Input Sensor Data Research Articles

Development of 3D UDTs for Traffic Monitoring in Unreal 5 Game Engine

Abstract. Digital Twins recently, are a much regarded research topic in geospatial engineering, offering great potential for optimization of a variety of interrelated processes in urban environments. A major component of a Digital Twin use case is real-time sensor data input and analysis, which is used as foundation for simulations that lead to decision-support for authorities or even automatic response into the physical environment. The use of game engines as platform for urban Digital Twin use cases is promising due to superior visualization and animation capabilities, that facilitate diverse monitoring use cases for highly dynamic sensor data inputs. This contribution evaluates the fit for purpose of the Unreal Engine 5 game engine for digital twin use cases, by conducting a case study on urban traffic monitoring in the city of Liverpool Australia. The case study proved, that the necessary connections of static data provided via a geodatabase and dynamic real-time data from public portals can be integrated into the engine to establish an interactive Digital Twin use cases for evaluating traffic user interactions.

Integration of BWT scrambling and data compression in an innovative system enhances protection and versatile management of sensor feeds (SEC)

The proposed Sensor Data Encryption with Compression (SEC) system is presented as an innovative solution for sensor data processing, aiming to achieve optimal efficiency while improving security and adaptability by integrating robust cryptography with advanced compression techniques. The system's objective is to create a reliable framework for managing sensitive sensor data by effectively tackling challenges in data processing through a balanced combination of strong cryptography and sophisticated compression methods. The SEC system utilizes advanced encryption and compression techniques, including the scrambling of Burrows-Wheeler Transform (BWT), Move-to-Front (MTF) encoding, Run-Length Encoding (RLE), and Huffman coding, to attain exceptional compression efficiency while maintaining data integrity. The practical implementation of the SEC system shows an impressive 85% enhancement in data compression efficiency, as evidenced by reduced Bits per Character (BPC) across various sensor data inputs, along with improved resistance to cryptanalysis due to an increased unicity distance. Consequently, the SEC system demonstrates itself to be a robust and efficient solution, achieving superior data compression and enhanced cryptographic resilience, thereby addressing critical challenges in sensor data processing.

Enhanced Fault Diagnosis in IoT: Uniting Data Fusion with Deep Multi-Scale Fusion Neural Network

One of the biggest challenges is figuring out when industrial IoT devices break. Notwithstanding these difficulties, one of the many advantages of using real-time sensor data from the Industrial Internet of Things (IIoT) is the ability to monitor events and respond promptly. The IIoT's sensor numbers may be analysed, combined with data from other sources, and applied quickly to make decisions. This manuscript proposes Enhanced Fault Diagnosis in IoT, Uniting Data Fusion with Deep Multi-Scale Fusion Neural Network (FD-IoT-DMSFNN). Initially, input sensor data are taken from the CWRU Dataset. Then, sensor data is normalised using Multivariate Fast Iterative Filtering. Then, the normalised sensor data are extensively dispersed and diverse; hence, the data outlier detection is performed using the Deep isolation forest (DIF) approach. Therefore, this manuscript proposes a Mexican Axolotl Optimization (MAO) for tuning the weight parameter of DMSFNN, which exhibits an enhanced fault detection process. Python is used to implement the recommended strategy. The proposed method's performance yields a lower completion time of 6.5%, 1.8%, and 4.13%; higher prediction accuracy of 2.26%, 6.71%, and 8.32% compared with existing approaches such as towards deep domain adaptability training methods for industrial IoT edge device soft real-time fault diagnosis (FD-IoT-LSTM), Intelligent Fault Quantitative Identification for the Industrial Internet of Things (IIoT) utilising a particular deep dual reinforcement learning model with insufficient samples (FD-IoT-DRL) and IIoT- based fault identification model for industrial use (FD-IoT-ML-LSTM) respectively.

