Search

Sine cosine algorithm (SCA) is a recently developed meta-heuristic method based on the characteristics of sine and cosine functions. However, SCA algorithm may suffer premature convergence in solving complex optimization problems. To mitigate this limitation, a modified SCA (MSCA) is developed to achieve an effective balance between exploration and exploitation. Specifically, the conversion parameter can be configured adaptively by using the individual fitness information in the evolution process to guide the individual search behavior. After that, an inertia weight is embedded in the position updating equation to further improve the search accuracy and accelerate the convergence speed. The performance of MSCA algorithm is investigated on 13 benchmark functions and the chaotic time series prediction problem. Experimental results demonstrate that MSCA algorithm is more effective than the other optimization methods. [ABSTRACT FROM AUTHOR]

Copyright of China Mining Magazine is the property of China Mining Magazine Co., Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Featured Application: The proposed method can be utilized to determine whether an aircraft encountered turbulence during or after flight, rather than relying on EDR estimation to ascertain turbulence encounters. By integrating swarm intelligence and machine learning and adopting a data-driven approach to turbulence identification, the method addresses previous challenges encountered in turbulence identification, thereby enhancing the efficacy of aviation safety. This approach demonstrates a certain degree of applicability in improving aviation safety. Turbulence is a significant operational aviation safety hazard during all phases of flight. There is an urgent need for a method of airline turbulence identification in aviation systems to avoid turbulence hazards to aircraft during flight. Integrating flight data and machine learning significantly enhances the efficacy of turbulence identification. Nevertheless, present studies encounter issues including unstable model performance, challenges in data feature extraction, and parameter optimization. Hence, it is imperative to propose a superior approach to enhance the accuracy of turbulence identification along airline. The paper presents a combined swarm intelligence and machine learning model based on data mining for identifying airline turbulence. Based on the theory of swarm-intelligence-based optimization algorithm, the optimal parameters of Categorical Boosting (CatBoost) are obtained by introducing the whale optimization algorithm (WOA), and the corresponding WOA-CatBoost fusion model is established. Then, the Recursive Feature Elimination algorithm (RFE) is used to eliminate the data with lower feature weights, extract the effective features of the data, and the combination with the WOA brings robust optimization effects, whereby the accuracy of CatBoost increased by 11%. The WOA-CatBoost model can perform accurate turbulence identification from QAR data, comparable to that with established EDR approaches and outperforms traditional machine learning models. This discovery highlights the effectiveness of combining swarm intelligence and machine learning algorithms in turbulence monitoring systems to improve aviation safety. [ABSTRACT FROM AUTHOR]

Copyright of Railway Construction Technology is the property of Railway Construction Technology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In order to solve the complex adjustment of XGBoost model parameters and further improve the accuracy of water quality data prediction，a predictin algorithm based on Gray Wolf Optimization（GWO） to optimize XGBoost. First，taking advantage of the strong convergence ability of the grey wolf optimization algorithm for global parameter search，we use GWO to optimize the hyperparameters of XGBoost（the maximum number of spanning trees，the learning rate，and the maximum depth of the tree） to obtain the best prediction performance of XGBoost. Secondly，the data reliability was improved by pre-processing the critical water quality data，and various algorithms are used for comparative analysis experiments. The results showed that compared with LSTM and XGBoost without GWO optimization parameters，the optimized XGBoost model had better nonlinear prediction ability，and the determination factor R2 of the model reached above 0.85.

High-flow hydraulic servo systems are extensively employed in contemporary industrial applications due to their considerable flow capacity and cost-effectiveness. Nonetheless, hydraulic servo systems frequently encounter unpredictable internal and external disturbances, and high-flow proportional directional valves always have unsatisfactory hydraulic characteristics, compromising the precision and robustness of high-flow hydraulic servo systems. This study proposes a novel control strategy integrating the Soft Actor-Critic (SAC) reinforcement learning algorithm with Adaptive Robust Control (ARC) to enhance system performance. This approach features a two-tiered controller: the upper controller utilizes the SAC algorithm to learn and adapt to the dynamics of the hydraulic servo system, iteratively refining the lower controller’s hyperparameters. Meanwhile, grounded in the ARC strategy, the lower controller executes real-time control of the hydraulic servo system. The simulation and experimental results demonstrate that the proposed control strategy can effectively adjust the control hyperparameters according to the learned system dynamic and tracking errors. Consequently, this approach enhances control precision amidst varying external and internal disturbances. Moreover, this control strategy is anticipated to realize high-flow, high-precision, and high-robust hydraulic servo systems, which can be used in various fields such as marine and offshore engineering.

