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Spectrum Sensing for Cognitive Radio Based on Feature Extraction and Deep learning 11

Spectrum Sensing for Cognitive Radio Based on Feature Extrac...

引用

2022 11th international conference on Information and Electronics engineering, ICIEE 2022

作者： Geng, Yue Huang, Jingyi Yang, Jianxin Zhang, Sen School of Electrical and Electronic Engineering Nanyang Technological University Singapore639798 Singapore College of Physics and Optoelectronic Engineering Shenzhen University Shenzhen518061 China School of Mechanical Engineering & Automation Beihang University Beijing100191 China School of Measurement-Control and Communication Engineering Harbin University of Science and Technology Harbin150080 China

In cognitive radio, spectrum sensing is used to determine whether the primary user is using the spectrum based on the signal received on a specific frequency band, thereby determining whether the secondary user can use the spectrum. the main problem faced by spectrum sensing is how to identify the existence of the primary signal under the condition of low signal-to-noise ratio (SNR). Compared with traditional technologies, deep learning methods can identify the features of input data more efficiently and accurately. Based on convolutional neural network (CNN), this paper regard spectrum sensing as a binary classification problem. In the method we proposed, different features of received are extracted, and a dataset of feature matrices obtained under different SNRs is constructed for the training of the CNN network. Experiment results show that under the condition of low signal-to-noise ratio, the performance of our method is improved compared with the traditional method, and the combination of different features can improve the sensing accuracy. © Published under licence by IOP Publishing Ltd.

关键词： Cognitive radio

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Applying blockchain technology for improvement of the educational process in terms of data processing 11

Applying blockchain technology for improvement of the educat...

引用

11th Majorov international conference on Software engineering and Computer Systems, MICSECS 2019

作者： Zimina, Dina Mouromtsev, Dmitry ITMO University Saint-Petersburg197101 Russia

the paper considers some new methods of using blockchain technology as a tool of improving educational process management. Two e-learning problems are described. the first one is low ratings of completing MOOCs. the second one is the need for more detailed and accurate reports. Blockchain technology and its applications in education is considered. Blockchain's ability of documenting events is reviewed particularly. Two cases of using blockchain in this way are performed. the first one is making educational reports more detailed and easier to design. Another one is using blockchain for creating any student model in adaptive education to make electronic educational courses more effective and to increase ratings of completing MOOCs. A blockchain's option of documenting timestamps and users' addresses automatically is specially mentioned. Both cases are described mathematically and united in one model. Using Ethereum blockchain for the evaluation because of its smart-contract's mechanism is justified. Sequence diagram showing application workflow, is presented. Input data from the real e-learning course are processed with using statistics to get the most frequently used system events. Application tools for working with the Ethereum blockchain are presented. the process of developing, deploying and mining smart-contract is shown. Output report is presented. Implementation is described, the ways of future learn are reported. Copyright © 2019 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 international (CC BY 4.0).

关键词： Smart contract

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Development of a Risk Assessment and Early Warning System for Pharmaceutical Cold Chain Transportation Based on Big data 11th

Development of a Risk Assessment and Early Warning System fo...

引用

11th international conference on Applications and Techniques in Cyber Intelligence, ATCI 2023

作者： Liu, Juan College of Chemistry and Environment Shandong Institute of Commerce and Technology Shandong Jinan250000 China

Cold chain logistics has become a core link in ensuring drug quality and safety, especially for temperature sensitive drugs such as vaccines and biologics. this paper aims to develop a drug cold chain transportation risk assessment and early warning system based on big data, utilizing advanced technologies such as big data analysis and machine learning to achieve real-time monitoring, intelligent analysis, and early warning of various risk factors in the drug cold chain transportation process. this paper proposes a comprehensive architecture that includes data collection layer, data transmission layer, data processing and storage layer, analysis engine, warning and response module, user interface, and system integration interface. this paper collects key environmental data in real-time through sensors, such as temperature and humidity, and uses a secure data transmission layer to transmit the data to the central processing system. the construction of the risk assessment model is based on a series of quantifiable risk indicators, including temperature deviation, humidity deviation, location deviation, etc., and through data standardization and weight allocation, combined with a machine learning model to perform risk scoring and grading. the early warning mechanism implementation part includes early warning rules and threshold settings as well as real-time monitoring and dynamic warning to ensure that the system can respond to potential risks in a timely manner. the results of the system performance evaluation show that the risk evolution prediction deviation rate remains at a very low level, with a maximum of 1.4%, the false alarm rate is very low, with a maximum of 0.15% and a minimum of 0.03%, and the risk situational awareness sensitivity coefficient remains at a high level, with a minimum of 0.8. these results indicate that the developed system performs well in terms of risk prediction accuracy, warning timeliness and risk perception sensitivity. © 2024 the Autho

关键词： Risk assessment

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Application of Spatio-Temporal Big data Technology in Territorial Spatial Planning 8

Application of Spatio-Temporal Big Data Technology in Territ...

