Heterogeneous Fusion Research Articles

Construction of rice supply chain supervision model driven by blockchain smart contract

The outbreak of the COVID-19 and the Russia Ukraine war has had a great impact on the rice supply chain. Compared with other grain supply chains, rice supply chain has more complex structure and data. Using digital means to realize the dynamic supervision of rice supply chain is helpful to ensure the quality and safety of rice. This study aimed to build a dynamic supervision model suited to the circulation characteristics of the rice supply chain and implement contractualization, analysis, and verification. First, based on an analysis of key information in the supervision of the rice supply chain, we built a dynamic supervision model framework based on blockchain and smart contracts. Second, under the logical framework of a regulatory model, we custom designed three types of smart contracts: initialization smart contract, model-verification smart contract, and credit-evaluation smart contract. To implement the model, we combined an asymmetric encryption algorithm, virtual regret minimization algorithm, and multisource heterogeneous fusion algorithm. We then analyzed the feasibility of the algorithm and the model operation process. Finally, based on the dynamic supervision model and smart contract, a prototype system is designed for example verification. The results showed that the dynamic supervision model and prototype system could achieve the real-time management of the rice supply chain in terms of business information, hazard information, and personnel information. It could also achieve dynamic and credible supervision of the rice supply chain’s entire life cycle at the information level. This new research is to apply information technology to the digital management of grain supply chain. It can strengthen the digital supervision of the agricultural product industry.

SLAM Overview: From Single Sensor to Heterogeneous Fusion

After decades of development, LIDAR and visual SLAM technology has relatively matured and been widely used in the military and civil fields. SLAM technology enables the mobile robot to have the abilities of autonomous positioning and mapping, which allows the robot to move in indoor and outdoor scenes where GPS signals are scarce. However, SLAM technology relying only on a single sensor has its limitations. For example, LIDAR SLAM is not suitable for scenes with highly dynamic or sparse features, and visual SLAM has poor robustness in low-texture or dark scenes. However, through the fusion of the two technologies, they have great potential to learn from each other. Therefore, this paper predicts that SLAM technology combining LIDAR and visual sensors, as well as various other sensors, will be the mainstream direction in the future. This paper reviews the development history of SLAM technology, deeply analyzes the hardware information of LIDAR and cameras, and presents some classical open source algorithms and datasets. According to the algorithm adopted by the fusion sensor, the traditional multi-sensor fusion methods based on uncertainty, features, and novel deep learning are introduced in detail. The excellent performance of the multi-sensor fusion method in complex scenes is summarized, and the future development of multi-sensor fusion method is prospected.

Feature selection based on double-hierarchical and multiplication-optimal fusion measurement in fuzzy neighborhood rough sets

In fuzzy neighborhood rough sets (FNRSs), uncertainty measurement performs mainly classification-hierarchical and multiplication-simple fusion, so the corresponding feature selection has advancement space. This paper aims to improve uncertainty measurement and feature selection via FNRSs. Two measurement strategies regarding class-hierarchical fusion and multiplication-optimal fusion are proposed, and three measure-based heuristic feature selection algorithms are developed. Concretely, fuzzy neighborhood self-information (FNSI) and joint entropy (FNJE) constitute two bases of heterogeneous fusion, and their multiplication fusion induces both the existing measure FNSIJE (which is based on classification-level fusion) and a new measure CFNSIJE (which is based on class-level fusion); furthermore, FNSIJE and CFNSIJE are extended to the optimal measures FNSIJEE and CFNSIJEE, respectively, by exponential parameterization. The four types of fusion measures acquire their calculation algorithms and granulation nonmonotonicity and systematically motivate four heuristic feature selection algorithms, i.e., the current FNSIJE-FS and the new CFNSIJE-FS, FNSIJEE-FS, and CFNSIJEE-FS. By using examples and experiments, relevant uncertainty measurement and granulation nonmonotonicity are validated, while the novel selection algorithms demonstrate better classification performances. This study establishes the hierarchical fusion and exponential expansion to acquire robust uncertainty measurement and optimal feature selection, and the measurement, nonmonotonicity, and selection have strong generalization for information fusion and rough-set learning.

