Gap Metric Research Articles

Influence of initial belt torso contact on the kinematics and kinetics of booster-seated ATDs in frontal-oblique impacts

Objective Varying initial belt torso contact (i.e., belt gap) on belt-positioning boosters may have implications for potential shoulder belt slip-off in low-speed evasive vehicle maneuvers and differences in dynamic outcomes in frontal sled tests. This study evaluated the influence of initial booster belt gap and belt fit conditions on the kinematic and kinetic outcomes during frontal oblique impacts. Methods Frontal oblique (+15° from frontal) sled tests (n = 18; 23.6 ± 0.1 g at 12.4 ± 0.1 ms) were conducted using the Q-Series 6-year-old (Q6) and 10-year-old (Q10) and the Large Omni Directional Child (LODC) anthropomorphic test devices (ATDs). Various initial belt fit and belt gap conditions were investigated by evaluating each ATD on 2 high-back (HB), 3 low-back (LB), and 1 low-profile (Low) booster. Initial belt fit and belt gap were quantified, and boosters were categorized as “smaller gap” or “larger gap” for comparison. Results Larger-gap boosters produced greater peak lumbar FY and MZ (HB: −23.2 ± 8.8 Nm, LB: −23.6 ± 9.7 Nm) compared to smaller-gap boosters (HB: −12.6 ± 4.4 Nm, LB/Low: −12.4 ± 7.2 Nm) for the LODC and Q10. Peak axial torso rotations were also observed for larger-gap LB (38.6°) compared to smaller-gap LB boosters (23.8°), and the LODC experienced greater peak thoracic rotations on larger-gap boosters compared to smaller-gap boosters. These results suggest that ATDs on larger-gap boosters experienced greater torso rotation and lumbar MZ due to lack of initial contact between the shoulder belt and inferior torso. No ATDs experienced complete shoulder belt slip-off; however, larger-gap boosters displayed more visual evidence of outboard shoulder belt positioning at the time of peak forward head excursion. Conclusion This study provides a novel investigation on the role of initial belt fit and belt gap metrics on the dynamic response of booster-seated ATDs in frontal oblique impacts. Larger-gap boosters allowed the torso to undergo greater axial rotation before restraint was provided by the shoulder belt to the lower torso. Increased shoulder rotations may indicate greater propensity for shoulder belt slip-off in more severe crashes, in oblique maneuvers, or with variations in initial occupant posture. These results suggest the importance of continued evaluation of the implications of initial belt gap provided by boosters and the importance of evaluating lumbar FY and MZ and useful metrics for discrimination of differences in ATD response across booster designs.

Institution level awarding gap metrics for identifying educational inequity: useful tools or reductive distractions?

AbstractEquity is increasingly seen as a core value for higher education systems around the world. (In)equity is often measured through construction of achievement gaps, quantifying the relative outcomes of two populations of students. Institution-level gaps are embedded in the policy landscape of HE, becoming performance metrics in their own right. These gap metrics increasingly inform the actions of governments, regulators, institutions and educators. This theoretical article scrutinises the technical and conceptual construction of achievement gaps through using the dominant UK conception of the institution level degree classification ‘awarding gap’. Drawing on Adam’s Equity Theory of Motivation, Rawls’s Distributive Justice and the Capability Approach as theoretical perspectives, I highlight multiple structural weaknesses in the conception of the awarding gap. I illustrate the implications of this metric by analysing simulated awarding gap data for a fictional institution, and through the perspectives of five idealised stakeholders. I identify multiple technical and theoretical limitations of the institution level awarding gap metric, including examples where the threshold-based nature of the awarding gap fails to capture statistical differences between groups, thereby undermining its utility in identifying inequity. I call on the sector to develop metrics that more accurately capture (in)equity of outcomes and align better with theoretical frameworks, thereby creating more powerful explanatory metrics that can inform meaningful action.

