Simulated Task Research Articles

Gate-Level Circuit Partitioning Algorithm Based on Clustering and an Improved Genetic Algorithm.

Gate-level circuit partitioning is an important development trend for improving the efficiency of simulation in EDA software. In this paper, a gate-level circuit partitioning algorithm, based on clustering and an improved genetic algorithm, is proposed for the gate-level simulation task. First, a clustering algorithm based on betweenness centrality is proposed to quickly identify clusters in the original circuit and achieve the circuit coarse. Next, a constraint-based genetic algorithm is proposed which provides absolute and probabilistic genetic strategies for clustered circuits and other circuits, respectively. This new genetic strategy guarantees the integrity of clusters and is effective for realizing the fine partitioning of gate-level circuits. The experimental results using 12 ISCAS '89 and ISCAS '85 benchmark circuits show that the proposed algorithm is 5% better than Metis, 80% better than KL, and 61% better than traditional genetic algorithms for finding the minimum number of connections between subsets.

Experimental Characterization of an NEG Pump of Novel Size—A Major Step toward Its Application in DEMO Neutral Beam Injectors

A future nuclear fusion plant DEMO needs neutral beam injection (NBI) systems requiring a vacuum pumping system with a very high pumping speed, in the order of several 1000 m3/s. Large customized cryopumps are actually used to meet the requirements. A promising concept for future NBI systems is based on high capacity getter materials. The ZAO® alloy, developed by SAES Getters, Italy, provides a drastically improved performance for the pumping of hydrogen compared to conventional getter materials. This paper describes the experimental characterization of a large pump of scalable size with 15 kg of ZAO® and the achieved results, in particular the systematic investigation of sorption characteristics and regeneration behaviors. Major findings include a very good repeatability of the sorption performance, a reduced pumping speed at higher pressures only above the NBI relevant level, an improved performance (+20%) with elevated getter temperature and an isotope independent sticking coefficient for hydrogen. Furthermore, improved operation experience and regeneration prediction tools have been developed. Employing the experimental results, a simulation task was performed and the sticking factor of the getter cartridge was determined with 7%.

Comparison of 2D and autostereoscopic 3D visualization during mixed reality simulation.

In general minimally invasive surgical procedures, surgeons are tied to 2D visualization, leading to the loss of depth perception. This can lead to large mental load for the surgeons and may be responsible for the long learning curve. To restore the sense of depth, this study investigated the use and benefits of an autostereoscopic (3D) display during a simulated laparoscopic task. A mixed reality simulator was developed for comparing the performance of participants while using 2D and autostereoscopic 3D visualization. An electromagnetic sensor was mounted on a physical instrument, and its pose was mapped to the virtual instrument. The virtual scene was developed using Simulation Open Framework Architecture (SOFA). Finite element modeling was used to calculate interaction forces, which were then mapped to visual soft tissue deformation. Ten non-expert participants completed a virtual laparoscopic task, where the subjects were asked to contact eighteen target areas distributed on the surface of the vagina, both in 2D and 3D. Results showed an improvement with 3D vision in task completion time (-16%), total traveled distance (-25%) and errors made (-14%). There was no difference in the average contact forces between the vagina and the instrument. Only the difference in time and forces were shown to be statistically significant. Overall, autostereoscopic 3D showed superiority over conventional 2D visualization. The traveled trajectory increased in 2D as the instrument was retracted more between the targets to avoid contact. The 2D and 3D deformation upon contact seems not to contribute differently to force perception. However, the participants only had visual feedback, but no haptic feedback. Therefore, it could be interesting to include haptic feedback in a future study.

