Sequence-to-Point Learning Based on Spatio-Temporal Attention Fusion Network for Non-Intrusive Load Monitoring

Gcha-Net: Global Context and Hybrid Attention Network for Automatic Liver Segmentation

A Soft Sensing Method of Billet Surface Temperature Based on ILGSSA-LSSVM

Abstract It is difficult to measure the surface temperature of continuous casting billet, which results in the lack of important feedback parameters for further scientific control of billet quality. This paper proposes a sparrow search algorithm(SSA) to optimize the least square support vector machine(LSSVM) model for surface temperature prediction of billet, which is improved by Logistic chaotic mapping and Golden sine algorithm (Improve Logistic Golden Sine Sparrow Search Algorithm LSSVM, ILGSSA-LSSVM). Based on LSSVM, the optimal solution of sparrow population is improved by improved Logistic chaos mapping and Golden sine algorithm as the value of penalty factor \(\gamma\)and kernel parameter\(\sigma\). Global optimization method is adopted to find the optimal parameter combination, which reduces the influence of blindly determining parameters on the prediction accuracy. The results show that the maximum error of ILGSA-LSSVM soft Sensing model is 3.85733℃, the minimum error is 0.0174℃, and the average error is 0.05805℃. The performance of ILGSA-LSSVM soft Sensing model is better than that of traditional least square support vector machine, BP neural network and gray Wolf optimized least square support vector machine.

Response map evaluation for RGBT tracking

Inner Wall Temperature Distribution Measurement of The Ladle Based on Cavity Effective Emissivity Correction

Abstract Inner wall temperature of ladle is closely related to the quality of steelmaking and control of steel-making tapping temperature. This article adopts a rotating platform to drive an infrared temperature sensor and a laser sensor to scan the temperature field distribution of the ladle inner wall at the hot repair station, where the scanning laser sensor obtains coordinates of each measured point. Because of measuring errors of infrared thermal radiation caused by emissivity uncertainty of the ladle inner wall surface, this article proposes a method for temperature measurement based on Monte Carlo model for effective emissivity correction of each measured point. In the model, we consider the ladle and fire baffle as a cavity. By calculation of the model, the effect of distance from the fire baffle to the ladle and the material surface emissivity of the ladle inner wall on the effective emissivity of the cavity are obtained. After that, the effective emissivity of each measured point is determined. Then the scanning temperature of each measured point is corrected to real temperature. By field measuring test and verification contrast, the results show that: the maximum absolute error of the method in this article is 4.7℃, the minimum error is 0.6℃, and the average error is less than 2.8℃. The method in this article achieves high measurement accuracy and contributes to the control of metallurgical process based on temperature information.

Fuzzy region-based active contour driven by global and local fitting energy for image segmentation

Multi-scale channel-attention model for speaker recognition with comparable loss functions

Level Set Method in Medical Imaging Segmentation

A Color Distance Model Based on Visual Recognition

In computer vision, Euclidean Distance is generally used to measure the color distance between two colors. And how to deal with illumination change is still an important research topic. However, our evaluation results demonstrate that Euclidean Distance does not perform well under illumination change. Since human eyes can recognize similar or irrelevant colors under illumination change, a novel color distance model based on visual recognition is proposed. First, we find that various colors are distributed complexly in color spaces. We propose to divide the HSV space into three less complex subspaces, and study their specific distance models. Then a novel hue distance is modeled based on visual recognition, and the chromatic distance model is proposed in line with our visual color distance principles. Finally, the gray distance model and the dark distance model are studied according to the natures of their subspaces, respectively. Experimental results show that the proposed model outperforms Euclidean Distance and the related methods and achieves a good distance measure against illumination change. In addition, the proposed model obtains good performance for matching patches of pedestrian images. The proposed model can be applied to image segmentation, pedestrian reidentification, visual tracking, and patch or superpixel-based tasks.

Local Kernel-Induced Fitting Variational Model for MRI Image Segmentation

Globally convex variational model for multiphsae image segmentation

A convex approaches toward global minimization for fast multiphase image segmentation

Influencing Factors Analysis in Simulation of Head-windscreen Laminated Glass Impacting

Multiphase Image Segmentation from a Statistical Framework

An Experimental Comparison of Semi-supervised Learning Algorithms for Multispectral Image Classification

Parametric kernel-driven active contours for image segmentation

Efficient and robust fragments-based multiple kernels tracking

Robust fragments-based tracking with adaptive feature selection