Incremental Algorithm Research Articles

A weather optimal heading-based incremental feedback control for dynamic positioning of ships

In this brief, the weather optimal heading-based incremental feedback control algorithm is built for dynamic positioning (DP) of ships in the presence of model uncertainties and unknown environmental disturbances induced by wind, waves and currents. The proposed control algorithm has two parts, i.e., the weather optimal heading portion and the control portion. An improved ZPC-W approach with the deviation of the lateral control force is designed in order to enhance the convergence rate of the desired weather optimal heading and avoid overshoot as well. In addition, based on the iterative sliding mode function, a concise incremental feedback control scheme is designed with the pitch ratio of the thruster as the control input. It is measurable in the practical plant and has nothing to do with ship models with good stability and strong robustness. Lastly, simulation studies are performed to illustrate the performance and the superiorities of the proposed control algorithm.

A survey of high utility sequential patterns mining methods

In recent years, there has been an increasing demand for high utility sequential pattern (HUSP) mining. Different from high utility itemset mining, the “combinatorial explosion” problem of sequence data makes it more challenging. This survey aims to provide a general, comprehensive, and structured overview of the state-of-the-art methods of HUSP from a novel perspective. Firstly, from the perspective of serial and parallel, the data structure used by the mining methods are illustrated and the pros and cons of the algorithms are summarized. In order to protect data privacy, many HUSP hiding algorithms have been proposed, which are classified into array-based, chain-based and matrix-based algorithms according to the key technologies. The hidden strategies and evaluation metrics adopted by the algorithms are summarized. Next, a taxonomy of the most common and the state-of-the-art approaches for incremental mining algorithms is presented, including tree-based and projection-based. In order to deal with the latest sequence in the data stream, the existing algorithms often use the window model to update dynamically, and the algorithms are divided into methods based on sliding windows and landmark windows for analysis. Afterwards, a summary of derived high utility sequential pattern is presented. Finally, aiming at the deficiencies of the existing HUSP research, the next work that the author plans to do is given.

Evaluation of a comprehensive power management system with maximum power point tracking algorithm for multiple microbial fuel cell energy harvesting

This study presents a comprehensive power management system (PMS) capable of tracking the maximum power point (MPP) and harvesting the energy from up to five microbial fuel cells (MFCs). The harvested energy from the MFCs was used to power the electronics, and in cases where this power was insufficient, alternative backup power options can be used. The voltage can be increased up to 3.3 V, and a hysteresis-based control approach was utilised to regulate the output voltage. The MPP of each MFC was determined using a variable step size incremental conductance algorithm that controls the duty cycle of the synchronous boost converters. No additional electronic components are necessary for the operation of the N and P-channel MOSFETs. The efficiency of the PMS relies on the target output voltage and the power output characteristics of the MFCs. Efficiencies of up to 87 % were achieved by combining the outputs of each MFC boost converter. To save energy, some electronic components are disabled when not in use, and the maximum power consumption of the PCB is below 5.8 mW at an output voltage of 3.3 V. The PMS is applied to simulated and real tubular MFCs under various operating conditions.

MOSER: Scalable Network Motif Discovery Using Serial Test

Given a graph G , a motif (e.g., 3-node clique) is a fundamental building block for G. Recently, motif-based graph analysis has attracted much attention due to its efficacy in tasks such as clustering, ranking, and link prediction. These tasks require Network Motif Discovery (NMD) at the early stage to identify the motifs of G. However, existing NMD solutions have two drawbacks: (1) Lack of theoretical guarantees on the quality of the samples generated using the existing methods, and (2) inefficient algorithms, which are not scalable for large graphs. These limitations hinder the exploration of motifs for analyzing large graphs. To address the above issues, we propose a novel solution named MOSER ( MO tif Discovery using SER ial Test). This novel NMD framework leverages a significance testing method known as the serial test, which differs from the existing solutions. We further propose two fast incremental subgraph counting algorithms, allowing MOSER to scale to larger graphs than ever possible before. Extensive experimental results show that using MOSER can improve the state-of-the-art up to 5 orders of magnitude in efficiency and that the motifs found by MOSER facilitate downstream tasks such as link prediction.

