Self-adaptive Weight Research Articles

Parameter Identification for Photovoltaic Models Using an Improved Learning Search Algorithm

As a renewable energy resource, solar photovoltaics (PV's) possess a promising future. Thus, it is important to simulate, evaluate and control the PV systems. To identify the parameters for the photovoltaic (PV) models, an improved learning search optimization algorithm (ILSA) is proposed in this paper. The proposed ILSA has the following three key features: (i) the constant self-adjustment rate changes with the iteration. (ii) a new part with a self-adaptive weighting of the current best and worst solution is introduced to learning patterns to guide the iterative direction. (iii) a perturbation method is added to avoid the algorithm falling into local optimum. In order to assess the effectiveness of ILSA relative to other state-of-the-art algorithms, single diode, double diode, and PV model are used for test. Our experimental results reveal that the ILSA performs well in terms of the accuracy of optimization solutions and effectiveness.

Gesture-based human-robot interface for dual-robot with hybrid sensors

PurposeThe purpose of this paper is the research of a novel gesture-based dual-robot collaborative interaction interface, which achieves the gesture recognition when both hands overlap. This paper designs a hybrid-sensor gesture recognition platform to detect the both-hand data for dual-robot control.Design/methodology/approachThis paper uses a combination of Leap Motion and PrimeSense in the vertical direction, which detects both-hand data in real time. When there is occlusion between hands, each hand is detected by one of the sensors, and a quaternion-based algorithm is used to realize the conversion of two sensors corresponding to different coordinate systems. When there is no occlusion, the data are fused by a self-adaptive weight fusion algorithm. Then the collision detection algorithm is used to detect the collision between robots to ensure safety. Finally, the data are transmitted to the dual robots.FindingsThis interface is implemented on a dual-robot system consisting of two 6-DOF robots. The dual-robot cooperative experiment indicates that the proposed interface is feasible and effective, and it takes less time to operate and has higher interaction efficiency.Originality/valueA novel gesture-based dual-robot collaborative interface is proposed. It overcomes the problem of gesture occlusion in two-hand interaction with low computational complexity and low equipment cost. The proposed interface can perform a long-term stable tracking of the two-hand gestures even if there is occlusion between the hands. Meanwhile, it reduces the number of hand reset to reduce the operation time. The proposed interface achieves a natural and safe interaction between the human and the dual robot.

Forecasting influenza activity using self-adaptive AI model and multi-source data in Chongqing, China.

BackgroundEarly detection of influenza activity followed by timely response is a critical component of preparedness for seasonal influenza epidemic and influenza pandemic. However, most relevant studies were conducted at the regional or national level with regular seasonal influenza trends. There are few feasible strategies to forecast influenza activity at the local level with irregular trends.MethodsMulti-source electronic data, including historical percentage of influenza-like illness (ILI%), weather data, Baidu search index and Sina Weibo data of Chongqing, China, were collected and integrated into an innovative Self-adaptive AI Model (SAAIM), which was constructed by integrating Seasonal Autoregressive Integrated Moving Average model and XGBoost model using a self-adaptive weight adjustment mechanism. SAAIM was applied to ILI% forecast in Chongqing from 2017 to 2018, of which the performance was compared with three previously available models on forecasting.FindingsILI% showed an irregular seasonal trend from 2012 to 2018 in Chongqing. Compared with three reference models, SAAIM achieved the best performance on forecasting ILI% of Chongqing with the mean absolute percentage error (MAPE) of 11·9%, 7·5%, and 11·9% during the periods of the year 2014–2016, 2017, and 2018 respectively. Among the three categories of source data, historical influenza activity contributed the most to the forecast accuracy by decreasing the MAPE by 19·6%, 43·1%, and 11·1%, followed by weather information (MAPE reduced by 3·3%, 17·1%, and 2·2%), and Internet-related public sentiment data (MAPE reduced by 1·1%, 0·9%, and 1·3%).InterpretationAccurate influenza forecast in areas with irregular seasonal influenza trends can be made by SAAIM with multi-source electronic data.

