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Previous research indicated a need to improve pilot training with regard to understanding of autopilot logic and behavior, especially in non-routine situations. Therefore, we tested the effect of problem-based exploratory training on pilots' understanding of autopilot functions. Using a moving-base flight simulator, general aviation pilots (n = 45) were trained to diagnose failures either without foreknowledge and guidance (exploratory group), without foreknowledge but with some guidance (exploratory-guidance group) or with foreknowledge and full guidance (control group). They subsequently performed six test scenarios in which their understanding of the effects of failures was tested by requiring them to deduce the failures and select autopilot modes that were still functioning. Those who received exploratory training with guidance were significantly more likely than the other groups to diagnose failures correctly. The exploratory training group also selected the most appropriate functioning autopilot modes significantly faster than the control group. The results suggest that exploratory training with an appropriate level of guidance is useful for gaining a practical understanding of autopilot logic and behavior. Exploratory training may help to improve transfer of training to operational practice, and prevent automation surprises and accidents. [ABSTRACT FROM AUTHOR]

• Survey of regular users of Tesla, GM, and Nissan's partially automated systems. • Group differences were observed based on driver characteristics and system designs. • Most drivers were more likely to do secondary activities while using the technology. • Many people have a false sense of security and poor understanding of their systems. • User safeguards combat distraction and are accepted by most, but not all, drivers. Introduction: Little is known about regular users' perceptions of partial (Level 2) automation or how those perceptions affect behind-the-wheel behavior. Method: A mixed mode (phone and online) survey explored the habits, expectations, and attitudes among regular users of General Motors Super Cruise (n = 200), Nissan/Infiniti ProPILOT Assist (n = 202), and Tesla Autopilot (n = 202). Results: All three groups reported being more likely to engage in non-driving-related activities while using their systems than while driving unassisted. Super Cruise and Autopilot users especially were more likely to report engaging in activities that involved taking their hands off the wheel or their eyes off the road. Many Super Cruise and Autopilot users also said they could perform secondary (non-driving-related) tasks better and more often while using their systems, while fewer ProPILOT Assist users shared this opinion. Super Cruise users were most likely and ProPILOT Assist users least likely to think that secondary activities were safer to perform while using their systems. While some drivers said they found user safeguards (e.g., attention reminders, lockouts) annoying and tried to circumvent them, most people said they found them helpful and felt safer with them. Large percentages of users (53% Super Cruise, 42% Autopilot and 12% ProPILOT Assist) indicated they were comfortable treating their systems as self-driving. Conclusions: Some regular users have a poor understanding of their technology's limits. System design appears to contribute to user perceptions and behavior. However, owner populations also differ, which means habits, attitudes, and expectations may not generalize. Most people value user safeguards, but some implementations may not be effective for everyone. Practical Applications: Multifaceted, proactive user-centric safeguards are needed to shape proper behavior and understanding about drivers' roles and responsibilities while using partial driving automation. [ABSTRACT FROM AUTHOR]

This article applies a two‐process "neural autopilot" model to field data. The autopilot model hypothesizes that habitual choice occurs when the reward from a behavior has low numerical "doubt" (i.e., reward prediction errors are small). The model toggles between repeating a previous choice (habit) when doubt is low and making a goal‐directed choice when doubt is high. The model has ingredients established in animal learning and cognitive neuroscience and is simple enough to make nonobvious predictions. In two empirical applications, we fit the model to field data on purchases of canned tuna and posting on the Chinese social media site Weibo. This style of modeling is called "structural" because there is a theoretical model of how different variables influence choices by agents (the "structure"), which tightly restricts how hidden variables lead to observed choices. There is empirical support for the model, more strongly for tuna purchases than for Weibo posting, relative to a baseline "reduced‐form" model in which current choices are correlated with past choices without a mechanistic (structural) explanation. An interesting set of predictions can also be derived about how consumers react to different kinds of changes in prices and qualities of goods (this is called "counterfactual analysis"). [ABSTRACT FROM AUTHOR]

Although a shield tunneling machine is intended to excavate a tunnel along its planned alignment, deviations occur between the planned alignment and the measured alignment, which must be corrected. These operations are time‐consuming, and it is difficult to correct deviations. However, related studies have been unable to automatically calculate the optimum operation parameters. Therefore, this study sought to use machine learning to develop an autopilot model, which is a method to automatically calculate optimal operation parameters of the shield machine for straight and curved sections of the planned alignment in both plan and longitudinal views using support vector regression. As a result, it could automatically calculate operation parameters that predict a shield machine that excavates with the same or better excavating accuracy of a skilled operator and contributes to the automation of shield machine operation. [ABSTRACT FROM AUTHOR]

A good situation awareness (SA) of the navigator is essential for the safety of the ship, especially in coastal areas. In this study, the Unity 3D engine was used to simulate the navigation of a coastal trading vessel along predefined routes in the Baltic Sea. The SA of the helmsman, who was either Chinese or European, was assessed several times with the SAGAT test (Endsley, 1995b, 20-21) a nd c ompared b etween l ow a nd h igh workload conditions and between manual and autopilot navigation. High workload and automated navigation both reduced SA significantly and in an additive manner. No difference was found between Chinese and European participants. In contrast to previous accident analyses of SA, we found that SA level 3 (projection of future states) was most strongly affected by both factors, while SA levels 1 (perception of relevant information) and 2 (comprehension of the current situation) suffered to a lesser extent. Further research is needed to establish specific relationships between types of automation on ships, types of workload, and SA problems in order to design countermeasures. [ABSTRACT FROM AUTHOR]

