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10th International Conference on information and Automation for Sustainability, ICIAfS 2021

作者： Samantha Rajapaksha, U.U. Jayawardena, Chandimal MacDonald, Bruce A. Sri Lanka Institute of Information Technology Department of Information Technology Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Computer System and Engineering Malabe Sri Lanka The University of Auckland Department of Electrical Computer and Software Engineering Auckland New Zealand

ISBN: (纸本)9781665444293

Human Robot Interaction (HRI) is one of the biggest research field in the Robotics research world. Understanding the semantic meaning of the user instruction is very important to establish the communication between user and robot. When a user instructs to all heterogeneous service Robots with high level instructions who are working at different locations in smart house, all robots need to operate by understanding the semantic of the instruction and complete the task uniformly without considering underline software and hardware implementations. Ontology is used to extract the semantic meaning of the instructions. Each robot is assigned a specific task for specific time period for each day. User can issue commands to all robots at different time slots but the same command can be issued with different sentence by different users. We have used ontology to represents the semantic of the sentence with different commands. We have used three robots (TurtleBot, TiaGo and Husky) for our experiment in Gazebo simulator. Robot Operating system (ROS) is the middleware which is hiding more complex implementation of different functions for different robots and provide the interoperability for heterogeneous robot operations. The ROS nodes and topics can be different for different robots, therefore our implementation can solve this issue by autonomous robot registration algorithm which is working with Robot ontology. © 2021 IEEE.

关键词： Ontology

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Automatic Comment Generation via Multi-Pass Deliberation

arXiv

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arXiv 2022年

作者： Mu, Fangwen Chen, Xiao Shi, Lin Wang, Song Wang, Qing Institute of Software Chinese Academy of Sciences Beijing China Lassonde School of Engineering York University Toronto Canada Laboratory for Internet Software Technologies Institute of Software CAS China University of Chinese Academy of Sciences China Science & Technology on Integrated Information System Laboratory Institute of Software CAS China

Deliberation is a common and natural behavior in human daily life. For example, when writing papers or articles, we usually first write drafts, and then iteratively polish them until satisfied. In light of such a human cognitive process, we propose DECOM, which is a multi-pass deliberation framework for automatic comment generation. DECOM consists of multiple Deliberation Models and one Evaluation Model. Given a code snippet, we first extract keywords from the code and retrieve a similar code fragment from a pre-defined corpus. Then, we treat the comment of the retrieved code as the initial draft and input it with the code and keywords into DECOM to start the iterative deliberation process. At each deliberation, the deliberation model polishes the draft and generates a new comment. The evaluation model measures the quality of the newly generated comment to determine whether to end the iterative process or not. When the iterative process is terminated, the best-generated comment will be selected as the target comment. Our approach is evaluated on two real-world datasets in Java (87K) and Python (108K), and experiment results show that our approach outperforms the state-of-the-art baselines. A human evaluation study also confirms the comments generated by DECOM tend to be more readable, informative, and useful. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep neural networks

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A Transformer-Based Time Series Prediction Model with Contrastive Learning for Nuclear Power system State Monitoring

A Transformer-Based Time Series Prediction Model with Contra...

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Prognostics and system Health Management Conference (PHM-Qingdao)

作者： Pengfei Gou Daiwei Zheng Lei Tang Mengjuan Liu School of Information and Software Engineering University of Electronic Science and Technology of China UESTC Chengdu China Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institute of China NPIC Chengdu China

ISBN: (数字)9798350354010

ISBN: (纸本)9798350354027

Prognostics and Health Management (PHM) is currently the mainstream solution for the condition monitoring and maintenance of industrial systems. In PHM, the system condition is monitored by monitoring the operational status parameters of the system to diagnose equipment faults, predict the remaining useful life, etc. Utilizing sequential operational status parameters to complete prediction and classification tasks is usually modeled as a time series processing task. Therefore, a high-performance time series prediction model is crucial for PHM. This paper focuses on the use of time series prediction models to achieve the prediction of key state parameters of the nuclear power system. However, there are still some challenges when directly applying existing time series prediction models to industrial systems. First, obtaining operational data on the nuclear power system is relatively difficult, resulting in a small amount of training data, which causes many models to face the risk of insufficient training or overfitting. Second, most of the existing time series prediction models are composed of high- and low-frequency decoupling modules, while the operational data of nuclear power systems often have fewer high-frequency components, making it difficult for many existing SOTA models to achieve good results. In order to address these challenges, we have developed a Transformer-based time series prediction model with contrastive learning for monitoring the status of nuclear power systems. We evaluated the new model on three nuclear power simulation datasets and four power transformer datasets. The experimental results show that the new model's performance is superior to baseline models. We also proved the effectiveness of our improvement through ablation experiments.

