Errors In Data Research Articles

Effects of Equatorial Plasma Bubbles on Multi-GNSS Signals: A Case Study over South China

Equatorial plasma bubbles (EPBs) occur frequently in low-latitude areas and have a non-negligible impact on navigation satellite signals. To systematically analyze the effects of a single EPB event on multi-frequency signals of GPS, Galileo, GLONASS, and BDS, all-sky airglow images over South China are jointly used to visually determine the EPB structure and depletion degree. The results reveal that scintillations, or GNSS signal fluctuations, are directly linked to EPBs and that the intensity of scintillation is positively correlated with the airglow depletion intensity. The center of the airglow depletion often corresponds to stronger GNSS scintillation, while the edge of the bubble, which is considered to have the largest density gradient, corresponds to relatively smaller scintillation instead. This work also systematically analyzes the responses of multi-constellation and multi-frequency signals to EPBs. The results show that the L2 and L5 frequencies are more susceptible than the L1 frequency is. For different constellations, Galileo’s signal has the best tracking stability during an EPB event compared with GPS, GLONASS, and BDS. The results provide a reference for dual-frequency signal selection in precise positioning or TEC calculation, that is, L1C and L2L for GPS, L1C and L5Q for Galileo, L1P and L2C for GLONASS, and L1P and L5P for BDS. Notably, BDS-2 is significantly weaker than BDS-3. And inclined geosynchronous orbit (IGSO) satellites have abnormal data error rates, which should be related to the special signal path trajectory of the IGSO satellite.

High resolution chlorophyll-a in-situ fluorescence sensors versus in-vitro chlorophyll-a measurements in mesocosms with contrasting nutrient and temperature treatments

Harmful algal blooms (HABs) are a significant threat to freshwater ecosystems, and monitoring for changes in biomass is therefore important. Fluorescence in-situ sensors enable rapid and high frequency real-time data collection and have been widely used to determine chlorophyll-a (Chla) concentrations that are used as an indicator of the total algal biomass. However, conversion of fluorescence to equivalent Chla concentrations is often complicated due to biofouling, phytoplankton composition and the type of equipment used. Here, we validated measurements from 24 Chla and 12 phycocyanin (cyanobacteria indicator) fluorescence in-situ sensors (Cyclops-7F, Turner Designs) against spectrophotometrically (in-vitro) determined Chla and tested a data-cleaning procedure for eliminating data errors and impacts of non-photochemical quenching. The test was done across a range of freshwater plankton communities in 24 mesocosms (i.e. experimental tanks) with a 2x3 (high and low nutrient x ambient, IPCC-A2 and IPCC-A2+50% temperature scenarios) factorial design. For most mesocosms (tanks), we found accurate (r2 ≥ 0.7) calibration of in-situ Chla fluorescence data using simple linear regression. An exception was tanks with high in-situ phycocyanin fluorescence, for which multiple regressions were employed, which increased the explained variance by >16%. Another exception was the low Chla concentration tanks (r2 < 0.3). Our results also show that the high frequency in-situ fluorescence data recorded the timing of sudden Chla variations, while less frequent in-vitro sampling sometimes missed these or, when recorded, the duration of changes was inaccurately determined. Fluorescence in-situ sensors are particularly useful to detect and quantify sudden phytoplankton biomass variations through high frequency measurements, especially when using appropriate data-cleaning methods and accounting for factors that can impact the fluorescence readings.

Artificial Intelligence Approach in Aerospace for Error Mitigation

Many of the reports created at assembly lines, where all components of an aircraft are installed, frequently indicate that errors threaten safety. The proposed methodology in this study evaluates error prediction and risk mitigation to prevent failures and their consequences. The results linked to a typical electrical harness manufacture of a military aircraft estimated reductions of 93% in time and 90% in error during the creation of engineering manufacturing processes using AI techniques. However, traditional risk assessments methods struggle to identify and mitigate errors effectively. Thus, developing an advanced methodology to ensure systems safety is needed. This paper addresses how innovative AI technology solutions can overcome these challenges, mitigate error risks, and enhance safety in aerospace. Technologies, such as artificial intelligence, predictive algorithms, machine learning, and automation, can play a key role in enhancing safety. The aim of this study is to develop a model that considers the factors that can potentially contribute to error creation, through an artificial intelligence (AI) approach. The specific AI techniques used such as support vector machine, random forest, logistic regression, K-nearest neighbor, and XGBoost (Python 3.8.5) show good performance for use in error mitigation. We have compared the modeled values obtained in this study with the experimental ones. The results confirm that the best metrics are obtained by using support vector machine and logistic regression. The smallest deviation between the measured and modeled values for these AI methods do not exceed 5%. Furthermore, using advancements in machine learning methods can enhance error mitigation in aerospace. The use of AutoML can play a key role in automatically finding an appropriate model which provides the best performance metrics and therefore the most reliable forecast for data prediction and error mitigation.

