Incremental Discriminant Analysis on Interval-Valued Parameters for Emitter Identification

Emitter identification has been widely recognized as one crucial issue for communication, electronic reconnaissance, and radar intelligence analysis. However, the measurements of emitter signal parameters typically take the form of uncertain intervals rather than precise values. In addition, the measurements are generally accumulated dynamically and continuously. As a result, one imminent task has become how to carry out discriminant analysis of interval-valued parameters incrementally for emitter identification. Existing machine learning approaches for interval-valued data analysis are unfit for this purpose as they generally assume a uniform distribution and are usually restricted to static data analysis. To address the above problems, we bring forward an incremental discriminant analysis method on interval-valued parameters (IDAIP) for emitter identification. Extensive experiments on both synthetic and real-life data sets have validated the efficiency and effectiveness of our method

Improving garment thermal insulation property by combining two non-contact measuring tools

To investigate the effect of air gaps on the heat transfer performance of clothing, the method using the combination of two non-contact measuring tools (infrared thermal camera and 3D body scanner) has been developed considering the quantification of the air gap thickness and clothing surface temperature of different body parts without contacting clothing surface directly. The results show that the air gaps over middle and lower back of upper body have the largest thickness in all body parts, while the front and back shoulders have the smallest air gap thickness. The one-way analysis of variance shows that air gap thickness under shoulder segments has no significant difference in terms of size. Furthermore, clothing surface temperatures of shoulder and chest decrease gradually along with air gap thickness; clothing surface temperatures of front abdomen, front waist, pelvis and hip segments decrease initially but begin to increase when the air gap is above 1.5cm; clothing surface temperatures of middle back and back waist continually increase with the air gap thickness. Based on the comprehensive analyzation of the distributed features of air gap thickness and clothing surface temperature of different body parts, a revised clothing pattern with lower regional temperature and higher thermal insulation is put forward

Effect of Graphene Embedment on Fiber–Matrix Interface and Tensile Properties of FRCM Composites

This paper presents the results of an experimental study to investigate the effects of two types of graphene, dried (DG) and hydrated graphene (HG), when enhancing the interfacial and tensile mechanical properties of fabric-reinforced cementitious matrix (FRCM) composites. The inclusion of DG and HG could produce an improvement in the tensile strength of the FRCM composites by increasing the tensile strength of the mortar paste and the amount of fibers that participate in load bearing due to the increased penetration of mortar (cement hydrates) into the fiber bundle. The better dispersion of HG produces better results than DG. The maximum increases in the overall tensile strengths of the FRCM composites with DG and HG are 18% and 31%, respectively, with the majority of these improvements coming from the increase in the number of fibers that participate in load bearing. The microstructure images indicate increases of up to 20% and 44% in the mortar penetration thickness into the fiber bundles using DG and HG, respectively.<br/

Machine learning-based prediction models for patients no-show in online outpatient appointments

With the development of information and communication technologies, all public tertiary hospitals in China began to use online outpatient appointment systems. However, the phenomenon of patient no-shows in online outpatient appointments is becoming more serious. The objective of this study is to design a prediction model for patient no-shows, thereby assisting hospitals in making relevant decisions, and reducing the probability of patient no-show behavior. We used 382,004 original online outpatient appointment records, and divided the data set into a training set (N1 = 286,503), and a validation set (N2 = 95,501). We used machine learning algorithms such as logistic regression, k-nearest neighbor (KNN), boosting, decision tree (DT), random forest (RF) and bagging to design prediction models for patient no-show in online outpatient appointments. The patient no-show rate of online outpatient appointment was 11.1% (N = 42,224). From the validation set, bagging had the highest area under the ROC curve and AUC value, which was 0.990, followed by random forest and boosting models, which were 0.987 and 0.976, respectively. In contrast, compared with the previous prediction models, the area under ROC and AUC values of the logistic regression, decision tree, and k-nearest neighbors were lower at 0.597, 0.499 and 0.843, respectively. This study demonstrates the possibility of using data from multiple sources to predict patient no-shows. The prediction model results can provide decision basis for hospitals to reduce medical resource waste, develop effective outpatient appointment policies, and optimize operations

Multi-Objective Topology Optimization for Curved Arm of Multifunctional Billet Tong Based on Characterization of Working Conditions

A windlass driven heavy duty multifunctional billet tong was designed for large-scale forging and casting to reduce the number of auxiliary material handling devices in manufacturing workshops. To improve its mechanical performance and safety, a novel multi-objective topology optimization method for its curved arm is proposed in this paper. Firstly, the influence of different open angles and working frequencies for the curved arm was simplified to a multi-objective optimization problem. A comprehensive evaluation function was constructed using the compromise programming method, and a mathematical model of multi-objective topology optimization was established. Meanwhile, a radar chart was employed to portray the comparative measures of working conditions, the weight coefficient for each working condition was determined based on the corresponding enclosed areas, combining the stress indices, the displacement indices and the frequency indices of all working conditions. The optimization results showed that the stiffness and strength of the curved arm can be improved while its weight can be reduced by 10.77%, which shows that it is feasible and promising to achieve a lightweight design of the curved arm of a billet tong. The proposed method can be extended to other equipment with complex working conditions

Multi-Objective Topology Optimization for Curved Arm of Multifunctional Billet Tong Based on Characterization of Working Conditions

A windlass driven heavy duty multifunctional billet tong was designed for large-scale forging and casting to reduce the number of auxiliary material handling devices in manufacturing workshops. To improve its mechanical performance and safety, a novel multi-objective topology optimization method for its curved arm is proposed in this paper. Firstly, the influence of different open angles and working frequencies for the curved arm was simplified to a multi-objective optimization problem. A comprehensive evaluation function was constructed using the compromise programming method, and a mathematical model of multi-objective topology optimization was established. Meanwhile, a radar chart was employed to portray the comparative measures of working conditions, the weight coefficient for each working condition was determined based on the corresponding enclosed areas, combining the stress indices, the displacement indices and the frequency indices of all working conditions. The optimization results showed that the stiffness and strength of the curved arm can be improved while its weight can be reduced by 10.77%, which shows that it is feasible and promising to achieve a lightweight design of the curved arm of a billet tong. The proposed method can be extended to other equipment with complex working conditions