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Records with Subject: Intelligent Systems
119. LAPSE:2020.0217
Extreme Learning Machine-Based Model for Solubility Estimation of Hydrocarbon Gases in Electrolyte Solutions
February 12, 2020 (v1)
Subject: Intelligent Systems
Keywords: Big Data, chemical process model, data science, deep learning, electrolyte solution, extreme learning machines, hydrocarbon gases, Machine Learning, Natural Gas, prediction model, solubility
Calculating hydrocarbon components solubility of natural gases is known as one of the important issues for operational works in petroleum and chemical engineering. In this work, a novel solubility estimation tool has been proposed for hydrocarbon gases—including methane, ethane, propane, and butane—in aqueous electrolyte solutions based on extreme learning machine (ELM) algorithm. Comparing the ELM outputs with a comprehensive real databank which has 1175 solubility points yielded R-squared values of 0.985 and 0.987 for training and testing phases respectively. Furthermore, the visual comparison of estimated and actual hydrocarbon solubility led to confirm the ability of proposed solubility model. Additionally, sensitivity analysis has been employed on the input variables of model to identify their impacts on hydrocarbon solubility. Such a comprehensive and reliable study can help engineers and scientists to successfully determine the important thermodynamic properties, which are key f... [more]
120. LAPSE:2020.0176
GC-MS Fingerprints Profiling Using Machine Learning Models for Food Flavor Prediction
February 12, 2020 (v1)
Subject: Intelligent Systems
Keywords: convolutional neural network, fingerprint modeling, GC-MS/O profiling, Machine Learning, odor compounds
Food flavor quality evaluation is attracting continuous attention, but a suitable evaluation system is severely lacking. Gas chromatography-mass spectrometry/olfactometry (GC-MS/O) is widely used to solve the food flavor evaluation problem, but the olfactometry evaluation is unfeasible to be carried out in large batches and is unreliable due to potential issue of an operator or systematic laboratory effect. Thus, a novel fingerprint modeling and profiling process was proposed based on several machine learning models including convolutional neural network (CNN). The fingerprint template was created by the data analysis of existing GC-MS spectrum dataset. Then the fingerprint image generation program was applied for structuring the complex instrumental data. Food olfactometry result was obtained by a machine learning method based on CNN using fingerprint image as the input. The case study on peanut oil samples demonstrated the model accuracy of around 93%. By structure optimization and f... [more]
121. LAPSE:2020.0137
A Reference-Model-Based Artificial Neural Network Approach for a Temperature Control System
February 2, 2020 (v1)
Subject: Intelligent Systems
Keywords: artificial neural networks, I-PD control, reference model, temperature control
Artificial neural networks (ANNs), which have excellent self-learning performance, have been applied to various applications, such as target detection and industrial control. In this paper, a reference-model-based ANN controller with integral-proportional-derivative (I-PD) compensation has been proposed for temperature control systems. To improve the ANN self-learning efficiency, a reference model is introduced for providing the teaching signal for the ANN. System simulations were carried out in the MATLAB/SIMULINK environment and experiments were carried out on a digital-signal-processor (DSP)-based experimental platform. The simulation and experimental results were compared with those for a conventional I-PD control system. The effectiveness of the proposed method was verified.