Distinct Antibacterial Activities of Nanosized Cationic Liposomes against Gram-Negative Bacteria Correlate with Their Heterogeneous Fusion Interactions

Distinct Antibacterial Activities of Nanosized Cationic Liposomes against Gram-Negative Bacteria Correlate with Their Heterogeneous Fusion Interactions

Construction of Edge Computing Platform Using 3D LiDAR and Camera Heterogeneous Sensing Fusion for Front Obstacle Recognition and Distance Measurement System

This research aims to utilise heterogeneous sensor fusion using 3D Light Detection and Ranging (LiDAR) and cameras, combined with an object recognition system and a ranging system, to construct an edge computing platform such that a vehicle equipped with the platform can perform computations offline in real time. This work comprises two main sections: the first is heterogeneous fusion, and the second is obstacle recognition and ranging detection. To achieve heterogeneous sensor fusion, 3D–3D point matching was used to find rigid body transformation between two sensors and finally project the LiDAR 3D point cloud image onto the 2D image. For object recognition, YOLOv4-Tiny was used as the detection network. A lightweight network architecture and high computational speed could be effectively used on edge computing hardware with limited performance. Further, by drawing the bounding box, we could detect the point cloud within the bounding box to estimate the distance to the obstacle. For detecting distance, we conducted experiments in two ways: ‘minimum point in box’ and ‘median point in box’ and compared the results. With heterogeneous sensor fusion, object recognition and the ranging system, detecting the category and distance of obstacles ahead of the vehicle was possible in real time. Furthermore, integrating the edge computing platform architecture enabled moving the entire system offline, making it an independent system that returns results in real time. Finally, a dynamic test was conducted on a road. The experiment showed that the detection speed of YOLOv4-Tiny in the dynamic test was higher than 60 FPS, and the accuracy rate surpassed 70%. Furthermore, the distance detection error of the 3D LiDAR was less than 3 cm, which is sufficiently accurate to be applied to complex environments on roads.

Energy-Efficient Deployment in Static and Mobile Heterogeneous Multi-Hop Wireless Sensor Networks

We study a heterogeneous wireless sensor network (WSN) where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> heterogeneous access points (APs) gather data from densely deployed sensors and transmit their sensed information to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula> heterogeneous fusion centers (FCs) via multi-hop wireless communication. The heterogeneous optimal deployment of APs and FCs is modeled as an optimization problem with total wireless communication power consumption of the network as its objective function. We consider both static WSNs, where APs and FCs retain their deployed position, and mobile WSNs where APs and FCs can move from their initial deployment to their optimal locations. Based on the derived necessary conditions for the optimal deployment in static WSNs, we propose an iterative algorithm to deploy APs and FCs. In addition, we study the necessary conditions of the optimal movement-efficient deployment in mobile WSNs with constrained movement energy and present iterative algorithms to find such deployments, accordingly. Simulation results show that our proposed deployment algorithms outperform the existing methods in the literature, and achieve a lower total wireless communication power in both static and mobile WSNs.

Identification of mine water inrush source based on multiple heterogeneous fusion: a case study in Lilou Coal Mine, China

Identification of mine water inrush source based on multiple heterogeneous fusion: a case study in Lilou Coal Mine, China

Research on Dance Movement Recognition Based on Multi-Source Information

A huge number of scientific research institutions and scholars are now researching this topic in depth, with promising results. Meanwhile, research development in dance visual frequency movement detection is rather modest due to the high complexity of dance movement and the challenges of human body self-shielding in dance performance. Aiming at the problem of the combination of motion recognition and dance video, the feature extraction, representation, and motion recognition methods based on dance video are emphatically studied. This paper studies an effective feature extraction method according to the characteristics of dance movements. Firstly, each dance movement video in the data set is separated into equal sections, and the edge characteristics of all video pictures in each segment are gathered into one image, from which the direction gradient histogram features are extracted. Secondly, a group of directional gradient histogram feature vectors is used to represent the local appearance information and shape features of the video dance moves. In view of the existing problem of heterogeneous feature fusion, this paper chooses the multi-core learning method to fuse the three kinds of features for dance movement recognition. Finally, the effectiveness of the proposed dance movement detection algorithm is tested using the Dance DB data set from the University of Cyprus and the Folk Dance data set from my laboratory. Experimental results show that the proposed algorithm can maintain a certain recognition rate for relatively complex dance movements and can still ensure a certain accuracy when the background and target are easily confused. This also confirms the efficacy of the movement recognition system used in this paper for recognizing dance movements.