Enhancing the Solar PV Plant Based on Incremental Optimization Algorithm

Enhancing the Solar PV Plant Based on Incremental Optimization Algorithm

Rough-Set-Based Rule Induction with the Elimination of Outdated Big Data: Case of Renewable Energy Equipment Promotion

As developing economies become more industrialized, the energy problem has become a major challenge in the twenty-first century. Countries around the world have been developing renewable energy to meet the Sustainable Development Goals (SDGs) of the United Nations (UN) and the 26th UN Climate Change Conference of the Parties (COP26). Leaders of enterprises have been made aware of the need to protect the environment and have been practicing environmental marketing strategies and green information systems (GISs) as part of ESG practices. With the rapid growth of the available data from renewable electricity suppliers, the analyses of multi-attribute characteristics across different fields of studies use data mining to obtain viable rule induction and achieve adaptive management. Rough set theory is an appropriate method for multi-attribute classification and rule induction. Nevertheless, past studies for Big Data analytics have tended to focus on incremental algorithms for dynamic databases. This study entails rough set theory from the perspective of the decrement decay alternative rule-extraction algorithm (DAREA) to explore rule induction and present case evidence with managerial implications for the emerging renewable energy industry. This study innovates rough set research to handle data deletion in a Big Data system and promotes renewable energy with valued managerial implications.

Feature incremental learning with causality

With the emerging of new data collection ways, the features are incremental and accumulated gradually. Due to the expansion of feature spaces, it is more common that there are unknown biases between the distribution of training and testing datasets. It is known as the unknown data selection bias, which belongs to the learning scenario with non-i.i.d samples. The performance of traditional approaches, which need the i.i.d. assumption, will be aggravated seriously. How to design an algorithm to address the problem of data selection bias in this feature incremental scenario is crucial but rarely studied. In this paper, we propose a feature incremental classification algorithm with causality. Firstly, we embed the confounding variable balance algorithm in causal learning into the prediction modeling and utilize the logical regression algorithm with balancing regular terms as a baseline. Then, to satisfy the special requirement of feature increment, we design a new regularizer, which maintains the consistency of the regression coefficients between the data in the current and previous stages. It retains the correlation between the old features and labels. Finally, we propose the Multiple Balancing Logistic Regression model (MBRLR) to jointly optimize the balancing regularizer and weighted logistic regression model with multiple feature sets. We also present theoretical results to show that our proposed algorithm can make precise and stable predictions. Besides, the numerical results also demonstrate that our MBRLR algorithm is superior to other methods.

Analysis and Design of Modified Incremental Conductance-Based MPPT Algorithm for Photovoltaic System

This study discusses the design of the Maximum Power Point Tracking (MPPT) technique for photovoltaic (PV) systems employing a modified incremental conductance (IncCond) algorithm to extract maximum power from a PV module. A PV module, a DC-DC converter, and a resistive load constitute the PV system. In the scientific literature, it is well-documented that typical MPPT algorithms have significant drawbacks, such as fluctuations around the MPP and poor tracking during a sudden change in atmospheric conditions. To solve the deficiencies of conventional methodology, a novel modified IncCond method is proposed in this study. The simulation results demonstrate that the updated IncCond algorithm presented allows for less oscillation around the maximum power point (MPP), a rapid dynamic response, and superior performance.

Statement of Retraction: Incremental Outlier Feature Clustering Algorithm in Blockchain Networks Based on Big Data Analysis

Statement of Retraction: Incremental Outlier Feature Clustering Algorithm in Blockchain Networks Based on Big Data Analysis

Transformer-BLS: An efficient learning algorithm based on multi-head attention mechanism and incremental learning algorithms

Due to its efficient model calibration given by unique incremental learning capability, broad learning system (BLS) has made impressive progress in image analytical tasks such as image classification and object detection. Inspired by this incremental remodel success, we proposed a novel transformer-BLS network to achieve a trade-off between model training speed and accuracy. Specially, we developed sub-BLS layers with the multi-head attention mechanism and combining these layers to construct a transformer-BLS network. In particular, our proposed transformer-BLS network provides four different incremental learning algorithms that enable the proposed model can realize the increments of its feature nodes, enhancement nodes, input data and sub-BLS layers, respectively, without the need of the full-weight update in this model. Furthermore, we validated the performance of our transformer-BLS network and its four incremental learning algorithms on a variety of image classification datasets. The results demonstrated that the proposed transformer-BLS maintains classification performance on both the MNIST and Fashion-MNIST datasets, while saving 2/3 of the training time. These findings imply that the proposed method has the potential in significant reducing model training complexity with this incremental remodel system, while simultaneously improving the increment learning performance of the original BLS within such contexts, especially in the classification task of some datasets.

