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The Application of Artificial Intelligence in Chinese News Media 2021

The Application of Artificial Intelligence in Chinese News M...

引用

2nd international conference on Artificial Intelligence and Information systems (ICAIIS )

作者： Wang, Yuting Lanzhou Univ Sch Journalism & Commun Lanzhou Peoples R China

ISBN: (纸本)9781450390200

From the creation of writing robot by Tencent Finance in 2015 to the "Creative Brain" published by People's Daily in 2020, the model of "Artificial Intelligence + News Production" has entered most mainstream media in China. This paper studies the use of artificial intelligence in news production, focusing on three aspects of robot writing, AI virtual news anchor and news distribution system, in order to observe the application of artificial intelligence in Chinese news media. The investigation found that the application of artificial intelligence in Chinese news media started late and lacked intelligence. The development of artificial intelligence was restricted due to the limitation of Chinese language and the inconsistency of technical standards. However, the Chinese news media has high acceptance of artificial intelligence, and has also creatively developed news products such as AI virtual anchor, which will develop rapidly in the future under the push of policy and market in the news field.

关键词： Artificial intelligence AI Virtual News Anchor NLP technology Ethics

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Regulatory analysis of barriers to SYNERGY's innovation 2

Regulatory analysis of barriers to SYNERGY's innovation

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2nd international conference on Energy Transition in the Mediterranean Area, SyNERGY MED 2022

作者： Psara, Kyriaki Papadimitriou, Christina Efstratiadi, Marily Tsakanikas, Sotiris Papadopoulos, Panos Tsitsanis, Tasos University of Cyprus Department of Electrical and Electronic Engineering Nicosia Cyprus Eidhoven University of Technology Electrical Engineering Electrical Energy Systems Eindhoven Netherlands Ellin Verd Smart Energy Systems Athens Greece Suite5 Data Intelligence Solutions Limited Limassol Cyprus

ISBN: (数字)9781665461078

ISBN: (纸本)9781665461078

The integrated electricity ecosystem suggests different active actors throughout the energy value chain. Different actors that interact with each other assume vast amounts of data with different characteristics that need to be processed, analyzed, and exchanged. Under this reality, different issues arise related to how data should be managed, protected, or exchanged. This paper presents a comprehensive analysis on the existence and importance of various regulatory obstacles regarding innovative energy services, data exchanges and synergies promoted by the SYNERGY project. In this framework, a survey-based data gathering exercise was designed, formulated, and conducted across the project's demo partners to assess certain aspects related to the European regulations. During the analysis we have also identified best practices and recommendations, towards enabling proactive decisions both referring to the design of the SYNERGY platform/applications and the way demonstration activities will be implemented to overcome regulatory barriers. © 2022 IEEE.

关键词： Electronic data interchange

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Comparison of 4 Vector Polygon Clipping Algorithms in the Spatial Overlay Analysis of GIS Using Simple Feature Model 2

Comparison of 4 Vector Polygon Clipping Algorithms in the Sp...

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2nd international conference on Geographic Information and Remote Sensing technology, GIRST 2023

作者： Zhang, Panpan Teng, Xin Fan, Junfu Meng, Xue Zhao, Qiwei Kang, WanChao National Ocean Technology Center Tianjin China School of Civil and Architectural Engineering Shandong University of Technology Zibo China

ISBN: (纸本)9781510668478

Vector polygon overlay analysis is one of the most basic, crucial and complex analysis methods in geographic information systems (GIS). The method is usually the basic algorithm for the constitutions of many high-level spatial analysis models. With the extensive and in-depth applications of vector spatial data structures based on simple feature models, nontopological vector polygon overlay analysis algorithms have attracted more attention from scholars. Many different types of polygon clipping algorithms used for overlay analysis have emerged. The Vatti algorithm, Greiner-Hormann algorithm, Liu algorithm and Martinez algorithm are 4 acknowledged algorithms for arbitrary vector polygon clipping with reasonable time consumption. Most previous studies focused on the optimization and improvement methods of a single polygon clipping algorithm and lacked comprehensive analysis and comparison between them based on real spatial vector data. In this paper, the 4 polygon clipping algorithms mentioned above are implemented based on the Geospatial Data Abstraction Library (GDAL)/OpenGIS Simple Features Reference Implementation (OGR) model. The spatial complexity and time consumption of the 4 polygon clipping algorithms are tested and analyzed statistically using 4 different Boolean operations, including the intersection, union, difference and symmetrical difference. Experimental results show that: 1) Regarding the spatial complexity, Liu’s and Vatti’s algorithms are the lowest and the highest, respectively, while Greiner-Hormann’s and Martinez’s algorithms are between the other two. 2) Regarding the time complexity, Vatti’s algorithm is the highest as the square order, Martinez’s algorithm is the linear logarithmic order, and the time complexities of Liu’s and Greiner-Hormann’s algorithms are between the linear order and the square order. 3) Regarding the time consumption of these algorithms, when four Boolean operations are carried out between two polygon layers, Martinez’s al

关键词： Vectors

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Estimation of Equipment Remaining Useful Life based on WTEE-RNN Method 2

Estimation of Equipment Remaining Useful Life based on WTEE-...

