A Computer Vision-Based System for Metal Sheet Pick Counting

Inventory counting is crucial to manufacturing industries in terms of inventory management,production,and procurement planning.Many companies currently require workers to manually count and track the status of materials,which are repetitive and non-value-added activities but incur significant costs to the companies as well as mental fatigue to the employees.This research aims to develop a computer vision system that can automate the material counting activity without applying any marker on the material.The type of material of interest is metal sheet,whose shape is simple,a large rectangular shape,yet difficult to detect.The use of computer vision technology can reduce the costs incurred fromthe loss of high-value materials,eliminate repetitive work requirements for skilled labor,and reduce human error.A computer vision system is proposed and tested on a metal sheet picking process formultiple metal sheet stacks in the storage area by using one video camera.Our results show that the proposed computer vision system can count the metal sheet picks under a real situation with a precision of 97.83%and a recall of 100%.

A Computer Vision-Based Model for Automatic Motion Time Study

Motion time study is employed by manufacturing industries to determine operation time.An accurate estimate of operation time is crucial for effective process improvement and production planning.Traditional motion time study is conducted by human analysts with stopwatches,which may be exposed to human errors.In this paper,an automated time study model based on computer vision is proposed.The model integrates a convolutional neural network,which analyzes a video of a manual operation to classify work elements in each video frame,with a time study model that automatically estimates the work element times.An experiment is conducted using a grayscale video and a color video of a manual assembly operation.The work element times from the model are statistically compared to the reference work element time values.The result shows no statistical difference among the time data,which clearly demonstrates the effectiveness of the proposed model.

AN ENSEMBLE MODEL OF ARIMA AND ANN WITH RESTRICTED BOLTZMANN MACHINE BASED ON DECOMPOSITION OF DISCRETE WAVELET TRANSFORM FOR TIME SERIES FORECASTING

Time series forecasting research area mainly focuses on developing effective forecasting models toimprove prediction accuracy. An ensemble model composed of autoregressive integrated movingaverage （ARIMA）, artificial neural network （ANN）, restricted Boltzmann machines （RBM）, anddiscrete wavelet transform （DWT） is presented in this paper. In the proposed model, DWT firstdecomposes time series into approximation and detail. Then Khashei and Bijari＇s model, which is anensemble model of ARIMA and ANN, is applied to the approximation and detail to extract their bothlinear and nonlinear components and fit the relationship between the components as a function insteadof additive relationship. Furthermore, RBM is used to perform pre-training for generating initialweights and biases based on inputs feature for ANN. Finally, the forecasted approximation and detailare combined to obtain final forecasting. The forecasting capability of the proposed model is testedwith three well-known time series： sunspot, Canadian lynx, exchange rate time series. The predictionperformance is compared to the other six forecasting models. The results indicate that the proposedmodel gives the best performance in all three data sets and all three measures （i.e. MSE, MAE andMAPE）.