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Automated Driving Capabilities using advanced software tools to improve optimization for Autonomous Vehicle in Industry 4.0 1

Automated Driving Capabilities using advanced software tools...

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1st international conference on Advances in Electrical, Electronics and Computational Intelligence, ICAEECI 2023

作者： Saini, Vaibhav Jain, Ayushi Shah, Pritesh Sekhar, Ravi Warrior, Preeti Verolt Engineering Pvt Ltd Pune India Pune Campus Maharashtra Pune412115 India Army Institute of Technology Pune411015 India

ISBN: (纸本)9798350342796

Autonomous vehicles are still evolving in Industry 4.0 and moving closer to becoming a reality. As technology advances in the context of intelligent world capabilities for self-driving cars, systems become more complicated and humans as engineers must employ several techniques to produce a product with fewer flaws. The graphical representation of technological developments is rising quickly over time and will reach its apex soon. Since it is extremely difficult to create a product from zero in this market, developers may benefit greatly from the industry's cutting-edge tools like MATLAB & Simulink for creation, analysis, simulation, testing, and verification. These tools for software development offer excellent solutions at every stage of creating a strong and efficient targeted goal. The self-driving car and its advancements, including ADAS levels, functional building blocks, smart sensors, sensor fusion capabilities, and the integration of these physical systems with cutting-edge software tools to address problems that arise in the real world, are the focus of this research. Additionally, autonomous cars can use interdisciplinary elements to strengthen them. Finally, we have proposed a strategy that entails building an effective architecture, employing data optimization techniques for efficient data processing, using the V-Model testing and verification cycle for autonomous cars, and integrating Auto Code generation into independent standalone application deployment features. © 2023 ieee.

关键词： ADAS Auto Code generation Autonomous Vehicle data optimization MATLAB & Simulink Self-driving vehicle sensor fusion Simulation Smart sensors V-Model testing Cycle

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Random Forest, DT And SVM Machine Learning Classifiers for Seed with Advanced WSN sensor Node 1

Random Forest, DT And SVM Machine Learning Classifiers for S...

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1st ieee international conference on Automation, computing and Renewable systems, ICACRS 2022

作者： Shingade, Sachin D. Mudhalwadkar, Rohini Prashant Masal, Komal M. Departement of Technology DOT SPPU & MITAOE Pune India Dept. of Instrumentation and Control Govt. COEP Pune India Department of Technology SPPU & PICT Pune India

ISBN: (纸本)9781665460842

Random Forest- RF, Support Vector Machine - SVM and Decision Tree - DT Machine learning classifiers proposed and implemented to develop seed prediction model in Visual studio code environment using python along with advanced Wireless sensor Network's raino-meter sensor node. Wireless sensor network's infrastructure consists of sensors and nodes. Use of a wireless system has the potential for significant savings of workforce, resources and time. By adding Soil pH, Temperature, Humidity and Rainfall sensor in the network one can predict the crop seed to be cultivated in the current time by analyzing the environmental conditions in the farm. Aim of this work is to develop model using data analytics and machine learning techniques. Prediction model is trained on historic environmental data of crops. Based on the history and current environmental data crop recommendation is proposed. Basic data collection nodes with the sensor network provides the required information about environmental parameters. Random Forest- RF, Support Vector Machine - SVM and Decision Tree - DT Machine learning classifiers implemented and results shows Random Forest is the best with 95.12 % accuracy, 94.94 % precision, 94.85 % recall and 95.12 % F1 score. This system helps to provide the correct advice at the correct time. © 2022 ieee

关键词： Forecasting

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Blockchain Technology:- A New Technology for Creating distributed and Trusted computing Environment 1

Blockchain Technology:- A New Technology for Creating Distri...

