Non-adaptive Method Research Articles

Frequency Increment Design Method of MR-FDA-MIMO Radar for Interference Suppression

In the present complex electromagnetic environment, radar target detection is threatened by different kinds of interferences, especially mainlobe deceptive interference, which occupies the same energy distributions of targets spatially, meaning that targets and interferences cannot be discriminated. To make matters worse, the number of suppressible interferences is limited by the number of physical array elements, leading to the degradation of the suppression performance of traditional radar. In this work, we propose a frequency-increment-based interference suppression method for minimum redundancy frequency diverse array multiple-input multiple-output (MR-FDA-MIMO) radar, which effectively solves the aforementioned two problems. The interference suppression method consists of two steps: (i) in the sidelobe barrage interference suppression stage, the interference-plus-noise covariance matrix is reconstructed to overcome the influence of the true targets and mainlobe deceptive interference on the performance of the beamformer; (ii) in the mainlobe deceptive interference suppression stage, a nonadaptive beamforming method is employed to suppress mainlobe deceptive interference and overcome the impact of insufficient virtual samples on interference suppression performance. Additionally, we design a frequency-increment-based MR-FDA-MIMO radar, fully utilizing the advantages of the virtual array to enhance interference suppression performance and increase the number of interferences. Numerical experiments undertaken demonstrate the effectiveness of the algorithm under different scenarios.

Optimizing human-robot handovers: the impact of adaptive transport methods.

Humans are increasingly coming into direct physical contact with robots in the context of object handovers. The technical development of robots is progressing so that handovers can be better adapted to humans. An important criterion for successful handovers between robots and humans is the predictability of the robot for the human. The better humans can anticipate the robot's actions, the better they can adapt to them and thus achieve smoother handovers. In the context of this work, it was investigated whether a highly adaptive transport method of the object, adapted to the human hand, leads to better handovers than a non-adaptive transport method with a predefined target position. To ensure robust handovers at high repetition rates, a Franka Panda robotic arm with a gripper equipped with an Intel RealSense camera and capacitive proximity sensors in the gripper was used. To investigate the handover behavior, a study was conducted with n = 40 subjects, each performing 40 handovers in four consecutive runs. The dependent variables examined are physical handover time, early handover intervention before the robot reaches its target position, and subjects' subjective ratings. The adaptive transport method does not result in significantly higher mean physical handover times than the non-adaptive transport method. The non-adaptive transport method does not lead to a significantly earlier handover intervention in the course of the runs than the adaptive transport method. Trust in the robot and the perception of safety are rated significantly lower for the adaptive transport method than for the non-adaptive transport method. The physical handover time decreases significantly for both transport methods within the first two runs. For both transport methods, the percentage of handovers with a physical handover time between 0.1 and 0.2 s increases sharply, while the percentage of handovers with a physical handover time of >0.5 s decreases sharply. The results can be explained by theories of motor learning. From the experience of this study, an increased understanding of motor learning and adaptation in the context of human-robot interaction can be of great benefit for further technical development in robotics and for the industrial use of robots.

Physics-informed neural networks with adaptive localized artificial viscosity

Physics-informed Neural Network (PINN) is a promising tool that has been applied in a variety of physical phenomena described by partial differential equations (PDE). However, it has been observed that PINNs are difficult to train in certain “stiff” problems, which include various nonlinear hyperbolic PDEs that display shocks in their solutions. Recent studies added a diffusion term to the PDE, and an artificial viscosity (AV) value was manually tuned to allow PINNs to solve these problems. In this paper, we propose three approaches to address this problem, none of which rely on an a priori definition of the artificial viscosity value. The first method learns a global AV value, whereas the other two learn localized AV values around the shocks, by means of a parametrized AV map or a residual-based AV map. We applied the proposed methods to the inviscid Burgers equation and the Buckley-Leverett equation, the latter being a classical problem in Petroleum Engineering. The results show that the proposed methods are able to learn both a small AV value and the accurate shock location and improve the approximation error over a nonadaptive global AV alternative method.

