Journal articles on the topic 'Automatic Function Prediction'
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Wrzeszczynski, K. O., Y. Ofran, B. Rost, R. Nair, and J. Liu. "Automatic prediction of protein function." Cellular and Molecular Life Sciences (CMLS) 60, no. 12 (December 1, 2003): 2637–50. http://dx.doi.org/10.1007/s00018-003-3114-8.
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Makrodimitris, Stavros, Roeland C. H. J. van Ham, and Marcel J. T. Reinders. "Automatic Gene Function Prediction in the 2020’s." Genes 11, no. 11 (October 27, 2020): 1264. http://dx.doi.org/10.3390/genes11111264.
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The current rate at which new DNA and protein sequences are being generated is too fast to experimentally discover the functions of those sequences, emphasizing the need for accurate Automatic Function Prediction (AFP) methods. AFP has been an active and growing research field for decades and has made considerable progress in that time. However, it is certainly not solved. In this paper, we describe challenges that the AFP field still has to overcome in the future to increase its applicability. The challenges we consider are how to: (1) include condition-specific functional annotation, (2) predict functions for non-model species, (3) include new informative data sources, (4) deal with the biases of Gene Ontology (GO) annotations, and (5) maximally exploit the GO to obtain performance gains. We also provide recommendations for addressing those challenges, by adapting (1) the way we represent proteins and genes, (2) the way we represent gene functions, and (3) the algorithms that perform the prediction from gene to function. Together, we show that AFP is still a vibrant research area that can benefit from continuing advances in machine learning with which AFP in the 2020s can again take a large step forward reinforcing the power of computational biology.
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Amidi, Shervine, Afshine Amidi, Dimitrios Vlachakis, Nikos Paragios, and Evangelia I. Zacharaki. "Automatic single- and multi-label enzymatic function prediction by machine learning." PeerJ 5 (March 29, 2017): e3095. http://dx.doi.org/10.7717/peerj.3095.
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The number of protein structures in the PDB database has been increasing more than 15-fold since 1999. The creation of computational models predicting enzymatic function is of major importance since such models provide the means to better understand the behavior of newly discovered enzymes when catalyzing chemical reactions. Until now, single-label classification has been widely performed for predicting enzymatic function limiting the application to enzymes performing unique reactions and introducing errors when multi-functional enzymes are examined. Indeed, some enzymes may be performing different reactions and can hence be directly associated with multiple enzymatic functions. In the present work, we propose a multi-label enzymatic function classification scheme that combines structural and amino acid sequence information. We investigate two fusion approaches (in the feature level and decision level) and assess the methodology for general enzymatic function prediction indicated by the first digit of the enzyme commission (EC) code (six main classes) on 40,034 enzymes from the PDB database. The proposed single-label and multi-label models predict correctly the actual functional activities in 97.8% and 95.5% (based on Hamming-loss) of the cases, respectively. Also the multi-label model predicts all possible enzymatic reactions in 85.4% of the multi-labeled enzymes when the number of reactions is unknown. Code and datasets are available athttps://figshare.com/s/a63e0bafa9b71fc7cbd7.
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Vega Yon, George G., Duncan C. Thomas, John Morrison, Huaiyu Mi, Paul D. Thomas, and Paul Marjoram. "Bayesian parameter estimation for automatic annotation of gene functions using observational data and phylogenetic trees." PLOS Computational Biology 17, no. 2 (February 18, 2021): e1007948. http://dx.doi.org/10.1371/journal.pcbi.1007948.
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Gene function annotation is important for a variety of downstream analyses of genetic data. But experimental characterization of function remains costly and slow, making computational prediction an important endeavor. Phylogenetic approaches to prediction have been developed, but implementation of a practical Bayesian framework for parameter estimation remains an outstanding challenge. We have developed a computationally efficient model of evolution of gene annotations using phylogenies based on a Bayesian framework using Markov Chain Monte Carlo for parameter estimation. Unlike previous approaches, our method is able to estimate parameters over many different phylogenetic trees and functions. The resulting parameters agree with biological intuition, such as the increased probability of function change following gene duplication. The method performs well on leave-one-out cross-validation, and we further validated some of the predictions in the experimental scientific literature.
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Sawada, Kenji, Seiichi Shin, Kenji Kumagai, and Hisato Yoneda. "Optimal Scheduling of Automatic Guided Vehicle System via State Space Realization." International Journal of Automation Technology 7, no. 5 (September 5, 2013): 571–80. http://dx.doi.org/10.20965/ijat.2013.p0571.
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This paper considers dynamical system modeling of transportation systems in semiconductor manufacturing based on state space realization. Utilizing this method, we consider an optimal scheduling problem for an Automatic Guided Vehicle (AGV) transfer problem, which is to control AGV congestion at transport rail junctions. Our scheduling algorithm is based on model-predictive control in which the cycle of measurement, prediction and optimization is repeated. Its optimization is recast as an Integer Linear Programming (ILP) problem. Since little attention has been given to AGV scheduling based on model-predictive control, no method is, to our knowledge, known for determining appropriate cost functions. Here, we focus on throughput maximization and shortest transit time problems and show corresponding cost function settings. We also propose a visualization algorithm of AGV scheduling via state space realization, presenting numerical examples.
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Ogawa, Chikara, Yasunori Minami, Masahiro Morita, Teruyo Noda, Soichi Arasawa, Masako Izuta, Atsushi Kubo, et al. "Prediction of Embolization Area after Conventional Transcatheter Arterial Chemoembolization for Hepatocellular Carcinoma Using SYNAPSE VINCENT." Digestive Diseases 34, no. 6 (2016): 696–701. http://dx.doi.org/10.1159/000448859.
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Purpose: Transcatheter arterial chemoembolization (TACE) is one of the most effective therapeutic options for hepatocellular carcinoma (HCC) and it is important to protect residual liver function after treatment as well as the effect. To reduce the liver function deterioration, we evaluated the automatic software to predict the embolization area of TACE in 3 dimensions. Materials and Methods: Automatic prediction software of embolization area was used in chemoembolization of 7 HCCs. Embolization area of chemoembolization was evaluated within 1 week CT findings after TACE and compared simulated area using automatic prediction software. Results: The maximal diameter of these tumors is in the range 12-42 mm (24.6 ± 9.5 mm). The average time for detecting tumor-feeding branches was 242 s. The total time to detect tumor-feeding branches and simulate the embolization area was 384 s. All cases could detect all tumor-feeding branches of HCC, and the expected embolization area of simulation with automatic prediction software was almost the same as the actual areas, as shown by CT after TACE. Conclusion: This new technology has possibilities to reduce the amount of contrast medium used, protect kidney function, decrease radiation exposure, and improve the therapeutic effect of TACE.
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Zacharaki, Evangelia I. "Prediction of protein function using a deep convolutional neural network ensemble." PeerJ Computer Science 3 (July 17, 2017): e124. http://dx.doi.org/10.7717/peerj-cs.124.
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Background The availability of large databases containing high resolution three-dimensional (3D) models of proteins in conjunction with functional annotation allows the exploitation of advanced supervised machine learning techniques for automatic protein function prediction. Methods In this work, novel shape features are extracted representing protein structure in the form of local (per amino acid) distribution of angles and amino acid distances, respectively. Each of the multi-channel feature maps is introduced into a deep convolutional neural network (CNN) for function prediction and the outputs are fused through support vector machines or a correlation-based k-nearest neighbor classifier. Two different architectures are investigated employing either one CNN per multi-channel feature set, or one CNN per image channel. Results Cross validation experiments on single-functional enzymes (n = 44,661) from the PDB database achieved 90.1% correct classification, demonstrating an improvement over previous results on the same dataset when sequence similarity was not considered. Discussion The automatic prediction of protein function can provide quick annotations on extensive datasets opening the path for relevant applications, such as pharmacological target identification. The proposed method shows promise for structure-based protein function prediction, but sufficient data may not yet be available to properly assess the method’s performance on non-homologous proteins and thus reduce the confounding factor of evolutionary relationships.
