Remaining useful life prediction for lithium-ion battery based on the particle filter considering temperature effect

Temperature would affect the degradation process of lithium-ion battery. Therefore, considering the influence of temperature, this paper proposes method to predict the Remaining useful life (RUL) of the lithium-ion battery based on Arrhenius and double exponential model. And update the parameter by particle filter. Firstly, we establish a capacity degradation model with considering the influence of temperature, which is based on Arrhenius model and double exponential model. And then, in order to obtain the initial value of the parameters, we process the fitted the lithium-ion battery degradation data. Next, we use the particle filter (PF) algorithm to update the model parameters to realize the capacity estimation and the RUL prediction. Finally, according the experiment, we prove that the accuracy of the method proposed in this paper is better than that the method without considering the influence of temperature change. The result shows that the lithium-ion battery capacity degradation model established in this paper has great potential in the RUL prediction of the lithium-ion battery.

MODEL HYBRID NONLINEAR REGRESSION LOGISTIC (NLR) –DOUBLE EXPONENSIAL SMOOTHING (DES) DAN PENERAPANNYA PADA JUMLAH KASUS KUMULATIF COVID-19 DI INDONESIA DAN BELANDA

The economic relationship between Indonesia and the Netherlands is a good trade relationship, but the spread of COVID-19 disrupts the two countries' economies. Both countries need to have an explanation regarding the condition of COVID-19 to raise economic market sentiment. Based on this, Hybrid and non-hybrid models are used to predict the dispersion conditions and compare them through the MAPE value. The double-exponential nonlinear logistic regression hybrid model on the cumulative number of COVID-19 is not suitable for use in the Netherlands COVID-19 cases but is suitable for use in the cumulative number of COVID-19 cases Indonesia. The hybrid nonlinear regression logistic-double exponential model is one way to optimize MAPE, especially in training data. Based on the hybrid non-client regression logistic model, the peak incidence of Covid-19 in the Netherlands is estimated at 22 November 2020, and the hybrid nonlinear regression logistic-Double exponential model predicts that the peak of Covid-19 occurs in Indonesia on 28 November 2020. the Netherlands wave is around 2.83 percent and Indonesia 1.62 percent. Therefore the decline in Indonesia is predicted to be faster, but the Netherlands will reach the peak of the Indonesian news wave.

Modeling COVID-19 Pandemic Data with Beta-Double Exponential Model

Objective: This paper examines and upgrades a two-parameter double exponential distribution to a four-parameter beta double exponential model by compounding the baseline distribution and beta link function to fits and analyse deaths-cases data set of the recent outbreak of the global pandemic coronavirus disease (COVID-19) for both Africa and Non-Africa countries. The new proposed model, although complex in its mathematical structure, yet flexible to implement and its robustness to accommodate non-normal data is an extra advantage to statistical theory and other fields. Methodology: The statistical properties: the density function, cumulative distribution function, survival function, hazard function, moments, moments generating function, skewness and kurtosis of the developed model were presented. Maximum likelihood method is used for parameters estimation procedure. The new model is validated and compared with some frontier similar extant parametric family of beta distributions using graphs, Kolmogorov Smirnov (KS) Statistic, Log-likelihood and model criteria statistics like Akaike Information Criteria (AIC), Bayesian Information Criteria (BIC) and Consistent Akaike Information Criteria (CAIC) as tools for comparison. Results: The graphs, KS, LogL and model criteria statistics values showed that the proposed model fits the COVID-19 pandemic data better than other competing models since the model has lower values as stated: The values from non-African countries KS = 0.1208, LogL = 278.4168, AIC = 560.8336, BIC = 576.1147 and CAIC = 577.1147. Also, from African countries are: KS = 0.0759, LogL = 144.0245, AIC = 292.0490, BIC = 303.9302 and CAIC = 304.9302. Conclusion: The proposed model showed its applicability and flexibility over other models considered in this work. Therefore, this implies that the new model can be used for modeling other infectious disease data and real data in many fields.

