Performance Of Optimization Method Research Articles

An integrated AHP‐ELECTRE and deep reinforcement learning methods for handover performance optimization in an LTE‐A networks

AbstractEmblematic growth of telecommunications networks has also produced high data rates and low user equipment (UE) latency. Also, the third generation partnership project launched long term evolution (LTE) and LTE advance (A), which supports different types of network traffic including video, voice, and so on. A handover is needed to provide semantic interloper ability is the process to move the UE power from serving evolved node B (eNB) to the subsequent eNB without interruption. Consequently, high quality of service (QoS), reliability relies on a smooth execution. If eNBs cannot be selected from available eNBs in an optimized manner to transfer during UE mobility, the criteria for quality of service can be violated. In order to accommodate QoS requires to be more configured for smooth synchronization, eNB range, and trigger points. This article aims to automate handover by means of sophisticated program based on the analytical hierarchy process (AHP) with ELECTRE and deep reinforcement (DR) learning methods. Network performance is scrutinized and simulation is utilized to visualize the proposed scheme. The experiments demonstrated that the proposed system helps to reduce the handover failure rate and handover ping‐pong. Thus, the proposed AHP‐ELECTRE‐DR‐learning method has achieved better results in delay, throughput, fairness index, and packet loss than the existing works like AHP‐TOPSIS‐Q‐learning for handover optimization in LTE‐A, auto tuning optimization for reducing frequent handovers and handover failure in LTE‐A, and particle swarm optimization with adaptive neuro‐fuzzy inference system for LTE and LTE‐A networks, respectively.

Computer-aided efficient design and performance optimization of cutting head for roadheader

The cutting head is the core working mechanism of the roadheader for coal-rock materials cutting. The efficient and high performance design of cutting head is the key to improve the road head digging and mining technology. In this paper, based on cutting head design theory and virtual prototype technology, we propose a computer-aided structure design and performance optimization method for cutting head. We compile the calculation code and realize the reading and storing of relevant data through Excel. In particular, to obtain more realistic cutting performance data of the cutting head, we construct a coupling model of cutting head cutting rock wall based on virtual prototype technology, and then establish a database matching structural parameters, working parameters, coal-rock properties and cutting performance through extensive simulations. Based on the method, we complete the design of EBZ220 roadheader cutting head. We show that our method can realize the fast and efficient design of cutting head, and the designed cutting head has good working performance.

Equation-oriented methods for optimizing Rankine cycles using radial inflow turbine

This paper presents methods for optimizing the design and the performance of Rankine cycles using radial inflow turbines. Both methods follow a novel equation-oriented approach and involve a single mathematical problem that is solved by an efficient gradient-based algorithm. The capabilities of the proposed methods were demonstrated through a case study for power generation from the batch-wise casting process at a representative ferroalloy plant. More specifically, the proposed methods were used to design and analyze three Rankine cycles with CO2 as the working fluid. The design optimization method converged in most cases to essentially the same solution regardless of the start values of the independent variables. The performance optimization method demonstrated that the control approaches with variable rotational turbine speed improved the turbine off-design efficiency over the control approaches with a constant rotational speed. Moreover, the control approaches with variable inlet guide vanes improved the thermodynamic performance of the cycle by facilitating operation at a higher pressure than the control approaches with a fixed geometry turbine. Considering the flexibility, robustness and the computational cost of the proposed methods, they can be regarded as a powerful tool for the preliminary design and performance prediction of Rankine cycles.

Research on Performance Optimization and Application in Smart Home for Hyperledger Fabric.

With the popularity of smart home services, smart home devices are also increasing significantly. At the same time, security problems of smart home services are becoming more and more important. With the characteristics of non-tampering and multi-party consensus mechanism, Blockchain technology provides powerful capabilities in security protection. In this paper, we introduce the widely used permission Blockchain Fabric for smart home services. As the high requirements of performance, we firstly study the methods of performance optimization for Fabric. Then, a Fabric-based smart home security control system is designed. Based on this system, the smart home system is able to provide access control and security control for smart home devices. The experimental results show that the system works best when the number of concurrent registrations of smart home device is under 6000.

An Intelligent Optimization for Building Design Based on BP Neural Network and SPEA-II Multiobjective Algorithm.