Gap-MK-DCCA-Based Intelligent Fault Diagnosis for Nonlinear Dynamic Systems

In intelligent process monitoring and fault detection of the modern process industry, conventional methods mostly consider singular characteristics of systems. To tackle the problem of suboptimal incipient fault detection in nonlinear dynamic systems with non-Gaussian distributed data, this paper proposes a methodology named Gap-Mixed Kernel-Dynamic Canonical Correlation Analysis. Initially, the Gap metric is employed for data preprocessing, followed by fault detection utilizing the Mixed Kernel-Dynamic Canonical Correlation Analysis. Ultimately, fault identification is conducted through a contribution method based on the T2 statistic. Furthermore, a comparative analysis was conducted using Canonical Variate Analysis, Dynamic Canonical Correlation Analysis, and Mixed Kernel-Dynamic Canonical Correlation Analysis on the Tennessee Eastman process. Experimental results indicate varying degrees of improvements in the detection rate, false alarm rate, missed detection rate, and detection time compared to the comparative methods, demonstrating the industrial value and academic significance of the method.

Distributed data-driven fault detection for industrial interconnected systems with unknown topology structure

Driven by the enhancing requirements for ensuring stable operation of complex industrial processes, this paper investigates a distributed data-driven fault detection (FD) method for industrial interconnected systems. The topology structure of the industrial interconnected system is presumed to be unknown. To this end, the input/output (I/O) data-set model for the interconnected system and the distributed input/output data-set models for the subsystems are derived first. Then, the topology structure of the interconnected system is reconstructed through data-driven realization of gap metric by utilizing the subspace Identification method. Subsequently, the distributed data-driven fault detection method with enhanced information exchange topology is developed. Finally, we validate the feasibility and efficiency of the proposed method by conducting a case study on a chemical process consisting of four continuous-stirred tank reactors (CSTRs).

Systematic model bank determination approach for nonlinear systems using gap metric and stability margin

This paper introduces a novel systematic approach for designing multimodel controller tailored to nonlinear systems. Our methodology focuses on the precise selection of local controllers, aligning their performance with specific requirements while minimizing redundancy. We achieve this through a two-fold process: first, the creation of an initial multimodel bank using the Self-Organizing Map (SOM) algorithm. Then, we employ gap metrics to assess the similarity between linear subsystems and hierarchical agglomerative clustering to group local models. This process leads to the identification of model sets for aggregation. Finally, we use stability margins as a guidance to get the reduced bank to elaborate the multimodel controller, ensuring robust stability. The main advantages of our method lie in the elimination of redundancy, simplification of the controller structure and the guarantee of robust stability. Three highly nonlinear processes are studied to demonstrate the efficiency of the proposed multimodel control approach.

Comparison of child and ATD belt fit and posture on belt-positioning boosters during self-selected, holding device, and nominal conditions

ObjectivePediatric anthropomorphic test devices (ATDs) are important tools for the assessment of child occupant protection and should represent realistic child belt fit and posture on belt-positioning boosters. Previous comparisons have been made to children in either self-selected or nominal postural conditions. This study compares belt fit and postural measurements between pediatric ATDs and a single cohort of children assuming different postures on boosters: self-selected, holding a portable electronic device, and nominal. MethodsA cohort of children (n = 25) were evaluated in a stationary vehicle on five boosters and in three postural conditions: nominal, self-selected, and a representative holding electronic device position. The Hybrid III 6- and 10-year-old and Q-Series 6- and 10-year-old ATDs were evaluated in the same five boosters and in two postural conditions: nominal and a representative holding electronic device position. A 3D coordinate measurement device was used to quantify belt fit (shoulder belt score, lap belt score, maximum gap size, and gap length) and anatomic landmark positions (head, suprasternale, ASIS, and patella). Landmark positions and belt fit were compared between ATDs and children for each booster and postural condition, and Pearson correlations (r) were assessed across boosters. ResultsATDs generally represented Nominal child postures across boosters. In the Device condition, ATDs were seldom able to be positioned to represent both the torso and head position of children, due to limited ATD spinal flexibility. When the torso position was matched, the ATD head was more rear by 63 mm. Correlations between Nominal child and ATD belt fit and belt gap metrics were generally weak and not significant, with the exception of lap belt score (all ATDs p < 0.07, r = 0.8549–0.9857). DiscussionATDs were generally able to represent realistic child postures and lap belt fit in Nominal and short duration Self-selected postures in a laboratory setting. However, these results display the potential difficulty of utilizing ATDs to represent more naturalistic child postures, especially the more forward head positions and flexed spinal posture associated with utilizing a portable electronic device.

