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检索条件"任意字段=International Conference on Big Data Intelligence and Computing, DataCom 2022"

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A Spatiotemporal Coding Letters Recognition Circuit Based on Organic Neuromorphic Synaptic Transistor 5

A Spatiotemporal Coding Letters Recognition Circuit Based on...

引用

5th international conference on Circuits and Systems, ICCS 2023

作者： Li, Jian Jin, Yandi Zheng, Chaoyue Zhang, Ting Chen, Ximing Song, Wanke Li, Shibin School of Optoelectronic Science and Engineering University of Electronic Science and Technology of China Chengdu China University of Electronic Science and Technology of China Huzhou China

ISBN: (纸本)9798350308273

With the rapid development of the artificial intelligence and big data technology, data centers need to throughput and process massive data information under immense pressure. Low power and efficient parallel data processing still face significant challenges. In this work, we propose a spatiotemporal coding letters recognition (SCLR) circuit system based on an organic neuromorphic synaptic transistor (ONST) as the front-end reservoir computing (RC) device combined with the back-end feedback type trans-impedance amplifier (TIA), successive approximation register (SAR) ADC circuit and MCU module for efficient spatiotemporal information processing in real-time. The dynamic conductance inhibition process caused by the holes trapping behavior in the front-end RC device allows the ONST to preserve the memory information of the input signal. The back-end circuit modules can realize the functions of current-to-voltage conversion and voltage amplification., and the correct letter output can be achieved by the MCU. Our work potentially paves a way in the field of the future low-power consumption intelligent edge machine vision system. © 2023 IEEE.

关键词： big data

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Construction of Smart Campus in Universities Under the Background of big data intelligence 4th

Construction of Smart Campus in Universities Under the Backg...

引用

4th international conference on Innovative computing, IC 2021

作者： Sun, Wengao Xi’an Traffic Engineering Institute Xi’an710300 China

ISBN: (纸本)9789811642579

"Smart campus" refers to the integration of different application service systems to create an intelligent teaching and learning environment, and based on the Internet, to build a life suitable for university management, teaching, research, and life. This article mainly introduces the construction of intelligent campus in colleges and universities in the context of big data artificial intelligence. This article proposes a smart campus construction system for the school’s lack of overall planning, lack of standards and information rules, lack of comprehensive system planning, and lack of comprehensive decision-making and information services. In addition, this article researches and explores the construction of smart campuses in well-known universities at home and abroad, as well as the study of big data and intelligent systems. The experimental results of this paper show that with the construction of future smart campuses, traditional construction management theory has certain problems in the construction model. Combining the mature project management theory with the latest information technology, and proposing a management model for the smart campus scene in colleges and universities has certain theoretical and practical significance. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Information services

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Tamper-Proof Storage of User Movement Logs in Smart Parks 7

Tamper-Proof Storage of User Movement Logs in Smart Parks

引用

7th international conference on Communication, Image and Signal Processing, CCISP 2022

作者： Niu, Yiru Jiang, Hong Huang, Xiaobing Zhao, Qi Xiang, Hong Xia, Xiaofeng Zhao, Yue Science and Technology on Communication Security Laboratory No.30 Research Institute of China Electronics Technology Group Corporation Chengdu610041 China No.30 Research Institute of China Electronics Technology Group Corporation Chengdu610041 China Qianghua Times Technology Co Ltd. Chengdu610041 China School of Big Data and Software Engineering Chongqing University Chongqing400044 China

ISBN: (数字)9781665459594

ISBN: (纸本)9781665459594

With the introduction of the smart city concept and the rapid development of various emerging technologies such as cloud computing, Internet of Things, big data and artificial intelligence, traditional parks have also ushered in new opportunities and are developing in a smarter direction. Since the park is an open area with frequent movement of people and equipment, it is prone to security problems. At this point, logs will be a good aid for incident accountability. In this paper, we mainly use Merkle hash tree and distributed storage for secure storage of logs. Meanwhile, to ensure the non-repudiation of logs, the signatures of users and log-generating devices are required when generating logs. After analysis, the scheme proposed in this paper can achieve tamper-proof and traceability of logs. © 2022 IEEE.

关键词： Parks

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Multigranularity Multiclass-Layer Markov Random Field Model for Improving Remote Sensing Image Classification 23

Multigranularity Multiclass-Layer Markov Random Field Model ...

