Rate Adaptation Research Articles

Intelligent in-process enhancement technique for machining efficiency in CNC machine tools based on spindle power

In the realm of mechanical machining, adaptive machining techniques offer an efficient method. However, existing adaptive machining technologies cannot automatically identify the type of workpiece to be machined, nor can they set different control targets based on tool load-bearing capacity. This often requires manual and cumbersome operations, making the application of adaptive machining technology difficult to popularize. To address these challenges, this paper proposes an intelligent and efficient adaptive machining method. Specifically, a workpiece automatic classification algorithm based on machine tool spindle power is introduced. This algorithm classifies the machined workpieces according to the collected spindle power data, enhancing the convenience of adaptive machining technology. Also based on spindle power data, a time-series segmentation method is proposed, dividing spindle power into different subsequences according to the tools used in machining, which enhances the accuracy of adaptive machining technology. Furthermore, an adaptive feed rate control algorithm is designed based on fuzzy control theory, realizing intelligent and efficient adaptive machining of workpieces to improve machining efficiency. Finally, experiments are conducted to validate the effectiveness of the intelligent and efficient adaptive machining method. The proposed method effectively addresses the drawbacks of existing adaptive machining method, which require complex parameter settings. It simplifies the operational process, enhances the intelligence, and improves the practical applicability of adaptive machining techniques. This represents an innovative contribution to the field of adaptive machining techniques.

LSTM network optimization and task network construction based on heuristic algorithm

This work aims to advance the security management of complex networks to better align with evolving societal needs. The work employs the Ant Colony Optimization algorithm in conjunction with Long Short-Term Memory neural networks to reconstruct and optimize task networks derived from time series data. Additionally, a trend-based noise smoothing scheme is introduced to mitigate data noise effectively. The approach entails a thorough analysis of historical data, followed by applying trend-based noise smoothing, rendering the processed data more scientifically robust. Subsequently, the network reconstruction problem for time series data originating from one-dimensional dynamic equations is addressed using an algorithm based on the principles of Stochastic Gradient Descent (SGD). This algorithm decomposes time series data into smaller samples and yields optimal learning outcomes in conjunction with an adaptive learning rate SGD approach. Experimental results corroborate the remarkable fidelity of the weight matrix reconstructed by this algorithm to the true weight matrix. Moreover, the algorithm exhibits efficient convergence with increasing data volume, manifesting shorter time requirements per iteration while ensuring the attainment of optimal solutions. When the sample size remains constant, the algorithm’s execution time is directly proportional to the square of the number of nodes. Conversely, as the sample size scales, the SGD algorithm capitalizes on the availability of more information, resulting in improved learning outcomes. Notably, when the noise standard deviation is 0.01, models predicated on SGD and the Least-Squares Method (LSM) demonstrate reduced errors compared to instances with a noise standard deviation of 0.1, highlighting the sensitivity of LSM to noise. The proposed methodology offers valuable insights for advancing research in complex network studies.

Multi-market P2P trading of cooling–heating-power-hydrogen integrated energy systems: An equilibrium-heuristic online prediction optimization approach

In this paper, an equilibrium-heuristic online prediction optimization approach is proposed for multi-market peer-to-peer (P2P) electricity–hydrogen trading of integrated energy systems (IESs) with uncertainties. First, the IES, consisting of a hydrogen energy storage subsystem and a combined cooling, heating, and power subsystem, is constructed in the distribution network to improve energy utilization and market efficiency. Then, a bi-level optimization model for IESs, participating in the P2P electricity–hydrogen energy trading, the real-time electricity, and the ancillary service markets, is proposed, in which the top-level model can be formulated as a P2P electricity–hydrogen trading pricing model through the social welfare maximization problem, and the lower-level model is used to maximize the IES operating profit. To effectively solve the bi-level model, the game equilibrium-based ADMM distributed algorithm is used to obtain the P2P trading volume and prices of the top-level model, and a new hybrid heuristic algorithm, called hybrid sand cat swarm optimization and improved honey badger algorithm (SCIHB), is proposed to solve the lower level model. SCIHB utilizes a hybrid mechanism and an adaptive learning rate parameter to balance local exploitation and global exploration. Furthermore, a new online learning probabilistic prediction method based on the hidden Markov model and wavelet transform is introduced to describe the uncertainties of wind power, electricity prices, and frequency regulation prices. Finally, case studies are conducted on an IEEE 33-bus test system, and the numerical results verify the effectiveness of the proposed P2P trading model and solution approach.

