Multi-sensor Information Fusion Research Articles

Multi-sensor Data Analysis for Aerial Image Semantic Segmentation and Vectorization

Abstract. One of the urgent and constantly in demand problems is updating maps. Maps, representing geo-information in vector form, have undoubted advantages in compactness and ”readability” compared to aerial photographs. The issue of maps actuality is critically important for rational urban planning, precision farming, the relevance of the cadastre and other geospatial applications. Various sources of data are used for maps updating, with aerial imagery being the main and rich source of information. Automatic processing of aerial photographs makes it possible to efficiently extract vector information, providing operational monitoring and accounting for changes that have appeared. The presented study addresses the problem of multi sensor information fusion in order to obtain accurate vector information. We use aerial images as a main data source and additionally the data of laser scanning and ground survey to increase performance of automatic image semantic segmentation and vectorization. The proposed framework is demonstrated on the task of forest monitoring.

Interference Fading Suppression with Fault-tolerant Kalman Filter in Phase-sensitive OTDR

A multi-sensor information fusion algorithm based on fault-tolerant Kalman filter is proposed in phase-sensitive optical time-domain reflectometer (Φ-OTDR) system, for achieving fading-free distributed vibration sensing. Firstly, a fault-tolerant dual-core complementary array model is designed. The Rayleigh scattering signal denoising, and vibration existence judgment of localization points are carried out to obtain the differentiated frequency demodulation results of the sensing points of the dual-core fiber array. Then a fault-tolerant control strategy is used to determine the sensor weight coefficients and vibration judgment coefficients during data fusion processing, and array data fusion is carried out based on time series data using Kalman filter to realize error value identification and filling. The advantage of this method is the combination of redundant data in a complementary way to improve the system stability. The frequency response ranges from 10Hz to 2400Hz and the localization accuracy is 98.33%. The influence of key parameters on the frequency demodulation performance of fault-tolerant Kalman filter is discussed, and a standard deviation of 14.6Hz and an average error of 7.6Hz are obtained. The demodulated frequency data matrix obtained by the classical demodulation method has a demodulation error probability of 89.18%, which proves the widespread existence of demodulation errors in vibration signals. The fusion error of demodulation frequency is reduced to 0.25Hz, the frequency demodulation accuracy reaches 100%, and the demodulation error caused by interference attenuation can be completely eliminated. This system based on fault-tolerant Kalman filter has the characteristics of simple multiplexing structure, interference fading resistance and stable demodulation performance.

Fault Diagnosis of Hydraulic Components Based on Multi-Sensor Information Fusion Using Improved TSO-CNN-BiLSTM.

In order to realize the accurate and reliable fault diagnosis of hydraulic systems, a diagnostic model based on improved tuna swarm optimization (ITSO), optimized convolutional neural networks (CNNs), and bi-directional long short-term memory (BiLSTM) networks is proposed. Firstly, sensor selection is implemented using the random forest algorithm to select useful signals from six kinds of physical or virtual sensors including pressure, temperature, flow rate, vibration, motor power, and motor efficiency coefficient. After that, fused features are extracted by CNN, and then, BiLSTM is applied to learn the forward and backward information contained in the data. The ITSO algorithm is adopted to adaptively optimize the learning rate, regularization coefficient, and node number to obtain the optimal CNN-BiLSTM network. Improved Chebyshev chaotic mapping and the nonlinear reduction strategy are adopted to improve population initialization and individual position updating, further promoting the optimization effect of TSO. The experimental results show that the proposed method can automatically extract fusion features and effectively utilize multi-sensor information. The diagnostic accuracies of the plunger pump, cooler, throttle valve, and accumulator are 99.07%, 99.4%, 98.81%, and 98.51%, respectively. The diagnostic results of noisy data with 0 dB, 5 dB, and 10 dB signal-to-noise ratios (SNRs) show that the ITSO-CNN-BiLSTM model has good robustness to noise interference.

Machine Tool Wear Prediction Technology Based on Multi-Sensor Information Fusion.

The intelligent monitoring of cutting tools used in the manufacturing industry is steadily becoming more convenient. To accurately predict the state of tools and tool breakages, this study proposes a tool wear prediction technique based on multi-sensor information fusion. First, the vibrational, current, and cutting force signals transmitted during the machining process were collected, and the features were extracted. Next, the Kalman filtering algorithm was used for feature fusion, and a predictive model for tool wear was constructed by combining the ResNet and long short-term memory (LSTM) models (called ResNet-LSTM). Experimental data for thin-walled parts obtained under various machining conditions were utilized to monitor the changes in tool conditions. A comparison between the ResNet and LSTM tool wear prediction models indicated that the proposed ResNet-LSTM model significantly improved the prediction accuracy compared to the individual LSTM and ResNet models. Moreover, ResNet-LSTM exhibited adaptive noise reduction capabilities at the front end of the network for signal feature extraction, thereby enhancing the signal feature extraction capability. The ResNet-LSTM model yielded an average prediction error of 0.0085 mm and a tool wear prediction accuracy of 98.25%. These results validate the feasibility of the tool wear prediction method proposed in this study.

