Vast amounts of heterogeneous data on marine observations have been accumulated due to the rapid development of ocean observation technology.Several state-of-art methods are proposed to manage the emerging Internet of...Vast amounts of heterogeneous data on marine observations have been accumulated due to the rapid development of ocean observation technology.Several state-of-art methods are proposed to manage the emerging Internet of Things(IoT)sensor data.However,the use of an inefficient data management strategy during the data storage process can lead to missing metadata;thus,part of the sensor data cannot be indexed and utilized(i.e.,‘data swamp’).Researchers have focused on optimizing storage procedures to prevent such disasters,but few have attempted to restore the missing metadata.In this study,we propose an AI-based algorithm to reconstruct the metadata of heterogeneous marine data in data swamps to solve the above problems.First,a MapReduce algorithm is proposed to preprocess raw marine data and extract its feature tensors in parallel.Second,load the feature tensors are loaded into a machine learning algorithm and clustering operation is implemented.The similarities between the incoming data and the trained clustering results in terms of clustering results are also calculated.Finally,metadata reconstruction is performed based on existing marine observa-tion data processing results.The experiments are designed using existing datasets obtained from ocean observing systems,thus verifying the effectiveness of the algorithms.The results demonstrate the excellent performance of our proposed algorithm for the metadata recon-struction of heterogenous marine observation data.展开更多
In the applications of water regime monitoring, incompleteness, and inaccuracy of sensor data may directly affect the reliability of acquired monitoring information. Based on the spatial and temporal correlation of wa...In the applications of water regime monitoring, incompleteness, and inaccuracy of sensor data may directly affect the reliability of acquired monitoring information. Based on the spatial and temporal correlation of water regime monitoring information, this paper addresses this issue and proposes an information fusion method to implement data rectification. An improved Back Propagation (BP) neural network is used to perform data fusion on the hardware platform of a stantion unit, which takes Field-Programmable Gate Array (FPGA) as the core component. In order to verify the effectiveness, five measurements including water level, discharge and velocity are selected from three different points in a water regime monitoring station. The simulation results show that this method can recitify random errors as well as gross errors significantly.展开更多
For complementarity and redundancy of multi-sensor data fusion (MSDF) system,it is an effective approach for multiple components measurement.In order to measure nutrient solution on-line,a dynamic and complex system ...For complementarity and redundancy of multi-sensor data fusion (MSDF) system,it is an effective approach for multiple components measurement.In order to measure nutrient solution on-line,a dynamic and complex system under greenhouse environment,sensors should have intelligent properties including self-calibration and self-compensation. Meanwhile,it is necessary for multiple sensors to cooperate and interact for enhancing reliability of multi-sensor system. Because of the properties of multi-agent system (MAS),it is an appropriate tool to study MSDF system.This paper proposed an architecture of MSDF system based on MAS for the multiple components measurement of nutrient solution.The sensor agent's structure and function modules are analyzed and described in detail,the formal definitions are given,too.The relations of the sensors are modeled to implement reliability diagnosis of the multi-sensor system,so that the reliability of nutrient control system is enhanced.This study offers an effective approach for the study of MSDF.展开更多
HY-2 satellite is the first marine dynamic environment satellite of China.In this study,global evaporation and water vapor transport of the global sea surface are calculated on the basis of HY-2 multi-sensor data from...HY-2 satellite is the first marine dynamic environment satellite of China.In this study,global evaporation and water vapor transport of the global sea surface are calculated on the basis of HY-2 multi-sensor data from April 1 to 30,2014.The algorithm of evaporation and water vapor transport is discussed in detail,and results are compared with other reanalysis data.The sea surface temperature of HY-2 is in good agreement with the ARGO buoy data.Two clusters are shown in the scatter plot of HY-2 and OAFlux evaporation due to the uneven global distribution of evaporation.To improve the calculation accuracy,we compared the different parameterization schemes and adopted the method of calibrating HY-2 precipitation data by SSM/I and Global Precipitation Climatology Project(GPCP)data.In calculating the water vapor transport,the adjustment scheme is proposed to match the balance of the water cycle for data in the low latitudes.展开更多
As the differences of sensor＇s precision and some random factors are difficult to control,the actual measurement signals are far from the target signals that affect the reliability and precision of rotating machinery...As the differences of sensor＇s precision and some random factors are difficult to control,the actual measurement signals are far from the target signals that affect the reliability and precision of rotating machinery fault diagnosis.The traditional signal processing methods,such as classical inference and weighted averaging algorithm usually lack dynamic adaptability that is easy for trends to cause the faults to be misjudged or left out.To enhance the measuring veracity and precision of vibration signal in rotary machine multi-sensor vibration signal fault diagnosis,a novel data level fusion approach is presented on the basis of correlation function analysis to fast determine the weighted value of multi-sensor vibration signals.The approach doesn＇t require knowing the prior information about sensors,and the weighted value of sensors can be confirmed depending on the correlation measure of real-time data tested in the data level fusion process.It gives greater weighted value to the greater correlation measure of sensor signals,and vice versa.The approach can effectively suppress large errors and even can still fuse data in the case of sensor failures because it takes full advantage of sensor＇s own-information to determine the weighted value.Moreover,it has good performance of anti-jamming due to the correlation measures between noise and effective signals are usually small.Through the simulation of typical signal collected from multi-sensors,the comparative analysis of dynamic adaptability and fault tolerance between the proposed approach and traditional weighted averaging approach is taken.Finally,the rotor dynamics and integrated fault simulator is taken as an example to verify the feasibility and advantages of the proposed approach,it is shown that the multi-sensor data level fusion based on correlation function weighted approach is better than the traditional weighted average approach with respect to fusion precision and dynamic adaptability.Meantime,the approach is adaptable and easy to use,can be applied to other areas of vibration measurement.展开更多