Examining the challenges of blood pressure estimation via photoplethysmogram

The use of observed wearable sensor data (e.g., photoplethysmograms [PPG]) to infer health measures (e.g., glucose level or blood pressure) is a very active area of research. Such technology can have a significant impact on health screening, chronic disease management and remote monitoring. A common approach is to collect sensor data and corresponding labels from a clinical grade device (e.g., blood pressure cuff) and train deep learning models to map one to the other. Although well intentioned, this approach often ignores a principled analysis of whether the input sensor data have enough information to predict the desired metric. We analyze the task of predicting blood pressure from PPG pulse wave analysis. Our review of the prior work reveals that many papers fall prey to data leakage and unrealistic constraints on the task and preprocessing steps. We propose a set of tools to help determine if the input signal in question (e.g., PPG) is indeed a good predictor of the desired label (e.g., blood pressure). Using our proposed tools, we found that blood pressure prediction using PPG has a high multi-valued mapping factor of 33.2% and low mutual information of 9.8%. In comparison, heart rate prediction using PPG, a well-established task, has a very low multi-valued mapping factor of 0.75% and high mutual information of 87.7%. We argue that these results provide a more realistic representation of the current progress toward the goal of wearable blood pressure measurement via PPG pulse wave analysis. For code, see our project page: https://github.com/lirus7/PPG-BP-Analysis

Kalman filter-based sensor fusion for Ackermann steering mobile robots

Abstract Autonomous navigation technologies are continuously improving in terms of performance and safety. A key subfield of autonomous navigation for mobile robots is localization or precise positioning, involving the robot understanding its position relative to key points of interest in the environment. Traditionally, localization is purely dependent on the quality of input received from onboard sensors and therefore localization quality degrades in the presence of high sensor error or other faults. Sensor fusion is useful to combat this. Sensor fusion is used to combine various data types from different sensors to gain a lower overall uncertainty or error in the resulting data, when compared to the separate data from each individual sensor. In this study a four-wheel mobile robot with front-wheel driving and steering is localized during navigation. The investigation is performed in simulation and with a physical prototype. The mobile robot has several sensors including a wheel encoder on each wheel, an inertial measurement unit as well as an indoor GPS system. A designed Kalman Filter called The Combined Kalman Filter has been designed to fuse the sensor input data in order to output the position data required to localize the robot in (X, Y) cartesian coordinates. This is completed in the presence of sensor bias error and noise with the output path compared against a pre-selected ground truth trajectory. The proposed filter is based on the Extended Kalman Filter to contend with the non-linear model arising from the four-wheel steerable robot with the steering system following the Ackermann steering condition. The robot model and sensors are simulated in MATLAB for a chosen ground truth trajectory with results highlighting any detected faults and final accurate position information. It was seen that the estimated filter output had high accuracy when compared with the ground truth. Compared with other investigations the proposed filter has reduced mean error and minimal deviations compared with the ground truth path. In addition, this filtering model can be used in general for the localization of other actuated steering vehicles and aims to widen the research in this field as the majority of research in this area leverages differential drive robots which require less complex modeling.

A Data-driven Deep Learning Approach for Remaining Useful Life in the ion mill etching Process

Prognostics and Health Management (PHM) is regarded as an essential element in the scope of intelligent manufacturing. Precise forecasting of the remaining useful life (RUL) of an ion mill is crucial in order to enhance the overall efficiency of the ion mill etching (IME) procedure. This paper proposed a Data-driven Deep Learning (DL) framework that integrates a Temporal Convolution Network (TCN), Long Short-Term Memory (LSTM), and self-attention mechanism to improve the accuracy of RUL prediction in the ion mill etching Process. Initially, sensor input data is divided into two parallel paths - one with TCN blocks for capturing long-range dependencies, and the other with LSTM layers for extracting temporal patterns. The outputs from both paths are then merged and input into an LSTM layer for enhanced learning, followed by a self-attention mechanism to highlight important features then fully connected layer for predicting RUL. The efficacy of this suggested model was assessed through the utilization of the 2018 PHM Data Challenge Dataset and juxtaposed against various Deep Learning models to demonstrate its efficacy. The results from the experiments indicate that ATCN-LSTM serves as a robust option for estimating the RUL in the ion mill etching Process as it outperformed all other models that were compared. The source code is publicly accessible at https://github.com/ion-mill-etching-Process.