• Being familiar with a road scene is the default mode of how we behave in traffic. • Four consecutive empirical studies revealed a pattern of increased automaticity. • Familiarity led to disengagement, monitoring rather than attending the road. • When studying what is seen this common cognitive mode impacts research methodology. • It is proposed to regard route familiarity as a context that enables distraction. Most trips are made, and most travel is done on roads and paths well-known. To provide insight in the cognitive processes involved in visual information processing of familiar traffic environments this paper considers a series of four consecutive empirical studies. The aim of this paper is 1) to discuss the implications of these insights on research methodologies used to measure driver attention and distraction and 2) to provide recommendations for policymaking, road design and vehicle design. This line of work consists of three studies on car driver behaviour (two driving simulator studies and a video-based study) and an observational study on pedestrian behaviour. The recurring theme in the results of these studies is that the progressive exposure to the same traffic environment enables participants to automatise their behavioural performance in traffic up to the point that it could be executed at skill-based level. As a result, attention is easily diverted away from traffic participation, while participating in traffic. Participants could act without thinking about it, so they didn't always even remember what they had done. People get used to familiar traffic environments so much that they don't have to think about walking or driving with much conscious focus. Hence, when studying road user behaviour and particularly driver distraction, it is crucial to mimic these natural circumstances as closely as possible. It is therefore proposed that within driver distraction research, route familiarity should be regarded as a context that enables distraction. What is more, is that the results point in the direction of a familiarity paradox : deviating from what is very familiar likely results in involuntary distraction, but being very familiar may lead to underload resulting in (voluntarily) diverting attention elsewhere. [ABSTRACT FROM AUTHOR]

The aim of the SYRIACA project was to test the capability of a social robot to perform specific tasks in healthcare settings, reducing infection risks for patients and caregivers. The robot was piloted in an Intensive Hematological Unit, where the patients' and healthcare operators' acceptability of the robot was evaluated. The robot's functions, including logistics, surveillance, entertainment, and remote visits, were well accepted. Patients expressed interest in having multiple interactions with the robot, which testifies to its engaging potential and that it provides useful services. During remote visits, the robot reduced perceived stress among patients, alleviating feelings of isolation. The successful implementation of the robot suggests its potential to enhance safety and well-being in healthcare settings. [ABSTRACT FROM AUTHOR]

To realize automatic harvesting of the jujube, the 4ZZ-4A2 full-hydraulic self-propelled jujube harvester was designed and manufactured. For achieving the jujube harvester autopilot, a novel algorithm for visual navigation path detection was proposed. The centerline of tree row lines was taken as the navigation path. The method included four main parts: image preprocessing, image segmentation, tree row lines access, and navigation path access. The methods of threshold segmentation, noise removal, and border smoothing were utilized on the image in Lab color space for the image segmentation. The least square method was employed to fit the tree row lines, and the centerline was obtained as the navigation path. Experimental results indicated that the average false detection rate was 3.98%, and the average detection speed was 41 fps. The algorithm meets the requirements of the jujube harvester autopilot in terms of accuracy and speed. It also can lay the foundation for accomplishing the jujube harvester vision-based autopilot. [ABSTRACT FROM AUTHOR]

Primary productivity response to climatic drivers varies temporally, indicating state-dependent interactions between climate and productivity. Previous studies primarily employed equation-based approaches to clarify this relationship, ignoring the state-dependent nature of ecological dynamics. Here, using 40 y of climate and productivity data from 48 grassland sites across Mongolia, we applied an equation- free, nonlinear time- series analysis to reveal sensitivity patterns of productivity to climate change and variability and clarify underlying mechanisms. We showed that productivity responded positively to annual precipitation in mesic regions but negatively in arid regions, with the opposite pattern observed for annual mean temperature. Furthermore, productivity responded negatively to decreasing annual aridity that integrated precipitation and temperature across Mongolia. Productivity responded negatively to interannual variability in precipitation and aridity in mesic regions but positively in arid regions. Overall, interannual temperature variability enhanced productivity. These response patterns are largely unrecognized; however, two mechanisms are inferable. First, time- delayed climate effects modify annual productivity responses to annual climate conditions. Notably, our results suggest that the sensitivity of annual productivity to increasing annual precipitation and decreasing annual aridity can even be negative when the negative time- delayed effects of annual precipitation and aridity on productivity prevail across time. Second, the proportion of plant species resistant to water and temperature stresses at a site determines the sensitivity of productivity to climate variability. Thus, we highlight the importance of nonlinear, state-dependent sensitivity of productivity to climate change and variability, accurately forecasting potential biosphere feedback to the climate system. [ABSTRACT FROM AUTHOR]

This article proposes an event-triggered distributed cooperative guidance law for multiple missiles to simultaneously attack a stationary target with consideration of autopilot lag and unknown disturbances. In order to achieve a consensus on each missile's impact time and save communication costs, the guidance law is designed on the basis of an auxiliary variable consisting of the time-to-go expression and its time derivative. Driven by the properly designed event-triggered mechanism, the information interaction among missiles performs in a need-based scheme and there is no need to continuously monitor the consensus errors between neighbor missiles. By this means, the communication burden is significantly reduced while satisfactory guidance performance is still guaranteed. The proposed guidance law can be extended to a communication failure case where one of the missiles suddenly cannot receive information from its neighbors. An integral sliding mode controller is integrated with the nominal guidance law to eliminate the adverse effects caused by unknown disturbances. Numerical simulations are carried out to illustrate the effectiveness of the proposed guidance design. [ABSTRACT FROM AUTHOR]

