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Most of the recent image segmentation methods have tried to achieve the utmost segmentation results using large-scale pixel-level annotated data sets. However, obtaining these pixel-level annotated training data is usually tedious and expensive. In this work, we address the task of semisupervised semantic segmentation, which reduces the need for large numbers of pixel-level annotated images. We propose a method for semisupervised semantic segmentation by improving the confidence of the predicted class probability map via two parts. First, we build an adversarial framework that regards the segmentation network as the generator and uses a fully convolutional network as the discriminator. The adversarial learning makes the prediction class probability closer to 1. Second, the information entropy of the predicted class probability map is computed to represent the unpredictability of the segmentation prediction. Then, we infer the label-error map of the segmentation prediction and minimize the uncertainty on misclassified regions for unlabeled images. In contrast to existing semisupervised and weakly supervised semantic segmentation methods, the proposed method results in more confident predictions by focusing on the misclassified regions, especially the boundary regions. Our experimental results on the PASCAL VOC 2012 and PASCAL-CONTEXT data sets show that the proposed method achieves competitive segmentation performance.

Light plays an important role in determining plant architecture, which greatly influences crop yield. However, the precise mechanisms by which light signaling regulates bud outgrowth remain to be identified. Here, we show that light regulates bud outgrowth via both HY5 and brassinosteroid (BR)-dependent pathways in tomato. Inactivation of the red-light photoreceptor PHYB, or deficiencies in PHYB or the blue-light photoreceptor CRY1a, inhibits bud outgrowth and leads to decreased accumulation of HY5 protein and increased transcript level of BRANCHED1 ( BRC1 ), a central integrator of branching signals. HY5, functioning as a mobile systemic signal from leaves, promotes bud outgrowth by directly suppressing BRC1 transcript and activating the transcript of BR biosynthesis genes within the lateral buds in tomato. Furthermore, BRC1 prevents the accumulation of cytokinin (CK) and gibberellin (GA) by directly inhibiting the transcript of CK synthesis gene LOG4 , while increasing the transcript levels of CK and GA degradation genes ( CKX7 , GA2ox4 , and GA2ox5 ), leading to an arrest of bud outgrowth. Moreover, bud outgrowth occurs predominantly in the day due to the suppression of BRC1 transcript by HY5. These findings demonstrate that light-inducible HY5 acts as a systemic signaling factor in fine-tuning the bud outgrowth of tomato.

Phytosulfokine (PSK) is a danger-associated molecular pattern recognized by PHYTOSULFOKINE RECEPTOR 1 (PSKR1) and initiates intercellular signaling to coordinate different physiological processes, especially in the defense response to the necrotrophic fungus Botrytis cinerea. The activity of peptide receptors is largely influenced by different posttranslational modifications, which determine intercellular peptide signal outputs. To date, the posttranslational modification to PHYTOSULFOKINE RECEPTOR 1 (PSKR1) remains largely unknown. Here, we show that tomato (Solanum lycopersicum) PSKR1 is regulated by the ubiquitin/proteasome degradation pathway. Using multiple protein–protein interactions and ubiquitylation analyses, we identified that plant U-box E3 ligases PUB12 and PUB13 interacted with PSKR1, among which PUB13 caused PSKR1 ubiquitylation at Lys-748 and Lys-905 sites to control PSKR1 abundance. However, this posttranslational modification was attenuated upon addition of PSK. Moreover, the disease symptoms observed in PUB13 knock-down and overexpression lines demonstrated that PUB13 significantly suppressed the PSK-initiated defense response. This highlights an important regulatory function for the turnover of a peptide receptor by E3 ligase-mediated ubiquitylation in the plant defense response.

Phase-shifting profilometry (PSP) via sequential fringe projection is applicable for high-accuracy 3-D measurements of static objects. However, moving objects cannot be static or quasi-static during one projection cycle, thereby causing surface misalignment and phase errors in 3-D measurements. This article proposes a motion-estimated PSP based on the fringe-oriented synthetic phase correlation algorithm (FO-SPCA), which achieves automatic 3-D measurements of objects in nonuniform 2-D motion. To robustly estimate the motion of objects among fringe images, the proposed fringe-oriented feature extraction technique obtains the surface features by eliminating the modulation influence of sinusoidal illumination. Furthermore, the FO-SPCA is developed to accurately detect the subpixel displacements of moving objects according to object surface features. With the subpixel displacements, the alignment procedure is completed through a reverse 2-D transformation and then the phase errors are compensated accordingly. The simulation and experimental results verify the feasibility and robustness of the motion-estimated PSP for the in situ 3-D measurements of nonuniform moving objects.

