A Classification Method for Transmission Line Icing Process Curve Based on Hierarchical K-Means Clustering

Icing forecasting for transmission lines is of great significance for anti-icing strategies in power grids, but existing prediction models have some disadvantages such as application limitations, weak generalization, and lack of global prediction ability. To overcome these shortcomings, this paper suggests a new conception about a segmental icing prediction model for transmission lines in which the classification of icing process plays a crucial role. In order to obtain the classification, a hierarchical K-means clustering method is utilized and 11 characteristic parameters are proposed. Based on this method, 97 icing processes derived from the Icing Monitoring System in China Southern Power Grid are clustered into six categories according to their curve shape and the abstracted icing evolution curves are drawn based on the clustering centroid. Results show that the processes of ice events are probably different and the icing process can be considered as a combination of several segments and nodes, which reinforce the suggested conception of the segmental icing prediction model. Based on monitoring data and clustering, the obtained types of icing evolution are more comprehensive and specific, and the work lays the foundation for the model construction and contributes to other fields.

Download Full-text

A Model Based on BP Neural Network for Audible Noise Prediction

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.986-987.1356 ◽

2014 ◽

Vol 986-987 ◽

pp. 1356-1359

Author(s):

You Xian Peng ◽

Bo Tang ◽

Hong Ying Cao ◽

Bin Chen ◽

Yu Li

Keyword(s):

Neural Network ◽

Prediction Model ◽

Transmission Lines ◽

Bp Neural Network ◽

Prediction Models ◽

Absolute Error ◽

Research Area ◽

Noise Prediction ◽

Bp Network ◽

Model Based

Audible noise prediction is a hot research area in power transmission engineering in recent years, especially come down to AC transmission lines. The conventional prediction models at present have got some problems such as big errors. In this paper, a prediction model is established based on BP network, in which the input variables are the four factors in the international common expression of power line audible noise and the noise value is the output. Take multiple measured power lines as an example, a train is made by the BP network and then the prediction model is set up in the hidden layer of the network. Using the trained model, the audible noise values are predicted. The final results show that the average absolute error in absolute terms of the values by the audible noise prediction model based on BP neural network is 1.6414 less than that predicted by the GE formula.

Download Full-text

The impact of Data structure on classification ability of financial failure prediction model

SHS Web of Conferences ◽

10.1051/shsconf/20207405024 ◽

2020 ◽

Vol 74 ◽

pp. 05024

Author(s):

Lucia Svabova ◽

Lucia Michalkova

Keyword(s):

Data Structure ◽

Prediction Model ◽

Statistical Methods ◽

Prediction Models ◽

Global Perspective ◽

Data Set ◽

Prediction Ability ◽

Financial Difficulties ◽

The Creation ◽

The Impact

The creation of prediction models to reveal the threat of financial difficulties of the companies is realized by the application of various multivariate statistical methods. From a global perspective, prediction models serve to classify a company into a group of prosperous or non-prosperous companies, or to quantify the probability of financial difficulties in the company. In many countries around the world, real financial data about the companies are used in developing these prediction models. In Slovakia, standard data from the financial statements and annual reports of Slovak companies are used for the creation of the company’s failure model. Since in this case there are generally large data files, it is necessary to pre-process the data by the selected methods before the prediction model is constructed. A database of the companies needs to be prepared for the subsequent application of statistical methods, and it is also highly appropriate to focus globally on the detection of potential extreme and remote observations. Therefore, the article will focus on quantifying the impact of the data structure detected, for example, the occurrence of extreme and remote observations in the data set, on the resulting overall classification of the prediction ability of the models created.

Download Full-text

Short-term wind speed prediction based on CEEMDAN-SE-improved PIO-GRNN model

Measurement and Control ◽

10.1177/0020294020981400 ◽

2020 ◽

pp. 002029402098140

Author(s):

Jiale Ding ◽

Guochu Chen ◽

Yongmin Huang ◽

Zhiquan Zhu ◽

Kuo Yuan ◽

...

Keyword(s):

