A novel bidirectional DiPLS based LSTM algorithm and its application in industrial process time series prediction

Wang, Yongjian; Bao, Dinghua; Qin, S. Joe

doi:10.1016/j.chemolab.2023.104878

Cited by 6 publications

(3 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This leads to improved prediction accuracy compared to models using fixed time intervals. Wang et al [85] presented a new algorithm called BiDiPLS-LSTM, which uses DiPLS to process both forward and backward time series data. This approach extracts dynamic latent variables (DLV) from the most predictable data for the target variable.…”

Section: Time Series-based Modelingmentioning

confidence: 99%

Dynamic Operation Optimization of Complex Industries Based on a Data-Driven Strategy

Tian,

Zhao,

Xie

et al. 2024

Processes

View full text Add to dashboard Cite

As industrial practices continue to evolve, complex process industries often exhibit characteristics such as multivariate correlation, dynamism, and nonlinearity, making traditional mechanism modeling inadequate in terms of addressing the intricacies of complex industrial problems. In recent years, with advancements in control theory and industrial practices, there has been a substantial increase in the volume of industrial data. Data-driven dynamic operation optimization techniques have emerged as effective solutions for handling complex industrial processes. By responding to dynamic environmental changes and utilizing advanced optimization algorithms, it is possible to achieve dynamic operational optimization in industrial processes, thereby reducing costs and emissions, improving efficiency, and increasing productivity. This correlates nicely with the goals set forth by conventional process operation optimization theories. Nowadays, this dynamic, data-driven strategy has shown significant potential in complex process industries characterized by multivariate correlations and nonlinear behavior. This paper approaches the subject from a data-driven perspective by establishing dynamic optimization models for complex industries and reviewing the state-of-the-art time series forecasting models to cope with changing objective functions over time. Meanwhile, aiming at the problem of concept drift in time series, this paper summarizes new concept drift detection methods and introduces model update methods to solve this challenge. In addressing the problem of solving dynamic multi-objective optimization problems, the paper reviews recent developments in dynamic change detection and response methods while summarizing commonly used as well as the latest performance measures for dynamic multi-objective optimization problems. In conclusion, a discussion of the research progress and challenges in the relevant domains is undertaken, followed by the proposal of potential directions for future research. This review will help to deeply understand the importance and application prospects of data-driven dynamic operation optimization in complex industrial fields.

show abstract

Section: Time Series-based Modelingmentioning

confidence: 99%

Dynamic Operation Optimization of Complex Industries Based on a Data-Driven Strategy

Tian,

Zhao,

Xie

et al. 2024

Processes

View full text Add to dashboard Cite

show abstract

“…Long short-term memory (LSTM) networks, a subtype of recurrent neural networks, are capable of storing enormous volumes of data in a short period [37]. LSTMs recognize longterm dependencies, sequential patterns, and temporal correlations in data, making them ideal for handling text, modeling context, and detecting cybercrime [38]. Because CNN and LSTM are both potent deep learning models that work well with sequence prediction problems including spatial inputs, such as text data, they were selected for this model.…”

Section: B Lstm (Long Short-term Memory)mentioning

confidence: 99%

A Hybrid CNN-LSTM and XGBoost Approach for Crime Detection in Tweets Using an Intelligent Dictionary

Abdalrdha,

Al-Bakry,

Farhan

2023

RIA

View full text Add to dashboard Cite

As social media grows, recognizing and managing illicit content, including threats, harassment, hate speech, armed robbery, drug smuggling, blackmail, and other crimes, is crucial. The present study uses machine learning and deep learning to create an intelligent lexicon for identifying crime-related material in Twitter tweets. The Aho-Corasick technique effectively creates a dictionary for extensive text corpus search, categorization, and keyword-based action execution. This strategy overcomes the evolution of language dynamics and criminal vocabulary to improve crime-related information detection. This paper aims to gather accurate data to help law enforcement identify and prevent specific crimes. For tweet preprocessing and extraction of relevant information like textual patterns and other distinctive qualities, natural language processing (NLP) technologies are prioritized. The paper describes labeling tweets into crime categories. This dataset trains supervised learning models to categorize tweets as criminal or not. XGBoost and Hybrid CNN-LSTM are combined for this. The suggested technique is assessed using precision, recall, F1-Score, accuracy, and MAP accuracy measures. These metrics measure the model's crime-related tweet identification accuracy. The Arabic tweets dataset, encompassing 18493 tweets and 10 features, is utilized for model testing. After training, the Hybrid CNN-LSTM model demonstrated an accuracy of 99.84% and a macro F1-Score of 98.20%. When the XGBoost method was employed, the traditional machine learning model achieved a peak F1 macro score of 99.36% and a maximum accuracy of 100%. The results suggest that while the deep learning models outperform machine learning models in the F1-Score, XGBoost exhibits superior accuracy. The paper presents a comprehensive strategy for crime detection in tweets, potentially offering a significant tool for law enforcement agencies.

show abstract

“…Therefore, many methods are used in the diagnosis, monitoring, and control of the combustion process, among which can be highlighted the Fourier transform [43], wavelet analysis [44], or recurrent neural networks [45][46][47][48][49][50]. Diagnostics of the combustion process can also use classification [51][52][53][54] and prediction [55,56].…”

Section: Introductionmentioning

confidence: 99%

Application of Fuzzy Neural Networks in Combustion Process Diagnostics

Grądz,

Wójcik,

Gromaszek

et al. 2023

Energies

View full text Add to dashboard Cite

Coal remains one of the key raw materials used in the energy industry to generate electricity and heat. As a result, diagnostics of the combustion process is still an important topic of scientific research. Correct implementation of the process allows the emission of pollutants into the atmosphere to be kept at a compliant level. Therefore, it is important to conduct the process in a manner that will not exceed these standards. A preliminary analysis of the measurement signals was carried out, and signal predictions of flame intensity changes were determined using the autoregressive moving average (ARMA) model. Different fuzzy neural network architectures have been investigated. Binary and multi-class classifications of flame states were conducted. The best results were obtained from the ANFIS_grid partition model, producing an accuracy of 95.46% for binary classification and 79.08% for multi-class classification. The accuracy of the recognition of flame states and the high convergence of the determined predictions with measurement signals validate the application of the proposed approach in diagnosing or controlling the combustion process of pulverized coal and its mixtures with biomass. Expert decisions determine the range of acceptable states.

show abstract

A novel bidirectional DiPLS based LSTM algorithm and its application in industrial process time series prediction

Cited by 6 publications

References 28 publications

Dynamic Operation Optimization of Complex Industries Based on a Data-Driven Strategy

Dynamic Operation Optimization of Complex Industries Based on a Data-Driven Strategy

A Hybrid CNN-LSTM and XGBoost Approach for Crime Detection in Tweets Using an Intelligent Dictionary

Application of Fuzzy Neural Networks in Combustion Process Diagnostics

Contact Info

Product

Resources

About