A Dynamic Hierarchical Improved Tyrannosaurus Optimization Algorithm with Hybrid Topology Structure

Aiming at the problems of the Tyrannosaurus optimization algorithm, of poor search accuracy, insufficient global search capability, and ease of falling into local optimality, a dynamic hierarchical improved Tyrannosaurus optimization algorithm (DHTROA) with hybrid topology structure is proposed. Initially, a chaotic opposition-based learning approach is selected to start the population, ensuring a more uniform distribution of prey across the solution area and boosting population diversity; later, a dynamic hybrid bi-population strategy is introduced to divide the initial population into an ‘advantaged group’ and a ‘disadvantaged group’ to improve the efficiency of individual information exchange. Finally, the ‘advantaged group’ and ‘disadvantaged group’ are hunted synchronously; for the ‘advantaged group’, the position update is carried out using the cellular ring topology strategy, and for the ‘disadvantaged group’, the original algorithm is run in accordance with the main loop process. For the problem of the constant running rate of the Tyrannosaurus in the original algorithm, an adaptive running rate strategy is proposed, which enhances the ability of global optimization, and at the same time, the shortcomings of the original algorithm’s ‘failure’ strategy are improved in order to enhance the original algorithm to jump out of extrema. DHTROA was tested for performance with nine optimization algorithms in different dimensions of the CEC2017 test function. The efficiency of these enhancements was confirmed through the Wilcoxon rank sum test and Friedman test, while DHTROA was utilized for six engineering optimization challenges of differing complexities. The experimental results show that DHTROA has improved greatly in convergence speed, optimality search accuracy, global search ability, and stability, and the excellent engineering optimization performance also proves the excellent robustness of DHTROA.

Adaptive QoE‐based video delivery by optimizing the bitrate of the video course using network bandwidth and user bandwidth

SummaryWith the increase in internet connectivity worldwide, people have resorted to various video streaming applications for entertainment, education, healthcare, and so on. However, this has resulted in huge internet traffic, which is predominantly occurring due to the increased transmission of videos over wireless networks to mobile devices, Transmission of ultra‐high‐definition videos can be effortlessly carried out, although the video quality perceived by the users is normally lesser than the anticipated quality. In video streaming applications, it is a pre‐requisite to provide users with high quality of experience (QoE) but this is generally affected by the dynamic variations in the network conditions. This work presents a novel QoE‐based video delivery system in a collaborative e‐learning platform. Here, a novel deep learning structure recurrent neural network‐long short term memory (RNN‐LSTM) is developed for adaptive bit rate (ABR) selection, thereby providing users with videos of high QoE. Various network, time, and buffer‐based parameters are considered while selecting the bit rate. Additionally, the proposed RNN‐LSTM is assessed for its superiority in providing high QoE videos based on measures, like QoE, buffer size, and bit rate, and is observed to attain values of 0.975, 45.654, and 90.766 b/s, respectively.

Settlement Forecast of Marine Soft Soil Ground Improved with Prefabricated Vertical Drain-Assisted Staged Riprap Filling

By comparing different settlement forecast methods, eight methods were selected considering the creep of marine soft soils in this case study, including the Hyperbolic Method (HM), Exponential Curve Method (ECM), Pearl Growth Curve Modeling (PGCM), Gompertz Growth Curve Modeling (GGCM), Grey (1, 1) Model (GM), Grey Verhulst Model (GVM), Back Propagation of Artificial Neural Network (BPANN) with Levenberg–Marquardt Algorithm (BPLM), and BPANN with Gradient Descent of Momentum and Adaptive Learning Rate (BPGD). Taking Lingni Seawall soil ground improved with prefabricated vertical drain-assisted staged riprap filling as an example, forecasts of the short-term, medium-term, long-term, and final settlements at different locations of the soft ground were performed with the eight selected methods. The forecasting values were compared with each other and with the monitored data. When relative errors were between 0 and −1%, both the forecasting accuracy and engineering safety were appropriate and reliable. It was concluded that the appropriate forecast methods were different not only due to the time periods during the settlement process, but also the locations of soft ground. Among these methods, only BPGD was appropriate for all the time periods and locations, such as at the edge of the berm, and at the center of the berm and embankment.