Codewords used to determine fringe orders are significant for absolute phase retrieval based on the phase-coding method. However, because of inevitable interference from image blurring, image noise, and system nonlinearity, it is challenging to significantly increase the number of codewords for high-accuracy phase retrieval. To address this challenge, this article proposes a joint coding strategy to perform the coding of high-density stripes. More specifically, codewords are simultaneously embedded into the phase domain and the intensity domain of composite fringe patterns, which greatly extends the encoding range of valid codewords. Moreover, to improve the anti-interference capability of the proposed coding strategy, a regional correction technique (RCT) is presented to remove jump phase errors induced by the inevitable interference. The results of experiments indicate that the proposed method is effective and accurate for both complex objects and a standard sphere.

The 3-D measurement methods based on fringe projection intensity adjustment (FPIA) have been widely adopted to measure the multireflectivity scenes. However, the signal-to-noise ratios (SNRs) of the captured fringe patterns in the FPIA-based methods are reduced because of a large range of reflectivity variations (LRRVs). In our research, a reflection model and a crosstalk model are built to analyze the disturbances induced by LRRVs on the SNRs in the captured fringe patterns, and then, a 3-D measurement method based on nonlinear FPIA (NLFPIA) is proposed to alleviate the LRRV-induced disturbances and improve the measurement accuracy. Moreover, to improve the efficiency of the proposed 3-D measurement method, we present a cyclic utilization of fringe patterns (CUFPs) technique to decrease the number of projection patterns. Experimental results show that the proposed NLFPIA-based 3-D measurement method reduces the root-mean-square error (RMSE) in the measurements of the two standard ceramic geometries by 52.9845% and 36.6393% compared with the FPIA-based method.

Herbivory severely affects plant growth, posing a threat to crop production. Calcium ion (Ca

Automatic defect classification in photovoltaic (PV) modules is gaining significant attention due to the limited application of manual/visual inspection. However, the automatic classification of defects in crystalline silicon solar cells is a challenging task due to the inhomogeneous intensity of cell cracks and complex background. The present study is carried out for automatic defects classification of PV cells in electroluminescence images. Two machine learning approaches, features extraction-based support vector machine (SVM) and convolutional neural network (CNN) are used for the solar cell defect classifications. Suitable hyperparameters, algorithm optimisers, and loss functions are used to achieve the best performance. Solar cell defects are divided into seven classes such as one non-defective and six defective classes. Feature extraction algorithms such as histograms of oriented gradients (HOG), KAZE, Scale-Invariant Feature Transform (SIFT) and speeded-up-robust features (SURF) are used to train the SVM classifier. Finally, the performance results are compared. It is concluded that CNN's accuracy for solar cell defect classification is 91.58% which outperforms the state-of-the-art methods. With features extraction-based SVM, accuracies of 69.95, 71.04, 68.90, and 72.74% are obtained for HOG, KAZE, SIFT, and SURF, respectively. The present study may contribute to making a PV system more efficient for classifying defects to improve the power system efficiency.

Fruits are excellent sources of essential vitamins and health-boosting minerals. Recently, regulation of fruit ripening by both internal and external cues for the improvement of fruit quality and shelf life has received considerable attention. Rosmarinic acid (RA) is a kind of natural plant-derived polyphenol, widely used in the drug therapy and food industry due to its distinct physiological functions. However, the role of RA in plant growth and development, especially at the postharvest period of fruits, remains largely unknown. Here, we demonstrated that postharvest RA treatment delayed the ripening in tomato fruits. Exogenous application of RA decreased ripening-associated ethylene production and inhibited the fruit color change from green to red based on the decline in lycopene accumulation. We also found that the degradation of sucrose and malic acid during ripening was significantly suppressed in RA-treated tomato fruits. The results of metabolite profiling showed that RA application promoted the accumulation of multiple amino acids in tomato fruits, such as aspartic acid, serine, tyrosine, and proline. Meanwhile, RA application also strengthened the antioxidant system by increasing both the activity of antioxidant enzymes and the contents of reduced forms of antioxidants. These findings not only unveiled a novel function of RA in fruit ripening, but also indicated an attractive strategy to manage and improve shelf life, flavor, and sensory evolution of tomato fruits.