Self-adaptive Power Flow Optimization Method for Multi-terminal SNOP Considering the Difference of Risk of Node Voltage beyond Limit

Soft Normally Open Point (SNOP) is a power electronic device used widely in power distribution network. It can solve many problems by adjusting reactive power compensation and active power transmission. The uncertainty and fluctuation of distributed generation (DG) may cause many problems to the distribution network. In order to balance the safety and economy in power flow optimization better, a self-adaptive power flow optimization method for active distribution network with multi-terminal SNOP considering difference of the risk of node voltage beyond limit is put forward. It takes both weighted voltage deviation and the network loss into objective function to construct the optimal operation model and mainly has two improvements: one is considering initial voltage deviation and real-time penetration of the DG to establish the self-adaptive weight model of the sum of voltage deviation in the objective function; the other is putting forward the weighting strategy for node voltage deviation which decided by the electrical distance, DG position and correlation between source output and load. Then, taking three improved IEEE33 node feeders as an example to verify the validity of the strategy. The results validate that the self-adaptive strategy can reduce the loss and improve the regulation of voltage better and is useful in distribution network under different penetration.

A novel active learning framework for classification: Using weighted rank aggregation to achieve multiple query criteria

Multiple query criteria active learning methods have a higher potential performance than conventional active learning methods in which only one criterion is deployed for sample selection. A central issue related to multiple query criteria active learning methods concerns the development of an integration criteria strategy that makes full use of all criteria. The conventional integration criteria strategies adopted in relevant research facilitate the desired effects, but several limitations still must be addressed. For instance, some of the strategies are not sufficiently scalable during the design process, and the number and type of criteria involved are dictated. Thus, it is challenging for the user to integrate other criteria into the original process unless modifications are made to the algorithm. Other strategies are too dependent on empirical parameters, which can be acquired only by experience or cross-validation and thus lack generality; additionally, these strategies are counter to the intention of active learning, as samples need to be labeled in the validation set before the active learning process can begin.To address these limitations, we propose a novel multiple query criteria active learning method for classification tasks that employs a third strategy via weighted rank aggregation. The proposed method serves as a heuristic means to select high-value samples of high scalability and generality and is implemented through a three-step process: (1) the transformation of the sample selection to sample ranking and scoring, (2) the computation of the self-adaptive weights of each criterion, and (3) the weighted aggregation of each sample rank list. Ultimately, the sample at the top of the aggregated ranking list is the most comprehensively valuable and must be labeled. Several experiments generating 419 wins, 226 ties and 55 losses against other state-of-the-art multiple query criteria-based methods are conducted to verify that the proposed method can achieve superior results.

Integrating spectral and textural attributes to measure magnitude in object-based change vector analysis

ABSTRACTWith the increasing accessibility of high or very high spatial resolution remote sensing images, more and more change detection works are conducted on the unit of image object, i.e. the object-based change detection (OBCD). Change vector analysis (CVA) is a promising tool for unsupervised OBCD because it can provide reasonable interpretation of the change and an insight into the type of change. However, various features and attributes of image object produce complex high dimensional feature space that poses new challenges to measure the overall change magnitude and direction in CVA. This paper presents a new approach to measure the overall change magnitude of an image object by integrating its spectral and textural attributes, self-adaptively. The new approach was compared with the standard CVA magnitude, Mahalanobis distance, and a recent Self-Adaptive Weight CVA (SAW-CVA) magnitude for unsupervised OBCD with the same OTSU auto-thresholding method. Two cases were investigated where Case I used bi-temporal WorldView multispectral images (4-band) and Case II employed bi-temporal three-band images obtained from Google Earth. Results in the two cases both demonstrated that the new approach outperforms the others for unsupervised OBCD. The percentage correct classification of the new approach, SAW-CVA, standard CVA, and Mahalanobis are 80.28%, 77.93%, 72.77%, and 61.50% in Case I, and 91.80%, 90.16%, 88.52%, and 90.16% in Case II. Further analysis indicated that the new approach gives more reasonable self-adaptive weights and it does not require the empirical self-adaptive index as compared with the recent SAW-CVA method.

