An Online Model Correction Method Based on an Inverse Problem:Part I—Model Error Estimation by Iteration

Errors inevitably exist in numerical weather prediction （NWP） due to imperfect numeric and physical parameterizations. To eliminate these errors, by considering NWP as an inverse problem, an unknown term in the prediction equations can be estimated inversely by using the past data, which are presumed to represent the imperfection of the NWP model （model error, denoted as ME）. In this first paper of a two-part series, an iteration method for obtaining the MEs in past intervals is presented, and the results from testing its convergence in idealized experiments are reported. Moreover, two batches of iteration tests were applied in the global forecast system of the Global and Regional Assimilation and Prediction System （GRAPES-GFS） for July-August 2009 and January-February 2010. The datasets associated with the initial conditions and sea surface temperature （SST） were both based on NCEP （National Centers for Environmental Prediction） FNL （final） data. The results showed that 6th h forecast errors were reduced to 10% of their original value after a 20-step iteration. Then, off-line forecast error corrections were estimated linearly based on the 2-month mean MEs and compared with forecast errors. The estimated error corrections agreed well with the forecast errors, but the linear growth rate of the estimation was steeper than the forecast error. The advantage of this iteration method is that the MEs can provide the foundation for online correction. A larger proportion of the forecast errors can be expected to be canceled out by properly introducing the model error correction into GRAPES-GFS.

An Online Model Correction Method Based on an Inverse Problem:PartⅡ——Systematic Model Error Correction

An online systematic error correction is presented and examined as a technique to improve the accuracy of real-time numerical weather prediction, based on the dataset of model errors （MEs） in past intervals. Given the analyses, the ME in each interval （6 h） between two analyses can be iteratively obtained by introducing an unknown tendency term into the prediction equation, shown in Part I of this two-paper series. In this part, after analyzing the 5-year （2001-2005） GRAPES- GFS （Global Forecast System of the Global and Regional Assimilation and Prediction System） error patterns and evolution, a systematic model error correction is given based on the least-squares approach by firstly using the past MEs. To test the correction, we applied the approach in GRAPES-GFS for July 2009 and January 2010. The datasets associated with the initial condition and SST used in this study were based on NCEP （National Centers for Environmental Prediction） FNL （final） data. The results indicated that the Northern Hemispheric systematically underestimated equator-to-pole geopotential gradient and westerly wind of GRAPES-GFS were largely enhanced, and the biases of temperature and wind in the tropics were strongly reduced. Therefore, the correction results in a more skillful forecast with lower mean bias and root-mean-square error and higher anomaly correlation coefficient.

Research on the Relationship between Income and Consumption of the Urban Residents in Hunan Province on the Basis of Error Correction Model

By using error correction model, I conduct co-integration analysis on the research of the relationship between the per capita practical consumption and per capita practical disposable income of urban residents in Hunan Province from 1978 to 2009. The results show that there is a co-integration relationship between the per capita practical consumption and the practical per capita disposable income of urban residents, and based on these, the corresponding error correction model is established. Finally, corresponding countermeasures and suggestions are put forward as follows: broaden the income channel of urban residents; create goods consuming environment; perfect socialist security system.

Parameters Sensitivity Analysis and Correction for Concrete Damage Plastic Model

In order to understand the effect of hardening ductility parameters and softening ductility parameters of the concrete damage plastic model in LS-DYNA,a sensitivity and reliability analysis of these parameters through a convenient cube unit test was conducted. The results showed that the peak strength strain was independent of the hardening ductility parameter DH,but affected by AH,BH,and CH. The softening ductility was mainly related to the softening ductility parameter AS,but not affected by the damage ductility exponent BS. In case that the model with default parameters failed to match the AS-controlled damage softening phase,an optimized model with an AS correction was developed. The corrected model with the AS value of 2 matched well with the code model,and exhibited good feasibility in predicting the stress-strain curve of different grades of concrete. Moreover,the practicability of the corrected model was further validated by the conventional triaxial test. The simulated curve exhibited favorable consistence with the trial curve. Therefore,the model with parameter correction could provide a prospective reference for predicting the mechanical properties of concrete.

