scholarly journals Intelligent Rework Process Management System under Smart Factory Environment

2020 ◽  
Vol 12 (23) ◽  
pp. 9883
Author(s):  
Da-Seol Jo ◽  
Tae-Woong Kim ◽  
Jun-Woo Kim
Keyword(s):  
Process Management ◽  
Sustainable Manufacturing ◽  
Information And Communications Technology ◽  
Classification Model ◽  
Defective Items ◽  
Latent Defects ◽  
Rpm System ◽  
Novel Approach ◽  
Reduction Effect ◽  
Rework Process

Rework for defective items is very common in practical shopfloors; however, it generally causes unnecessary energy consumptions and operational costs. In order to address this problem, we propose a novel approach called the intelligent rework process management (i-RPM) system. The proposed system is based on intelligent rework policy, which provides a preventive rework procedure for items with latent defects. Such items can be detected before quality tests by applying conventional classification techniques. Moreover, training sets for the classification algorithms can be collected by using modern information and communications technology (ICT) infrastructures. Items with latent defects are not allowed to proceed to the following processes under intelligent rework policy. Instead, they are returned to the preceding processes for rework in order to avoid unnecessary losses on the shopfloor. Consequently, the proposed system helps to achieve a sustainable manufacturing system. Nevertheless, misclassification by the classification model can degrade the performance of intelligent rework policy. Therefore, the i-RPM system is designed to compare rework policies based on classification accuracy and choose the best one of them. For illustration, we applied the i-RPM system to the rework procedure of a steel manufacturer located in Busan, South Korea, and our experiment results revealed that the cost reduction effect of the intelligent rework policy is affected by several input parameters.

scholarly journals Application of Innovative TGA/Chemometric Approach for Forensic Purposes: The Estimation of the Time since Death in Contaminated Specimens

Diagnostics ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 121
Author(s):  
Roberta Risoluti ◽  
Giuseppina Gullifa ◽  
Vittorio Fineschi ◽  
Paola Frati ◽  
Stefano Materazzi
Keyword(s):  
Multidisciplinary Approach ◽  
Vitreous Humor ◽  
Classification Model ◽  
Forensic Sciences ◽  
Time Since Death ◽  
Time Of Death ◽  
Novel Approach ◽  
Chemometric Approach ◽  
Postmortem Examinations

Chronothanatology has always been a challenge in forensic sciences. Therefore, the importance of a multidisciplinary approach for the characterization of matrices (organs, tissues, or fluids) that respond linearly to the postmortem interval (PMI) is emerging increasingly. The vitreous humor is particularly suitable for studies aimed at assessing time-related modifications because it is topographically isolated and well-protected. In this work, a novel approach based on thermogravimetry and chemometrics was used to estimate the time since death in the vitreous humor and to collect a databank of samples derived from postmortem examinations after medico–legal evaluation. In this study, contaminated and uncontaminated specimens with tissue fragments were included in order to develop a classification model to predict time of death based on partial least squares discriminant analysis (PLS-DA) that was as robust as possible. Results demonstrate the possibility to correctly predict the PMI even in contaminated samples, with an accuracy not lower than 70%. In addition, the correlation coefficient of the measured versus predicted outcomes was found to be 0.9978, confirming the ability of the model to extend its feasibility even to such situations involving contaminated vitreous humor.

scholarly journals A Novel Approach to Oil Layer Recognition Model Using Whale Optimization Algorithm and Semi-Supervised SVM

Symmetry ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 757
Author(s):  
Yongke Pan ◽  
Kewen Xia ◽  
Li Wang ◽  
Ziping He
Keyword(s):  
Optimization Algorithm ◽  
Oil And Gas ◽  
The Other ◽  
Whale Optimization Algorithm ◽  
Classification Model ◽  
Support Vector ◽  
Recognition Model ◽  
Novel Approach ◽  
Whale Optimization ◽  
Oil And Gas Reservoir

The dataset distribution of actual logging is asymmetric, as most logging data are unlabeled. With the traditional classification model, it is hard to predict the oil and gas reservoir accurately. Therefore, a novel approach to the oil layer recognition model using the improved whale swarm algorithm (WOA) and semi-supervised support vector machine (S3VM) is proposed in this paper. At first, in order to overcome the shortcomings of the Whale Optimization Algorithm applied in the parameter-optimization of the S3VM model, such as falling into a local optimization and low convergence precision, an improved WOA was proposed according to the adaptive cloud strategy and the catfish effect. Then, the improved WOA was used to optimize the kernel parameters of S3VM for oil layer recognition. In this paper, the improved WOA is used to test 15 benchmark functions of CEC2005 compared with five other algorithms. The IWOA–S3VM model is used to classify the five kinds of UCI datasets compared with the other two algorithms. Finally, the IWOA–S3VM model is used for oil layer recognition. The result shows that (1) the improved WOA has better convergence speed and optimization ability than the other five algorithms, and (2) the IWOA–S3VM model has better recognition precision when the dataset contains a labeled and unlabeled dataset in oil layer recognition.

