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Records with Subject: Intelligent Systems
Showing records 1 to 25 of 140. [First] Page: 1 2 3 4 5 Last
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]
Gaussian Process Methodology for Multi-Frequency Marine Controlled-Source Electromagnetic Profile Estimation in Isotropic Medium
Muhammad Naeim Mohd Aris, Hanita Daud, Sarat Chandra Dass, Khairul Arifin Mohd Noh
December 3, 2019 (v1)
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]
A Comparison of Clustering and Prediction Methods for Identifying Key Chemical−Biological Features Affecting Bioreactor Performance
Yiting Tsai, Susan A. Baldwin, Lim C. Siang, Bhushan Gopaluni
November 24, 2019 (v1)
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]
An Improved Eclat Algorithm Based on Tissue-Like P System with Active Membranes
Linlin Jia, Laisheng Xiang, Xiyu Liu
November 24, 2019 (v1)
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.
A Review of Computational Methods for Clustering Genes with Similar Biological Functions
Hui Wen Nies, Zalmiyah Zakaria, Mohd Saberi Mohamad, Weng Howe Chan, Nazar Zaki, Richard O. Sinnott, Suhaimi Napis, Pablo Chamoso, Sigeru Omatu, Juan Manuel Corchado
November 24, 2019 (v1)
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]
Temporal Feature Selection for Multi-Step Ahead Reheater Temperature Prediction
Ning Gui, Jieli Lou, Zhifeng Qiu, Weihua Gui
September 23, 2019 (v1)
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]
Online Operation Risk Assessment of the Wind Power System of the Convolution Neural Network (CNN) Considering Multiple Random Factors
Qingwu Gong, Si Tan, Yubo Wang, Dong Liu, Hui Qiao, Liuchuang Wu
September 23, 2019 (v1)
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]
Ear Detection and Localization with Convolutional Neural Networks in Natural Images and Videos
William Raveane, Pedro Luis Galdámez, María Angélica González Arrieta
September 23, 2019 (v1)
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.
Online Decision-Support Tool “TECHoice” for the Equipment Technology Choice in Sterile Filling Processes of Biopharmaceuticals
Haruku Shirahata, Sara Badr, Yuki Shinno, Shuta Hagimori, Hirokazu Sugiyama
September 23, 2019 (v1)
Keywords: MATLAB Production Server, multi-objective decision-making, parenteral manufacturing, process design, single-use technology, software development
In biopharmaceutical manufacturing, a new single-use technology using disposable equipment is available for reducing the work of change-over operations compared to conventional multi-use technology that use stainless steel equipment. The choice of equipment technologies has been researched and evaluation models have been developed, however, software that can extend model exposure to reach industrial users is yet to be developed. In this work, we develop and demonstrate a prototype of an online decision-support tool for the multi-objective evaluation of equipment technologies in sterile filling of biopharmaceutical manufacturing processes. Multi-objective evaluation models of equipment technologies and equipment technology alternative generation algorithms are implemented in the tool to support users in choosing their preferred technology according to their input of specific production scenarios. The use of the tool for analysis and decision-support was demonstrated using four productio... [more]
Development and Application of a Data-Driven System for Sensor Fault Diagnosis in an Oil Processing Plant
Nayher Clavijo, Afrânio Melo, Maurício M. Câmara, Thiago Feital, Thiago K. Anzai, Fabio C. Diehl, Pedro H. Thompson, José Carlos Pinto
September 23, 2019 (v1)
Keywords: canonical variate analysis, conditional-based maintenance, fault diagnosis, fiscal meters, real oil and gas processing facility
Predictive analytics is usually cited as one of the most important pillars of the digital transformation. For the oil industry, specifically, it is a common belief that issues like integrity and maintenance could benefit from predictive analytics. This paper presents the development and the application of a process-monitoring tool in a real process facility. The PMA (Predictive Maintenance Application) system is a data-driven application that uses a multivariate analysis in order to predict the system behavior. Results show that the use of a multivariate approach for process monitoring could not only detect an early failure at a metering system days before the operation crew, but could also successfully identify, among hundreds of variables, the root cause of the abnormal situation. By applying such an approach, a better performance of the monitored equipment is expected, decreasing its downtime.
Deep Learning-Based Pose Estimation of Apples for Inspection in Logistic Centers Using Single-Perspective Imaging
Lino Antoni Giefer, Juan Daniel Arango Castellanos, Mohammad Mohammadzadeh Babr, Michael Freitag
September 13, 2019 (v1)
Keywords: deep learning, lie algebra, logistic centers, pose estimation, quality inspection
Fruit packaging is a time-consuming task due to its low automation level. The gentle handling required by some kinds of fruits and their natural variations complicates the implementation of automated quality controls and tray positioning for final packaging. In this article, we propose a method for the automatic localization and pose estimation of apples captured by a Red-Green-Blue (RGB) camera using convolutional neural networks. Our pose estimation algorithm uses a cascaded structure composed of two independent convolutional neural networks: one for the localization of apples within the images and a second for the estimation of the three-dimensional rotation of the localized and cropped image area containing an apple. We used a single shot multi-box detector to find the bounding boxes of the apples in the images. Lie algebra is used for the regression of the rotation, which represents an innovation in this kind of application. We compare the performances of four different network ar... [more]
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