scholarly journals Public Environment Emotion Prediction Model Using LSTM Network

2020 ◽  
Vol 12 (4) ◽  
pp. 1665 ◽  
Author(s):  
Qiang Zhang ◽  
Tianze Gao ◽  
Xueyan Liu ◽  
Yun Zheng
Keyword(s):  
Optimization Algorithm ◽  
Short Term Memory ◽  
Time Series Prediction ◽  
Prediction Method ◽  
Atmospheric Environment ◽  
Public Attention ◽  
Neural Network Models ◽  
The Public ◽  
Whale Optimization ◽  
Public Environment

Public environmental sentiment has always played an important role in public social sentiment and has a certain degree of influence. Adopting a reasonable and effective public environmental sentiment prediction method for the government’s public attention in environmental management, promulgation of local policies, and hosting characteristics activities has important guiding significance. By using VAR (vector autoregressive), the public environmental sentiment level prediction is regarded as a time series prediction problem. This paper studies the development of a mobile “impression ecology” platform to collect time spans in five cities in Lanzhou for one year. In addition, a parameter optimization algorithm, WOA (Whale Optimization Algorithm), is introduced on the basis of the prediction method. It is expected to predict the public environmental sentiment more accurately while predicting the atmospheric environment. This paper compares the decision performance of LSTM (Long Short-Term Memory) and RNN (Recurrent Neural Network) models on the public environment emotional level through experiments, and uses a variety of error assessment methods to quantitatively analyze the prediction results, verifying the LSTM’s performance in prediction performance and level decision-making effectiveness and robustness.

State of health prediction based on multi-kernel relevance vector machine and whale optimization algorithm for lithium-ion battery

2021 ◽  
pp. 014233122110420
Author(s):  
Shuai Wang ◽  
Xiaochen Zhang ◽  
Wengxiang Chen ◽  
Wei Han ◽  
Shoubin Zhou ◽  
...  
Keyword(s):  
Lithium Ion Battery ◽  
Optimization Algorithm ◽  
Prediction Method ◽  
Lithium Ion ◽  
Relevance Vector Machine ◽  
Whale Optimization Algorithm ◽  
Data Sets ◽  
State Of Health ◽  
Whale Optimization ◽  
Maximal Relevance

The state of health (SOH) reflects the health status of the lithium-ion battery and is expected to accurately predicted, so as the corresponding maintenance measures can be taken to ensure the safe operation of the battery. This paper proposed a SOH prediction method based on multi-kernel relevance vector machine (RVM) and whale optimization algorithm (WOA). Firstly, the original features were obtained from the battery voltage and temperature data in charging and discharging phases. Secondly, the minimal-redundancy-maximal-relevance (mRMR) algorithm was introduced to select the optimal feature set. Then, the online model and offline model based on multi-kernel RVM and WOA were constructed. Finally, a hybrid model which combines the online model and offline model was proposed to prediction the SOH of the lithium-ion battery. The performance of the proposed method was evaluated with two kinds of data sets. The experimental results showed that the proposed method obtained higher prediction accuracy in both long-term and short-term periods than other methods.

scholarly journals TD-LSTM: Temporal Dependence-Based LSTM Networks for Marine Temperature Prediction

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3797 ◽  
Author(s):  
Jun Liu ◽  
Tong Zhang ◽  
Guangjie Han ◽  
Yu Gou
Keyword(s):  
Short Term Memory ◽  
Global Climate ◽  
Time Series Prediction ◽  
Prediction Method ◽  
Observation Data ◽  
Ocean Temperature ◽  
Temporal Dependence ◽  
Temperature Prediction ◽  
Argo Data ◽  
Matrix Fusion

Changes in ocean temperature over time have important implications for marine ecosystems and global climate change. Marine temperature changes with time and has the features of closeness, period, and trend. This paper analyzes the temporal dependence of marine temperature variation at multiple depths and proposes a new ocean-temperature time-series prediction method based on the temporal dependence parameter matrix fusion of historical observation data. The Temporal Dependence-Based Long Short-Term Memory (LSTM) Networks for Marine Temperature Prediction (TD-LSTM) proves better than other methods while predicting sea-surface temperature (SST) by using Argo data. The performances were good at various depths and different regions.

