Implicit Control Research Articles

Probability cueing induced bias does not modulate attention-capture by brief abrupt-onset cues

ABSTRACT Salient objects, such as abrupt-onsets, can capture attention even when nearly invisible. While the influence of explicit factors on such capture (e.g., task-goals) has been extensively investigated, the role of implicit control settings (e.g., probability cueing) is relatively unknown. We examined whether probability cueing affects attentional capture by masked onset cues. The target was more likely to appear on one hemifield of the display than the other. Saccade latencies indicated that capture by masked cues was identical across high and low-probability hemifields (Experiment 1). The same cues, when unmasked, captured attention more when presented in the high-probability hemifield (Experiment 2). We argue that capture by masked cues is not sensitive to implicitly learned biases, such as probability cueing. These findings support theories proposing that attentional control settings influence masked visual orienting only when set up explicitly.

The effect of implicit (vs explicit) rejection on the behavioral intentions of online daters

PurposeOnline dating facilitates both dater interactions and rejections. Given the vast offer of potential mates and daters' limited time, several rejections may occur. On online dating platforms, most of these rejections are simply the absence of a reply (ignoring). The purpose of this paper is to compare the impact of implicit rejection (ignoring) vs explicit rejection (declining) on the behavioral intentions of daters, considering self-esteem as a moderator.Design/methodology/approachExperiment 1 investigated the effect of the extent of rejection (implicit vs explicit vs control) on the behavioral intentions of online daters. Experiment 2 assessed observers' recommended actions to a male (vs female) online dater following rejection (implicit vs explicit vs control).FindingsImplicit rejections generate greater behavioral intentions than explicit rejections. Both daters (study 1) and observers of the dating scenario (study 2) indicated greater intent to revise their profiles (study 1) or recommend a profile revision (study 2) when implicitly (vs explicitly) rejected by interaction partners. Self-esteem moderated the effect of the extent of rejection. Higher levels of self-esteem eliminate and lower levels of self-esteem intensify the effect of the extent of rejection on behavioral intentions. Additionally, observers' recommendations based on the extent of rejection depend on the rejected dater's gender.Originality/valueIgnoring is a frequent practice among dating platform users, and this paper provides an original contribution to better understand the differences stemming from implicit or explicit rejection of online daters.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-06-2020-0207

Data-Driven Safety-Critical Control: Synthesizing Control Barrier Functions With Koopman Operators

Control barrier functions (CBFs) are a powerful tool to guarantee safety of autonomous systems, yet they rely on the computation of control invariant sets, which is notoriously difficult. A backup strategy employs an implicit control invariant set computed by forward integrating the system dynamics. However, this integration is prohibitively expensive for high dimensional systems, and inaccurate in the presence of unmodelled dynamics. We propose to learn discrete-time Koopman operators of the closed-loop dynamics under a backup strategy. This approach replaces forward integration by a simple matrix multiplication, which can mostly be computed offline. We also derive an error bound on the unmodeled dynamics in order to robustify the CBF controller. Our approach extends to multi-agent systems, and we demonstrate the method on collision avoidance for wheeled robots and quadrotors.

A Robust Hybrid Position/Force Control Considering Motor Torque Saturation

This paper proposes a robust hybrid position/force control (HPFC) system for multi-degree of freedom manipulators (MDoFMs) with torque constraints on each joint. General HPFC systems can control the interactive contact forces and positions of manipulators in various environments. In HPFC systems, to improve the control performance for MDoFMs, motor torque saturation should be considered. Thus, a robust HPFC system that considers torque constraints by predictive functional control (PFC) is proposed in this paper. The proposed method simultaneously handles the response characteristics and torque constraints of actuators by using PFC as joint space position controllers. Additionally, the robustness of actuators against external forces is enhanced, and the model parameter errors are compensated by the disturbance observer technique. Moreover, implicit force control law and inverse kinematics are introduced for the workspace position/force control to implement the joint space position controllers. Consequently, the stability and robustness of the actuators are considered, and suitable position/force control can be performed even if the torque is saturated. The validity of the proposed method is tested with three planar manipulator joints with a uniaxial force sensor.