Introduction/purpose: Simulated annealing is a powerful technique widely used in optimization problems. One critical aspect of using simulated annealing effectively is a proper and optimal adjustment of its parameters. This paper presents a novel approach to efficiently adjust the parameters of simulated annealing to enhance its performance and convergence speed. Methods: Since the simulated algorithm is inspired by the cooling Metropolis process, the basic idea is to simulate and analyze this process using a mathematical model. The proposed work tends to properly imitate the Metropolis cooling process in the algorithmic field. By intelligently adjusting the temperature schedule, temperature reduction and cooling rate, the algorithm optimizes the balance between exploration and exploitation, leading to improved convergence and higher-quality solutions. Results: To evaluate the effectiveness of this approach, it was applied first on a chosen sample function to be minimized, and then on some usual known optimization functions. The results demonstrate that our approach, called Optimal Adjusting of Simulated Annealing parameters (OASA), achieves superior performance compared to traditional static parameter settings and other existing approaches, showing how to well adjust the parameters of the simulated annealing algorithm to improve its efficiency in terms of solution quality and processing time. Conclusion: Adjusting the algorithm parameters could have a significant contribution in the optimization field even for other metaheuristics.

Accurate assessment of greenhouse gas emissions from wastewater treatment plants is crucial for mitigating climate change. N2O is a potent greenhouse gas that is emitted from wastewater treatment plants during the biological denitrification process. In this study, we developed and evaluated deep learning models for predicting N2O emissions from a WWTP in Switzerland. Six key parameters were selected to obtain the optimal LSTM model by adjusting experimental parameter conditions. The optimal parameter condition was achieved with 150 neurons, the tanh activation function, the RMSprop optimization algorithm, a learning rate of 0.001, no dropout regularization, and a batch size of 128. Under the same conditions, we compared the performance of recurrent neural networks (RNNs) and Long Short-Term Memory (LSTM) networks. We found that LSTM models outperformed RNN models in predicting N2O emissions. The optimal LSTM model achieved a 36% improvement in mean absolute error (MAE), a 19% improvement in root mean squared error (RMSE), and a 6.92% improvement in R2 score compared to the RNN model. Additionally, LSTM models demonstrated better resilience to sudden changes in the target sequence, exhibiting a 9.54% higher percentage of explained variance compared to RNNs. These results highlight the potential of LSTM models for accurate and robust prediction of N2O emissions from wastewater treatment plants, contributing to effective greenhouse gas mitigation strategies. [ABSTRACT FROM AUTHOR]

Introduction/purpose: Simulated annealing is a powerful technique widely used in optimization problems. One critical aspect of using simulated annealing effectively is a proper and optimal adjustment of its parameters. This paper presents a novel approach to efficiently adjust the parameters of simulated annealing to enhance its performance and convergence speed. Methods: Since the simulated algorithm is inspired by the cooling Metropolis process, the basic idea is to simulate and analyze this process using a mathematical model. The proposed work tends to properly imitate the Metropolis cooling process in the algorithmic field. By intelligently adjusting the temperature schedule, temperature reduction and cooling rate, the algorithm optimizes the balance between exploration and exploitation, leading to improved convergence and higher-quality solutions. Results: To evaluate the effectiveness of this approach, it was applied first on a chosen sample function to be minimized, and then on some usual known optimization functions. The results demonstrate that our approach, called Optimal Adjusting of Simulated Annealing parameters (OASA), achieves superior performance compared to traditional static parameter settings and other existing approaches, showing how to well adjust the parameters of the simulated annealing algorithm to improve its efficiency in terms of solution quality and processing time. Conclusion: Adjusting the algorithm parameters could have a significant contribution in the optimization field even for other metaheuristics. [ABSTRACT FROM AUTHOR]

Copyright of Industrial Water Treatment is the property of CNOOC Tianjin Chemical Research & Design Institute and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

• The mechanism including the adjustable positive stiffness configuration is adopted. • The dynamic property with deviation of mass load is analyzed. • The influence of the asymmetric stiffness is studied at small displacement. • The isolation performance with various mass load is evaluated. Generally, the performance of the quasi-zero-stiffness (QZS) isolator is very sensitive to the deviation of mass load. Due to the ultra-low stiffness, even a slight deviation could lead to a large offset from the designed equilibrium. The performance degradation for low frequency vibration would be caused by the increase of the stiffness around the actual equilibrium. In this paper, a QZS isolator with the deviation of mass load is studied. The isolator comprises a nonlinear positive stiffness configuration with a pair of torsion springs, oblique bars and linear bearings, and a negative stiffness configuration with a pair of oblique bars connected to horizontal springs. The dynamic equation of the isolator with the deviation of mass load is derived. The amplitude-frequency property and force transmissibility are investigated. It is found that the deviation of mass load could significantly change the dynamic properties of the QZS isolator. To improve the isolation performance, the parameters of the positive stiffness configuration are considered to be adjustable. The results show that the adjustability can help remain the initial equilibrium, and reduce the response amplitude and force transmissibility in low frequency. The negative effect of the deviation of mass load on the QZS isolator can be decreased to a great extent. [ABSTRACT FROM AUTHOR]

Turbulence is a significant operational aviation safety hazard during all phases of flight. There is an urgent need for a method of airline turbulence identification in aviation systems to avoid turbulence hazards to aircraft during flight. Integrating flight data and machine learning significantly enhances the efficacy of turbulence identification. Nevertheless, present studies encounter issues including unstable model performance, challenges in data feature extraction, and parameter optimization. Hence, it is imperative to propose a superior approach to enhance the accuracy of turbulence identification along airline. The paper presents a combined swarm intelligence and machine learning model based on data mining for identifying airline turbulence. Based on the theory of swarm-intelligence-based optimization algorithm, the optimal parameters of Categorical Boosting (CatBoost) are obtained by introducing the whale optimization algorithm (WOA), and the corresponding WOA-CatBoost fusion model is established. Then, the Recursive Feature Elimination algorithm (RFE) is used to eliminate the data with lower feature weights, extract the effective features of the data, and the combination with the WOA brings robust optimization effects, whereby the accuracy of CatBoost increased by 11%. The WOA-CatBoost model can perform accurate turbulence identification from QAR data, comparable to that with established EDR approaches and outperforms traditional machine learning models. This discovery highlights the effectiveness of combining swarm intelligence and machine learning algorithms in turbulence monitoring systems to improve aviation safety.

