Adaptive Rule Research Articles

GNSS 불용 구간 내 특수 임무용 드론 자세 보정을 위한 영상 정보 기반 적응형 항법 알고리즘

In the field of UAV (Unmanned Aerial Vehicle), drone is noticed that it can replace human role in unknown/hazardous area. Drone’s position is calculated with GNSS (Global Navigation Satellite System) and INS (Inertial Navigation System), however, environment like under bridge, GNSS signal is not reliable, which makes INS diverge. In this situation, drone’s orientation is important for suppressing the divergence of position by INS. Roll and pitch angle can be estimated by employing G-slave mode attitude compensation algorithm. However, yaw angle cannot be estimated well. In this paper, we propose indirect EKF (Extended Kalman Filter) based adaptive navigation algorithm that uses line information from image. In the algorithm, we calculate the relative yaw angle from line information. To get reliable yaw angle, we introduce angle compensation algorithm that corrects the distorted line information by roll and pitch angle. Furthermore, we propose outlier mitigation and adaptation rule based on distance between inlier-outlier features and the number of features for classifying the reliability of the yaw information from the image. Through the simulation, it is confirmed that the proposed algorithm estimates yaw angle sufficiently. After that, we conduct experiment and it is validated that proposed algorithm can reduced heading error with 25.32%.

Adaptive Shewhart Control Charts Under Fuzzy Parameters with Tuned Particle Swarm Optimization Algorithm

In recent years, adaptive control charts in which the design parameters depend on the observed samples have been successfully used as efficient alternatives for traditional control charts with constant parameters. In crisp run control rules, the process state may change very sharply from in-control to out-of-control conditions which increase the rate of false alarms. To overcome this drawback, this paper presents an adaptive Shewhart-type control chart, where the design parameters (sample size ([Formula: see text]), sampling interval ([Formula: see text]), and control limit coefficients ([Formula: see text] and [Formula: see text])) are defined with linguistic variables. To accomplish that, the chart parameters are determined based on the location of eight previous chart statistics using a set of fuzzy rules in a continuous environment. In order to improve the sensitivity of the proposed control chart in detecting small shifts in both location and scale parameters, the adaptive procedure is designed by integration of fuzzy Western Electric rules and fuzzy adaptive sampling rules. After designing the control charts using a fuzzy inference system (FIS), in order to provide an economic design of the proposed control chart, a tuned Particle Swarm Optimization (PSO) algorithm is employed to determine the optimal values corresponding to membership functions of the control chart parameters. Finally, using simulation studies, the capability of the proposed control chart is analyzed and compared with common charts in the literature. The results confirm that under different shifts in location and scale parameters, the proposed control chart outperforms other charts in terms of both economic and statistical criteria.

Stability of Interval Type-3 Fuzzy Controllers for Autonomous Vehicles

Economic efficient Autonomous Road Vehicles (ARVs) are invariably subjected to uncertainties and perturbations. Therefore, control of vehicle systems requires stability to withstand the effect of variations in the nominal performance. Lateral path-tracking is a substantial task of ARVs, especially in critical maneuvering and cornering with variable speed. In this study, a new controller on the basis of interval type-3 (T3) fuzzy logic system (FLSs) is designed. The main novelties and advantages are as follows. (1) The uncertainty is a main challenge in the path-following problem of ARVs. However, in the fuzzy-based approaches, the bounds of uncertainty are assumed to be known. However, in the our suggested approach, the bounds of uncertainties are also fuzzy sets and type-3 FLSs with online adaptation rules are suggested to handle the uncertainties. (2) The approximation errors (AEs) and perturbations are investigated and tackled by the compensators. (3) The bounds of estimation errors are also uncertain and are estimated by the suggested adaptation laws. (4) The stability is ensured under unknown dynamics, perturbations and critical maneuvers. (5) Comparison with the benchmarking techniques and conventional fuzzy approaches verifies that the suggested path-following scheme results in better maneuver performance.