Thermo-mechanical loads induce stresses in photovoltaic (PV) modules, leading to crack formation. In this context, the understanding of module's thermo-mechanical behavior is important. To investigate the thermo-mechanical behavior of smart wire connected technology (SWCT) and busbarPV modules throughout their entire life, the present study is conducted that probes the stress distribution and deformation during production, transportation, and subsequent mechanical and thermal loading stages in a consecutive step-by-step manner using finite element modellingapproach. Pre-stresses and non-linearitiesare considered in simulation models. Stresses and displacements experienced by different parts/layers are examined, and crack sensitive regions are identified. In addition, the SWCTand busbarmodules are compared, and it is found that SWCTinterconnection is relatively a less stress inducing process and less susceptible to thermal and dynamic affects. During production stage, stresses of 39.3 MPaand 40.4 MPaare generated in SWCTcells and copper wires respectively; while, stresses of 60 MPaand 87 MPaare generated in busbarcells and busbarrespectively. Similarly, lower stresses are induced in SWCTPV modules during subsequent stages. The comparison results show advantages of SWCTmodule in terms of mechanical stability which can lead to improve the performance and reliability of PV modules.

Background and objectives To explore the relationship between dietary patterns, physical activity and lipid-related indices in Chinese Population. Methods and study design This study included 21,472 (72.3% men) participants aged 16 to 78 years. Data of anthropometric measurements, biochemical tests and questionnaires were collected through a physical examination. Diet patterns were identified through factor analysis and five patterns were retained (“meat,” “high-energy,” “high-protein,” “healthy” and “traditional Chinese”). Physical activity was classified into low, moderate, or high. Abnormalities in lipid indices were assessed using the Adult Treatment Panel III criterion. Results Higher factor scores of “high-protein” pattern and “healthy” pattern were found to be related to favorable lipid indices. Quartiles 3 and 4 of “meat” pattern showed increased risks of having elevates total cholesterol and low-density lipoprotein cholesterol concentrations. Participants with higher levels of physical activity showed lowest risk of abnormal lipid profiles. All the associations were equally established among men, while most were no longer significant among women. Conclusions Higher physical activity level and a dietary pattern consists of high-quality protein foods, vegetables and fruits were associated with favorable lipid profiles, and these lifestyle factors were related to the risk of dyslipidemia in a sex-specific way.

To examine the association of dietary diversity and physical activity with lipidrelated indices in a Beijing population.This cross-sectional study included 21,472 participants (72.3% men) aged 16 to 78 years. Data were obtained through a physical examination that included anthropometric measurements, biochemical tests, and questionnaires. The dietary diversity score (0-8) was calculated according to the baseline consumption frequencies of eight food groups (cereals, fruits, vegetables, meat, eggs, fish, dairy, and legumes). Physical activity level was classified as low, moderate, or high according to International Physical Activity Questionnaire scoring protocol. Abnormalities in lipid-related indices were assessed using the criteria of the National Cholesterol Education Program Adult Treatment Panel III guidelines.Compared with individuals with poor dietary diversity (score 0-5), higher dietary diversity was associated with lower risk of abnormal levels of triglycerides and high-density lipoprotein cholesterol. Dairy intake was associated with a lower risk of having a high triglyceride-to-high-density lipoprotein cholesterol ratio after adjusting for potential covariates. Participants with the lowest risk of abnormal lipid profiles were those who had high levels of physical activity.Dietary diversity and physical activity level were associated with lipid-related indices. Therefore, to maintain healthy lipid profiles in the general population, improving dietary diversity and physical activity is essential.

