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Records with Subject: Intelligent Systems
Showing records 26 to 50 of 218. [First] Page: 1 2 3 4 5 6 Last
Prediction Model of Suspension Density in the Dense Medium Separation System Based on LSTM
Cheng Zheng, Jianjun Deng, Zhixin Hong, Guanghui Wang
December 22, 2020 (v1)
Keywords: dense medium separation, LSTM, prediction model, suspension density
In the dense medium separation system of coal preparation plant, the fluctuation of raw coal ash and lag of suspension density adjustment often causes the instability of product quality. To solve this problem, this study established a suspension density prediction model for the dense medium separation system based on Long Short-Term Memory (LSTM). First, the historical data in the dense medium separation system of a coal preparation plant were collected and preprocessed. Moving average and cubic exponential smoothing methods were used to replace abnormal data and to fill in the missing data, respectively. Second, a LSTM network was used to construct the density prediction model, and the optimal number of time steps, hidden layers, and nodes was determined. Finally, the model was employed on a testing set for prediction, and a Back-Propagation (BP) network without a time series was used for comparison. Root Mean Squared Error (RMSE) were the minimum when the number of the hidden layers,... [more]
Application of Systems Engineering Principles and Techniques in Biological Big Data Analytics: A Review
Q. Peter He, Jin Wang
December 17, 2020 (v1)
Keywords: biological big data, dynamic analysis, feature engineering, Machine Learning, overfitting, systems engineering
In the past few decades, we have witnessed tremendous advancements in biology, life sciences and healthcare. These advancements are due in no small part to the big data made available by various high-throughput technologies, the ever-advancing computing power, and the algorithmic advancements in machine learning. Specifically, big data analytics such as statistical and machine learning has become an essential tool in these rapidly developing fields. As a result, the subject has drawn increased attention and many review papers have been published in just the past few years on the subject. Different from all existing reviews, this work focuses on the application of systems, engineering principles and techniques in addressing some of the common challenges in big data analytics for biological, biomedical and healthcare applications. Specifically, this review focuses on the following three key areas in biological big data analytics where systems engineering principles and techniques have be... [more]
EEG Synchronization Analysis for Seizure Prediction: A Study on Data of Noninvasive Recordings
Paolo Detti, Giampaolo Vatti, Garazi Zabalo Manrique de Lara
November 9, 2020 (v1)
Keywords: data classification, EEG data, epilepsy, synchronization measures, threshold-based classifier
Objective: Epilepsy is a neurological disorder arising from anomalies of the electrical activity in the brain, affecting ~65 million individuals worldwide. Prediction methods, typically based on machine learning methods, require a large amount of data for training, in order to correctly classify seizures with small false alarm rates. Methods: In this work, we present a new database containing EEG scalp signals of 14 epileptic patients acquired at the Unit of Neurology and Neurophysiology of the University of Siena, Italy. Furthermore, a patient-specific seizure prediction method, based on the detection of synchronization patterns in the EEG, is proposed and tested on the data of the database. The use of noninvasive EEG data aims to explore the possibility of developing a noninvasive monitoring/control device for the prediction of seizures. The prediction method employs synchronization measures computed over all channel pairs and a computationally inexpensive threshold-based classificat... [more]
Performance Prediction Model of Solid Oxide Fuel Cell System Based on Neural Network Autoregressive with External Input Method
Shan-Jen Cheng, Jing-Kai Lin
November 9, 2020 (v1)
Keywords: multi-step prediction, NNARX model, SOFC, Taguchi orthogonal array
