Sensor Data Validation Research Articles

Spatiotemporal Analysis of Complex Emission Dynamics in Port Areas Using High-Density Air Sensor Network.

Cargo terminals, as pivotal hubs of mechanical activities, maritime shipping, and land transportation, are significant sources of air pollutants, exhibiting considerable spatiotemporal heterogeneity due to the complex and irregular nature of emissions. This study employed a high-density air sensor network with 17 sites across four functional zones in two Shanghai cargo terminals to monitor NO and NO2 concentrations with high spatiotemporal resolution post sensor data validation against regulatory monitoring stations. Notably, NO and NO2 concentrations within the terminal surged during the night, peaking at 06:00 h, likely due to local regulations on heavy-duty diesel trucks. Spatial analysis revealed the highest NO concentrations in the core operational areas and adjacent roads, with significantly lower levels in the outer ring, indicating strong emission sources and limited dispersion. Employing the lowest percentile method for baseline extraction from high-resolution data, this study identified local emissions as the primary source of NO, constituting over 80% of total emissions. Elevated background concentrations of NO2 suggested a gradual oxidation of NO into NO2, with local emissions contributing to 32-70% of the total NO2 concentration. These findings provide valuable insights into the NO and NO2 emission characteristics across different terminal areas, aiding decision-makers in developing targeted emission control policies.

Touch–based potentiometric sensors for simultaneous detection of urea and ammonium from fingertip sweat

Urea is a biomarker for diagnosis and management of multiple disorders. The measurement of urea could help in the diagnosis of patients with kidney dysfunction. Despite their attractive analytical performances, the existing urea measurement methods are still limited to sophisticated and time–consuming or require invasive blood sampling. Herein, a reliable noninvasive, simple, and disposable touch–based potentiometric sensor is fabricated for rapid monitoring of fingertip sweat urea. Novel touch–based urea detection relies on immobilized urease recognition and a solid–contact ammonium–ion–selective electrode, along with a screen–printed carbon working electrode modified carboxylated carbon nanotubes for enhancing sensor’s sensitivity. The fingertip sweat under sedentary rest conditions can be instantaneously collected on customized porous polyvinyl alcohol hydrogel transporting sweat to working electrode surface where the hydrolysis of urea is catalyzed, generating bicarbonate and ammonium. The resulting ammonium can be detected with ion–selective transducer, enabling indirect monitoring of sweat urea. Potential interference from co–existing ammonium in human sweat is addressed by developing a dual disposable potentiometric sensor, consisting of urea and ammonium sensors on a single strip platform allowing for simultaneous touch–based detection of sweat urea and ammonium. The resulting dual disposable sensing system offers rapid urea and ammonium monitoring within a group of healthy human subjects, along with a good correlation with R2 = 0.974 between the sweat urea and capillary blood urea levels. Additional confirmation among a broad sample of individuals along with the validation of sensor data through centralized laboratory tests will establish the practicality of simultaneously monitoring urea and ammonium levels.

Variational Autoencoders for Biomedical Signal Morphology Clustering and Noise Detection.

Accurate estimation of physiological biomarkers using raw waveform data from non-invasive wearable devices requires extensive data preprocessing. An automatic noise detection method in time-series data would offer significant utility for various domains. As data labeling is onerous, having a minimally supervised abnormality detection method for input data, as well as an estimation of the severity of the signal corruptness, is essential. We propose a model-free, time-series biomedical waveform noise detection framework using a Variational Autoencoder coupled with Gaussian Mixture Models, which can detect a range of waveform abnormalities without annotation, providing a confidence metric for each segment. Our technique operates on biomedical signals that exhibit periodicity of heart activities. This framework can be applied to any machine learning or deep learning model as an initial signal validator component. Moreover, the confidence score generated by the proposed framework can be incorporated into different models' optimization to construct confidence-aware modeling. We conduct experiments using dynamic time warping (DTW) distance of segments to validated cardiac cycle morphology. The result confirms that our approach removes noisy cardiac cycles and the remaining signals, classified as clean, exhibit a 59.92% reduction in the standard deviation of DTW distances. Using a dataset of bio-impedance data of 97885 cardiac cycles, we further demonstrate a significant improvement in the downstream task of cuffless blood pressure estimation, with an average reduction of 2.67 mmHg root mean square error (RMSE) of Diastolic Blood pressure and 2.13 mmHg RMSE of systolic blood pressure, with increases of average Pearson correlation of 0.28 and 0.08, with a statistically significant improvement of signal-to-noise ratio respectively in the presence of different synthetic noise sources. This enables burden-free validation of wearable sensor data for downstream biomedical applications.

