Path Estimation Research Articles

A Widely Applicable Modeling and Efficient Simulation Method for Power Electronics Grids Based on Unit Switching Circuits

As an essential part of modern power systems, power electronics have improved system performance. They also brought new dynamic and transient problems and posed significant challenges to precise and efficient simulation. Traditional modeling and solving methods fail to balance between the simulation accuracy and efficiency, and they are not flexible enough to adapt to converters with various new topologies. This article proposed a widely applicable modeling and simulation method based on the unit switching circuit that greatly promote the simulation efficiency and modeling simplicity by converting switching circuits into constant sub-grid equations with injection functions. Critical issues for engineering applications, such as the blocking state and the triggering deadband of converters, were also discussed and addressed by boundary balance and conduction path estimation. The error and stability of the solving method were qualitatively analyzed, with further discussions on the validity and numerical features. Several cases, including common and general cases and a real project of power electronic microgrid, tested and verified the accuracy, generalization ability, and efficiency of the proposed method.

Anthropogenic Coal Ash as a Contaminant in a Micro-meteoritic Underwater Search

Chemical composition for spherules recovered from the search area of CNEOS 2014-01-08 in the Pacific Ocean has been recently released. A three-order of magnitude difference from CI-chondrites has been identified for elements beryllium, lanthanum and uranium in five samples. The lack of consensus regarding atmospheric survival and precision of path estimates motivate an examination of possible contaminants. Contents of nickel, beryllium, lanthanum and uranium are examined in the context of a known anthropogenic source of contamination, and found to be consistent with coal ash as suggested from a publicly available coal chemical composition database (COALQUAL). The meteoritic origin is disfavored.

Mental Health During Early Adolescence and Later Cardiometabolic Risk: A Prospective Study of US Latinx Youth

PurposeRising rates of cardiometabolic risk and mental health problems are serious public health concerns for US adolescents, particularly those of Latinx origin. This research examines how Latinx youth's internalizing symptoms during early adolescence are related to sleep problems, overweight/obesity, sedentary behavior, physical activity, healthy diet, and hypertension or diabetes risk during middle and late adolescence. MethodsParticipants included 547 adolescents listed as “Hispanic” on 2017-18 middle school enrollment lists in a suburban Atlanta, GA school district. Survey data collected at baseline (2018) and four years later (2022) were analyzed using Structural Equation Model. Path estimates from baseline internalizing symptoms to later health behaviors and physical health outcomes adjusted for demographics, the follow-up measure of internalizing symptoms, and correlations among outcome variables. Missing data were handled using Full Information Maximum Likelihood. ResultsAt baseline, the 244 (44.6%) male and 303 (55.4%) female participants had a mean (standard deviation) age in years of 13.31 (0.97). Early adolescent internalizing symptoms were associated positively with later sleep problems (ß = 0.36 [95% confidence interval (CI), 0.24–0.48]), overweight/obesity (adjusted odds ratio, 2.57; 95% CI, 1.29–5.15), sedentary behavior (ß = 0.19 [95% CI, 0.09–0.30]), and internalizing symptoms (ß = 0.48 [95% CI, 0.39–0.56]) and inversely with later physical activity (ß = −0.16 [95% CI, −0.27 to −0.05]) and a healthy diet (ß = −0.21 [95% CI, −0.32 to −0.09]). DiscussionLatinx youth's internalizing symptoms during early adolescence not only track into later adolescence, but they also relate to health behaviors and outcomes underlying cardiometabolic risk during middle and late adolescence.

New Partial Least Square Third-order Latent Variable Models

As a special case of higher-order model, third-order model has the abilities to reflect the relations among more different levels of indicators. More specially, third-order here means that there exist three different orders of latent variables and certain amounts of observed variables. The main challenge for third-order model lies in the estimation of unknown path and loading coefficients due to the complex structure inside. Our paper proposes a new estimation algorithm for third-order model under the framework of partial least square. On the above basis, we further develop a new partial least square third-order model with quantile regression and propose a new modified algorithm correspondingly. This kind of model can capture the relations among different variables at different quantiles. In addition, we develop another new partial least square third-order model with varying coefficients, which reflecting the dynamic relations among different latent variables and observed variables.

