Pattern Projection Method Research Articles

High-precision defocus detection through bevel-axial stripe pattern projection method

High-precision defocus detection through bevel-axial stripe pattern projection method

Pattern Projection Calibrations on Subseasonal Forecasts of Surface Air Temperature over East Asia

Abstract Subseasonal forecasts have recently attracted widespread interest yet remain a challenging issue. A statistical Kalman filter pattern projection method (KFPPM), which absorbs the projection conception of the raw covariance pattern projection (COVPPM) and the adaptive adjustments of the Kalman filter, is proposed to calibrate the single-model forecasts of the daily maximum and minimum temperatures (Tmax and Tmin) for lead times of 8–42 days over East Asia in 2018 derived from the UKMO control (CTL) forecast. The Kalman filter–based gridly calibration (KFGC) is carried out in parallel as a benchmark, which could improve the forecast skills to a certain extent. The COVPPM effectively calibrates the temperature forecasts at the early stage and displays better performances than the CTL and KFGC. However, with the growing lead times, it shows speedily decreasing skills and can no longer produce positive adjustments over the areas outside the plateaus. By contrast, the KFPPM consistently outperforms the other calibrations and reduces the forecast errors by almost 1.0° and 0.5°C for Tmax and Tmin, respectively, both retaining superiorities to the random climatology benchmark till the lead time of 24 days. The optimization of KFPPM maintains throughout the whole range of the subseasonal time scale, showing the most conspicuous improvements distributed over the Tibetan Plateau and its surroundings. Though the postprocessing procedures are more skillful in calibrating Tmax forecasts than Tmin forecasts, the Tmax forecasts are still characterized by lower skills than the latter. Case experiments further demonstrate the abovementioned features and imply the potential capability of KFPPM in improving forecast skills and disaster preventions for extreme temperature events.

An Augmented Reality-Based System for Ultrasonically Measuring the Thickness of Thin-Walled Parts

Manual thickness measurement of thin-walled parts is largely demanded in industrial applications. Repetitive manual interventions and poor consistency of measurement data are two of the critical existing problems for manual thickness measurement, which reduce the measuring accuracy and efficiency. To address these problems, this article combines digital ultrasonic thickness with an augmented reality (AR)-based system to assist the manual thickness measurement of the thin-walled parts. A simple adaptive pattern projection method is proposed to replace the manual drawing of grids and thickness states on the part. A multifunctional tool is developed to automatically visualize and record the measured thickness data in real-time, which guarantees the consistency of measurement data. The communication within the proposed system is established to enhance the human–machine interaction experience, allowing the operator to focus on the thickness measurement itself. The accuracy of the projection position is analyzed and the maximum deviation is 1.5 mm. A case study is conducted to validate the proposed system. The proposed system largely improves digitization and decreases the workload of manual thickness measurement. The measurement time is shortened compared to the conventional thickness measurement method.

Statistical Calibrations of Surface Air Temperature Forecasts over East Asia using Pattern Projection Methods

AbstractIn this study, two pattern projection methods, i.e., the Stepwise Pattern Projection Method (SPPM) and the newly proposed Neighborhood Pattern Projection Method (NPPM), are investigated to improve forecast skills of daily maximum and minimum temperatures (Tmax and Tmin) over East Asia with lead times of 1–7 days. Meanwhile, the decaying averaging method (DAM) is conducted in parallel for comparison. These post-processing methods are found to effectively calibrate the temperature forecasts on the basis of the raw ECMWF output. Generally, the SPPM is slightly inferior to the DAM, while its insufficiency decreases with increasing lead times. The NPPM shows manifest superiority for all lead times, with the mean absolute errors of Tmax and Tmin decreased by ~0.7°C and ~0.9°C, respectively. Advantages of the two pattern projection methods are both mainly concentrated on the high-altitude areas such as the Tibetan Plateau, where the raw ECMWF forecasts show most conspicuous biases. In addition, aiming at further assessments of these methods on extreme event forecasts, two case experiments are carried out towards a heat wave and a cold surge, respectively. The NPPM is retained as the optimal with the highest forecast skills, which reduces most of the biases to < 2°.C for both Tmax and Tmin over all the lead days. In general, the statistical pattern projection methods are capable of effectively eliminating spatial biases in forecasts of surface air temperature. Compared with the initial SPPM, the NPPM not only produces more powerful forecast calibrations, but also provides more pragmatic calculations and greater potential economic benefits in practical applications.

