Real-time Identification System Research Articles

Real-Time Fault Identification System for a Retrofitted Ultra-Precision CNC Machine from Equipment's Power Consumption Data: A Case Study of an Implementation

Real-Time Fault Identification System for a Retrofitted Ultra-Precision CNC Machine from Equipment's Power Consumption Data: A Case Study of an Implementation

Low-Complexity Adaptive Background Compensation of Coherent Optical Transmitters

Next generation optical communications will demand coherent transceivers with data rates in the 1.6 Terabit-per-second range and beyond, requiring both high symbol rate and large constellations. In this scenario coherent systems become increasingly sensitive to transmitter imperfections such as In-phase/Quadrature time skew, amplitude and phase errors, bandwidth limitations and mismatches, etc. These impairments must be compensated to achieve satisfactory performance. In this work, we propose a novel and efficient technique for the precompensation of such impairments, requiring minimum additional hardware in current transmitter designs. This scheme is <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">adaptive</i> , as it readjusts itself to cope with time and environment dependent impairments, and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">background</i> , as interruptions of normal operation are not required for the continuous recalibration of the system. Naturally, such <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in-field</i> adaptation demands the real-time system identification of the coherent optical transmitter, which is performed from the sole information provided by a low bandwidth photodiode/analog-to-digital converter chain at the transmitter output. This fact represents a meaningful advance with respect to the usual channel estimation methods, which require the full bandwidth coherent demodulation of the optical output. A theoretical foundation for the proposed scheme is presented. Numerical simulations of a realistic coherent optical transmitter show excellent results for high symbol rate modulation formats.

Fractional derivative truncation approximation for real-time applications

Fractional derivatives are non local operators as they are well-suited for modeling long-memory phenomena such as heat transfers or fluid dynamics. However, such non-locality implies a constant knowledge of the full past of the functions to differentiate. In the context of real-time (online) system identification, such a global property may limit the implementation as calculations can become slower as time grows. Also, instead of using the full data length to compute fractional order derivatives, truncated fractional derivatives are used. However, such a truncation windowing will bring inaccuracy on the computation of fractional derivatives. This study deals with the relationship between the signal frequency content, the fractional derivative truncated approximation as well as the fractional system relaxation in order to get a good approximation of the truncated fractional derivative and consequently, to help providing real-time exploitable system identification algorithms that uses such truncated fractional derivatives.

Real-Time Model Predictive Control and System Identification Using Differentiable Simulation

Transferring a controller from a simulated environment to a physical system is regarded as a challenging problem in robotics. We present a method for continuous improvement of modeling and control <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">after</i> deploying the robot to a dynamically-changing target environment. We develop a differentiable physics simulation framework that simultaneously performs online system identification and optimal control using the incoming observations from the target environment in real time. To ensure robust system identification against noisy observations, we devise an algorithm to assess the confidence of our estimated parameters using numerical analysis of the dynamic equations. To ensure real-time optimal control, we adapt start time of the optimization window so that the optimized actions can be replenished ahead of consumption, while staying as up-to-date with new information as possible. The constantly re-planning based on a constantly improving model allows the robot to swiftly adapt to the changing environment using real-world data in a sample-efficient way. Thanks to a fast differentiable physics simulator, both system identification and control can be solved efficiently in real time. We demonstrate our method on a set of examples in simulation and on a real robot. Our method can outperform all baseline methods in different experiments.

Detection of Certain Objects Wearing Masks in Real Time To Prevent the Spread of the Virus (Yolov3)

A significant increase in the spread of the corona virus (COVID-19) in the community is currently happening due to people not following the health protocol rules set by the ministry of health. One of the rules is to require people to wear masks while they are outside the home. Measures need to be implemented in anticipation of situations where people do not wear masks in public spaces. Therefore, the establishment of a mask detection system is chosen as a solution in order to solve the mentioned problem above. A real-time identification system for people wearing masks is proposed to be developed in this paper. The system utilizes Yolov3 with Darknet -53 as a deep learning mask detector and OpenCV as a real-time computer vision library, so that people doing activities in a public space captured by a video can be recognized and detected when they do not wear masks . In implementing deep learning, a data set of 4000 images is divided into two classes, i.e.,2000 images with masks f or data testing purposes and another 2000 images without masks for training custom objects. The Extreme Programming (XP) method as part of the Agile Process Model is adopted for system development. Computer language support and the latest system development tools have made it possible to utilize this method in an effort to develop this system rapidly. Requirement Analysis is conducted to obtain required processes before designing system. Writing code and testing the system will be the next step before the system is declared ready to be implemented in the public space. By adopting the XP development method, all of the above steps can be implemented repeatedly until the system delivers the expected results