Effect of control domains of fractures and caves on reactive transport in porous media

It is often highly difficult to predict the main flow path of reactive fluids in porous media with complexly distributed fractures and/or caves. Based on Darcy's law, this research defines the control domain of fractures/caves that represents the interference range of fractures/caves. This control domain can be used to identify the connectivity possibility between fractures/caves and predict the flow paths of reactive fluids at the optimal flow rate. Furthermore, after simplifying the geometry of fractures/caves, a threshold geometric aspect ratio of 20 is set to distinguish fractures and caves, concerning the flow mechanisms, by clarifying the ranges of control domains. Moreover, the control domain theory is combined with the two-scale continuum model for acidizing carbonate rocks to estimate the flow paths of acid in fractured-vuggy carbonate rocks at an optimal flow rate, thereby validating the accuracy of the reactive fluid main flow path estimation based on the control domain theory. The primary criterion to determine the reactive transport in porous media with complex fracture/cave distribution is the overlap degrees of control domains of adjacent isobaric bodies along their width and length directions, while the directions of isobaric bodies offer supplementary material. If the control domains of two isobaric bodies overlap with each other perpendicular to the flow direction, these isobaric bodies have higher odds of connected fluid flow.

Updating the secondary path using deep learning models to enhance the performance of active road noise control

This research investigates the importance of accurately updating the secondary path estimation to enhance the performance of active road noise control (ARNC), particularly in response to variable conditions within a vehicle. The study analyzes the impact of four spatial conditions on the secondary path and introduces prediction models using Delaunay triangulation-based interpolation and deep learning methods, using multi-layer perceptron (MLP) networks. MLP networks were constructed based on the representation domain chosen for the secondary path. The deep learning methods demonstrated higher prediction accuracy and smaller data storage requirements compared to the interpolation method. Among these deep learning methods, the approach that stood out as the best performer was representing the secondary path through principal component analysis (PCA) and learning the weight of each basis vector. Through this technique, it was experimentally confirmed that the performance of ARNC under variable conditions in the vehicle improved through the accurate update of real-time secondary path estimation. These advantages are particularly prominent when dealing with high frequencies, which are likely to be the foundational technology for the frequency expansion of ARNC.

A low-complexity multi-channel active noise control system using local secondary path estimation and clustered control strategy for vehicle interior engine noise

Conventional multi-channel active noise control (ANC) system based on the adaptive notch filtered-x least mean square algorithm is typically used for active control of vehicle interior engine noise with prominent harmonic characteristics. However, due to the large estimated secondary path length and the centralized control strategy, the system has high computational complexity and fragile stability. To alleviate this problem, an efficient multi-channel ANC system using the local secondary path (LSP) estimation and clustered control strategy is proposed in this paper. Based on the modified LSP estimation method, the system may utilize a set of low-order but accurate LSP models to simplify the convolution operations of reference filtering. Besides, the proposed clustered control strategy combines the advantages of centralized and decentralized control strategies, and thus the system not only enjoys good noise attenuation performance and stability, but also its computational burden is further greatly reduced. Numerical simulations are performed to evaluate the noise attenuation performance and frequency mismatch effect of the proposed multi-channel ANC system. In addition, a series of real vehicle ANC experiments are conducted. The results show that, under stationary work conditions, the interior overall, 2nd order, 4th order, 6th order engine noises may be attenuated by 12.91 dB(A), 32.98 dB(A), 15.34 dB(A) and 8.74 dB(A), respectively, and under nonstationary work conditions, the maximum overall noise reductions at the four error microphones may reach 6.40 dB(A), 6.14 dB(A), 12.48 dB(A) and 11.07 dB(A), respectively. This fully confirms the effectiveness of the proposed multi-channel ANC system in real-world applications.

MBT3D: Deep learning based multi-object tracker for bumblebee 3D flight path estimation.