Traditional methods often ignore the direct influences of vehicle vibration on the flywheel battery system, which leads to an inaccurate analysis of the dynamic performance of the flywheel battery system and its control effect. Therefore, to make up for the deficiencies of existing studies, a more accurate dynamic performance analysis method and efficient control parameter adjustment algorithm for flywheel batteries based on automotive direct action are proposed in this study. First, the influence of road conditions and vehicle driving conditions on the stability of a vehicle is analyzed primarily. Then, the vibration signal generated by the vehicle is transmitted to the vehicle's magnetic flywheel battery system for analysis, and the accuracy of the analysis process is realized. Then, according to the stability analysis results for the direct action of the vehicle and the actual PID controller, the control parameter adjustment algorithm is summarized using the curve-fitting method. Finally, a performance test is carried out on the mobile experimental platform. Good experimental results show that the flywheel can quickly return to its equilibrium position and effectively reduce the influence of interference from road conditions and different working conditions and improve the robustness. Therefore, the correctness of the theoretical analysis and parameter adjustment method proposed in this paper was effectively verified. [ABSTRACT FROM AUTHOR]

Perceptual grouping along well-established Gestalt laws provides one set of traditional methods that provide a tiny set of meaningful parameters to be adjusted for each application field. More complex and challenging tasks require a hierarchical setting, where the results aggregated by a first grouping process are later subject to further processing on a larger scale and with more abstract objects. This can be several steps deep. An example from the domain of forestry provides insight into the search for suitable parameter settings providing sufficient performance for the machine-vision module to be of practical use within a larger robotic control setting in this application domain. This sets a stark contrast in comparison to the state-of-the-art deep-learning neural nets, where many millions of obscure parameters must be adjusted properly before the performance suffices. It is the opinion of the author that the huge freedom for possible settings in such a high-dimensional inscrutable parameter space poses an unnecessary risk. Moreover, few-parameter learning is getting along with less training material. Whereas the state-of-the-art networks require millions of images with expert labels, a single image can already provide good insight into the nature of the parameter domain of the Gestalt laws, and a domain expert labeling just a handful of salient contours in said image yields already a proper goal function, so that a well working sweet spot in the parameter domain can be found in a few steps. As compared to the state-of-the-art neural nets, a reduction of six orders of magnitude in the number of parameters results. Almost parameter-free statistical test methods can reduce the number of parameters to be trained further by one order of magnitude, but they are less flexible and currently lack the advantages of hierarchical feature processing.

Copyright of Electric Power is the property of Electric Power Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

To solve the problem that weak signals are difficult to detect accurately in low signal-to-noise ratios, this paper presents a method to achieve effective detection of weak signals, applying the method of stochastic resonance to bistable systems. The principle of the method is that by transferring part of the noise energy to the signal energy, enabling the detection of weak signals at low signal-to-noise ratios. It makes it easier to extract the signal at the receiving end. This model designs a parametrized conditioning system based on the factors influencing the output power spectrum and output SNR of a stochastic resonant system. Based on the experimental results, the influence of parameters a and b on the model can be analysed, and the optimal noise intensity range of the system can be found. At the receiving end of the system, the constellation diagram and BER are used as a measure of system performance. Simulation experiments show that stochastic resonance can effectively enhance the energy of weak signals under low signal-to-noise conditions, and the demodulation performance of the system is significantly better than that of the system without the use of stochastic resonance.

Deep learning has achieved good results in the field of image recognition due to the key role of the optimizer in a deep learning network. In this work, the optimizers of dynamical system models are established, and the influence of parameter adjustments on the dynamic performance of the system is proposed. This is a useful supplement to the theoretical control models of optimizers. First, the system control model is derived based on the iterative formula of the optimizer, the optimizer model is expressed by differential equations, and the control equation of the optimizer is established. Second, based on the system control model of the optimizer, the phase trajectory process of the optimizer model and the influence of different hyperparameters on the system performance of the learning model are analyzed. Finally, controllers with different optimizers and different hyperparameters are used to classify the MNIST and CIFAR-10 datasets to verify the effects of different optimizers on the model learning performance and compare them with related methods. Experimental results show that selecting appropriate optimizers can accelerate the convergence speed of the model and improve the accuracy of model recognition. Furthermore, the convergence speed and performance of the stochastic gradient descent (SGD) optimizer are better than those of the stochastic gradient descent-momentum (SGD-M) and Nesterov accelerated gradient (NAG) optimizers. [ABSTRACT FROM AUTHOR]