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1st international conference on Applied Artificial Intelligence and computing, ICAAIC 2022

作者： Tyagi, Amit Kumar Chandrasekaran, Saravanan Sreenath, N. School of Computer Science and Engineering Vellore Institute of Technology Tamilnadu Chennai600127 India Department of Computer Science & Engineering Tamil Nadu Chennai India Puducherry Technological University Department of Computer Science and Engineering Puducherry India

ISBN: (纸本)9781665497107

In these (several) past decades (1950 to 2018), the world is moved from wired things to wireless devices or electronics devices/ technology. This increment or development in networking technology faces several incidents like issues of leaking privacy of users, issue of surveillance and security breaches in systems. This paper discusses about compromising users' privacy as various issues, challenges and questions (raised in existed models). Note that due to these issues or breaches or loopholes, unknown users/ third-parties collect and control large amounts of personal data, also they may use this data against respective user, for example, for financial use, for blackmailing, etc. Hence, this article discusses about a new technology (i.e., Blockchain), on which every industry is trusting on. How this new technology has been growing as fastest technology only in the past decade to build among users and organization? Such answers have been given in this paper. Blockchain provides decentralized, distributed personal data management system which ensures users own and control their data or protect user's data any kind of breaches. © 2022 ieee.

关键词： Blockchain

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An Adaptive Fuzzy-Based Clustering Model for Healthcare Wireless sensor Networks 1st

An Adaptive Fuzzy-Based Clustering Model for Healthcare Wire...

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1st international conference Ambient Intelligence in Health Care (ICAIHC)

作者： Chithaluru, Premkumar Jena, Lambodar Singh, Debabrata Teja, K. M. V. Ravi Koneru Lakshmaiah Educ Fdn Dept Comp Sci & Engn Vaddeswaram 522302 Andhra Pradesh India SOA Univ ITER Dept CA Bhubaneswar Odisha India Koneru Lakshmaiah Educ Fdn Dept Mech Engn Vaddeswaram 522302 Andhra Pradesh India

ISBN: (纸本)9789811960703;9789811960680;9789811960673

Healthcare wireless sensor networks (WSNs) have made their way into a wide range of applications and technologies with significantly different requirements and features in recent years. The integration of sensor nodes into intelligent sensing, information processing and information exchange infrastructures form healthcare WSNs will have a significant impact on a variety of applications, including telemedicine, habitat monitoring, structure health monitoring, human-centric applications and medical applications, among others. Monitoring, identification of events and responding to an event which requires a continuous access to real-time information either partial or fully distributed environment is a challenging issue. In order to overcome the challenges of healthcare wireless sensor networks (H-WSN), fuzzy-based clustering provides a cost-effective and efficient solution. Most of the problems in WSN are real time based that require fast computation, real-time optimal solution and need to be adaptive to the situation of the events and data traffic to achieve desired goals. Hence, neural networks and fuzzy sets would form appropriate candidates for implementing most of the computations involved in the issues of resource management in sensor networks. A real-time event detection is simulated and implemented on Crossbow mote (sensor node) using TinyOS.

关键词： Fuzzy H-WSN Habitat monitoring Human-centric applications Neural network structure health monitoring Telemedicine

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Backup Placement in WSNs in the Network Management distributed Setting 41

Backup Placement in WSNs in the Network Management Distribut...

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ieee 41st international conference on distributed computing systems (ICDCS)

作者： Oren, Gal Barenboim, Leonid Technion Israel Inst Technol Dept Comp Sci Haifa Israel Nucl Res Ctr Negev Sci Comp Ctr Beer Sheva Israel Open Univ Israel Dept Math & Comp Sci Raanana Israel

ISBN: (纸本)9781665449328

sensor nodes are inherently a cheap piece of hardware - due to the common need to use many of them over a large area - and usually contain a small amount of RAM and flash memory, which are insufficient in case of high degree of data sampling. An overloaded sensor can harm the data integrity, or even completely reject incoming messages. The problem gets even worse when data should be received from many nodes, as missing data becomes a more common phenomenon as deployed WSNs grow in scale. As a solution, we consider the Backup Placement problem in networks in the network management distributed setting: Given a network graph G = (V, E), the goal of each vertex v is an element of V is selecting a neighbor, such that the maximum number of vertices in V that select the same vertex is minimized. In cases of an overflow, our distributed Adaptive Clustering algorithm (D-ACR) reconfigures the network, by adaptively and hierarchically re-clustering parts of it, based on the rate of incoming data packages in order to minimize the energy-consumption, and prevent premature death of nodes. In the network management setting of a grid, we proved that a backup placement of load at most O(1) can be maintained within O(log(2) n) update time, and an O(n log(2) n) energy consumption using the constructed ACR tree.

