Operational Sensor Data Research Articles

Towards Semantic Sensor Data: An Ontology Approach.

In order to optimize intelligent applications driven by various sensors, it is vital to properly interpret and reuse sensor data from different domains. The construction of semantic maps which illustrate the relationship between heterogeneous domain ontologies plays an important role in knowledge reuse. However, most mapping methods in the literature use the literal meaning of each concept and instance in the ontology to obtain semantic similarity. This is especially the case for domain ontologies which are built for applications with sensor data. At the instance level, there is seldom work to utilize data of the sensor instances when constructing the ontologies’ mapping relationship. To alleviate this problem, in this paper, we propose a novel mechanism to achieve the association between sensor data and domain ontology. In our approach, we first classify the sensor data by making them as SSN (Semantic Sensor Network) ontology instances, and map the corresponding instances to the concepts in the domain ontology. Secondly, a multi-strategy similarity calculation method is used to evaluate the similarity of the concept pairs between the domain ontologies at multiple levels. Finally, the set of concept pairs with a high similarity is selected by the analytic hierarchy process to construct the mapping relationship between the domain ontologies, and then the correlation between sensor data and domain ontologies are constructed. Using the method presented in this paper, we perform sensor data correlation experiments with a simulator for a real world scenario. By comparison to other methods, the experimental results confirm the effectiveness of the proposed approach.

Suomi-NPP OMPS Nadir Mapper’s Operational SDR Performance

The ozone mapping and profiler suite (OMPS) is carried onboard the Suomi National Polar-orbiting Partnership satellite that was launched on October 28, 2011. The OMPS mission objectives concern atmospheric ozone concentrations and their variations in the earth atmosphere. A successful thorough on-orbit sensor calibration enabled current validated operational sensor data record (SDR) stage, providing quality sensor data that meet the requirements and users' expectations. The calibration coefficients derived from this paper have been successfully used in a life-cycle SDR reprocessing to improve sensor data quality. In this paper, we present our qualitative analyses and the results of OMPS nadir mapper SDR on-orbit calibration, and address current SDR data quality in relation to instrument detector performance, stray light correction, and wavelength registration. Our OMPS SDR calibration experience sets a reference for the successor instruments for accurate long-term monitoring of ozone total column concentrations.

Sensor Data Analysis On Monitoring And Control System Of Temperature And Humidity Based On Android In Soybean Seed Storage Room Using Nodemcu

soybean seeds may be damaged during storage time. Temperature and humidity of soybean seed storage room, is one of the external factors of damage to the seed. The system consists of monitoring and control temperature and humidity in some rooms used as soybean seed storage room samples. This study discusses and perform sensor data analysis using several types of temperature and humidity sensors based on Internet of Things. Sensor nodes generate data and processed by a microcontroller NodeMCU ESP8266, and the results data is then transmitted by the Internet network using MQTT broker and stored in the database. Results data is then analyzed to monitor the condition of soybean seed storage room. SHT30 sensor has the most excellent temperature accuracy of 98, 21%. DHT22 sensor has the most excellent moisture accuracy of 95.74%. Data sending to the database has a good level of dataloss category for node 1 is 3.39%, 4.33% for node 2, and 3.22% for node 3. Air conditioner control system using Android can keep the room temperature state in the range of 18-23oC and humidity of 40-60% with an air conditioning remote control setting at 20oC on an area of 36 m2.

Characterization of Long-Term Stability of Suomi NPP Cross-Track Infrared Sounder Spectral Calibration

Since early 2012, the cross-track infrared sounder (CrIS) onboard the Suomi National Polar-Orbiting Partnership satellite has continually provided the hyperspectral infrared observations for profiling atmospheric temperature, moisture, and greenhouse gases. The CrIS radiance data are also directly assimilated into global numerical weather prediction models to improve the medium-range forecasts. These important applications require accurate CrIS calibration. Since CrIS radiometric accuracy depends on the accurate spectral calibration, it is important to reduce the spectral uncertainty and increase the calibration stability for weather and climate applications. In this paper, the accuracy of CrIS spectral calibration and its stability are assessed using the operational sensor data record (SDR) data generated by the interface data processing segment (IDPS). A spectral validation method is developed and applied to clear scene data over ocean from September 22, 2012, to April 19, 2016. It is shown that CrIS metrology laser wavelength varies within 3 ppm, as measured by the Neon calibration subsystem. While the current CrIS operational algorithm is designed to have a spectra error less than 2 ppm, the actual spectral errors are about 4 ppm due to the IDPS software bugs. A new correction method is applied to fix the bugs and to further improve CrIS spectral calibration. It is found that the CrIS spectral calibration accuracy is less than 1 ppm in both normal and full spectral resolution SDR data sets.

