scholarly journals Feasibility of Using Electrical Impedance Spectroscopy for Assessing Biological Cell Damage during Freezing and Thawing

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4129
Author(s):  
Sisay Mebre Abie ◽  
Ørjan Grøttem Martinsen ◽  
Bjørg Egelandsdal ◽  
Jie Hou ◽  
Frøydis Bjerke ◽  
...  
Keyword(s):  
Phase Angle ◽  
Short Term Memory ◽  
Cell Damage ◽  
Electrical Impedance Spectroscopy ◽  
Electric Impedance ◽  
Freezing And Thawing ◽  
Short Term ◽  
Long Short Term Memory ◽  
Impedance Parameters ◽  
Deep Learning Model

This study was performed to test bioimpedance as a tool to detect the effect of different thawing methods on meat quality to aid in the eventual creation of an electric impedance-based food quality monitoring system. The electric impedance was measured for fresh pork, thawed pork, and during quick and slow thawing. A clear difference was observed between fresh and thawed samples for both impedance parameters. Impedance was different between the fresh and the frozen-thawed samples, but there were no impedance differences between frozen-thawed samples and the ones that were frozen-thawed and then stored at +3 °C for an additional 16 h after thawing. The phase angle was also different between fresh and the frozen-thawed samples. At high frequency, there were small, but clear phase angle differences between frozen-thawed samples and the samples that were frozen-thawed and subsequently stored for more than 16 h at +3 °C. Furthermore, the deep learning model LSTM-RNN (long short-term memory recurrent neural network) was found to be a promising way to classify the different methods of thawing.

scholarly journals Application of Rough and Fuzzy Set Theory for Prediction of Stochastic Wind Speed Data Using Long Short-Term Memory

Atmosphere ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 924
Author(s):  
Moslem Imani ◽  
Hoda Fakour ◽  
Wen-Hau Lan ◽  
Huan-Chin Kao ◽  
Chi Ming Lee ◽  
...  
Keyword(s):  
Deep Learning ◽  
Wind Speed ◽  
Set Theory ◽  
Rough Set ◽  
Fuzzy Set Theory ◽  
Short Term Memory ◽  
Learning Model ◽  
Short Term ◽  
Long Short Term Memory ◽  
Deep Learning Model

Despite the great significance of precisely forecasting the wind speed for development of the new and clean energy technology and stable grid operators, the stochasticity of wind speed makes the prediction a complex and challenging task. For improving the security and economic performance of power grids, accurate short-term wind power forecasting is crucial. In this paper, a deep learning model (Long Short-term Memory (LSTM)) has been proposed for wind speed prediction. Knowing that wind speed time series is nonlinear stochastic, the mutual information (MI) approach was used to find the best subset from the data by maximizing the joint MI between subset and target output. To enhance the accuracy and reduce input characteristics and data uncertainties, rough set and interval type-2 fuzzy set theory are combined in the proposed deep learning model. Wind speed data from an international airport station in the southern coast of Iran Bandar-Abbas City was used as the original input dataset for the optimized deep learning model. Based on the statistical results, the rough set LSTM (RST-LSTM) model showed better prediction accuracy than fuzzy and original LSTM, as well as traditional neural networks, with the lowest error for training and testing datasets in different time horizons. The suggested model can support the optimization of the control approach and the smooth procedure of power system. The results confirm the superior capabilities of deep learning techniques for wind speed forecasting, which could also inspire new applications in meteorology assessment.

Pressure prediction and abnormal working conditions detection of water supply network based on LSTM

2020 ◽  
Vol 20 (3) ◽  
pp. 963-974 ◽  
Author(s):  
Zhe Xu ◽  
Zhihao Ying ◽  
Yuquan Li ◽  
Bishi He ◽  
Yun Chen
Keyword(s):  
Water Supply ◽  
Short Term Memory ◽  
Learning Performance ◽  
Support Vector ◽  
Supply Network ◽  
Short Term ◽  
Water Supply Network ◽  
Vector Machines ◽  
Long Short Term Memory ◽  
Deep Learning Model

Abstract In this study, a deep learning model based on LSTM (Long Short-Term Memory) is used to predict the state of a water supply network due to its highly complex nonlinearity. The inputs of the model include state information on the pressures at measuring points, as well as control information on the water supply pressure and flow at each entry point. In order to enhance the performance of the model in feature extraction and identification and improve prediction accuracy, a parallel LSTM tandem DNN deep neural network model (PLDNN) is proposed. The experimental results indicate that the model has better learning performance and accuracy compared with traditional prediction methods (artificial neural networks, support vector machines, etc.) and general LSTM models.

