Статті в журналах з теми "LSTM. ESN"
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Chattopadhyay, Ashesh, Pedram Hassanzadeh, and Devika Subramanian. "Data-driven predictions of a multiscale Lorenz 96 chaotic system using machine-learning methods: reservoir computing, artificial neural network, and long short-term memory network." Nonlinear Processes in Geophysics 27, no. 3 (July 2, 2020): 373–89. http://dx.doi.org/10.5194/npg-27-373-2020.
Повний текст джерелаАнотація:
Abstract. In this paper, the performance of three machine-learning methods for predicting short-term evolution and for reproducing the long-term statistics of a multiscale spatiotemporal Lorenz 96 system is examined. The methods are an echo state network (ESN, which is a type of reservoir computing; hereafter RC–ESN), a deep feed-forward artificial neural network (ANN), and a recurrent neural network (RNN) with long short-term memory (LSTM; hereafter RNN–LSTM). This Lorenz 96 system has three tiers of nonlinearly interacting variables representing slow/large-scale (X), intermediate (Y), and fast/small-scale (Z) processes. For training or testing, only X is available; Y and Z are never known or used. We show that RC–ESN substantially outperforms ANN and RNN–LSTM for short-term predictions, e.g., accurately forecasting the chaotic trajectories for hundreds of numerical solver's time steps equivalent to several Lyapunov timescales. The RNN–LSTM outperforms ANN, and both methods show some prediction skills too. Furthermore, even after losing the trajectory, data predicted by RC–ESN and RNN–LSTM have probability density functions (pdf's) that closely match the true pdf – even at the tails. The pdf of the data predicted using ANN, however, deviates from the true pdf. Implications, caveats, and applications to data-driven and data-assisted surrogate modeling of complex nonlinear dynamical systems, such as weather and climate, are discussed.
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Mirza, Sami F., and Abdulbasit K. Al-Talabani. "Efficient Kinect Sensor-based Kurdish Sign Language Recognition Using Echo System Network." ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY 9, no. 2 (October 13, 2021): 1–9. http://dx.doi.org/10.14500/aro.10827.
Повний текст джерелаАнотація:
Sign language assists in building communication and bridging gaps in understanding. Automatic sign language recognition (ASLR) is a field that has recently been studied for various sign languages. However, Kurdish sign language (KuSL) is relatively new and therefore researches and designed datasets on it are limited. This paper has proposed a model to translate KuSL into text and has designed a dataset using Kinect V2 sensor. The computation complexity of feature extraction and classification steps, which are serious problems for ASLR, has been investigated in this paper. The paper proposed a feature engineering approach on the skeleton position alone to provide a better representation of the features and avoid the use of all of the image information. In addition, the paper proposed model makes use of recurrent neural networks (RNNs)-based models. Training RNNs is inherently difficult, and consequently, motivates to investigate alternatives. Besides the trainable long short-term memory (LSTM), this study has proposed the untrained low complexity echo system network (ESN) classifier. The accuracy of both LSTM and ESN indicates they can outperform those in state-of-the-art studies. In addition, ESN which has not been proposed thus far for ASLT exhibits comparable accuracy to the LSTM with a significantly lower training time.
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Pei, Jiaxin, and Jian Wang. "Multisensor Prognostic of RUL Based on EMD-ESN." Mathematical Problems in Engineering 2020 (November 24, 2020): 1–12. http://dx.doi.org/10.1155/2020/6639171.
Повний текст джерелаАнотація:
This paper presents a prognostic method for RUL (remaining useful life) prediction based on EMD (empirical mode decomposition)-ESN (echo state network). The combination method adopts EMD to decompose the multisensor time series into a bunch of IMFs (intrinsic mode functions), which are then predicted by ESNs, and the outputs of each ESN are summarized to obtain the final prediction value. The EMD can decompose the original data into simpler portions and during the decomposition process, much noise is filtered out and the subsequent prediction is much easier. The ESN is a relatively new type of RNN (recurrent neural network), which substitutes the hidden layers with a reservoir remaining unchanged during the training phase. The characteristic makes the training time of ESN is much shorter than traditional RNN. The proposed method is applied to the turbofan engine datasets and is compared with LSTM (Long Short-Term Memory) and ESN. Extensive experimental results show that the prediction performance and efficiency are much improved by the proposed method.
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Chen, Xiaojuan, Haiyang Zhang, and Hongwu Qin. "Lowering Nitrogen Oxide Emissions in a Coal-Powered 1000-MW Boiler." Journal of Sensors 2021 (August 8, 2021): 1–11. http://dx.doi.org/10.1155/2021/9958972.
Повний текст джерелаАнотація:
Burning of coal in power plants produces excessive nitrogen oxide (NOx) emissions, which endanger people’s health. Proven and effective methods are highly needed to reduce NOx emissions. This paper constructs an echo state network (ESN) model of the interaction between NOx emissions and the operational parameters in terms of real historical data. The grey wolf optimization (GWO) algorithm is employed to improve the ESN model accuracy. The operational parameters are subsequently optimized via the GWO algorithm to finally cut down the NOx emissions. The experimental results show that the ESN model of the NOx emissions is more accurate than both of the LSTM and ELM models. The simulation results show NOx emission reduction in three selected cases by 16.5%, 15.6%, and 10.2%, respectively.
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Sheng, Hui, Min Liu, Jiyong Hu, Ping Li, Yali Peng, and Yugen Yi. "LA-ESN: A Novel Method for Time Series Classification." Information 14, no. 2 (January 26, 2023): 67. http://dx.doi.org/10.3390/info14020067.
Повний текст джерелаАнотація:
Time-series data is an appealing study topic in data mining and has a broad range of applications. Many approaches have been employed to handle time series classification (TSC) challenges with promising results, among which deep neural network methods have become mainstream. Echo State Networks (ESN) and Convolutional Neural Networks (CNN) are commonly utilized as deep neural network methods in TSC research. However, ESN and CNN can only extract local dependencies relations of time series, resulting in long-term temporal data dependence needing to be more challenging to capture. As a result, an encoder and decoder architecture named LA-ESN is proposed for TSC tasks. In LA-ESN, the encoder is composed of ESN, which is utilized to obtain the time series matrix representation. Meanwhile, the decoder consists of a one-dimensional CNN (1D CNN), a Long Short-Term Memory network (LSTM) and an Attention Mechanism (AM), which can extract local information and global dependencies from the representation. Finally, many comparative experimental studies were conducted on 128 univariate datasets from different domains, and three evaluation metrics including classification accuracy, mean error and mean rank were exploited to evaluate the performance. In comparison to other approaches, LA-ESN produced good results.
