Data Prediction Method Research Articles

Power data prediction method of new energy system based on wavelet transform and adaptive hybrid optimization

Abstract New energy power generation is easily affected by climate, seasons, holidays and other factors, and has different fluctuation characteristics. Aiming at the problems of low prediction accuracy and weak generalization ability of power data with different characteristics, a new energy generation data prediction method based on wavelet transform and adaptive hybrid optimization is proposed. First, the power data is transformed by wavelets to obtain sequencing of different types of information. Then LSTM model and autoregressive moving average model are used to fit and predict different sequences. Finally, the prediction sequence is reconstructed to obtain the prediction result of the power data. This method takes the actual data of a power system as the data set, which not only has high prediction accuracy, but also has strong generalization ability.

基于外部知识辅助的人群健康数据预测方法

基于外部知识辅助的人群健康数据预测方法

Predictive analysis of brain imaging data based on deep learning algorithms

Abstract This paper analyzes the effectiveness of predictive analysis of brain imaging data based on deep learning algorithms, and improves the prediction accuracy and efficiency of brain imaging data through improved methods. The first step is to measure the local consistency of the brain imaging data using Kendall’s concordance coefficient (KCC), and to analyze the differences between the datasets using the two-sample t-test. Secondly, a batch normalized convolutional neural network (BN-CNN)-based prediction method for brain imaging data has been developed. This method extracts spatial and temporal features in two convolutional layers, followed by a fully connected layer for classification. Experimental results show that this method is helpful in predicting missing structural data in brain imaging. Secondly, a batch normalized convolutional neural network (BN-CNN) based brain imaging data prediction method is developed, which extracts spatial and temporal features in two convolutional layers. Then it connects to a fully connected layer for classification. The experimental results show that this method’s structural similarity index (SSIM) and feature similarity index (FSIM) in brain imaging data prediction of missing data reaches 0.9446 and 0.9465, respectively, which is significantly better than that of other GAN benchmarks. In applying the method to epilepsy and Parkinson’s cases, this algorithm is used to epilepsy and Parkinson’s cases, and a two-sample t-test analyzes the differences in the data sets. In the application of epilepsy and Parkinson’s cases, the algorithm in this paper has an average prediction accuracy of 93.37%, effectively reducing the rate of incorrect predictions. Deep learning algorithms are highly efficient and accurate in predicting brain imaging data, which is crucial for future clinical diagnosis and treatment.

Lightweight RepVGG-Based Cross-Modality Data Prediction Method for Solid Rocket Motors.

Solid rocket motors (SRMs) have been popularly used in the current aerospace industry. Performance indicators, such as pressure and thrust, are of great importance for rocket monitoring and design. However, the measurement of such signals requires high economic and time costs. In many practical situations, the thrust measurement error is large and requires manual correction. In order to address this challenging problem, a lightweight RepVGG-based cross-modality data prediction method is proposed for SRMs. An end-to-end data prediction framework is established by transforming data across different modalities. A novel RepVGG deep neural network architecture is built, which is able to automatically learn features from raw data and predict new time-series data of different modalities. The effectiveness of the proposed method is extensively validated with the field SRM data. The accurate prediction of the thrust data can be achieved by exploring the pressure data. After calculation, the percentage error between the predicted data and the actual data is less than 5%. The proposed method offers a promising tool for cross-modality data prediction in real aerospace industries for SRMs.

Research on Earthquake Data Prediction Method Based on DIN–MLP Algorithm

This paper proposes a recommendation algorithm that combines MLP with the DIN model and conducts simulation experiments in the field of earthquake missing data prediction. The original DIN model may face challenges and weaknesses in earthquake monitoring data prediction, such as a limited capability in handling data loss or anomalies in seismic monitoring stations. To overcome these issues, we innovatively treat seismic monitoring stations as special users and historical data patterns as recommended items. Based on the DIN model, we implement data processing and prediction for seismic monitoring stations and introduce an attention mechanism based on MLP neural networks in the model, while leveraging the prior knowledge base to enhance predictive capabilities. Compared to the original DIN model, our proposed approach not only recommends sequence combinations that meet the demands of seismic monitoring stations but also enhances the matching between station behavior attributes and historical data characteristics, thereby significantly improving prediction accuracy. To validate the effectiveness of our method, we conducted comparative experiments. The results show that the GAUC achieved by the DIN–MLP model reaches 0.69, which is an 11 percent point improvement over the original DIN model. This highlights the remarkable advantages of our algorithm in earthquake missing data prediction. Furthermore, our research reveals the potential of the DIN–MLP algorithm in practical applications, providing more accurate data processing and time series combination recommendations for the field of earthquake monitoring stations, thus contributing to the improvement of monitoring efficiency and accuracy.