Cloud Computing (CC) is the most promising and advanced technology to store data and offer online services in an effective manner. When such fast evolving technologies are used in the protection of computerbased systems from cyberattacks, it brings several advantages compared to conventional data protection methods. Some of the computer-based systems that effectively protect the data include Cyber-Physical Systems (CPS), Internet of Things (IoT), mobile devices, desktop and laptop computer, and critical systems. Malicious software (malware) is nothing but a type of software that targets the computer-based systems so as to launch cyberattacks and threaten the integrity, secrecy, and accessibility of the information. The current study focuses on design of Optimal Bottleneck driven Deep Belief Network-enabled Cybersecurity Malware Classification (OBDDBNCMC) model. The presentedOBDDBN-CMCmodel intends to recognize and classify the malware that exists in IoT-based cloud platform. To attain this, Zscore data normalization is utilized to scale the data into a uniform format. In addition, BDDBN model is also exploited for recognition and categorization of malware. To effectually fine-tune the hyperparameters related to BDDBN model, GrasshopperOptimizationAlgorithm (GOA) is applied. This scenario enhances the classification results and also shows the novelty of current study. The experimental analysis was conducted upon OBDDBN-CMC model for validation and the results confirmed the enhanced performance ofOBDDBNCMC model over recent approaches. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of fault tolerant control (FTC) for autonomous underwater vehicles (AUVs) with multiple thrusters in the presence of current disturbances, thruster faults, and modelling uncertainty. This paper focuses on the problems of reducing the energy consumption caused by the chattering of control signals and improving the tracking accuracy of an AUV operating in deep-sea environments. In view of the problem of large energy consumption in some other methods, a fault tolerant control method for multiple-thruster AUVs based on a finite-time extended state observer (FTESO) is proposed. More specifically, a FTESO based on an integral sliding mode surface is designed to estimate the generalized uncertainty compounded using current disturbances, thruster faults, and modelling uncertainty. The fast finite-time uniformly ultimately bounded stability of the proposed FTESO is analyzed. Then, based on the estimated value of FTESO, an FTC method based on non-singular fast terminal sliding mode surfaces is developed for AUVs. The finite-time convergence of the closed-loop control system is proved theoretically. In this design, two different sliding mode surfaces are used to design FTESO and FTC, in order to avoid the appearance of singularities. Moreover, a parameter adjustment method is designed to improve tracking accuracy. Finally, comparative numerical simulations show that the proposed control scheme is effective at reducing energy consumption and improving tracking accuracy. [ABSTRACT FROM AUTHOR]

Bu çalışmada, enjektör imal eden bir firmanın taşlama makinesindeki insan faktörüne bağlı ıskartaların azaltılması amaçlanmıştır. İnsan faktörüne bağlı ıskartalar, makinenin taş değişimi, arıza gibi çeşitli nedenlerle durmasından sonra makine operatörünün, makine ve ürün parametrelerinde yaptığı ayarlamalardan kaynaklanmaktadır. Iskartaların azaltılması amacıyla iki aşamalı bir çözüm yaklaşımı önerilmiştir. İlk aşamada, makine öğrenmesi kullanılarak kalite tahminlenmiştir. Kalite tahminleme, bir sınıflandırma problemi olarak ele alınmıştır. Farklı sınıflandırma algoritmaları modellenerek en yüksek performansa sahip Destek Vektör Makineleri (DVM) algoritması seçilmiştir. İkinci aşamada ise, DVM kullanılarak kaliteli ürün ile sonuçlanması beklenen uygun parametre değerleri belirlenmiştir. Güncel veri dikkate alınarak parametre değerlerinin otomatik olarak revize edilmesi ve operatöre sunulması amacıyla bir öneri sistemi geliştirilmiştir. Bu öneri sistemi ile, taşlama işleminin insan etmenine olan bağlılığının ortadan kaldırılarak dijitalleşmesi amaçlanmıştır.

Traditional methods often ignore the direct influences of vehicle vibration on the flywheel battery system, which leads to an inaccurate analysis of the dynamic performance of the flywheel battery system and its control effect. Therefore, to make up for the deficiencies of existing studies, a more accurate dynamic performance analysis method and efficient control parameter adjustment algorithm for flywheel batteries based on automotive direct action are proposed in this study. First, the influence of road conditions and vehicle driving conditions on the stability of a vehicle is analyzed primarily. Then, the vibration signal generated by the vehicle is transmitted to the vehicle’s magnetic flywheel battery system for analysis, and the accuracy of the analysis process is realized. Then, according to the stability analysis results for the direct action of the vehicle and the actual PID controller, the control parameter adjustment algorithm is summarized using the curve-fitting method. Finally, a performance test is carried out on the mobile experimental platform. Good experimental results show that the flywheel can quickly return to its equilibrium position and effectively reduce the influence of interference from road conditions and different working conditions and improve the robustness. Therefore, the correctness of the theoretical analysis and parameter adjustment method proposed in this paper was effectively verified.