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Sun, Yuanqiang, Jianping Chen, Pengbing Yan, Jun Zhong, Yuxin Sun, and Xinyu Jin. "Lithology Identification of Uranium-Bearing Sand Bodies Using Logging Data Based on a BP Neural Network." Minerals 12, no. 5 (April 27, 2022): 546. http://dx.doi.org/10.3390/min12050546.
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Lithology identification is an essential fact for delineating uranium-bearing sandstone bodies. A new method is provided to delineate sandstone bodies by a lithological automatic classification model using machine learning techniques, which could also improve the efficiency of borehole core logging. In this contribution, the BP neural network model for automatic lithology identification was established using an optimized gradient descent algorithm based on the neural network training of 4578 sets of well logging data (including lithology, density, resistivity, natural gamma, well-diameter, natural potential, etc.) from 8 boreholes of the Tarangaole uranium deposit in Inner Mongolia. The softmax activation function and the cross-entropy loss function are used for lithology classification and weight adjustment. The lithology identification prediction was carried out for 599 samples, with a prediction accuracy of 88.31%. The prediction results suggest that the model is efficient and effective, and that it could be directly applied for automatic lithology identification in sandstone bodies for uranium exploration.
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Sun, Ling Fang, Hong Gang Xie, and Li Hong Qiao. "Research on the Fouling Prediction Based on Hybrid Kernel Function Relevance Vector Machine." Advanced Materials Research 204-210 (February 2011): 31–35. http://dx.doi.org/10.4028/www.scientific.net/amr.204-210.31.
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The research on the fouling prediction of heat exchanger is significantly to improve operational efficiency and economic benefits of the plants. Based on the relevance vector machine with Gaussian kernel function, polynomial kernel function and hybrid kernel function, simulation research on the fouling prediction was introduced. We construct a six-inputs and one-output network model according to the fouling monitor principle and parameters with MATLAB, all training data came from the Automatic Dynamic Simulator of Fouling and input the network after normalized processing and reclassification. Simulations show that the root mean square error of fouling prediction with hybrid kernel function is less than simple kernel function, and has the better prediction precision.
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Brata, Adam Hendra, Deron Liang, and Sholeh Hadi Pramono. "Software Development of Automatic Data Collector for Bus Route Planning System." International Journal of Electrical and Computer Engineering (IJECE) 5, no. 1 (February 1, 2015): 150. http://dx.doi.org/10.11591/ijece.v5i1.pp150-157.
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<p>Public transportation is important issue in Taiwan. Recently, mobile application named Bus Route Planning was developed to help the user to get information about public transportation using bus. But, this application often gave the user inaccurate bus information and this application has less attractive GUI. To overcome those 2 problems, it needed 2 kinds of solutions. First, a more accurate time prediction algorithm is needed to predict the arrival time of bus. Second, augmented reality technology can be used to make a GUI improvement. In this research, Automatic Data Collector system was proposed to give support for those 2 solutions at once. This proposed system has 3 main functionalities. First, data collector function to provide some data sets that can be further analyzed as an base of time prediction algorithm. Second, data updater functions to provide the most updated bus information for used in augmented reality system. Third, data management function to gave the system better functionality to supported those 2 related systems. This proposed Automatic Data Collector system was developed using batch data processing scenario and SQL native query in Java programming language. The result of testing shown this data processing scenario was very effective to made database manipulation especially for large-sized data.</p>
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Deghboudj, Imen, and Samir Ladaci. "Automatic voltage regulator performance enhancement using a fractional order model predictive controller." Bulletin of Electrical Engineering and Informatics 10, no. 5 (October 1, 2021): 2424–32. http://dx.doi.org/10.11591/eei.v10i5.2435.
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In this paper, a new design method for fractional order model predictive control (FO-MPC) is introduced. The proposed FO-MPC is synthesized for the class of linear time invariant system and applied for the control of an automatic voltage regulator (AVR). The main contribution is to use a fractional order system as prediction model, whereas the plant model is considered as an integer order one. The fractional order model is implemented using the singularity function approach. A comparative study is given with the classical MPC scheme. Numerical simulation results on the controlled AVR performances show the efficiency and the superiority of the fractional order MPC.
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Funk, Christopher S., Indika Kahanda, Asa Ben-Hur, and Karin M. Verspoor. "Evaluating a variety of text-mined features for automatic protein function prediction with GOstruct." Journal of Biomedical Semantics 6, no. 1 (2015): 9. http://dx.doi.org/10.1186/s13326-015-0006-4.
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Chen, Xue, Yuanyuan Shi, Yanjun Wang, and Yuanjuan Cheng. "Deep Learning-Based Image Automatic Assessment and Nursing of Upper Limb Motor Function in Stroke Patients." Journal of Healthcare Engineering 2021 (August 24, 2021): 1–6. http://dx.doi.org/10.1155/2021/9059411.
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This paper mainly introduces the relevant contents of automatic assessment of upper limb mobility after stroke, including the relevant knowledge of clinical assessment of upper limb mobility, Kinect sensor to realize spatial location tracking of upper limb bone points, and GCRNN model construction process. Through the detailed analysis of all FMA evaluation items, a unique experimental data acquisition environment and evaluation tasks were set up, and the results of FMA prediction using bone point data of each evaluation task were obtained. Through different number and combination of tasks, the best coefficient of determination was achieved when task 1, task 2, and task 5 were simultaneously used as input for FMA prediction. At the same time, in order to verify the superior performance of the proposed method, a comparative experiment was set with LSTM, CNN, and other deep learning algorithms widely used. Conclusion. GCRNN was able to extract the motion features of the upper limb during the process of movement from the two dimensions of space and time and finally reached the best prediction performance with a coefficient of determination of 0.89.
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Zhao, J. Q., J. Yang, P. X. Li, and J. M. Lu. "Semi-automatic Road Extraction from SAR images using EKF and PF." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-7/W4 (June 26, 2015): 227–30. http://dx.doi.org/10.5194/isprsarchives-xl-7-w4-227-2015.
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Recently, the use of linear features for processing remote sensing images has shown its importance in applications. As one of typical linear targets, road is a hot spot of remote sensing image interpretation. Since extracting road by manual processing is too expensive and time consuming, researches based on automatic and semi-automatic have become more and more popular. Such interest is motivated by the requirements for civilian and military applications, such as road maps, traffic monitoring, navigation applications, and topographic mapping. How to extract road accurately and efficiently from SAR images is a key problem. In this paper, through analyzing characteristics of road, semi-automatic road extraction based on Extend Kalman Filtering (EKF) and Particles Filtering (PF), is presented. These two methods have the same algorithm flow which is an iterative approach based on prediction and update. The specific procedure as follows: at prediction stage, we obtain prior probability density function by the prior stage and prediction model, and through prior probability density function and the new measurement, at update stage we obtain the posterior probability density function which is the optimal estimation of road system state. Both EKF and PF repeat the steps above until the extracting tasks are finished. We use these two methods to extract road respectively. The effectiveness of the proposed method is demonstrated through the experiments from Howland by UAVSAR in L-band. And through contrast experiments, we discover that extracting difference complexity of road based on different methods can improve accuracy and efficiency. The results show that EKF has better performance on road with middle noise and PF has better performance on road with high noise.
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Reis, A. P., G. Brocart, M. Belghiti, N. Le Brusq, S. Messoudi, B. M. Le Guienne, L. Laffont, et al. "40 Toward a standardised annotation of morphokinetical parameters for an automatic early prediction of the in vitro development potential of bovine embryos." Reproduction, Fertility and Development 31, no. 1 (2019): 146. http://dx.doi.org/10.1071/rdv31n1ab40.
