Semantic Sequence Labeling Model of Power Dispatching Based on Deep Long Short Term Memory Network

2021 ◽  
Author(s):  
Hu Feifei ◽  
Zeng Shibo ◽  
Hong Danke ◽  
Zhang Situo ◽  
Song yongwei ◽  
...  
Keyword(s):  
Decision Making ◽  
Short Term Memory ◽  
Mechanism Analysis ◽  
Short Term ◽  
Term Memory ◽  
Control Center ◽  
Fine Grained ◽  
Sequence Labeling ◽  
Long Short Term Memory ◽  
Power Dispatching

As the decision-making brain for power system operation, grid regulation and operation is a comprehensive decision-making control that combines a large amount of data, mechanism analysis, operating procedures and professional experience, and a new generation of artificial intelligence development ideas and evolution characterized by data-driven and knowledge-guided. The directions are very close. However, the current scheduling control is still based on experience and manual analysis. The massive and diverse data of the control center and the lack of logical models between the plans require a large amount of experience and knowledge associations by the control personnel. There are more repetitive human brain labor and relatively low intelligence. Therefore, deep learning is applied to the learning of power control knowledge, and a semantic understanding network based on deep Long Short Term Memory is proposed. It uses sequence labeling to extract in-depth semantic related information of different keywords and query questions, and finds key information about language problems in order to achieve fine-grained and precise query. Experiments show that the proposed network model is superior to the previous methods, and it achieves better performance in the joint extraction of fine-grained evaluation words and evaluation objects, extracts the key information and deep semantic information of query problems and corresponding cases, and realizes power scheduling based on voice interaction The model can be effectively applied in the field of power dispatching and solve a large number of problems in power dispatching and control.

scholarly journals Ekstraksi Event Berbasis Paragraf dari Artikel Berita Bahasa Indonesia

2018 ◽  
Author(s):  
Masayu Leylia Khodra ◽  
Yudi Wibisono
Keyword(s):  
Random Fields ◽  
Conditional Random Fields ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Sequence Labeling ◽  
Long Short Term Memory ◽  
Bahasa Indonesia

Dengan banyaknya artikel berita online yang terbit setiap saat, sistem ekstraksi event dapat membantu pembaca berita dengan memberikan informasi terstruktur dari setiap artikel berita. Ekstraksi event dari artikel berita merupakan proses mendapatkan informasi terstruktur 5W1H yaitu siapa (who) melakukan apa (what), kapan (when), dimana (where), mengapa (why), dan bagaimana (how). Ekstraksi 5W1H ini merupakan salah satu jenis ekstraksi informasi. Model ekstraksi 5W1H dibangun dengan pendekatan berbasis sequence labeling berbasis skema BIO (Begin Inside Outside). Karena setiap paragraf berisi satu pokok pikiran, idealnya satu instans frame 5W1H dihasilkan dari satu paragraf, dan satu artikel berita direpresentasikan dengan sejumlah instans frame 5W1H. Oleh karena itu, makalah ini membahas pembangunan model ekstraksi event 5W1H berbasis paragraf. Pemodelan dilakukan dengan menggunakan korpus 610 teks paragraf yang diambil dari 57 artikel berita yang telah dianotasi secara manual dengan informasi 5W1H. Pemodelan memanfaatkan arsitektur bidirectional LSTMs (long short term memory) dan CRF (conditional random fields). Pada tahap evaluasi, kinerja model yang dicapai adalah F1 0.62

scholarly journals Castration-Resistant Prostate Cancer Outcome Prediction Using Phased Long Short-Term Memory with Irregularly Sampled Serial Data

2020 ◽  
Vol 10 (6) ◽  
pp. 2000 ◽  
Author(s):  
Jihwan Park ◽  
Mi Jung Rho ◽  
Hyong Woo Moon ◽  
Ji Youl Lee
Keyword(s):  
Prostate Cancer ◽  
Decision Making ◽  
Outcome Prediction ◽  
Short Term Memory ◽  
Castration Resistant Prostate Cancer ◽  
Short Term ◽  
Term Memory ◽  
Castration Resistant ◽  
Serial Data ◽  
Long Short Term Memory

It is particularly desirable to predict castration-resistant prostate cancer (CRPC) in prostate cancer (PCa) patients, and this study aims to predict patients’ likely outcomes to support physicians’ decision-making. Serial data is collected from 1592 PCa patients, and a phased long short-term memory (phased-LSTM) model with a special module called a “time-gate” is used to process the irregularly sampled data sets. A synthetic minority oversampling technique is used to overcome the data imbalance between two patient groups: those with and without CRPC treatment. The phased-LSTM model is able to predict the CRPC outcome with an accuracy of 88.6% (precision-recall: 91.6%) using 120 days of data or 94.8% (precision-recall: 96.9%) using 360 days of data. The validation loss converged slowly with 120 days of data and quickly with 360 days of data. In both cases, the prediction model takes four epochs to build. The overall CPRC outcome prediction model using irregularly sampled serial medical data is accurate and can be used to support physicians’ decision-making, which saves time compared to cumbersome serial data reviews. This study can be extended to realize clinically meaningful prediction models.