People often have good intentions but fail to adhere to them. Implementation intentions, a form of strategic planning, can help people to close this intention–behavior gap. Their effectiveness has been proposed to depend on the mental formation of a stimulus–response association between a trigger and target behavior, thereby creating an "instant habit." If implementation intentions do indeed lead to reliance on habitual control, then this may come at the cost of reduced behavioral flexibility. Furthermore, we would expect a shift from recruitment of corticostriatal brain regions implicated in goal-directed control toward habit regions. To test these ideas, we performed a fMRI study in which participants received instrumental training supported by either implementation or goal intentions, followed by an outcome revaluation to test reliance on habitual versus goal-directed control. We found that implementation intentions led to increased efficiency early in training, as reflected by higher accuracy, faster RTs, and decreased anterior caudate engagement. However, implementation intentions did not reduce behavioral flexibility when goals changed during the test phase, nor did it affect the underlying corticostriatal pathways. In addition, this study showed that "slips of action" toward devalued outcomes are associated with reduced activity in brain regions implicated in goal-directed control (ventromedial prefrontal cortex and lateral orbitofrontal cortex) and increased activity of the fronto-parietal salience network (including the insula, dorsal anterior cingulate cortex, and SMA). In conclusion, our behavioral and neuroimaging findings suggest that strategic if–then planning does not lead to a shift from goal-directed toward habitual control. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to optimize the simultaneous flight performance of a hexarotor unmanned aerial vehicle (UAV) by using simultaneous perturbation stochastic approximation (i.e. SPSA), deep neural network and proportional integral derivative (i.e. PID) according to varying arm length (i.e. morphing). Design/methodology/approach: In this paper, proper PID gain coefficients and morphing ratio were obtained using the stochastic optimization method, also known as SPSA to maximize flight efficiency. Because it is difficult to establish an analytical connection between the morphing ratio and hexarotor moments of inertia, the deep neural network was used to obtain the moments of inertia according to the morphing ratio. By using SPSA and deep neural network, the best performance indexes were obtained and both longitudinal and lateral flight simulations were performed with the obtained data. Findings: With SPSA, the best PID coefficients and morphing ratio are obtained for both longitudinal and lateral flight. Because the hexarotor solid body model changes according to the morphing ratio, the moment of inertia values used in the simulations also change. According to the morphing ratio, the moment of inertia values was obtained with the deep neural network over a created data set. Research limitations/implications: It takes a long time to obtain the morphing ratio suitable for the hexarotor model and the PID gain coefficients suitable for this morphing ratio. However, this situation can be overcome with the proposed SPSA. In addition, it takes a long time to obtain the appropriate moments of inertia according to the morphing ratio. However, in this case, it was overcome using the deep neural network. Practical implications: Determining the morphing ratio and PID gain coefficients using the optimization method, as well as determining the moments of inertia using the deep neural network, is very useful as it can increase the efficiency of hexarotor flight and flight efficiently with different arm lengths. With the proposed method, the hexarotor design performance criteria (i.e. rise time, settling time and overshoot) values were significantly improved compared to similar studies. Social implications: Determining the hexarotor flight parameters using SPSA and deep neural network provides advantages in terms of time, cost and applicability. Originality/value: The hexarotor flight efficiency is improved with the proposed SPSA and deep neural network approaches. In addition, the desired flight parameters can be obtained more quickly and reliably with the proposed approaches. The design performance criteria were also improved, enabling the hexarotor UAV to follow the given trajectory in the best way and providing convenience for end users. SPSA was preferred because it converged faster than other methods. While other methods perform 2n operations per iteration, SPSA only performs two operations. To obtain the moment of inertia, many physical parameter values of the UAV are required in the existing methods. In the proposed method, by creating a date set, only arm length and moment of inertia were estimated without the need to obtain physical parameters with the deep neural network structure. [ABSTRACT FROM AUTHOR]

Many photonic and electronic molecular properties, as well as chemical and biochemical reactivities are controlled by fast intramolecular vibrational energy redistribution (IVR). This fundamental ultrafast process limits coherence time in applications from photochemistry to single quantum level control.While time-resolved multidimensional IR-spectroscopy can resolve the underlying vibrational interaction dynamics, as a nonlinear optical technique it has been challenging to extend its sensitivity to probe small molecular ensembles, achieve nanoscale spatial resolution, and control intramolecular dynamics. Here, we demonstrate a concept how mode-selective coupling of vibrational resonances to IR nanoantennas can reveal intramolecular vibrational energy transfer. In time-resolved infrared vibrational nanospectroscopy, we measure the Purcell-enhanced decrease of vibrational lifetimes of molecular vibrations while tuning the IR nanoantenna across coupled vibrations. At the example of a Re-carbonyl complex monolayer, we derive an IVR rate of (25 ± 8) cm−1 corresponding to (450 ± 150) fs, as is typical for the fast initial equilibration between symmetric and antisymmetric carbonyl vibrations. We model the enhancement of the cross-vibrational relaxation based on intrinsic intramolecular coupling and extrinsic antenna-enhanced vibrational energy relaxation. The model further suggests an anti-Purcell effect based on antenna and laser–field–driven vibrational mode interference which can counteract IVR-induced relaxation. Nanooptical spectroscopy of antenna-coupled vibrational dynamics thus provides for an approach to probe intramolecular vibrational dynamics with a perspective for vibrational coherent control of small molecular ensembles. [ABSTRACT FROM AUTHOR]

Currently, the presence of data uncertainty and noise raises critical issues to be handled on both theoretical and computational grounds. Therefore, robustification and robust optimization have gained attention from theoretical and practical points of view for the establishment of a modeling framework in mathematical optimization to immunize solutions against diverse uncertainties. Data of both the input and output variables, underlying the problems to be addressed, are affected by the noise of different kinds, such that standard statistical models alone may not be sufficient to ensure trustworthy results due to the complexity of the model. Consequently, we propose including parametric uncertainties reflecting future scenarios in multivariate adaptive regression splines (MARS), which, in turn, show great promise for fitting nonlinear multivariate functions, where additive and interactive effects of the predictors are employed to assess the response variable. We robustify MARS through the usage of robust optimization techniques. Due to the large complexity of the underlying model in prior studies, we applied a so-called weak robustification. Now, we exploit a geometrical and combinatorial approach to allow for a more complete robustification, by formulating Robust MARS (RMARS) under Cross-Polytope Uncertainty. In this study, we use RMARS for energy data and demonstrate its superior performance through a simulation study. [ABSTRACT FROM AUTHOR]