A Robust Conformer-Based Speech Recognition Model for Mandarin Air Traffic Control

This study aims to address the deviation in downstream tasks caused by inaccurate recognition results when applying Automatic Speech Recognition(ASR)technology in the Air Traffic Control(ATC)field.This paper presents a novel cascaded model architecture,namely Conformer-CTC/Attention-T5(CCAT),to build a highly accurate and robust ATC speech recognition model.To tackle the challenges posed by noise and fast speech rate in ATC,the Conformer model is employed to extract robust and discriminative speech representations from raw waveforms.On the decoding side,the Attention mechanism is integrated to facilitate precise alignment between input features and output characters.The Text-To-Text Transfer Transformer(T5)language model is also introduced to handle particular pronunciations and code-mixing issues,providing more accurate and concise textual output for downstream tasks.To enhance the model’s robustness,transfer learning and data augmentation techniques are utilized in the training strategy.The model’s performance is optimized by performing hyperparameter tunings,such as adjusting the number of attention heads,encoder layers,and the weights of the loss function.The experimental results demonstrate the significant contributions of data augmentation,hyperparameter tuning,and error correction models to the overall model performance.On the Our ATC Corpus dataset,the proposed model achieves a Character Error Rate(CER)of 3.44%,representing a 3.64%improvement compared to the baseline model.Moreover,the effectiveness of the proposed model is validated on two publicly available datasets.On the AISHELL-1 dataset,the CCAT model achieves a CER of 3.42%,showcasing a 1.23%improvement over the baseline model.Similarly,on the LibriSpeech dataset,the CCAT model achieves a Word Error Rate(WER)of 5.27%,demonstrating a performance improvement of 7.67%compared to the baseline model.Additionally,this paper proposes an evaluation criterion for assessing the robustness of ATC speech recognition systems.In robustness evaluation experiments based on this criterion,the proposed model demonstrates a performance improvement of 22%compared to the baseline model.

Design of weak current measurement system and research on temperature impact

A dedicated weak current measurement system was designed to measure the weak currents generated by the neutron ionization chamber.This system incorporates a second-order low-pass filter circuit and the Kalman filtering algorithm to effectively filter out noise and minimize interference in the measurement results.Testing conducted under normal temperature conditions has demonstrated the system's high precision performance.However,it was observed that temperature variations can affect the measurement performance.Data were collected across temperatures ranging from -20 to 70℃,and a temperature correction model was established through linear regression fitting to address this issue.The feasibility of the temperature correction model was confirmed at temperatures of -5 and 40℃,where relative errors remained below 0.1% after applying the temperature correction.The research indicates that the designed measurement system exhibits excellent temperature adaptability and high precision,making it particularly suitable for measuring weak currents.

Power output correction model of narrow linewidth ring fiber laser filtered with whispering gallery mode resonator

A whispering gallery mode resonator(WGMR)filter can narrow laser linewidth while significantly changing the output power characteristics of fiber laser system.It is found that traditional laser output power model is invalid.We report a correction model of a narrow linewidth fiber laser filtered with a WGMR to analyze its power.We believe that the loss of the laser system and the threshold gain increase caused by the WGMR filter lead to the predominate amplified spontaneous emission during the original laser period.According to that,we assume the correction coefficient is an exponential decay related to the Er-doped fiber length in the large loss situation,and we verify it experimentally.As a result,the correction model is valid for WGMR-filtered fiber laser.