Robust and transferable end-to-end navigation against disturbances and external attacks: an adversarial training approach

Purpose This paper aims to facilitate the research and development of resilient navigation approaches, explore the robustness of adversarial training to different interferences and promote their practical applications in real complex environments. Design/methodology/approach In this paper, the authors first summarize the real accidents of self-driving cars and develop a set of methods to simulate challenging scenarios by introducing simulated disturbances and attacks into the input sensor data. Then a robust and transferable adversarial training approach is proposed to improve the performance and resilience of current navigation models, followed by a multi-modality fusion-based end-to-end navigation network to demonstrate real-world performance of the methods. In addition, an augmented self-driving simulator with designed evaluation metrics is built to evaluate navigation models. Findings Synthetical experiments in simulator demonstrate the robustness and transferability of the proposed adversarial training strategy. The simulation function flow can also be used for promoting any robust perception or navigation researches. Then a multi-modality fusion-based navigation framework is proposed as a light-weight model to evaluate the adversarial training method in real-world. Originality/value The adversarial training approach provides a transferable and robust enhancement for navigation models both in simulation and real-world.

Advancing Fall Detection Utilizing Skeletal Joint Image Representation and Deformable Layers

Falls are a significant concern among the elderly population, with 25% of individuals over 65 years old experiencing a fall severe enough to require a visit to the emergency department each year. Early detection of falls can prevent serious injuries and complications, making it an important problem to address. There are various methods for detecting falls, utilizing different types of sensor input data. However, when considering factors such as ease of setup, accessibility, and accuracy, utilizing cameras for fall detection is a highly effective approach. In this study, a novel video-based fall detection algorithm that relies on skeleton joints is introduced. The results of pose estimation are preprocessed into an image representation and ShuffleNet V2 model with the addition of a Deformable Layer is employed for classification. Experiments were carried out on four distinct datasets: URFD, UP-Fall Detection, Le2i, and NTU RGB+D 60, which encompass individuals engaged in various activities, including falls. The results showcase exceptional performance across all these datasets, affirming the efficacy of the approach in accurately detecting falls in video footage.

A comparative study on deep learning models for condition monitoring of advanced reactor piping systems

Advanced nuclear reactors offer innovative applications due to their portability, reliability, resiliency, and high capacity factors. To operate them on a wider scale, reducing maintenance life-cycle costs while ensuring their integrity is essential. Autonomous operations in advanced nuclear reactors using augmented Digital Twin (DT) technology can serve as a cost-effective solution by increasing awareness about the system’s health. A key component of nuclear DT frameworks is the condition monitoring of safety systems, such as piping-equipment systems, which involves acquiring and monitoring the plant’s sensor data. This research proposes a condition monitoring methodology utilizing deep learning algorithms, such as multilayer perceptions (MLP) and convolutional neural networks (CNNs), to detect degradation and its severity in nuclear piping-equipment systems. Sensor signals are processed to obtain the power spectral density and the Short-Time Fourier transform, and feature extraction methodologies are proposed to develop degradation-sensitive data repositories. The performance of MLP, one-dimensional (1D) CNN, and 2D CNN within the proposed condition monitoring framework is compared using a finite element model of a 3D piping system subjected to seismic loads as the application case study. Various approaches, such as dropout, k-Fold validation, regularization, and early stopping of training the network, are investigated to avoid overfitting the models to the input sensor data. The predictive capability and computational capacity of the deep learning algorithms are also compared to detect degradation in the Z-pipe system of the Experimental Breeder Reactor II (EBRII). The Z-pipe system is subjected to harmonic excitations that represent normal operating loads, such as pump-induced vibrations. The findings of the study indicate that the proposed artificial intelligence (AI)-driven condition monitoring framework demonstrates superior prediction accuracies with a 2D CNN, whereas the MLP exhibits higher computational efficiency.