Volatility-based diversity awareness for distributed data storage of Mobile Crowd Sensing

With the widespread utilization of sensors and the rapid development of Mobile Crowd Sensing (MCS), the framework of distributed data storage, which makes use of the limited storage capacity of sensing devices, is providing an alternative to cloud-based data storage. Since the construction of the distributed storage framework for MCS has to be completed by Compressed Sensing (CS) theory, a reasonable measurement allocation becomes a no-brainer if we want to obtain more recovery precision of data without wasting measurement resources. To more efficiently address the above problems, we present a novel strategy of Volatility-Based Diversity Awareness for CS measurement allocation called VBDA, which collectively achieves lower computational complexity, adaptive sampling rate allocation for non-visual data, and high-quality reconstruction. We first process the target monitoring region in blocks. Then, we calculate the degree of data diversity of each area using the volatility, which is used to assess the importance of the different areas. Worthy note, based on the volatility calculation, we can obtain the magnitude of the variation in diversity that is only present in the real data, even when using very ambiguous recovered data. This is primarily due to the volatility unique design concept, which attempts to offset partially identical errors in adjacent recovered data using volatility calculations. Finally, to rationally allocate measurement resources, a volatility-based sampling rate allocation scheme is proposed. We further provide an implementation for practical deployments of VBDA in various related contexts. Numerous experimental show that the VBDA performs excellently. In comparison to the current state-of-the-art strategy, it improves data recovery accuracy by 12.2% without the use of any prior knowledge, while being adaptable to many different distribution types of data.

Structure of Spread Spectrum Telemetry Radio Link for LEO Small Satellite

The paper considers the design of telemetry radio link for low Earth orbit (LEO) satellites, particularly based on CubeSat format. A significant limitation of such spacecrafts are the available dimensions for antennas and the transmitting power, which leads to low energy level at the ground receiving point of transmitted signal. The article presents the physical level structure for data frame based on direct sequence spread spectrum technique, which allows for additional signal gain to achieve the required level of link performance. Using such principle also make possible to implement adaptive data rate control depending on dynamics in signal-to-noise ratio when the distance from the satellite to the ground point changes. For proposed data frame structure, an analysis of received information size during a communication session was carried out, which is parameterized on the maximum observable elevation angle of the satellite. The issue of datalink layer interaction between devices is also considered.

Modeling spatial evolution of multi-drug resistance under drug environmental gradients.

Multi-drug combinations to treat bacterial populations are at the forefront of approaches for infection control and prevention of antibiotic resistance. Although the evolution of antibiotic resistance has been theoretically studied with mathematical population dynamics models, extensions to spatial dynamics remain rare in the literature, including in particular spatial evolution of multi-drug resistance. In this study, we propose a reaction-diffusion system that describes the multi-drug evolution of bacteria based on a drug-concentration rescaling approach. We show how the resistance to drugs in space, and the consequent adaptation of growth rate, is governed by a Price equation with diffusion, integrating features of drug interactions and collateral resistances or sensitivities to the drugs. We study spatial versions of the model where the distribution of drugs is homogeneous across space, and where the drugs vary environmentally in a piecewise-constant, linear and nonlinear manner. Although in many evolution models, per capita growth rate is a natural surrogate for fitness, in spatially-extended, potentially heterogeneous habitats, fitness is an emergent property that potentially reflects additional complexities, from boundary conditions to the specific spatial variation of growth rates. Applying concepts from perturbation theory and reaction-diffusion equations, we propose an analytical metric for characterization of average mutant fitness in the spatial system based on the principal eigenvalue of our linear problem, λ1. This enables an accurate translation from drug spatial gradients and mutant antibiotic susceptibility traits to the relative advantage of each mutant across the environment. Our approach allows one to predict the precise outcomes of selection among mutants over space, ultimately from comparing their λ1 values, which encode a critical interplay between growth functions, movement traits, habitat size and boundary conditions. Such mathematical understanding opens new avenues for multi-drug therapeutic optimization.

Weight Decay With Tailored Adam on Scale-Invariant Weights for Better Generalization.

Weight decay (WD) is a fundamental and practical regularization technique in improving generalization of current deep learning models. However, it is observed that the WD does not work effectively for an adaptive optimization algorithm (such as Adam), as it works for SGD. Specifically, the solution found by Adam with the WD often generalizes unsatisfactorily. Though efforts have been made to mitigate this issue, the reason for such deficiency is still vague. In this article, we first show that when using the Adam optimizer, the weight norm increases very fast along with the training procedure, which is in contrast to SGD where the weight norm increases relatively slower and tends to converge. The fast increase of weight norm is adverse to WD; in consequence, the Adam optimizer will lose efficacy in finding solution that generalizes well. To resolve this problem, we propose to tailor Adam by introducing a regularization term on the adaptive learning rate, such that it is friendly to WD. Meanwhile, we introduce first moment on the WD to further enhance the regularization effect. We show that the proposed method is able to find solution with small norm and generalizes better than SGD. We test the proposed method on general image classification and fine-grained image classification tasks with different networks. Experimental results on all these cases substantiate the effectiveness of the proposed method in help improving the generalization. Specifically, the proposed method improves the test accuracy of Adam by a large margin and even improves the performance of SGD by 0.84% on CIFAR 10 and 1.03 % on CIFAR 100 with ResNet-50. The code of this article is public available at xxx.

