Short-term Memory Algorithm Research Articles

Enhancement of Coronary Heart Disease Prediction using Stacked Long Short Term Memory

The high incidence of death caused by coronary heart disease has become a global concern in the world of health, where patients with coronary heart disease are no longer only adults and the elderly, yet there are now so many cases of coronary heart disease experienced by underage patients. As a result, it is critical to be able to prevent and reduce the number of instances. One of them is the ability to predict a person's risk of coronary heart disease so that patients can be treated and provided early therapy. The risk of coronary heart disease will be predicted in this study utilizing Stacked long short-term memory algorithms. By appling this algorithm, the accuracy of 81.3% from previous study can be increased to 91.8% by this study.

Deep learning based optimal energy management framework for community energy storage system

This paper proposes a deep learning-based integrated framework for multiple cooperative households to achieve optimal energy distribution. The corresponding energy generation and consumption problems are formulated by a long short-term memory algorithm is combined with an optimization algorithm to produce an optimal solution. In this study, a PV-community energy storage system (CESS) integrated is considered where the scheduling decision of the CESS and utility grid can be subsequently achieved through formulated constraints. The test results demonstrate the efficacy and robustness of the proposed system that achieves superior performance on effective renewable energy usages of maximum 31.74% in a home environment.

Implementation of Convolutional Neural Network and Long Short-Term Memory Algorithms in Human Activity Recognition Based on Visual Processing Video

Human Activity Recognition (HAR) is an interesting research topic, especially in identifying human movement actions focusing on video-based security surveillance. Symptom of an illness from a movement. The use of HAR in this research is the key to better understanding the various semantics contained in the video to find out the pattern of a human movement, especially in sports movements. In this study, a combination of the CNN and LSTM method algorithms was applied by using several variations of the model parameter values on the dropout layer and batch size to convert the pattern in the video into image form to produce a HAR model. Data processing at the convolution layer is used to extract spatial features in the frame. The extraction results are fed to the LSTM layer on each network for modeling the temporal sequence of human movement. In this way, the network on the model will learn spatiotemporal features directly in end-to-end data training tests to produce a robust model. The test data used are 10 sports activities obtained from related research from the University of Central Florida (UCF). The results showed that the performance was quite good, although there were still errors in the classification of sports activities because they had similarities in the movements of the activities carried out. The classification results show a loss value of 0.4 and an accuracy of 0.94. In further research, what needs to be corrected is the loss value which is still high so that several times the test results show an error in the classification of sports activities that have similarities in the movements of the activities.

Architecture-Driven Physics-Informed Deep Learning for Temperature Prediction in Laser Powder Bed Fusion Additive Manufacturing With Limited Data

Abstract Physics-informed deep learning (PIDL) is one of the emerging topics in additive manufacturing (AM). However, the success of previous PIDL approaches is generally significantly dependent on the existence of massive datasets. As the data collection in AM is usually challenging, a novel Architecture-driven PIDL structure named APIDL based on the deep unfolding approach for limited data scenarios has been proposed in the current study for predicting thermal history in the laser powder bed fusion process. The connections in this machine learning architecture are inspired by iterative thermal model equations. In other words, each iteration of the thermal model is mapped to a layer of the neural network. The hyper-parameters of the APIDL model are tuned, and its performance is analyzed. The APIDL for 1000 points with 80:20 split ratio achieves testing mean absolute percentage error (MAPE) of 2.8% and R2 value of 0.936. The APIDL is compared with the artificial neural network, extra trees regressor (ETR), support vector regressor, and long short-term memory algorithms. It was shown that the proposed APIDL model outperforms the others. The MAPE and R2 of APIDL are 55.7% lower and 15.6% higher than the ETR, which had the best performance among other pure machine learning models.

