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Records with Subject: Intelligent Systems
Showing records 97 to 121 of 261. [First] Page: 1 2 3 4 5 6 7 8 9 Last
A Recognition Method of the Hydrophobicity Class of Composite Insulators Based on Features Optimization and Experimental Verification
Lin Yang, Jikai Bi, Yanpeng Hao, Lupeng Nian, Zijun Zhou, Licheng Li, Yifan Liao, Fuzeng Zhang
June 23, 2020 (v1)
Keywords: composite insulator, features, hydrophobic image, hydrophobicity class, Optimization, recognition model
The hydrophobicity of composite insulators is a great significance to the safe and stable operation of transmission lines. In this paper, a recognition method of the hydrophobicity class (HC) of composite insulators based on features optimization was proposed. Through the spray method, many hydrophobic images of water droplets on the insulator surface at various hydrophobicity classes (HCs) were taken. After processing of the hydrophobic images, seven features were extracted: the number n, mean eccentricity Eav and coverage rate k₁ of the water droplets, and the coverage rate k₂, perimeter Lmax, shape factor fc, and eccentricity Emax of the maximum water droplet. Then, the maximum value Δxmax, the minimum value Δxmin, and the average value Δxav of the change rate of each feature value between adjacent HCs, and the volatility Δs of each feature value, were used as the evaluation indexes for features optimization. After this features optimization, the five features that are most closely... [more]
A New Scheme to Improve the Performance of Artificial Intelligence Techniques for Estimating Total Organic Carbon from Well Logs
Pan Wang, Suping Peng
June 23, 2020 (v1)
Keywords: artificial intelligence techniques, geophysical logs, least square support vector machine (LSSVM), organic shale, particle swarm optimization (PSO), total organic carbon (TOC)
Total organic carbon (TOC), a critical geochemical parameter of organic shale reservoirs, can be used to evaluate the hydrocarbon potential of source rocks. However, getting TOC through core analysis of geochemical experiments is costly and time-consuming. Therefore, in this paper, a TOC prediction model was built by combining the data from a case study in the Ordos Basin, China and core analysis with artificial intelligence techniques. In the study, the data of samples were optimized based on annealing algorithm (SA) and genetic algorithm (GA), named SAGA-FCM method. Then, back propagation algorithm (BPNN), least square support vector machine (LSSVM), and least square support vector machine based on particle swarm optimization algorithm (PSO-LSSVM) were built based on the data from optimization. The results show that the intelligence model constructed based on core samples data after optimization has much better performance in both training and validation accuracy than the model const... [more]
A Fuzzy Gravitational Search Algorithm to Design Optimal IIR Filters
Danilo Pelusi, Raffaele Mascella, Luca Tallini
June 23, 2020 (v1)
Keywords: fuzzy systems, gravitational search algorithm, IIR filters, optimization algorithms
The goodness of Infinite Impulse Response (IIR) digital filters design depends on pass band ripple, stop band ripple and transition band values. The main problem is defining a suitable error fitness function that depends on these parameters. This fitness function can be optimized by search algorithms such as evolutionary algorithms. This paper proposes an intelligent algorithm for the design of optimal 8th order IIR filters. The main contribution is the design of Fuzzy Inference Systems able to tune key parameters of a revisited version of the Gravitational Search Algorithm (GSA). In this way, a Fuzzy Gravitational Search Algorithm (FGSA) is designed. The optimization performances of FGSA are compared with those of Differential Evolution (DE) and GSA. The results show that FGSA is the algorithm that gives the best compromise between goodness, robustness and convergence rate for the design of 8th order IIR filters. Moreover, FGSA assures a good stability of the designed filters.
Hybrid GA-PSO Optimization of Artificial Neural Network for Forecasting Electricity Demand
Atul Anand, L Suganthi
June 23, 2020 (v1)
Keywords: ANN, electricity demand, forecasting, GA, hybrid optimization, PSO
In the present study Artificial Neural Network (ANN) has been optimized using a hybrid algorithm of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The hybrid GA-PSO algorithm has been used to improve the estimation of electricity demand of the state of Tamil Nadu in India. The ANN-GA-PSO model uses gross domestic product (GSDP); electricity consumption per capita; income growth rate and consumer price index (CPI) as predictors that affect the electricity demand. Using the historical demand data of 25 years from 1991 till 2015 it is found that ANN-GA-PSO models have higher accuracy and performance reliability than single optimization models such as ANN-PSO or ANN-GA. In addition, the paper also forecasts the electricity demand of the state based on “as-it-is” scenario and the scenario based on milestones set by the “Vision-2023” document of the state.