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Li, Xin, Fengrong Bi, Lipeng Zhang, Xiao Yang, and Guichang Zhang. "An Engine Fault Detection Method Based on the Deep Echo State Network and Improved Multi-Verse Optimizer." Energies 15, no. 3 (February 7, 2022): 1205. http://dx.doi.org/10.3390/en15031205.
Повний текст джерелаАнотація:
This paper aims to develop an efficient pattern recognition method for engine fault end-to-end detection based on the echo state network (ESN) and multi-verse optimizer (MVO). Bispectrum is employed to transform the one-dimensional time-dependent vibration signal into a two-dimensional matrix with more impact features. A sparse input weight-generating algorithm is designed for the ESN. Furthermore, a deep ESN model is built by fusing fixed convolution kernels and an autoencoder (AE). A novel traveling distance rate (TDR) and collapse mechanism are studied to optimize the local search of the MVO and speed it up. The improved MVO is employed to optimize the hyper-parameters of the deep ESN for the two-dimensional matrix recognition. The experiment result shows that the proposed method can obtain a recognition rate of 93.10% in complex engine faults. Compared with traditional deep belief networks (DBNs), convolutional neural networks (CNNs), the long short-term memory (LSTM) network, and the gated recurrent unit (GRU), this novel method displays superior performance and could benefit the fault end-to-end detection of rotating machinery.
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Tian, Ye, Yue-Ping Xu, Zongliang Yang, Guoqing Wang, and Qian Zhu. "Integration of a Parsimonious Hydrological Model with Recurrent Neural Networks for Improved Streamflow Forecasting." Water 10, no. 11 (November 14, 2018): 1655. http://dx.doi.org/10.3390/w10111655.
Повний текст джерелаАнотація:
This study applied a GR4J model in the Xiangjiang and Qujiang River basins for rainfall-runoff simulation. Four recurrent neural networks (RNNs)—the Elman recurrent neural network (ERNN), echo state network (ESN), nonlinear autoregressive exogenous inputs neural network (NARX), and long short-term memory (LSTM) network—were applied in predicting discharges. The performances of models were compared and assessed, and the best two RNNs were selected and integrated with the lumped hydrological model GR4J to forecast the discharges; meanwhile, uncertainties of the simulated discharges were estimated. The generalized likelihood uncertainty estimation method was applied to quantify the uncertainties. The results show that the LSTM and NARX better captured the time-series dynamics than the other RNNs. The hybrid models improved the prediction of high, median, and low flows, particularly in reducing the bias of underestimation of high flows in the Xiangjiang River basin. The hybrid models reduced the uncertainty intervals by more than 50% for median and low flows, and increased the cover ratios for observations. The integration of a hydrological model with a recurrent neural network considering long-term dependencies is recommended in discharge forecasting.
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Youssef, Samuel M., MennaAllah Soliman, Mahmood A. Saleh, Mostafa A. Mousa, Mahmoud Elsamanty, and Ahmed G. Radwan. "Modeling of Soft Pneumatic Actuators with Different Orientation Angles Using Echo State Networks for Irregular Time Series Data." Micromachines 13, no. 2 (January 29, 2022): 216. http://dx.doi.org/10.3390/mi13020216.
Повний текст джерелаАнотація:
Modeling of soft robotics systems proves to be an extremely difficult task, due to the large deformation of the soft materials used to make such robots. Reliable and accurate models are necessary for the control task of these soft robots. In this paper, a data-driven approach using machine learning is presented to model the kinematics of Soft Pneumatic Actuators (SPAs). An Echo State Network (ESN) architecture is used to predict the SPA’s tip position in 3 axes. Initially, data from actual 3D printed SPAs is obtained to build a training dataset for the network. Irregular-intervals pressure inputs are used to drive the SPA in different actuation sequences. The network is then iteratively trained and optimized. The demonstrated method is shown to successfully model the complex non-linear behavior of the SPA, using only the control input without any feedback sensory data as additional input to the network. In addition, the ability of the network to estimate the kinematics of SPAs with different orientation angles θ is achieved. The ESN is compared to a Long Short-Term Memory (LSTM) network that is trained on the interpolated experimental data. Both networks are then tested on Finite Element Analysis (FEA) data for other θ angle SPAs not included in the training data. This methodology could offer a general approach to modeling SPAs with varying design parameters.
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Feitosa, Allan Rivalles Souza, Henrique Figuerôa Lacerda, Wellington Pinheiro dos Santos, and Abel Guilhermino da Silva Filho. "Household appliance usage recommendation based on demand forecasting and multiobjective optimization." Research, Society and Development 11, no. 1 (January 3, 2022): e13411124515. http://dx.doi.org/10.33448/rsd-v11i1.24515.
Повний текст джерелаАнотація:
Accelerated population growth in the 21st century and increased demand for energy sources, associated with climate change, have resulted in two main challenges: the search for sustainable energy sources and the need to find more efficient ways to use extant sustainable sources. The forecasting module provides an estimate of the future usage of these appliances and it is the source of the recommended module’s suggestion. Time Series Forecasting techniques, such as Autoregressive Integrated Moving Average, LongShort Term Memory (LSTM), Gated Recurrent Units, Echo State Networks (ESN), and Support Vector Regression, were tested for the predictive module. Multiobjective optimization techniques such as NonSorted Genetic Algorithm II (NSGA II), MultiObjective Particle Swarm Optimization (MOPSO), Speed constrained Multi-objective Particle Swarm Optimization (SMOPSO), and Strength Pareto Evolutionary Algorithm two (SPEA2), for example, were tested for the Recommendation Module. The Forecasting and Recommendation module experiments were performed independently. In the Forecasting Module, the results and statistical tests revealed LSTM as the best suited technique for forecasting the loads of the majority of the appliances tested (in this case seven) in terms of root mean square error. In the experiments performed for the recommendation module, NSGA II showed a higher overall performance compared to other metrics in terms of hyper volume of the Pareto Front generated. This work presents the potential of using both Predictive Models and MultiObjective Optimization Techniques combined to reduce energy usage in household environments.
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Rajamoorthy, Rajasekaran, Hemachandira V. Saraswathi, Jayanthi Devaraj, Padmanathan Kasinathan, Rajvikram Madurai Elavarasan, Gokulalakshmi Arunachalam, Tarek M. Mostafa, and Lucian Mihet-Popa. "A Hybrid Sailfish Whale Optimization and Deep Long Short-Term Memory (SWO-DLSTM) Model for Energy Efficient Autonomy in India by 2048." Sustainability 14, no. 3 (January 25, 2022): 1355. http://dx.doi.org/10.3390/su14031355.
