A novel semi-active control algorithm for adaptive variable stiffness intelligent structures that combines the fuzzy control strategy with Long Short-Term Memory (LSTM) is proposed in this study, with the aim of mitigating structural responses under earthquake excitations. The combined Fuzzy-LSTM strategy utilizes an LSTM neural network to predict the displacement and velocity responses of the structure at the next moment, and uses the predicted results as input for fuzzy control to obtain active control forces. Due to the difference between the displacement-velocity response and the fuzzy control domain, the genetic algorithm is used to optimize scaling factors of displacement and velocity responses and the control force, which is implemented by exploiting stiffness variability and structural inter-shift. The Fuzzy-LSTM strategy can compensate for the time delay effect on active control caused by stiffness variations, algorithmic computations and sensor measurements, and therefore achieves a better control effect through prediction. The semi-active independently variable stiffness (SAIVS) device is used to implement the presented control algorithm, which is a diamond device consisting of four linear springs and an electromagnetic actuator. The SAIVS device can achieve a continuous and smooth stiffness variation and meet the application requirements of the developed algorithm. Therefore, active control forces can be obtained through structural stiffness variations and interlayer displacements under seismic excitation, to achieve an approximate active control effect through a semi-active method. A 10-story building is presented as a case study for numerical simulations. Results show that the Fuzzy-LSTM strategy can significantly improve the control effect compared to the conventional fuzzy control strategy, and therefore achieve a better seismic response mitigation performance. 

Seismic control of adaptive variable stiffness intelligent structures using fuzzy control strategy combined with LSTM

An earthquake is a tremor felt on the surface of the earth created by the movement of the major pieces of its outer shell. Till now, many attempts have been made to forecast earthquakes, which saw some success, but these attempted models are specific to a region. In this paper, an earthquake occurrence and location prediction model is proposed. After reviewing the literature, long short-term memory (LSTM) is found to be a good option for building the model because of its memory-keeping ability. Using the Keras tuner, the best model was selected from candidate models, which are composed of combinations of various LSTM architectures and dense layers. This selected model used seismic indicators from the earthquake catalog of Bangladesh as features to predict earthquakes of the following month. Attention mechanism was added to the LSTM architecture to improve the model’s earthquake occurrence prediction accuracy, which was 74.67%. Additionally, a regression model was built using LSTM and dense layers to predict the earthquake epicenter as a distance from a predefined location, which provided a root mean square error of 1.25.

/pdf/attention-based-bi-directional-long-short-term-memory-5cfvgki52g.pdf

Attention-Based Bi-Directional Long-Short Term Memory Network for Earthquake Prediction

Earthquakes, as natural phenomena, have consistently caused damage and loss of human life throughout history. Earthquake prediction is an essential aspect of any society's plans and can increase public preparedness and reduce damage to a great extent. Despite advances in computing systems and deep learning methods, no substantial achievements have been made in earthquake prediction. One of the most important reasons is that the earthquake's nonlinear and chaotic behavior makes it hard to train the deep learning method. To tackle this drawback, this study tries to take an effective step in improving the performance of prediction results by employing a novel method in earthquake prediction. This method employs a deep learning model based on convolutional neural networks (CNN), bi-directional long short-term memory (BiLSTM), and an attention mechanism, as well as a zero-order hold (ZOH) pre-processing methodology. This study aims to predict the maximum magnitude and number of earthquakes in the next month with the least error. The proposed method was evaluated by an earthquake dataset from nine distinct regions of China. The results reveal that the proposed method outperforms other prediction methods in terms of performance and generalization. 

pdf/a-cnn-bilstm-model-with-attention-mechanism-for-earthquake-3oopgbm12s.pdf

A CNN-BiLSTM model with attention mechanism for earthquake prediction

The Internet of Things (IoT) has created a novel ecosystem for sensing and actuation throughout our world, enabling intelligently controlled autonomous systems to conserve energy, water crops, manage factories, and provide situation awareness on an unprecedented scale As IoT progresses, the interest in IoT search engines, that is, search engines to find IoT devices and retrieve IoT data, has grown While basic examples of IoT search engines exist, considerable challenges prevent the full realization of an efficient and intelligent IoT search engine that provides universal data service, scalable data communication and retrieval, and efficient querying of massively distributed heterogeneous devices and data In this article, we first propose a generic framework for the IoT search engine, and then present a naming service for the IoT system, an essential component for an effective IoT search engine We also outline some research challenges and possible solutions for building efficiency and intelligence in the IoT search engine Further, we present a case study and seek to address a particular aspect of the query process for IoT search, namely efficient and timely query processing Given the now obvious advances in machine learning, the potential for deep learning-based prediction to improve resource use, and thus query retrieval, is clear In detail, we utilize Long-Short-Term Memory (LSTM) neural network architecture to predict aggregated query volumes to be preemptively applied and stored for immediate response Combining several realistic IoT datasets, we explore the efficacy of simultaneously predicting multiple targets for predictive query retrieval

/pdf/towards-efficient-and-intelligent-internet-of-things-search-10vwhed452.pdf

Towards Efficient and Intelligent Internet of Things Search Engine

An attention-based LSTM network for large earthquake prediction

Over the last few years, many works have been done in earthquake prediction using different techniques and precursors in order to warn of earthquake damages and save human lives. Plenty of works have failed to sufficiently predict earthquakes, because of the complexity and the unpredictable nature of this task. Therefore, in this work we use the powerful deep learning technique. A useful algorithm that captures complex relationships in time series data. The technique is called long short-term memory (LSTM). The work employs this method in two cases of study; the first learns all the datasets in one model, the second case learns the correlations on two divided groups considering their range of magnitude. The results show that learning decomposed datasets gives more well-functioning predictions since it exploits the nature of each type of seismic events.

LSTM-based Models for Earthquake Prediction

A location-dependent earthquake prediction using recurrent neural network algorithms

Coronavirus 2019 (COVID-19) has reached the stage of an international epidemic with a major socioeconomic negative impact Considering the weakness of the healthy structure and the limited availability of test kits, particularly in emerging countries, predicting the spread of COVID-19 is expected to help decision-makers to improve health management and contribute to alleviating the related risks In this article, we studied the effectiveness of machine learning techniques using Morocco as a case-study We studied the performance of six multi-step models derived from both Machine Learning and Deep Learning regards multiple scenarios by combining different time lags and three COVID-19 datasets(periods): confinement, deconfinement, and hybrid datasets The results prove the efficiency of Deep Learning models and identify the best combinations of these models and the time lags enabling good predictions of new cases The results also show that the prediction of the spread of COVID-19 is a context sensitive problem © 2021 International Information and Engineering Technology Association All rights reserved

/pdf/artificial-intelligence-enabled-and-period-aware-forecasting-589dkx01zd.pdf

Artificial intelligence-enabled and period-aware forecasting COVID-19 spread

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LSTM-based Models for Earthquake Prediction

An attention-based LSTM network for large earthquake prediction

A location-dependent earthquake prediction using recurrent neural network algorithms

Artificial intelligence-enabled and period-aware forecasting COVID-19 spread