Wind Speed ◽

Prediction Model ◽

Prediction Models ◽

Mean Squared Error ◽

General Regression Neural Network ◽

Percentage Error ◽

Short Term ◽

Prediction Ability ◽

Wind Speed Prediction ◽

Speed Prediction

In this paper, a short-term wind speed prediction model, called CEEMDAN-SE-Improved PIO-GRNN, is proposed to optimize the accuracy of the short-term wind speed forecast. This model is established on account of the optimized General Regression Neural Network (GRNN) method optimized by three algorithms, which are Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Sample Entropy (SE), and Pigeon Inspired Optimization (PIO), separately. Firstly, decomposing the original wind speed sequences into several subsequences with different complexity by CEEMDAN. Then, the complexity of each subsequence is judged by SE and the similar subsequences are combined into a new sequence to reduce the scale of calculation. Afterwards, the GRNN model optimized by improved PIO is used to predict each new sequence. Finally, the predicted results are superposed as the eventual predicted value. Implementing the prediction for the wind speed data of a wind field in north China within 30 days by applying the different prediction models, namely, GRNN, CEEMDAN-GRNN, Improved PIO-GRNN, and CEEMDAN-SE-Improved PIO-GRNN which are proposed in this paper. Comparing the prediction curves of different models with the fitting curve of the actual wind speed shows that the optimal fitting effect and minimum error value are included in CEEMDAN-SE-Improved PIO-GRNN model. Specifically, the values of mean squared error (MSE), mean absolute error (MAE) and weighted mean absolute percentage error (WMAPE) separately decrease by 0.6222, 0.3334, and 8.5766%, which compare with the single prediction model GRNN. Meanwhile, diebold-mariano (DM) test shows that the prediction ability of the two models is significantly different. The above statements indicate the proposed model does great advance in the precision of short-term wind speed prediction.

Download Full-text

Logit and Probit application for the prediction of bankruptcy in Slovak companies

Equilibrium ◽

10.24136/eq.v12i4.40 ◽

2017 ◽

Vol 12 (4) ◽

pp. 775-791 ◽

Cited By ~ 11

Author(s):

Maria Kovacova ◽

Tomas Kliestik

Keyword(s):

Logistic Regression ◽

Prediction Model ◽

Statistical Methods ◽

Prediction Models ◽

Slovak Republic ◽

Relevant Literature ◽

Value Added ◽

Bankruptcy Prediction ◽

Data Set ◽

Prediction Ability

Research background: Prediction of bankruptcy is an issue of interest of various researchers and practitioners since the first study dedicated to this topic was published in 1932. Finding the suitable bankruptcy prediction model is the task for economists and analysts from all over the world. forecasting model using. Despite a large number of various models, which have been created by using different methods with the aim to achieve the best results, it is still challenging to predict bankruptcy risk, as corporations have become more global and more complex. Purpose of the article: The aim of the presented study is to construct, via an empirical study of relevant literature and application of suitable chosen mathematical statistical methods, models for bankruptcy prediction of Slovak companies and provide the comparison of overall prediction ability of the two developed models. Methods: The research was conducted on the data set of Slovak corporations covering the period of the year 2015, and two mathematical statistical methods were applied. The methods are logit and probit, which are both symmetric binary choice models, also known as conditional probability models. On the other hand, these methods show some significant differences in process of model formation, as well as in achieved results. Findings & Value added: Given the fact that mostly discriminant analysis and logistic regression are used for the construction of bankruptcy prediction models, we have focused our attention on the development bankruptcy prediction model in the Slovak Republic via logistic regression and probit. The results of the study suggest that the model based on a logit functions slightly outperforms the classification accuracy of probit model. Differences were obtained also in the detection of the most significant predictors of bankruptcy prediction in these types of models constructed in Slovak companies.

Download Full-text

A Study on the Clustering of Bidding Company Trends Using Machine Learning Based G2B Data

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i3.33.18522 ◽

2018 ◽

Vol 7 (3.33) ◽

pp. 51

Author(s):

Min Sun Kim ◽

Eun Soo Choi ◽

Min Soo Kang

Keyword(s):

Machine Learning ◽

Prediction Model ◽

Prediction Models ◽

Public Procurement ◽

Data Sets ◽

Model Learning ◽

The Public ◽

Electronic Procurement ◽

Procurement System

KONEPS is the National Comprehensive Electronic Procurement System of the Public Procurement Service. If KONEPS can know the bidding possibility and trend before bidding, it will be more efficient for companies to bid. In this paper, we used in the experiment was the data of "Progress Bidding Classification" of the Procurement Information Open Portal. And preprocessing process was performed to facilitate prediction model learning. Prior to learning, preprocessed 1,158 data sets were normalized to match the range of data or to make the distribution similar. After normalization we select the number of cluster. As a result of K-Means Clustering, Biddropping is 77 ~ 80%, Budget Allocated is about 2 billion Won(₩), Biddropping is 83 ~ 87%, Budget Allocated is about 1 billion won, bid dropping is 87 ~ 90% Budget Allocated is distributed around 500 million won. And can be confirmed that the cluster is divided based on the number of enterprise 58. Through the results, it is possible to study the tendering trends through the community by learning the prediction models of the bidder companies, the number of bidders, and the tendency of the bidding business, and it will help KONEPS to develop the next generation ISP.