This work presents the Multi-Bees-Tracker (MBT3D) algorithm, a Python framework implementing a deep association tracker for Tracking-By-Detection, to address the challenging task of tracking flight paths of bumblebees in a social group. While tracking algorithms for bumblebees exist, they often come with intensive restrictions, such as the need for sufficient lighting, high contrast between the animal and background, absence of occlusion, significant user input, etc. Tracking flight paths of bumblebees in a social group is challenging. They suddenly adjust movements and change their appearance during different wing beat states while exhibiting significant similarities in their individual appearance. The MBT3D tracker, developed in this research, is an adaptation of an existing ant tracking algorithm for bumblebee tracking. It incorporates an offline trained appearance descriptor along with a Kalman Filter for appearance and motion matching. Different detector architectures for upstream detections (You Only Look Once (YOLOv5), Faster Region Proposal Convolutional Neural Network (Faster R-CNN), and RetinaNet) are investigated in a comparative study to optimize performance. The detection models were trained on a dataset containing 11359 labeled bumblebee images. YOLOv5 reaches an Average Precision of AP = 53, 8%, Faster R-CNN achieves AP = 45, 3% and RetinaNet AP = 38, 4% on the bumblebee validation dataset, which consists of 1323 labeled bumblebee images. The tracker's appearance model is trained on 144 samples. The tracker (with Faster R-CNN detections) reaches a Multiple Object Tracking Accuracy MOTA = 93, 5% and a Multiple Object Tracking Precision MOTP = 75, 6% on a validation dataset containing 2000 images, competing with state-of-the-art computer vision methods. The framework allows reliable tracking of different bumblebees in the same video stream with rarely occurring identity switches (IDS). MBT3D has much lower IDS than other commonly used algorithms, with one of the lowest false positive rates, competing with state-of-the-art animal tracking algorithms. The developed framework reconstructs the 3-dimensional (3D) flight paths of the bumblebees by triangulation. It also handles and compares two alternative stereo camera pairs if desired.

Cascade algorithms for combined acoustic feedback cancelation and noise reduction

This paper presents three cascade algorithms for combined acoustic feedback cancelation (AFC) and noise reduction (NR) in speech applications. A prediction error method (PEM)-based adaptive feedback cancelation (PEM-based AFC) algorithm is used for the AFC stage, while a multichannel Wiener filter (MWF) is applied for the NR stage. A scenario with M microphones and 1 loudspeaker is considered, without loss of generality. The first algorithm is the baseline algorithm, namely the cascade M-channel rank-1 MWF and PEM-AFC, where a NR stage is performed first using a rank-1 MWF followed by a single-channel AFC stage using a PEM-based AFC algorithm. The second algorithm is the cascade (M+1)-channel rank-2 MWF and PEM-AFC, where again a NR stage is applied first followed by a single-channel AFC stage. The novelty of this algorithm is to consider an (M+1)-channel data model in the MWF formulation with two different desired signals, i.e., the speech component in the reference microphone signal and in the loudspeaker signal, both defined by the speech source signal but not equal to each other. The two desired signal estimates are later used in a single-channel PEM-based AFC stage. The third algorithm is the cascade M-channel PEM-AFC and rank-1 MWF where an M-channel AFC stage is performed first followed by an M-channel NR stage. Although in cascade algorithms where NR is performed first and then AFC the estimation of the feedback path is usually affected by the NR stage, it is shown here that by performing a rank-2 approximation of the speech correlation matrix this issue can be avoided and the feedback path can be correctly estimated. The performance of the algorithms is assessed by means of closed-loop simulations where it is shown that for the considered input signal-to-noise ratios (iSNRs) the cascade (M+1)-channel rank-2 MWF and PEM-AFC and the cascade M-channel PEM-AFC and rank-1 MWF algorithms outperform the cascade M-channel rank-1 MWF and PEM-AFC algorithm in terms of the added stable gain (ASG) and misadjustment (Mis) as well as in terms of perceptual metrics such as the short-time objective intelligibility (STOI), perceptual evaluation of speech quality (PESQ), and signal distortion (SD).

Research on the Cooperative Target State Estimation and Tracking Optimization Method of Multi-UUV.