Bayesian Modeling of Cognitive Impairment in the Presence of Retest Effects

Diagnosing the early onset of neuropathologies, such as mild cognitive impairment (MCI), requires repeated evaluation of cognitive skills several times per year -- a measurement design known as a “burst design.” Detecting the often subtle cognitive decline in the presence of retest effects requires careful statistical modeling. The double exponential model offers a modeling framework to account for retest gains across measurement bursts, as well as warm-up effects within a burst, while quantifying change across bursts in peak performance. This talk highlights how a Bayesian multilevel implementation of the double exponential model allows for flexible extensions of this framework in terms of accommodating different timescales (nesting) and person-level predictors and drawing intuitive inferences on cognitive change with Bayesian posterior probabilities. We will use reaction time data to show how individual differences in asymptotic performance and change can be related to predictors such as age and MCI status. Part of a symposium sponsored by the Measurement, Statistics, and Research Design Interest Group.

Active carbon pool-size is enhanced by long-term manure application

We studied the dynamics of soil organic carbon (SOC)-pool mineralisation in agricultural soil. A laboratory incubation experiment was conducted using the soil from a long-term experiment involving the following fertilisation regimes: no fertilisation (CK); mineral (NPK); organic (M), and combined organic-inorganic fertilisers (MNPK). SOC mineralisation rate decreased as follows: MNPK > M > NPK > CK. Cumulative SOC mineralisation (C_m) ranged between 730.15 and 3 022.09 mg/kg in CK and MNPK, respectively; 8.81% (CK) to 20.45% (MNPK) of initial SOC was mineralised after a 360-day incubation. Soil C_m values were significantly higher under NPK, M, and MNPK compared to those under the CK treatment. Dynamic variation in C_m with incubation time fitted a double exponential model. Active carbon pools accounted for 2.06–6.51% of total SOC and the average mean resistant time (MRT₁) was 28.76 days, whereas slow carbon pools accounted for 93.49–97.94% of SOC, with an average MRT₂ of 8.53 years. The active carbon pool in fertilised soils was larger than in CK; furthermore, it was larger in M- and MNPK- than under NPK-treated plots. SOC decomposed more easily in long-term fertilised plots than in non-fertilised plots.

Price forecasting and Seasonality of Soybean in Amravati District of Maharashtra India

Soybean crop has contributed to improve the financial strength of the Indian farmers. It usually fetches higher income to the farmers owing to the massive export market for Soybean de-oiled cake. In state of Maharashtra Soybean is cultivated extensively in Amravati district. So the present studies explore the seasonality and price forecasting issue for Soybean crop. The is based on the secondary data. The monthly wholesale prices and arrivals data for the study collected from the agmarknet.gov.in for the period January 2008 to December 2017. To analyze the data we use statistical techniques like seasonality and exponential smoothing for price forecasting. The processing of data is done through MS- Excel and MINITAB Software. The study gives an overview of the different time series analytical methods, which can be used for price forecasting. The present study is undertaken precisely to fill the research gap and results of this study found an inverse relationship between price and market arrivals of soybean. The arrivals were recorded very high from October to January and seasonal indices of price were elevated during August in which arrivals were found stumpy. The assessment of all three Exponential Smoothing models was carried out in the procedure based on the Double Exponential model with MAD (168.3) and MAPE (6.14) values, which were considered in the smallest amount. The accuracy of proportion among the forecasted and actual price value of soybean was found in between 80.52 to 85.55 percent. It was pragmatic that the Double Exponential model was the most appropriate for forecasting the soybean.

SIMULATION OF TIME SERIES FOR WHEAT PRODUCTION AND A SHARE OF A SELF-DYNAMIC RATE IN THE FUTURE: DOUBLE EXPONENTIAL FORECASTING

The article proposes a time series model for forecasting the annual production and consumption of wheat in Egypt, based on data and economic indicators for the period from 1995 to 2015. The main objective of this work is to predict future trends in wheat production and consumption and use several prediction methods to analyze the development of the industry in Egypt until 2030. The study was conducted to select a suitable model for predicting these processes among three methods that depend on the values of three accuracy measures (MAPE, MAD and MSD) using the moving average model, the exponential smoothing model and the double exponential model. As a result, it was revealed that the double exponential model is the most suitable model for forecasting the future trend of wheat production and consumption due to smaller values of prediction errors. Recommendations were also formulated to improve food security until 2030, which are presented in order to improve land management and productivity, reduce agricultural waste and create a strategic wheat stock to address local supply problems.