Adaptive Neural Network Optimized Control Using Reinforcement Learning of Critic-Actor Architecture for a Class of Non-Affine Nonlinear Systems

In this article, an optimized tracking control using critic-actor reinforcement learning (RL) strategy is investigated for a class of non-affine nonlinear continuous-time systems. Since the non-affine system is with the implicit control input in dynamic equation, it is a more general modeling form than the affine case, hence this also makes the optimized control more challenging and rewarding. However, most existing RL-based optimal controllers are very complex in algorithm because their actor and critic training laws obtained by implementing gradient descent on the square of Bellman residual error, which equals to the approximation of Hamilton-Jacobi-Bellman (HJB) equation, hence these methods are difficult to be extended to non-affine systems. In this optimized control, the RL algorithm is produced from implementing gradient descent to a simple positive-definite function, which is derived from HJB equation’s partial derivative. As a result, the proposed control algorithm can be significantly simple so as to alleviate the computational burden. Finally, a typical numerical simulation is carried out, and the results also further confirm effectiveness of the proposed control scheme.

Frontal Theta Oscillation as a Mechanism for Implicit Gender Stereotype Control: Electrophysiological Evidence From an Extrinsic Affective Simon Task.

Previous research has indicated that frontal midline theta (FMθ) reflects a domain-general cognitive control mechanism of the prefrontal cortex. Brain imaging studies have shown that the inhibition of implicit stereotypes was dependent on this domain-general cognitive control mechanism. Based on this knowledge, the present study investigated the neural oscillatory correlates of implicit gender stereotype control in an extrinsic affective Simon task (EAST) using electrophysiological methods. Participants in this task conducted verification to white gender names and colored gender traits, and their behavioral response and electroencephalogram (EEG) were recorded during their performances. As predicted, stereotype-inconsistent trials resulted in reduced response accuracies and slower response times than stereotype-consistent trials. For the event-related potential (ERP) results, the enhanced performance of stereotype-inconsistent trials was accompanied by an enhanced N400 amplitude but an attenuated late positive potential amplitude. In contrast, early attentional components such as P2 and N2 as well as their amplitudes were impacted by the experimental manipulations and individual differences in gender factors. In addition, based on time–frequency (TF) analysis, we found that the enhanced performance of stereotype-inconsistent trials was also accompanied by an event-related synchronization on the frontal theta oscillation. This frontal theta appeared at a late processing stage and persisted across a time window from N400 to late positive potential. Additionally, this enhanced frontal theta effect was not modulated by the experimental manipulations and individual differences in gender factors. Based on these findings, the feature of frontal theta oscillation in the implicit gender stereotype control process was discussed.

Design of global smooth implicit control Lyapunov function for multiple-integrator system with input constraint

In this study, we propose a new method to design a smooth implicit control Lyapunov function and a smooth control law for a multi-integrator system with an input constraint. The proposed controller is almost linear near the origin, and it tends to a homogeneous controller of the previous implicit Lyapunov function methods as the state goes to infinity. The proposed method can be used for the backstepping method with a restricted virtual input, which is a nonlinear function of the state, and the desired virtual input is designed as a bounded function. Furthermore, we apply our method to the control of a magnetic levitation system, and a simulation result presents the advantage of the proposed method.

Simulating services from power-to-heat components in integrated energy systems

The integration of thermal and electrical systems offers potential for exchanging system services. Studies of integrated energy systems typically represent the system quasi-statically, and use implicit control schemes, e.g. through optimisation formulations. However, properly characterising services offered between these systems and their cross-domain impact, requires both modelling these systems at time-scales congruent with operation, and explicitly modelling their control flow. By modelling both domain- and control systems explicitly, we show how a co-simulation tool-chain allows examining the impact of flexibility provision in one domain on another, as well as evaluating cross-domain control strategies. We further examine the impact time resolution has on the perceived benefit of cross-domain service provision, specifically whether quasi-static studies lead to systematic over- or under-estimation of the benefits from cross-domain integration.