Cascading-Failures Effect on Heterogeneous Internet of Things Systems under Targeted Selective Attack

With the rapid development of the Internet of Things (IoT), the physical system and the network space are further deeply integrated, forming a larger-scale IoT heterogeneous fusion system. The attack mode considered in the security mechanism research of traditional large-scale complex systems is relatively simple; only simple attack types such as random attacks on physical systems or network systems are considered. In addition, existing attack modalities such as selectivity, locality, and distribution cannot fully consider the characteristics of security threats in the IoT system. In this paper, for large-scale heterogeneous IoT system scenarios, attackers can attack network systems or physical systems through cyberspace. We conduct situational awareness analysis on important traffic nodes or backbone nodes and study the cascading failures of two interdependent heterogeneous space systems. In view of the existence of such targeted attack threats in large-scale IoT heterogeneous systems, we focus on security assessment and risk prediction issues. First, this paper analyzes and models different IoT heterogeneous systems. Then using the penetration theory, we analyze the cascading failure process step by step and obtain the critical threshold for system collapse failure. Finally, we further verify the correctness of the theoretical values through simulation to effectively analyze and illustrate the reliability of the parameters affecting the system risk. The experimental results show that the large-scale IoT heterogeneous system presents a first-order discontinuous transition value near the critical threshold and the power-law index of the SF network has little effect on the system security.

Ensemble Classifier with Heterogenous Fusion Center for Cooperative Spectrum Sensing in Cognitive Radio

Cooperative spectrum sensing (CSS) in a cognitive radio uses a fusion center, which receives local sensing decisions from multiple secondary users to predict whether primary user is present or absent. Therefore, an ensemble classifier with heterogenous fusion center (EC-HFC) is proposed in this work, where the ensemble classifier comprise three classification algorithms such as logistic regression (LR), support vector machine (SVM), and gaussian naive bayes (GNB). In addition, voting classifier with its variants also employed for finding the best suitable classifier. Further, the performance metrics such as accuracy, F1-score, area under the curve (AUC), probability of detection and probability of false alarm are computed for evaluating the performance of proposed ensemble classifier-based fusion center for cooperative spectrum sensing in cognitive radio. Finally, the obtained receiver operating characteristics (ROC) and extensive simulation results shows that proposed fusion center resulted in superior performance as compared to individual secondary users.

Bearing Fault Diagnosis Method Based on Multidomain Heterogeneous Information Entropy Fusion and Model Self-Optimisation

Incomplete diagnostic information, inadequate multisource sensor information, weak diagnosis models, and subjective experience result in difficulty in predicting rotating machinery faults. To overcome these limitations, we proposed a multiple domain and heterogeneous information entropy fusion model based on an optimisation of bearing fault diagnosis. The spatiotemporal approach uses a multiscene domain fusion strategy based on heterogeneous sensors (HSMSF) to extract feature fusion strategies and analyses the characteristics of the bearing fault features by multichannel processes with convolutional neural networks to vibration signals. After the mapping of multiple quality characteristics, the high-quality features are combined with each other, and the adaptive entropy weighted fusion method is used to analyse and make decisions on sensor information from different detection points. Nineteen key model parameters that were required for HSMSF construction were selected by adaptive optimisation using the chaos elitist modified sparrow search algorithm (CEI-SSA), and a self-learning diagnostic model that is suitable for multiple detection points was constructed. The validity and feasibility of the proposed fault diagnosis method were verified experimentally on two common reference-bearing datasets, CWRU and IMS, and compared with other fault diagnosis methods.

Target recognition of heterogeneous fusion images based on improved YOLO model deep learning algorithm

Aiming at the problem of target recognition of heterogeneous fusion images, the algorithm flow and network structure of the YOLO convolutional neural network model are analyzed and explained based on the deep learning of the convolutional neural network. Combining the characteristics of the fusion image, the interface, structure and parameters of the YOLOv3 convolutional neural network are improved and adjusted while the recognition effect is guaranteed, the YOLO model is streamlined, and the training is successfully implemented and the training weights are output. The algorithm is effective and stable, and the recognition speed is faster.