Data‐Driven Method for Probabilistic Response Prediction of Cable‐Stayed Bridges

This study proposes a data‐driven method for predicting the probabilistic response of cable‐stayed bridges. The proposed method is used to construct an optimal prediction model based on a data‐driven machine‐learning method. In addition, the accuracy and efficiency of the prediction model are improved by considering the correlation coefficients between the input sensor data and the output of the target response. The proposed method is comprised of two steps: the selection of meaningful features and the construction of a probabilistic prediction model employing Gaussian process regression. The proposed method is applied to an in‐service cable‐stayed bridge in the Republic of Korea using actual measurement data from various sensors. For comparison purposes, two parametric studies are performed, and the effects of the proposed feature‐selection procedure are investigated based on the normalized correlation coefficients. Consequently, the proposed feature‐selection method is proven to increase the accuracy and efficiency of the prediction.

Information Quality Improvement With Task Selection Algorithm For IoT Energy Harvesting Devices

The purpose of study is to propose a task selection algorithm that both keeps information quality and saves power consumption in IoT energy harvesting devices. The proposed algorithm not only keeps stable information quality but saves power loss also. The sensor node operation is divided into four tasks depending on the input data including battery capacity, solar panel charging current, and input sensor data variation. The task selector based on a neural network consists of an input layer, a hidden layer of 20 neurons, and an output layer. The proposed algorithm is different from the predefined task algorithm, which mainly focused on deep sleep mode or scheduled tasks. Our proposed algorithm helps the sensor node to be more adaptive to the environment based on real-time execution at each node. The collected information amount varies according to the input data variation. The experiment results show that the proposed algorithm collects higher quality information at large input data variation. The battery lifetime is also improved by up to 22%.

Automated Gesture Recognition Using African Vulture Optimization with Deep Learning for Visually Impaired People on Sensory Modality Data

Gesture recognition for visually impaired persons (VIPs) is a useful technology for enhancing their communications and increasing accessibility. It is vital to understand the specific needs and challenges faced by VIPs when planning a gesture recognition model. But, typical gesture recognition methods frequently depend on the visual input (for instance, cameras); it can be vital to discover other sensory modalities for input. The deep learning (DL)-based gesture recognition method is effective for the interaction of VIPs with their devices. It offers a further intuitive and natural way of relating with technology, creating it more available for everybody. Therefore, this study presents an African Vulture Optimization with Deep Learning-based Gesture Recognition for Visually Impaired People on Sensory Modality Data (AVODL-GRSMD) technique. The AVODL-GRSMD technique mainly focuses on the utilization of the DL model with hyperparameter tuning strategy for a productive and accurate gesture detection and classification process. The AVODL-GRSMD technique utilizes the primary data preprocessing stage to normalize the input sensor data. The AVODL-GRSMD technique uses a multi-head attention-based bidirectional gated recurrent unit (MHA-BGRU) method for accurate gesture recognition. Finally, the hyperparameter optimization of the MHA-BGRU method can be performed by the use of African Vulture Optimization with Deep Learning (AVO) approach. A series of simulation analyses were performed to demonstrate the superior performance of the AVODL-GRSMD technique. The experimental values demonstrate the better recognition rate of the AVODL-GRSMD technique compared to that of the state-of-the-art models.

