Error Correction Strategies Research Articles

Integrated Positioning System of Kiwifruit Orchard Mobile Robot Based on UWB/LiDAR/ODOM.

To address the issue of low positioning accuracy of mobile robots in trellis kiwifruit orchards with weak signal environments, this study investigated an outdoor integrated positioning method based on ultra-wideband (UWB), light detection and ranging (LiDAR), and odometry (ODOM). Firstly, a dynamic error correction strategy using the Kalman filter (KF) was proposed to enhance the dynamic positioning accuracy of UWB. Secondly, the particle filter algorithm (PF) was employed to fuse UWB/ODOM/LiDAR measurements, resulting in an extended Kalman filter (EKF) measurement value. Meanwhile, the odometry value served as the predicted value in the EKF. Finally, the predicted and measured values were fused through the EKF to estimate the robot's pose. Simulation results demonstrated that the UWB/ODOM/LiDAR integrated positioning method achieved a mean lateral error of 0.076 m and a root mean square error (RMSE) of 0.098 m. Field tests revealed that compared to standalone UWB positioning, UWB-based KF positioning, and LiDAR/ODOM integrated positioning methods, the proposed approach improved the positioning accuracy by 64.8%, 13.8%, and 38.3%, respectively. Therefore, the proposed integrated positioning method exhibits promising positioning performance in trellis kiwifruit orchards with potential applicability to other orchard environments.

Resilient Pseudorange Error Prediction and Correction for GNSS Positioning in Urban Areas

Positioning, navigation and timing (PNT) is essential for internet of things (IoT) communications and location-based services. Although GNSS can provide accurate PNT in open areas, obtaining reliable PNT is still a considerable technical challenge in complex urban environments. This is because the GNSS signals are more likely to be affected by multipath interference and non-line of sight (NLOS) reception issues arising from the obstructions and reflections in built environments. These introduce range measurement errors that degrade the GNSS positioning accuracy. This paper proposes two resilient pseudorange error prediction and correction strategies to improve the GNSS positioning accuracy in urban environments. In particular, considering the carrier-to-noise density (C/N0), satellite elevation angle and local positional information, the random forest based pseudorange error prediction and correction models are constructed in two variations, including: 1) the point-based correction (PBC), and 2) the grid-based correction (GBC). The final improved positioning solution is then calculated by using the least square method (LSM) of the corrected pseudoranges. Kinematic test results in urban environments show that both variations of the proposed model can improve the positioning accuracy by 42.9% and 40.8% in horizontal, and by 60.1% and 63.3% in 3D, respectively, compared to the positioning results obtained by traditional method without pseudorange error corrections. The improvements are 41.1% and 38.9% in horizontal, and 45.7% and 50.0% in 3D, respectively, compared with traditional elevation angle weighting method.

Role of energy consumption, tourism and economic growth in carbon emission: evidence from Kuwait

Even though the Paris Agreement and the Sustainable Development Goals say that low-carbon economic growth is essential, more research must be done to determine how the tourism sector affects carbon productivity. So, to see if increased energy usage, tourism, and economic growth jointly raise carbon productivity in Kuwait, this study uses a vector error correction strategy to look at the years 1995–2020. Predictions about how sustainable tourism will affect energy efficiency, and carbon productivity improvements are also an excellent way to learn more about this subject. As the amount of carbon dioxide in the air increases, tourism will go down by 0.13 percent. Inverse cointegration is the term for this phenomenon. However, the vector error correction model showed that carbon emissions go down as the economy grows and people pay more attention to how much energy they use. Nevertheless, Granger’s theory of cause and effect says that carbon emissions, energy use, and economic growth can only lead to more tourism in one way.