In order to solve the tracking accuracy problem of the redundant manipulator, a PI control method with Henry gas solubility optimization parameter regulator (PI-HGSO) is proposed in this paper. This method consists of the controller and the parameter regulator. The characteristic is that the position deviation of a manipulator is equivalent to a specific function; namely, the proportional-integral (PI) controller is used to adjust the deviation input. The error can be better corrected by the processing of the PI controller so that the inverse kinematics solution of the minimum error can be realized. At the same time, the parameter selection of PI controllers has always been a difficulty in controller design. To address the problem, Henry gas solubility optimization (HGSO) is selected as a parameter regulator to optimize the parameters and obtain the optimal controller, thereby achieving high-precision trajectory tracking. Experiments on 9-DOF redundant manipulator show that our method achieves competitive tracking accuracy in contrast with others. Meanwhile, the efficiency and accuracy of the PI controller are greatly guaranteed by using HGSO to automatically optimize controller parameters instead of making approximate adjustments through infinite manual trial and error. Therefore, the feasibility and competitive superiority of PI-HGSO is fully proved in trajectory planning of redundant manipulators. [ABSTRACT FROM AUTHOR]

Fast iterative soft threshold algorithm (FISTA) is one of the algorithms for the reconstruction part of compressed sensing (CS). However, FISTA cannot meet the increasing demands for accuracy and efficiency in the signal reconstruction. Thus, an improved algorithm (FIPITA, fast iterative parametric improved threshold algorithm) based on mended threshold function, restart adjustment mechanism and parameter adjustment is proposed. The three parameters used to generate the gradient in the FISTA are carefully selected by assessing the impact of them on the performance of the algorithm. The developed threshold function is used to replace the soft threshold function to reduce the reconstruction error and a restart mechanism is added at the end of each iteration to speed up the algorithm. The simulation experiment is carried out on one-dimensional signal and the FISTA, RadaFISTA and RestartFISTA are used as the comparison objects, with the result that in one case, for example, the residual rate of FIPITA is about 6.35% lower than those three and the number of iterations required to achieve the minimum error is also about 102 less than that of FISTA. [ABSTRACT FROM AUTHOR]

• Vibro-electric coupling model of the VEH under square wave excitation is established. • The parameter adjusting priority of the VEH system is proposed. • The maximum output power of the proposed system is 2.7 W. • The proposed VEH is suitable for multi-vibration scenarios. Harvesting energy from surrounding vibrations is an effective method for powering various low-power electronic devices. The energy output performance of vibration energy harvesters under some special excitations remains to be investigated. In this paper, a vibration energy harvester (VEH) system based on a rack and pinion mechanism and a bevel gear set is proposed, and a detailed electromechanical coupling modeling of the system under general excitation and square wave excitation is carried out. The energy output characteristics of the proposed system under square wave excitation are explored through simulation and experiment. When the energy output is the target, the priority of adjusting the constituent parameters in the equivalent mass parameter, equivalent damping parameter and equivalent stiffness parameter of the system can be obtained based on the simulation results. The experimental results under periodic square wave excitation force show that the maximum output power of the proposed system is 2.7 W at an amplitude of 4 kN and a frequency of 3 Hz. This is a 55 % increase in the maximum power compared to the sinusoidal excitation under the same conditions. It is demonstrated that the proposed system has great potential to power various low-power electronic devices. [Display omitted] [ABSTRACT FROM AUTHOR]

In this work, aiming at the problem of cooperative task assignment for multiple unmanned aerial vehicles (UAVs) in actual combat, battlefield tasks are divided into reconnaissance tasks, strike tasks and evaluation tasks, and a cooperative task-assignment model for multiple UAVs is built. Meanwhile, heterogeneous UAV-load constraints and mission-cost constraints are introduced, the UAVs and their constraints are analyzed and the mathematical model is established. The exploration performance and convergence performance of the harmony search algorithm are analyzed theoretically, and the more general formulas of exploration performance and convergence performance are proved. Based on theoretical analysis, an algorithm called opposition-based learning parameter-adjusting harmony search is proposed. Using the algorithm to test the functions of different properties, the value range of key control parameters of the algorithm is given. Finally, four algorithms are used to simulate and solve the assignment problem, which verifies the effectiveness of the task-assignment model and the excellence of the designed algorithm. Simulation results show that while ensuring proper assignment, the proposed algorithm is very effective for the multi-objective optimization of heterogeneous UAV-cooperation mission planning with multiple constraints. [ABSTRACT FROM AUTHOR]

In this paper, a target detection approach (Faster R-CNN) based on convolutional neural networks is applied to the training and recognition of typical defects in TOFD welding seam images. Before training and recognition, a total of 162 ultrasonic TOFD welding seam images containing five typical defects are collected. The ultrasonic TOFD welding seam image dataset required for neural network model training is established on the basis of the collected images. The neural network model is trained including pre-training on ImageNet, RPN training alone, Faster R-CNN training alone, and joint RPN and Faster R-CNN training. During training, the parameters in the program are adjusted, and, then, the convergence of the neural network models and recognition performance are compared after the same training iterations. It is found that the neural network model has a tendency to converge only when the batch size is 10 and the learning rate is 0.001. Under this parameter configuration condition, the program conducts training with more iterations and is used to identify welding seam defects. The results show that the program is accurate in locating typical defects in the images, and the recognition confidence for all kinds of defects is more than 0.9. Compared with the other parameter configuration conditions after the same training iterations, the program has the highest recognition confidence in identifying all types of defects when the batch size is 10 and the learning rate is 0.001. [ABSTRACT FROM AUTHOR]