Prognosis of Tissue Stiffness Through Multilayer Perceptron Technique With Adaptive Learning Rate in Minimal Invasive Surgical Procedures

Prognosis of Tissue Stiffness Through Multilayer Perceptron Technique With Adaptive Learning Rate in Minimal Invasive Surgical Procedures

Mixed-Input Residual Network for Air Temperature Forecasting by Low-Power Embedded Devices

Accuracy and model compactness are essential requirements for weather forecasting models designed for operation on low-power embedded devices. This study developed Mixed-Input Residual Network (MIRNet), a compact temperature-forecasting deep neural network model. MIRNet integrates stacked bidirectional long short-term memory layers using concatenated 1-dimensional and 2-dimensional convolutional layers to improve model accuracy. MIRNet was trained and tested on two datasets: one, IfeData, comprising historical weather data from Ile-Ife, Nigeria and the other a standard weather forecasting dataset called the Jena dataset. Training was carried out using 100 epochs of data partitioned in the standard 80:20 ratio, with an adaptable learning rate strategy. The model was tested for Nth-hour-ahead prediction for 1N24; where N are natural numbers, and performance quantified using metrics such as mean absolute percentage error (MAPE) and mean square error (MSE). The model was also implemented on a Raspberry Pi 4 device with a 1.8 GHz 64-bit quad-core ARM Cortex-A72 processor. The model achieved a MSE of 1.00 x 10-3 on the IfeData dataset, and 1.23x10-4 on the Jena dataset for 1-hour ahead forecasting. This is currently the best verifiable result achieved on the Jena dataset by any prediction model globally. For Nth hour ahead forecasting, MIRNet achieved an MSE generally below 2.0x10-3 for all values of N on the standard Jena dataset. The MSE of MIRNet for N-sequential 1-hour ahead and single Nth hour predictions using the Jena dataset reveal quadratic and linear relationships with N respectively. The model compares favourably with existing models for multi-hour predictions. The developed model is compact and has good forecasting properties.

Power and Discrete Rate Adaptation in Wideband NOMA in Frequency-Selective Channels

Power and Discrete Rate Adaptation in Wideband NOMA in Frequency-Selective Channels

Fractional order adaptive sliding mode control method of manipulator

A new controller (NAFOFTSMC) is designed and successfully applied to the manipulator system. Compared with traditional integer-order sliding mode control methods (IOSMC), the fractional order sliding mode control method (FOSMC) adopted additional degrees of freedom and, to some extent, weakens the drawback of sliding mode control chattering problem. Fractional-order adaptive rate compensation is utilized to compensate for the total disturbance set of the system. The new sliding surface reduces the derivative signals that are difficult to obtain due to external interference and other factors in the robotic arm system. It also reduces the unknown parameters for adjustment, increases the proportion term, and thus enables the system to have higher control accuracy and shorter adjustment time. While designing the controller, the stability is proved at the end of the fourth section. At the same time, this control method was compared and analyzed with IOSMC, simple FOSMC, and a Pre-improved controller (AFOFTSMC).

2852: Balancing plan robustness and adaptation rate for proton therapy of lung cancer patients

2852: Balancing plan robustness and adaptation rate for proton therapy of lung cancer patients

Novel Advanced Artificial Neural Network-Based Online Stator and Rotor Resistance Estimator for Vector-Controlled Speed Sensorless Induction Motor Drives

This paper presents a new approach for the online estimation of stator and rotor resistance of induction motors for speed sensorless vector-controlled drives, using feed-forward artificial neural networks with advanced adaptive learning rates. For the rotor resistance estimation, a neural network model based on rotor speed and stator currents is developed. The rotor flux linkages acquired from the voltage model are compared with the neural network model. The feed-forward neural network employs an adaptive learning rate as the function of the obtained error during training for quick convergence with minimal estimation error. A two-layered neural network model based on the stator voltage and current equations is developed for the stator resistance estimation. The d-q axes stator currents obtained from the developed model are compared with the acquired d-q axes stator currents. For the fast convergence with minimal estimation error, an adaptive learning rate as the function of error is adopted during training. Furthermore, the neural network estimates the induction motor’s speed. The simulation and experimental results justify that the developed algorithms track variation in the resistances quickly and precisely along with the speed as compared with the conventional constant learning rate algorithm, leading to reliable operation of the drive.