In a typical digital fringe projection (DFP) system, the shadows in the fringe images cause errors in the phase map. We propose a novel discriminative repair approach to remove the shadow-induced error in the phase map. The proposed approach first classifies the shadow area in the phase map obtained by the DFP into two categories: valid shadow area and invalid shadow area. Then the valid shadow area is repaired by a proposed neighboring information fusion phase estimation (NIFPE) method, which fuses the phase gradient into the result of kernel density estimation (KDE) through the Kalman filter (KF) algorithm. The invalid shadow area is repaired by a proposed background phase matching (BPM) method. The experimental results demonstrate that the shadow-induced error in the phase map can be removed, which verifies the effectiveness of the proposed approach.

The presented dual-drive long-travel precise positioning stage is employed in grating ruling engine and capable of extended travel and high precision. Based on the characteristic of grating ruling engine, a dual-drive positioning system is developed. The primary positioning stage is an open-loop system and designed to obtain long-travel range. The secondary positioning stage with a control system can guarantee the positioning precision being less than 10 nm. After modeling the fine positioning system, a single-neuron-based PID controller is employed to implement the control system. The experiment results demonstrate that in the long-travel mode, the stage is capable of performing positioning with an extended move range and accuracy being less than 10 nm. Meanwhile, in the short-travel mode, the stage performs positioning with a limited range of 120 μm and accuracy being less than 7 nm. The main sources of the positioning deviation including clearance, vibration, and the measurement error are analyzed in detail.

Arbitrary two-dimensional (2-D) motion introduces coordinate errors and phase errors to three-dimensional (3-D) shape measurement of objects in phase-shifting profilometry (PSP). This paper presents a new robust 3-D reconstruction method for arbitrary 2-D moving objects by introducing an adaptive reference phase map and the motion estimation based on fence image. First, a composite fence image is used to track object motion. Second, to obtain the transformation matrixes and remove the coordinate errors among object images, the angle extraction technique and the 1-D hybrid phase correlation method (1-D HPCM) are integrated to automatically estimate the sub-pixel motion of objects. Third, the phase errors are compensated to obtain the rough absolute phase map of objects by combining the transformation matrixes with the reference phase map. Finally, the absolute phase map is refined to reconstruct the 3-D surfaces of moving objects with adaptive reference phase map. The proposed computational framework can accurately and automatically realize 3-D shape measurement of arbitrary objects with 2-D movement. The results of experiment verify the effectiveness of our computational framework.

The quality of ground surface pregrouting (GSPG) is commonly qualitatively evaluated using single factor; however, the quality evaluation involves numerous facets, and a quantitative evaluation is rare. The aim of this study was to quantitatively evaluate the quality of a GSPG based on three aspects. The fuzzy analytic hierarchy process (FAHP) was adopted to obtain the final score and quality classification of the GSPG. Based on three aspects, namely, integrity, continuity, and sturdiness, a series of field tests was also conducted, qualitatively evaluating the quality of the GSPG preliminary, to obtain the required test data and verify the FAHP results. The results of the FAHP showed that the quality of the GSPG was 86.42, which could be classified as “Good”, whereas the field tests exhibited that the GSPG was effective, thereby verifying that the FAHP was reliable. In addition, the proposed method provided a detailed comprehension of the quality evaluation of GSPG and a frame of reference for analogous engineering.

It is essential to enhance the ability of wind speeds forecasting for wind energy and wind resource planning. For this purpose, a hybrid strategy has been proposed based on spatio-temporal covariance model which combined the spatio-temporal ordinary kriging (STOK) technology with autoregressive integrated moving average (ARIMA) regression smoothing method. This is because wind speed time series exhibits a long-term dependency. In the case study, both STOK method and ARIMA method are employed and their performances are compared. The ARIMA model can obtain a necessary and sufficient smoothing condition for them to be smoothed. Meanwhile, further theoretical analysis is provided to discuss why the STOK method is potentially more accurate than the ARIMA method for wind speed time series prediction. Results show that the proposed method outperforms the Non-Sep-Gneiting model by 9% and 7.2% in terms of mean absolute error (MAE) and root-mean-square error (RMSE).