scholarly journals Seismic Events Prediction Using Deep Temporal Convolution Networks

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Yue Geng ◽  
Lingling Su ◽  
Yunhong Jia ◽  
Ce Han
Keyword(s):  
Time Series ◽  
Coal Mine ◽  
Short Term Memory ◽  
Time Series Prediction ◽  
Real Life ◽  
Prediction Method ◽  
Machine Learning Algorithms ◽  
Seismic Events ◽  
Prediction Task

Seismic events prediction is a crucial task for preventing coal mine rock burst hazards. Currently, this task attracts increasing research enthusiasms from many mining experts. Considering the temporal characteristics of monitoring data, seismic events prediction can be abstracted as a time series prediction task. This paper contributes to address the problem of long-term historical dependence on seismic time series prediction with deep temporal convolution neural networks (CNN). We propose a dilated causal temporal convolution network (DCTCNN) and a CNN long short-term memory hybrid model (CNN-LSTM) to forecast seismic events. In particular, DCTCNN is designed with dilated CNN kernels, causal strategy, and residual connections; CNN-LSTM is established in a hybrid modeling way by utilizing advantage of CNN and LSTM. Based on these manners, both of DCTCNN and CNN-LSTM can extract long-term historical features from the monitoring seismic data. The proposed models are experimentally tested on two real-life coal mine seismic datasets. Furthermore, they are also compared with one traditional time series prediction method, two classic machine learning algorithms, and two standard deep learning networks. Results show that DCTCNN and CNN-LSTM are superior than the other five algorithms, and they successfully complete the seismic prediction task.

Neural Network Techniques for Time Series Prediction: A Review

2019 ◽  
Vol 3 (3) ◽  
Author(s):  
Muhammad Faheem Mushtaq ◽  
Urooj Akram ◽  
Muhammad Aamir ◽  
Haseeb Ali ◽  
Muhammad Zulqarnain
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Time Series Data ◽  
Weather Prediction ◽  
Time Series Prediction ◽  
Series Data ◽  
Prediction Problem ◽  
Neural Network Models ◽  
Physical Time

It is important to predict a time series because many problems that are related to prediction such as health prediction problem, climate change prediction problem and weather prediction problem include a time component. To solve the time series prediction problem various techniques have been developed over many years to enhance the accuracy of forecasting. This paper presents a review of the prediction of physical time series applications using the neural network models. Neural Networks (NN) have appeared as an effective tool for forecasting of time series.  Moreover, to resolve the problems related to time series data, there is a need of network with single layer trainable weights that is Higher Order Neural Network (HONN) which can perform nonlinearity mapping of input-output. So, the developers are focusing on HONN that has been recently considered to develop the input representation spaces broadly. The HONN model has the ability of functional mapping which determined through some time series problems and it shows the more benefits as compared to conventional Artificial Neural Networks (ANN). The goal of this research is to present the reader awareness about HONN for physical time series prediction, to highlight some benefits and challenges using HONN.

Performance Evaluation of Fiber Wireless (FiWi) Access Network using Position Optimization of ONUs

2020 ◽  
Vol 10 ◽  
Author(s):  
Nitin Chouhan ◽  
Uma Rathore Bhatt ◽  
Raksha Upadhyay
Keyword(s):  
Power Consumption ◽  
Optimization Algorithm ◽  
Optical Network ◽  
Access Network ◽  
Whale Optimization Algorithm ◽  
Wireless Access ◽  
Delivery Ratio ◽  
Packet Delivery ◽  
Whale Optimization ◽  
Wireless Access Network

: Fiber Wireless Access Network is the blend of passive optical network and wireless access network. This network provides higher capacity, better flexibility, more stability and improved reliability to the users at lower cost. Network component (such as Optical Network Unit (ONU)) placement is one of the major research issues which affects the network design, performance and cost. Considering all these concerns, we implement customized Whale Optimization Algorithm (WOA) for ONU placement. Initially whale optimization algorithm is applied to get optimized position of ONUs, which is followed by reduction of number of ONUs in the network. Reduction of ONUs is done such that with fewer number of ONUs all routers present in the network can communicate. In order to ensure the performance of the network we compute the network parameters such as Packet Delivery Ratio (PDR), Total Time for Delivering the Packets in the Network (TTDPN) and percentage reduction in power consumption for the proposed algorithm. The performance of the proposed work is compared with existing algorithms (deterministic and centrally placed ONUs with predefined hops) and has been analyzed through extensive simulation. The result shows that the proposed algorithm is superior to the other algorithms in terms of minimum required ONUs and reduced power consumption in the network with almost same packet delivery ratio and total time for delivering the packets in the network. Therefore, present work is suitable for developing cost-effective FiWi network with maintained network performance.

