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检索条件"任意字段=3rd IEEE International Conference on Distributed Computing in Sensor Systems"

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Wireless sensor network to connect isolated nodes using link assessment technique 3

Wireless sensor network to connect isolated nodes using link...

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3rd international conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2021

作者： Sundaram, B. Barani Pandey, Amit Abiko, Aschalew Tirulo Vijaykumar, Janga Genale, Adola Haile Karthika, P. Bule Hora University College of Informatics Bule hora Ethiopia College of Informatics CSI Executive Member CSRSCD BHU Ethiopia Wachemo University School of Computing and Informatics Hosana Ethiopia Bule Hora University College of Informatics Dept. of Information Technology Bule Hora Ethiopia Bule Hora University Department of Information Science Bule hora Ethiopia Kalasalingam Academy of Research and Education Department of Computer Applications Krishnankoil India

ISBN: (纸本)9780738111834

A simultaneous geography control effort is implemented for full-reachability systems and a genetic algorithm with dynamic transformation to find the breakthrough in a blaze-scene. The proposed scheme efficiently takes care of the issue of separate node for transmission run. The suggested technique makes it possible to improve the display of the genetic algorithm in order to maintain a strategic distance from the possible arrangements which fall into nearby ideal. Different experimental simulations have to be confirmed the performance quality, reliability and timeliness of designed approaches. In the Wireless sensor established network, can connect at least two sensors in a network sensors and deliver jointly via the obtained transmission extend to form the network of total accessibility. Accordingly, excellent blaze exhaust system with complete network accessibility the proposed approaches in this paper established topology and real-time routing methods. This device supports the protection and security of individuals in a blaze scene. © 2021 ieee.

关键词： Network topology

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Crop Growth Monitoring and Leaf Area Index Estimation Using Wireless sensor Network and CNN 3

Crop Growth Monitoring and Leaf Area Index Estimation Using ...

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3rd international conference on Inventive Research in computing Applications, ICIRCA 2021

作者： Gilbert Rozario, S. Vasanthi, V. Rathinam College of Arts and Science Department of Computer Science Coimbatore India Sri Krishna Adithya College of Arts and Science Department of It and Bca Coimbatore India

ISBN: (纸本)9780738146270

Agriculture was the critical improvement in the ascent of human civilization. Leaf area is viewed as a significant marker in the investigation and evaluation of the development and assessment of the production potential of a crop. Leaf area index is an important factor for calculating the amount of energy used in the photosynthesis process, evapotranspiration, crop growth monitoring, nutrition requirement, and change in crop water requirement. Leaf area index is directly related to yield prediction. It also helps to estimate the percentage of crop growth affected by pests and diseases. Leaf area index is calculated using destructive sampling method are removing several plants from the field and placing the plant in leaf area index calculating machine. This type of sampling method is an expensive and time taking process. The camera sensor captures the crop image and transmits it to the base station to estimate the leaf area index through identifying potentially constitute the number of pixels in the leaf. These green pixels are used to calculate the leaf area in the image. The important features of this cost-efficient method are its suitability for precise, user-friendly, and non-destructive crop sample methods. This method can estimate the leaf area index for a crop without any expensive tools. The convolutional neural network is used for calculating leaf area index. Wireless sensor networks place an important role in data management and future data analysis. Crop growth monitoring is a reliable process because wireless sensor networks provide frequent data collecting without human interaction. © 2021 ieee.

关键词： Crops

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Battery Lifetime Enhancement in Wireless sensor Networks using Artificial Intelligence Techniques 3

Battery Lifetime Enhancement in Wireless Sensor Networks usi...