Research on Intelligent Monitoring of Urban Environment under the Environment of the Internet of Things

With the development of the economy, human society has gradually strengthened the emphasis on environmental protection. Environmental monitoring, early warning, and governance have become important worldwide tasks. Traditional urban environmental monitoring relies on environmental monitoring stations. Manual data collection, processing, and analysis often consume a lot of manpower, and at the same time, efficiency and accuracy are lacking. The Internet of Things and sensors enable people to carry out unmanned collection, transmission, and analysis. This paper discusses the research on intelligent monitoring of the urban environment under the Internet of Things, designs the scheme from the software and hardware perspective, and proposes a time series data prediction method based on the Gaussian process. It is hoped that this article can provide some references for applying the collected massive environmental data.

Application of Biographical Data in Student’s Major Selection

The research studies describe that students utilize Information and Communication Technology (ICT) widely to improve their academic performance. In the classroom, students use ICT assistive technologies via laptops and smartphones for academic and non-academic activities. The ICT tool interactions are applied to developing an effective learning environment that is used to support the student’s learning and understanding in a specific context. The utilization of ICT motivates the students to utilize the technologies in the classroom environment. The ICT training policies help resolve the fundamental issues that students come across, particularly high school students going to college. However, most students do not know enough about their major tendencies and feel lost when deciding on a major. Our study aimed to apply ICT biographical data as a tool for major selection. Based on the rationale of psychometrics and valuable evidence, some studies show that the average high school score is the best predictor of the average college score. The biographical data prediction method is the pre-university life history of students of different majors. Compiling questionnaires takes the college academic performance of students as the studying criterion and weights projects on the biographical data table to develop college students’ biographical information blank and its norm system to provide services for student’s choice of major. Various results show that biographical information blank items are diverse, and the impurity of the content may lead to low internal reliability (α coefficient is usually between 0.60 and 0.80) but a high test–retest validity coefficient (usually between 0 and 0.90). In contrast, its validity has predictive validity because it is independent of each score. Furthermore, since biographical information blanks comprise verifiable and unverifiable items, the ideal subjects answered more reliably because they were accountable for their responses. Studies show that the description of individual life history was moderately associated with the results recorded by the psychologist.

Novel production prediction model of gasoline production processes for energy saving and economic increasing based on AM-GRU integrating the UMAP algorithm

Gasoline, as an extremely important petroleum product, is of great significance to ensure people's living standards and maintain national energy security. In the actual gasoline industrial production environment, the point information collected by industrial devices usually has the characteristics of high dimension, high noise and time series because of the instability of manual operation and equipment operation. Therefore, it is difficult to use the traditional method to predict and optimize gasoline production. In this paper, a novel production prediction model using an attention mechanism (AM) based gated recurrent unit (GRU) (AM-GRU) integrating the uniform manifold approximation and projection (UMAP) is proposed. The data collected in the industrial plant are processed by the box plot to remove the data outside the quartile. Then, the UMAP is used to remove the strong correlation between the data, which can improve the running speed and the performance of the AM-GRU. Compared with the existing time series data prediction method, the superiority of the AM-GRU is verified based on University of California Irvine (UCI) benchmark datasets. Finally, the production prediction model of actual complex gasoline production processes for energy saving and economic increasing based on the proposed method is built. The experiment results show that compared with other time series data prediction models, the proposed model has better stability and higher accuracy with reaching 0.4171, 0.9969, 0.2538 and 0.5038 in terms of the mean squared error, the average absolute accuracy, the mean squared error and the root mean square error. Moreover, according to the optimal scheme of the raw material, the inefficiency production points can be expected to increase about 0.69 tons of the gasoline yield and between about $645.1 and $925.6 of economic benefits of industrial production.

