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Morris Hansen made seminal contributions to the early development of sampling theory, including convincing government survey administrators to use probability sampling as opposed to nonprobability (NP) methods like quota sampling. He codified many of the early results in design-based sampling theory in his 1953 two-volume set co-authored with Hurwitz and Madow. Since those developments, the explicit use of models has proliferated in sampling for use in basic point estimation, nonresponse and noncoverage adjustment, imputation, and a variety of other areas. This paper summarizes some of the early developments, controversies in the design-based versus model-based debate, and uses of models for inference from probability and NP samples. [ABSTRACT FROM AUTHOR]

Daerah penelitian ini terletak di Cekungan Bonaparte, Australia. Dalam memodelkan bawah permukaan dengan metode seismik inversi impedansi akustik, peneliti kebanyakan hanya menggunakan seismik inversi berbasis model based. Pada penelitian ini, penulis telah melakukan pemodelan impedansi akustik (IA) bawah permukaan dengan menggunakan metode seismik post-stack berbasis rekursif, model based, dan sparse spike. Penelitian ini bertujuan untuk mengetahui model impedansi akustik serta persebaran porositas dari hubungan log porositas dan impedansi akustik (IA) hasil inversi pada zona target daerah penelitian. Penelitian ini dilakukan menggunakan data seismik 3D post stack dan 4 data sumur yaitu MKS-1, MKS-2, MKS-3 dan MKS-4. Berdasarkan hasil pengolahan diperoleh bahwa persebaran impedansi akustik pada area target dengan seismik inversi berbasis rekursif, model based, dan sparse spike memiliki rentang IA sekitar 10000 ((ft/s)*(g/cc)) – 50000 ((ft/s)*(g/cc)) yang merupakan kisaran impedansi akustik untuk litologi sand berfluida – clean sand, serta memiliki persebaran porositas prediksi dari hasil seismik inversi memiliki rentang sekitar 0,037 – 0,176. Berdasarkan analisis kuantitatif terlihat bahwa seismik inversi sparse spike paling cocok dalam memodelkan parameter impedansi akustik dan porositas prediksi daerah penelitian dengan standar deviasi error sebesar 1867,167 untuk impedansi akustik prediksi dan 0,010 untuk porositas prediksi.

Fault detection and diagnosis (FDD) is of utmost importance in ensuring the safety and reliability of electric vehicles (EVs). The EV's power train and energy storage, namely the electric motor drive and battery system, are critical components that are susceptible to different types of faults. Failure to detect and address these faults in a timely manner can lead to EV malfunctions and potentially catastrophic accidents. In the realm of EV applications, Permanent Magnet Synchronous Motors (PMSMs) and lithium-ion battery packs have garnered significant attention. Consequently, fault detection methods for PMSMs and their drives, as well as for lithium-ion battery packs, have become a prominent area of research. An effective FDD approach must possess qualities such as accuracy, speed, sensitivity, and cost-effectiveness. Traditional FDD techniques include model-based and signal-based methods. However, data-driven approaches, including machine learning-based methods, have recently gained traction due to their promising capabilities in fault detection. This paper aims to provide a comprehensive overview of potential faults in EV motor drives and battery systems, while also reviewing the latest state-of-the-art research in EV fault detection. The information presented herein can serve as a valuable reference for future endeavors in this field. [ABSTRACT FROM AUTHOR]

Immunization Information Systems are confidential computerized population-based systems that collect data from vaccination providers on individual vaccinations administered along with limited patient-level characteristics. Through a data use agreement, Centers for Disease Control and Prevention obtains the individual-level data and aggregates the number of vaccinations for geographical statistical areas defined by the US Census Bureau (counties or equivalent statistical entities) for each vaccine included in system. Currently, 599 counties, covering 11 states, collect and report data using a uniform protocol. We combine these data with inter-decennial population counts from the Population Estimates Program in the US Census Bureau and several covariates from a variety of sources to develop model-based estimates for each of the 3,142 counties in 50 states and the District of Columbia and then aggregate to the state and national levels. We use a hierarchical Bayesian model and Markov Chain Monte Carlo methods to obtain draws from the posterior predictive distribution of the vaccination rates. We use posterior predictive checks and cross-validation to assess the goodness of fit and to validate the models. We also compare the model-based estimates to direct estimates from the National Immunization Surveys. [ABSTRACT FROM AUTHOR]