Deep Learning Model for Forecasting Financial Sales Based on Long Short-Term Memory Networks

2019 ◽  
pp. 498-506
Author(s):  
Pablo F. Ordoñez-Ordoñez ◽  
Martha C. Suntaxi Sarango ◽  
Cristian Narváez ◽  
Maria del Cisne Ruilova Sánchez ◽  
Mario Enrique Cueva-Hurtado
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Learning Model ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Deep Learning Model

scholarly journals Optimization of Air Traffic Management Efficiency Based on Deep Learning Enriched by the Long Short-term Memory(LSTM) and Extreme Learning Machine(ELM)

2021 ◽  
Author(s):  
Mahdi Yousefzadeh Aghdam ◽  
Seyed Reza Kamel ◽  
Seyed Javad Mahdavi Chabok ◽  
maryam khairabadi
Keyword(s):  
Deep Learning ◽  
Traffic Management ◽  
Short Term Memory ◽  
Air Traffic Management ◽  
Air Traffic ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Learning Machine ◽  
Deep Learning Model

Abstract Air traffic management refers to the activities required for the efficient and safe management of the national air system (NAS) for each country. This concept has been widely assessed due to its complexity and sensitivity for the beneficiaries, including passengers, airlines, regulatory agencies, and other organizations. To date, various methods (e.g., statistical and fuzzy techniques) and data mining algorithms (e.g., neural network) have been used to solve the issues of air traffic management (ATM) and delay the minimization problems. However, each of these techniques has some disadvantages, such as overlooking the data, computational complexities, and uncertainty. The present study aimed to increase ATM efficiency using the deep learning approach. The main research objective was to propose a deep learning model with the application of a long short-term memory-based deep learning model in order to increase the predictive accuracy in short daily and long-term annual windows by enhancing deep learning (two-dimensional). In addition, the deep model output was transferred to the extreme learning machine fast learning deep neural machine in order to calculate the estimated time of arrival real-time based on other similar input data, including the NAS data, bureau of transportation statistics system, and automatic dependent surveillance-broadcast system. The final results indicated the increased accuracy of ATM compared to other studies.

Regularized Deep LSTM Autoencoder for Phonological Deviation Assessment

2020 ◽  
pp. 2152002
Author(s):  
Sheena Christabel Pravin ◽  
M. Palanivelan
Keyword(s):  
Short Term Memory ◽  
Drop Out ◽  
Severity Assessment ◽  
Short Term ◽  
Term Memory ◽  
Proposed Model ◽  
Phonological Features ◽  
Decoder Architecture ◽  
Long Short Term Memory ◽  
Deep Learning Model

In this paper, the Deep Long-short term memory Autoencoder (DLAE), a regularized deep learning model, is proposed for the automatic severity assessment of phonological deviations which are crucial stuttering markers in children. This automatic noninvasive severity assessment plays a paramount role in prevenient diagnosis, progress inference, and post-care for the patients with specific speech disorder. The proposed model is an implementation of a multi-layered Autoencoder in the Encoder–Decoder architecture of the Long-Short Term Memory (LSTM) model with hierarchically appended hidden layers and hidden units. The DLAE has definite advantage over the baseline Autoencoders. During the training phase, the proposed DLAE reconstructs the phonological features in an unsupervised fashion and the latent bottleneck features are extracted from the Encoder. The trained and regularized DLAE model with drop out is then used to predict the severity of the phonological deviation with high precision and classification accuracy compared to the baseline models.

Sign in / Sign up

Export Citation Format

Share Document