A Multi-Source Data Fusion Network for Wood Surface Broken Defect Segmentation.

Wood surface broken defects seriously damage the structure of wooden products, these defects have to be detected and eliminated. However, current defect detection methods based on machine vision have difficulty distinguishing the interference, similar to the broken defects, such as stains and mineral lines, and can result in frequent false detections. To address this issue, a multi-source data fusion network based on U-Net is proposed for wood broken defect detection, combining image and depth data, to suppress the interference and achieve complete segmentation of the defects. To efficiently extract various semantic information of defects, an improved ResNet34 is designed to, respectively, generate multi-level features of the image and depth data, in which the depthwise separable convolution (DSC) and dilated convolution (DC) are introduced to decrease the computational expense and feature redundancy. To take full advantages of two types of data, an adaptive interacting fusion module (AIF) is designed to adaptively integrate them, thereby generating accurate feature representation of the broken defects. The experiments demonstrate that the multi-source data fusion network can effectively improve the detection accuracy of wood broken defects and reduce the false detections of interference, such as stains and mineral lines.

Identification and Positioning of Abnormal Maritime Targets Based on AIS and Remote-Sensing Image Fusion.

The identification of maritime targets plays a critical role in ensuring maritime safety and safeguarding against potential threats. While satellite remote-sensing imagery serves as the primary data source for monitoring maritime targets, it only provides positional and morphological characteristics without detailed identity information, presenting limitations as a sole data source. To address this issue, this paper proposes a method for enhancing maritime target identification and positioning accuracy through the fusion of Automatic Identification System (AIS) data and satellite remote-sensing imagery. The AIS utilizes radio communication to acquire multidimensional feature information describing targets, serving as an auxiliary data source to complement the limitations of image data and achieve maritime target identification. Additionally, the positional information provided by the AIS can serve as maritime control points to correct positioning errors and enhance accuracy. By utilizing data from the Jilin-1 Spectral-01 satellite imagery with a resolution of 5 m and AIS data, the feasibility of the proposed method is validated through experiments. Following preprocessing, maritime target fusion is achieved using a point-set matching algorithm based on positional features and a fuzzy comprehensive decision method incorporating attribute features. Subsequently, the successful fusion of target points is utilized for positioning error correction. Experimental results demonstrate a significant improvement in maritime target positioning accuracy compared to raw data, with over a 70% reduction in root mean square error and positioning errors controlled within 4 pixels, providing relatively accurate target positions that essentially meet practical requirements.

A Soft Sensor Model of Sintering Process Quality Index Based on Multi-Source Data Fusion.

In complex industrial processes such as sintering, key quality variables are difficult to measure online and it takes a long time to obtain quality variables through offline testing. Moreover, due to the limitations of testing frequency, quality variable data are too scarce. To solve this problem, this paper proposes a sintering quality prediction model based on multi-source data fusion and introduces video data collected by industrial cameras. Firstly, video information of the end of the sintering machine is obtained via the keyframe extraction method based on the feature height. Secondly, using the shallow layer feature construction method based on sinter stratification and the deep layer feature extraction method based on ResNet, the feature information of the image is extracted at multi-scale of the deep layer and the shallow layer. Then, combining industrial time series data, a sintering quality soft sensor model based on multi-source data fusion is proposed, which makes full use of multi-source data from various sources. The experimental results show that the method effectively improves the accuracy of the sinter quality prediction model.

Real-Time Target Detection System for Intelligent Vehicles Based on Multi-Source Data Fusion.

To improve the identification accuracy of target detection for intelligent vehicles, a real-time target detection system based on the multi-source fusion method is proposed. Based on the ROS melodic software development environment and the NVIDIA Xavier hardware development platform, this system integrates sensing devices such as millimeter-wave radar and camera, and it can realize functions such as real-time target detection and tracking. At first, the image data can be processed by the You Only Look Once v5 network, which can increase the speed and accuracy of identification; secondly, the millimeter-wave radar data are processed to provide a more accurate distance and velocity of the targets. Meanwhile, in order to improve the accuracy of the system, the sensor fusion method is used. The radar point cloud is projected onto the image, then through space-time synchronization, region of interest (ROI) identification, and data association, the target-tracking information is presented. At last, field tests of the system are conducted, the results of which indicate that the system has a more accurate recognition effect and scene adaptation ability in complex scenes.

