Self-adaptive Prediction Research Articles

A novel high-precision and self-adaptive prediction method for ship energy consumption based on the multi-model fusion approach

The accurate prediction of energy consumption is significant for ship energy efficiency optimization. However, the existing prediction methods of ship energy consumption based on a single algorithm have limitations in adaptability and accuracy. Therefore, a novel high-precision and self-adaptive prediction method based on the multi-model fusion approach is investigated in this paper. Firstly, the data processing and analysis are carried out. Then, the Stacking-based fusion model is established and the prediction performance is analyzed. On this basis, an adaptive fusion model based on the Self-adaptive Parameter Optimization (SPO) method is established. Finally, an Intelligent Selection based Self-adaptive Hybrid (ISSH) method is proposed. The study results indicate that the proposed ISSH method can predict ship energy consumption more accurately, with the Mean Square Error (MSE) reduced by 66.7 % and the Mean Absolute Error (MAE) reduced by 12.7 % compared to the optimal single prediction model. In addition, the ISSH-based fusion model can reduce the MSE by 50.0 % and the MAE by 9.9 %, compared to the Stacking-based fusion model without parameter optimization. Moreover, the ISSH method can achieve self-adaptive prediction of ship energy consumption under diverse scenarios by adopting the intelligent selection strategy (ISS) method of basic models, which is significant to achieve dynamic optimization of ship energy efficiency.

Towards artificial intelligence-based disease prediction algorithms that comprehensively leverage and continuously learn from real-world clinical tabular data systems.

This manuscript presents a proof-of-concept for a generalizable strategy, the full algorithm, designed to estimate disease risk using real-world clinical tabular data systems, such as electronic health records (EHR) or claims databases. By integrating classic statistical methods and modern artificial intelligence techniques, this strategy automates the production of a disease prediction model that comprehensively reflects the dynamics contained within the underlying data system. Specifically, the full algorithm parses through every facet of the data (e.g., encounters, diagnoses, procedures, medications, labs, chief complaints, flowsheets, vital signs, demographics, etc.), selects which factors to retain as predictor variables by evaluating the data empirically against statistical criteria, structures and formats the retained data into time-series, trains a neural network-based prediction model, then subsequently applies this model to current patients to generate risk estimates. A distinguishing feature of the proposed strategy is that it produces a self-adaptive prediction system, capable of evolving the prediction mechanism in response to changes within the data: as newly collected data expand/modify the dataset organically, the prediction mechanism automatically evolves to reflect these changes. Moreover, the full algorithm operates without the need for a-priori data curation and aims to harness all informative risk and protective factors within the real-world data. This stands in contrast to traditional approaches, which often rely on highly curated datasets and domain expertise to build static prediction models based solely on well-known risk factors. As a proof-of-concept, we codified the full algorithm and tasked it with estimating 12-month risk of initial stroke or myocardial infarction using our hospital's real-world EHR. A 66-month pseudo-prospective validation was conducted using records from 558,105 patients spanning April 2015 to September 2023, totalling 3,424,060 patient-months. Area under the receiver operating characteristic curve (AUROC) values ranged from .830 to .909, with an improving trend over time. Odds ratios describing model precision for patients 1-100 and 101-200 (when ranked by estimated risk) ranged from 15.3 to 48.1 and 7.2 to 45.0, respectively, with both groups showing improving trends over time. Findings suggest the feasibility of developing high-performing disease risk calculators in the proposed manner.

Adaptive tourism forecasting using hybrid artificial intelligence model: a case study of Xi’an international tourist arrivals

Accurate forecasting of tourist demand is important to both business practitioners and government policymakers. In the past decade of rapid development of deep learning, many artificial intelligence methods or deep learning models have been built to improve prediction accuracy. But data-driven end-to-end deep network models usually require large data sets to support. For tourism forecasting, the sample is insufficient and many models are difficult to apply. In this article, we propose a novel hybrid model GM-LSTM, which combines the advantages of gray models and neural networks to achieve self-adaptive prediction with small samples. Specifically, the overall trend of tourism demand is captured by a first-order gray model and the non-linear residual fluctuation is characterized using a long short-term memory (LSTM) network with a rolling mechanism. The model is validated through a case study of up to 38 years of data on annual international tourist arrivals in Xi’an, China. The proposed GM-LSTM model achieved a predicted MAPE value of 11.88%, outperforming other time series models. The results indicate that our proposed hybrid model is accurate and efficient.