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In a previous work, we proposed an algorithm to select a subset of discriminant morphokinetical parameters within a larger predefined set (116 parameters) and to predict 6 major bovine embryo profiles of in vitro development. The algorithm relies on flexible combinations of the discriminant parameters selected within the four first cleavage cycles. The retained profiles were arrested embryos (embryos without mitotic activity, showing signs of life); dead embryos (embryos with all cells dead); anarchic embryos (embryos with abnormal morphological and/or kinetical development: some of these embryos can result in a blastocyst); not hatched blastocysts (blastocysts not hatching by 8 dpi); hatching blastocysts (blastocysts hatching in vitro from 7.3 to 8 dpi); and early hatching blastocysts (blastocysts hatching from 6 to 7.2 dpi) (Reis et al. 2018 Anim. Reprod.). The aim of the present work was to develop a ready-to-use software (EasyPickAndPredict) allowing the extension of this methodology to other embryologists. The software of high portability was built with the JAVA language and embedded with the classifier (R language, R Foundation for Statistical Computing, Vienna, Austria) and a user’s help for annotation (Adobe Premiere Pro CS5, San Jose, CA, USA). The predictive software is easy to handle, fast to load, and has high portability. The “manual annotation” function is based on click actions to annotate the discriminant parameters. The “prediction” function calls the embedded classifier. The “report” function creates customised reports including the embryo classification, the summary of the measures, and the accuracy of the prediction (vote system). The “help” function calls an audiovisual guideline with annotation specifications for all the morphokinetic parameters currently described (including the discriminant subset of parameters). This document includes help to produce a good time-lapse video (16min 51s); annotation specifications for the cleavage cycles 1 to 4 (36 min 26s); and a summary with examples of the 6 major embryonic morphokinetical profiles (16min 47s). The predictive graphical interface is easy to manipulate and automatically extracts the value of each discriminant parameter on the time-lapse picture as validated by the user click. The functions “manual annotation”, “automatic prediction”, “help”, and “report” supply embryologists with a standard approach to predict and analyse morphokinetical profiles of their embryos. This standardised approach is necessary to improve the capacity of comparison of morphokinetical works in different laboratories and enhance knowledge about in vitro-produced bovine embryos.
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Ying, X. Y., X. Y. Qin, J. H. Chen, and J. Gao. "Generating Residential Layout Based on AI in the View of Wind Environment." Journal of Physics: Conference Series 2069, no. 1 (November 1, 2021): 012061. http://dx.doi.org/10.1088/1742-6596/2069/1/012061.
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Abstract There is a contradiction between the high-density residential area development form and comfortable outdoor physical environment. The existing studies on wind environment of high-rise residential areas only provide the guidance for the simple general layouts, which cannot cope with the fact that most high-rise residential areas are mixed of point buildings and board buildings, and it would cost a lot of time and resources to carry out computer simulation of each layout. This paper presents a new tool, which uses the automatic optimization function of genetic algorithm and the prediction function of fully convolutional neural network to integrates three functions: the automatic generation of high-rise residential layout, the simulation of wind environment and the comparison for optimization, to learn plan scheduling and obtain the optimal solution for high-rise residential layout under specific plot ratio and plot conditions, provides guidance for today’s fast-paced architectural design.
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Yi, Gangman. "An Optimized Prediction Model Based on Feature Probability for Functional Identification of Large-Scale Ubiquitous Data." Mathematical Problems in Engineering 2015 (2015): 1–7. http://dx.doi.org/10.1155/2015/647296.
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Recently, there is a growing interest in the sequence analysis. In particular, the next generation sequencing (NGS) technique fragments the base sequence and analyzes the functions thereof. Its essential role is to arrange pieces of the base sequence together based on sequencing and to define the functions. The organization of unarranged piece of sequence is one of the active research areas; moreover, definition of gene function automatically is a popular research topic. The previous studies about the automatic gene function have mainly utilized the method that automatically defines protein functions by using the similarities of base sequence or the disclosed database and the protein interaction or context free method. This study aims to predict the category of protein whose function was not defined after learning automatically with GO by extracting the characteristics of protein inside the cluster. This study conducts clustering by using the protein interaction that is generated by the similarities of base sequence under the assumption that the proteins inside the cluster have similar function. The proposed method is to show an optimized result in accordance with the option after finding the option value that can give the outperformed prediction of GO, which classifies the functions based on the IPR and keywords inside the same cluster as the unique features.
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Horejs, O., M. Mares, and A. Mlcoch. "SMART SENSOR FOR ENHANCEMENT OF A MULTI-SPINDLE AUTOMATIC LATHE THERMAL ERROR COMPENSATION MODEL." MM Science Journal 2021, no. 3 (June 30, 2021): 4706–12. http://dx.doi.org/10.17973/mmsj.2021_7_2021079.
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The development of a smart sensor is proposed to improve the thermal error compensation model of a multi-spindle automatic lathe. The smart sensor is capable of gathering real-time information about rotating spindle drum temperatures. Thereafter, the temperature obtained by the smart sensor is applied as input to the thermal error compensation model based on the transfer function instead of an indigenous temperature measured on the stationary part of the multi-spindle automatic lathe. Using spindle drum temperature as the model input increases the prediction of thermal displacements in the X-axis by 16%.
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Kabir, Anowarul, and Amarda Shehu. "GOProFormer: A Multi-Modal Transformer Method for Gene Ontology Protein Function Prediction." Biomolecules 12, no. 11 (November 18, 2022): 1709. http://dx.doi.org/10.3390/biom12111709.
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Protein Language Models (PLMs) are shown to be capable of learning sequence representations useful for various prediction tasks, from subcellular localization, evolutionary relationships, family membership, and more. They have yet to be demonstrated useful for protein function prediction. In particular, the problem of automatic annotation of proteins under the Gene Ontology (GO) framework remains open. This paper makes two key contributions. It debuts a novel method that leverages the transformer architecture in two ways. A sequence transformer encodes protein sequences in a task-agnostic feature space. A graph transformer learns a representation of GO terms while respecting their hierarchical relationships. The learned sequence and GO terms representations are combined and utilized for multi-label classification, with the labels corresponding to GO terms. The method is shown superior over recent representative GO prediction methods. The second major contribution in this paper is a deep investigation of different ways of constructing training and testing datasets. The paper shows that existing approaches under- or over-estimate the generalization power of a model. A novel approach is proposed to address these issues, resulting in a new benchmark dataset to rigorously evaluate and compare methods and advance the state-of-the-art.
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Zhi, Tongle, Chengjie Meng, and Linshan Fu. "Design of Intelligent Rehabilitation Evaluation Scale for Stroke Patients Based on Genetic Algorithm and Extreme Learning Machine." Journal of Sensors 2022 (March 19, 2022): 1–8. http://dx.doi.org/10.1155/2022/9323152.
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The rehabilitation of stroke patients is a long-term process. To realize the automation and quantification of upper limb rehabilitation assessment of stroke patients, an automatic prediction model of rehabilitation evaluation scale was established by extreme learning machine (ELM) according to Fugl-Meyer motor function assessment (FMA). Four movements in the shoulder and elbow joints of FMA were selected. Two acceleration sensors fixed on the forearm and upper arm of the hemiplegic side were used to collect the motion data of 35 patients. After preprocessing and feature extraction, the feature selection was carried out based on genetic algorithm and ELM, and the single-action model and comprehensive prediction model were established, respectively. The results show that the model can accurately and automatically predict the shoulder and elbow score of FMA, and the root mean square error of prediction is 2.16. This method breaks through the limitations of subjectivity, time-consuming and dependence on rehabilitation doctors in the traditional evaluation. It can be easily used in the assessment of long-term rehabilitation.
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Zhu, Qiang, Artem Oganov, and Guangrui Qian. "Computational Materials design by Evolutionary Structure Prediction." Acta Crystallographica Section A Foundations and Advances 70, a1 (August 5, 2014): C1539. http://dx.doi.org/10.1107/s2053273314084605.
