scholarly journals Research on the Construction of Intelligent Community Emergency Service Platform Based on Convolutional Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Yu Chen ◽  
Zhong Tang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Emergency Service ◽  
Question Answering ◽  
Health Management ◽  
Health And Safety ◽  
Semantic Matching ◽  
Service Platform ◽  
Logistics Network ◽  
Intelligent Community

Aiming at the shortcomings of the existing community emergency service platform, such as single function, poor scalability, and strong subjectivity, an intelligent community emergency service platform based on convolutional neural network was constructed. Firstly, the requirements analysis of the emergency service platform was carried out, and the functional demand of the emergency service platform was analyzed from the aspects of community environment, safety, infrastructure, health management, emergency response, and so on. Secondly, through logistics network, big data, cloud computing, artificial intelligence, and all kinds of applications, the intelligent community emergency service platform was designed. Finally, a semantic matching emergency question answering system based on convolutional neural network was developed to provide key technical support for the emergency preparation stage of intelligent community. The results show that the intelligent community emergency service platform plays an important role in preventing community emergency events and taking active and effective measures to ensure the health and safety of community residents.

Pencarian Question-Answer Menggunakan Convolutional Neural Network Pada Topik Agama Berbahasa Indonesia

2018 ◽  
Vol 10 (1) ◽  
pp. 57-64 ◽  
Author(s):  
Rizqa Raaiqa Bintana ◽  
Chastine Fatichah ◽  
Diana Purwitasari
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Question Answering ◽  
Mean Average Precision ◽  
Average Precision ◽  
Community Based ◽  
Network Methods ◽  
The Mean ◽  
Index Terms ◽  
Search Information

Community-based question answering (CQA) is formed to help people who search information that they need through a community. One condition that may occurs in CQA is when people cannot obtain the information that they need, thus they will post a new question. This condition can cause CQA archive increased because of duplicated questions. Therefore, it becomes important problems to find semantically similar questions from CQA archive towards a new question. In this study, we use convolutional neural network methods for semantic modeling of sentence to obtain words that they represent the content of documents and new question. The result for the process of finding the same question semantically to a new question (query) from the question-answer documents archive using the convolutional neural network method, obtained the mean average precision value is 0,422. Whereas by using vector space model, as a comparison, obtained mean average precision value is 0,282. Index Terms—community-based question answering, convolutional neural network, question retrieval

scholarly journals Convolutional recurrent neural network with template based representation for complex question answering

2020 ◽  
Vol 10 (3) ◽  
pp. 2710
Author(s):  
A. Chandra Obula Reddy ◽  
K. Madhavi
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Question Answering ◽  
Internal Representation ◽  
Semantic Matching ◽  
Softmax Classifier ◽  
Questions And Answers ◽  
Fully Connected ◽  
The Given ◽  
Complex Question

Complex Question answering system is developed to answer different types of questions accurately. Initially the question from the natural language is transformed to an internal representation which captures the semantics and intent of the question. In the proposed work, internal representation is provided with templates instead of using synonyms or keywords. Then for each internal representation, it is mapped to relevant query against the knowledge base. In present work, the Template representation based Convolutional Recurrent Neural Network (T-CRNN) is proposed for selecting answer in Complex Question Answering (CQA) framework. Recurrent neural network is used to obtain the exact correlation between answers and questions and the semantic matching among the collection of answers. Initially, the process of learning is accomplished through Convolutional Neural Network (CNN) which represents the questions and answers separately. Then the representation with fixed length is produced for each question with the help of fully connected neural network. In order to design the semantic matching between the answers, the representation of Question Answer (QA) pair is given into the Recurrent Neural Network (RNN). Finally, for the given question, the correctly correlated answers are identified with the softmax classifier.

scholarly journals Fault Diagnosis for Rotating Machinery Based on Convolutional Neural Network and Empirical Mode Decomposition

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Yuan Xie ◽  
Tao Zhang
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Empirical Mode Decomposition ◽  
Spatial Information ◽  
Health Management ◽  
Rotating Machinery ◽  
Intrinsic Mode Functions ◽  
Vibration Signals ◽  
Mode Decomposition

The analysis of vibration signals has been a very important technique for fault diagnosis and health management of rotating machinery. Classic fault diagnosis methods are mainly based on traditional signal features such as mean value, standard derivation, and kurtosis. Signals still contain abundant information which we did not fully take advantage of. In this paper, a new approach is proposed for rotating machinery fault diagnosis with feature extraction algorithm based on empirical mode decomposition (EMD) and convolutional neural network (CNN) techniques. The fundamental purpose of our newly proposed approach is to extract distinguishing features. Frequency spectrum of the signal obtained through fast Fourier transform process is trained in a designed CNN structure to extract compressed features with spatial information. To solve the nonstationary characteristic, we also apply EMD technique to the original vibration signals. EMD energy entropy is calculated using the first few intrinsic mode functions (IMFs) which contain more energy. With features extracted from both methods combined, classification models are trained for diagnosis. We carried out experiments with vibration data of 52 different categories under different machine conditions to test the validity of the approach, and the results indicate it is more accurate and reliable than previous approaches.

scholarly journals An Integrative Framework for Online Prognostic and Health Management Using Internet of Things and Convolutional Neural Network

Sensors ◽  
2019 ◽  
Vol 19 (10) ◽  
pp. 2338 ◽  
Author(s):  
Yuanju Qu ◽  
Xinguo Ming ◽  
Siqi Qiu ◽  
Maokuan Zheng ◽  
Zengtao Hou
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Internet Of Things ◽  
Convolutional Neural Network ◽  
Health Management ◽  
Added Value ◽  
Time Data ◽  
Integrative Framework ◽  
Operational Level ◽  
Prognostic And Health Management

With the development of the internet of things (IoTs), big data, smart sensing technology, and cloud technology, the industry has entered a new stage of revolution. Traditional manufacturing enterprises are transforming into service-oriented manufacturing based on prognostic and health management (PHM). However, there is a lack of a systematic and comprehensive framework of PHM to create more added value. In this paper, the authors proposed an integrative framework to systematically solve the problem from three levels: Strategic level of PHM to create added value, tactical level of PHM to make the implementation route, and operational level of PHM in a detailed application. At the strategic level, the authors provided the innovative business model to create added value through the big data. Moreover, to monitor the equipment status, the health index (HI) based on a condition-based maintenance (CBM) method was proposed. At the tactical level, the authors provided the implementation route in application integration, analysis service, and visual management to satisfy the different stakeholders’ functional requirements through a convolutional neural network (CNN). At the operational level, the authors constructed a self-sensing network based on anti-inference and self-organizing Zigbee to capture the real-time data from the equipment group. Finally, the authors verified the feasibility of the framework in a real case from China.

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