/vitamin D (controversial guidelines) and influenza/pneumococcal vaccinations (no robust information in the database), the difference decreased: 7.5±2.7 (MF) versus 5.98±2.55 (GS) (p<0.00001). The remaining difference was driven by, for example, missing nitro spray in coronary heart disease/acute coronary syndrome as the related information was rarely found in the database, but notoriously detected by MF. Three hundred and forty errors from those 100 patients with the largest score deviation accounted for 68% of excess errors by MF. MF was more sensitive to detect medication errors than GS, all frequent errors only detected by MF were plausible, and almost no adaptations of the MF algorithm seem indicated. This automated tool to check medication appropriateness according to the FORTA list is now validated and represents the first clinically directed algorithm in this context. It should ease the application of FORTA and help to implement the proven beneficial effects of FORTA on clinical endpoints.

A feature enhanced EEG compression model using asymmetric encoding–decoding network * *This work was supported by the National Natural Science Foundation of China (No. NSFC62227801) and the Lingang Laboratory, Grant No. LG-TKN-202205-01.

Objective. Recently, the demand for wearable devices using electroencephalography (EEG) has increased rapidly in many fields. Due to its volume and computation constraints, wearable devices usually compress and transmit EEG to external devices for analysis. However, current EEG compression algorithms are not tailor-made for wearable devices with limited computing and storage. Firstly, the huge amount of parameters makes it difficult to apply in wearable devices; secondly, it is tricky to learn EEG signals’ distribution law due to the low signal-to-noise ratio, which leads to excessive reconstruction error and suboptimal compression performance. Approach. Here, a feature enhanced asymmetric encoding–decoding network is proposed. EEG is encoded with a lightweight model, and subsequently decoded with a multi-level feature fusion network by extracting the encoded features deeply and reconstructing the signal through a two-branch structure. Main results. On public EEG datasets, motor imagery and event-related potentials, experimental results show that the proposed method has achieved the state of the art compression performance. In addition, the neural representation analysis and the classification performance of the reconstructed EEG signals also show that our method tends to retain more task-related information as the compression ratio increases and retains reliable discriminative information after EEG compression. Significance. This paper tailors an asymmetric EEG compression method for wearable devices that achieves state-of-the-art compression performance in a lightweight manner, paving the way for the application of EEG-based wearable devices.

Heat transfer error analysis of high-temperature wall temperature measurement using thermocouple

Thermocouples are widely employed as the temperature measuring element for an aero-engine. However, due to the unique characteristics of the aero-engine test environment, there exist significant temperature measurement errors of the thermocouple. The steady-state heat transfer error of the thermocouple can be attributed to convection heat transfer error, wire heat transfer error, and radiation heat transfer error. The flow and heat transfer process of wall temperature measurements using armored K-type thermocouples were simulated numerically by Fluent software at the Mach number of 0.1–0.5 and the temperature of 700∼1200 K. The results indicate that convective error accounts for more than 55 % of the total transfer error. To reduce excessive temperature measurement errors, a novel airflow stagnation cover structure around the thermocouple was presented. When the diameter of the stagnation cover is 2 mm, the error of the thermocouple is reduced to less than 1 %.

Iris Recognition based on Statistically Bound Spatial Domain Zero Crossing and Neural Networks