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Crystal structure prediction (CSP) has been viewed as a major challenge in condensed matter science for a long time. Until recently, we developed a USPEX method based on evolutionary algorithms, and it proved to be a powerful tool enabling accurate and reliable prediction of structures from the beginning. How does it work - and why? In this lecture, I will summarize the principles, recent developments, and some applications of the USPEX code. 1) Optimizing chemical compositional space for compounds and co-crystals. A scheme is proposed to allow the automatic search for all the stable compounds with variation of chemical compositions. This function can be applied to study binary/ternary systems composed of both atomic/molecular blocks (Na-Cl, Mg-O, CaCl2-H2O, etc) [1]. 2) Predicting structures containing complex inorganic/organic molecular motifs. We designed a constrained evolutionary algorithm [2]. The key feature of this new approach is that each motif is treated as a building block which significantly reduces the search space. This method has been applied to a wide range of systems including inorganic complex, small molecular crystals, pharmaceuticals and even polymers crystals. 3) Predicting low dimensional system is different from predicting the bulk crystals. Surface brings another independent thermodynamic parameter, chemical potential. Since the stability of surface configuration depends on the chemical potential, the established phase diagram for multi-component system is quite different from that of bulk crystals [3].
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Anwar, Farhat, Rounakul Islam Boby, Hasmah Mansor, Sabahat Hussain, and Afsah Sharmin. "Development of Approximate Prediction Model for 3-DOF Helicopter and Benchmarking with Existing Controllers." Indonesian Journal of Electrical Engineering and Computer Science 8, no. 2 (November 1, 2017): 502. http://dx.doi.org/10.11591/ijeecs.v8.i2.pp502-510.
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<p>Recent trend of living is getting modernized rapidly by the involvement of automatic systems. Within the aviation industry, automatic systems had become heavily reliable by the end of the nineteen centuries. The systems usually require controllable devices with desired control algorithm known as controller. Controllers can be replaced with, almost every mechanical automation aspect where, safety is a serious issue. But it is not easy to adapt a controller with a specific model at the beginning. It is important to predict the model before a controller works on the model and the controller parameters need to be adapted to get maximum efficiency. A 3-DOF (Three Degrees of Freedom) airframe model is an advanced benchmark model of real 3-DOF helicopter. It has the same uncommon model dynamics with nonlinearities, strong duel motor cross coupling system, uncertain characteristics, disturbances dependent, unmodeled dynamics and many more. The 3-DOF airframe model is a well-known platform for controller performance benchmarking. This research paper shows the development of an approximate prediction model of a Three Degrees of Freedom helicopter model and uses the proposed approximate model to observe the performance of an existent hybrid controller. The hybrid controller is the combination of two different controllers named Quantitative Feedback Theory (QFT) controller and Adaptive controller. To achieve the research objective, the proposed mathematical model of this airframe was used to develop transfer function and simulate with the hybrid controller in MATLAB. The performance of the controller based on the proposed heliframe model of 3-DOF helicopter have also been reported added within this paper.</p>
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Xia, Yuan, Jingbo Zhou, Zhenhui Shi, Chao Lu, and Haifeng Huang. "Generative Adversarial Regularized Mutual Information Policy Gradient Framework for Automatic Diagnosis." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 01 (April 3, 2020): 1062–69. http://dx.doi.org/10.1609/aaai.v34i01.5456.
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Automatic diagnosis systems have attracted increasing attention in recent years. The reinforcement learning (RL) is an attractive technique for building an automatic diagnosis system due to its advantages for handling sequential decision making problem. However, the RL method still cannot achieve good enough prediction accuracy. In this paper, we propose a Generative Adversarial regularized Mutual information Policy gradient framework (GAMP) for automatic diagnosis which aims to make a diagnosis rapidly and accurately. We first propose a new policy gradient framework based on the Generative Adversarial Network (GAN) to optimize the RL model for automatic diagnosis. In our framework, we take the generator of GAN as a policy network, and also use the discriminator of GAN as a part of the reward function. This generative adversarial regularized policy gradient framework can try to avoid generating randomized trials of symptom inquires deviated from the common diagnosis paradigm. In addition, we add mutual information to enhance the reward function to encourage the model to select the most discriminative symptoms to make a diagnosis. Experiment evaluations on two public datasets show that our method beats the state-of-art methods, not only can achieve higher diagnosis accuracy, but also can use a smaller number of inquires to make diagnosis decision.
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Lv, Xiao Yan, Si Long Sun, and Hong Liu. "Stock Price Prediction Model Based on BA Neural Network and its Applications." Advanced Materials Research 989-994 (July 2014): 1646–51. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.1646.
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In view of the deficiency of the basic back-propagation (BP) algorithm based on steepest descent method. Bat algorithm (BA) in intelligent optimization is introduced into the training process of feed-forward neural networks, capturing the optimal solution of the objective function with a small population size and less number of iterations, and a prediction model based on BA feed-forward neural network (BA-NN) is given. By the empirical study of stock price prediction in Sany Heavy Industry, the results show that this method has advantages of frequency tuning and dynamic control of exploration and exploitation by automatic switching to intensive exploitation if necessary.
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Fu, Zhuang, Yanzheng Zhao, and Qinghua Yang. "A Prediction Model of the Adhesive Coating Thickness on a Space Solar Cell." Journal of Manufacturing Science and Engineering 128, no. 2 (October 27, 2005): 576–79. http://dx.doi.org/10.1115/1.2162909.
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This paper presents a prediction model of the adhesive coating thickness on a space solar cell. The model describes the non-Newtonian characteristics of the adhesive when it is bonded to the cover glass and the space solar cell in a non-vacuumed environment. The coating thickness is a function of the differential pressure inside the syringe and its relative speed. It can be indirectly controlled by the two feedbacks. Compared with other systems, the automatic one has better effect on the manufacture of the qualified space solar cell array.
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Kai, Hong. "Automatic Recommendation Algorithm for Video Background Music Based on Deep Learning." Complexity 2021 (February 2, 2021): 1–11. http://dx.doi.org/10.1155/2021/6696986.
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As one of the traditional entertainment items, video background music has gradually changed from traditional consumption to network consumption, which naturally also has the problem of information overload. From the perspective of model design and auxiliary information, this paper proposes a tightly coupled fusion model based on deep learning and collaborative filtering to alleviate the problem of poor prediction accuracy due to sparse matrix in the scoring prediction problem. In the use of auxiliary information, this paper uses crawler technology to obtain auxiliary information on the user side and the video background music side and compensates for the model’s sensitivity to the sparsity of the score matrix from a data perspective. In terms of model design, this paper conducts auxiliary information mining based on the diversity and structural differences of auxiliary information, uses an improved stack autoencoder to learn user’s interests, and uses convolutional neural networks to mine hidden features of video background music. Based on the idea of probabilistic matrix decomposition, the tightly coupled fusion of multiple deep learning models and collaborative filtering is realized. By comprehensively considering user’s interest and video background music characteristics, the collaborative filtering process is supervised, and the optimized prediction result is finally obtained. The performance test and function test of the system were carried out, respectively, to verify the effectiveness of the hybrid recommendation algorithm and the effect of the system for recommendation. Through experimental analysis, it is proved that the algorithm designed in this paper can improve the recommendation quality and achieve the expected goal.
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Jiang, Fan, Jitong Ma, Baosen Wang, Feifei Shen, and Lingling Yuan. "Ocean Observation Data Prediction for Argo Data Quality Control Using Deep Bidirectional LSTM Network." Security and Communication Networks 2021 (October 11, 2021): 1–11. http://dx.doi.org/10.1155/2021/5665386.
