A Metadata Reconstruction Algorithm Based on Heterogeneous Sensor Data for Marine Observations

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.

Enhancing Human Action Recognition with Adaptive Hybrid Deep Attentive Networks and Archerfish Optimization

In recent years,wearable devices-based Human Activity Recognition(HAR)models have received significant attention.Previously developed HAR models use hand-crafted features to recognize human activities,leading to the extraction of basic features.The images captured by wearable sensors contain advanced features,allowing them to be analyzed by deep learning algorithms to enhance the detection and recognition of human actions.Poor lighting and limited sensor capabilities can impact data quality,making the recognition of human actions a challenging task.The unimodal-based HAR approaches are not suitable in a real-time environment.Therefore,an updated HAR model is developed using multiple types of data and an advanced deep-learning approach.Firstly,the required signals and sensor data are accumulated from the standard databases.From these signals,the wave features are retrieved.Then the extracted wave features and sensor data are given as the input to recognize the human activity.An Adaptive Hybrid Deep Attentive Network(AHDAN)is developed by incorporating a“1D Convolutional Neural Network(1DCNN)”with a“Gated Recurrent Unit(GRU)”for the human activity recognition process.Additionally,the Enhanced Archerfish Hunting Optimizer(EAHO)is suggested to fine-tune the network parameters for enhancing the recognition process.An experimental evaluation is performed on various deep learning networks and heuristic algorithms to confirm the effectiveness of the proposed HAR model.The EAHO-based HAR model outperforms traditional deep learning networks with an accuracy of 95.36,95.25 for recall,95.48 for specificity,and 95.47 for precision,respectively.The result proved that the developed model is effective in recognizing human action by taking less time.Additionally,it reduces the computation complexity and overfitting issue through using an optimization approach.

Analysis of kinematic data and determination of ground reaction force of foot in slow squat

In the present paper, the ground reaction force （GRF） acting on foot in slow squat was determined through a force measuring system, and at the same time, the kinematic data of human squat were obtained by analyzing the photographed image sequences. According to the height and body weight, six healthy volunteers were selected, three men in one group and the other three women in another group, and the fundamental parameters of subjects were recorded, including body weight, height and age, etc. Based on the anatomy characteristics, some markers were placed on the right side of joints. While the subject squatted at slow speed on the force platform, the ground reaction forces on the forefoot and heel for each foot were obtained through calibrated force platform. The analysis results show that the reaction force on heel is greater than that on forefoot, and double feet have nearly constant force. Moreover, from processing and analyzing the synchronously photographed image sequences in squat, the kinematic data of human squat were acquired, including mainly the curves of angle, angular velocity and angular acceleration varied with time for knee, hip and ankle joints in a sagittal plane. The obtained results can offer instructive reference for photographing and analyzing the movements of human bodies, diagnosing some diseases, and establishing in the future appropriate mathematical models for the human motion.

AN INFORMATION FUSION METHOD FOR SENSOR DATA RECTIFICATION

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.

Application of data fusion on multi-function earth drill

taking the bucket of multi function earth drill as an example, combining with the conception of multi sensor integration and data fusion, adopting the terrene column chart and digging torque formula as control dependence, the detecting method of the earth drill’s working state is introduced. Multi sensor data fusion is done with the aid of BP neural network in Matlab. The data to be interfused are pre processed and the program of simulation and “point checking” is given.

Sensor Registration Based on Neural Network in Data Fusion

The contents of sensor registration in the multi-sensor data fusion system are introduced, and some existing methods are analyzed. Then, one approach to sensor registration based on BP neural network is proposed. Here the measurements from radar are transformed from the polar coordinate system to the Cartesian coordinate through a BP neural network. With this approach, the systematic errors are removed as well as the coordinate is transformed. The efficiency of this method is demonstrated by simulation, and the result show that this approach could remove the systematic errors effectively and the DAR are closer to real position than DBR.