With the continuous development of the field of building optimization, more and more optimization methods have sprung up, among which there are many kinds of intelligent optimization algorithms. This kind of intelligent optimization algorithm usually relies on the traditional building performance simulation method to obtain the building performance index for optimization. However, intelligent optimization algorithms generally require large-scale calculations. At the same time, the time required for building performance simulation is often limited by the complexity of building models and the configuration of computers, which leads to a long time for performance optimization, which cannot give efficient and accurate feedback to designers in engineering. Building performance optimization methods based on intelligent optimization algorithms are mainly used in scientific research and are difficult to put into practical projects. Therefore, this paper builds an accurate and efficient platform for building performance prediction and optimization to help designers make decisions combined with BP neural network and the SPEA-II multiobjective optimization algorithm. Besides, the optimization results of the case are quantitatively and qualitatively analyzed and presented in visual form based on the BP neural network prediction model. Quantitative analysis includes the evolution process of solution set, convergence process, and comprehensive quality evaluation of solution set. Qualitative analysis includes Pareto frontier and optimal architectural scheme analysis. Finally, the conclusion shows that the platform prediction and optimization can give accurate and reliable optimal solution, and the optimal building scheme is reasonable and has high engineering application value.

Intelligent Design Model of Urban Landscape Space Based on Optimized BP Neural Network

Urban landscape space planning is an important application field of landscape ecology. With the continuous development of the field of architectural optimization, more and more optimization methods have sprung up, including various intelligent optimization algorithms. Such intelligent optimization algorithms usually rely on traditional building performance simulation methods to obtain building performance indicators for optimization in the optimization process. However, intelligent optimization algorithms generally require large-scale calculations. At the same time, the time required for building performance simulation is often limited by the complexity of the building model and the configuration of the computer, which leads to too long performance optimization time for designers in the project. With efficient and accurate feedback, building performance optimization methods based on intelligent optimization algorithms are mainly used in scientific research and are difficult to invest in actual projects. Because the traditional BP neural network has its own limitations and its insufficient sample size and weak generalization ability in complex prediction problems, this paper uses the learning algorithm of optimizing the BP neural network to propose an urban landscape space intelligent design model. This article introduces the artificial neural network, a new application technology with the assistance of a geographic information system, and establishes a BP neural network model for urban landscape ecological planning. Seven elements of distance and number of residential points are used as input variables, patch density, fractal dimension, the Shannon diversity index, and aggregation degree are selected as output changes, and 20 samples are carefully collected to train the network. The results show that the network convergence effect is ideal and the generalization ability is strong, which provides a new simulation analysis method for landscape ecological planning.

Performance Optimization Method of Community Sports Facilities Configuration Based on Linear Planning Model

The conventional community sports facility allocation methods have high minimum costs, and a new community sports facility allocation performance optimization method is designed based on a linear programming model in order to reduce the performance capital investment. The standardized community sports facility allocation performance objective function is established, and a pairwise model is built to divide the feasible and optimal solutions, and the feasible solutions and their constraints are found out. Establish a community sports facility configuration performance optimization model, delineate the boundaries of the optimal solution by bifurcation, and set the feasible domain of the performance optimization model. The performance optimization algorithm is designed to improve the speed of community sports facility allocation performance optimization. In the experiment, the method is compared with data‐driven method, digital management method, and evolutionary game algorithm, which requires less capital investment in spring, autumn, and winter than digital management method and evolutionary game algorithm, and the minimum capital investment in summer is less than the other three methods. From the above experimental data, it is clear that the performance optimization method has the smallest minimum capital investment and achieves the purpose of reducing the input cost.

Robust Widely Linear Beamforming With Multiple Uncertainty Sets

The widely linear minimum variance distortionless response (WL-MVDR) beamformer has a better performance than the conventional MVDR beamformer when the received signals are potentially noncircular. However, it is sensitive to the extended steering vector (ESV) mismatches which can be caused by direction of arrival (DOA) errors, noncircularity coefficient errors, array imperfections and so on. To improve the robustness against large ESV mismatches, a robust WL beamformer based on the worst-case performance optimization (WCPO) method with multiple uncertainty sets is proposed. The resultant beamformer has the mathematical form of a nonconvex optimization problem, which can be converted into a semidefinite programming (SDP) problem and solved iteratively. Simulation results show that, in comparison with several representative robust WL beamformers, the proposed method can achieve better performances, especially under large ESV mismatch conditions.