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2nd international conference on Computational Modeling, Simulation and Data Analysis, CMSDA 2022

作者： Li, Wenhuai Cai, Jiejin Li, Hansheng Yu, Fengwan Ding, Peng School of Electric Power South China University of Technology Guangzhou China China Nuclear Power Research Institute Co. LTD Guangdong Technol. Res. Ctr. of Advanced Nuclear Energy Artificial Intelligence and Virtual Reality Shenzhen China

ISBN: (纸本)9798350345759

Nuclear power plants have a large number of safety or non-safety related mechanical equipment. Knowing when and where these equipment failure or near failure is one of the key problems in nuclear safety and nuclear health management and maintenance, that is to predict the remaining useful life for specific equipment. A prediction model labeled as Weibull time to event recurrent neural network (WTTE-RNN) is proposed to effectively obtain the prediction and estimation of time to event (such as customer churn, patient survival, machine failure). The model is effective even under some missing-spots in the training data (i.e. deleted data). The purpose of this research is to study the main challenges faced by the application of WTTE-RNN to the nuclear equipment, and identify feasible optimization directions. The results may lay a foundation for the WTTE-RNN application to the remaining useful life. © 2022 IEEE.

关键词： Recurrent neural networks

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Motion control of an internally-actuated cubical robot balancing on its edge 2

Motion control of an internally-actuated cubical robot balan...

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2nd IEEE international conference on Control, Measurement and Instrumentation, CMI 2021

作者： Patil, Saurabh Chatterjee, Sananda Bhaumik, Subhasis Indian Institute of Engineering Science and Technology School of Mechatronics and Robotics Shibpur Howrah West Bengal India

ISBN: (纸本)9781728193427

The Internally-Actuated robots have recently gained popularity due to their ability of achieving controlled mobility (via moving or spinning internal inertial elements) even in milli/ micro gravity environment. In this paper such an internally actuated robot, cubical in shape, is considered and the dynamical equation of the robot is obtained. Aiming to achieve features like controlled tumbling and balancing on the edges, which are necessary for such robots to effectively execute their popular applications such as exploration of smaller solar-system bodies and self assembly of group robots, a stabilizing controller is proposed. The performance and robustness results of the proposed controller are also presented. The comparison results obtained, indicate the superiority of the proposed controller over the ones available in the literature. © 2021 IEEE.

关键词： Controllers

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Innovative Research of Blockchain technology in the field of Computer Monitoring of Hydropower Station 2

Innovative Research of Blockchain Technology in the field of...

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2nd international conference on Electronics and Sustainable Communication systems, ICESC 2021

作者： Majia, Luolun Dehong teachers'College Institute of Science and Engineering Mangshi678400 China

ISBN: (纸本)9781665428675

Early power stations in China have been in operation for a long time. Due to the limitations of design, construction materials and technological level at that time, many hydroelectric generators have some defects. The computer monitoring and control system of a hydropower station refers to the measurement, control, protection and monitoring of the entire equipment of a hydropower station completed and realized by the computer system. The computer monitoring and control system of hydropower station replaces the traditional control equipment, fault recording equipment, monitoring and measuring equipment and relay protection equipment. This paper studies the full homomorphic encryption algorithm, introduces the zero-knowledge proof technology into the block chain trading protocol based on the full homomorphic encryption, and constructs the block chain that protects data privacy. © 2021 IEEE.

关键词： Smart contract

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Monte Carlo Modeling of Employment Distribution of College Graduates based on Data Mining technology 2

Monte Carlo Modeling of Employment Distribution of College G...

引用

2nd international conference on Electronics and Sustainable Communication systems, ICESC 2021

作者： Lyu, Bu Wang, Na Chen, Jianhui School of Economics Management Bengbu University Bengbu233030 China

ISBN: (纸本)9781665428675

This study considers the university as the sample to follow general process of knowledge discovery. Also applying a data mining technology constructs the prediction model of graduates' employment. This research work has made a scientific analysis of the academic data and employment related data of college graduates over the years, and finds out the available knowledge and potential laws, which can provide reference for colleges and universities to deal with the situation of increasing employment pressure and prominent employment structural contradictions. This provides a scientific data-based reference for students' career guidance work to make correct decisions. © 2021 IEEE.

关键词： Data mining

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Research on Distributed Intelligent Protection Strategy of distribution network based on Reinforcement Learning 2

Research on Distributed Intelligent Protection Strategy of d...

引用

2nd IEEE international conference on Information technology, Big Data and Artificial Intelligence, ICIBA 2021

作者： Cui, Shijie Zeng, Peng Hu, Ende Shenyang Institute of Automation Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang110016 China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China State Grid Shaoxing Power Supply Company Shaoxing312099 China

ISBN: (纸本)9781665428767

In the context of building a new power system with new energy as the main part, the operation mode of distribution network is more complex, and the traditional distribution network protection strategy can hardly meet the demand, so this paper proposes a distributed intelligent protection strategy for distribution network based on reinforcement learning. This paper analyzes the impact of new energy resources access on distribution network relay protection, and presents an intelligent protection algorithm model based on deep reinforcement learning. The structure of intelligent protection device is designed, and the distribution network distributed protection scheme based on intelligent protection device is proposed. Finally, the algorithm training process is given, and the experimental environment is built. © 2021 IEEE.