Independent Benthic Microbial Fuel Cells Powering Sensors and Acoustic Communications with the MARS Underwater Observatory

AbstractMost oceanographic instruments on the seafloor have no connections with the surface and therefore have to run on batteries and store data until recovery. To demonstrate a developing technology, sensors and acoustic modems were powered with energy harvested from the seafloor, and data were relayed acoustically in near–real time to the Monterey Accelerated Research System (MARS) observatory in Monterey Bay, California, and to surface research vessels. MARS is a cabled observatory in deep water (~890 m) at the edge of Monterey Canyon. An acoustic modem was attached to the MARS node and configured to send out commands to, and relay data received from, remote modems. Two benthic microbial fuel cells (BMFCs) positioned approximately 0.5 km away from MARS supplied power to the remote modems and sensors. At their peak performance, these BMFCs produced continuous power densities of ~35 mW m−2 (footprint area). The modems utilized in this study contained an integrated power management platform (PMP) designed to manage and store the electrical energy generated by each BMFC and to record BMFC performance parameters and sensor data on an hourly basis. Temperature and either oxygen or conductivity sensors were chosen because of their common use and environmental relevance. Acoustically transmitted data records show that the BMFCs renewed energy stores and that the oceanographic sensors measured dissolved oxygen, temperature, and conductivity reliably throughout the operational life of each BMFC system (~6 months). These systems remained in place for more than 12 months.

News & information: Impressions of the 9th IEEE AESS workshop "sensor data fusion - trends, solutions, applications" (SDF 2014), October 8-9, 2014, Bonn, Germany

Before any thoughts of technical realization or scientific reflections on it, all living creatures perform Sensor Data Fusion: They combine sensations from mutually complementary sense organs with their past experiences and the communications they receive from other creatures. By doing so, they generate “situation pictures” of their particular environment, the very basis for behaving appropriately to reach certain goals or to avoid harm.

Recommendation of Counteractions for Prevention of Critical Events in Sub-Surface Drilling Environments

Sub-Surface Drilling is the process of making boreholes into the Earth, which can reach depths of many kilometers. One of the major purposes of such boreholes is the exploration of oil or gas bearing formations with the goal to recover the content of such reservoirs. Problems in drilling operations pose serious risks for the crew and the environment and can cause significant financial losses. Critical events usually do not arise abruptly, but develop over time before they escalate. In this work, the authors present a system that integrates sensor data and machine learning algorithms into a decision support system (DSS), thus helping to avoid critical events by monitoring and recommending preventive measures. The authors describe how the DSS is implemented as a distributed system and how data-driven decision support processes are implemented and integrated into the system. The DSS detects drilling operations by recognizing temporal patterns in the sensor data and uses a combination of detected operational rig-states and sensor data to predict and recommend preventive measures for the stuck pipe problem. The sensor data, detection results and predictions are distributed to all stakeholders and displayed in appropriate user interfaces.

Empirical Mode Decomposition Technique With Conditional Mutual Information for Denoising Operational Sensor Data

This paper presents a new approach for denoising sensor signals using the Empirical Mode Decomposition (EMD) technique and the Information-theoretic method. The EMD technique is applied to decompose a noisy sensor signal into the so-called intrinsic mode functions (IMFs). These functions are of the same length and in the same time domain as the original signal. Therefore, the EMD technique preserves varying frequency in time. Assuming the given signal is corrupted by high-frequency (HF) Gaussian noise implies that most of the noise should be captured by the first few modes. Therefore, our proposition is to separate the modes into HF and low-frequency (LF) groups. We applied an information-theoretic method, namely, mutual information to determine the cutoff for separating the modes. A denoising procedure is applied only to the HF group using a shrinkage approach. Upon denoising, this group is combined with the original LF group to obtain the overall denoised signal. We illustrate our approach with simulated and real-world cargo radiation data sets. The results are compared to two popular denoising techniques in the literature, namely discrete Fourier transform (DFT) and discrete wavelet transform (DWT). We found that our approach performs better than DWT and DFT in most cases, and comparatively to DWT in some cases in terms of: 1) mean square error; 2) recomputed signal-to-noise ratio; and 3) visual quality of the denoised signals.