This paper presents a state of the art machine learning-based approach for automation of a varied class of Internet of things(Io T) analytics problems targeted on 1-dimensional(1-D) sensor data. As feature recommendat...This paper presents a state of the art machine learning-based approach for automation of a varied class of Internet of things(Io T) analytics problems targeted on 1-dimensional(1-D) sensor data. As feature recommendation is a major bottleneck for general Io Tbased applications, this paper shows how this step can be successfully automated based on a Wide Learning architecture without sacrificing the decision-making accuracy, and thereby reducing the development time and the cost of hiring expensive resources for specific problems. Interpretation of meaningful features is another contribution of this research. Several data sets from different real-world applications are considered to realize the proof-of-concept. Results show that the interpretable feature recommendation techniques are quite effective for the problems at hand in terms of performance and drastic reduction in development time.展开更多
This paper proposes a scheme to obtain location and vital health information using ZigBee system. ZigBee systems are wireless communication systems defined by IEEE 802.154. In the proposed scheme, location information...This paper proposes a scheme to obtain location and vital health information using ZigBee system. ZigBee systems are wireless communication systems defined by IEEE 802.154. In the proposed scheme, location information is obtained using the Link Quality Indication (LQI) function of a ZigBee system, which represents the received signal strength. And, the vital health information are collected from the electrocardiogram monitor, the pulse and blood pressure device, attached to the patient’s body. This information is then transmitted to an outside network by ZigBee systems. In this way, vital health information can be transmitted as ZigBee sensor data while patients with the ZigBee terminal are moving. In the experiments using actual ZigBee devices, the proposed scheme could obtain accurate location and vital health information from the sensor data. Moreover, to achieve high reliability in the actual service, the collected amount of sensor data was confirmed by the theoretic calculation, when a ZigBee terminal passed through ZigBee routers. These results indicate that the proposed scheme can be used to detect the accurate location of the ZigBee terminal. And over 99% of the sensor data on vital health information was obtained when the ZigBee terminal passed through approximately four ZigBee routers.展开更多
The coal-rock interface recognition method based on multi-sensor data fusiontechnique is put forward because of the localization of single type sensor recognition method. Themeasuring theory based on multi-sensor data...The coal-rock interface recognition method based on multi-sensor data fusiontechnique is put forward because of the localization of single type sensor recognition method. Themeasuring theory based on multi-sensor data fusion technique is analyzed, and hereby the testplatform of recognition system is manufactured. The advantage of data fusion with the fuzzy neuralnetwork (FNN) technique has been probed. The two-level FNN is constructed and data fusion is carriedout. The experiments show that in various conditions the method can always acquire a much higherrecognition rate than normal ones.展开更多
The authors have applied a systems analysis approach to describe the musculoskeletal system as consisting of a stack of superimposed kinematic hier-archical segments in which each lower segment tends to transfer its m...The authors have applied a systems analysis approach to describe the musculoskeletal system as consisting of a stack of superimposed kinematic hier-archical segments in which each lower segment tends to transfer its motion to the other superimposed segments. This segmental chain enables the derivation of both conscious perception and sensory control of action in space. This applied systems analysis approach involves the measurements of the complex motor behavior in order to elucidate the fusion of multiple sensor data for the reliable and efficient acquisition of the kinetic, kinematics and electromyographic data of the human spatial behavior. The acquired kinematic and related kinetic signals represent attributive features of the internal recon-struction of the physical links between the superimposed body segments. In-deed, this reconstruction of the physical links was established as a result of the fusion of the multiple sensor data. Furthermore, this acquired kinematics, kinetics and electromyographic data provided detailed means to record, annotate, process, transmit, and display pertinent information derived from the musculoskeletal system to quantify and differentiate between subjects with mobility-related disabilities and able-bodied subjects, and enabled an inference into the active neural processes underlying balance reactions. To gain insight into the basis for this long-term dependence, the authors have applied the fusion of multiple sensor data to investigate the effects of Cerebral Palsy, Multiple Sclerosis and Diabetic Neuropathy conditions, on biomechanical/neurophysiological changes that may alter the ability of the human loco-motor system to generate ambulation, balance and posture.展开更多
In recent years, as embedded devices become smaller, cheaper and more diverse, the demand for urban sensing systems that present valuable information to users is increasing. However, in achieving urban sensing systems...In recent years, as embedded devices become smaller, cheaper and more diverse, the demand for urban sensing systems that present valuable information to users is increasing. However, in achieving urban sensing systems, the communication channel from the sensors to the data centers pose a problem, especially in respect to the cost of furnishing IP/mobile networks for each and every one of the sensor nodes. Many existing researches attempt to tackle this problem, but they generally limit either the types of sensors used or the distances among the sensors. In this paper, we propose a new sensor data collection system model in which mobile relay nodes transport the sensor data to the data center. We ran simulations under conditions imitating the real world to verify the practicality of the proposed system. This simulation uses data accumulated from traffic surveys to closely imitate pedestrians in the real world. We evaluated that the proposed system has sufficient ability to use in urban sensing systems that are not under the real-time constraint.展开更多
Due to the large scale and complexity of civil infrastructures, structural health monitoring typically requires a substantial number of sensors, which consequently generate huge volumes of sensor data. Innovative sens...Due to the large scale and complexity of civil infrastructures, structural health monitoring typically requires a substantial number of sensors, which consequently generate huge volumes of sensor data. Innovative sensor data compression techniques are highly desired to facilitate efficient data storage and remote retrieval of sensor data. This paper presents a vibration sensor data compression algorithm based on the Differential Pulse Code Modulation (DPCM) method and the consideration of effects of signal distortion due to lossy data compression on structural system identification. The DPCM system concerned consists of two primary components: linear predictor and quantizer. For the DPCM system considered in this study, the Least Square method is used to derive the linear predictor coefficients and Jayant quantizer is used for scalar quantization. A 5-DOF model structure is used as the prototype structure in numerical study. Numerical simulation was carried out to study the performance of the proposed DPCM-based data compression algorithm as well as its effect on the accuracy of structural identification including modal parameters and second order structural parameters such as stiffness and damping coefficients. It is found that the DPCM-based sensor data compression method is capable of reducing the raw sensor data size to a significant extent while having a minor effect on the modal parameters as well as second order structural parameters identified from reconstructed sensor data.展开更多