Purpose: To improve the MRI reconstruction performance of model‐based networks and to alleviate their large demand for GPU memory. Methods: A model‐based neural network with enhanced deep learned regularizers (MEDL‐Net) was proposed. The MEDL‐Net is separated into several submodules, each of which consists of several cascades to mimic the optimization steps in conventional MRI reconstruction algorithms. Information from shallow cascades is densely connected to latter ones to enrich their inputs in each submodule, and additional revising blocks (RB) are stacked at the end of the submodules to bring more flexibility. Moreover, a composition loss function was designed to explicitly supervise RBs. Results: Network performance was evaluated on a publicly available dataset. The MEDL‐Net quantitatively outperforms the state‐of‐the‐art methods on different MR image sequences with different acceleration rates (four‐fold and six‐fold). Moreover, the reconstructed images showed that the detailed textures are better preserved. In addition, fewer cascades are required when achieving the same reconstruction results compared with other model‐based networks. Conclusion: In this study, a more efficient model‐based deep network was proposed to reconstruct MR images. The experimental results indicate that the proposed method improves reconstruction performance with fewer cascades, which alleviates the large demand for GPU memory. [ABSTRACT FROM AUTHOR]

Cognitive maps enable efficient inferences from limited experience that can guide novel decisions. We tested whether the hippocampus (HC), entorhinal cortex (EC), and ventromedial prefrontal cortex (vmPFC)/medial orbitofrontal cortex (mOFC) organize abstract and discrete relational information into a cognitive map to guide novel inferences. Subjects learned the status of people in two unseen 2D social hierarchies, with each dimension learned on a separate day. Although one dimension was behaviorally relevant, multivariate activity patterns in HC, EC, and vmPFC/mOFC were linearly related to the Euclidean distance between people in the mentally reconstructed 2D space. Hubs created unique comparisons between the hierarchies, enabling inferences between novel pairs. We found that both behavior and neural activity in EC and vmPFC/mOFC reflected the Euclidean distance to the retrieved hub, which was reinstated in HC. These findings reveal how abstract and discrete relational structures are represented, are combined, and enable novel inferences in the human brain.

Train wheel flats are formed when wheels slip on rails. Crucial for passenger comfort and the safe operation of train systems, early detection and quantification of wheel-flat severity without interrupting railway operations is a desirable and challenging goal. Our method involves identifying the wheel-flat size by using a model updating strategy based on dynamic measurements. Although measurement and modelling uncertainties influence the identification results, they are rarely taken into account in most wheel-flat detection methods. Another challenge is the interpretation of time series data from multiple sensors. In this article, the size of the wheel flat is identified using a model-falsification approach that explicitly includes uncertainties in both measurement and modelling. A two-step important point selection method is proposed to interpret high-dimensional time series in the context of inverse identification. Perceptually important points, which are consistent with the human visual identification process, are extracted and further selected using joint entropy as an information gain metric. The proposed model-based methodology is applied to a field train track test in Singapore. The results show that the wheel-flat size identified using the proposed methodology is within the range of true observations. In addition, it is also shown that the inclusion of measurement and modelling uncertainties is essential to accurately evaluate the wheel-flat size because identification without uncertainties may lead to an underestimation of the wheel-flat size. [ABSTRACT FROM AUTHOR]

Anomaly detection of multi-dimensional time-series data is a key research area, and the analysis of control, switching, and other state signals (i.e., industrial state quantity time series) is of particular importance to the operational sciences. When only the limited values of industrial state quantities are taken in the discrete set, there is no continuous change trend, making it difficult to achieve good results when applying analogue anomaly detection methods directly. In this study, assuming a correlation between the time series of state and analogue quantities in industrial systems, a model for anomaly detection in state quantity time-series data is built through a correlation supported by long short-term memory, and the model is verified using real physical process data. These results demonstrate that the proposed method is superior to extant industrial time-series models. Thus far, no studies focusing on the anomaly detection of two-state quantity time-series outliers have been performed. We believe that the research problem addressed herein and the proposed method contribute an interesting design methodology for the anomaly detection of time-series data in IIoT. [ABSTRACT FROM AUTHOR]

T1 mapping is increasingly used in clinical practice and research studies. With limited scan time, existing techniques often have limited spatial resolution, contrast resolution and slice coverage. High fat concentrations yield complex errors in Look–Locker T1 methods. In this study, a dual‐echo 2D radial inversion‐recovery T1 (DEradIR‐T1) technique was developed for fast fat–water separated T1 mapping. The DEradIR‐T1 technique was tested in phantoms, 5 volunteers and 28 patients using a 3 T clinical MRI scanner. In our study, simulations were performed to analyze the composite (fat + water) and water‐only T1 under different echo times (TE). In standardized phantoms, an inversion‐recovery spin echo (IR‐SE) sequence with and without fat saturation pulses served as a T1 reference. Parameter mapping with DEradIR‐T1 was also assessed in vivo, and values were compared with modified Look–Locker inversion recovery (MOLLI). Bland–Altman analysis and two‐tailed paired t‐tests were used to compare the parameter maps from DEradIR‐T1 with the references. Simulations of the composite and water‐only T1 under different TE values and levels of fat matched the in vivo studies. T1 maps from DEradIR‐T1 on a NIST phantom (Pcomp = 0.97) and a Calimetrix fat–water phantom (Pwater = 0.56) matched with the references. In vivo T1 was compared with that of MOLLI: Rcomp2=0.77; Rwater2=0.72. In this work, intravoxel fat is found to have a variable, echo‐time‐dependent effect on measured T1 values, and this effect may be mitigated using the proposed DRradIR‐T1. [ABSTRACT FROM AUTHOR]