China’s Monetary Policy Impacts on Money and Stock Markets

This study investigated the impact of China’s monetary policy on both the money market and stock markets,assuming that non-policy variables would not respond contemporaneously to changes in policy variables.Monetary policy adjustments are swiftly observed in money markets and gradually extend to the stock market.The study examined the effects of monetary policy shocks using three primary instruments:interest rate policy,reserve requirement ratio,and open market operations.Monthly data from 2007 to 2013 were analyzed using vector error correction(VEC)models.The findings suggest a likely presence of long-lasting and stable relationships among monetary policy,the money market,and stock markets.This research holds practical implications for Chinese policymakers,particularly in managing the challenges associated with fluctuation risks linked to high foreign exchange reserves,aiming to achieve autonomy in monetary policy and formulate effective monetary strategies to stimulate economic growth.

i4OilS pill, an Operational Marine Oil Spill Forecasting Model for Bohai Sea

Oil spill models can effectively simulate the trajectories and fate of oil slicks, which is an essential element in contingency planning and effective response strategies prepared for oil spill accidents. However, when applied to offshore areas such as the Bohai Sea, the trajectories and fate of oil slicks would be affected by time-varying factors in a regional scale, which are assumed to be constant in most of the present models. In fact, these factors in offshore regions show much more variation over time than in the deep sea, due to offshore bathymetric and climatic characteristics. In this paper, the challenge of parameterizing these offshore factors is tackled. The remote sensing data of the region are used to analyze the modification of wind-induced drift factors, and a well-suited solution is established in parameter correction mechanism for oil spill models. The novelty of the algorithm is the self-adaptive modification mechanism of the drift factors derived from the remote sensing data for the targeted sea region, in respect to empirical constants in the present models. Considering this situation, a new regional oil spill model(i4Oil Spill) for the Bohai Sea is developed, which can simulate oil transformation and fate processes by Eulerian-Lagrangian methodology. The forecasting accuracy of the proposed model is proven by the validation results in the comparison between model simulation and subsequent satellite observations on the Penglai 19-3 oil spill accident. The performance of the model parameter correction mechanism is evaluated by comparing with the real spilled oil position extracted from ASAR images.

Analytical modeling of sandwich beam for piezoelectric bender elements

Piezoelectric bender elements are widely used as electromechanical sensors and actuators, An analytical sandwich beam model for piezoelectric bender elements was developed based on the first-order shear deformation theory （FSDT）, which assumes a single rotation angle for the whole cross-section and a quadratic distribution function for coupled electric potential in piezoelectric layers, and corrects the effect of transverse shear strain on the electric displacement integration. Free vibration analysis of simplysupported bender elements was carried out and the numerical results showed that, solutions of the present model for various thickness-to-length ratios are compared well with the exact two-dimensional solutions, which presents an efficient and accurate model for analyzing dynamic electromechanical responses of bender elements.

Relationship Between Agricultural Credits and Agricultural Economy Based on Error Correct Model in Heilongjiang Province

Heilongjiang is a large agriculture province.Problems of agriculture,rural areas and farmers are urgent to be solved.The development of agriculture needs the support of agricultural credits,because finance is the center of agriculture economy.However,the low comparative advantage in agriculture and pursuit of the capital interests which aggravate the conflicts of supply and demand of agricultural funds.Lacking of fund is the main factor that constrains the development of agricultural economy.In order to analyze the economic effect of agricultural credits on agricultural economy,an error correction model was set up to research the relationship between them,which based on the least square methods.Through the study of the contribution from agricultural credits to total value of agricultural out-put,the empirical evidence for developing the rural financial vigorously was provided,in order to promote the agricultura leconomic development.

Water level updating model for flow calculation of river networks

Complex water movement and insufficient observation stations are the unfavorable factors in improving the accuracy of flow calculation of river networks. A water level updating model for river networks was set up based on a three-step method at key nodes, and model correction values were collected from gauge stations. To improve the accuracy of water level and discharge forecasts for the entire network, the discrete coefficients of the Saint-Venant equations for river sections were regarded as the media carrying the correction values from observation locations to other cross-sections of the river network system. To examine the applicability, the updating model was applied to flow calculation of an ideal river network and the Chengtong section of the Yangtze River. Comparison of the forecast results with the observed data demonstrates that this updating model can improve the forecast accuracy in both ideal and real river networks.

