Multiple Model Algorithm Research Articles

Spatial heterogeneity of ecosystem service bundles and the driving factors in the Beijing-Tianjin-Hebei region

Defining a multi-service spatial pattern and ecosystem packages is the key to ensuring regional ecological security and sustainable development. Ecosystem service bundles can effectively discern the interactions among multiple services and regionalize ecological functional zones. Aiming at a range of environmental and ecological challenges in the Beijing-Tianjin-Hebei region, such as water scarcity, soil erosion, land degradation, poor forest and grassland quality, and shrinking agricultural land, the spatial pattern and driving forces of eight crux ecosystem services, including product supply, soil conservation, water conservation, carbon storage, sand fixation, environment purification, biological diversity and recreation culture, were analyzed by combining multiple model algorithms. Additionally, spatial system clustering was employed to identify ecosystem services bundles and optimize the ecological functional zoning. The results revealed significant spatial heterogeneity of the eight ecosystem services in the Beijing-Tianjin-Hebei region. High product supply concentrated in plain regions, while the rest seven ecosystem services were dominant in in mountainous areas. The region can be classified into four service bundles: ecological conservation service (ECS), ecological restoration service (ERS), product supply service (PSS) and ecological fragile service (EFS). ECS and ERS are dominated by regulating and supporting services, which are primarily driven by climatic factors and NDVI. ERS exhibits relatively low service level and is the crucial area for future ecological restoration. PSS is dominated by food supply, with land use change being the primary driving force. At present, with the rapid urbanization, various ecosystem services provided by EFS is inadequate. In the future, urban ecological construction should be strengthened to enhance regional ecosystem services capabilities.

Multi-dimensional early warning of the entire supply chain of power materials based on RFID technology

Early warning system needs to process a large amount of real-time data, and carry out in-depth analysis and mining of these data to identify potential risks and hidden dangers. However, existing data processing and analysis capabilities may not be able to meet this demand. To this end, a multi-dimensional early warning of the entire supply chain of power materials based on RFID technology is designed. According to the real-time failure probability of power materials, the probability of early warning accidents is calculated and classified, and the risk is graded based on these probabilities. Through the methods of bonus points, deduction points and grade evaluation, the risk early warning indicators of different stages in the whole supply chain of power materials were quantified. The objective and rationality of risk assessment can be ensured by means of comprehensive weight. A multi-dimensional early warning system based on RFID technology is established, combining multiple linear regression model and particle swarm optimization algorithm to determine the time window of multi-dimensional early warning, and carry out dynamic monitoring and early warning of supply chain. The experimental results show that the early warning effect of the design method can reach 95% and the highest early warning effect can reach 98% at 10 seconds. The average warning error is only 2.91%, and the average warning time is only 1.34 seconds, which is more accurate in identifying the number of first-level risks, second-level risks and third-level risks.

Improved Variable Structure Interacting Multimodels for Target Trajectory Tracking and Extrapolation

To improve the lengthy computation time of conventional variable structure interacting multiple model (VSIMM) algorithm and increase the precision of target prediction and extrapolation, the target state and flight intent information captured by the Automatic Dependent Surveillance-Broadcast (ADS-B) are used as the model’s prior information; combining this information with VSIMM theoretical framework, we purpose an intent variable structure interacting multiple model (INT-VSIMM) algorithm. Firstly, the motion pattern of the target in the flight phase of the flight path is decomposed, and complete sets of motion models are established. Secondly, according to the principle of directed graph switching, a model set switching method is designed, which is mainly based on “hard” switching and supplemented by “soft” switching. Finally, the INT-VSIMM algorithm is used to track the trajectory of the target aircraft, and short-term trajectory extrapolation is performed based on the target state estimation. The simulation results show that the target tracking performance computational time based on the INT-VSIMM algorithm is superior to the comparative existing methods, and the extrapolated trajectory has less error in the short term, which can satisfy the needs of conflict detection.