Development of a Shape Meter Employing the LED Dot Pattern Projection Method for a Hot Strip Finishing Mill

Development of a Shape Meter Employing the LED Dot Pattern Projection Method for a Hot Strip Finishing Mill

Assessment of the Sea Surface Temperature Predictability Based on Multimodel Hindcasts

Abstract Based on historical forecasts of four individual forecasting systems, we conducted multimodel ensembles (MME) to predict the sea surface temperature anomaly (SSTA) variability and assessed these methods from a deterministic and probabilistic point of view. To investigate the advantages and drawbacks of different deterministic MME methods, we used simple averaged MME with equal weighs (SCM) and the stepwise pattern projection method (SPPM). We measured the probabilistic forecast accuracy by Brier skill score (BSS) combined with its two components: reliability (Brel) and resolution (Bres). The results indicated that SCM showed a high predictability in the tropical Pacific Ocean, with a correlation exceeding 0.8 with a 6-month lead time. In general, the SCM outperformed the SPPM in the tropics, while the SPPM tend to show some positive effect on the correction when at long lead times. Corrections occurred for the spring predictability barrier of ENSO, in particular for improvements when the correlation was low or the RMSE was large using the SCM method. These qualitative results are not susceptible to the selection of the hindcast periods, it is as a rule rather by chance of these individual systems. Performance of our probabilistic MME was better than the Climate Forecast System version2 (CFSv2) forecasts in forecasting COLD, NEUTRAL, and WARM SSTA categories for most regions, mainly due to the contribution of Brel, indicating more adequate ensemble construction strategies of the MME system superior to the CFSv2.

Development of Shape Meter Employing LED Dot Pattern Projection Method for Hot Strip Finishing Mill

Development of Shape Meter Employing LED Dot Pattern Projection Method for Hot Strip Finishing Mill

Adaptive pixel-to-pixel projection intensity adjustment for measuring a shiny surface using orthogonal color fringe pattern projection

Three-dimensional (3D) shape measurement based on fringe pattern projection techniques has been commonly used in various fields. One of the remaining challenges in fringe pattern projection is that camera sensor saturation may occur if there is a large range of reflectivity variation across the surface that causes measurement errors. To overcome this problem, a novel fringe pattern projection method is proposed to avoid image saturation and maintain high-intensity modulation for measuring shiny surfaces by adaptively adjusting the pixel-to-pixel projection intensity according to the surface reflectivity. First, three sets of orthogonal color fringe patterns and a sequence of uniform gray-level patterns with different gray levels are projected onto a measured surface by a projector. The patterns are deformed with respect to the object surface and captured by a camera from a different viewpoint. Subsequently, the optimal projection intensity at each pixel is determined by fusing different gray levels and transforming the camera pixel coordinate system into the projector pixel coordinate system. Finally, the adapted fringe patterns are created and used for 3D shape measurement. Experimental results on a flat checkerboard and shiny objects demonstrate that the proposed method can measure shiny surfaces with high accuracy.