Повний текст джерелаАнотація:
In order to formulate the long-term and short-term development plans to meet the energy needs, there is a great demand for accurate energy forecasting. Energy autonomy helps to decompose a large-scale grid control into a small sized decisions to attain robustness and scalability through energy independence level of a country. Most of the existing energy demand forecasting models predict the amount of energy at a regional or national scale and failed to forecast the demand for power generation for small-scale decentralized energy systems, like micro grids, buildings, and energy communities. A novel model called Sailfish Whale Optimization-based Deep Long Short- Term memory (SWO-based Deep LSTM) to forecast electricity demand in the distribution systems is proposed. The proposed SWO is designed by integrating the Sailfish Optimizer (SO) with the Whale Optimization Algorithm (WOA). The Hilbert-Schmidt Independence Criterion (HSIC) is applied on the dataset, which is collected from the Central electricity authority, Government of India, for selecting the optimal features using the technical indicators. The proposed algorithm is implemented in MATLAB software package and the study was done using real-time data. The optimal features are trained using Deep LSTM model. The results of the proposed model in terms of install capacity prediction, village electrified prediction, length of R & D lines prediction, hydro, coal, diesel, nuclear prediction, etc. are compared with the existing models. The proposed model achieves percentage improvements of 10%, 9.5%,6%, 4% and 3% in terms of Mean Squared Error (MSE) and 26%, 21%, 16%, 12% and 6% in terms of Root Mean Square Error (RMSE) for Bootstrap-based Extreme Learning Machine approach (BELM), Direct Quantile Regression (DQR), Temporally Local Gaussian Process (TLGP), Deep Echo State Network (Deep ESN) and Deep LSTM respectively. The hybrid approach using the optimization algorithm with the deep learning model leads to faster convergence rate during the training process and enables the small-scale decentralized systems to address the challenges of distributed energy resources. The time series datasets of different utilities are trained using the hybrid model and the temporal dependencies in the sequence of data are predicted with point of interval as 5 years-head. Energy autonomy of the country till the year 2048 is assessed and compared.
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Zheng, Guoxiao, Weifang Sun, Hao Zhang, Yuqing Zhou, and Chen Gao. "Tool wear condition monitoring in milling process based on data fusion enhanced long short-term memory network under different cutting conditions." Eksploatacja i Niezawodnosc - Maintenance and Reliability 23, no. 4 (August 23, 2021): 612–18. http://dx.doi.org/10.17531/ein.2021.4.3.
Повний текст джерелаАнотація:
Tool wear condition monitoring (TCM) is essential for milling process to ensure the machining quality, and the long short-term memory network (LSTM) is a good choice for predicting tool wear value. However, the robustness of LSTM- based method is poor when cutting condition changes. A novel method based on data fusion enhanced LSTM is proposed to estimate tool wear value under different cutting conditions. Firstly, vibration time series signal collected from milling process are transformed to feature space through empirical mode decomposition, variational mode decomposition and fourier synchro squeezed transform. And then few feature series are selected by neighborhood component analysis to reduce dimension of the signal features. Finally, these selected feature series are input to train the bidirectional LSTM network and estimate tool wear value. Applications of the proposed method to milling TCM experiments demonstrate it outperforms significantly SVR- based and RNN- based methods under different cutting conditions.
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Widiputra, Harya. "GA-Optimized Multivariate CNN-LSTM Model for Predicting Multi-channel Mobility in the COVID-19 Pandemic." Emerging Science Journal 5, no. 5 (October 1, 2021): 619–35. http://dx.doi.org/10.28991/esj-2021-01300.
Повний текст джерелаАнотація:
The primary factor that contributes to the transmission of COVID-19 infection is human mobility. Positive instances added on a daily basis have a substantial positive association with the pace of human mobility, and the reverse is true. Thus, having the ability to predict human mobility trend during a pandemic is critical for policymakers to help in decreasing the rate of transmission in the future. In this regard, one approach that is commonly used for time-series data prediction is to build an ensemble with the aim of getting the best performance. However, building an ensemble often causes the performance of the model to decrease, due to the increasing number of parameters that are not being optimized properly. Consequently, the purpose of this study is to develop and evaluate a deep learning ensemble model, which is optimized using a genetic algorithm (GA) that incorporates a convolutional neural network (CNN) and a long short-term memory (LSTM). A CNN is used to conduct feature extraction from mobility time-series data, while an LSTM is used to do mobility prediction. The parameters of both layers are adjusted using GA. As a result of the experiments conducted with data from the Google Community Mobility Reports in Indonesia that ranges from the beginning of February 2020 to the end of December 2020, the GA-Optimized Multivariate CNN-LSTM ensemble outperforms stand-alone CNN and LSTM models, as well as the non-optimized CNN-LSTM model, in terms of predicting human movement in the future. This may be useful in assisting policymakers in anticipating future human mobility trends. Doi: 10.28991/esj-2021-01300 Full Text: PDF
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Seo, Yeongung, Seungyoung Park, Myungjin Kim, and Sungbin Lim. "ESS Operation Scheduling Scheme Using LSTM for Peak Demand Reduction." Journal of KIISE 46, no. 11 (November 30, 2019): 1165–73. http://dx.doi.org/10.5626/jok.2019.46.11.1165.
Повний текст джерела
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Islam, Umar, Rami Qays Malik, Amnah S. Al-Johani, Muhammad Riaz Khan, Yousef Ibrahim Daradkeh, Ijaz Ahmad, Khalid A. Alissa, Zulkiflee Abdul-Samad, and Elsayed M. Tag-Eldin. "A Novel Anomaly Detection System on the Internet of Railways Using Extended Neural Networks." Electronics 11, no. 18 (September 6, 2022): 2813. http://dx.doi.org/10.3390/electronics11182813.
Повний текст джерелаАнотація:
The Internet of Railways (IoR) network is made up of a variety of sensors, actuators, network layers, and communication systems that work together to build a railway system. The IoR’s success depends on effective communication. A network of railways uses a variety of protocols to share and transmit information amongst each other. Because of the widespread usage of wireless technology on trains, the entire system is susceptible to hacks. These hacks could lead to harmful behavior on the Internet of Railways if they spread sensitive data to an infected network or a fake user. For the previous few years, spotting IoR attacks has been incredibly challenging. To detect malicious intrusions, models based on machine learning and deep learning must still contend with the problem of selecting features. k-means clustering has been used for feature scoring and ranking because of this. To categorize attacks in two datasets, the Internet of Railways and the University of New South Wales, we employed a new neural network model, the extended neural network (ENN). Accuracy and precision were among the model’s strengths. According to our proposed ENN model, the feature-scoring technique performed well. The most accurate models in dataset 1 (UNSW-NB15) were based on deep neural networks (DNNs) (92.2%), long short-term memory LSTM (90.9%), and ENN (99.7%). To categorize attacks, the second dataset (IOR dataset) yielded the highest accuracy (99.3%) for ENN, followed by CNN (87%), LSTM (89%), and DNN (82.3%).
