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Articles 1 - 6 of 6
Full-Text Articles in Physical Sciences and Mathematics
Lstm Model For Trajectory Design Of Missile-Borne Bfsar Imaging Guidance, Bohao Li, Yunjie Wu
Lstm Model For Trajectory Design Of Missile-Borne Bfsar Imaging Guidance, Bohao Li, Yunjie Wu
Journal of System Simulation
Abstract: In the multi-platform path planning of multi-missile cooperative guidance algorithm used for missile-borne Bi-static Forward-looking Synthetic Aperture Radar (BFSAR) imaging, it is necessary to plan and adjust the transmitter trajectory according to the space state of the receiver and the path constraints between the receiver and the transmitter.An effective LSTM model is constructed to study and train the trajectory path generated by the existing algorithms, and the direct mapping relationship between the receiver space state, path constraint and the trajectory of the transmitter is obtained. The simulation results not only show that LSTM is feasible in multi-missile path …
An Lstm-Based Motion Recognition Method For Power Operation And Maintenance, Peizhen Liu, Yuxiang Jia, Shihong Xia
An Lstm-Based Motion Recognition Method For Power Operation And Maintenance, Peizhen Liu, Yuxiang Jia, Shihong Xia
Journal of System Simulation
Abstract: The security of power operation and maintenance has always been a subject of great social concern. In order to avoid serious consequences caused by the fault of staffs, a motion recognition method for power operation and maintenance jobs based on LSTM (Long Short-Term Memory) is proposed, which covers the whole process from data collection, data processing to motion classification and recognition, then it can recognise and supervise the behavior of staffs who are at work. In addition, a simulation experiment is conducted between the deep learning algorithm LSTM and the traditional machine learning algorithm KNN based on the newly …
Forecasting Building Energy Consumption With Deep Learning: A Sequence To Sequence Approach, Ljubisa Sehovac, Cornelius Nesen, Katarina Grolinger
Forecasting Building Energy Consumption With Deep Learning: A Sequence To Sequence Approach, Ljubisa Sehovac, Cornelius Nesen, Katarina Grolinger
Electrical and Computer Engineering Publications
Energy Consumption has been continuously increasing due to the rapid expansion of high-density cities, and growth in the industrial and commercial sectors. To reduce the negative impact on the environment and improve sustainability, it is crucial to efficiently manage energy consumption. Internet of Things (IoT) devices, including widely used smart meters, have created possibilities for energy monitoring as well as for sensor based energy forecasting. Machine learning algorithms commonly used for energy forecasting such as feedforward neural networks are not well-suited for interpreting the time dimensionality of a signal. Consequently, this paper uses Recurrent Neural Networks (RNN) to capture time …
Detecting Crispr Arrays Using Long-Short Term Memory Network, Shantanu Deshmukh
Detecting Crispr Arrays Using Long-Short Term Memory Network, Shantanu Deshmukh
Master's Projects
CRISPR (Clustered Regularly Interspaced Short Palindromic Repeat) is a se- quence found in the DNA sequence of an organism. It provides provides immunity to the organism. Recently, it was found that the CRISPR-based immunity mechanism can be manipulated to perform genome editing. The problem is, it is hard to know the specificity of this system and in turn, making it highly specific is difficult. More re- search is required to improve this CRISPR-based genome editing. Detecting CRISPR arrays in the DNA sequence is the first step towards this research. In this work, a CRISPR array detection pipeline, CRISPRLstm, is proposed. …
Improving Vix Futures Forecasts Using Machine Learning Methods, James Hosker, Slobodan Djurdjevic, Hieu Nguyen, Robert Slater
Improving Vix Futures Forecasts Using Machine Learning Methods, James Hosker, Slobodan Djurdjevic, Hieu Nguyen, Robert Slater
SMU Data Science Review
The problem of forecasting market volatility is a difficult task for most fund managers. Volatility forecasts are used for risk management, alpha (risk) trading, and the reduction of trading friction. Improving the forecasts of future market volatility assists fund managers in adding or reducing risk in their portfolios as well as in increasing hedges to protect their portfolios in anticipation of a market sell-off event. Our analysis compares three existing financial models that forecast future market volatility using the Chicago Board Options Exchange Volatility Index (VIX) to six machine/deep learning supervised regression methods. This analysis determines which models provide best …
Emotion Forecasting In Dyadic Conversation : Characterizing And Predicting Future Emotion With Audio-Visual Information Using Deep Learning, Sadat Shahriar
Emotion Forecasting In Dyadic Conversation : Characterizing And Predicting Future Emotion With Audio-Visual Information Using Deep Learning, Sadat Shahriar
Legacy Theses & Dissertations (2009 - 2024)
Emotion forecasting is the task of predicting the future emotion of a speaker, i.e., the emotion label of the future speaking turn–based on the speaker’s past and current audio-visual cues. Emotion forecasting systems require new problem formulations that differ from traditional emotion recognition systems. In this thesis, we first explore two types of forecasting windows(i.e., analysis windows for which the speaker’s emotion is being forecasted): utterance forecasting and time forecasting. Utterance forecasting is based on speaking turns and forecasts what the speaker’s emotion will be after one, two, or three speaking turns. Time forecasting forecasts what the speaker’s emotion will …