Abstract:This paper proposes a method for characterizing the junction temperature of light-emitting diodes (LEDs) by using two parameters, and the selected reference method is used to eliminate the self-heating effect of white LEDs. The constant current source is used to drive, which improves the practicability and reduces the measurement cost. The junction temperature of cold and warm white LED is measured with a small current of 50—400 mA as the driving current. The studied ambient temperature range is 30—80 °C. The results show that the relationship between the spectrum valley value of the calibration function, the full width at half maximum (FWHM), driving current, and junction temperature can be combined with a high degree of the fitting. Compared with the measurement results of the forward voltage method, the maximum error of the measurement of the two-parameter joint characterization junction temperature method is only 2.38 °C. It is a low-cost, practical, and effective junction temperature measurement method for white LEDs.

Abstract:In order to improve the precision of static load pressure recognition and identify the position of the applied force accurately, a fiber Bragg grating (FBG) flexible sensor array is proposed in this work. Numerical analysis for the package thickness (4 mm) and package position (2 mm from the bottom) of the FBG flexible sensor is performed using COMSOL, and optimal package thickness (4 mm) and package position (2 mm from the bottom) are selected in the analysis. By using 12-FBGs layout method and random forest algorithm, the position and load prediction model is established. The results show that the average error of the distance between the prediction points of coordinates X-Y and static load F and the real sample points is 0.092. Finally, to verify the proposed models, the pressure sensing experiments of the flexible FBG array are carried out on this basis. The weights of 100 g to 1 000 g are applied to different regions of the flexible sensor array one by one in accordance with a certain trajectory. The variation of each FBG wavelength was taken as the input of the stochastic forest prediction model, and the coordinate position and the static load size F were taken as the output to establish the prediction model. The minimum distance error between the actual point and the predicted point was calculated by experiment as 0.03491. The maximum is 0.2481, and the mean error is 0.1515. It is concluded that the random forest prediction model has a good prediction effect on the pressure sensing of the flexible FBG sensing array.

Abstract:Controlled NOT (CNOT) gate is well known because of its several advantages in quantum computing and information processing. In the area of quantum computing, several methods of CNOT gates were established in last few years. In this paper, we propose a new approach of implementation of tristate CNOT operation with light as information carrying signal. To do this, the frequency encoding method has been exploited for successful realization of the CNOT gate with light.

Abstract:Organosilicone thin films have been deposited by plasma polymerization (pp) in a plasma enhanced chemical vapor deposition (PECVD) system using hexamethyldisilazane (HMDSN:C6H19Si2N) as a monomer precursor, at different biases of the stainless-steel substrate holder. The substrate bias affected film thickness, surface morphology, chemical composition and photoluminescence (PL) emission. For a negatively biased substrate, it is found that the film thickness is the minimum, while the porosity and PL emission are the maximum. For a positively biased substrate, the thickness and the ratio of Si/N are the maximum which correspond to a blue shift of the PL emission in comparison with the case of non-biased grounded substrate. In addition, the characterization of the plasma using a single cylindrical Langmuir probe has been performed to obtain information about both the electron density and the positive ion energy, where it can be concluded that the ion energy plays a major role in determining film thickness.

Abstract:Great challenges and demands are presented by increasing edge computing services for current elastic optical networks (EONs) to deal with serious diversity and complexity of these services. To improve the match degree between edge computing and optical network, the services awareness function is necessary for EON. This article proposes a Naive long short-term memory (Naive-LSTM) based services awareness strategy of the EON, where the Naive-LSTM model makes full use of the most simplified structure and conducts discretization of the LSTM model. Moreover, the proposed algorithm can generate the probability output result to determine the quality of service (QoS) policy of EONs. After well learning operation, these Naive-LSTM classification agents in edge nodes of EONs are able to perform services awareness by obtaining data traffic characteristics from services traffics. Test results show that the proposed approach is feasible and efficient to improve edge computing ability of EONs.

Abstract:If there are a lot of inputs, the readability of the “If-then” fuzzy rule is reduced, and the complexity of the fuzzy neural network structure will be increased. Hence, to optimize the structure of the fuzzy rule based neural network, a group Lasso based redundancy-controlled feature selection (input pruning) method is proposed. For realizing feature selection, the linear/nonlinear redundancy between features is considered, and the Pearson's correlation coefficient is employed to construct the additive redundancy-controlled regularizer in the error function. In addition, considering the past gradient information, a novel parameter optimization method is presented. Finally, we demonstrate the effectiveness of our method on two benchmark classification datasets.