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Reinforcement Learning (RL) comes with the promise of automating network management. However, due to its trial-and-error learning approach, model-based RL (MBRL) is not applicable in some network management scenarios. This paper explores the potential of using Automated Planning (AP) to achieve this MBRL in the functional areas of network management. In addition, a comparison of several integration strategies of AP and RL is depicted. We also describe an architecture that realizes a cognitive management control loop by combining AP and RL. Our experiments evaluate on a simulated environment evidence that the combination proposed improves model-free RL but demonstrates lower performance than Deep RL regarding the reward and convergence time metrics. Nonetheless, AP-based MBRL is useful when the prediction model needs to be understood and when the high computational complexity of Deep RL can not be used. [ABSTRACT FROM AUTHOR]

Fault detection and diagnosis (FDD) is of utmost importance in ensuring the safety and reliability of electric vehicles (EVs). The EV’s power train and energy storage, namely the electric motor drive and battery system, are critical components that are susceptible to different types of faults. Failure to detect and address these faults in a timely manner can lead to EV malfunctions and potentially catastrophic accidents. In the realm of EV applications, Permanent Magnet Synchronous Motors (PMSMs) and lithium-ion battery packs have garnered significant attention. Consequently, fault detection methods for PMSMs and their drives, as well as for lithium-ion battery packs, have become a prominent area of research. An effective FDD approach must possess qualities such as accuracy, speed, sensitivity, and cost-effectiveness. Traditional FDD techniques include model-based and signal-based methods. However, data-driven approaches, including machine learning-based methods, have recently gained traction due to their promising capabilities in fault detection. This paper aims to provide a comprehensive overview of potential faults in EV motor drives and battery systems, while also reviewing the latest state-of-the-art research in EV fault detection. The information presented herein can serve as a valuable reference for future endeavors in this field.

Effective planning involves knowing where different actions take us. However, natural environments are rich and complex, leading to an exponential increase in memory demand as a plan grows in depth. One potential solution is to filter out features of the environment irrelevant to the task at hand. This enables a shared model of transition dynamics to be used for planning over a range of different input features. Here, we asked human participants (13 male, 16 female) to perform a sequential decision-making task, designed so that knowledge should be integrated independently of the input features (visual cues) present in one case but not in another. Participants efficiently switched between using a low-dimensional (cue independent) and a high-dimensional (cue specific) representation of state transitions. fMRI data identified the medial temporal lobe as a locus for learning state transitions. Within this region, multivariate patterns of BOLD responses were less correlated between trials with differing input features but similar state associations in the high dimensional than in the low dimensional case, suggesting that these patterns switched between separable (specific to input features) and shared (invariant to input features) transition models. Finally, we show that transition models are updated more strongly following the receipt of positive compared with negative outcomes, a finding that challenges conventional theories of planning. Together, these findings propose a computational and neural account of how information relevant for planning can be shared and segmented in response to the vast array of contextual features we encounter in our world. [ABSTRACT FROM AUTHOR]

Introduction In major depressive disorder (MDD), reward-based decision-making (DM) is frequently impaired: e.g. patients don’t engage in pleasant activities as much as healthy subjects. Put differently, previous and expected future rewards have less reinforcing effects on DM. This study investigated two experimentally well-observable reward-based DM modes, namely model-based (based on cognitive models of the environment) and model-free (based on previous experience) DM. Objectives We hypothesized that model-based training can improve reward-based DM in patients with MDD. Answers to these questions could enhance the development of cognitive-behavioral therapeutic interventions. Methods 27 patients with MDD were recruited and assessed with psychometry. All patients performed the „two-step Markov decision-task“ (Daw, 2011), which allows the simultaneous investigation of model-based and model-free DM via computational modelling. All subjects performed the task 4 times: at the beginning and at the end of 2 assessment days (session-interval: 4 days). Subjects were randomly allocated to an intervention group, which performed model-based training, and a control group, which performed model-free training. The main outcomes of training effect were the influence of model-based reward expectations on decisions (quantified by computational modelling parameters) and overall monetary reward-success. Results In all patients, the influence of model-based reward expectations on decisions increased after training. However, there was no significant effect of group allocation. Furthermore, patients in the intervention group did not achieve significantly higher overall monetary reward. Conclusions Results suggest that in MDD, the influence of model-based reward expectations on decisions can be improved regardless of specific training type. Future studies should investigate the effects on everyday functioning. Disclosure No significant relationships.