Download Full-text

Antibacterial Activity Prediction Model of Traditional Chinese Medicine Based on Combined Data-Driven Approach and Machine Learning Algorithm: Constructed and Validated

Frontiers in Microbiology ◽

10.3389/fmicb.2021.763498 ◽

2021 ◽

Vol 12 ◽

Author(s):

Jin-Tong Li ◽

Ya-Wen Wei ◽

Meng-Yu Wang ◽

Chun-Xiao Yan ◽

Xia Ren ◽

...

Keyword(s):

Antibacterial Activity ◽

Prediction Model ◽

Antibacterial Agents ◽

Prediction Models ◽

Learning Algorithm ◽

Classification And Regression Tree ◽

China National Knowledge Infrastructure ◽

Prediction Ability ◽

Linear Discriminant ◽

Natural Medicine

Traditional Chinese medicines (TCMs), as a unique natural medicine resource, were used to prevent and treat bacterial diseases in China with a long history. To provide a prediction model of screening antibacterial TCMs for the design and discovery of novel antibacterial agents, the literature about antibacterial TCMs in the China National Knowledge Infrastructure (CNKI) and Web of Science database was retrieved. The data were extracted and standardized. A total of 28,786 pieces of data from 904 antibacterial TCMs were collected. The data of plant medicine were the most numerous. The result of association rules mining showed a high correlation between antibacterial activity with cold nature, bitter and sour tastes, hemostatic, and purging fire efficacies. Moreover, TCMs with antibacterial activity showed a specific aggregation in the phylogenetic tree; 92% of them came from Tracheophyta, of which 74% were mainly concentrated in rosids, asterids, Liliopsida, and Ranunculales. The prediction models of anti-Escherichia coli and anti-Staphylococcus aureus activity, with AUC values (the area under the ROC curve) of 77.5 and 80.0%, respectively, were constructed by the Neural Networks (NN) algorithm after Bagged Classification and Regression Tree (Bagged CART) and Linear Discriminant Analysis (LDA) selection. The in vitro experimental results showed the prediction accuracy of these two models was 75 and 60%, respectively. Four TCMs (Cirsii Japonici Herba Carbonisata, Changii Radix, Swertiae Herba, Callicarpae Formosanae Folium) were proposed for the first time to show antibacterial activity against E. coli and/or S. aureus. The results implied that the prediction model of antibacterial activity of TCMs based on properties and families showed certain prediction ability, which was of great significance to the screening of antibacterial TCMs and can be used to discover novel antibacterial agents.

Download Full-text

Bioactivity Prediction Based on Matched Molecular Pair and Matched Molecular Series Methods

Current Pharmaceutical Design ◽

10.2174/1381612826666200427111309 ◽

2020 ◽

Vol 26 (33) ◽

pp. 4195-4205

Author(s):

Xiaoyu Ding ◽

Chen Cui ◽

Dingyan Wang ◽

Jihui Zhao ◽

Mingyue Zheng ◽

...

Keyword(s):

Prediction Model ◽

Large Scale ◽

Prediction Models ◽

Predictive Accuracy ◽

Lead Optimization ◽

Consensus Method ◽

Molecular Pair ◽

Bioactivity Prediction ◽

Compound Synthesis ◽

Consensus Modeling

Background: Enhancing a compound’s biological activity is the central task for lead optimization in small molecules drug discovery. However, it is laborious to perform many iterative rounds of compound synthesis and bioactivity tests. To address the issue, it is highly demanding to develop high quality in silico bioactivity prediction approaches, to prioritize such more active compound derivatives and reduce the trial-and-error process. Methods: Two kinds of bioactivity prediction models based on a large-scale structure-activity relationship (SAR) database were constructed. The first one is based on the similarity of substituents and realized by matched molecular pair analysis, including SA, SA_BR, SR, and SR_BR. The second one is based on SAR transferability and realized by matched molecular series analysis, including Single MMS pair, Full MMS series, and Multi single MMS pairs. Moreover, we also defined the application domain of models by using the distance-based threshold. Results: Among seven individual models, Multi single MMS pairs bioactivity prediction model showed the best performance (R2 = 0.828, MAE = 0.406, RMSE = 0.591), and the baseline model (SA) produced the most lower prediction accuracy (R2 = 0.798, MAE = 0.446, RMSE = 0.637). The predictive accuracy could further be improved by consensus modeling (R2 = 0.842, MAE = 0.397 and RMSE = 0.563). Conclusion: An accurate prediction model for bioactivity was built with a consensus method, which was superior to all individual models. Our model should be a valuable tool for lead optimization.