Simulation of Spacecraft Moon Landing Control in Virtual Environment Complexes

The paper considers the task for simulation of final stage spacecraft landing on the Moon in virtual environment systems. To solve this task, methods and algorithms are proposed for the lunar module motion control with the implementation of fast attitude maneuvers and minimum fuel consumption during its deceleration. The spacecraft control is based on virtual sensors feedback and makes it possible to implement stabilization, reorientation, deceleration, maneuvers, hovering and soft landing of the spacecraft on the Moon. The work involves virtual reality technologies with the implementation of human interaction with a computer-synthesized environment. In this case, to control the spacecraft in manual mode, virtual hands are used that copy the movements of the operator’s hands and act on the elements of virtual controls (joystick, buttons, etc.) inside the spacecraft model. Approbation of methods and algorithms proposed in the paper was carried out in our software package of virtual environment system on the example for landing simulation of virtual model Orel spacecraft in semi-automatic mode. In this software package the spacecraft control in manual mode is implemented by data which transit from Oculus Rift CV1 VR headset and Oculus Touch controllers designed for tracking the operator’s head and hands, as well as displaying synthesized stereopair to his eyes. The simulation of spacecraft landing on the Moon was carried out for stages that begin immediately after the basic deceleration at an altitude of about 2 km and including the free fall of the lunar vehicle, its verticalization, horizontal and vertical deceleration, hovering, and soft landing. The results of approbation showed the adequacy and quality of the solutions proposed in the paper, which can be further used to create simulators designed to train cosmonauts how to control a spacecraft during landing on the Moon.

Efficient Uncertainty Propagation in Model-Based Reinforcement Learning Unmanned Surface Vehicle Using Unscented Kalman Filter

This article tackles the computational burden of propagating uncertainties in the model predictive controller-based policy of the probabilistic model-based reinforcement learning (MBRL) system for an unmanned surface vehicles system (USV). We proposed filtered probabilistic model predictive control using the unscented Kalman filter (FPMPC-UKF) that introduces the unscented Kalman filter (UKF) for a more efficient uncertainty propagation in MBRL. A USV control system based on FPMPC-UKF is developed and evaluated by position-keeping and target-reaching tasks in a real USV data-driven simulation. The experimental results demonstrate a significant superiority of the proposed method in balancing the control performance and computational burdens under different levels of disturbances compared with the related works of USV, and therefore indicate its potential in more challenging USV scenarios with limited computational resources.

Assessing the development of mental fatigue during simulated flights with concurrent EEG-fNIRS measurement

Mental fatigue (MF) can impair pilots’ performance and reactions to unforeseen events and is therefore an important concept within aviation. The physiological measurement of MF, especially with EEG and, in recent years, fNIRS, has gained much attention. However, a systematic investigation and comparison of the measurements is seldomly done. We induced MF via time on task during a 90-min simulated flight task and collected concurrent EEG-fNIRS, performance and self-report data from 31 participants. While their subjective MF increased linearly, the participants were able to keep their performance stable over the course of the experiment. EEG data showed an early increase and levelling in parietal alpha power and a slower, but steady increase in frontal theta power. No consistent trend could be observed in the fNIRS data. Thus, more research on fNIRS is needed to understand its possibilities and limits for MF assessment, and a combination with EEG is advisable to compare and validate results. Until then, EEG remains the better choice for continuous MF assessment in cockpit applications because of its high sensitivity to a transition from alert to fatigued, even before performance is impaired.

A geometric optimal control approach for imitation and generalization of manipulation skills

Daily manipulation tasks are characterized by regular features associated with the task structure, which can be described by multiple geometric primitives related to actions and object shapes. Only using Cartesian coordinate systems cannot fully represent such geometric descriptors. In this article, we consider other candidate coordinate systems and propose a learning approach to extract the optimal representation of an observed movement/behavior from these coordinates. This is achieved by using an extension of Gaussian distributions on Riemannian manifolds, which is used to analyze a small set of user demonstrations statistically represented in different coordinate systems. We formulate the skill generalization as a general optimal control problem based on the (iterative) linear quadratic regulator ((i)LQR), where the Gaussian distribution in the proper coordinate systems is used to define the cost function. We apply our approach to object grasping and box-opening tasks in simulation and on a 7-axis Franka Emika robot using open-loop and feedback control, where precision matrices result in the automatic determination of feedback gains for the controller from very few demonstrations represented in multiple coordinate systems. The results show that the robot can exploit several geometries to execute the manipulation task and generalize it to new situations. The results show high variation along the do-not-matter direction, while maintaining the invariant characteristics of the task in the coordinate system(s) of interest. We then tested the approach in a human–robot shared control task. Results show that the robot can modify its grasping strategy based on the geometry of the object that the user decides to grasp.