Novel Multimodal Biometric Feature Extraction for Precise Human Identification

In recent years,biometric sensors are applicable for identifying impor-tant individual information and accessing the control using various identifiers by including the characteristics like afingerprint,palm print,iris recognition,and so on.However,the precise identification of human features is still physically chal-lenging in humans during their lifetime resulting in a variance in their appearance or features.In response to these challenges,a novel Multimodal Biometric Feature Extraction(MBFE)model is proposed to extract the features from the noisy sen-sor data using a modified Ranking-based Deep Convolution Neural Network(RDCNN).The proposed MBFE model enables the feature extraction from differ-ent biometric images that includes iris,palm print,and lip,where the images are preprocessed initially for further processing.The extracted features are validated after optimal extraction by the RDCNN by splitting the datasets to train the fea-ture extraction model and then testing the model with different sets of input images.The simulation is performed in matlab to test the efficacy of the modal over multi-modal datasets and the simulation result shows that the proposed meth-od achieves increased accuracy,precision,recall,and F1 score than the existing deep learning feature extraction methods.The performance improvement of the MBFE Algorithm technique in terms of accuracy,precision,recall,and F1 score is attained by 0.126%,0.152%,0.184%,and 0.38%with existing Back Propaga-tion Neural Network(BPNN),Human Identification Using Wavelet Transform(HIUWT),Segmentation Methodology for Non-cooperative Recognition(SMNR),Daugman Iris Localization Algorithm(DILA)feature extraction techni-ques respectively.

Study on key technologies of GNSS-based train state perception for traincentric railway signaling

The application of Global Navigation Satellite Systems(GNSSs)in the intelligent railway systems is rapidly developing all over the world.With the GNSs-based train positioning and moving state perception,the autonomy and flexibility of a novel train control system can be greatly enhanced over the existing solutions relying on the track-side facilities.Considering the safety critical features of the railway signaling applications,the GNSS stand-alone mode may not be sufficient to satisfy the practical requirements.In this paper,the key technologies for applying GNSS in novel train-centric railway signaling systems are investigated,including the multi-sensor data fusion,Virtual Balise(VB)capturing and messaging,train integrity monitoring and system performance evaluation.According to the practical characteristics of the novel train control system under the moving block mode,the details of the key technologies are introduced.Field demonstration results of a novel train control system using the presented technologies under the practical railway operation conditions are presented to illustrate the achievable performance feature of autonomous train state perception using BeiDou Navigation Satellite System(BDS)and related solutions.It reveals the great potentials of these key technologies in the next generation train control system and other GNSS-based railway implementations.

Construction and Optimization of TRNG Based Substitution Boxes for Block Encryption Algorithms

Internet of Things is an ecosystem of interconnected devices that are accessible through the internet.The recent research focuses on adding more smartness and intelligence to these edge devices.This makes them susceptible to various kinds of security threats.These edge devices rely on cryptographic techniques to encrypt the pre-processed data collected from the sensors deployed in the field.In this regard,block cipher has been one of the most reliable options through which data security is accomplished.The strength of block encryption algorithms against different attacks is dependent on its nonlinear primitive which is called Substitution Boxes.For the design of S-boxes mainly algebraic and chaos-based techniques are used but researchers also found various weaknesses in these techniques.On the other side,literature endorse the true random numbers for information security due to the reason that,true random numbers are purely non-deterministic.In this paper firstly a natural dynamical phenomenon is utilized for the generation of true random numbers based S-boxes.Secondly,a systematic literature review was conducted to know which metaheuristic optimization technique is highly adopted in the current decade for the optimization of S-boxes.Based on the outcome of Systematic Literature Review(SLR),genetic algorithm is chosen for the optimization of s-boxes.The results of our method validate that the proposed dynamic S-boxes are effective for the block ciphers.Moreover,our results showed that the proposed substitution boxes achieve better cryptographic strength as compared with state-of-the-art techniques.

Study on Federated Architecture for GPS/INS/TRN Integrated Navigation System

Based on the information fusion theory, a kind of integrated navigation system integration for cruise missile is presented in this paper. Besides, the way with which the system is integrated and the related data fusion technique are discussed. Information-fusion-based hybrid navigation system integration can fully utilize information provided by all kinds of navigation sensor subsystem and can improve the precision of the system effectively. Simultaneously, the reconstructing ability ensures the system of great reliability.

A TimeImageNet Sequence Learning for Remaining Useful Life Estimation of Turbofan Engine in Aircraft Systems

Internet of Things systems generate a large amount of sensor data that needs to be analyzed for extracting useful insights on the health status of the machine under consideration.Sensor data of all possible states of a system are used for building machine learning models.These models are further used to predict the possible downtime for proactive action on the system condition.Aircraft engine data from run to failure is used in the current study.The run to failure data includes states like new installation,stable operation,first reported issue,erroneous operation,and final failure.In the present work,the non-linear multivariate sensor data is used to understand the health status and anomalous behavior.The methodology is based on different sampling sizes to obtain optimum results with great accuracy.The time series of each sensor is converted to a 2D image with a specific time window.Converted Images would represent the health of a system in higher-dimensional space.The created images were fed to Convolutional Neural Network,which includes both time variation and space variation of each sensed parameter.Using these created images,a model for estimating the remaining life of the aircraft is developed.Further,the proposed net is also used for predicting the number of engines that would fail in the given time window.The current methodology is useful in avoiding the health index generation for predicting the remaining useful life of the industrial components.Better accuracy in the classification of components is achieved using the TimeImagenet-based approach.