Improved Firefly Algorithm for the Optimal Coordination of Directional Overcurrent Relays

In an electrical power network linear and non-linear models are used for directional overcurrent relay (DOCR) coordination issue by applying different heuristic techniques. Nature inspired algorithms (NIA) have found great interest in power system optimization issues. This paper proposes the recently developed meta-heuristic technique known as Firefly Algorithm (FA) that mimics the flashing behavior of fireflies. The implementation of the proposed algorithm has been utilized to solve the coordination of DOCR problems. The main aim of this paper is to find out the optimum values of the Time Dial Setting (TDS) to minimize the relay operating time. The modifications to original FA has been implemented in this paper to solve the DOCR coordination issues. Self-adaptive weight and experience-based learning strategy are added in the original FA, named as improved firefly algorithm (IFA). In IFA, a self-adaptive weight is presented to change the propensity of moving the best solution and ignoring the worst solution. In addition, an experience-based learning system is created and utilized arbitrarily to keep up the populace-assorted variety and improve the exploration capacity. The IFA has been tested on IEEE 6 and 30-bus systems and tested on IEEE 9-bus system for numerical DOCRs and the results had been compared with results of Whale optimization algorithm to validate the performance of IFA in case of numerical DOCR. The obtained results show that the IFA provides efficient and promising results compared to other meta-heuristic techniques mentioned in the literature. The IFA has been successfully implemented on MATLAB software programming.

Edge-Preserving Stereo Matching Using Minimum Spanning Tree

Despite that the accuracy and efficiency of stereo matching technology have significantly improved in the past decades, the issue of edge-blurring remains a challenge to most of the existing approaches. In this paper, we propose a minimum spanning tree (MST) based stereo matching method by using the image edge and segmentation optimization to preserve the image boundary. We first exploit a fast disparity range estimation method by combining the Surf and Akaze feature points to improve the computational efficiency. Second, we utilize the image edges and brightness information to generate a self-adaptive weight function, which is able to significantly improve the accuracy of MST aggregating in the regions of complex texture and boundaries with similar color distribution. Third, we employ the image segmentation to extract the invalid regions of the estimated disparity map, and propose a post-processing scheme to refine the disparity result. Finally, we run our method on several Middlebury and KITTI datasets. The comparison results between our method and other state-of-the-art approaches demonstrate that the proposed method has high accuracy for disparity computation and is especially robust to the edge-blurring.

Applications of IBA for photovoltaic array under partially shaded condition

In photovoltaic systems, the output power curve of solar battery has multiple peaks, under the partially shaded condition. Traditional maximum power point tracking (MPPT) search method often traps in local extremum, which causes the loss of the global maximum power point even generates oscillation and leads to instability of output. An improved bat algorithm (IBA) is proposed and used to find global optimal point, by introducing chaos search strategy in initial arrangement which can improve the uniformity and ergodicity. The self-adapting weight is introduced to enhance the global searching ability of previous processing and the local searching ability of late processing, and Levy flight is introduced in the same time to create the saltation velocity to jump out the local extremum. Dynamic contraction is also used to decrease the search section more effectively, so as to avoid premature convergence of the population affected by the local extremum. The simulation shows that modified bat algorithm can find the global optimal point fast, with high precision, under the partially shaded condition.

Contrast Limited Adaptive Histogram Equalization-Based Fusion in YIQ and HSI Color Spaces for Underwater Image Enhancement

To improve contrast and restore color for underwater images without suffering from insufficient details and color cast, this paper proposes a fusion algorithm for different color spaces based on contrast limited adaptive histogram equalization (CLAHE). The original color image is first converted from RGB space to two different spaces: YIQ and HSI. Then, the algorithm separately applies CLAHE in YIQ and HSI color spaces to obtain two different enhanced images. After that, the YIQ and HSI enhanced images are respectively converted back to RGB space. When the three components of red, green, and blue are not coherent in the YIQ-RGB or HSI-RGB images, the three components will have to be harmonized with the CLAHE algorithm in RGB space. Finally, using a 4-direction Sobel edge detector in the bounded general logarithm ratio operation, a self-adaptive weight selection nonlinear image enhancement is carried out to fuse the YIQ-RGB and HSI-RGB images together to achieve the final image. The experimental results showed that the proposed algorithm provided more detail enhancement and higher values of color restoration than other image enhancement algorithms. The proposed algorithm can effectively reduce noise interference and observably improve the image quality for underwater images.