122. LAPSE:2020.0068
Multivariate Analysis and Machine Learning for Ripeness Classification of Cape Gooseberry Fruits
January 7, 2020 (v1)
Subject: Intelligent Systems
Keywords: Cape gooseberry, color space combination, color space selection, food engineering
This paper explores five multivariate techniques for information fusion on sorting the visual ripeness of Cape gooseberry fruits (principal component analysis, linear discriminant analysis, independent component analysis, eigenvector centrality feature selection, and multi-cluster feature selection.) These techniques are applied to the concatenated channels corresponding to red, green, and blue (RGB), hue, saturation, value (HSV), and lightness, red/green value, and blue/yellow value (L*a*b) color spaces (9 features in total). Machine learning techniques have been reported for sorting the Cape gooseberry fruits’ ripeness. Classifiers such as neural networks, support vector machines, and nearest neighbors discriminate on fruit samples using different color spaces. Despite the color spaces being equivalent up to a transformation, a few classifiers enable better performances due to differences in the pixel distribution of samples. Experimental results show that selection and combination o... [more]
123. LAPSE:2020.0051
Development of New Algorithm for Aniline Point Estimation of Petroleum Fraction
January 7, 2020 (v1)
Subject: Intelligent Systems
Keywords: algorithm, aniline point, estimation, multiple linear regression, petroleum fraction
The aniline point (AP) is an important physical property of a petroleum fraction. The AP gives an indication of the aromatic hydrocarbon content in a hydrocarbon mixture and can also be an indicator of the ignition point of a diesel fraction. In this study, common estimation methods were introduced and evaluated, and their limitations were analyzed. Multiple linear regression was used in constructing a quantitative function to solve for the AP using the average boiling point and specific gravity. The iterative modification algorithm of the ternary interaction algorithm was used to obtain the predicted value of the petroleum fraction AP, and the proposed algorithm was tested using 127 actual petroleum fractions. The average estimation deviation of the proposed method was 3.55%; hence, compared to the commonly used estimation methods, the prediction accuracy was significantly improved. This method offers important practical value in the calculation of the petroleum fraction AP and other... [more]
124. LAPSE:2019.1640
Novel Parallel Heterogeneous Meta-Heuristic and Its Communication Strategies for the Prediction of Wind Power
December 16, 2019 (v1)
Subject: Intelligent Systems
Keywords: communication strategies, dynamic change, heterogeneous, neural network, parallel, prediction, wind power
Wind and other renewable energy protects the ecological environment and improves economic efficiency. However, it is difficult to accurately predict wind power because of the randomness and volatility of wind. This paper proposes a new parallel heterogeneous model to predict the wind power. Parallel meta-heuristic saves computation time and improves solution quality. Four communication strategies, which include ranking, combination, dynamic change and hybrid, are introduced to balance exploration and exploitation. The dynamic change strategy is to dynamically increase or decrease the members of subgroup to keep the diversity of the population. The benchmark functions show that the algorithms have excellent performance in exploration and exploitation. In the end, they are applied to successfully realize the prediction for wind power by training the parameters of the neural network.
125. LAPSE:2019.1638
Wind Power Short-Term Forecasting Hybrid Model Based on CEEMD-SE Method
December 16, 2019 (v1)
Subject: Intelligent Systems
Keywords: complementary ensemble empirical mode decomposition, hybrid forecasting model, improved extreme learning machine with kernel, sample entropy, wind power forecasting
Accurately predicting wind power is crucial for the large-scale grid-connected of wind power and the increase of wind power absorption proportion. To improve the forecasting accuracy of wind power, a hybrid forecasting model using data preprocessing strategy and improved extreme learning machine with kernel (KELM) is proposed, which mainly includes the following stages. Firstly, the Pearson correlation coefficient is calculated to determine the correlation degree between multiple factors of wind power to reduce data redundancy. Then, the complementary ensemble empirical mode decomposition (CEEMD) method is adopted to decompose the wind power time series to decrease the non-stationarity, the sample entropy (SE) theory is used to classify and reconstruct the subsequences to reduce the complexity of computation. Finally, the KELM optimized by harmony search (HS) algorithm is utilized to forecast each subsequence, and after integration processing, the forecasting results are obtained. The... [more]
126. LAPSE:2019.1623
Bioinspired Hybrid Model to Predict the Hydrogen Inlet Fuel Cell Flow Change of an Energy Storage System
December 16, 2019 (v1)
Subject: Intelligent Systems
Keywords: Artificial Neural Networks, fuel cell, hybrid systems, hydrogen energy, intelligent systems, power management
The present research work deals with prediction of hydrogen consumption of a fuel cell in an energy storage system. Due to the fact that these kind of systems have a very nonlinear behaviour, the use of traditional techniques based on parametric models and other more sophisticated techniques such as soft computing methods, seems not to be accurate enough to generate good models of the system under study. Due to that, a hybrid intelligent system, based on clustering and regression techniques, has been developed and implemented to predict the necessary variation of the hydrogen flow consumption to satisfy the variation of demanded power to the fuel cell. In this research, a hybrid intelligent model was created and validated over a dataset from a fuel cell energy storage system. Obtained results validate the proposal, achieving better performance than other well-known classical regression methods, allowing us to predict the hydrogen consumption with a Mean Absolute Error (MAE) of 3.73... [more]