Sensor-Ball system based on IEEE 1451 for monitoring the condition of power transmission lines

The energy industry is developing it safety methods so that it can prevent accidents, diagnose the conditions of power utilities and identify any problems that might arise. In other words, the industry is trying to increase the reliability of power utilities and improve the quality of power utilities by conducting monitoring and diagnosis tasks in real-time by building a sensor network in order to monitor the status of power utilities. This study has applied IEEE 1451, an interface standard of sensor network, for the diagnosis, information exchange and compatibility of the Sensor-Ball, which is a sensor unit that constructs a sensor network for the status monitoring of power transmission lines. In order to accomplish these objectives, we applied IEEE 1451.0 and IEEE 1451.5 to the Sensor-Ball, suggested a reference model, and represented the characteristic information of the Sensor-Ball by use of IEEE 1451 TEDS (Transducer Electronic Data Sheet). By constructing a Sensor-Ball system that has a connected sensor for measuring the atmospheric temperature and line slope, we have identified the operational status and sensor data of TEDS applied through a monitoring program.

Construcción de espectros de oleaje en el mar Caribe a partir de mediciones satelitales

Construction of wave spectra using significant wave height from Jason-1 satellite has been explored employing the JONSWAP preconceived spectra, to be used on data assimilation on a third generation wave model operated in the Oceanographic and Hydrographic Research Center-CIOH (Centro de Investigaciones Oceanográficas e Hidrográficas). Information from an operational data product, the Operational Sensor Data Record (OSDR) available within a 3-5 hour delay was employed, on which a correction to eliminate systematic bias and necessary quality control were applied to achieve a higher accuracy offered on the Interim Geophysical Data Record (IGDR), a more detailed data product available within a 2-3 day delay. Two directional wave buoys installed by Colombia's Maritime Authority were used to verify the wave spectra construction results, finding a great coincidente between the buoy and constructed spectra.

Self-Organizing Maps Application in a Remote Water Quality Monitoring System

This work was developed in the context of a system for remote water quality monitoring based on a wireless local area network (WLAN) and includes a Kohonen self-organizing map (K-SOM) implementation in order to perform sensor data validation and reconstruction and sensor failure and pollution event detections. Simulation and experimental results are presented.

Impact of Multisatellite Altimeter Data Assimilation on Wave Analysis and Forecast

Satellite altimetry has become an important discipline in the development of sea-state forecasting or more generally in operational oceanography. Météo-France Marine and Oceanography Division is much involved in altimetry, in which it is also one of the main operational customers. Sea-state forecasts are produced every day with the help of numerical models assimilating Fast Delivery Product altimeter data from ESA ERS-2 satellite, available in real-time (3–5 h). These forecasts are transmitted to seamen as part of safety mission of persons and properties, or specific assistance for particular operations. With the launch of ENVISAT (from ESA, launched on 1 March 2002, to take over the ERS mission) and JASON-1 (from CNES/NASA, launched on 7 December 2001, successor of TOPEX/Poseidon), we have an unprecedented opportunity of improved coverage with the availability in quasi-real-time of data from several altimeters. The objective of this study is to evaluate the impact of using multisources of altimeter data in real-time, to improve wave model analyses and forecasts, at global scale. Since July 2003, Météo-France injects the wind/wave JASON-1 Operational Sensor Data Record on the WMO Global Transmitting System, making them available in near real-time to the international meteorological community. Similarly, fast delivery altimeter data of ENVISAT will improve coverage and contribute to the constant progress of marine meteorology. For this purpose, significant wave height time series were generated using the Wave Model WAM and the assimilation of altimeter wave heights from two satellites ERS-2 and JASON-1. The results were then compared to Geosat Follow-On (GFO, U.S. Navy Satellite) and moored buoy wave data. It is shown that the impact of data assimilation, when two (ERS-2 and JASON-1) or three (ERS-2 with JASON-1 and GFO) sources of data are used instead of one (ERS-2), in term of significant wave height, is larger in wave model analyses but smaller in wave model forecasts. However, there is no improvement in terms of wave periods, both in the analysis and forecast periods.