Extending the coverage of user-diversified IM-DD PON by FDM: a mathematical model with experimental verification.

Future passive optical networks (PONs) call for more flexibility to support diversified users with various rate demands and link qualities. Using traditional time-division multiplexing (TDM), the concept of a flexible rate PON was proposed to accommodate more users with link diversity by rate adaptation. In this Letter, we reveal the PON coverage can be further extended through frequency-division multiplexing (FDM) in the presence of multiuser diversity, namely, (i) there exist users with frequency-dependent link conditions and (ii) the link conditions exhibit disparity among users. We build a mathematical model and propose an optimization algorithm based on the binary tree search to optimize diversity gain. We experimentally verify its feasibility by studying the diversity gain concerning chromatic dispersion, optical path loss, and signal-to-noise ratio (SNR) variation in a 200G-class intensity-modulation direct-detection (IM-DD) system.

Cross layered adaptive rate optimized irregular LDPC for WSN

This paper proposes a cross layered adaptive rate optimized technique that uses irregular low-density parity-check (LDPC) codes over an additive white Gaussian noise (AWGN) channel in wireless sensor networks (WSNs). Irregular LDPC is used as error control code. Cross layer design along with error control code gives the energy balance in the network. This new algorithm uses the physical layer parameters such as coherence time of the channel, BER and SNR and the routing layer parameter such as demanded data rate to determine the rate of the LDPC coder. The algorithm adaptively changes the code rates as ¾, ½, and 1/3. We have conducted the simulations to verify our methods using MATLAB and find that the proposed scheme increases the network performance in terms of energy balance and quality of data by using irregular LDPC codes in the cross-layer environment.

Mapping Method of Human Arm Motion Based on Surface Electromyography Signals.

This paper investigates a method for precise mapping of human arm movements using sEMG signals. A multi-channel approach captures the sEMG signals, which, combined with the accurately calculated joint angles from an Inertial Measurement Unit, allows for action recognition and mapping through deep learning algorithms. Firstly, signal acquisition and processing were carried out, which involved acquiring data from various movements (hand gestures, single-degree-of-freedom joint movements, and continuous joint actions) and sensor placement. Then, interference signals were filtered out through filters, and the signals were preprocessed using normalization and moving averages to obtain sEMG signals with obvious features. Additionally, this paper constructs a hybrid network model, combining Convolutional Neural Networks and Artificial Neural Networks, and employs a multi-feature fusion algorithm to enhance the accuracy of gesture recognition. Furthermore, a nonlinear fitting between sEMG signals and joint angles was established based on a backpropagation neural network, incorporating momentum term and adaptive learning rate adjustments. Finally, based on the gesture recognition and joint angle prediction model, prosthetic arm control experiments were conducted, achieving highly accurate arm movement prediction and execution. This paper not only validates the potential application of sEMG signals in the precise control of robotic arms but also lays a solid foundation for the development of more intuitive and responsive prostheses and assistive devices.

Adaptive Controller Based on Estimated Parameters for Quadcopter Trajectory Tracking

This paper presents a trajectory control system design for a quadcopter, an unmanned aerial vehicle (UAV), which is based on estimated parameters that are assumed to exhibit random walk behavior. Initially, the rotational dynamic model of the UAV is formulated using the Newton Euler method in terms of angular velocity about the x, y, and z axes. This model is then simplified into three separated-first-order linear differential equations, with coefficients derived from the combined effects of inertia, aerodynamic drag, and gyroscopic effects, referred to as lumped parameters. A Proportional-Integral (PI) controller with feed-forward design is then developed to control this simplified model. To adapt the controller to the lumped parameters that exhibit random walk behavior, each simplified equation is restructured into a processing and measurement model. The states of these models are estimated by using the Unscented Kalman Filter (UKF). These estimated values are then utilized to adjust the PI gains and compensate the signal of the designed angular velocity controller, transforming it into an adaptive controller. The entire UAV controller comprises two main parts, an inner loop for adaptive angular rate control and an outer loop serving as an attitude-thrust controller. The proposed controller is simulated using Simulink, with circular and square trajectories. The simulation results demonstrate that the quadcopter successfully follows the desired circular and square paths. The steady-state error for the x and y axes in the square trajectory is less than 0.05 meters within 5 seconds, and for the z axis, it is less than 0.02 meters within 2.5 seconds. The controller gains do not require adjustment when changing trajectories. Moreover, the estimated parameters remain nearly constant at steady state.