Three Dimensional Surface Profile Measurement Based on Grating Pattern Projection Method Using Liquid Crystal Grating

Three Dimensional Surface Profile Measurement Based on Grating Pattern Projection Method Using Liquid Crystal Grating

Assessment of APCC multimodel ensemble prediction in seasonal climate forecasting: Retrospective (1983–2003) and real‐time forecasts (2008–2013)

AbstractSince 2007, the Asia‐Pacific Economic Cooperation (APEC) Climate Center (APCC) has monthly issued multimodel ensemble (MME) seasonal predictions for 3 months, with 1 month lead time, and disseminated it to APEC member economies. This paper gives a comprehensive documentation of the current status of the APCC operational multimodel performance, with a large set of retrospective (1983–2003) and real‐time (2008–2013) predictions of temperature and precipitation. In order to investigate the enhancement in seasonal predictability that can be achieved by empirically weighted MME (using multiple regression) and calibrated MME (by correcting single‐model prediction using a stepwise pattern projection method) schemes, operationally implemented at the APCC, we compare them with a simple averaged MME (with equal weightings), for predicting seasonal mean temperature and precipitation 1 month ahead. The results indicate that the simple averaged MME consistently outperforms the multiple regression‐based MMEs, when considering all aspects of the predictions from operational prediction systems (i.e., in different variables, regions, and seasons) whereas the calibrated MME shows the capability to reduce errors and improve forecast skills in a large proportion of cases. The possible causes of the failure and success of the different MME methods implemented in the APCC operations are discussed.

Improvement of Initialized Decadal Predictions over the North Pacific Ocean by Systematic Anomaly Pattern Correction

The prediction skill and errors in surface temperature anomalies in initialized decadal hindcasts from phase 5 of the Coupled Model Intercomparison Project (CMIP5) are assessed using six ocean–atmosphere coupled models initialized every year from 1961 to 2008. The initialized hindcasts show relatively high prediction skill over the regions where external forcing dominates, indicating that a large portion of the prediction skill is due to the long-term trend. After removing the linear trend, high prediction skill is shown near the centers of action of the dominant decadal climate oscillations, such as the Pacific decadal oscillation (PDO) and Atlantic multidecadal oscillation (AMO). Low prediction skill appears over the tropical and eastern North Pacific Ocean where the predicted anomaly patterns associated with the PDO are systematically different in model and observations. By statistically correcting those systematic errors using a stepwise pattern projection method (SPPM) based on the data in an independent training period, the prediction skill of sea surface temperature (SST) is greatly enhanced over the North Pacific Ocean. The SST prediction skill over the North Pacific Ocean after the SPPM error correction is as high as that over the North Atlantic Ocean. In addition, the prediction skill in a single model after correction exceeds the skill of the multimodel ensemble (MME) mean before correction, implying that the MME method is not as effective in addressing systematic errors as the SPPM correction.

High-speed three-dimensional shape measurements using multiwavelength spatiotemporal phase shifting

Phase shifting using digital light processing (DLP) projectors enables high-speed three-dimensional (3-D) shape measurements based on a pattern projection method. However, faster phase shifting is required in industry to reduce the measurement time. For this purpose, it is necessary to precisely control the fringe pattern, but conventional DLP projectors afford limited control of the pattern due to their low-refresh rate (typically 120 Hz). Here, a multiwavelength spatiotemporal phase-shifting technique is proposed for faster 3-D shape measurements using a 3CCD camera. The projector consists of a single micro-electro-mechanical system mirror and three laser diodes of different wavelengths. The intensity modulation is transformed from the time to the spatial domain. The phases of the fringe patterns are independently controlled at each wavelength. Images are simultaneously captured of the projected patterns deformed in accord with the surface profile of the objective. The method is validated using a gray code technique for the height measurement of a sample in large steps.©The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part

Generation of sinusoidal fringes with a holographic phase grating and a phase-only spatial light modulator

A variety of pattern projection methods for the three-dimensional capture of objects is based on the generation of purely sinusoidal fringes. This is not an easy task, especially when a portable non-interferometric system for outdoor usage is required. The use of phase gratings with coherent illumination as a possible solution has the advantage of providing good stability and a large measurement volume. In this work, we analyze the quality of fringes projected with two sinusoidal phase gratings. The first grating is recorded on a silver-halide holographic plate by means of a Michelson interferometer. The spatial resolution of the silver-halide material used is greater than 6000 lines per millimeter, and the recorded grating is practically analogous to a smooth variation of the phase profile. The second grating is formed as a sinusoidal phase variation on a liquid crystal-on-silicon phase-only reflective display with a resolution of 1920×1080 pixels, a pixel pitch of 8 μm and 256 phase levels. The frequency content of the fringes projected with both gratings is analyzed and compared on the basis of the calculated Fresnel diffraction pattern, taking into account that the sinusoidal phase distribution in the case of a spatial light modulator is both sampled and quantized. Experimental fringe patterns projected using both gratings are also provided.