Capital mobility in Latin American and Caribbean countries: new evidence from dynamic common correlated effects panel data modeling

This study investigates the degree of capital mobility in a panel of 16 Latin American and 4 Caribbean countries during 1960 to 2017 against the backdrop of the Feldstein-Horioka hypothesis by applying recent panel data techniques.This is the first study on capital mobility in Latin American and Caribbean countries to employ the recently developed panel data procedure of the dynamic common correlated effects modeling technique of Chudik and Pesaran(J Econ 188:393–420,2015)and the error-correction testing of Gengenbach,Urbain,and Westerlund(Panel error correction testing with global stochastic trends,2008,J Appl Econ 31:982–1004,2016).These approaches address the serious panel data econometric issues of crosssection dependence,slope heterogeneity,nonstationarity,and endogeneity in a multifactor error-structure framework.The empirical findings of this study reveal a low average(mean)savings–retention coefficient for the panel as a whole and for most individual countries,as well as indicating a cointegration relationship between saving and investment ratios.The results indicate that there is a relatively high degree of capital mobility in the Latin American and Caribbean countries in the short run,while the long-run solvency condition is maintained,which is due to reduced frictions in goods and services markets causing increase competition.Increased capital mobility in these countries can promote economic growth and hasten the process of globalization by creating a conducive economic environment for FDI in these countries.

On ionosphere-delay processing methods for single-frequency precise-point positioning

In single-frequency precise-point positioning of a satellite,ionosphere delay is one of the most important factors impacting the accuracy. Because of the instability of the ionosphere and uncertainty of its physical properties, the positioning accuracy is seriously limited when using a precision-limited model for correction. In order to reduce the error, we propose to introduce some ionosphere parameter for real-time ionosphere-delay estimation by applying various mapping functions. Through calculation with data from the IGS（ International GPS Service） tracking station and comparison among results of using several different models and mapping functions, the feasibility and effectiveness of the new method are verified.

Assessing the spillover effects of U.S.monetary policy normalization on Nigeria sovereign bond yield

This study examines the spillover effects of U.S.monetary policy normalization on Nigeria 10-Year Treasury bond yield between 2011 and 2017,using the vector error correction model approach.Our results reveal that domestic factors,such as exchange rate and inflation,rather than the U.S.10-Year sovereign bond yield,are the key drivers of Nigeria 10-Year bond yield.Additionally,the spillover effect from the U.S.monetary policy was amplified by oil price shocks and changes in Nigeria’s monetary policy rates.Our counterfactual analysis confirms the findings.

Soft-Sensing Method of Water Temperature Measurement for Controlled Cooling System

Aiming at the water temperature measuring problem for controlled cooling system of rolling plant,a new water temperature measuring method based on soft-sensing method with a water temperature model of on-line self correction parameter was built.A water temperature compensation factor model was also built to improve coiling temperature control precision.It was proved that the model meets production requirements.The soft-sensing technique has extensive applications in the field of metal forming.