About 40 000 individuals depend on assisted living in long-term care facilities in Norway. Around 80% of these have a cognitive impairment or suffer from dementia. This actualizes the need for activities that are tailored to individual needs. For some users, technology-assisted participation in communal activities can be an alternative approach to increasing their quality of life. To gain insight about the experiences of residents and healthcare professionals in long-term care facilities when interacting with the social robot Pepper. This is a qualitative pilot study. After a series of interventions with the robot in a long-term care facility, data were collected through individual interviews with healthcare professional and residents. These were analyzed through a qualitative content analysis. A thematic analysis identified three major themes: 1) Activity, joy and ambivalence, 2) challenges when introducing social robots in contexts of care and 3) thoughts about the future. Although employees and residents report that they enjoyed interactions with the social robot, highlighting opportunities for novel types of activities and action that differed from the daily routine, the subjects articulated several concerns and challenges. Developments in intelligent social robots is still in its infancy, despite much hype. [ABSTRACT FROM AUTHOR]

A two-loop acceleration autopilot is designed using the twin-delayed deep deterministic policy gradient (TD3) strategy to avoid the tedious design process of conventional tactical missile acceleration autopilots and the difficulty of meeting the performance requirements of the full flight envelope. First, a deep reinforcement learning model for the two-loop autopilot is developed. The flight state information serves as the state, the to-be-designed autopilot control parameters serve as the action, and a reward mechanism based on the stability margin index is designed. The TD3 strategy is subsequently used to offline learn the control parameters for the entire flight envelope. An autopilot control parameter fitting model that can be directly applied to the guidance loop is obtained. Finally, the obtained fitting model is combined with the impact angle constraint in the guidance system and verified online. The simulation results demonstrate that the autopilot based on the TD3 strategy can self-adjust the control parameters online based on the real-time flight state, ensuring system stability and achieving accurate acceleration command tracking. [ABSTRACT FROM AUTHOR]

In this study, a novel framework for the flight control of a morphing unmanned aerial vehicle (UAV) based on linear parameter-varying (LPV) methods is proposed. A high-fidelity nonlinear model and LPV model of an asymmetric variable-span morphing UAV were obtained using the NASA generic transport model. The left and right wing span variation ratios were decomposed into symmetric and asymmetric morphing parameters, which were then used as the scheduling parameter and the control input, respectively. LPV-based control augmentation systems were designed to track the normal acceleration, angle of sideslip, and roll rate commands. The span morphing strategy was investigated considering the effects of morphing on various factors to aid the intended maneuver. Autopilots were designed using LPV methods to track commands for airspeed, altitude, angle of sideslip, and roll angle. A nonlinear guidance law was coupled with the autopilots for three-dimensional trajectory tracking. A numerical simulation was performed to demonstrate the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

A consistent approach to the development of tuning rules for course-keeping and path-tracking PID controllers for a ship autopilot are presented. The consistency comes from the observation that for each of the controllers the controlled plant can be modelled by an integrator with inertia. In the case of the course controller, it is the well-known Nomoto model. The PID controller may be implemented in series or parallel form, the consequence of which is a 2nd or 3rd order of the system, specified by a double or triple closed-loop time constant. The new tuning rules may be an alternative to the standard ones given in [1,2]. It is shown that, whereas the reference responses for the standard and new rules are almost the same, the new rules provide better suppression of disturbances such as wind, waves or current. The parallel controller is particularly advantageous. The path-tracking PID controller can provide better tracking accuracy than the conventional PI. Simulated path-tracking trajectories generated by a cascade control system are presented. The novelty of this research is in the theory, specifically in the development of new tuning rules for the two PID autopilot controllers that improve disturbance suppression. [ABSTRACT FROM AUTHOR]

Viscoelastic flows are pervasive in a host of natural and industrial processes, where the emergence of nonlinear and time-dependent dynamics regulates flow resistance, energy consumption, and particulate dispersal. Polymeric stress induced by the advection and stretching of suspended polymers feeds back on the underlying fluid flow, which ultimately dictates the dynamics, instability, and transport properties of viscoelastic fluids. However, direct experimental quantification of the stress field is challenging, and a fundamental understanding of how Lagrangian flow structure regulates the distribution of polymeric stress is lacking. In this work, we show that the topology of the polymeric stress field precisely mirrors the Lagrangian stretching field, where the latter depends solely on flow kinematics.We develop a general analytical expression that directly relates the polymeric stress and stretching in weakly viscoelastic fluids for both nonlinear and unsteady flows, which is also extended to special cases characterized by strong kinematics. Furthermore, numerical simulations reveal a clear correlation between the stress and stretching field topologies for unstable viscoelastic flows across a broad range of geometries. Ultimately, our results establish a connection between the Eulerian stress field and the Lagrangian structure of viscoelastic flows. This work provides a simple framework to determine the topology of polymeric stress directly from readily measurable flow field data and lays the foundation for directly linking the polymeric stress to flow transport properties. [ABSTRACT FROM AUTHOR]

in recent years, UAVs (Unmanned Aerial Vehicle) have become playing an active role and have been involved in a number of fields such as surveillance, photography, agriculture, transportation and communications. For this reason, the research institution is working to develop linear and nonlinear controllers to make these UAVs more stable and effective while performing various tasks assigned to them. In this paper, a longitudinal autopilot was designed for a solar UAV (sky sailor) using two controllers, the first is SLC (Successive Loop Closure) which is a classic controller that is based on successive loops with a PID controller, and the second method is the TECS (Total Energy Control System) controller that depends on the total specific energy rate and the energy distribution rate to control the airspeed and altitude of the UAV. After detailing the working principle and tuning of each controller they were applied to the non-linear model of UAV using MATLAB Simulink. Through the results obtained from the simulations, we conclude that the TECS controller is better than the SLC controller in terms of stability and energy economy, being an ideal choice for solar UAVs to increase their endurance, and for civil aircraft to reduce the cost of flights. [ABSTRACT FROM AUTHOR]