Abstract Background Large area forest inventories often use regular grids (with a single random start) of sample locations to ensure a uniform sampling intensity across the space of the surveyed populations. A design-unbiased estimator of variance does not exist for this design. Oftentimes, a quasi-default estimator applicable to simple random sampling (SRS) is used, even if it carries with it the likely risk of overestimating the variance by a practically important margin. To better exploit the precision of systematic sampling we assess the performance of five estimators of variance, including the quasi default. In this study, simulated systematic sampling was applied to artificial populations with contrasting covariance structures and with or without linear trends. We compared the results obtained with the SRS, Matérn’s, successive difference replication, Ripley’s, and D’Orazio’s variance estimators. Results The variances obtained with the four alternatives to the SRS estimator of variance were strongly correlated, and in all study settings consistently closer to the target design variance than the estimator for SRS. The latter always produced the greatest overestimation. In populations with a near zero spatial autocorrelation, all estimators, performed equally, and delivered estimates close to the actual design variance. Conclusion Without a linear trend, the SDR and DOR estimators were best with variance estimates more narrowly distributed around the benchmark; yet in terms of the least average absolute deviation, Matérn’s estimator held a narrow lead. With a strong or moderate linear trend, Matérn’s estimator is choice. In large populations, and a low sampling intensity, the performance of the investigated estimators becomes more similar.

This article introduces a control design framework for modern dc–dc topologies with high-order dynamics. In particular, model-based and model-free approaches using high-order controllers are introduced. The model-based approach permits the use of a minimum number of sensors, even for converters with a high number of components (e.g., multilevel, quadratic, input/output LC filter-based converters, etc.). This setting does not require estimation of state variables and its gain tuning can be achieved by selecting time-response specifications. The proposed model-free approach control exhibits the same, as well as some additional characteristics. Namely, controllers can be synthesized without the requirement of an explicit dynamic model, and gain tuning with guaranteed stability is directly achieved from measurement data. The latter controller is implemented in discrete time, which facilitates a digital implementation. The proposed approaches are validated through the control design and closed-loop implementation of a sixth-order topology. [ABSTRACT FROM AUTHOR]

Investing in strangers in a socio-economic exchange is risky, as we may be uncertain whether they will reciprocate. Nevertheless, the potential rewards for cooperating can be great. Here, we used a cross sectional sample (n = 784) to study how the challenges of cooperation versus defection are negotiated across an important period of the lifespan: from adolescence to young adulthood (ages 14 to 25). We quantified social behaviour using a multi round investor-trustee task, phenotyping individuals using a validated model whose parameters characterise patterns of real exchange and constitute latent social characteristics. We found highly significant differences in investment behaviour according to age, sex, socio-economic status and IQ. Consistent with the literature, we showed an overall trend towards higher trust from adolescence to young adulthood but, in a novel finding, we characterized key cognitive mechanisms explaining this, especially regarding socio-economic risk aversion. Males showed lower risk-aversion, associated with greater investments. We also found that inequality aversion was higher in females and, in a novel relation, that socio-economic deprivation was associated with more risk averse play.

Reinforcement Learning (RL) comes with the promise of automating network management. However, due to its trial-and-error learning approach, model-based RL (MBRL) is not applicable in some network management scenarios. This paper explores the potential of using Automated Planning (AP) to achieve this MBRL in the functional areas of network management. In addition, a comparison of several integration strategies of AP and RL is depicted. We also describe an architecture that realizes a cognitive management control loop by combining AP and RL. Our experiments evaluate on a simulated environment evidence that the combination proposed improves model-free RL but demonstrates lower performance than Deep RL regarding the reward and convergence time metrics. Nonetheless, AP-based MBRL is useful when the prediction model needs to be understood and when the high computational complexity of Deep RL can not be used.

