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Multi-Sensor Data fusion and CNN-LSTM Model for Human Activity Recognition System

SENSORS 2023年第10期23卷 4750-4750页

作者： Zhou, Haiyang Zhao, Yixin Liu, Yanzhong Lu, Sichao An, Xiang Liu, Qiang Beijing Inst Petrochem Technol Acad Artificial Intelligence Beijing 102617 Peoples R China Beijing Acad Safety Engn & Technol Beijing 102617 Peoples R China

Human activity recognition (HAR) is becoming increasingly important, especially with the growing number of elderly people living at home. However, most sensors, such as cameras, do not perform well in low-light environments. To address this issue, we designed a HAR system that combines a camera and a millimeter wave radar, taking advantage of each sensor and a fusion algorithm to distinguish between confusing human activities and to improve accuracy in low-light settings. To extract the spatial and temporal features contained in the multisensor fusion data, we designed an improved CNN-LSTM model. In addition, three data fusion algorithms were studied and investigated. Compared to camera data in low-light environments, the fusion data significantly improved the HAR accuracy by at least 26.68%, 19.87%, and 21.92% under the data level fusion algorithm, feature level fusion algorithm, and decision level fusion algorithm, respectively. Moreover, the data level fusion algorithm also resulted in a reduction of the best misclassification rate to 2%similar to 6%. These findings suggest that the proposed system has the potential to enhance the accuracy of HAR in low-light environments and to decrease human activity misclassification rates.

关键词： human activity recognition multi-sensor data fusion fusion algorithm CNN-LSTM

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Power Grid Material Demand Forecasting Based on Pearson Feature Selection and Multi-Model fusion

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FRONTIERS IN ENERGY RESEARCH 2022年 10卷

作者： Dai, Zhou Wang, Gang Bian, Ruien Deng, Chaozhi South China Univ Technol Sch Elect Power Engn Guangzhou Peoples R China China Southern Power Grid Mat Co Ltd Guangzhou Peoples R China

The demand projection of power grid materials can furnish an effective support for the management of power grid materials. Due to variations in the data distribution of individual districts and diversity of materials, a single forecasting model is incapable of accurately predicting the demand for all types of materials. Moreover, for the data-driven network model, the effect of the model has a strong correlation with the quality of its input parameters. To address these problems, this study proposes a power grid material demand forecasting model based on feature selection and multi-model fusion. The first step in this regard is the usage of Pearson coefficient in the selection of main characteristic parameters from original parameters and using them as the input of the network model. Then, stacking fusion algorithm is used to fuse multiple basic models. At last, the proposed method mentioned in this study is tested on a real dataset. The results depict that the proposed method can fully integrate the advantages of various basic models with higher accuracy and generalization ability.

关键词： power grid materials demand prediction feature selection fusion algorithm gradient boosting decision tree eXtreme gradient boosting tree long- and short-term memory network

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Multiple depots vehicle routing based on the ant colony with the genetic algorithm

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JOURNAL OF INDUSTRIAL ENGINEERING AND MANAGEMENT-JIEM 2013年第4期6卷 1013-1026页

作者： Liu, ChunYing Yu, Jijiang Heze Univ Heze Peoples R China

Purpose: the distribution routing plans of multi-depots vehicle scheduling problem will increase exponentially along with the adding of customers. So, it becomes an important studying trend to solve the vehicle scheduling problem with heuristic algorithm. On the basis of building the model of multi-depots vehicle scheduling problem, in order to improve the efficiency of the multiple depots vehicle routing, the paper puts forward a fusion algorithm on multiple depots vehicle routing based on the ant colony algorithm with genetic algorithm. Design/methodology/approach: to achieve this objective, the genetic algorithm optimizes the parameters of the ant colony algorithm. The fusion algorithm on multiple depots vehicle based on the ant colony algorithm with genetic algorithm is proposed. Findings: simulation experiment indicates that the result of the fusion algorithm is more excellent than the other algorithm, and the improved algorithm has better convergence effective and global ability. Research limitations/implications: in this research, there are some assumption that might affect the accuracy of the model such as the pheromone volatile factor, heuristic factor in each period, and the selected multiple depots. These assumptions can be relaxed in future work. Originality/value: In this research, a new method for the multiple depots vehicle routing is proposed. The fusion algorithm eliminate the influence of the selected parameter by optimizing the heuristic factor, evaporation factor, initial pheromone distribute, and have the strong global searching ability. The Ant Colony algorithm imports cross operator and mutation operator for operating the first best solution and the second best solution in every iteration, and reserves the best solution. The cross and mutation operator extend the solution space and improve the convergence effective and the global ability. This research shows that considering both the ant colony and genetic algorithm together can improve the ef