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3rd international conference on Advances in computing, Communication Control and Networking, ICAC3N 2021

作者： Irfan, S. Ramesh, K.S. Swaroop Kumar, N. Maheswary, A. KIOT Wollo Universoty Dept. of ECE Kombolcha Ethiopia KLEF Vaddeswaram Dept. of ECE A.P Guntur India Siddharth Institute of Science and Technology ECE A.P Puttur India Sree Venkateswara College of Engineering and Technology ECE A.P Chittoor India

ISBN: (纸本)9781665438117

A Wireless sensor Network (WSN) is a state of art technology for improving the lifetime of battery in conjunction with artificial intelligence techniques. A WSN is an intensive group of different miniature devices called sensor nodes, which help to monitor external or internal environmental conditions like luminosity and weather parameters such as pressure, sound, temperature, and humidity. These nodes are embedded with various devices like sensing components, computer-based input and output devices, radio transceivers and other power related components. These are inherently interconnected resource based constraints like limited storage capacity, communication bandwidth and limited processing speed. There is a need to adapt self-organizing nodes mechanism to match the application in order to minimize the intermediate nodes power consuming and longevity of the battery used in sensor network. These monitor dynamic paths change more often in over times, so sensor networks often adopt artificial intelligence techniques like neural networks to solve the need, such as to reduce the intermediate nodes power consuming and maximization of resource utilization sources and enhance the life period of the sensor network. This manuscript presents an absence of middle nodes inactive time, power consumption is minimized, therefore, the life time of battery to be maximized and better utilization of resources are possible. © 2021 ieee.

关键词： Artificial intelligence

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Smart Transportation systems: The Role of IoT and sensor-Based Solutions 3

Smart Transportation Systems: The Role of IoT and Sensor-Bas...

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3rd ieee international conference on Automation and Computation, AUTOCOM 2025

作者： Agarwal, Ankita Seth, Kanika Sahoo, Shreeya Swagatika Parmar, Yuvraj Dasarathy, A.K. Siva Sangari, A. Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Centre for Cyber Security Odisha Bhubaneswar India Chitkara University Chitkara Centre for Research and Development Himachal Pradesh Baddi174103 India Department of Civil Engineering Karnataka Bangalore India Sathyabama Institute of Science and Technology Department of Information Technology Tamil Nadu Chennai India

ISBN: (纸本)9798331542375

Smart transportation systems are sophisticated technology solutions that deploy the Internet of Things (IoT) and sensor-based data platforms to transform one's existing erratic transportation network into an efficient system. The Internet of Things, which combines all devices and systems connected to the Internet, is changing our management and operation of transportation systems. Real-time data from sensors installed on vehicles, infrastructure, and other transport assets is one of the biggest benefits IoT can offer to transportation. This data will reduce traffic congestion, improve road safety, and help buses move smoothly in terms of traffic flow. A good way of illustrating this is through sensors equipped with traffic lights that communicate to the cars, where they can adjust between red and green light times while in action, thus causing fewer accidents on the road. loT likewise can lessen energy consumption and emissions, driving by way of the sustainability lens on transportation systems. Smart traffic management systems can adjust routes for vehicles, saving fuel and emissions. Cars and even electric vehicles can be configured using the same platform through this system will deliver higher efficiency for managing charging stations. Additionally, IoT and sensor integration are important for the growth of autonomous vehicles. These vehicles need immediate input from cameras, radars, and LiDARs to move around safely. IoT will allow these vehicles to communicate, and the transportation infrastructure will lead to a safer means of transport. © 2025 ieee.

关键词： Traffic congestion

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indexPDT: A High Scalable distributed Classification Approach with Novel Cache Structure for Geo-location

indexPDT: A High Scalable Distributed Classification Approac...

