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Although scientific inquiry with simulations may enhance learning, learners often face challenges creating high demand for self-regulation due to an abundance of information in simulations and supplementary instructional texts. In this research, participants engaged in simulation-based inquiry about principles of electric circuits supplemented by domain-specific expository text provided on-demand. They received just-in-time inquiry prompts for inquiry behaviors, guidance to self-explain electrical principles, both, or neither. We examined how these interventions influenced participants' access of text information and achievement. Undergraduates (N = 80) were randomly assigned to one of four groups: (1) inquiry prompts and principle-based self-explanation (SE) guidance, (2) inquiry prompts without principle-based SE guidance, (3) principle-based SE guidance without inquiry prompts, or (4) control. Just-in-time inquiry prompts facilitated learning rules. However, there was no main effect of principle-based self-explanation guidance nor an interaction involving both interventions. Effects of just-in-time inquiry prompts were moderated by prior knowledge. Although principle-based self-explanation guidance promoted re-examination of text-based domain information, reading time did not affect posttest scores. These findings have important implications for instructional design of computer-based adaptive guidance in simulation-based inquiry learning. [ABSTRACT FROM AUTHOR]

This paper illustrates how the combination of teacher and computer guidance can strengthen collaborative revision and identifies opportunities for teacher guidance in a computer-supported collaborative learning environment. We took advantage of natural language processing tools embedded in an online, collaborative environment to automatically score student responses using human-designed knowledge integration rubrics. We used the automated explanation scores to assign adaptive guidance to the students and to provide real-time information to the teacher on students’ learning. We study how one teacher customizes the automated guidance tools and incorporates it with her in-class monitoring system to guide 98 student pairs in meaningful revision of two science explanations embedded in an online plate tectonics unit. Our study draws on video and audio recordings of teacher-student interactions during instruction as well as on student responses to pretest, embedded and posttest assessments. The findings reveal five distinct strategies the teacher used to guide student pairs in collaborative revision. The teacher’s strategies draw on the automated guidance to personalize guidance of student ideas. The teacher’s guidance system supported all pairs to engage in two rounds of revision for the two explanations in the unit. Students made more substantial revisions on posttest than on pretest yet the percentage of students who engaged in revision overall remained small. Results can inform the design of teacher professional development for guiding student pairs in collaborative revision in a computer-supported environment.

A novel algorithm for autonomous landing guidance computation is presented. The trajectory is expressed in polynomial form of minimum order to satisfy a set of 17 boundary constraints, depending on 2 parameters: time-of-flight and initial thrust magnitude. The consequent control acceleration is expressed in terms of differential algebraic (DA) variables, expanded around the point of the domain along the nominal trajectory followed at the retargeting epoch. The DA representation of the objective and constraints gives additional information about their sensitivity to variations of the optimization variables, which is exploited to find the desired fuel minimum solution (if existing) robustly and with a very light computational effort. • A novel algorithm for autonomous planetary powered landing guidance computation. • Trajectory is expressed as minimum order polynomials to satisfy boundary constraints. • Control acceleration is expressed in terms of differential algebraic variables. • Differential algebra objects carry sensitivity info wrt optimization variables. • Fuel minimum solution is found robustly and with a very light computational effort. [ABSTRACT FROM AUTHOR]

Objectives In order to guide a large-scale high-speed unmanned underwater vehicle (UUV) along the desired path smoothly in 3D space while taking the task requirements of its control performance and hydrodynamics into consideration, a fuzzy line-of-sight (LOS) guidance law with robustness is proposed.Methods Based on decoupling control, an integrated separate PID algorithm is designed for velocity, heading, depth and pitch. The dynamic characteristics are then modified by introducing filters for commands and state feedback. Finally, the control performance under varying velocities is tested by conducting lake trials.Results The experimental results show that, under the velocities of 6, 9, 13 kn, the vehicle can follow the desired path and depth within a reasonable margin of error, which demonstrates that the proposed control system is valid.Conclusions Besides, the achievements lay a theoretical foundation for the motion control algorithm design of next-generation unmanned underwater test vehicles.