Identification of Potential Parkinson's Disease Drugs Based on Multi-Source Data Fusion and Convolutional Neural Network.

Parkinson's disease (PD) is a serious neurodegenerative disease. Most of the current treatment can only alleviate symptoms, but not stop the progress of the disease. Therefore, it is crucial to find medicines to completely cure PD. Finding new indications of existing drugs through drug repositioning can not only reduce risk and cost, but also improve research and development efficiently. A drug repurposing method was proposed to identify potential Parkinson's disease-related drugs based on multi-source data integration and convolutional neural network. Multi-source data were used to construct similarity networks, and topology information were utilized to characterize drugs and PD-associated proteins. Then, diffusion component analysis method was employed to reduce the feature dimension. Finally, a convolutional neural network model was constructed to identify potential associations between existing drugs and LProts (PD-associated proteins). Based on 10-fold cross-validation, the developed method achieved an accuracy of 91.57%, specificity of 87.24%, sensitivity of 95.27%, Matthews correlation coefficient of 0.8304, area under the receiver operating characteristic curve of 0.9731 and area under the precision-recall curve of 0.9727, respectively. Compared with the state-of-the-art approaches, the current method demonstrates superiority in some aspects, such as sensitivity, accuracy, robustness, etc. In addition, some of the predicted potential PD therapeutics through molecular docking further proved that they can exert their efficacy by acting on the known targets of PD, and may be potential PD therapeutic drugs for further experimental research. It is anticipated that the current method may be considered as a powerful tool for drug repurposing and pathological mechanism studies.

Simulation of average monthly ozone exposure concentrations in China: A temporal and spatial estimation method.

BACKGROUND: Ozone has adverse effects on human health, it is necessary to obtain the refined ozone exposure concentration. At present, most of existing ozone exposure research is based on ground air quality monitoring station (MS) which gather urban area information only. It is diffcult to estimate the ozone in the areas where MSs are scarce. OBJECTIVE: By combining accurate but uneven data of outdoor ozone exposure concentrations based on MSs and unbiased coverage data based on RS in China, we can improve the accuracy of simulation of average monthly ozone exposure concentrations in monitor-free area. Since ozone concentrations are usually low at night, ozone exposure is assessed during the day (e.g., 10:00-18:00). METHODS: We proposed a space-time geostatistical kriging interpolation based on the composite space/time mean trend model (CSTM) to predict ozone exposure in mainland China, having obtained a refined ozone exposure concentration interpolation map from an MS. We verified the accuracy of the interpolation results and remote sensing (RS) data, while simultaneously determining the distance threshold (according to the data accuracy) to improve the accuracy of the hybrid map. RESULTS: We used a refined smoothing filter to reduce the influence of spatial and seasonal trends on ozone concentration. We found a cutoff separation distance of 175 km at which the two data showed an equal estimation accuracy, and the estimation result was fused with RS data through the distance threshold. Finally, The multi-source data with the best accuracy were fused to obtain the refined map. In China, ozone exposure concentration mainly gathers in the northern and eastern regions as well as part of the central mainland. CONCLUSIONS: RS data can be used to characterize ground ozone exposure concentrations when 24th-layer data and MS data for monitoring ozone exposure concentrations are combined to estimate temporal and spatial ozone exposure in China. Ozone exposure in China can be explored further to provide suggestions for human health and regional economic development.

Quality Evaluation of Rock Mass Using RMR14 Based on Multi-Source Data Fusion.