Analysis of body undulation using dynamic model with frictional force for myriapod robot

Myriapoda, having multitudes of legs and an elongated body, can dexterously travel on natural environments. Myriapod locomotion has an advantage over wheeled and tracked vehicles on a rough terrain, because each leg can discretely contact the ground at several points. The authors have attempted, therefore, to develop a light, simple, and adaptive myriapod robot, i-CentiPot, in accordance with the implicit control law given by passive dynamics, which achieved significant mobility against unpredictable environment. The physical model, i-CentiPot 01, showed that the body undulation emerged because of the leg wave, which was similar to that of a real centipede. However, the mechanism of the body undulation was not revealed. This study, therefore, aims at developing a dynamic model of the myriapod robot using frictional force and elucidating how it locomotes.

Should I stay or should I go? How local-global implicit temporal expectancy shapes proactive motor control: An hdEEG study

In this study, we investigated the effect of global temporal prediction on the brain capability to implicitly adjust proactive motor control. We used the Dynamic Temporal Prediction (DTP), in which local and global predictions of an imperative stimulus were manipulated by using different stimulus-onset asynchronies (SOAs), presented with several distribution probabilities. At a behavioural level, the results show a performance adjustment (reaction time decrease) depending on the implicit use of global prediction. At a neurophysiological level, three separate computational steps underlying motor control were investigated. First, the expectancy implementation was associated with global probability-dependent contingent negative variation (CNV) modulation supported by the recruitment of a frontoparietal network involving the anterior cingulate, the left intraparietal sulcus, the occipital, and the premotor areas. Second, the response implementation was modulated by the global prediction fostering stimulus processing (P3 increase) at the motor response level, as suggested by both oscillatory (beta desynchronization), as well as source analysis (frontal cortical network). Third, the expectancy violation lead to a negativity increase (omission-detection potential) time locked to the global rule violation and additionally, to delta and theta power increase interpreted as inhibitory control and rule violation detection, respectively. The expectancy violation further engaged a left lateralized network including the temporal parietal junction (TPJ) and the motor cortex, suggesting involvement of attentional reorienting and a motor adjustment. Finally, these findings provide new insights on the neurocognitive mechanisms underlying proactive motor control, suggesting an overlapping between implicit and explicit processes.

A least‐squares finite element method based on the Helmholtz decomposition for hyperbolic balance laws

AbstractIn this paper, a least‐squares finite element method for scalar nonlinear hyperbolic balance laws is proposed and studied. The approach is based on a formulation that utilizes an appropriate Helmholtz decomposition of the flux vector and is related to the standard notion of a weak solution. This relationship, together with a corresponding connection to negative‐norm least‐squares, is described in detail. As a consequence, an important numerical conservation theorem is obtained, similar to the famous Lax–Wendroff theorem. The numerical conservation properties of the method in this paper do not fall precisely in the framework introduced by Lax and Wendroff, but they are similar in spirit as they guarantee that when L2 convergence holds, the resulting approximations approach a weak solution to the hyperbolic problem. The least‐squares functional is continuous and coercive in an H−1‐type norm, but not L2‐coercive. Nevertheless, the L2 convergence properties of the method are discussed. Convergence can be obtained either by an explicit regularization of the functional, that provides control of the L2 norm, or by properly choosing the finite element spaces, providing implicit control of the L2 norm. Numerical results for the inviscid Burgers equation with discontinuous source terms are shown, demonstrating the L2 convergence of the obtained approximations to the physically admissible solution. The numerical method utilizes a least‐squares functional, minimized on finite element spaces, and a Gauss–Newton technique with nested iteration. We believe that the linear systems encountered with this formulation are amenable to multigrid techniques and combining the method with adaptive mesh refinement would make this approach an efficient tool for solving balance laws (this is the focus of a future study).

Expanding the Bounds of Seated Virtual Workspaces

Mixed Reality (MR), Augmented Reality (AR) and Virtual Reality (VR) headsets can improve upon existing physical multi-display environments by rendering large, ergonomic virtual display spaces whenever and wherever they are needed. However, given the physical and ergonomic limitations of neck movement, users may need assistance to view these display spaces comfortably. Through two studies, we developed new ways of minimising the physical effort and discomfort of viewing such display spaces. We first explored how the mapping between gaze angle and display position could be manipulated, helping users view wider display spaces than currently possible within an acceptable and comfortable range of neck movement. We then compared our implicit control of display position based on head orientation against explicit user control, finding significant benefits in terms of user preference, workload and comfort for implicit control. Our novel techniques create new opportunities for productive work by leveraging MR headsets to create interactive wide virtual workspaces with improved comfort and usability. These workspaces are flexible and can be used on-the-go, e.g., to improve remote working or make better use of commuter journeys.

