Joint Algorithm Research Articles

Energy-Efficient Federated Learning Over UAV-Enabled Wireless Powered Communications

Since the invention in 2016, federated learning (FL) has been a key concept of artificial intelligence, in which the data of FL users needs not to be uploaded to the central server. However, performing FL tasks may not be feasible due to the unavailability of terrestrial communications and the battery limitation of FL users. To address these issues, we make use of unmanned aerial vehicles (UAVs) and wireless powered communications (WPC) for FL networks. In order to enable sustainable FL solutions, the UAV equipped with edge computing and WPC capabilities is deployed as an aerial energy source as well as an aerial server to perform FL tasks. We propose a joint algorithm of UAV placement, power control, transmission time, model accuracy, bandwidth allocation, and computing resources, namely energy-efficient FL (E2FL), aiming at minimizing the total energy consumption of the aerial server and users. The E2FL overcomes the original nonconvex problem by an efficient algorithm. We show that sustainable FL solutions can be provided via UAV-enabled WPC through various simulation results. Moreover, the outperformance of E2FL in terms of energy efficiency over several benchmarks emphasizes the need for a joint resource allocation framework rather than optimizing a subset of optimization factors.

Dynamic Mechanism and Decision Analysis of Urban Exhibition Tourism Development Based on High-Performance Computing.

MICE tourism has received the attention and support from relevant departments and tourism industry with the growth rate of recent years. In this paper, the dynamics mechanism of urban exhibition tourism development is studied by using high-performance computing, and the dynamics mechanism model of exhibition tourism is constructed. To improve the performance, the solution constructed in the adaptive joint algorithm is used as the initial solution, and then the initial solution is improved using the method proposed by the new neighborhood strategy. It promotes the development of MICE tourism and urban MICE tourism by combining research on theoretical aspects related to tourism development, such as the relationship between tourism development and exhibition, and the development dynamics and stages of MICE tourism. The research results show that, in the assessment of the development mechanism of urban MICE tourism, tourism resources, economic, and policy factors have a relatively large weight, among which the weight coefficient of tourism resources factor is 0.2411. It can be seen that the development power of MICE tourism should start from tourism resources, so that the development power mechanism of urban MICE tourism can be optimized and improved, which not only provides new methods and new ideas for the transformation and upgrading of tourism industry but also promotes tourism enterprises and related departments to creatively develop tourism resources and design competitive tourism products, so as to improve the supply quality of MICE tourism, and then continuously promote the process of supply-side reform of MICE tourism.

Application Analysis of Combining BP Neural Network and Logistic Regression in Human Resource Management System.

Human resource management involves a variety of data processing, and the process is complicated. In order to improve the effect of human resource management, this paper combines BP neural network and logistic regression analysis to construct an intelligent human resource management system and uses backpropagation learning to adjust training errors and determine connection weights. Moreover, this paper estimates the probability of a certain event through regression analysis, predicts and analyzes the human resource management process, and builds an intelligent human resource management system with the support of joint algorithms. In order to explore the reliability of the joint algorithm proposed in this paper, the effectiveness of the algorithm proposed in this paper is verified through simulation tests. The experimental research results show that the human resource management system based on BP neural network and logistic regression proposed in this paper has good practical effects.

Many-objective charging optimization for electric vehicles considering demand response and multi-uncertainties based on Markov chain and information gap decision theory