Innovative approach to predict soil moisture using the backpropagation‐Elman neural network

AbstractAccurate moisture prediction aids in taking early preventive measures to minimize damage caused by severe storms, droughts, and floods. The study collected 24 types of statistical data from the Xilingol Grassland, Inner Mongolia, spanning from 2011 to 2021. By integrating Elman, backpropagation (BP), and soil moisture prediction model (Elman‐BP) neural networks, soil moisture to 2 m was predicted. The soil moisture mechanism model analysis revealed that soil moisture is related to water supply rather than directly correlated with time. Based on these findings, a two‐stage soil moisture prediction model was established. In the first stage, the Elman neural network model predicted future precipitation data based on observed values. In the second stage, soil moisture was predicted using a BP neural network model based on predicted precipitation data. Comparing the mean squared errors of training and prediction data across the three models revealed that the Elman‐BP neural network had superior predictive accuracy. The maximum relative errors for the trained Elman neural network, BP neural network, and Elman‐BP model were 31.35%, 20.98%, and 3.94%. Overall, results showed a link between soil moisture and water supply and that soil moisture decreased with increasing temperatures. Soil moisture at 10‐ and 40‐cm depths was increased with increasing rainfall. Furthermore, a delay exists between the 40‐ and 100‐cm soil depths. Moreover, soil moisture stability increased with depth.

Global Navigation Satellite System Receiver Positioning in Harsh Environments via Clock Bias Prediction by Empirical Mode Decomposition and Back Propagation Neural Network Method.

This paper proposes a novel method to improve the clock bias short-term prediction accuracy of navigation receivers then solve the problem of low positioning accuracy when the satellite signal quality deteriorates. Considering that the clock bias of a navigation receiver is equivalent to a virtual satellite, the predicted value of clock bias is used to assist navigation receivers in positioning. Consequently, a combined prediction method for navigation receiver clock bias based on Empirical Mode Decomposition (EMD) and Back Propagation Neural Network (BPNN) analysis theory is demonstrated. In view of systematic errors and random errors in the clock bias data from navigation receivers, the EMD method is used to decompose the clock bias data; then, the BPNN prediction method is used to establish a high-precision clock bias prediction model; finally, based on the clock bias prediction value, the three-dimensional positioning of the navigation receiver is realized by expanding the observation equation. The experimental results show that the proposed model is suitable for clock bias time series prediction and providing three-dimensional positioning information meets the requirements of navigation application in the harsh environment of only three satellites.

Fault diagnosis of gearboxin wind turbine based on EMD-DCGAN

INTRODUCTION: Wind turbine gearbox fault diagnosis is of great significance for the safe and stable operation of wind turbines. The accuracy of wind turbine gearbox fault diagnosis can be effectively improved by using complete wind turbine gearbox fault data and efficient fault diagnosis algorithms.A wind turbine gearbox fault diagnosis method based on EMD-DCGAN method is proposed in this paper.
 OBJECTIVES: It can solve the problem when the sensor fails or the data transmission fails, it will lead to errors in the wind turbine gearbox fault data, which in turn will lead to a decrease in the wind turbine gearbox fault diagnosis accuracy.
 METHODS: Firstly, the outliers in the sample data need to be detected and removed. In this paper, the EMD method is used to eliminate outliers in the wind turbine gearbox fault data samples with the aim of enhancing the true continuity of the samples; secondly, in order to make up for the lack of missing samples, a data enhancement algorithm based on a GAN network is proposed in the paper, which is able to effectively perfect the missing items of the sample data; lastly, in order to improve the accuracy of wind turbine gearbox faults, a DCGAN neural network-based fault diagnosis method is proposed, which effectively combines the data dimensionality reduction feature of deep learning method and the data enhancement feature of generative adversarial network, and can improve the accuracy and speed of fault diagnosis.
 RESULTS and CONCLUSIONS: The experimental results show that the proposed method can effectively identify wind turbine gearbox fault conditions, and verify the effectiveness of the algorithm under different sample data conditions.