State of the Art on Neural Rendering

AbstractEfficient rendering of photo‐realistic virtual worlds is a long standing effort of computer graphics. Modern graphics techniques have succeeded in synthesizing photo‐realistic images from hand‐crafted scene representations. However, the automatic generation of shape, materials, lighting, and other aspects of scenes remains a challenging problem that, if solved, would make photo‐realistic computer graphics more widely accessible. Concurrently, progress in computer vision and machine learning have given rise to a new approach to image synthesis and editing, namely deep generative models. Neural rendering is a new and rapidly emerging field that combines generative machine learning techniques with physical knowledge from computer graphics, e.g., by the integration of differentiable rendering into network training. With a plethora of applications in computer graphics and vision, neural rendering is poised to become a new area in the graphics community, yet no survey of this emerging field exists. This state‐of‐the‐art report summarizes the recent trends and applications of neural rendering. We focus on approaches that combine classic computer graphics techniques with deep generative models to obtain controllable and photorealistic outputs. Starting with an overview of the underlying computer graphics and machine learning concepts, we discuss critical aspects of neural rendering approaches. Specifically, our emphasis is on the type of control, i.e., how the control is provided, which parts of the pipeline are learned, explicit vs. implicit control, generalization, and stochastic vs. deterministic synthesis. The second half of this state‐of‐the‐art report is focused on the many important use cases for the described algorithms such as novel view synthesis, semantic photo manipulation, facial and body reenactment, relighting, free‐viewpoint video, and the creation of photo‐realistic avatars for virtual and augmented reality telepresence. Finally, we conclude with a discussion of the social implications of such technology and investigate open research problems.

Effects of Implicit and Explicit Instruction on Morphological Awareness and Knowledge

In the present study, we explored how implicit and explicit learning affects L2 learners’ morphological awareness and knowledge. To this end, 10th grade high school students (N = 170) in Korea were assigned to one of the three conditions: implicit learning, explicit learning, and control. Participants in the implicit condition learned the meaning of derivatives containing target derivational morphemes (e.g,, sub-) and distractor words, whereas their counterparts in the explicit condition learned the same lexical items and also received morphological instructions. Next, morphological awareness and knowledge was measured by using one- and seven-week delayed post-tests. Overall results revealed superiority of explicit learning over the other two conditions in all post-tests. Results also revealed that although implicit learning may enhance morphological awareness and knowledge to a statistically significant degree, the magnitude of effects computed by Cohen’s d showed small or medium-sized effects. Given that the present study is the first to explore the impact of implicit and explicit learning on morphological awareness and knowledge in EFL settings, the present findings may make important contributions to current second language acquisition literature.

An Implicit Function-Based Adaptive Control Scheme for Noncanonical-Form Discrete-Time Neural-Network Systems

This article proposes a new implicit function-based adaptive control scheme for the discrete-time neural-network systems in a general noncanonical form. Feedback linearization for such systems leads to the output dynamics nonlinear dependence on the system states, the control input, and uncertain parameters, which leads to the nonlinear parametrization problem, the implicit relative degree problem, and the difficulty to specify an analytical adaptive controller. To address these problems, we first develop a new adaptive parameter estimation strategy to deal with all uncertain parameters, especially, those of nonlinearly parameterized forms, in the output dynamics. Then, we construct a key implicit function equation using available signals and parameter estimates. By solving the equation, a unique adaptive control law is derived to ensure asymptotic output tracking and closed-loop stability. Alternatively, we design an iterative solution-based adaptive control law which is easy to implement and ensure output tracking and closed-loop stability. The simulation study is given to demonstrate the design procedure and verify the effectiveness of the proposed adaptive control scheme.

