Step Size Adjustment Research Articles

Delayed-X harmonic synthesizer cancelling narrow bandpass noises by control signals via aural cartilage conduction

When a transducer is put on an aural cartilage, the transmitted vibration generates sound in the external auditory canal (i.e., cartilage conduction). A hearing device based on the cartilage conduction does not need occluding the external auditory canal. When an active noise control can adapt only to unwanted noise, the device enables natural conversations with unprocessed voice in the noisy environment (selective cancelation). To accomplish the selective cancelation, we proposed delayed-X harmonics synthesizer (DXHS) algorithm to minimize the damage for the speech quality. A target noise in this study was bandpass noise through a gammatone filter (center frequency: 500 Hz) overlapping the speech spectrum. After tracing the spectral envelope of the noise by using linear predictive coding (LPC), the DXHS outputted five pure tones distributed around the peak of envelope, and each step size parameter was adjusted according to the amplitude of the envelope. As results, our proposed method showed the noise reduction of 11.3 dB maintaining the speech quality in the computer simulations. During the adaptation, the step size adjustment enabled round off the spectral peak of the bandpass noise to avoid sharpening it.

Adaptive ray tracing in freeform gradient-index media using an index directional derivative.

Freeform gradient index (F-GRIN) media are increasingly used in optical systems. The existing ray tracing methods for F-GRIN have low calculation efficiency and require manual adjustment of step size, making the design process complex. To address this, we propose an adaptive ray tracing (ART) method for F-GRIN. ART obtains an initial step size based on an index directional derivative and the element's length. During the subsequent transmission process, the step size is adaptively adjusted. The accuracy and speed of ART were verified through multiple comparison tests. ART can reduce manual participation and significantly improve efficiency in the optical design of F-GRIN.

Use estimated signal and noise to adjust step size for image restoration

Image deblurring is a challenging inverse problem, especially when there is additive noise to the observation. To solve such an inverse problem in an iterative manner, it is important to control the step size for achieving a stable and robust performance. We designed a method that controls the progress of iterative process in solving the inverse problem without the need for a user-specified step size. The method searches for an optimal step size under the assumption that the signal and noise are two independent stochastic processes. Experiments show that the method can achieve good performance in the presence of noise and imperfect knowledge about the blurring kernel. Tests also show that, for different blurring kernels and noise levels, the difference between two consecutive estimates given by the new method tends to remain more stable and stay in a smaller range, as compared to those given by some existing techniques. This stable feature makes the new method more robust in the sense that it is easier to select a stopping threshold for the new method to use in different scenarios.

Advanced Perturb and Observe Algorithm for Maximum Power Point Tracking in Photovoltaic Systems with Adaptive Step Size

Maximum power point tracking (MPPT) algorithms are commonly used in photovoltaic (PV) systems to optimize the power output from the solar panels. Among the various MPPT algorithms, the perturb and observe (P&O) algorithm is a popular choice due to its simplicity and effectiveness. However, the basic P&O algorithm has some limitations such as oscillations and steady-state error under rapidly changing irradiance conditions. The enhanced algorithm includes a modified perturbation step and a dynamic step size adjustment scheme. This reduces the oscillations and improves the tracking accuracy. In the dynamic step size adjustment scheme, the step size is adjusted based on the rate of change of the PV output power. This improves the tracking performance under rapidly changing irradiance conditions. In order to prove the performance of the designed control algorithm, we will test it under simple climatic conditions of fixed temperature (30°C) and variable irradiation in the form of steps (500W/m² and 2000w/m²) and see the system response. The performance of the enhanced P&O algorithm has been evaluated using MATLAB simulations.