This paper constructs an electric vehicle (EV) charging optimization model considering demand response and the uncertainties of source and load. First, after obtaining the charging load by using the Markov chain, the charging time is divided into peaks, flats, and valleys, and the charging load is adjusted through price-based demand response. Secondly, aiming at minimizing user charging cost, greenhouse gas (GHG) emissions, and load fluctuation, and maximizing the revenue of power supplier, a deterministic EV charging optimization model is established. Third, by modeling information gap decision theory (IGDT), the uncertainty of wind and photovoltaic powers (WP and PV) is introduced into the charging optimization model to analyze the impact of WP and PV's fluctuations on risk aversion decision makers. Finally, a joint algorithm for solving a many-objective problem based on ε-constraint and NSGA-II algorithm is proposed. The results of case study show that: (1) rolling forecast based on Markov chain has a better effect on dealing with the uncertainty of charging load than Monte Carlo; (2) the price-based demand response combined with time division model can be used to shave peaks and fill valleys and reduce user charging cost; (3) optimized by using the deterministic model, the charging load curve is smoother; (4) with the uncertainty of power generation involved in the many-objective optimization, the PV's fluctuation is greater, and the power supplier revenue increases compared with the deterministic model, but the GHG emissions also increase; (5) In the same range of expected benefit changes, the fluctuation values acceptable to decision makers are different for different objective functions; (6) the user response, the normalized number of the selected sub-objective, and the maximum acceptable benefit deviation have different influences on the ordered charging strategy; (7) the joint algorithm proposed in this paper is an effective method to solve many-objective problems that contain conflictive aims.

Research on methods for extracting aging characteristics and health status of lithium-ion batteries based on small samples

The number of aging samples is small with regard to the small sample characteristics of lithium-ion battery aging data, and the extraction of effective aging features is difficult to perform, which reduces the generalization of the battery aging behavior model as well as the accuracy of battery state of health (SOH) estimation. According to the incremental capacity analysis, a gray relational analysis (GRA) method based on PCA (principal component analysis) was proposed in this study, which was combined with AdaBoost-SVR (support vector regression) to achieve accurate SOH measurement under small sample conditions. Accordingly, the combination of GRA and PCA was able to fully mine the aging characteristic information from limited battery aging samples while improving the generalization ability of the battery aging behavior model. Moreover, based on AdaBoost-SVR, it was able to perform adaptive weighted sampling on small data samples, and through multiple iterations of the SVR training model, the full use of small sample information was realized, ensuring the accuracy of SOH estimation. Finally, by employing multiple sets of battery aging training sets and test sets for experimental verification, the joint algorithm based on GRA-PCA and AdaBoost-SVR was proven to achieve the accurate extraction of battery aging characteristics and precise tracking of capacity decline.

SOH prediction for lithium-ion battery based on improved support vector regression

ABSTRACT As a good energy storage element, lithium-ion batteries are used widely in electric vehicles and electric energy storage systems now, but its own State of Health (SOH) is difficult to measure directly. The Support Vector Regression (SVR) method can use the cell characteristics extracted from the experiment to predict the SOH of the cell.However,the method itself is difficult to determine the hyperparameters and has a large impact on the prediction accuracy.For this reason, this paper proposes the joint algorithm of Bat Algorithms (BA) and Support Vector Regression to predict the SOH of lithium-ion batteries, uses the BA algorithm to find the optimal SVR hyperparameters and uses Grey Relationship Analysis (GRA) to verify their high correlation with the battery SOH correlation.The proposed method was validated using a publicly available experimental dataset, and the results showed that the method has higher prediction accuracy compared with the conventional GA-SVR and PSO-SVR.

Polarization Voltage Characterization of Lithium-Ion Batteries Based on a Lumped Diffusion Model and Joint Parameter Estimation Algorithm

Polarization is a universal phenomenon that occurs inside lithium-ion batteries especially during operation, and whether it can be accurately characterized affects the accuracy of the battery management system. Model-based approaches are commonly adopted in studies of the characterization of polarization. Towards the application of the battery management system, a lumped diffusion model with three parameters was adopted. In addition, a joint algorithm composed of the Particle Swarm Optimization algorithm and the Levenberg-Marquardt method is proposed to identify model parameters. Verification experiments showed that this proposed algorithm can significantly improve the accuracy of model output voltages compared to the Particle Swarm Optimization algorithm alone and the Levenberg-Marquardt method alone. Furthermore, to verify the real-time performance of the proposed method, a hardware implementation platform was built, and this system’s performance was tested under actual operating conditions. Results show that the hardware platform is capable of realizing the basic function of quantitative polarization voltage characterization, and the updating frequency of relevant parameters can reach 1 Hz, showing good real-time performance.