关键词： Training Time series analysis Training data Contrastive learning Predictive models Data models Power systems Power transformers Prognostics and health management Monitoring

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A Teaching Materials Recommendation system Considering Individual Characteristics

A Teaching Materials Recommendation System Considering Indiv...

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IIAI International Conference on Advanced Applied Informatics (IIAIAAI)

作者： Chengjiu Yin Ryoya Shiroki Lin Lin Huiyong Li Research Institute for Information Technology Kyushu University Fukuoka Japan Graduate School of System Informatics Kobe University Kobe Japan School of Software Technology and DUT-RU International School of Information Science and Engineering Dalian University of Technology Dalian China

ISBN: (数字)9798350377903

ISBN: (纸本)9798350377910

In this study, we proposed and implemented a combined recommendation system by utilizing both learning log data, which contributes to improving learning performance, and learning style questionnaire, which contributes to increasing learning motivation. We also conducted an empirical experiment to evaluate the effect of the proposed recommendation system on learning performance, read efficiency, and perception of the proposed system. Students learned the same teaching material from the Commercial Law course. They were divided into three groups and received a page recommendation of the teaching material: recommended by only VARK data, recommended by only learning log, and recommended by both of V ARK data and learning log data. The findings show that the students who received the combined recommendation increased their mean values of learning performance and reading efficiency in the experiment and reported the highest improvement in usefulness and satisfaction on the system. On the other hand, the students who received the log data-only recommendation didn't increase their mean values of learning performance and reading efficiency and reported the lowest improvement of usefulness and satisfaction on the system. The students' post-questionnaires on their learning motivation and suggestions for learning recommendation system are also discussed.

关键词： Surveys Commercial law Education Informatics Recommender systems Testing

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Improving the search for information from Kazakh-language content in search systems

Improving the search for information from Kazakh-language co...

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Modeling, Simulation & Intelligent Computing (MoSICom), International Conference on

作者： Zhibek Alibiyeva Nurzhan Mukazhanov Ainur Akhmediyarova Dina Oralbekova Dinara Kassymova Department Of Software Engineering Satbayev University Almaty Kazakhstan Department of Cybersecurity information processing and storage Satbayev University Almaty Kazakhstan Institute of Information and Computational Technologies of the Committee of Science of the Ministry of Science and Higher Education of the Republic of Kazakhstan Almaty Kazakhstan Department of Information and Communication Technologies Mukhametzhan Tynyshbayev ALT University Almaty Kazakhstan

ISBN: (数字)9798331533311

ISBN: (纸本)9798331533328

The presented paper is devoted to exploring the ways to enhance the possibility of finding Kazakh-language text in search engines and improve the quality of search results. There have not been many studies conducted on data retrieval in the Kazakh language, and major search engines do not prioritize processing languages with small audiences, such as Kazakh. During the study, an analysis was conducted on the search results for Kazakh-language data in two search engines frequently used in Kazakhstan: *** and ***. The analysis revealed that these search engines are unable to sufficiently process textual data in Kazakh. To address this issue, the study proposed utilizing morphological analysis and identifying basic forms of searching words. Further, a test search engine was developed taking into account the identified shortcomings, and its results were compared with the results of other search engines.

关键词： Analytical models Web services Computational modeling Web pages Search engines Data retrieval Search problems Hardware Indexing

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Quadrature Demodulation as a Technique to Measure Signal Parameters with Increased Accuracy

Quadrature Demodulation as a Technique to Measure Signal Par...