关键词： genetic algorithm ant colony algorithm multiple depots vehicle routing fusion algorithm

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POTENTIAL INFLUENCE DIAGRAMS

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INTERNATIONAL JOURNAL OF APPROXIMATE REASONING 1994年第3期10卷 251-285页

作者： NDILIKILIKESHA, PC The Fuqua School of Business Duke University Durham North Carolina USA

This study introduces potential influence diagrams, a generalization of standard influence diagrams in which each chance node is associated with an arbitrary nonnegative function (called a potential) instead of a conditional probability table. This generalization is motivated primarily by computational considerations;it allows us to remove chance nodes directly without reversing arcs. We use this transformation to develop a new reduction algorithm for computing optimal strategies. By avoiding arc reversals and the divisions associated with them, the proposed algorithm improves significantly the efficiency of reduction algorithms, and it brings them closer to competing algorithms based on other representations. In particular, the proposed algorithm is equivalent to Shenoy's fusion algorithm, equivalent in the sense that it performs the same numerical computations. We also show that it is equivalent to an instance of inward propagation in a rooted join tree, thus bridging the gap between reduction and join-tree algorithms. Finally, the proposed algorithm has the advantage of solving decision problems directly in their influence diagram representation-a representation that has been successful in structuring and assessing complex decision problems.

关键词： INFLUENCE DIAGRAM POTENTIAL ARC REVERSAL CHANCE NODE ABSORPTION DECISION NODE REMOVAL ADMISSIBLE REDUCTION JOIN-TREE algorithm fusion algorithm REDUCTION algorithm

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Improvement of Ladar Imaging Quality Based on fusion Technology

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LASERS IN ENGINEERING 2012年第1-2期23卷 19-28页

作者： Li, L-J. Lu, W. Qi, W. Chinese Harbin Inst Technol Natl Key Lab Tunable Laser Technol Harbin 150001 Peoples R China

Based on morphological edge detection technology a fusion algorithm to improve imaging quality of ladar range image is proposed in this paper. The algorithm uses a generalized anti-noise dilation edge detection operator to extract the coordinates and amplitude of a target edge from a range image. After finding the uncertain points near the range image edge the ratio algorithm is used to make the above uncertain points and the corresponding intensity information fused. In the laser radar image processing stage the lost detail information in the range image is restored, which makes the distance accuracy and signal to noise ratio (SNR) of laser radar system increased.

关键词： Ladar coherent imaging laser radar laser range image fusion algorithm signal to noise ratio (SNR)

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Nonlinear correction method of pressure sensor based on data fusion

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MEASUREMENT 2022年 199卷

作者： Yue, Yuan-Long Xu, Shi-Jiang Zuo, Xin China Univ Petr Beijing 102249 Peoples R China