This work studied two sub-problems of the cooperative state estimation and cooperative optimization of tracking paths in multiple unmanned underwater vehicle (multi-UUV) cooperative target tracking. The mathematical model of each component of the multi-UUV cooperative target tracking system was established. According to the target bearing-only information obtained by each unmanned underwater vehicle's (UUV) detection, the extended Kalman filter algorithm based on interacting with multiple model bearing-only data was used to estimate the target state in a distributed way, and the federal fusion algorithm was used to fuse the estimated results of each UUV. The fused target state was predicted, and, based on the predicted target state, to achieve the persistent tracking of the target, the particle swarm optimization algorithm was used for the online collaborative optimization of the UUV tracking path. The simulation results showed that the multi-UUV distributed fusion filtering algorithm could obtain a better target state estimation effect, and the online path collaborative optimization method based on the prediction of the target state could achieve persistent target tracking.

Estimating resilience of manufacturing plants to physical disruptions: Model and application

The concept of Resilience is gaining an increasing importance in industrial systems, service networks and supply chains, as it extends traditional risk assessment methods by capturing both shortterm and long-term effects of accidents and disruptions. Resilience is, in fact, both a qualitative property and a performance indicator that measures the system's ability to survive disruptive events, and rapidly restore its functionality after the disruptive event has occurred. However, while literature about systems resilience is ample, comparatively scarce attention has been devoted to the evaluation of production systems resilience. As a contribution to fill this gap, in this paper a quantitative method for resilience estimation specifically aimed at manufacturing plants is developed. The method focuses on computing resilience of the manufacturing process, according to its structure, when resources are subjected to physical damage resulting from disruptive events such as sabotage, technical accidents or natural disasters. Overall, the paper allows to assess the economic loss and duration of production interruption in any kind of manufacturing plant when subjected to damage produced by the above causes. In particular, the method enables direct assessment of the initial capacity loss following a prescribed disruptive event, and explicit estimation of the time-dependent capacity recovery path based on the actual process structure and sequence of resources restoration tasks, also allowing the quantification of resulting economic losses. In the paper, after model development, an application example is provided to show model capabilities. Facility designers and emergency managers can use this model as a decision support tool for risk assessment, and to plan emergency actions and plant refurbishment aimed at resilience enhancement.

Angular Irradiation Methods for DOI Calibration of Light-Sharing Detectors—A Perspective for PET In-System Calibration

Typical positron emission tomography (PET) detectors consist of one-layer segmented scintillators coupled to silicon photomultipliers (SiPMs). Light-sharing detectors, e.g., semi-monoliths, additionally provide depth-of-interaction (DOI) estimation, performing best when calibrated individually. To establish those designs in large PET systems, scalable (re-)calibration methods are needed, possibly transferable to assembled systems. Here, two DOI calibration methods, potentially allowing in-system calibration, are evaluated and compared with an established calibration scheme. Both methods are based on angular detector irradiation using a fan-beam slit collimator and gradient tree boosting (GTB) for 3-Dimensional (3D) position estimation. The positioning performance was assessed for irradiation angles between 0∘ (lateral) and 90∘ (detector normal). With lateral irradiation, a unique DOI position is given, whereas with angular irradiation a gamma-individual reference position must be retrieved. The first method employs one angular beam and calculates DOI from the beam path and planar position estimation. The second method uses two intersecting beams. The intersection defines DOI for gamma interactions that are spatially localized there. Those gamma photons are identified by light distribution comparison using a nearest-neighbor routine. The methods were evaluated on a semimonolithic LYSO slab detector (32×3.9×19 mm slabs). Both methods performed similarly to the benchmark lateral irradiation within 1 %, and 6 %, respectively, for shallow irradiation angles up to 45∘.

An Ensemble Filter for Indoor Positioning Technology of Mobile Home Service with Agile iBeacon Deployment

In this article, we undertake a thorough investigation into the theory of indoor location, adopt an effective and quick positioning algorithm, and make use of a network of lowpower iBeacons. An iBeacon-based indoor positioning system (IPS) is presented to investigate how to utilize iBeacon for accurate location and whether it can successfully replace the current dominant positioning technology based on the analysis that was conducted. However, the first things that should be taken into account at this point in the design of house robots for indoor environments are how to quickly and precisely gather target node location information as well as how to regulate and plan a course. This article examined the more popular and often used indoor positioning techniques, provided a succinct summary of current indoor positioning technologies and regulators, and examined ultrasonic locating technology in depth. Based on this, a system for mobile home service robots was developed, and simulation tests were performed to assess the accuracy of node locating, node reception and arrival times, the best level of route planning, and navigation and path estimation errors in both absolute and comparative terms. Additionally, we go into detail about the difficulties in developing a practical IPS, the solutions that are already available, a thorough performance comparison, and some potential future IPS development trends.