The measurement of the real altitude of aircraft is usually done using an aviation radio altimeter (ALT). A radio altimeter provides crucial information about the instantaneous (radio) altitude of aircraft, helicopter, or unmanned aerial vehicle, to the pilot or another assistance system, such as an autopilot or an anti-collision system. However, this flight altitude measurement is affected by several errors, methodological errors and the operating frequency and modulation parameters instability, or the Doppler shift error. This article is focused on the evaluation of how the Doppler effect error develops during the operation of an ALT and its potential use as an information carrier concerning a possible loss of radio altitude, leading to dangerous situations. This paper briefly explains in a theoretical and practical way how this error develops and how it can affect the process of creation of height impulses. Practical experiments were conducted and evaluated in this research, and a theoretical design of a simple circuit capable of signalization of radio altitude loss presented. As the Doppler shift error was previously recognized solely as a measurement error, it could be used in a new function as a source of supplemental warning information. [ABSTRACT FROM AUTHOR]

Policies are assumed to rarely change after enactment. Punctuated equilibrium theory argues this is because law makers shift attention away from policies after enactment, letting them run on autopilot. I argue that the periods between punctuations deserves study, and that what law makers do is design institutions to make policies path dependent so they can only change within limited parameters—if at all. It may also be that interest groups opposing new policies are still able to shape the way they evolve, pushing against intended paths. In this article I test the influence of path dependence versus group opposition in state charter school policies. I find some evidence of path dependence, but I also find that, to some extent, opposing interests can still change these laws after enactment. Furthermore, I find these policies became increasingly similar over time, partially because of path dependence, but also because of the influence of opposing interests. Related Articles: Ertas, Nevbahar. 2015. "Policy Narratives and Public Opinion concerning Charter Schools." Politics & Policy 43(3): 426–51. https://doi.org/10.1111/polp.12120. Kwon, Sung‐Wook, and Sylvia Gonzalez‐Gorman. 2019. "Influence of Local Political Institutions on Policy Punctuation in Three Policy Areas." Politics & Policy 47(2): 300–25. https://doi.org/10.1111/polp.12295. Patrick, Barbara. 2016. "Performance Policies and Workforce Development: Can the Market Model Be Successfully Implemented in the Public Arena?" Politics & Policy 44(6): 1116–57. https://doi.org/10.1111/polp.12185. [ABSTRACT FROM AUTHOR]

The perceived autonomy is thus related to not only how autonomy is perceived but also how working with human-machine aggregates from a broader perspective of interactional and situational outcomes, socio-cognitive organization, culture and, thus, of the ethical issues that are central to AI. Whilst the media is intrigued by the humanoid robot Ai-Da addressing the British House of Lords committee on the future of the arts, design, fashion and music industries, telling a committee that artificial intelligence can be a "threat and opportunity" to artists (The Guardian [8]), we also note concerns of the media on the risk of creating a generation of racist and sexist robots'. In the end, it is not important how the AI machine can be aligned with human values or visualising as how human values are fully aligned with the AI machine, converging to the post-human world, what it is important to know is that human values are diverse, social, cultural and contextual, and they do not fit into the logic of the AI machine. [Extracted from the article]

This paper proposes an algorithm for missile manoeuvring based on a hierarchical proximal policy optimization (PPO) reinforcement learning algorithm, which enables a missile to guide to a target and evade an interceptor at the same time. Based on the idea of task hierarchy, the agent has a two-layer structure, in which low-level agents control basic actions and are controlled by a high-level agent. The low level has two agents called a guidance agent and an evasion agent, which are trained in simple scenarios and embedded in the high-level agent. The high level has a policy selector agent, which chooses one of the low-level agents to activate at each decision moment. The reward functions for each agent are different, considering the guidance accuracy, flight time, and energy consumption metrics, as well as a field-of-view constraint. Simulation shows that the PPO algorithm without a hierarchical structure cannot complete the task, while the hierarchical PPO algorithm has a 100% success rate on a test dataset. The agent shows good adaptability and strong robustness to the second-order lag of autopilot and measurement noises. Compared with a traditional guidance law, the reinforcement learning guidance law has satisfactory guidance accuracy and significant advantages in average time and average energy consumption. [ABSTRACT FROM AUTHOR]

Neural circuits can produce similar activity patterns from vastly different combinations of channel and synaptic conductances. These conductances are tuned for specific activity patterns but might also reflect additional constraints, such as metabolic cost or robustness to perturbations. How do such constraints influence the range of permissible conductances? Here we investigate how metabolic cost affects the parameters of neural circuits with similar activity in a model of the pyloric network of the crab Cancer borealis. We present a machine learning method that can identify a range of network models that generate activity patterns matching experimental data and find that neural circuits can consume largely different amounts of energy despite similar circuit activity. Furthermore, a reduced but still significant range of circuit parameters gives rise to energy-efficient circuits. We then examine the space of parameters of energy-efficient circuits and identify potential tuning strategies for low metabolic cost. Finally, we investigate the interaction between metabolic cost and temperature robustness. We show that metabolic cost can vary across temperatures but that robustness to temperature changes does not necessarily incur an increased metabolic cost. Our analyses show that despite metabolic efficiency and temperature robustness constraining circuit parameters, neural systems can generate functional, efficient, and robust network activity with widely disparate sets of conductances. [ABSTRACT FROM AUTHOR]

