Progressive Learning without Forgetting

Learning from changing tasks and sequential experience without forgetting the obtained knowledge is a challenging problem for artificial neural networks. In this work, we focus on two challenging problems in the paradigm of Continual Learning (CL) without involving any old data: (i) the accumulation of catastrophic forgetting caused by the gradually fading knowledge space from which the model learns the previous knowledge; (ii) the uncontrolled tug-of-war dynamics to balance the stability and plasticity during the learning of new tasks. In order to tackle these problems, we present Progressive Learning without Forgetting (PLwF) and a credit assignment regime in the optimizer. PLwF densely introduces model functions from previous tasks to construct a knowledge space such that it contains the most reliable knowledge on each task and the distribution information of different tasks, while credit assignment controls the tug-of-war dynamics by removing gradient conflict through projection. Extensive ablative experiments demonstrate the effectiveness of PLwF and credit assignment. In comparison with other CL methods, we report notably better results even without relying on any raw data

A Hybrid Forecasting Model Based on Bivariate Division and a Backpropagation Artificial Neural Network Optimized by Chaos Particle Swarm Optimization for Day-Ahead Electricity Price

In the electricity market, the electricity price plays an inevitable role. Nevertheless, accurate price forecasting, a vital factor affecting both government regulatory agencies and public power companies, remains a huge challenge and a critical problem. Determining how to address the accurate forecasting problem becomes an even more significant task in an era in which electricity is increasingly important. Based on the chaos particle swarm optimization (CPSO), the backpropagation artificial neural network (BPANN), and the idea of bivariate division, this paper proposes a bivariate division BPANN (BD-BPANN) method and the CPSO-BD-BPANN method for forecasting electricity price. The former method creatively transforms the electricity demand and price to be a new variable, named DV, which is calculated using the division principle, to forecast the day-ahead electricity by multiplying the forecasted values of the DVs and forecasted values of the demand. Next, to improve the accuracy of BD-BPANN, chaos particle swarm optimization and BD-BPANN are synthesized to form a novel model, CPSO-BD-BPANN. In this study, CPSO is utilized to optimize the initial parameters of BD-BPANN to make its output more stable than the original model. Finally, two forecasting strategies are proposed regarding different situations

The Combination Forecasting of Electricity Price Based on Price Spikes Processing: A Case Study in South Australia

Electricity price forecasting holds very important position in the electricity market. Inaccurate price forecasting may cause energy waste and management chaos in the electricity market. However, electricity price forecasting has always been regarded as one of the largest challenges in the electricity market because it shows high volatility, which makes electricity price forecasting difficult. This paper proposes the use of artificial intelligence optimization combination forecasting models based on preprocessing data, called “chaos particles optimization (CPSO) weight-determined combination models.” These models allow for the weight of the combined model to take values of [-1,1]. In the proposed models, the density-based spatial clustering of applications with noise (DBSCAN) algorithm is used to identify outliers, and the outliers are replaced by a new data-produced linear interpolation function. The proposed CPSO weight-determined combination models are then used to forecast the projected future electricity price. In this case study, the electricity price data of South Australia are simulated. The results indicate that, while the weight of the combined model takes values of [-1,1], the proposed combination model can always provide adaptive, reliable, and comparatively accurate forecast results in comparison to traditional combination models

Swarm Intelligence-Based Hybrid Models for Short-Term Power Load Prediction

Swarm intelligence (SI) is widely and successfully applied in the engineering field to solve practical optimization problems because various hybrid models, which are based on the SI algorithm and statistical models, are developed to further improve the predictive abilities. In this paper, hybrid intelligent forecasting models based on the cuckoo search (CS) as well as the singular spectrum analysis (SSA), time series, and machine learning methods are proposed to conduct short-term power load prediction. The forecasting performance of the proposed models is augmented by a rolling multistep strategy over the prediction horizon. The test results are representative of the out-performance of the SSA and CS in tuning the seasonal autoregressive integrated moving average (SARIMA) and support vector regression (SVR) in improving load forecasting, which indicates that both the SSA-based data denoising and SI-based intelligent optimization strategy can effectively improve the model’s predictive performance. Additionally, the proposed CS-SSA-SARIMA and CS-SSA-SVR models provide very impressive forecasting results, demonstrating their strong robustness and universal forecasting capacities in terms of short-term power load prediction 24 hours in advance

Numerical investigation of seismic behaviour of railway embankments in cold regions