Bayesian inference of plasma parameters from collective Thomson scattering technique on a gas-puff near stagnation

The Collective Thomson scattering technique has been implemented to study the stagnation of a single liner gas-puff. The plasma parameters are determined by theoretically modelling the scattering form factor in combination with Bayesian inference to provide the set of the most probable parameters that describe the experimental data. Analysis of the data reveal that incoming flows are able to interpenetrate partially. Estimation of the mean free path shows a gradual transition from a weakly collisional to a collisional regime as the plasma gets to the axis. Furthermore, we find that the ion energy at mathrm{r}=2.5,mathrm{mm} is {13.6}_{-0.9}^{+1.0},mathrm{keV} and is mostly kinetic in nature and represents {98}_{-9}^{+10} % of the total energy. This kinetic energy is far greater than the value on axis of {3.7}_{-0.5}^{+0.4},mathrm{keV} which is {84}_{-14}^{+15} % of the total energy. Energy transfer to the electrons and radiation losses are found to be negligible by this time. A possible explanation for this energy imbalance is the presence of an azimuthal magnetic field larger than sim 4.7,mathrm{T} that deflect the ions vertically. The uncertainties quoted represent 68% credible intervals.

Certification of Bottleneck Task Assignment With Shortest Path Criteria

Minimising the longest travel distance for a group of mobile robots with interchangeable goals requires knowledge of the shortest length paths between all robots and goal destinations. Determining the exact length of the shortest paths in an environment with obstacles is NP-hard however. In this paper, we investigate when polynomial-time approximations of the shortest path search are sufficient to determine the optimal assignment of robots to goals. In particular, we propose an algorithm in which the accuracy of the path planning is iteratively increased. The approach provides a certificate when the uncertainties on estimates of the shortest paths become small enough to guarantee the optimality of the goal assignment. To this end, we apply results from assignment sensitivity assuming upper and lower bounds on the length of the shortest paths. We then provide polynomial-time methods to find such bounds by applying sampling-based path planning. The upper bounds are given by feasible paths, the lower bounds are obtained by expanding the sample set and leveraging the knowledge of the sample dispersion. We demonstrate the application of the proposed method with a multi-robot path-planning case study.

The effect of ultrasound wave path estimation to defect characterization capability in half-skip total focusing method

The total focusing method (TFM) is a post-processing imaging technique applied on full matrix capture (FMC) ultrasonic inspection (UT) dataset. In TFM the ultrasonic wave energy is synthetically focused on every pixel in the image region of interest (ROI). In terms of half-skip TFM (HSTFM), wave mode conversion happens when the wave rebounds at interface, such as specimen backwall. This paper aims to propose and evaluate a method that involves Snell’s law to address accurate estimation of distanceof-flight (DOF) of wave propagation when wave mode conversion appears in HSTFM. This HSTFM algorithm is applied to both experimental and simulated FMC dataset that inspects a surface-breaking notch for notch image reconstruction. Comparisons between images with and without considering Snell’s law in wave mode conversion show visible difference that could lead to misinterpretations in characterizing the defect. The sensitivity of TFM to varying defect features such as defect tilt angle is also studied using simulated FMC datasets.