Real-time vibrotactile feedback system for reducing trunk flexion exposure during construction tasks

Workplaces are increasingly evaluating the use of wearables for ergonomic assessment and providing biofeedback as a real-time postural intervention to improve workers’ posture. However, the effectiveness of such intervention systems has yet to be thoroughly investigated in different types of industrial tasks. This study tested the immediate effects of using vibrotactile feedback in simulated construction work tasks including lifting-lowering, shoveling, and tying rebar, to investigate the potential for such an intervention as a way to instruct workers in reducing excessive trunk flexion exposures. Fourteen male participants completed simulated work tasks with three different feedback locations, namely, no feedback, back, and wrist. The results demonstrate that the 95th, 90th, and 50th percentiles of trunk flexion angles are significantly lower for lifting-lowering and shoveling tasks when the feedback system is used. No significant postural changes were observed for the rebar tying task at any combination of percentile and feedback location. The rating of perceived exertion (RPE) for each task did not differ significantly between feedback conditions. Productivity remained the same with the feedback for lifting-lowering and rebar tying, while it was significantly reduced (4.5% of working rate reduction) in shoveling. Participants rated the wrist as the most preferred feedback location. The results of this study suggest that vibrotactile feedback has potential as an effective postural intervention for ergonomic risk factors in selected construction work tasks. Implications for the future design of real-time wearable, sensor-based vibrotactile feedback systems for postural control intervention during dynamic work tasks are discussed.

Optical neuroimaging and neurostimulation in surgical training and assessment: A state-of-the-art review.

Functional near-infrared spectroscopy (fNIRS) is a non-invasive optical neuroimaging technique used to assess surgeons' brain function. The aim of this narrative review is to outline the effect of expertise, stress, surgical technology, and neurostimulation on surgeons' neural activation patterns, and highlight key progress areas required in surgical neuroergonomics to modulate training and performance. A literature search of PubMed and Embase was conducted to identify neuroimaging studies using fNIRS and neurostimulation in surgeons performing simulated tasks. Novice surgeons exhibit greater haemodynamic responses across the pre-frontal cortex than experts during simple surgical tasks, whilst expert surgical performance is characterized by relative prefrontal attenuation and upregulation of activation foci across other regions such as the supplementary motor area. The association between PFC activation and mental workload follows an inverted-U shaped curve, activation increasing then attenuating past a critical inflection point at which demands outstrip cognitive capacity Neuroimages are sensitive to the impact of laparoscopic and robotic tools on cognitive workload, helping inform the development of training programs which target neural learning curves. FNIRS differs in comparison to current tools to assess proficiency by depicting a cognitive state during surgery, enabling the development of cognitive benchmarks of expertise. Finally, neurostimulation using transcranial direct-current-stimulation may accelerate skill acquisition and enhance technical performance. FNIRS can inform the development of surgical training programs which modulate stress responses, cognitive learning curves, and motor skill performance. Improved data processing with machine learning offers the possibility of live feedback regarding surgeons' cognitive states during operative procedures.

Performance changes during repeated military occupational test and its associations to physical performance

ABSTRACT The present study investigated performance changes during three runs (1 min recovery) of repeated military simulation task test (RMST) and its associations with physical performance. Voluntary male soldiers (N = 114) participated in a series of measurements of physical performance. Lower body explosive force production, anaerobic endurance and upper body strength endurance together explained 58% of the variance in the first RSMT (p < 0.001). The same variables explained the variance in the second and third runs of RSMT by 60% and 51%, but explosive force production was replaced with aerobic endurance, assessed by the 3.2 km loaded run (p < 0.001). This study demonstrated that the role of explosive power of the lower body decreased and military specific aerobic endurance increased when occupational performance was assessed under acute fatigue even during a short high-intensity test. These results may benefit tactical strength and conditioning coaches in training optimization for improved occupational performance in military.