Short-term wind speed prediction is an essential task for wind resource and wind energy planning. However, most of this literature does not take into account the spatio-termporal correlation of wind data from the geographical field. For this reason, we propose an integrated spatio-temporal kriging and functional kriging strategy to exploit such spatio-temporal correlation into the wind speed prediction. First, the deterministic trend component in wind data is estimated to be removed. The residuals are used for spatio-temporal modeling and prediction. Based on the spatio-temporal kriging framework, four spatio-temporal covariance models (product-sum model, separable exponential product model, separable and nonseparable Gneiting models) are considered which describe the spatio-temporal correlation of wind data. In particular, the flexibility of using the nonseparable Gneiting model is highlighted. More specifically, four spatio-temporal random fields are modeled from the 12 wind monitoring stations over Ireland. We also use an involved weighted least squares method for estimating parameters of the four covariance models involved in the spatio-temporal kriging strategy. We apply the fitted covariance models to generate day-ahead wind speed predictions at both observed and nonobserved locations where wind station already exist but also to nearby locations. Leave-one-out cross-validation is applied to check the significance of the difference among the four models, these spatio-temporal ordinary kriging (STOK), functional ordinary kriging (FOK) and autoregressive integrated moving average (ARIMA) methods are compared for day-ahead wind speed predictions. Forecasting results indicate that the predicting accuracy is improved almost 33.5% using FOK compared with three approaches which confirm the effectiveness of the functional kriging method in the paper.

Accurate wind speed prediction plays an important role in the planning and development of wind power plant. Wind speed prediction can reduce the impact of wind power grid connection and improve the control of wind power systems. In this study, wind speed is considered as a geographical variable. We firstly used exploratory methods to examine the spatio-temporal dependence on wind field. The variogram was taken into account in analysing the spatial structure in the rectangular grid. Then the exponential model was adopted to fit the anisotropic feature of variogram. The de-trended data have been computed by using a geostatistical kriging estimator. The results were compared with the leave-one-out cross-validation (LOOCV) method. The accuracy of the proposed method was verified to obtain the best estimated results.

Sparse component analysis (SCA) has been extensively studied to solve undetermined blind source separation problem in various fields over the last decades. This paper proposes a SCA algorithm based on hierarchical Hough transform. The hyperplanes clustering within the mixture space are revealed as local maxima in the parameter space after Hough transform is performed. Then, the local maxima are picked up, and the mixing matrix is calculated. The grid resolution in which the parameter space is divided plays an important role on the estimation error and the computational load. Therefore, the parameter space is divided into hypercubes recursively from low to high resolution, and Hough transform is performed only on the hypercubes with votes exceeding a selected threshold. The grid resolution selection problem is solved, and the computational load is reduced a lot in the meantime. After the mixing matrix is obtained, the sources are recovered with $$\ell _1$$l1-norm optimization. Numerical simulation and a speech separation application illustrate the superior performance of the proposed algorithm.

This paper presents a theoretical analysis for a graphene-based FET real-time detector of the target bacteriaE. coliK12. The motivation for this study is to design a sensor device for detection of bacteria in food and water in order to guarantee food safety. Graphene is chosen as our material for sensor design, which has outstanding electrical, physical, and optical performance. In our sensor structure, graphene-based solution gate field effect transistor (FET) is the device model; fabrication and functionalization protocol are presented together in this paper. What is more, a real-time signal display system is the accompanied equipment for our designed biosensor device. In this system, the sensor bias current signalIdswould change obviously when the target bacteria are attached to the sensor surface. And the bias currentIdsincreases when theE. coliconcentration increases. In the latter part, a theoretical interpretation of the sensor signal is to explain the bias currentIdsincreasing after theE. coliK12 attachment.

The prediction of wind speed plays a key role in the determination of the optimal reserve capacity for different time instants and the reduction of the standby cost of wind power grid. Short-term wind speed prediction can reduce the impact of wind power grid and improve the control of wind turbines. The geostatistics kriging as an unbiased estimation method applied to predict the wind speed in a two-dimensional vertical rectangular grid. The wind data were as a geographical variable considered. We firstly used exploratory methods to examine the spatial variability of wind speed and its dependence on geographical variables. The covariance and variogram models took into account in analysing the spatial structure of wind speed at three different instants of time. Then the product model adopted to fit wind data due to the variogram. The accuracy of the method was assessed through a cross validation. The prediction results were comparable with the observed values. The prediction error was about ±0.15 and the correlation coefficient was about 0.85. This study demonstrated that the kriging method could be success fully used in the short-time wind speed prediction in space and time direction.