Dynamic contact and stiffness characteristic analysis of angular contact ball bearings under combined external loads

A nonlinear dynamic model of angular contact ball bearing under combined external loads is established based on the nonlinear elastic Hertz contact theory and raceless control theory. The established dynamic model is solved by employing variable step size Newton Raphson iteration method and the validation of model is verified by comparing the proposed results with the corresponding results come from the existing literatures. The solution of the presented model has high accuracy compared with the existing experimental results. In addition, the computational efficiency of the proposed model is improved significantly by introducing iteration step size adjustment factor. According to the proposed model, the dynamic contact and stiffness characteristics of angular contact ball are studied systematically by investigating the effects of combined external load working conditions on the contact parameters including contact angle and contact force and the stiffness parameters including diagonal stiffness and off-diagonal stiffness. This research can provide the theoretical basis and technique guidance for the designation and manufacture of angular contact ball bearing under combined external loads.

Adaptive step size quantized simulation method for gas–electricity integrated energy systems

Gas–electricity integrated energy systems (GE-IES) offers a promising solution for enhancing energy efficiency and accommodating renewable energy sources. Accurate dynamic simulation is essential for optimizing and controlling GE-IES. However, the presence of various local controllers introduces prominent discrete characteristics, posing challenges for the dynamic simulation of the GE-IES. This paper investigates the dynamic simulation method in GE-IES with discrete characteristics. Firstly, we propose an adaptive step size simulation method based on quantized state system theory. This method maintains the event-driven characteristics of the quantized state integration algorithms, while enhancing computational speed through adaptive step size adjustments. Secondly, we establish an event-driven simulation framework that facilitates interactions of different subsystems during the dynamic simulation, improving the compatibility with various models and solving algorithms. Finally, we validate the accuracy, efficiency, and scalability of the proposed method and the framework using two typical GE-IES cases with different scales. Simulation results demonstrate the effectiveness on the dynamic simulation of GE-IES and highlight the feasibility of natural gas networks in consuming and storing surplus renewable energy.

Variational Adiabatic Transport of Tensor Networks

We discuss a tensor network method for constructing the adiabatic gauge potential—the generator of adiabatic transformations—as a matrix product operator, which allows us to adiabatically transport matrix product states. Adiabatic evolution of tensor networks offers a wide range of applications, of which two are explored in this paper: improving tensor network optimization and scanning phase diagrams. By efficiently transporting eigenstates to quantum criticality and performing intermediary density-matrix renormalization group (DMRG) optimizations along the way, we demonstrate that we can compute ground and low-lying excited states faster and more reliably than a standard DMRG method at or near quantum criticality. We demonstrate a simple automated step size adjustment and detection of the critical point based on the norm of the adiabatic gauge potential. Remarkably, we are able to reliably transport states through the critical point of the models we study. Published by the American Physical Society 2024

An Intelligent Apple Identification Method via the Collaboration of YOLOv5 Algorithm and Fast-Guided Filter Theory

Apple-picking robot can promote the development of smart agriculture, and accurate object recognition in complex natural environments using deep learning algorithms is critical. However, research has shown that changes in illumination and object occlusion remain significant challenges for recognition. In order to improve the accuracy of apple apple-picking robot’s identification and positioning of apples in natural environment, a method using YOLOv5 (You Only Look Once, YOLO) combined with fast-guided filter is proposed. By introducing a fast-guided filtering module, the ability to extract image features is improved, and the problem of inaccurate occlusion targets and edge detection is solved; [Formula: see text]-means clustering algorithm is introduced in improving YOLOv5, which can realize automatic adjustment of image size and step size; BiFPN structure is introduced in Neck network to add weighted feature fusion to highlight the detailed features. The results show that the algorithm proposed in this paper can well remove noise information such as occlusion edge blurring in apple images in a natural light environment. In the real orchard environment, the apple recognition accuracy rate reached 97.8%, the recall rate was 97.3% and the recognition rate was about 26.84[Formula: see text]fps. The results show that this research based on YOLOv5 and fast-guided filtering can realize fast and accurate identification of apple fruits in natural environment, and meet the practical application requirements of real-time target detection.