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International Scientific Symposium on Metrology and Metrology Assurance (MMA)

作者： Anna A. Krylovich Sergey A. Podobuev Vadim A. Loginov Dmitry S. Zolkin Nikolay A. Serov Andrey N. Serov Diagnostic Information Technologies Department National Research University “Moscow Power Engineering Institute” Moscow Russian Federation Computer Engineering Department National Research University “Moscow Power Engeneering Institute” Moscow Russian Federation Division for Design of Information Retrieval System Federal Institute of Industrial Property (FIPS Rospatent) Moscow Russian Federation

ISBN: (数字)9798350352832

ISBN: (纸本)9798350352849

To solve modern problems related to spectrum measurement, the most common use of discrete Fourier transform algorithms. The main problem of using these algorithms is associated with the so-called “spectrum leakage” effect, the occurrence of which limits the maximum achievable accuracy from the point of view of the methodological component of the error. An alternative solution to the spectrum measurement problem is to use a quadrature demodulator. The approach itself has great potential in terms of the final accuracy achieved. The use of additional modifications discussed in the paper can further improve it. The paper discusses the advantages and disadvantages of all the considered modifications, and obtains analytical expressions that allow us to estimate the final achievable error. An assessment of possible influencing quantities was carried out and their influence on the measurement errors of amplitude, initial phase and frequency was assessed. Considerations are given for developing a simulation model of such a demodulator and its modifications under consideration. A ready-made model which is performed in Simulink environment is presented. The correctness of the obtained analytical relationships was checked by comparing the results of simulation modeling and analytical calculations.

关键词： Analytical models Measurement errors Accuracy Phase measurement software packages Low-pass filters Metrology Frequency measurement Integrated circuit modeling Demodulation

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Hybrid Power Generation Forecasting Using an Intellectual Evolutionary Energy-Preserve Rate Clustering Technique 1

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10th International Conference on Innovations in Computer Science and engineering, ICICSE 2022

作者： Webber, Julian L. Jayagopal, Vellingiri Mehbodniya, Abolfazl Sengan, Sudhakar Velayutham, Priya Rangasamy, Rajasekar Stalin David, D. Doha Kuwait Department of Software & System Engineering School of Information Technology & Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India Department of Computer Science and Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli627152 India Department of Computer Science and Engineering Paavai Engineering College Tamil Nadu Namakkal637018 India Department of Computer Science and Engineering GITAM School of Technology GITAM University Karnataka Bengaluru561203 India Department of Information Technology Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College Tamil Nadu Chennai600072 India

ISBN: (数字)9789811974557

ISBN: (纸本)9789811974540

The significant growth of Wind Power (WP) has significant challenges due to the inconsistency and uncertainty of Wind Power Generation (WPG) to ensure the power system. Solar energy and WP calculation are great ways to overcome the problem. Mainly voltage and frequency fluctuations and harmonics have a high impact on the power generation and distribution system;in any high case, the power quality of grid systems is reduced. This can be primarily solved with proper design, advanced rapid response control facilities and optimization of power systems. A new type of Multi-term Wind Energy (WE) with an IEEE 30 bus forecasting system based on digital weather forecasting and error modification methods is presented. From the Evolutionary Energy- Preserve Rate Clustering (EEPRC) Technique-based technique for the evaluates the probability ratio in Solar and wind error energy prediction, the error assessment model is proposed, which has the advantage of estimating from real wind farms and statistical Weather Forecast (WF) data and has proven to be a nominal method for improving the predictive accuracy of Multi-term WPG. The primary step of the Energy-Preserve Rate Clustering (EPRC) method is to calculate the uncertainty of real-time data with the predicted value. The computed values are helpful for the increases in Solar WPG production. The proposed wind forecasting system is utilized in the Coastal area-based energy generation. Also, it has been demonstrated that wind speed and WPG with its probability ratio have been evaluated with the EEPRC-based forecasting method, especially for its powerful predictive capability for the complex properties of the controller function. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Solar energy

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Estimation of The Throughput of The Channel for Measuring The Distance of Short-Range Radio engineering systems 41

Estimation of The Throughput of The Channel for Measuring Th...

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41st IEEE International Conference on Electronics and Nanotechnology, ELNANO 2022

作者： Svyd, Iryna Obod, Ivan Maltsev, Oleksandr Romanov, Oleksandr Zhuk, Oleksandr Nesmiian, Oleksii Kharkiv National University of Radio Electronics Dept. of Microprocessor Technologies and Systems Kharkiv Ukraine Institute of Telecommunication System National Technical University of Ukraine 'Igor Sikorsky Kyiv Polytechnic Institute' Kyiv Ukraine Military Institute of Telecommunications and Information Technologies Named after Heroes of Kruty Kyiv Ukraine Kharkiv National University of Air Forces Dept. of Mathematical and Software of Automated Control Systems Kyiv Ukraine