To this day,diesel generator(DG)continues to play an indispensable role in all industries and smart engines and engines with eco-friendly technologies are being developed.However,with the advent of the unmanned automa...To this day,diesel generator(DG)continues to play an indispensable role in all industries and smart engines and engines with eco-friendly technologies are being developed.However,with the advent of the unmanned automation era,countermeasures are required when DG sensors are non-functional.Therefore,an optimized AI model for backing up sensor data,which is necessary for the safety of smart engines equipped with eco-friendly facilities,was developed.To develop an AI model for this purpose,an experiment was conducted to obtain the engine and emission data to be used and 11 models were created.By predicting 16 variables related to the engine performance and emissions using a total of five sensor data,including three sensors essential for the engine safety,the proposed AI model could back up data when some sensors failed.Moreover,various hyperparameter tunings were applied and compared to maximize the model performance.Consequently,the decision tree(DT)-based models and genetic algorithm showed a good performance,and the weighted average of ensemble(DT)model showed the best performance with R^(2) value of 0.9981,and a SMAPE value of 0.7244.Additionally,to confirm the generalization performance of the model,the prediction performance of the models was measured using new data,and the blending of ensemble(ALL)model had the best performance with R^(2) value of 0.9266,and a SMAPE value of 5.585.Finally,the application of the concept used to develop the AI model and the future direction of the work were discussed.展开更多
Wireless sensor network deployment optimization is a classic NP-hard problem and a popular topic in academic research.However,the current research on wireless sensor network deployment problems uses overly simplistic ...Wireless sensor network deployment optimization is a classic NP-hard problem and a popular topic in academic research.However,the current research on wireless sensor network deployment problems uses overly simplistic models,and there is a significant gap between the research results and actual wireless sensor networks.Some scholars have now modeled data fusion networks to make them more suitable for practical applications.This paper will explore the deployment problem of a stochastic data fusion wireless sensor network(SDFWSN),a model that reflects the randomness of environmental monitoring and uses data fusion techniques widely used in actual sensor networks for information collection.The deployment problem of SDFWSN is modeled as a multi-objective optimization problem.The network life cycle,spatiotemporal coverage,detection rate,and false alarm rate of SDFWSN are used as optimization objectives to optimize the deployment of network nodes.This paper proposes an enhanced multi-objective mongoose optimization algorithm(EMODMOA)to solve the deployment problem of SDFWSN.First,to overcome the shortcomings of the DMOA algorithm,such as its low convergence and tendency to get stuck in a local optimum,an encircling and hunting strategy is introduced into the original algorithm to propose the EDMOA algorithm.The EDMOA algorithm is designed as the EMODMOA algorithm by selecting reference points using the K-Nearest Neighbor(KNN)algorithm.To verify the effectiveness of the proposed algorithm,the EMODMOA algorithm was tested at CEC 2020 and achieved good results.In the SDFWSN deployment problem,the algorithm was compared with the Non-dominated Sorting Genetic Algorithm II(NSGAII),Multiple Objective Particle Swarm Optimization(MOPSO),Multi-Objective Evolutionary Algorithm based on Decomposition(MOEA/D),and Multi-Objective Grey Wolf Optimizer(MOGWO).By comparing and analyzing the performance evaluation metrics and optimization results of the objective functions of the multi-objective algorithms,the algorithm outperforms the other algorithms in the SDFWSN deployment results.To better demonstrate the superiority of the algorithm,simulations of diverse test cases were also performed,and good results were obtained.展开更多
As households are equipped with smart meters,supervised Machine Learning(ML)models and especially Non-Intrusive Load Monitoring(NILM)disaggregation algorithms are becoming increasingly important.To be robust,these mod...As households are equipped with smart meters,supervised Machine Learning(ML)models and especially Non-Intrusive Load Monitoring(NILM)disaggregation algorithms are becoming increasingly important.To be robust,these models require a large amount of data,which is difficult to collect.Consequently,the generation of meaningful synthetic data is becoming more relevant.We use a simulation framework to generate multiple datasets using different techniques and evaluate their quality statistically by measuring the performance of NILM models for transferability.We demonstrate that the method of data generation is crucial to train ML models in a meaningful way.The experiments conducted reveal that adding noise to the synthetic smart meter data is essential to train robust NILM models for transferability.The best results are obtained when this noise is derived from unknown appliances for which no ground truth data is available.Since we observed that NILM models can provide unstable results,we develop a reliable evaluation methodology,based on Cochran’s sample size.Finally,we compare the quality of the generated synthetic data with real data and observe that multiple NILM models trained on synthetic data perform significantly better than those trained on real data.展开更多
This study investigates a consistent fusion algorithm for distributed multi-rate multi-sensor systems operating in feedback-memory configurations, where each sensor's sampling period is uniform and an integer mult...This study investigates a consistent fusion algorithm for distributed multi-rate multi-sensor systems operating in feedback-memory configurations, where each sensor's sampling period is uniform and an integer multiple of the state update period. The focus is on scenarios where the correlations among Measurement Noises(MNs) from different sensors are unknown. Firstly, a non-augmented local estimator that applies to sampling cases is designed to provide unbiased Local Estimates(LEs) at the fusion points. Subsequently, a measurement-equivalent approach is then developed to parameterize the correlation structure between LEs and reformulate LEs into a unified form, thereby constraining the correlations arising from MNs to an admissible range. Simultaneously, a family of upper bounds on the joint error covariance matrix of LEs is derived based on the constrained correlations, avoiding the need to calculate the exact error cross-covariance matrix of LEs. Finally, a sequential fusion estimator is proposed in the sense of Weighted Minimum Mean Square Error(WMMSE), and it is proven to be unbiased, consistent, and more accurate than the well-known covariance intersection method. Simulation results illustrate the effectiveness of the proposed algorithm by highlighting improvements in consistency and accuracy.展开更多