DeepPlace: Deep reinforcement learning for adaptive flow rule placement in Software-Defined IoT Networks

In this paper, we propose a novel and adaptive flow rule placement system based on deep reinforcement learning, namely DeepPlace, in Software-Defined Internet of Things (SDIoT) networks. DeepPlace can provide a fine-grained traffic analysis capability while assuring QoS of traffic flows and proactively avoiding the flow-table overflow issue in the data plane. Specifically, we first investigate the traffic forwarding process in an SDIoT network, i.e., routing and flow rule placement tasks. We design a cost function for the routing to set up traffic flow paths in the data plane. Next, we propose an adaptive flow rule placement approach to maximize the number of match-fields in a flow rule at SDN switches. To deal with the dynamics of IoT traffic flows, we model the system operation by using the Markov decision process (MDP) with a continuous action space and formulate its optimization problem. Subsequently, we develop a deep deterministic policy gradient-based algorithm to help the system obtain the optimal policy. The evaluation results demonstrate that DeepPlace can efficiently maintain a significant number of match-fields in a flow rule, i.e., approximately 86% of the maximum level, while minimizing the QoS violation ratio of traffic flows, i.e., 6.7%, in a highly dynamic traffic scenario, which outperforms three other existing solutions, i.e., FlowMan, FlowStat, and DeepMatch.

Automatically interactive group VIKOR decision making mechanism based on BSO-SNA

In order to improve the reliability of alternative ranking and group consensus adaptively, this paper develops an automatically interactive group decision-making (GDM) framework under the VIKOR environment. The proposed framework contains an interaction network mechanism based on social network analysis (SNA) and an opinion interaction mechanism based on Brain Strom Optimization (BSO) algorithm. Firstly, to connect isolated decision-makers (DMs), interaction relationships among DMs are built based on the SNA and similarity measurement. Secondly, the BSO algorithm is introduced to design the opinion interaction mechanism, including subgroup identification model and preference iteration model. The preference iteration model contains an adaptive preference update rule (UR), iteration stop rule (ISR), and solution selection rule (SSR) to enhance group consensus adaptively rather than force DMs to change the preference. Thirdly, the proposed interactive group VIKOR (G-VIKOR) method is applied in SARS-cov-2 nucleic acid detection sites evaluation. Finally, consistency and comparison analysis are developed to illustrate the validity and robustness of evaluation results.

Neural‐network‐based control for discrete‐time nonlinear systems with denial‐of‐service attack: The adaptive event‐triggered case

AbstractThis article investigates a neural network (NN)‐based control problem for unknown discrete‐time nonlinear systems with a denial‐of‐service (DoS) attack and an adaptive event‐triggered mechanism (ETM). The considered DoS attacks are described by the occurrence frequency and durations and hence more general in comparison with existing stochastic models. To the addressed problem, a novel adaptive rule adjusting the triggering threshold of ETM is constructed to govern the communication schedule, and an NN‐based observer is designed to identify the system dynamics where a piecewise update rule of NN weights is adopted to handle the challenge of the complex time series coming from both ETM and DoS attacks. In light of this kind of protocol‐ and attack‐induced switched systems, a sufficient condition dependent on the occurrence frequency and durations of DoS attacks is elaborately established via the analysis of input‐to‐state stability. Furthermore, an iteration adaptive dynamic programming approach is proposed to handle the addressed control issue, and the boundedness is discussed to both the estimation errors of the Luenberger‐type observer and the identified errors of NN weights of observer networks as well as actor‐critic networks with the help of the Lyapunov theory. Finally, a simulation example is utilized to illustrate the usefulness of the proposed controller design scheme.