Energy-Efficient Sensory Data Collection Based on Spatiotemporal Correlation in IoT Networks

The Internet of Things (IoT) is currently in a stage of rapid development. Hundreds of millions of sensing nodes and intelligent terminals undertake the tasks of sensing and transmitting data. Data collection is the key to realizing data analysis and intelligent application of IoT. The life cycle of IoT is limited by the energy of the IoT nodes in the network. A complex computing model will bring serious or even unbearable burdens to IoT nodes. In this study, we use the data prediction method to explore time correlation data and adjust the appropriate spatial sampling rate on the basis of the spatial correlation of sensory data to further reduce data. Specifically, the improved and optimized DNA-binding protein (DBP) data prediction method can increase the time interval of sensing data to further reduce energy consumption. Based on the spatial characteristics of the sensing data, substituting the data of similar nodes can reduce the sampling rate. The probabilistic wake-up strategy is also adopted to adjust the spatial correlation of the sensing data. On the basis of node priority, an optimized greedy algorithm is proposed to select the appropriate dominating node for eliminating redundant nodes and improving network energy utilization. Experiments have proven that our scheme reduces network energy consumption under the premise of ensuring data reliability.

Digital Twin-Assisted Real-Time Traffic Data Prediction Method for 5G-Enabled Internet of Vehicles

The development of Internet of Vehicles (IoV) has produced a considerable amount of real-time traffic data. These traffic data constitute a kind of digital twin that connects the physical vehicles and their virtual representation via 5G communications. Generally, through analyzing the digital twin traffic data, traffic administrators can optimize traffic scheduling and alleviate traffic jams. However, the exceptions of IoV sensors inevitably raise an issue of traffic data sparsity and consequently influence scientific traffic scheduling decisions. Inspired by this drawback, in this article, a digital twin-assisted real-time traffic data prediction method is proposed by analyzing the traffic flow and velocity data monitored by IoV sensors and transmitted through 5G. At last, we conduct a set of experiments based on a traffic dataset collected by Nanjing city of China. Reported results show the feasibility of our proposal in smart traffic flow and velocity prediction that call for a quick response and high accuracy.

Data Analysis and Prediction Modeling Based on Deep Learning in E-Commerce

Due to the low efficiency of traditional data analysis methods for massive e-commerce data analysis, an e-commerce data analysis and prediction method based on the GBDT deep learning model was proposed. Purchase behavior is divided into another category, which transforms the problem of e-commerce data analysis and prediction into a binary classification problem. At the same time, we extract 107 features that can reflect the user behavior and construct the GBDT model. The characteristics include counting class, sorting class, time difference class, conversion rate class, and so on. It follows from the above that the analysis and prediction of e-commerce data are realized. In addition, the results show that when the learning rate of GBDT model parameters is 0.05, the number of basic learners is 200, the tree depth is 20, the threshold is 0.5, the model prediction effect is best, and the F1 value can reach 0.12. Compared with the traditional prediction model based on logistic regression and neural network, the proposed GBDT model is more suitable for e-commerce data analysis and prediction.

A Combinational Data Prediction Model for Data Transmission Reduction in Wireless Sensor Networks

Background: Data prediction methods in wireless sensor networks (WSN) has been emerged as a significant way to reduce the redundant data transfers and in extending the overall network’s lifetime. Nowadays, two types of data prediction algorithms are in use. The first focus on reassembling historical data and providing backward models, resulting in unmanageable delays. The second is concerned with future data forecasting and gives forward models, that involves increased data transmissions. Method: Here, we develop a Combinational Data Prediction Model (CDPM) that can build prior data to control delays as well as anticipate future data to reduce excessive data transmission. To implement this paradigm in WSN applications two algorithms are implemented. The first algorithm creates step by step optimal models for sensor nodes (SNs). The other predicts ang regenerates readings of the sensed data by the base stations (BS). Comparison: To evaluate the performance of our proposed CDPM data-prediction method, a WSN based real application is simulated using a real data-set. The performance of CDPM is also compared with HLMS, ELR and P-PDA algorithms. Results: The CDPM model displayed significant transmission suppression (16.49%, 19.51% and 20.57%%), reduced energy consumption (29.56%, 50.14%, 61.12%) and improved accuracy (15.38%, 21.42%, 31.25%) when compared with HLMS, ELR and P-PDA algorithms respectively. The delay caused by CDPM training is also controllable in data collection. Conclusion: Results advised that the efficacy of the proposed CDPM over a single forward or backward model in terms of decreased data transmission, improved energy efficiency, and regulated latency.

