Dynamic Weight Factor Research Articles

Target Tracking with Underwater Sensor Networks Based on Grubbs Criterion and Improved Particle Filter Algorithm

When the Underwater Wireless Sensor Network (UWSN) performs target tracking, the contributions of the measured values of the nodes are different, and the battery energy carried by the sensor node is limited. Therefore, a good node fusion weight method and node planning mechanism can obtain better tracking performance. A distributed particle filter target tracking algorithm based on Grubbs criterion and Mutual Information Entropy Weighted (GMIEW) fusion is proposed to solve the above problem in this paper. Firstly, the Grubbs criterion is used to analyze and verify the information obtained by the sensor nodes before the information fusion, and the interference information and error information are removed. Secondly, in the process of calculating the importance weight of particle filter, the dynamic weighting factor is introduced. The mutual information entropy between the measured value of the sensor node and the target state is used to reflect the amount of target information provided by the sensor node, so as to obtain the corresponding weighting factor of each node. Finally, the improved cluster-tree network topology is used to track the target in three-dimensional space. Simulation results show that the proposed algorithm improves greatly the accuracy of underwater sensor measurement data for target tracking prediction and reduces the tracking error.

A Self-Adaptive FCM for the Optimal Fuzzy Weighting Exponent

Fuzzy weighting exponent [Formula: see text] is an important parameter of fuzzy [Formula: see text]-means (FCM), closely related to the performance of the algorithm. First, an improved fuzzy correlation degree was put forward to measure the relevance between the clusters, based on which a new cluster validity function was defined to evaluate the quality of the fuzzy partition. Then a self-adaptive FCM for the optimal value of [Formula: see text] was proposed with the aid of the global search ability of improved particle swarm algorithm to find out both the final clustering centroids and the optimal value of fuzzy weighting exponent automatically. The improved particle swarm algorithm updated the speed and the position based on dynamic inertia weight and learning factors, and introduced mutation of genetic algorithm to keep the diversity of the particles, preventing premature convergence. The experimental results showed that the proposed algorithm automatically calculated the optimal value of [Formula: see text] and meanwhile achieved better clustering results.

Dynamic assessment elements and their prospective solutions in dynamic life cycle assessment of buildings

Life cycle assessment (LCA) is a mature and widely used tool for quantifying environmental performance in the building sector. Temporal variations in building characteristics over their long life cycles significantly influence LCA results. Therefore, in recent years, dynamic LCA (DLCA) has become an emerging research field. However, DLCA research is still in its infancy, as demonstrated by the lack of systematic analysis regarding the dynamic aspects that should be considered. By examining the data transformation pathway in accordance with the standard LCA framework, this study formalizes four identified dynamic assessment elements (DAEs), namely dynamic consumption, dynamic basic inventory datasets, dynamic characterization factors, and dynamic weighting factors. A DLCA framework for buildings is proposed to enable the incorporation of DAEs into the static LCA framework. Furthermore, to better support the DLCA applications, viable prospective solutions and the availability of related data regarding the four DAEs are discussed. A case study shows that the assessment results are sensitive to the DAEs at varying degrees, and the DLCA model potentially offers a more realistic evaluation result. This study is expected to serve as a reference and provide guidelines for developing further in-depth DLCA models and methods that consider complex temporal variations.

Centralised model‐predictive decoupled active–reactive power control for three‐level neutral point clamped photovoltaic inverter with preference selective index‐based objective prioritisation

This study presents a single-stage grid-tied three-level neutral point clamped photovoltaic inverter with a centralised model-predictive decoupled active–reactive power control. The proposed centralised model predictive control (CMPC) incorporates the constraints of maximum power extraction, dc-link capacitor voltage balancing and active–reactive power tracking in a single objective function. The dc-link voltage of the inverter is regulated to its reference for extracting the maximum power. In order to eliminate the impact of reactive power exchange on floating dc-link voltage regulation, a decoupled active–reactive power control is used in the CMPC. Furthermore, a preference selective index-based dynamic weighting factor selection approach is introduced to maintain the relative importance between the power tracking and dc-link capacitor voltage balancing. The proposed control approach eliminates the outer dc-link voltage control loop and also, the empirical approach required for the selection of weighting factors. As a result, it ensures an optimal control action in each sampling period to improve the steady-state and dynamic tracking performance of the control objectives. The proposed control approach is experimentally verified by using a 1.2 kW laboratory-scale prototype and the results are presented to demonstrate its effectiveness compared to the classical proportional–integral-based model predictive control.