Abstract:This research suggests a methodology to optimize Elman neural network based on improved slime mould algorithm (ISMA) to anticipate the aero optical imaging deviation. The improved Tent chaotic sequence is added to the SMA to initialize the population to accelerate the algorithm's speed of convergence. Additionally, an improved random opposition-based learning was added to further enhance the algorithm's performance in addressing problems that the SMA has such as weak convergence ability in the late iteration and an easy tendency to fall into local optimization in the optimization process when solving the optimization problem. Finally, the algorithm model is compared to the Elman neural network and the SMA optimization Elman neural network model. The three models are assessed using four evaluation indicators, and the findings demonstrate that the ISMA optimization model can anticipate the aero optical imaging deviation in an accurate way.

Abstract:Edge computing plays an active role in empowering the power industry as a key technology for establishing data-driven Internet of things (IoT) applications. Traditional defect diagnosis mainly relies on regular inspection of equipment by operation and maintenance personnel at all levels, and its accuracy relies on the human experience. In actual production, the image data of some dashboard damage types are easy to collect in large quantities, while some dashboard damage types occur less frequently and are more difficult to collect. The use of edge computing nodes allows flexible and fast collection of smart meter data and transmission of the reduced data or results to a cloud computing center. In this study, we provide a fresh balanced training approach to address the issue of learning from unbalanced data. In the equilibrium training phase, a new impact balance loss is introduced to reduce the influence of samples on the overfitting decision boundary. Experimental results show that the proposed balance loss function effectively improves the performance of various types of imbalance learning methods.

Abstract:Gaze estimation has become an important field of image and information processing. Estimating gaze from full-face images using convolutional neural network (CNN) has achieved fine accuracy. However, estimating gaze from eye images is very challenging due to the less information contained in eye images than in full-face images, and it’s still vital since eye-image-based methods have wider applications. In this paper, we propose the discretization-gaze network (DGaze-Net) to optimize monocular three-dimensional (3D) gaze estimation accuracy by feature discretization and attention mechanism. The gaze predictor of DGaze-Net is optimized based on feature discretization. By discretizing the gaze angle into K bins, a classification constraint is added to the gaze predictor. In the gaze predictor, the gaze angle is pre-applied with a binned classification before regressing with the real gaze angle to improve gaze estimation accuracy. In addition, the attention mechanism is applied to the backbone to enhance the ability to extract eye features related to gaze. The proposed method is validated on three gaze datasets and achieves encouraging gaze estimation accuracy.

Abstract:Fundus images are commonly used to capture changes in fundus structures and the severity of fundus lesions, and are the basis for detecting and treating ophthalmic diseases as well as other important diseases. This study proposes an automatic diagnosis method for multiple fundus lesions based on a deep graph neural network (GNN). 2 083 fundus images were collected and annotated to develop and evaluate the performance of the algorithm. First, high-level semantic features of fundus images are extracted using deep convolutional neural networks (CNNs). Then the features are input into the GNN to model the correlation between different lesions by mining and learning the correlation between lesions. Finally, the input and output features of the GNN are fused, and a multi-label classifier is used to complete the automatic diagnosis of fundus lesions. Experimental results show that the method proposed in this study can learn the correlations between lesions to improve the diagnostic performance of the algorithm, achieving better performance than the original ResNet and DenseNet models in both qualitative and quantitative evaluation.

Abstract:High-velocity small-sized space debris with a diameter of 1—10 cm can cause huge damage to orbiting satellites and spacecraft. In recent years, the technology of actively removing small-sized space debris by high-energy pulsed laser irradiation has attracted widespread attention from scholars around the world, who strive for giving the maximum protection to the safety of the low-earth orbit environment. This paper focuses on exploring the dynamic behavior of centimeter-sized space debris under space-based pulsed laser irradiation. For this purpose, a fluid-structure-thermal-plasma multiphysics coupling model is built for space debris, and the effect law of plasma plumes produced by space debris after laser irradiation at different time is drawn. The simulation and measurement results are compared for analysis, verifying the validity and reliability of the proposed method and the built simulation model. The findings of this study are expected to provide an important theoretical reference and guidance for the research on the application of pulsed lasers to the active removal of centimeter-sized space debris.