Copyright of Cotton Textile Technology is the property of Cotton Textile Technology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Simulation calibration of modern engines to test bench measurements, mainly matching the indicated pressure curves in the combustion chamber, is a time consuming task that requires high user experience to manage a multitude of adjustment parameters. In the scope of this work an automated process for calibrating simulations at motored engine operating points is presented. This is achieved using characteristic pressure deviation curves, based on 1D simulations of a simplified base model. They incorporate the dimensionless deviation of the cylinder pressure originating from one parameter. A set of these curves is scaled and superposed to recreate the original pressure deviation between simulation and measurement. The scaling factor is used to quantify each parameter's suggested adjustment value. This work presents the workflow of creating the characteristic pressure deviation curves, matching the deviation between measurement and simulation and determining the adjustment values. Further, the methodology is tested for interference of different parameters. A series of applications, ranging from 1D and 3D CFD simulation test cases to real world applications in different engines, concludes this work. [ABSTRACT FROM AUTHOR]

In recent decades, the small-scale pressure swing adsorption (PSA) oxygen generator has been widely used in the fields of home medical and hospital oxygen supply, anoxic environments, and plateau environments due to its cost effectiveness, operational flexibility, and adequate O2 volume fraction. The flexible optimization of PSA oxygen generation in response to changes in product demand is an important factor in its practical performance. To study the influence of a variable product flow rate on O2 volume fraction in the small-scale PSA oxygen generator, experimental equipment was set up, which consisted of a modified Skarstrom-cycle two-bed PSA system. The research results show that variations in the parameters at the lower product flow rate (≤10.37 L·min−1) may have a negative effect on oxygen countercurrent mixing, which can impair oxygen generation, and at higher product flow rates (≥13.57 L·min−1) may cause the negative effect of nitrogen breakthrough, which decreases the working capacity of the adsorbents in the bed. The O2 volume fraction at the lower product flow rate was improved by increasing the ratio of total oxygen in the purge gas to the total oxygen in the feed gas (P/F) and by decreasing the ratio of the highest adsorption pressure to the lowest desorption pressure (θ) during a cycle to suppress oxygen countercurrent mixing. The O2 volume fraction at the higher product flow rate was improved by increasing the P/F and θ values to improve the working capacity of the adsorbents in the bed. Accordingly, adjustments are made in the P/F and θ values at the lower and higher product flow rates to achieve optimal oxygen generation performances, enhancing the O2 volume fraction from 92.4% and 74.0% to 95.7% and 74.9% at the respective product flow rates of 3.55 L·min−1 and 19.88 L·min−1. This work is meaningful for the optimization of the parameters of the PSA oxygen production process at variable product flow rates.

Copyright of Revista de Ciencia y Tecnología is the property of Revista de Ciencia y Tecnologia and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A problem of reducing interval uncertainty is considered by an approach of cutting off equal parts from the left and right. The interval contains admissible values of an observed object’s parameter. The object’s parameter cannot be measured directly or deductively computed, so it is estimated by expert judgments. Terms of observations are short, and the object’s statistical data are poor. Thus an algorithm of flexibly reducing interval uncertainty is designed via adjusting the parameter by expert procedures and allowing to control cutting off. While the parameter is adjusted forward, the interval becomes progressively narrowed after every next expert procedure. The narrowing is performed via division-by-q dichotomization cutting off the q−1-th parts from the left and right. If the current parameter’s value falls outside of the interval, forward adjustment is canceled. Then backward adjustment is executed, where one of the endpoints is moved backwards. Adjustment is not executed when the current parameter’s value enclosed within the interval is simultaneously too close to both left and right endpoints. If the value is “trapped” like that for a definite number of times in succession, the early stop fires.

We previously developed a novel composite wheel-leg-track explosive ordnance disposal (EOD) robot with high mobility, able to switch between a track or a self-balancing motion mode according to environmental conditions, named Scorpio. In this paper, we propose an adaptive nonlinear control algorithm for improving the stability of the robot in the self-balancing mode. First, a model of the dynamics of the robot was established, with which we designed the nonlinear cascade controller for combined balance and motion control. With our system, the attitude of the robot is estimated using a Kalman filtering algorithm. Based on this, an adaptive adjustment algorithm amends the parameters of the controller in real time according to the state of the robot, for improved stability. In addition, we formulated an adaptive zero-offset angle identification algorithm to compensate for deviations caused by changes to the robot's center of gravity (due to changes to its mechanical structure), ensuring that this stability could be maintained. Results of experiments conducted to verify their operation show that self-balancing control of Scorpio can be achieved with the proposed algorithms.