Monte Carlo integration of C^r functions with adaptive variance reduction: an asymptotic analysis

The theme of the present paper is numerical integration of C^r functions using randomized methods. We consider variance reduction methods that consist in two steps. First the initial interval is partitioned into subintervals and the integrand is approximated by a piecewise polynomial interpolant that is based on the obtained partition. Then a randomized approximation is applied on the difference of the integrand and its interpolant. The final approximation of the integral is the sum of both. The optimal convergence rate is already achieved by uniform (nonadaptive) partition plus the crude Monte Carlo; however, special adaptive techniques can substantially lower the asymptotic factor depending on the integrand. The improvement can be huge in comparison to the nonadaptive method, especially for functions with rapidly varying rth derivatives, which has serious implications for practical computations. In addition, the proposed adaptive methods are easily implementable and can be well used for automatic integration.

Quantum metrology in a lossless Mach–Zehnder interferometer using entangled photon inputs for a sequence of non-adaptive and adaptive measurements

Using multi-photon entangled input states, we estimate the phase uncertainty in a noiseless Mach–Zehnder interferometer using photon-counting detection. We assume a flat prior uncertainty and use Bayesian inference to construct a posterior uncertainty. By minimizing the posterior variance to get the optimal input states, we first devise an estimation and measurement strategy that yields the lowest phase uncertainty for a single measurement. N00N and Gaussian states are determined to be optimal in certain regimes. We then generalize to a sequence of repeated measurements, using non-adaptive and fully adaptive measurements. N00N and Gaussian input states are close to optimal in these cases as well, and optimal analytical formulae are developed. Using these formulae as inputs, a general scaling formula is obtained, which shows how many shots it would take on average to reduce phase uncertainty to a target level. Finally, these theoretical results are compared with a Monte Carlo simulation using frequentist inference. In both methods of inference, the local non-adaptive method is shown to be the most effective practical method to reduce phase uncertainty.

Optimal Cold Atom Thermometry Using Adaptive Bayesian Strategies

Precise temperature measurements on systems of few ultracold atoms is of paramount importance in quantum technologies, but can be very resource-intensive. Here, we put forward an adaptive Bayesian framework that substantially boosts the performance of cold atom temperature estimation. Specifically, we process data from real and simulated release--recapture thermometry experiments on few potassium atoms cooled down to the microkelvin range in an optical tweezer. From simulations, we demonstrate that adaptively choosing the release--recapture times to maximise information gain does substantially reduce the number of measurements needed for the estimate to converge to a final reading. Unlike conventional methods, our proposal systematically avoids capturing and processing uninformative data. We also find that a simpler non-adaptive method exploiting all the a priori information can yield competitive results, and we put it to the test on real experimental data. Furthermore, we are able to produce much more reliable estimates, especially when the measured data are scarce and noisy, and they converge faster to the real temperature in the asymptotic limit. Importantly, the underlying Bayesian framework is not platform-specific and can be adapted to enhance precision in other setups, thus opening new avenues in quantum thermometry.

Climatological parameters estimation based on artificial intelligence techniques with particle swarm optimization and deep neural networks

Climate forecasting plays an important role for human life in many areas such as water management, agriculture, natural hazards including drought and flood, tourism, business, and regional investment. Estimating these data is a difficult task as the time series climate parameter values vary monthly and seasonally. Therefore, predicting climate parameters based on learning and artificial intelligence is important to long-term efficient results in these fields. For this purpose, in this study, a time-series based Long Short-Term Memory (LSTM) deep neural network is proposed to predict future climate in Çankırı and Adıyaman cities in Turkey. With the help of this network, the average temperature, relative humidity, and precipitation values, which are known as the most effective climate parameters, have been estimated. An improved Particle Swarm Optimization (PSO) technique is also proposed to optimize input weight values of the LSTM deep network, and reduce the estimation errors. The proposed algorithm is compared with deep models of LSTM variants based on Root Mean Square Error (RMSE), Mean Absolute Deviation (MADE), and Mean Absolute Percentage Error (MAPE) metrics. The proposed adaptive LSTM-PSO and non-adaptive LSTM-PSO models achieved at RMSE 0.98 and 1.05 for temperature, 1.19 and 1.27 for relative humidity, and 4.21 and 4.67 for precipitation estimation, respectively. The RMSE is %7 lower with the proposed adaptive LSTM-PSO method than proposed non-adaptive LSTM-PSO method.