With the development of autopilot, the performance of intelligent vehicles is constrained by their inability to perceive blind and beyond visual range areas. Vehicle-to-infrastructure cooperative perception has become an effective method for achieving reliable and higher-level autonomous driving. A vehicle-to-infrastructure cooperative beyond visual range and non-blind area method, based on heterogeneous sensors, was proposed in this study. Firstly, a feature map receptive field enhancement module with spatial dilated convolution module (SDCM), based on spatial dilated convolution, was proposed and embedded into the YOLOv4 algorithm. The YOLOv4-SDCM algorithm with SDCM module achieved a 1.65% mAP improvement in multi-object detection performance with the BDD100K test set. Moreover, the backbone of CenterPoint was improved with the addition of self-calibrated convolutions, coordinate attention, and residual structure. The proposed Centerpoint-FE (Feature Enhancement) algorithm achieved a 3.25% improvement in mAP with the ONCE data set. In this paper, a multi-object post-fusion matching method of heterogeneous sensors was designed to realize the vehicle-to-infrastructure cooperative beyond visual range. Experiments conducted at urban intersections without traffic lights demonstrated that the proposed method effectively resolved the problem of beyond visual range perception of intelligent vehicles. [ABSTRACT FROM AUTHOR]

As automated vehicles become more common, there is a need for precise measurement and definition of when and in what ways a driver can use a mobile phone in L2 autonomous driving mode, for how long it can be used, the complexity of the call content, and the accumulated mental workload. This study uses a 2 (driving mode) × 2 (call content complexity) × 6 (driving stage) three-factor mixed experimental design to investigate the effect of these factors on the driver's mental workload by measuring the driver's performance on Detection response tasks, pupil diameter, and EEG components in various brain regions in the alpha band. The results showed that drivers' mental workload levels converge between manual and automatic driving modes as the duration of driving increases, regardless of the level of complexity of the mobile phone conversation. This suggests that mobile phone conversations can also disrupt the driver's cognitive resource balance in L2 automatic driving mode, as it increases mental workload while also impairing the normal functioning of brain functions such as cognitive control, problem solving, and judgment, thereby compromising driving safety. [ABSTRACT FROM AUTHOR]

This article presents an adaptive method for ArduPilot-based autopilots of fixed-wing unmanned aerial vehicles (UAVs). ArduPilot is a popular open-source unmanned vehicle software suite. We explore how to augment the PID loops embedded inside ArduPilot with a model-free adaptive control method. The adaptive augmentation, adopted for both attitude and total energy control, uses input/output data without requiring an explicit model of the UAV. The augmented architecture is tested in a software-in-the-loop UAV platform in the presence of several uncertainties (unmodeled low-level dynamics, different payloads, time-varying wind, and changing mass). The performance is measured in terms of tracking errors and control efforts of the attitude and total energy control loops. Extensive experiments with the original ArduPilot, the proposed augmentation, and alternative autopilot strategies show that the augmentation can significantly improve the performance for all payloads and wind conditions: the UAV is less affected by wind and exhibits more than 70% improved tracking, with more than 7% reduced control effort. [ABSTRACT FROM AUTHOR]

This article focuses on the analysis of generalized control coupling effect of spinning guided projectiles. A concept of so-called equivalent dynamics is first proposed in this article to avoid nonlinearity and facilitate understanding of control coupling for spinning guided projectiles. First, the coupling induced by an arbitrary-order dynamic is formulated. Two useful theorems showing the property of the concept are presented standby. Second, considering the main elements involved in the control loop, i.e., autopilot, actuator, and inertial measurement unit, a comprehensive model in the spinning frame is established for spinning guided projectiles. With aid of the second theorem, the equivalent dynamics of the multiple elements are introduced into the model, in which the closed-loop characteristic equation is described with the complex summation method. Then, the closed-loop complex steady gain is analyzed and an integrated decoupling is applied to eliminate the coupling effects completely. This can be implemented readily via presetting a leading angle in engineering. Furthermore, it is noted that for this high-order system, numerical method solving the characteristic equation is the only way to obtain dynamic stability conditions. Finally, a case study is conducted to demonstrate the effectiveness and robustness of the decoupling approach. In addition, the stability problem is figured out with numerical method. [ABSTRACT FROM AUTHOR]

This paper describes the optimization of target categorization tracking and mathematical modelling to control the autopilot in the missile guidance system, which can recognize any type of target using intelligent devices and image processing. A typical inaccuracy in a reliable connection to the flight path can be reduced by using imaging techniques and new cell communication-based fruit fly swarm optimization within the object recognition processes. The suggested approach is able to locate a target among a batch of visual images captured from a camera, despite the inclusion of two targeting artefacts and their nearly similar layout. Furthermore, it has been proposed to not only evaluate movement objects but also track artefacts with consistent rotation speeds and steady speeds. Aside from adjusting the yaw, pitch, and roll axes, a simulation result for auto-pilot regulation has now been used to control a flight path. Using MATLAB and Simulink software, a simulated missile model was created that moves at 328 m/s toward the target and hits it in 3.25 seconds. [ABSTRACT FROM AUTHOR]

This article deals with a human–machine cooperative system for the control of a smart wheelchair for people with motor disabilities. The choice of a traded control mode is first argued. The paper then pursues two objectives. The first is to describe the design of the cooperative system by focusing on the dialogue and the interaction between the pilot and the robot. The second objective is to introduce a new cooperative mode. In this one, three features are proposed: two semi-autonomous features, a wall following and a doorway crossing, during which the user can intervene punctually to rectify a trajectory or a path, and an assisted mode where, conversely, the machine intervenes in a manual control to avoid obstacles. This mode of intervention of an entity, human or machine, supervising a movement controlled by the other is referred as "combined control." Examples of scenarios exploiting the cooperative capabilities of the system are presented and discussed. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to make a more precise prediction of the life of servo motors used in aircraft. The variation of the axial load created by the wave washer for bearing, which is one of the factors affecting the bearing life of the electric motors and servo motors used in aircraft, was analyzed. Design/methodology/approach: In electric motors and servo motors that body as stator, the height of the stator stack affects the compression amount of the wave washer spring. Working with electric motor and servo manufacturers, production-related variations in stator height were determined by making multiple measurements. The reaction forces resulting from these compression amounts were simulated by mathematical modeling with the finite element method, and also experimentally measured on real parts. Findings: Results obtained with finite element method and real experiments were compared. By adding the force differences to the general operating conditions, the effect on the bearing life was theoretically determined. In a servo motor with this type of construction, the difference in stator height created different axial loads on the motor shaft. The difference of these loads affected the motor bearing life. Research limitations/implications: The results implicated in terms of flight safety, maintenance operation and resource efficiency. Practical implications: The results of this study are effective in determining the maintenance intervals more clearly. This study can be used for the design criteria of aircraft servo motors. These servos, which are especially used to move the flight control surfaces, contribute to flight safety as the life expectancy will be clearer. Social implications: This study may be effective in preventing aviation accidents caused by servo motors. It can make maintenance management more efficient. Originality/value: This study investigated the effect of aircraft servo motor design inputs on servo motor life, considering the production. [ABSTRACT FROM AUTHOR]