An accurate performance prediction model for the solid oxide fuel cell (SOFC) system not only contributes to the realization of the operating condition but also plays a role in long-term prediction performance. Accordingly, a research study has been developed to suitably deal with the time-series model and accurately build the performance prediction model of SOFC system based on neural network autoregressive with external input (NNARX) method. The architecture regressor parameters of the NNARX model were efficiently determined using the Taguchi orthogonal array (OA) method for optimal sets. The identified and evaluated optimal parameter levels were used to conduct an analysis of variance (ANOVA) to prove correctness. Moreover, a series of statistics criteria and multi-step prediction were also employed for investigating the uncertainty of the predicted model and solve the overfitting and under fitting problems; further. These criteria were also used to determine the performance of the... [more]
Visual-Based Multi-Section Welding Path Generation Algorithm
Hong Lu, Mingtian Ma, Shu Liu, Essa Alghannam, Yue Zang, Shuo Li, Weixin Zhang
November 9, 2020 (v1)
Keywords: non-standard groove, path generation algorithm, vision system, welding
As an important form of additive manufacturing, welding is widely used in steel components welding work of construction, shipbuilding and other fields. In this study, an intelligent welding path generation algorithm based on multi-section interpolation is proposed in order to deal with non-standard multi-pass welding grooves which are difficult to be handled by automatic welding equipment in the construction site. Firstly, the non-standard grooves are classified and the reasons for their occurrence are discussed. Secondly, an automatic welding additive manufacturing system framework is discussed and an appropriate detection method is selected. Then, combining with the welding standard of non-standard grooves and the characteristics of the welding process, a multi-section interpolation-based welding path generation algorithm is proposed. Finally, a visual experiment platform was built to detection the typical non-standard groove and the welding experiment is implemented to verify the fe... [more]
A Workflow Incorporating an Artificial Neural Network to Predict Subsurface Porosity for CO2 Storage Geological Site Characterization
George Koperna, Hunter Jonsson, Richie Ness, Shawna Cyphers, JohnRyan MacGregor
November 9, 2020 (v1)
Keywords: Carbon Capture Storage, Machine Learning, Petrophysics
The large scale and complexity of Carbon, Capture, Storage (CCS) projects necessitates time and cost saving strategies to strengthen investment and widespread deployment of this technology. Here, we successfully demonstrate a novel geologic site characterization workflow using an Artificial Neural Network (ANN) at the Southeast Regional Carbon Anthropogenic Test in Citronelle, Alabama. The Anthropogenic Test Site occurs within the Citronelle oilfield which contains hundreds of wells with electrical logs that lack critical porosity measurements. Three new test wells were drilled at the injection site and each well was paired with a nearby legacy well containing vintage electrical logs. The test wells were logged for measurements of density porosity and cored over the storage reservoir. An Artificial Neural Network was developed, trained, and validated using patterns recognized between the between vintage electrical logs and modern density porosity measurements at each well pair. The tra... [more]
Reckoning the Dearth of Bioinformatics in the Arena of Diabetic Nephropathy (DN)—Need to Improvise
Jae-Wook Oh, Manikandan Muthu, Steve W. Haga, Vimala Anthonydhason, Piby Paul, Sechul Chun
October 26, 2020 (v1)
Keywords: bioinformatics, diabetes, diabetic nephropathy, microalbumin, proteomics
Diabetic nephropathy (DN) is a recent rising concern amongst diabetics and diabetologist. Characterized by abnormal renal function and ending in total loss of kidney function, this is becoming a lurking danger for the ever increasing population of diabetics. This review touches upon the intensity of this complication and briefly reviews the role of bioinformatics in the area of diabetes. The advances made in the area of DN using proteomic approaches are presented. Compared to the enumerable inputs observed through the use of bioinformatics resources in the area of proteomics and even diabetes, the existing scenario of skeletal application of bioinformatics advances to DN is highlighted and the reasons behind this discussed. As this review highlights, almost none of the well-established tools that have brought breakthroughs in proteomic research have been applied into DN. Laborious, voluminous, cost expensive and time-consuming methodologies and advances in diagnostics and biomarker dis... [more]
Product Quality Detection through Manufacturing Process Based on Sequential Patterns Considering Deep Semantic Learning and Process Rules
Liguo Yao, Haisong Huang, Shih-Huan Chen
October 6, 2020 (v1)