Multi-focus image fusion based on multi-scale sparse representation

Although colorful information in natural scenes can be collected, due to the limitation of camera depth of field, it is hard to capture an image with all-in-focus. Sparse representation (SR)-based methods have shown their powerful potentiality and ability in multi-focus image fusion. However, because of sparse coding and information compress, the existing fusion methods based on SR are imperfect to seize the rich details and significant texture information in source images. As a result, a fusion method based on multi-scale sparse representation for registered multi-focus images (MIF-MsSR) is proposed in this paper, where an adaptive fusion rule for sparse coefficients is presented. At first, source images are processed by multi-scale decomposition and sub-images with different scales can be obtained. According to image features with different richness in these sub-images, dictionaries with different sizes and redundancy are thereby trained. By comprehensively considering the relationships of focused areas, out-of-focused areas and boundary areas between the source images, an adaptive fusion rule based on l0−max and Sum Modified Laplacian (SML) is proposed. Finally, a fused image with all-in-focus can be obtained by sparse reconstruction and inverse multi-scale decomposition. Excessive experiments on multi-focus images have demonstrated that the proposed MIF-MsSR not only reserves the integrity of the information in source images, but also has better fusion performance on subjective and objective indicators than other state-of-the-art methods.

Multifocus image fusion using convolutional dictionary learning with adaptive contrast enhancement

Multifocus image fusion technology uses a mathematical model to integrate multifocus areas to obtain full-focus, clear images. The fusion method based on convolutional sparse representation (CSR) trains and “learns” translation-invariant filters, thereby addressing the missing signal infrastructure and high redundancy of the patch-based method. Convolutional dictionary learning and CSR rely on the alternating-direction method of multipliers and ignore model matching between the training and testing phases, leading to convergence difficulties due to the tricky parameter tuning. The block proximal extrapolated gradient method using the majorization and gradient-based restarting scheme (reG-BPEG-M) adopts the driving force coefficient formula and adaptive restart rule to solve the model mismatch problem. We introduce reG-BPEG-M into multifocus image fusion to update filters and sparse code using two-block and multiblock schemes. Compared with other state-of-the-art fusion methods, our strategy reduces model mismatch and improves the convergence of fusion for gray and color multifocus images.

Robust adaptive controller–observer scheme for robot manipulators: a Bernstein–Stancu approach

AbstractThis article introduces a robust adaptive controller–observer structure for robotic manipulators such that the need for joints speed measurement is removed. Besides, it is presumed that the system model has uncertainties and is subject to disturbances, and the proposed method must eliminate the impact of these factors on the system response. According to this, for the first time in the robotics field, a model-free scheme is developed based on the Bernstein–Stancu polynomial. The universal approximation property of the Bernstein–Stancu polynomial enables it to accurately estimate the lumped uncertainty, including unmodeled dynamics and disturbances. Moreover, to increase the efficiency of the controller–observer structure, adaptive rules have been proposed to update polynomial coefficients. The boundedness of all system errors is proven using the Lyapunov theorem. Finally, the proposed robust Adaptive controller–observer is applied on a planer robot, and the results are precisely analyzed. The results of the proposed approach are also compared with two state-of-art powerful approximation methods.

Optimal adaptive computed torque control for haptic-teleoperation system with uncertain dynamics

This study aimed to eliminate dynamic uncertainty, one of the main problems of haptic teleoperation robotic systems. The optimal adaptive computed torque control method was used to overcome this problem. As is known, excellent stability and transparency are required in teleoperation systems. However, dynamic uncertainty that causes stability problems in the control of these systems also causes poor performance. In conventional adaptive computed torque control methods, updating the parameters of the system is generally discussed, but updating the control coefficients of vital importance in the control of the system is not considered. In the proposed method, an adaptation rule has been created to update uncertain parameters. In addition, the gray wolf optimization algorithm, one of the current optimization algorithms, has been proposed and applied to obtain the control coefficients of the system. The position tracking stability of the system was examined by using Lyapunov stability analysis method. As a result, both simulation and real-time optimal adaptive computational torque control method were used and bilateral position and force control was performed and the performance results of the system are obtained graphically and examined. Optimal adaptive computed torque control method obtained using the gray wolf optimization algorithm was used first in the literature search and success results have been obtained. In this regard, the authors have the idea that this work is an innovative aspect of both simulation and real time with the optimal adaptive computed torque control method.