In this article, we propose a novel hierarchical framework to resolve within-visual-range (WVR) air-to-air combat under complex nonlinear 6 degrees-of-freedom (6-DOF) dynamics of the aircraft and missile. The decision process is constructed with two layers from the top to the bottom and adopts reinforcement learning to solve them separately. The top layer designs a new combat policy to decide the autopilot commands (such as the target heading, velocity, and altitude) and missile launch according to the current combat situation. Then the bottom layer uses a control policy to answer the autopilot commands by calculating the actual input signals (deflections of the rudder, elevator, aileron, and throttle) for the aircraft. For the combat policy, we present a new learning method called "E2L" that can mimic the knowledge of the expert under the two-layer decision frame to inspire the intelligence of the agent in the early stage of training. This method establishes a cross coordination of behavior clone (BC) and proximal policy optimization (PPO). Under the mechanism, the agent is alternately updated around the latest strategy, using BC with gradient clipping and PPO with Kullback–Leibler divergence loss and the modified BC demonstration trajectories, which can learn competitive combat strategies more stably and quickly. Sufficient experimental results show that the proposed method can achieve better combat performance than the baselines. [ABSTRACT FROM AUTHOR]

Developing a fully automatic auxiliary flying system with robot maneuvering is feasible. This study develops a control vision system that can read all kind of needle-type meters. The vision device in this study implements a modified YOLO-based object detection model to recognize the airspeed readings from the needle-type dashboard. With this approach, meter information in the cockpit is replaced by a single camera and a powerful edge-computer for future autopilot maneuvering purpose. A modified YOLOv4-tiny model by adding the Spatial Pyramid Pooling (SPP) and the Bidirectional Feature Pyramid Network (BAFPN) to the Neck region of the convolutional neural networks (CNN) structure is implemented. The Taguchi method for acquiring a set of optimum hyperparameters for the CNN is applied. An improved deep learning network with higher Mean Average precision (mAP) compared with conventional YOLOv4-tiny and possessing a higher Frames Per Second (FPS) value than YOLOv4 is deployed successfully. Established a self-control system using a camera to receive airspeed indications from the designed virtual needle-type dashboard. Moreover, the dashboard's pointer is controlled by applying the proposed control method, which contains PID control in addition to the pointer's rotation angle recognition. A modified YOLOv4-tiny model with a fabricated system for visual dynamical recognition control is implemented successfully. The feasibility of bettering mean accuracy precision and frame per second in achieving autopilot maneuvering is verified. [ABSTRACT FROM AUTHOR]

This paper designs a novel adaptive robust optimal sliding mode control (SMC) strategy for constrained input missile system with uncertain input and unknown disturbance. First, a smooth function is employed to map the constrained input. Based on SMC theory, a fast adaptive SMC scheme is designed to reduce the effects of unknown input and approximate error which has two advantages. One is that the proposed scheme is independent of the uncertain information. The other is that the proposed scheme forces the states to converge with exponential rate as they are far away from the sliding mode surface, and the states move with linear rate when they are closed to the sliding mode surface. Then, a zero-sum differential games strategy-based adaptive dynamic programming (ADP) technique is employed to obtain the optimal performance of sliding mode dynamics system. The loop-system and estimation weight error can be proved stable by utilising Lyapunov theory. Finally, the missile autopilot system is used to indicate the effectiveness of the designed approach. [ABSTRACT FROM AUTHOR]

In the terminal guidance phase of intercepting aerial target, a guidance law, satisfying impact angle constraint, acceleration constraint, and second-order dynamic characteristic of interceptor autopilot, is designed based on the prescribed performance control and dynamic surface control method. Firstly, a two-dimensional plane interception model considering the dynamic characteristics of autopilot and input constraints is established. Furthermore, for the nonconstrained system, based on the dynamic surface control, the prescribed performance reaching law is used to ensure that the line-of-sight angle and the line-of-sight angle rate can converge to the prescribed range in finite time. At the same time, an adaptive law is used to estimate the uncertainty caused by target maneuver and autopilot. Then, aiming at the acceleration constraint, an auxiliary system is established to convert the restricted system into a nonrestricted system. The finite-time convergence of the terminal line-of-sight tracking error, the line-of-sight rate, and the uniform ultimate boundedness of the system are rigorously proved by Lyapunov stability theory. Finally, the simulation results show that the proposed guidance law can make the interceptor have better interception performance when attacking targets with different maneuvering forms. [ABSTRACT FROM AUTHOR]

In order to improve the decision-making and control effect of autonomous vehicles, in this paper, combined with literature research and process analysis, the control algorithm of autopilot vehicle is analyzed, and the driving process is analyzed combined with the flow method. In order to improve the effect of autonomous driving, with the support of improved algorithms, an integrated decision-making control system for autonomous vehicles under multi-task constraints in intelligent traffic scenarios is constructed, and system performance is improved by simulating autonomous driving decisions in a variety of complex situations. Moreover, this paper designs the road driving model according to actual needs, sets the functional modules of the entire system, and build the overall framework of the system. Finally, in order to study the integrated decision-making effect of this system, this paper conducts test research by designing a simulation test method. From the simulation test results, it can be seen that the intelligent decision-making system for autonomous vehicles constructed in this paper has certain effects. [ABSTRACT FROM AUTHOR]