TransCloudSeg: Ground-Based Cloud Image Segmentation With Transformer

Cloud image segmentation plays an important role in ground-based cloud observation. Recently, most existing methods for ground-based cloud image segmentation learn feature representations using the convolutional neural network (CNN), which results in the loss of global information because of the limited receptive field size of the filters in the CNN. In this article, we propose a novel deep model named TransCloudSeg, which makes full use of the advantages of the CNN and transformer to extract detailed information and global contextual information for ground-based cloud image segmentation. Specifically, TransCloudSeg hybridizes the CNN and transformer as the encoders to obtain different features. To recover and fuse the feature maps from the encoders, we design the CNN decoder and the transformer decoder for TransCloudSeg. After obtaining two sets of feature maps from two different decoders, we propose the heterogeneous fusion module to effectively fuse the heterogeneous feature maps by applying the self-attention mechanism. We conduct a series of experiments on Tianjin Normal University large-scale cloud detection database and Tianjin Normal University cloud detection database, and the results show that our method achieves a better performance than other state-of-the-art methods, thus proving the effectiveness of the proposed TransCloudSeg.

Multi-source heterogeneous fusion entropy ratio distance feature of bearing performance degradation based on DTW

The key to the evaluation of bearing performance degradation is to extract sensitive characteristic indicators that can reflect the degradation process. In order to solve the problem of insufficient sensitivity of the bearing multi-source signals feature, a fusion entropy ratio feature based on dynamic time warping is proposed. First, the segmented approximate entropy, sample entropy and fuzzy entropy of the bearing performance degradation process are extracted, and then the mean clustering is used to obtain the standard entropy vector of the health state. In order to eliminate the influence of working environment and entropy value on state evaluation, a concept of entropy ratio is defined by the ratio of the entropy vector of each state to the standard entropy vector of the health state. Finally, using dynamic time warping has the advantage of accurately describing the similarity between vectors from a global perspective, and the multi-source heterogeneous fusion entropy ratio distance feature is constructed to describe the degraded state of bearing performance. Experiments show that this feature is more sensitive to the bearing performance degradation than other features, such as fusion entropy ratio feature based on Euclidean distance, single entropy ratio feature based on dynamic time bending.

Heterogeneous Signal Fusion Method in Driving Fatigue Detection Signals

Driving fatigue is a physiological phenomenon that often occurs during driving. When the driver enters a fatigue state, they will become distracted and unresponsive, which can easily lead to traffic accidents. The driving fatigue detection method based on a single information source has poor stability in a specific driving environment and has great limitations. This work helps with being able to judge the fatigue state of the driver more comprehensively and achieving a higher accuracy rate of driving fatigue detection. This work mainly introduces research into different signal fusion methods to detect fatigue drive. This work will take the normal driver’s breathing signal, eye signals, and steering wheel signal as research objects and collect and isolate the characteristics of the fatigue detection signal. Research was then conducted on different signal fusion methods for the detected depth of breath. Change of steering angle, eyelid closure, and blinking marks and the fatigue driving experiment was designed to evaluate the results of different data fusion methods. Experimental results show that the detection accuracy of the heterogeneous signal fusion method in fatigue detection is as high as 80%.

Multiple Optimizations-Based ESRFBN Super-Resolution Network Algorithm for MR Images

Magnetic resonance (MR) images can detect small pathological tissue with the size of 3–5 image pixels at an early stage, which is of great significance in the localization of pathological lesions and the diagnosis of disease. High-resolution MR images can provide clearer structural details and help doctors to analyze and diagnose the disease correctly. In this paper, MR super-resolution based on the multiple optimizations-based Enhanced Super Resolution Feed Back Network (ESRFBN) is proposed. The method realizes network optimization from the three perspectives of network structure, data characteristics and heterogeneous network integration. Firstly, a super-resolution network structure based on multi-slice input optimization is proposed to make full use of the structural similarity between samples. Secondly, aiming at the problem that the L1 or L2 loss function is based on a per-pixel comparison of differences, without considering human visual perception, the optimization method of multiple loss function cascade is proposed, which combines the L1 loss function to retain the color and brightness characteristics and the MS-SSIM loss function to retain the contrast characteristics of the high-frequency region better, so that the depth model has better characterization performance; thirdly, in view of the problem that large deep learning networks are difficult to balance model complexity and training difficulty, a heterogeneous network fusion method is proposed. For multiple independent deep super-resolution networks, the output of a single network is integrated through an additional fusion layer, which broadens the width of the network, and can effectively improve the mapping and characterization capabilities of high- and low-resolution features. The experimental results on two super-resolution scales and on MR images datasets of four human body parts show that the proposed large-sample space learning super-resolution method effectively improves the super-resolution performance.