Robust sliding-mode control for maximum power point tracking of photovoltaic power systems with quantized input signal

A robust sliding-mode controller is proposed for maximum power point tracking in photovoltaic power generation system. Thanks to using hysteresis quantized inputs, the chattering phenomena, as the main weakness of the sliding mode controllers, is removed. Also, the external disturbances, model uncertainties, environmental and load variation effects are attenuated in the proposed controller scheme. The upper bound of the disturbance is assumed to be unknown and is estimated with an appropriate adaptive rule. Using the Lyapunov theory, the stability of the proposed scheme is verified and analytically proved. MATLAB simulation results confirm that the undesired effects of disturbances and chattering are simultaneously removed, as is proved in the theoretical analysis.

An Adaptive STDP Learning Rule for Neuromorphic Systems.

The promise of neuromorphic computing to develop ultra-low-power intelligent devices lies in its ability to localize information processing and memory storage in synaptic circuits much like the synapses in the brain. Spiking neural networks modeled using high-resolution synapses and armed with local unsupervised learning rules like spike time-dependent plasticity (STDP) have shown promising results in tasks such as pattern detection and image classification. However, designing and implementing a conventional, multibit STDP circuit becomes complex both in terms of the circuitry and the required silicon area. In this work, we introduce a modified and hardware-friendly STDP learning (named adaptive STDP) implemented using just 4-bit synapses. We demonstrate the capability of this learning rule in a pattern recognition task, in which a neuron learns to recognize a specific spike pattern embedded within noisy inhomogeneous Poisson spikes. Our results demonstrate that the performance of the proposed learning rule (94% using just 4-bit synapses) is similar to the conventional STDP learning (96% using 64-bit floating-point precision). The models used in this study are ideal ones for a CMOS neuromorphic circuit with analog soma and synapse circuits and mixed-signal learning circuits. The learning circuit stores the synaptic weight in a 4-bit digital memory that is updated asynchronously. In circuit simulation with Taiwan Semiconductor Manufacturing Company (TSMC) 250 nm CMOS process design kit (PDK), the static power consumption of a single synapse and the energy per spike (to generate a synaptic current of amplitude 15 pA and time constant 3 ms) are less than 2 pW and 200 fJ, respectively. The static power consumption of the learning circuit is less than 135 pW, and the energy to process a pair of pre- and postsynaptic spikes corresponding to a single learning step is less than 235 pJ. A single 4-bit synapse (capable of being configured as excitatory, inhibitory, or shunting inhibitory) along with its learning circuitry and digital memory occupies around 17,250 μm2 of silicon area.

Citalopram for treatment of cocaine use disorder: A Bayesian drop-the-loser randomized clinical trial

BackgroundMedication development research for cocaine use disorder (CUD) has been a longstanding goal in addiction research, but has not resulted in an FDA-approved treatment. Rising cocaine use rates underscore the need for efficient adaptive designs. This study compared differences between two doses of the selective serotonin reuptake inhibitor (SSRI) citalopram (versus placebo) on duration of cocaine abstinence and applied adaptive decision rules to select the ‘best efficacy’ dose. MethodsA double-blind, placebo-controlled, randomized Bayesian drop-the-loser (DTL) trial with three arms compared placebo to citalopram 20 mg and 40 mg. Adults (N = 107) with CUD attended thrice-weekly clinic visits for 9 weeks. The primary outcome was longest duration of abstinence (LDA), based on continuous cocaine-negative urine drug screens (UDS). The secondary outcome was probability of cocaine-negative UDS during treatment. A planned interim analysis performed at approximately 50% of recruitment dropped the least-effective active medication. Bayesian inference was used for all analyses with a pre-specified posterior probability (PP) threshold PP ≥ 95% considered statistically reliable evidence ResultsCitalopram 40 mg satisfied interim efficacy criteria and was retained for the second half of the trial. For LDA, analyses indicated PP = 82% and PP = 65% of benefit for 40 mg and 20 mg, respectively (each relative to placebo). The odds of having cocaine-negative UDS decreased in all groups over 9 weeks but remained higher for 40 mg (PP = 97.4%) ConclusionsNeither dose met the 95% PP threshold for the primary outcome; however, 40 mg provided moderate-to-strong evidence for positive effects on LDA and cocaine-negative UDS. The 40 mg dose was declared the “winner” in this DTL trial.