Investing in strangers in a socio-economic exchange is risky, as we may be uncertain whether they will reciprocate. Nevertheless, the potential rewards for cooperating can be great. Here, we used a cross sectional sample (n = 784) to study how the challenges of cooperation versus defection are negotiated across an important period of the lifespan: from adolescence to young adulthood (ages 14 to 25). We quantified social behaviour using a multi round investor-trustee task, phenotyping individuals using a validated model whose parameters characterise patterns of real exchange and constitute latent social characteristics. We found highly significant differences in investment behaviour according to age, sex, socio-economic status and IQ. Consistent with the literature, we showed an overall trend towards higher trust from adolescence to young adulthood but, in a novel finding, we characterized key cognitive mechanisms explaining this, especially regarding socio-economic risk aversion. Males showed lower risk-aversion, associated with greater investments. We also found that inequality aversion was higher in females and, in a novel relation, that socio-economic deprivation was associated with more risk averse play. [ABSTRACT FROM AUTHOR]

When direct experience is unavailable, animals and humans can imagine or infer the future to guide decisions. Behavior based on direct experience versus inference may recruit partially distinct brain circuits. In rodents, the orbitofrontal cortex (OFC) contains neural signatures of inferred outcomes, and OFC is necessary for behavior that requires inference but not for responding driven by direct experience. In humans, OFC activity is also correlated with inferred outcomes, but it is unclear whether OFC activity is required for inference-based behavior. To test this, we used noninvasive network-based continuous theta burst stimulation (cTBS) in human subjects (male and female) to target lateral OFC networks in the context of a sensory preconditioning task that was designed to isolate inference-based behavior from responding that can be based on direct experience alone. We show that, relative to sham, cTBS targeting this network impairs reward-related behavior in conditions in which outcome expectations have to be mentally inferred. In contrast, OFC-targeted stimulation does not impair behavior that can be based on previously experienced stimulus-outcome associations. These findings suggest that activity in the targeted OFC network supports decision-making when outcomes have to be mentally simulated, providing converging cross-species evidence for a critical role of OFC in model-based but not model-free control of behavior. [ABSTRACT FROM AUTHOR]

Abstract There is considerable physiological and clinical evidence of harm and increased risk of death associated with dysglycemia in critical care. However, glycemic control (GC) currently leads to increased hypoglycemia, independently associated with a greater risk of death. Indeed, recent evidence suggests GC is difficult to safely and effectively achieve for all patients. In this review, leading experts in the field discuss this evidence and relevant data in diabetology, including the artificial pancreas, and suggest how safe, effective GC can be achieved in critically ill patients in ways seeking to mimic normal islet cell function. The review is structured around the specific clinical hurdles of: understanding the patient’s metabolic state; designing GC to fit clinical practice, safety, efficacy, and workload; and the need for standardized metrics. These aspects are addressed by reviewing relevant recent advances in science and technology. Finally, we provide a set of concise recommendations to advance the safety, quality, consistency, and clinical uptake of GC in critical care. This review thus presents a roadmap toward better, more personalized metabolic care and improved patient outcomes.

In an effort to improve plant growth and to achieve high yield and/or quality, irrigation scheduling (IS) seeks to provide plants with appropriate quantities of water at appropriate times. To better understand irrigation scheduling's main processes and principles, its four most common methods of operation—(1) evapotranspiration and water balance (ET-WB), (2) soil moisture (Θ) status, (3) plant water status, and (4) models—along with their pros and cons are introduced and compared. Irrigation applications, including software, programs, and associated controllers are introduced. Given that some of these methods focus on Θ or plant responses to soil moisture, the determination of target soil moisture levels, along with estimates (either calculated or measured) of current soil moisture status are key to both scheduling irrigations, and the precise replenishment of soil moisture to target levels. Accordingly, factors in the soil-crop-atmosphere system affecting soil moisture must be considered in the scheduling process. As all four types of IS methods focus on soil water content, which serves as a bridge between irrigation management and crop water requirements for growth, future scheduling methods should focus on the management of soil moisture based on an advanced understanding of its effects on crop growth either by the integration of existing IS methods or the development of new models, using intelligent algorithms. Using these approaches, more practical, accurate, and easily adaptable IS applications should be developed for real-time farming operations. Weather station networks and online data access should be enhanced to better serve these IS applications. [ABSTRACT FROM AUTHOR]

Background: Large area forest inventories often use regular grids (with a single random start) of sample locations to ensure a uniform sampling intensity across the space of the surveyed populations. A design-unbiased estimator of variance does not exist for this design. Oftentimes, a quasi-default estimator applicable to simple random sampling (SRS) is used, even if it carries with it the likely risk of overestimating the variance by a practically important margin. To better exploit the precision of systematic sampling we assess the performance of five estimators of variance, including the quasi default. In this study, simulated systematic sampling was applied to artificial populations with contrasting covariance structures and with or without linear trends. We compared the results obtained with the SRS, Matérn’s, successive difference replication, Ripley’s, and D’Orazio’s variance estimators. Results: The variances obtained with the four alternatives to the SRS estimator of variance were strongly correlated, and in all study settings consistently closer to the target design variance than the estimator for SRS. The latter always produced the greatest overestimation. In populations with a near zero spatial autocorrelation, all estimators, performed equally, and delivered estimates close to the actual design variance. Conclusion: Without a linear trend, the SDR and DOR estimators were best with variance estimates more narrowly distributed around the benchmark; yet in terms of the least average absolute deviation, Matérn’s estimator held a narrow lead. With a strong or moderate linear trend, Matérn’s estimator is choice. In large populations, and a low sampling intensity, the performance of the investigated estimators becomes more similar. [ABSTRACT FROM AUTHOR]