Two-Stage Optimization Method for Sizing Stack and Battery Modules of a Fuel Cell Vehicle Based on a Power Split Control

Currently, hybrid and battery electric vehicles are the best-selling green cars commercially available. However, there is a growing interest in fuel cell electric vehicles (FCVs). Nevertheless, due to the unidirectional nature of energy transformation in an FCV, an auxiliary energy storage system (ESS) is required to cope with peak power demand and recover braking energy. In this paper, we propose a joint algorithm for sizing both the fuel cell (FC) stack and an auxiliary storage system, taking into account the power split strategy between the two energy sources. Moreover, a novel power split method is introduced based on the lumped resistance of both the FC stack and lithium-ion battery modules. Several simulation results implementing different driving cycles prove that the proposed sizing procedure is able to reduce the fuel consumption of the FCV and increase the expected lifetime of both the FC stack and lithium-ion battery modules according to a given power split strategy.

Symbol error probability optimization in cognitive amplify and forward cooperative relaying networks

This paper considers a resource scheduling problem of a cooperative cognitive radio network based on orthogonal frequency division multiplexing technology, in which primary and secondary users enjoy different priorities. Our goal is to minimize the symbol error probability of secondary users while assuring the quality of service for primary users due to higher priority and payment guarantees. Different from the usual energy efficiency, spectrum efficiency, sum rate or throughput issues, the symbol error probability is recruited as the optimization goal. According to different power rationing and quality of service requirements, the joint algorithm of power and subcarrier allocation is divided into four cases: total power budget and total interference level, total power budget and separate interference level, separate power budget and total interference level as well as separate power budget and separate interference level. For each scenario, a detailed analysis is carried out to obtain a closed-form solution of the power allocation, and the decision basis of the path selection is strictly proved. Simulation results show that the joint algorithm of power allocation, path selection and subcarrier pairing has excellent performance in relay location deployment, convergence analysis and symbol error probability reduction.

Estimation of Lithium‐Ion Battery State of Charge for Electric Vehicles Using an Adaptive Joint Algorithm

AbstractAs a new means of transportation, electric vehicles (EVs) have a lot of potential. On the other hand, EVs that employ lithium‐ion batteries face certain difficulties in forecasting the battery's health and remaining useful life. This paper uses the adaptive joint algorithm approach to calculate the battery's online parameters and accurate state of charge (SOC). To establish the battery online parameters, the forgetting factor recursive least square (FFRLS) technique is utilized, and the extended Kalman filter (EKF), unscented Kalman filter (UKF) are employed to estimate accurate SOC. Compared to the EKF/UKF method, the joint algorithm (FFRLS‐UKF) approach produces better results. The results are validated using the urban dynamometer driving schedule cycle and the ECE extra‐urban driving cycle (low powered vehicles) to determine the performance of the proposed algorithm. The error of the estimated SOC has fallen from 3.3% to 2%. The proposed adaptive joint algorithm has substantially improved the system's accuracy and provides better results than the EKF/UKF technique. Furthermore, the random variable noise is also supplied to the test data to ensure that the proposed method is robust.

Transient Electromagnetic 1‐Dimensional Inversion Based on the Quantum Particle Swarms Optimization‐Smooth Constrained Least Squares Joint Algorithm and Its Application in Karst Exploration

Before the construction of the bridge bored pile in the karst area, geological conditions of the excavation area should be investigated. In order to avoid the karst caves in underground space making adverse impacts on the construction, bearing capacity, and stability of pile foundation, in this paper, we use the transient electromagnetic method to detect the karst development in the bearing layer of the pile foundation, which is different from the traditional karst survey method. To improve the interpretation accuracy of transient electromagnetic detection for karst caves, the quantum particle swarm optimization (QPSO) algorithm was combined with the smooth constrained least squares (CLS) algorithm, and the transient electromagnetic inversion based on the QPSO‐CLS joint algorithm was generated. Better inversion results were achieved by the proposed method in this study. Based on the inversion calculation results of simulation data and field test data, it is further demonstrated that the QPSO‐CLS joint algorithm has high optimization efficiency without manually setting the initial model. The interpretation results are consistent with the theoretical model and drilling logging results, which proves the adaptability of the proposed algorithm.