An iterative method for the thermally coupled incompressible magnetohydrodynamics equations at high parameter

We design an iterative method based on linearization approach for the thermally coupled stationary incompressible magnetohydrodynamics equations at high physical parameters. The iterative method includes solving the considered equations by the linearization approach and applying the error correction strategy to reduce the iterative error arising from the linearization approach for solving the nonlinear equations. The iterative method not only can improve computational capability for solving the considered equations at high physical parameters but also can save computational time and iterative step. Moreover, we prove the stability of the iterative method and derive the iterative error estimates. Finally, some numerical tests are implemented to illustrate the efficiency of the presented method.

A Research on Error Correction Strategies in English Class of Senior High School from the Perspective of Gender and Personalities based on Affective Filter Hypothesis Taking SJZ Foreign Language School as an Example

According to the Compulsory Education English Curriculum Standard (2022), senior high school students are encouraged to actively speak English in English in and out of the class. Teachers' inappropriate error correction methods will increase students' English-speaking anxiety and reduce their enthusiasm for oral English learning. Based on Affective Filter Hypothesis, this study uses Foreign Language Speaking Anxiety Self-Schema (FLSAS) as the measuring tool, and try to find out the attitude of students toward oral error correction in English classes and to what extent do gender and personality affect this attitude. The subjects were 130 students from ten parallel classes in senior 2 of Shijiazhuang Foreign Language School. The instruments of quantitative study consisted of one questionnaire, with a total of 17 items used to investigate students English-speaking anxiety. 130 valid questionnaires were collected, and then the data collected from the questionnaires were further analyzed by SPSS, by which the results have shown that different personalities have relative influence on students attitudes toward oral error correction in English classes. Discussions about error correction strategies that teachers can adopt are put forward based on the results.

Spatial profiling of microbial communities by sequential FISH with error-robust encoding

Spatial analysis of microbiomes at single cell resolution with high multiplexity and accuracy has remained challenging. Here we present spatial profiling of a microbiome using sequential error-robust fluorescence in situ hybridization (SEER-FISH), a highly multiplexed and accurate imaging method that allows mapping of microbial communities at micron-scale. We show that multiplexity of RNA profiling in microbiomes can be increased significantly by sequential rounds of probe hybridization and dissociation. Combined with error-correction strategies, we demonstrate that SEER-FISH enables accurate taxonomic identification in complex microbial communities. Using microbial communities composed of diverse bacterial taxa isolated from plant rhizospheres, we apply SEER-FISH to quantify the abundance of each taxon and map microbial biogeography on roots. At micron-scale, we identify clustering of microbial cells from multiple species on the rhizoplane. Under treatment of plant metabolites, we find spatial re-organization of microbial colonization along the root and alterations in spatial association among microbial taxa. Taken together, SEER-FISH provides a useful method for profiling the spatial ecology of complex microbial communities in situ.

A New General Framework for Response Prediction of Composite Structures Based on Digital Twin with Three Effective Error Correction Strategies

A New General Framework for Response Prediction of Composite Structures Based on Digital Twin with Three Effective Error Correction Strategies

Efficient diagnostics for quantum error correction

Fault-tolerant quantum computing will require accurate estimates of the resource overhead under different error correction strategies, but standard metrics such as gate fidelity and diamond distance have been shown to be poor predictors of logical performance. We present a scalable experimental approach based on Pauli error reconstruction to predict the performance of concatenated codes. Numerical evidence demonstrates that our method significantly outperforms predictions based on standard error metrics for various error models, even with limited data. We illustrate how this method assists in the selection of error correction schemes.

Rethinking statistical learning as a continuous dynamic stochastic process, from the motor systems perspective.