Fault Detection and Interactive Multiple Models Optimization Algorithm Based on Factor Graph Navigation System

Accurate and stable positioning is significant for vehicle navigation systems, especially in complex urban environments. However, urban canyons and dynamic interference make vehicle sensors prone to disturbance, leading to vehicle positioning errors and even failures. To address these issues, an adaptive loosely coupled IMU/GNSS/LiDAR integrated navigation system based on factor graph optimization with sensor weight optimization and fault detection is proposed. First, the factor nodes and system framework are constructed based on error models of sensors, and the optimization method principle is derived. Second, the interactive multiple-model algorithm based on factor graph optimization (IMMFGO) is utilized to calculate and adjust sensor weights for global optimization, which will reduce the impact of disturbed sensors. Finally, a multi-stage fault detection, isolation, and recovery (MSFDIR) strategy is implemented based on the IMMFGO results and IMU pre-integration measurements, which can detect significant sensor faults and optimize the system structure. Vehicle experiments show that our IMMFGO method generally obtains better performance in positioning accuracy by 23.7% compared to adaptive factor graph optimization (AFGO) methods, and the MSFDIR strategy possesses the capability of fault sensor detection, which provides an essential reference for multi-source vehicle navigation systems in urban canyons.

Joint waveform design and resource allocation strategy for cognitive radar target situation awareness

AbstractTraditional radar systems use fixed patterns and constant electromagnetic wave transmission to illuminate targets, but they often do not effectively use prior information about targets and consume significant radar resources. Cognitive radar has emerged as a way to improve resource efficiency and address these shortcomings. A joint waveform design and resource allocation strategy for cognitive radar that incorporates target situational awareness is proposed. This method integrates the interacting multiple model algorithm and the Unscented Kalman Particle Filter to achieve target situation awareness as prior knowledge. By combining the target attitude and the frequency response function of the target radar cross section at different time points in the prior knowledge, a joint beam control and power allocation strategy is formulated and transformed into an optimization problem. In addition, a cognitive pulse‐to‐pulse frequency agile waveform design method is proposed to support multiple target tracking under complex motion models. Simulation experiments demonstrate the effectiveness of this approach in obtaining accurate target situation information, achieving beam control, and optimizing power allocation. The designed waveforms can enhance radar target detection performance and improve low probability of intercept characteristics by adjusting the pulse repetition interval. This method has significant technical value.

Reweighting Interacting Multiple-Model Algorithm to Overcome Model Competition for Target Tracking in the Hybrid System

Reweighting Interacting Multiple-Model Algorithm to Overcome Model Competition for Target Tracking in the Hybrid System

Data-driven online tracking filter architecture: A LightGBM implementation

State estimation has been dominated by traditional algorithms based on the Bayesian framework, and Bayesian filtering has proven to be an effective component with a wide range of applications in various fields. On the other hand, Bayesian framework is inherently limited by the precise model prior information, while struggling to cope with the challenges such as Markov assumptions that do not adequately describe the actual motion modes, and model transition decision delay in the multiple model algorithm. To alleviate this, we redefine the state filtering problem from a data-driven perspective as a non-probabilistic mapping problem from measurement sequence to target state, replacing the iteration of model derivation with supervised learning regression. Specifically, we introduce a data-driven tracking filter architecture (DTF) that includes spatio-temporal feature processing and decoupling of target trend and sensor uncertainty features. Its fast implementation based on LightGBM is further given, considering the interpretability of the model. Simulation results show that the proposed method approaches to the theoretical optimal solution of the Kalman filter in an ideal linear Gaussian scenario. In challenging maneuvering target tracking scenarios, the proposed model is superior in performance compared with other existing methods. Finally, we validate the effectiveness of the method in radar tracking and real-world video scenes.

Prediction of pregnancy-related complications in women undergoing assisted reproduction, using machine learning methods