A Novel Type-2 Fuzzy Logic for Improved Risk Analysis of Proton Exchange Membrane Fuel Cells in Marine Power Systems Application
Sajjad Bahrebar, Frede Blaabjerg, Huai Wang, Navid Vafamand, Mohammad-Hassan Khooban, Sima Rastayesh, Dao Zhou
June 23, 2020 (v1)
Keywords: failure mode and effect analysis (FMEA), general type II fuzzy logic, Proton Exchange Membrane Fuel Cell (PEMFC), risk priority number (RPN)
A marine energy system, which is fundamentally not paired with electric grids, should work for an extended period with high reliability. To put it in another way, by employing electrical utilities on a ship, the electrical power demand has been increasing in recent years. Besides, fuel cells in marine power generation may reduce the loss of energy and weight in long cables and provide a platform such that each piece of marine equipment is supplied with its own isolated wire connection. Hence, fuel cells can be promising power generation equipment in the marine industry. Besides, failure modes and effects analysis (FMEA) is widely accepted throughout the industry as a valuable tool for identifying, ranking, and mitigating risks. The FMEA process can help to design safe hydrogen fueling stations. In this paper, a robust FMEA has been developed to identify the potentially hazardous conditions of the marine propulsion system by considering a general type-2 fuzzy logic set. The general type... [more]
A Novel Nonlinear Combined Forecasting System for Short-Term Load Forecasting
Chengshi Tian, Yan Hao
June 23, 2020 (v1)
Keywords: combined model, forecasting performance, nonlinear forecasting, short-term load forecasting
Short-term load forecasting plays an indispensable role in electric power systems, which is not only an extremely challenging task but also a concerning issue for all society due to complex nonlinearity characteristics. However, most previous combined forecasting models were based on optimizing weight coefficients to develop a linear combined forecasting model, while ignoring that the linear combined model only considers the contribution of the linear terms to improving the model’s performance, which will lead to poor forecasting results because of the significance of the neglected and potential nonlinear terms. In this paper, a novel nonlinear combined forecasting system, which consists of three modules (improved data pre-processing module, forecasting module and the evaluation module) is developed for short-term load forecasting. Different from the simple data pre-processing of most previous studies, the improved data pre-processing module based on longitudinal data selection is succ... [more]
State-of-Charge Estimation of Battery Pack under Varying Ambient Temperature Using an Adaptive Sequential Extreme Learning Machine
Cheng Siong Chin, Zuchang Gao
June 23, 2020 (v1)
Keywords: adaptive online sequential extreme learning machine, battery cell, extreme learning machine, state-of-charge
An adaptive online sequential extreme learning machine (AOS-ELM) is proposed to predict the state-of-charge of the battery cells at different ambient temperatures. With limited samples and sequential data for training during the initial design stage, conventional neural network training gives higher errors and longer computing times when it maps the available inputs to SOC. The use of AOS-ELM allows a gradual increase in the dataset that can be time-consuming to obtain during the initial stage of the neural network training. The SOC prediction using AOS-ELM gives a smaller root mean squared error in testing (and small standard deviation in the trained results) and reasonable training time as compared to other types of ELM-based learnings and gradient-based machine learning. In addition, the subsequent identification of the cells’ static capacity and battery parameters from actual experiments is not required to estimate the SOC of each cell and the battery stack.