An explicit algorithm for solving monotone variational inequalities

In this paper, we are interested in the generalized variational inequality problem in real Hilbert spaces. We propose an explicit proximal method which requires only one proximal step and one mapping evaluation per iteration and also uses an adaptive step-size rule that enables to avoid the prior knowledge of the Lipschitz constant of the involved mapping. Weak convergence of the proposed scheme is established under standard assumptions. Under strong monotonicity, we present the R-linear convergence rate of our new method. Intensive numerical experiments illustrate the advantages and the applicability of our scheme. Moreover, our work generalizes theoretically several recent results in this field.

Design of Computerized Adaptive Testing Module into Our Dynamic Adaptive Hypermedia System

By the end of the 20th century, the most common and traditional paper and pencil tests (PBT) were faced with an increasing set of difficulties and drawbacks. Proceed and meet the measurement and evaluation needs of the 21st century; it is mandatory to have radical and qualitative changes. The accelerating pace of technological innovation in Education shows a clear path to computer-based testing, which offers a more engaging and innovative testing environment, as well as the ability to obtain instant results and feedback, the use of dynamic, multimedia learning objects (LO), and the implementation of adaptive, individualized assessment approaches. In this sense, this paper presents an adaptive formative assessment framework in which test adaptation is based on multiple criteria. The presented system bases its adaptation on the test takers' proficiency, schemata, desirable learning goal, cognitive level, and learning style. The main objective of this paper is to discuss the value of the adaptive testing system. Therefore, it presents the design and implementation of the system and the adaptive rules employed.

Underwater Vehicle Positioning by Correntropy-Based Fuzzy Multi-Sensor Fusion.

The ability of the underwater vehicle to determine its precise position is vital to completing a mission successfully. Multi-sensor fusion methods for underwater vehicle positioning are commonly based on Kalman filtering, which requires the knowledge of process and measurement noise covariance. As the underwater conditions are continuously changing, incorrect process and measurement noise covariance affect the accuracy of position estimation and sometimes cause divergence. Furthermore, the underwater multi-path effect and nonlinearity cause outliers that have a significant impact on positional accuracy. These non-Gaussian outliers are difficult to handle with conventional Kalman-based methods and their fuzzy variants. To address these issues, this paper presents a new and improved adaptive multi-sensor fusion method by using information-theoretic, learning-based fuzzy rules for Kalman filter covariance adaptation in the presence of outliers. Two novel metrics are proposed by utilizing correntropy Gaussian and Versoria kernels for matching theoretical and actual covariance. Using correntropy-based metrics and fuzzy logic together makes the algorithm robust against outliers in nonlinear dynamic underwater conditions. The performance of the proposed sensor fusion technique is compared and evaluated using Monte-Carlo simulations, and substantial improvements in underwater position estimation are obtained.

