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international conference on smart computing (smartCOMP)

作者： Afiya Ayman Michael Wilbur Amutheezan Sivagnanam Philip Pugliese Abhishek Dubey Aron Laszka University of Houston Vanderbilt University Chattanooga Area Regional Transportation Authority

ISBN: (数字)9781728169972

ISBN: (纸本)9781728169989

Due to increasing concerns about environmental impact, operating costs, and energy security, public transit agencies are seeking to reduce their fuel use by employing electric vehicles (EVs), However, because of the high upfront cost of EVs, most agencies can afford only mixed fleets of internal-combustion and electric vehicles. Making the best use of these mixed fleets presents a challenge for agencies since optimizing the assignment of vehicles to transit routes, scheduling charging, etc. require accurate predictions of electricity and fuel use. Recent advances in sensor-based technologies, data analytics, and machine learning enable remedying this situation; however, to the best of our knowledge, there exists no framework that would integrate all relevant data into a route-level prediction model for public transit. In this paper, we present a novel framework for the data-driven prediction of route-level energy use for mixed-vehicle transit fleets, which we evaluate using data collected from the bus fleet of CARTA, the public transit authority of Chattanooga, TN. We present a data collection and storage framework, which we use to capture system-level data, including traffic and weather conditions, and high-frequency vehicle-level data, including location traces, fuel or electricity use, etc. We present domain-specific methods and algorithms for integrating and cleansing data from various sources, including street and elevation maps. Finally, we train and evaluate machine learning models, including deep neural networks, decision trees, and linear regression, on our integrated dataset. Our results show that neural networks provide accurate estimates, while other models can help us discover relations between energy use and factors such as road and weather conditions.

关键词： Roads Neural networks Machine learning Electric vehicles Fuels Security Meteorology

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Large-Scale intelligent Microservices

Large-Scale Intelligent Microservices

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IEEE international conference on Big data

作者： Mark Hamilton Nick Gonsalves Christina Lee Anand Raman Brendan Walsh Siddhartha Prasad Dalitso Banda Lucy Zhang Lei Zhang William T. Freeman Cognitive Services Research Microsoft MIT Boston USA Cognitive Services Research Microsoft Boston USA Applied AI Microsoft Bellevue USA Grey Systems Lab Microsoft Boston USA Applied AI Microsoft MIT Boston USA Cognitive Services Research Microsoft Bellevue USA CSAIL MIT Boston USA

ISBN: (数字)9781728162515

ISBN: (纸本)9781728162522

Deploying Machine Learning (ML) algorithms within databases is a challenge due to the varied computational footprints of modern ML algorithms and the myriad of database technologies each with their own restrictive syntax. We introduce an Apache Spark-based micro-service orchestration framework that extends database operations to include web service primitives. Our system can orchestrate web services across hundreds of machines and takes full advantage of cluster, thread, and asynchronous parallelism. Using this framework, we provide large scale clients for intelligent services such as speech, vision, search, anomaly detection, and text analysis. This allows users to integrate ready-to-use intelligence into any datastore with an Apache Spark connector. To eliminate the majority of overhead from network communication, we also introduce a low-latency containerized version of our architecture. Finally, we demonstrate that the services we investigate are competitive on a variety of benchmarks, and present two applications of this framework to create intelligent search engines, and real time auto race analytics systems.

关键词： Machine learning algorithms databases Web services Ecosystems Cluster computing Parallel processing Throughput

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2nd Workshop on Edge Intelligence and computing for IoT Communications and Applications

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太赫兹科学与电子信息学报 2020年第5期 957-957页

in 7th EAI international conference on IoT as a Service (IoTAAS 2020)November 19-20, 2020, Xi’an, China Website:http://***/Scope and Topics of Interests Nowadays, billions of IoT devices, e.g., sensors and RFIDs, arise around us providing not only computing intensive,but also delay-sensitive services, ranging from augmented/virtual realities to distributed data analysis and artificial intelligence. Unfortunately, in many application scenarios, the low response latency for IoT services are achieved at the cost of computing-complexity that far exceeds the capabilities of IoT devices. To feed this trend, multiple computing paradigms emerge, such as mobile transparent computing, edge computing, fog computing and big data analytics based framework. These paradigms employ more resourceful edge devices, e.g., small-scale servers, smart phones and laptops,to assist the low-end IoT devices. By offloading the computing-intensive tasks to the edge devices, it is expected to converge the data collection at IoT devices and the data processing at edge devices to provision computing-intensive and delay-sensitive services. However, lots of issues remain in the application of edge computing which impede its flourish in IoTs.

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data Fusion in Autonomous Vehicles Research, Literature Tracing from Imaginary Idea to smart Surrounding Community

Data Fusion in Autonomous Vehicles Research, Literature Trac...

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international conference on Fog and Mobile Edge computing (FMEC)

作者： Shadi AlZu'bi Yaser Jararweh Al Zaytoonah University of Jordan Amman Jordan Jordan University of Science and Technology Irbid Jordan

ISBN: (数字)9781728172163

ISBN: (纸本)9781728172170

Many significant challenges related to transportation systems have been raised recently, these include the high accidents rate, road congestion, emission of gases, and environment pollution. Furthermore, transportation crashes caused injuries in accident. Researchers investigated virtual methodologies to automate the transportation process that known as intelligent Transport System (ITS). In the last 20 years, ITS have been employed efficiently to enhance the performance of transportation systems, improve security of travel, and provide several choices for travelers. The idea of virtual technologies integration is a novel in transportation field. Collecting data from multi-sources played a significant improvement in ITS, this can help stakeholders for processing different forms of data. This huge amount of data can significantly help in the revolution of ITS. This paper focused on several Autonomous vehicles (AVs) related technologies that employed efficiently in ITS. This review integrates and synthesizes monitoring AVs using data fusion from different data sources. Furthermore, recent technologies and software toolboxes are presented in this paper to facilitate the researchers job in improving the data and sensor fusion in AVs.