Longitudinal associations between weight stigma and disordered eating across the weight spectrum

Weight stigma reflects discrimination or stereotyping based on weight, and this construct is associated with body dissatisfaction, low self-esteem, and eating pathology. Recent research suggests that internalizing weight stigma (i.e., endorsing negative stereotypes about one's weight) mediates associations between experienced weight stigma and disordered eating. However, much of this research has been cross-sectional and limited data exist on associations between weight stigma constructs and eating pathology across the weight spectrum. The current study explored whether internalized weight stigma differentially mediates the relationship between experiencing weight stigma and disordered eating symptoms over time in higher-weight versus non-higher-weight individuals. Undergraduate students (N = 661, 80 % Female, 28.5 % higher weight) completed surveys at three time points over six months. Multigroup path analyses tested whether the effects of experienced weight stigma and internalized weight stigma on binge eating, food restriction, and body dissatisfaction differed between the higher-weight and non-higher-weight groups. All models showed improved fit when path estimates were allowed to vary between groups. Mediation analyses indicated a significant indirect effect of experienced weight stigma on binge eating via internalized weight stigma among the higher-weight group but not the non-higher-weight group. Only internalized weight stigma was directly associated with body dissatisfaction across weight status. A direct effect of experienced weight stigma on restriction was found in the non-higher-weight group. Findings suggest that, in general, weight stigma negatively affects body image and eating behavior, although specific effects may vary depending on one's weight.

Hybrid UWB-Inertial TDoA-Based Target Tracking With Concentrated Anchors

In this paper, hybrid radio/inertial mobile target tracking for accurate and smooth path estimation is considered. The proposed tracking approach builds upon an Ultra WideBand (UWB)-based positioning algorithm, based on the Linear Hyperbolic Positioning System (LinHPS), with Time Difference of Arrival (TDoA) processing and anchors concentrated on a single hotspot at the center of the environment where the target moves. First, we design an Adaptive Radio-based Extended Kalman Filter (AREKF), which does not require a priori statistical knowledge of the noise in the target movement model and estimates the measurement noise covariance, at each sampling time, according to a proper LookUp Table (LUT). In order to improve the performance of AREKF, we incorporate inertial data collected from the target and propose three “hybrid” radio/inertial algorithms, denoted as Hybrid Inertial Measurement Unit (IMU)-aided Radio-based EKF (HIREKF), Hybrid Noisy Control EKF (HNCEKF), and Hybrid Control EKF (HCEKF). Our results on experimentally acquired paths show that the proposed algorithms achieve an average instantaneous position estimation error on the order of a few centimeters. Moreover, the minimum target path length estimation error, obtained with HCEKF, is on the order of 6% and 1% for two paths with lengths equal to approximately 17 m and 46 m, respectively.

Marketing Capabilities, Market Orientation and Food Security of Biofortified Cassava Producers in Nigeria

This paper proposed that marketing capability is a critical component of market orientation. As such, knowledge of intelligence and competitive strategy, aspects of market capability obtained through training is essential for market orientation. Cross-sectional survey was used. Data were collected with a structured questionnaire and analysed using the structural equation model (SEM). The structural model and path estimates indicate that all the constructs intervene in food security, implying that a systemic approach rather than individual strength can help achieve food security. The study's findings support the perspective that marketing capabilities have spill-over effects that improve market orientation and food security performance. Therefore, BoP producers through training can generate significant marketing capabilities that can improve the food market. It highlights the importance of producers adopting a market-oriented approach. The study recommended a multi-stakeholder engagement model toward integrating biofortification into public and private policies and programmes. This would encourage investment and continuous training of producers on how to improve communication and customer relationship management to stimulate consumption and supply.

EFFECTS OF TEACHING QUALITY, TEACHING COMPETENCE, AND MATHEMATICS CONNECTION ON MATHEMATICS ACHIEVEMENT MOTIVATION AMONG SENIOR HIGH SCHOOL STUDENTS IN GHANA

This survey assessed the direct effects of teaching quality, teaching competence and mathematics connection on mathematics achievement motivation in Senior high School students in Ghana. A descriptive survey was used in the study, which included 329 senior high school students from Barekese Senior High School. Prior to the main model estimation, preliminary studies such as Confirmatory Factor Analysis, convergent validity, discriminant validity, and internal consistency (Cronbach’s Alpha) were computed. Amos (v.23) was used to do structural equation modeling (SEM) in order to assess the various assumptions. The results of path estimations revealed that mathematics connection positively and significantly influenced mathematics achievement motivation. However, teaching quality and teaching competence has no significant impact on mathematics achievement motivation.