127. LAPSE:2019.1617
Wavelet Analysis of the Effect of Injection Strategies on Cycle to Cycle Variation GDI Optical Engine under Clean and Fouled Injector
December 16, 2019 (v1)
Subject: Intelligent Systems
Keywords: cycle to cycle variation, fouled injector, GDI engine, wavelet analysis
High fluctuation in cyclic variations influences engine combustion negatively, leading to higher fuel consumption, lower performance, and drivability problems. This paper examines the impacts of injection strategies (injection pressure, injection timing and injection duration) on the cyclic variation of gasoline spark ignition (SI) optical engine under clean and fouled injectors. The principal oscillatory modes of the cycle to cycle variation have been identified, and the engine cycles over which these modes may persist are described. Through the wavelet power spectrum, the presence of short, intermediate and long-term periodicities in the pressure signal have been detected. It was noticed that depending on the clean and fouled injector, the long and intermediate-term periodicities may span many cycles, whereas the short-period oscillations tend to appear intermittently. Information of these periodicities could be helpful to promote efficient control strategies for better combustion. T... [more]
128. LAPSE:2019.1596
Automatic Hybrid Attack Graph (AHAG) Generation for Complex Engineering Systems
December 16, 2019 (v1)
Subject: Intelligent Systems
Keywords: Hybrid Attack Graph, Level-of-Resilience, stability, topology
Complex Engineering Systems are subject to cyber-attacks due to inherited vulnerabilities in the underlying entities constituting them. System Resiliency is determined by its ability to return to a normal state under attacks. In order to analyze the resiliency under various attacks compromising the system, a new concept of Hybrid Attack Graph (HAG) is introduced. A HAG is a graph that captures the evolution of both logical and real values of system parameters under attack and recovery actions. The HAG is generated automatically and visualized using Java based tools. The results are illustrated through a communication network example.
129. LAPSE:2019.1529
Data Analysis and Neuro-Fuzzy Technique for EOR Screening: Application in Angolan Oilfields
December 10, 2019 (v1)
Subject: Intelligent Systems
Keywords: artificial intelligence (AI), enhanced oil recovery (EOR), neural network (NN), neuro-fuzzy (NF), reservoir screening
In this work, a neuro-fuzzy (NF) simulation study was conducted in order to screen candidate reservoirs for enhanced oil recovery (EOR) projects in Angolan oilfields. First, a knowledge pattern is extracted by combining both the searching potential of fuzzy-logic (FL) and the learning capability of neural network (NN) to make a priori decisions. The extracted knowledge pattern is validated against rock and fluid data trained from successful EOR projects around the world. Then, data from Block K offshore Angolan oilfields are then mined and analysed using box-plot technique for the investigation of the degree of suitability for EOR projects. The trained and validated model is then tested on the Angolan field data (Block K) where EOR application is yet to be fully established. The results from the NF simulation technique applied in this investigation show that polymer, hydrocarbon gas, and combustion are the suitable EOR techniques.
130. LAPSE:2019.1504
A Data-Driven Learning-Based Continuous-Time Estimation and Simulation Method for Energy Efficiency and Coulombic Efficiency of Lithium Ion Batteries
December 10, 2019 (v1)
Subject: Intelligent Systems
Keywords: back propagation (BP) neural network, continuous-time efficiency estimation, coulombic efficiency, Energy Efficiency, lithium titanate battery
Lithium ion (Li-ion) batteries work as the basic energy storage components in modern railway systems, hence estimating and improving battery efficiency is a critical issue in optimizing the energy usage strategy. However, it is difficult to estimate the efficiency of lithium ion batteries accurately since it varies continuously under working conditions and is unmeasurable via experiments. This paper offers a learning-based simulation method that employs experimental data to estimate the continuous-time energy efficiency and coulombic efficiency of lithium ion batteries, taking lithium titanate batteries as an example. The state of charge (SOC) regions and discharge current rates are considered as the main variables that may affect the efficiencies. Over eight million empirical datasets are collected during a series of experiments performed to investigate the efficiency variation. A back propagation (BP) neural network efficiency estimation and simulation model is proposed to estimate t... [more]
131. LAPSE:2019.1486
A Novel Multi-Objective Optimal Approach for Wind Power Interval Prediction
December 10, 2019 (v1)
Subject: Intelligent Systems
Keywords: artificial bee colony algorithm, kernel extreme learning machine, prediction intervals, variational mode decomposition, wind power prediction
Numerous studies on wind power forecasting show that random errors found in the prediction results cause uncertainty in wind power prediction and cannot be solved effectively using conventional point prediction methods. In contrast, interval prediction is gaining increasing attention as an effective approach as it can describe the uncertainty of wind power. A wind power interval forecasting approach is proposed in this article. First, the original wind power series is decomposed into a series of subseries using variational mode decomposition (VMD); second, the prediction model is established through kernel extreme learning machine (KELM). Three indices are taken into account in a novel objective function, and the improved artificial bee colony algorithm (IABC) is used to search for the best wind power intervals. Finally, when compared with other competitive methods, the simulation results show that the proposed approach has much better performance.