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Lindemann, Benjamin, Nasser Jazdi, and Michael Weyrich. "Detektion von Anomalien zur Qualitätssicherung basierend auf Sequence-to-Sequence LSTM Netzen." at - Automatisierungstechnik 67, no. 12 (November 18, 2019): 1058–68. http://dx.doi.org/10.1515/auto-2019-0076.
Повний текст джерелаАнотація:
Zusammenfassung Unvorhersehbare Prozessereignisse und Anomalien sind Treiber erhöhter Ineffizienzen in Form von schwankender Produktqualität. In diesem Beitrag wird ein datengetriebener Ansatz zur Qualitätsoptimierung vorgestellt, auf dessen Basis Anomalien charakterisiert werden, die zur Entwurfszeit des Systems nicht bekannt waren. Es wird eine Netzarchitektur in Form eines Sequence-to-Sequence Netzes mit Long Short-Term Memory (LSTM) Zellen vorgestellt. Dadurch kann vorhergesagt werden, welche Anpassung am Stellgrößenverhalten vorgenommen werden muss, um erwartete Anomalien zu kompensieren. Dadurch wird das Qualitätsergebnis langfristig in der Toleranz gehalten. Der Ansatz wird prototypisch anhand von zwei Prozessketten der diskreten Fertigung umgesetzt und evaluiert.
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Santos, Rolando A., and Brian W. Sloboda. "Macroeconomic Forecasting Examining the COVID-19 Pandemic Using Selected Countries: A Machine Learning LSTM (Long Term Short Term Memory) Approach." European Scientific Journal, ESJ 18, no. 12 (April 30, 2022): 1. http://dx.doi.org/10.19044/esj.2022.v18n12p1.
Повний текст джерелаАнотація:
The disease COVID-19 caused by the virus SARS-CoV-2 has initially disrupted the Chinese economy after the first cases were reported in December 2019 in Wuhan city in Hubei province of China. The virus continued to spread throughout the rest of the world. This spread of the virus led to the official designation of the COVID-19 pandemic by the World Health Organization (WHO) in late February 2020, which resulted in the disruption of these economies due to the stringent lockdowns and restrictions in travel disease's evolution. The disruptive economic impact is highly uncertain, making it difficult for policymakers to craft an appropriate policy response to these macroeconomic disruptions. To better understand possible economic outcomes, this paper explores the use of the machine learning approach LSTM to assess the economic forecast in some selected countries. The empirical results from this paper demonstrate that there are temporary disruptions in macroeconomics in the short run and these economies rebound. The recovery of each selected country may be different as the forecast would imply.
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Jieyang, Peng, Wang Dongkun, Andreas Kimmig, Mikhail A. Langovoy, Wang Jiahai, and Jivka Ovtcharova. "Ein hybrides RNN-Modell für die mittel- bis langfristige Vorhersage des Strombedarfs unter Berücksichtigung von Wettereinflüssen." at - Automatisierungstechnik 69, no. 1 (January 1, 2021): 73–83. http://dx.doi.org/10.1515/auto-2020-0033.
Повний текст джерелаАнотація:
Zusammenfassung Im täglichen Stadtbetrieb sollte die Stromversorgung unterbrechungsfrei sein, was das moderne Energiemanagement vor Herausforderungen stellt. Die Prognose des Energiebedarfs kann die Strategie des Energiemanagements optimieren und die Energieeffizienz verbessern. Das traditionelle LSTM-Modell, das auf einer Codierungs-Decodierungs-Struktur basiert, codiert alle historischen Informationen als Vektor fester Länge, was zum Informationsverlust führt, wenn der vorhergesagte Wert von den Merkmalen abhängt die weit in der Vergangenheit liegen. Dies ist bei Energieprognosen aufgrund der Periodizität des Energieverbrauchs üblich. Um das oben genannte Problem zu lösen und das Potenzial der Betriebsdaten von Kraftwerken für Energievorhersagen vollständig auszuschöpfen, wird in diesem Artikel ein Energievorhersagemodell vorgeschlagen, das auf dem Aufmerksamkeitsmechanismus basiert. Ausgehend von der traditionellen Codierungs-Decodierungs-Architektur wird der räumliche und zeitliche Aufmerksamkeitsmechanismus eingeführt, um die räumlichen und zeitlichen Eigenschaften, die für den vorhergesagten Wert am relevantesten sind, adaptiv auszuwählen. Die experimentellen Ergebnisse zeigen, dass bei der Vorhersage des Strombedarfs von Shanghai für die nächsten 100 Tage, der Fehler des Hybridmodells 25,8 % niedriger ist als der des traditionellen LSTM-Modells. Darüber hinaus zeigt der Fehlertrend des Hybridmodells im Laufe der Zeit auch eine stärkere Stabilität als das herkömmliche Modell.
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Brenner, Claire, Jonathan Frame, Grey Nearing, and Karsten Schulz. "Schätzung der Verdunstung mithilfe von Machine- und Deep Learning-Methoden." Österreichische Wasser- und Abfallwirtschaft 73, no. 7-8 (May 17, 2021): 295–307. http://dx.doi.org/10.1007/s00506-021-00768-y.
Повний текст джерелаАнотація:
ZusammenfassungDie Verdunstung ist ein entscheidender Prozess im globalen Wasser‑, Energie- sowie Kohlenstoffkreislauf. Daten zur räumlich-zeitlichen Dynamik der Verdunstung sind daher von großer Bedeutung für Klimamodellierungen, zur Abschätzung der Auswirkungen der Klimakrise sowie nicht zuletzt für die Landwirtschaft.In dieser Arbeit wenden wir zwei Machine- und Deep Learning-Methoden für die Vorhersage der Verdunstung mit täglicher und halbstündlicher Auflösung für Standorte des FLUXNET-Datensatzes an. Das Long Short-Term Memory Netzwerk ist ein rekurrentes neuronales Netzwerk, welchen explizit Speichereffekte berücksichtigt und Zeitreihen der Eingangsgrößen analysiert (entsprechend physikalisch-basierten Wasserbilanzmodellen). Dem gegenüber gestellt werden Modellierungen mit XGBoost, einer Entscheidungsbaum-Methode, die in diesem Fall nur Informationen für den zu bestimmenden Zeitschritt erhält (entsprechend physikalisch-basierten Energiebilanzmodellen). Durch diesen Vergleich der beiden Modellansätze soll untersucht werden, inwieweit sich durch die Berücksichtigung von Speichereffekten Vorteile für die Modellierung ergeben.Die Analysen zeigen, dass beide Modellansätze gute Ergebnisse erzielen und im Vergleich zu einem ausgewerteten Referenzdatensatz eine höhere Modellgüte aufweisen. Vergleicht man beide Modelle, weist das LSTM im Mittel über alle 153 untersuchten Standorte eine bessere Übereinstimmung mit den Beobachtungen auf. Allerdings zeigt sich eine Abhängigkeit der Güte der Verdunstungsvorhersage von der Vegetationsklasse des Standorts; vor allem wärmere, trockene Standorte mit kurzer Vegetation werden durch das LSTM besser repräsentiert, wohingegen beispielsweise in Feuchtgebieten XGBoost eine bessere Übereinstimmung mit den Beobachtung liefert. Die Relevanz von Speichereffekten scheint daher zwischen Ökosystemen und Standorten zu variieren.Die präsentierten Ergebnisse unterstreichen das Potenzial von Methoden der künstlichen Intelligenz für die Beschreibung der Verdunstung.