Three passive arm-support exoskeletons have inconsistent effects on muscle activity, posture, and perceived exertion during diverse simulated pseudo-static overhead nutrunning tasks

Arm-support exoskeletons (ASEs) are an emerging technology with the potential to reduce physical demands during diverse tasks, especially overhead work. However, limited information is available about the effects of different ASE designs during overhead work with diverse task demands. Eighteen participants (gender-balanced) performed lab-based simulations of a pseudo-static overhead task. This task was performed in six different conditions (3 work heights × 2 hand force directions), with each of three ASEs and in a control condition (i.e., no ASE). Using ASEs generally reduced the median activity of several shoulder muscles (by ∼12–60%), changed working postures, and decreased perceived exertion in several body regions. Such effects, though, were often task-dependent and differed between the ASEs. Our results support earlier evidence of the beneficial effects of ASEs for overhead work but emphasize that: 1) these effects depend on the task demands and ASE design and 2) none of the ASE designs tested was clearly superior across the tasks simulated.

Motion Smoothness-Based Assessment of Surgical Expertise: The Importance of Selecting Proper Metrics.

The smooth movement of hand/surgical instruments is considered an indicator of skilled, coordinated surgical performance. Jerky surgical instrument movements or hand tremors can cause unwanted damages to the surgical site. Different methods have been used in previous studies for assessing motion smoothness, causing conflicting results regarding the comparison among surgical skill levels. We recruited four attending surgeons, five surgical residents, and nine novices. The participants conducted three simulated laparoscopic tasks, including peg transfer, bimanual peg transfer, and rubber band translocation. Tooltip motion smoothness was computed using the mean tooltip motion jerk, logarithmic dimensionless tooltip motion jerk, and 95% tooltip motion frequency (originally proposed in this study) to evaluate their capability of surgical skill level differentiation. The results revealed that logarithmic dimensionless motion jerk and 95% motion frequency were capable of distinguishing skill levels, indicated by smoother tooltip movements observed in high compared to low skill levels. Contrarily, mean motion jerk was not able to distinguish the skill levels. Additionally, 95% motion frequency was less affected by the measurement noise since it did not require the calculation of motion jerk, and 95% motion frequency and logarithmic dimensionless motion jerk yielded a better motion smoothness assessment outcome in distinguishing skill levels than mean motion jerk.

Relations between Math Achievement, Math Anxiety, and the Quality of Parent–Child Interactions While Solving Math Problems

In the current study, we used a multi-method approach to understand the quality of math homework-helping interactions between parents and their children and how parents’ and children’s own math achievement and math anxiety relate to the quality of the interaction. Forty Canadian parents and their children (ages 10–12 years; grades 5 to 7) completed self-report measures of math and general anxiety. Parents and children completed standardized assessments of math achievement and were then recorded as they engaged in a simulated math homework interaction. Coders assessed parent–child interaction quality during the interaction. Parent–child dyads generally performed well on the simulated math homework task. Nevertheless, task performance was correlated with the quality of the interaction, with high-quality interactions associated with high accuracy on the math task. Furthermore, the variability in the quality of the interaction was associated with parents’ and children’s math achievement and with the math anxiety of the children, but not the parents. Identifying the elements that influence parent–child interactions in math-related situations is essential to developing effective interventions to scaffold children’s math learning and attitudes.

Comparison of Attempts Needed for Veterinary Students to Reach Proficiency in a Basic and Advanced Robotic Simulator Task.

