Search

Workforce assignment (WA) is a crucial aspect of production planning and control. However, current global crises have led to workforce absence and skill shortages. To tackle these challenges, a short-term WA approach is necessary for manufacturing systems flexibility and workforce health. Conventional methods for WA face complexity issues due to complex manufacturing systems, requiring substantial computation time. A Quantum Annealing-based WA approach is proposed in this paper to allocate tasks and shifts to workers. The proposed method is validated in a use case, showing improved solution quality compared to traditional methods.

Service provision processes in technical product-service systems (PSS) should be scheduled before they are performed according to the real process conditions. Incorrectly scheduled service provision processes will result in value losses for both customers and providers of PSS. We developed a quantum annealing (QA)-based service provision process scheduling approach and implemented it in a case study in the commercial vehicle industry. For small-size problems, the results show that our QA-based method delivers comparable results to the conventional computational method. For large-size problems, our QA-based method outperforms the conventional method in terms of computational efficiency and optimization accuracy.

An efficient production planning and control is inevitable for the economic operation of a manufacturing system. As an essential part of production planning and control, process scheduling aims to assign processes to the available resources of a manufacturing system under consideration of its objectives. This forms an optimization problem also known as job shop scheduling which can be solved with computer aided techniques. Exact solution methods are only practical up to a certain number of functional units and processes, therefore, approximation methods are used in industry. However, as the problem size increases, the computational solving time increases significantly and the solution quality decreases in equal measure. In order to react to the current effects of global crises, such as disruptions in supply chains, approaches for fast and efficient rescheduling are needed. A dynamic shop job scheduling approach using Quantum Annealing bears the potential to close this research gap. Previous work has shown that Quantum Annealing is able to solve static job shop scheduling problems within seconds while finding good solutions. However, in a flexible environment such as a manufacturing system, the static approach is not suitable for process scheduling. Therefore, a dynamic Quantum Annealing based approach for job shop scheduling with consideration of machine breakdowns and new job arrivals is proposed. The approach monitors a manufacturing system and reacts to changes in the job pool or availabilities in functional units with rescheduling. The method is tested with several use cases involving small and large-scale problems and is compared with a simulated annealing approach. Thereby, the Quantum Annealing based computations show better results regarding solution quality and computing time. Moreover, the dynamic approach bears the potential for industrial application, especially as a supplement to a conventional advanced planning system.

A novel analytical model of the calibration error impact on the Earth radiance measurement from thermal emissive bands of sensors is developed. The goal is to assess the impact of calibration errors, to evaluate those errors and to perform correction. This model is applied in the correction of bias in Advanced Very High Resolution Radiometer (AVHRR) channels 4 and 5 observed from the intercomparison with Infrared Atmospheric Sounding Interferometer measurements. A two‐step regression is used to separate the effects of calibration radiance errors from calibration coefficient errors. In the first step, the calibration radiance error, primarily due to internal calibration target (ICT) imperfections and temperature measurement error, is evaluated using the bias from selected Earth scenes with brightness temperatures close to the ICT temperature. The effects from the ICT imperfections and temperature measurement errors are analyzed collectively. The resulting estimation of the calibration radiance error is 0.30% for channel 4 and 0.33% for channel 5. After correcting the Earth scene radiance for these effects, the errors in the offset and nonlinear coefficient of instrument response are evaluated through the second step of the regression. A weighting function is used to account for the nonuniformity in the data distribution over the Earth radiance range. After the evaluation of the errors, removal of their effects can be achieved either through corrections of the calibration coefficients or correction of the measured radiance. The results are useful for the improvement of the AVHRR IR channel calibration algorithm. This model and two‐step regression approach can also be applied to other similar broadband thermal infrared radiometric sensors. A novel model to assess radiometric calibration impact on Earth measurement of thermal emissive band with application to AVHRRDerived errors in AVHRR bands 4 and 5 calibration from the brightness temperature‐dependent bias observed during intercomparison with IASIProvided algorithm for calibration improvement and bias correction in AVHRR thermal emissive band L1B products