Small Fault Diagnosis With Gap Metric

This article proposes a novel data-driven gap metric fault detection and isolation (FDI) approach for small multiplicative fault. First, the scheme of model-based fault classification and gradation is developed by means of the gap metric. Subsequently, the data-driven gap metric is utilized to detect a small fault via the mechanism model. Furthermore, fault detectability criterion is derived with the help of the developed fault detectability indicator. The relationship between fault detectability indicator and fault detection index is then investigated to analyze fault detection performance. To enhance fault isolability, a solution of appropriate fault cluster center model and radius is provided under the condition of fault isolation. Third, a gap metric fault-tolerant control strategy is exploited to guarantee system stability when a large fault is diagnosed by the developed FDI approach. The speed regulation of dc-motor and dc–dc converter are used for simulation and experiment verifications. Moreover, the comparison results and Monte Carlo simulation demonstrate the superiority and reliability of the proposed method.

Flexible operation of the pressurized water reactor nuclear power system using multi-model predictive control over a wide nonlinear operating range

The nonlinearity problem caused by the large load variation is a significant challenge for the pressurized water reactor nuclear power system (PWRNPS) to realize its flexible operation over a wide operating range when it participates in peak regulation in the power grid. Thus, this work proposes a novel multi-model predictive control (MMPC) strategy for PWRNPS to address its nonlinearity problem in multivariable control within a wide range of conditions, in which the coupling effect between the average coolant temperature and the steam turbine power is considered. Given the insufficient understanding of the nonlinearity of PWRNPS in previous work, an in-depth investigation in this regard is performed to support the control design, in which the nonlinearity of PWRNPS at the system level is analyzed both on quantitative and qualitative aspects for the first time. Considering the captured uneven nonlinearity distribution of PWRNPS, a new approach integrating hierarchical clustering with gap metric is proposed to guide the decomposition of the operating space of PWRNPS systematically, based on which MMPC is designed to realize the nonlinearity control of PWRNPS under the large load variation. Three types of simulations demonstrate the effectiveness of the proposed methods.

Closed-Loop Fault Diagnosis of SDR Using Gap Metric, PCA, and Kalman’s Principle

The measurement of the pressure in the gas generator (GG) plays a decisive role in the closed-loop regulation of the gas flow in a solid ducted rocket ramjet (SDR). Therefore, the fault detection and isolation (FDI) of the pressure sensors is particularly significant. Especially in the GG with double pressure feedback, there are issues such as less available data and sensor fault transmission, which make the FDI more difficult. In this paper, for the GG with double pressure feedback, firstly, the “Consistency Index” was constructed based on the principal component analysis (PCA) algorithm, which made it easier to evaluate the consistency of the measured values. Secondly, based on Kalman’s principle, the redundancy information of the system was used to estimate the pressure in the GG. Finally, the gap metric between the measured pressure and the estimated pressure was employed to characterize the health of the sensors. Compared with the MWPCA algorithm, it was shown that our proposed algorithm was more accurate in fault diagnosis and could avoid the problem of missing alarm when two sensors had consistent faults, which would provide strong support for the safe operation of the SDR and could further promote its application in long-endurance aircrafts.

A Mixed Integer Linear Programming Model with Heuristic Improvements for Single-Track Railway Rescheduling Problem

A rescheduling algorithm for trains on a single-track railway was developed in case of disturbances that would cause conflicts between trains. This algorithm is based on mixed integer linear programming (MILP) with speed-up routines. The model considers station capacities explicitly (i.e., the number of available tracks for meeting and overtaking operations). Because the model is too hard for the solvers (CPLEX in this study) to tackle, three speed-up routines were devised when rescheduling trains. These routines are a greedy heuristic to reduce the solution space, using the lazy constraint attribute of the solver and a multiobjective approach to find a good initial feasible solution that conforms to actual railway operation. The algorithm was tested on a hypothetical rail line for different sizes of timetable instances with disturbed trains in a maximum two-hour time horizon. It managed to solve the hardest instances within a three-minute time limit thus minimizing the total weighted delay of rescheduled trains. The optimality gap metric is used to show the effectiveness and efficiencies of the speed-up heuristics developed.