Lattice modelling of early-age creep of 3D printed segments with the consideration of stress history

We propose a new numerical method to analyze the early-age creep of 3D printed segments with the consideration of stress history. The integral creep strain evaluation formula is first expressed in a summation form using superposition principle. The experimentally derived creep compliance surface is then employed to calculate the creep strain in the lattice model with a combination of stored stress history. These strains are then converted into element forces and applied to the analyzed object. The entire numerical analysis consists of a sequence of linear analyses, and the viscosity is modelled using imposed local forces. The model is based on the incremental algorithm and one of the main advantages is the straightforward implementation of stress history consideration. The creep test with incremental compressive loading is utilized to validate this model. The modelling results are in good agreement with experimental data, demonstrating the feasibility of the lattice model in early-age creep analysis under incremental compressive loading. To understand the impact of early-age creep on structural viscoelastic deformation during the printing process, additional analyses of a printed segment are carried out. These simulation results highlight the need to consider creep for accurate prediction of viscoelastic deformation during the printing process.

Incremental permutation feature importance (iPFI): towards online explanations on data streams

Explainable artificial intelligence has mainly focused on static learning scenarios so far. We are interested in dynamic scenarios where data is sampled progressively, and learning is done in an incremental rather than a batch mode. We seek efficient incremental algorithms for computing feature importance (FI). Permutation feature importance (PFI) is a well-established model-agnostic measure to obtain global FI based on feature marginalization of absent features. We propose an efficient, model-agnostic algorithm called iPFI to estimate this measure incrementally and under dynamic modeling conditions including concept drift. We prove theoretical guarantees on the approximation quality in terms of expectation and variance. To validate our theoretical findings and the efficacy of our approaches in incremental scenarios dealing with streaming data rather than traditional batch settings, we conduct multiple experimental studies on benchmark data with and without concept drift.

Incremental Online Machine Learning for Detecting Malicious Nodes in Vehicular Communications Using Real-Time Monitoring

Detecting malicious activities in Vehicular Ad hoc Networks (VANETs) is an important research field as it can prevent serious damage within the network and enhance security and privacy. In this regard, a number of approaches based on machine learning (ML) algorithms have been proposed. However, they encounter several challenges due to data being constantly generated over time; this can impact the performance of models trained on fixed datasets as well as cause the need for real-time data analysis to obtain timely responses to potential threats in the network. Therefore, it is crucial for machine learning models to learn and improve their predictions or decisions in real time as new data become available. In this paper, we propose a new approach for attack detection in VANETs based on incremental online machine learning. This approach uses data collected from the monitoring of the VANET nodes’ behavior in real time and trains an online model using incremental online learning algorithms. More specifically, this research addresses the detection of black hole attacks that pose a significant threat to the Ad hoc On Demand Distance Vector (AODV) routing protocol. The data used for attack detection are gathered from simulating realistic VANET scenarios using the well-known simulators Simulation of Urban Mobility (SUMO) and Network Simulator (NS-3). Further, key features which are relevant in capturing the behavior of VANET nodes under black hole attack are monitored over time. The performance of two online incremental classifiers, Adaptive Random Forest (ARF) and K-Nearest Neighbors (KNN), are assessed in terms of Accuracy, Recall, Precision, and F1-score metrics, as well as training and testing time. The results show that ARF can be successfully applied to classify and detect black hole nodes in VANETs. ARF outperformed KNN in all performance measures but required more time to train and test compared to KNN. Our findings indicate that incremental online learning, which enables continuous and real-time learning, can be a potential method for identifying attacks in VANETs.

Characterization of random walks on space of unordered trees using efficient metric simulation

The simple random walk on Zp shows two drastically different behaviors depending on the value of p: it is recurrent when p∈{1,2} while it escapes (with a rate increasing with p) as soon as p≥3. This classical example illustrates that the asymptotic properties of a random walk provides some information on the structure of its state space. This paper aims to explore analogous questions on space made up of combinatorial objects with no algebraic structure. We take as a model for this problem the space of unordered unlabeled rooted trees endowed with Zhang edit distance. To this end, it defines the canonical unbiased random walk on the space of trees and provides an efficient algorithm to evaluate its escape rate. Compared to Zhang algorithm, it is incremental and computes the edit distance along the random walk approximately 100 times faster on trees of size 500 on average. The escape rate of the random walk on trees is precisely estimated using intensive numerical simulations, out of reasonable reach without the incremental algorithm.