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Natasya and Abba Suganda Girsang. "Modified EDA and Backtranslation Augmentation in Deep Learning Models for Indonesian Aspect-Based Sentiment Analysis." Emerging Science Journal 7, no. 1 (November 7, 2022): 256–72. http://dx.doi.org/10.28991/esj-2023-07-01-018.
Повний текст джерелаАнотація:
In the process of developing a business, aspect-based sentiment analysis (ABSA) could help extract customers' opinions on different aspects of the business from online reviews. Researchers have found great prospective in deep learning approaches to solving ABSA tasks. Furthermore, studies have also explored the implementation of text augmentation, such as Easy Data Augmentation (EDA), to improve the deep learning models’ performance using only simple operations. However, when implementing EDA to ABSA, there will be high chances that the augmented sentences could lose important aspects or sentiment-related words (target words) critical for training. Corresponding to that, another study has made adjustments to EDA for English aspect-based sentiment data provided with the target words tag. However, the solution still needs additional modifications in the case of non-tagged data. Hence, in this work, we will focus on modifying EDA that integrates POS tagging and word similarity to not only understand the context of the words but also extract the target words directly from non-tagged sentences. Additionally, the modified EDA is combined with the backtranslation method, as the latter has also shown quite a significant contribution to the model’s performance in several research studies. The proposed method is then evaluated on a small Indonesian ABSA dataset using baseline deep learning models. Results show that the augmentation method could increase the model’s performance on a limited dataset problem. In general, the best performance for aspect classification is achieved by implementing the proposed method, which increases the macro-accuracy and F1, respectively, on Long Short-Term Memory (LSTM) and Bidirectional LSTM models compared to the original EDA. The proposed method also obtained the best performance for sentiment classification using a convolutional neural network, increasing the overall accuracy by 2.2% and F1 by 3.2%. Doi: 10.28991/ESJ-2023-07-01-018 Full Text: PDF
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Bestaeva, Salome. "Rural Tourism Business as an Economic Instrument for the Development of Economically Backward Regions." European Scientific Journal, ESJ 18, no. 29 (September 30, 2022): 1. http://dx.doi.org/10.19044/esj.2022.v18n29p1.
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The disease COVID-19 caused by the virus SARS-CoV-2 has initially disrupted the Chinese economy after the first cases were reported in December 2019 in Wuhan city in Hubei province of China. The virus continued to spread throughout the rest of the world. This spread of the virus led to the official designation of the COVID-19 pandemic by the World Health Organization (WHO) in late February 2020, which resulted in the disruption of these economies due to the stringent lockdowns and restrictions in travel disease's evolution. The disruptive economic impact is highly uncertain, making it difficult for policymakers to craft an appropriate policy response to these macroeconomic disruptions. To better understand possible economic outcomes, this paper explores the use of the machine learning approach LSTM to assess the economic forecast in some selected countries. The empirical results from this paper demonstrate that there are temporary disruptions in macroeconomics in the short run and these economies rebound. The recovery of each selected country may be different as the forecast would imply.
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Huang, Yu, Lichao Yang, and Zuntao Fu. "Reconstructing coupled time series in climate systems using three kinds of machine-learning methods." Earth System Dynamics 11, no. 3 (September 18, 2020): 835–53. http://dx.doi.org/10.5194/esd-11-835-2020.
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Abstract. Despite the great success of machine learning, its application in climate dynamics has not been well developed. One concern might be how well the trained neural networks could learn a dynamical system and what will be the potential application of this kind of learning. In this paper, three machine-learning methods are used: reservoir computer (RC), backpropagation-based (BP) artificial neural network, and long short-term memory (LSTM) neural network. It shows that the coupling relations or dynamics among variables in linear or nonlinear systems can be inferred by RC and LSTM, which can be further applied to reconstruct one time series from the other. Specifically, we analyzed the climatic toy models to address two questions: (i) what factors significantly influence machine-learning reconstruction and (ii) how do we select suitable explanatory variables for machine-learning reconstruction. The results reveal that both linear and nonlinear coupling relations between variables do influence the reconstruction quality of machine learning. If there is a strong linear coupling between two variables, then the reconstruction can be bidirectional, and both of these two variables can be an explanatory variable for reconstructing the other. When the linear coupling among variables is absent but with the significant nonlinear coupling, the machine-learning reconstruction between two variables is direction dependent, and it may be only unidirectional. Then the convergent cross mapping (CCM) causality index is proposed to determine which variable can be taken as the reconstructed one and which as the explanatory variable. In a real-world example, the Pearson correlation between the average tropical surface air temperature (TSAT) and the average Northern Hemisphere SAT (NHSAT) is weak (0.08), but the CCM index of NHSAT cross mapped with TSAT is large (0.70). And this indicates that TSAT can be well reconstructed from NHSAT through machine learning. All results shown in this study could provide insights into machine-learning approaches for paleoclimate reconstruction, parameterization scheme, and prediction in related climate research.Highlights: i The coupling dynamics learned by machine learning can be used to reconstruct time series. ii Reconstruction quality is direction dependent and variable dependent for nonlinear systems. iii The CCM index is a potential indicator to choose reconstructed and explanatory variables. iv The tropical average SAT can be well reconstructed from the average Northern Hemisphere SAT.
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Chen, Min-Rong, Guo-Qiang Zeng, Kang-Di Lu, and Jian Weng. "A Two-Layer Nonlinear Combination Method for Short-Term Wind Speed Prediction Based on ELM, ENN, and LSTM." IEEE Internet of Things Journal 6, no. 4 (August 2019): 6997–7010. http://dx.doi.org/10.1109/jiot.2019.2913176.
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Martín Guerrero, JM, C. Ortiz Moyano, and C. Rodríguez Alonso. "Underwater mucosectomy of a 25 mm IIa-LSTG homogeneous lession at the transverse colon." Revista Andaluza de Patología Digestiva 44, no. 2 (April 30, 2021): 56–58. http://dx.doi.org/10.37352/2021442.3.