A deep learning and Grad-Cam-based approach for accurate identification of the fall armyworm (Spodoptera frugiperda) in maize fields

Population monitoring and early warning to the world's important invasive pest, the fall armyworm (FAW), Spodoptera frugiperda (J.E. Smith), is a basis for its management in the field. Deep learning is a useful intelligence approach for identifying Spodoptera frugiperda; however, it has a few limitations, such as too many model parameters, and the mismatch between training in simple backgrounds and complex real scenarios. Herein, we established a two-stage classification approach called MaizePestNet, which is based on EfficientNet, to address these concerns. It combines the Gradient-weighted Class Activation Mapping (Grad-CAM) technique for removing background interference with the knowledge distillation strategy for reducing model parameters and size. A dataset of adults and larvae of 36 common maize field pests, including Spodoptera frugiperda, was collected. The accuracy, precision, recall, and F1-score of the new model were acquired as 93.85%, 93.56%, 96.23%, and 93.85% through training and testing on the dataset. The new model’s accuracy was 5.65% and 9.04% higher than that of the ResNet101 and DenseNet161 models, respectively; the model size was 90% lower and 85% lower than the ResNet101 and DenseNet161 models, respectively. A WeChat applet and online real-time identification system (http://migrationinsect.cn/maizepestidentification) based on our classification model were developed for practical applications to accurately identify Spodoptera frugiperda adults and larvae. This study gave public an accurate, efficient, and straightforward method for detecting Spodoptera frugiperda in maize fields, and the findings can guide and assist plant protection agencies and farmers in monitoring, early warning, and control activities of the pest.

A Real-Time Recognition System of Driving Propensity Based on AutoNavi Navigation Data.

Driving propensity is the driver’s attitude towards the actual traffic situation and the corresponding decision-making or behavior during the driving process. It is of great significance to improve the accuracy of safety early warning and reduce traffic accidents. In this paper, a real-time identification system of driving propensity based on AutoNavi navigation data is proposed. The main work includes: (1) A dynamic data acquisition method of AutoNavi navigation is proposed to obtain the time, speed and acceleration of the driver during the navigation process. (2) The dynamic data collection method of AutoNavi navigation is analyzed and verified through the dynamic data obtained in the real vehicle experiment. The principal component analysis method is used to process the experimental data to extract the driving propensity characteristics variables. (3) The fruit fly optimization algorithm combined with GRNN (generalized neural network) and the feature variable set are used to build a FOA-GRNN-based model. The results show that the overall accuracy of the model can reach 94.17%. (4) A driving propensity identification system is constructed. The system has been verified through real vehicle test experiments. This paper provides a novel and convenient method for building personalized intelligent driver assistance systems in practical applications.

Does the traffic volume of a port determine connectivity? Revisiting port connectivity measures with high-frequency satellite data

Seaports play an important role in the global shipping network. Shipping participants often attach great importance to the measurement of container port connectivity, as it reflects countries' access to world markets. As a result, various port connectivity index systems have been proposed by members of the shipping industry and scholars. In recent years, technological developments especially the advancement of high coverage and real-time Automatic Identification System (AIS) data, have provided a chance to improve the scope and frequency of the existing index systems. An improved system is expected to reflect the dynamic changes in a port's connectivity which may be induced by either local disruptions or shocks in the wider economy. This study builds a monthly container port connectivity index system by applying big data mining techniques, graph theory, and principal component analysis (PCA) to AIS data, taking both port factors and shipping network factors into consideration. AIS records from 2020 are used to calculate the connectivity score of 25 major container ports. We also compare our system with the connectivity index commonly used in the shipping industry, the Liner Shipping Connectivity Index (LSCI). Our results show that the measurement of connectivity can be improved over indices that depend primarily on indicators of traffic volume. Ports like Antwerp and Tanjung Pelepas rank high in the proposed system due to their sound performance on their accessibility and strategic position in the local region instead of their traffic volume. The monthly index system is also proven to reflect timely changes in the shipping industry through its accurate portrayal of changes in port connectivity during the COVID-19 outbreak.

Accurate fish-freshness prediction label based on red cabbage anthocyanins

Biosafe colorimetric labels that can accurately evaluate food freshness have been widely investigated in recent years. Here, red cabbage anthocyanin labels and back propagation (BP) neural network are combined to form a system for monitoring fish freshness. Anthocyanins extracted from red cabbage were used as color response pigments and carboxymethyl chitosan/oxidized sodium alginate (CMCS/OSA) as the solid matrix. They were dispersed in silica sol to obtain colorimetric labels using the screen-printing approach. The label is recognized by the mobile phone to obtain freshness information, rather than the traditional method with the color card. The labels underwent color gradation during the storage period which was driven by response of anthocyanins to changes in pH. Computers are more sensitive to changes in color than the human eye. The labels are divided into three categories according to the freshness of the fish. BP neural network trained with labeled red cabbage anthocyanin label images predicted fish freshness with an overall accuracy of 92.6%. Integrating a BP neural network into a smartphone application forms a simple system for fast label scanning and real-time identification of fish freshness. The system can be used for food quality control throughout the supply chain.