Transformers Health Index Assessment Based on Neural-Fuzzy Network
Emran Jawad Kadim, Norhafiz Azis, Jasronita Jasni, Siti Anom Ahmad, Mohd Aizam Talib
June 23, 2020 (v1)
Keywords: condition assessment, health index (HI), Neural-Fuzzy (NF), transformers
In this paper, an assessment on the health index (HI) of transformers is carried out based on Neural-Fuzzy (NF) method. In-service condition assessment data, such as dissolved gases, furans, AC breakdown voltage (ACBDV), moisture, acidity, dissipation factor (DF), color, interfacial tension (IFT), and age were fed as input parameters to the NF network. The NF network were trained individually based on two sets of data, known as in-service condition assessment and Monte Carlo Simulation (MCS) data. HI was also obtained from the scoring method for comparison with the NF method. It is found that the HI of transformers that was obtained by NF trained by MCS method is closer to scoring method than NF trained by in-service condition assessment method. Based on the total of 15 testing transformers, NF trained by MCS data method gives 10 transformers with the same assessments as scoring method as compared to eight transformers given by NF trained by in-service condition data method. Analysis b... [more]
Wind Speed Prediction with Spatio⁻Temporal Correlation: A Deep Learning Approach
Qiaomu Zhu, Jinfu Chen, Lin Zhu, Xianzhong Duan, Yilu Liu
June 23, 2020 (v1)
Keywords: convolutional neural networks, deep learning, Machine Learning, spatio-temporal correlation, wind speed prediction
Wind speed prediction with spatio⁻temporal correlation is among the most challenging tasks in wind speed prediction. In this paper, the problem of predicting wind speed for multiple sites simultaneously is investigated by using spatio⁻temporal correlation. This paper proposes a model for wind speed prediction with spatio⁻temporal correlation, i.e., the predictive deep convolutional neural network (PDCNN). The model is a unified framework, integrating convolutional neural networks (CNNs) and a multi-layer perceptron (MLP). Firstly, the spatial features are extracted by CNNs located at the bottom of the model. Then, the temporal dependencies among these extracted spatial features are captured by the MLP. In this way, the spatial and temporal correlations are captured by PDCNN intrinsically. Finally, PDCNN generates the predicted wind speed by using the learnt spatio⁻temporal correlations. In addition, three error indices are defined to evaluate the prediction accuracy of the model on the... [more]
A New Hybrid Prediction Method of Ultra-Short-Term Wind Power Forecasting Based on EEMD-PE and LSSVM Optimized by the GSA
Peng Lu, Lin Ye, Bohao Sun, Cihang Zhang, Yongning Zhao, Jingzhu Teng
June 23, 2020 (v1)
Keywords: ensemble empirical mode decomposition-permutation entropy (EEMD-PE), heuristic algorithm, least squares support vector machine (LSSVM), wind power prediction
Wind power time series data always exhibits nonlinear and non-stationary features, making it very difficult to accurately predict. In this paper, a novel hybrid wind power time series prediction model, based on ensemble empirical mode decomposition-permutation entropy (EEMD-PE), the least squares support vector machine model (LSSVM), and gravitational search algorithm (GSA), is proposed to improve accuracy of ultra-short-term wind power forecasting. To process the data, original wind power series were decomposed by EEMD-PE techniques into a number of subsequences with obvious complexity differences. Then, a new heuristic GSA algorithm was utilized to optimize the parameters of the LSSVM. The optimized model was developed for wind power forecasting and improved regression prediction accuracy. The proposed model was validated with practical wind power generation data from the Hebei province, China. A comprehensive error metric analysis was carried out to compare the performance of our me... [more]
The Integration of Collaborative Robot Systems and Their Environmental Impacts
Lucian Stefanita Grigore, Iustin Priescu, Daniela Joita, Ionica Oncioiu
June 23, 2020 (v1)
Keywords: autonomous robot, continuous execution, integrated solution, mobility, Planning, programming
Today, industrial robots are used in dangerous environments in all sectors, including the sustainable energy sector. Sensors and processors collect and transmit information and data from users as a result of the application of robot control systems and sensory feedback. This paper proposes that the estimation of a collaborative robot system’s performance can be achieved by evaluating the mobility of robots. Scenarios have been determined in which an autonomous system has been used for intervention in crisis situations due to fire. The experimental model consists of three autonomous vehicles, two of which are ground vehicles and the other is an aerial vehicle. The conclusion of the research described in this paper highlights the fact that the integration of robotic systems made up of autonomous vehicles working in unstructured environments is difficult and at present there is no unitary analytical model.
