Prediction Algorithm of Spectral Reflectance of Spot Color Ink Based on Color Parallel and Superposition Model

2012 ◽  
Vol 430-432 ◽  
pp. 1176-1182
Author(s):  
Chao Rong Lin ◽  
Jun Fei Xu ◽  
Jin Lin Xu
Keyword(s):  
Prediction Model ◽  
Spectral Reflectance ◽  
Difficult Problem ◽  
Current Paper ◽  
Prediction Algorithm ◽  
Superposition Model ◽  
Yule Model ◽  
Computational Difficulty ◽  
Color Prediction ◽  
Spot Color

Spectral prediction of spot color ink has always been a difficult problem in the field of color research. The current paper adopted color parallel and superposition models to deal with the Clapper-Yule model and to simplify the prediction model of spectral reflectance of spot color ink. respectively. Based on the experimental verification. the simplified method has a certain practical application value. and the spot color prediction of general presswork can meet the requirements fully. The most important consideration is that the simplification can largely reduce the computational difficulty. Moreover. the current paper compared the accuracy of color parallel model with the superposition model to simplify the spectral prediction model of spot color ink. The result showed that the color superposition model has higher accuracy in simplifying the spectral prediction model of spot color ink.

scholarly journals A Genetic Algorithm Optimized RNN-LSTM Model for Remaining Useful Life Prediction of Turbofan Engine

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 285
Author(s):  
Kwok Tai Chui ◽  
Brij B. Gupta ◽  
Pandian Vasant
Keyword(s):  
Genetic Algorithm ◽  
Feature Extraction ◽  
Prediction Model ◽  
Prediction Models ◽  
Remaining Useful Life ◽  
Prediction Algorithm ◽  
Short Term ◽  
Turbofan Engine ◽  
Term Prediction ◽  
Useful Life

Understanding the remaining useful life (RUL) of equipment is crucial for optimal predictive maintenance (PdM). This addresses the issues of equipment downtime and unnecessary maintenance checks in run-to-failure maintenance and preventive maintenance. Both feature extraction and prediction algorithm have played crucial roles on the performance of RUL prediction models. A benchmark dataset, namely Turbofan Engine Degradation Simulation Dataset, was selected for performance analysis and evaluation. The proposal of the combination of complete ensemble empirical mode decomposition and wavelet packet transform for feature extraction could reduce the average root-mean-square error (RMSE) by 5.14–27.15% compared with six approaches. When it comes to the prediction algorithm, the results of the RUL prediction model could be that the equipment needs to be repaired or replaced within a shorter or a longer period of time. Incorporating this characteristic could enhance the performance of the RUL prediction model. In this paper, we have proposed the RUL prediction algorithm in combination with recurrent neural network (RNN) and long short-term memory (LSTM). The former takes the advantages of short-term prediction whereas the latter manages better in long-term prediction. The weights to combine RNN and LSTM were designed by non-dominated sorting genetic algorithm II (NSGA-II). It achieved average RMSE of 17.2. It improved the RMSE by 6.07–14.72% compared with baseline models, stand-alone RNN, and stand-alone LSTM. Compared with existing works, the RMSE improvement by proposed work is 12.95–39.32%.

scholarly journals Intelligent manufacturing of color blended yarn: Color matching algorithm and manufacturing process through computer numerically controlled ring spinning system

2021 ◽  
Vol 16 ◽  
pp. 155892502110065
Author(s):  
Peng Cui ◽  
Yuan Xue ◽  
Yuexing Liu ◽  
Xianqiang Sun
Keyword(s):  
Intelligent Manufacturing ◽  
Prediction Algorithm ◽  
Ring Spinning ◽  
Blended Yarn ◽  
Spun Yarn ◽  
Computer Numerically Controlled ◽  
Ring Spun Yarn ◽  
High Production ◽  
Color Prediction ◽  
Textile Products

Yarn-dyed textiles complement digital printing textiles, which hold promise for high production and environmentally friendly energy efficiencies. However, the complicated structures of color-blended yarns lead to unpredictable colors in textile products and become a roadblock to developing nonpollution textile products. In the present work, we propose a framework of intelligent manufacturing of color blended yarn by combining the color prediction algorithm with a self-developed computer numerically controlled (CNC) ring spinning system. The S-N model is used for the prediction of the color blending effect of the ring-spun yarn. The optimized blending ratios of ring-spun yarn are obtained based on the proposed linear model of parameter W. Subsequently, the CNC ring-spinning frame is used to manufacture color-blended yarns, which can configure the constituent fibers in such a way that different sections of yarn exhibit different colors.

