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Road extraction in rural areas is one of the most fundamental tasks in the practical application of remote sensing. In recent years, sample-driven methods have achieved state-of-the-art performance in road extraction tasks. However, sample-driven methods are prohibitively expensive and laborious, especially when dealing with rural roads with irregular curvature changes, narrow widths, and diverse materials. The template matching method can overcome these difficulties to some extent and achieve impressive road extraction results. This method also has the advantage of the vectorization of road extraction results, but the automation is limited. Straight line sequences can be substituted for curves, and the use of the color space can increase the recognition of roads and nonroads. A model-driven-to-sample-driven road extraction method for rural areas with a much higher degree of automation than existing template matching methods is proposed in this study. Without prior samples, on the basis of the geometric characteristics of narrow and long roads and using the advantages of straight lines instead of curved lines, the road center point extraction model is established through length constraints and gray mean contrast constraints of line sequences, and the extraction of some rural roads is completed through topological connection analysis. In addition, we take the extracted road center point and manual input data as local samples, use the improved line segment histogram to determine the local road direction, and use the panchromatic and hue, saturation, value (HSV) space interactive matching model as the matching measure to complete the road tracking extraction. Experimental results show that, for different types of data and scenarios on the premise, the accuracy and recall rate of the evaluation indicators reach more than 98%, and, compared with other methods, the automation of this algorithm has increased by more than 40%.

Variable displacement hydraulic pumps are applied to a wide range of fields for energy saving, but the displacement control is easily influenced by changes in dynamic characteristics depending on the operating point, and the control valve and pump displacement have constraints. Therefore, high control performance cannot be obtained without considering these nonlinearities. In a previous study, we designed a pump displacement control system based on a model predictive control (MPC) method that can consider various constraints at the design step. However, the previously presented control system requires the pre-designed reference trajectory of the pump displacement at the design step. Furthermore, the pump displacement cannot track to other reference trajectories. In this study, an extended MPC proposed in a previous study is combined with an adaptive model matching-based MPC with an inverse optimization method, proposed as a control system by the authors. This compensates for modeling errors and optimizes the weights of the evaluation function to achieve tracking to arbitrary time-varying reference trajectories using a virtual reference signal. To improve tracking performance, variable control input constraints, which are also proposed in our previous study, are introduced. The tracking performance of this control system for arbitrary time-varying reference trajectories have been verified by experiments. The experimental results have shown that the proposed control system achieves high tracking accuracy for an arbitrary time-varying reference trajectory and significantly reduces the man-hours for the parameter design of the control system.

BackgroundAlthough the efficacy of self-help cognitive-behavioral therapy (CBT) for depression has been well established, its feasibility in primary care settings is limited because of time and resource constraints. The goal of this study was to identify common elements of empirically supported (i.e., proven effective in controlled research) self-help CBTs and frameworks for effective use in practice.MethodsRandomized controlled trials (RCTs) for self-help CBTs for depression in primary care were systematically identified in Pubmed, PsycINFO, and CENTRAL. The distillation and matching model approach was used to abstract commonly used self-help techniques (practice elements). Study contexts associated with unique combinations of intervention elements were explored, including total human support dose (total face-to-face, telephone, and personalized email contact time recommended by the protocol), effective symptom domain (depression vs. general psychological distress), and severity of depression targeted by the study. Relative contribution to intervention success was estimated for individual elements and human support by conditional probability (CP, proportion of the number of times each element appeared in a successful intervention to the number of times it was used in the interventions identified by the review).ResultsTwenty-one interventions (12 successful) in 20 RCTs and 21 practice elements were identified. Cognitive restructuring, behavioral activation, and homework assignment were elements appearing in > 80% of successful interventions. The dose of human support was positively associated with the proportion of interventions that were successful in a significant linear fashion (CPs: interventions with no support, 0.20; 1–119 min of support, 0.60; 120 min of support, 0.83; p = 0.042). In addition, human support increased the probability of success for most of the extracted elements. Only social support activation, homework assignment, and interpersonal skills were highly successful (CPs ≥ 0.60) when minimal support was provided.DiscussionThese findings suggest that human support is an important component in creating an evidence-informed brief self-help program compatible with primary care settings.

Model matching algorithms are used to identify common elements in input models, which is a fundamental precondition for many software engineering tasks, such as merging software variants or views. If there are multiple input models, an n-way matching algorithm that simultaneously processes all models typically produces better results than the sequential application of two-way matching algorithms. However, existing algorithms for n-way matching do not scale well, as the computational effort grows fast in the number of models and their size. We propose a scalable n-way model matching algorithm, which uses multi-dimensional search trees for efficiently finding suitable match candidates through range queries. We implemented our generic algorithm named RaQuN   (Range Queries on N input models) in Java and empirically evaluate the matching quality and runtime performance on several datasets of different origins and model types. Compared to the state of the art, our experimental results show a performance improvement by an order of magnitude, while delivering matching results of better quality.

High-precision displacement control for water-hydraulic artificial muscles is challenging because of their strong hysteresis characteristics, which are difficult to be modeled precisely. Recently, data-driven control methods have attracted considerable attention because they do not explicitly use mathematical models, making the design much easier. In our previous work, we proposed a fictitious reference iterative tuning (FRIT)-based model predictive control (FMPC), which combines data-driven and model-based methods for the muscle, and showed its effectiveness because it can also consider input constraints. However, the problem in which control performance strongly depends on prior input-output data remains unsolved. Adaptive FRIT (A-FRIT) based on directional forgetting has also been proposed; however, achieving the desired transient performance is difficult because it cannot consider the input constraints, and there are no design parameters that directly determine the control performance. This paper proposes a novel data-driven adaptive model matching-based controller that combines MPC with the A-FRIT. The experimental results show that the proposed method can significantly improve the control performance and achieve high robustness against inappropriate initial experimental data while considering the input constraints in the design phase.