Naturalistic observations show that decisions to avoid or escape predators occur at different spatiotemporal scales and that they are supported by different computations and neural circuits. At their extremes, proximal threats are addressed by a limited repertoire of reflexive and myopic actions, reflecting reduced decision and state spaces and model-free (MF) architectures. Conversely, distal threats allow increased information processing supported by model-based (MB) operations, including affective prospection, replay, and planning. However, MF and MB computations are often intertwined, and under conditions of safety the foundations for future effective reactive execution can be laid through MB instruction of MF control. Together, these computations are associated with distinct population codes embedded within a distributed defensive circuitry whose goal is to determine and realize the best policy. Decisions to avoid or escape predators occur at different spatiotemporal scales, resulting in different computations and neural circuits. At their extremes, surprising or proximal threats will reduce decision and state space and utilize model-free architectures, while distant threats allow increased information processing supported by model-based operations. Model-free and model-based computations, however, are often intertwined. Furthermore, under conditions of safety the foundations for effective reactive execution in the future can be laid through model-based instruction of model-free control. Prospective planning can also be enabled. Together, these computations reflect distinct population codes embedded within a distributed defensive circuitry whose goal is to determine and realize the best policy. [ABSTRACT FROM AUTHOR]

This article presents new model-based adaptive motion control algorithms for an underactuated aerial robotic manipulator comprised of a conventional multirotor unmanned aerial vehicle (UAV) and a multilink serial robotic arm. Two control strategies are proposed to allow the manipulator to operate in the joint and task spaces. The proposed controllers incorporate the combined dynamics of the UAV base and the serial arm, and properly account for the two degrees of underactuation in the plane of the propellers. The control developments follow the so-called method of virtual decomposition, which by employing a Newtonian formulation of the UAV–manipulator dynamics, sidesteps the complexities associated with the derivation and parametrization of a lumped Lagrangian dynamics model. The algorithms are guaranteed to produce feasible control commands as the constraints associated with the underactuation are explicitly considered in the control calculations. A Lyapunov analysis demonstrates the stability of the overall system and the convergence of the motion tracking errors. Experimental results show the effectiveness of the proposed control strategies. [ABSTRACT FROM AUTHOR]

Abstract For addressing the energy waste resulted by over-ventilation or under-ventilation in conventional demand-controlled ventilation system, an air balancing strategy is proposed to solve the over-ventilation and under-ventilation problems of the multi-zone demand-controlled ventilation system. In this study, an energy-saving oriented mathematical model is constructed to simulate the non-linear behavior of the multi-zone ventilation system and Bayesian linear regression supervised machine learning algorithm is used to estimate the unknown parameters of the constructed model. On the basis of the developed model, the damper control method is established to determine the position of the damper according to the desired airflow rate to ensure the system well-balanced. Therefore, with the constructed system model and the damper control method, the system can be well-balanced to overcome the disadvantages of over-ventilation and under-ventilation, and consumes less energy compared to the system that are not balanced. The performance of the proposed air balancing strategy for demand-controlled ventilation system is practically tested in an experimental rig with five terminals and validated by comparing to the demand-controlled ventilation strategy without air balancing. The experimental results demonstrate that the proposed strategy achieved the desired airflow rate within 4.6% maximum absolute percentage error, and also achieved a maximum value 14.3% for fan power reduction compared to conventional the strategy without air balancing. Highlights • A model is built to simulate the non-linear relations of the ventilation system. • Bayesian linear regression algorithm is used to train the developed model. • Damper positon control method is developed to obtain the damper operating angle. [ABSTRACT FROM AUTHOR]

In this paper a novel fault detection, isolation, and identification (FDI&E) scheme using a coupling diagnosis method with the integration of the model-based method and unsupervised learning algorithm is proposed and developed for monitoring gas turbine sensor faults, which represents an integration of Square Root Cubature Kalman Filters (SRCKF) and an improved Density-Based Spatial Clustering of Application with Noise (DBSCAN) algorithm. A detection indicator produced by SRCKF with a specific hypothesis is used for extracting sensor fault features against process and measurement noise, as well as operating conditions. Then, an improved DBSCAN is implemented based on a voting scheme to detect and isolate the faulty sensors. Finally, a residual-based fault estimation scheme is proposed to track sensor fault evolution and help to judge the types of faults. Moreover, the observability of the model involved is analyzed to verify the stable operation of the FDI&E scheme. Various experiments for single and concurrent sensor fault scenarios in a dual-spool gas turbine prototype during a whole flight mission are conducted to demonstrate the effectiveness of the proposed FDI&E scheme. Moreover, comparative studies confirm the superiority of our proposed FDI&E scheme than the existing methods in terms of promptness and robustness of the sensor FDI.