143 Validation Strategies of Sensor Data in the Context of Heat Stress Detection

Abstract Evaluating genetically heat stress (HS) of cattle is still a major challenge, also because of the need to have access to HS sensitive phenotypes relating their reaction to temperature and humidity as described by the temperature-humidity index (THI). A solution could be to add the information of data from sensors. However, activity (ACT) and rumination (RUM) sensors reflect no “production” [i.e., milk yields (MY), milk composition, but also intake or growth] related aspects which is suboptimal. Therefore, the objectives of this study were thus to assess the possible usefulness for adding sensors data for HS genetic evaluation based on a dairy situation. But the context described can be easily enlarged and include beef cattle and pigs. Strategy was illustrated using daily SenseHub collar records that were obtained from October 2019 to July 2022 in six dairy herds in Wallonia in Southern Belgium. A total of 453 Walloon Holstein cows were followed during this period for activity time. Meteorological data and a HS sensitive “production” trait (here MY) were also obtained from 2015 to 2022 for 1,740 cows from the same herds. In total 32,154 MY, 130,867 ACT and 130,848 RUM records were available. The thresholds at which the different traits start to be affected by HS were estimated at a THI of 63, 64 and 66, respectively, for MY, ACT and RUM. A three-trait random regression reaction norm model was fitted with these different thresholds. Estimated heritability values were 0.19 ± 0.03, 0.14 ± 0.06 and 0.21 ± 0.05 respectively for MY, ACT and RUM using AIREML. Ratios of variance associated to THI and constant genetic effect were 0.002, 0.02 and 0.04 indicating a stronger reaction by RUM but also ACT than MY to THI. However, it is uncertain if the reactions of these sensor traits are reflected in the production traits. Estimated genetic correlations of regressions on THI for ACT and RUM showed values of 0.51 ± 0.70 and 0.08 ± 0.54 with MY and 0.77 ± 0.69 between ACT and RUM. The first eigenvector of the correlation matrix explained 65% of the total variability and standardized coefficients of this eigenvector were 0.42, 0.69 and 0.59 for MY, ACT and RUM. A total of 50% of the variance associated with the regression on THI for MY could be explained by the regressions on THI for ACT and RUM. Adding the constant genetic effects for ACT and RUM only marginally increased to 51% the ability to explain the effect of THI on MY. Based on these results, reactions of different indicator traits, here sensor data, may reflect a different reaction than production and therefore economically important traits. Additional research, also beyond dairy cattle, should continue to address this topic that is very important for the validation of sensor data detecting HS.

Data Communication Stability Test a Data Acquisition System (DAQ) for Inpatient Rooms

In inpatient services, patient beds are usually limited by curtains or partitions to maintain the patient's safety. At the time of data collection, the barrier or barrier is considered to the effectiveness of the results and validation of medical device sensor data. This study aims to explain the results of the reliability testing of Bluetooth and the internet on hardware and CovWatch applications. This study is action research; comparison is used using devices (tools) with different specifications. Testing internet and Bluetooth connectivity from the CovWatch unit and applications installed on the Samsung A01 devices, Redmi Note 10 and Oppo A57, have mixed results. Hardware CovWatch and Samsung A01 are considered the best in acquiring vital sign data, while Redmi Note 10 and OPPO A57 are not good because some data cannot be obtained so that it does not appear on the monitor unit.

AI-based validation of wastewater treatment plant sensor data using an open data exchange architecture

Typically, within the context of treatment plant-wide data, the quality of data can be impacted by sensor faults, sensor calibration issues, fouling of and obstruction to the sensors and connectivity problems between sensors, actuators and the data management system, therefore hampering advanced data driven monitoring and control of (critical) water operations. Here, a smart data validation scheme is proposed that validates sensor data from a wastewater treatment plant and is tightly integrated with the open-source data exchange system called FIWARE, an EU supported framework. The data validation application and FIWARE setup are integrated, tested and deployed at the water utility, Waternet. The validation scheme is based on an anomaly detector using (statistical) threshold techniques and a data reconciliation part that aggregates deep learning based autoencoder model predictions whenever an anomaly is detected. The autoencoder models proved to have a high accuracy and good reconciliation performance considering the variability of the signal. Furthermore, (near) real-time validated and raw data signals are relayed towards a dashboard. Finally, the validated data can be used as a screening for data ingested by another AI-based model that enables monitoring and smart control of the wastewater treatment plant in order to minimise greenhouse gas emissions and energy consumption while meeting effluent water quality standards.