scholarly journals Prediction Model for Occupational Incidents in Chemical and Gas Industries

2019 ◽  
Vol 8 (4) ◽  
pp. 3836-3840
Keyword(s):  
Prediction Model ◽  
Technical Information ◽  
Workplace Safety ◽  
Machine Learning Algorithms ◽  
Streaming Data ◽  
Industrial Safety ◽  
Prediction Algorithm ◽  
Scoring Algorithm ◽  
Inspection Data ◽  
The Right

Understanding occupational incidents is one of the important measures in workplace safety strategy. Analyzing the trends of the occupational incident data helps to identify the potential pain points and helps to reduce the loss. Optimizing the Machine Learning algorithms is a relatively new trend to fit the prediction model and algorithms in the right place to support human beneficial factors. The aim of this research is to build a prediction model to identify the occupational incidents in chemical and gas industries. This paper describes the architecture and approach of building and implementing the prediction model to predict the cause of the incident which can be used as a key index for achieving industrial safety in specific to chemical and gas industries. The implementation of the scoring algorithm coupled with prediction model should bring unbiased data to obtain logical conclusion. The prediction model has been trained against FACTS (Failure and Accidents Technical information system) is an incidents database which have 25,700 chemical industrial incidents with accident descriptions for the years span from 2004 to 2014. Inspection data and sensor logs should be fed on top of the trained dataset to verify and validate the implementation. The outcome of the implementation provides insight towards the understanding of the patterns, classifications, and also contributes to an enhanced understanding of quantitative and qualitative analytics. Cutting edge cloud-based technology opens up the gate to process the continuous in-streaming data, process it and output the desired result in real-time. The primary technology stack used in this architecture is Apache Kafka, Apache Spark Streaming, KSQL, Data frames, and AWS Lambda functions. Lambda functions are used to implement the scoring algorithm and prediction algorithm to write out the results back to AWS S3 buckets. Proof of concept implementation of the prediction model helps the industries to see through the incidents and will layout the base platform for the various safety-related implementations which always benefits the workplace's reputation, growth, and have less attrition in human resources.

Research on Method of Metro Monitoring Model Based on Grey Prediction Algorithm

2014 ◽  
Vol 548-549 ◽  
pp. 641-645
Author(s):  
Mao Hua Liu ◽  
Xiu Bo Sun
Keyword(s):  
Prediction Model ◽  
Safety Monitoring ◽  
Prediction Algorithm ◽  
Grey Prediction ◽  
Monitoring Point ◽  
Value Analysis ◽  
Grey Prediction Model ◽  
Monitoring Model ◽  
Surface Monitoring ◽  
Set Up

Grey prediction model is a model to predict the trend maturely, its application in the subway safety monitoring is of great significance. Set up by MATLAB software to complete the grey prediction model, and take the surface monitoring point for example, Comparing the prediction value with the actual measured value, analysis by the accuracy, obtain the trend of surface change around the subway station.

scholarly journals Improving Accuracy of the Kalman Filter Algorithm in Dynamic Conditions Using ANN-Based Learning Module

Symmetry ◽  
2019 ◽  
Vol 11 (1) ◽  
pp. 94 ◽  
Author(s):  
Israr Ullah ◽  
Muhammad Fayaz ◽  
DoHyeun Kim
Keyword(s):  
Kalman Filter ◽  
Prediction Model ◽  
Historical Data ◽  
Mean Squared Error ◽  
External Factors ◽  
Prediction Algorithm ◽  
Dynamic Conditions ◽  
Learning Module ◽  
Prediction Algorithms ◽  
Kalman Filter Algorithm