132. LAPSE:2019.1480
Image Recognition of Icing Thickness on Power Transmission Lines Based on a Least Squares Hough Transform
December 10, 2019 (v1)
Subject: Intelligent Systems
Keywords: geometric calculation model, Hough transform, icing thickness, least squares, power transmission line
In view of the shortcomings of current image detection methods for icing thickness on power transmission lines, an image measuring method for icing thickness based on remote online monitoring was proposed. In this method, a Canny operator is used to get the image edge, in addition, a Hough transform and least squares are combined to solve the problems of traditional Hough transform in the parameter space whereby it is easily disturbed by the image background and noises, and eventually the edges of iced power transmission lines and un-iced power transmission lines are accurately detected in images which have low contrast, complex grayscale, and many noises. Furthermore, based on the imaging principle of the camera, a new geometric calculation model for icing thickness is established by using the radius of power transmission line as a reference, and automatic calculation of icing thickness is achieved. The results show that proposed image recognition method is rarely disturbed by noises... [more]
133. LAPSE:2019.1380
Forecasting Models of Electricity Prices
December 10, 2019 (v1)
Subject: Intelligent Systems
This book contains the successful invited submissions [1,2,3,4,5,6,7,8,9,10,11] to a Special Issue of Energies on the subject area of “Forecasting Models of Electricity Prices”.
134. LAPSE:2019.1322
Neural-Network-Based Building Energy Consumption Prediction with Training Data Generation
December 10, 2019 (v1)
Subject: Intelligent Systems
Keywords: building modelling, energy management, mean impact value (MIV), neural network (NN), receiver operating characteristic (ROC)
The importance of neural network (NN) modelling is evident from its performance benefits in a myriad of applications, where, unlike conventional techniques, NN modeling provides superior performance without relying on complex filtering and/or time-consuming parameter tuning specific to applications and their wider ranges of conditions. In this paper, we employ NN modelling with training data generation based on sensitivity analysis for the prediction of building energy consumption to improve performance and reliability. Unlike our previous work, where insignificant input variables are successively screened out based on their mean impact values (MIVs) during the training process, we use the receiver operating characteristic (ROC) plot to generate reliable data with a conservative or progressive point of view, which overcomes the issue of data insufficiency of the MIV method: By properly setting boundaries for input variables based on the ROC plot and their statistics, instead of complet... [more]
135. LAPSE:2019.1316
Load State Identification Method for Ball Mills Based on Improved EWT, Multiscale Fuzzy Entropy and AEPSO_PNN Classification
December 10, 2019 (v1)
Subject: Intelligent Systems
Keywords: EWT, load identification, multiscale fuzzy entropy, PNN
To overcome the difficulty of accurately determining the load state of a wet ball mill during the grinding process, a method of mill load identification based on improved empirical wavelet transform (EWT), multiscale fuzzy entropy (MFE), and adaptive evolution particle swarm optimization probabilistic neural network (AEPSO_PNN) classification is proposed. First, the concept of a sliding frequency window is introduced based on EWT, and the adaptive frequency window EWT algorithm, which is used to decompose the vibration signals recorded under different load states to obtain the intrinsic mode components, is proposed. Second, a correlation coefficient threshold is used to select the sensitive mode components that characterize the state of the original signal for signal reconstruction. Finally, the MFE of the reconstructed signal is used as the characteristic vector to characterize the load state of the mill, and the partial mean value of MFE is calculated. The results show that the mean... [more]
136. LAPSE:2019.1310
Model-Based Monitoring of Occupant’s Thermal State for Adaptive HVAC Predictive Controlling
December 10, 2019 (v1)
Subject: Intelligent Systems
Keywords: adaptive controlling, machine-learning, prediction, thermal comfort, thermal sensation