Price Prediction of Cryptocurrency based on LSTM Model: Evidence from Ethereum

Contemporarily, under the impacts of COVID-19 and regional conflicts with radicalness fiscal policy, the prices of cryptocurrency have been fluctuated dramatically. Among various types of cryptocurrency, Ethereum is one of the most volatility assets. In order to avoid risks as well as gain extra return in the crypto market, it is necessary to construct accurate prediction approach. In this paper, the Long Short-Term Memory algorithm will be used to predict the future price of Ethereum by learning Ethereum's past price direction data. price trend by learning Ethereum's past price trend data. Based on the analysis, the predicted values of the trained model fit well with the actual data, with the regression evaluation index R2 of 97.08% and MAPE of 6.89%. According to the results, it is feasible to predict the future price trend through the past price trend data. Nevertheless, it should be noted that the stochastic process in data training might lead to the instability of model performances. Hence, it is necessary to train the data several time to select the best models. Overall, these results shed light on guiding further exploration of cryptocurrency price forecasting in terms of the state-of-art neural networks.

Sparse sensor reconstruction of vortex-impinged airfoil wake with machine learning

Reconstruction of unsteady vortical flow fields from limited sensor measurements is challenging. We develop machine learning methods to reconstruct flow features from sparse sensor measurements during transient vortex–airfoil wake interaction using only a limited amount of training data. The present machine learning models accurately reconstruct the aerodynamic force coefficients, pressure distributions over airfoil surface, and two-dimensional vorticity field for a variety of untrained cases. Multi-layer perceptron is used for estimating aerodynamic forces and pressure profiles over the surface, establishing a nonlinear model between the pressure sensor measurements and the output variables. A combination of multi-layer perceptron with convolutional neural network is utilized to reconstruct the vortical wake. Furthermore, the use of transfer learning and long short-term memory algorithm combined in the training models greatly improves the reconstruction of transient wakes by embedding the dynamics. The present machine-learning methods are able to estimate the transient flow features while exhibiting robustness against noisy sensor measurements. Finally, appropriate sensor locations over different time periods are assessed for accurately estimating the wakes. The present study offers insights into the dynamics of vortex–airfoil interaction and the development of data-driven flow estimation.Graphic abstract

Daily streamflow prediction based on the long short-term memory algorithm: a case study in the Vietnamese Mekong Delta

Abstract The objective of this study is the development of a state-of-the-art method based on long short-term memory (LSTM), support vector machine (SVM), and random forest (RF) to predict the streamflow in the Mekong Delta in Vietnam, an area crucial to Vietnam's food security. Water level and flow data from 2014 to 2018 at the Tan Chau station and Can Tho (on the Hau River) were used as the input data of the prediction model. Three different ranges of data – from the preceding 4, 8, and 12 days – were used to predict streamflow for both 1 and 7 days ahead, resulting in six individual predictions. Various statistical indices, namely root-mean-square error, mean absolute error (MAE), and the coefficient of determination (R2), were used to assess the predictive ability of the model. The results showed that the SVM and random forest models were successful in improving the performance of the LSTM model, with R2 > 80%. For a prediction of 1 day ahead, the proposed models gave an R2 value of 2–5% higher than a prediction of 7 days ahead. These results highlighted that LSTM is a robust technique for characterizing and predicting time series behaviors in hydrology applications.

Real-Time Liquid Rate and Water Cut Prediction From the Electrical Submersible Pump Sensors Data Using Machine-Learning Algorithms.

This study presents a novel data-driven approach for calculating multiphase flow rates in electrical submersible pumped wells. Traditional methods for estimating flow rates at test separators fail to identify production trends and require additional costs for maintenance. In response, virtual flow metering (VFM) has emerged as an attractive research area in the oil and gas industry. This study introduces a robust workflow utilizing symbolic regression, extreme gradient boosted trees, and a deep learning model that includes a pipeline of convolutional neural network (CNN) layers and long short-term memory algorithm (LSTM) layers to predict liquid rate and water cut in real time based on pump sensors' data. The novelty of this approach lies in offering a cost-effective and accurate alternative to the usage of multiphase physical flow meters and production testing. Additionally, the study provides insights into the potential of data-driven methods for VFM in electrical submersible pumped wells, highlighting the effectiveness of the proposed approach. Overall, this study contributes to the field by introducing a new, data-driven method for accurately predicting multiphase flow rates in real time, thereby providing a valuable tool for monitoring and optimizing production processes in the oil and gas industry.