Algorithms for Automatic Data Validation and Performance Assessment of MOX Gas Sensor Data Using Time Series Analysis

The following work presents algorithms for semi-automatic validation, feature extraction and ranking of time series measurements acquired from MOX gas sensors. Semi-automatic measurement validation is accomplished by extending established curve similarity algorithms with a slope-based signature calculation. Furthermore, a feature-based ranking metric is introduced. It allows for individual prioritization of each feature and can be used to find the best performing sensors regarding multiple research questions. Finally, the functionality of the algorithms, as well as the developed software suite, are demonstrated with an exemplary scenario, illustrating how to find the most power-efficient MOX gas sensor in a data set collected during an extensive screening consisting of 16,320 measurements, all taken with different sensors at various temperatures and analytes.

Application of autonomous sensor technology to estimate selenium exposure and a site-specific selenium threshold in a Canadian boreal lake.

There is an increasing trend in the use of real-time sensor technology to remotely monitor aquatic ecosystems. Commercially available probes, however, are currently not able to measure aqueous selenium (Se) concentrations. Because of the well-described bioaccumulation potential and associated toxicity of Se in oviparous vertebrates, it is crucial to monitor Se concentrations at sites receiving continuous effluent Se input. This study aimed to estimate Se concentrations in a boreal lake (McClean Lake) downstream from a Saskatchewan uranium mill using real-time electrical conductivity (EC) data measured by autonomous sensors. Additionally, this study aimed to derive a site-specific total aqueous Se (TSe) threshold based on Se concentrations in periphyton and benthic macroinvertebrates sampled from the same lake. To characterize effluent distribution within the lake, eight Smart Water (Libelium) sensor units were programmed to report EC and temperature for five and seven consecutive weeks in 2018 and 2019, respectively. In parallel, periphyton and benthic macroinvertebrates were sampled with Hester-Dendy's artificial substrate samplers (n = 4) at the same sites and subsequently analyzed for Se concentrations. Electrical conductivity was measured with a handheld field meter for sensor data validation and adjusted to the median lake water temperature (13 °C) registered for the deployment periods. Results demonstrated good accuracy of sensor readings relative to handheld field meter readings and the successful use of real-time EC in estimating TSe exposure (r = 0.87; r2 = 0.84). Linear regression equations derived for Se in detritivores versus Se in periphyton and Se in periphyton versus sensor-estimated TSe were used to estimate a site-specific TSe threshold of 0.7 µg/L (±0.2). Moreover, mean Se concentrations in periphyton (16.7 ± 4.4 µg/g dry weight [d.w.]) and benthic detritivores (6.0 ± 0.4 µg/g d.w.) from one of the exposure sites helped identify an area with potential for high Se bioaccumulation and toxicity in aquatic organisms in McClean Lake. Integr Environ Assess Manag 2023;19:395-411. © 2022 SETAC.

Validation of foot plantar pressure sensor data used to estimate standing, sitting, and moving durations in one working day

BackgroundMaintaining immovable postures for long durations might be a cause of low back pain. However, the relation between low back pain and the maintenance of postures for long durations has been unclear. Therefore, the durations of several postures in one working day should be measured to evaluate the risk of low back pain, although the available measuring methods are limited. To the best of our knowledge, no study has reported the development and investigation of a foot plantar pressure sensor for measurement of standing, sitting, and moving durations in daily work routines. Thus, in this study, we aimed to develop a foot plantar pressure sensor that could withstand long-term loads in the workplace. Furthermore, we aimed to evaluate the estimated results of standing, sitting, and moving durations among factory workers using the developed foot plantar pressure sensor. MethodsThe developed foot plantar pressure sensor obtained a percentage difference within ±5% to estimate standing, sitting, and moving durations in the laboratory. We measured foot plantar pressures of 20 factory workers to estimate standing, sitting, and moving activity in one working day using data obtained by the foot plantar pressure sensor. The estimated standing, sitting, and moving durations were compared with the human estimation of photo data obtained by a wearable camera. ResultsThe agreement rate (Cohen's kappa coefficient) was 0.75 between the evaluation using the foot plantar pressure sensor data and human estimation using a wearable camera. Cohen's kappa coefficient was 0.81 in subjects who sat for ≥30% during daily work and 0.68 in subjects who sat for <30%. ConclusionOur foot plantar pressure sensor effectively measured the standing, sitting, and moving durations in daily work that requires various movements and assumption of postures.