As speed of communication data path is drastically improved in this decade due to the high data rate, evolutional technology is demanded to address the fast communication implementation. In this paper, we focus on data compression technology to speed up the communication data path. We have proposed a stream-based data compression called ASE coding. It compresses data stream based on the instantaneous data entropy without buffering and stalling for the compression processes. It is also suitable for hardware implementation. However, the stream-based data compression works heuristically with sensitive parameters that affect to the data compression ratio. If the parameters are statically configured, it does not follow the dynamic data entropy, and thus, the data compression performance becomes unstable. In this paper, we will disseminate the parameters, discuss the behaviors of those parameters and propose its autonomous adjustment methods. We will also propose adjustment algorithms for those parameters that follow the data entropy of the input data stream autonomously. Through experimental evaluations applying the algorithms, we will confirm the parameters are adjusted with depending on the data entropy in the data stream. And then, the compression ratio becomes stable as the compressor exploits the minimal entropy adaptively.

This paper investigates the problem of fault tolerant control (FTC) for autonomous underwater vehicles (AUVs) with multiple thrusters in the presence of current disturbances, thruster faults, and modelling uncertainty. This paper focuses on the problems of reducing the energy consumption caused by the chattering of control signals and improving the tracking accuracy of an AUV operating in deep-sea environments. In view of the problem of large energy consumption in some other methods, a fault tolerant control method for multiple-thruster AUVs based on a finite-time extended state observer (FTESO) is proposed. More specifically, a FTESO based on an integral sliding mode surface is designed to estimate the generalized uncertainty compounded using current disturbances, thruster faults, and modelling uncertainty. The fast finite-time uniformly ultimately bounded stability of the proposed FTESO is analyzed. Then, based on the estimated value of FTESO, an FTC method based on non-singular fast terminal sliding mode surfaces is developed for AUVs. The finite-time convergence of the closed-loop control system is proved theoretically. In this design, two different sliding mode surfaces are used to design FTESO and FTC, in order to avoid the appearance of singularities. Moreover, a parameter adjustment method is designed to improve tracking accuracy. Finally, comparative numerical simulations show that the proposed control scheme is effective at reducing energy consumption and improving tracking accuracy.

This work utilizes an impedance estimation methodology based in the discrete Fourier transform, expanding its use toward conventional applications associated with voltage source converters. Such applications include the integration of renewable energy resources with the grid, reactive power compensation and motor driving. Furthermore, the methodology utilized in this paper includes a control loop that does not require previous knowledge of the grid to enable the method. Simulation results are utilized to validate the study. [ABSTRACT FROM AUTHOR]

In order to solve the tracking accuracy problem of the redundant manipulator, a PI control method with Henry gas solubility optimization parameter regulator (PI-HGSO) is proposed in this paper. This method consists of the controller and the parameter regulator. The characteristic is that the position deviation of a manipulator is equivalent to a specific function; namely, the proportional-integral (PI) controller is used to adjust the deviation input. The error can be better corrected by the processing of the PI controller so that the inverse kinematics solution of the minimum error can be realized. At the same time, the parameter selection of PI controllers has always been a difficulty in controller design. To address the problem, Henry gas solubility optimization (HGSO) is selected as a parameter regulator to optimize the parameters and obtain the optimal controller, thereby achieving high-precision trajectory tracking. Experiments on 9-DOF redundant manipulator show that our method achieves competitive tracking accuracy in contrast with others. Meanwhile, the efficiency and accuracy of the PI controller are greatly guaranteed by using HGSO to automatically optimize controller parameters instead of making approximate adjustments through infinite manual trial and error. Therefore, the feasibility and competitive superiority of PI-HGSO is fully proved in trajectory planning of redundant manipulators.

Abstract Satellite remote-sensing products with high spatial and time resolution are expected to provide alternative data sources for data-sparse regions. This study clarifies if the satellite–gauge product outperforms the satellite-only product by comparing remote-sensing precipitation products: one that incorporates rain gauge data (GSMaP-Gauge) and one that uses satellite only (GSMaP-MVK). The appropriateness of those two commonly used high-resolution products as the input to the conceptual hydrological model Hydrologiska Byråns Vattenbalansavdelning for stream flow prediction was also investigated. In addition, we also analyzed the deviations of model parameters due to the bias in remote-sensing precipitation inputs compared to standard ground measurements. The results indicated that GSMaP-Gauge was superior, with satisfactory to good performances in predicting stream flow in both temperate and subtropical basins (Hyeonsan, Fuji, and Da). However, its performance was slightly worse than GSMaP-MVK in the Upper-Cau basin, which was explained by the poor quality of the adjusted data source due to sparse data and the satellite–gauge blending algorithm of GSMaP-Gauge. Better parameter agreements with the observations of GSMaP-Gauge than GSMaP-MVK were found in the Hyeonsan and Da river basins where GSMaP-Gauge showed almost consistent relationship of monthly rainfall compared to ground measurements.