Design of Multi-mode Heterogeneous Fusion Terminal for Power System

With the increasing demand of users and power supply quality in the power grid market, this puts forward higher requirements on the power system. However, the traditional power system has many limitations. People need to be able to upgrade on the basis of the traditional power grid to adapt to changing requirements. The purpose of this paper is to study the design of multi-mode heterogeneous fusion terminal in power system. This article is based on two wireless network technology solutions, GPRS wireless public network and LTE-230 wireless private network, a hardware development platform with STM32F107VCT6 microprocessor as the core, and a μ C/OS-III embedded real-time operating system software development platform, has designed the multi-mode heterogeneous fusion terminal of the electricity information collection system. This article deeply analyzes the demand for multi-mode heterogeneous fusion terminals of the electricity consumption information collection system, which is mainly used for the collection of electricity consumption information and the marketing and distribution automation management of power services; then it analyzes the work of the GPRS network and the LTE-230 network Principle and network system structure. This article designs the communication driver and application program, and analyzes the software architecture in detail. According to the logic processing structure of the internal data of the multi-mode heterogeneous fusion terminal, this article designs the data forwarding process in detail. In this paper, protocol conversion test, interface test and communication test are carried out on multi-mode heterogeneous fusion terminal. The test results show that when the system buffer K is 2 or 4, that is, if the average size of each message in the system is 100 Bytes, and the buffer is set to 800 Bytes, the system loss rate will approach zero.

Heterogeneous Multi-Sensor Fusion With Random Finite Set Multi-Object Densities

This paper addresses the density based multi-sensor cooperative fusion using random finite set (RFS) type multi-object densities (MODs). Existing fusion methods use scalar weights to characterize the relative information confidence among the local MODs, and in this way the portion of contribution of each local MOD to the fused global MOD can be tuned via adjusting these weights. Our analysis shows that the fusion mechanism of using a scalar coefficient can be oversimplified for practical scenarios, as the information confidence of an MOD is complex and usually space-varying due to the imperfection of sensor ability and the various impacts from surveillance environment. Consequently, severe fusion performance degradation can be observed when these scalar weights fail to reflect the actual situation. We make two contributions towards addressing this problem. Firstly, we propose a novel heterogeneous fusion method to perform the information averaging among local RFS MODs. By factorizing each local MODs into a number of smaller size sub-MODs, it can transform the original complicated fusion problem into a much easier parallelizable multi-cluster fusion problem. Secondly, as the proposed fusion strategy is a general procedure without any particular model assumptions, we further derive the detailed heterogeneous fusion equations, with centralized network architecture, for both the probability hypothesis density (PHD) filter and the multi-Bernoulli (MB) filter. The Gaussian mixture implementations of the proposed fusion algorithms are also presented. Various numerical experiments are designed to demonstrate the efficacy of the proposed fusion methods.

An Asynchronous Heterogeneous Fusion Framework for RF Localization Ensembles

An Asynchronous Heterogeneous Fusion Framework for RF Localization Ensembles

Energy-Efficient Node Deployment in Heterogeneous Two-Tier Wireless Sensor Networks With Limited Communication Range

We study a heterogeneous two-tier wireless sensor network in which N heterogeneous access points (APs) collect sensing data from densely distributed sensors and then forward the data to M heterogeneous fusion centers (FCs). This heterogeneous node deployment problem is modeled as an optimization problem with the total power consumption of the network as its cost function. The necessary conditions of the optimal AP and FC node deployment are explored in this paper. We provide a variation of Voronoi Diagram as the optimal cell partition for this network and show that each AP should be placed between its connected FC and the geometric center of its cell partition. In addition, we propose a heterogeneous two-tier Lloyd algorithm to optimize the node deployment. Furthermore, we study the sensor deployment when the communication range is limited for sensors and APs. Simulation results show that our proposed algorithms outperform the existing clustering methods like Minimum Energy Routing, Agglomerative Clustering, Divisive Clustering, Particle Swarm Optimization, Relay Node placement in Double-tiered Wireless Sensor Networks, and Improved Relay Node Placement, on average.