Comparative Study Of Five Mppt Controls In Two-Stage Three-Phase Grid-Connected Photovoltaic Systems Under Partial Shading Conditions

Abstract One of the maximum popular renewable electricity assets is photovoltaics. Grid-connected photovoltaic structures are designed to generate as a lot strength as possible. Photovoltaic systems have nonlinear traits imposed with the aid of environmental factors consisting of radiation and temperature, making it hard to operate at the most power factor. It may additionally be tough to extract the maximum amount of electricity due to the formation of nearby maxima because of other factors which include shading or degradation. There are several MPPT algorithms and approaches that may be used for this. Publications with comparative analyses have also been released. However, in most of these works, comparisons are based on simulations or literature review. From the simplest to the most complex MPPT methods, empirical validation remains important. The two simplest and most widely used MPPT techniques are the perturbation, open-loop, and incremental conductance algorithms. The three most challenging ones are sliding mode control, backstepping controller, and particle swarm optimization. Therefore, five MPPT algorithms are empirically studied in this paper. Under test settings, Matlab/Simulink was used to conduct experimental experiments. The findings demonstrate that under ordinary operating circumstances, the backstepping algorithm is the only one capable of finding the global MPP under the influence of local shadowing.

Dynamic Graph-Based Adaptive Learning for Online Industrial Soft Sensor With Mutable Spatial Coupling Relations

Accurate online soft sensor in complex industrial processes remains challenging because underlying spatial coupling relations among process variables have not been effectively mined and exploited. Recently, some deep learning-based studies construct static graphs to explicitly represent underlying spatial coupling relations among process variables, but they neglect the fact that spatial coupling relations have mutable characteristics, leading to the poor performance in online soft sensor. Therefore, we propose a novel deep learning model to address this issue to achieve accurate soft sensor. Specifically, a dynamic graph is proposed to realize adaptive learning and automatic inference for mutable spatial coupling relations, so that the proposed model is endowed with the ability to real-timely sense spatial coupling relations in online industrial soft sensor. Then, based on the dynamic graph, a new multi-hop attention graph convolutional network is proposed to systematically aggregate various crucial node feature representations during graph convolution processes to capture fine-grained spatial dependence features, thereby achieving effective modeling for variation patterns of process variables. Finally, a new multivariate incremental training algorithm is designed for deep learning models to further improve the prediction performance. Verification study on a coal mill rig demonstrates the feasibility and effectiveness of the proposed model.

Stream Convolution for Attribute Reduction of Concept Lattices

Attribute reduction is a crucial research area within concept lattices. However, the existing works are mostly limited to either increment or decrement algorithms, rather than considering both. Therefore, dealing with large-scale streaming attributes in both cases may be inefficient. Convolution calculation in deep learning involves a dynamic data processing method in the form of sliding windows. Inspired by this, we adopt slide-in and slide-out windows in convolution calculation to update attribute reduction. Specifically, we study the attribute changing mechanism in the sliding window mode of convolution and investigate five attribute variation cases. These cases consider the respective intersection of slide-in and slide-out attributes, i.e., equal to, disjoint with, partially joint with, containing, and contained by. Then, we propose an updated solution of the reduction set for simultaneous sliding in and out of attributes. Meanwhile, we propose the CLARA-DC algorithm, which aims to solve the problem of inefficient attribute reduction for large-scale streaming data. Finally, through the experimental comparison on four UCI datasets, CLARA-DC achieves higher efficiency and scalability in dealing with large-scale datasets. It can adapt to varying types and sizes of datasets, boosting efficiency by an average of 25%.