Near-Real–Time GPS-Based Orbit Determination and Sea Surface Height Observations from the Jason-1 MissionSpecial Issue: Jason-1 Calibration/Validation

The Jason-1 Operational Sensor Data Record (OSDR) is intended as a wind and wave product that is aimed towards near-real–time (NRT) meteorological applications. However, the OSDR provides most of the information that is required to determine altimetric sea surface heights in NRT. The exceptions include a sufficiently accurate orbit altitude, and pressure fields to determine the dry troposphere path delay correction. An orbit altitude field is provided on the OSDR but has accuracies that range between 8–25 cm (RMS). However, tracking data from the on-board BlackJack GPS receiver are available with sufficiently short latency for use in the computation of NRT GPS-based orbit solutions. The orbit altitudes from these NRT orbit solutions have typical accuracies of < 3.0 cm (RMS) with a latency of 1–3 h, and < 2.5 cm (RMS) with a latency of 3–5 h. Meanwhile, forecast global pressure fields from the National Center for Environmental Prediction (NCEP) are available for the NRT computation of the dry troposphere correction. In combination, the Jason-1 OSDR, the NRT GPS-based orbit solutions, and the NCEP pressure fields can be used to compute sea surface height observations from the Jason-1 mission with typical latencies of 3–5 h, and have differences with those from the 2–3 day latency Interim Geophysical Data Records of < 5 cm (RMS). The NRT altimetric sea surface height observations are potentially of benefit to forecasting, tactical oceanography, and natural hazard monitoring.

Statistical Evaluation of the Jason-1 Operational Sensor Data RecordSpecial Issue: Jason-1 Calibration/Validation

The Jason-1 satellite altimeter mission represents a first step towards operational oceanography from satellite altimeter missions. An operational data product, the Operational Sensor Data Record (OSDR), provides measurements from the on-board altimeter and radiometer within 3–5 h of real time. This data product is a wind and wave product that is aimed towards near-real–time meteorological applications. A higher accuracy and more detailed data product, the Interim Geophysical Data Record (IGDR), that is better suited to detailed scientific studies of ocean topography, is available no sooner than 2–3 days from real time. The measurements reported on the OSDR primarily differ from those on the IGDR in that the OSDR reports measurements derived from on-board processing of the altimeter waveforms, while ground retracking of the waveforms is performed for the IGDR. The altimeter-derived measurements on the OSDR are validated through a statistical evaluation of the differences between data on the OSDR and IGDR. In doing so, the impact of ground retracking of the altimeter waveforms is also illustrated.

Distributed contract networks of sensor agents with adaptive reconfiguration: modelling, simulation, implementation and experiments

As both sensors and communication technologies are becoming more powerful at decreasing costs, sensor networks are constantly growing in complexity, i.e. in the information they generate and in the need for coordinating network resources. In this paper we comparatively explore the performance of multi-level hierarchical networks and flat networks, i.e. arrays of sensor agents with specific competences but without predefined communication or control structures. We develop a model of the performance of these types of networks for analysing the throughput of sensing tasks depending on a variety of network and sensor parameters. This analytical model serves as the basis for numerical simulation runs, which reveal the specific advantages of the different network structures. Motivated by these results, we develop an adaptive control scheme, which enables the network to organise itself at run-time so as to achieve the highest result precision in minimum time with minimum communication overhead, maximum parallelism and maximum fault tolerance. This control scheme is a computer-operational version of the contract network, originally proposed for problem solving in distributed artificial intelligence, with agents teaming up dynamically after a negotiation phase following a task specification. The metaphor of contracting also holds the potential to integrate agents that control actuators, e.g. cooperating robots, by assigning appropriate competences to them. Hence the design of sensorimotor systems, whose behaviour is adaptively controlled in a situation-dependent way by a team of different sensors, is a straightforward extension. Moreover, it is also easy to extend the network by adding agents that do not have a sensing component and can only perform sensor data processing tasks. We introduce a complete software framework for implementing these kinds of networks. It consists of a formal specification of the requirements that the sensory component of an agent has to meet and a comprehensive library of C++ objects encapsulating all of the negotiation protocol and communications routines. In the experimental section of the paper we show how several uncalibrated cameras are used to estimate the trajectory of a manipulator in order to guide the manipulator towards a target in real time. We also illustrate how agent-teams may easily reconfigure during task execution in the case of unexpected events. We then go on to show how complex vision tasks can be broken down into tasks that identical agents may work on in parallel. Finally we show how fusion for vision at a symbolic, i.e. object description level, may contribute to solve the hard problem of recognising occluded objects from different view points. We conclude the paper by summarising our results and sketching future research, in particular the automatic mutual reprogramming of agents using a specifically developed ontology.