The uncertainties in quality evaluations of rock mass are embedded in the underlying multi-source data composed by a variety of testing methods and some specialized sensors. To mitigate this issue, a proper method of data-driven computing for quality evaluation of rock mass based on the theory of multi-source data fusion is required. As the theory of multi-source data fusion, Dempster-Shafer (D-S) evidence theory is applied to the quality evaluation of rock mass. As the correlation between different rock mass indices is too large to be ignored, belief reinforcement and Murphy's average belief theory are introduced to process the multi-source data of rock mass. The proposed method is designed based on RMR14, one of the most widely used quality-evaluating methods for rock mass in the world. To validate the proposed method, the data of rock mass is generated randomly to realize the data fusion based on the proposed method and the conventional D-S theory. The fusion results based on these two methods are compared. The result of the comparison shows the proposed method amplifies the distance between the possibilities at different ratings from 0.0666 to 0.5882, which makes the exact decision more accurate than the other. A case study is carried out in Daxiagu tunnel in China to prove the practical value of the proposed method. The result shows the rock mass rating of the studied section of the tunnel is in level III with the maximum possibility of 0.9838, which agrees with the geological survey report.

Adaptive Neuro-Fuzzy Fusion of Multi-Sensor Data for Monitoring a Pilot's Workload Condition.

To realize an early warning of unbalanced workload in the aircraft cockpit, it is required to monitor the pilot's real-time workload condition. For the purpose of building the mapping relationship from physiological and flight data to workload, a multi-source data fusion model is proposed based on a fuzzy neural network, mainly structured using a principal components extraction layer, fuzzification layer, fuzzy rules matching layer, and normalization layer. Aiming at the high coupling characteristic variables contributing to workload, principal component analysis reconstructs the feature data by reducing its dimension. Considering the uncertainty for a single variable to reflect overall workload, a fuzzy membership function and fuzzy control rules are defined to abstract the inference process. An error feedforward algorithm based on gradient descent is utilized for parameter learning. Convergence speed and accuracy can be adjusted by controlling the gradient descent rate and error tolerance threshold. Combined with takeoff and initial climbing tasks of a Boeing 737-800 aircraft, crucial performance indicators-including pitch angle, heading, and airspeed-as well as physiological indicators-including electrocardiogram (ECG), respiration, and eye movements-were featured. The mapping relationship between multi-source data and the comprehensive workload level synthesized using the NASA task load index was established. Experimental results revealed that the predicted workload corresponding to different flight phases and difficulty levels showed clear distinctions, thereby proving the validity of data fusion.

An Improved Multi-Source Data Fusion Method Based on the Belief Entropy and Divergence Measure.

Dempster-Shafer (DS) evidence theory is widely applied in multi-source data fusion technology. However, classical DS combination rule fails to deal with the situation when evidence is highly in conflict. To address this problem, a novel multi-source data fusion method is proposed in this paper. The main steps of the proposed method are presented as follows. Firstly, the credibility weight of each piece of evidence is obtained after transforming the belief Jenson-Shannon divergence into belief similarities. Next, the belief entropy of each piece of evidence is calculated and the information volume weights of evidence are generated. Then, both credibility weights and information volume weights of evidence are unified to generate the final weight of each piece of evidence before the weighted average evidence is calculated. Then, the classical DS combination rule is used multiple times on the modified evidence to generate the fusing results. A numerical example compares the fusing result of the proposed method with that of other existing combination rules. Further, a practical application of fault diagnosis is presented to illustrate the plausibility and efficiency of the proposed method. The experimental result shows that the targeted type of fault is recognized most accurately by the proposed method in comparing with other combination rules.

THz Spectroscopic Investigation of Wheat-Quality by Using Multi-Source Data Fusion.

In order to improve the detection accuracy for the quality of wheat, a recognition method for wheat quality using the terahertz (THz) spectrum and multi-source information fusion technology is proposed. Through a combination of the absorption and the refractive index spectra of samples of normal, germinated, moldy, and worm-eaten wheat, support vector machine (SVM) and Dempster-Shafer (DS) evidence theory with different kernel functions were used to establish a classification fusion model for the multiple optical indexes of wheat. The results showed that the recognition rate of the fusion model for wheat samples can be as high as 96%. Furthermore, this approach was compared to the regression model based on single-spectrum analysis. The results indicate that the average recognition rates of fusion models for wheat can reach 90%, and the recognition rate of the SVM radial basis function (SVM-RBF) fusion model can reach 97.5%. The preliminary results indicated that THz-TDS combined with DS evidence theory analysis was suitable for the determination of the wheat quality with better detection accuracy.

An autoregressive integrated moving average and long short-term memory (ARIM-LSTM) hybrid model for multi-source epidemic data prediction.