Fast iterative soft threshold algorithm (FISTA) is one of the algorithms for the reconstruction part of compressed sensing (CS). However, FISTA cannot meet the increasing demands for accuracy and efficiency in the signal reconstruction. Thus, an improved algorithm (FIPITA, fast iterative parametric improved threshold algorithm) based on mended threshold function, restart adjustment mechanism and parameter adjustment is proposed. The three parameters used to generate the gradient in the FISTA are carefully selected by assessing the impact of them on the performance of the algorithm. The developed threshold function is used to replace the soft threshold function to reduce the reconstruction error and a restart mechanism is added at the end of each iteration to speed up the algorithm. The simulation experiment is carried out on one-dimensional signal and the FISTA, RadaFISTA and RestartFISTA are used as the comparison objects, with the result that in one case, for example, the residual rate of FIPITA is about 6.35% lower than those three and the number of iterations required to achieve the minimum error is also about 102 less than that of FISTA.

Abstract A simple parameter nudging procedure is described that systematically reduces near‐analysis time errors in the surface net shortwave flux in the Navy ESPC (Earth System Prediction Capability) system, a global coupled forecast system that is the product of a continuing development effort at the U. S. Naval Research Laboratory. The procedure generates geographically varying perturbations to one of the cloud fraction parameters in the atmospheric model component of the system during the data assimilation cycle, resulting in large improvements in near‐analysis time surface net shortwave flux biases. After a several week spin‐up period, the global RMSE of the succeeding 10‐day mean bias computed for lead times of 6–12 hours is reduced by 40 percent. Results from application of the approach in a series of 45‐day integrations show that improvements are realized at longer forecast lead times as well. The global RMSE of the surface net shortwave flux averaged over these integrations improves by 37 percent for lead times from 1–5 days, decreasing to 18 percent for lead times from 31–45 days. The corresponding longwave flux errors are slightly degraded, ranging from a 2 percent increase for lead times from 1–5 days to a 0.5 percent increase for lead times from 31–45 days. Global‐mean reductions in ground and sea surface temperature errors are obtained through most of the 45‐day integration period due to improvements over ocean and polar regions. Potential steps for extension and operational application of the method are discussed.

A simple parameter nudging procedure is described that systematically reduces near‐analysis time errors in the surface net shortwave flux in the Navy ESPC (Earth System Prediction Capability) system, a global coupled forecast system that is the product of a continuing development effort at the U. S. Naval Research Laboratory. The procedure generates geographically varying perturbations to one of the cloud fraction parameters in the atmospheric model component of the system during the data assimilation cycle, resulting in large improvements in near‐analysis time surface net shortwave flux biases. After a several week spin‐up period, the global RMSE of the succeeding 10‐day mean bias computed for lead times of 6–12 hours is reduced by 40 percent. Results from application of the approach in a series of 45‐day integrations show that improvements are realized at longer forecast lead times as well. The global RMSE of the surface net shortwave flux averaged over these integrations improves by 37 percent for lead times from 1–5 days, decreasing to 18 percent for lead times from 31–45 days. The corresponding longwave flux errors are slightly degraded, ranging from a 2 percent increase for lead times from 1–5 days to a 0.5 percent increase for lead times from 31–45 days. Global‐mean reductions in ground and sea surface temperature errors are obtained through most of the 45‐day integration period due to improvements over ocean and polar regions. Potential steps for extension and operational application of the method are discussed. Plain Language Summary: The net radiation budget at the earth's surface is of key importance for weather and climate, and clouds are a primary agent in regulating this budget. This study presents a simple but powerful method to gradually adjust the cloud cover in weather and seasonal forecast models during the process of forecast initiation in a way that brings the model surface radiation budget into better agreement with satellite observations. Improvements to ground and sea surface temperature forecasts are highlighted in addition to reductions in surface radiation budget errors. Key Points: A method is presented to nudge a cloud fraction parameter in a global coupled forecast system to reduce surface net shortwave flux biases.Results from a series of 45‐day test forecasts are presented demonstrating the efficacy of the approach.Further tests are required to determine operational applicability using a suitable near real‐time source of surface radiative flux data. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Vibration suppression in servo systems is significant in high-precision motion control. This paper describes a vibration-suppression method based on input shaping and adaptive model-following control. First, a zero vibration input shaper is used to suppress the vibration caused by an elastic load to obtain an ideal position output. Then, a configuration that combines input shaping with model-following control is developed to suppress the vibration caused by changes of system parameters. Finally, analyzing the percentage residual vibration reveals that it is effective to employ the sum of squared position error as a criterion. Additionally, a golden-section search is used to adjust the parameters of a compensator in an online fashion to adapt to the changes in the vibration frequency. A comparison with other input shaper methods shows the effectiveness and superiority of the developed method. [ABSTRACT FROM AUTHOR]

Traditionally, transport planning model systems are estimated and calibrated in an unstructured way, which does not allow for interactions among included parameters to be considered. Furthermore, the computational burden of model systems plays a key role in choosing a calibration approach, and usually forces modellers to calibrate demand-side and network models separately. Also, trial-and-error methods and expert opinion are currently the backbones of transport model calibration, which leaves room for error in the calibrated parameters. This paper addresses these challenges and suggests a structured approach for determining optimal calibrated transport model parameters. This approach involves joint estimation and calibration of demand and network models, with a major focus on avoiding any manipulation of the OD matrix. The approach can be applied to static or dynamic traffic assignments. The approach is applied by calibrating GTAModel—an example of a large-scale agent-based model system from Toronto, Canada. [ABSTRACT FROM AUTHOR]