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With the rapid development of maritime technologies, a huge amount of ocean data has been acquired through the state-of-the-art ocean equipment to get better understanding and development of ocean. The prediction and correction of oceanic observation data play a fundamental and important role in the oceanic relevant applications, including both civilian and military fields. On the basis of Argo data, aiming at predicting and correcting the oceanic observation data, we propose an ocean temperature and salinity prediction approach in this paper. In our approach, firstly, bounded nonlinear function is utilized for dataset quality control, which can effectively eliminate the influence of spikes or outliers in Argo data. Then, RBF neural network is used for high-resolution Argo dataset construction. Finally, a bidirectional LSTM framework is proposed to predict and analyze the ocean temperature and salinity on the basis of BOA Argo data. Experimental results demonstrate that the proposed bidirectional LSTM framework can accurately predict the ocean temperature and salinity and enable outstanding performance in oceanic observation data prediction and correction. The proposed approach is also important for the realization of Argo dataset automatic quality control.
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Amilpur, Santhosh, and Raju Bhukya. "EDeepSSP: Explainable deep neural networks for exact splice sites prediction." Journal of Bioinformatics and Computational Biology 18, no. 04 (July 22, 2020): 2050024. http://dx.doi.org/10.1142/s0219720020500249.
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Splice site prediction is crucial for understanding underlying gene regulation, gene function for better genome annotation. Many computational methods exist for recognizing the splice sites. Although most of the methods achieve a competent performance, their interpretability remains challenging. Moreover, all traditional machine learning methods manually extract features, which is tedious job. To address these challenges, we propose a deep learning-based approach (EDeepSSP) that employs convolutional neural networks (CNNs) architecture for automatic feature extraction and effectively predicts splice sites. Our model, EDeepSSP, divulges the opaque nature of CNN by extracting significant motifs and explains why these motifs are vital for predicting splice sites. In this study, experiments have been conducted on six benchmark acceptors and donor datasets of humans, cress, and fly. The results show that EDeepSSP has outperformed many state-of-the-art approaches. EDeepSSP achieves the highest area under the receiver operating characteristic curve (AUC_ROC) and area under the precision-recall curve (AUC_PR) of 99.32% and 99.26% on human donor datasets, respectively. We also analyze various filter activities, feature activations, and extracted significant motifs responsible for the splice site prediction. Further, we validate the learned motifs of our model against known motifs of JASPAR splice site database.
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Hao, Ying, Mingshun Guo, and Yijing Guo. "Predicting technological innovation in new energy vehicles based on an improved radial basis function neural network for policy synergy." PLOS ONE 17, no. 8 (August 25, 2022): e0271316. http://dx.doi.org/10.1371/journal.pone.0271316.
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Policy synergy is necessary to promote technological innovation and sustainable industrial development. A radial basis function (RBF) neural network model with an automatic coding machine and fractional momentum was proposed for the prediction of technological innovation. Policy keywords for China’s new energy vehicle policies issued over the years were quantified by the use of an Latent Dirichlet Allocation (LDA) model. The training of the neural network model was completed by using policy keywords, synergy was measured as the input layer, and the number of synchronous patent applications was measured as the output layer. The predictive efficacies of the traditional neural network model and the improved neural network model were compared again to verify the applicability and accuracy of the improved neural network. Finally, the influence of the degree of synergy on technological innovation was revealed by changing the intensity of policy measures. This study provides a basis for the relevant departments to formulate industrial policies and improve innovation performance by enterprises.
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Zhao, Xinxin, and Zhijun Li. "Data-Driven Predictive Control Applied to Gear Shifting for Heavy-Duty Vehicles." Energies 11, no. 8 (August 16, 2018): 2139. http://dx.doi.org/10.3390/en11082139.
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In this paper, the data-driven predictive control method is applied to the clutch speed tracking control for the inertial phase of the shift process. While the clutch speed difference changes according to the predetermined trajectory, the purpose of improving the shift quality is achieved. The data-driven predictive control is implemented by combining the subspace identification with the model predictive control. Firstly, the predictive factors are constructed from the input and output data of the shift process via subspace identification, and then the factors are applied to a prediction equation. Secondly, an optimization function is deduced by taking the tracking error and the increments of inputs into accounts. Finally, the optimal solutions are solved through quadratic programming algorithm in Matlab software, and the future inputs of the system are obtained. The control algorithm is applied to the upshift process of an automatic transmission, the simulation results show that the algorithm is in good performance and satisfies the practical requirements.
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Zeng, Jie, Panayiotis C. Roussis, Ahmed Salih Mohammed, Chrysanthos Maraveas, Seyed Alireza Fatemi, Danial Jahed Armaghani, and Panagiotis G. Asteris. "Prediction of Peak Particle Velocity Caused by Blasting through the Combinations of Boosted-CHAID and SVM Models with Various Kernels." Applied Sciences 11, no. 8 (April 20, 2021): 3705. http://dx.doi.org/10.3390/app11083705.
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This research examines the feasibility of hybridizing boosted Chi-Squared Automatic Interaction Detection (CHAID) with different kernels of support vector machine (SVM) techniques for the prediction of the peak particle velocity (PPV) induced by quarry blasting. To achieve this objective, a boosting-CHAID technique was applied to a big experimental database comprising six input variables. The technique identified four input parameters (distance from blast-face, stemming length, powder factor, and maximum charge per delay) as the most significant parameters affecting the prediction accuracy and utilized them to propose the SVM models with various kernels. The kernel types used in this study include radial basis function, polynomial, sigmoid, and linear. Several criteria, including mean absolute error (MAE), correlation coefficient (R), and gains, were calculated to evaluate the developed models’ accuracy and applicability. In addition, a simple ranking system was used to evaluate the models’ performance systematically. The performance of the R and MAE index of the radial basis function kernel of SVM in training and testing phases, respectively, confirm the high capability of this SVM kernel in predicting PPV values. This study successfully demonstrates that a combination of boosting-CHAID and SVM models can identify and predict with a high level of accuracy the most effective parameters affecting PPV values.
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Shao, Zhenfeng, Penghao Tang, Zhongyuan Wang, Nayyer Saleem, Sarath Yam, and Chatpong Sommai. "BRRNet: A Fully Convolutional Neural Network for Automatic Building Extraction From High-Resolution Remote Sensing Images." Remote Sensing 12, no. 6 (March 24, 2020): 1050. http://dx.doi.org/10.3390/rs12061050.
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Building extraction from high-resolution remote sensing images is of great significance in urban planning, population statistics, and economic forecast. However, automatic building extraction from high-resolution remote sensing images remains challenging. On the one hand, the extraction results of buildings are partially missing and incomplete due to the variation of hue and texture within a building, especially when the building size is large. On the other hand, the building footprint extraction of buildings with complex shapes is often inaccurate. To this end, we propose a new deep learning network, termed Building Residual Refine Network (BRRNet), for accurate and complete building extraction. BRRNet consists of such two parts as the prediction module and the residual refinement module. The prediction module based on an encoder–decoder structure introduces atrous convolution of different dilation rates to extract more global features, by gradually increasing the receptive field during feature extraction. When the prediction module outputs the preliminary building extraction results of the input image, the residual refinement module takes the output of the prediction module as an input. It further refines the residual between the result of the prediction module and the real result, thus improving the accuracy of building extraction. In addition, we use Dice loss as the loss function during training, which effectively alleviates the problem of data imbalance and further improves the accuracy of building extraction. The experimental results on Massachusetts Building Dataset show that our method outperforms other five state-of-the-art methods in terms of the integrity of buildings and the accuracy of complex building footprints.
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Cao, Yi, Xiaolei Hou, and Nan Chen. "Short-Term Forecast of OD Passenger Flow Based on Ensemble Empirical Mode Decomposition." Sustainability 14, no. 14 (July 13, 2022): 8562. http://dx.doi.org/10.3390/su14148562.