A Binary Artificial Rabbit Optimization Algorithm for Improved Cognitive Radio Spectrum Allocation

A binary artificial rabbit optimization algorithm is proposed to maximize the cognitive radio (CR) network performance and fairness among users by addressing the issue of low spectrum resource utilization during the CR spectrum allocation process. Building upon the graph coloring spectrum allocation model, Sigmoid is introduced to transform the algorithm solution space. Incorporating the Lévy flight strategy for adaptive step size adjustment enhances the algorithm’s flexibility and convergence accuracy. Moreover, a selective opposition strategy based on Spearman’s coefficient is integrated into the algorithm to improve population diversity and convergence accuracy through reverse learning. Applying the binary artificial rabbit optimization algorithm to the CR spectrum allocation problem in the same CR network environment, we compare network efficiency and inter-user fairness through simulation experiments with the artificial rabbits optimization algorithm seagull optimization algorithm and particle swarm optimization algorithms. The experimental results show that the binary artificial rabbit optimization algorithm has higher convergence performance and global exploration ability, improves the overall network efficiency and user fairness of CR networks, and alleviates the problem of low spectrum resource utilization.

HK-SPSA based performance optimization method for steam generator liquid level control

Steam generator level control systems play a crucial role in ensuring safe, economical, and stable operation of nuclear power plants. However, traditional control systems exhibit limited efficiency in commissioning and are susceptible to human error. Additionally, traditional parameter tuning methods are typically experienced-based, cumbersome, and time-consuming, making it challenging to obtain optimal parameters. To address these issues, we propose a hybrid knowledge-guided improved simultaneous perturbation stochastic approximation algorithm (HK-SPSA) to optimize the control parameters of steam generator level control systems. Firstly, the algorithm utilizes iteration point adjacency information to approximate the current optimization process status and guide the adaptive adjustment of the iteration step size. Secondly, it utilizes composite gradient information generated by the estimated historical and current gradients to guide the optimization direction and the tuning of iteration step size. Simulation experiments have shown that HK-SPSA improves optimization performance and reduces the costs of optimization and adjustment compared to traditional methods.

Methods for Minimizing the Savage Function with Various Constraints

Introduction. When making decisions under uncertainty, Savage's criterion is sometimes used, or the criterion of minimizing regrets [1]. Usually, in the literature, this decision situation is described in matrix language. In other words, both the number of decision alternatives and the number of states of nature are finite. Of particular interest are situations where the admissible domain of decision variants is a convex set, and the regrets with respect to each state of nature are expressed by convex functions. In this paper, we propose numerical methods for minimizing Savage's regret function, constructed based on the subgradient projection method with automatic step size adjustment [2, 3]. The convergence of these methods is demonstrated. Goal. In the article, the Savage function is defined as a function that expresses the maximum regret value, assumed to be a convex function with respect to the decision factors. This function measures the effectiveness of each decision relative to the set of states of nature. It is important to note that computing the values of these functions is complex because of the need to know the optimal solution for each state of nature. This difficulty is successfully overcome in the process of solving the problem of minimizing functions on convex sets, thanks to parallel solutions of m "internal" algorithms based on the number of states of nature, and one external algorithm, aimed at minimizing the Savage function. Each of the m+1 algorithms represent modifications of the subgradient projection method with a programmable way of adjusting the step size. Depending on the complexity of the constraints and the required precision, three theorems have been proven, confirming the convergence of the investigated methods. Results obtained. Constructive numerical algorithms have been developed for determining optimal decision alternatives under uncertainty, when the number of states of nature is finite, the admissible domain of control factors is convex and compact, and the Savage regret function serves as a decision criterion. The convergence of the corresponding algorithms to the set of optimal solutions has been proven, without knowing the exact values of the Savage function. Instead, estimates obtained from parallel runs of algorithms were used, aimed at determining optimal solutions for each state of nature. Conclusions. Uncertainty poses significant difficulties in designing and making decisions. Any decision made under uncertainty represents a certain risk or a certain regret. In cases where the number of states of nature is finite, the decision domain is convex, the target function with respect to each state of nature is convex, and the Savage regret function is adopted as the decision criterion, the decision-making problem can be successfully solved using numerical algorithms based on the generalized gradient method. The implementation of the algorithm is relatively simple, and the fields of application can be very diverse. Keywords: uncertainty, decisions, Savage function, optimization.