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Fadli, Faisal, Saib Suwilo, and Muhammad Zarlis. "Model Prediksi Data Besar Distribusi Produk Farmasi: Analisis Kinerja Model Deep Learning." CSRID (Computer Science Research and Its Development Journal) 14, no. 1 (February 10, 2022): 68. http://dx.doi.org/10.22303/csrid.14.1.2021.79-91.
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<p><em>Seiring dengan berjalannya bisnis perusahaan, masalah dalam penyimpanan dan pengolahan data besar pun akan semakin kompleks. data yang tidak terorganisir dapat menyebabkan perusahaan gagal dalam memaksimalkan strategi penjualan. Salah satu pendekatan untuk memaksimalkan strategi penjualan tersebut adalah dengan peramalan.penelitian ini bertujuan untuk mengurangi tingkat persediaan pelanggan jangka pendek dan membantu dalam menentukan target penjualan yang realistis di masa depan dengan mengusulkan metode pembelajaran mendalam berdasarkan segmentasi pelanggan. Kerangka analisa diusulkan menggunakan teknik Robust Principal Component Analysis (RPCA) untuk mengurangi dimensi kumpulan dataset, kemudian algoritma K-Means Clustering diterapkan untuk mengidentifikasi kelompok populasi guna melihat beberapa kluster yang dapat sangat mewakili karakteristik basis pelanggan perusahaan yang ada. Terakhir lapisan CNN dan LSTM digabungkan untuk memperkirakan penjualan masa depan. Hasil peramalan dievaluasi menggunakan M</em><em>ean Absolute Error</em><em> (MAE) dan </em><em>Root Mean Square Error</em><em> (RMSE). Pendekatan yang diusulkan guna mengisi celah masalah yang terjadi karena kurangnya informasi mengenai kurangnya informasi tentang kinerja bisnis dalam hal kategorisasi produk.</em></p>
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Lee, Yao Hui A., and Wen Cheng J. Wei. "Processing and Characterization of La2O3-SiO2-B2O3 (LSB) Based Glass-Ceramics for LTCC Application." Key Engineering Materials 280-283 (February 2007): 935–40. http://dx.doi.org/10.4028/www.scientific.net/kem.280-283.935.
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Synthesis of La2O3-SiO2-B2O3 (LSB) based glass-ceramics using glass melting method has een investigated in this study. XRD result showed that some LSB glass systems in this study were ntirely amorphous phases. In addition, TMA results revealed that the LSB/mullite (LSBM) glassceramics ith a mass ratio of 60/40 could be densified at 850oC, which matches the requirements for theLTCC application. Moreover, dispersive behavior of the LSB glass powder with six kinds of commercial ispersants in MEK and toluene solvent had been studied. Furthermore, tape-casting process used for ow-temperature-cofired-ceramic (LTCC) chips was conducted in order to do crystal phase identification, microstructure analysis, and dielectric property measurement.
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Guilarte López-Mañas, J., F. Valverde López, and E. Fernández. "Fragment mucosectomy (PEMR) of a 45 mm rectal lesion (mixed nodular LST-G)." Revista Andaluza de Patología Digestiva 44, no. 3 (June 30, 2021): 98–100. http://dx.doi.org/10.37352/2021443.2.
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Tanno, Nobuhiro, Kazuo Koyanagi, Satoshi Tabuchi, Hideyuki Tawara, Makoto Nishimura, Osamu Togawa, Shin Arai, et al. "A case of laterally-spreading tumor in a colonic interposition after esophageal cancer treated by endoscopic submucosal dissection." Progress of Digestive Endoscopy 77, no. 2 (2010): 60–61. http://dx.doi.org/10.11641/pde.77.2_60.
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Chen, Xiaojuan, and Haiyang Zhang. "Grey Wolf Optimization–Based Deep Echo State Network for Time Series Prediction." Frontiers in Energy Research 10 (March 11, 2022). http://dx.doi.org/10.3389/fenrg.2022.858518.
Повний текст джерелаАнотація:
The Echo State Network (ESN) is a unique type of recurrent neural network. It is built atop a reservoir, which is a sparse, random, and enormous hidden infrastructure. ESN has been successful in dealing with a variety of non-linear issues, including prediction and classification. ESN is utilized in a variety of architectures, including the recently proposed Multi-Layer (ML) architecture. Furthermore, Deep Echo State Network (DeepESN) models, which are multi-layer ESN models, have recently been proved to be successful at predicting high-dimensional complicated non-linear processes. The proper configuration of DeepESN architectures and training parameters is a time-consuming and difficult undertaking. To achieve the lowest learning error, a variety of parameters (hidden neurons, input scaling, the number of layers, and spectral radius) are carefully adjusted. However, the optimum training results may not be guaranteed by this haphazardly created work. The grey wolf optimization (GWO) algorithm is introduced in this study to address these concerns. The DeepESN based on GWO (GWODESN) is utilized in trials to forecast time series, and therefore the results are compared with the regular ESN, LSTM, and ELM models. The findings indicate that the planned model performs the best in terms of prediction.
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Jirak, Doreen, Stephan Tietz, Hassan Ali, and Stefan Wermter. "Echo State Networks and Long Short-Term Memory for Continuous Gesture Recognition: a Comparative Study." Cognitive Computation, October 7, 2020. http://dx.doi.org/10.1007/s12559-020-09754-0.
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Abstract Recent developments of sensors that allow tracking of human movements and gestures enable rapid progress of applications in domains like medical rehabilitation or robotic control. Especially the inertial measurement unit (IMU) is an excellent device for real-time scenarios as it rapidly delivers data input. Therefore, a computational model must be able to learn gesture sequences in a fast yet robust way. We recently introduced an echo state network (ESN) framework for continuous gesture recognition (Tietz et al., 2019) including novel approaches for gesture spotting, i.e., the automatic detection of the start and end phase of a gesture. Although our results showed good classification performance, we identified significant factors which also negatively impact the performance like subgestures and gesture variability. To address these issues, we include experiments with Long Short-Term Memory (LSTM) networks, which is a state-of-the-art model for sequence processing, to compare the obtained results with our framework and to evaluate their robustness regarding pitfalls in the recognition process. In this study, we analyze the two conceptually different approaches processing continuous, variable-length gesture sequences, which shows interesting results comparing the distinct gesture accomplishments. In addition, our results demonstrate that our ESN framework achieves comparably good performance as the LSTM network but has significantly lower training times. We conclude from the present work that ESNs are viable models for continuous gesture recognition delivering reasonable performance for applications requiring real-time performance as in robotic or rehabilitation tasks. From our discussion of this comparative study, we suggest prospective improvements on both the experimental and network architecture level.