Application of Physical Model Test-Based Long Short-Term Memory Algorithm as a Virtual Sensor for Nitrogen Oxide Prediction in Diesel Engines

Application of Physical Model Test-Based Long Short-Term Memory Algorithm as a Virtual Sensor for Nitrogen Oxide Prediction in Diesel Engines

Thermal error compensation method of truss robot beam structure based on mechanism and data drive

As the main supporting component of the truss robot, the thermal deformation of the beam often has a great influence on the overall thermal error of the truss robot due to its large span. In order to improve the thermal error prediction accuracy of long-span truss robot, a thermal error prediction method based on multiple linear regression and long short-term memory network is proposed based on mechanism and data drive. Firstly, the multiple linear regression model is used to predict the thermal error, and the prediction error data processing. Secondly, the long short-term memory network is established. In order to improve the performance of the long short-term memory network more effectively, an improved particle swarm optimization algorithm is proposed to optimize the hyper-parameters of the long short-term memory network. Finally, the improved particle swarm optimization–long short-term memory network is used to correct the prediction error of the multiple linear regression model. The experimental results show that the combined thermal error prediction model based on multiple linear regression and improved particle swarm optimization–long short-term memory algorithm has higher prediction accuracy than multiple linear regression model and long short-term memory network. The method has stable prediction accuracy and can provide a basis for thermal error compensation.

Learning-Empowered Resource Allocation for Air Slicing in UAV-Assisted Cellular V2X Communications

In this article, we propose a resource allocation scheme for air slicing in unmanned aerial vehicle (UAV)-assisted cellular vehicle-to-everything (V2X) communications. We consider a scenario, where multiple flexible UAVs are deployed as the aerial base station (BS) to assist terrestrial BS for providing service to vehicular users with the objective of maximizing the bandwidth efficiency while concurrently guaranteeing the transmission rate and the latency by adopting network slicing. Due to the uncertainty of the stochastic environment in the scenario, we formulate the optimization problem to be a stochastic game, which is an extension of game theory to Markov decision process-like environments for the case of multiple adaptive agents are involved to compete goals simultaneously. Nevertheless, the dynamic nature of both UAVs and vehicles pose the difficulty of perceiving and interacting with the unknown environment, the long short-term memory algorithm is used for extracting the features of the observation and making forecast on the mobility of UAVs and vehicles. Simulation results adduce the validity of the proposed scheme as compared with two benchmark schemes: Deep Q-network and deep deterministic policy gradient.

Monitoring the respiratory behavior of multiple cows based on computer vision and deep learning

Automatic respiration monitoring of dairy cows in modern farming not only helps to reduce manual labor but also increases the automation of health assessment. It is common for cows to congregate on farms, which poses a challenge for manual observation of cow status because they physically occlude each other. In this study, we propose a method that can monitor the respiratory behavior of multiple cows. Initially, 4,000 manually labeled images were used to fine-tune the YOLACT (You Only Look At CoefficienTs) model for recognition and segmentation of multiple cows. Respiratory behavior in the resting state could better reflect their health status. Then, the specific resting states (lying resting, standing resting) of different cows were identified by fusing the convolutional neural network and bidirectional long and short-term memory algorithms. Finally, the corresponding detection algorithms (lying and standing resting) were used for respiratory behavior monitoring. The test results of 60 videos containing different interference factors indicated that the accuracy of respiratory behavior monitoring of multiple cows in 54 videos was >90.00%, and that of 4 videos was 100.00%. The average accuracy of the proposed method was 93.56%, and the mean absolute error and root mean square error were 3.42 and 3.74, respectively. Furthermore, the effectiveness of the method was analyzed for simultaneous monitoring of respiratory behavior of multiple cows under movement, occlusion disturbance, and behavioral changes. It was feasible to monitor the respiratory behavior of multiple cows based on the proposed algorithm. This study could provide an a priori technical basis for respiratory behavior monitoring and automatic diagnosis of respiratory-related diseases of multiple dairy cows based on biomedical engineering technology. In addition, it may stimulate researchers to develop robots with health-sensing functions that are oriented toward precision livestock farming.