引用

23rd IEEE international conference on High Performance computing and Communications, 7th IEEE international conference on data Science and Systems, 19th IEEE international conference on Smart City and 7th IEEE international conference on Dependability in Sensor, Cloud and big data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Wang, Jun Fu, Haoyu Zhen, Cheng Wang, Leiguang Dai, Qinling Southwest Forestry University Faculty of Forestry Yunnan Kunming China School of Mathematics and Statistics Henan University Henan Kaifeng China Institutes of Big Data and Artifificial Intelligence Southwest Forestry University Yunnan Kunming China Art and Design College Southwest Forestry University Yunnan Kunming China

ISBN: (纸本)9781665494571

The classification of remote sensing images has always been one of the important tasks in the field of remote sensing. However, with the improvement of sensors, the spatial resolution of the remote sensing images we obtained has been further improved, so that the images after the classification of high-resolution remote sensing images will appear jagged and salt-and pepper noise phenomena. In order to further improve the accuracy of classification, many post-processing methods have been proposed. Markov random field model (MRF) is an excellent post-processing method for classified images, because it can effectively consider the spatial relationship between objects. In this article, a new MRF method is proposed for the post-processing of classified images. It not only considers therelationship between pixels and objects, but also considers the relationship between different semantic scales. This method is based on pixel and object modeling, and each granularity in the model has different semantic information. The optimization result is finally obtained through iterative transfer and update of labels between different granularities and different semantic scales. Study show that this method has better performance on the images obtained by the Gaofen-2 satellite than other post-processing methods. Kappa increased from the initial 0.8636 to 0.9547. © 2021 IEEE.

关键词： Image sensors Satellites Semantics Sensor phenomena and characterization Task analysis Spatial resolution Remote sensing

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Advanced Model for Improving IoT Security Using Blockchain Technology 4

Advanced Model for Improving IoT Security Using Blockchain T...

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4th international conference on Smart Systems and Inventive Technology, ICSSIT 2022

作者： Chauhan, Chetan Ramaiya, Manoj Kumar Department of CSE SIRT SAGE University M.P. Indore India Institute of Advance Computing SAGE University M.P. Indore India

ISBN: (纸本)9781665401180

In order to process and transmit large amounts of data generated by a wide range of devices, the advancement of artificial intelligence (AI) is critical. Anonymous users abuse the decentralisation of the blockchain when it is used to manage IoT devices and their associated large data. One of the most common uses of the platform was for illicit transactions and criminal activity. Since it's built into the hash function, no large hard forks are necessary. There are no changes made to its hash code. Intelligent design would allow any errors in the chain to be corrected uniformly and automatically, but this has not been implemented. big data or any other data-driven business is discouraged from using a new blockchain pattern due to this fact. to propose a advanced blockchain model (SRB) in support of intelligent chain editing in this article as a solution. Hash and an ephemeral trapdoor prevent editorial power from being misused. © 2022 IEEE

关键词： Internet of things

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Survey on Methodology of Intrusion Detection in Industrial Control System Based on Artificial intelligence

Survey on Methodology of Intrusion Detection in Industrial C...

引用

international conference on Computers and Artificial intelligence Technologies (CAIT)

作者： Li, Ligang Fu, Zhenyu Zou, Gaokai Mu, Zongjun Zhang, Qiaoxia Wang, Guangmin Wang, Pan Chaoyang Power Supply Co State Grid Liaoning Elect Power Co Ltd Chaoyang Liaoning Peoples R China Xuji Grp Co Ltd Xuchang Henan Peoples R China

ISBN: (纸本)9798350396171

With the increasingly close connection between the industrial control system and the Internet, the industrial control system are facing more and more cyber threats. Intrusion detection technology is one of the important technologies to protect the security of industrial control systems. With the development of artificial intelligence, many researchers have applied artificial intelligence algorithms based on machine learning and deep learning in the industrial control intrusion detection systems. Compared with the traditional methods, the detection accuracy based on machine learning is higher, but the procedure of manual feature extraction limits its practical application. The detection method based on deep learning has a strong feature extraction ability, but its computational complexity is high. In many cases, the detection effect is not ideal in industrial control scenes with real-time requirements. Based on the analysis of the related research, this paper not only summarizes the current research status of intrusion detection in industrial control system based on artificial intelligence, but also deeply discusses the challenges faced in this field. It is pointed out that the intrusion detection algorithm for the industrial control system should be further improved according to the characteristics of the industrial control system, such as real-time requirement, limited computing resources, high data dimension, and high data noise.

关键词： industrial control system intrusion detection artificial intelligence machine learning deep learning

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Research on Evaluation Model of Railway Construction Drawings 11

Research on Evaluation Model of Railway Construction Drawing...

引用

11th Joint international Information Technology and Artificial intelligence conference, ITAIC 2023

作者： Wang, Chao Li, Hui Liu, Beisheng Lyu, Xiangru Institute of Computing Technologies China Academy of Railway Sciences Corporation Limited Beijing China Beijing Jingwei Information Technology Co. Ltd. Beijing China

ISBN: (纸本)9798350333664

During the construction drawing design stage of railway construction projects, assessing and evaluating the design units is an important task. In the process of informatization, it is crucial to establish an evaluation model that meets industry standards to support complex evaluation work and ensure fairness. This study proposes an improved model for railway construction drawing evaluation, which optimizes model parameters based on big data analysis and is applied for the first time in the construction drawing evaluation process of railway construction projects. The application results show that the improved evaluation model can effectively reduce the evaluation errors caused by interference factors. At present, the evaluation model has been popularized in the railway construction projects across the whole country, reflecting the practicality of this method. © 2023 IEEE.