Prediction algorithms enable computers to learn from historical data in order to make accurate decisions about an uncertain future to maximize expected benefit or avoid potential loss. Conventional prediction algorithms are usually based on a trained model, which is learned from historical data. However, the problem with such prediction algorithms is their inability to adapt to dynamic scenarios and changing conditions. This paper presents a novel learning to prediction model to improve the performance of prediction algorithms under dynamic conditions. In the proposed model, a learning module is attached to the prediction algorithm, which acts as a supervisor to monitor and improve the performance of the prediction algorithm continuously by analyzing its output and considering external factors that may have an influence on its performance. To evaluate the effectiveness of the proposed learning to prediction model, we have developed the artificial neural network (ANN)-based learning module to improve the prediction accuracy of the Kalman filter algorithm as a case study. For experimental analysis, we consider a scenario where the Kalman filter algorithm is used to predict actual temperature from noisy sensor readings. the Kalman filter algorithm uses fixed process error covariance R, which is not suitable for dynamic situations where the error in sensor readings varies due to some external factors. In this study, we assume variable error in temperature sensor readings due to the changing humidity level. We have developed a learning module based on ANN to estimate the amount of error in current readings and to update R in the Kalman filter accordingly. Through experiments, we observed that the Kalman filter with the learning module performed better (4.41%–11.19%) than the conventional Kalman filter algorithm in terms of the root mean squared error metric.

scholarly journals Response of plant reflectance spectrum to simulated dust deposition and its estimation model

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Jiyou Zhu ◽  
Xinna Zhang ◽  
Weijun He ◽  
Xuemei Yan ◽  
Qiang Yu ◽  
...  
Keyword(s):  
Prediction Model ◽  
Spectral Reflectance ◽  
Coal Dust ◽  
Short Wave ◽  
Soil Dust ◽  
Wave Direction ◽  
Dust Deposition ◽  
Cement Dust ◽  
Blue Edge ◽  
Red Edge

Abstract To quantitatively reflect the relationship between dust and plant spectral reflectance. Dust from different sources in the city were selected to simulate the spectral characteristics of leaf dust. Taking Euonymus japonicus as the research object. Prediction model of leaf dust deposition was established based on spectral parameters. Results showed that among the three different dust pollutants, the reflection spectrum has 6 main reflection peaks and 7 main absorption valleys in 350–2500 nm. A steep reflection platform appears in the 692–763 nm band. In 760–1400 nm, the spectral reflectance gradually decreases with the increase of leaf dust coverage, and the variation range was coal dust > cement dust > pure soil dust. The spectral reflectance in 680–740 nm gradually decreases with the increase of leaf dust coverage. In the near infrared band, the fluctuation amplitude and slope of its first derivative spectrum gradually decrease with the increase of leaf dust. The biggest amplitude of variation was cement dust. With the increase of dust retention, the red edge position generally moves towards short wave direction, and the red edge slope generally decreases. The blue edge position moved to the short wave direction first and then to the long side direction, while the blue edge slope generally shows a decreasing trend. The yellow edge position moved to the long wave direction first and then to the short wave direction (coal dust, cement dust), and generally moved to the long side direction (pure soil dust). The yellow edge slope increases first and then decreases. The R2 values of the determination coefficients of the dust deposition prediction model have reached significant levels, which indicated that there was a relatively stable correlation between the spectral reflectance and dust deposition. The best prediction model of leaf dust deposition was leaf water content index model (y = 1.5019x − 1.4791, R2 = 0.7091, RMSE = 0.9725).

Spectral Prediction Model for Halftone Prints by Enhanced Clapper-Yule Model

2012 ◽  
Vol 457-458 ◽  
pp. 1405-1408
Author(s):  
Tao Meng ◽  
Chun Mei Zhang ◽  
Mi Dan Li ◽  
Yi Xiao Song ◽  
Tai Sun ◽  
...  
Keyword(s):  
Prediction Model ◽  
Fluorescent Imaging ◽  
New Model ◽  
Color Separation ◽  
Extended Application ◽  
Yule Model ◽  
Dot Gain ◽  
The Way

The classical Clapper-Yule model and its improved models will both introduced the extended application which supports rough printing and halftone color fluorescent imaging. The characteristics of the new model were analyzed and the prospect of the Clapper-Yule mode was discussed. We proposed a new model, which was an enhancement of the classical Clapper-Yule model, which simulate optical dot gain of halftone prints by taking into account lateral scattering within the paper bulk and multiple internal reflections. The model we propose also takes into account the reflectance of inks at surface of the specific colors at specific rates. The model opens the way towards color separation of images to be reproduced. Several designs printed on an offset press demonstrate their applicability and their benefits.

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