Short-Term Load Forecasting Using Smart Meter Data: A Generalization Analysis
Aida Mehdipour Pirbazari, Mina Farmanbar, Antorweep Chakravorty, Chunming Rong
June 23, 2020 (v1)
Keywords: deep learning, generalization analysis, Machine Learning, short-term load forecasting, smart meters
Short-term load forecasting ensures the efficient operation of power systems besides affording continuous power supply for energy consumers. Smart meters that are capable of providing detailed information on buildings energy consumption, open several doors of opportunity to short-term load forecasting at the individual building level. In the current paper, four machine learning methods have been employed to forecast the daily peak and hourly energy consumption of domestic buildings. The utilized models depend merely on buildings historical energy consumption and are evaluated on the profiles that were not previously trained on. It is evident that developing data-driven models lacking external information such as weather and building data are of great importance under the situations that the access to such information is limited or the computational procedures are costly. Moreover, the performance evaluation of the models on separated house profiles determines their generalization abili... [more]
Can Machine Learning Predict Stress Reduction Based on Wearable Sensors’ Data Following Relaxation at Workplace? A Pilot Study
Alessandro Tonacci, Alessandro Dellabate, Andrea Dieni, Lorenzo Bachi, Francesco Sansone, Raffaele Conte, Lucia Billeci
June 22, 2020 (v1)
Keywords: autonomic nervous system, ECG, galvanic skin response, heart rate, heart rate variability, Machine Learning, mindfulness, neural networks, relaxation, signal processing, skin conductance, wearable sensors, yoga
Nowadays, psychological stress represents a burdensome condition affecting an increasing number of subjects, in turn putting into practice several strategies to cope with this issue, including the administration of relaxation protocols, often performed in non-structured environments, like workplaces, and constrained within short times. Here, we performed a quick relaxation protocol based on a short audio and video, and analyzed physiological signals related to the autonomic nervous system (ANS) activity, including electrocardiogram (ECG) and galvanic skin response (GSR). Based on the features extracted, machine learning was applied to discriminate between subjects benefitting from the protocol and those with negative or no effects. Twenty-four healthy volunteers were enrolled for the protocol, equally and randomly divided into Group A, performing an audio-video + video-only relaxation, and Group B, performing an audio-video + audio-only protocol. From the ANS point of view, Group A sub... [more]
Infrared Infusion Monitor Based on Data Dimensionality Reduction and Logistics Classifier
Xiaoli Wang, Haonan Zhou, Yong Song
June 22, 2020 (v1)
Keywords: data dimensionality reduction, drop count, logistics classifier
This paper presents an infrared infusion monitoring method based on data dimensionality reduction and a logistics classifier. In today’s social environment, nurses with hospital infusion work are under excessive pressure. In order to improve the information level of the traditional medical process, hospitals have introduced a variety of infusion monitoring devices. The current infusion monitoring equipment mainly adopts the detection method of infrared liquid drop detection to realize non-contact measurements. However, a large number of experiments have found that the traditional infrared detection method has the problems of low voltage signal amplitude variation and low signal-to-noise ratio (SNR). Conventional threshold judgment or signal shaping cannot accurately judge whether droplets exist or not, and complex signal processing circuits can greatly increase the cost and power consumption of equipment. In order to solve these problems, this paper proposes a method for the accurate m... [more]
Semi-Supervised Ensemble Classification Method Based on Near Neighbor and Its Application
Chuang Li, Yongfang Xie, Xiaofang Chen
June 10, 2020 (v1)
Keywords: adaboost, aluminum electrolysis, ensemble learning, multivariate adaptive regression splines, semi-supervised learning
Semi-supervised learning can be used to solve the problem of insufficient labeled samples in the process industry. However, in an actual scenario, traditional semi-supervised learning methods usually do not achieve satisfactory performance when the small number of labeled samples is subjective and inaccurate and some do not consider how to develop a strategy to expand the training set. In this paper, a new algorithm is proposed to alleviate the above two problems, and consequently, the information contained in unlabeled samples can be fully mined. First, the multivariate adaptive regression splines (MARS) and adaptive boosting (Adaboost) algorithms are adopted for co-training to make the most of the deep connection between samples and features. In addition, the strategies, pseudo-labeled dataset selection algorithm based on near neighbor degree (DSSA) and pseudo-labeled sample detection algorithm based on near neighbor degree selection (SPDA) are adopted to enlarge the dataset of label... [more]