Real-time, sample-by-sample estimation of multiple signal waveforms from acoustic array data using B-splines

This work presents a real-time signal processing algorithm that estimates time-domain waveforms of multiple plane wave signals on a sample-by-sample basis from data measured by an acoustic array. Moreover, the algorithm provides sample-by-sample estimates of the direction-of-arrival (DOA) of the waveforms. In this case sample-by-sample means that as each sample of data is measured by the array estimates of the waveforms and their directions-of-arrival are updated. While the basic idea can be based on almost any finite-dimensional approximation to a function space, this algorithm makes use of B-spline basis for the signal waveforms. Other features of the approach include no restrictions on signal waveform shape, automatic estimation of the number of signals present, automatic step-size adjustment for correction of signal waveform and direction-of-arrival parameters to achieve good performance while guaranteeing stability, and the use of circular (spherical) statistical models for 2-D (3-D) DOA estimates. The approach contrasts with blind source separation (BSS) methods that are based on non-Gaussian statistical assumptions and that do not assume a known array geometry nor a propagation model. This method is closely related to algorithms based on block-of-data by block-of-data processing but does not require stitching results from block processing to create time-domain waveform estimates.

A bi-layer optimization method of the grid-connected microgrid based on the multi-strategy of the beluga whale algorithm

Endeavoring to enhance the penetration rate of renewable energy sources, concurrently ensuring economic and operational stability, this study proposes a novel bi-layer optimization method of the wind–solar-storage AC/DC microgrid (MG). First, by incorporating a superordinate electric and seasonal hydrogen hybrid energy storage system (E&SHESS), the topology structure of the microgrid is established. Subsequently, to rectify the intrinsic limitations of the conventional beluga whale optimization (BWO) algorithm, this paper proposes a multi-strategy hybrid improvement to BWO (MHIBWO). This innovative improvement integrates an MTent strategy, a step size adjustment mechanism, and a crisscross strategy. Then, constructing a bi-layer iterative model based on the topology, annual net income and grid-connected friendliness are introduced as optimization objectives for the outer and inner layers, respectively, utilizing MHIBWO and CPLEX for resolution. Through a nested iteration of the two layers, the model outputs the capacity scheme with the best performance of economy and stability. Finally, the simulation unequivocally demonstrated the superiority of MHIBWO and the model proposed. In addition, based on the real data of the Elia power station, the validity of the method in operation is tested using the fuzzy C-means algorithm (FCMA) to extract and aggregate typical days, thereby presenting a sophisticated solution for the field of microgrids optimization configuration.

Improved RRT* Algorithm for Disinfecting Robot Path Planning.

In this paper, an improved APF-GFARRT* (artificial potential field-guided fuzzy adaptive rapidly exploring random trees) algorithm based on APF (artificial potential field) guided sampling and fuzzy adaptive expansion is proposed to solve the problems of weak orientation and low search success rate when randomly expanding nodes using the RRT (rapidly exploring random trees) algorithm for disinfecting robots in the dense environment of disinfection operation. Considering the inherent randomness of tree growth in the RRT* algorithm, a combination of APF with RRT* is introduced to enhance the purposefulness of the sampling process. In addition, in the context of RRT* facing dense and restricted environments such as narrow passages, adaptive step-size adjustment is implemented using fuzzy control. It accelerates the algorithm's convergence and improves search efficiency in a specific area. The proposed algorithm is validated and analyzed in a specialized environment designed in MATLAB, and comparisons are made with existing path planning algorithms, including RRT, RRT*, and APF-RRT*. Experimental results show the excellent exploration speed of the improved algorithm, reducing the average initial path search time by about 46.52% compared to the other three algorithms. In addition, the improved algorithm exhibits faster convergence, significantly reducing the average iteration count and the average final path cost by about 10.01%. The algorithm's enhanced adaptability in unique environments is particularly noteworthy, increasing the chances of successfully finding paths and generating more rational and smoother paths than other algorithms. Experimental results validate the proposed algorithm as a practical and feasible solution for similar problems.

