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共 769 条记录，以下是161-170 订阅

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The Role of the Internet of Things in Monitoring Air Pollution-A Survey

The Role of the Internet of Things in Monitoring Air Polluti...

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International Conference on information technology (ICIT)

作者： Ahmad Abusukhon Bareeq Alghannam Computer Science Dep. Faculty of Science and Information Technology Al-Zaytoonah University of Jordan Amman Jordan Department of Computer Science and Information System College of Business Studies The Public Authority for Applied Education and Training Kuwait City Kuwait

Air pollution is one of the most common problems that the world is facing today. In fact, there are numerous causes of air pollution, including the large number of industries and automobiles that emit carbon dioxide (CO2) and carbon monoxide. Pollution is increasing on a daily basis, making air quality monitoring and management a major challenge for governments in both developed and developing countries. Air pollution affects human health as well as the atmosphere, causing climate change. One of the techniques used for monitoring air pollution is the Internet of Things (IoT). In this paper, we demonstrate various techniques for monitoring air quality using IoT and make a comparison based on various factors elicited from the previous work.

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Improving Distribution Systems Maintenance Management, by Predicting Reliability Indices Using Fully Connected Neural Networks

Improving Distribution Systems Maintenance Management, by Pr...

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Smart Grid Conference (SGC)

作者： Mohammad-Hossein Tananaeifar Mahdis Alimadaday SeyedMohammad-Mahdi Ghadamgahi Mohammad Ghafourian-Nasiri Alireza Fereidunian Construction Engineering and Management Dep. Amirkabir University of Technology Tehran Iran Industrial Engineering Dep. Tarbiat Modares University Tehran Iran Computer Engineering Dep. Kharazmi University Tehran Iran Electrical Engineering Faculty K. N. Toosi University of Technology Tehran Iran

ISBN: (数字)9798350333961

ISBN: (纸本)9798350333978

Due to the increasing amount of industrial data worldwide, deep learning solutions have become extensively popular for preventive maintenance programs. Preventive maintenance reduces the costs of equipment failures, thus crafting detailed preventive maintenance programs are very effective. Neural networks are intelligent computing techniques inspired by biological neurons. Neural networks are one of the most common and practical machine learning algorithms that are utilized in many industrial applications. In this research, a fully connected neural network is developed to predict the systems reliability indices, i.e. the number of failures occurring in feeders and the amount of energy not served (ENS) in electricity distribution feeders. As a result, a data-driven improvement is provided for the preventive maintenance program.

关键词： Costs Machine learning algorithms Neurons Energy measurement Organizations Smart grids Reliability

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Algorithms for Enhanced Indoor Positioning and Tracking based on a 60-GHz Radar Platform

Algorithms for Enhanced Indoor Positioning and Tracking base...

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Workshop on Metrology for Industry 4.0 and IoT

作者： F. Shamsfakhr M. Corrà A. Ferrari D. Macii L. Palopoli D. Fontanelli Dep. of Industrial Engineering University of Trento Italy Tretec S.r.L. Trento Italy VNG Ingegneria Trento Italy Dep. of Information Engineering and Computer Science University of Trento Italy

ISBN: (数字)9781665410939

ISBN: (纸本)9781665410946

Wireless technologies are increasingly adopted for indoor localisation and tracking. Among them, radars based on 60 GHz phased-array transceivers are becoming a viable and low-cost solution, which delivers high accuracy with a low computational cost while preserving people privacy. In this paper, we propose two techniques for radar-based people tracking and localisation that tackle important effects such as delayed and spurious detection. We also offer a thorough and scientifically sound experimental analysis of the performance of the proposed techniques using an off-the-shelf phased-array device (i.e., the System-on-Chip (SOC) TI IWR6843).

关键词： Wireless communication Performance evaluation Privacy Target tracking Radar measurements Radar Radar tracking

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Basic Study for Transfer Learning for Autonomous Driving in Car Race of Model Car 6

Basic Study for Transfer Learning for Autonomous Driving in ...

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6th International Conference on Business and Industrial Research, ICBIR 2021

作者： Chiba, Shohei Sasaoka, Hisayuki National Institute of Technology Asahikawa College Advanced Course of Production System Engineering Asahikawa Japan National Institute of Technology Asahikawa College Dep. of Electrical and Computer Engineering Asahikawa Japan

ISBN: (纸本)9781665433495

Reinforcement learning, deep learning, and deep reinforcement learning can effectively acquire action rules for the autonomous motion of objects. However, some researchers have reported that the machine learning process requires a large amount of learning time. Besides, the process needs to consider the similarity of the environment between the training target and the test target. There is no such thing as driving only on a course learned in advance in actual autonomous driving. In this study, we have used a transfer learning algorithm for autonomous drivings for model cars. The training target for acquiring the learning model and the actual driving courses are changed. In this study, we report on the effectiveness of transfer learning using a model car as the basis for a learning model acquired by reinforcement learning. © 2021 IEEE.