Piezoresistive sensors are widely used in the design of underwater pressure transmitters, whose nonlinear output is easily affected by the temperature of oil and gas medium. Most of the published literature focuses on hardware, software, or hybrid correction methods, but they are inflexible, have low accuracy and large temperature drift, especially can not retain the nonlinear characteristics of the sensor itself. This paper presents a nonlinear correction method for pressure sensors based on data fusion. Firstly, the pressure and temperature coupling model is established by using ANSYS software, and the extra thermal stress caused by temperature is analyzed, which makes the pressure sensor have strong nonlinear. Secondly, a nonlinear correction method combining polynomial fitting, BP neural network activated by wavelet function, and BP optimization algorithm based on particle swarm optimization (PF-W_BP-PSO_BP) is proposed. This method combines the advantages of numerical calculation and machine learning, retains the nonlinear characteristics of sensor output and restrains the influence of temperature, and proves that this method is superior to the existing correction methods. Finally, pressure and temperature data acquisition experiments are designed, which verifies the scientific nature and effectiveness of the nonlinear correction method in this paper. The method has been applied to the development project of a pressure and temperature integrated transmitter 1500 m underwater, and its practicability has been verified.

关键词： PGA900 Pressure sensor Neural network Nonlinear correction fusion algorithm

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The Biomass Proxy: Unlocking Global Agricultural Monitoring through fusion of Sentinel-1 and Sentinel-2

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REMOTE SENSING 2024年第5期16卷 835页

作者： Burger, Rogier Aouizerats, Benjamin den Besten, Nadja Guillevic, Pierre Catarino, Filipe van der Horst, Teije Jackson, Daniel Koopmans, Regan Ridderikhoff, Margot Robson, Greg Zajdband, Ariel de Jeu, Richard Planet Labs PBC Wilhelminastr 43A NL-2011 VK Haarlem Netherlands

The Biomass Proxy is a new cloud-free vegetation monitoring product that offers timely and analysis-ready data indicative of above-ground crop biomass dynamics at 10m spatial resolution. The Biomass Proxy links the consistent and continuous temporal signal of the Sentinel-1 Cross Ratio (CR), a vegetation index derived from Synthetic Aperture Radar backscatter, with the spatial information of the Sentinel-2 Normalized Difference Vegetation Index (NDVI), a vegetation index derived from optical observations. A global scaling relationship between CR and NDVI forms the basis of a novel fusion methodology based on static and dynamic combinations of temporal and spatial responses of CR and NDVI at field level. The fusion process is used to mitigate the impact on product quality of low satellite revisit periods due to acquisition design or persistent cloud coverage, and to respond to rapid changes in a timely manner to detect environmental and management events. The resulting Biomass Proxy provides time series that are continuous, unhindered by clouds, and produced uniformly across all geographical regions and crops. The Biomass Proxy offers opportunities including improved crop growth monitoring, event detection, and phenology stage detection.

关键词： agricultural monitoring crop biomass fusion algorithm radar optical field scale vegetation index remote sensing

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A novel lithium-ion battery state of charge estimation method based on the fusion of neural network and equivalent circuit models

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APPLIED ENERGY 2023年第1期348卷

作者： Tang, Aihua Huang, Yukun Liu, Shangmei Yu, Quanqing Shen, Weixiang Xiong, Rui Chongqing Univ Technol Sch Vehicle Engn Chongqing 400054 Peoples R China Harbin Inst Technol Sch Automot Engn Weihai 264209 Shandong Peoples R China Swinburne Univ Technol Sch Sci Comp & Engn Technol Melbourne Vic 3122 Australia Beijing Inst Technol Sch Mech Engn Joint Lab Adv Energy Storage & Applicat Beijing 100081 Peoples R China

Accurate estimating the state of charge (SOC) can improve battery reliability, safety, and extend battery service life. The existing battery models used for SOC estimation inadequately capture the dynamic characteristics of a battery in a wide temperature over the full SOC range, leading to significant inaccuracies in SOC estimation, especially in low temperature and low SOC. A novel SOC estimation approach is developed based on a fusion of neural network model and equivalent circuit model. Firstly, the weight-SOC-temperature relationship is established by obtaining the weights of the equivalent circuit model and the neural network model offline using the standard deviation weight assignment method. Following that, an online adaptive weight correction approach is implemented to update the weight-SOC-temperature relationship. Finally, a novel multi-algorithm fusion technique is utilized to achieve SOC estimation accuracy within 1%. The results clearly demonstrate that the developed approach achieves twice the accuracy of the existing approach, highlighting its superior effectiveness.