A compression algorithm is proposed in this paper for reducing the size of sensor data. By using a dictionary-based lossless compression algorithm, sensor data can be compressed efficiently and interpreted without dec...A compression algorithm is proposed in this paper for reducing the size of sensor data. By using a dictionary-based lossless compression algorithm, sensor data can be compressed efficiently and interpreted without decompressing. The correlation between redundancy of sensor data and compression ratio is explored. Further, a parallel compression algorithm based on MapReduce [1] is proposed. Meanwhile, data partitioner which plays an important role in performance of MapReduce application is discussed along with performance evaluation criteria proposed in this paper. Experiments demonstrate that random sampler is suitable for highly redundant sensor data and the proposed compression algorithms can compress those highly redundant sensor data efficiently.展开更多
With the advancement of industrial internet of things(IIoT),wireless medical sensor networks(WMSNs)have been widely introduced in modern healthcare systems to collect real-time medical data from patients,which is know...With the advancement of industrial internet of things(IIoT),wireless medical sensor networks(WMSNs)have been widely introduced in modern healthcare systems to collect real-time medical data from patients,which is known as HealthIIoT.Considering the limited computing and storage capabilities of lightweight HealthIIoT devices,it is necessary to upload these data to remote cloud servers for storage and maintenance.However,there are still some serious security issues within outsourcing medical sensor data to the cloud.One of the most signifcant challenges is how to ensure the integrity of these data,which is a prerequisite for providing precise medical diagnosis and treatment.To meet this challenge,we propose a novel and efcient public auditing scheme,which is suitable for cloud-assisted HealthIIoT system.Specifcally,to address the contradiction between the high real-time requirement of medical sensor data and the limited computing power of HealthIIoT devices,a new online/ofine tag generation algorithm is designed to improve preprocessing efciency;to protect medical data privacy,a secure hash function is employed to blind the data proof.We formally prove the security of the presented scheme,and evaluate the performance through detailed experimental comparisons with the state-of-the-art ones.The results show that the presented scheme can greatly improve the efciency of tag generation,while achieving better auditing performance than previous schemes.展开更多
The use of sensors for monitoring livestock has opened up new possibilities for the management of livestock in extensive grazing systems.The work presented in this paper aimed to develop a model for predicting the met...The use of sensors for monitoring livestock has opened up new possibilities for the management of livestock in extensive grazing systems.The work presented in this paper aimed to develop a model for predicting the metabolisable energy intake(MEI)of sheep by using temperature,pitch angle,roll angle,distance,speed,and grazing time data obtained directly from wearable sensors on the sheep.A Deep Belief Network(DBN)algorithm was used to predict MEI,which to our knowledge,has not been attempted previously.The results demonstrated that the DBN method could predict the MEI for sheep using sensor data alone.The mean square error(MSE)values of 4.46 and 20.65 have been achieved using the DBN model for training and testing datasets,respectively.We also evaluated the influential sensor data variables,i.e.,distance and pitch angle,for predicting the MEI.Our study demonstrates that the application of machine learning techniques directly to on-animal sensor data presents a substantial opportunity to interpret biological interactions in grazing systems directly from sensor data.We expect that further development and refinement of this technology will catalyse a step-change in extensive livestock management,as wearable sensors become widely used by livestock producers.展开更多
Sensors installed on a ship return high quality data that can be used for ship bunker fuel efficiency analysis.However,important information about weather and sea conditions the ship sails through,such as waves,sea cu...Sensors installed on a ship return high quality data that can be used for ship bunker fuel efficiency analysis.However,important information about weather and sea conditions the ship sails through,such as waves,sea currents,and sea water temperature,is often absent from sensor data.This study addresses this issue by fusing sensor data and publicly accessible meteorological data,constructing nine datasets accordingly,and experimenting with widely adopted machine learning(ML)models to quantify the relationship between a ship's fuel consumption rate(ton/day,or ton/h)and its voyage-based factors(sailing speed,draft,trim,weather conditions,and sea conditions).The best dataset found reveals the benefits of fusing sensor data and meteorological data for ship fuel consumption rate quantification.The best ML models found are consistent with our previous studies,including Extremely randomized trees(ET),Gradient Tree Boosting(GB)and XGBoost(XG).Given the best dataset from data fusion,their R^(2) values over the training set are 0.999 or 1.000,and their R^(2) values over the test set are all above 0.966.Their fit errors with RMSE values are below 0.75 ton/day,and with MAT below 0.52 ton/day.These promising results are well beyond the requirements of most industry applications for ship fuel efficiency analysis.The applicability of the selected datasets and ML models is also verified in a rolling horizon approach,resulting in a conjecture that a rolling horizon strategy of“5-month training t 1-month test/applicatoin”could work well in practice and sensor data of less than five months could be insufficient to train ML models.展开更多
A Cyber-Physical System （CPS） integrates physical devices （i.e.,sensors） with cyber （i.e.,informational） components to form a context sensitive system that responds intelligently to dynamic changes in real-world...A Cyber-Physical System （CPS） integrates physical devices （i.e.,sensors） with cyber （i.e.,informational） components to form a context sensitive system that responds intelligently to dynamic changes in real-world situations.Such a system has wide applications in the scenarios of traffic control,battlefield surveillance,environmental monitoring,and so on.A core element of CPS is the collection and assessment of information from noisy,dynamic,and uncertain physical environments integrated with many types of cyber-space resources.The potential of this integration is unbounded.To achieve this potential the raw data acquired from the physical world must be transformed into useable knowledge in real-time.Therefore,CPS brings a new dimension to knowledge discovery because of the emerging synergism of the physical and the cyber.The various properties of the physical world must be addressed in information management and knowledge discovery.This paper discusses the problems of mining sensor data in CPS：With a large number of wireless sensors deployed in a designated area,the task is real time detection of intruders that enter the area based on noisy sensor data.The framework of IntruMine is introduced to discover intruders from untrustworthy sensor data.IntruMine first analyzes the trustworthiness of sensor data,then detects the intruders＇ locations,and verifies the detections based on a graph model of the relationships between sensors and intruders.展开更多
基金supported by the Shandong Province Natural Science Foundation(No.ZR2020QF028).