Thermodynamic and cloud evolution in a cold-air outbreak during HALO-(AC)3: quasi-Lagrangian observations compared to the ERA5 and CARRA reanalyses

Abstract. Arctic air masses undergo intense transformations when moving southward from closed sea ice to warmer open waters in marine cold-air outbreaks (CAOs). Due to the lack of measurements of diabatic heating and moisture uptake rates along CAO flows, studies often depend on atmospheric reanalysis output. However, the uncertainties connected to those datasets remain unclear. Here, we present height-resolved airborne observations of diabatic heating, moisture uptake, and cloud evolution measured in a quasi-Lagrangian manner. The investigated CAO was observed on 1 April 2022 during the HALO-(AC)3 campaign. Shortly after passing the sea-ice edge, maximum diabatic heating rates over 6 K h−1 and moisture uptake over 0.3 gkg-1h-1 were measured near the surface. Clouds started forming and vertical mixing within the deepening boundary layer intensified. The quasi-Lagrangian observations are compared with the fifth-generation global reanalysis (ERA5) and the Copernicus Arctic Regional Reanalysis (CARRA). Compared to these observations, the mean absolute errors of ERA5 versus CARRA data are 14 % higher for air temperature over sea ice (1.14 K versus 1.00 K) and 62 % higher for specific humidity over ice-free ocean (0.112 g kg−1 versus 0.069 g kg−1). We relate these differences to issues with the representation of the marginal ice zone and corresponding surface fluxes in ERA5, as well as the cloud scheme producing excess liquid-bearing, precipitating clouds, which causes a too-dry marine boundary layer. CARRA's high spatial resolution and demonstrated higher fidelity towards observations make it a promising candidate for further studies on Arctic air mass transformations.

Efficient Dynamic Distributed Resource Slicing in 6G Multi-Access Edge Computing Networks With Online ADMM and Message Passing Graph Neural Networks

We consider the problem of resource slicing in the 6 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> generation multi-access edge computing (6G-MEC) network. The network includes many non-stationary space-air-ground-sea nodes with dynamic, unstable connections and resources, where any node can be in one of two hidden states: i) reliable – when the node generates/propagates no data errors; ii) unreliable – when the node can generate/propagate random errors. We show that solving this problem is challenging, since it represents a non-deterministic polynomial-time (NP) hard dynamic combinatorial optimization problem depending on the unknown distribution of hidden nodes' states and time-varying parameters (connections and resources of nodes) which can only be observed locally. To tackle these challenges, we develop a new deep learning (DL) model based on the message passing graph neural network (MPNN) to estimate hidden nodes' states in dynamic network environments. We then propose a novel algorithm based on the integration of MPNN-based DL and online alternating direction method of multipliers (ADMM) – extension of the well-known classical “static” ADMM to dynamic settings, where the slicing problem is solved distributedly, in real time, based on local information. We prove that our algorithm converges to a global optimum of our problem with a superior performance even in the highly-dynamic, unreliable scenarios.

The design of a quality improvement dashboard for monitoring spinal cord and column injuries

BackgroundInteractive dashboards are a powerful tool for dynamic visualization and monitoring of patient performance and serve as a useful to for optimal decision-making. The National Spinal Column and Cord Injury Registry of Iran (NSCIR-IR) was designed to efficiently display and broadcast important patient care data. This has been achieved through an electronic dashboard display (graph and visual displays), rather than traditional static paper reports (text). ObjectivesThe objective of this study was to design and develop an electronic visual dashboard as a display system to monitor the quality of care in the NSCIR-IR collaborating centers. MethodsThe indicators chosen were 20 pre-hospital and in-hospital quality of care (QoC) assessment tool indicators. A structured query was created from the NSCIR-IR system database to create the dashboard database. The Microsoft Power BI software was used. After data cleaning, filtering of erroneous records, and modeling, visual displays were designed and evaluated. ResultsThe dashboard reported on quality of care (QoC) for 2,745 patients registered in NSCIR-IR. 17% of registered cases had at least one data error in the quality of care indicators. These errors were automatically filtered by the system. The two most prominent weaknesses in (QoC indicators were delay in patient transfer by EMS (Mean and SD were 9.54 ± 13.8 h) and timing of surgical spinal cord decompression (114.5 ± 45.3 h). ConclusionsElectronic dashboards provide efficient and concise data summaries “at a glance”. However, their value and accuracy are dependent on the entered data quality. Identifying data source errors and correcting them continuously led to improved quality of data.