Flexibilization 4.0 for production manufacturing optimization

The continuous variations of requirements and the versatility of customers have a real impact on the company performance. Digital transformation corresponds to one lever for making the company more competitive. New technologies and tools are required as recommended in Industry 4.0 concepts for making this transformation a success. This paper focuses on production manufacturing problems such as human-machine collaboration, to achieve a high level of performance while maintaining a wide range of operational complexity, guaranteeing the flexibility of the workstations and thus the company’s ability to respond to varied demands. A sustainable digital transformation methodology has been elaborated for solving problems related to human-robot collaboration during the production process, to reduce lead time and decreasing series changing time. After a literature review on artificial intelligence (AI), collaborative robotics and production performance optimization methods, this article describes the developed sustainable digital transformation methodology and focuses on operational problems that could be solved in this frame. Then, the results obtained on an electronic card Industry 4.0 demonstration platform will be presented.

Effect of N <sub>2</sub>O treatment on performance of back channel etched metal oxide thin film transistors

In this paper, the rare earth element praseodymium-doped indium tin zinc oxide semiconductor is used as the channel layer of the thin film transistor, and the aluminum oxide-based wet back channel etched thin film transistor is successfully prepared. The effect of N <sub>2</sub>O plasma treatment on the back-channel interface of thin film transistor is studied, and the effect of treatment power and time on device performance are studied in detail. The results show that the good device performance can be obtained under certain power and time treatment, and the prepared device has good thermal stability of positive bias and negative bias under light conditions. The results from high-resolution transmission electron microscopy show that the amorphous structure of the metal oxide semiconductor material can effectively resist the wet etchant, and that no obvious component segregation phenomenon is found. Further, X-ray photoelectric spectroscopy tests show that N <sub>2</sub>O plasma treatment can form an oxygen-rich, low-carrier-concentration interface layer at the interface. On the one hand, it can effectively resist the damage of the back channel caused by the plasma of plasma enhanced chemical vapor deposition (PECVD), and on the other hand, it acts as a passivation body of hydrogen from PECVD plasma, suppressing the generation of low-level donor state of hydrogen. This study provides an important reference for low-cost, high-efficiency thin film transistor performance optimization methods.

A Performance Optimization Method Based on the Flow Field Structure of the Vortex Tubes with Ann

A Performance Optimization Method Based on the Flow Field Structure of the Vortex Tubes with Ann

Distributed deep reinforcement learning-based coordination performance optimization method for proton exchange membrane fuel cell system

A proton exchange membrane fuel cell (PEMFC) is a multi-input multi-output nonlinear complicated system, the output power and operating efficiency of which are affected by various operating variables. To achieve coordinated control of multiple operating variables and thus improve the stability, performance, and efficiency of the PEMFC power system, a data-driven power coordination management method is proposed. Furthermore, a metaverse-based multiagent double delay deep deterministic policy gradient (MET-MADDPG) algorithm is also presented. The design of this algorithm is structured on the concept of the metaverse in that it employs different metaverses during pre-learning and combines imitation learning and curriculum learning to enable agents in different combinations to be fully trained in different environments to improve coordination strategy robustness. In this algorithm, the hydrogen controller, air controller, water pump controller and radiator controller are regarded as four agents; the objective of the training is to enable agents with their different goals to coordinate. The effectiveness of the proposed algorithm is demonstrated in a series of experiments in which it is compared with existing algorithms.

Review on heat pipe based solar collectors: Classifications, performance evaluation and optimization, and effectiveness improvements

Heat pipe based solar collectors (HP–SCs) have emerged as a promising way to improve the performance of solar thermal collectors (STCs) and PV/T collectors, a technology that leads to a proliferation of scientific studies and literature. To date, several review papers have been published summarizing studies relevant to either HP-STCs or HP-PV/T. As the update development of HP-STCs, HP-PV/T technology shows common features from structural design to operation principles. There exist some degrees of inevitable relationships between such two kinds of HP-SCs. Therefore, it is meaningful to conduct a comprehensive overview of studies about these two kinds of HP-SCs. This paper aims to summarize the classification, performance evaluation and optimization, and effective improvements related to both HP-STCs and HP-PV/T collectors. Diverse performance evaluation and optimization methods, and effective improvement measurements for various HP-SCs are presented and discussed. This is followed by conclusive remarks of their application situations, performance analyses and comparison, and effective improvements. Finally, new research directions and potentials in this field are identified and recommended.

