Adaptive Exponential Smoothing Models for Reliability Estimation

An experimental comparison of exponential and adaptive smoothing forecasting models using actual operating data

In this paper, we review the state of art of short-term traffic forecasting models, outlining their basic ideas, related works, advantages and disadvantages of each model. An improved adaptive exponential smoothing (IAES) model is also proposed to overcome the drawbacks of previous adaptive exponential smoothing model. Then comparing experiments are carried out under normal traffic condition and abnormal traffic condition to evaluate the performance of four main branches of forecasting models on direct travel time data obtained by license plate matching. The results of experiments show the forecasting performance of IASE is superior to other models in shorter forecasting horizon (one and two step forecasting).

A number of multiple-parameter adaptive exponential smoothing models have been proposed and demonstrated over the last two decades for short range forecasting. There have been conflicting results on their performance and no systematic study has been conducted to compare them in a controlled environment. The work reported here fills this void by testing a set of well known multiple-parameter adaptive procedures against the three-parameter Winters' model. First, sets of synthetic time series with known characteristics are used to compare performance for the different approaches using the standard deviation of forecast errors. Second, the information gathered at this point is used to predict the technique's performance on six empirical time series. And third, general guidelines are presented for model selection.

Empirical Bayes estimates in exponential reliability model

Advances in micro robots in non-invasive medicine have enabled physicians to perform diagnostic and therapeutic procedures with higher resolution and lower risk than before. However, navigation and precise localisation of such micro robots inside human body still remains a challenge. This is mostly due to the 1) lack of precise communication channel models, 2) inhomogeneity of the propagation medium and 3) non-geometric boundaries of the tissues morphometric parameters. In this study, we derive novel intra-body path loss channel models for wave propagation in wireless capsule endoscopy, i.e., propagation through the gastrointestinal tract and the abdominal wall. We formulate an adaptive attenuation parameter as a function of permittivity, conductivity and the thickness of various layers between the transmitter and the receiver. The standard deviation of modelling error of the path loss using our adaptive channel model is smaller than 50% of that of existing channel models. We further analyse the sensitivity of the path loss model to the variations of thickness of different abdominal wall layers. We finally show that the thickness of the fat layer has the greatest influence on the total attenuation parameter of the path loss model and therefore, we modify our adaptive model accordingly.

A new hybrid scheme which contains multiple fixed and adaptive Takagi-Sugeno (T-S) identification models is proposed in this paper in order to control efficiently a class of unknown nonlinear dynamical fuzzy systems. One state feedback fuzzy controller corresponds to each T-S model, producing its signal according to the certainty equivalence approach. A performance index and an appropriate switching rule are used to determine the T-S model which best approximates the plant and consequently to pick the best available controller at every time instant. There are two kind of adaptive models: one free running adaptive model and one reinitialized adaptive model which uses the parameters of the best fixed model at every time instant. Lyapunov stability theory is used in order to obtain the adaptive law for every adaptive model's parameters ensuring the asymptotic stability of the system. Singularity problems in the control signal are avoided by modifying the adaptive law and the Next Best Controller Logic (NBCL) ensures the controller's feasibility. A computer simulation example is given to verify the theoretical results.

This paper describes a general methodology for the analysis of differential gene expression based on microarray data. First, we characterize the data by a linear statistical model that accounts for relevant sources of variation in the data and then we consider estimation of the model parameters. Because microarray studies typically involve thousands of genes, we propose a two-stage method for parameter estimation. The interaction terms for genes and experimental conditions in this model capture all relevant information about differential gene expression in the microarray data. We propose a mixture distribution model for a summary statistic of differential expression that consists of null and alternative component distributions. The mixture model suggests two methods for identifying genes exhibiting differential expression. One is a frequentist method that identifies distinguished genes and the other an empirical Bayes procedure that yields estimated posterior probabilities of differential expression, conditional on observed microarray readings. An extensive case application involving juvenile cystic kidney disease in mice is used to illustrate the methodology. The application controls for variation arising from array, color channel, experimental condition (tissue type), and gene, with the analysis of variance (ANOVA) model including both main effects to normalize the expression data and all interaction terms involving genes. The gene expression profile is found to vary by tissue type as expected, but also by color channel, which was less expected. A concluding section discusses some outstanding research questions related to the analysis of microarray data.