关键词： Deep learning

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Intelligent Recognition of Immune Cells: Analysis and Research

Intelligent Recognition of Immune Cells: Analysis and Resear...

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IEEE International Conference on e-Business engineering (ICEBE)

作者： Chih-En Li Wei-Lun Liu Kun-Yi Lai Yu-Hsiang Chiang Xiaowei Yan Hao-Xiang Chen Hsu-Ching Li Wan-Shu Cheng Department of Electrical Engineering National Kaohsiung University of Science and Technology Kaohsiung Taiwan Institute for Systems Biology Seattle Washington U.S.A Dep. of Computer Science and Information Management Providence University Taichung Taiwan

Immunotherapy utilizes the potential of the patient's immune system to fight disease. T-lymphocytes have become the central focus of the immune system in the fight against cancer. One of the fundamental studies in immunotherapy is how to accurately identify various types of immune cells. Therefore, the study of immune-related cells will be an important reference for the treatment of COVID19 or other related diseases. In this paper, a series of experiments were conducted with COVID-19 patients as the starting point to design a strategy to identify the important subtypes of CD$4^{+}$T cells. In this paper, an intelligent machine learning algorithm is proposed according to the results of different experiments of linear and nonlinear dimension reduction algorithms, integrating the important characteristics of these two algorithms and improving the performance. Our experimental results show that the algorithm designed based on the front-end experiments can effectively distinguish the important lymphocytes. The proposed algorithm not only improves the performance, but also the dimension reduction procedure keeps the important features of both global and regional data. The experimental results of this paper will be beneficial for the subsequent analysis of the clustering in identifying important subtypes of CD$4^{+}$T cells. Most importantly, this wm significantly improve the procedure of manually integrating and labeling the recorded data after several tedious and monotonous analyses with external software.

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Challenges and Solutions of 5G technology 4

Challenges and Solutions of 5G Technology

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4th International Iraqi Conference on engineering technology and Their Applications, IICETA 2021

作者： Thabit, Ahmed A. Abd, Bassam H. Alkhayyat, Ahmed Computer Communication Department Al-Rafidain University College Baghdad Iraq University of Technology Baghdad Iraq Dep. of Computer Technical Engineering College of Technical Engineering Islamic University Najaf Iraq

ISBN: (纸本)9781665494618

Mobile access innovation is experiencing a progressive change. Every mobile generation witnessed fast and huge execution upgrades. These are proportional with the requests of the massive data increasing over the last years. There is significant strain to characterize the vital necessities of 5G, create 5G principles and perform innovation preliminaries as fast as could really be expected. 5G needs a progressive jump forward regarding information rates, inactivity, huge availability, network unwavering quality and energy efficiency. These capacities are focused at acknowledging rapid network, IoT, increased augmented experience, the material web, etc. The prerequisites for 5G are need to be done by new range in the microwave groups and using enormous data transfer capacities accessible in mm-wave groups, expanding spatial levels of opportunity by means of huge radio wire clusters and 3D MIMO and new waveforms that give versatility and adaptability in order to fulfill fluctuating needs of 5G administrations. In this paper, we introduce a review for 5G exploration, normalization preliminaries, and organization challenges. Because of gigantic extent of 5G frameworks, it is important to give some bearing in an instructional exercise article, and in this outline, the center is generally client driven, as opposed to gadget driven. Notwithstanding studying the condition of play in the region, we distinguish driving innovations, assessing their qualities and shortcomings, and framework the critical difficulties ahead, with research proving grounds conveying promising execution. © 2021 IEEE.

关键词： Data transfer

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Smart Monitoring and Disease Detection for Robotic Harvesting of Tomatoes 3

Smart Monitoring and Disease Detection for Robotic Harvestin...

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3rd International Conference on Advancements in Computing, ICAC 2021

作者： Pasindu, Imesh Viraj, Sumeera Dilshan, Ravindu Kalhara, Akshith Senaweera, Oshada De Silva, Rajitha Jayawardena, Chandimal Sri Lanka Institute of Information Technology Dep. of Computer Systems Engineering Malabe Sri Lanka Lincoln Institute for Agri-Food Technology University of Lincoln Lincoln Agri-Robotics Centre United Kingdom Sri Lanka Institute of Information Technology Faculty of Computing Malabe Sri Lanka

ISBN: (纸本)9781665408622

Tomato is a one of the most popular produced and extensively consumed vegetables in the world. Typical agricultural systems make extensive use of human labor which is more costly and less effective. This research explores the minimization of human labor through automation. The diseases infected by tomato plants are hard to detect. Identifying these diseases in advance would save the cultivation of the disease from spreading, thereby saving the crop. It is also a difficult task to recognize the ripe harvest and experienced labor is required. The efficiency of the harvesting method will be increased by automating the identification process of ripened fruits. Manually picking tomatoes can cause some harm to the fruits during plucking due to inconsistencies in human labor. Such damage will be reduced through a better implemented robotic scheme. This paper presents the development of autonomous system for tomato harvesting and disease detection. © 2021 IEEE.