Keywords: deep semantic learning, frequent pattern mining, manufacturing process diagnostics, manufacturing process rule, product quality detection
Companies accumulate a large amount of production process data during product manufacturing. Sequence data from the mining production process can enable a company to evaluate the manufacturing process, to find the key factors affecting product quality, and to improve product quality. However, the production process mainly exists in the form of text. To solve this problem, we propose a novel frequent pattern mining algorithm (EABMC) based on the text context semantics and rules of the manufacturing process to remove redundant sequences and to obtain good mining results. In this algorithm, first, we use embeddings from language models (ELMo ) to improve the process of text similarity matching and to classify similar semantic processes into one class. Then, the manufacturing process unit (MPU) is proposed by extracting the characteristics of manufacturing process data according to the constraints of the manufacturing process and other conditions. The above two steps cause the complex manu... [more]
Modelling Acetification with Artificial Neural Networks and Comparison with Alternative Procedures
Jorge E. Jiménez-Hornero, Inés María Santos-Dueñas, Isidoro García-García
October 6, 2020 (v1)
Keywords: acetification, artificial neural networks, bioreactor systems, Modelling, multilayer perceptron, vinegar
Modelling techniques allow certain processes to be characterized and optimized without the need for experimentation. One of the crucial steps in vinegar production is the biotransformation of ethanol into acetic acid by acetic bacteria. This step has been extensively studied by using two predictive models: first-principles models and black-box models. The fact that first-principles models are less accurate than black-box models under extreme bacterial growth conditions suggests that the kinetic equations used by the former, and hence their goodness of fit, can be further improved. By contrast, black-box models predict acetic acid production accurately enough under virtually any operating conditions. In this work, we trained black-box models based on Artificial Neural Networks (ANNs) of the multilayer perceptron (MLP) type and containing a single hidden layer to model acetification. The small number of data typically available for a bioprocess makes it rather difficult to identify the m... [more]
The Neural Network Revamping the Process’s Reliability in Deep Lean via Internet of Things
Ahmed M. Abed, Samia Elattar, Tamer S. Gaafar, Fadwa Moh. Alrowais
September 23, 2020 (v1)
Keywords: circulation number, deep learning, DMAIC, eddy waste control, Reynolds number
Deep lean is a novel approach that is concerned with the profound analysis for waste’s behavior at hidden layers in manufacturing processes to enhance processes’ reliability level at the upstream. Ideal Standard Co. for bathtubs suffered from defects and cost losses in the spraying section, due to differences in the painting cover thickness due to bubbles, caused by eddies, which move toward the bathtubs through hoses. These bubbles and their movement are considered as a form of lean’s waste. The spraying liquid inside the tanks and hoses must move with uniform velocity, viscosity, pressure, feed rate and suitable Reynolds circulation values to eliminate the eddy causes. These factors are tackled through the adoption Internet of Things (IoT) technologies that are aided by neural networks (NN) when an abnormal flow rate is detected using sensor data in real-time that can reduce the defects. The NN aimed at forecasting eddies’ movement lines that carry bubbles and works on being blasted... [more]
Image-Based Model for Assessment of Wood Chip Quality and Mixture Ratios
Thomas Plankenbühler, Sebastian Kolb, Fabian Grümer, Dominik Müller, Jürgen Karl
September 23, 2020 (v1)
Keywords: Biomass, biomass power plant, fuel quality, image analysis, Machine Learning, regression modeling
This article focuses on fuel quality in biomass power plants and describes an online prediction method based on image analysis and regression modeling. The main goal is to determine the mixture fraction from blends of two wood chip species with different qualities and properties. Starting from images of both fuels and different mixtures, we used two different approaches to deduce feature vectors. The first one relied on integral brightness values while the latter used spatial texture information. The features were used as input data for linear and non-linear regression models in nine training classes. This permitted the subsequent prediction of mixture ratios from prior unknown similar images. We extensively discuss the influence of model and image selection, parametrization, the application of boosting algorithms and training strategies. We obtained models featuring predictive accuracies of R2 > 0.9 for the brightness-based model and R2 > 0.8 for the texture based one during the valid... [more]