The brain integrates streams of sensory input and builds accurate predictions, while arriving at stable percepts under disparate time scales. This stochastic process bears different unfolding dynamics for different people, yet statistical learning (SL) currently averages out, as noise, individual fluctuations in data streams registered from the brain as the person learns. We here adopt a new analytical approach that instead of averaging out fluctuations in continuous electroencephalographic (EEG)-based data streams, takes these gross data as the important signals. Our new approach reassesses how individuals dynamically learn predictive information in stable and unstable environments. We find neural correlates for two types of learners in a visuomotor task: narrow-variance learners, who retain explicit knowledge of the regularity embedded in the stimuli. They seem to use an error-correction strategy steadily present in both stable and unstable environments. This strategy can be captured by current optimization-based computational frameworks. In contrast, broad-variance learners emerge only in the unstable environment. Local analyses of the moment-by-moment fluctuations, naïve to the overall outcome, reveal an initial period of memoryless learning, well characterized by a continuous gamma process starting out exponentially distributed whereby all future events are equally probable, with high signal (mean) to noise (variance) ratio. The empirically derived continuous Gamma process smoothly converges to predictive Gaussian signatures comparable to those observed for the error-corrective mode that is captured by current optimization-driven computational models. We coin this initially seemingly purposeless stage exploratory. Globally, we examine a posteriori the fluctuations in distributions' shapes over the empirically estimated stochastic signatures. We then confirm that the exploratory mode of those learners, free of expectation, random and memoryless, but with high signal, precedes the acquisition of the error-correction mode boasting smooth transition from exponential to symmetric distributions' shapes. This early naïve phase of the learning process has been overlooked by current models driven by expected, predictive information and error-based learning. Our work demonstrates that (statistical) learning is a highly dynamic and stochastic process, unfolding at different time scales, and evolving distinct learning strategies on demand.

Complementary ensemble empirical mode decomposition and independent recurrent neural network model for predicting air quality index

Establishing a scientific and effective air quality prediction model is of great scientific value and practical significance for protecting people’s health and promoting social harmony and stability. However, existing prediction models have certain shortcomings in various aspects. To address these shortcomings, this paper combines different methods to achieve better prediction accuracy. Outlier analysis is done for air quality index (AQI) using an isolation forest algorithm. An air quality prediction system that consists of data preprocessing, optimization, prediction, and modification is established. The complementary ensemble empirical mode decomposition (CEEMD), modified particle swarm optimization (MPSO) algorithm, independent recurrent neural network (IndRNN), and nonlinear correction strategy are employed for the prediction. We select five cities with different AQI as the experiment sites, and three experiments are designed to test the accuracy of the prediction model. The results reveal that (1) by using CEEMD data decomposition technology to deal with the non-stationarity and nonlinearity of the original data, the prediction accuracy of the original cyclic neural network model can be improved by about 15%. (2) The prediction system with the CEEMD-MPSO-IndRNN model as the core prediction module and with a nonlinear error correction strategy has good scalability and robustness for air quality prediction. (3) The performance of the air quality prediction model constructed with systematic thought is better than other comparable models, and it can effectively predict the time series data of different cities and frequencies.

Multi-step-ahead copper price forecasting using a two-phase architecture based on an improved LSTM with novel input strategy and error correction

Accurate copper price forecasting plays a vital role in many aspects of economics. However, the complicated fluctuations of copper price make it a challenging job. This study develops a novel architecture based on long short-term memory model optimized by genetic algorithm (GA-LSTM) and error correction strategy for multi-step-ahead copper price forecasting. The proposed architecture includes the following two sub-phases: (1) initial forecasting of copper price; (2) error correction. In the first phase, to improve the forecast accuracy, a number of recent copper price data and some selected influencing factors are combined to construct a hybrid input of GA-LSTM model. The hybrid input strategy simultaneously considers the evolution rule of historical price data and the causal relationship between the influencing factors and copper price data, which provides more information for GA-LSTM model. One real copper price data series with a time span of 30 years is used for validating the forecasting ability of the proposed architecture and a same length of iron ore price data series is also adopted for testifying the generalization ability and robustness of the proposed architecture. The experiment results illustrate that the proposed architecture outperforms the benchmark models considered in this study.