Purpose The iris pattern is an important biological feature of the human body. The recognition of an individual based on an iris pattern is gaining more popularity due to the uniqueness of the pattern among the people. Iris recognition systems have received attention very much due to their rich iris texture which gives robust standards for identifying individuals. Notwithstanding this, there are several challenges in unrestricted recognition environments. Methods This article discusses a highly error-resistant technique to implement a biometric recognition system based on the iris portion of the human eye. All iris recognition algorithms of the current day face a major problem of localization errors and the enormous time involved in this localization process. Spatial domain zero crossing may be the simplest and least complex method for localization. Yet, it has not been used due to its high sensitivity to erroneous edges, as a consequence of which more complex and time-consuming algorithms have taken its place. Appropriate statistical bounds imposed on this process help this method to be the least erroneous and time-consuming. Errors were reduced to 0.022% using this approach on the CASIA v1 &amp; v2 datasets. Time consumption in this stage was the least compared to other algorithms. At the comparison stage, most algorithms use multiple comparisons to account for translation and rotation errors. This is time-consuming and very resource-hungry. Results The current approach discusses a robust method based on a single comparison, which works with a correct recognition of over 99.78% which is clearly demonstrated by tests. Conclusions The technique is to use a neural network trained to recognize special statistical and regional parameters unique to every person’s iris. The algorithm also gives sufficient attention to consider illumination errors, elliptical pupils, excess eyelash errors and bad contrast.

Fixed-time trajectory tracking for autonomous surface vehicles via output feedback under various constraints

This paper proposes a novel fixed-time output feedback trajectory tracking control method suitable for autonomous surface vehicles (ASVs). Firstly, various complex uncertainty terms, such as unknown external disturbances, unmodeled errors, and actuator faults are considered in this paper. And these terms are uniformly converted into the total uncertainty terms through model transformation for convenience. Then, a fixed-time extended state observer (FTESO) with the newly expanded state is designed to observe and estimate the total uncertainty terms. Next, the saturation dynamic filter (SDF) is adopted to address the input saturation, and the saturation factors of SDF are incorporated into the trajectory error calculation to reduce the torque mutation caused by an excessive error in the control input. Finally, a fixed-time control law is proposed by taking into account the ASV system error and FTESO estimation state. Simulation experiments show that this method has excellent control performance, and this conclusion is validated through stability proof and case comparison.

Factoring 15 with a remote quantum computer: a complete guide for beginners

We present, for the non-specialist, an experimental implementation of Shor’s factoring algorithm. We are unaware of any other single reference that explains, in a beginner-friendly way, complete circuits that implement Shor’s algorithm. We perform the experiment with IBM quantum processors, which are remotely accessible online for free. We reproducibly factor 15 with one 7-qubit processor (ibm_perth), while four other quantum processors exhibit excessive error.

Assessing the usability of an immersive virtual reality grocery store in healthy controls

BackgroundImmersive virtual reality (IVR) as a research platform to study human behaviors is an emerging field and may be useful for studying self-care management, especially in the gap between formal healthcare recommendations and day-to-day living. Self-care activities, such as grocery shopping, can be challenging for people with chronic illness. We developed an IVR environment that simulates a real-life grocery store and conducted a usability study to demonstrate the safety and acceptability of IVR as an experimental environment. MethodsThis study was a three-arm randomized control trial involving 24 participants, conducted as a usability study to evaluate aspects of the experimental condition including the effectiveness of a training exposure, the occurrence of undesirable effects associated with IVR, and participants’ experiences of realism, immersion, and spatial presence. The experiment, using a head mounted device and handheld controllers, included a 10-minute training exposure, followed by one of three unique 30-minute experimental conditions which exposed participants to different combinations of tasks and stimuli, and a post-experience interview. We measured controller errors, undesirable symptoms associated with IVR, and the perception of realism, immersion, and spatial presence. ResultsParticipants used controllers effectively to interact within the IVR environment. Hand controller use errors were fewer during the experimental conditions compared to the training exposure. Minimal undesirable IVR symptoms were reported. Presence was rated in the middle range with no significant differences based on experimental condition. Overall, user experience feedback was positive. ConclusionsWe demonstrated that participants could engage in our IVR environment without excessive error or experiencing undesirable effects and confirmed that the virtual experience attained a level of presence necessary to effectively engage in the study. These findings give us confidence that this IVR intervention designed to explore instrumental activities of daily living is safe, effective and provides a credible, controlled simulated community-like setting.

