As an emerging application of biomimetic intelligence, attention mechanism in deep learning has been actively studied for intelligent monitoring. The present paper proposes an attention-based recurrent neural network ...
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As an emerging application of biomimetic intelligence, attention mechanism in deep learning has been actively studied for intelligent monitoring. The present paper proposes an attention-based recurrent neural network that can make multi-step prediction of the target parameters using historical multivariate sensory time series. Sequence-to-sequence learning is achieved through a developed rnn encoding-decoding architecture, which embeds a multi-layer attention mechanism. The attention layers incorporate inter-site correlations over spatially distributed observation sites, parameter-wise dependencies among heterogeneous parameters, and temporal correlations over time variation. The analysis of the developed methodology is demonstrated using real-world data collected by an air quality monitoring network. The experimental results show that the proposed deep neural network model can provide superior prediction performance compared to the state-of-the-art baseline models.
This work utilizes the potential of NLP and machine learning for the challenging task of human-machine communication. A task of robot movement is selected as the context of the research work where the goal is to creat...
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ISBN:
(纸本)9781538652541
This work utilizes the potential of NLP and machine learning for the challenging task of human-machine communication. A task of robot movement is selected as the context of the research work where the goal is to create a software system that receives natural language input movement command from human and produces the set of precise trajectory information for the robot to perform. The proposed system consists of Pre-processing function, Command classification, Parameter classification, Post-processing function where rnn encoder-decoder is used for the implementation of the classification process. The system was trained using a dataset of 1,600 unique entries. The experiment results show that the average accuracy in case of single movement command is 79.23% whereas the average accuracy in case of multiple command in one sentence is 73.65%
Surface Mounted Device (SMD) assembly machine manufactures various products on a flexible manufacturing line. An anomaly detection model that can adapt to the various manufacturing environments very fast is required. ...
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Surface Mounted Device (SMD) assembly machine manufactures various products on a flexible manufacturing line. An anomaly detection model that can adapt to the various manufacturing environments very fast is required. In this paper, we proposed a fast adaptive anomaly detection model based on a Recurrent Neural Network (rnn) encoder-decoder with operating machine sounds. rnn encoder-decoder has a structure very similar to Auto-encoder (AE), but the former has significantly reduced parameters compared to the latter because of its rolled structure. Thus, the rnn encoder-decoder only requires a short training process for fast adaptation. The anomaly detection model decides abnormality based on Euclidean distance between generated sequences and observed sequence from machine sounds. Experimental evaluation was conducted on a set of dataset from the SMD assembly machine. Results showed cutting-edge performance with fast adaptation.
The number of natural language queries submitted to search engines is increasing as search environments get diversified. However, legacy search engines are still optimized for short keyword queries. Thus, the use of n...
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ISBN:
(纸本)9781450350228
The number of natural language queries submitted to search engines is increasing as search environments get diversified. However, legacy search engines are still optimized for short keyword queries. Thus, the use of natural language queries at legacy search engines degrades the retrieval performance of the engines. This paper proposes a novel method to translate a natural language query into a keyword query relevant to the natural language query for retrieving better search results without change of the engines. The proposed method formulates the translation as a generation task. That is, the method generates a keyword query from a natural language query by preserving the semantics of the natural language query. A recurrent neural network encoder-decoder architecture is adopted as a generator of keyword queries from natural language queries. In addition, an attention mechanism is also used to cope with long natural language queries.
The aftermath of the COVID-19 pandemic has led to a global surge in inflation rates across the world, eroding consumer purchasing power and sparking concerns of potential recession. A critical gauge of inflation is th...
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In order to tackle the problem of abstractive summarization of long multi-sentence texts, it is critical to construct an efficient model, which can learn and represent multiple compositionalities better. In this paper...
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In order to tackle the problem of abstractive summarization of long multi-sentence texts, it is critical to construct an efficient model, which can learn and represent multiple compositionalities better. In this paper, we introduce a temporal hierarchical pointer generator network that can represent multiple compositionalities in order to handle longer sequences of texts with a deep structure. We demonstrate how a multilayer gated recurrent neural network organizes itself with the help of an adaptive timescale in order to represent the compositions. The temporal hierarchical network is implemented with a multiple timescale architecture where the timescale of each layer is also learned during the training process through error backpropagation through time. We evaluate our proposed model using an Introduction-Abstract summarization dataset from scientific articles and the CNN/Daily Mail summarization benchmark dataset. The results illustrate that, we successfully implement a summary generation system for long texts by using the multiple timescale with adaptation concept. We also show that we have improved the summary generation system with our proposed model on the benchmark dataset. (C) 2019 Elsevier Ltd. All rights reserved.
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