Application of Hybrid Particle Swarm Optimization Algorithm in the Optimal Water Operation of Cascade Reservoirs in Dry Season

In this paper, a hybrid particle swarm optimization (HPSO) algorithm is proposed to solve the problem of optimal water operation of cascade reservoirs in dry season. Based on the basic particle swarm optimization (PSO) algorithm, chaos algorithm is introduced to traverse the search space to generate the initial population and improve the global searching ability of the algorithm. A self-adaptive inertial weighting method based on optimized inertial weighting coefficient is adopted to improve the ability of particle individual search and avoid local optimum. The proposed algorithm is applied to the optimal water operation in dry season of cascade reservoirs on the mainstream of Xijiang River. The results show that the HPSO algorithm can effectively improve the guarantee degree of ecological flow and suppressing salinity flow in the control reach of Wuzhou station under different typical dry year scenarios.

Path Planning for Robot Using the Particle Swarm Optimization with Self-adaptive Inertia Weight

Path Planning for Robot Using the Particle Swarm Optimization with Self-adaptive Inertia Weight

Enterprise benefit game model of collaborative supply chain in logistics industry park

Multi-objective harmony optimization algorithm dominated by α in negotiation supply chain of self-adaptive weighted constraint penalty is proposed for high dimension of objective optimization and low algorithm accuracy and computational efficiency in the collaborative supply chain between enterprises in Logistics Industry Park. Firstly, the collaborative scheduling model of supply chain is researched, the collaborative scheduling multi-objective optimization model is constructed, the single collaborative scheduling optimization model is established with the method of self-adaptive weight, and the collaborative scheduling multi-objective compensation model is set up by means of integrated constraint; secondly, the multi-objective evolutionary algorithm is improved, elite gradient selection strategy dominated by α is constructed for the problem of missing guidance in Pareto solution, and multi-objective harmony search optimization algorithm dominated by α is established by combining with harmony search algorithm; finally, scheduling effect of distributors is improved and good results of α dominating set is obtained under the premise that the manufacturers scheduling is ensured.

A robust complex-domain state estimator using synchrophasor measurements

Phasor measurement units (PMUs) have been widely deployed in modern power grids, which provides a great opportunity for high-precision and real-time power system state estimation. If power networks are completely observable by PMUs, linear state estimation can be achieved with massive synchronous data. In this paper, the analysis of phasor error is presented and a complex number weighted least squares (CWLS) algorithm for state estimation is proposed, which shows high computational efficiency and stronger robustness to phasor errors. Meanwhile, self-adaptive weight and bad data identification techniques are developed to enhance the CWLS’s performance. Test results on various IEEE test systems and the realistic power grid validate the effectiveness and superiority of the proposed CWLS algorithm, outperforming the conventional weighted least squares (WLS) state estimation method as well as other existing industrial-grade approaches.

Parameters identification of photovoltaic models using an improved JAYA optimization algorithm

Parameters identification of photovoltaic (PV) models based on measured current-voltage characteristic curves is significant for the simulation, evaluation, and control of PV systems. To accurately and reliably identify the parameters of different PV models, an improved JAYA (IJAYA) optimization algorithm is proposed in the paper. In IJAYA, a self-adaptive weight is introduced to adjust the tendency of approaching the best solution and avoiding the worst solution at different search stages, which enables the algorithm to approach the promising area at the early stage and implement the local search at the later stage. Furthermore, an experience-based learning strategy is developed and employed randomly to maintain the population diversity and enhance the exploration ability. A chaotic elite learning method is proposed to refine the quality of the best solution in each generation. The proposed IJAYA is used to solve the parameters identification problems of different PV models, i.e., single diode, double diode, and PV module. Comprehensive experiment results and analyses indicate that IJAYA can obtain a highly competitive performance compared with other state-of-the-state algorithms, especially in terms of accuracy and reliability.

Coupled Monte Carlo simulation and Copula theory for uncertainty analysis of multiphase flow simulation models.

Simulation-optimization techniques are effective in identifying an optimal remediation strategy. Simulation models with uncertainty, primarily in the form of parameter uncertainty with different degrees of correlation, influence the reliability of the optimal remediation strategy. In this study, a coupled Monte Carlo simulation and Copula theory is proposed for uncertainty analysis of a simulation model when parameters are correlated. Using the self-adaptive weight particle swarm optimization Kriging method, a surrogate model was constructed to replace the simulation model and reduce the computational burden and time consumption resulting from repeated and multiple Monte Carlo simulations. The Akaike information criterion (AIC) and the Bayesian information criterion (BIC) were employed to identify whether the t Copula function or the Gaussian Copula is the optimal Copula function to match the relevant structure of the parameters. The results show that both the AIC and BIC values of the t Copula function are less than those of the Gaussian Copula function. This indicates that the t Copula function is the optimal function for matching the relevant structure of the parameters. The outputs of the simulation model when parameter correlation was considered and when it was ignored were compared. The results show that the amplitude of the fluctuation interval when parameter correlation was considered is less than the corresponding amplitude when parameter estimation was ignored. Moreover, it was demonstrated that considering the correlation among parameters is essential for uncertainty analysis of a simulation model, and the results of uncertainty analysis should be incorporated into the remediation strategy optimization process.