Research on Fan Operation Evaluation and Error State Judgment Relying on Improved Neural Network and Intelligent Computing

Fan, as the most commonly used mechanical equipment, is widely used. In order to solve the problem of fan bearing fault diagnosis, this paper analyzes the main factors affecting fan spindle speed and power generation in operation. The input and output parameters of the performance prediction model are determined. The performance prediction model of wind turbine is established by using generalized regression neural network, and the smoothing factor of GRNN is optimized by comparing the prediction accuracy of the model. Based on this model, the sliding data window method is used to calculate the residual evaluation index of wind turbine speed and power in real time. When the evaluation index continuously exceeds the pre-set threshold, the abnormal state of wind turbine can be judged. In order to obtain wind turbine blades with better aerodynamic performance, a blade aerodynamic performance optimization method based on quantum heredity is proposed. The B é zier curve control point is used as the design variable to represent the continuous chord length and torsion angle distribution of the blade, the blade shape optimization model aiming at the maximum power is established, and the quantum genetic algorithm is used to optimize the chord length and torsion angle of the blade under different constraints. The optimization results of quantum genetic algorithm and classical genetic algorithm are compared and analyzed. Under the same parameters and boundary conditions, the proposed blade aerodynamic optimization method based on quantum genetic optimization is better than the classical genetic optimization method, and can obtain better blade aerodynamic shape and higher wind energy capture efficiency. This method makes up for the shortcomings of traditional fault diagnosis methods, improves the recognition rate of fault types and the accuracy of fault diagnosis, and the diagnosis effect is good.

Estimates of hydroelectric energy generation in Turkey with Jaya algorithm-optimized artificial neural networks

The main purpose of this study was to establish an artificial neural network (ANN) model trained by a Jaya algorithm, and use the model to predict Turkey’s future hydroelectric energy generation (HEG). Population, gross domestic product (GDP), installed capacity, energy consumption, gross electricity energy demand (GEED), and average yearly temperature (AYT) data were inputted as independent variables in the model. ANN-Jaya was compared with ANN models trained by the other two high performance optimization methods, namely back-propagation (BP) and artificial bee colony (ABC) algorithms, to test its accuracy. The ANN-Jaya model converged to smaller error values than were obtained with the ANN-BP and ANN-ABC models for both the training and test datasets. When the average relative error (RE) values calculated for the test set are taken into account, ANN-Jaya performs 19.3% better than ANN-ABC and 31.2% better than ANN-BP. Therefore, Turkey’s HEG projections were made out to the year 2030 using an ANN-Jaya model in a low and a high energy demand scenario. According to the developed projections, HEG values in Turkey in 2030 will be in the range of 104.81–124.66 TWh. The present results affirm that HEG can be modeled accurately with an ANN-Jaya technique and this method was shown to be advantageous for predicting future HEG.

Decoding Electrophysiological Signals with Organic Electrochemical Transistors.

The organic electrochemical transistor (OECT) has unique characteristics that distinguish it from other transistors and make it a promising electronic transducer of biological events. High transconductance, flexibility, and biocompatibility render OECTs ideal for detecting electrophysiological signals. Device properties such as transconductance, response time, and noise level should, however, be optimized to adapt to the needs of various application environments including in vitro cell culture, human skin, and inside of a living system. This review includes an overview of the origin of electrophysiological signals, the working principles of OECTs, and methods for performance optimization. While covering recent research examples of the use of OECTs in electrophysiology, a perspective is provided for next-generation bioelectric sensors and amplifiers for electrophysiology applications.