Conventional indoor climate design and control approaches are based on static thermal comfort/sensation models that view the building occupants as passive recipients of their thermal environment. Recent advances in wearable sensing technologies and their generated streaming data are providing a unique opportunity to understand the user’s behaviour and to predict future needs. Estimation of thermal comfort is a challenging task given the subjectivity of human perception; this subjectivity is reflected in the statistical nature of comfort models, as well as the plethora of comfort models available. Additionally, such models are using not-easily or invasively measured variables (e.g., core temperatures and metabolic rate), which are often not practical and undesirable measurements. The main goal of this paper was to develop dynamic model-based monitoring system of the occupant’s thermal state and their thermoregulation responses under two different activity levels. In total, 25 participan... [more]
137. LAPSE:2019.1250
Gaussian Process Methodology for Multi-Frequency Marine Controlled-Source Electromagnetic Profile Estimation in Isotropic Medium
December 3, 2019 (v1)
Subject: Intelligent Systems
Keywords: computer experiment, electromagnetic profile estimation, Gaussian process, multiple frequency marine controlled-source electromagnetic technique, uncertainty quantification
The marine controlled-source electromagnetic (CSEM) technique is an application of electromagnetic (EM) waves to image the electrical resistivity of the subsurface underneath the seabed. The modeling of marine CSEM is a crucial and time-consuming task due to the complexity of its mathematical equations. Hence, high computational cost is incurred to solve the linear systems, especially for high-dimensional models. Addressing these problems, we propose Gaussian process (GP) calibrated with computer experiment outputs to estimate multi-frequency marine CSEM profiles at various hydrocarbon depths. This methodology utilizes prior information to provide beneficial EM profiles with uncertainty quantification in terms of variance (95% confidence interval). In this paper, prior marine CSEM information was generated through Computer Simulation Technology (CST) software at various observed hydrocarbon depths (250−2750 m with an increment of 250 m each) and different transmission frequencies (0.12... [more]
138. LAPSE:2019.1203
A Comparison of Clustering and Prediction Methods for Identifying Key Chemical−Biological Features Affecting Bioreactor Performance
November 24, 2019 (v1)
Subject: Intelligent Systems
Keywords: bioinformatics, Machine Learning, statistics
Chemical−biological systems, such as bioreactors, contain stochastic and non-linear interactions which are difficult to characterize. The highly complex interactions between microbial species and communities may not be sufficiently captured using first-principles, stationary, or low-dimensional models. This paper compares and contrasts multiple data analysis strategies, which include three predictive models (random forests, support vector machines, and neural networks), three clustering models (hierarchical, Gaussian mixtures, and Dirichlet mixtures), and two feature selection approaches (mean decrease in accuracy and its conditional variant). These methods not only predict the bioreactor outcome with sufficient accuracy, but the important features correlated with said outcome are also identified. The novelty of this work lies in the extensive exploration and critique of a wide arsenal of methods instead of single methods, as observed in many papers of similar nature. The results show... [more]
139. LAPSE:2019.1144
An Improved Eclat Algorithm Based on Tissue-Like P System with Active Membranes
November 24, 2019 (v1)
Subject: Intelligent Systems
Keywords: eclat algorithm, frequent pattern mining, membrane computing, tissue-like P systems
The Eclat algorithm is a typical frequent pattern mining algorithm using vertical data. This study proposes an improved Eclat algorithm called ETPAM, based on the tissue-like P system with active membranes. The active membranes are used to run evolution rules, i.e., object rewriting rules, in parallel. Moreover, ETPAM utilizes subsume indices and an early pruning strategy to reduce the number of frequent pattern candidates and subsumes. The time complexity of ETPAM is decreased from O(t2) to O(t) as compared with the original Eclat algorithm through the parallelism of the P system. The experimental results using two databases indicate that ETPAM performs very well in mining frequent patterns, and the experimental results using four databases prove that ETPAM is computationally very efficient as compared with three other existing frequent pattern mining algorithms.