A dynamic risk-early-warning methodology of distribution system faults incorporating spatiotemporal imbalanced data distributions

Power distribution systems are susceptible to external environmental disturbances. The early warning of potential fault risks in both spatial and temporal scales can assist in maintenance planning and overhaul scheduling for distribution systems, thus their overall reliability consequently. To achieve it, this paper proposes a self-adaptive prediction model for future failure risks in distribution systems, namely the association rules exploration with conditional filter and fitness regulation (AREcffr). In this approach, electrical attributes along with surrounding condition factors are both implemented as inputs. Then, to cope with the commonly-occurred imbalanced data distribution in both two scales when distinguishing risky factors, the conditional importance diagnostic threshold setting and importance diagnostic standard calculation methods set are designed. In that case, the included high-risk-low-probability (HRLP) time series and condition factors in the sparsely distributed input data can be taken into the assessment. Next, to conduct more reasonable measurements of risk levels for those selected risky components, a component importance measure (CIM)-based relative weight analysis model, which is according to the variation of total system risks caused by each risky component rather than its appearance frequency or database share, is established. Finally, an empirical research is presented, and the flexibility and advantages of this risk-early-warning model can be validated and demonstrated in consequence.

Fuzzy self-adaptive prediction method for data transmission congestion of multimedia network

Fuzzy self-adaptive prediction method for data transmission congestion of multimedia network

Design of Smart Vehicular Traffic Network for Self-Adaptive Prediction Service

Design of Smart Vehicular Traffic Network for Self-Adaptive Prediction Service

An Action Dependent Heuristic Dynamic Programming Approach for Algal Bloom Prediction With Time-Varying Parameters

Algal bloom is a nonlinear and time-varying process, which brings challenges for the accurate prediction. For the existing mechanism model of algae ignores the external key factors, we propose an algae growth model (AGM) optimized by action dependent heuristic dynamic programming (ADHDP). This model has the structure of information interaction with the outside, which can predict algal bloom with well adaptive ability. In this paper, chlorophyll-a concentration is used as the representative factor of algal bloom. We use ADHDP approach to map the external key factors to the time-varying parameters, so the AGM can be adjusted to realize the self-adaptive prediction with the changes in external environments. Compared with different prediction methods, the simulation result shows that the ADHDP-AGM prediction model can accurately predict the chlorophyll-a concentration under different data distributions. Moreover, the prediction process shows that the time-varying parameters in AGM conform to the evolution trend of chlorophyll-a concentration in fact, which further improves the interpretability of prediction model. It provides a new perspective for building a data-driven prediction model with clear physical significance, and makes the mechanism research and data science further fusion.

Autonomous supervision and optimization of product quality in a multi-stage manufacturing process based on self-adaptive prediction models