MPPIF-Net: Identification of Plasmodium Falciparum Parasite Mitochondrial Proteins Using Deep Features with Multilayer Bi-directional LSTM
Samee Ullah Khan, Ran Baik
September 23, 2020 (v1)
Keywords: bi-directional LSTM, Machine Learning, mitochondrial protein, plasmodium falciparum
Mitochondrial proteins of Plasmodium falciparum (MPPF) are an important target for anti-malarial drugs, but their identification through manual experimentation is costly, and in turn, their related drugs production by pharmaceutical institutions involves a prolonged time duration. Therefore, it is highly desirable for pharmaceutical companies to develop computationally automated and reliable approach to identify proteins precisely, resulting in appropriate drug production in a timely manner. In this direction, several computationally intelligent techniques are developed to extract local features from biological sequences using machine learning methods followed by various classifiers to discriminate the nature of proteins. Unfortunately, these techniques demonstrate poor performance while capturing contextual features from sequence patterns, yielding non-representative classifiers. In this paper, we proposed a sequence-based framework to extract deep and representative features that are... [more]
An Artificial Intelligence Approach to Predict the Thermophysical Properties of MWCNT Nanofluids
Balaji Bakthavatchalam, Nagoor Basha Shaik, Patthi Bin Hussain
September 15, 2020 (v1)
Keywords: Artificial Neural Networks, experimental data, nanofluids, prediction, thermophysical properties
Experimental data of thermal conductivity, thermal stability, specific heat capacity, viscosity, UV−vis (light transmittance) and FTIR (light absorption) of Multiwalled Carbon Nanotubes (MWCNTs) dispersed in glycols, alcohols and water with the addition of sodium dodecylbenzene sulfonate (SDBS) surfactant for 0.5 wt % concentration along a temperature range of 25 °C to 200 °C were verified using Artificial Neural Networks (ANNs). In this research, an ANN approach was proposed using experimental datasets to predict the relative thermophysical properties of the tested nanofluids in the available literature. Throughout the designed network, 65% and 25% of data points were comprehended in the training and testing set while the other 10% was utilized as a validation set. The parameters such as temperature, concentration, size and time were considered as inputs while the thermophysical properties were considered as outputs to develop ANN models of further predictions with unseen datasets. Th... [more]
Investigating Vapour Cloud Explosion Dynamic Fatality Risk on Offshore Platforms by Using a Grid-Based Framework
Usama Muhammad Niazi, Mohammad Shakir Nasif, Masdi Muhammad, Faisal Khan
September 15, 2020 (v1)
Keywords: Bayesian inference, CFD modelling, grid-based approach, human fatality dynamic risk, vapour cloud explosions
The reliability of petroleum offshore platform systems affects human safety and well-being; hence, it should be considered in plant design and operation in order to determine its effect on human fatality risk. Methane Vapour Cloud Explosions (VCE) in offshore platforms are known to be one of the fatal potential accidents that can be attributed to failure in plant safety systems. Traditional Quantitative Risk Analysis (QRA) lacks in providing microlevel risk assessment studies and are unable to update risk with the passage of time. This study proposes a grid-based dynamic risk analysis framework for analysing the effect of VCEs on the risk of human fatality in an offshore platform. Flame Acceleration Simulator (FLACS), which is a Computational Fluid Dynamics (CFD) software, is used to model VCEs, taking into account different wind and leakage conditions. To estimate the dynamic risk, Bayesian Inference (BI) is utilised using Accident Sequence Precursor (ASP) data. The proposed framework... [more]
A Feed-Forward Back Propagation Neural Network Approach to Predict the Life Condition of Crude Oil Pipeline
Nagoor Basha Shaik, Srinivasa Rao Pedapati, Syed Ali Ammar Taqvi, A. R. Othman, Faizul Azly Abd Dzubir
August 29, 2020 (v1)
Keywords: artificial neural networks, deterioration, estimated repair factor, life prediction, pipeline