A PCA-based Data Prediction Method

A PCA-based Data Prediction Method

An investigation of various machine learning techniques for mobile call data analysis for reducing call drop

Mobile call drop is a big concern for all telecommunications operators. In telecommunications, a cell phone call drop is a condition where calls have been cut off before the speakers have ended their conversation tone and before one of them has hung up. Therefore, the goal of this research is to design a predictive model that can estimate the mobile call drop data set of the mobile network. In order to address the disadvantage of simple statistical approaches, we have proposed and used data mining techniques, methods and methodologies in this study. In this paper, we propose a decision tree based on the mobile call drop data classification and prediction method. Experimental research is also being performed to test the proposed technique.

Estimation of Arc Welding Pressure Pipeline Weld Peaking Parameters Based on Data Prediction

Peaking parameter is the key content in the regular inspection of the pressure pipeline. Solving the problem of the peaking measurement method defined by a standard cannot be applied to a situation in which there exists a weld surface with reinforcement and misalignment. In this paper, a peaking estimation method based on data prediction was proposed to estimate the contour information of the base metal at the weld joint using the contour point set data of the base metal part of the weld. Herein, the longitudinal weld peaking estimation method based on a piecewise logistic regression (PLR) and the girth weld peaking estimation method based on a piecewise Bayesian linear regression (PBLR) were studied, and the midpoint of the two symmetrical points of the base metal on either side of the weld was used as a reference for calculating the peaking. Finally, we collected the surface profile data of longitudinal weld pressure pipes with diameters of 155 mm, 255 mm, 550 mm, and 600 mm and the surface profile data of girth weld pressure pipes with diameters of 120 mm, 130 mm, 140 mm, and 170 mm. This weld seam data used the data estimation method proposed in this article and traditional long short-term memory and fitting methods. The results showed that the proposed data prediction method could accurately predict the position of the base metal, and the theoretical mean absolute error of the peaking estimation based on the PBLR and PLR could attain 0.06 mm and 0.07 mm, respectively, which meets the parameter measurement requirements of related verification fields.

The impact of vertical greenery system on building thermal performance in tropical climates

The thermal benefits of Vertical Greenery System (VGS) in providing thermal comfort, reducing internal building temperature, and lowering operational energy consumption are not widely known. There is a lack of research and technical knowledge on the effects of vertical greenery systems related to thermal performance, especially in tropical climates, such as that of Malaysia. Therefore, this paper addresses this gap by investigating the effect of VGS on heat transfer and the thermal performance of hypothetical buildings. In this paper, a data prediction method is used to identify the overall thermal transfer value (OTTV) from several hypothetical case studies. A variety of combinations of variables have been used to identify the best design with the lowest OTTV reduction through VGS. From the calculation, Linear Greenery System achieved the highest OTTV reduction with an average of 6.87%, followed by Modular Green Wall (6.82%), Double-skin Green Facades (2.97%), and Direct Green Facades (1.32%). Therefore, this paper can conclude that Linear Green Wall is the best greenery system for reducing heat transfer for the tropical climate of Malaysia.