In modern manufacturing facilities, there are basically two essential phases for assuring high production quality with low (or even zero) defects and waste in order to save costs for companies. The first phase concerns the early recognition of potentially arising problems in product quality, the second phase concerns proper reactions upon the recognition of such problems. In this paper, we address a holistic approach for handling both issues consecutively within a predictive maintenance framework at an on-line production system. Thereby, we address multi-stage functionality based on (i) data-driven forecast models for (measure-able) product quality criteria (QCs) at a latter stage, which are established and executed through process values (and their time series trends) recorded at an early stage of production (describing its progress), and (ii) process optimization cycles whose outputs are suggestions for proper reactions at an earlier stage in the case of forecasted downtrends or exceeds of allowed boundaries in product quality. The data-driven forecast models are established through a high-dimensional batch time-series modeling problem. In this, we employ a non-linear version of PLSR (partial least squares regression) by coupling PLS with generalized Takagi–Sugeno fuzzy systems (termed as PLS-fuzzy). The models are able to self-adapt over time based on recursive parameters adaptation and rule evolution functionalities. Two concepts for increased flexibility during model updates are proposed, (i) a dynamic outweighing strategy of older samples with an adaptive update of the forgetting factor (steering forgetting intensity) and (ii) an incremental update of the latent variable space spanned by the directions (loading vectors) achieved through PLS; the whole model update approach is termed as SAFM-IF (self-adaptive forecast models with increased flexibility). Process optimization is achieved through multi-objective optimization using evolutionary techniques, where the (trained and updated) forecast models serve as surrogate models to guide the optimization process to Pareto fronts (containing solution candidates) with high quality. A new influence analysis between process values and QCs is suggested based on the PLS-fuzzy forecast models in order to reduce the dimensionality of the optimization space and thus to guarantee high(er) quality of solutions within a reasonable amount of time (→ better usage in on-line mode). The methodologies have been comprehensively evaluated on real on-line process data from a (micro-fluidic) chip production system, where the early stage comprises the injection molding process and the latter stage the bonding process. The results show remarkable performance in terms of low prediction errors of the PLS-fuzzy forecast models (showing mostly lower errors than achieved by other model architectures) as well as in terms of Pareto fronts with individuals (solutions) whose fitness was close to the optimal values of three most important target QCs (being used for supervision): flatness, void events and RMSEs of the chips. Suggestions could thus be provided to experts/operators how to best change process values and associated machining parameters at the injection molding process in order to achieve significantly higher product quality for the final chips at the end of the bonding process.

Equipment remaining useful life prediction oriented symbiotic simulation driven by real-time degradation data

As an emerging simulation technology in the field of system modeling and simulation, the equipment symbiotic simulation has become research emphasis. In the field of equipment maintenance support, the outstanding problem of equipment remaining useful life (RUL) prediction is analyzed, i.e., the stable model parameters without self-evolution ability, which has become the primary factor that hinders self-adaptive prediction of equipment RUL. Combined with parallel systems theory, the equipment RUL prediction oriented symbiotic simulation framework is proposed on the basis of modeling analysis and Wiener state space model (SSM) is taken as the basic simulation model in the framework. Driven by the dynamic injected equipment degradation observation data, the model parameters are updated online by using expectation maximum (EM) algorithm and the data assimilation between simulation outputs and observation data is executed by using Kalman filter, so as to realize dynamic evolution of the simulation model. The simulation model evolution which makes the simulation outputs close to equipment real degradation state provides high fidelity model and data for predicting equipment RUL accurately. The framework is verified by the performance degradation data of a bearing. The simulation results show that the symbiotic simulation method can accurately simulate the equipment performance degradation process and the self-adaptive prediction of equipment RUL is realized on the basis of improving prediction accuracy, proving the feasibility and effectiveness of symbiotic simulation method.

An autonomic prediction suite for cloud resource provisioning

One of the challenges of cloud computing is effective resource management due to its auto-scaling feature. Prediction techniques have been proposed for cloud computing to improve cloud resource management. This paper proposes an autonomic prediction suite to improve the prediction accuracy of the auto-scaling system in the cloud computing environment. Towards this end, this paper proposes that the prediction accuracy of the predictive auto-scaling systems will increase if an appropriate time-series prediction algorithm based on the incoming workload pattern is selected. To test the proposition, a comprehensive theoretical investigation is provided on different risk minimization principles and their effects on the accuracy of the time-series prediction techniques in the cloud environment. In addition, experiments are conducted to empirically validate the theoretical assessment of the hypothesis. Based on the theoretical and the experimental results, this paper designs a self-adaptive prediction suite. The proposed suite can automatically choose the most suitable prediction algorithm based on the incoming workload pattern.

A Self-Adaptive Prediction Algorithm for Cloud Workloads

It is difficult to analyze the workload in complex cloud computing environments with a single prediction algorithm as each algorithm has its own shortcomings. A self-adaptive prediction algorithm combining the advantages of linear regression (LR) and a BP neural network to predict workloads in clouds is proposed in this paper. The main idea of the self-adaptive prediction algorithm is to choose the better prediction method of the future workload. Some experiments of prediction algorithms are conducted with workloads on the public cloud servers. The experimental results show that the proposed algorithm has a relatively high accuracy on the workload predictions compared with the BP neural network and LR. Furthermore, in order to use the proposed algorithm in a cloud data center, a dynamic scheduling architecture of cloud resources is designed to improve resource utilization and reduce energy consumption.