IoT Enabled Sensory Monitoring System for Fog Optimal Resource Provisioning Method in Health Monitoring System

Fog is data management and analytics service. In this paper gains and most effective novel approach to provide IoT enabled services in healthcare application using Fog Computing. In this research the data is collected from Google Scholar, Science Director and MEDLINE database. IoT based Fog Computing techniques are proposed for delivering quality of services to the user. Optimal Resource Provisioning method is proposed to find edges, service level agreements and administration services for IoT client. The DeepQ residue information processing technique is applied for connecting data centre of the cloud and computing paradigms technique is finding the depth reference of Fog levels. The proposed Optimal resource provisioning algorithm is examining the dataset and TensorFlow tool is used for simulating environment. Fog computing layer consist of IoT sensor data inputs, data centres for the cloud and connected layers for simulations. The Deep belief network is generated based on above inputs using 256 X 256 X 3 layer system and 5000 trained data, 1000 test data are taken for simulations. Each dataset simulation is recording using supervised and unsupervised learning methods. Based on above results IoT enable Fog Computing data management and analytics systems provided 95% accuracy and the compared with existing computing techniques our proposed systems shows better efficiency with respect to safety and convenience.

SBFEM and Bayesian inference for efficient multiple flaw detection in structures

Bayesian inference is a powerful technique for damage/flaw detection in critical structures. This paper explores the application of Bayesian inference to identify the flaws (voids/inclusions) and its parameters, assuming no information about the flaws is known a priori. Multiple flaws are represented by a parameter vector that contains the locations and the geometric information. In the inverse problem framework, the Scaled Boundary Finite Element Method (SBFEM) with quadtree decomposition is used to solve the forward problem. The flaw parameters are statistically quantified using the Bayesian inference. The likelihood function and prior information update the joint distribution of the number of flaws and their corresponding flaw parameters. The sampling to estimate the posterior distribution of the flaw parameters is based on the trans-dimensional Reversible Jump Markov Chain Monte Carlo (RJMCMC). Also, the impact of additive Gaussian noise on the observed data is investigated. Numerical examples include identifying multiple voids of different shapes such as circle and ellipse and identifying voids and inclusions, using the proposed approach with input sensor data at different noise levels.

True sparse PCA for reducing the number of essential sensors in virtual metrology

In the semiconductor industry, virtual metrology (VM) is a cost-effective and efficient technique for monitoring the processes from one wafer to another. This technique is implemented by generating a predictive model that uses real-time data from equipment sensors in conjunction with measured wafer quality characteristics. Before establishing a prediction model for the VM system, appropriate selection of relevant input variables should be performed to maintain the efficiency of subsequent analyses considering the large dimensionality of the sensor data inputs. However, wafer production processes usually employ multiple sensors, which leads to cost escalations. Herein, we propose a variant of the sparse principal component analysis (PCA) called true sparse PCA (TSPCA). The proposed method uses a small number of input variables in the first few principal components. The main contribution of the proposed TSPCA is reducing the number of essential sensors. Our experimental results demonstrate that compared to the existing sparse PCA methods, the proposed approach can reduce the number of sensors required while explaining an approximately equivalent amount of variance.

A Hardware-in-the-Loop Simulator to Optimize Autonomous Sailboat Performance in Real Ocean Conditions

In this work, a hardware-in-the-loop (HIL) simulator is designed to diagnose the behavior of an autonomous sailboat as it navigates between waypoints. At its core, the HIL simulator includes the sailboat pilot on an embedded system. The sensor data input to the embedded system is fed by a navigation simulator that takes into account the different forces on the sailboat due to the wind, waves and current conditions. The HIL simulator is then tested for a navigation route from sea trials published in 2014, and the behavior of the automated pilot is compared to its behavior when the vessel is driven by a crew. As demonstrated, the automated system can outperform the man-operated vessel. The tool is also used to diagnose weaknesses in the sailboat autopilot algorithm that can be improved in the future.