Outlier Detection and Forecasting for Bridge Health Monitoring Based on Time Series Intervention Analysis

The method of time series analysis,applied by establishing appropriate mathematical models for bridge health monitoring data and making forecasts of structural future behavior,stands out as a novel and viable research direction for bridge state assessment.However,outliers inevitably exist in the monitoring data due to various interventions,which reduce the precision of model fitting and affect the forecasting results.Therefore,the identification of outliers is crucial for the accurate interpretation of the monitoring data.In this study,a time series model combined with outlier information for bridge health monitoring is established using intervention analysis theory,and the forecasting of the structural responses is carried out.There are three techniques that we focus on:(1)the modeling of seasonal autoregressive integrated moving average(SARIMA)model;(2)the methodology for outlier identification and amendment under the circumstances that the occurrence time and type of outliers are known and unknown;(3)forecasting of the model with outlier effects.The method was tested with a case study using monitoring data on a real bridge.The establishment of the original SARIMA model without considering outliers is first discussed,including the stationarity,order determination,parameter estimation and diagnostic checking of the model.Then the time-by-time iterative procedure for outlier detection,which is implemented by appropriate test statistics of the residuals,is performed.The SARIMA-outlier model is subsequently built.Finally,a comparative analysis of the forecasting performance between the original model and SARIMA-outlier model is carried out.The results demonstrate that proper time series models are effective in mining the characteristic law of bridge monitoring data.When the influence of outliers is taken into account,the fitted precision of the model is significantly improved and the accuracy and the reliability of the forecast are strengthened.

Self-Optimizing Flexible Assembly Systems

Today＇s production systems are demanded to exhibit an increased flexibility and mutability in order to deal with dynamically changing conditions, objectives and an increasing number of product variants within industrial turbulent environments. Flexible automated systems are requested in order to improve dynamic production efficiency, e.g. robot-based hardware and PC-based controllers, but these usually induce a significantly higher production complexity, whereby the efforts for planning and programming, but also setups and reconfiguration, expand. In this paper a definition and some concepts of self-optimizing assembly systems are presented to describe possible ways to reduce the planning efforts in complex production systems. The concept of self-optimization in assembly systems will be derived from a theoretical approach and will be transferred to a specific application scenario---the automated assembly of a miniaturized solid state laser--where the challenges of unpredictable influences from e.g. component tolerances can be overcome by the help of self-optimization.

Patient Centered Real-Time Mobile Health Monitoring System

In this paper, we introduce a system architecture for a patient centered mobile health monitoring (PCMHM) system that deploys different sensors to determine patients’ activities, medical conditions, and the cause of an emergency event. This system combines and analyzes sensor data to produce the patients’ detailed health information in real-time. A central computational node with data analyzing capability is used for sensor data integration and analysis. In addition to medical sensors, surrounding environmental sensors are also utilized to enhance the interpretation of the data and to improve medical diagnosis. The PCMHM system has the ability to provide on-demand health information of patients via the Internet, track real-time daily activities and patients’ health condition. This system also includes the capability for assessing patients’ posture and fall detection.

Field Investigation of Vehicle Acceleration at the Stop Line with a Dynamic Vision Sensor

This article presents a study of vehicle acceleration distribution at a traffic signal stop line in an urban environment. Accurate representation of vehicle acceleration behavior provides important inputs to traffic simulation models especially when traffic related emissions need to be estimated. A smart eye TDS （traffic data sensor） system was used to record vehicle trajectories, which were extracted to calculate vehicle acceleration profiles. This paper presents the acceleration distributions obtained from over 300 passenger-car acceleration cycles observed on site from the stop line up to a maximum speed of 40 km/h. These distributions are compared with the outputs from a traffic micro simulation tool modeling a similar stop line scenario. The comparison shows that measured accelerations present wider distribution and lower values than the micro simulation. This result highlights the importance of using acceleration distribution calibrated with real-world measured data rather than default values in order to estimate accurate emission levels.