Abstract Rotations for Uniform Adaptive Control and Soft Modeling of Mechanical Devices

The model-based controllers generally suffer from the lack of precise dynamic models. Making reliable analytical models can be evaded by soft modeling techniques, while the consequences of modeling imprecisions are tackled by either robust or adaptive techniques. In robotics, the prevailing adaptive techniques are based on Lyapunov’s “direct method” that normally uses special error metrics and adaptation rules containing fragments of the Lyapunov function. The soft models and controllers need massive parallelism and suffer from the curse of dimensionality. A different adaptive approach based on Banach’s fixed point theorem and using special abstract rotations was recently suggested. Similar rotations were suggested to develop particular neural network-like soft models, too. Presently, via integrating these approaches, a uniform adaptive controlling and modeling methodology is suggested with especial emphasis on the effects of the measurement noises. Its applicability is investigated via simulations for a two degree of freedom mechanical system in which one of the generalized coordinates is under control, while the other one belongs to a coupled parasite dynamical system. The results are promising for allowing the development of relatively coarse soft models and a simple adaptive rule that can be implemented in embedded systems.

Gait Phase Estimation Based on User–Walker Interaction Force

Smart walkers have been developed for assistance and rehabilitation of elderly people and patients with physical health conditions. A force sensor mounted under the handle is widely used in smart walkers to establish a human–machine interface. The interaction force can be used to control the walker and estimate gait parameters using methods such as the Kalman filter for real-time estimation. However, the estimation performance decreases when the peaks of the interaction force are not captured. To improve the stability and accuracy of gait parameter estimation, we propose an online estimation method to continuously estimate the gait phase and cadence. A multiple model switching mechanism is introduced to improve the estimation performance when gait is asymmetric, and an adaptive rule is proposed to improve the estimation robustness and accuracy. Simulations and experiments demonstrate the effectiveness and accuracy of the proposed gait parameter estimation method. Here, the average estimation error for the gait phase is 0.691 rad when the gait is symmetric and 0.722 rad when it is asymmetric.

Normalized Combinations of Proportionate Affine Projection Sign Subband Adaptive Filter

The proportionate affine projection sign subband adaptive filter (PAP-SSAF) has a better performance than the affine projection sign subband adaptive filter (AP-SSAF) when we eliminate the echoes. Still, the robustness of the PAP-SSAF algorithm is insufficient under unknown environmental conditions. Besides, the best balance remains to be found between low steady-state misalignment and fast convergence rate. In order to solve this problem, we propose a normalized combination of PAP-SSAF (NCPAP-SSAF) based on the normalized adaption schema. In this paper, a power normalization adaptive rule for mixing parameters is proposed to further improve the performance of the NCPAP-SSAF algorithm. By using Nesterov’s accelerated gradient (NAG) method, the mixing parameter of the control combination can be obtained with less time consumed when we take the l1-norm of the subband error as the cost function. We also test the algorithmic complexity and memory requirements to illustrate the rationality of our method. In brief, our study contributes a novel adaptive filter algorithm, accelerating the convergence speed, reducing the steady-state error, and improving the robustness. Thus, the proposed method can be utilized to improve the performance of echo cancellation. We will optimize the combination structure and simplify unnecessary calculations to reduce the algorithm’s computational complexity in future research.

Constrained plasticity reserve as a natural way to control frequency and weights in spiking neural networks

Biological neurons have adaptive nature and perform complex computations involving the filtering of redundant information. However, most common neural cell models, including biologically plausible, such as Hodgkin–Huxley or Izhikevich, do not possess predictive dynamics on a single-cell level. Moreover, the modern rules of synaptic plasticity or interconnections weights adaptation also do not provide grounding for the ability of neurons to adapt to the ever-changing input signal intensity. While natural neuron synaptic growth is precisely controlled and restricted by protein supply and recycling, weight correction rules such as widely used STDP are efficiently unlimited in change rate and scale. The present article introduces new mechanics of interconnection between neuron firing rate homeostasis and weight change through STDP growth bounded by abstract protein reserve, controlled by the intracellular optimization algorithm. We show how these cellular dynamics help neurons filter out the intense noise signals to help neurons keep a stable firing rate. We also examine that such filtering does not affect the ability of neurons to recognize the correlated inputs in unsupervised mode. Such an approach might be used in the machine learning domain to improve the robustness of AI systems.