Multi-scale retinex with color restoration image enhancement based on Gaussian filtering and guided filtering

In order to restore image color and enhance contrast of remote sensing image without suffering from color cast and insufficient detail enhancement, a novel improved multi-scale retinex with color restoration (MSRCR) image enhancement algorithm based on Gaussian filtering and guided filtering was proposed in this paper. Firstly, multi-scale Gaussian filtering functions were used to deal with the original image to obtain the rough illumination components. Secondly, accurate illumination components were acquired by using the guided filtering functions. Then, combining with four-direction Sobel edge detector, a self-adaptive weight selection nonlinear image enhancement was carried out. Finally, a series of evaluate metrics such as mean, MSE, PSNR, contrast and information entropy were used to assess the enhancement algorithm. The results showed that the proposed algorithm can suppress effectively noise interference, enhance the image quality and restore image color effectively.

Robust sparse representation based face recognition in an adaptive weighted spatial pyramid structure

The sparse representation based classification methods has achieved significant performance in recent years. To fully exploit both the holistic and locality information of face samples, a series of sparse representation based methods in spatial pyramid structure have been proposed. However, there are still some limitations for these sparse representation methods in spatial pyramid structure. Firstly, all the spatial patches in these methods are directly aggregated with same weights, ignoring the differences of patches reliability. Secondly, all these methods are not quite robust to poses, expression and misalignment variations, especially in under-sampled cases. In this paper, a novel method named robust sparse representation based classification in an adaptive weighted spatial pyramid structureRSRC-ASP) is proposed. RSRC-ASP builds a spatial pyramid structure for sparse representation based classification with a self-adaptive weighting strategy for residuals aggregation. In addition, three strategies, local-neighbourhood representation local intra-class Bayesian residual criterion and local auxiliary dictionary are exploited to enhance the robustness of RSRC-ASP. Experiments on various data sets show that RSRC-ASP outperforms the classical sparse representation based classification methods especially for under-sampled face recognition problems.

A parallel multisplitting method with self-adaptive weightings for solving H-matrix linear systems

In this paper, a parallel multisplitting iterative method with the self-adaptive weighting matrices is presented for the linear system of equations when the coefficient matrix is an H-matrix. The zero pattern in weighting matrices is determined in advance, while the non-zero entries of weighting matrices are determined by finding the optimal solution in a hyperplane of α points generated by the parallel multisplitting iterations. Especially, the nonnegative restriction of weighting matrices is released. The convergence theory is established for the parallel multisplitting method with self-adaptive weightings. Finally, a numerical example shows that the parallel multisplitting iterative method with the self-adaptive weighting matrices is effective.

A Hierarchical Maritime Target Detection Method for Optical Remote Sensing Imagery

Maritime target detection from optical remote sensing images plays an important role in related military and civil applications and its weakness lies in its compromised performance under complex uncertain conditions. In this paper, a novel hierarchical ship detection method is proposed to overcome this issue. In the ship detection stage, based on Entropy information, we construct a combined saliency model with self-adaptive weights to prescreen ship candidates from across the entire maritime domain. To characterize ship targets and further reduce the false alarms, we introduce a novel and practical descriptor based on gradient features, and this descriptor is robust against clutter introduced by heavy clouds, islands, ship wakes as well as variation in target size. Furthermore, the proposed method is effective for not only color images but also gray images. The experimental results obtained using real optical remote sensing images have demonstrated that the locations and the number of ships can be determined accurately and that the false alarm rate is greatly decreased. A comprehensive comparison is performed between the proposed method and the state-of-the-art methods, which shows that the proposed method achieves higher accuracy and outperforms all the competing methods. Furthermore, the proposed method is robust under various backgrounds of maritime images and has great potential for providing more accurate target detection in engineering applications.