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Li, Songyang, Weiling Luan, Chang Wang, Ying Chen, and Zixian Zhuang. "Degradation prediction of proton exchange membrane fuel cell based on Bi-LSTM-GRU and ESN fusion prognostic framework." International Journal of Hydrogen Energy, August 2022. http://dx.doi.org/10.1016/j.ijhydene.2022.07.230.
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Kłosowski, Grzegorz, Tomasz Rymarczyk, Konrad Niderla, Monika Kulisz, Łukasz Skowron, and Manuchehr Soleimani. "Using an LSTM network to monitor industrial reactors using electrical capacitance and impedance tomography – a hybrid approach." Eksploatacja i Niezawodność – Maintenance and Reliability 25, no. 1 (January 27, 2023). http://dx.doi.org/10.17531/ein.2023.1.11.
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The article presents a new concept for monitoring industrial tank reactors. The presented concept allows for faster and more reliable monitoring of industrial processes, which increases their reliability and reduces operating costs. The innovative method is based on electrical tomography. At the same time, it is non-invasive and enables the imaging of phase changes inside tanks filled with liquid. In particular, the hybrid tomograph can detect gas bubbles and crystals formed during industrial processes. The main novelty of the described solution is the simultaneous use of two types of electrical tomography: impedance and capacitance. Another novelty is the use of the LSTM network to solve the tomographic inverse problem. It was made possible by taking the measurement vector as a data sequence. Research has shown that the proposed hybrid solution and the LSTM algorithm work better than separate systems based on impedance or capacitance tomography.
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Dongwei, Gu, Nie Ruihua, Han Wenbo, Chen Guang, and Jia Ligang. "Research on preventive maintenance strategy of Coating Machine based on dynamic failure rate." Eksploatacja i Niezawodność – Maintenance and Reliability, February 1, 2023. http://dx.doi.org/10.17531/ein.2023.1.20.
Повний текст джерелаАнотація:
In this paper, a dynamic preventive maintenance strategy is proposed for the problem of high maintenance cost rate due to excessive maintenance caused by unreasonable maintenance threshold setting when complex electromechanical equipment maintenance strategy is formulated. Increasing failure rate factor and decreasing service age factor are introduced to describe the evolution rules of failure rate during the maintenance of the coating machine, and the BP-LSTM (BP-Long Short Term Memory Network, BP-LSTM) model is combined to predict the failure rate of the coating machine. A Dynamic preventive maintenance Model (DM) that relies on dynamic failure rate thresholds to classify the three preventive maintenance modes of minor, medium and major repairs is constructed. A dynamic preventive maintenance strategy optimization process based on Genetic-Particle Swarm Optimization (GPSO) algorithm with the lowest cost rate per unit time in service phase is built to solve the difficult problem of dynamic failure rate threshold finding. Based on the historical operating data of the coating machine, a case study of the dynamic preventive maintenance strategy of the coating machine was conducted to verify the effectiveness of the model and the developed maintenance strategy proposed in this paper. The results show that the maintenance strategy developed in this paper can ensure better economy and applicability.
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Heydari, Azim, Meysam Majidi Nezhad, Davide Astiaso Garcia, Farshid Keynia, and Livio De Santoli. "Air pollution forecasting application based on deep learning model and optimization algorithm." Clean Technologies and Environmental Policy, April 14, 2021. http://dx.doi.org/10.1007/s10098-021-02080-5.
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AbstractAir pollution monitoring is constantly increasing, giving more and more attention to its consequences on human health. Since Nitrogen dioxide (NO2) and sulfur dioxide (SO2) are the major pollutants, various models have been developed on predicting their potential damages. Nevertheless, providing precise predictions is almost impossible. In this study, a new hybrid intelligent model based on long short-term memory (LSTM) and multi-verse optimization algorithm (MVO) has been developed to predict and analysis the air pollution obtained from Combined Cycle Power Plants. In the proposed model, long short-term memory model is a forecaster engine to predict the amount of produced NO2 and SO2 by the Combined Cycle Power Plant, where the MVO algorithm is used to optimize the LSTM parameters in order to achieve a lower forecasting error. In addition, in order to evaluate the proposed model performance, the model has been applied using real data from a Combined Cycle Power Plant in Kerman, Iran. The datasets include wind speed, air temperature, NO2, and SO2 for five months (May–September 2019) with a time step of 3-h. In addition, the model has been tested based on two different types of input parameters: type (1) includes wind speed, air temperature, and different lagged values of the output variables (NO2 and SO2); type (2) includes just lagged values of the output variables (NO2 and SO2). The obtained results show that the proposed model has higher accuracy than other combined forecasting benchmark models (ENN-PSO, ENN-MVO, and LSTM-PSO) considering different network input variables. Graphic abstract
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BİLGİLİ, Mehmet, Şaban ÜNAL, Aliihsan ŞEKERTEKİN, and Cahit GÜRLEK. "Machine Learning Approaches for One-Day Ahead Soil Temperature Forecasting." Tarım Bilimleri Dergisi, January 31, 2023, 221–38. http://dx.doi.org/10.15832/ankutbd.997567.
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Present study investigates the capabilities of six distinct machine learning techniques such as ANFIS network with fuzzy c-means (ANFIS-FCM), grid partition (ANFIS-GP), subtractive clustering (ANFIS-SC), feed-forward neural network (FNN), Elman neural network (ENN), and long short-term memory (LSTM) neural network in one-day ahead soil temperature (ST) forecasting. For this aim, daily ST data gathered at three different depths of 5 cm, 50 cm, and 100 cm from the Sivas meteorological observation station in the Central Anatolia Region of Turkey was used as training and testing datasets. Forecasting values of the machine learning models were compared with actual data by assessing with respect to four statistic metrics such as the mean absolute error, root mean square error (RMSE), Nash−Sutcliffe efficiency coefficient, and correlation coefficient (R). The results showed that the ANFIS-FCM, ANFIS-GP, ANFIS-SC, ENN, FNN and LSTM models presented satisfactory performance in modeling daily ST at all depths, with RMSE values ranging 0.0637-1.3276, 0.0634-1.3809, 0.0643-1.3280, 0.0635-1.3186, 0.0635-1.3281, and 0.0983-1.3256 °C, and R values ranging 0.9910-0.9999, 0.9903-0.9999, 0.9910-0.9999, 0.9911-0.9999, 0.9910-0.9999 and 0.9910-0.9998 °C, respectively.
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Lu, Quanying, Shaolong Sun, Hongbo Duan, and Shouyang Wang. "Analysis and forecasting of crude oil price based on the variable selection-LSTM integrated model." Energy Informatics 4, S2 (September 2021). http://dx.doi.org/10.1186/s42162-021-00166-4.