This paper presents a short tutorial introduction to disturbance observer-based control approaches for the quadrotors. With this tutorial, researchers, engineers and students would be able to implement disturbance observer-based model-in-loop simulations and experiments more easily to design robust autopilot system for the quadrotors. To achieve this, first of all, the modeling and controlling of a quadrotor are explained and all linear disturbance observer-based control approaches in the literature are adapted in its overall nonlinear architecture. Disturbance observer-based control design steps are given in detail by design challenges. To show their disturbance rejection capabilities and practical applicability, two flight simulation scenarios are carried out. For all simulation cases, we only take into account the external disturbances in rotational motions. While we give the attitude trajectory commands to quadrotor attitude control architecture in the first scenario, we issue both way-point and trajectory commands to an outer loop controlling the translational motions in the second one. Presented disturbance observer-based control approaches have successfully completed the given reference commands in the presence of the external disturbances even under the measurement noise. Moreover, simulation experiments have shown that UDEBC approach transmits the external disturbance and measurement noise effects to the actuators directly. As a result, for UDEBC approach, it should be kept in mind that flight accidents may occur due to excessive ESC heating. Baseline attitude controller without disturbance observer-based control approach have failed to follow the given reference commands. The simulation studies have also proved the practical applicability of these methods, which are successful even under measurement noise. [ABSTRACT FROM AUTHOR]

This paper discusses the development of a single-lift and dual-lift helicopter underslung load transportation system for practical applications. A control law is developed to damp the load swing and stabilize the oscillation while performing the transportation task. For the dual-lift system, the load transportation is achieved by using a load distribution controller, developed for this purpose, to maintain equal load distribution among the vehicles. The load damping and load distribution controllers require accurate measurement of load states, which is achieved through the design and development of innovative, simple, and lightweight sensors units, namely, load tension measurement unit (LTMU) and load swing measurement unit (LSMU). LTMU sensor consists of a unique design that utilizes a flexi-force sensor, capable of measuring compressive load, for measurement of cable tension. The cable inclination in the longitudinal and lateral directions is measured by the LSMU sensor. These units are integrated with the helicopter autopilot for autonomous flight. The performance of the developed system is experimentally validated in the outdoor environment with single and dual-lift systems. [ABSTRACT FROM AUTHOR]

Multi-rotor flying vehicles (MRFVs) are increasingly becoming the preferred means of transportation in various civilian and military applications, such as mine detection, reconnaissance missions, agriculture, and load transfer to areas inaccessible to ground vehicles. In these applications, the payload is suspended underneath the vehicle and subjected to large oscillations because of external disturbances or the vehicle's acceleration. This study proposes an anti-swing controller (ASC) based on time-delayed feedback of the load swing angles to be implemented in MRFVs with suspended load and operated by a PX4 autopilot. The PX4 autopilot is now widely used in commercial drones. The implementation of the proposed ASC does not require any modifications to the original PX4 firmware because it adds additional displacements to the original trajectories of MRFVs. The effectiveness of the ASC is evaluated by simulating a quadrotor as an example of MRFVs with different flight conditions. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to overcome the main obstacle to compare the merits of the different control strategies for fixed-wing unmanned aerial vehicles (UAVs) to assess autopilot performances. Up to now, the published studies of control strategies have been carried out over disperse models, thus being complicated, if not impossible, to compare the merits of each proposal. The authors present a worked benchmark for autopilots studies, consisting of generalized models obtained by merging UAVs' parameters gathered from selected literature (journals) with other parameters directly obtained by the authors to include some relevant UAVs whose models are not provided in the literature. To obtain them it has been used a dedicated software (from U.S. Air Force). Design/methodology/approach: The proposed models have been constructed by averaging both the main aircraft defining parameters (model derivatives) and pole-zero locations of longitudinal transfer functions. The suitability of the used methodologies has been checked from their capability to fit the short period and the phugoid modes. Previous analytical model arrangement has been required to match a uniform set of parameters, as the inner state variables are neither the same along the different published models nor between the additional models the authors have here contributed. Besides, moving models between the space state representation and transfer function is not just a simple averaging process, as neither the parameters nor the model orders are the same in the different published works. So, the junction of the models to a common set of parameters requires some residual's computation and transient responses assessment (even Fourier analysis has been included to preserve the dominance of the phugoid) to keep the main properties of the models. The least mean squares technique has been used to have better fittings between SISO model parameters with state–space ones. Findings: Both the SISO (Laplace) and state-space models for the longitudinal transfer function of an "averaged" fixed-wing UAV are proposed. Research limitations/implications: More complicated situations, such as strong wind conditions, need another kind of models, usually based on finite element method simulation. These particular models apply fluid dynamics to study aerostructural aircraft aspects, such as flutter and other aerolastic aspects, the behavior under icing conditions or other distributed parameter problems. Even some models aim to control other aspects than the autopilot, such as the trajectory prediction. However, these models are not the most suitable for the basic UAV autopilot design (early design), so they are outside the objective of this paper. Obviously, the here-considered UAVs are not all the existing ones, but the number is large enough to consider the result as a reliable and realistic representation. The presented study may be seen as a stepping stone, allowing to include other UAVs in future works. Practical implications: The proposed models can be used as benchmarks, or as a previous step to produce improved benchmarks, in order to have a common and realistic scenario the compare the benefits of the different control actions in UAV autopilots continuously presented in the published research. Originality/value: A work with the scope of the presented one, merging model parameters from literature with other (often referred in papers and websites) whose parameters have been obtained by the authors has been never published. [ABSTRACT FROM AUTHOR]