The COVID-19 pandemic has far-reaching impacts on the global economy and public health. To prevent the recurrence of pandemic outbreaks, the development of short-term prediction models is of paramount importance. We propose an ARIMA-LSTM (autoregressive integrated moving average and long short-term memory) model for predicting future cases and utilize multi-source data to enhance prediction performance. Firstly, we employ the ARIMA-LSTM model to forecast the developmental trends of multi-source data separately. Subsequently, we introduce a Bayes-Attention mechanism to integrate the prediction outcomes from auxiliary data sources into the case data. Finally, experiments are conducted based on real datasets. The results demonstrate a close correlation between predicted and actual case numbers, with superior prediction performance of this model compared to baseline and other state-of-the-art methods.

Realizing the soft infrastructure mixing of Shanghai's catering industry based on land expansion and population growth.

This study takes Shanghai's restaurants as a case study of urban soft infrastructure, employing big data sets from third-party website platforms for multi-source data fusion, to deeply analyze the impact of urban land expansion and population dynamics on the availability, affordability, acceptability, and accessibility of restaurants. The case study reveals that, despite the Shanghai municipal authorities' focus on mitigating overcrowding in the central urban areas, soft infrastructure such as restaurants in new urban districts remains at a relative disadvantage. The decentralization of soft infrastructure to peripheral urban areas has not met policy expectations, presenting a spatial imbalance characterized by greater provision in the main urban areas than in new urban districts, and higher in Puxi than in Pudong. The single-threshold model uncovers that the positive impact of land urbanization and population dynamics on restaurant convenience undergoes a transformation after reaching a certain critical point, where the linear relationship and synchronous growth shift. By controlling the development area of construction land and the population density within regions, a dynamic combination of availability, affordability, acceptability, and accessibility of restaurants can be achieved. This forms a spatio-temporal management strategy that integrates land, population, and comfort facilities, potentially alleviating the inequity of comfort facilities in Shanghai and the central urban areas' siphoning effect.

Deep learning-based prediction of effluent quality of a constructed wetland.

Data-driven approaches that make timely predictions about pollutant concentrations in the effluent of constructed wetlands are essential for improving the treatment performance of constructed wetlands. However, the effect of the meteorological condition and flow changes in a real scenario are generally neglected in water quality prediction. To address this problem, in this study, we propose an approach based on multi-source data fusion that considers the following indicators: water quality indicators, water quantity indicators, and meteorological indicators. In this study, we establish four representative methods to simultaneously predict the concentrations of three representative pollutants in the effluent of a practical large-scale constructed wetland: (1) multiple linear regression; (2) backpropagation neural network (BPNN); (3) genetic algorithm combined with the BPNN to solve the local minima problem; and (4) long short-term memory (LSTM) neural network to consider the influence of past results on the present. The results suggest that the LSTM-predicting model performed considerably better than the other deep neural network-based model or linear method, with a satisfactory R2. Additionally, given the huge fluctuation of different pollutant concentrations in the effluent, we used a moving average method to smooth the original data, which successfully improved the accuracy of traditional neural networks and hybrid neural networks. The results of this study indicate that the hybrid modeling concept that combines intelligent and scientific data preprocessing methods with deep learning algorithms is a feasible approach for forecasting water quality in the effluent of actual engineering.

PL-TARMI: A deep learning framework for pixel-level traffic crash risk map inference.

A citywide traffic crash risk map is of great significance for preventing future traffic crashes. However, the fine-grained geographic traffic crash risk inference is still a challenging task, mainly due to the complex road network structure, human behavior and high data requirements. In this work, we propose a deep-learning framework PL-TARMI, which leverages easily accessible data to achieve accurate fine-grained traffic crash risk map inference. Specifically, we integrate the satellite image and road network image, combine with other accessible data (e.g., point of interest distribution, human mobility data, traffic data, etc.) as input, and finally obtain the pixel-level traffic crash risk map, which could provide more reasonable traffic crash prevention guidance with a lower cost. Extensive experiments on real-world datasets demonstrate the effectiveness of PL-TARMI.

Conversion and fusion method of multi-source and different populations maintainability prior data.