关键词： Backup Placement WSNs Network Management distributed Setting

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Distance Estimation for Collision Avoidance of Micro Aerial Vehicles using LiDAR sensor 1

Distance Estimation for Collision Avoidance of Micro Aerial ...

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1st ieee international conference on Automation, computing and Renewable systems, ICACRS 2022

作者： Pinnamaraju, Hemanth Verma Kapu, Prashanth Reddy Juturu, Adi Narayana Anbarasu, B. School of Aeronautical Sciences Hindustan Institute of Technology & Science Chennai India

ISBN: (纸本)9781665460842

This research work proposes a novel method to prevent collision in the micro aerial vehicles by calculating the distance of the obstacle prior to the collision. For distance measurement we have used the LiDAR sensor which uses the time of flight principle and, identified its operating ranges that works perfectly for the micro aerial vehicles and analyzed the errors in various operating ranges. Determining accuracy using the Root Mean Square Error and Absolute Mean Error as two parameters for three types of data classified as All calculated data, very close values and deviating range of the Result plotting graphs and calculating errors for the same. This proposed research helps in understanding the LiDAR sensor in better way for the exploration of the range of operation of the LiDAR sensor and its applications in collision avoidance. Though the algorithm that we have done is in the Arduino UNO the same can be implemented in Pixhawk module and the control signal can be used to change the direction of the micro aerial vehicle where ever necessary. © 2022 ieee

关键词： Errors

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Research on Informatization Module of College Renovation Project Management Based on Positioning System and Height sensor Network 1

Research on Informatization Module of College Renovation Pro...

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1st international conference on Applied Artificial Intelligence and computing, ICAAIC 2022

作者： Ling, Yipin Wang, Jinbei Huang, Cong Shenzhen University Logistics Support Department Shenzhen518000 China Public Works Bureau of Longhua District of Shenzhen Shenzhen518000 China

ISBN: (纸本)9781665497107

Relying on the management practice of renovation projects in a university, and based on the perspective of internal control of renovation projects, this paper analyzes the problems existing in the management of renovation projects in universities, and puts forward countermeasures for the management of renovation projects from the aspects of establishing regulations, pre-project planning, bidding management, and team building. After the design and implementation of the sensor node hardware, the wireless sensor network operating system Contiki OS is deeply analyzed. The necessity of strengthening information management in colleges and universities under the new situation is discussed, and the implementation of college information management is analyzed from the perspective of highly sensory networks. Process and results, and finally put forward suggestions to promote the information management of colleges and universities. © 2022 ieee.

关键词： Informatization

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A Parallel Workflow for Polar Sea-Ice Classification using Auto-labeling of Sentinel-2 Imagery

A Parallel Workflow for Polar Sea-Ice Classification using A...

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1st international conference on Smart Energy systems and Artificial Intelligence (SESAI)

作者： Iqrah, Jurdana Masuma Wang, Wei Xie, Hongjie Prasad, Sushil K. Univ Texas San Antonio Dept Comp Sci San Antonio TX 78249 USA Univ Texas San Antonio Dept Earth & Planetary Sci San Antonio TX USA

ISBN: (纸本)9798350364613;9798350364606

The observation of the advancing and retreating pattern of polar sea ice cover stands as a vital indicator of global warming. This research aims to develop a robust, effective, and scalable system for classifying polar sea ice as thick/snow-covered, young/thin, or open water using Sentinel-2 (S2) images. Since the 52 satellite is actively capturing high-resolution imagery over the earth's surface, there are lots of images that need to be classified. One major obstacle is the absence of labeled 52 training data (images) to act as the ground truth. We demonstrate a scalable and accurate method for segmenting and automatically labeling S2 images using carefully determined color thresholds. We employ a parallel workflow using PySpark to scale and achieve 9-fold data loading and 16-fold map-reduce speedup on auto-labeling S2 images based on thin cloud and shadow filtered color-based segmentation to generate label data. The auto-labeled data generated from this process are then employed to train a U-Net machine learning model, resulting in good classification accuracy. As training the U-Net classification model is computationally heavy and time-consuming, we distribute the U-Net model training to scale it over 8 GPLJs using the Horovod framework over a DGX cluster with a 7.2 lx speedup without affecting the accuracy of the model. Using the Antarctic's Ross Sea region as an example, the U-Net model trained on autolabeled data achieves a classification accuracy of 98.97% for auto-labeled training datasets when the thin clouds and shadows from the S2 images are filtered out.