Improved salp swarm algorithm based on weight factor and adaptive mutation

ABSTRACTSalp Swarm Algorithm (SSA) is a novel swarm intelligent algorithm with good performance. However, like other swarm-based algorithms, it has insufficiencies of low convergence precision and slow convergence speed when dealing with high-dimensional complex optimisation problems. In response to this concerning issue, in this paper, we propose an improved SSA named as WASSA. First of all, dynamic weight factor is added to the update formula of population position, aiming to balance global exploration and local exploitation. In addition, in order to avoid premature convergence and evolution stagnation, an adaptive mutation strategy is introduced during the evolution process. Disturbance to the global extremum promotes the population to jump out of local extremum and continue to search for an optimal solution. The experiments conducted on a set of 28 benchmark functions show that the improved algorithm presented in this paper displays obvious superiority in convergence performance, robustness as well as the ability to escape local optimum when compared with SSA.

Dynamically Weighted Optimal Switching Vector Model Predictive Control of Power Converters

In this paper, new techniques are presented to improve the performance of the optimal switching vector model predictive control. A well-defined analytic technique is proposed for the real-time adjustment of dynamic weighting factors, as well as a new objective function formulation. These techniques provide a dynamic controller behavior, automatically adapted to distinct operating points. This ensures better steady-state performance in different conditions while improving simultaneously the dynamic response. The proposed controller presents a lower computational load than the conventional formulation and its design requires less extensive testing. General guidelines are presented to guide the user in the controller design. Simulation and experimental results are presented, demonstrating the advantages and superior performance of the proposed controller.

Sensor-Networked Underwater Target Tracking Based on Grubbs Criterion and Improved Particle Filter Algorithm

For target tracking in underwater wireless sensor networks (WSNs), the contributions of the measured values of each sensor node are different for data fusion, so a better weighted nodes fusion and participation planning mechanism can obtain better tracking performance. A distributed particle filter based target tracking algorithm with Grubbs criterion and mutual information entropy weighted fusion (GMIEW) is proposed in this paper. The Grubbs criterion is adopted to analyze and verify the information obtained by sensor nodes before the information fusion, and accordingly some interference information or error information can be excluded from the data set. In the process of calculating importance weight in particle filter, dynamic weighting factor is introduced. The mutual information entropy between the measured value of the sensor nodes and the target state is used to reflect the amount of target information provided by sensor nodes, thus a dynamic weighting factor corresponding to each node can be obtained. The simulation results show that the proposed algorithm effectively improves the accuracy of prediction of target tracking system.

Improved Cuckoo Search algorithm for numerical function optimization

<p style='text-indent:20px;'>Cuckoo Search (CS) is a recently proposed metaheuristic algorithm to solve optimization problems. For improving its performance both on the efficiency of searching and the speed of convergence, we proposed an improved Cuckoo Search algorithm based on the teaching-learning strategy (TLCS). For a better balance between intensification and diversification, both a dynamic weight factor and an out-of-bound project strategies are also introduced into TLCS. The results of numerical experiment demonstrate that our improved TLCS performs better than the basic CS and other two improved CS methods appearing in literatures.