An optimized backpropagation neural network models for the prediction of nanomaterials concentration for purification industrial wastewater

In this paper, an optimized backpropagation neural network (BPNN) prediction models have been proposed to estimate the concentration of titanium dioxide (TiO2) nanomaterials required for the removal of Acid Black 24 dye, a type of textile dye, from industrial wastewater. The research gap addressed in this study lies in the investigation of various optimization algorithms for BPNN models and their profound impact on the accuracy of nanomaterial concentration predictions, thus contributing to the advancement of nanotechnology-based wastewater treatment research and its practical applications in industrial settings. Although several studies have predicted the amount of nanomaterial needed to degrade the dyes in industrial wastewater using BPNN, few have investigated the effects of optimized BPNN and how greatly the amount of degradation can vary. Therefore, six different optimization algorithms, including genetic algorithms (GAs), ant colony optimization (ACO), particle swarm optimization (PSO), evaluation strategy (ES), biogeography-based optimization (BBO), and population-based incremental learning algorithm (PBIL), are applied to optimize and evaluate the BPNN models. Experimental data obtained from chemical laboratory experiments are used to train and test the models, with different experiments conducted for various factors like TiO2, H2SO4, NaIO4, K2S2O8, dye, and silver nitrate. The performance of the optimized BPNN models is assessed using metrics such as mean squared error (MSE), root means squared error (RMSE), and R-squared (R2). The results demonstrate the effectiveness of the proposed models, with ACO yielding the best outcomes across all models. The R2 values for the TiO2, H2SO4, NaIO4, and K2S2O8 models after optimization indicate perfect predictions, whereas the R2 values before optimization ranged from 0.888 to 0.984. PSO performs well for most models except for K2S2O8, GA improves most models except for the silver nitrate model, ES enhances only the Dye model, and PBIL does not affect the TiO2 model. Overall, the optimized BPNN models exhibit impressive accuracy in estimating the TiO2 concentration required for removing Acid Black 24 dye from industrial wastewater.

Thermomechanical shakedown analysis considering temperature-dependent yield stress based on the primal-dual eigenstress-driven method

Many industrial structural components are loaded repeatedly in high-temperature environments. The shakedown limit under variable thermomechanical loads is a critical factor in thermomechanical elastoplastic structure design assessment. The shakedown limit is weakened due to material strength reduction at high-temperature conditions.This paper develops an efficient shakedown analysis Primal-dual Eigenstress-driven Method(PEM) algorithm for temperature-dependent yield condition, which overcomes the obstacle of the original PEM method [1] which can only deal with constant material properties. The developed temperature-dependent PEM algorithm proposes a two-level iteration algorithm to determine the shakedown limit and the time-independent residual stress field. At the inner level the theoretical framework tailored to the temperature dependence problem is derived to solve the Karush-Kuhn-Tucker (KKT) conditions of the extended temperature-dependent shakedown theorem and establish an increment algorithm for forming residual stress fields. Besides, the lower bound safe multipliers are computed to verify the residual stress field obtained by inner-level algorithm. At the outer level an efficient fixed-point algorithm is applied to search the shakedown multiplier, where the corresponding updating strategy for the yield stress with temperature is nested. For practicability, the proposed algorithm is seamlessly integrated into commercial platform ANSYS, which is applicable to industrial structures. Four numerical examples from multiple perspectives show the effectiveness of developed PEM method.

A Self-Adaptive Double Q-Backstepping Trajectory Tracking Control Approach Based on Reinforcement Learning for Mobile Robots

When a mobile robot inspects tasks with complex requirements indoors, the traditional backstepping method cannot guarantee the accuracy of the trajectory, leading to problems such as the instrument not being inside the image and focus failure when the robot grabs the image with high zoom. In order to solve this problem, this paper proposes an adaptive backstepping method based on double Q-learning for tracking and controlling the trajectory of mobile robots. We design the incremental model-free algorithm of Double-Q learning, which can quickly learn to rectify the trajectory tracking controller gain online. For the controller gain rectification problem in non-uniform state space exploration, we propose an incremental active learning exploration algorithm that incorporates memory playback as well as experience playback mechanisms to achieve online fast learning and controller gain rectification for agents. To verify the feasibility of the algorithm, we perform algorithm verification on different types of trajectories in Gazebo and physical platforms. The results show that the adaptive trajectory tracking control algorithm can be used to rectify the mobile robot trajectory tracking controller’s gain. Compared with the Backstepping-Fractional-Older PID controller and Fuzzy-Backstepping controller, Double Q-backstepping has better robustness, generalization, real-time, and stronger anti-disturbance capability.