文摘Vast amounts of heterogeneous data on marine observations have been accumulated due to the rapid development of ocean observation technology.Several state-of-art methods are proposed to manage the emerging Internet of Things(IoT)sensor data.However,the use of an inefficient data management strategy during the data storage process can lead to missing metadata;thus,part of the sensor data cannot be indexed and utilized(i.e.,‘data swamp’).Researchers have focused on optimizing storage procedures to prevent such disasters,but few have attempted to restore the missing metadata.In this study,we propose an AI-based algorithm to reconstruct the metadata of heterogeneous marine data in data swamps to solve the above problems.First,a MapReduce algorithm is proposed to preprocess raw marine data and extract its feature tensors in parallel.Second,load the feature tensors are loaded into a machine learning algorithm and clustering operation is implemented.The similarities between the incoming data and the trained clustering results in terms of clustering results are also calculated.Finally,metadata reconstruction is performed based on existing marine observa-tion data processing results.The experiments are designed using existing datasets obtained from ocean observing systems,thus verifying the effectiveness of the algorithms.The results demonstrate the excellent performance of our proposed algorithm for the metadata recon-struction of heterogenous marine observation data.
基金Supported by the National Natural Science Foundation of China (No. 60774092, No. 60901003)the Specialized Research Fund for the Doctoral Program of Higher Education (No. 20070294027)
文摘In the applications of water regime monitoring, incompleteness, and inaccuracy of sensor data may directly affect the reliability of acquired monitoring information. Based on the spatial and temporal correlation of water regime monitoring information, this paper addresses this issue and proposes an information fusion method to implement data rectification. An improved Back Propagation (BP) neural network is used to perform data fusion on the hardware platform of a stantion unit, which takes Field-Programmable Gate Array (FPGA) as the core component. In order to verify the effectiveness, five measurements including water level, discharge and velocity are selected from three different points in a water regime monitoring station. The simulation results show that this method can recitify random errors as well as gross errors significantly.
文摘For complementarity and redundancy of multi-sensor data fusion (MSDF) system,it is an effective approach for multiple components measurement.In order to measure nutrient solution on-line,a dynamic and complex system under greenhouse environment,sensors should have intelligent properties including self-calibration and self-compensation. Meanwhile,it is necessary for multiple sensors to cooperate and interact for enhancing reliability of multi-sensor system. Because of the properties of multi-agent system (MAS),it is an appropriate tool to study MSDF system.This paper proposed an architecture of MSDF system based on MAS for the multiple components measurement of nutrient solution.The sensor agent's structure and function modules are analyzed and described in detail,the formal definitions are given,too.The relations of the sensors are modeled to implement reliability diagnosis of the multi-sensor system,so that the reliability of nutrient control system is enhanced.This study offers an effective approach for the study of MSDF.
基金the financial support from the National Natural Science Foundation of China (No. 4197 6017)the Ministry of Science and Technology of China (No. 2016YFC1401405)the National Natural Science Foundation of China (No. U1406401)
文摘HY-2 satellite is the first marine dynamic environment satellite of China.In this study,global evaporation and water vapor transport of the global sea surface are calculated on the basis of HY-2 multi-sensor data from April 1 to 30,2014.The algorithm of evaporation and water vapor transport is discussed in detail,and results are compared with other reanalysis data.The sea surface temperature of HY-2 is in good agreement with the ARGO buoy data.Two clusters are shown in the scatter plot of HY-2 and OAFlux evaporation due to the uneven global distribution of evaporation.To improve the calculation accuracy,we compared the different parameterization schemes and adopted the method of calibrating HY-2 precipitation data by SSM/I and Global Precipitation Climatology Project(GPCP)data.In calculating the water vapor transport,the adjustment scheme is proposed to match the balance of the water cycle for data in the low latitudes.
基金supported by National Hi-tech Research and Development Program of China (863 Program, Grant No. 2007AA04Z433)Hunan Provincial Natural Science Foundation of China (Grant No. 09JJ8005)Scientific Research Foundation of Graduate School of Beijing University of Chemical and Technology,China (Grant No. 10Me002)
文摘As the differences of sensor＇s precision and some random factors are difficult to control,the actual measurement signals are far from the target signals that affect the reliability and precision of rotating machinery fault diagnosis.The traditional signal processing methods,such as classical inference and weighted averaging algorithm usually lack dynamic adaptability that is easy for trends to cause the faults to be misjudged or left out.To enhance the measuring veracity and precision of vibration signal in rotary machine multi-sensor vibration signal fault diagnosis,a novel data level fusion approach is presented on the basis of correlation function analysis to fast determine the weighted value of multi-sensor vibration signals.The approach doesn＇t require knowing the prior information about sensors,and the weighted value of sensors can be confirmed depending on the correlation measure of real-time data tested in the data level fusion process.It gives greater weighted value to the greater correlation measure of sensor signals,and vice versa.The approach can effectively suppress large errors and even can still fuse data in the case of sensor failures because it takes full advantage of sensor＇s own-information to determine the weighted value.Moreover,it has good performance of anti-jamming due to the correlation measures between noise and effective signals are usually small.Through the simulation of typical signal collected from multi-sensors,the comparative analysis of dynamic adaptability and fault tolerance between the proposed approach and traditional weighted averaging approach is taken.Finally,the rotor dynamics and integrated fault simulator is taken as an example to verify the feasibility and advantages of the proposed approach,it is shown that the multi-sensor data level fusion based on correlation function weighted approach is better than the traditional weighted average approach with respect to fusion precision and dynamic adaptability.Meantime,the approach is adaptable and easy to use,can be applied to other areas of vibration measurement.