COVID-19 impact on maritime traffic and corresponding pollutant emissions. The case of the Port of Barcelona

The impact of the SARS-CoV pandemic has gone well beyond health concerns, reaching the maritime industry. The study on the environmental impact of shipping industry during COVID-19 pandemic can provide useful insights to propose new management policies regarding shipping operations, both in-port and on the route. We present a case study centred in the Port of Barcelona covering a 30 nautical miles range in the period March to July 2020, during which different levels of restrictions and stringent lockdown measures were enforced. In this paper, we assess the impact of COVID-19 on maritime traffic and its related emissions in port cities using real-time Automatic Identification System (AIS) data. Interestingly, results show that the decline in maritime traffic is not correlated with a decrease in maritime emissions due to changes in vessel operation. During lockdown (March to June 2020), we observed a 27.9% reduction in the number of port calls compared to the pre-lockdown scenario, whereas pollutant emissions show a moderate decrease (1.8% for CO2), no significant reduction (SO2 and PM) or a slight increase (1.3% for NOx). This can be directly assigned to changes in vessel operation mode, i.e. vessels switched from Underway to At Anchor or Moored status, during which auxiliary engines are used at higher loads.

Model Construction Based on Real-Time System Identification Method (Proposal of a Model Construction Method for Anomaly Detection of Control Systems)

Model Construction Based on Real-Time System Identification Method (Proposal of a Model Construction Method for Anomaly Detection of Control Systems)

Truncation of fractional derivative for online system identification

Fractional derivatives are non local operators that has compacity property in terms of parameter number for modeling diffusive phenomenon with very few parameters. One of its main properties is its non-local behavior, as it can be exploited to model long-memory phenomena such as heat transfers. However, such non-locality implies a constant knowledge of the full past of the function to be differentiated. In the context of real-time system identification, this may limit the experiences as calculations become slower as time progresses. This study deals with the relationship between frequency content of a signal and its truncation error in order to obtain real-time exploitable algorithms.

Centralized System Identification of Multi-Rail Power Converter Systems Using an Iterative Decimation Approach

This paper presents an iterative decimation approach to significantly alleviate the computational burden of centralized controllers applying real-time recursive system identification algorithms in multi-rail power converters. The proposed approach uses an adaptive update rate as opposed to the fixed update rate used in conventional adaptive filters. Also, the step size/forgetting factors vary at different iteration stages. As a result, a reduced computational burden and faster model update can be achieved. Besides, recursive algorithms, such as Recursive Least Square (RLS), Fast Affine Projection (FAP) and Kalman Filter (KF), contain two important updates per iteration cycle; Covariance Matrix Approximation (CMA) update and Gradient Vector (GV) update. Usually, the CMA update requires the greater computational effort than the GV update. Therefore, in circumstances where the sampled data in the regressor does not experience significant fluctuations, re-using the CMA, calculated from the last iteration cycle for the current update can result in computational cost savings for real-time system identification. In this paper, both iteration rate adjustment and CMA re-cycling are combined and applied to simultaneously identify the power converter models in a three-rail power conversion architecture.

Early Detection of Freezing of Gait during Walking Using Inertial Measurement Unit and Plantar Pressure Distribution Data.

Freezing of gait (FOG) is a sudden and highly disruptive gait dysfunction that appears in mid to late-stage Parkinson’s disease (PD) and can lead to falling and injury. A system that predicts freezing before it occurs or detects freezing immediately after onset would generate an opportunity for FOG prevention or mitigation and thus enhance safe mobility and quality of life. This research used accelerometer, gyroscope, and plantar pressure sensors to extract 861 features from walking data collected from 11 people with FOG. Minimum-redundancy maximum-relevance and Relief-F feature selection were performed prior to training boosted ensembles of decision trees. The binary classification models identified Total-FOG or No FOG states, wherein the Total-FOG class included data windows from 2 s before the FOG onset until the end of the FOG episode. Three feature sets were compared: plantar pressure, inertial measurement unit (IMU), and both plantar pressure and IMU features. The plantar-pressure-only model had the greatest sensitivity and the IMU-only model had the greatest specificity. The best overall model used the combination of plantar pressure and IMU features, achieving 76.4% sensitivity and 86.2% specificity. Next, the Total-FOG class components were evaluated individually (i.e., Pre-FOG windows, Freeze windows, transition windows between Pre-FOG and Freeze). The best model detected windows that contained both Pre-FOG and FOG data with 85.2% sensitivity, which is equivalent to detecting FOG less than 1 s after the freeze began. Windows of FOG data were detected with 93.4% sensitivity. The IMU and plantar pressure feature-based model slightly outperformed models that used data from a single sensor type. The model achieved early detection by identifying the transition from Pre-FOG to FOG while maintaining excellent FOG detection performance (93.4% sensitivity). Therefore, if used as part of an intelligent, real-time FOG identification and cueing system, even if the Pre-FOG state were missed, the model would perform well as a freeze detection and cueing system that could improve the mobility and independence of people with PD during their daily activities.