In this work, an integrated contactless multiple hand feature acquisition system is designed. The system can capture palmprint, palm vein, and palm dorsal vein images simultaneously. Moreover, the images are captured in a contactless manner, that is, users need not to touch any part of the device when capturing. Palmprint is imaged under visible illumination while palm vein and palm dorsal vein are imaged under near infrared (NIR) illumination. The capturing is controlled by computer and the whole process is less than 1 second, which is sufficient for online biometric systems. Based on this device, this paper also implements a contactless hand-based multimodal biometric system. Palmprint, palm vein, palm dorsal vein, finger vein, and hand geometry features are extracted from the captured images. After similarity measure, the matching scores are fused using weighted sum fusion rule. Experimental results show that although the verification accuracy of each uni-modality is not as high as that of state-of-the-art, the fusion result is superior to most of the existing hand-based biometric systems. This result indicates that the proposed device is competent in the application of contactless multimodal hand-based biometrics.

Vein recognition is becoming an effective method for personal recognition. Vein patterns lie under the skin surface of human body, and hence provide higher reliability than other biometric traits and hard to be damaged or faked. This paper proposes a novel vein feature representation method call orientation of local binary pattern (OLBP) which is an extension of local binary pattern (LBP). OLBP can represent the orientation information of the vein pixel which is an important characteristic of vein patterns. Moreover, the OLBP can also indicate on which side of the vein centerline the pixel locates. The OLBP feature maps are encoded by 4-bit binary values and an orientation distance is developed for efficient feature matching. Based on OLBP feature representation, we construct a hand vein recognition system employing multiple hand vein patterns include palm vein, dorsal vein, and three finger veins (index, middle, and ring finger). The experimental results on a large database demonstrate the effectiveness of the proposed approach.

The Global Space-based Inter-Calibration System (GSICS) geostationary (GEO) vs. low earth orbit (LEO) inter-calibration correction products have been routinely generated for years at NOAA to improve and harmonize the data quality of the operational GOES satellite for a better global weather monitoring, prediction and climate change studies. In this study, the collocated GOES-13 and GOES-15 Imager infrared (IR) data are used to validate the GSICS GEO-LEO Imager inter-calibration correction products. To compensate the impact of difference in the spectral response function (SRF) on the GSICS corrected GEO radiance, two radiative transfer models (RTM) with different atmospheric profiles are used to simulate the relations between the two GEO radiance values. The results of GEO-GEO inter-calibration shows that the mean Tb difference between GOES-13 and GOES-15 is less than 0.2K(Ch2), 0.65K(Ch3), 0.08K(Ch4) and 0.35K(Ch6). The two RTM models with different atmospheric profiles have significantly different impacts on the Tb difference at the two absorptive channels, Ch3 and Ch6, indicating that the impact of different optical path is not well addressed in this study. Future study should apply the double difference using the RTM as transfer to compensate for the SRF and viewing/optical path difference at each collocated pixel.

It is well known that the varying geometrical relationships between the Sun and the Earth throughout the year affect to some degree the performance of the instruments onboard Earth orbiting satellites. Following the commissioning of MetOp-A, EUMETSAT and NOAA have continued monitoring the long term trends in in-orbit performance of AVHRR, HIRS and AMSU-A. The data acquired since the launch of the satellite has allowed studying how the yearly seasonal variations, as well as aging, have affected the instrument performance. This paper presents the evolution of the performance of the AVHRR, HIRS and AMSU-A for more than four years since the launch of the MetOp-A satellite.