Micro-Doppler Trajectory Estimation of Human Movers by Viterbi–Hough Joint Algorithm

Micro-Doppler Trajectory Estimation of Human Movers by Viterbi–Hough Joint Algorithm

A Joint Algorithm for Base Station Deactivation and Mobile User Reassignment in Green Cellular Networks

A Joint Algorithm for Base Station Deactivation and Mobile User Reassignment in Green Cellular Networks

A Hybrid Predicting Model for the Daily Photovoltaic Output Based on Fuzzy Clustering of Meteorological Data and Joint Algorithm of GAPS and RBF Neural Network

Photovoltaic (PV) output is greatly affected by meteorological factors. If it has no efficient meteorological factors, the prediction accuracy for PV is a little low. Although the Radial Basis Function (RBF) network is already widely utilized in photovoltaic prediction, its prediction error is too large. An algorithm for forecasting the evaluation of the short-term PV output based on fuzzy clustering of meteorological data and a joint algorithm of the Genetic Algorithm Programming System (GAPS) and Radial Basis Function (RBF) is proposed in this paper to increase the prediction accuracy. Selecting the three main types of meteorological data, including atmospheric turbidity, relative humidity, and solar irradiance, as clustering feature vectors of the cluster class and clustering that historical PV outputting data into three groups by an improved fuzzy c-means clustering (IFCM) method are significant in this study. Finally, this research implemented the computational simulation for a real case. Its results show that the proposed model and algorithm work well and can reduce the dimension of the model and improve the prediction accuracy.

Multifractal analysis for spatial characterization of high resolution Sentinel-2/MAJA products in Southwestern France

Multifractal analysis was applied to evidence spatial properties, in particular scaling behaviour, of Sentinel-2 surface reflectances and optical indexes acquired over the Southwest region of France. Considering the significant impact of clouds on the spatial properties of optical products, no study to our knowledge has focused on the multifractal analysis of a series of images. Images with minimal cloud cover were identified for the whole year 2016 based on the MACCS-ATCOR Joint Algorithm (MAJA) cloud and shadow detection process. This allowed us to access the dynamics of the scaling properties of surface reflectances, contributing to demonstrate the potential of multifractals for spatial characterization. Different scaling regimes, separated by significant scale breaks, were evident in these data. Through simulations from the Universal Multifractal model, we could explain the scaling properties and especially the position of scale breaks observed in the images studied by two types of phenomena. First, a transition scale located between hundreds of meters and one kilometre was detected on power spectra, structure functions and statistical moments. This specific scale was related to the average size of the agricultural plots of the studied region, revealing the direct link between the scaling behaviour observed and the physical properties of the surface. Second, an instrumental phenomenon was detected on the power spectra, resulting in a systematic transition scale observed near the resolution of the sensor and revealing the impact of the acquisition system on the high frequencies of optical images. In this research work, theoretical and/or empirical validation arguments were provided as to the origin of the scales detected, contributing to demonstrate the value of multifractals to extract image texture from surface reflectance products.

Low complexity state-of-charge estimation for lithium-ion battery pack considering cell inconsistency

The estimation accuracy of State-of-Charge (SOC) has great significance for cell charge/discharge management, balance control, and safety management. However, some inconsistencies among cells may lead to model mismatch, which will have a significant impact on the estimation results. Furthermore, it is difficult to achieve an accurate SOC estimation with less computational burden when the battery pack contains hundreds (or thousands) of cells. This paper constructs a battery pack model that consists of a mean-model and several difference-models. The mean-model represents the overall performance of the battery pack. The difference-model describes the inconsistencies of SOC, internal resistance, and coulombic efficiency among cells. In addition, a joint algorithm is proposed to estimate SOC with low complexity. It includes a dual time-scale adaptive extended Kalman filter (AEKF) and a SOC correction method. Comparing with the conventional filter method of SOC estimation, it can effectively simplify a large number of matrix operations, and greatly reduce the overall computational complexity. The experimental results show that the established model can well describe the dynamic characteristics of the battery pack, and the proposed method can track the changing of SOC at a low computational cost. ● The CE inconsistency among cells is further considered in modeling. ● The estimated SOC tracks the changing of actual value well. ● The method is proposed to estimate the SOCs of many cells rapidly. ● The method requires low computation cost. ● The method is suitable for the real-time application.