Pipelines are like a lifeline that is vital to a nation’s economic sustainability; as such, pipelines need to be monitored to optimize their performance as well as reduce the product losses incurred in the transportation of petroleum chemicals. A significant number of pipes would be underground; it is of immediate concern to identify and analyse the level of corrosion and assess the quality of a pipe. Therefore, this study intends to present the development of an intelligent model that predicts the condition of crude oil pipeline cantered on specific factors such as metal loss anomalies (over length, width and depth), wall thickness, weld anomalies and pressure flow. The model is developed using Feed-Forward Back Propagation Network (FFBPN) based on historical inspection data from oil and gas fields. The model was trained using the Levenberg-Marquardt algorithm by changing the number of hidden neurons to achieve promising results in terms of maximum Coefficient of determination (R2) va... [more]
Progressive System: A Deep-Learning Framework for Real-Time Data in Industrial Production
Yifeng Liu, Wei Zhang, Wenhao Du
August 5, 2020 (v1)
Keywords: deep-learning, few-shot learning, image classification, real-time systems
Deep learning based on a large number of high-quality data plays an important role in many industries. However, deep learning is hard to directly embed in the real-time system, because the data accumulation of the system depends on real-time acquisitions. However, the analysis tasks of such systems need to be carried out in real time, which makes it impossible to complete the analysis tasks by accumulating data for a long time. In order to solve the problems of high-quality data accumulation, high timeliness of the data analysis, and difficulty in embedding deep-learning algorithms directly in real-time systems, this paper proposes a new progressive deep-learning framework and conducts experiments on image recognition. The experimental results show that the proposed framework is effective and performs well and can reach a conclusion similar to the deep-learning framework based on large-scale data.
Measuring Performance Metrics of Machine Learning Algorithms for Detecting and Classifying Transposable Elements
Simon Orozco-Arias, Johan S. Piña, Reinel Tabares-Soto, Luis F. Castillo-Ossa, Romain Guyot, Gustavo Isaza
August 5, 2020 (v1)
Keywords: classification, deep learning, detection, Machine Learning, metrics, transposable elements
Because of the promising results obtained by machine learning (ML) approaches in several fields, every day is more common, the utilization of ML to solve problems in bioinformatics. In genomics, a current issue is to detect and classify transposable elements (TEs) because of the tedious tasks involved in bioinformatics methods. Thus, ML was recently evaluated for TE datasets, demonstrating better results than bioinformatics applications. A crucial step for ML approaches is the selection of metrics that measure the realistic performance of algorithms. Each metric has specific characteristics and measures properties that may be different from the predicted results. Although the most commonly used way to compare measures is by using empirical analysis, a non-result-based methodology has been proposed, called measure invariance properties. These properties are calculated on the basis of whether a given measure changes its value under certain modifications in the confusion matrix, giving co... [more]
MobileNetV2 Ensemble for Cervical Precancerous Lesions Classification
Cătălin Buiu, Vlad-Rareş Dănăilă, Cristina Nicoleta Răduţă
July 17, 2020 (v1)
Keywords: biomedical image processing, cervical cancer, computer-aided diagnosis, deep learning, ensemble, machine learning algorithms, MobileNetV2, transfer learning
Women’s cancers remain a major challenge for many health systems. Between 1991 and 2017, the death rate for all major cancers fell continuously in the United States, excluding uterine cervix and uterine corpus cancers. Together with HPV (Human Papillomavirus) testing and cytology, colposcopy has played a central role in cervical cancer screening. This medical procedure allows physicians to view the cervix at a magnification of up to 10%. This paper presents an automated colposcopy image analysis framework for the classification of precancerous and cancerous lesions of the uterine cervix. This framework is based on an ensemble of MobileNetV2 networks. Our experimental results show that this method achieves accuracies of 83.33% and 91.66% on the four-class and binary classification tasks, respectively. These results are promising for the future use of automatic classification methods based on deep learning as tools to support medical doctors.