Surgical training of students is one of the most difficult aspects of veterinary medical education. Competing interests of house officers, owner's wishes, and ethical concerns have led to increasing use of inanimate models for basic surgical skills training. Robotic simulators have benefits for psychomotor training but have not been previously investigated in veterinary medicine. Understanding the necessary practice time for new training devices is important when determining their potential value. The aim of this study was to compare the number of attempts needed for veterinary students to reach proficiency in both a basic and advanced robotic simulator task, and to assess the predictive nature of performance variables. Each student performed a basic and advanced tasks on the Mimic dV-Trainer™ until proficiency was reached. Students required a median of eight attempts (95% CI = 7-8, range: 6-11) to reach proficiency for the basic task versus 22 attempts (95% CI = 20-26, range: 11-62) for the advanced task. The median time required to complete training for the basic and advanced task was 13.5 minutes (range: 8-24 minutes) and 26.5 minutes (range: 11-82 minutes) respectively. The difference in task attempts supports the training protocol and confirms proficiency can be attained in a short period of time. The number of attempts to reach proficiency correlated with specific performance variables which can be used by educators to aid in training students on a robotic simulator. Continued investigations on robotic simulators should be performed to investigate their use in improving psychomotor skills in veterinary students.

A time-accurate inflow coupling for zonal LES

Generating turbulent inflow data is a challenging task in zonal large eddy simulation (zLES) and often relies on predefined DNS data to generate synthetic turbulence with the correct statistics. The more accurate, but more involved alternative is to use instantaneous data from a precursor simulation. Using instantaneous data as an inflow condition allows to conduct high fidelity simulations of subdomains of, e.g. an aircraft including all non-stationary or rare events. In this paper, we introduce a toolchain that is capable of interchanging highly resolved spatial and temporal data between flow solvers with different discretization schemes. To accomplish this, we use interpolation algorithms suitable for scattered data in order to interpolate spatially. In time, we use one-dimensional interpolation schemes for each degree of freedom. The results show that we can get stable simulations that map all flow features from the source data into a new target domain. Thus, the coupling is capable of mapping arbitrary data distributions and formats into a new domain while also recovering and conserving turbulent structures and scales. The necessary time and space resolution requirements can be defined knowing the resolution requirements of the used numerical scheme in the target domain.

A Trajectory Generation Method for Multi-Rotor UAV Based on Adaptive Adjustment Strategy

In order to fly safely and autonomously in complex environments, UAVs need to be able to plan their trajectories in real-time. This paper proposes an improved B-spline-based trajectory generation method that can generate safe, smooth, and kinodynamically feasible trajectories in real-time. This paper firstly introduces the principle of error upper bound of the B-spline curve and proposes a new trajectory safety assurance method; then, the loss function of trajectory is constructed based on safety, smoothness, and flight time; finally, a parameter adaptive trajectory optimization method is proposed, so that obtain the safe trajectory. Compared with the existing methods, the proposed method has two important improvements: (1) it solves the problem of overly conservative safety distance estimation at control points, improves the trajectory smoothness, and reduces the required flight time; (2) it proposes a trajectory optimization method with adaptive adjustment of safety distance parameters, which improves the quality and success rate of the planned trajectory. We validate our proposed method in simulation and real-world tasks, and the test results show that the method proposed in this paper can significantly improve the quality of the generated trajectory.

Development and validation of a task load index for process control room operators (PCRO-TLX)

Process control room operators (PCRO) perform a range of complex cognitive safety-critical tasks. The aim of this exploratory sequential mixed methods study was to develop an occupation specific tool to measure the task load of PCRO using NASA Task Load Index (TLX) methodology. Participants were 30 human factors experts and 146 PCRO at two refinery complexes in Iran. Dimensions were developed via a cognitive task analysis, a research review, and three expert panels. Six dimensions were identified: perceptual demand, performance, mental demand, time pressure, effort, and stress. Data from 120 PCRO confirmed that the developed PCRO-TLX has acceptable psychometric properties, and a comparison with the NASA-TLX confirmed that perceptual, not physical, demand was relevant for measuring workload in PCRO. There was a positive convergence of scores of the Subjective Workload Assessment Technique and the PCRO-TLX. This reliable tool (α = 0.83) is recommended for risk assessing the task load of PCRO. Practitioner summary: There are benefits of having a specific tool to measure task load in safety critical roles. Thus, we developed and validated an easy-to-use targeted tool, the PCRO-TLX, for process control room operatives. Timely use and response will assure optimal production alongside health and safety in an organisation. Abbreviations: PCRO: process control room operator; TLX: task load index; PCRO-TLX: process control room operator task load index; NASA-TLX: National Aeronautics and Space Administration task load index; SWAT: subjective workload assessment technique; DALI: driving activity load index; SURG-TLX: surgery task load index; SIM-TLX: virtual reality simulation task load index; VACP: visual, auditory, cognitive and psychomotor; CVI: content validity index; CVR: content validity ratio; RMSEA: root mean square of error approximation; GFI: goodness of fit index; AGFI: adjusted goodness of fit index; CFI: comparative fit index; ANOVA: analysis of variance; CI: confidence interval