A New Improved Learning Algorithm for Convolutional Neural Networks
Jie Yang, Junhong Zhao, Lu Lu, Tingting Pan, Sidra Jubair
May 18, 2020 (v1)
Keywords: CIFAR-10, convolutional neural networks, loss function, MNIST
The back-propagation (BP) algorithm is usually used to train convolutional neural networks (CNNs) and has made greater progress in image classification. It updates weights with the gradient descent, and the farther the sample is from the target, the greater the contribution of it to the weight change. However, the influence of samples classified correctly but that are close to the classification boundary is diminished. This paper defines the classification confidence as the degree to which a sample belongs to its correct category, and divides samples of each category into dangerous and safe according to a dynamic classification confidence threshold. Then a new learning algorithm is presented to penalize the loss function with danger samples but not all samples to enable CNN to pay more attention to danger samples and to learn effective information more accurately. The experiment results, carried out on the MNIST dataset and three sub-datasets of CIFAR-10, showed that for the MNIST data... [more]
Enhancing Failure Mode and Effects Analysis Using Auto Machine Learning: A Case Study of the Agricultural Machinery Industry
Sami Sader, István Husti, Miklós Daróczi
April 14, 2020 (v1)
Keywords: auto machine learning, failure mode effects analysis, Industry 4.0, risk priority number
In this paper, multiclass classification is used to develop a novel approach to enhance failure mode and effects analysis and the generation of risk priority number. This is done by developing four machine learning models using auto machine learning. Failure mode and effects analysis is a technique that is used in industry to identify possible failures that may occur and the effects of these failures on the system. Meanwhile, risk priority number is a numeric value that is calculated by multiplying three associated parameters namely severity, occurrence and detectability. The value of risk priority number determines the next actions to be made. A dataset that includes a one-year registry of 1532 failures with their description, severity, occurrence, and detectability is used to develop four models to predict the values of severity, occurrence, and detectability. Meanwhile, the resulted models are evaluated using 10% of the dataset. Evaluation results show that the proposed models have... [more]
Systematic Boolean Satisfiability Programming in Radial Basis Function Neural Network
Mohd. Asyraf Mansor, Siti Zulaikha Mohd Jamaludin, Mohd Shareduwan Mohd Kasihmuddin, Shehab Abdulhabib Alzaeemi, Md Faisal Md Basir, Saratha Sathasivam
April 14, 2020 (v1)
Keywords: Hopfield Neural Network, logic programming, Optimization, Radial Basis Function Neural Network, satisfiability
Radial Basis Function Neural Network (RBFNN) is a class of Artificial Neural Network (ANN) that contains hidden layer processing units (neurons) with nonlinear, radially symmetric activation functions. Consequently, RBFNN has extensively suffered from significant computational error and difficulties in approximating the optimal hidden neuron, especially when dealing with Boolean Satisfiability logical rule. In this paper, we present a comprehensive investigation of the potential effect of systematic Satisfiability programming as a logical rule, namely 2 Satisfiability (2SAT) to optimize the output weights and parameters in RBFNN. The 2SAT logical rule has extensively applied in various disciplines, ranging from industrial automation to the complex management system. The core impetus of this study is to investigate the effectiveness of 2SAT logical rule in reducing the computational burden for RBFNN by obtaining the parameters in RBFNN. The comparison is made between RBFNN and the exist... [more]
Short-Term Wind Power Prediction Using GA-BP Neural Network Based on DBSCAN Algorithm Outlier Identification
Pei Zhang, Yanling Wang, Likai Liang, Xing Li, Qingtian Duan
March 25, 2020 (v1)
Keywords: DBSCAN algorithm, GA-BP neural network, linear regression method, outlier identification, short-term wind power prediction
Accurately predicting wind power plays a vital part in site selection, large-scale grid connection, and the safe and efficient operation of wind power generation equipment. In the stage of data pre-processing, density-based spatial clustering of applications with noise (DBSCAN) algorithm is used to identify the outliers in the wind power data and the collected wind speed data of a wind power plant in Shandong Province, and the linear regression method is used to correct the outliers to improve the prediction accuracy. Considering the important impact of wind speed on power, the average value, the maximum difference and the average change rate of daily wind speed of each historical day are used as the selection criteria to select similar days by using DBSCAN algorithm and Euclidean distance. The short-term wind power prediction is carried out by using the similar day data pre-processed and unprocessed, respectively, as the input of back propagation neural network optimized by genetic al... [more]