An Evaluation of Engine Faults Diagnostics Using Artificial Neural Networks

Application of artificial neural network (ANN)-based method to perform engine condition monitoring and fault diagnosis is evaluated. Back-propagation, feedforward neural nets are employed for constructing engine diagnostic networks. Noise-contained training and testing data are generated using an influence coefficient matrix and the data scatters. The results indicate that under high-level noise conditions ANN fault diagnosis can only achieve a 50–60 percent success rate. For situations where sensor scatters are comparable to those of the normal engine operation, the success rates for both four-input and eight-input ANN diagnoses achieve high scores which satisfy the minimum 90 percent requirement. It is surprising to find that the success rate of the four-input diagnosis is almost as good as that of the eight-input. Although the ANN-based method possesses certain capability in resisting the influence of input noise, it is found that a preprocessor that can perform sensor data validation is of paramount importance. Autoassociative neural network (AANN) is introduced to reduce the noise level contained. It is shown that the noise can be greatly filtered to result in a higher success rate of diagnosis. This AANN data validation preprocessor can also serve as an instant trend detector which greatly improves the current smoothing methods in trend detection. It is concluded that ANN-based fault diagnostic method is of great potential for future use. However, further investigations using actual engine data have to be done to validate the present findings.

Input Force Reconstruction Using a Time Domain Technique

A time domain method is presented for estimating the discrete input forces acting on a structure based upon its measured response. The structure is essentially transformed into its own loads transducer. A set of inverse system Markov parameters, in which the roles of input and output are reversed, is estimated from forward system Markov parameters using a linear predictive scheme. Inputs and acceleration outputs are assumed to be collocated to maintain minimum phase. Subsequently, any set of measured operational sensor data of any time duration can be convolved with the inverse Markov parameters to produce estimates of the input forces. This problem is ill-posed, so a regularization technique is employed to stabilize computations. A stability analysis is performed to illustrate the effects of the regularization. Predicted pseudo-forces qualitatively approximate the actual input forces and, when applied back to the structure, produce accelerations which accurately match the measured operational sensor data.

EVALUATION OF THE ACCURACY OF VOLUMETRIC LEAK DETECTION METHODS FOR UNDERGROUND STORAGE TANKS CONTAINING GASOLINE1

ABSTRACT A United States Environmental Protection Agency (EPA) research program evaluated the current performance of commercially available volumetric test methods for the detection of small leaks in underground gasoline storage tanks. The evaluations were performed at the EPA Risk Reduction Engineering Laboratory's Underground Storage Tank Test Apparatus in Edison, New Jersey. The methodology used for evaluation made it possible to determine and resolve most of the technological and engineering issues associated with volumetric leak detection, as well as to define the current practice of commercially available test methods. The approach used (1) experimentally validated models of the important sources of ambient noise that affect volume changes in nonleaking and leaking tanks, (2) a large data base of product-temperature changes that result from the delivery of product to a tank at a different temperature than the product in the tank, and (3) a mathematical model of each test method to estimate the performance of that method. The test-method model includes the instrumentation noise, the configuration of the sensors, the test protocol, the data analysis algorithms, and the detection criterion. Twenty-five commercially available volumetric leak detection systems were evaluated. The leak rate measurable by these systems ranged from 0.26 to 6.78 L/h (0.07 to 1.79 gal/h), with a probability of detection of 0.95 and a probability of false alarm of 0.05. Five methods achieved a performance between 0.19 L/h (0.05 gal/h) and 0.57 L/h (0.15 gal/h). Only one method was able to detect leaks less than 0.57 L/h (0.15 gal/h) if the probability of detection was increased to 0.99 and the probability of false alarm was decreased to 0.01. The measurable leak rates ranged from 0.45 to 12.94 L/h (0.12 to 3.42 gal/h) with these more stringent detection and false alarm parameters. The performance of the methods evaluated was primarily limited by test protocol, operational sensor configuration, data analysis, and calibration, rather than by hardware. The experimental analysis and model calculations suggested that substantial performance improvements can be realized by making procedural changes. With modifications, it is estimated that more than 60 percent of the methods should be able to achieve a probability of detection of 0.99 and a probability of false alarm of 0.01 for leak rates between 0.19 L/h (0.05 gal/h) and 0.56 L/h (0.15 gal/h), and 100 percent should be able to achieve this performance for leak rates of approximately 0.76 L/h (0.20 gal/h).