文摘This paper presents a state of the art machine learning-based approach for automation of a varied class of Internet of things(Io T) analytics problems targeted on 1-dimensional(1-D) sensor data. As feature recommendation is a major bottleneck for general Io Tbased applications, this paper shows how this step can be successfully automated based on a Wide Learning architecture without sacrificing the decision-making accuracy, and thereby reducing the development time and the cost of hiring expensive resources for specific problems. Interpretation of meaningful features is another contribution of this research. Several data sets from different real-world applications are considered to realize the proof-of-concept. Results show that the interpretable feature recommendation techniques are quite effective for the problems at hand in terms of performance and drastic reduction in development time.
文摘This paper proposes a scheme to obtain location and vital health information using ZigBee system. ZigBee systems are wireless communication systems defined by IEEE 802.154. In the proposed scheme, location information is obtained using the Link Quality Indication (LQI) function of a ZigBee system, which represents the received signal strength. And, the vital health information are collected from the electrocardiogram monitor, the pulse and blood pressure device, attached to the patient’s body. This information is then transmitted to an outside network by ZigBee systems. In this way, vital health information can be transmitted as ZigBee sensor data while patients with the ZigBee terminal are moving. In the experiments using actual ZigBee devices, the proposed scheme could obtain accurate location and vital health information from the sensor data. Moreover, to achieve high reliability in the actual service, the collected amount of sensor data was confirmed by the theoretic calculation, when a ZigBee terminal passed through ZigBee routers. These results indicate that the proposed scheme can be used to detect the accurate location of the ZigBee terminal. And over 99% of the sensor data on vital health information was obtained when the ZigBee terminal passed through approximately four ZigBee routers.
基金This project is supported by Provincial Youth Science Foundation of Shanxi China (No.20011020)National Natural Science Foundation of China (No.59975064).
文摘The coal-rock interface recognition method based on multi-sensor data fusiontechnique is put forward because of the localization of single type sensor recognition method. Themeasuring theory based on multi-sensor data fusion technique is analyzed, and hereby the testplatform of recognition system is manufactured. The advantage of data fusion with the fuzzy neuralnetwork (FNN) technique has been probed. The two-level FNN is constructed and data fusion is carriedout. The experiments show that in various conditions the method can always acquire a much higherrecognition rate than normal ones.
文摘The authors have applied a systems analysis approach to describe the musculoskeletal system as consisting of a stack of superimposed kinematic hier-archical segments in which each lower segment tends to transfer its motion to the other superimposed segments. This segmental chain enables the derivation of both conscious perception and sensory control of action in space. This applied systems analysis approach involves the measurements of the complex motor behavior in order to elucidate the fusion of multiple sensor data for the reliable and efficient acquisition of the kinetic, kinematics and electromyographic data of the human spatial behavior. The acquired kinematic and related kinetic signals represent attributive features of the internal recon-struction of the physical links between the superimposed body segments. In-deed, this reconstruction of the physical links was established as a result of the fusion of the multiple sensor data. Furthermore, this acquired kinematics, kinetics and electromyographic data provided detailed means to record, annotate, process, transmit, and display pertinent information derived from the musculoskeletal system to quantify and differentiate between subjects with mobility-related disabilities and able-bodied subjects, and enabled an inference into the active neural processes underlying balance reactions. To gain insight into the basis for this long-term dependence, the authors have applied the fusion of multiple sensor data to investigate the effects of Cerebral Palsy, Multiple Sclerosis and Diabetic Neuropathy conditions, on biomechanical/neurophysiological changes that may alter the ability of the human loco-motor system to generate ambulation, balance and posture.
文摘In recent years, as embedded devices become smaller, cheaper and more diverse, the demand for urban sensing systems that present valuable information to users is increasing. However, in achieving urban sensing systems, the communication channel from the sensors to the data centers pose a problem, especially in respect to the cost of furnishing IP/mobile networks for each and every one of the sensor nodes. Many existing researches attempt to tackle this problem, but they generally limit either the types of sensors used or the distances among the sensors. In this paper, we propose a new sensor data collection system model in which mobile relay nodes transport the sensor data to the data center. We ran simulations under conditions imitating the real world to verify the practicality of the proposed system. This simulation uses data accumulated from traffic surveys to closely imitate pedestrians in the real world. We evaluated that the proposed system has sufficient ability to use in urban sensing systems that are not under the real-time constraint.
文摘Due to the large scale and complexity of civil infrastructures, structural health monitoring typically requires a substantial number of sensors, which consequently generate huge volumes of sensor data. Innovative sensor data compression techniques are highly desired to facilitate efficient data storage and remote retrieval of sensor data. This paper presents a vibration sensor data compression algorithm based on the Differential Pulse Code Modulation (DPCM) method and the consideration of effects of signal distortion due to lossy data compression on structural system identification. The DPCM system concerned consists of two primary components: linear predictor and quantizer. For the DPCM system considered in this study, the Least Square method is used to derive the linear predictor coefficients and Jayant quantizer is used for scalar quantization. A 5-DOF model structure is used as the prototype structure in numerical study. Numerical simulation was carried out to study the performance of the proposed DPCM-based data compression algorithm as well as its effect on the accuracy of structural identification including modal parameters and second order structural parameters such as stiffness and damping coefficients. It is found that the DPCM-based sensor data compression method is capable of reducing the raw sensor data size to a significant extent while having a minor effect on the modal parameters as well as second order structural parameters identified from reconstructed sensor data.
基金supported by the Autonomous Ship Technology Development Program[K_G012001614003]funded by the Ministry of Trade,Industry,&Energy(MOTIE,Korea)the National Research Foundation of Korea(NRF)funded by the Ministry of Education[2022R1F1A1073764].