Pixel-by-pixel phase unwrapping (PPU) has been employed to rapidly achieve three-dimensional (3-D) shape measurement without additional projection patterns. However, the maximum measurement depth range that traditional PPU can handle is within 2π in phase domain; thus PPU fails to measure the dynamic object surface when the object moves in a large depth range. In this paper, we propose a novel adaptive pixel-by-pixel phase unwrapping (APPU), which extends PPU to an unlimited depth range. First, with PPU, temporary phase maps of objects are obtained referring to the absolute phase map of a background plane. Second, we quantify the difference between the image edges of the temporary phase maps and the practical depth edges of dynamic objects. Moreover, according to the degree of the edge difference, the temporary phase maps are categorized into two classes: failed phase maps and relative phase maps. Third, by combining a mobile reference phase map and the edge difference quantization technique, the failed phase maps are correspondently converted into relative phase maps. Finally, the relative phase maps are innovatively transformed into the absolute phase maps using a new shadow-informed depth estimation method (SDEM). The proposed approach is suitable for high-speed 3-D shape measurement without depth limitations or additional projection patterns.

Sparse component analysis (SCA) has been widely used for blind source separation(BSS) for many years. Recently, SCA has been applied to operational modal analysis (OMA), which is also known as output-only modal identification. This paper considers the sparsity of sources’ time-frequency (TF) representation and proposes a new TF-domain SCA under the OMA framework. First, the measurements from the sensors are transformed to the TF domain to get a sparse representation. Then, single-source-points (SSPs) are detected to better reveal the hyperlines which correspond to the columns of the mixing matrix. The K-hyperline clustering algorithm is used to identify the direction vectors of the hyperlines and then the mixing matrix is calculated. Finally, basis pursuit de-noising technique is used to recover the modal responses, from which the modal parameters are computed. The proposed method is valid even if the number of active modes exceed the number of sensors. Numerical simulation and experimental verification demonstrate the good performance of the proposed method.

This paper presents a system design of mechanical robotic manipulator with its own sensor end effectors. For the end effectors, they would be fixed with a fabricated micro-nano sensor. This system’s application is to detect gas density and recognize the unknown gas at the target place by executing a robotic arm action. The gas to be detected is driven to the sensor surface, and the sensor surface’s vibration signal is tested. So the paper includes the mechanical modeling of the robotic arm, and the device fabrication of the end sensor, the material vibration analysis, and the vibration signal process. In our paper we use this system to conduct NO2 measurement. Finally, the system and the gas sensor’s overall performance is summarized and discussed.

An improved active disturbance rejection controller (ADRC) is designed to eliminate the influences of the current-loop for the pitching axis control system of an aerial camera. The improved ADRC is composed of a tracking differentiator (TD), an improved extended state observer (ESO), an improved nonlinear state error feedback (NLSEF), and a disturbance compensation device (DCD). The TD is used to arrange transient process. The improved ESO is utilized to observe the state extended by nonlinear dynamics, model uncertainty, and external disturbances. Overtime variation of the current-loop can be predicted by the improved ESO. The improved NLSEF is adopted to restrain the residual errors of the current-loop. The DCD is used to compensate the overtime variation of the current-loop in real time. The improved ADRC is designed based on a new nonlinear functionnewfal(·). This function exhibits enhanced continuity and smoothness compared to previously available nonlinear functions. Thus, the new nonlinear function can effectively decrease the high-frequency flutter phenomenon. The improved ADRC exhibits improved control performance, and disturbances of the current-loop can be eliminated by the improved ADRC. Finally, simulation experiments are performed. Results show that the improved ADRC displayed better performance than the proportional integral (PI) control strategy and traditional ADRC.