An Adjective Selection Personality Assessment Method Using Gradient Boosting Machine Learning
Bruno Fernandes, Alfonso González-Briones, Paulo Novais, Miguel Calafate, Cesar Analide, José Neves
July 17, 2020 (v1)
Keywords: Affective Computing, gradient boosting, Machine Learning, personality assessment
Goldberg’s 100 Unipolar Markers remains one of the most popular ways to measure personality traits, in particular, the Big Five. An important reduction was later preformed by Saucier, using a sub-set of 40 markers. Both assessments are performed by presenting a set of markers, or adjectives, to the subject, requesting him to quantify each marker using a 9-point rating scale. Consequently, the goal of this study is to conduct experiments and propose a shorter alternative where the subject is only required to identify which adjectives describe him the most. Hence, a web platform was developed for data collection, requesting subjects to rate each adjective and select those describing him the most. Based on a Gradient Boosting approach, two distinct Machine Learning architectures were conceived, tuned and evaluated. The first makes use of regressors to provide an exact score of the Big Five while the second uses classifiers to provide a binned output. As input, both receive the one-hot enc... [more]
AOC-OPTICS: Automatic Online Classification for Condition Monitoring of Rolling Bearing
Hassane Hotait, Xavier Chiementin, Lanto Rasolofondraibe
July 17, 2020 (v1)
Keywords: classification, condition monitoring, OPTICS, rolling bearing
Bearings are essential components in rotating machines. They ensure the rotation and power transmission. So, these components are essential elements for industrial machines. Thus, real-time monitoring is required to detect a possible anomaly, diagnose the failure of rolling bearing and follow its evolution. This paper presents a methodology for automatic online implementation of fault diagnosis of rolling bearings, by AOC-OPTICS (automatic online classification monitoring based on ordering points to identify clustering structure, OPTICS). The algorithm consists of three phases namely: initialization, detection and follow-up. These phases use the combination of features extraction methods, smart ranking, features weighting and classification by the OPTICS method. Two methods have been integrated in the dimension reduction step to improve the efficiency of detection and the followed of the defect (relief method and t-distributed stochastic neighbor embedding method). Thus, the determinat... [more]
Applying Differential Neural Networks to Characterize Microbial Interactions in an Ex Vivo Gastrointestinal Gut Simulator
Misael Sebastián Gradilla-Hernández, Alejandro García-González, Anne Gschaedler, Enrique J. Herrera-López, Marisela González-Avila, Ricardo García-Gamboa, Carlos Yebra Montes, Rita Q. Fuentes-Aguilar
July 17, 2020 (v1)
Keywords: differential neural network, generalized Lotka-Volterra model, microbial interactions, mixed cultures
The structure of mixed microbial cultures—such as the human gut microbiota—is influenced by a complex interplay of interactions among its community members. The objective of this study was to propose a strategy to characterize microbial interactions between particular members of the community occurring in a simulator of the human gastrointestinal tract used as the experimental system. Four runs were carried out separately in the simulator: two of them were fed with a normal diet (control system), and two more had the same diet supplemented with agave fructans (fructan-supplemented system). The growth kinetics of Lactobacillus spp., Bifidobacterium spp., Salmonella spp., and Clostridium spp. were assessed in the different colon sections of the simulator for a nine-day period. The time series of microbial concentrations were used to estimate specific growth rates and pair-wise interaction coefficients as considered by the generalized Lotka-Volterra (gLV) model. A differential neural netw... [more]
Election Algorithm for Random k Satisfiability in the Hopfield Neural Network
Saratha Sathasivam, Mohd. Asyraf Mansor, Mohd Shareduwan Mohd Kasihmuddin, Hamza Abubakar
July 17, 2020 (v1)
Keywords: election algorithm, exhaustive search, Genetic Algorithm, Hopfield neural network, random k satisfiability
