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Full-Text Articles in Engineering
Artificial Intelligence Method For The Forecast And Separation Of Total And Hvac Loads With Application To Energy Management Of Smart And Nze Homes, Rosemary E. Alden, Huangjie Gong, Evan S. Jones, Cristinel Ababei, Dan M. Ionel
Artificial Intelligence Method For The Forecast And Separation Of Total And Hvac Loads With Application To Energy Management Of Smart And Nze Homes, Rosemary E. Alden, Huangjie Gong, Evan S. Jones, Cristinel Ababei, Dan M. Ionel
Electrical and Computer Engineering Faculty Publications
Separating the HVAC energy use from the total residential load can be used to improve energy usage monitoring and to enhance the house energy management systems (HEMS) for existing houses that do not have dedicated HVAC circuits. In this paper, a novel method is proposed to separate the HVAC dominant load component from the house load. The proposed method utilizes deep learning techniques and the physical relationship between HVAC energy use and weather. It employs novel long short-term memory (LSTM) encoder-decoder machine learning (ML) models, which are developed based on future weather data input in place of weather forecasts. In …
A Fast Full-Search Adaptive Vector Quantizer For Video Coding, Scott E. Budge, Christian B. Peel
A Fast Full-Search Adaptive Vector Quantizer For Video Coding, Scott E. Budge, Christian B. Peel
Electrical and Computer Engineering Faculty Publications
This paper presents a novel VQ structure which provides very good quality encoding for video sequences and exploits the computational savings gained from a fast-search algorithm. It uses an adaptive-search, variable-length encoding method which allows for very fast matching of a wide range of transmission rates. Both the encoding quality and the computational benefits from the fast-search algorithm are presented. Simulations show that full-search tree residual VQ (FTRVQ) can provide up to 3 dB improvement over a similar RVQ encoder on video sequences.
Adaptive-Rate Tree-Structured Residual Vector Quantization, Christian B. Peel, X. Liu, Scott E. Budge
Adaptive-Rate Tree-Structured Residual Vector Quantization, Christian B. Peel, X. Liu, Scott E. Budge
Electrical and Computer Engineering Faculty Publications
Full-search vector quantization (VQ) provides optimal results only with high memory and computational cost. We describe the computational and memory requirements of tree-structured VQ, residual VQ (RVQ), and tree-structured RVQ. We present multiple-rate, adaptive-search implementations of these VQ structures, and simulation results with video sequences. Tree-structured RVQ provides up to 1.5 db PSNR quality improvements over RVQ, as well as significant perceptual improvement. These algorithms maintain many of the benefits of full-search VQ, while providing trade-offs between computational, storage, and performance requirements.