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AbstractIn recent years, the crude oil market has entered a new period of development and the core influence factors of crude oil have also been a change. Thus, we develop a new research framework for core influence factors selection and forecasting. Firstly, this paper assesses and selects core influence factors with the elastic-net regularized generalized linear Model (GLMNET), spike-slab lasso method, and Bayesian model average (BMA). Secondly, the new machine learning method long short-term Memory Network (LSTM) is developed for crude oil price forecasting. Then six different forecasting techniques, random walk (RW), autoregressive integrated moving average models (ARMA), elman neural Networks (ENN), ELM Neural Networks (EL), walvet neural networks (WNN) and generalized regression neural network Models (GRNN) were used to forecast the price. Finally, we compare and analyze the different results with root mean squared error (RMSE), mean absolute percentage error (MAPE), directional symmetry (DS). Our empirical results show that the variable selection-LSTM method outperforms the benchmark methods in both level and directional forecasting accuracy.
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Liu, Yang, Shuaibing Du, and Lihu Wang. "Flood forecasting and uncertainty analysis based on the combination of improved adaptive noise learning model and density estimation." Water Supply, November 22, 2022. http://dx.doi.org/10.2166/ws.2022.403.
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Abstract The strong randomness exhibited by the runoff series makes the accuracy of the flood forecasting still needs to be improved. Mode mixing can be dealt with using complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), and the endpoint effect of CEEMDAN can be successfully dealt with using the mutual information criterion. To increase the computational effectiveness of broad learning (BL), orthogonal triangular matrix decomposition (QR) was used. A novel improved coupled CEEMDAN-QRBL flood forecasting model was created and applied to the prediction of daily runoff in Xiaolangdi reservoir based on the benefit of quick calculation of the model output layer. The findings indicate that the enhanced QRBL is 28.92% more computationally efficient than the BL model, and that the reconstruction error of CEEMDAN has been decreased by 48.22%. The MAE of the improved CEEMDAN-QRBL model is reduced by 12.36% and 16.31%, and the Ens is improved by 8.81% and 3.96%, respectively, when compared to the EMD-LSTM and CEEMDAN-GRU model. The predicted values of CEEMDAN-QRBL model have a suitable fluctuation range thanks to the use of nonparametric kernel density estimation (NPKDE), which might serve as a useful benchmark for the distribution of the regional water resources.
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Deo, Ravinesh C., Richard H. Grant, Ann Webb, Sujan Ghimire, Damien P. Igoe, Nathan J. Downs, Mohanad S. Al-Musaylh, Alfio V. Parisi, and Jeffrey Soar. "Forecasting solar photosynthetic photon flux density under cloud cover effects: novel predictive model using convolutional neural network integrated with long short-term memory network." Stochastic Environmental Research and Risk Assessment, April 6, 2022. http://dx.doi.org/10.1007/s00477-022-02188-0.
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AbstractForecast models of solar radiation incorporating cloud effects are useful tools to evaluate the impact of stochastic behaviour of cloud movement, real-time integration of photovoltaic energy in power grids, skin cancer and eye disease risk minimisation through solar ultraviolet (UV) index prediction and bio-photosynthetic processes through the modelling of solar photosynthetic photon flux density (PPFD). This research has developed deep learning hybrid model (i.e., CNN-LSTM) to factor in role of cloud effects integrating the merits of convolutional neural networks with long short-term memory networks to forecast near real-time (i.e., 5-min) PPFD in a sub-tropical region Queensland, Australia. The prescribed CLSTM model is trained with real-time sky images that depict stochastic cloud movements captured through a total sky imager (TSI-440) utilising advanced sky image segmentation to reveal cloud chromatic features into their statistical values, and to purposely factor in the cloud variation to optimise the CLSTM model. The model, with its competing algorithms (i.e., CNN, LSTM, deep neural network, extreme learning machine and multivariate adaptive regression spline), are trained with 17 distinct cloud cover inputs considering the chromaticity of red, blue, thin, and opaque cloud statistics, supplemented by solar zenith angle (SZA) to predict short-term PPFD. The models developed with cloud inputs yield accurate results, outperforming the SZA-based models while the best testing performance is recorded by the objective method (i.e., CLSTM) tested over a 7-day measurement period. Specifically, CLSTM yields a testing performance with correlation coefficient r = 0.92, root mean square error RMSE = 210.31 μ mol of photons m−2 s−1, mean absolute error MAE = 150.24 μ mol of photons m−2 s−1, including a relative error of RRMSE = 24.92% MAPE = 38.01%, and Nash Sutcliffe’s coefficient ENS = 0.85, and Legate and McCabe’s Index LM = 0.68 using cloud cover in addition to the SZA as an input. The study shows the importance of cloud inclusion in forecasting solar radiation and evaluating the risk with practical implications in monitoring solar energy, greenhouses and high-value agricultural operations affected by stochastic behaviour of clouds. Additional methodological refinements such as retraining the CLSTM model for hourly and seasonal time scales may aid in the promotion of agricultural crop farming and environmental risk evaluation applications such as predicting the solar UV index and direct normal solar irradiance for renewable energy monitoring systems.
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Mr. Abdul Khadar A, Modem Tharun Kumar, Sharath K N, Sukesh V N, and Tejaswini K N. "Intrusion Detection System Using K-Means and Edited Nearest Neighbour Algorithm." International Journal of Advanced Research in Science, Communication and Technology, June 23, 2022, 451–58. http://dx.doi.org/10.48175/ijarsct-5052.
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In imbalanced network traffic, malicious cyber-attacks can often hide in large amounts of normal data. It exhibits a high degree of stealth and obfuscation in cyberspace, making it difficult for Network Intrusion Detection System (NIDS) to ensure the accuracy and timeliness of detection. This paper researches machine learning and deep learning for intrusion detection in imbalanced network traffic. It proposes a novel Difficult Set Sampling Technique (DSSTE) algorithm to tackle the class imbalance problem. First, use the Edited Nearest Neighbor (ENN) algorithm to divide the imbalanced training set into the difficult set and the easy set. Next, use the K- Means algorithm to compress the majority samples in the difficult set to reduce the majority. Zoom in and out the minority samples’ continuous attributes in the difficult set synthesize new samples to increase the minority number. Finally, the easy set, the compressed set of majority in the difficult, and the minority in the difficult set are combined with its augmentation samples to make up a new training set. The algorithm reduces the imbalance of the original training set and provides targeted data augment for the minority class that needs to learn. It enables the classifier to learn the differences in the training stage better and improve classification performance. To verify the proposed method, we conduct experiments on the classic intrusion dataset NSL-KDD. We use classical classification models: random forest(RF), Support Vector Machine (SVM), XGBoost, Long and Short- term Memory (LSTM), Adaboost, AlexNet, Mini- VGGNet.