Spatial epidemiology is description and analysis of geographically indexed health related data which contains demographic, environmental, socioeconomic, behavioral factors. Spatial sampling techniques is to collect samples/data in two or three dimensional space. Design based sampling, Model based sampling, adaptive sampling, optimal sampling and kriging are some of the types of spatial sampling techniques. This article aims to discuss various types of spatial sampling techniques which will sensitize the medical and public health researchers about this Methodology. [ABSTRACT FROM AUTHOR]

Highlights • Review the literature of railway efficient railway operations from a data driven perspective. • Identify issues in current data and models related to data volume variety and validity. • Derive a roadmap for data driven applications in energy efficient railway operations. Abstract Railway systems must increase their performance and economic competitiveness to remain an effective and efficient transport mode. Energy efficiency goals are one of the main drivers for the future evolution of planning and operations of transport systems. An opportunity to improve energy efficiency together with reliability and feasibility of railway systems come from the huge amount of data being currently collected and available in the future. The hidden potential in large sets of data for improving energy efficiency can be fully exploited through novel, data-driven approaches. This paper discusses the relation of those future approaches with the current state of the art and challenges, highlighting natural advantages and possible weak points. We identify dimensions within the current literature describing the suitability of current approaches to embrace the data revolution, and the possible enhancements resulting from that. We refer to practical test cases based on real on-board monitoring of electric trains in Switzerland to identify current and future challenges in improving energy efficiency of train operations. We conclude with a discussion and a roadmap on the introduction of data-driven approaches for improving energy efficiency of railway systems. [ABSTRACT FROM AUTHOR]

Wireless mesh networks begin to be used as an infrastructure of cyber-physical systems. A critical issue in developing wireless cyber physical systems (WCPSs) is the limited amount of energy available in the nodes. Energy consumption analysis can help designer to conduct a power-aware design process. In this paper, we propose a model based energy consumption analysis framework at architecture level for WCPSs. We extract event chains from the architecture model. With the energy consumption model for processing each type of event, we can estimate the energy consumption for each control loop and each node, as well as the overall energy consumption. All these energy consumption indexes can help us to design a performance and energy consumption balanced WCPS. [ABSTRACT FROM AUTHOR]

An underwater manipulator is a complex system, highly non-linear and subject to disturbances caused by underwater effects. To obtain a reliable system, robust control strategies have to be designed for the manipulator. The main contribution of this paper is the development of the low-level position/force control structure for an underwater manipulator. The proposed control strategy is planned in the operational space and combines together the parallel control structure for position/force applications with the sliding mode theory and the manipulator model information. The dynamic model of the system incorporates the hydrodynamic effects and an approximation of the end-effector force contact with the environment. This paper presents a method for computing the interaction force at the end-effector in the absence of a force–torque sensor. The control structure is validated through a Lyapunov-stability approach and experimental results. The control structure is tested on a 6 degrees-of-freedom underwater manipulator interacting with the underwater environment. [ABSTRACT FROM AUTHOR]

Aim Multivariate mixture models offer the ability to streamline the typically multi-stage process of vegetation classification and mapping into a single, simultaneous analytical step. Our aim is to demonstrate the model's utility over large and diverse areas, with comparison to existing classification and mapping conventions. Location New South Wales, Australia. Methods We demonstrate a statistical model that uses both multivariate species data and environmental covariate data to simultaneously classify observations into groups and predict those groups out into environmental and geographical space. We used two large data sets to demonstrate the method: an ~810,000 km2 area with 4,715 sites and 488 species and a ~220,000 km2 area with 5,183 sites and 446 species. A range of topographic, terrain, climate and soil predictors were used as environmental variables, including future projected climate variables. Models can be fit with the R package ' RCPmod.' Results The method results in probabilistic memberships to vegetation assemblages, for both the classification and map. There was general agreement between our approach and existing vegetation classification conventions, but we explore some notable differences. We were able to examine the environmental gradients that define the predicted vegetation distributions, as well as make predictions about how the distribution of species assemblages might shift as a result of climate change. Main conclusions Our approach tightens the link between description of biodiversity patterns and their depiction in space, by considering both biotic and spatially explicit abiotic information simultaneously. The method allows uncertainty to be quantified objectively across a consistent set of groups for both vegetation classification and mapping, which is rarely the case in traditional multi-stage approaches. A simultaneous modelling approach increases capacity to make predictions into varying spatial, temporal and environmental dimensions, providing new ecological insights and increasing capacity for evidence-based decision making. [ABSTRACT FROM AUTHOR]