Distributed model predictive control‐oriented network decomposition based on full dynamic response

AbstractThis article addresses network decomposition for distributed model predictive control (DMPC), which includes two improvements. First, in the weighted input–output bipartite graph construction of a process network, a new measure called frequency affinity is proposed to characterize the input–output interaction considering the full dynamic response and structural information of a process. Then, in community detection, which is used to decompose the process network, the gap metric is added to quantify stability and the loss of control performance of each subsystem. Through the proposed decomposition, the obtained subsystems can be dynamically well‐decoupled since both transient and steady‐state responses are measured by the frequency affinity. As structural information is considered, the decomposition is consistent with the process physical topology. Furthermore, the utilization of gap metric can facilitate controller design for DMPC. Case studies on a reactor separator process and an air separation process demonstrate the effectiveness of the proposed decomposition method.

Design of Practical Scrambling Schemes for Physical-Layer Security

In physical-layer security, coding and modulation are employed to prevent successful eavesdropping without the need for secret keys. Most promising coding approaches apply channel coding in combination with information-bit scrambling, which leads to error multiplication at the descrambling process. In this letter, the strategies of shift-register based scrambling and block-wise scrambling (using matrix multiplications) are assessed for a Gaussian wiretap channel scenario w.r.t. the security gap metric. We show that the former leads to an unequal protection of communicated message bits. This becomes apparent only upon the evaluation of individual bit positions’ error rates and security gaps. In contrast, the more general block scrambling enables schemes that provide equal protection. An optimized scrambling matrix design is presented, which yields the best error-rate performance. In addition to the theoretical examination of error propagation in the descrambling process, numerical results that combine low-density parity-check codes and scrambling support those findings.

The role of paid media, earned media, and sales promotions in driving marcom sales performance in consumer services

Firms deploy an array of marketing communications (marcom) tools to influence sales performance, yet critics lament that such efforts can be incredibly inefficient. This research focuses on the impact of a range of marcom inputs on sales performance. It uses a multi-media data set assembled from four industry sources to examine the joint impact of paid media, earned media, and sales promotion activity. It does so through two Frontier methodologies (Stochastic Frontier (SFA) and Data Envelopment Analysis (DEA)) to benchmark sales performance. All firms within a single industry are compared to these sales performance benchmarks to generate a sales performance gap metric that pinpoints the most effective marcom activities. The results show that paid and earned media positively influence sales performance, however, excessive sales promotion activity has a negative effect. Several scenario analyses provide actionable strategic insights for managers to more efficiently allocate marketing communications efforts.

Fault Detection for Systems With Model Uncertainty and Disturbance via Coprime Factorization and Gap Metric.

The fault detection (FD) problem for systems with both model uncertainty and external disturbance is investigated in this article. First, the mathematical models of systems with model uncertainty and disturbance, systems with additive faults, and systems with multiplicative faults are established with both left and right coprime factorization. Then, an observer-based FD scheme is proposed and the FD thresholds are derived for both open-loop and closed-loop manners. The necessary conditions on multiplicative FD are obtained and the fault detectability analyses are carried out with the aid of the gap metric technique. Finally, the effectiveness of the proposed method is illustrated by a case study on a cart dynamic system.

Metric Learning in Freewill EEG Pre-Movement and Movement Intention Classification for Brain Machine Interfaces.

Decoding movement related intentions is a key step to implement BMIs. Decoding EEG has been challenging due to its low spatial resolution and signal to noise ratio. Metric learning allows finding a representation of data in a way that captures a desired notion of similarity between data points. In this study, we investigate how metric learning can help finding a representation of the data to efficiently classify EEG movement and pre-movement intentions. We evaluate the effectiveness of the obtained representation by comparing classification the performance of a Support Vector Machine (SVM) as a classifier when trained on the original representation, called Euclidean, and representations obtained with three different metric learning algorithms, including Conditional Entropy Metric Learning (CEML), Neighborhood Component Analysis (NCA), and the Entropy Gap Metric Learning (EGML) algorithms. We examine different types of features, such as time and frequency components, which input to the metric learning algorithm, and both linear and non-linear SVM are applied to compare the classification accuracies on a publicly available EEG data set for two subjects (Subject B and C). Although metric learning algorithms do not increase the classification accuracies, their interpretability using an importance measure we define here, helps understanding data organization and how much each EEG channel contributes to the classification. In addition, among the metric learning algorithms we investigated, EGML shows the most robust performance due to its ability to compensate for differences in scale and correlations among variables. Furthermore, from the observed variations of the importance maps on the scalp and the classification accuracy, selecting an appropriate feature such as clipping the frequency range has a significant effect on the outcome of metric learning and subsequent classification. In our case, reducing the range of the frequency components to 0–5 Hz shows the best interpretability in both Subject B and C and classification accuracy for Subject C. Our experiments support potential benefits of using metric learning algorithms by providing visual explanation of the data projections that explain the inter class separations, using importance. This visualizes the contribution of features that can be related to brain function.