Ultrasensitive sequencing of STR markers utilizing unique molecular identifiers and the SiMSen-Seq method

Massively parallel sequencing (MPS) is increasingly applied in forensic short tandem repeat (STR) analysis. The presence of stutter artefacts and other PCR or sequencing errors in the MPS-STR data partly limits the detection of low DNA amounts, e.g., in complex mixtures. Unique molecular identifiers (UMIs) have been applied in several scientific fields to reduce noise in sequencing. UMIs consist of a stretch of random nucleotides, a unique barcode for each starting DNA molecule, that is incorporated in the DNA template using either ligation or PCR. The barcode is used to generate consensus reads, thus removing errors. The SiMSen-Seq (Simple, multiplexed, PCR-based barcoding of DNA for sensitive mutation detection using sequencing) method relies on PCR-based introduction of UMIs and includes a sophisticated hairpin design to reduce unspecific primer binding as well as PCR protocol adjustments to further optimize the reaction. In this study, SiMSen-Seq is applied to develop a proof-of-concept seven STR multiplex for MPS library preparation and an associated bioinformatics pipeline. Additionally, machine learning (ML) models were evaluated to further improve UMI allele calling. Overall, the seven STR multiplex resulted in complete detection and concordant alleles for 47 single-source samples at 1ng input DNA as well as for low-template samples at 62.5pg input DNA. For twelve challenging mixtures with minor contributions of 10pg to 150pg and ratios of 1% to 15% relative to the major donor, 99.2% of the expected alleles were detected by applying the UMIs in combination with an ML filter. The main impact of UMIs was a substantially lowered number of artefacts as well as reduced stutter ratios, which were generally below 5% of the parental allele. In conclusion, UMI-based STR sequencing opens new means for improved analysis of challenging crime scene samples including complex mixtures.

Expert‐in‐the‐loop design of integral nuclear data experiments

AbstractNuclear data are fundamental inputs to radiation transport codes used for reactor design and criticality safety. The design of experiments to reduce nuclear data uncertainty has been a challenge for many years, but advances in the sensitivity calculations of radiation transport codes within the last two decades have made optimal experimental design possible. The design of integral nuclear experiments poses numerous challenges not emphasized in classical optimal design, in particular, constrained design spaces (in both a statistical and engineering sense), severely under‐determined systems, and optimality uncertainty. We present a design pipeline to optimize critical experiments that uses constrained Bayesian optimization within an iterative expert‐in‐the‐loop framework. We show a successfully completed experiment campaign designed with this framework that involved two critical configurations and multiple measurements that targeted compensating errors in 239Pu nuclear data.

Simulation method for filling stage in liquid composite molding based on a two-phase flow volume of fluid model

Liquid composite molding (LCM) is a process for manufacturing fiber-reinforced resin matrix composites. The chemical rheological properties of resin in the filling stage of LCM will produce a non-isothermal flow process that includes multi-field coupling of thermal field, flow field, and chemical field. During the filling stage, the resin is injected into a closed mold pre-laid with a fiber at a certain temperature and pressure to flow continuously and undergo a curing chemical reaction. The viscosity of resin changes constantly under the influence of temperature and the curing reaction, which affects the flow of resin. The simulation of this multi-field coupling filling stage in LCM can provide an effective reference for the design of process parameters. The simulation method of the filling stage in LCM is studied in this paper. The mathematical model of the filling stage is derived based on the air–resin two-phase flow volume of the fluid model. Based on the secondary development of the ANSYS FLUENT software, the mathematical model is solved to realize the simulation. The parameters of an alkali-free glass fiber plain weave fabric are measured and the calculation model of thermosetting resin viscosity is established by designed experiments, which are applied in simulation. The filling stage experiment of LCM is designed to verify the accuracy of simulation results. Meanwhile, by comparing with the traditional simulation method, it is found that the data error obtained by the simulation method in this paper is significantly reduced.