Promotion of driver compliance with V2V information in car-following tasks via multimodal display

The success of vehicle-to-vehicle (V2V) communication in improving the driving safety and efficiency primarily depends on driver compliance with the transmitted information. In this study, driver compliance with V2V information in simulated car-following tasks was measured and analyzed under different information display settings, such as baseline without information, visual information only, and multimodal information. Car-following data were collected from 30 participants in a driving simulation environment. The results revealed that V2V information could significantly reduce the driver's response time to the lead car’s speed change, resulting in an increase in the minimum time-to-collision when the lead car slowed down. Driver compliance was significantly higher under multimodal information settings than when using visual information alone. Moreover, driver compliance was higher when facing a decelerating lead car than when facing an accelerating lead car. No significant differences in driver compliance between the deceleration levels was observed; however, differences existed among different car-following preference groups. The findings of this study can be applied to V2V car-following models to improve the simulation accuracy for connected-vehicle traffic. Additionally, multimodal information presentation can provide a reference for V2V communication design.

Towards a Broad-Persistent Advising Approach for Deep Interactive Reinforcement Learning in Robotic Environments.

Deep Reinforcement Learning (DeepRL) methods have been widely used in robotics to learn about the environment and acquire behaviours autonomously. Deep Interactive Reinforcement 2 Learning (DeepIRL) includes interactive feedback from an external trainer or expert giving advice to help learners choose actions to speed up the learning process. However, current research has been limited to interactions that offer actionable advice to only the current state of the agent. Additionally, the information is discarded by the agent after a single use, which causes a duplicate process at the same state for a revisit. In this paper, we present Broad-Persistent Advising (BPA), an approach that retains and reuses the processed information. It not only helps trainers give more general advice relevant to similar states instead of only the current state, but also allows the agent to speed up the learning process. We tested the proposed approach in two continuous robotic scenarios, namely a cart pole balancing task and a simulated robot navigation task. The results demonstrated that the agent's learning speed increased, as evidenced by the rising reward points of up to 37%, while maintaining the number of interactions required for the trainer, in comparison to the DeepIRL approach.

Interior Individual Trajectory Simulation with Population Distribution Constraint

Individual trajectory generation plays an important role in simulation tasks, reconstructing fine-grained mobility behaviors that can be used to evaluate epidemic risks, congestion risks, or commercial profit. Previous research works adopt the Newton’s mechanic-based particle model as their core algorithm, such as the Social Force model. However, real-world human mobility behaviors hardly follow the particle models, especially in the interior scenes where interactions between pedestrians and environments matter. In this article, we propose a Social Force-based trajectory simulator for interior scenarios that improve both trajectory quality and generation speed for interior scenarios. First, we introduce prior scene knowledge to guide the generation process, where pedestrians are armed with exploration behaviors that follow the group-level distribution. It provides more flexibility to simulate complicated human behaviors rather than straight-line movements, generating high-quality individual trajectories. Experiments show that the correlation between the aggregated population distribution of generated trajectories and ground-truth distribution is improved by 11.84% by our method. Second, we optimize the algorithm procedure by introducing a caching mechanism for tenderized intermediate values, along with graph-processing-unit-based implementation. Compared with the baseline Social Force model, we reduced the time consumption by 95%. More importantly, based on our simulation paradigm, we quantitatively evaluate several common mobility interventions in our simulation scenario, which can shed light on better policy designs in public spaces.