Ant colony optimization has insufficient convergence and tends to fall into the local optima when solving the traveling salesman problem. This paper proposes a co-evolutionary multi-colony ant colony optimization (MCGACO) to overcome this deficiency and applies it to the Robot Path Planning. First, a dynamic grouping cooperation algorithm, combined with Ant Colony System and Max-Min Ant System, is introduced to form a heterogeneous multi-population structure. Each population co-evolves and complements each other to improve the overall optimization performance. Second, an adaptive guidance mechanism is proposed to accelerate convergence speed. The mechanism includes two parts: One is a dynamic evaluation network, which is used to evaluate and divide all solutions by the evaluation function. The other is a positive-negative incentive strategy, which can enhance the guiding role of solutions with higher evaluation value. Besides, to jump out of the local optima, an inter-specific co-evolution mechanism based on the game model is proposed. By dynamically determining the optimal communication combination, the diversity among populations can be well balanced. Finally, the experimental results demonstrate that MCGACO outperforms in terms of solution accuracy and convergence. Meanwhile, the proposed algorithm is also feasible for application in Robot Path Planning. [ABSTRACT FROM AUTHOR]

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This paper is about orchestrating the emergence of conceptual learning in a collaborative setting. We elaborate on the idea of critical moments in group learning, events which may lead to a particular development at the epistemic level regarding the shared object. We conjecture that teachers’ identification of critical moments may help them guide students to the emergence of conceptual learning. The complexity of small group settings in classrooms prevents teachers from noticing these critical moments, though. Here we present an environment, SAGLET (System for Advancing Group Learning in Educational Technologies), based on the VMT (Virtual Math Teams) environment (Stahl 2009), which allows teachers to observe multiple groups engaging in problem-solving in geometry. SAGLET capitalizes on machine learning techniques to inform teachers about on-line critical moments by sending them alerts, so that they can then decide whether (and how) to use the alerts in guiding their students. One teacher in an elementary school used SAGLET to help multiple groups of students solve difficult problems in geometry. We observed how the teacher mediated two cohorts of multiple groups at two different times in a mathematics classroom. We show that in both cases the teacher could detect the needs of the groups (partly thanks to the alerts) and could provide adaptive guidance for all the groups. [ABSTRACT FROM AUTHOR]

An adaptive predictor-corrector guidance algorithm is presented to improve the performance of atmospheric entry guidance. In the predictor, a virtual entry terminal (VET) is employed instead of an actual terminal to reduce the computational request. The VET is adaptively selected from a reference trajectory according to the vehicle's authority to cancel disturbances; thus, it is reachable by the vehicle in different cases. In the corrector, an effective modification method for the bank angle and the angle of attack is designed. With the predictor-corrector algorithm, the vehicle is able to reach the VET. After the VET, a reference trajectory tracking law is employed to fly the vehicle to the actual entry terminal. The adaptive entry guidance method is verified using Monte Carlo simulations. A comparison with the conventional predictorcorrector algorithm shows that the computation time is significantly reduced by employing the VET in the adaptive algorithm. [ABSTRACT FROM AUTHOR]

• We propose a novel progressive downsampling and adaptive guidance network for retaining more the strong edges and other high-frequency information of the blurry images, so as to make the network model learn a more effective dynamic scene deblurring mapping. • In the proposed network, we design a multiscale blended activation residual block to learn the nonlinear characteristics of dynamic scene blur, which can alleviate the performance saturation problem caused by a single activation function and improve multiscale feature extraction ability. • We propose a multisupervision strategy for making the proposed network learn more robust and effective features and making the network possess more stable training and faster convergence. The existing learning-based dynamic scene deblurring methods have made good progress to some extent. However, these methods are usually based on multiscale strategy, which has the following shortcomings: (1) The bilinear downsampling operation will cause some loss of important high-frequency information, e.g., strong edges, which also further affects the network learning a better deblurring mapping. (2) Existing methods only use a single activation function, which limits the ability of the network model to fit data and causes the network performance to be easily saturated. Therefore, we propose an end-to-end progressive downsampling and adaptive guidance network called PDAG-Net for solving above problems. The proposed PDAG-Net can retain more strong edges and other high-frequency information of a blurry image so as to make the network learn a more effective deblurring mapping between the input and label images. In the proposed PDAG-Net, we implement a multiscale blended activation residual block called MSBA-ResBlock for learning the nonlinear characteristics of dynamic scene blur, which can also alleviate the performance saturation problem caused by a single activation function and improve multiscale feature extraction ability. Finally, we propose a multisupervision strategy for obtaining more robust and effective features and making the network possess more stable trainging and faster convergence. Extensive experimental results on a public dataset indicate that the proposed network outperforms the state-of-the-art image deblurring methods. [ABSTRACT FROM AUTHOR]