Error Correction and Error Detection in Network

Errors manipulation explains how errors are handled and determined using the network, specifically on the data connection layer. We offer a top level view of error manipulate in this paintings, including mistakes detection and mistakes restore. Information hyperlink layer mistakes manipulate takes place there. We specially communicate approximately the varieties of error detection algorithms used to locate faults and the way to repair them so the receiver can get the real statistics. The essential requirement of each communique system in the realm of wi-fi conversation these days is the potential to ship and receive errorless statistics over any noisy channel. The assets of noise and interference have also grown as a result of the development in records transmission. Engineers have made several tries to address the demand for more dependable and effective strategies for detecting and correcting errors within the acquired statistics. Various techniques are hired to pick out and attach information transmission faults. This evaluation paper affordan a extensive range of error detection and correction strategies thathave been around for a while. More than one tend error in SRAM memory rise whilst the technology scaled down, inflicting unmarried cellular and more than one cellular upsets to emerge. Blunders-correcting codes, such the preliminary approach of the (7,four) hamming code, wherein 7 stands for the overall code word, four stands for statistics bits, and 3 stands for parity bits, had been positioned into use and their encoding and decoding processes have been examined. The main drawback of this hamming code is that it's far handiest suitable forsingle-bitt errors detection and rectification.

Multi-Step Ahead Short-Term Electricity Load Forecasting Using VMD-TCN and Error Correction Strategy

The electricity load forecasting plays a pivotal role in the operation of power utility companies precise forecasting and is crucial to mitigate the challenges of supply and demand in the smart grid. More recently, the hybrid models combining signal decomposition and artificial neural networks have received popularity due to their applicability to reduce the difficulty of prediction. However, the commonly used decomposition algorithms and recurrent neural network-based models still confront some dilemmas such as boundary effects, time consumption, etc. Therefore, a hybrid prediction model combining variational mode decomposition (VMD), a temporal convolutional network (TCN), and an error correction strategy is proposed. To address the difficulty in determining the decomposition number and penalty factor for VMD decomposition, the idea of weighted permutation entropy is introduced. The decomposition hyperparameters are optimized by using a comprehensive indicator that takes account of the complexity and amplitude of the subsequences. Besides, a temporal convolutional network is adopted to carry out feature extraction and load prediction for each subsequence, with the primary forecasting results obtained by combining the prediction of each TCN model. In order to further improve the accuracy of prediction for the model, an error correction strategy is applied according to the prediction error of the train set. The Global Energy Competition 2014 dataset is employed to demonstrate the effectiveness and practicality of the proposed hybrid model. The experimental results show that the prediction performance of the proposed hybrid model outperforms the contrast models. The accuracy achieves 0.274%, 0.326%, and 0.405 for 6-steps, 12-steps, and 24 steps ahead forecasting, respectively, in terms of the mean absolute percentage error.

The influence of a vibrotactile biofeedback system on postural dynamics during single-leg standing in healthy older adults

The effectiveness of sensory substitution technology, such as haptic-based vibrotactile biofeedback (VBF), has been verified for balance training and rehabilitation. However, whether VBF training changes postural dynamics in older people remains unknown. This study investigated the influence of VBF training on postural dynamics during single-leg standing in older adults, using detrended fluctuation analysis (DFA). Twenty older adults participated in this study. Measurement of postural sway comprised three phases: first measurement session as a baseline test, postural training (day 1), and second measurement session (day 2). The BF group received BF training during the balance training session, while the control group practiced single-leg stance without BF. The center of pressure (CoP) trajectory was recorded in the first measurement session (pre) and second measurement session (post) at 50 Hz. DFA revealed the presence of two linear scaling regions in the CoP, indicating the presence of fast- and slow-scale fluctuations. For the BF group, slow-scale postural dynamics revealed more anti-persistent behavior after training in the anterior-posterior direction. However, the control group showed a change toward more random dynamics after training. These different influences suggest that the BF system might improve error correction strategies during single-leg standing for older adults, while single-leg standing training without the BF system might cause the loss of controllability in single-leg standing. Further, the results of the DFA are discussed in the context of balance training using VBF.