关键词： Lithium-ion batteries fusion model State of charge estimation fusion algorithm Battery modeling

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On-the-Fly fusion of Remotely-Sensed Big Data Using an Elastic Computing Paradigm with a Containerized Spark Engine on Kubernetes

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SENSORS 2021年第9期21卷 2971-2971页

作者： Huang, Wei Zhou, Jianzhong Zhang, Dongying Huazhong Univ Sci & Technol Sch Civil & Hydraul Engn Wuhan 430074 Peoples R China

Remotely-sensed satellite image fusion is indispensable for the generation of long-term gap-free Earth observation data. While cloud computing (CC) provides the big picture for RS big data (RSBD), the fundamental question of the efficient fusion of RSBD on CC platforms has not yet been settled. To this end, we propose a lightweight cloud-native framework for the elastic processing of RSBD in this study. With the scaling mechanisms provided by both the Infrastructure as a Service (IaaS) and Platform as a Services (PaaS) of CC, the Spark-on-Kubernetes operator model running in the framework can enhance the efficiency of Spark-based algorithms without considering bottlenecks such as task latency caused by an unbalanced workload, and can ease the burden to tune the performance parameters for their parallel algorithms. Internally, we propose a task scheduling mechanism (TSM) to dynamically change the Spark executor pods' affinities to the computing hosts. The TSM learns the workload of a computing host. Learning from the ratio between the number of completed and failed tasks on a computing host, the TSM dispatches Spark executor pods to newer and less-overwhelmed computing hosts. In order to illustrate the advantage, we implement a parallel enhanced spatial and temporal adaptive reflectance fusion model (PESTARFM) to enable the efficient fusion of big RS images with a Spark aggregation function. We construct an OpenStack cloud computing environment to test the usability of the framework. According to the experiments, TSM can improve the performance of the PESTARFM using only PaaS scaling to about 11.7%. When using both the IaaS and PaaS scaling, the maximum performance gain with the TSM can be even greater than 13.6%. The fusion of such big Sentinel and PlanetScope images requires less than 4 min in the experimental environment.

关键词： fusion algorithm parallel computing big data Kubernetes cloud computing

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Multi-Sensor fusion and data analysis for operating conditions of low power transmission lines

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MEASUREMENT 2022年 190卷 1页

作者： Cao, Shipeng Fan, Qiao Yu, Wan Jin Wang, Li Tao Ni, Sha Chen, Jie Allcorehatress Beijing Technol Co Ltd Beijing Beijing 100193 Peoples R China Allcorehatress JiangSu Technol Co Ltd Nanjing 211106 Jiangsu Peoples R China

The research of the safety distance among drones and cable attracted wide attention with the rising deployment of unmanned aerial vehicle (UAV) to check for high-voltage overhead power systems. To increase the inspection operation's dependability and guarantee a safe and stable functioning of the transmission grid and inspections equipment, it is essential to determine the safety distance among the UAV and the driveway. Due to UAV patrol safety distances from overhead power lines, it is difficult to offer precise surfing information because of the lack of quantitative assistance. The author of the relevant study uses multi-sensor fusion data analysis (MFDA-LPTL). There have been discussions on the properties of large data sets, smart networks, and gigantic data sets before the emergence of low-cost power transmission lines and the benefits they may give. For example, using the adaptive weighted fusional method, which combines first-level data on homogenous sensor input based on critical UAVinfluencing factors such as maximum inspection speed, wind speed, positioning error, and drone size, can help achieve this goal. As a secondary benefit, the theory makes use of more robust evidence than before. However, the use of big data analytics in present smart grids must be expanded due to numerous challenges, such as the need for new technologies and increased public awareness. The experimental analysis shows that the proposed MFDA-LPTL model increases the performance ratio of 98.9%, efficiency ratio of 97.2%, reduces the detection failure analysis of 10.2%, processing time of 7.8%, positioning error rate of 12.3% compared to other existing methods.

关键词： Low power transmission line Big data fusion algorithm

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