Integrating multisensory information fusion and interaction technologies in smart healthcare systems

<p>The advent of intelligent medical systems has heralded a new era in healthcare, promising enhanced diagnostic accuracy, treatment efficacy, and personalized patient care. Central to these advancements is the application of multisensory information fusion and interaction technology, which integrates diverse data types—from imaging to auditory signals and electronic health records—to facilitate comprehensive patient assessments. This study examines the efficacy of such multisensory integration within an intelligent medical system framework, focusing on its impact on diagnostic accuracy and treatment effectiveness. A hypothetical dataset encompassing various sensory inputs for a cohort of patients was analyzed, revealing a significant improvement in diagnostic precision (average accuracy of 92.3%) and treatment outcomes, with a majority of interventions rated as highly effective. These findings underscore the potential of multisensory data fusion in revolutionizing medical diagnostics and treatment planning. Despite the promising results, limitations such as sample size and data quality were acknowledged, pointing towards the necessity for further research. This study not only corroborates the value of multisensory information fusion in enhancing healthcare delivery but also highlights the pathway for future advancements in intelligent medical systems. The article’s novelty lies in its approach to integrating multisensory data with AI technologies, leading to a more nuanced understanding of patient health. This method transcends traditional diagnostic techniques, allowing for a multifaceted analysis of medical conditions. It emphasizes the potential of this technology to detect diseases earlier and more accurately, tailor treatments to individual patient needs, and improve overall healthcare efficiency.</p>

Study on Multisensory Information Fusion and Interaction Techniques in Experience Design and Optimization of Intelligent Game Systems

Study on Multisensory Information Fusion and Interaction Techniques in Experience Design and Optimization of Intelligent Game Systems

Multi-sensor information fusion filtering for delayed nonlinear stochastic singular systems with packet disorders

The fusion filtering problem is investigated for a class of time-varying nonlinear singular systems with packet disorders (PDs). The key idea is to transfer the original singular systems with multiple sensors to full-order augmented nonsingular systems by utilizing the matrix transformation method. The phenomenon of PDs accompanied with random transmission delays (RTDs) is considered, which exists in the channel from sensor to filter. A set of independent and identically distributed random variables obeying known probability distribution are introduced to model the RTDs. A centralized fusion filter is designed, which employs the rounding function of the mathematical expectation of the RTDs. Furthermore, the nonlinear function is linearized by utilizing the method of Taylor series expansion. Then, the desired upper bounds of the estimation error covariance are guaranteed and then locally minimized through appropriately designing the corresponding filter gains. Finally, a numerical example with comparisons is carried out to verify the feasibility of the proposed fusion filtering method.

Research on sound signal filtering and processing delay based on multiple receivers

With the rapid development of modern communication and audio technology, sound signal processing has become more and more important. This paper deeply explores the potential of multi-receiver audio signal processing technology based on practical application scenarios, and studies the effect of beamforming technology and adaptive noise cancellation algorithm on audio signal quality improvement. Experimental results show that with proper technology selection and framework design, the audio signal processing effect in complex environments can be significantly improved. In addition, this paper also predicts the trend of signal filtering and processing in the future and puts forward suggestions for the application of deep learning in this field, the research of adaptive algorithms, the fusion of multi-sensor information, the optimization of computational efficiency, and the establishment of real scene simulation. Overall, sound signal processing has great potential and opportunities in practical applications, which are worthy of further research and exploration.

Research Progress on Quality Detection of Livestock and Poultry Meat Based on Machine Vision, Hyperspectral and Multi-Source Information Fusion Technologies

Presently, the traditional methods employed for detecting livestock and poultry meat predominantly involve sensory evaluation conducted by humans, chemical index detection, and microbial detection. While these methods demonstrate commendable accuracy in detection, their application becomes more challenging when applied to large-scale production by enterprises. Compared with traditional detection methods, machine vision and hyperspectral technology can realize real-time online detection of large throughput because of their advantages of high efficiency, accuracy, and non-contact measurement, so they have been widely concerned by researchers. Based on this, in order to further enhance the accuracy of online quality detection for livestock and poultry meat, this article presents a comprehensive overview of methods based on machine vision, hyperspectral, and multi-sensor information fusion technologies. This review encompasses an examination of the current research status and the latest advancements in these methodologies while also deliberating on potential future development trends. The ultimate objective is to provide pertinent information and serve as a valuable research resource for the non-destructive online quality detection of livestock and poultry meat.