Design and Development of a High-Speed UAS for beyond Visual Line-of-Sight Operations

This paper provides details on best practices for vehicle design for BVLOS missions, with a focus on robust control links and real-time health monitoring.The development work presented herein focuses on a specific mission to set the speed and distance world record for a fully autonomous vehicle, requiring development of analytic tools for estimating vehicle range and endurance, as well as models for predicting command and control link integrity while operating BVLOS. Range and endurance estimates are developed using a novel real-time system identification algorithm onboard the vehicle for aerodynamic parameter estimation, and propulsion system characterization. Flight-testing methods and results are presented demonstrating the capabilities of these algorithms, with results from a unique test-case involving an unintended fault during the record flight. In addition to the vehicle performance assessment, Friis-based models are introduced to provide a pseudo real-time communications link range assessment. The predictive communications range models were refined using specific antenna and system characterization data measured in a compact range facility resulting in a detailed link budget useful for addressing BVLOS command and control requirements. The combination of aircraft and radio-frequency performance prediction models with supporting flight-testing techniques applied to small unmanned aerial systems provides critical insight into the design of future vehicles, and validation of existing systems tasked with performing BVLOS missions.

Implementing a real-time image captioning service for scene identification using embedded system

Falling over or curling up on the ground potentially causes danger for the elderly, staying at home alone, once we rescue them lately. To ensure the safety of the elderly at home, most people monitor the home situation with mobile devices remotely. However, they are incapable of monitoring the home frequently while working outside. To this end, we design a scene identification system translating the semantic visual information into a human-readable sentence. Users can understand the scene instantly with a summarized sentence. In addition, our system features low inference time and easy deployment. Specifically, we implement a real-time scene identification system, equipped with an image captioning model, under the edge computing scenario. An image captioning model translates the captured scene image into a human-readable sentence. In a nutshell, we summarize an indoor scene in sentence form.

Recursive system identification for coefficient estimation of continuous-time fractional order systems

Fractional-order calculus has already proven to be effective in order to model diffusive phenomena, such as heat transfer or anomalous mass transfer. This has led to developments of fractional-order transfer function models and, as a consequence, system identification algorithms have been developed to identify the parameters for this type of structures. However, it may be necessary to perform real-time system identification in which the parameters evolve over time while more and more data is acquired. Extensions to recursive identification algorithms are presented for fractional-order transfer functions. Two methods are proposed for recursive estimation of the coefficients of continuous-time fractional order systems: the recursive least squares with state variable filters and the prediction-error method. These two methods are compared with different signal to noise ratio levels through Monte Carlo simulations.

Real-time system identification of an unmanned quadcopter system using fully tuned radial basis function neural networks

Real-time system identification of an unmanned quadcopter system using fully tuned radial basis function neural networks

Real-time system identification of an unmanned quadcopter system using fully tuned radial basis function neural networks

Real-time system identification of an unmanned quadcopter system using fully tuned radial basis function neural networks

Video Aficionado: We Know What You Are Watching

Users enjoy the convenience of watching videos on smart devices. However, video watching records can be exposed without users’ knowledge and be exploited to infer private information. In this paper, we design and implement a new side-channel attack system, named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">video aficionado</i> , which can identify video watching information without violating any access control policies on Android. Our system only needs to collect power consumption data of a video playing app, which does not require explicit user permission. The collected data is sent to a remote server, where noise is cleaned and identified by a multi-layer perceptron (MLP) trained classifier. We evaluate our proposed system through a set of carefully designed experiments. Experimental results demonstrate that our system can make an identification with 74.5 percent accuracy on average for each 20-second power measurement segment out of 3918 segments collected from 20 videos. To the best of our knowledge, video aficionado is the first real-time power consumption-based video identification system on smart devices.