The article presents a power detection and prediction system (PDPS) using fusion algorithms to be applied in the on-line power monitoring of the target device. The system contains multiple power detection units, a dat...
详细信息
ISBN:
(纸本)9783037856512
The article presents a power detection and prediction system (PDPS) using fusion algorithms to be applied in the on-line power monitoring of the target device. The system contains multiple power detection units, a data integration unit, a target device, a power source and a main controller. Each power detection unit measures the assigned power source on real-time, and uses four current sensors to measure the current variety. We use fusion algorithms to be applied in current and voltage detection. We can calculate the real-time power values according to the estimated values of current and voltage measurement values. The main controller predicts the power loading for each power detection unit using auto-regression algorithm, and calculates the error value between the prediction value and the measurement value for each detection value, and compares the relation value on various condition.
Smart sensors are becoming an integral part of the evolving technology landscape;their ability to share reduced data over networks enables live data fusion, which significantly improves sensor performance and situatio...
详细信息
ISBN:
(纸本)9781479977727
Smart sensors are becoming an integral part of the evolving technology landscape;their ability to share reduced data over networks enables live data fusion, which significantly improves sensor performance and situational awareness. A lightweight, mobile acoustic sensor network has been used as an infrastructure to layer multi-sensor fusion algorithms, for detection of impulsive events such as gunfire or explosions. The system can create actionable information within seconds, and can be used to direct assets such as unmanned aerial vehicles (UAVs) to specific coordinates, for eyes-on assessment in under a minute. The sensor array will be discussed in terms of its three primary components: the smart sensors, the synchronization network, and the fusion algorithms. Performance of the array from recent tests will be examined with respect to small arms and simulated mortar fire, and producing actionable information. In addition, test results will be discussed in context of autonomous control of UAV assets and potential applications.
GeoEye-1 is the first commercial satellite that collects images at nadir with 0.41m panchromatic and 1.65m multispectral resolution (panchromatic imagery sold to commercial customers is resampled to 0.5m resolution). ...
详细信息
ISBN:
(纸本)9780819483485
GeoEye-1 is the first commercial satellite that collects images at nadir with 0.41m panchromatic and 1.65m multispectral resolution (panchromatic imagery sold to commercial customers is resampled to 0.5m resolution). In this study nine fusion techniques and more especially the Ehlers, Gram-Schmidt, High Pass Filter, Local Mean Matching (LMM), Local Mean and Variance Matching (LMVM), Modified IHS (ModIHS), Pansharp, PCA and Wavelet were used for the fusion of Geoeye panchromatic and multispectral data. The panchromatic data have a spatial resolution of 0.5m while the multispectral data have a spatial resolution of 2.0m. The optical result, the statistical parameters and different quality indexes such as ERGAS, Q and entropy were examined and the results are presented. The broader area of Agrinio city in Western Greece was selected for this comparison. It has a complex geomorphology. At the west the area is flat and the elevation ranges between 5 and 20 meters. At the east there are many hills and the elevation rises to more than 450 meters. The area combines at the same time the characteristics of an urban and a rural area thus it is suitable for a comparison of different fusion algorithms.
Combining the multiscale capability from wavelet with the performance of real-time and recursion about Kalman filter, a multiscale sequential filter is proposed to process dynamic systems with multisensor. This filter...
详细信息
ISBN:
(纸本)0780393953
Combining the multiscale capability from wavelet with the performance of real-time and recursion about Kalman filter, a multiscale sequential filter is proposed to process dynamic systems with multisensor. This filter can not only absolutely achieve the effect obtained via conventional multisensor fusion approach, but also it has the advantages as wavelet and Kalman filter. Its multiscale characteristic can be used to analyze stochastic signal in different frequency subspace. Some similar methods existed do not possess these capabilities, such as real time and recursion. Computer simulation also shows that all estimate results from the new algorithm is comparable with that from traditional date fusion algorithms. Finally, the computable advantage is likewise validated by comparing the computer burden between the new algorithm and other two existed fusion algorithms.
Alarm fatigue is a top medical device hazard in patient monitoring that could be reduced by merging physiological information from multiple sensors, minimizing the impact of a single sensor failing. We developed a hea...
详细信息
Alarm fatigue is a top medical device hazard in patient monitoring that could be reduced by merging physiological information from multiple sensors, minimizing the impact of a single sensor failing. We developed a heart beat detection algorithm that utilizes multi-modal physiological signals (e.g. electrocardiogram, blood pressure, stroke volume, photoplethysmogram and electro-encephalogram) by merging the heart beats obtained from signal-specific detectors. We used the PhysioNet/Computing in Cardiology Challenge 2014 training set to develop the algorithm, and we refined it with a mix of signals from the multiparameter intelligent monitoring in intensive care (MIMIC II) database and artificially disrupted waveforms. The algorithm had an average sensitivity of 95.67% and positive predictive value (PPV) of 92.28% when applied to the PhysioNet/Computing in Cardiology Challenge 2014 200 record training set. On a refined dataset obtained by removing 5 records with arrhythmias and inconsistent reference annotations we obtained an average sensitivity of 97.43% and PPV of 94.17%. Algorithm performance was assessed with the Physionet Challenge 2014 test set that consisted of 200 records (each up to 10 min length) containing multiple physiological signals and reference annotations verified by the PhysioNet/Computing in Cardiology Challenge 2014 organizers. Our algorithm had a sensitivity of 92.74% and PPV of 87.37% computed over all annotated beats, and a record average sensitivity of 91.08%, PPV of 86.96% and an overall score (average of all 4 measures) of 89.53%. Our algorithm is an example of a data fusion approach that can improve patient monitoring and reduce false alarms by reducing the effect of individual signal failures.
暂无评论