Detection of Drivers’ Anxiety Invoked by Driving Situations Using Multimodal Biosignals
Seungji Lee, Taejun Lee, Taeyang Yang, Changrak Yoon, Sung-Phil Kim
March 12, 2020 (v1)
Keywords: driver anxiety, emotion detection, multimodal biosignals
It has become increasingly important to monitor drivers’ negative emotions during driving to prevent accidents. Despite drivers’ anxiety being critical for safe driving, there is a lack of systematic approaches to detect anxiety in driving situations. This study employed multimodal biosignals, including electroencephalography (EEG), photoplethysmography (PPG), electrodermal activity (EDA) and pupil size to estimate anxiety under various driving situations. Thirty-one drivers, with at least one year of driving experience, watched a set of thirty black box videos including anxiety-invoking events, and another set of thirty videos without them, while their biosignals were measured. Then, they self-reported anxiety-invoked time points in each video, from which features of each biosignal were extracted. The logistic regression (LR) method classified single biosignals to detect anxiety. Furthermore, in the order of PPG, EDA, pupil, and EEG (easiest to hardest accessibility), LR classified ac... [more]
Robust Condition Assessment of Electrical Equipment with One Class Support Vector Machines Based on the Measurement of Partial Discharges
Emilio Parrado-Hernández, Guillermo Robles, Jorge Alfredo Ardila-Rey, Juan Manuel Martínez-Tarifa
February 24, 2020 (v1)
Keywords: early fault prevention, electrical asset monitoring, noise characterization, One Class Support Vector Machines (OCSVM), partial discharge discrimination
This paper presents a system for the detection of partial discharges (PD) in industrial applications based on One Class Support Vector Machines (OCSVM). The study stresses the detection of Partial Discharges (PD) as they represent a major source of information related to degradation in the equipment. PD measurement is a widely extended technique for condition monitoring of electrical machines and power cables to avoid catastrophic failures and the consequent blackouts. One of the most important keystones in the interpretation of partial discharges is their separation from other signals considered as not-PD especially in low SNR measurements. In this sense, the OCSVM is an interesting alternative to binary SVMs since it does not need a training set with examples of all the output classes correctly labelled. On the contrary, the OCSVM learns a model of the signals acquired when the equipment is in PD-free mode, defined as a state where no degradation mechanism is active, so one only need... [more]
Short-Term Wind Power Prediction Based on Improved Grey Wolf Optimization Algorithm for Extreme Learning Machine
Jiale Ding, Guochu Chen, Kuo Yuan
February 12, 2020 (v1)
Keywords: extreme learning machine, improved grey wolf optimization algorithm, phase space reconstruction, variational mode decomposition
In order to improve the accuracy of wind power prediction and ensure the effective utilization of wind energy, a short-term wind power prediction model based on variational mode decomposition (VMD) and an extreme learning machine (ELM) optimized by an improved grey wolf optimization (GWO) algorithm is proposed. The original wind power sequence is decomposed into series of modal components with different center frequencies by the VMD method and some new sequences are obtained by phase space reconstruction (PSR). Then, the ELM model is established for different new time series, and the improved GWO algorithm is used to optimize its parameters. Finally, the output results are weighted and merged as the final predicted value of wind power. The root-mean-square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) of the proposed VMD-improved GWO-ELM prediction model in the paper are 5.9113%, 4.6219%, and 13.01% respectively, which are better than these of ELM,... [more]
Extreme Learning Machine-Based Model for Solubility Estimation of Hydrocarbon Gases in Electrolyte Solutions
Narjes Nabipour, Amir Mosavi, Alireza Baghban, Shahaboddin Shamshirband, Imre Felde
February 12, 2020 (v1)
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]
GC-MS Fingerprints Profiling Using Machine Learning Models for Food Flavor Prediction
Kexin Bi, Dong Zhang, Tong Qiu, Yizhen Huang
February 12, 2020 (v1)
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]
A Reference-Model-Based Artificial Neural Network Approach for a Temperature Control System
Song Xu, Seiji Hashimoto, YuQi Jiang, Katsutoshi Izaki, Takeshi Kihara, Ryota Ikeda, Wei Jiang
February 2, 2020 (v1)
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.
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