Compact camera for three-dimensional profilometry incorporating a single MEMS mirror

To overcome inherent problems with conventional three-dimensional profiling systems based on pattern-projection method, we propose incorporating a digital device, such as a single MEMS mirror in the projection optics. In this system, a projector is controlled to generate a projection pattern with an appropriate periodic structure and sinusoidal intensity distribution. The key aspect to this projection method is that sinusoidal signals are generated by a function generator; that is, the temporal sinusoidal intensity distribution is transformed from the time domain to the spatial domain. This flexible pattern-projection method permits phase-shifting techniques to be applied to industrial measurement and inspection. This apparatus is so compact as to have a dimensional size similar to a conventional digital camera [53 mm(H)×130 mm(W)×38 mm(D)]. Furthermore, it is lightweight (320 g) without a battery or circuit boards. Such a compact system can be used as a palm-top camera and potentially may be used in low cost measurement systems for three-dimensional profilometry.

Statistical downscaling of pattern projection using multi-model output variables as predictors

A pattern projection downscaling method is employed to predict monthly station precipitation. The predictand is the monthly precipitation at 1 station in China, 60 stations in Korea, and 8 stations in Thailand. The predictors are multiple variables from the output of operational dynamical models. The hindcast datasets span a period of 21 yr from 1983 to 2003. A downscaled prediction is made for each model separately within a leave-one-out cross-validation framework. The pattern projection method uses a moving window, which scans globally, in order to seek the most optimal predictor for each station. The final forecast is the average of the model downscaled precipitation forecasts using the best predictors and is referred to as DMME. It is found that DMME significantly improves the prediction skill by correcting the erroneous signs of the rainfall anomalies in coarse resolution predictions of general circulation models. The correlation coefficient between the prediction of DMME and the observation in Beijing of China reaches 0.71; the skill is improved to 0.75 for Korea and 0.61 for Thailand. The improvement of the prediction skills for the first two cases is attributed to three steps: coupled pattern selection, optimal predictor selection, and multi-model downscaled precipitation ensemble. For Thailand, we use the single-predictor prediction, which results in a lower prediction skill than the other two cases. This study indicates that the large-scale circulation variables, which are predicted by the current operational dynamical models, if selected well, can be used to make skillful predictions of local precipitation by means of appropriate statistical downscaling.

A palm-top camera for 3D profilometry using a single MEMS mirror

To realize a compact three-dimensional profile measurement system that is based on pattern projection method, we have proposed incorporating a recent digital device such as a MEMS scanner into projection optics. Due to this revision, first of all, such a small size system like a palm-top camera became attainable, and low cost measurement system has been potentially realized. In this system, we control a single MEMS scanner to produce the projection pattern with appropriate periodical structure and sinusoidal intensity distribution. Due to this flexibility in pattern projection, such a compact camera as a palm-top size is attainable. We adopted phase-shifting technique which is most popular technique applicable to profile measurement and inspection in automobile industry and others. This camera will be potentially as small as a photographic digital camera in dimensional size. In addition, we propose to use RGB color projection system to make up for the shortcomings such as short depth of field inherent to conventional systems and, at the same time, we aim to attain higher speed of measurement.