The high-speed, heavy-load, high-traffic density of railway demands the high reliability of a traction power supply system (TPSS). To achieve this, a diagnosis system is essential. This paper presents a reliable, general, and easy-to-maintain diagnosis system, based on the system model with the purpose of online fault detection, location, and recognition of the TPSS. Two kinds of model-based diagnosis (MBD) are combined to achieve high diagnosis efficiency and recognition ability of fault types. The model library and diagnosis engine are the two main parts of the diagnosis system, both of which have the two-level structure that contains a consistency-based level and an abductive level. In the consistency-based level, the model and diagnosis engine of consistency-based MBD are established, which contribute to the fault detection and diagnosis candidate generation. The minimal support environment offline searching algorithm and binary particle swarm optimization with a genetic algorithm are proposed to enhance the consistency-based reasoning. In the abductive level, the model and diagnosis engine of abductive MBD are utilized to locate the faults and recognize the fault types. With the diagnosis candidates, the abductive reasoning efficiency can be dramatically improved. In addition, to improve the fault location and recognition performance, the Bayes theorem is utilized in the abductive reasoning. As the system relies on the sensor information, a fault-tolerant strategy for fault reasoning is proposed to enhance the diagnosis system reliability. Finally, three cases are presented to illustrate the effectiveness and efficiency of this system. [ABSTRACT FROM PUBLISHER]

Bearingless machines are used for a variety of applications with demand for low mechanical loss, low wear and low contamination. These machines use contact-free magnetic suspension to levitate the rotor. The control of the machine requires precise radial and angular position information in order to ensure stable levitation. This information is usually obtained with two types of sensors: radial displacement sensors and angle sensors. Alternatively, an angle-sensorless control scheme can be used, reducing the complexity and the cost of the machine. While such a control is well known for conventional machines it is challenging to adapt it for bearingless machines. The reason is that most methods fail to provide the angle information at zero and at low speed but bearingless machines require knowledge about the rotor angle at all speeds in order to function. The theoretical mode of operation of a model based angle observer for zero and low speed operation of a bearingless machine was shown in previous publications. The observer obtains the rotor angle estimation error by analyzing the performance of the radial bearing and comparing it to the performance of a model with zero angle error. This observer can be used for operation at standstill and over the whole speed range. This paper provides a more detailed description of the non-idealities of the zero and low speed observer and presents results of machine operation without angle sensors. The generation of torque and force inside the machine is analyzed in more detail. Furthermore, it is shown how to combine the novel observer with a conventional, back electromotive force based, high speed angle observer. The experimentally verified results of this paper indicate that the novel observer can be used up to speeds at which back electromotive force estimation is possible. This allows the efficient, angle-sensorless operation of the machine over the whole speed range.

Computational psychiatry aims to apply mathematical and computational techniques to help improve psychiatric care. To achieve this, the phenomena under scrutiny should be within the scope of formal methods. As emotions play an important role across many psychiatric disorders, such computational methods must encompass emotions. Here, we consider formal valuation accounts of emotions. We focus on the fact that the flexibility of emotional responses and the nature of appraisals suggest the need for a model-based valuation framework for emotions. However, resource limitations make plain model-based valuation impossible and require metareasoning strategies to apportion cognitive resources adaptively. We argue that emotions may implement such metareasoning approximations by restricting the range of behaviors and states considered. We consider the processes that guide the deployment of the approximations, discerning between innate, model-free, heuristic, and model-based controllers. A formal valuation and metareasoning framework may thus provide a principled approach to examining emotions. [ABSTRACT FROM AUTHOR]

Infrastructure associated with coastal communities is likely to not only directly displace natural systems, but also leave environmental footprints' that stretch over multiple scales. Some coastal infrastructure will, there- fore, generate a hidden layer of habitat heterogeneity in sediment systems that is not immediately observable in classical impact assessment frameworks. We examine the hidden heterogeneity associated with one of the most ubiquitous coastal modifications; dense swing moorings fields. Using a model based geo-statistical framework we highlight the variation in sedimentology throughout mooring fields and reference locations. Moorings were correlated with patches of sediment with larger particle sizes, and associated metal(loid) concentrations in these patches were depressed. Our work highlights two important ideas i) mooring fields create a mosaic of habitat in which contamination decreases and grain sizes increase close to moorings, and ii) model- based frameworks provide an information rich, easy-to-interpret way to communicate complex analyses to stakeholders. [ABSTRACT FROM AUTHOR]