Evaluation of long-term power capture performance of a bistable point absorber wave energy converter in South China Sea

Nowadays, extensive research has been conducted to generate electricity from ocean waves, but there are not too many studies on the capture of wave energy in a specific ocean area. As the largest sea area in China's offshore waters, the South China Sea contains a large amount of wave energy and is regarded as a natural wave energy power plant. A comparative study was conducted to investigate the effects of wave energy converter (WEC) parameters on the long-term power capture performance of a bistable point absorber WEC installed in the South China Sea in this paper. Combined with the joint probability distribution of significant wave height Hs and peak wave period Tp in South China Sea, a mathematical model was developed for the evaluation of long-term power performance of WECs. The governing equations of the point absorber WEC were established using the hybrid frequency-time domain method. The dynamic response and captured power can be obtained after numerically solving the equations of motion with the 4th order Runge-Kutta Method. The results show that the bistable mechanism has great advantages in the long-term captured power of WEC. In general, a circular truncated cone-shaped buoy WEC with a larger buoy size and smaller nonlinear parameter tend to result in better long-term power capture performance. The insights given in this study provide a motivation and foundation for developing a complete set of performance optimization methods of point absorber WECs installed in South China Sea.

A comprehensive performance optimization method for the honeycomb buffer of a legged-type lander

PurposeIn the field of planetary exploration, the legged-type lander is a common landing buffer device. There are two important performance metrics for legged-type landers: the energy absorption capacity and landing stability. In this paper, a novel method is proposed to optimize the honeycomb buffer of a legged-type lander. Optimization design variables are the dimension parameters of honeycomb and the objective functions are the evaluation parameters of the above two performance metrics.Design/methodology/approachA multi-body dynamic model of a lander and a finite-element model of the metal honeycomb are established. Based on the simulation results of the finite-element model and the quartic polynomial, the surrogate models are established to evaluate the energy absorption capacity of honeycomb. Considering both the multi-body dynamic model and the surrogate models, the study designed the optimization flow of dimension parameters of honeycomb. Besides, the non-dominated sorting genetic algorithm II is used for iterative calculation.FindingsImages of surrogate models show the monotonous functional relationship between the honeycomb’s energy absorption characteristics and its dimension parameters. Optimization results show an apparent contradiction among the objective functions. Besides, according to the simulation results, this method can significantly improve the comprehensive performance of the lander.Originality/valueThe novel method can effectively reduce the cost of honeycomb compression tests and improve the lander’s design. Therefore, it can be used for optimizing buffers of other types of legged-type landers.

An improved aerodynamic performance optimization method of 3-D low Reynolds number rotor blade

Abstract In this paper, an improved aerodynamic performance optimization method for 3-D low Reynolds number (Re) rotor blade is proposed. A conventional optimization procedure of blade is usually divided into three parts, such as the parameterization method, the fitness value evaluation and the optimization algorithm. This work is mainly focused on the first two parts. The parametrization method, Camber-FFD, is presented based on the camber parametrization method and the free-form deformation algorithm (FFD). The shape of 3-D blade is parameterized by the incidence angles and the coordinates of the maximum camber points. The fitness value evaluation has been realized with the help of an adaptive topological back propagation multi-layer forward artificial neural network (BP-MLFANN). During the training of BP-MLFANN, the hybrid particle swarm optimization method combined with the modified very fast simulate annealing algorithm (HPSO-MVFSA) is adopted to determine the neural network topology adaptively. To verify the effectiveness of this aerodynamic optimization method, the aerodynamic performance of a 3-D low-Re blade, such as Blade D900, is optimized, and the results are compared and analyzed based on the experiments and simulations. It is proved that this aerodynamic optimization method is feasible.

An improved aerodynamic performance optimization method of 3-D low Reynolds number rotor blade

Abstract In this paper, an improved aerodynamic performance optimization method for 3-D low Reynolds number (Re) rotor blade is proposed. A conventional optimization procedure of blade is usually divided into three parts, such as the parameterization method, the fitness value evaluation and the optimization algorithm. This work is mainly focused on the first two parts. The parametrization method, Camber-FFD, is presented based on the camber parametrization method and the free-form deformation algorithm (FFD). The shape of 3-D blade is parameterized by the incidence angles and the coordinates of the maximum camber points. The fitness value evaluation has been realized with the help of an adaptive topological back propagation multi-layer forward artificial neural network (BP-MLFANN). During the training of BP-MLFANN, the hybrid particle swarm optimization method combined with the modified very fast simulate annealing algorithm (HPSO-MVFSA) is adopted to determine the neural network topology adaptively. To verify the effectiveness of this aerodynamic optimization method, the aerodynamic performance of a 3-D low-Re blade, such as Blade D900, is optimized, and the results are compared and analyzed based on the experiments and simulations. It is proved that this aerodynamic optimization method is feasible.