Fabric defect detection algorithm based on PHOG and SVM

In order to effectively improve the detection probabilityfor different types of fabrics and defects, a fabric defectdetection method based on pyramid histogram of edge orientationgradients (PHOG) and support vector machine (SVM) has beenproposed. The algorithm combines fabric texture statisticalmethod and machine learning method. It has two main parts,namely the feature extraction and classification. The detectionprocess mainly includes image segmentation, PHOG featureextraction, SVM model training and detection classification. Thesimulation results show that, based on the detection rate and thefalse alarm rate, the algorithm has a good detection andclassification effect, has a certain robustness, and can be appliedto the actual production department

Fabric defect detection algorithm based on PHOG and SVM

123-126In order to effectively improve the detection probability for different types of fabrics and defects, a fabric defect detection method based on pyramid histogram of edge orientation gradients (PHOG) and support vector machine (SVM) has been proposed. The algorithm combines fabric texture statistical method and machine learning method. It has two main parts, namely the feature extraction and classification. The detection process mainly includes image segmentation, PHOG feature extraction, SVM model training and detection classification. The simulation results show that, based on the detection rate and the false alarm rate, the algorithm has a good detection and classification effect, has a certain robustness, and can be applied to the actual production department

Novologue Therapy Improves Mitochondrial Bioenergetics and Modulates Transcriptome Changes in Diabetic Sensory Neurons

Diabetic peripheral neuropathy (DPN) is a prevalent diabetic complication with scarce treatment options. Impaired neuronal mitochondrial bioenergetics contributes to the pathophysiologic progression of DPN and may be a focal point for disease management. We have demonstrated that modulating Hsp90 and Hsp70 with the small-molecule drug KU-32 ameliorates psychosensory, electrophysiologic, morphologic, and bioenergetic deficits of DPN in animal models of type 1 diabetes. The current study used mouse models of type 1 and type 2 diabetes to determine the relationship of changes in sensory neuron mitochondrial bioenergetics to the onset of and recovery from DPN. The onset of DPN showed a tight temporal correlation with a decrease in mitochondrial bioenergetics in a genetic model of type 2 diabetes. In contrast, sensory hypoalgesia developed 10 weeks before the occurrence of significant declines in sensory neuron mitochondrial bioenergetics in the type 1 model. KU-32 therapy improved mitochondrial bioenergetics in both the type 1 and type 2 models, and this tightly correlated with a decrease in DPN. Mechanistically, improved mitochondrial function following KU-32 therapy required Hsp70, since the drug was ineffective in diabetic Hsp70 knockout mice. Our data indicate that changes in mitochondrial bioenergetics may rapidly contribute to nerve dysfunction in type 2 diabetes, but not type 1 diabetes, and that modulating Hsp70 offers an effective approach toward correcting sensory neuron bioenergetic deficits and DPN in both type 1 and type 2 diabetes. We also sought to determine whether KU-596, an analogue of KU-32, offers similar therapeutic potential for treating DPN. Similar to KU-32, KU-596 improved psychosensory and bioenergetic deficits of DPN in a dose-dependent manner. However, the drug could not improve DPN in Hsp70 KO mice. Transcriptomic analysis using RNA sequencing (RNA-Seq) of DRG from diabetic wild type (WT) and Hsp70 KO mice revealed that KU-596 modulated transcription of genes involved in inflammatory pathways independently of Hsp70. In contrast, the effects of KU-596 on genes involved in the production of reactive oxygen species (ROS) are Hsp70-dependent. Our data indicate that modulation of molecular chaperones offers an effective approach towards correcting nerve dysfunction, and that normalization of inflammatory pathways alone by novologue therapy seems to be insufficient to reverse the deficits associated with insensate DPN in our model of type 1 diabetes

A Nonlinear Model Order Reduction Framework for Dynamic Vapor Compression Cycles via Proper Orthogonal Decomposition

A computationally efficient and accurate modeling approach is critically important for designing and evaluating controls and fault detection and diagnosis (FDD) algorithms. This paper proposes a reduced order modeling approach for vapor compression cycles (VCC) that involves application of nonlinear model order reduction (MOR) methods to dynamic heat exchanger (HX) models to generate reduced order HX models. A reformulated finite volume HX model was first developed that matches the baseline MOR model structure. Then, a nonlinear MOR framework based on Proper Orthogonal Decomposition (POD) and a Discrete Empirical Interpolation Method (DEIM) was developed for generating nonlinear reduced order HX models. The proposed approach was implemented within a comprehensive VCC model. Reduced order HX models were constructed for a centrifugal chiller system and coupled to quasistatic models of a compressor and expansion valve to complete the reduced order VCC model. The reduced cycle model was implemented within the Modelicabased platform and used to predict loadchange transients over a wide range of operating conditions for comparison with measurements. The proposed reduced order modeling approach is computationally efficient and accurately captures cycle dynamics

Development Of A Fast Method For Retrieving Thermodynamic Properties To Accelerate Transient Vapor Compression Cycle Simulation

It has been previously demonstrated that the most significant computational requirements for vapor compression system models are associated with evaluation of thermodynamic properties. This is particularly important for transient models because properties are evaluated at each time step and the overall model can often run less than real time. The typical approach for evaluating thermodynamic properties involves the use of complicated equations of state (EOS), such as utilized in standard software tools such as RefProp and CoolProp. Overall computation speed can be significantly enhanced using interpolation methods that are based on pre-calculated thermodynamic properties. This paper presents an improved interpolation method to quickly and accurately retrieve refrigerant properties based on the neural networks. Since the approach has an explicit functional form, it is able to avoid the computation time to find nearest points in the thermodynamic database. Comparisons between the proposed method and Refprop are provided for a wide range of pressure and enthalpy to show its accuracy. Then, performance comparisons between the proposed and conventional interpolation methods for a transient vapor compression cycle simulation are provided

Symbol: Generating Flexible Black-Box Optimizers through Symbolic Equation Learning

Recent Meta-learning for Black-Box Optimization (MetaBBO) methods harness neural networks to meta-learn configurations of traditional black-box optimizers. Despite their success, they are inevitably restricted by the limitations of predefined hand-crafted optimizers. In this paper, we present \textsc{Symbol}, a novel framework that promotes the automated discovery of black-box optimizers through symbolic equation learning. Specifically, we propose a Symbolic Equation Generator (SEG) that allows closed-form optimization rules to be dynamically generated for specific tasks and optimization steps. Within \textsc{Symbol}, we then develop three distinct strategies based on reinforcement learning, so as to meta-learn the SEG efficiently. Extensive experiments reveal that the optimizers generated by \textsc{Symbol} not only surpass the state-of-the-art BBO and MetaBBO baselines, but also exhibit exceptional zero-shot generalization abilities across entirely unseen tasks with different problem dimensions, population sizes, and optimization horizons. Furthermore, we conduct in-depth analyses of our \textsc{Symbol} framework and the optimization rules that it generates, underscoring its desirable flexibility and interpretability.Comment: Published as a conference paper at ICLR 202