Application of sensor data based predictive maintenance and artificial neural networks to enable Industry 4.0

Possessing an efficient production line relies heavily on the availability of the production equipment. Thus, to ensure that the required function for critical equipment is in compliance, and unplanned downtime is minimized, succeeding with the field of maintenance is essential for industrialists. With the emergence of advanced manufacturing processes, incorporating predictive maintenance capabilities is seen as a necessity. Another field of interest is how modern value chains can support the maintenance function in a company. Accessibility to data from processes, equipment and products have increased significantly with the introduction of sensors and Industry 4.0 technologies. However, how to gather and utilize these data for enabling improved decision making within maintenance and value chain is still a challenge. Thus, the aim of this paper is to investigate on how maintenance and value chain data can collectively be used to improve value chain performance through prediction. The research approach includes both theoretical testing and industrial testing. The paper presents a novel concept for a predictive maintenance platform, and an artificial neural network (ANN) model with sensor data input. Further, a case of a company that has chosen to apply the platform, with the implications and determinants of this decision, is also provided. Results show that the platform can be used as an entry-level solution to enable Industry 4.0 and sensor data based predictive maintenance.

Fuzzy based Irrigation Control System for Indian Subcontinent

Water resource usage should be optimized as there is always a scarcity. This paper aims to provide an efficient way of water using sense and weather data and implementing a fuzzy decision model. An automated intelligent watering system is proposed in this paper using the internet of things and fuzzy logic. The weather data, coupled with Temperature, Relative Humidity, and soil moisture sensor data, is used to decide whether to switch on/off the motor. In-house-created prototypes of ground-moving robots have soil moisture, digital humidity, and temperature sensors implanted in them. The soil moisture sensor is attached to the Rack and pinion mechanism. The soil moisture sensor is pushed into the soil when the pinion rotates. It minimizes the use of sensors by using a distributed sensing method. Based on data obtained from sensors and meteorological information, the system will use this information to decide whether to Switch on/off the sprinkler motor. A fuzzy logic-based system decision is implemented on the input sensor and weather data, and the model will decide to switch on/off the actuator. An accuracy of 97% is achieved. The Android app is used to visualize sensor data, based on which the farmer can manually control the motor.

A method for rolling bearing fault diagnosis based on GSC-MDRNN with multi-dimensional input

The traditional fault diagnosis methods for rolling bearings through neural networks mostly use data sources collected by a single sensor and use single-dimensional data input, leading to fault features in bearings not be completely extracted. Moreover, traditional convolution often uses single-size convolution kernels, which are insufficient for fault feature extraction. In response to these problems, the global shortcut connection (GSC)-multichannel deep ResNet network model is proposed. First, a new residual structure, the GSC, is proposed to fuse two-dimensional and one-dimensional signal features. Second, involution is introduced into the field of fault diagnosis to address the problem of insufficient network feature extraction caused by using single-size convolution kernels. In addition, a convolutional block attention module can adaptively assign the weight of each channel feature to achieve adaptive channel fusion. The verification was performed on the four-category and eight-category data sets collected in the laboratory, and the results show that this method has a high fault recognition rate.

Estimating Chlorophyll-a Concentration from Hyperspectral Data Using Various Machine Learning Techniques: A Case Study at Paldang Dam, South Korea

Algal blooms have been observed worldwide and have had a serious impact on industries that use water resources, which is a problem for people and the environment. For this reason, an algae warning system is used to count the number of cyanobacterial cells and the concentration of chlorophyll-a. Several studies using multispectral or hyperspectral data to estimate chlorophyll concentration have recently been carried out. In the present study, a comparative approach was applied to estimate the concentration of chlorophyll-a at Paldang Dam, South Korea using hyperspectral data. We developed a framework for estimating chlorophyll-a using dimension reduction methods, such as principal component analysis and partial least squares, and various machine learning algorithms. We analyzed hyperspectral data collected during a field survey to locate peaks in the chlorophyll-a spectrum. The framework that used support vector regression achieved the highest R2 of 0.99, a mean square error (MSE) of 1.299 μg/cm3, and showed a small discrepancy between observed and real values relative to other frameworks. These findings suggest that by combining hyperspectral data with dimension reduction and a machine learning algorithm, it is possible to provide an accurate estimation of chlorophyll-a. Using this, chlorophyll-a can be obtained in real time through hyperspectral sensor data input from drones or unmanned aerial vehicles using the learned machine learning algorithm.