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The development of metro systems can be a good solution to many problems in urban transport and promote sustainable urban development. A metro system plays an important role in urban public transit, and the passenger-flow forecasting is fundamental to assisting operators in establishing an intelligent transport system (ITS). In order to accurately predict the passenger flow of urban metros in different periods and provide a scientific basis for schedule planning, a short-term metro passenger-flow prediction model is constructed by integrating ensemble empirical mode decomposition (EEMD) and long short-term memory neural network (LSTM) to solve the problem that the existing empirical mode decomposition (EMD) is prone to modal aliasing. According to the processed metro-card data, the time series of historical OD data of metro passenger flow is obtained. After EEMD modal decomposition, several intrinsic mode functions sub-items and residual items are obtained. Then, an LSTM network is constructed for prediction. The time step of the network is decided according to the partial autocorrelation functions. The prediction results of intrinsic mode function (IMF) and residual items are integrated to obtain prediction results. The station is classified according to the land types around the station, and the model is tested by using the metro automatic fare collection (AFC) data. In order to test the actual prediction, a different number of training set samples are selected to predict. The measured data of the next day is continuously added to the original training set to compare the prediction accuracy. The results show that the mean absolute percentage error (MAPE) and root mean square error (RMSE) of the EEMD-LSTM model are better than the EMD-LSTM in predicting the OD value of commercial–residential stations and scenic–residential stations. Compared with the EMD-LSTM model, the EEMD-LSTM model showed an average reduction by 3.112% in MAPE values and 15.889 in RMSE, indicating that the EEMD-LSTM has higher prediction accuracy, and EEMD-LSTM model has higher accuracy in short-term metro passenger-flow prediction. The average MAPE for the 35-to-42-day historical data sample decreased from 13.02% to 10.39% with a decreasing trend. It shows that the prediction accuracy keeps improving as the training set samples increase.
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Cui, Lei, Jun Feng, and Lin Yang. "Towards Fine Whole-Slide Skeletal Muscle Image Segmentation through Deep Hierarchically Connected Networks." Journal of Healthcare Engineering 2019 (June 27, 2019): 1–10. http://dx.doi.org/10.1155/2019/5191630.
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Automatic skeletal muscle image segmentation (MIS) is crucial in the diagnosis of muscle-related diseases. However, accurate methods often suffer from expensive computations, which are not scalable to large-scale, whole-slide muscle images. In this paper, we present a fast and accurate method to enable the more clinically meaningful whole-slide MIS. Leveraging on recently popular convolutional neural network (CNN), we train our network in an end-to-end manner so as to directly perform pixelwise classification. Our deep network is comprised of the encoder and decoder modules. The encoder module captures rich and hierarchical representations through a series of convolutional and max-pooling layers. Then, the multiple decoders utilize multilevel representations to perform multiscale predictions. The multiscale predictions are then combined together to generate a more robust dense segmentation as the network output. The decoder modules have independent loss function, which are jointly trained with a weighted loss function to address fine-grained pixelwise prediction. We also propose a two-stage transfer learning strategy to effectively train such deep network. Sufficient experiments on a challenging muscle image dataset demonstrate the significantly improved efficiency and accuracy of our method compared with recent state of the arts.
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Kenzhebek, Y., T. S. Imankulov, and D. Zh Akhmed-Zaki. "PREDICTION OF OIL PRODUCTION USING PHYSICS-INFORMED NEURAL NETWORKS." BULLETIN Series of Physics & Mathematical Sciences 76, no. 4 (December 15, 2021): 45–50. http://dx.doi.org/10.51889/2021-4.1728-7901.06.
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In recent years, modern information technologies have been actively used in various industries. The oil industry is no exception, since high-performance computing technologies, artificial intelligence algorithms, methods of collecting, processing and storing information are actively used to solve the problems of increasing oil recovery. Deep learning has made remarkable strides in a variety of applications, but its use for solving partial differential equations has only recently emerged. In particular, you can replace traditional numerical methods with a neural network that approximates the solution to a partial differential equation. Physically Informed Neural Networks (PINNs) embed partial differential equations into the neural network loss function using automatic differentiation. A numerical algorithm and PINN have been developed for solving the one-dimensional pressure equation from the Buckley-Leverett mathematical model. The results of numerical solution and prediction of the PINN neural network for solving the pressure equation are obtained.
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Lin, Chi Ying, and Yu Sheng Zeng. "Visual Servoing of Automatic Alignment System Using Model Predictive Control." Key Engineering Materials 625 (August 2014): 627–32. http://dx.doi.org/10.4028/www.scientific.net/kem.625.627.
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Over the past few decades, vision based alignment has been accepted as an important technique to achieve higher economic benefits for precision manufacturing and measurement applications. Also referred to as visual servoing, this technique basically applies the vision feedback information and drives the moving parts to the desired target location using some appropriate control laws. Although recently rapid development of advanced image processing algorithms and hardware have made this alignment process an easier task, some fundamental issues including inevitable system constraints and singularities, still remain as a challenging research topic for further investigation. This paper aims to develop a visual servoing method for automatic alignment system using model predictive control (MPC). The reason for using this optimal control for visual servoing design is because of its capability of handling constraints such as motor and image constraints in precision alignment systems. In particular, a microassembly system for peg and hole alignment application is adopted to illustrate the design process. The goal is to perform visual tracking of two image feature points based on a XYθ motor-stage system. From the viewpoint of MPC, this is an optimization problem that minimizes feature errors under given constraints. Therefore, a dynamic model consisting of camera parameters and motion stage dynamics is first derived to build the prediction model and set up the cost function. At each sample step the control command is obtained by solving a quadratic programming optimization problem. Finally, simulation results with comparison to a conventional image based visual servoing method demonstrate the effectiveness and potential use of this method.
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Diller, Gerhard Paul, Stefan Orwat, Julius Vahle, Ulrike M. M. Bauer, Aleksandra Urban, Samir Sarikouch, Felix Berger, Philipp Beerbaum, and Helmut Baumgartner. "Prediction of prognosis in patients with tetralogy of Fallot based on deep learning imaging analysis." Heart 106, no. 13 (March 11, 2020): 1007–14. http://dx.doi.org/10.1136/heartjnl-2019-315962.
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ObjectiveTo assess the utility of machine learning algorithms for automatically estimating prognosis in patients with repaired tetralogy of Fallot (ToF) using cardiac magnetic resonance (CMR).MethodsWe included 372 patients with ToF who had undergone CMR imaging as part of a nationwide prospective study. Cine loops were retrieved and subjected to automatic deep learning (DL)-based image analysis, trained on independent, local CMR data, to derive measures of cardiac dimensions and function. This information was combined with established clinical parameters and ECG markers of prognosis.ResultsOver a median follow-up period of 10 years, 23 patients experienced an endpoint of death/aborted cardiac arrest or documented ventricular tachycardia (defined as >3 documented consecutive ventricular beats). On univariate Cox analysis, various DL parameters, including right atrial median area (HR 1.11/cm², p=0.003) and right ventricular long-axis strain (HR 0.80/%, p=0.009) emerged as significant predictors of outcome. DL parameters were related to adverse outcome independently of left and right ventricular ejection fraction and peak oxygen uptake (p<0.05 for all). A composite score of enlarged right atrial area and depressed right ventricular longitudinal function identified a ToF subgroup at significantly increased risk of adverse outcome (HR 2.1/unit, p=0.007).ConclusionsWe present data on the utility of machine learning algorithms trained on external imaging datasets to automatically estimate prognosis in patients with ToF. Due to the automated analysis process these two-dimensional-based algorithms may serve as surrogates for labour-intensive manually attained imaging parameters in patients with ToF.
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Et.al, Vishaal Saravanan. "Automated Web Design And Code Generation Using Deep Learning." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 6 (April 10, 2021): 364–73. http://dx.doi.org/10.17762/turcomat.v12i6.1401.
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Excited by ground-breaking progress in automatic code generation, machine translation, and computer vision, further simplify web design workflow by making it easier and productive. A Model architecture is proposed for the generation of static web templates from hand-drawn images. The model pipeline uses the word-embedding technique succeeded by long short-term memory (LSTM) for code snippet prediction. Also, canny edge detection algorithm fitted with VGG19 convolutional neural net (CNN) and attention-based LSTM for web template generation. Extracted features are concatenated, and a terminal LSTM with a SoftMax function is called for final prediction. The proposed model is validated with a benchmark based on the BLUE score, and performance improvement is compared with the existing image generation algorithms.