Explicit and Implicit IDA-PBC Design and Implementation for a Portal Crane

The interconnection and damping assignment passivity-based control (IDA-PBC) is well-known for regulating the behavior of nonlinear systems. In underactuated mechanical systems (UMSs), its application requires the satisfaction of matching conditions, which in many cases demands to solve partial differential equations (PDEs). Only recently, the IDA-PBC method has been extended to UMSs in implicit representation, where the system dynamics are described by a set of differential-algebraic equations. In some system classes, this implicit model allows to circumvent the PDE obstacle and to construct an output-feedback law. This paper discusses the design and real-system implementation of the total energy shaping IDA-PBC with an optimal local performance for a portal crane system in implicit port-Hamiltonian representation. The implicit controller is compared with the simplified (explicit) IDA-PBC, introduced by Xue and Zhiyong (2017), which also shapes the total energy and avoids PDEs.

Dual multi-stage NMPC using sigma point principles

Dual control is a technique that addresses the trade-off between probing (excitation signals) and control actions, which results in a better estimation of the unknown parameters and therefore in a better (tracking or economic) performance. Multi-stage NMPC is a robust-control scheme that represents the uncertainty using a scenario tree that is often built by assuming parametric uncertainty and by taking into account the minimum, nominal and maximum values of the uncertain parameters. If the uncertainty set is not a box, this procedure augments the uncertainty set and results in a loss of performance. Here, we mitigate this problem by tightly approximating the uncertainty set using the so-called sigma points and computing an ellipsoidal over-approximation of the reachable set of the system using the unscented transformation. We also improve the performance by considering the future reduction of the ranges of the uncertainties due to control actions and measurements thereby achieving implicit dual control actions. The advantages of the proposed approach over the standard multi-stage NMPC scheme are demonstrated for a linear and a nonlinear (semi-batch reactor) simulation case study.

Robust adaptive control of voltage-type MLS based on wavelet neural network

In this paper, a novel robust adaptive controller based on wavelet neural network is proposed for voltage-type magnetic levitation system with unknown control direction. First, coordinate transformation is used to simplify the model of the voltage-type magnetic levitation system. Second, the mean value theorem and Nussbaum function are used to deal with the problem of the implicit control input of nonlinear function and unknown control direction, the approximation to the unknown nonlinear function is realized by wavelet neural network. Finally, all the signals of the closed loop system are bounded according to Lyapunov function method.Brdys:99

Implementing Augmented Reality (AR) on Phonics-based Literacy among Children with Autism

The implementation of Augmented Reality (AR) technology in education has created an interesting approach to enhance the effectiveness and attractiveness of teaching and learning for students in real-life scenarios. This medium offers unique affordances, combining physical and virtual worlds, with continuous and implicit user control of the point of view and interactivity. This paper introduces the technology of augmented reality and its capabilities in facilitating children with autism. AR is a technology that overlays digital information on a live view of the physical world to create a blended experience. AR provides unique experiences and opportunities to learn and interact with information in the physical world. Hence, AR can be one of the effective technologies available in developing tools for teaching and learning with the combination of the virtual world and real objects such as transportation, fruits, numbers, and alphabets. This will facilitate the autistic child to recognize the abstract concepts of the real objects and their descriptions. The purpose of this paper was to investigate the use of AR on mobile devices in fostering literacy in academic and learning skills for children with autism using the phonics learning method. This prototype helps the autistic child to capture and associate the graphics or pictures of the surrounding objects so as improving the literacy and learning skills of the children. The results show that the children can pronounce and to distinguish between vowels “a”, “i” and “u”. The children are also able to answer most of the questions in the exercises provided. The interactivity between the children and the application raises their attention and focus mainly in literacy and learning skills.

Government ’s control rights influence investor’s escalation of commitment in PPP project: Regulating effect of other-face concern

In public–private partnership (PPP) project, government’s control right (GCR) cannot be completely defined by contract information. Government has some advantageous information resources outside of the contract, such as status, supervision level, trust etc. These information lead investors to overestimate the GCR and aggravate the escalation of commitment (EOC), especially for investors who prefer the other-face concern. According to the degree of quantification of information owned by government in a PPP project, the study divided GCR into explicit control rights (ECR) and implicit control rights (ICR). In order to discuss the difference of actions that two types’ control right on investor’s EOC who have other-face concern. The study collected 176 valid data through scenario questionnaires and constructed the structural equation model with regulating effect. After verifying the model with AMOS. We found the ECR influence investor’s EOC significantly, while the ICR does not; the other-face concern only regulate the influence of ICR on investors’ EOC. These findings are helpful to reduce the behavioral risk of investors’ EOC in PPP project.