Retracted: The Application of Multisensor Information Fusion Technology in Environmental Restoration

Retracted: The Application of Multisensor Information Fusion Technology in Environmental Restoration

An Anti-Collision Algorithm for Self-Organizing Vehicular Ad-Hoc Network Using Deep Learning

The rapid increase of the number of motor vehicles over the last several decades has driven a corresponding increase in the severity of traffic congestion, which has exhibited a considerable human impact. Intelligent anti-collision control via inter-vehicle communication technology can facilitate vehicles adhering to spacing speed standards and improve road utilization and traffic efficiency. In this paper, we apply a deep learning image estimation model based on joint attention mechanism. The network framework uses a deep estimation network Yolov5 and a location based VANET information fast transmission strategy to work together. This paper mainly considers vehicle distance measurement technology as a point of entry to study multi-sensor information fusion for vehicle collision prevention technology based on the self-organizing vehicular ad-hoc network (VANET). This paper also proposes a solution based on the strategy of one-way transmission of shared information and dynamic valuation of a cluster distance threshold with vehicle density. The proposed vehicle anti-collision control algorithm is designed to realize dynamic vehicle control via inter-vehicle communication. In this paper, the two-way coupling of traffic flow and network simulator is used to randomly generate vehicle nodes on the road, and the behavior of the anti-collision system is simulated. The experimental results show that the predetermined control goal is achieved, which demonstrates the effectiveness of the proposed algorithm.

Semi-supervised multi-sensor information fusion tailored graph embedded low-rank tensor learning machine under extremely low labeled rate

This paper investigates a demanding and meaningful task of intelligent fault diagnosis, in which multi-sensors signals are fused for semi-supervised analysis with few labeled fault data. Exploring effective strategies to solve this task from an industrial or academic perspective remains a challenging and resource-consuming task. To this issue, this study develops a new low-rank tensor-based semi-supervised classifier, called graph embedded low-rank tensor learning machine (GE-LRTLM), which can effectively alleviate the foregoing difficulties and increase the diagnosis precision in engineering applications. First, multi-sensor and multi-channel vibration signals are converted into the pixel matrices that are stacked as the multi-sensor information fusion feature tensors, making the coupling relationship between multi-sensor signals remained and achieving a reasonable fusion of multi-source features. Additionally, an advanced tensor decomposition method, tensor nuclear norm (TNN), is introduced in GE-LRTLM model to obtain the low-rank structure information of each feature tensor, implementing the extraction of the most important feature and patterns from the tensor data while ensuring that the tensor data structure remains unchanged. Ultimately, the manifold regularization and tensor-based graph construction method are introduced to obtain potential label information from unlabeled samples, achieving better description of the geometry similarity and distribution of tensor data. Numerous semi-supervised experiments are conducted across multiple datasets, and the results demonstrate that the proposed method can achieve a classification accuracy of 97% even when the number of labeled samples is extremely limited. Simultaneously, it also verifies that the combination of the constructed labeled and unlabeled multi-sensor information fusion tensor samples can promote the improvement of model accuracy.

A belief Sharma-Mittal divergence with its application in multi-sensor information fusion

A belief Sharma-Mittal divergence with its application in multi-sensor information fusion

A fault diagnosis method based on feature-level fusion of multi-sensor information for rotating machinery

Traditionally, most fault diagnosis work on rotating machinery is carried out on single sensor datasets. However, the single feature source may suffer from missing or inaccurate features, which is especially sluggish for fault diagnosis tasks under noise interference. Feature-level fusion of multi-sensor information can obtain more comprehensive and abundant feature information, while improving the feature discrimination. Therefore, through feature-level fusion of multi-sensor information, a parallel multi-scale attentional convolutional neural network (PMSACNN) is proposed in this paper to achieve rotating machinery fault diagnosis. A dilated wide convolutional layer is designed to extract the short-time features of signals with noise by performing sparse sampling on them. The multi-scale structure is constructed to capture the diversity feature information of signals, and the feature-level stitching of multi-sensor information is realized by the parallel input mechanism. Feature fusion is achieved by adaptively correcting the importance of different channel features by using channel attention. The global averaging pooling operation is introduced to reduce the number of parameters and improve the efficiency of the model operation. The effectiveness of PMSACNN is verified by using the bearing dataset acquired from the mechanical comprehensive diagnosis simulation platform. The experimental results indicate that the proposed method outperforms the existing methods of this field in terms of fault diagnosis accuracy and noise immunity, which can improve the reliability and safety of rotating machinery.