Spatial—Temporal Traffic Flow Data Restoration and Prediction Method Based on the Tensor Decomposition

As an important part of urban big data, traffic flow data play a critical role in traffic management and emergency response. Traffic flow data contain multi-mode characteristics, which need to be deeply mined. To make full use of multi-mode characteristics, we use a 3-order tensor to represent the traffic flow data, considering “temporal-spatial-periodic” characteristics. To recover the missing data of traffic flow, we propose the Missing Data Completion Algorithm Based on Residual Value Tensor Decomposition (MDCA-RVTD), which combines linear regression, univariate spline, and CP decomposition. Then, we predict the future traffic flow data by using the proposed Traffic Flow Prediction Algorithm Based on Data Completion Strategy (TFPA-DCS). The experimental results show that recovering the missing data is helpful in improving the prediction accuracy. Additionally, the prediction accuracy of the proposed Algorithm is better than gray model and traditional tensor CP decomposition method.

Design of Novel Optical Fiber Communication Electronic System and Big Data Prediction Method of Its Loss

For the shortcomings of traditional data communication electronic mode and bus interface, optical fiber communication electronic technology and compact peripheral component interconnect (CPCI) bus interface mode with industry standard are adopted to design a kind of optical fiber communication electronic data transmission mode. The optical fiber communication electronic board based on CPCI bus constitutes a novel optical fiber communication electronic system. In the process of hardware design, the small form-factor pluggable (SFP) transceiver module is used to convert the photoelectric signal and complete the transmission of optical fiber data. Regarding the existence of differential signal in high-speed serial circuit of electronic board, the wiring is reasonably arranged. The hardware test platform of optical fiber communication electronic system is built based on PCI9054 CPCI bus and industrial personal computer (IPC) communication electronics. FPGA with embedded high-speed serial transceiver module is adopted to design the hardware logic function. Binary gray code is used to count. FIFO protocol and Aurora protocol are supported. Protocol IP core is customized to realize the full duplex electronic communication of high-speed data. Considering the loss problem of the system, the grey model GM(1, 1), ARIMA model and Elman neural network are employed as the big data level research scheme, which are combined into the loss prediction model according to a certain proportion. In the experiment, the test of DMA data reading and writing function is designed, and the embedded highspeed transceiver module and optical fiber communication electronic logic function are simulated. The results show that the symbol error rate (SER) is always 0, and the low-pass filtering characteristics of the channel are effectively compensated, which makes the signal quality stabler. Compared with the single big data level research scheme, the combined model has smaller prediction error and higher prediction accuracy.

Adaptive Synapse Arrangement in Cortical Learning Algorithm

The cortical learning algorithm (CLA) is a time-series data prediction method that is designed based on the human neocortex. The CLA has multiple columns that are associated with the input data bits by synapses. The input data is then converted into an internal column representation based on the synapse relation. Because the synapse relation between the columns and input data bits is fixed during the entire prediction process in the conventional CLA, it cannot adapt to input data biases. Consequently, columns not used for internal representations arise, resulting in a low prediction accuracy in the conventional CLA. To improve the prediction accuracy of the CLA, we propose a CLA that self-adaptively arranges the column synapses according to the input data tendencies and verify its effectiveness with several artificial time-series data and real-world electricity load prediction data from New York City. Experimental results show that the proposed CLA achieves higher prediction accuracy than the conventional CLA and LSTMs with different network optimization algorithms by arranging column synapses according to the input data tendency.

Using KNN Algorithm Predictor for Data Synchronization of Ultra-Tight GNSS/INS Integration

The INS system’s update rate is faster than that of the GNSS receiver. Additionally, GNSS receiver data may suffer from blocking for a few seconds for different reasons, affecting architecture integrations between GNSS and INS. This paper proposes a novel GNSS data prediction method using the k nearest neighbor (KNN) predictor algorithm to treat data synchronization between the INS sensors and GNSS receiver and overcome those GNSS receiver’s blocking, which may occur for a few seconds. The experimental work was conducted on a flying drone over a minor Hungarian (Mátyásföld, 47.4992 N, 19.1977 E) model airfield. The GNSS data are predicted by four different scenarios: the first is no blocking of data, and the other three have blocking periods of 1, 4, and 8 s, respectively. Ultra-tight architecture integration is used to perform the GNSS/INS integration to deal with the INS sensors’ inaccuracy and their divergence throughout the operation. The results show that using the GNSS/INS integration system yields better positioning data (in three axes (X, Y, and Z)) than using a stand-alone INS system or GNSS without a predictor.