An h-adaptive local discontinuous Galerkin method for second order wave equation: Applications for the underwater explosion shock hydrodynamics

The underwater explosion (UNDEX) phenomenon involves hydrodynamic behaviors with a wide range of spatial scales, from macroscopic cavitation regions to refined areas with fluid-structure interactions around the shock wavefront. Correct description of the refined shock wavefront is important for determining the shock wave position and pressure peak. In this work, the local discontinuous Galerkin (LDG) method for the two-dimensional wave equation, which allows for the local triangular meshes refinement and merging during the shock wave propagation process, is presented to model the UNDEX shock hydrodynamics. This article is an extension of our previous work, where we discretize the UNDEX total wave formulation in the LDG framework and simulate the UNDEX shock wave and cavitation phenomenon. In order to save computational cost (including CPU time and computational storage) and capture the shock wavefront more accurately, the LDG method is extended to be in combination with the mesh adaption technique. The troubled-cell indicator, depending on the local jump of the pressure, is adopted to identify elements which require to be refined or coarsened. Typical numerical examples are tested to assess the capability of the present h-adaptive LDG method for capturing the UNDEX shock wave. The numerical results clearly demonstrate that the mesh adaption technique enables us to dynamically increase the mesh resolution in close proximity to the flow discontinuities with a large pressure jump. The local increase of the mesh resolution results in a substantial increase in the accuracy of the pressure peak. In terms of the computational efficiency, the h-adaptive LDG method can save nearly half of the calculation time in comparison to the non-adaptive LDG method on uniform meshes, whose mesh resolution is the same as that of the maximum level of refinement of the h-adaptive LDG method.

Adapt or Perish? Exploring the Effectiveness of Adaptive DoF Control Interaction Methods for Assistive Robot Arms

Robot arms are one of many assistive technologies used by people with motor impairments. Assistive robot arms can allow people to perform activities of daily living (ADL) involving grasping and manipulating objects in their environment without the assistance of caregivers. Suitable input devices (e.g., joysticks) mostly have two Degrees of Freedom (DoF), while most assistive robot arms have six or more. This results in time-consuming and cognitively demanding mode switches to change the mapping of DoFs to control the robot. One option to decrease the difficulty of controlling a high-DoF assistive robot arm using a low-DoF input device is to assign different combinations of movement-DoFs to the device’s input DoFs depending on the current situation (adaptive control). To explore this method of control, we designed two adaptive control methods for a realistic virtual 3D environment. We evaluated our methods against a commonly used non-adaptive control method that requires the user to switch controls manually. This was conducted in a simulated remote study that used Virtual Reality and involved 39 non-disabled participants. Our results show that the number of mode switches necessary to complete a simple pick-and-place task decreases significantly when using an adaptive control type. In contrast, the task completion time and workload stay the same. A thematic analysis of qualitative feedback of our participants suggests that a longer period of training could further improve the performance of adaptive control methods.

Numerical simulation of mid-deep geothermal system based on dynamic mesh adaptive finite element

Numerical simulation is an important tool in the study of geothermal science and even the development and utilization of geothermal resources. Aiming at the development of middle and deep geothermal resources, this paper takes the numerical simulation of U-shaped heat exchange well as the research object. Based on Fourier’s law and heat conduction equation, the dynamic mesh adaptive finite element method is used to simulate the heat transfer process. The results show that the fixed mesh non-adaptive finite element method lacks a feasible error control mechanism, and the adaptive high-order finite element method with dynamic mesh and adaptive time step control can effectively simulate the process of geothermal energy transmission.