Download Full-text

Fire modelling in Tasmanian buttongrass moorlands. III. Dead fuel moisture

International Journal of Wildland Fire ◽

10.1071/wf01025 ◽

2001 ◽

Vol 10 (2) ◽

pp. 241 ◽

Cited By ~ 27

Author(s):

Jon B. Marsden-Smedley ◽

Wendy R. Catchpole

Keyword(s):

Prediction Model ◽

Regression Model ◽

Fire Management ◽

Prediction Models ◽

Dew Point ◽

Seasonal Effects ◽

Experimental Program ◽

Fuel Moisture ◽

Fire Behaviour ◽

Fire Modelling

An experimental program was carried out in Tasmanian buttongrass moorlands to develop fire behaviour prediction models for improving fire management. This paper describes the results of the fuel moisture modelling section of this project. A range of previously developed fuel moisture prediction models are examined and three empirical dead fuel moisture prediction models are developed. McArthur’s grassland fuel moisture model gave equally good predictions as a linear regression model using humidity and dew-point temperature. The regression model was preferred as a prediction model as it is inherently more robust. A prediction model based on hazard sticks was found to have strong seasonal effects which need further investigation before hazard sticks can be used operationally.

Download Full-text

Study on Quality Prediction of 2219 Aluminum Alloy Friction Stir Welding Based on Real-Time Temperature Signal

Materials ◽

10.3390/ma14133496 ◽

2021 ◽

Vol 14 (13) ◽

pp. 3496

Author(s):

Haijun Wang ◽

Diqiu He ◽

Mingjian Liao ◽

Peng Liu ◽

Ruilin Lai

Keyword(s):

Aluminum Alloy ◽

Friction Stir Welding ◽

Prediction Model ◽

Real Time ◽

Support Vector ◽

Weld Quality ◽

Prediction Ability ◽

Friction Stir ◽

Temperature Signal ◽

2219 Aluminum Alloy

The online prediction of friction stir welding quality is an important part of intelligent welding. In this paper, a new method for the online evaluation of weld quality is proposed, which takes the real-time temperature signal as the main research variable. We conducted a welding experiment with 2219 aluminum alloy of 6 mm thickness. The temperature signal is decomposed into components of different frequency bands by wavelet packet method and the energy of component signals is used as the characteristic parameter to evaluate the weld quality. A prediction model of weld performance based on least squares support vector machine and genetic algorithm was established. The experimental results showed that, when welding defects are caused by a sudden perturbation during welding, the amplitude of the temperature signal near the tool rotation frequency will change significantly. When improper process parameters are used, the frequency band component of the temperature signal in the range of 0~11 Hz increases significantly, and the statistical mean value of the temperature signal will also be different. The accuracy of the prediction model reached 90.6%, and the AUC value was 0.939, which reflects the good prediction ability of the model.

Download Full-text

A Genetic Algorithm Optimized RNN-LSTM Model for Remaining Useful Life Prediction of Turbofan Engine

Electronics ◽

10.3390/electronics10030285 ◽

2021 ◽

Vol 10 (3) ◽

pp. 285

Author(s):

Kwok Tai Chui ◽

Brij B. Gupta ◽

Pandian Vasant

Keyword(s):

Genetic Algorithm ◽

Feature Extraction ◽

Prediction Model ◽

Prediction Models ◽

Remaining Useful Life ◽

Prediction Algorithm ◽

Short Term ◽

Turbofan Engine ◽

Term Prediction ◽

Useful Life

Understanding the remaining useful life (RUL) of equipment is crucial for optimal predictive maintenance (PdM). This addresses the issues of equipment downtime and unnecessary maintenance checks in run-to-failure maintenance and preventive maintenance. Both feature extraction and prediction algorithm have played crucial roles on the performance of RUL prediction models. A benchmark dataset, namely Turbofan Engine Degradation Simulation Dataset, was selected for performance analysis and evaluation. The proposal of the combination of complete ensemble empirical mode decomposition and wavelet packet transform for feature extraction could reduce the average root-mean-square error (RMSE) by 5.14–27.15% compared with six approaches. When it comes to the prediction algorithm, the results of the RUL prediction model could be that the equipment needs to be repaired or replaced within a shorter or a longer period of time. Incorporating this characteristic could enhance the performance of the RUL prediction model. In this paper, we have proposed the RUL prediction algorithm in combination with recurrent neural network (RNN) and long short-term memory (LSTM). The former takes the advantages of short-term prediction whereas the latter manages better in long-term prediction. The weights to combine RNN and LSTM were designed by non-dominated sorting genetic algorithm II (NSGA-II). It achieved average RMSE of 17.2. It improved the RMSE by 6.07–14.72% compared with baseline models, stand-alone RNN, and stand-alone LSTM. Compared with existing works, the RMSE improvement by proposed work is 12.95–39.32%.

Download Full-text