In order to extract the maximum power from PV system, the maximum power point tracking (MPPT) technology has always been applied in PV system. At present, various MPPT control methods have been presented. The perturb and observe (P&O) and conductance increment methods are the most popular and widely used under the constant irradiance. However, these methods exhibit fluctuations among the maximum power point (MPP). In addition, the changes of the environmental parameters, such as cloud cover, plant shelter, and the building block, will lead to the radiation change and then have a direct effect on the location of MPP. In this paper, a feasible MPPT method is proposed to adapt to the variation of the irradiance. This work applies the glowworm swarm optimization (GSO) algorithm to determine the optimal value of a reference voltage in the PV system. The performance of the proposed GSO algorithm is evaluated by comparing it with the conventional P&O method in terms of tracking speed and accuracy by utilizing MATLAB/SIMULINK. The simulation results demonstrate that the tracking capability of the GSO algorithm is superior to that of the traditional P&O algorithm, particularly under low radiance and sudden mutation irradiance conditions.

Thermally induced error of nanopositioning systems is rarely studied specifically because most researchers strictly limit the ambient temperature in order to ignore the influences of thermal deformation. However, the control cost of a narrow temperature range is too high for widely application. This paper is addressed at modeling and compensation of thermal error with the purpose of ensuring the positioning accuracy with an extended temperature range. The finite element analysis and contrast experiments with different temperature ranges reveal the significant impact of temperature variation on positioning precision. A polynomial model based on the genetic algorithm is implemented to describe the relationship between deviations and temperatures. By utilizing the parametric study, the optimal parameters of the genetic algorithm are determined. Based on the thermal model, a compensation system has been developed. A constant proportional–integral–derivative (PID) controller and a single-neuron PID controller are employed for compensation, respectively. The results demonstrate that the single-neuron PID controller can effectively restrain thermal error and guarantee the location accuracy with extended temperature range. The positioning precision of the positioning stage with compensation system and temperature range of 20 + 0.3°C is improved to 12 nm, while the one of the original system with temperature range of 20 ± 0.01°C is 31 nm.

This paper proposed a novel in-plane displacement field measurement algorithm using an optical flow strategy. We built a linear illumination model between images before and after deformation to guarantee intensity invariability. We used image upsampling and a reliability-guided strategy to find the matching points accurate to 0.5 pixels in the reference and deformed images. The criterion to determine the reliability is zero-mean normalized cross-correlation coefficient. Afterward, we used the brightness constancy constraint combined with Lucas-Kanade optical flow constraint in a specific image region to obtain an overdetermined linear equation. We applied the noniterative least-squares algorithm to solve the equations and to achieve the displacement offsets. This research utilized multithread calculation to handle the complete cracking applications. We estimated the computing efficiency and calculation precision of the proposed method through a series of experimental speckle patterns. All results demonstrated the correctness, effectiveness, and robustness of the proposed method.

In this paper, we propose an automatic detection schema including three stages for weld defects in x-ray images. Firstly, the preprocessing procedure for the image is implemented to locate the weld region; Then a classification model which is trained and tested by the patches cropped from x-ray images is constructed based on deep neural network. And this model can learn the intrinsic feature of images without extra calculation; Finally, the sliding-window approach is utilized to detect the whole images based on the trained model. In order to evaluate the performance of the model, we carry out several experiments. The results demonstrate that the classification model we proposed is effective in the detection of welded joints quality.

To study the effect of body mass index (BMI) on lung function indexes in Chinese population. A cross-sectional study was performed on 10, 592 participants. The linear relationship between lung function and BMI was evaluated by multivariate linear regression analysis, and the correlation between BMI and lung function was assessed by Pearson correlation analysis. Correlation analysis showed that BMI was positively related with the decreasing of forced vital capacity (FVC), forced expiratory volume in one second (FEV1) and FEV1/FVC (P

This paper presents a system design of mechanical robotic manipulator with its own sensor end effectors. For the end effectors, they would be fixed with a fabricated micro-nano sensor. This system's application is to detect specific heart rates of various population by executing a robotic arm action to measure people's radial signals. So the paper includes the mechanical modeling of the robotic manipulator, the design of sensor surface, the material vibration analysis, and the vibration signal process.