A New Synergistic Forecasting Method for Short-Term Traffic Flow with Event-Triggered Strong Fluctuation

Directing against the shortcoming of low accuracy in short-term traffic flow prediction caused by strong traffic flow fluctuation, a novel method for short-term traffic forecasting based on the combination of improved grey Verhulst prediction algorithm and first-order difference exponential smoothing is proposed. Firstly, we constructed an improved grey Verhulst prediction model by introducing the Markov chain to its traditional version. Then, based on an introduced dynamic weighting factor, the improved grey Verhulst prediction method, and the first-order difference exponential smoothing technique, the new method for short-term traffic forecasting is completed in an efficient way. Finally, experiment and analysis are carried out in the light of actual data gathered from strong fluctuation environment to verify the effectiveness and rationality of our proposed scheme.

Dynamic LCA framework for environmental impact assessment of buildings

Traditional life cycle assessment (LCA) methods are used to conduct building environmental impact assessment (EIA) with little consideration of influential factors that vary in time and of variation in occupancy behaviors. Because the life cycle of a building is quite long, such details have significant influence on the accuracy of evaluation results. To fill in this gap and extend the LCA system, this paper developed a dynamic assessment framework based on LCA principles after reviewing the research progress of DLCA (dynamic LCA). The new framework identified four dynamic building properties (i.e., technological progress, variation in occupancy behavior, dynamic characteristic factors, and dynamic weighting factors) and considered them in corresponding assessment steps to realize real-time EIA. In addition, residential occupancy profiles were described at personal level, family level, and social level; and three potential quantification methods were introduced to explore the relationship between occupancy profiles and household energy consumption. The DLCA framework expands the connotation of the LCA system from a dynamic perspective, making it possible to present time-varying EIs of buildings over their long life cycle and guide occupancy behavior in time. This framework has the potential to be base for developing a useful tool for conducting forecast evaluation and promoting sustainability.

Multi-objective reliability-based design optimization approach of complex structure with multi-failure modes

To make multi-objective reliability-based design optimization (MORBDO) more effective for complex structure with multi-failure modes and multi-physics coupling, multiple response surface method (MRSM)-based artificial neural network (ANN) (ANN-MRSM) and dynamic multi-objective particle swarm optimization (DMOPSO) algorithm are proposed based on MRSM and MOPSO algorithm. The mathematical model of ANN-MRSM is established by using artificial neural network to fit the multiple response surface function. The DMOPSO algorithm is proposed by designing dynamic inertia weight and dynamic learning factors. The proposed approach is verified by the MORBDO of turbine blisk deformation and stress with respect to fluid–thermal-structure interaction from probabilistic analysis. The optimization design results show that the proposed approach has the promising potentials to improve computational efficiency with acceptable computational precision for the MORBDO of turbine blisk deformation and stress. Moreover, Pareto front curve and a set of viable design values of turbine blisk are obtained for the high-reliability high-performance design of turbine blisk. The presented efforts provide an effective approach for MORBDO of complex structures, and enrich mechanical reliability design theory as well.

On Optimal Control Model for the Treatment of Dual HIV-Parasitoid Pathogen Infection

Following the insurmountable and seeming incurable status for the most acclaimed infectious disease – HIV, and which have been worsened by its allies of infectious diseases, this paper projected using ordinary differential equations, a 4-Dimensional mathematical model that accounted for the percentage optimal benefits and the methodological application of chemotherapy - RTI, in the interaction of dual HIV- parasitoid pathogen infectivity with the human immune system. Simple analytical optimal control method was deployed, primed by the maximization of healthy immune system on the basis of control effect of chemotherapy on viruses’ infectivity. Using Pontryagin’s Maximum Principle, the study established the model dynamical optimal control as a composition of system state variables, coupled with four adjoint systems with corresponding initial and transversality conditions together with the optimal control function. The model was solved numerically and results indicated thus: benefits on cost function as highest when onset of infection were followed by high intensity chemotherapy schedule; while optimum control were achieved with prolong treatment administration. The study revealed that optimal control is a function of dynamic optimal weight factor and is independent of prolong treatment duration. The study therefore, advocates the incorporation of dual immunotherapies for the treatment of multiple virus infectivity.