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ieee international conference on High Performance computing and Communications (HPCC)

作者： Zhijie Sun Jing Li Jun Xie Binfan Zheng Li Zeng Rongqian Zhao Huawei Technologies Co. Ltd. Shenzhen China

Geo-location, also known as measurement report (MR) location, is a technique to determine the geographic location of user equipment (UE) and the behaviour attribute of telephone traffic based on wireless signals measured by the mobile communication network. The geographic location information can help to support network performance monitoring and evaluation. Considering accuracy and cost, we mainly adopt a hybrid location scheme combined with feature matching location and Weighted Centroid Correction Location (WCCL). As for feature matching location, over 20 billion samples gathered from tens of thousands of cells daily updated. Due to the vast data scale, feature analysis encounters a severe performance bottleneck. To address this problem, we design the indexed parallel decision tree (indexPDT) operator and integrate it into WindTensor, a self-innovated distributed machine learning (ML) engine. indexPDT is a classifier unit of the random forest (RF) algorithm with a novel cache structure. It performs structured cache processing on the dataset's meta-information, which can accompany the splitting of nodes. The cache structure can be quickly converted into statistical information to help find the optimal splitting point, effectively reducing memory usage and improving performance. Under the public datasets testing on 5 nodes, the mean speedup ratios are 86x and 3x compared with SparkML and XGBoost, respectively. In the Geo-location scenario, for a single cell, the speedup ratios are 82x and 4x compared with SparkML and XG Boost, respectively.

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A Stochastic Neighbor Embedding Approach for Cancer Prediction

A Stochastic Neighbor Embedding Approach for Cancer Predicti...

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3rd ieee international conference on Emerging Smart computing and Informatics (ieee-ESCI)

作者： Sharma, Nonita Mangla, Monika Mohanty, Sachi Nandan Satpaty, Suneeta NIT Jalandhar Jalandhar Punjab India Lokmanya Tilak Coll Engn Navi Mumbai India Coll Engn Pune Pune Maharashtra India Coll Engn Bhubaneswar Bhubaneswar India

ISBN: (纸本)9781728185194

Nowadays, cancer is the most prevalent cause of death worldwide. Breast cancer is the 2nd most common cancer registered globally and the leading death factor among women. So, if we predict cancer in the primitive stage, we can avoid death. Therefore, there is an imperative need of a tool that can quickly detect whether the breast cancer tumor is a malignant or a benign one. In this paper, two-dimensionality reduction methods, namely Principal Component Analysis (PCA) and t-distributed Stochastic Neighbor Embedding(t-SNE) are used. t-SNE visualizes high-dimensional data by reducing it in a two or three-dimensional map. It optimizes the cost function and produces much better scatter plots by decreasing the tendency to the crowd points in the center of the map. For classification, two machine learning algorithms, namely Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost), are employed. The experimental result reveals that t-SNE performed better as compared to PCA.

关键词： Principal Component Analysis t-distributed Stochastic Neighbor Embedding Machine Learning Support Vector Machine Dimensionality reduction

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2021 3rd international conference on Electrical Engineering, EECon 2021

2021 3rd International Conference on Electrical Engineering,...

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3rd international conference on Electrical Engineering, EECon 2021

ISBN: (纸本)9781665417129

The proceedings contain 23 papers. The topics discussed include: workshop on ‘soft robotics for biomedical applications’: a soft robotics approach to understand how the brain organizes the body to make real-time survival in natural environments more efficient;workshop on ‘soft robotics for biomedical applications’: facial expression rendering for high fidelity robotic patients;validation of Meteonorm 8 for energy estimation of solar power plants in Sri Lanka, using PVsyst software;optimum wind turbine design and analysis to harvest wind energy from fast-moving vehicles on highways;development of a software tool for power flow analysis in a distributed generation integrated radial distribution system;short-term wind power forecasting using a Markov model;application of machine learning algorithms for predicting vegetation related outages in power distribution systems;and influence of water and powder contaminants on dielectric strength of transformer oil.

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Fault Detection in ieee 9-Bus DC Microgrid System using Differential Current Method 3

Fault Detection in IEEE 9-Bus DC Microgrid System using Diff...