Design and validation of wireless strain test system for bridge based on the resistance strain sensor

In order to achieve an access to strain sensor data with wireless transmission in bridge engineering structure testing, a wireless strain test system is presented based on the resistance strain sensor of networks. The wireless bridge strain test system composed of master station and substation adopts the wireless method to realize the high accuracy data acquisition between the master station and the substation under a reliable communication protocol. The system has been tested in contrast with the present strain apparatus. Results show that the wireless system is high-reliable, and has many characteristics such as high efficiency, good precision, high stability with low cost, and good flexibility, without using the present communication network.

Cattle behaviour classification from collar, halter, and ear tag sensors

In this paper,we summarise the outcome of a set of experiments aimed at classifying cattle behaviour based on sensor data.Each animal carried sensors generating time series accelerometer data placed on a collar on the neck at the back of the head,on a halter positioned at the side of the head behind the mouth,or on the ear using a tag.The purpose of the study was to determine how sensor data from different placement can classify a range of typical cattle behaviours.Data were collected and animal behaviours(grazing,standing or ruminating)were observed over a common time frame.Statistical features were computed from the sensor data and machine learning algorithms were trained to classify each behaviour.Classification accuracies were computed on separate independent test sets.The analysis based on behaviour classification experiments revealed that different sensor placement can achieve good classification accuracy if the feature space(representing motion patterns)between the training and test animal is similar.The paper will discuss these analyses in detail and can act as a guide for future studies.

Sensor data compression based on MapReduce

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.

Using naturalistic driving data to identify driving style based on longitudinal driving operation conditions

Purpose–An individual’s driving style significantly affects overall traffic safety.However,driving style is difficult to identify due to temporal and spatial differences and scene heterogeneity of driving behavior data.As such,the study of real-time driving-style identification methods is of great significance for formulating personalized driving strategies,improving traffic safety and reducing fuel consumption.This study aims to establish a driving style recognition framework based on longitudinal driving operation conditions(DOCs)using a machine learning model and natural driving data collected by a vehicle equipped with an advanced driving assistance system(ADAS).Design/methodology/approach–Specifically,a driving style recognition framework based on longitudinal DOCs was established.To train the model,a real-world driving experiment was conducted.First,the driving styles of 44 drivers were preliminarily identified through natural driving data and video data;drivers were categorized through a subjective evaluation as conservative,moderate or aggressive.Then,based on the ADAS driving data,a criterion for extracting longitudinal DOCs was developed.Third,taking the ADAS data from 47 Kms of the two test expressways as the research object,six DOCs were calibrated and the characteristic data sets of the different DOCs were extracted and constructed.Finally,four machine learning classification(MLC)models were used to classify and predict driving style based on the natural driving data.Findings–The results showed that six longitudinal DOCs were calibrated according to the proposed calibration criterion.Cautious drivers undertook the largest proportion of the free cruise condition(FCC),while aggressive drivers primarily undertook the FCC,following steady condition and relative approximation condition.Compared with cautious and moderate drivers,aggressive drivers adopted a smaller time headway(THW)and distance headway(DHW).THW,time-to-collision(TTC)and DHW showed highly significant differences in driving style identification,while longitudinal acceleration(LA)showed no significant difference in driving style identification.Speed and TTC showed no significant difference between moderate and aggressive drivers.In consideration of the cross-validation results and model prediction results,the overall hierarchical prediction performance ranking of the four studied machine learning models under the current sample data set was extreme gradient boosting>multi-layer perceptron>logistic regression>support vector machine.Originality/value–The contribution of this research is to propose a criterion and solution for using longitudinal driving behavior data to label longitudinal DOCs and rapidly identify driving styles based on those DOCs and MLC models.This study provides a reference for real-time online driving style identification in vehicles equipped with onboard data acquisition equipment,such as ADAS.

Joint Design of Clustering and In-cluster Data Route for Heterogeneous Wireless Sensor Networks

A heterogeneous wireless sensor network comprises a number of inexpensive energy constrained wireless sensor nodes which collect data from the sensing environment and transmit them toward the improved cluster head in a coordinated way. Employing clustering techniques in such networks can achieve balanced energy consumption of member nodes and prolong the network lifetimes.In classical clustering techniques, clustering and in-cluster data routes are usually separated into independent operations. Although separate considerations of these two issues simplify the system design, it is often the non-optimal lifetime expectancy for wireless sensor networks. This paper proposes an integral framework that integrates these two correlated items in an interactive entirety. For that,we develop the clustering problems using nonlinear programming. Evolution process of clustering is provided in simulations. Results show that our joint-design proposal reaches the near optimal match between member nodes and cluster heads.