To use machine learning methods to develop prediction models of pregnancy complications in women who conceived with assisted reproductive techniques (ART). A nation-wide register-based cohort study with prospectively collected data. Swedish national registers and nationwide quality IVF register. all nulliparous women who achieved birth within the first 3 ART treatment cycles between 2008 and 2016 in Sweden. Characteristics before the use of ART, such as demographics and medical history, were considered potential predictors in the development of before treatment prediction models. ART treatment details were further included in after treatment prediction models. Potential diagnoses of preeclampsia, placental complications (previa, accreta, and abruption), and postpartum hemorrhage were identified using the International Classification of Diseases recorded in the Swedish Medical Birth and Patient registers, respectively. Multiple prediction model algorithms were performed and compared for each outcome and treatment cycle, including logistic regression, decision tree model, naïve Bayes classification, support vector machine, random forest, and gradient boosting. The performance of each model was assessed with C statistic, and nested cross-validation was used to aid model selection and hyperparameter tuning. A total of 14,732 women gave birth after the first (N = 7,302), second (N = 4,688), or third (N = 2,742) ART cycle, representing birth rates of 24.1%, 23.8%, and 22.0%. Overall prediction performance did not vary much across the different methods used. In the first cycle, the before treatment prediction performance was at best 66%, 66%, and 60% for preeclampsia, placental complications, and postpartum hemorrhage, respectively. Inclusion of after treatment characteristics conferred slight improvement (approximately 1%-5%), as did prediction in later cycles (approximately 1%-5%). The top influential and consistent predictors included age, region of residence, infertility diagnosis, and type of embryo transfer (fresh or frozen) in the later (2nd and 3rd) cycles. Body mass index was a top predictor of preeclampsia and was also influential for placental complications but not for postpartum hemorrhage. The combined use of demographics, medical history, and ART treatment information was not enough to confidently predict serious pregnancy complications in women who conceived with ART. Future studies are needed to assess if additional longitudinal follow-up during pregnancy can improve the prediction to allow clinical protocol development.

A Feature-Aided Multiple Model Algorithm for Maneuvering Target Tracking

A Feature-Aided Multiple Model Algorithm for Maneuvering Target Tracking

Battery State of Health Estimation Using the Sliding Interacting Multiple Model Strategy

Due to their nonlinear behavior and the harsh environments to which batteries are subjected, they require a robust battery monitoring system (BMS) that accurately estimates their state of charge (SOC) and state of health (SOH) to ensure each battery’s safe operation. In this study, the interacting multiple model (IMM) algorithm is implemented in conjunction with an estimation strategy to accurately estimate the SOH and SOC of batteries under cycling conditions. The IMM allows for an adaptive mechanism to account for the decaying battery capacity while the battery is in use. The proposed strategy utilizes the sliding innovation filter (SIF) to estimate the SOC while the IMM serves as a process to update the parameter values of the battery model as the battery ages. The performance of the proposed strategy was tested using the well-known B005 battery dataset available at NASA’s Prognostic Data Repository. This strategy partitions the experimental dataset to build a database of different SOH models of the battery, allowing the IMM to select the most accurate representation of the battery’s current conditions while in operation, thus determining the current SOH of the battery. Future work in the area of battery retirement is also considered.

Dynamic Regulation of Great Lakes Water Levels Using Multiple Model Algorithms

Dynamic Regulation of Great Lakes Water Levels Using Multiple Model Algorithms

A Novel Transformer-Aided Multiple Model Algorithm for Maneuvering Target Tracking

A Novel Transformer-Aided Multiple Model Algorithm for Maneuvering Target Tracking

Multiple maneuvering target tracking based on hierarchical Dirichlet process and hidden Markov model

This paper is concerned with multiple maneuvering target tracking problems. Although the interacting multiple model (IMM) algorithm can be used for maneuvering targets tracking, the performance is limited as the motion mode set of the target is fixed in advance. In this paper, we address the problem of multiple maneuvering targets with unknown maneuvering models in cluttered environments. A finite hidden Markov model (HMM) is developed with truncated Hierarchical Dirichlet process (HDP) to recognize the unknown and changing motion modes. Further, the HDP-HMM is introduced into multiple hypothesis tracking (MHT) and probabilistic multiple hypothesis tracking (PMHT) approaches for multiple maneuvering targets tracking with data association uncertainty. To joint estimate the HDP-HMM parameters and target states, the particle learning and variational inference approaches are used, and the solutions of HDP-HMM-MHT and HDP-HMM-PMHT algorithms are obtained based on Rao-Blackwellized particle filter and variational Bayesian methods, respectively. As the changes of maneuver models can be learned using HDP, the multi-target tracking accuracy of the proposed algorithm is improved. Simulation results show that the proposed algorithms outperform the traditional IMM-MHT method with fixed model sets.