A novel two-stage seasonal grey model for residential electricity consumption forecasting

Accurate electricity consumption forecasting plays a significant role in power production and supply and power dispatching. Thus, a new hybrid model combing a grey model with fractional order accumulation, called FGM (1, 1), with seasonal factors, sine cosine algorithm (SCA), and an error correction strategy is proposed in this research. To accurately predict the seasonal fluctuations, seasonal factors are used in this model; Then, with the aim of improving the prediction performance, a SFGM (1, 1) model optimized by SCA rather than least square method, namely SCA-SFGM (1, 1), is establish to forecast electricity consumption; Moreover, considering forecasting error sequence may contain useful information, an error correction strategy is introduced to model forecasting error time series to adjust the preliminary forecasts of SCA-SFGM (1, 1). Fourth, four comparison models, three measurement criteria and a statistical hypothesis testing method using monthly residential electricity consumption dataset from 2015 to 2020 are designed to verify the prediction performance of models; Lastly, experimental results show that the mean absolute percentage error (MAPE) of the proposed model is 4.1698%, which is much lower than 14.5642%, 6.5108%, 5.9472%, 5.7060% and 4.9219% of GM (1, 1), SARIMA, SGM (1, 1), SFGM (1, 1) and SCA-SFGM (1, 1) models, respectively, showing that the proposed model can not only effectively capture seasonal fluctuations, it also adds an operational candidate forecasting benchmark model in electricity markets.

Grammar System of TCFL Driven by Neural Network Technology.

With the economy's continued and stable growth, China's political and economic influence in the international community has grown, and more and more friends from all over the world are requesting to learn Chinese and visit China. The growth of information technology and curriculum integration has had a significant impact on TCFL (teaching Chinese as a foreign language). Facing the new situation will enable us to gain a fresh perspective on the current state of TCFL grammar system research. Through specific teaching practice, this paper verifies the effectiveness of teaching Chinese as a foreign language and cultural vocabulary. This paper proposes a grammar error correction scheme based on hybrid models—Transformer model and N-gram model—that dynamically combine the outputs of different neural modules to improve the model's ability to capture semantic information, with the goal of correcting Chinese grammar errors. Experiments show that the Transformer and N-gram model-based Chinese grammar error correction strategy performs well in the global effect, and the overall performance is the best in the detection and positioning levels. At the detection level, the model in this document has the highest error correction accuracy of 0.64 and the highest recall rate of 0.67. The results show that adding an attention mechanism to a grammatical error correction model can improve its computational efficiency.

An adaptive hybrid ensemble with pattern similarity analysis and error correction for short-term load forecasting

Forecasting future electricity consumption is one of the critical processes for addressing energy management and supply–demand balance in modern electrical systems. In this paper, an adaptive hybrid ensemble, CMKP-EG-SVR, is presented to solve the short-term load forecasting problem. For this purpose, the merits of the median filtering are exploited at an initial phase of data preprocessing to reduce the high-frequency parts and to detect and correct outliers and missing data. Besides, Pearson’s correlation, k-nearest neighbors algorithm and a particular calendar grouping based on day-type encoding are used as efficient tools for selecting relevant features and extracting similar patterns. To achieve diversification gains and improve the overall performance, several prediction engines based on support vector regression are proposed, in an adaptive weighting procedure, by our ensemble forecasting scheme. Finally, an error correction strategy based on a modified Gaussian function is also included for a further enhancement in prediction accuracy. The main idea behind this integration is to meet the multiple requirements and characteristics of accurate prediction using a unique and adaptive forecasting scheme. The electricity demand data from the Australian state of New South Wales are employed to describe an extensive evaluation under both normal and anomalous load conditions. The obtained results of the proposed CMKP-EG-SVR model demonstrate: (1) an average computation time of 4.47 s; (2) mean absolute percentage error values of the range of 1.03%–4.26%; (3) a superior performance over 24 state-of-the-art forecasting techniques.