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3rd ieee international conference on Energy, Power and Environment - Towards Clean Energy Technologies (ICEPE)

作者： Kumar, Satyavarta Affijulla, Shaik Panda, Gayadhar NIT Meghalaya Dept EE Shillong Meghalaya India

ISBN: (纸本)9781665425360

The fault current in DC microgrids changes quickly compared to conventional AC power systems. This is due to low passivity-based distributed energy resources in DC microgrids that transfer power through electronic converters. Additionally, due to the limited magnitude of fault current, fault detection and identification is an additional challenging task. This paper proposes a new effective protection system for rapid short-circuit detection and isolation of faults in a 9 bus DC microgrid system. This is accomplished by allowing, via a cumulative sum of differential forward and reverse current based method, the fault Isolation by a solid-state circuit breaker action. The faulty line is identified through boundary analysis of locally measured current and voltage signals. The defective branch is isolated by a trip at each node in the system. Finally, two tests of simulation results of the system under pole-pole fault in PSS (R) SINCAL have been designed which achieve the reliability of the method. The simulation result showed that the system fault can be isolated by a trip at each node in the system.

关键词： DC microgrids power converters PV module differential current protection

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Condition Monitoring and Identification Method for Multiple Filters of Gas Turbine

Condition Monitoring and Identification Method for Multiple ...

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Power systems and Electrical Technology (PSET), ieee international conference on

作者： Linfeng Xu Dengji Zhou Wang Xiao Chongyuan Shui Zewen Gu Cheng Wang Shanghai Jiao Tong University Shanghai China West Pipeline Company Pipe China Urumqi China

ISBN: (数字)9798350353204

ISBN: (纸本)9798350353211

Gas turbine inlet filters are a crucial component that maintains the purity and quality of the intake air in gas turbines. Besides, gas turbine inlet filters usually consist of multiple filter elements, each of which is expensive, making it significant to locate the fault before a replacement can be made. However, there is no existing method to online diagnose and locate the fault in gas turbine inlet filter. This paper proposed a monitoring and identification method for gas turbine inlet filters. Faulty filters can be identified by examining pressure drops detected by several sensors at various locations. This study conducted Computational Fluid Dynamics calculation of the gas turbine inlet system and validated the results by comparing them with actual pressure difference measurement data. Based on the CFD calculations, the sensor measurement points were arranged, and the localization of the faulty filter core region was achieved by utilizing the distribution characteristics of the pressure difference data from these measurement points.

关键词： Fault diagnosis Filters Power measurement Computational fluid dynamics sensor phenomena and characterization Pressure measurement Power system reliability Turbines Monitoring sensor arrays

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Fault Localization of Distribution Network Based on Reverse-Local Learning Based Particle Swarm Optimization Algorithm

Fault Localization of Distribution Network Based on Reverse-...

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Energy Engineering and Power systems (EEPS), international conference on

作者： Wei Zhang Yangjun Zhou Chenying Yi Shan Li Bin Feng Weixiang Huang Juntao Pan Yuanchao Zhou Lifang Wu Guangxi Key Laboratory of Intelligent Control and Maintenance of Power Equipment Electric Power Research Institute of Guangxi Power Grid Co. Ltd. Nanning China Guangxi Power Grid Co. Ltd. Nanning China Nanning Power Supply Bureau of Guangxi Power Grid Co. Ltd. Nanning China

ISBN: (数字)9798350375138

ISBN: (纸本)9798350375145

With the large-scale access of distributed generation, the power flow characteristics of distribution network have been greatly changed. The conventional fault localization methods for distribution networks are no longer applicable. To deal with the problems induced by large-scale access of distributed generation, a novel fault localization method based on improved particle swarm optimization algorithm is proposed in this paper. Aiming at the issues of potential misjudge, an objective function/ fitness value correction method is proposed to adapt to bidirectional power flow induced by distributed generation. Aiming at the issues of sluggish convergence and poor robustness in existing models, this paper introduces the Reverse-Local Learning based Particle Swarm Optimization algorithm to improve the optimization efficiency and algorithm stability. Case study is performed on the ieee-33 bus distribution network to demonstrate the accuracy and efficiency of the proposed fault localization method.

关键词： Location awareness Accuracy Distribution networks Power system stability Robustness Stability analysis distributed power generation Smart grids Particle swarm optimization Convergence

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