A Fault Diagnosis Method under Data Imbalance Based on Generative Adversarial Network and Long Short-Term Memory Algorithms for Aircraft Hydraulic System

Safe and stable operation of the aircraft hydraulic system is of great significance to the flight safety of an aircraft. Any fault may be a threat to flight safety and may lead to enormous economic losses and even human casualties. Hence, the normal status of the aircraft hydraulic system is large, but very few data samples relate to the fault status. This causes a data imbalance in the fault diagnosis of the aircraft hydraulic system, which directly affects the accuracy of aircraft fault diagnosis. To solve the data imbalance problem in the fault diagnosis of the aircraft hydraulic system, this paper proposes an improved GAN-LSTM algorithm by using the improved GAN method, which can stably and accurately generate high-quality simulated fault samples using a small number of fault data. First, the model of the aircraft hydraulic system was built using AMESim software, and the imbalanced fault data and normal status data were acquired. Then, the imbalanced data were used to train the GAN model until the system reached a Nash equilibrium. By comparing the time domain and frequency signal, it was found that the quality of the generated sample was highly similar to the real sample. Moreover, LSTM (long short-term memory) and some other data-driven intelligent fault diagnosis methods were used as classifiers. The accuracy of these fault diagnosis methods increased steadily when the number of fault samples was gradually increased until it reached a balance with the normal sample. Meanwhile, three different sample generation methods were compared and analyzed to find the method with the best data generation ability. Finally, the anti-noise performance of the LSTM-GAN method was analyzed; this model has superior noise immunity.

Optimization of photovoltaic panel deployment in centralized photovoltaic power plant under multiple factors

Solar energy is one of the main renewable energy sources and has rapidly developed in many countries. However, the photovoltaic (PV) output power will be different under various meteorological and geographical conditions. Therefore, this paper presents an optimization method for the deployment of PV panels in a centralized PV power plant considering multiple factors. Firstly, the whole planning area is divided into a certain amount of sub-areas according to a given area, and fuzzy C-means algorithm is used for terrain clustering according to the geographical characteristics of the sub-areas. Secondly, the correlation analysis between each meteorological factor and PV output power is carried out separately to select the main factors affecting PV output power, and then the expected annual PV output power under the joint action of several main meteorological factors in each terrain is calculated by dual-stage attention mechanism based long short-term memory algorithm. Finally, according to the expected annual PV output of each terrain, considering the constraints including cost, area and so on, the deployment optimization of PV panels is obtained to maximize the annual PV output of the whole PV power plant and minimize the construction cost. The results of case studies show that the proposed methods effectively improve the expected PV output power of the PV power plant and reduce the construction cost.

Hybridized Deep Learning Approach for Detecting Alzheimer's Disease.

Alzheimer's disease (AD) is mainly a neurodegenerative sickness. The primary characteristics are neuronal atrophy, amyloid deposition, and cognitive, behavioral, and psychiatric disorders. Numerous machine learning (ML) algorithms have been investigated and applied to AD identification over the past decades, emphasizing the subtle prodromal stage of mild cognitive impairment (MCI) to assess critical features that distinguish the disease's early manifestation and instruction for early detection and treatment. Identifying early MCI (EMCI) remains challenging due to the difficulty in distinguishing patients with cognitive normality from those with MCI. As a result, most classification algorithms for these two groups perform poorly. This paper proposes a hybrid Deep Learning Approach for the early detection of Alzheimer's disease. A method for early AD detection using multimodal imaging and Convolutional Neural Network with the Long Short-term memory algorithm combines magnetic resonance imaging (MRI), positron emission tomography (PET), and standard neuropsychological test scores. The proposed methodology updates the learning weights, and Adam's optimization is used to increase accuracy. The system has an unparalleled accuracy of 98.5% in classifying cognitively normal controls from EMCI. These results imply that deep neural networks may be trained to automatically discover imaging biomarkers indicative of AD and use them to identify the illness accurately.

An intelligent scheduling control method for smart grid based on deep learning.

Nowadays, data analysis is been the most important means to realize power scheduling in smart grids. However, the sharp increase in business data of grids has posed great challenges for this purpose. To deal with such issue, this paper utilizes deep learning to discover hidden rules from massive large-scale big data and particle swarm optimization (PSO) algorithm for generation of control decision. Therefore, an intelligent scheduling control method for smart grid based on deep learning is proposed in this paper. By modeling the historical data of the power company, the long short-term memory algorithm can effectively extract the effective features and realize the prediction of the coal consumption of the unit under certain conditions. At the same time, a kind of intelligent power scheduling algorithm is designed by using PSO, so as to save energy and reduce emissions as much as possible while fulfilling the real-time power generation task. Experiments on a real-world smart grid dataset show that the proposal can achieve a relatively good performance with respect to intelligent scheduling.