Self-adaptive prediction of cloud resource demands using ensemble model and subtractive-fuzzy clustering based fuzzy neural network.

In IaaS (infrastructure as a service) cloud environment, users are provisioned with virtual machines (VMs). To allocate resources for users dynamically and effectively, accurate resource demands predicting is essential. For this purpose, this paper proposes a self-adaptive prediction method using ensemble model and subtractive-fuzzy clustering based fuzzy neural network (ESFCFNN). We analyze the characters of user preferences and demands. Then the architecture of the prediction model is constructed. We adopt some base predictors to compose the ensemble model. Then the structure and learning algorithm of fuzzy neural network is researched. To obtain the number of fuzzy rules and the initial value of the premise and consequent parameters, this paper proposes the fuzzy c-means combined with subtractive clustering algorithm, that is, the subtractive-fuzzy clustering. Finally, we adopt different criteria to evaluate the proposed method. The experiment results show that the method is accurate and effective in predicting the resource demands.

S-wave velocity self-adaptive prediction based on a variable dry rock frame equivalent model

Seismic velocities are important reservoir parameters in seismic exploration. The Gassmann theory has been widely used to predict velocities of fluid-saturated isotropic reservoirs at low frequency. According to Gassmann theory, dry rock frame moduli are essential input parameters for estimating reservoir velocities. A variable dry rock frame equivalent model called VDEM based on the differential effective medium (DEM) theory is constructed in this paper to obtain the dry rock frame moduli. We decouple the DEM equations by introducing variable parameters, then simplify these decoupled equations to get the equivalent dry rock fame model. The predicted dry rock frame moduli by the VDEM are in good agreement with the laboratory data. The VDEM is also utilized to predict S-wave velocity combined with Gassmann theory. A self-adaptive inversion method is applied to fit the variable parameters with the constraint of P-wave velocity from well logging data. The S-wave velocity is estimated from these inversed parameters. A comparison between the self-adaptive method and the Xu-White model on S-wave velocity estimation is made. The results corroborate that the self-adaptive method is flexible and effective for S-wave velocity prediction.

Variational inequality model for cordon-based congestion pricing under side constrained stochastic user equilibrium conditions

A major objective of the practical implemented cordon-based congestion pricing schemes is to maintain the traffic conditions within the cordon area, which is rarely considered in most of the existing studies. Thus, this paper addresses the optimal toll charge pattern that can restrict the total inbound flow of each cordon to a predetermined threshold. The toll charges on all the entry links of one cordon are required to be identical, for the ease of implementation and users’ recognition. The users’ route choice behaviour is assumed to follow stochastic user equilibrium (SUE) with asymmetric link travel time functions. It is shown that such an optimal toll charge pattern can be attained by solving a SUE problem with side constraints. A variational inequality (VI) model is first proposed for the optimal toll pattern, where the monotone property of this model is rigorously proved. Then, a convergent self-adaptive prediction and correction method can be adopted for solving the VI model. It is shown that when used in practice, the solution method only needs traffic counts on entry links of each cordon.

Asymmetric stochastic user equilibrium problem with elastic demand and link capacity constraints

This paper focuses on model development and algorithm design for the general stochastic user equilibrium (SUE) problem with elastic demand, asymmetric link travel time functions and link capacity constraints. It first defines the generalised SUE conditions using generalised link travel time. An equivalent variational inequality (VI) model for these generalised SUE conditions is then developed and it is rigorously proven to be monotone and uniform Lipschitz-continuous. These two properties of the proposed VI model ensure the global convergence of the self-adaptive prediction–correction algorithm incorporating cost averaging method as a solution algorithm. Finally, a numerical example is utilised to assess the performance of the proposed VI model and solution algorithm.