This paper proposes a novel two-stage method for the design of a suboptimal model-matching controller in an output feedback closed-loop system (OFCLS) using the concept of squared magnitude function (SMF). A streamlined procedure for selection of a reference model, based on a linear quadratic regulator (LQR) with integral action (LQRI) having optimum values for the elements of the weighting matrices and the degree of interaction is proposed. The degrees of the numerator and denominator polynomials of the elements of the OFCLS transfer function matrix (TFM) are obtained from those of the plant and the chosen controller structure. In the first stage of the controller design, taking the LQRI-based closed-loop system (LCLS) as a reference model, the OFCLS is obtained using the approximate model-matching (AMM) technique based on the SMF concept. The approximation method involves a higher-order approximation for stable multiple-input-multiple-output (MIMO) lower-order systems. In the second stage, controller parameters are obtained using the exact model-matching (EMM) method with information about the OFCLS and plant TFMs. The proposed controller design method outperforms the method presented in the literature on integral squared error index. The simulation and experimental results illustrate the effectiveness of the proposed method.

We present a sustainable multimodal transit system that integrates taxi-sharing with subways to alleviate traffic congestion and restore the cooperative relationship between taxis and subways. This study proposes a two-phase matching model based on optimization theory, in which pick-up/drop-off sequences for participants, as well as their motivation to shift to a TSS service, were considered. For the transportation system, achieving a reduction in vehicle miles is considered to be the matching objective. We tested the matching model using empirical taxi global positioning system (GPS) data for a typical morning rush hour in Beijing. The optimization model performs well for large-scale data and the optimal solution can be calculated quickly, which is ideal in a dynamic system. Furthermore, several sensitive analysis experiments were conducted to evaluate the performance of the TSS system. We found that approximately 23.13% of taxi users can be served by TSS transit, total taxi mileage can be reduced by 20.17%, and carbon dioxide emissions may be reduced by 15.16%. The proposed model and findings demonstrate that the TSS service considered here is a feasible multimodal transit mode, with the advantages of flexibility and sustainability, and has great potential for improving social benefits.

The tap-water-driven McKibben artificial muscle has many advantages and is expected to be applied in mechanical systems that require a high degree of cleanliness. However, the muscle has strong asymmetric hysteresis characteristics that depend on the load, and these problems prevent its widespread use. In this study, a novel control method, model predictive control with a servomechanism based on inverse optimization with adaptive model matching, was developed. This control method was applied to the muscle by using a high-precision mathematical model employing an asymmetric Bouc-Wen model. The experimental results show that the proposed approach achieved a high tracking performance for a given reference frequency, with a mean absolute error of 0.13 mm in the steady-state response and with easier controller tuning. Furthermore, the contributions of the controller elements of the proposed method were evaluated. The results show that the contribution of the adaptive system was higher than that of the servo system. Furthermore, the effectiveness of adaptive model matching was verified.

ObjectivesTo determine whether intravenous iodinated contrast material administration increases the risk of acute kidney injury (AKI) in patients with nephrotic syndrome undergoing contrast-enhanced CT.MethodsPatients with nephrotic syndrome undergoing contrast-enhanced CT were retrospectively identified (n = 701). Control group consisted of patients with nephrotic syndrome receiving non-contrast CT (n = 1053). Two different 1:1 propensity score matching models using three or 10 variables were developed for each estimated glomerular filtration (eGFR) subgroup. Incidence of post-CT AKI for the two groups was assessed and compared by standard AKI criteria and Acute Kidney Injury Network (AKIN) criteria.ResultsAfter matching with three variables, the AKI incidence in the contrast-enhanced CT and non-contrast CT groups was 2.7% vs 2.5% (standard AKI criteria) and 4.2% vs. 6.7% (AKIN criteria) (p = 1.00 and 0.05), respectively. After matching with 10 variables, AKI incidences were 3.1% vs. 2.6% (standard AKI criteria) and 4.1% vs. 7.4% (AKIN criteria) (p = 0.72 and 0.03), respectively. AKI incidences of each eGFR subgroup in the contrast-enhanced CT group were not higher than in the non-contrast CT group (lowest p = 0.46).ConclusionIntravenous contrast material administration during CT was not found to be a risk factor for AKI in this large cohort of patients with nephrotic syndrome.Key points• AKI incidence of contrast-enhanced CT and non-contrast CT had no difference.• AKI incidences of eGFR subgroup in contrast-enhanced CT were not increased.• Studies without a non-contrast CT control group may overestimate CIN incidence.

Rule engines are widely used in engineering and academic fields because they can flexibly separate facts and rules from the rule matching process. Since the rule matching process is very time-consuming, and the traditional rule matching uses single-computer operation when many facts and rules exceed the computer’s memory and computational capacity limit, it will cause the application to crash and paralyze. To solve the above problems, this paper investigates the Spark framework and Rete algorithm to take advantage of Spark’s in-memory computation to alleviate the time-consuming problem of the traditional rule matching process. A high-performance distributed rule matching model is designed by combining the actual rule matching scenario and the development process. In addition, according to the form of rules and facts in the actual scenario, this paper effectively divides the matching process and improves the scalability of the rule matching model.