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Records with Subject: Intelligent Systems
Showing records 1 to 25 of 150. [First] Page: 1 2 3 4 5 Last
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.
Multivariate Analysis and Machine Learning for Ripeness Classification of Cape Gooseberry Fruits
Miguel De-la-Torre, Omar Zatarain, Himer Avila-George, Mirna Muñoz, Jimy Oblitas, Russel Lozada, Jezreel Mejía, Wilson Castro
January 7, 2020 (v1)
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]
Development of New Algorithm for Aniline Point Estimation of Petroleum Fraction
Kaiyue Wang, Xiaoyan Sun, Shuguang Xiang, Yushi Chen
January 7, 2020 (v1)
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]
Novel Parallel Heterogeneous Meta-Heuristic and Its Communication Strategies for the Prediction of Wind Power
Jeng-Shyang Pan, Pei Hu, Shu-Chuan Chu
December 16, 2019 (v1)
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.
Wind Power Short-Term Forecasting Hybrid Model Based on CEEMD-SE Method
Keke Wang, Dongxiao Niu, Lijie Sun, Hao Zhen, Jian Liu, Gejirifu De, Xiaomin Xu
December 16, 2019 (v1)
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]
Bioinspired Hybrid Model to Predict the Hydrogen Inlet Fuel Cell Flow Change of an Energy Storage System
Héctor Alaiz-Moretón, Esteban Jove, José-Luis Casteleiro-Roca, Héctor Quintián, Hilario López García, José Alberto Benítez-Andrades, Paulo Novais, Jose Luis Calvo-Rolle
December 16, 2019 (v1)
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]
Wavelet Analysis of the Effect of Injection Strategies on Cycle to Cycle Variation GDI Optical Engine under Clean and Fouled Injector
Omar I. Awad, Zhou Zhang, Mohammed Kamil, Xiao Ma, Obed Majeed Ali, Shijin Shuai
December 16, 2019 (v1)
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]
Automatic Hybrid Attack Graph (AHAG) Generation for Complex Engineering Systems
Mariam Ibrahim, Ahmad Alsheikh
December 16, 2019 (v1)
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.
Data Analysis and Neuro-Fuzzy Technique for EOR Screening: Application in Angolan Oilfields
Geraldo A. R. Ramos, Lateef Akanji
December 10, 2019 (v1)
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.
A Data-Driven Learning-Based Continuous-Time Estimation and Simulation Method for Energy Efficiency and Coulombic Efficiency of Lithium Ion Batteries
Yuechen Liu, Linjing Zhang, Jiuchun Jiang, Shaoyuan Wei, Sijia Liu, Weige Zhang
December 10, 2019 (v1)
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]
A Novel Multi-Objective Optimal Approach for Wind Power Interval Prediction
Mengyue Hu, Zhijian Hu, Jingpeng Yue, Menglin Zhang, Meiyu Hu
December 10, 2019 (v1)
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.
Image Recognition of Icing Thickness on Power Transmission Lines Based on a Least Squares Hough Transform
Jingjing Wang, Junhua Wang, Jianwei Shao, Jiangui Li
December 10, 2019 (v1)
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]
Forecasting Models of Electricity Prices
Javier Contreras
December 10, 2019 (v1)
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”.
Neural-Network-Based Building Energy Consumption Prediction with Training Data Generation
Sanghyuk Lee, Jaehoon Cha, Moon Keun Kim, Kyeong Soo Kim, Van Huy Pham, Mark Leach
December 10, 2019 (v1)
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]
Load State Identification Method for Ball Mills Based on Improved EWT, Multiscale Fuzzy Entropy and AEPSO_PNN Classification
Gaipin Cai, Xin Liu, Congcong Dai, Xiaoyan Luo
December 10, 2019 (v1)
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]
Model-Based Monitoring of Occupant’s Thermal State for Adaptive HVAC Predictive Controlling
Ali Youssef, Nicolás Caballero, Jean-Marie Aerts
December 10, 2019 (v1)
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]
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