Maintainability is an important universal quality characteristic that reflects the convenience, speed and economy of weapon and equipment maintenance. Making full use of multi-source data to accurately verify the degree to which the developed equipment meets the maintainability requirements is an important basis for equipment identification and acceptance. To solve the low reliability of equipment maintainability verification results caused by inaccurate comprehensive prior distribution obtained by fusing multi-source and different populations' prior data, a method of data conversion and fusion is proposed. A data conversion model based on the mean value ratio of failure mode maintenance data is constructed. The conversion factor is defined according to objective data to convert the different populations' prior data to the same populations. Next, a comparison of the prior distribution fitting performance of Bayes bootstrap, bootstrap, and two improved sample-resampling methods to are used obtain the closest fitting distribution to the true distribution. By constructing a multi-source data fusion model based on improved KL divergence, a symmetrical KL divergence is constructed to describe the similarity between each prior distribution and the field distribution for the weighted fusion of multi-source prior distribution in addition to determining and testing the normal comprehensive prior distribution. The results show that the conversion and fusion method effectively converts the multi-source and different populations' maintainability prior data and obtains an accurate, comprehensive prior distribution by fusion, laying the foundation for applying the Bayes test method to verify the quantitative index of equipment maintainability.

Prediction method of coal mine gas occurrence law based on multi-source data fusion.

To improve the prediction accuracy and time-consuming of coal mine gas occurrence law (OL), a new prediction method based on multi-source data fusion is proposed in this paper. Firstly, the method obtains the data of coal mine gas OL, determines the key data required in prediction through decision matrix, and preprocesses the data to reduce the influence of regular noise data. This paper analyzes the basic principle of multi-source data fusion, constructs the prediction model of coal mine gas OL with this technology, takes the optimal value of weighting factor as the input value of the model, and completes the design of coal mine gas OL prediction method based on multi-source data fusion. The experimental results show that the accuracy of this method can reach 98%, while that of the other two traditional methods is lower than the existing methods. This method has high accuracy and efficiency in predicting the coal mine gas OL.

A Methodology for Integrating Population Health Surveys Using Spatial Statistics and Visualizations for Cross-Sectional Analysis.

Large-scale population surveys are beneficial in gathering information on the performance indicators of public well-being, including health and socio-economic standing. However, conducting national population surveys for low and middle-income countries (LMIC) with high population density comes at a high economic cost. To conduct surveys at low-cost and efficiently, multiple surveys with different, but focused, goals are implemented through various organizations in a decentralized manner. Some of the surveys tend to overlap in outcomes with spatial, temporal or both scopes. Mining data jointly from surveys with significant overlap gives new insights while preserving their autonomy. We propose a three-step workflow for integrating surveys using spatial analytic workflow supported by visualizations. We implement the workflow on a case study using two recent population health surveys in India to study malnutrition in children under-five. Our case study focuses on finding hotspots and coldspots for malnutrition, specifically undernutrition, by integrating the outcomes of both surveys. Malnutrition in children under-five is a pertinent global public health problem that is widely prevalent in India. Our work shows that such an integrated analysis is beneficial alongside independent analyses of such existing national surveys to find new insights into national health indicators.

Extraction of urban built-up area based on the fusion of night-time light data and point of interest data.

The accurate extraction of urban built-up areas is an important prerequisite for urban planning and construction. As a kind of data that can represent urban spatial form, night-time light data has been widely used in the extraction of urban built-up areas. As one of the geographic open-source big data, point of interest (POI) data has a high spatial coupling with night-time light data, so researchers are beginning to explore the fusion of the two data in order to achieve more accurate extraction of urban built-up areas. However, the current research methods and theoretical applications of the fusion of POI data and night-time light data are still insufficient compared with the dramatically changing urban built-up areas, which needed to be further supplemented and deepened. This study proposes a new method to fuse POI data and night-time light data. The results before and after data fusion are compared, and the accuracy of urban built-up area extracted by different data and methods is analysed. The results show that the data fusion can avoid the shortage of single data and effectively improve the extraction accuracy of urban built-up areas, which is greatly helpful to supplement the study of data fusion in urban built-up areas, and also can provide decision-making guidance for urban planning and construction.