引用

8th international Technical conference on Frontiers of Hydraulic and Civil engineering Technology, HCET 2023

作者： Pu, Yu Li, Liang Li, Ruobing Ma, Zhongjian Cui, Di Xie, Wenpei Zhai, Hongting Zhang, Qingrui Shandong Urban and Rural Planning and Design Research Institute Co. Ltd. China Shandong University of Technology China Information & Telecommunications Company State Grid Shandong Electric Power Company China Golden Modern Information Industry Co. Ltd. China Integrated Electronic Systems Lab Co. Ltd. China Co. Ltd. China

ISBN: (纸本)9781643684642

Territorial spatial planning is the guide for national spatial development, the spatial blueprint for sustainable development, and the basic basis for various development, protection, and construction activities. With the development of machine learning and artificial intelligence, big data processing technology has also begun to integrate these technologies. these technologies can make big data processing more intelligent and automated, thereby providing more value for enterprises and individuals. Space-time big data technology is playing an increasingly important role in land planning. It is becoming a consensus to carry out 'accurate planning' by means of big data. However, how to obtain data and how to convert huge data into values and charts that can be used directly are still difficult problems. this paper takes the traffic planning research of Jinan City as an example. By continuously collecting traffic flow pictures of Internet maps for one month, using big data analysis technology to extract effective information, and using R language and ArcGIS to carry out time-space analysis, it provides theoretical support for the preparation of land spatial planning with multiple regulations. © 2023 the Authors.

关键词： Traffic congestion

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Make LLM a Testing Expert: Bringing Human-Like Interaction to Mobile GUI Testing via Functionality-Aware Decisions 24

Make LLM a Testing Expert: Bringing Human-Like Interaction t...

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44th ACM/IEEE international conference on Software engineering, ICSE 2024

作者： Liu, Zhe Chen, Chunyang Wang, Junjie Chen, Mengzhuo Wu, Boyu Che, Xing Wang, Dandan Wang, Qing State Key Laboratory of Intelligent Game Beijing China Institute of Software Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Technical University of Munich Munich Germany DiDi Global Inc Science & Technology on Integrated Information System Laboratory China

ISBN: (纸本)9798400702174

automated Graphical User Interface (GUI) testing plays a crucial role in ensuring app quality, especially as mobile applications have become an integral part of our daily lives. Despite the growing popularity of learning-based techniques in automated GUI testing due to their ability to generate human-like interactions, they still suffer from several limitations, such as low testing coverage, inadequate generalization capabilities, and heavy reliance on training data. Inspired by the success of Large Language Models (LLMs) like ChatGPT in natural language understanding and question answering, we formulate the mobile GUI testing problem as a Q&A task. We propose GPTDroid, asking LLM to chat with the mobile apps by passing the GUI page information to LLM to elicit testing scripts, and executing them to keep passing the app feedback to LLM, iterating the whole process. Within this framework, we have also introduced a functionality-aware memory prompting mechanism that equips the LLM with the ability to retain testing knowledge of the whole process and conduct long-term, functionality-based reasoning to guide exploration. We evaluate it on 93 apps from Google Play and demonstrate that it outperforms the best baseline by 32% in activity coverage, and detects 31% more bugs at a faster rate. Moreover, GPTDroid identifies 53 new bugs on Google Play, of which 35 have been confirmed and fixed. © 2024 ACM.

关键词： Graphical user interfaces

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Generalized non-metric multidimensional scaling

Generalized non-metric multidimensional scaling

引用

11th international conference on Artificial Intelligence and Statistics, AISTATS 2007

作者： Agarwal, Sameer Lanckriet, Gert Wills, Josh Kriegman, David Cayton, Lawrence Belongie, Serge Computer Science and Engineering University of Washington Seattle WA 98105 United States Electrical and Computer Engineering University of California San Diego CA 92093 United States Sony Pictures Imageworks Culver City CA 90232 United States Computer Science and Engineering University of California San Diego San Diego CA 92093 United States

We consider the non-metric multidimensional scaling problem: given a set of dissimilarities Δ, find an embedding whose inter-point Euclidean distances have the same ordering as Δ In this paper, we look at a generalization of this problem in which only a set of order relations of the form dijkl are provided. Unlike the original problem, these order relations can be contradictory and need not be specified for all pairs of dissimilarities. We argue that this setting is more natural in some experimental settings and propose an algorithm based on convex optimization techniques to solve this problem. We apply this algorithm to human subject data from a psychophysics experiment concerning how reflectance properties are perceived. We also look at the standard NMDS problem, where a dissimilarity matrix Δ is provided as input, and show that we can always find an order-respecting embedding of Δ.

关键词： Convex optimization

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Medical distress prediction based on Classification Rule Discovery using ant-miner algorithm

Medical distress prediction based on Classification Rule Dis...