scholarly journals Using physiological signals to predict temporal defense responses: a multi-modality analysis

2020 ◽  
Author(s):  
Tae-jun Choi ◽  
Honggu Lee
Keyword(s):  
Defense Response ◽  
Defense Responses ◽  
Physiological Signals ◽  
Classification Model ◽  
Post Traumatic Stress ◽  
Obsessive Compulsive ◽  
Compulsive Disorder ◽  
Novel Approach ◽  
Data Driven Approach ◽  
Response Classification

AbstractDefense responses are a highly conserved behavioral response set across species. Defense responses motivate organisms to detect and react to threats and potential danger as a precursor to anxiety. Accurate measurement of temporal defense responses is important for understanding clinical anxiety and mood disorders, such as post-traumatic stress disorder, obsessive compulsive disorder, and generalized anxiety disorder. Within these conditions, anxiety is defined as a state of prolonged defense response elicitation to a threat that is ambiguous or unspecific. In this study, we aimed to develop a data-driven approach to capture temporal defense response elicitation through a multi-modality data analysis of physiological signals, including electroencephalogram (EEG), electrocardiogram (ECG), and eye-tracking information. A fear conditioning paradigm was adopted to develop a defense response classification model. From a classification model based on 42 feature sets, a higher order crossing feature set-based model was chosen for further analysis with cross-validation loss of 0.0462 (SEM: 0.0077). To validate our model, we compared predicted defense response occurrence ratios from a comprehensive situation that generates defense responses by watching movie clips with fear awareness and threat existence predictability, which have been reported to correlate with defense response elicitation in previous studies. We observed that defense response occurrence ratios are correlated with threat existence predictability, but not with fear awareness. These results are similar to those of previous studies using comprehensive situations. Our study provides insight into measurement of temporal defense responses via a novel approach, which can improve understanding of anxiety and related clinical disorders for neurobiological and clinical researchers.

scholarly journals CROSS-LANGUAGE TEXT CLASSIFICATION WITH CONVOLUTIONAL NEURAL NETWORKS FROM SCRATCH

2017 ◽  
Vol 2 ◽  
pp. 24-33 ◽  
Author(s):  
Musbah Zaid Enweiji ◽  
Taras Lehinevych ◽  
Аndrey Glybovets
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Current Method ◽  
Classification Model ◽  
The Novel ◽  
Novel Approach ◽  
Multilingual Learning ◽  
Cross Language ◽  
Language Text ◽  
Language Classification

Cross language classification is an important task in multilingual learning, where documents in different languages often share the same set of categories. The main goal is to reduce the labeling cost of training classification model for each individual language. The novel approach by using Convolutional Neural Networks for multilingual language classification is proposed in this article. It learns representation of knowledge gained from languages. Moreover, current method works for new individual language, which was not used in training. The results of empirical study on large dataset of 21 languages demonstrate robustness and competitiveness of the presented approach.

scholarly journals Inventory Modeling for Imperfect Production Process with Inspection Errors, Sales Return, and Imperfect Rework Process

2017 ◽  
Vol 2 (4) ◽  
pp. 242-258 ◽  
Author(s):  
Aditi Khanna ◽  
Aakanksha Kishore ◽  
Chandra K. Jaggi
Keyword(s):  
Customer Service ◽  
Real Life ◽  
Type I ◽  
Defective Items ◽  
Optimal Production ◽  
Human Errors ◽  
Inspection Errors ◽  
Production Policy ◽  
Good Customer Service ◽  
Rework Process

In real life, due to certain machine problems, process deterioration and many other factors, production processes deliver imperfect quality items. So, the effect of these defectives cannot be ignored in terms of ensuring good customer service. In order to sustain today’s cut-throat competition, rework process of defective items becomes a rescue to compensate for the imperfections present in the production system. The present model attempts to explore the traditional imperfect environment with a more practical approach by incorporating the concept of inspection errors, along with an imperfect rework process. By considering human errors as unavoidable, Type-I and Type-II errors are also incorporated in the study. To prioritize on the customer satisfaction level, Sales returns are given full price refunds. An analytical method is employed to maximize the expected total profit per unit time to study the combined effect of aforementioned factors on the optimal production quantity. A numerical example along with a comprehensive sensitivity analysis has been presented to demonstrate the applicability of the model and also to observe the effects of key parameters on the optimal production policy respectively. The pertinence of the model can be found in most manufacturing industries like textile, electronics, furniture, footwear, crockery etc.