Robust Fault-Tolerant Control of Wave Equation Without Estimations of Plant and Failure Parameters

This paper describes a novel robust fault-tolerant control for a vibrating string (captured by a wave partial differential equation) with constraint to be clamped at the left end and free at the right one. The control input is a force at the right hand, which might be affected by an actuator failure modelled by a general multiplicative type with unknown values. Except for the actuator failure, some physical parameters of the plant are also unavailable. We propose a pure output feedback control law without any estimation terms to guarantee the prescribed performance constraint of the state on the boundary of the closed-loop system and the uniform boundedness of the state in the whole domain. By applying the backstepping technique to the boundary of the system, together with a barrier Lyapunov function, we develop a non-adaptive control method capable of dealing with the underlying problem. Effective control performance in the wave equation with the designed control strategy is verified via Lyapunov analysis and numerical simulation.

A Data-Driven Design for Gas Turbines Exit Temperature Spread Condition Monitoring System

One of the most complex and costly systems in the industry is the Gas turbine (GT). Because of the complexity of these assets, various indicators have been used to monitor the health condition of different parts of the gas turbine. Turbine exit temperature (TET) spread is one of the significant indicators that help monitor and detect faults such as overall engine deterioration and burner fault. The goal of this article is to use data-driven approaches to monitor TET data to detect faults early, as fault detection can have a significant impact on gas turbine reliability and availability. In this study, the TET data of v94.2 GT is measured by six temperature transmitters to show a detailed profile. According to the statistical tests, TET data are high dimensional and time-dependent in the real world industry. Hence, three distinctive methods in the field of the gas turbine are proposed in this study for early fault detection. Conventional principal component analysis (PCA), moving window PCA (MWPCA), and incremental PCA (IPCA) were implemented on TET data. According to the results, the conventional PCA model is a non-adaptive method, and the false alarm rate is high due to the incompatibility of this approach and the process. The MWPCA based on V-step-ahead and IPCA approaches overcame the non-stationary problem and reduced the false alarm rate. In fact, these approaches can distinguish between the normal time-varying and slow ramp fault processes. The results showed that IPCA could detect fault situations faster than MWPCA based on V-step-ahead in this study.

A new non-adaptive optimization method: Stochastic gradient descent with momentum and difference

A new non-adaptive optimization method: Stochastic gradient descent with momentum and difference

Rapid, large-scale, and effective detection of COVID-19 via non-adaptive testing

Pooling of samples can increase lab capacity when using Polymerase chain reaction (PCR) to detect diseases such as COVID-19. However, pool testing is typically performed via an adaptive testing strategy which requires a feedback loop in the lab and at least two PCR runs to confirm positive results. This can cost precious time. We discuss a non-adaptive testing method where each sample is distributed in a prescribed manner over several pools, and which yields reliable results after one round of testing. More precisely, assuming knowledge about the overall incidence rate, we calculate explicit error bounds on the number of false positives which scale favourably with pool size and sample multiplicity. This allows for hugely streamlined PCR testing and cuts in detection times for a large-scale testing scenario. A viable consequence of this method could be real-time screening of entire communities, frontline healthcare workers and international flight passengers, for example, using the PCR machines currently in operation.