ISBN: (数字)9781665469227

ISBN: (纸本)9781665469234

In the presented work, the relative throughput of both a ground-based range transponder and the entire short-range radio engineering system is estimated under the action in the range channel of the total flow of range request signals. And also considered the requesting signals for unauthorized use of the rangefinder channel transponder by the interested party under the action of chaotic impulse noise flow. The evaluation was carried out on the basis of the representation of the channel for measuring the range of short-range radio engineering systems in the form of a non-synchronous network. We assume that the range request signals are serviced by an open single-channel queuing system with refusing according to the service scheme of the first correctly received range request signal. © 2022 IEEE.

关键词： Transponders

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Research on Forecasting Flowering Phase of Pear Tree Based on Neural Network

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Computers, Materials & Continua 2021年第9期68卷 3431-3446页

作者： Zhenzhou Wang Yinuo Ma Pingping Yu Ning Cao Heiner Dintera School of Information Science and Engineering Hebei University of Science and TechnologyShijiazhuang050000China School of Internet of Things and Software Technology Wuxi Vocational College of Science and TechnologyWuxi214028China German-Russian Institute of Advanced Technologies Karan420126Russia

Predicting the blooming season of ornamental plants is significant for guiding adjustments in production decisions and providing viewing periods and *** current strategies for observation of ornamental plant booming periods are mainly based on manpower and experience,which have problems such as inaccurate recognition time,time-consuming and energy ***,this paper proposes a neural network-based method for predicting the flowering phase of pear ***,based on the meteorological observation data of Shijiazhuang Meteorological Station from 2000 to 2019,three principal components(the temperature factor,weather factor,and humidity factor)with high correlation coefficient with the flowering phase of pear tree are obtained by using the principal component analysis ***,the three components are used as input factors for the BP neural network.A BP neural network prediction model is constructed based on genetic algorithm *** crossover operator and mutation operator in the adaptive genetic algorithm are ***,the meteorological sample data from 2013 to 2019 are used to test and verify the algorithm in this *** results demonstrate that,the model can solve the local optimization problem of the BP neural network *** prediction results of the flowering phase of pear tree are evaluated in terms of relevance and prediction *** are superior to the traditional effective accumulated temperature and the prediction results of the stepwise regression *** method can provide more reliable forecast information for the blooming period,which can provide decision-making reference for improving the development of tourism industry.

关键词： Pear flower flowering phase principal component analysis BP neural network prediction model

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NLP for Sentiment Analysis in Social Media Posts to Detect Suspicious Behaviour

NLP for Sentiment Analysis in Social Media Posts to Detect S...

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2024 International Conference on Intelligent Algorithms for Computational Intelligence systems, IACIS 2024

作者： Awasthi, Abhishek Bdair, Mohammad Kumar, A. Nandha Thapa, Sanjog Nalini Devi, S. Kumar, Bharathi Ramesh Sporta Technology pvt Ltd Software Developer Maharashtra India University of East London School of Architecture Computing & Engineering London United Kingdom Bannari Amman Institute of Technology Department of Eee Sathyamangalam India Lamar University Department of Information System and Analysis TX United States St. Joseph's Institute of Technology Department of Management Studies Chennai India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Mathematics Chennai India

ISBN: (纸本)9798350360660

In an era where social media is an almost omnipresent means of communication, online safety is crucial. Still, conventional sentiment analysis methods sometimes fall short of accurately spotting dubious conduct. A novel approach utilizing advanced Natural Language Processing (NLP) techniques, especially Deep Learning (DL) models like BERT (Bidirectional Encoder Representations from Transformers), is proposed to overcome these challenges. DL is used by the suggested method to identify nuanced attitudes in social media posts, which raises the accuracy of identifying suspicious activities. That is not the case with conventional systems that depend on basic algorithms. With detection rates of 88%, 85%, and 90% for hate speech, violent statements, and extreme opinions, respectively, the system accomplishes remarkable outcomes via meticulous data gathering, preprocessing, and model training. Through comparison, it outperforms existing systems in terms of F1 score (0.92), recall (0.91), precision (0.94), and accuracy (0.92). The proposed system effectively reduces potential risks and encourages a safer online environment by utilizing the effective analysis of sentiment in social media posts, therefore indicating a major advancement in online security. © 2024 IEEE.

关键词： Tweets

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