面向对海监视的舰船目标跟踪与航迹融合数据集

Real-time trajectory association and trajectory fusion in maritime surveillance pose great challenges and remain hot issues in security, regional situation monitoring, and long-range precision strikes for both military and civilian applications. High-quality datasets play a pivotal role in advancing research in target tracking and fusion technologies within this domain. This paper addresses the data requirements for technological research in target tracking and fusion, as well as the limitations of currently available datasets, including data scarcity, inadequate scene design specificity, uniform data formats, and incomplete data descriptions. We used simulation software to emulate multi-sensor multi-target detection data in complex scenarios, so as to present a dataset tailored for typical maritime surveillance scenarios—targeting ships using 2D radar and Electronic Support Measures (ESM) sensors. The simulation software comprises a scenario generator and a sensor simulator, creating a mature target tracking scenario simulation environment with realistic detection data modeling capabilities. The dataset includes data from 2D radar and ESM sensors, covering typical maritime ship categories, supporting configurations with radiation sources. It is designed for a variety of scenarios such as high-speed motion, dense traffic, multi-sensor data fusion, specific ship detection, and cross-positioning. The dataset contains a total of 368,155 target tracks from 101 ships, spanning a duration of 15,000 seconds. The data format conforms to actual equipment reporting scenarios, while the detection error model accurately reflects real-world conditions. Accuracy assessment and data completeness are ensured through various methods including normality testing of data error, scenario testing of detection rate and false alarm rate, as well as field research. This dataset can provide fundamental data for algorithmic research and validation in the ship's target tracking, trajectory fusion and other related areas.

Transient analysis of temperature field and fission gas about the arc plate fuel element

Obtaining the temperature field and fission gas release of fuel elements are crucial to the study on fuel behaviors and ensure the safety of elements. In order to enhance fuel efficiency, providing a design idea to make the fast reactor with high flux and multi-function, we have designed a core of the lead-based fast reactor with arc plate fuel elements. Based on above, a transient performance analysis program of arc plate fuel elements (TPAPAFE) was developed in this paper, for analyzing the thermal and fission gas module of arc plate fuel element by calculating transient variation in the maximum pellet temperature, internal and external coolant outlet temperature, and fission gas release share. The modular verification was done by comparing the results of TPAPAFE with others. Besides, the result of the arc plate fuel analyzed by TPAPAFE shows that the code is feasible and rational, and the single arc plate is so thin that coolant outlet temperatures on both sides of the plate are almost the same. Meanwhile, the error in temperature data between TPAPAFE and the steady-state program is only 5%. Furthermore, fission gas release fraction increased linearly with pellet temperature and bubble growth.

Determination of Timewise-Source Coefficient in Time-Fractional Reaction-Diffusion Equation from First Order Heat Moment

     This article aims to determine the time-dependent heat coefficient together with the temperature solution for a type of semi-linear time-fractional inverse source problem by applying a method based on the finite difference scheme and Tikhonov regularization. An unconditionally stable implicit finite difference scheme is used as a direct (forward) solver. While by the MATLAB routine lsqnonlin from the optimization toolbox, the inverse problem is reformulated as nonlinear least square minimization and solved efficiently. Since the problem is generally incorrect or ill-posed that means any error inclusion in the input data will produce a large error in the output data. Therefore, the Tikhonov regularization technique is applied to obtain stable and accurate results. Finally, to demonstrate the accuracy and effectiveness of our scheme, two benchmark test problems have been considered, and its good working with different noise levels.

Непрерывная зависимость решений линеаризованной начально-краевой задачи транспорта многокомпонентных наносов от входных данных задачи

We consider a chain of problems linearized on a time grid that describe the processes of transport of multicomponent sediments in relation to coastal marine systems. In the author&amp;#x27;s previous works, the conditions for the existence and uniqueness of a solution to this class of problems were determined, while simultaneously determining the requirements for the smoothness classes of the input data of the problem. The result of this study is related to obtaining an a priori estimate of the norm for solving the problem depending on the integral esti-mates of the right-hand side, boundary conditions and the norm of the initial condition. The obtained a priori estimates allow us to further carry out computationally stable calculations with respect to small errors in the initial data and to errors that arise during calculations.

A Method for Identifying Gross Errors in Dam Monitoring Data

Real and effective monitoring data are crucial in assessing the structural safety of dams. Gross errors, resulting from manual mismeasurement, instrument failure, or other factors, can significantly impact the evaluation process. It is imperative to eliminate such anomalous data. However, existing methods for detecting gross errors in concrete dam deformation often focus on analyzing a single monitoring effect quantity. This can lead to sudden jumps in values of effect quantity caused by changes in environmental variables being mistakenly identified as gross error. Therefore, a method based on Fuzzy C-Means clustering algorithm (FCM) partitioning and density clustering algorithm (Ordering Points To Identify the Clustering Structure, OPTICS) combined with Local Outlier Factor (LOF) algorithm for gross error identification is proposed. Firstly, the FCM algorithm is used to achieve the division of measurement point areas. Then, the OPTICS and LOF algorithms are jointly utilized to determine the gross errors. Finally, the real gross errors are identified by comparing the time of occurrence of the gross errors at measurement points in the same area. Through the case study, the results indicate that the method can effectively identify spurious, gross errors in the monitoring effect quantity caused by environmental mutations. The accuracy of gross error detection is significantly improved, and the rate of misjudgment of gross errors is reduced.