Efficient Predefined-Time Adaptive Neural Networks for Computing Time-Varying Tensor Moore-Penrose Inverse.

This article proposes predefined-time adaptive neural network (PTANN) and event-triggered PTANN (ET-PTANN) models to efficiently compute the time-varying tensor Moore-Penrose (MP) inverse. The PTANN model incorporates a novel adaptive parameter and activation function, enabling it to achieve strongly predefined-time convergence. Unlike traditional time-varying parameters that increase over time, the adaptive parameter is proportional to the error norm, thereby better allocating computational resources and improving efficiency. To further enhance efficiency, the ET-PTANN model combines an event trigger with the evolution formula, resulting in the adjustment of step size and reduction of computation frequency compared to the PTANN model. By conducting mathematical derivations, the article derives the upper bound of convergence time for the proposed neural network models and determines the minimum execution interval for the event trigger. A simulation example demonstrates that the PTANN and ET-PTANN models outperform other related neural network models in terms of computational efficiency and convergence rate. Finally, the practicality of the PTANN and ET-PTANN models is demonstrated through their application for mobile sound source localization.

A route planning for oil sample transportation based on improved A* algorithm

The traditional A* algorithm suffers from issues such as sharp turning points in the path, weak directional guidance during the search, and a large number of computed nodes. To address these problems, a modified approach called the Directional Search A* algorithm along with a path smoothing technique has been proposed. Firstly, the Directional Search A* algorithm introduces an angle constraint condition through the evaluation function. By converting sharp turns into obtuse angles, the path turning points become smoother. This approach reduces the occurrence of sharp turns in the path, resulting in improved path smoothness. Secondly, the algorithm enhances the distance function to strengthen the directional guidance during the path search. By optimizing the distance function, the algorithm tends to prefer directions that lead towards the target, which helps reduce the search space and shorten the overall path planning time. Additionally, the algorithm removes redundant nodes along the path, resulting in a more concise path representation. Lastly, the algorithm proposes an improved step size adjustment method to optimize the number of path nodes obtained. By appropriately adjusting the step size, the algorithm further reduces the number of nodes, leading to improved path planning efficiency. By applying these methods together, the Directional Search A* algorithm effectively addresses the limitations of the traditional A* algorithm and produces smoother and more efficient path planning results. Simulation experiments comparing the modified A* algorithm with the traditional A* algorithm were conducted using MATLAB. The experimental results demonstrate that the improved A* algorithm can generate shorter paths, with reduced planning time and smoother trajectories. This indicates that the Directional Search A* algorithm, incorporating the angle constraint condition in the evaluation function and the direction-guided strategy, outperforms the traditional A* algorithm in path planning and provides better solutions to the existing issues.