关键词： Reinforcement learning

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CARDIOCARE platform: A beyond the state of the art approach for the management of elderly multimorbid patients with breast cancer therapy induced cardiac toxicity

CARDIOCARE platform: A beyond the state of the art approach ...

引用

2023 IEEE international conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Tsiouris, Kostas M. Kalliatakis, Grigoris Mazzocco, Ketti Seruga, Bostjan Marias, Kostas Karanasiou, Georgia Antoniades, Athos Papakonstantinou, Andri Conti, Constanza Tsiknakis, Manolis Sfakianakis, Stelios Lakkas, Lampros Filippatos, Gerasimos Bucur, Anca Fotiadis, Dimitrios I. Manikis, George Mauri, Davide Constantinidou, Anastasia Pacella, Elsa University of Ioannina Unit of Medical Technology and Intelligent Information Systems Ioannina Greece Foundation for Research and Technology - Hellas Computational Bio-Medicine Laboratory Institute of Computer Science Heraklion Greece European Institute of Oncology Cardiology Division Milan Italy Institute of Oncology Ljubljana Department of Medical Oncology Ljubljana Slovenia Stremble Ventures Ltd Limassol Cyprus Karolinska University Hospital Endocrine Tumors and Sarcoma Theme Cancer Dept. for Breast Cancer Sweden Istituto di Management Sanitario S.r.l Milano Italy Foundation for Research and Technology - Hellas Institute of Computer Science Heraklion Greece University of Ioannina 2nd Dept. of Cardiology School of Medicine Ioannina Greece National and Kapodistrian University of Athens Athens Greece Attikon University Hospital School of Medicine Dept. of Cardiology Athens Greece Philips Research Europe Eindhoven Netherlands University of Ioannina Dept. of Medical Oncology Ioannina Greece Bank of Cyprus Oncology Centre Nicosia Cyprus European Society of Cardiology Brussels Belgium

ISBN: (纸本)9798350337488

Breast cancer (BC) is the most common cancer in women in Europe and worldwide, with a high prevalence in middle-aged and older women. The last years, the evolution in the existing treatment approaches have contributed to improved clinical outcomes and survival rates. Nevertheless, BC therapy-related cardiotoxicity, poses a severe impact in the short- and long-term Quality of Life (QoL) and associated survival of the BC patients. This study demonstrates how the CARDIOCARE platform and the developed risk stratification models provides healthcare professionals with a valuable tool for effectively managing BC patients, preventing treatment induced cardiotoxicity and improving their QoL. This is accomplished through the integration of multi-source patient-specific data from patient-oriented mobile applications and wearable sensors, and by the employment of beyond the state-of-the-art data mining and machine learning approaches. © 2023 IEEE.

关键词： Breast cancer cardiotoxicity machine learning mobile application risk stratification models

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A Motion Imagery EEG Signal Recognition Algorithm Based on Power Spectral Density combined with Particle Swarm Optimization Algorithm Optimized Support Vector Machine 2

A Motion Imagery EEG Signal Recognition Algorithm Based on P...

引用

2nd international conference on Networking systems of AI, INSAI 2022

作者： Lin, Ruijing Dong, Chaoyi Ma, Pengfei Chen, Xiaoyan Liu, Huanzi Lei, Dongyang College of Electric Power Inner Mongolia University of technology Inner Mongolia Key Laboratory of Mechanical and Electrical Control Hohhot China

ISBN: (纸本)9781665491785

For the purpose of addressing the issue of low classification accuracy resulting from low signal-to-noise ratios in electroencephalogram (EEG) signals, in this paper, a classification algorithm for motion imagery EEG signals (PSD-PSO-SVM) utilizing power spectral density analysis (PSD) combined with particle swarm optimization method (PSO) improved support vector machine (SVM) is proposed. A first step of the algorithm is to extract features of the EEG signal in the frequency domain by PSD, and the energy spectral densities of 0-30Hz in delta, theta, alpha, and beta frequencies are selected as their frequency domain features. Following that, a support vector machine is utilized for the classification of the extracted features. A solution is provided for the problem that performance in classification of traditional SVM is greatly influenced by its k-function parameters. It is the parameters of the kernel function of the SVM that are optimized by using the global optimisation-seeking capability of PSO to achieve optimal performance in classification. Finally, the algorithm validity was assessed by analyzing both the laboratory dataset (IMUT data) and the open dataset III developed for the 2003 competition on brain-computer interfaces (III BCI 2003). These findings show that PSD-PSO-SVM can perform classifications up to a level of 80.63%. It is proposed in this article that the proposed algorithm is compared to other optimization SVM algorithms, such as genetic algorithm optimization SVM (GA-SVM), and shows that PSO-SVM outperforms GA - SVM with an average classification accuracy of 5.63%. Thus the PSD-PSO-SVM was shown to have good classification performance. © 2022 IEEE.

关键词： Support vector machines

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