Research on Point Cloud Structure Detection of Manhole Cover Based on Structured Light Camera

This study introduced an innovative approach for detecting structural anomalies in road manhole covers using structured light cameras. Efforts have been dedicated to enhancing data quality by commencing with the acquisition and preprocessing of point cloud data from real-world manhole cover scenes. The RANSAC algorithm is subsequently employed to extract the road plane and determine the height of the point cloud structure. In the presence of non-planar point cloud exhibiting abnormal heights, the DBSCAN algorithm is harnessed for cluster segmentation, aiding in the identification of individual objects. The method culminates with the introduction of a sector fitting detection model, adept at effectively discerning manhole cover features within the point cloud and delivering comprehensive height and structural information. Experimental findings underscore the method’s efficacy in accurately gauging the degree of subsidence in manhole cover structures, with data errors consistently maintained within an acceptable range of 8 percent. Notably, the measurement speed surpasses that of traditional methods, presenting a notably efficient and dependable technical solution for road maintenance.

Математическое обеспечение информационно-управляющей системы для хранения продукции с ограниченным сроком хранения

The work presents a solution to the problem of developing mathematical support for an information and control system for storing products with a limited shelf life using the example of storing sugar beets in field conditions and in heaps at sugar factories based on experimental experiments published in the open press data. An end-to-end ranking of methods for storing root crops was carried out, starting with a root lying separately on the field (rank 0.1 points), from simple windrows and heaps (rank 0.2 points) to specialized root storage and ventilated piles covered with special bactericidal coatings with reflective and protective properties (rank 4 points). An end-to-end ranking of mechanical damage to sugar beet roots has been implemented, starting from 1 point (the root crop is practically undamaged) to 0.1 point (fragments and tails). The studied hybrids were ranked according to sugar content. Based on the rankings, taking into ac-count storage time and temperature, mathematical models (mm) of the dependence of technological indica-tors of beet raw materials, including losses of beet mass and sucrose, on storage parameters were built for each study. Extensive computational experiments were per-formed using an interactive identification and optimization system (isio), which is based on a genetic algorithm and the Hooke Jeeves nonlinear programming method. The errors of the developed models do not exceed the errors of the experimental data used to construct them.

Learning-driven hybrid scaling for multi-type services in cloud

In order to deal with the fast changing requirements of container based services in clouds, auto-scaling is used as an essential mechanism for adapting the number of provisioned resources with the variable service workloads. However, the latest auto-scaling approaches lack the comprehensive consideration of variable workloads and hybrid auto-scaling for multi-type services. Firstly, the historical data based proactive approaches are widely used to handle complex and variable workloads in advance. The decision-making accuracy of proactive approaches depends on the prediction algorithm, which is affected by the anomalies, missing values and errors in the historical workload data, and the unexpected workload cannot be handled. Secondly, the trigger based reactive approaches are seriously affected by workload fluctuation which causes the frequent invalid scaling of service resources. Besides, due to the existence of scaling time, there are different completion delays of different scaling actions. Thirdly, the latest approaches also ignore the different scaling time of hybrid scaling for multi-type services including stateful services and stateless services. Especially, when the stateful services are scaled horizontally, the neglected long scaling time causes the untimely supply and withdrawal of resources. Consequently, all three issues above can lead to the degradation of Quality of Services (QoS) and the inefficient utilization of resources. This paper proposes a new hybrid auto-scaling approach for multi-type services to resolve the impact of service scaling time on decision making. We combine the proactive scaling strategy with the reactive anomaly detection and correction mechanism. For making a proactive decision, the ensemble learning model with the structure improved deep network is designed to predict the future workload. On the basis of the predicted results and the scaling time of different types of services, the auto-scaling decisions are made by a Deep Reinforcement Learning (DRL) model with heterogeneous action space, which integrates horizontal and vertical scaling actions. Meanwhile, with the anomaly detection and correction mechanism, the workload fluctuation and unexpected workload can be detected and handled. We evaluate our approach against three different proactive and reactive auto-scaling approaches in the cloud environment, and the experimental results show the proposed approach can achieve the better scaling behavior compared to state-of-the-art approaches.