IMPACT OF SUMMER WARMING ON DYNAMICS-STATISTICS-COMBINED METHOD TO PREDICT THE SUMMER TEMPERATURE IN CHINA

Based on NCEP/NCAR daily reanalysis data, climate trend rate and other methods are used to quantitatively analyze the change trend of China's summer observed temperature in 1983—2012. Moreover, a dynamics-statistics-combined seasonal forecast method with optimal multi-factor portfolio is applied to analyze the impact of this trend on summer temperature forecast. The results show that: in the three decades, the summer temperature shows a clear upward trend under the condition of global warming, especially over South China, East China, Northeast China and Xinjiang Region, and the trend rate of national average summer temperature was 0.27℃ per decade. However, it is found that the current business model forecast(Coupled Global Climate Model) of National Climate Centre is unable to forecast summer warming trends in China, so that the post-processing forecast effect of dynamics-statistics-combined method is relatively poor. In this study, observed temperatures are processed first by removing linear fitting trend, and then adding it after forecast to offset the deficiency of model forecast indirectly. After test, ACC average value in the latest decade was 0.44 through dynamics-statistics-combined independent sample return forecast. The temporal correlation(TCC) between forecast and observed temperature was significantly improved compared with direct forecast results in most regions, and effectively improved the skill of the dynamics-statistics-combined forecast method in seasonal temperature forecast.

The impact of bank lending on Palestine economic growth:an econometric analysis of time series data

Banking is an essential sector of Palestine’s economy.More credits provided by banks are considered to have a positive impact on economic growth so that the overall objective of this study is to examine the impact of bank lending on economic growth in Palestine.The study employs the Augmented Dickey-Fuller to test for stationarity in the time series,The Johansen co-integration,Vector Autoregressive Model and Vector Error Correction Model are employed to identify the long-run and short-run dynamics among the variables,and Granger causality test in order to determine the direction of causality.The study finds that a long run relationship exists among the variables and insignificant short run relationship.Also,the study findings show that there is unidirectional causality and runs from GDP to bank lending.The insignificant contribution of bank lending to GDP is attributed to the fact that banks are not highly interested in lending to the production sector of the economy due to the high level of risk.However,the primary empirical evidence reveals that bank lending doesn’t cause economic growth,but economic growth causes bank lending.

Impact of Sub-Economic on Money Supply in Nigeria: An Autoregressive Distribution Lag (ARDL) Approach

The escalation in dollar rates and the price instability in the Nigerian economy went through some significant structural and institutional changes such as the liberalization of the external trade, the elimination of price and interest rate controls, and the adoption of a managed float exchange rate system as well as the changes in monetary policy including innovations in the banking sector. Hence, the study examines the impact of financial development on money demand in Nigeria by means of ARDL approach. It examined the quarterly returns of M2, exchange rate (EXR), inflation rate (IFR), currency in credits to private sector (CPS) and circulation (CIC). The data span from 1991 to 2018. The study utilizes regression model techniques where the regression model’s residual is tested for Cointegration using Engle-Granger residual approach, the significances of the variable’s co-movement are checked by pairwise Granger Causality tests and ARDL and VECM are estimated in order to account for the short run and long run relationship among the variables. From the empirical results, Engle-Granger residuals and pairwise Granger Causality tests confirm cointegration among variables. The ARDL and VECM confirm the long run relation between money demand (M2) and financial development variables: CPS and CIC. ARDL models (short run relationship) are estimated for exchange rate and inflation rate. Long run (VECM) analysis has confirmed significance of financial development variables (CPS and CIC) with positive sign;implies that money demand function is stable in long run. The VECM granger causality results reveal that bidirectional causality exists between currency in circulation and money demand in both short and long run. Unidirectional causal relationship exists between credits to private sector and money demand in both short and long run. Hence, government should pay more attention on financial development and ensure a coordination of both fiscal and monetary policy.

Predicting Bank Interests When Monetary Rates Are Close to Zero

Monetary policies, either actual or perceived, cause changes in monetary interest rates. These changes impact the economy through financial institutions, which react to changes in the monetary rates with changes in their administered rates, on both deposits and lendings. The dynamics of administered bank interest rates in response to changes in money market rates is essential to examine the impact of monetary policies on the economy. Chong et al. (2006) proposed an error correction model to study such impact, using data previous to the recent financial crisis. In this paper we examine the validity of the model in the recent time period, characterized by very low monetary rates. The current state of close-to-zero monetary rates is of particular relevance, as it has never been studied before. Our main contribution is a novel, more parsimonious, model and a predictive performance assessment methodology, which allows comparing it with the error correction model.