Relative efficiency appraisal of discrete choice modeling algorithms using small-scale maximum likelihood estimator through empirically tailored computing environment

Discrete choice models are widely used in multiple sectors such as transportation, health, energy, and marketing, etc., where the model estimation is usually carried out by using commercial software. Nonetheless, tailored computer codes offer modellers greater flexibility and control of unique modelling situation. Aligned with empirically tailored computing environment, this research discusses the relative performance of six different algorithms of a discrete choice model using three key performance measures： convergence time, number of iterations, and iteration time. The computer codes are developed by using Visual Basic Application （VBA）. Maximum likelihood function （MLF） is formulated and the mathematical relationships of gradient and Hessian matrix are analytically derived to carry out the estimation process. The estimated parameter values clearly suggest that convergence criterion and initial guessing of parameters are the two critical factors in determining the overall estimation performance of a custom-built discrete choice model.

A fast detection algorithm for ceramic ball surface defects based on fringe reflection

A ceramic ball is a basic part widely used in precision bearings.There is no perfect testing equipment for ceramic ball surface defects at present.A fast visual detection algorithm for ceramic ball surface defects based on fringe reflection is designed.By means of image preprocessing,grayscale value accumulative differential positioning,edge detection,pixel-value row difference and template matching,the algorithm can locate feature points and judge whether the spherical surface has defects by the number of points.Taking black silicon nitride ceramic balls with a diameter of 6.35 mm as an example,the defect detection time for a single gray scale image is 0.78 s,and the detection limit is 16.5μm.

An improved Vibe algorithm for illumination mutations

The visual background extractor(Vibe)algorithm can lead to a large area of false detection in the extracted foreground target when the illumination is mutated.An improved Vibe method based on the YCbCr color space and improved three-frame difference is proposed in this paper.The algorithm detects the illumination mutation frames accurately based on the difference between the luminance components of two frames adjacent to a video frame.If there exists a foreground moving target in the previous frame of the mutated frame,three-frame difference method is utilized;otherwise,Vibe method using current frame is used to initialize background.Improved three-frame differential method based on the difference in brightness between two frames of the video changes the size of the threshold adaptively to reduce the interference of noise on the foreground extraction.Experiment results show that the improved Vibe algorithm can not only suppress the“ghost”phenomenon effectively but also improve the accuracy and completeness of target detection,as well as reduce error rate of detection when the illumination is mutated.

Application of Multivariate Reinforcement Learning Engine in Optimizing the Power Generation Process of Domestic Waste Incineration

Garbage incineration is an ideal method for the harmless and resource-oriented treatment of urban domestic waste.However,current domestic waste incineration power plants often face challenges related to maintaining consistent steam production and high operational costs.This article capitalizes on the technical advantages of big data artificial intelligence,optimizing the power generation process of domestic waste incineration as the entry point,and adopts four main engine modules of Alibaba Cloud reinforcement learning algorithm engine,operating parameter prediction engine,anomaly recognition engine,and video visual recognition algorithm engine.The reinforcement learning algorithm extracts the operational parameters of each incinerator to obtain a control benchmark.Through the operating parameter prediction algorithm,prediction models for drum pressure,primary steam flow,NOx,SO2,and HCl are constructed to achieve short-term prediction of operational parameters,ultimately improving control performance.The anomaly recognition algorithm develops a thickness identification model for the material layer in the drying section,allowing for rapid and effective assessment of feed material thickness to ensure uniformity control.Meanwhile,the visual recognition algorithm identifies flame images and assesses the combustion status and location of the combustion fire line within the furnace.This real-time understanding of furnace flame combustion conditions guides adjustments to the grate and air volume.Integrating AI technology into the waste incineration sector empowers the environmental protection industry with the potential to leverage big data.This development holds practical significance in optimizing the harmless and resource-oriented treatment of urban domestic waste,reducing operational costs,and increasing efficiency.

Grayscale image statistics of COVID-19 patient CT scans characterize lung condition with machine and deep learning

Background:Grayscale image attributes of computed tomography(CT)of pulmonary scans contain valuable information relating to patients with respiratory ailments.These attributes are used to evaluate the severity of lung conditions of patients confirmed to be with and without COVID-19.Method:Five hundred thirteen CT images relating to 57 patients(49 with COVID-19;8 free of COVID-19)were collected at Namazi Medical Centre(Shiraz,Iran)in 2020 and 2021.Five visual scores(VS:0,1,2,3,or 4)are clinically assigned to these images with the score increasing with the severity of COVID-19-related lung conditions.Eleven deep learning and machine learning techniques(DL/ML)are used to distinguish the VS class based on 12 grayscale image attributes.Results:The convolutional neural network achieves 96.49%VS accuracy(18 errors from 513 images)successfully distinguishing VS Classes 0 and 1,outperforming clinicians’visual inspections.An algorithmic score(AS),involving just five grayscale image attributes,is developed independently of clinicians’assessments(99.81%AS accuracy;1 error from 513 images).Conclusion:Grayscale CT image attributes can be successfully used to distinguish the severity of COVID-19 lung damage.The AS technique developed provides a suitable basis for an automated system using ML/DL methods and 12 image attributes.