关键词： big data analysis construction drawing design unit assessment evaluation model Railway construction project

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Artificial intelligence and Smart Cities: A Bibliometric Analysis

Artificial Intelligence and Smart Cities: A Bibliometric Ana...

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2022 international conference on Machine Learning, big data, Cloud and Parallel computing, COM-IT-CON 2022

作者： Gupta, Ashulekha Gupta, Sanjay Memoria, Minakshi Kumar, Rajiv Kumar, Sanjeev Singh, Devendra Tyagi, Shagun Ansari, Nabila Graphic Era Deemed to Be University Department of Management Studies Dehradun India Swami Rama Himalayan University HimalayanSchool of Biosciences Dehradun India Uttaranchal Institute of Technology Uttaranchal University Department of Computer Science and Engineering Uttaranchal Dehradun India Uttaranchal Institute of Management Uttaranchal University Department of Computer Applications Dehradun India Himgiri Zee University Department of Computer Science & Information Technology Dehradun India Uttaranchal Institute of Management Uttaranchal University Dehradun India

ISBN: (数字)9781665496025

ISBN: (纸本)9781665496025

Smart cities use pioneering technology like Machine-Learning (ML), Artificial-intelligence (AI) and Internet of -Things (IoT) to connect people and places. This report includes a bibliometric analysis of works related to AI technologies used in smart city applications in order to provide a comprehensive perspective. The study's goal is to identify the most common AI approaches utilized in smart city contexts, as well as how the smart city AI research area changes over time. To investigate the problem, we used both qualitative and quantitative methodologies. To discover relevant publications published in scientific journals, we used the Scopus database. This research looks at 197 publications published between 2013 and 2021. We used the Biliometrix library and biblioshiny package, which was created in R Studio, for the bibliometric analysis. Our findings reveal that every tier of a smart city project employs a diverse set of AI technologies. Several supervised and unsupervised machine learning methods are used to operate the instrumentation, middleware, and application layers. According to the bibliometric study, AI and IoT in terms of smart cities is a rapidly emerging research field. Researchers from all over the world have expressed a strong desire to explore and collaborate in this interdisciplinary topic. © 2022 IEEE.

关键词： Internet of things

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Spatial-temporal-spectral transformer for 3D human pose estimation 23

Spatial-temporal-spectral transformer for 3D human pose esti...

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作者： Wu, Yongpeng Kong, Dehui Wang, Shaofan Li, Jinghua Yin, Baocai Beijing Institute of Artificial Intelligence Beijing University of Technology Beijing Key Laboratory of Multimedia and Intelligent Software Technology Faculty of Information Technology Beijing100124 China

ISBN: (纸本)9781665494571

Human motion exhibits a high spatial-temporal correlation, and further exploration of the intrinsic correlation of joint motion trajectories is beneficial to improve the performance of 3D pose estimation. Therefore, we propose a novel spatial-temporal-spectral transformer for high-quality 3D human pose estimation in videos, which mainly includes the spatial-temporal transformer at the joint level and the spectral transformer at the joint trajectory level. The former explores the dependencies of the joint level from the skeleton graphic structure and the sequence to obtain a richer feature representation. The latter explores the dependence of joint motion trajectories in the spectral domain. To obtain a more accurate 3D pose estimation of the center frame, a multi-layer stride convolution module is used to realize the estimation from the full frame to the center frame. In addition, since the 2D and 3D pose sequences have the same motion trajectory in the xy plane, we add the consistency constraint to obtain more accurate estimation results. Extensive experiments show that the proposed framework achieves state-of-the-art performance on Human3.6M. © 2021 IEEE.

关键词： Three-dimensional displays Correlation Smart cities Pose estimation Transformers Feature extraction Trajectory

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Analysis and Discussion on Standard Cost Allocation Model in State Grid 6th

Analysis and Discussion on Standard Cost Allocation Model in...

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6th international conference on Smart computing and Communication (SmartCom)

作者： Jin, Shaojun Pan, Jun Chen, Qian Li, Bo Jinhua Power Supply Co State Grid Zhejiang Elect Power Co Jinhua 321000 Zhejiang Peoples R China

ISBN: (纸本)9783030977740;9783030977733

With the rapid development of smart grid, the traditional financial system gradually does not meet the needs of the current power grid financial decision-making. As a new data processing means, data analysis and statistical regression can make up for some shortcomings of the traditional financial system. This paper discusses the application of big data analysis and artificial intelligence theory in the activity-based transformation of power grid standard cost, in order to provide new ideas for power grid cost management. Firstly, the stepwise regression analysis and forward driver selection method are used to select the most significant driver variables from many driver variables, and then on this basis, the selected variables are visually analyzed to further eliminate the high correlation between variables. Finally, the machine learning model in artificial intelligence and lifting algorithm are used to optimize and discuss the cost allocation model. The experimental results show that the average absolute error of the total cost calculated by the model is 6% lower than the actual total cost. big data analysis and AI can more efficiently process power grid financial data, which is of great help to improve the accuracy of calculation.

关键词： Cost allocation Standard cost Artificial intelligence Stepwise regression Decision tree

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