A Power-Efficient and Self-Adaptive Prediction Engine for H.264/AVC Decoding

Prediction, including intra prediction and inter prediction, is the most critical issue in H.264/AVC decoding in terms of processing cycles and computation complexity. These two predictions demand a huge number of memory accesses and account for up to 80% of the total decoding cycles. In this paper, we present the design and VLSI implementation of a novel power-efficient and highly self-adaptive prediction engine that utilizes a 4 times 4 block level pipeline. Based on the different prediction requirements, the prediction pipeline stages, as well as the correlated memory accesses and datapaths, are fully adjustable, which helps to reduce unnecessary decoding operations and energy dissipation while retaining the fixed real-time throughput. Compared with conventional designs, this paper has the advantage of higher efficiency and lower power consumption due to the elimination of all redundant operations and the wide employment of the pipeline and parallel processing. Under different prediction modes, our design is able to decode each macroblock within 500 cycles. A prototype H.264/AVC baseline decoder chip that utilizes the proposed prediction engine is fabricated with UMC 0.18-mu CMOS 1P6 M technology. The prediction engine contains 79 K gates and 2.8 kb single-port on-chip SRAM, and occupies half of the whole chip area. When running at 1.5 MHz for QCIF 30 f/s real-time decoding, the prediction engine dissipates 268 muW at a 1.8-V power supply.

An self-adaptive LQP method for constrained variational inequalities

In this paper, we present a logarithmic-quadratic proximal (LQP) type prediction–correction methods for solving constrained variational inequalities VI ( S , f ) , where S is a convex set with linear constraints. The computational load in each iteration is quite tiny. However, the number of iterations is significantly dependent on a parameter which balances the primal and dual variables. We then propose a self-adaptive prediction–correction method that adjusts the scalar parameter automatically. Under certain conditions, the global convergence of the proposed method is established. In order to demonstrate the efficiency of the proposed method, we provide numerical results for a convex nonlinear programming and traffic equilibrium problems.

Real-time operation guide system for sintering process with artificial intelligence

In order to optimize the sintering process, a real-time operation guide system with artificial intelligence was developed, mainly including the data acquisition online subsystem, the sinter chemical composition controller, the sintering process state controller, and the abnormal conditions diagnosis subsystem. Knowledge base of the sintering process controlling was constructed, and inference engine of the system was established. Sinter chemical compositions were controlled by the strategies of self-adaptive prediction, internal optimization and center on basicity. And the state of sintering was stabilized centering on permeability. In order to meet the needs of process change and make the system clear, the system has learning ability and explanation function. The software of the system was developed in Visual C++ programming language. The application of the system shows that the hitting accuracy of sinter compositions and burning through point prediction are more than 85%; the first-grade rate of sinter chemical composition, stability rate of burning through point and stability rate of sintering process are increased by 3%, 9% and 4%, respectively.

The Structure and Functions of CCSCAD Package

A large control systems CAD package - CCSCAD had been established by cooperative efforts. There are 16 functional subpackages included in the package, which have the functions of time series analysis, system identification and parameter estimation, selfadaptive prediction, SISO and MIMO system analysis and synthesis both in time domain and frequency domain methods, systems simulation, SISO and MIMO adaptive control etc. All the functional subpackages and mathematical libraries are dispatched by general management program, and both the interactive dialogue operation mode and macro-command file operation mode had been implemented. Three computer aided teaching subpackage also had been included. CCSCAD package had been used in a lot of engineering projects.

Self-tuning predictors with application to river flow prediction

The paper presents developments of recursive self-adaptive prediction algorithms, called ‘self-tuning predictors’, using some common estimation techniques, and their application to prediction of flow discharge of the river Tigris at Baghdad, Iraq. Four kinds of predictors, viz the least-square predictor, the minimum-variance predictor, a predictor using stochastic approximation, and a two-stage predictor, have been developed. Using available data for the River Tigris, prediction results have been obtained for the average daily discharge, the average monthly discharge and the average yearly discharge. In each type of prediction a number of models have been tried. The various prediction results are presented in graphical and in tabular forms for comparison.