Data Authentication for Web of Things (WoT) by Using Modified Secure Hash Algorithm-3 (SHA-3) and Salsa20 Algorithm

The development of the Web as a highly integrated user interface to real link things has brought many challenges and problems to study, which has led to a rapidly increasing area of research called the Web of things (WoT). Present Web of things (WoT) research is a trigger for the Internet of Things (IoT) growth, opening up opportunities to create ambient areas where people and things interact seamlessly via the Web. This proliferation created concerns among users about the increased usage of Web of Things (WoT) without ensuring that the data generated by its devices are maintained. Many ways to maintain authentication by using lightweight speed algorithms to encrypt and validate the relevant parameters. In the authentication of information, many algorithms have been developed to ensure that the data generated from a physical sensor to the user environment is authentic. These include SHA-1, SHA-2, SHA-3, etc. In Web of Things (WoT), it must be essential to ensure data validity and the continuation of data monitoring to ensure encryption and authentication speed. Therefore, the provision of fast algorithms is an essential requirement for the Web of Things (WoT). This paper includes a modification of the Secure Hash Algorithm 3 (SHA-3) with another high-speed algorithm (Salsa20), which creates a high-speed and secure algorithm in the sensor data validation process. The expanded logistic method would also produce the Secure Hash Algorithm 3 (SHA-3) algorithm's initial values unknown and not identifiable by the intruder. Correspondingly, the National Institute of Standards and Technology (NIST) fifteen statistical tests successfully surpassed the randomness of a proposed method

A Novel Method for Sensor Data Validation based on the analysis of Wavelet Transform Scalograms

Sensor data validation has become an important issue in the operation and control of energy production plants. An undetected sensor malfunction may convey inaccurate or misleading information about the actual plant state, possibility leading to unnecessary downtimes and, consequently, large financial losses. The objective of this work is the development of a novel sensor data validation method to promptly detect sensor malfunctions. The proposed method is based on the analysis of data regularity properties, through the joint use of Continuous Wavelet Transform and image analysis techniques. Differently from the typical sensor data validation techniques which detect a sensor malfunction by observing variations in the relationships among measurements provided by different sensors, the proposed method validates the data collected by a given sensor only using historical data collected from the sensor itself. The proposed method is shown able to correctly detect different types and intensities of sensor malfunctions from energy production plants.

Sensor data validation and fault diagnosis using Auto-Associative Neural Network for HVAC systems

The Heating, Ventilation, and Air conditioning (HVAC) system is a major system in buildings for conditioning the indoor environment. Sensor data validation and fault diagnosis for HVAC systems are essentially important to secure a reliable and efficient operation since sensor measurements are vital for the HVAC closed-loop control system. The aim of this work is to address this matter by developing a data-driven approach using the system's normal operation data and without the need for the knowledge of the mathematical model of the system. It is based on an Auto-Associative Neural Network (AANN) that is structured and trained to construct an input-output mapping model based on data dimensionality reduction that is capable of validating sensor measurements in terms of sensor error correction, missing data replacement, noise filtering, and inaccuracy correction. It can be used for both single and multiple sensor faults diagnosis by monitoring the consistency between the actual and the AANN-estimated sensor reading. The validation of the proposed method is demonstrated on data obtained from a 3-zone HVAC system simulated in TRNSYS. The evaluation results show the effectiveness of the proposed approach and an improvement in terms of data validation and diagnostic accuracy when compared with a PCA-based method.

Automated near real-time validation and exploitation of optical sensor data for improved orbital safety

The orbital safety of operational spacecraft, regardless of the mission, relies on timely and actionable observations to maintain so-called “custody” of all trackable Resident Space Objects (RSOs), including space debris, that might pose a hazard to safe, secure, and sustainable operations. For operations in and around the Geosynchronous Earth Orbit (GEO) regime, electro-optical (EO) observations are the most prevalent observation type available for tracking and determining RSO orbits. The quality (both accuracy and precision) of the data affects the inferable kinematic, physical, and other characteristics of RSOs and, in particular, measurement biases will result in inaccurate orbital trajectories and subsequent predictions. Physically meaningful conjunction assessments rely on not only accurate orbit state prediction, but also the “realistic” covariances associated with said predictions. In this paper we demonstrate an automated near real-time (NRT) assessment of measurement biases with an appropriately implemented Unscented Schmidt Kalman Filter (USKF) [14]. Hypothesized biases that are deterministic but statistically unobservable in the measurement data and cannot be estimated are accounted for as so-called “consider” parameters. The method presented herein is assessed and quantified using both simulated and actual measurement data. This method will enable the exploitation and mining of so-called “non-traditional” sensor data to maximize Space Situational Awareness (SSA) in a robust and timely fashion toward improvement of orbital safety. The ultimate goal is to provide decision-making evidence required solve problems preventing the space domain from being safe, secure, and sustainable.