Enhancing earth-to-satellite FSO system spectrum efficiency with adaptive M-ary PSK and SIMO in presence of scintillation and beam wander

In this paper, the performance of the free-space optical (FSO) system from ground-to-satellite is analyzed considering the combined effect of atmospheric turbulence and beam wandering employing M-ary phase-shift keying (MPSK). Key parameters of the vertical connection, such as satellite altitude, zenith angle, and beam size, are investigated. In order to improve the spectrum efficiency, an adaptive transmission approach is applied to ensure efficient channel capacity usage. The procedure depends on changing the modulation order of the MPSK scheme according to the instantaneous channel state and the acceptable bit error rate. Furthermore, a single-input multiple-output (SIMO) system with maximal ratio combining (MRC) diversity is used to mitigate the effects of atmospheric turbulence and beam wandering. Closed-form and asymptotic expressions are presented for the average bit error rate (BER) for a single-input single-output (SISO) and SIMO techniques. Moreover, closed-form expression is deducted for the upper bound bandwidth efficiency. The accuracy of the derived closed-form and asymptotic expressions are verified by adopting appropriate simulation cases and parameters. The results show that the optimal value of the beam size ratio (BSZ) is 1.6. Also, the results prove that the adaptive technique improves spectrum efficiency by more four times the non-adaptive method at the same BER threshold of 10-2. By using a 1×4 SIMO system, the same adapted spectrum efficiency is achieved with a reduction of 4 dB in SNR versus 1×2 SIMO system and 8 dB in SNR versus SISO system. A 1×4 SIMO system achieves a threshold bit error rate of 5 dB reduction in SNR compared to a 1×2 SIMO system and a 10 dB reduction in SNR compared to SISO system.

H-DrunkWalk

Large-scale micro-aerial vehicle (MAV) swarms provide promising solutions for situational awareness in applications such as environmental monitoring, urban surveillance, search and rescue, and so on. However, these scenarios do not provide localization infrastructure and limit cost and size of on-board capabilities of individual nodes, which makes it challenging for nodes to autonomously navigate to suitable preassigned locations. In this article, we present H-DrunkWalk , a collaborative and adaptive technique for heterogeneous MAV swarm navigation in environments not formerly preconditioned for operation. Working with heterogeneous MAV swarm, the H-DrunkWalk achieves high accuracy through collaboration but still maintains a low cost of the entire swarm. The heterogeneous MAV swarm consists of two types of nodes: (1) basic MAVs with limited sensing, communication, computing capabilities and (2) advanced MAVs with premium sensing, communication, computing capabilities. The key focus behind this networked MAV swarm research is to (1) rely on collaboration to overcome limitations of individual nodes and efficiently achieve system-wide sensing objectives and (2) fully take advantage of advanced MAVs to help basic MAVs improve their performance. The evaluations based on real MAV testbed experiments and large-scale physical-feature-based simulations show that compared to the traditional non-collaborative and non-adaptive method (dead reckoning with map bias), our system achieves up to 6× reductions in location estimation errors, and as much as 3× improvements in navigation success rate under the given time and accuracy constraints. In addition, by comprehensively considering the environment, heterogeneous structure, and quality of location estimation, our H-DrunkWalk brings 2× performance improvement (on average) as that of a hardware upgrade.

Numerical calculation of 5-loop QED contributions to the electron anomalous magnetic moment

Preliminary numerical results of calculating the 5-loop QED contributions to the electron (and muon) anomalous magnetic moment are presented. The results include the total contribution of the 5-loop Feynman diagrams without lepton loops and the contributions of nine gauge-invariant classes that form that set. A discrepancy with known results is revealed. The contributions of the gauge-invariant classes are presented for the first time. The method of the computation is briefly described (with the corresponding references). The calculation is based on the following elements:(i)a subtraction procedure for removing infrared (IR) and ultraviolet (UV) divergences point by point in Feynman parametric representation before integration;(ii)a nonadaptive Monte Carlo integration method that is founded on probability density functions (PDF) that are constructed for each Feynman diagram individually using its combinatorial structure;(iii)a GPU-based numerical integration with the help of the supercomputer “Govorun” (JINR, Dubna, Russia).