Automated Planning of Process Models

2011 ◽  
pp. 169-194 ◽  
Author(s):  
Bernd Heinrich ◽  
Mathias Klier ◽  
Steffen Zimmermann
Keyword(s):  
Business Process ◽  
Process Modeling ◽  
Financial Services ◽  
Business Process Management ◽  
Process Management ◽  
Automated Planning ◽  
Process Models ◽  
Control Structures ◽  
Manual Task ◽  
Novel Approach

Companies need to adapt their processes quickly in order to react to changing customer demands or new regulations, for example. Process models are an appropriate means to support process setup but currently the (re)design of process models is a time-consuming manual task. Semantic Business Process Management, in combination with planning approaches, can alleviate this drawback. This means that the workload of (manual) process modeling could be reduced by constructing models in an automated way. Since existing, traditional planning algorithms show drawbacks for the application in Semantic Business Process Management, we introduce a novel approach that is suitable especially for the Semantic-based Planning of process models. In this chapter, we focus on the semantic reasoning, which is necessary in order to construct control structures, such as decision nodes, which are vital elements of process models. We illustrate our approach by a running example taken from the financial services domain. Moreover, we demonstrate its applicability by a prototype and provide some insights into the evaluation of our approach.

Artificial Intelligence in Business Processes

2022 ◽  
pp. 205-230
Author(s):  
S. Asif Basit
Keyword(s):  
Neural Networks ◽  
Process Management ◽  
Business Processes ◽  
Control Process ◽  
Shop Floor ◽  
Business Environments ◽  
Novel Approach ◽  
Governing Factor ◽  
Artificial Neurons ◽  
Process Interaction

The aim of this chapter is to establish that the principles used by neural networks can be applied to business process management. The similarity between artificial neurons and business processes, and hence between neural networks and process landscapes, will be demonstrated. This novel approach leads to an emphasis on process interactions and their effect on actions as a major governing factor in controlling process outputs. Stigmergic interaction in biological systems is explored in the context of business processes, and its potential to understand process interaction is investigated. In order to verify the use of stigmergy in business environments, a pilot study is described in which shop floor business processes in a retailing environment are observed and described using a stigmergic framework. Establishing the viability of using stigmergic interaction to control process actions and outputs is the first step towards designing neural process networks.

scholarly journals Predicting Currency Crises: A Novel Approach Combining Random Forests and Wavelet Transform

2018 ◽  
Vol 11 (4) ◽  
pp. 86 ◽  
Author(s):  
Lei Xu ◽  
Takuji Kinkyo ◽  
Shigeyuki Hamori
Keyword(s):  
Exchange Rate ◽  
Wavelet Transform ◽  
Real Exchange Rate ◽  
Random Forests ◽  
Currency Crises ◽  
Predictive Accuracy ◽  
Classification Model ◽  
Novel Approach ◽  
Variable Importance Measures ◽  
High Level

We propose a novel approach that combines random forests and the wavelet transform to model the prediction of currency crises. Our classification model of random forests, built using both standard predictors and wavelet predictors, and obtained from the wavelet transform, achieves a demonstrably high level of predictive accuracy. We also use variable importance measures to find that wavelet predictors are key predictors of crises. In particular, we find that real exchange rate appreciation and overvaluation, which are measured over a horizon of 16–32 months, are the most important.

scholarly journals Application of Electrical Network Frequency of Digital Recordings for Location-Stamp Verification

2019 ◽  
Vol 9 (15) ◽  
pp. 3135 ◽  
Author(s):  
Mrinmoy Sarkar ◽  
Dhiman Chowdhury ◽  
Celia Shahnaz ◽  
Shaikh Anowarul Fattah
Keyword(s):  
Power Distribution ◽  
Performance Evaluations ◽  
Classification Model ◽  
Electrical Network ◽  
Support Vector ◽  
Distribution Grid ◽  
Audio Signals ◽  
Novel Approach ◽  
The World ◽  
Digital Recordings

Electrical network frequency (ENF) is a signature of a power distribution grid. It represents the deviation from the nominal frequency (50 or 60 Hz) of a power system network. The variations in ENF sequences within a grid are subject to load fluctuations within that particular grid. These ENF variations are inherently located in a multimedia signal, which is recorded close to the grid or directly from the mains power line. Thus, the specific location of a recording can be identified by analyzing the ENF sequences of the multimedia signal in absence of the concurrent power signal. In this article, a novel approach to location-stamp authentication based on ENF sequences of digital recordings is presented. ENF patterns are extracted from a number of power and audio signals recorded in different grid locations across the world. The extracted ENF signals are decomposed into low outliers and high outliers frequency segments and potential feature vectors are determined for these ENF segments by statistical and signal processing analysis. Then, a multi-class support vector machine (SVM) classification model is developed to verify the location-stamp information of the recordings. The performance evaluations corroborate the efficacy of the proposed framework.

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