Remote Sensing Inversion and Mapping of Typical Forest Stand Age in the Loess Plateau

The accuracy of vegetation indices (VIs) in estimating forest stand age is significantly inadequate due to insufficient consideration of the differences in the physiological functions of forest ecosystems, which limits the accuracy of carbon sink simulation. In this study, remote sensing inversion and mapping of forest stand age were carried out on the Loess Plateau under consideration of the remote sensing mechanism of VIs and the physiological function and canopy structure of the forest using multiple linear regression (MLR) and random forest (RF) models. The main conclusions are as follows: (1) The canopy reflectance of different forest stands has a significant change pattern, and the older the forest stands, the lower the NIR reflectance. The relationship between forest stands and red edge is the most significant, and r is 0.53, and the relationship between Simple Ratio Index (SR), near-infrared reflectance of vegetation (NIRv), normalized difference vegetation index (NDVI), Global Vegetation Index and forest stands is more nonlinear than linear. (2) Principal component analysis (PCA) of canopy spectral information shows that SR, NDVI and red edge (B5) could explain 98% of all spectral information. SR, NDVI and red edge (B5) were used to construct a multiple linear regression model and random forest (RF) algorithm model, and RF has high estimation accuracy (R2 = 0.63). (3) The accuracy of the model was evaluated using reference data, and it was found that the accuracy of the RF model (R2 = 0.63) was higher than that of the linear regression model (R2 = 0.61), but both models underestimated the forest stand age when the forest stand age was greater than 50a, which may be caused by the saturation of the reflectance of the old forest canopy. The RF model was used to generate the dataset of forest stand information in the Loess Plateau, and it was found that the forest is dominated by young forests (<20a), accounting for 38.26% of the forest area, and the average age of forests in the Loess Plateau is 56.1a. This study not only improves the method of forest stand age estimation, but also provides data support for vegetation construction in the Loess Plateau.

Information fusion for online estimation of the behavior of traffic participants using belief function theory

Motion planning algorithms for automated vehicles need to assess the intended behavior of other Traffic Participants (TPs), in order to predict the likely future trajectory of TPs and plan the motion consequently. Information resulting from several sources, like sensors, must be gathered and combined into a reliable estimate of the intended behavior of TPs. Such estimates must be sufficiently steady and quantify the inherent uncertainty around the assessment. We present a novel information fusion algorithm to combine information from different sources into a coherent and reliable estimate. To explicitly account for the uncertainty of estimates, we leverage the Belief Function Theory and evaluate and handle possible disagreements between estimates individually provided by the sources. The algorithm is flexible and can also handle sources that do not discern between some of the considered behaviors and are only capable of assessing the probability of unions or clusters of different behaviors. We discuss the strengths of the approach through simulations in SUMO, comparing it to the Interactive Multiple Model algorithm.

An Improved Interacting Multiple Model Algorithm With Adaptive Transition Probability Matrix Based on the Situation

An Improved Interacting Multiple Model Algorithm With Adaptive Transition Probability Matrix Based on the Situation

Adaptive filtering and smoothing algorithm based on variable structure interactive multiple model

For maneuvering target tracking, a novel adaptive variable structure interactive multiple model filtering and smoothing (AVSIMMFS) algorithm is proposed in this paper. Firstly, an accurate model of the variable structure interactive multiple model algorithm is established. Secondly, by constructing a new model subset based on the original model subsets, the matching accuracy between the model subset and the actual maneuvering mode of the target is improved. Then, the AVSIMMFS algorithm is obtained by smoothing the filtered data of the new model subset. Because of the combination of forward filtering and backward smoothing, the target tracking accuracy is further improved. Finally, in order to verify the effectiveness of the algorithm, the simulation is carried out on two cases. The simulation results show that the tracking performance of AVSIMMFS algorithm is better than other methods and has lower calculation cost.