关键词： Polar Sea Ice Sentinel-2 Sea Ice Classification Auto-labeling Parallel Processing distributed Deep Learning Synchronous Data Parallel

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strategic Placement of PV and WTG in Low Power Distribution System for Improving System Efficacy 1

Strategic Placement of PV and WTG in Low Power Distribution ...

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1st international conference on Intelligent Controller and computing for Smart Power, ICICCSP 2022

作者： Patnaik, Samarjit Sarangi, Baisakh Tripathy, Ruchira Nayak, Manas Ranjan Viswavandya, Meera Biju Patnaik University of Technology Department of Electrical Engineering India Odisha University of Technology and Research Department of Electrical Engineering India

ISBN: (数字)9781665472586

ISBN: (纸本)9781665472586

It is common for distributed generation (DG) systems to have a capacity of several megawatts, which may be utilized to generate electricity close to users. Consider that DGs are linked to the network at ideal locations. As a result, they will reduce network losses, improve voltage profile, increase network reliability, and gain from investment deferral for distribution feeder up-gradation. The simultaneous deployment of wind turbine generators (WTG) and solar photovoltaic (PV) generators are addressed in this work, considering their uncertain power generation characteristics for a day. DGs' placement and sizing issues were then addressed using Tunicate Swarm Algorithm (TSA), an evolutionary algorithm;incorporated into the ieee 33-bus radial distribution system. Simulated findings demonstrate an improved voltage profile, increased minimum security margin by 7.8%, network loss reduction of 22.7%, and feeder investment deferral by a minimum of 13 years for distribution feeder up-gradation. © 2022 ieee.

关键词： distributed power generation

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Optimization-Based Collision Avoidance in Underwater Wireless sensor Network 1

Optimization-Based Collision Avoidance in Underwater Wireles...

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1st ieee international conference on Applied Intelligence and Sustainable computing, ICAISC 2023

作者： Sugumaran, S. Buvaneswari, B. Senthilkumar, K.S. Jeevitha, R. Ramachandra, A.C. Vishnu Institute of Technology ECE Department Bhimavaram India Panimalar Engineering College Department of Information Technology Chennai India Sri Ramakrishna College of Arts & Science Department of Computer Applications Coimbatore India KPR Institute of Engineering and Technology Computer Science and Engineering Arasur India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350323795

The Underwater Wireless sensor Network (UWSN) is crucial for private, military, and commercial maritime operations, like environmental monitoring, underwater research, scientific data collection, and underwater wireless communications. The existing protocols for Terrestrial Wireless sensor Networks (TWSNs) could be better in terms of power efficiency, reliability, and effectiveness. Due to their unique characteristics. The energy-efficient convergent depth variation routing (ECDVR) algorithm is introduced to evaluate and reduce power consumption by calculating the distance between each node point and node depth variation. This algorithm considers the two-hop neighbour depth variation when sending data to a neighbor node with a higher forward angle toward the receiver. It calculates the receive time contribution of the Adaptive Time Difference of Arrival (ATDoA). Adaptive Time Difference of Arrival (ATDoA) The arrival time of a signal makes it simple to estimate the time difference between two signals at a node. The storage node-to-receiver approach is the one that has been proposed for determining the node's remaining power for each information exchange. In the subsequent UWSN transmission, the sink allocates the node's higher power when the node reduces its power. Extending the packet's lifetime and shortening the time it takes to reach its destination boosts system performance. It outperforms the compared algorithms regarding node lifetime, dead node count, power consumption, and propagation delay. © 2023 ieee.

关键词： Energy efficiency

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