An Approach to NMT Re-Ranking Using Sequence-Labeling for Grammatical Error Correction

2020 ◽  
Vol 24 (4) ◽  
pp. 557-567
Author(s):  
Bo Wang ◽  
◽  
Kaoru Hirota ◽  
Chang Liu ◽  
Yaping Dai ◽  
...  
Keyword(s):  
Error Correction ◽  
Short Term Memory ◽  
Conditional Random Field ◽  
Short Term ◽  
Neural Machine Translation ◽  
Term Memory ◽  
Sequence Labeling ◽  
Grammatical Error ◽  
Small Set ◽  
Long Short Term Memory

An approach to N-best hypotheses re-ranking using a sequence-labeling model is applied to resolve the data deficiency problem in Grammatical Error Correction (GEC). Multiple candidate sentences are generated using a Neural Machine Translation (NMT) model; thereafter, these sentences are re-ranked via a stacked Transformer following a Bidirectional Long Short-Term Memory (BiLSTM) with Conditional Random Field (CRF). Correlations within the sentences are extracted using the sequence-labeling model based on the Transformer, which is particularly suitable for long sentences. Meanwhile, the knowledge from a large amount of unlabeled data is acquired through the pre-trained structure. Thus, completely revised sentences are adopted instead of partially modified sentences. Compared with conventional NMT, experiments on the NUCLE and FCE datasets demonstrate that the model improves the F0.5 score by 8.22% and 2.09%, respectively. As an advantage, the proposed re-ranking method has the advantage of only requires a small set of easily computed features that do not need linguistic inputs.

scholarly journals Primary user characterization for cognitive radio wireless networks using long short-term memory

2018 ◽  
Vol 14 (11) ◽  
pp. 155014771881182
Author(s):  
Johana Hernández ◽  
Danilo López ◽  
Nelson Vera
Keyword(s):  
Decision Making ◽  
Cognitive Radio ◽  
Short Term Memory ◽  
Fuzzy Inference ◽  
Primary User ◽  
Short Term ◽  
Neural Structure ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Inference Systems

Cognitive radio is a paradigm that proposes managing the radio electric spectrum dynamically by integrating the spectrum sensing, decision-making, sharing, and mobility stages. In the decision-making stage, the best available channel is selected for transmitting secondary user data in an opportunistic fashion, and the success of that stage depends on the efficiency of the primary user characterization model. Use of the long short-term memory technique based on the deep learning concept is proposed in order to reduce the forecasting error present in the future estimation of primary users in the GSM and WiFi frequency bands. The results show that long short-term memory has the capacity needed to improve channel use forecasting significantly more than other methods such as multilayer perceptron neural networks, Bayesian networks, and adaptive neuro-fuzzy inference systems (ANFIS-Grid). It is concluded that although long short-term memory exhibits better performance generating forecasts for time series, computing complexity is higher due to the existence of input, forget, and output gates within the neural structure; therefore, implementation is feasible in cognitive radio networks based on centralized network topologies.

Improving Fine-Grained Opinion Mining Approach with a Deep Constituency Tree-Long Short Term Memory Network and Word Embedding

2020 ◽  
Vol 13 ◽  
Author(s):  
Dalila Bouras ◽  
Mohamed Amroune ◽  
Hakim Bendjenna ◽  
Issam Bendib
Keyword(s):  
Opinion Mining ◽  
Short Term Memory ◽  
State Of The Art ◽  
Syntactic Structure ◽  
Product Reviews ◽  
Short Term ◽  
Term Memory ◽  
Fine Grained ◽  
Wide Range ◽  
Long Short Term Memory

Objective: One key task of fine-grained opinion mining on product review is to extract product aspects and their corresponding opinion expressed by users. Previous work has demonstrated that precise modeling of opinion targets within the surrounding context can improve performances. However, how to effectively and efficiently learn hidden word semantics and better represent targets and the context still needs to be further studied. Recent years have seen a revival of the long short-term memory (LSTM), with its effectiveness being demonstrated on a wide range of problems. However, LSTM based approaches are still limited to linear data processing since it processes the information sequentially. As a result, they may perform poorly on user-generated texts, such as product reviews, tweets, etc., whose syntactic structure is not precise.To tackle this challenge, <P> Methods: In this research paper, we propose a constituency tree long short term memory neural network-based approach. We compare our model with state-of-the-art baselines on SemEval 2014 datasets. <P> Results: Experiment results show that our models obtain competitive performances compared to various supervised LSTM architectures. <P> Conclusion: Our work contributes to the improvement of state-of-the-art aspect-level opinion mining methods and offers a new approach to support human decision-making process based on opinion mining results.

scholarly journals Long short-term memory stacking model to predict the number of cases and deaths caused by COVID-19

2021 ◽  
pp. 1-14
Author(s):  
Filipe Fernandes ◽  
Stéfano Frizzo Stefenon ◽  
Laio Oriel Seman ◽  
Ademir Nied ◽  
Fernanda Cristina Silva Ferreira ◽  
...  
Keyword(s):  
Decision Making ◽  
Time Series ◽  
Large Volume ◽  
High Efficiency ◽  
Short Term Memory ◽  
Control Measures ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

The long short-term memory (LSTM) is a high-efficiency model for forecasting time series, for being able to deal with a large volume of data from a time series with nonlinearities. As a case study, the stacked LSTM will be used to forecast the growth of the pandemic of COVID-19, based on the increase in the number of contaminated and deaths in the State of Santa Catarina, Brazil. COVID-19 has been spreading very quickly, causing great concern in relation to the ability to care for critically ill patients. Control measures are being imposed by governments with the aim of reducing the contamination and the spreading of viruses. The forecast of the number of contaminated and deaths caused by COVID-19 can help decision making regarding the adopted restrictions, making them more or less rigid depending on the pandemic’s control capacity. The use of LSTM stacking shows an R2 of 0.9625 for confirmed cases and 0.9656 for confirmed deaths caused by COVID-19, being superior to the combinations among other evaluated models.

Incorporating Financial News for Forecasting Bitcoin Prices Based on Long Short-Term Memory Networks

2020 ◽  
Author(s):  
Abdolreza Nazemi ◽  
Johannes Jakubik ◽  
Andreas Geyer-Schulz ◽  
Frank J. Fabozzi
Keyword(s):  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Financial News ◽  
Long Short Term Memory
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