scholarly journals A Survey of the Knowledge of Truffles among Polish Foresters and Implications for Environmental Education

Forests ◽  
2019 ◽  
Vol 10 (5) ◽  
pp. 365
Author(s):  
Dorota Hilszczańska ◽  
Aleksandra Rosa-Gruszecka ◽  
Bogusław Kosel ◽  
Jakub Horak ◽  
Marta Siebyła
Keyword(s):  
Environmental Education ◽  
Forest Ecology ◽  
Public Attention ◽  
Hypogeous Fungi ◽  
The Public ◽  
Age Cohorts ◽  
The Past ◽  
Ecology Education ◽  
And Gender ◽  
Tuber Spp

While the use of truffles in Poland has a long tradition, for historical reasons this knowledge was almost lost. Currently, truffles and truffle orchards are again receiving public attention. For example, the Polish State Forests supported the establishment of truffle orchards by the Forestry Research Institute. In recent years, knowledge concerning these unique hypogeous fungi has been disseminated systematically through scientific and popular publications, films, and electronic media. This study investigates the awareness of economically and culinary valued truffle fungi (Tuber spp.) among more than 1400 Polish foresters. The results show that 70% of interviewees were familiar with historical and contemporary information about growing and using truffles in Poland. Based on respondents’ age, education, type of work, and gender we attempted to identify whether these elements were associated with the state of knowledge about truffles. The results indicated that younger foresters were better informed about the presence of truffles in Poland and also about their use in the past in Polish cuisine. Environmental education was an important source of knowledge about truffle harvesting and the soils that are conducive to truffle development. Foresters who have provided forest ecology education and who are 36–65 years of age generally possessed better knowledge about truffles than other age cohorts. More than 30% of respondents expressed interest in educational courses to improve their knowledge of truffles. The results point to the need for forestry education concerning truffles and indicate the need for fostering sustainable agroforestry-centered initiatives disseminating this knowledge to the public.

scholarly journals Multi-Objective Whale Optimization Algorithm for Computation Offloading Optimization in Mobile Edge Computing

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2628
Author(s):  
Mengxing Huang ◽  
Qianhao Zhai ◽  
Yinjie Chen ◽  
Siling Feng ◽  
Feng Shu
Keyword(s):  
Optimization Algorithm ◽  
Edge Computing ◽  
Solution Set ◽  
Whale Optimization Algorithm ◽  
Computation Offloading ◽  
Mobile Edge Computing ◽  
Multi Objective ◽  
Whale Optimization ◽  
Artificial Immune System Algorithm

Computation offloading is one of the most important problems in edge computing. Devices can transmit computation tasks to servers to be executed through computation offloading. However, not all the computation tasks can be offloaded to servers with the limitation of network conditions. Therefore, it is very important to decide quickly how many tasks should be executed on servers and how many should be executed locally. Only computation tasks that are properly offloaded can improve the Quality of Service (QoS). Some existing methods only focus on a single objection, and of the others some have high computational complexity. There still have no method that could balance the targets and complexity for universal application. In this study, a Multi-Objective Whale Optimization Algorithm (MOWOA) based on time and energy consumption is proposed to solve the optimal offloading mechanism of computation offloading in mobile edge computing. It is the first time that MOWOA has been applied in this area. For improving the quality of the solution set, crowding degrees are introduced and all solutions are sorted by crowding degrees. Additionally, an improved MOWOA (MOWOA2) by using the gravity reference point method is proposed to obtain better diversity of the solution set. Compared with some typical approaches, such as the Grid-Based Evolutionary Algorithm (GrEA), Cluster-Gradient-based Artificial Immune System Algorithm (CGbAIS), Non-dominated Sorting Genetic Algorithm III (NSGA-III), etc., the MOWOA2 performs better in terms of the quality of the final solutions.

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