Approximation of functions from Korobov spaces by shallow neural networks

In this paper, we consider the problem of approximating functions from a Korobov space on [−1,1]d by ReLU shallow neural networks and present a rate O(m−25(1+2d)log⁡m) of uniform approximation by networks of m hidden neurons. This is achieved by combining a novel Fourier analysis approach and a probability argument. We apply our approximation theory to a learning algorithm for regression based on ReLU shallow neural networks and derive learning rates of order O(N−4(d+2)9d+8log⁡N) for the excess generalization error with the sample size N when the regression function lies in the Korobov space.

Event-Based Remote State Estimation for Nonlinear Systems: A Box Particle Filtering Method.

This article is concerned with the problem of event-based remote state estimation for nonlinear/non-Gaussian systems on a wireless network with limited bandwidth. To reduce unnecessary data transmissions, a novel event-triggering mechanism is developed by using the least-square technique. Based on this, an event-triggered box particle filtering scheme is designed to realize the minimum mean-squared error estimation at the remote estimator end, in which the posterior probability density functions are calculated separately according to the information of the event-triggered indicator to avoid the problem of excessive estimation error. Different from the existing approaches, the proposed algorithm does not depend on any Gaussian assumptions and reduces the computational complexity under the premise of ensuring the estimation performance. Finally, two simulation examples are performed to demonstrate the validity of the proposed algorithm.

Lost Domain Generalization Is a Natural Consequence of Lack of Training Domains

We show a hardness result for the number of training domains required to achieve a small population error in the test domain. Although many domain generalization algorithms have been developed under various domain-invariance assumptions, there is significant evidence to indicate that out-of-distribution (o.o.d.) test accuracy of state-of-the-art o.o.d. algorithms is on par with empirical risk minimization and random guess on the domain generalization benchmarks such as DomainBed. In this work, we analyze its cause and attribute the lost domain generalization to the lack of training domains. We show that, in a minimax lower bound fashion, any learning algorithm that outputs a classifier with an ε excess error to the Bayes optimal classifier requires at least poly(1/ε) number of training domains, even though the number of training data sampled from each training domain is large. Experiments on the DomainBed benchmark demonstrate that o.o.d. test accuracy is monotonically increasing as the number of training domains increases. Our result sheds light on the intrinsic hardness of domain generalization and suggests benchmarking o.o.d. algorithms by the datasets with a sufficient number of training domains.

An efficient constraint method for solving planning problems under end-effector constraints

Purpose In response to the challenge of reduced efficiency or failure of robot motion planning algorithms when faced with end-effector constraints, this study aims to propose a new constraint method to improve the performance of the sampling-based planner. Design/methodology/approach In this work, a constraint method (TC method) based on the idea of cross-sampling is proposed. This method uses the tangent space in the workspace to approximate the constrained manifold pattern and projects the entire sampling process into the workspace for constraint correction. This method avoids the need for extensive computational work involving multiple iterations of the Jacobi inverse matrix in the configuration space and retains the sampling properties of the sampling-based algorithm. Findings Simulation results demonstrate that the performance of the planner when using the TC method under the end-effector constraint surpasses that of other methods. Physical experiments further confirm that the TC-Planner does not cause excessive constraint errors that might lead to task failure. Moreover, field tests conducted on robots underscore the effectiveness of the TC-Planner, and its excellent performance, thereby advancing the autonomy of robots in power-line connection tasks. Originality/value This paper proposes a new constraint method combined with the rapid-exploring random trees algorithm to generate collision-free trajectories that satisfy the constraints for a high-dimensional robotic system under end-effector constraints. In a series of simulation and experimental tests, the planner using the TC method under end-effector constraints efficiently performs. Tests on a power distribution live-line operation robot also show that the TC method can greatly aid the robot in completing operation tasks with end-effector constraints. This helps robots to perform tasks with complex end-effector constraints such as grinding and welding more efficiently and autonomously.