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Sun, Pei Lu, Ping Yu Jiang, Bao Quan Chen, and Lei Zhang. "Ct-SELECTOR: A Web Application for Cutting-Tool Selection in a CNC Machining Workshop." Advanced Materials Research 889-890 (February 2014): 1170–73. http://dx.doi.org/10.4028/www.scientific.net/amr.889-890.1170.
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Recently in most Chinese CNC machining enterprises, the selection of cutting tools still depends on the experience of individual workers, which is hard to be inherited by others. Faced with this situation, we developed a web application for selecting proper cutting tools in CNC machining workshop, named ct-SELECTOR for short. This article introduces the main functions of the ct-SELECTOR and analyzed the three key enable technologies for realizing each function: (1) the automatic cutting-tool selection and match, (2) the optimal recommendation and management of metal cutting parameters and (3) the management and off-line prediction of tool-life. Finally, the developing architecture of the ct-SELECTOR is demonstrated, and a running example is illustrated to verify the validity and practicability of this system.
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Chua, Fang-Fang, Tek-Yong Lim, Bushra Tajuddin, and Amarilis Putri Yanuarifiani. "Incorporating Semi-Automated Approach for Effective Software Requirements Prioritization: A Framework Design." Journal of Informatics and Web Engineering 1, no. 1 (March 16, 2022): 1–15. http://dx.doi.org/10.33093/jiwe.2022.1.1.1.
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Software Requirements Prioritization (SRP) is one of the crucial processes in software requirements engineering. It presents a challenging task to decide among the pool of requirements and the variance of the stakeholder’s needs in prioritizing requirements. Semi-automated requirements prioritization is implemented in both manual and automatic processes. When prioritizing requirements, these aspects such as importance, time, cost and risk, should be taken into account. The emergence of machine learning is advancing to improve and automate the SRP process whereby decision making can be performed with minimal human intervention. Incorporating machine learning approaches in prioritization techniques can be implemented in the ranking process and classifying the priority group of the software requirements. A Semi-Automated Requirements Prioritization framework (SARiP), which implements semi-automatic process in requirements prioritization is proposed. SARiP concentrates on the areas related to prediction of requirements priority group and ranks requirements using classification tree and ranking algorithm. SARiP has been successfully evaluated in the government sector domain by the i-Tegur team from the Department of Information Technology, Ministry of Housing and Local Government of Malaysia (KPKT). 80% of the participants agreed that SARiP is extremely likely to help the participants in prioritizing the requirements for their projects. All participants agreed that SARiP is reliable and useful. Recording the requirements and results for the prioritization will be considered for future work and traceability function will be included to trace the requirements changes.
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Stroppiana, Daniela, Gloria Bordogna, Matteo Sali, Mirco Boschetti, Giovanna Sona, and Pietro Alessandro Brivio. "A Fully Automatic, Interpretable and Adaptive Machine Learning Approach to Map Burned Area from Remote Sensing." ISPRS International Journal of Geo-Information 10, no. 8 (August 13, 2021): 546. http://dx.doi.org/10.3390/ijgi10080546.
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The paper proposes a fully automatic algorithm approach to map burned areas from remote sensing characterized by human interpretable mapping criteria and explainable results. This approach is partially knowledge-driven and partially data-driven. It exploits active fire points to train the fusion function of factors deemed influential in determining the evidence of burned conditions from reflectance values of multispectral Sentinel-2 (S2) data. The fusion function is used to compute a map of seeds (burned pixels) that are adaptively expanded by applying a Region Growing (RG) algorithm to generate the final burned area map. The fusion function is an Ordered Weighted Averaging (OWA) operator, learnt through the application of a machine learning (ML) algorithm from a set of highly reliable fire points. Its semantics are characterized by two measures, the degrees of pessimism/optimism and democracy/monarchy. The former allows the prediction of the results of the fusion as affected by more false positives (commission errors) than false negatives (omission errors) in the case of pessimism, or vice versa; the latter foresees if there are only a few highly influential factors or many low influential ones that determine the result. The prediction on the degree of pessimism/optimism allows the expansion of the seeds to be appropriately tuned by selecting the most suited growing layer for the RG algorithm thus adapting the algorithm to the context. The paper illustrates the application of the automatic method in four study areas in southern Europe to map burned areas for the 2017 fire season. Thematic accuracy at each site was assessed by comparison to reference perimeters to prove the adaptability of the approach to the context; estimated average accuracy metrics are omission error = 0.057, commission error = 0.068, Dice coefficient = 0.94 and relative bias = 0.0046.
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Jiang, Bao Yi, Zhi Ping Li, Cheng Wen Zhang, and Xi Gang Wang. "Optimization Algorithms Used in Reservoir History Matching." Advanced Materials Research 748 (August 2013): 614–18. http://dx.doi.org/10.4028/www.scientific.net/amr.748.614.
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Numerical reservoir models are constructed from limited available static and dynamic data, and history matching is a process of changing model parameters to find a set of values that will yield a reservoir simulation prediction of data that matches the observed historical production data. To minimize the objective function involved in the history matching procedure, we need to apply the optimization algorithms. This paper is based on the optimization algorithms used in automatic history matching. Several optimization algorithms will be compared in this paper.
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Cartella, Francesco, Jan Lemeire, Luca Dimiccoli, and Hichem Sahli. "Hidden Semi-Markov Models for Predictive Maintenance." Mathematical Problems in Engineering 2015 (2015): 1–23. http://dx.doi.org/10.1155/2015/278120.
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Realistic predictive maintenance approaches are essential for condition monitoring and predictive maintenance of industrial machines. In this work, we propose Hidden Semi-Markov Models (HSMMs) with (i) no constraints on the state duration density function and (ii) being applied to continuous or discrete observation. To deal with such a type of HSMM, we also propose modifications to the learning, inference, and prediction algorithms. Finally, automatic model selection has been made possible using the Akaike Information Criterion. This paper describes the theoretical formalization of the model as well as several experiments performed on simulated and real data with the aim of methodology validation. In all performed experiments, the model is able to correctly estimate the current state and to effectively predict the time to a predefined event with a low overall average absolute error. As a consequence, its applicability to real world settings can be beneficial, especially where in real time the Remaining Useful Lifetime (RUL) of the machine is calculated.
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Korsunovs, Aleksandrs, Felician Campean, Gaurav Pant, Oscar Garcia-Afonso, and Efe Tunc. "Evaluation of zero-dimensional stochastic reactor modelling for a Diesel engine application." International Journal of Engine Research 21, no. 4 (April 29, 2019): 592–609. http://dx.doi.org/10.1177/1468087419845823.
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Prediction of engine-out emissions with high fidelity from in-cylinder combustion simulations is still a significant challenge early in the engine development process. This article contributes to this fast evolving body of knowledge by focusing on the evaluation of NO x emission prediction capability of a probability density function–based stochastic reactor engine models for a Diesel engine. The research implements a systematic approach to the study of the stochastic reactor engine model performance, underpinned by a detailed space-filling design of experiments (DoE)-based sensitivity analysis of both external and internal parameters, evaluating their effects on the accuracy in matching physical measurements of both in-cylinder conditions and NO x output. The approach proposed in this article introduces an automatic stochastic reactor engine model calibration methodology across the engine operating envelope, based on a multi-objective optimization approach. This aims to exploit opportunities for internal stochastic reactor engine model parameters tuning to achieve good overall modelling performance as a trade-off between physical in-cylinder measurements accuracy and the output NO x emission predictions error. The results from the case study provide a valuable insight into the effectiveness of the stochastic reactor engine model, showing good capability for NO x emissions prediction and trends, while pointing out the critical sensitivity to the external input parameters and modelling conditions.