Spectral Performance and Calibration of the Suomi NPP OMPS Nadir Profiler Sensor

AbstractThe Ozone Mapping and Profiler Suite (OMPS) is one of five instruments on board the Suomi National Polar‐orbiting Partnership (Suomi NPP) satellite. As part of OMPS, the Nadir Profiler (NP) spectrometer measures Earth's albedo in ultraviolet wavelengths from 250 to 310 nm to profile Earth atmospheric ozone concentration. Since launch in 2011, many changes in the NP radiometric and spectrometric calibration have been made to improve sensor data quality. The most challenging issue is to characterize instrument on‐orbit spectral calibration and to maintain a stable spectral performance to meet sensor design criteria. Validation of the NP sensor data found an average of 3–5% error in the sensor albedo measurements that exceeded 2% requirement, creating 1–2% uncertainty in ozone profile. Data analysis attributes the error to prelaunch calibration uncertainty, ground‐to‐orbit dichroic changes, and on‐orbit detector wavelength registration drifts. A recent update of calibration significantly improved spectral wavelength knowledge as well as the accuracy of the measured solar spectra. As a result, the wavelength‐dependent albedo error is significantly reduced and generally meets the radiometric uncertainty requirement of 2%.

Sensor placement determination in system health monitoring process based on dual information risk and uncertainty criteria

Validity of sensor data is a challenge in system monitoring due to stochastic nature of failure occurrence. The quantity and location of sensors affect the system health information, while sensor malfunction causes misleading results about the system condition. Occurred economic losses are irrecoverable expenses in respect to the monitoring system as well as the system failure. In this study, a dual index approach is proposed for the determination of sensor placement scenarios based on two criteria: (1) uncertainty of sensor information and (2) risk of sensor failure. With about variation of environmental factors conditions (e.g. temperature) and their failure threshold characterization, system failure model is developed and analyzed by a proposed efficient Monte Carlo simulation. Statistical variance of sensor information about estimating of system state as the quantitative uncertainty measure of choice in this research is estimated according to the information value that each possible sensor placement scenario provides through sensor information. In the next phase, risk index is determined based on sensor malfunction and corresponding quantifiable losses through both failure costs and maintenance expenditure. All feasible combinations of sensor failures are considered in the risk model. Finally, a combinatorial criterion is determined through information entropy calculation, which considers both indexes proposed above simultaneously. Sensor placement scenarios are comparatively prioritized based on this criterion. Accordingly, the technical directions are provided for suitability of the criteria for prioritizing sensor arrangement in various systems with different reliability-based characteristics. As a case study, determination of sensor placement is demonstrated on a typical steam turbine. According to the low variation of both information uncertainty and risk indexes, it is concluded that the combinatorial index is the proper criterion for sensor placement determination in health monitoring process of the steam turbine.

Data Validation and Reconstruction for Performance Enhancement and Maintenance of Water Networks

Abstract: In a real water network, a telecontrol system must periodically acquire, store and validate data gathered by sensor measurements in order to achieve accurate monitoring of the whole network in real time. For each sensor measurement, data are usually represented by one-dimensional time series. These values, known as raw data, need to be validated before further use to assure the reliability of the results obtained when using them. In real operation, problems affecting the communication system, lack of reliability of sensors, or other inherent errors often arise, generating missing or false data during certain periods of time. These wrong data must be detected and replaced by estimated data. Thus, it is important to provide the data system with procedures that can detect such problems and assist the user in monitoring and processing the incoming data. Data validation is an essential step to improve data reliability. The validated data represent measurements of the variables in the required form where unnecessary information from raw data has been removed. In this paper, a methodology for data validation and reconstruction of sensor data in a water network is used to analyze the performance of the sectors of a water network. Finally, from this analysis several indicators of the components (sensors, actuators and pipes) and of the sectors themselves can be derived in order to organize useful plans for performance enhancement and maintenance. Nice practices have been developed during a large period in the water network of the company ATLL Concessionaria de la Generalitat de Catalunya, S.A.