引用

international conference on (ISCO) intelligent Systems and Control

作者： M. Durgadevi R. Kalpana Dept.of CSE Pondicherry Engineering College Puducherry

ISBN: (纸本)9781509027187

Enormous data mining techniques were used for disease prediction among which only a few have employed feature selection. the prediction knowledge for disease diagnosis highly depends on the subjective knowledge of the experts. Developing a disease prediction model in time can help us to overcome the medical distress. In this paper, three feature selection strategies namely, HS, MS and TS are devised to obtain the valuable subset of relevant features for reducing the dimensionality of multiple attributes. this work proposed a modified ant-miner algorithm to extract the classification rules from the data. three benchmarked datasets (Cleveland, Pima and Wisconsin) from the UCI machine learning repository were used to analyze effectiveness of the proposed model. the obtained results clearly shows that the modified ant-miner outperforms the other top data mining classification algorithms like the CN2, RBF, Adaboost and Bagging in terms of accuracy. thus the proposed model is capable of producing good results with fewer features and serves as a suitable tool for eliciting and representing the expert's decision rules with an effective support for solving disease prediction problem.

关键词： Diabetes Diseases Classification algorithms Prediction algorithms data mining Predictive models Medical diagnostic imaging

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Multiple convolutional neural network for feature extraction 1

引用

11th international conference on intelligent Computing, ICIC 2015

作者： Yang, Guo-Wei Jing, Hui-Fang College of Information Engineering Nanchang Hangkong University Nanchang330063 China School of Technology Nanjing Audit University Nanjing211815 China

ISBN: (数字)9783319221861

ISBN: (纸本)9783319221854

Recent theoretical studies indicate that Deep Neural Network has been applied to many image processing tasks. However, learning in deep architectures is still difficult. One of the neural network, Convolutional Neural Network (CNN) has gained great success in image recognition and it builds features by automatic learning. More importantly, CNN can operate directly on the gray image, so it can be directly used for processing classification of the image. In order to utilize CNN to recognize plant leaf, a hierarchical model based on CNN is proposed in this paper. We firstly do some pre-processing, such as illumination changes, rotation and leaf distortion. After that, we applied the method of CNN to extract the features of leaves pictures. One focus on our network is about the depth of CNN, which affects the ability of capability of convolution. thus, we try our best to choose the best depth of CNN with several experiments. Moreover, in order to destroy the symmetry of networks, the strategies used in this paper is to add a mathematical formula for feature map connection between convolutional layer and sampling layer. the experimental results show that the proposed method is quite effective and feasible. And we also applied other classification methods to the ICL dataset. By contrast, our classification is much better than other methods. © Springer international Publishing Switzerland 2015.

关键词： Feature extraction

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Visualization and Analysis of Mapping Knowledge Domains for AI Education System Studies

Visualization and Analysis of Mapping Knowledge Domains for ...

引用

Information and Education Technology (ICIET), international conference on

作者： Yakun Wang Fuxiang Yu College of Digital Economics Nanning University Nanning China Business School Hangzhou City University Hangzhou China

the development of Artificial Intelligence (AI) technology brings opportunities and challenges to the education system. It is changing the way educational information is disseminated and at the same time triggering a change in the educational model. Based on the database of CNKI, this paper uses the visualization tool CiteSpace software to map the knowledge structure of AI education system research and conducts a bibliometric analysis of the relevant literature collected in this field. the results showed that 1) In the past 20 years, the emphasis of studies AI education system in China has shifted from a singular artificial intelligence to smart technology, decision-making systems, learning analysis, big data, and knowledge engineering. the qualities, such as increasing study horizon and growing research scope, are highlighted. 2) AI helps to upgrade the elements of education, intelligent education for large-scale, personalized education, and the development path of AI education technology are research hotspots. 3) Smart education, Artificial intelligence, Talent training, Emotional computing, and Smart classroom are the strongest bursting keywords in the last two years, indicating the recent research frontiers.

关键词： Training Knowledge engineering Education Decision making learning (artificial intelligence) Educational technology Big data

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Fuzzy-sliding model reference learning control of inverted pendulum with big bang — Big crunch optimization method

Fuzzy-sliding model reference learning control of inverted p...

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international conference on intelligent Systems Design and Applications (ISDA)

作者： M. Aliasghary I. Eksin M. Guzelkaya Control Engineering Department Faculty of Electronics Engineering Istanbul Technical University Istanbul Turkey

In this paper, a fuzzy-sliding model reference learning controller is proposed in which optimal scaling factors are assigned for the fuzzy sliding mode controllers. As the name of this study suggest the method is a breeding or hybrid combination of the fuzzy-sliding mode control (FSMC) and fuzzy model reference learning control (FMRLC) which inherits the benefits of these two methods. the main advantage of the proposed controller is that the number of rules has been reduced dramatically in comparison with the traditional FMRLC since fuzzy-sliding mode controllers are invoked in place of standard fuzzy logic controllers. the input and output scaling factors of fuzzy sliding mode controllers are adjusted using big bang - big crunch optimization method to provide an optimal result. the simulations for the proposed method are done on the inverted pendulum system. the results of these simulations demonstrate that the FS-MRLC achieves a robust performance with minimum number of fuzzy rules.

关键词： Adaptation models Fuzzy logic Simulation Robustness Fuzzy sets Sliding mode control Mathematical model

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