A Partial Weighted Utility Measure for Fuzzy Association Rule Mining

Background/Objectives: Association rules are generated from frequent item set by Association mining. The generation of frequent item set makes a great impact on decision making. The objective of the work is to introduce a new measure called SUF (Skill Utility Factor) to extract meaningful hidden item set and develop a hybrid algorithm FPWUM (Fuzzy Partial Weighted Utility Mining) for decision making. Methods/Statistical Analysis: The traditional measures support and confidence is augmented with SUF which can be useful for Human resource personnel to easily predict the work-force calibre in an organization. Using different methods like association rule mining, fuzzy logic and weighted utility mining has improved the prediction of attributes relations efficient and faster. Findings: The FPWUM extracts more efficient hidden frequent item sets through which many new and interesting rules are generated. Since the application of attribute’s weight are handled wisely and improvising factor is used only for hidden item set the model process time is reduced fairly. The idea of integrating the conventional measures and the SUF is a unique technique. The approach works well on real time dataset compared to the conventional models. The comparative result shows the algorithm’s ability. Improvements/ Applications: The algorithm uses predefined weighting scheme. It can be enhanced by using dynamic intelligent weighting factor.

PID Control of Multisources Complex Excitations Active Vibration Isolation System: An Improved Particle Swarm Optimization Algorithm

Active vibration isolation technology is the key technique to solve the vibration isolation problems related to the multisources complex excitations vibration isolation system. The electromagnetic actuators-based multisources complex excitations active vibration isolation system is built. Additionally, in view of the complex structure and strong coupling of the system, the least-squares method to identify and obtain the mathematical model of the vibration isolation system is adopted. Furthermore, this paper also sets up the acceleration feedback-based PID control model for multisources complex excitations active vibration isolation system, proposes an improved particle swarm optimization (PSO) algorithm of dynamic inertia weight factors used to optimize parameters of the built PID control model, and conducts simulation analysis. The simulation results show that, compared with the passive system before the control, the multisources complex excitations active vibration isolation system under the PID control has the far less peak-to-peak amplitude of acceleration which is transmitted to the foundation and has the much better vibration isolation effect. Finally, the paper conducts experimental verification, which demonstrates that active vibration control effect is identical to the simulation results and the vibration control effect is significantly improved.

A novel LTE scheduling algorithm for green technology in smart grid.

Smart grid (SG) application is being used nowadays to meet the demand of increasing power consumption. SG application is considered as a perfect solution for combining renewable energy resources and electrical grid by means of creating a bidirectional communication channel between the two systems. In this paper, three SG applications applicable to renewable energy system, namely, distribution automation (DA), distributed energy system-storage (DER) and electrical vehicle (EV), are investigated in order to study their suitability in Long Term Evolution (LTE) network. To compensate the weakness in the existing scheduling algorithms, a novel bandwidth estimation and allocation technique and a new scheduling algorithm are proposed. The technique allocates available network resources based on application’s priority, whereas the algorithm makes scheduling decision based on dynamic weighting factors of multi-criteria to satisfy the demands (delay, past average throughput and instantaneous transmission rate) of quality of service. Finally, the simulation results demonstrate that the proposed mechanism achieves higher throughput, lower delay and lower packet loss rate for DA and DER as well as provide a degree of service for EV. In terms of fairness, the proposed algorithm shows 3%, 7 % and 9% better performance compared to exponential rule (EXP-Rule), modified-largest weighted delay first (M-LWDF) and exponential/PF (EXP/PF), respectively.

Recovering a Function from Circular Means or Wave Data on the Boundary of Parabolic Domains

Determining a function from its circular means with restricted centers is crucial for many modern medical imaging and remote sensing applications. Examples include photo- and thermoacoustic tomography, ultrasound imaging, and synthetic aperture radar. In this paper we derive an explicit inversion formula for the case where the centers of the circles of integration are restricted to the boundary of parabolic domains. A similar result is obtained for recovering the initial data of the wave equation from data on the boundary of a parabolic domain. These results show that the universal back-projection formulas, previously known to be exact for half-spaces, discs, and ellipsoids, are also exact for parabolic domains. We further present numerical simulations supporting our theoretical results. There, we also include two previously proposed strategies (dynamic aperture length correction and weight factor correction) accounting for the truncation of the parabola in practical applications.