Election Algorithm (EA) is a novel variant of the socio-political metaheuristic algorithm, inspired by the presidential election model conducted globally. In this research, we will investigate the effect of Bipolar EA in enhancing the learning processes of a Hopfield Neural Network (HNN) to generate global solutions for Random k Satisfiability (RANkSAT) logical representation. Specifically, this paper utilizes a bipolar EA incorporated with the HNN in optimizing RANkSAT representation. The main goal of the learning processes in our study is to ensure the cost function of RANkSAT converges to zero, indicating the logic function is satisfied. The effective learning phase will affect the final states of RANkSAT and determine whether the final energy is a global minimum or local minimum. The comparison will be made by adopting the same network and logical rule with the conventional learning algorithm, namely, exhaustive search (ES) and genetic algorithm (GA), respectively. Performance eval... [more]
Implementation Criteria for Intelligent Systems in Motor Production Line Process Management
Yao-Chin Lin, Ching-Chuan Yeh, Wei-Hung Chen, Kai-Yen Hsu
July 7, 2020 (v1)
Keywords: intelligent systems, motor production lines, process management
In this study, the factors that affect the implementation of intelligent systems in motor production lines are analyzed. A motor production line located in Vietnam is used as the research object. The research methods include secondary data collection, field study, and interviews. This study demonstrates the following: firstly, the implementation of intelligent systems in motor production lines is heading toward Industry 4.0. Secondly, it is proposed that three functional systems—robot arm, image recognition, and big data analysis—can be introduced in the motor production line. This study analyzes the process involved in coil and motor production lines and attempts to combine intelligent system functions. It is expected that in the future, manpower will be reduced, production line productivity will increase, and intelligent production lines will be proposed. The factors that affect the introduction of intelligent systems in motor production lines are improved, and the importance of inte... [more]
Intelligent Setting Method of Reagent Dosage Based on Time Series Froth Image in Zinc Flotation Process
Zhaohui Tang, Liyong Tang, Guoyong Zhang, Yongfang Xie, Jinping Liu
July 7, 2020 (v1)
Keywords: cumulative distribution function, flotation process, reagent dosage, time series froth image
It is well known that the change of the reagent dosage during the flotation process will cause the froth image to change continuously with time. Therefore, an intelligent setting method based on the time series froth image in the zinc flotation process is proposed. Firstly, the sigmoid kernel function is used to estimate the cumulative distribution function of bubble size, and the cumulative distribution function shape is characterized by sigmoid kernel function parameters. Since the reagent will affect the froth image over a period of time, the time series of bubble size cumulative distribution function is processed by the ELMo model and the dynamic feature vectors are output. Finally, XGBoost is used to establish the nonlinear relationship modeling between reagent dosage and dynamic feature vectors. Industrial experiments have proved the effectiveness of the proposed method.
Integrating Support Vector Regression with Genetic Algorithm for Hydrate Formation Condition Prediction
Jie Cao, Shijie Zhu, Chao Li, Bing Han
July 2, 2020 (v1)
Keywords: gas hydrate, Genetic Algorithm, outlier detection, support vector machine
To predict the natural gas hydrate formation conditions quickly and accurately, a novel hybrid genetic algorithm−support vector machine (GA-SVM) model was developed. The input variables of the model are the relative molecular weight of the natural gas (M) and the hydrate formation pressure (P). The output variable is the hydrate formation temperature (T). Among 10 gas samples, 457 of 688 data points were used for training to identify the optimal support vector machine (SVM) model structure. The remaining 231 data points were used to evaluate the generalisation capability of the best trained SVM model. Comparisons with nine other models and analysis of the outlier detection revealed that the GA-SVM model had the smallest average absolute relative deviation (0.04%). Additionally, the proposed GA-SVM model had the smallest amount of outlier data and the best stability in predicting the gas hydrate formation conditions in the gas relative molecular weight range of 15.64−28.97 g/mol and the... [more]
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