关键词： Harvesting

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A Novel SCD11 CNN Model Performance Evaluation with Inception V3, VGG16 and ResNet50 Using Surface Crack Dataset

A Novel SCD11 CNN Model Performance Evaluation with Inceptio...

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International Conference on Unmanned Vehicle Systems-Oman (UVS)

作者： Adil Hussain Kashif Naseer Qureshi Raja Waseem Anwar Ayesha Aslam School of Electronics and Control Engineering Chang'an University Xi'An China Department of Computer Engineering and Electronics University of Limerick Limerick Ireland Dep of Computer Science German University of Technology in Oman Muscat Oman School of Information Engineering Chang'an University Xi'An China

Image processing techniques can be used to detect potential defects in images of infrastructure elements that have been collected or scanned. Aside from image processing, machine learning technologies are increasingly being applied to increase crack detection performance and resilience. A novel Surface Crack Detection Convolutional Neural Network (CNN) named the SCD11 CNN model is proposed. The main aim of this research paper is to improve efficiency and minimize the loss rate. The proposed CNN model is compared with the CNNs, including Inception V3, VGG16, and ResNet50, using a Surface Cracks Dataset. The accuracy and loss of the proposed model are compared. The results show that the proposed SCD11 CNN model performs better in test accuracy than Inception V3 and VGG16 and has a slightly lower accuracy than ResNet50.

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Multi-objective parameter-less population pyramid in solving the real-world and theoretical problems 21

Multi-objective parameter-less population pyramid in solving...

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2021 Genetic and Evolutionary Computation Conference, GECCO 2021

作者： Przewozniczek, Michal W. Dziurzanski, Piotr Zhao, Shuai Indrusiak, Leandro S. Dep. of Computational Intelligence Wroclaw University of Science and Technology Wroclaw Poland Fac. of Computer Science and Information Technology West Pomeranian University of Technology Szczecin Poland University of York Department of Computer Science York United Kingdom

ISBN: (纸本)9781450383516

Many real-world problems are notoriously multi-objective and NP-hard. Hence, there is a constant striving for optimizers capable of solving such problems effectively. In this paper, we examine the Multi-Objective Parameter-less Population Pyramid (MO-P3). MO-P3 is based on the Parameter-less Population Pyramid (P3) that was dedicated to solving single-objective problems. P3 employs linkage learning to decompose the problem and uses this information during its run. P3 maintains many different linkage information sets, which is the key to effectively solve the problems of the overlapping nature, i.e., the problems whose variables form a large and complicated network of dep.ndencies rather than additively separable blocks. MO-P3 inherits the features of its predecessor and employs both linkage learning and linkage diversity maintenance to effectively solve hard multi-objective problems, which includes both: well-known test problems and NP-hard real-world problems. © 2021 Owner/Author.

关键词： Genetic algorithms

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Intelligent disease detection system for greenhouse with a robotic monitoring system 2

Intelligent disease detection system for greenhouse with a r...

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2nd International Conference on Advancements in Computing, ICAC 2020

作者： Fernando, Sandunika Nethmi, Ranusha Silva, Ashen Perera, Ayesh De Silva, Rajitha Abeygunawardhana, Pradeep K.W. Dep. of Computer Systems Engineering Sri Lanka Institute of Information Technology Malabe10115 Sri Lanka

ISBN: (纸本)9781728184128

Greenhouse farming plays a significant role in the agricultural industry because of its controlled climatic features. Recent examinations have stated that the mean creation of the yields under greenhouses is lessening due to disease events in the plants. These foods have become an imposing undertaking because these plants are being assaulted by different bacterial diseases, micro-organisms, and pests. The chemicals are applied to the plants intermittently without thinking about the necessity of each plant. Several problems have occurred in the greenhouse environment due to these causes. Therefore, there is a huge necessity for a system to detect diseases at an early stage. This research focused on designing a system to detect disease, which causes yellowish in greenhouse plants. Plant yellowing can be considered a significant problem of plants that grow under greenhouse-controlled environments. Through this research is focused on the most important and one of the most attention-grabbing crop tomato. There are specific diseases that cause yellowish the tomato plant, and they have been identified. The techniques utilized for early recognition of infection are image processing, machine learning, and deep learning. Index Terms-Greenhouses, Disease diagnosis, Image processing, Machine Learning, Deep Learning, Tomato Farming. © 2020 IEEE.

关键词： Greenhouses

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