An ensemble method for short-term wind power prediction considering error correction strategy

With a high proportion of renewable energy injected into the power grid, the accurate wind power prediction of wind farms is key to improving power quality and ensuring the stable operation of the power grid. In this paper, an ensemble learning prediction model considering the rolling error correction strategy used for short-term wind power prediction is proposed. Firstly, the ensemble learning prediction model integrating multiple Gradient Boosting Trees (GBDTs) based on Bayesian optimization is established, and the characteristics of prediction errors are analyzed. To further improve the prediction accuracy, a daily rolling error correction strategy based on the Swing Window segmentation method, Spearman correlation coefficient and Quantile Regression is established to obtain the optimal compensation amount. Finally, the corrected short-term wind power prediction results are obtained. The two-year dataset collected from a regional wind farm in China is used as the benchmark test. Different prediction models and error correction strategies are compared, and the effectiveness of the proposed method is comprehensively evaluated. The results from different seasons show that the proposed prediction method has good stability, and the proposed error correction strategy has good generalization ability.

Data-driven assisted chance-constrained energy and reserve scheduling with wind curtailment

Chance-constrained optimization (CCO) has been widely used for uncertainty management in power system operation. With the prevalence of wind energy, it becomes necessary to consider wind curtailment as a dispatch variable in CCO. However, the wind curtailment will cause an impulse for the uncertainty distribution, yielding challenges for explicit modeling of chance constraint. In this paper, a novel data-driven framework is developed to handle this issue. By modeling the wind curtailment as a cap enforced on the wind power output, the proposed framework constructs a data-driven surrogate to describe the relationship between wind curtailment and the tightening boundary of chance constraints. This allows us to reformulate the CCO with wind curtailment as a mixed-integer second-order cone programming (MI-SOCP) problem. An error correction strategy is developed by solving a convex linear programming (LP) to improve the modeling accuracy. The notable characteristic of the proposed method is that the chance constraints with wind curtailment can be explicitly and accurately handled without any distribution assumptions, which provides great convenience for the power dispatch under a high percentage of renewables. Case studies performed on the PJM 5-bus and IEEE 118-bus systems demonstrate that the proposed method is capable of accurately accounting the influence of wind curtailment dispatch in CCO. It shows that the proposed method outperforms existing methods by producing more economic and secure solutions for the chance-constrained energy and reserve scheduling problem under uncertainties.

Implementing Error Correction Strategies to Enhance Arts & Science College Students’ Writing Skills

The purpose of the study is to implement error correction strategies to enhance arts & Science college students’ writing skills. Participants of the study were final year students of UG and PG from Dhanalakshmi Srinivasan Arts and Science College for Women, Perambalur. An Online Google form and a survey questionnaire were administered in this study to collect data. Five point likert scale was incorporated in the questionnaire. Respondent’s size is two hundred and seventeen (217) female students. The problem statement of the study is that the female students of Arts and Science College struggle to frame a correct sentence with proper sentence structure. They are unable to frame a sentence with complete meaning. The hypothesis of the study is to enhance the arts and science female students' writing skills through the gallery walk technique and three error correction techniques. The objectives of the study are i) to enhance the writing skills of arts and science college female students, ii) To develop the ability of students to understand the importance of sentence making, iii) To bring awareness about the gallery walk technique, self, peer, and teacher correction, and its benefits. The research questions are i) Will the students develop their writing skills through self-correction, peer-correction, and teacher-correction strategies? ii) Will students enhance sentence structure and sentence pattern through the gallery walk technique? iii) Will students improve their language proficiency through texts and images? The collaborative theory is utilized in this study. The findings of the study are that through gallery walk and self-correction, peer-correction, and teacher-correction strategies, students have shown involvement and improvement.