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Mir, Adil Aslam, Fatih Vehbi Çelebi, Muhammad Rafique, M. R. I. Faruque, Mayeen Uddin Khandaker, Kimberlee Jane Kearfott, and Pervaiz Ahmad. "Correction to: Anomaly Classification for Earthquake Prediction in Radon Time Series Data Using Stacking and Automatic Anomaly Indication Function." Pure and Applied Geophysics 178, no. 6 (June 2021): 2395. http://dx.doi.org/10.1007/s00024-021-02764-5.
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Munir, Hafiz Shahbaz, Shengbing Ren, Mubashar Mustafa, Chaudry Naeem Siddique, and Shazib Qayyum. "Attention based GRU-LSTM for software defect prediction." PLOS ONE 16, no. 3 (March 4, 2021): e0247444. http://dx.doi.org/10.1371/journal.pone.0247444.
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Software defect prediction (SDP) can be used to produce reliable, high-quality software. The current SDP is practiced on program granular components (such as file level, class level, or function level), which cannot accurately predict failures. To solve this problem, we propose a new framework called DP-AGL, which uses attention-based GRU-LSTM for statement-level defect prediction. By using clang to build an abstract syntax tree (AST), we define a set of 32 statement-level metrics. We label each statement, then make a three-dimensional vector and apply it as an automatic learning model, and then use a gated recurrent unit (GRU) with a long short-term memory (LSTM). In addition, the Attention mechanism is used to generate important features and improve accuracy. To verify our experiments, we selected 119,989 C/C++ programs in Code4Bench. The benchmark tests cover various programs and variant sets written by thousands of programmers. As an evaluation standard, compared with the state evaluation method, the recall, precision, accuracy and F1 measurement of our well-trained DP-AGL under normal conditions have increased by 1%, 4%, 5%, and 2% respectively.
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Feng, Ke, Zhi Wei Han, Jian Feng Cao, Yi Wen Kong, and Shui Gen Wang. "Industrial Application of the Packaged Soft-Reduction Technology for Slab Continuous Casting." Advanced Materials Research 538-541 (June 2012): 1222–27. http://dx.doi.org/10.4028/www.scientific.net/amr.538-541.1222.
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Based on the conception of integration and optimization of process, mechanical, electric and hydraulic system, the advanced CISDI packaged soft-reduction technology for slab continuous casting with our full intellectual property rights has been developed successfully, which consists of the metallurgical process models with strong function and advanced arithmetic, the segment equipments with optimized structure and high strength, the basic automation program with full inspection and control function, the reliable stable hydraulic system and automatic system. It contains two control modules, viz., the static soft-reduction function & dynamic soft-reduction function, and three gap-adjustment modes, viz., the position-control mode, pressure-control mode & soft-clamping mode. From the opinions of equipment protection and production safety, some functional mechanism such as segment ordinal action, gap self-adapting adjustment, forced protection for breakout prediction, and etc., have been adopted. The whole system has been put into the industrial practice on the No.6 slab caster of Liusteel China, which has shown the smooth caster operation, the steady running of system, and the good metallurgical effects on improvement of internal quality of slab like center segregation and center porosity. The industrial application of CISDI soft-reduction technology has brought the rising of output proportion of high-class steel grades and the increasing of qualified rate for slab quality inspection, which has resulted in the distinct economic benefits for Liusteel group.
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Hu, Jianming, Xiyang Zhi, Wei Zhang, Longfei Ren, and Lorenzo Bruzzone. "Salient Ship Detection via Background Prior and Foreground Constraint in Remote Sensing Images." Remote Sensing 12, no. 20 (October 15, 2020): 3370. http://dx.doi.org/10.3390/rs12203370.
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Automatic ship detection in complicated maritime background is a challenging task in the field of optical remote sensing image interpretation and analysis. In this paper, we propose a novel and reliable ship detection framework based on a visual saliency model, which can efficiently detect multiple targets of different scales in complex scenes with sea clutter, clouds, wake and islands interferences. Firstly, we present a reliable background prior extraction method adaptive for the random locations of targets by computing boundary probability and then generate a saliency map based on the background prior. Secondly, we compute the prior probability of salient foreground regions and propose a weighting function to constrain false foreground clutter, gaining the foreground-based prediction map. Thirdly, we integrate the two prediction maps and improve the details of the integrated map by a guided filter function and a wake adjustment function, obtaining the fine selection of candidate regions. Afterwards, a classification is further performed to reduce false alarms and produce the final ship detection results. Qualitative and quantitative evaluations on two public available datasets demonstrate the robustness and efficiency of the proposed method against four advanced baseline methods.
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Nursimulu, Nirvana, Alan M. Moses, and John Parkinson. "Architect: A tool for aiding the reconstruction of high-quality metabolic models through improved enzyme annotation." PLOS Computational Biology 18, no. 9 (September 8, 2022): e1010452. http://dx.doi.org/10.1371/journal.pcbi.1010452.
Full textAbstract:
Constraint-based modeling is a powerful framework for studying cellular metabolism, with applications ranging from predicting growth rates and optimizing production of high value metabolites to identifying enzymes in pathogens that may be targeted for therapeutic interventions. Results from modeling experiments can be affected at least in part by the quality of the metabolic models used. Reconstructing a metabolic network manually can produce a high-quality metabolic model but is a time-consuming task. At the same time, current methods for automating the process typically transfer metabolic function based on sequence similarity, a process known to produce many false positives. We created Architect, a pipeline for automatic metabolic model reconstruction from protein sequences. First, it performs enzyme annotation through an ensemble approach, whereby a likelihood score is computed for an EC prediction based on predictions from existing tools; for this step, our method shows both increased precision and recall compared to individual tools. Next, Architect uses these annotations to construct a high-quality metabolic network which is then gap-filled based on likelihood scores from the ensemble approach. The resulting metabolic model is output in SBML format, suitable for constraints-based analyses. Through comparisons of enzyme annotations and curated metabolic models, we demonstrate improved performance of Architect over other state-of-the-art tools, notably with higher precision and recall on the eukaryote C. elegans and when compared to UniProt annotations in two bacterial species. Code for Architect is available at https://github.com/ParkinsonLab/Architect. For ease-of-use, Architect can be readily set up and utilized using its Docker image, maintained on Docker Hub.
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Wang, Lipeng, Zhi Zhang, Qidan Zhu, and Ran Dong. "Longitudinal automatic carrier landing system guidance law using model predictive control with an additional landing risk term." Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering 233, no. 3 (December 20, 2017): 1089–105. http://dx.doi.org/10.1177/0954410017746432.
Full textAbstract:
This paper used a model predictive control with an additional term to develop a modified longitudinal guidance law to reduce landing risk in an automatic carrier landing system. The landing risk model was established by using a longitudinal trajectory and touchdown point predictive principle. A traditional MPC was then involved in designing a modified automatic carrier landing system guidance law for the proposed model. The nonlinear landing mathematic model of an F/A-18 carrier-based aircraft was initially established. Considering the processed procedure in the model predictive control algorithm, the corresponding linear landing model was derived on the basis of the equilibrium states of the F/A-18. Second, landing trajectory in the longitudinal plane was analysed so that the predictive principle of the trajectory trend was reasonably addressed. Depending on the experimental sample data of a pilot model, some linear imitating envelopes are transformed from the corresponding nonlinear trajectory clusters. Furthermore, a touchdown point prediction model was further established based on the predicted trajectory and touchdown point. Third, the traditional model predictive control was introduced to integrate the landing risk term in the performance cost function to develop a novel modified algorithm that not only guides the aircraft to automatically approach and land on the carrier, but also eliminates landing risk during the final carrier approach. Linear matrix inequalities were imported to substitute algebraic inequalities derived from this new algorithm to increase calculating speed. A simulation mission was conducted on a semi-physical platform and compared with the traditional model predictive control without the additional term. The theoretical results validated the correctness and robustness of the modified algorithm and its capability to eliminate landing risk during terminal carrier approach.