Optical Quality Control of High-Strength Structures

High-strength structure components can be built by the application of an advantageously designed geometry. These structures are challenging to manufacture due to their complex shape. This motivates the design of a new system for quality assurance of the geometric properties of these structures. Firstly, different measurement approaches and their usability for the measurement of high-strength structure components are discussed. Then an optimized solution is suggested. A pattern projection method is introduced, whereas the patterns adapt locally to the measured surface. The basic object of the research are methods of the optic three-dimensional measurement of structural elements with the help of adaptable pattern projection that allows to adjust the measuring process to the local geometry and reflecting characteristics of structural elements. This technique takes care of the special properties of high-strength structure components within the production process. It is described, how a measurement speed can be achieved that can catch up with a common production frequency without interrupting the flow of the parts. Furthermore, the measurement can take place in an industrial environment, because it is relatively tolerant to movements of the measured structure. Therefore, the measurement procedure is divided into two steps. Within the adaptation-step the luminescence and the resolution of the projected pattern is adapted to the measured object. The actual picture is taken and calculated in a second step in order to be robust against vibrations. The result of the measurement is an optimized point cloud of several million points. The paper concludes with a description of the data processing procedures necessary in order to evaluate the correctness of the measured structure.

A High-speed Digital Laser Grating Projection System for the Measurement of 3-dimensional Shapes

In the non-contact 3-dimensional (3D) shape measurements, the fringe pattern projection method based on the phase-shifting technique has been considered very effective for its high speed and accuracy. The digital fringe projector in particular has great flexibility in generating fringe patterns since the patterns can be controlled easily by the computer program. In this work, we have developed a high-speed digital laser grating projection system using a laser diode and a polygon mirror, and evaluated its performance. It has been demonstrated that all the optical measurements required to find out the profile of a 3D object could be carried out within 31 ms, which confirmed the validity of our 3D measurement system. The result implies the more important fact that the speed in 3D measurement can be enhanced remarkably since, in our novel system, there is no device like a LCD or DMD whose response time limits the measurement speed.

Statistical Downscaling of Precipitation in Korea Using Multimodel Output Variables as Predictors

Abstract A pattern projection downscaling method is applied to predict summer precipitation at 60 stations over Korea. The predictors are multiple variables from the output of six operational dynamical models. The hindcast datasets span a period of 21 yr from 1983 to 2003. A downscaled prediction was made for each model separately within a leave-one-out cross-validation framework. The pattern projection method uses a moving window, which scans globally, in order to seek the most optimal predictor for each station. The final forecast is the average of six model downscaled precipitation forecasts using the best predictors and will be referred to as “DMME.” It is found that DMME significantly improves the prediction skill by correcting the erroneous signs of the rainfall anomalies in coarse-resolution predictions of general circulation models. Although Korea’s precipitation is strongly influenced by local mountainous terrain, DMME performs well at 59 stations with correlation skill significant at the 95% confidence level. The improvement of the prediction skill is attributed to three steps: coupled pattern selection, optimal predictor selection, and the multimodel downscaled precipitation ensemble. This study indicates that the large-scale circulation variables, which are predicted by the current operational dynamical models, if selected, can be used to make skillful predictions of the local precipitation by using appropriate statistical downscaling methods.

Systematic Error Correction of Dynamical Seasonal Prediction of Sea Surface Temperature Using a Stepwise Pattern Project Method

Abstract Every dynamical climate prediction model has significant errors in its mean state and anomaly field, thus degrading its performance in climate prediction. In addition to correcting the model’s systematic errors in the mean state, it is also possible to correct systematic errors in the predicted anomalies by means of dynamical or statistical postprocessing. In this study, a new statistical model has been developed based on the pattern projection method in order to empirically correct the dynamical seasonal climate prediction. The strength of the present model lies in the objective and automatic selection of optimal predictor grid points. The statistical model was applied to systematic error correction of SST anomalies predicted by Seoul National University’s (SNU) coupled GCM and evaluated in terms of temporal correlation skill and standardized root-mean-square error. It turns out that the statistical error correction improves the SST prediction over most regions of the global ocean with most forecast lead times up to 6 months. In particular, the SST predictions over the western Pacific and Indian Ocean are improved significantly, where the SNU coupled GCM shows a large error.