文摘To this day,diesel generator(DG)continues to play an indispensable role in all industries and smart engines and engines with eco-friendly technologies are being developed.However,with the advent of the unmanned automation era,countermeasures are required when DG sensors are non-functional.Therefore,an optimized AI model for backing up sensor data,which is necessary for the safety of smart engines equipped with eco-friendly facilities,was developed.To develop an AI model for this purpose,an experiment was conducted to obtain the engine and emission data to be used and 11 models were created.By predicting 16 variables related to the engine performance and emissions using a total of five sensor data,including three sensors essential for the engine safety,the proposed AI model could back up data when some sensors failed.Moreover,various hyperparameter tunings were applied and compared to maximize the model performance.Consequently,the decision tree(DT)-based models and genetic algorithm showed a good performance,and the weighted average of ensemble(DT)model showed the best performance with R^(2) value of 0.9981,and a SMAPE value of 0.7244.Additionally,to confirm the generalization performance of the model,the prediction performance of the models was measured using new data,and the blending of ensemble(ALL)model had the best performance with R^(2) value of 0.9266,and a SMAPE value of 5.585.Finally,the application of the concept used to develop the AI model and the future direction of the work were discussed.
基金supported by the National Natural Science Foundation of China under Grant Nos.U21A20464,62066005Innovation Project of Guangxi Graduate Education under Grant No.YCSW2024313.
文摘Wireless sensor network deployment optimization is a classic NP-hard problem and a popular topic in academic research.However,the current research on wireless sensor network deployment problems uses overly simplistic models,and there is a significant gap between the research results and actual wireless sensor networks.Some scholars have now modeled data fusion networks to make them more suitable for practical applications.This paper will explore the deployment problem of a stochastic data fusion wireless sensor network(SDFWSN),a model that reflects the randomness of environmental monitoring and uses data fusion techniques widely used in actual sensor networks for information collection.The deployment problem of SDFWSN is modeled as a multi-objective optimization problem.The network life cycle,spatiotemporal coverage,detection rate,and false alarm rate of SDFWSN are used as optimization objectives to optimize the deployment of network nodes.This paper proposes an enhanced multi-objective mongoose optimization algorithm(EMODMOA)to solve the deployment problem of SDFWSN.First,to overcome the shortcomings of the DMOA algorithm,such as its low convergence and tendency to get stuck in a local optimum,an encircling and hunting strategy is introduced into the original algorithm to propose the EDMOA algorithm.The EDMOA algorithm is designed as the EMODMOA algorithm by selecting reference points using the K-Nearest Neighbor(KNN)algorithm.To verify the effectiveness of the proposed algorithm,the EMODMOA algorithm was tested at CEC 2020 and achieved good results.In the SDFWSN deployment problem,the algorithm was compared with the Non-dominated Sorting Genetic Algorithm II(NSGAII),Multiple Objective Particle Swarm Optimization(MOPSO),Multi-Objective Evolutionary Algorithm based on Decomposition(MOEA/D),and Multi-Objective Grey Wolf Optimizer(MOGWO).By comparing and analyzing the performance evaluation metrics and optimization results of the objective functions of the multi-objective algorithms,the algorithm outperforms the other algorithms in the SDFWSN deployment results.To better demonstrate the superiority of the algorithm,simulations of diverse test cases were also performed,and good results were obtained.
基金funded by the German Ministry for Economics Affairs and Climate Action(BMWK)within the project ForeSightNEXT.
文摘As households are equipped with smart meters,supervised Machine Learning(ML)models and especially Non-Intrusive Load Monitoring(NILM)disaggregation algorithms are becoming increasingly important.To be robust,these models require a large amount of data,which is difficult to collect.Consequently,the generation of meaningful synthetic data is becoming more relevant.We use a simulation framework to generate multiple datasets using different techniques and evaluate their quality statistically by measuring the performance of NILM models for transferability.We demonstrate that the method of data generation is crucial to train ML models in a meaningful way.The experiments conducted reveal that adding noise to the synthetic smart meter data is essential to train robust NILM models for transferability.The best results are obtained when this noise is derived from unknown appliances for which no ground truth data is available.Since we observed that NILM models can provide unstable results,we develop a reliable evaluation methodology,based on Cochran’s sample size.Finally,we compare the quality of the generated synthetic data with real data and observe that multiple NILM models trained on synthetic data perform significantly better than those trained on real data.
基金supported by the National Natural Science Foundation of China (Nos. 62276204, 62203343)。
文摘This study investigates a consistent fusion algorithm for distributed multi-rate multi-sensor systems operating in feedback-memory configurations, where each sensor's sampling period is uniform and an integer multiple of the state update period. The focus is on scenarios where the correlations among Measurement Noises(MNs) from different sensors are unknown. Firstly, a non-augmented local estimator that applies to sampling cases is designed to provide unbiased Local Estimates(LEs) at the fusion points. Subsequently, a measurement-equivalent approach is then developed to parameterize the correlation structure between LEs and reformulate LEs into a unified form, thereby constraining the correlations arising from MNs to an admissible range. Simultaneously, a family of upper bounds on the joint error covariance matrix of LEs is derived based on the constrained correlations, avoiding the need to calculate the exact error cross-covariance matrix of LEs. Finally, a sequential fusion estimator is proposed in the sense of Weighted Minimum Mean Square Error(WMMSE), and it is proven to be unbiased, consistent, and more accurate than the well-known covariance intersection method. Simulation results illustrate the effectiveness of the proposed algorithm by highlighting improvements in consistency and accuracy.
基金supported by the National Natural Science Foundation of China(60933011,61170258)
文摘A compression algorithm is proposed in this paper for reducing the size of sensor data. By using a dictionary-based lossless compression algorithm, sensor data can be compressed efficiently and interpreted without decompressing. The correlation between redundancy of sensor data and compression ratio is explored. Further, a parallel compression algorithm based on MapReduce [1] is proposed. Meanwhile, data partitioner which plays an important role in performance of MapReduce application is discussed along with performance evaluation criteria proposed in this paper. Experiments demonstrate that random sampler is suitable for highly redundant sensor data and the proposed compression algorithms can compress those highly redundant sensor data efficiently.