Minimal determination of a pure qutrit state and four-measurement protocol for pure qudit state

Finding the least measurement settings to determine an arbitrary pure state has been long known as the Pauli problem. Peres conjectured that two unbiased bases may be sufficient to determine a pure state up to some finite ambiguities. Here we find that the Peres conjecture is correct in the case d = 3 where a pure state is determined up to at most six candidates states and is incorrect in the case of d = 4 for which a counterexample is constructed. We observe that the target state can be picked out from candidates states by an adaptive two-outcome measurement. We thus provided a minimal qutrit tomography protocol constituted of three measurements in contrast to the four measurements required in previous non-adaptive method. We also reduce a five-measurement protocol for a pure qudit state into three full-dimensional measurements and one two-outcome measurement protocol, which exhibits robustness to the usual white noise thus also applies to nearly pure state.

Locomotion Mode Recognition With Robotic Transtibial Prosthesis in Inter-Session and Inter-Day Applications

Locomotion mode recognition across multiple sessions and days is an indispensable step towards the practical use of the robotic transtibial prosthesis. In this study, we proposed an adaptive recognition strategy to against the time-varying features of on-prosthesis mechanical signals in inter-session and inter-day recognition tasks. The strategy was designed with an automatic training algorithm which could update the classifiers with the data of the most recent completed gait cycles to seize the changes of the features brought by external disturbances. After implementation, we measured multiple experimental sessions on six transtibial amputees with intervals from a few hours (inter-session experiment) to several months (108 days at most in inter-day experiment). In each session, they performed five locomotion modes and eight locomotion transitions using the robotic prosthesis. Between each two experimental sessions, the subjects were required to doff the robotic prosthesis (including the socket). The proposed adaptive recognition algorithm significantly improved recognition accuracies in both experiments. In inter-session experiment, the proposed method increased the recognition accuracy from 89.3% to 92.8% than previous non-adaptive recognition method. In inter-day experiments, it increased the recognition accuracy from 60% to 88.8%. If taking three modes (level walking, stair ascending/descending) and four locomotion transitions into calculation, the recognizer produced an accuracy up to 96.6% (swing phase) for static mode and an accuracy of 96.9% for locomotion transitions on inter-day tasks without manual intervenes. Compared with state-of-the-art, our study extends the ability of the robotic transtibial prosthesis in locomotion mode recognition across multiple sessions and days. Future efforts are worth being paid in this direction to get more promising results.

Wavelet-based adaptive delayed detached eddy simulations for wall-bounded compressible turbulent flows

A novel wavelet-based adaptive delayed detached eddy simulation (W-DDES) approach for simulations of wall-bounded compressible turbulent flows is proposed. The new approach utilizes anisotropic wavelet-based mesh refinement and its effectiveness is demonstrated for flow simulations using the Spalart–Allmaras DDES model. A variable wavelet thresholding strategy blending two distinct thresholds for the Reynolds-averaged Navier–Stokes (RANS) and large-eddy simulation (LES) regimes is used. A novel mesh adaptation on mean and fluctuating quantities with different wavelet threshold levels is proposed. The new strategy is more accurate and efficient compared to the adaptation on instantaneous quantities using a priori defined uniform thresholds. The effectiveness of the W-DDES method is demonstrated by comparing the results of the W-DDES simulations with results already available in the literature. Supersonic plane channel flow for two different configurations is tested as benchmark wall-bounded flows. Both the accuracy indicated by the threshold and efficiency in terms of degrees of freedom for the novel adaptation strategy are successfully gained compared with the wavelet-based adaptive LES method. Moreover, the newly proposed W-DDES resolves the typical log-layer match issue encountered in the conventional non-adaptive DDES method mainly due to the use of wavelet-based adaptive mesh refinement. The W-DDES capability for simulations of complex turbulent flows is validated by two other flow configurations – a subsonic channel flow with periodic hill constrictions and a supersonic flow over a compression ramp inducing the shock wave–turbulent boundary layer interaction. The current study serves as a crucial step towards construction of a unified wavelet-based adaptive hierarchical RANS/LES modelling framework, capable of performing simulations of varying fidelities from no-modelling direct numerical simulations to full-modelling RANS simulations.