140. LAPSE:2019.1139
A Review of Computational Methods for Clustering Genes with Similar Biological Functions
November 24, 2019 (v1)
Subject: Intelligent Systems
Keywords: biological functions detection, gene clustering, informative genes, swarm intelligence
Clustering techniques can group genes based on similarity in biological functions. However, the drawback of using clustering techniques is the inability to identify an optimal number of potential clusters beforehand. Several existing optimization techniques can address the issue. Besides, clustering validation can predict the possible number of potential clusters and hence increase the chances of identifying biologically informative genes. This paper reviews and provides examples of existing methods for clustering genes, optimization of the objective function, and clustering validation. Clustering techniques can be categorized into partitioning, hierarchical, grid-based, and density-based techniques. We also highlight the advantages and the disadvantages of each category. To optimize the objective function, here we introduce the swarm intelligence technique and compare the performances of other methods. Moreover, we discuss the differences of measurements between internal and external... [more]
141. LAPSE:2019.1052
Temporal Feature Selection for Multi-Step Ahead Reheater Temperature Prediction
September 23, 2019 (v1)
Subject: Intelligent Systems
Keywords: deep neural network, delay order prediction, Genetic Algorithm, reheat steam temperature, temporal feature selection
Accurately predicting the reheater steam temperature over both short and medium time periods is crucial for the efficiency and safety of operations. With regard to the diverse temporal effects of influential factors, the accurate identification of delay orders allows effective temperature predictions for the reheater system. In this paper, a deep neural network (DNN) and a genetic algorithm (GA)-based optimal multi-step temporal feature selection model for reheater temperature is proposed. In the proposed model, DNN is used to establish a steam temperature predictor for future time steps, and GA is used to find the optimal delay orders, while fully considering the balance between modeling accuracy and computational complexity. The experimental results for two ultra-super-critical 1000 MW power plants show that the optimal delay orders calculated using this method achieve high forecasting accuracy and low computational overhead. Moreover, it is argued that the similarities of the two re... [more]
142. LAPSE:2019.1043
Online Operation Risk Assessment of the Wind Power System of the Convolution Neural Network (CNN) Considering Multiple Random Factors
September 23, 2019 (v1)
Subject: Intelligent Systems
Keywords: CNN, equipment failure rate, load fluctuations, online operation risk assessment, operation pattern, uncertain wind power output
In order to solve the problem of the inaccuracy of the traditional online operation risk assessment model based on a physical mechanism and the inability to adapt to the actual operation of massive online operation monitoring data, this paper proposes an online operation risk assessment of the wind power system of the convolution neural network (CNN) considering multiple random factors. This paper analyzes multiple random factors of the wind power system, including uncertain wind power output, load fluctuations, frequent changes in operation patterns, and the electrical equipment failure rate, and generates the sample data based on multi-random factors. It uses the CNN algorithm network, offline training to obtain the risk assessment model, and online application to obtain the real-time online operation risk state of the wind power system. Finally, the online operation risk assessment model is verified by simulation using the standard network of 39 nodes of 10 machines New England syst... [more]
143. LAPSE:2019.1036
Ear Detection and Localization with Convolutional Neural Networks in Natural Images and Videos
September 23, 2019 (v1)
Subject: Intelligent Systems
Keywords: computer vision, convolutional neural network, ear detection, image recognition, video analysis
The difficulty in precisely detecting and locating an ear within an image is the first step to tackle in an ear-based biometric recognition system, a challenge which increases in difficulty when working with variable photographic conditions. This is in part due to the irregular shapes of human ears, but also because of variable lighting conditions and the ever changing profile shape of an ear’s projection when photographed. An ear detection system involving multiple convolutional neural networks and a detection grouping algorithm is proposed to identify the presence and location of an ear in a given input image. The proposed method matches the performance of other methods when analyzed against clean and purpose-shot photographs, reaching an accuracy of upwards of 98%, but clearly outperforms them with a rate of over 86% when the system is subjected to non-cooperative natural images where the subject appears in challenging orientations and photographic conditions.