U ovom radu provedene su iscrpne rasprave i analize primjenom numeričkih tehnika, a s ciljem da se posve ispita seizmičko ponašanje željezničkog nasipa Qinghai-Tibet. Točnije, provedena je analiza jednodimenzionalnog ekvivalentnog linearnog odziva tla u područjima permafrosta. Na temelju toga, seizmički odziv tipičnog željezničkog nasipa dalje se ispitao nelinearnim dinamičkim proračunom metodom konačnih elemenata. To je rezultiralo određivanjem nelinearnog ponašanja tla na području permafrosta (stalno smrznuto tlo), a raspravljalo se o dinamičkom ubrzanju, brzini i pomaku nasipa te se predvidjela približna kvantitativna ocjena. Rezultati upućuju na to da dinamički odziv nasipa ima izrazito nelinearna svojstva. Koeficijent vršnog ubrzanja tla na kruni nasipa veći je nego na prirodnoj površini tla, a označava povećanje od 73 % u odnosu na koeficijent na prirodnoj površini tla. Kada seizmički intenzitet postigne određenu vrijednost, područje plastičnosti postupno se pojavljivalo na nasipu, a postoji i kontinuirano proširenje područja plastičnosti koje je povezano s povećanjem vršnog ubrzanja ulaznog seizmičkog vala. Rezultati istraživanja mogu dati uvide i imati značajne implikacije za daljnje istraživanje hladnih područja.To investigate more fully seismic behaviour of the Qinghai-Tibet railway embankment, a comprehensive discussion and a781nalysis is conducted in this paper by applying a numerical technique. Specifically, the one dimensional equivalent linear ground response analysis was conducted in permafrost regions. On this basis, the seismic response of a typical railway embankment was further studied by applying the nonlinear dynamic finite element analysis method. As a result, nonlinear behaviour of permafrost sites was determined, and the dynamic acceleration, velocity and displacement of the embankment was discussed and the quantitative assessment was approximately estimated. The results indicate that the dynamic response of the embankment has distinct nonlinear characteristics. The peak ground acceleration coefficient at the embankment shoulder is larger than the natural ground surface, marking a 73% increase compared to the coefficient on the natural ground surface. When the seismic intensity reaches a certain value, a plastic zone gradually appears in the embankment, and a continuous extension of the plastic zone can be noted with an increase in peak acceleration of the input seismic wave. The findings of this research may provide an additional insight and have significant implications for further research of cold regions

Comparison of left ventricular mechanical dyssynchrony parameters between exercise and adenosine triphosphate stress tests using gated single-photon emission computed tomography myocardial perfusion imaging

Background Left ventricular mechanical dyssynchrony (LVMD) can be induced after stress test. However, no studies have compared the influence of different stress-inducing methods on LVMD parameters. aims The aim of the study was to determine whether there is a difference between exercise and adenosine triphosphate (ATP) stress tests in terms of changes in LVMD parameters assessed using gated single-photon emission computed tomography myocardial perfusion imaging (GSPECT MPI). methods A total of 190 patients who underwent 99mTc-sestamibi GSPECT MPI were consecutively enrolled. Treadmill exercise and ATP stress tests were performed in 95 patients each. Normal myocardial perfusion was defined as the summed stress score (SSS) ≤3 and summed rest score (SRS) ≤3, myocardial ischemia as SSS \u3e3 and SRS ≤3, and myocardial infarction as SSS \u3e3 and SRS \u3e3. Parameters of LVMD, including phase standard deviation (PSD), phase bandwidth (PBW), skewness, and kurtosis were compared. Subtraction was made between values during stress and rest phases to acquire ∆PSD, ∆PBW, ∆skewness, and ∆kurtosis. results There were no differences in LVMD parameters between the exercise and ATP groups. The same results were obtained in the normal perfusion, ischemia, and infarction subgroups. Furthermore, no differences were observed in ∆PSD (median [interquartile range, IQR], 0.25 [-2.3 to 3.1] vs 0.42 (-1.7 to 3.1]; P = 0.73), ∆PBW (median [IQR], 1 [-7 to 11] vs 1 [-6 to 11]; P = 0.95), ∆skewness (mean [SD], -0.06 [0.63] vs 0 [0.81]; P= 0.53), and ∆kurtosis (median [IQR], -0.47 [-4.2 to 4.3] vs -0.42 [-4.8 to 5.2]; P= 0.73) between the exercise and ATP stress-inducing methods. conclusions There are no differences between the exercise and ATP stress tests in terms of changes in LVMD parameters. Thus, the 2 methods can be used alternatively