Advances in automated scoring technologies have the potential to support student learning during inquiry instruction by providing timely and adaptive guidance on individual students' responses. To identify which forms of automated guidance can be beneficial for inquiry learning, we compared reflective guidance to directive guidance for student-generated concept diagrams in web-based inquiry instruction. Eleven intact classes were randomly assigned to either a reflective guidance or a directive guidance condition. After creating a concept diagram showing energy flow in life science during the inquiry instruction, the directive group was told specific ways to improve their diagram, while the reflective group was told to revisit a relevant visualization step to locate useful information. The results from the concept diagrams, as well as individual tests, show that both forms of automated guidance helped students add target energy concepts, but reflective guidance was significantly more effective than directive guidance in improving students' coherent understanding of how energy flows in life science. Analyses of log data revealed that the reflective group was more likely to revisit the visualization step as suggested in the guidance, which significantly enhanced student learning. Detailed analyses suggest that revisiting relevant materials to find useful information challenged students to identify gaps in their understanding and distinguish among multiple ideas. This study shows the value of designing reflective automated guidance for helping students engage in evidence-gathering practices and enhance their understanding of scientific concepts. The findings suggest promising directions for the design of automated adaptive guidance to support complex science learning. © 2016 Wiley Periodicals, Inc. J Res Sci Teach 53:1003-1035, 2016 [ABSTRACT FROM AUTHOR]

The entry-glide guidance strategy for hypersonic vehicles that can satisfy both terminal and path constraints is investigated in this paper. We propose a quasi-equilibrium glide adaptive guidance methodology based on the quasi-equilibrium glide condition (QEGC), which innovatively utilizes the quasi-equilibrium glide phenomenon in lifting entry. With the aid of QEGC, both range and terminal velocity can be predicted analytically with high precision. The path constraints are converted into angle of attack constraints, which has been difficult to realize by using traditional predictive guidance methods. The algorithm is independent of the standard trajectory. All the guidance commands, including the bank angle and the angle of attack, are calculated analytically in real time, which endows the algorithm with sufficient adapbility. The results of a CAV-H vehicle guidance test show that the algorithm leads the vehicle along a quasi-equilibrium glide trajectory satisfying both the terminal and path constraints and has sufficient flexibility for occasional mission changes. Furthermore, the robustness of the guidance algorithm under disturbances is validated through a Monte Carlo simulation. [ABSTRACT FROM AUTHOR]

In this paper an adaptive guidance law based on the characteristic model is designed to track a reference drag acceleration for reentry vehicles like the Shuttle. The characteristic modeling method of linear constant systems is extended for single-input and single-output (SISO) linear time-varying systems so that the characteristic model can be established for reentry vehicles. A new nonlinear differential golden-section adaptive control law is presented. When the coefficients belong to a bounded closed convex set and their rate of change meets some constraints, the uniformly asymptotic stability of the nonlinear differential golden-section adaptive control system is proved. The tracking control law, the nonlinear differential golden-section control law, and the revised logical integral control law are integrated to design an adaptive guidance law based on the characteristic model. This guidance law overcomes the disadvantage of the feedback linearization method which needs the precise model. Simulation results show that the proposed method has better performance of tracking the reference drag acceleration than the feedback linearization one. [ABSTRACT FROM AUTHOR]

Abstract: For a class of linear dynamical systems with unknown parameters a direct model reference adaptive control framework is developed that provides stable adaptation in the presence of input constraints. The proposed design methodology, termed “positive -modification”, has the ability to protect the control law from actuator position saturation. The reference system is adaptively modified to ensure global asymptotic tracking for open-loop stable systems. For unstable systems an estimate for the domain of attraction is derived based on the input saturation magnitude and system parameters. Simulation of a benchmark example verifies the theoretical statements. [Copyright &y& Elsevier]

Abstract: We demonstrate that a three-dimensional entry guidance with accurate ground-track control can be achieved using a combined strategy of proportional down-range maneuvering and linear quadratic (LQ) technique, thereby precluding the necessity of sophisticated nonlinear programming. It is also shown that heating rate and load-factor constraints can be met by a careful but simple selection of LQ gains. Although the speed, flight path angle, and down-range accuracy are comparable to that of a planar guidance strategy, the present technique also demonstrates an accurate cross-range control, which is not possible with a planar strategy. It is shown that adaptive maneuvering produces a superior performance than that of a nonlinear tracking control derived using feedback linearization. The method can handle much larger deviations in the entry conditions than those published previously, and has the important advantage of a relatively simple implementation in a realistic entry scenario. Furthermore, the LQ technique can take advantage of the good robustness properties of a linear Kalman filter based upon the measurement of longitude, latitude, and speed, wherein little deterioration is observed in the closed-loop performance. [Copyright &y& Elsevier]