The Advanced Very High Resolution Radiometer (AVHRR) visible and near-infrared channels must be calibrated after launch to maintain the accuracy of data derived from these channels for quantitative utilizations. The postlaunch calibration of these channels can only be carried out vicariously. The National Oceanic and Atmospheric Administration (NOAA) – National Environmental Satellite, Data, an Information Service (NESDIS) has been using the Libyan Desert as reference for operational calibration of AVHRR visible and near-infrared channels since 1995. A previous algorithm was successful correcting for the long-term instrument degradation in recalibration but had difficulty updating instrument calibration in near-real-time operation. This paper describes the operational calibration algorithm implemented since 2003, which overcomes the existing shortcomings by reducing target contamination and accounting for the effects of target bidirectional reflectance distribution function. Application of the algorithm shortens the latency of postlaunch calibration from 3 to 4 years for NOAA-14 and NOAA-16 to less than 2 years for NOAA-17 and to a few months for later satellites. Compared with the previous algorithm, the current algorithm enhances the calibration precision from 1.7% to 0.9% for channel 1.

Abstract: This paper presents a novel approach for palmprint authentication based on the fusion of the phase and orientation information. In the proposed approach, the phase information (called FusionCode or PhaseCode) of a palmprint is extracted by using four 2D Gabor filters with different orientations, and at the same time the orientation information (called OrientationCode) of the palmprint is also extracted. The PhaseCode and the OrientationCode are fused to make a new feature, called the Palmprint Phase and Orientation Code (PPOC). At the matching stage, a modified Hamming distance is defined to measure the similarity of two PPOCs. This approach is tested on a palmprint database containing 7,605 samples and the experimental results show that the accuracy and speed of the PPOC approach can satisfy the requirements of a real time biometric system.

This paper describes the results from a collaborative study between the European Space Operations Center, the European Organization for the Exploitation of Meteorological Satellites, the National Oceanic and Atmospheric Administration, and the Cooperative Institute for Meteorological Satellite Studies investigating the relationship between satellite-derived monthly mean fields of wind and humidity in the upper troposphere for March 1994. Three geostationary meteorological satellites GOES-7, Meteosat-3, and Meteosat-5 are used to cover an area from roughly 160°W to 50°E. The wind fields are derived from tracking features in successive images of upper-tropospheric water vapor (WV) as depicted in the 6.5-μ absorption band. The upper-tropospheric relative humidity (UTH) is inferred from measured water vapor radiances with a physical retrieval scheme based on radiative forward calculations.Quantitative information on large-scale circulation patterns in the upper troposphere is possible with the dense spatial coverage of the WV wind vectors. The monthly mean wind field is used to estimate the large-scale divergence; values range between about −5 × 10−6 and 5 × 10−6 sec−1when averaged over a scale length of about 1000–2000 km. The spatial patterns of the UTH field and the divergence of the wind field closely resemble one another, suggesting that UTH patterns are principally determined by the large-scale circulation.Since the upper-tropospheric humidity absorbs upwelling radiation from lower-tropospheric levels and therefore contributes significantly to the atmospheric greenhouse effect, this work implies that studies on the climate relevance of water vapor should include threedimensional modeling of the atmospheric dynamics. The fields of UTH and WV winds are useful parameters for a climate-monitoring system based on satellite data. The results from this 1-month analysis suggest the desirability of further GOES and Meteosat studies to characterize the changes in the upper-tropospheric moisture sources and sinks over the past decade.

The emissivity model for rough sea surface [Remote Sensing Environ.24, 313–329 (1988)] is inspected in light of the measured surface emissivity. In the presence of moderate wind (5 m/s or less), the emissivity model is found to be adequate for small to moderate view angles. For large view angles, the discrepancy between the computed and the measured emissivity is large, but one can reduce this considerably by incorporating the reflected sea surface emission into the emissivity model. In addition, examination of the spectral variation of the observed and computed emissivity suggests the need for refined measurements of the complex refractive index. An improved model is constructed to calculate the rough sea surface emissivity that can be used to provide accurate estimates of sea surface skin temperatures from remotely sensed radiometric measurements. An important feature of the improved model is that the computed sea surface emissivity is only weakly dependent on wind speed for most view angles used in practice.