Stealthy multiplicative attacks against cyber‐physical systems: A gap metric approach

AbstractIn this article, a stealthy multiplicative attack design scheme for cyber‐physical systems equipped with parity space based detector is proposed, with the aid of gap metric method. First, a novel stealthiness index defined by the gap metric is presented for multiplicative attacks, and the security analysis issue of parity space based detector is formulated. Then, the attack subspace satisfying the stealthiness constraint is constructed, and the subspace rotation is applied to parameterize the multiplicative attacks. Moreover, a joint optimization method is presented to design the stealthy multiplicative attacks. Finally, simulations on a flight vehicle demonstrate the effectiveness of the proposed methods.

Efficiency Enhancements of Wind Energy Conversion Systems Using Soft Switching Multiple Model Predictive Control

The intermittent nature of wind speed imposesa nonlinear behavior on the dynamics of the wind turbine. Consequently, relying on one linear controller tuned to a single specific operating point cannot guarantee a feasible performance in the whole operating region despite providing a fast solution. Besides, wind speed variations cause oscillations in the output power of the wind turbine. To tackle these issues, the Soft Switching Multiple Model Predictive Control (SSMMPC) technique is introduced in which the nonlinear dynamical model of the wind turbine is approximated by defining multiple linear models around various operating points. The gap metric is used as a mathematical tool to construct a bank of multiple linear models that results in a bank of linear controllers. By assuming a relatively small gap threshold value between the neighboring linear models, the soft switching is guaranteed. In this way, there will be no unexpected impulsive behavior in the control input during switching from one MPC to another. The closed loop system stability is studied using Lyapunov theory and a novel switching stability proof based on probabilities is provided. The SSMMPC performance is investigated and compared with a TSR-based controller and a bidirectional control method through simulations using the NREL 10 kW wind turbine model and experimental results on a 7.5 kW wind turbine drivetrain. The results corroborate the improvements that can be attained by using SSMMPC in terms of more captured energy, better maximum power point tracking quality, lower generator torque oscillations, and smoother output power curve.

Poverty Profile and Health Dynamics of Indigenous People

This paper examines the critical aspect of health dynamics in the context of poverty and development of Indigenous people (Agta Isarog &amp; Agta Tabangnon) in Mt. Isarog, Southern Luzon, the Philippines. The datasets were gathered from the Community-Based Monitoring System (CBMS) of Goa Municipality, complemented by IP Censuses of 2018-2019. The core poverty indicators were analyzed in aggregated and disaggregated approaches. The poverty of each locality differs yet a large portion of the entire households and population of indigenous people are living below the poverty and food thresholds. In addition, the poverty incidence, gap, and severity using headcount ratios, gap metrics, squared gap statistics, and Watts indices were evaluated. It has been revealed that the poverty of Indigenous people is moderate to intense and manageable through intervention programs and policy initiatives. It then subsequently characterized the variables of health dynamics which vary per locality, and have been impacting poverty across all barangays. To confirm whether health dynamics predict poverty occurrences, Logistic regression models were estimated in an individual and consolidated manner. Results confirm that health dynamics significantly predict poverty outcomes.

Unmet Aspirations and Urban Malaise

This article analyses the gap between human aspirations concerning self-enhancement and corresponding outcomes in ten western European countries. Utilizing individual data for 14,300 respondents from the European Social Survey, four self-enhancement gap metrics are created: (1) the Ambition gap; (2) the Success gap; (3) the Wealth gap; and (4) the Authority gap. The findings suggest that subjective well-being (SWB) appears to be higher in rural than in urban communities. One reason for lower SWB among urban residents relates to their higher aspirations in certain areas of life. However, urban areas are apparently able to meet the financial expectations of their inhabitants far better than rural areas are, whereas an unmet craving for, e.g., success in rural areas appears not to affect SWB at all. Overall, there is a strong association between unmet aspirations and lower satisfaction with life. The added value of this paper is that it goes beyond existing explanations of the reasons behind urban malaise in developed economies.

Optimal Robustness in the Gap Metric for Ltv Systems

Optimal Robustness in the Gap Metric for Ltv Systems