Simulation Test of The Aerodynamic Environment of A Missile-Borne Pulsed Laser Forward Detection System at High Flight Speed

When a missile-borne pulsed laser forward detection system flies at supersonic speed, the laser beam will be distorted by the uneven outflow field, resulting in a significant reduction in ranging accuracy. In this paper, the impact of high flight speed on a pulsed laser detection system is studied. First, a new ray tracing method with adaptive step size adjustment is proposed, which greatly improves the computational efficiency. Second, the aerodynamic environment of a munition flying at high speed is simulated by an intermittent transonic and supersonic wind tunnel to obtain the schlieren data of the flow field at various Mach numbers. The schlieren data present a shock wave structure similar to that of the simulation. In addition, the variation patterns of the pulsed laser echo waveform of the model under different aerodynamic conditions are studied to evaluate the detectability and operational stability of the laser detection system under static conditions. The test results match the simulation results well, and the two offer relatively consistent shock wave structures, which verifies the correctness and effectiveness of the flow field simulation model. The test echo waveforms are in good agreement with the simulated echo waveforms; the relative errors between the peak values of test and simulated echo waveforms at various Mach numbers do not exceed 20%, and the correlation coefficients between the test and simulated echo waveforms all exceed 0.7, indicating high correlations between the two.

Development of a Higher-Order 𝒜-Stable Block Approach with Symmetric Hybrid Points and an Adaptive Step-Size Strategy for Integrating Differential Systems Efficiently

This article introduces a computational hybrid one-step technique designed for solving initial value differential systems of a first order, which utilizes second derivative function evaluations. The method incorporates three intra-step symmetric points that are calculated to provide an optimum version of the suggested scheme. By combining the hybrid and block methodologies, an efficient numerical method is achieved. The hybrid nature of the algorithm determines that the first Dahlquist barrier is overcome, ensuring its effectiveness. The proposed technique exhibits an eighth order of convergence and demonstrates A-stability characteristics, making it particularly well suited for handling stiff problems. Additionally, an adjustable step size variant of the algorithm is developed using an embedded-type technique. Through numerical experiments, it is shown that the suggested approach outperforms some other well-known methods with similar properties when applied to initial-value ordinary differential problems.

A new and effective nonparametric variable step-size normalized least-mean-square algorithm and its performance analysis

This paper presents a new and effective nonparametric variable step-size normalized least-mean-square (VSS-NLMS) algorithm which provides excellent performance in various signal-to-noise ratio (SNR) scenarios. The step-size update expressions are based on steady-state step-size which derives from the filter weight update equations. Specifically, the step-size adjustment rules employ the square root of the estimated error power and the system noise power to enhance the effectiveness of the proposed algorithm. Detailed transient-state and steady-state analyses are presented for the Gaussian white input signal and suggest that the steady-state performance of the proposed algorithm is independent of the smoothing parameter (used in the step-size update expressions). In addition, performance comparisons between the proposed algorithm and other well-known VSS-NLMS algorithms show the improvements of the proposed algorithm. Furthermore, theoretical analyses and simulated behaviors of the proposed algorithm are in good agreement for both transient-state and steady-state. Through simulation results, the irrelevance between the steady-state performance and the smoothing parameter is verified.

An Improved Constant Modulus Algorithm for Blind Signal Adaptation in Wireless Communications

While the Constant Modulus Algorithm (CMA) is the most often used blind signal equalization or adaptation technique, it converges slowly and produces excessive Side Lobe Level (SLL) radiation, which wastes energy and interferes with other equipment. Additionally, interfering signals may be picked up by side lobes, increasing the noise level in the receiver. A normalized constant-modulus method with adjustable step size was devised, which considerably increased its convergence rate. The Blackman window was also applied to the CMA to lower the SLL. The designed beamformer improved convergence rate by 40%, as observed by lower Mean Square Error (MSE) to Signal to Interference plus Noise Ratio (SINR) values. As a result, the beam former is a viable alternative in an environment where channel conditions are constantly changing. Furthermore, the use of the Blackman window had the ability to minimize the Peak Side Lobe Level (PSLL) and resulted in a 5 dB improvement over CMA for the SLL rejection gain. As compared to the conventional CMA, the Improved CMA displayed the highest reduction in PSLL with quick convergence capabilities and saved a lot of energy wastage due to side lobes minimization. These characteristics make the ICMA a potential choice for advanced wireless applications