Multiple-Model Sensor and Components Fault Diagnosis in Gas Turbine Engines Using Autoassociative Neural Networks

In this paper the problem of fault diagnosis in an aircraft jet engine is investigated by using an intelligent-based methodology. The proposed fault detection and isolation (FDI) scheme is based on the multiple model approach and utilizes autoassociative neural networks (AANNs). This methodology consists of a bank of AANNs and provides a novel integrated solution to the problem of both sensor and component fault detection and isolation even though possibly both engine and sensor faults may occur concurrently. Moreover, the proposed algorithm can be used for sensor data validation and correction as the first step for health monitoring of jet engines. We have also presented a comparison between our proposed approach and another commonly used neural network scheme known as dynamic neural networks to demonstrate the advantages and capabilities of our approach. Various simulations are carried out to demonstrate the performance capabilities of our proposed fault detection and isolation scheme.

Suomi NPP CrIS measurements, sensor data record algorithm, calibration and validation activities, and record data quality

AbstractThe Cross‐Track Infrared Sounder (CrIS) is a Fourier Transform Michelson interferometer instrument launched on board the Suomi National Polar‐Orbiting Partnership (Suomi NPP) satellite on 28 October 2011. CrIS provides measurements of Earth view interferograms in three infrared spectral bands at 30 cross‐track positions, each with a 3 × 3 array of field of views. The CrIS ground processing software transforms the measured interferograms into calibrated and geolocated spectra in the form of Sensor Data Records (SDRs) that cover spectral bands from 650 to 1095 cm−1, 1210 to 1750 cm−1, and 2155 to 2550 cm−1 with spectral resolutions of 0.625 cm−1, 1.25 cm−1, and 2.5 cm−1, respectively. During the time since launch a team of subject matter experts from government, academia, and industry has been engaged in postlaunch CrIS calibration and validation activities. The CrIS SDR product is defined by three validation stages: Beta, Provisional, and Validated. The product reached Beta and Provisional validation stages on 19 April 2012 and 31 January 2013, respectively. For Beta and Provisional SDR data, the estimated absolute spectral calibration uncertainty is less than 3 ppm in the long‐wave and midwave bands, and the estimated 3 sigma radiometric uncertainty for all Earth scenes is less than 0.3 K in the long‐wave band and less than 0.2 K in the midwave and short‐wave bands. The geolocation uncertainty for near nadir pixels is less than 0.4 km in the cross‐track and in‐track directions.

On Co-Scheduling of Update and Control Transactions in Real-Time Sensing and Control Systems: Algorithms, Analysis, and Performance

Maintaining sensor data validity while exercising timely control is crucial in real-time sensing and control systems. The goal of scheduling algorithms deployed in such systems is to maintain the validity of real-time sensor data so as to maximize the schedulability of update transactions with minimum update workload so that control actions occur on time. In this paper, we first propose a dynamic scheduling algorithm, called Deferrable Scheduling with Least Actual Laxity First (DS-LALF). DS-LALF is designed by extending the deferrable scheduling algorithm, DS-FP which is designed for fixed priority systems. We develop a schedulability test algorithm for DS-LALF based on pattern analysis and a pattern search algorithm to find the shortest and earliest pattern in the schedule. Then, based on DS-LALF, a co-scheduling algorithm called Co-LALF-to schedule update transactions and control transactions in a real-time sensing and control system together-is developed 1) to meet the deadlines of all the control transactions and 2) to maximize the quality of data (QoD) utilized by the control transactions. Co-LALFschedules the jobs in the ascending order of their actual laxities and defers the release times of update jobs as long as the corresponding sensor data are maintained within the required quality. Experimental results show that DS-LALF incurs lower update workload compared with DS-FP and ML, and its schedulability is close to DS-FP but is much better than ML and DS-EDF. The experimental results also show that Co-LALF is effective in improving the overall performance by ensuring better QoD for the real-time data while meeting the deadline constraints of all the control transactions.

Fault Diagnosis Technology Research on Radar Equipment Based on Sensor Data Validation and Detection Fusion

Aiming at many fault characteristic parameters of modern radar and the problem that general diagnosis method is difficult to locate fault accurately. The dynamic mathematical model of radar equipment fault diagnosis system is built. The uncertainty theoretic analysis of fault diagnosis result is studied from two aspects, data processing channel and fault model. This paper combines sensor data validation with detection fusion technology, and then puts forward the fault diagnosis technology based on sensor data validation and detection fusion. The structure of the diagnostic system model is given. Case analysis proves that the proposed model and method has a significant role in improving detection performance, reducing the Bayes risk and improve fault diagnosis rate.