Attenuation Compensation and Q Estimation of Nonstationary Data Using Semi-Supervised Learning

Traditional inverse Q filtering methods for post-stack seismic data attenuation compensation (AC) have the drawback of instability or under-compensation. Besides, the quality factor (Q) should be known as a prerequisite, which is commonly estimated using the attribute difference between the reference and observed wavelets of pre-stack VSP data. The alternating iterative AC and Q estimation method is also researched for post-stack data, while the instability or huge computation becomes a defect. In this letter, we propose a simultaneous AC and Q estimation method for nonstationary post-stack seismic data based on semi-supervised learning. Specifically, we choose the Long Short-Term Memory algorithm which is sensitive to time series and can characterize seismic signal nonlinearly with high accuracy. The proposed AC and Q estimation method employs the Q information from well-logs and the compensated high-resolution data for supervised learning, and uses nonstationary seismic data beyond wells for self-supervised learning, without the wavelet extraction procedure. The synthetic data analysis and field data applications prove the feasibility of the designed semi-supervised method in improving the vertical resolution and Q estimation. The field data impedance inversion after AC further demonstrates its effectiveness.

Deep Learning-Based Autonomous Excavation: A Bucket-Trajectory Planning Algorithm

The increased risk to the safety of excavator personnel and difficulty in training them, combined with a manpower shortage, have led to an increased demand for machine automation. This study applies a long short-term memory algorithm for automating a bucket-tip trajectory planning AI system. Unlike other autonomous excavation techniques, the proposed approach in this study performs the bucket-trajectory planning of the excavator without prior knowledge of nonlinear bucket–soil interaction dynamics during excavation, which requires precise adjustment of parameters with the heuristic analysis of correlation between them. Based on data acquisition from excavation of excavator experts, this method uses the three-dimensional point cloud of terrain and bucket motion data in excavation process for training and application of the AI. Especially, we transform the point cloud, which comprises massive number of points and increases the computation complexity, into the much smaller number of values, which are sufficient for representing the three-dimensional shape of target terrain. To ensure safety against collision with underground obstacles, a collision avoidance algorithm is applied to prevent crashes during excavations along the given path, based on continuous monitoring of the pressure in the excavator’s hydraulic cylinder. Comparative experiments reveal that the bucket-tip trajectory planning AI system with the collision avoidance algorithm generates a traceable trajectory for the machine controller, equipped in the excavator, and yields the desired excavation volume and lead time without collision, regardless of the topographic change caused through successive excavations.

Price Prediction with Bidirectional Long Short-Term Memory Algorithm for High-Value Commercial Crops

Price Prediction with Bidirectional Long Short-Term Memory Algorithm for High-Value Commercial Crops

Assessing the Performance of a Long Short-Term Memory Algorithm in the Dataset with Missing Values

This study was conducted to assess the performance of a long short-term memory algorithm (LSTM), which was suitable for time series prediction, in the multivariate dataset with missing values. The full dataset for the adopted LSTM model was prepared by running a popular watershed model Hydrological Simulation Program-Fortran (HSPF) in the upper Nam River Basin for 3 years from 2016 to 2018, excluding a one-year warm-up period, on a daily time step. The accuracy of prediction for the LSTM model was evaluated in response to various interpolation methods as well as changes in the number of missing values (for dependent variables) and independent variables (containing a fixed number of missing values for either single or multiple variables). Note that the entire dataset is divided into training and test datasets at a ratio of 7:3. Results showed that different interpolation methods resulted in a considerable variation in performance of the LSTM model. Out of them, StructTS and RPART were selected as the best imputation methods recovering missing values for discharge and total phosphorus, respectively. The prediction error of the LSTM model increased gradually with increasing the number of missing values from 300 to 700. The LSTM model, however, appeared to maintain its performance fairly well even in data sets with a large amount of missing values as long as adequate interpolation methods were adopted for each dependent variable. The performance of the LSTM model degraded further as the number of independent variables containing the fixed number of missing values increased from 1 to 7. We believe that the proposed methodology can be used not only to reconstruct missing values in a real-time monitoring dataset with excellent performance, but also to improve the accuracy of prediction for (time series) deep learning models.