Measuring Bus Stops toward a Sustainable Urban Environment

This paper describes how a bus stop measuring system can be developed not only to achieve transit operation efficiency but also to collaborate proactively with urban planners and traffic engineers on transit–land use interface activities. The measuring system was developed by identifying transit performance variants at the bus stop level with the use of a dynamic composite weighting factor approach on a geographic information system platform. A tier structure framework was used to categorize the composite scores of bus stops into six groups. Specific transit improvement actions were then tailored on the basis of their bus stop characteristics. Two project application examples are presented in this paper. One is a bus stop thinning project for speed improvement, and the other is for prioritizing capital project improvements. As various active transportation programs emerge to encourage local jurisdictions to plan transit connectivity with other community gathering places, this bus stop measuring approach presents a con tinued process to monitor their performance. It is also used to strengthen integration with other street improvement activities, including pedestrian paths and bikeways, to create a safe and sustainable urban environment.

Coordination Scheme and Behavior Fusion Based on Fuzzy Weighting Factors for a Mobile Robotic Navigation

This paper presents a behavior fuzzy coordination scheme for a mobile robot platform. The control architecture is composed of three basic behaviors: obstacle avoidance, left wall following and left wall following. The mechanism of fusion behavior was performed on a weighting system that acquires information from the environment and calculates fuzzy dynamic weighting factors adjusted to the region in which the robot travels. These factors influence the output of each behavior giving strength to the most appropriate action according to the current situation of the environment. Subsequently, the weighted outputs of each behavior are normalized by the command fusion, achieving a cooperative solution to meeting the robot navigation target.

Forecasting Optimal Solar Energy Supply in Jiangsu Province (China): A Systematic Approach Using Hybrid of Weather and Energy Forecast Models

The idea of aggregating information is clearly recognizable in the daily lives of all entities whether as individuals or as a group, since time immemorial corporate organizations, governments, and individuals as economic agents aggregate information to formulate decisions. Energy planning represents an investment-decision problem where information needs to be aggregated from credible sources to predict both demand and supply of energy. To do this there are varying methods ranging from the use of portfolio theory to managing risk and maximizing portfolio performance under a variety of unpredictable economic outcomes. The future demand for energy and need to use solar energy in order to avoid future energy crisis in Jiangsu province in China require energy planners in the province to abandon their reliance on traditional, “least-cost,” and stand-alone technology cost estimates and instead evaluate conventional and renewable energy supply on the basis of a hybrid of optimization models in order to ensure effective and reliable supply. Our task in this research is to propose measures towards addressing optimal solar energy forecasting by employing a systematic optimization approach based on a hybrid of weather and energy forecast models. After giving an overview of the sustainable energy issues in China, we have reviewed and classified the various models that existing studies have used to predict the influences of the weather influences and the output of solar energy production units. Further, we evaluate the performance of an exemplary ensemble model which combines the forecast output of two popular statistical prediction methods using a dynamic weighting factor.

Artificial fish swarm parallel algorithm based on multi-core cluster

Concerning the problems of low accuracy,limitations of stagnation and slow convergence speed in the later evolution process of Artificial Fish Swarm Algorithm(AFSA),a Parallel Dynamic weigh Niches Artificial Fish Swarm(PDNAFS) algorithm based on multi-core cluster was proposed. Firstly,the advantages and disadvantages of AFSA were analyzed,and dynamic weighting factor strategy and niche mechanism were adopted,hence a new Dynamic weigh Niches Artificial Fish Swarm(DN-AFS) algorithm was put forward. Then parallel design and analysis of DN-AFS algorithm based on parallel programming model(MPI + OpenMP) were introduced. Finally,the simulation experiments on multi-core cluster environment were given. The experimental results show that PDN-AFS can effectively improve the convergence speed and optimization performance of the complex multimodal function optimization problem,and achieve high speed ratio.