Numerical simulation analysis of load-bearing capacity at joints of symmetrical cantilever-assembled bridges based on the logistic regression model

Abstract Bridge joint bearing capacity analysis is difficult to fit the requirements of the era of big data. The traditional way of bridge-bearing capacity numerical analysis exists in the wave of digital development of big data information, the Internet of Things, and artificial intelligence technology like a wild development. The traditional way of under the problem: is efficiency, engineering analysis is not accurate, data information paper, work responsibility is not in place, technical equipment, etc. This paper revolves around the numerical simulation analysis of the load-bearing capacity at the joints of symmetrical cantilever-assembled bridges based on the logistic regression model by finding literature to understand the concept and algorithm of the logistic regression model, construct logistic regression model based on dichotomous dependent variable logistic regression algorithm, logistic regression model parameter estimation, logistic regression. The three aspects of the model evaluation index are studied. The numerical simulation analysis of the load-bearing capacity at the symmetrical cantilever joint based on the logistic regression model, the shear strength calculation at the bridge joint, and the bridge longitudinal bar slip deformation are calculated, respectively, and then the actual value of the bridge load bearing capacity is derived. Simulation in the bridge bearing capacity at 0kN, 5kN, 10kN, 15kN, 20kN, 25kN, and 30kN, the model algorithm evaluation index under the three indicators of prediction rate, accuracy, deviation of the verification analysis. The results show that the prediction rate of the logistic regression model algorithm based on the load carrying capacity up to 30kN is 84%, the accuracy rate is 94%, and the deviation is 11%, and the three index values have better performance compared with the one-dimensional linear regression model algorithm and the multiple linear regression model algorithms. This study is beneficial to extend the bridge structural analysis function and make the bridge load capacity assessment more automatic; it can facilitate the comparison of historical data of bridge tests, longitudinal change of indicators at different testing time points, and grasp the current situation and change trend of bridges. Thus it is of great historical significance to the development of the Chinese bridge infrastructure industry.

Green Energy Consumption Path Selection and Optimization Algorithms in the Era of Low Carbon and Environmental Protection Digital Trade

In order to better study the chosen path of the consumption model of public green energy and more accurately predict consumers’ green energy consumer behavior, we take new energy vehicles as an example to explore the driving mechanism and internal mechanism of the public green energy consumption model from the perspective of motivation. We propose an ensemble learning model based on a stacking strategy. The model uses XGBoost, random forest and gradient lifting decision trees as primary learners to transform features, and uses logistic regression as a meta-learner to predict users’ consumer behavior. The experimental results show that this feature engineering method can significantly improve the accuracy rate in multiple model algorithms, and the prediction effect of the ensemble learning model is better than that of a single model, with the accuracy rate of 82.81%. In conclusion, the ensemble learning model based on a stacking strategy can effectively predict the public’s consumer behavior. This provides a theoretical basis and policy recommendations for promoting green energy products represented by new energy vehicles, thereby improving the practical path for proposing green energy consumption.

Tracking of Maneuvering Targets Using a Variable Structure Multiple Model Algorithm

Introduction. In recent years, much attention has been paid to the development of trajectory filtering methods for tracking maneuvering targets. Multi-model (MM) algorithms are widely used for filtering maneuvering targets. Conventional MM algorithms are characterized by a fixed structure. However, highly maneuvering targets require a sufficiently large set of models covering the entire range of possible maneuvers, although an increase in the number of models cannot ensure an increase in the accuracy of tracking. To overcome these problems, multiple model algorithms with a variable structure (VSMM) were proposed. This article proposes two VSMM algorithms for tracking maritime targets performing a coordinated turn at constant speed. These are algorithms with a variable set of models based on adaptive grid and switching grid methods.Aim. To develop an adaptive trajectory tracking algorithm that uses a constant turn model to track maneuvering surface objects.Materials and methods. The resulting algorithm is based on the theory of grid adaptation in multi-model estimation methods and is used to estimate the components of the coordinate and velocity vectors of surface maneuvering targets. The algorithm efficiency was evaluated using computer statistical modeling in the MATLAB environment.Results. The structure of an adaptive VSMM algorithm was described. Simulations were carried out to confirm the algorithm efficiency. In the considered simulation scenarios, the algorithm produces effective estimates of the coordinate vectors and speed of surface maneuvering targets.Conclusion. Adaptive algorithms improve the efficiency of target tracking in comparison with multi-model algorithms with a fixed structure, at the same time as saving computational resources.