Retracted: Application of 5G Internet of Things Multisensor Information Fusion Model in Piano Performance

Retracted: Application of 5G Internet of Things Multisensor Information Fusion Model in Piano Performance

MIFDELN: A multi-sensor information fusion deep ensemble learning network for diagnosing bearing faults in noisy scenarios

Owing to the harsh operating environment of rolling bearings, acquired vibration signals contain strong noise interference, which makes it challenging for conventional methods to effectively extract fault information directly from the original signal. Additionally, obtaining satisfactory diagnostic results using traditional methods based on a single sensor is often difficult because the complementary information between different sensors is ignored. To address these challenges, this paper proposes a multi-sensor information fusion deep ensemble learning network (MIFDELN) for diagnosing bearing faults. First, the collected multi-sensor signals are organically fused using the weighted fusion strategy based on the composite index, which not only avoids the selection of poor-quality sensor signals but also suppresses part of the strong noise interference in the original signal. Second, the cross-scale attention feature extraction module (CAFEM) is presented to automatically learn discriminative features from the fused signals and reduce the impact of useless feature information through cross-scale learning and attentional weight enhancement. Subsequently, a weighted topology learning module (WTLM) is introduced to further excavate the spatial structure features and strengthen the distinctness of the learned features. Finally, the softmax classifier is employed to finalize the fault identification. Two experiments were conducted to verify the effectiveness and superiority of the proposed approach. The results indicated that the proposed approach performs best in extracting discriminating features with the highest accuracy and greatest robustness compared with several representative multi-sensor fusion technologies.

Multi-sensor information fusion localization of rare-earth suspended permanent magnet maglev trains based on adaptive Kalman algorithm.

Since the positioning accuracy of sensors degrades due to noise and environmental interference when a single sensor is used to localize a suspended rare-earth permanent magnetically levitated train, a multi-sensor information fusion method using multiple sensors and self-correcting weighting is proposed for permanent magnetic levitated train localization. A decay memory factor is introduced to reduce the weight of the influence of historical measurement data on the fusion estimation, thus enhancing the robustness of the fusion algorithm. The Kalman filtering results suffer from inaccuracy when process noise is present in the system. In this paper, we use a covariance adaptive scheme that replaces the prediction step of the Kalman filter with covariance. It uses the covariance adaptive scheme to search the posterior sequence online and reconstruct the prior error covariance. Since the process noise covariance is not used in the new adaptive scheme, the negative impact of the mismatch noise statistics is greatly reduced. Simulation and experimental results show that the use of multi-sensor information fusion and covariance adaptive Kalman algorithm has significant advantages in terms of adaptability, accuracy and simplicity.

Intelligent Perception in Healing Landscape Design Based on Multi-sensor Information Fusion

Intelligent Perception in Healing Landscape Design Based on Multi-sensor Information Fusion

Diagnosing root causes of faults based on alarm flood classification using transfer entropy and multi-sensor fusion approaches

Process abnormalities can result in serious accidents that may lead to unexpected loss of life and property. Early fault detection and diagnosis are essential to prevent these accidents. In the efficient operation of industrial systems, alarm systems play a crucial role. Developing new sensors and alarm adjustment networks has increased the possibility of generating multiple alarms. The alarm flood is the most important type of this problem. In this study, a new algorithm is proposed to diagnose the root cause of the fault through the classification of alarm floods. The main novelty of this algorithm is to use transfer entropy as a criterion to detect the similarity between the alarm flood sequences. The other innovations include calculating transfer entropy between the process variable and the alarm data, multi-sensor information fusion for large-scale plants and simultaneous alarms, and proposing an online version of the algorithm for the early prediction of the type of fault occurring. Finally, the Tennessee Eastman Process system, as a simulator, and the Saveh rotary cement kiln, as a real industrial system, are applied to evaluate the proposed method.

Multiscale cyclic frequency demodulation-based feature fusion framework for multi-sensor driven gearbox intelligent fault detection

Accurate fault detection is extremely important to ensure stable gearbox operation. Data-driven schemes using cyclic spectral have received significant attention due to their robust demodulation performance. However, these schemes are mainly applied to process single sensor signals, and they are unable to accurately obtain precise fault features. This paper proposed a novel multiscale cyclic frequency demodulation (MCFD)-based feature fusion framework for multi-sensor driven gearbox intelligent fault diagnosis. Firstly, the MCFD is proposed to analyze the vibration signals from multi-sensor driven gearbox, which acquires the multi-sensor mode information without setting parameters in advance. Thereafter, the grey relational degree between the multi-sensor mode information and original signal is calculated, and its results are normalized to obtain the relationship coefficients. Finally, the acquired coefficients are performed for multi-sensor information fusion to form the covariance matrix for gearbox fault diagnosis. The effectiveness of the proposed feature fusion framework is validated using the gearbox case. The comparative experiments indicate that this framework outperforms comparative algorithms for multi-sensor driven gearbox fault diagnosis.