Providing reliable predictive traffic information is a crucial element for successful operation of intelligent transportation systems. However, there are difficulties in providing accurate predictions mainly because of limitations in processing data associated with existing traffic surveillance systems and the lack of suitable prediction techniques. This study examines different aggregation intervals to characterize various levels of traffic dynamic representations and to investigate their effects on prediction accuracy. The relationship between data aggregation and predictability is explored by predicting travel times obtained from the inductive signature-based vehicle reidentification system on the I-405 freeway detector test bed in Irvine, California. For travel time prediction, this study employs three techniques: adaptive exponential smoothing, adaptive autoregressive model using Kalman filtering, and recurrent neural network with genetically optimized parameters. Finally, findings are discussed on s...

This paper adopts the adaptive neural internal model control strategy to design a control system for complex and uncertain process with delay. Because of the shortage of the classic internal model control system the performance of the control system will slip back if there exist large errors between the real plant and the model, an adaptive mechanism based on single neuron which can tune the parameters of the internal model and that of controller in the control system on line is designed. The multimodel approach is exploited to represent the direct model by a model basis made up of five linear models, and to construct a controller trained by the corresponding reverse models. The simulation results shows that the adaptive internal model control system designed in this paper has good performance of overcoming deviations of model parameters.

There are a lot of network monitors which are capable of performing network packets deep inspection (DPI) as a set of information security check. These steps include intrusion detection system check, exfiltration, detection and parental filtering. However, it is not allowed to use such a slow mechanism as DPI in industrial networks. Hence, architectors have to choose only one of two capabilities of the system: system is required to be fast and fail safe even without any protection mechanisms such as encryption and/or signatures. The proposed approach describes a model of abnormal activity detection, which uses two algorithms to work with industrial network traffic: one is based on Brown's adaptive prediction model and another one based on Support Vector Machine (SVM) predict method. Not typical events detection is demonstrated on test traffic captures.

Reliable energy access in rural schools is necessary for educational and community development. Solar hybrid systems, particularly Direct Current (DC) Coupled designs, offer a sustainable solution. However, they face significant scalability, maintenance complexity, and charge controller dependency challenges. This paper addresses these issues and presents an adaptive model to optimize DC Coupled solar hybrid systems. Applying Basic Qualitative Inquiry, semi-structured interviews were conducted during this study with five experts, including site officers, maintenance contractors, and a senior electrical engineer. The results reveal that rigid system design, high upgrading costs, and charge controller inefficiencies limit scalability and long-term performance. System reliability will suffer from a lack of maintenance, especially battery management and overheating prevention. The Adaptive DC Coupled System Solution Model (ADCSM) was proposed to overcome these challenges, including adaptive system restructuring, AC-DC hybrid integration, advanced charge controller optimization technologies, and intelligent monitoring technologies. This paradigm improves the system's flexibility, efficiency, and sustainability and provides a cost-effective and scalable strategy for policymakers, engineers, and researchers to manage rural energy. Keywords: Solar Hybrid Systems, DC Coupled, Scalability Challenges, Charge Controller Optimization, Renewable Energy in Rural Schools.

The exponential excess Gibbs energy model revisited

Adaptive nonlinear state-space modelling for the prediction of daily mean PM 10 concentrations

The Mean Time to Failure (MTTF) is a critical metric for assessing the reliability of non-repairable systems, and it plays a significant role in incident management. However, accurately estimating MTTF can be challenging due to the limited availability and quality of historical experimental data on a group of identical parts or systems. To address this challenge, we propose an adaptive surrogate modeling method, which approximate the system’s performance functions with a more computationally efficient model to predict the MTTF during the design stage. In this paper, we develop an adaptive Gaussian process (GP) approach that combines with Monte Carlo simulation (MCS) to predict MTTF. The proposed method initially trains a GP model for the system’s performance function, and then an MTTF surrogate model can be obtained. With a learning function, these models are updated dynamically based on new information as it becomes available. We demonstrate the effectiveness of our method on series system, parallel system, and mixed system with two examples, and the results show that the adaptive surrogate modeling method can accurately predict the MTTF of the system. Our method has the potential to improve the reliability and safety of complex system and reduces the need for costly performance testing.

To improve network security situation prediction accuracy, an adaptive network security situation prediction model based on alarm elements was proposed. Firstly, we used the entropy correlation method to generate the network security situation time series according to Alarm Frequency (AF), Alarm Criticality (AC) and Alarm Severity (AS). Then, the initial situation predicted value is calculated through sliding adaptive cubic exponential smoothing. Finally, based on the error state, we built the time-varying weighted Markov chain to predict the error value and modify the initial predicted value. The experimental results show that the network security situation prediction results of this model have a better fit with the real results than other models.