基金supported in part by the National Natural Science Foundation of China(Grant No.U1405254)the Natural Science Foundation of Fujian Province of China(No.2018J01093)+1 种基金the Open Project Program of Wuhan National Laboratory for Optoelectronics(No.2018 WNLOKF009)the Scientifc Research Funds of Huaqiao University(No.605-50Y19028).
文摘With the advancement of industrial internet of things(IIoT),wireless medical sensor networks(WMSNs)have been widely introduced in modern healthcare systems to collect real-time medical data from patients,which is known as HealthIIoT.Considering the limited computing and storage capabilities of lightweight HealthIIoT devices,it is necessary to upload these data to remote cloud servers for storage and maintenance.However,there are still some serious security issues within outsourcing medical sensor data to the cloud.One of the most signifcant challenges is how to ensure the integrity of these data,which is a prerequisite for providing precise medical diagnosis and treatment.To meet this challenge,we propose a novel and efcient public auditing scheme,which is suitable for cloud-assisted HealthIIoT system.Specifcally,to address the contradiction between the high real-time requirement of medical sensor data and the limited computing power of HealthIIoT devices,a new online/ofine tag generation algorithm is designed to improve preprocessing efciency;to protect medical data privacy,a secure hash function is employed to blind the data proof.We formally prove the security of the presented scheme,and evaluate the performance through detailed experimental comparisons with the state-of-the-art ones.The results show that the presented scheme can greatly improve the efciency of tag generation,while achieving better auditing performance than previous schemes.
文摘The use of sensors for monitoring livestock has opened up new possibilities for the management of livestock in extensive grazing systems.The work presented in this paper aimed to develop a model for predicting the metabolisable energy intake(MEI)of sheep by using temperature,pitch angle,roll angle,distance,speed,and grazing time data obtained directly from wearable sensors on the sheep.A Deep Belief Network(DBN)algorithm was used to predict MEI,which to our knowledge,has not been attempted previously.The results demonstrated that the DBN method could predict the MEI for sheep using sensor data alone.The mean square error(MSE)values of 4.46 and 20.65 have been achieved using the DBN model for training and testing datasets,respectively.We also evaluated the influential sensor data variables,i.e.,distance and pitch angle,for predicting the MEI.Our study demonstrates that the application of machine learning techniques directly to on-animal sensor data presents a substantial opportunity to interpret biological interactions in grazing systems directly from sensor data.We expect that further development and refinement of this technology will catalyse a step-change in extensive livestock management,as wearable sensors become widely used by livestock producers.
基金the IAMU(International Association of Maritime Universities)research project titled“Data fusion and machine learning for ship fuel efficiency analysis:a small but essential step towards green shipping through data analytics”(Research Project No.20210205_AMC).
文摘Sensors installed on a ship return high quality data that can be used for ship bunker fuel efficiency analysis.However,important information about weather and sea conditions the ship sails through,such as waves,sea currents,and sea water temperature,is often absent from sensor data.This study addresses this issue by fusing sensor data and publicly accessible meteorological data,constructing nine datasets accordingly,and experimenting with widely adopted machine learning(ML)models to quantify the relationship between a ship's fuel consumption rate(ton/day,or ton/h)and its voyage-based factors(sailing speed,draft,trim,weather conditions,and sea conditions).The best dataset found reveals the benefits of fusing sensor data and meteorological data for ship fuel consumption rate quantification.The best ML models found are consistent with our previous studies,including Extremely randomized trees(ET),Gradient Tree Boosting(GB)and XGBoost(XG).Given the best dataset from data fusion,their R^(2) values over the training set are 0.999 or 1.000,and their R^(2) values over the test set are all above 0.966.Their fit errors with RMSE values are below 0.75 ton/day,and with MAT below 0.52 ton/day.These promising results are well beyond the requirements of most industry applications for ship fuel efficiency analysis.The applicability of the selected datasets and ML models is also verified in a rolling horizon approach,resulting in a conjecture that a rolling horizon strategy of“5-month training t 1-month test/applicatoin”could work well in practice and sensor data of less than five months could be insufficient to train ML models.
基金supported in part by the U.S. Army Research Laboratory under Cooperative Agreement Nos. W911NF-09-2-0053 (NS-CTA) and W911NF-11-20086 (Cyber-Security)the U.S. Army Research Office under Cooperative Agreement No. W911NF-13-1-0193, DTRAU.S. National Science Foundation grants CNS-0931975, IIS-1017362, IIS-1320617, and IIS1354329
文摘A Cyber-Physical System （CPS） integrates physical devices （i.e.,sensors） with cyber （i.e.,informational） components to form a context sensitive system that responds intelligently to dynamic changes in real-world situations.Such a system has wide applications in the scenarios of traffic control,battlefield surveillance,environmental monitoring,and so on.A core element of CPS is the collection and assessment of information from noisy,dynamic,and uncertain physical environments integrated with many types of cyber-space resources.The potential of this integration is unbounded.To achieve this potential the raw data acquired from the physical world must be transformed into useable knowledge in real-time.Therefore,CPS brings a new dimension to knowledge discovery because of the emerging synergism of the physical and the cyber.The various properties of the physical world must be addressed in information management and knowledge discovery.This paper discusses the problems of mining sensor data in CPS：With a large number of wireless sensors deployed in a designated area,the task is real time detection of intruders that enter the area based on noisy sensor data.The framework of IntruMine is introduced to discover intruders from untrustworthy sensor data.IntruMine first analyzes the trustworthiness of sensor data,then detects the intruders＇ locations,and verifies the detections based on a graph model of the relationships between sensors and intruders.