Joint Diagnosis of RIS and BS for RIS-Aided Millimeter-Wave System

Recently, the reconfigurable intelligent surface (RIS)-aided communication system has emerged as a promising candidate for future millimeter-wave wireless communications. Due to the short wavelength of millimeter wave, the antennas on the base station (BS) and the elements on the RIS can be densely packed. It usually causes the BS and RIS to be blocked by rain, snow, or dust, which will change the channel’s characteristics and decrease the performance of communication system. In order to solve this problem, we propose an iterative compressed sense based algorithm for joint estimating the blockage coefficients of RIS and BS. Then, for the complete blockage scenario, we propose a low complexity algorithm for estimating the blockage coefficients. Our simulation results demonstrate the superior performance of the proposed algorithm to existing ones.

An Adaptive Heart Rate Monitoring Algorithm for Wearable Healthcare Devices

This paper focuses on developing an adaptive heart rate monitoring algorithm for wrist-based rehabilitation systems. Due to the characteristics of the wrist, the heartbeat measurements are unstable. To improve the preprocessing efficiency and perform measurement calibration, a novel joint algorithm incorporating automatic multiscale-based peak detection and fuzzy logic control (AMPD-Fuzzy) is proposed. The monitoring approach consists of two phases: (1) Preprocessing and (2) Detection and Calibration. Phase 1 explores the parameter settings, threshold, and decision rules. Phase 2 applies fuzzy logic control and the Laplacian model to provide signal reshaping. Experimental results show that the proposed algorithm can effectively achieve heart rate monitoring for wearable healthcare devices.

Research on online parameter identification and SOC estimation methods of lithium‐ion battery model based on a robustness analysis

Under complex working conditions in variable temperatures, the accuracy of SOC is reduced due to the low robustness of the lithium-ion battery model online parameter identification method as well as the SOC estimation approach. Given this problem, a parameter identification method called FF-AGLS (alternative generalized least squares with forgetting factor) is proposed. The proposed method was combined with the robust H∞-CKF (cubature Kalman filter) based on singular value decomposition (SVD) in order to achieve an accurate estimation of lithium-ion battery SOC. FF-AGLS, which adopts unbiased estimation, has strong parameter tracking ability in low temperatures and low SOC regions, as well as high model parameter identification accuracy. As a result, combining the H∞ filter with SVD-CKF can maintain the robustness of the algorithm when the model parameters are uncertain, which may solve issues related to the decrease in SOC estimation accuracy caused by temperature changes. Finally, a series of experiments were conducted on the proposed method at different temperatures, while its performance was verified with the current under different working conditions. Accordingly, the joint algorithm based on FF-AGLS and H∞-SVD-CKF was able to accurately track model parameters and SOC with a strong degree of robustness.

Lithium-Ion Battery SOC Estimation Based on Adaptive Forgetting Factor Least Squares Online Identification and Unscented Kalman Filter

In order to improve the estimation accuracy of the battery state of charge (SOC) based on the equivalent circuit model, a lithium-ion battery SOC estimation method based on adaptive forgetting factor least squares and unscented Kalman filtering is proposed. The Thevenin equivalent circuit model of the battery is established. Through the simulated annealing optimization algorithm, the forgetting factor is adaptively changed in real-time according to the model demand, and the SOC estimation is realized by combining the least-squares online identification of the adaptive forgetting factor and the unscented Kalman filter. The results show that the terminal voltage error identified by the adaptive forgetting factor least-squares online identification is extremely small; that is, the model parameter identification accuracy is high, and the joint algorithm with the unscented Kalman filter can also achieve a high-precision estimation of SOC.