关键词： Roads Automobiles Autonomous vehicles Robot sensing systems Cameras data integration

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Secure and Fault-tolerant Advanced Metering Infrastructure

Secure and Fault-tolerant Advanced Metering Infrastructure

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international conference on Power System Technology (POWERCON)

作者： Dodla Sidhartha Lagineni Mahendra Katta Jagan Mohan R.K. Senthil Kumar B.S. Bindhumadhava Cente for Development of Advanced Computing Knowledge Park No 1 Old Madras Road Byappanahalli Bangalore INDIA Center for Development of Advanced Computing Knowledge Park No 1 Old Madras Road Byappanahalli Bangalore INDIA

ISBN: (数字)9781728163505

ISBN: (纸本)9781728163512

A smart grid consists of many digital operations that bring the grid into an intelligent system. One of the key components in the smart grid is the advanced metering infrastructure (AMI). AMI helps to acquire the data from Interface Energy Meters (IEMs), which are geographically dispersed across substations. AMI includes Interface Energy Meters, data concentrator units, IT infrastructure and various communication mediums for meter data transfer like Power-line communication carries (PLCC), Fiber Optics, Very Small Aperture Terminal (VSAT), GSM/GPRS/3G/4G, etc. Instantaneous profile, event profile, load profile and billing profile of meter data is used for load and renewable energy forecasting, scheduling, outage management, power system planning and efficient management of power grid. Any fault/error in AMI components leads to data unavailability. The Communication between the IEMs and control center is vulnerable for various communication channel attacks like meter spoofing attack, authentication attacks, Man-In-The-Middle (MITM) attacks, reply attacks, etc. High availability, reliability and integrity of meter data are a need of the hour for efficient management of power systems operations. The proposed solution helps to achieve fault-tolerant AMI with high-secure data communication between IEMs and control centers by retaining interoperability.

关键词： Meters Fault tolerance Renewable energy sources Substations Fault tolerant systems smart grids Stakeholders

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COVID-19: Geospatial Analysis of the Pandemic—A Case Study of Bihar State, India, Using data Derived from Remote Sensing Satellites and COVID-19 National Geoportal 9th

COVID-19: Geospatial Analysis of the Pandemic—A Case Study ...

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9th international conference on Frontiers in intelligent computing: Theory and Applications, FICTA 2021

作者： Kumari, Pallavi Sharma, Richa Rathore, Virendra Singh Department of Remote Sensing Birla Institute of Technology Ranchi Mesra India Birla Institute of Technology Ranchi Mesra India

ISBN: (纸本)9789811666230

The state of Bihar has a sizeable population which is spread all over the nation especially the skilled and non-skilled labourers. This populace contributes towards human resource as service providers for nation building in various sectors across the length and breadth of India. The declaration by the World Health Organization (WHO) of the spread of COVID-19 virus as a pandemic brought a nationwide lockdown from 23rd of March 2020 to curb its spread. These daily wage workers were stranded far and wide without any resources. The transport communication was also withdrawn initially against the spread. Now, as soon as the conditions became conducive for the migrant labours to return to the native state of Bihar, there was a fear for disaster due to these returning migrant labour force as they probably could become a vector for the spread of COVID-19 pandemic in the state. The state government in consonance with the Central Government formed strict protocols to be adhered to, for these returning migrants. The present paper statistically analyses the spread of this pandemic once the migrant populace began returning to their home state. It investigates whether the influx of so many humans from various parts of the country would become the hub of the spread of the virus causing infectious hot spots or not. Simultaneously, as many researchers were trying to correlate the presence of atmospheric nitrogen dioxide (NO2) with the spread of COVID-19 virus, the paper tried to relate the amount of NO2 present over the study area on the day the maximum number of cases were reported in the study area. Evaluation of atmospheric nitrogen dioxide (NO2) used for the present paper was derived from satellite data. Time series analysis of this NO2 data was done. This enabled us to identify the peak day and the day when the NO2 levels were minimum. Incidentally, the number of COVID-19 cases reported synchronized with the NO2 levels in the atmosphere. Spatial auto-correlation was performed u

关键词： Coronavirus

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Development of IoT/Cloud Integration Frameworks for Autonomous Networked Robots (ANR)

Development of IoT/Cloud Integration Frameworks for Autonomo...

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international Workshop on Research and Education in Mechatronics (REM)

作者： Maki K. Habib Chimsom. I. Chukwuemeka Robotics Control and Smart Systems (RCSS) The American University in Cairo (AUC) Cairo Egypt

ISBN: (数字)9781728162249

ISBN: (纸本)9781728162256

ANR are ubiquitous systems featured by the interaction of multirobots and WSNs, where data is exchanged between these systems leading to wider coverage and perception. Also, the integration of ANR systems with IoT/cloud platform increases their efficiency of the ANR system through expanded data storage for remote data analytics and cloud-based action triggering on their operational environments. This paper presents the development of IoT/cloud integration frameworks supporting ANR functionalities. The developed framework constitutes a base station (BS) IoT/cloud and sensor node (SN) IoT/cloud framework. The BS framework involves the use of IoT gateways to connect to IoT/cloud platforms while in the SN framework connects the sensor node IoTs to the IoT/cloud platform. In the BS framework, the sensor nodes communicate their data to the BS IoT gateway. The BS IoT gateway then communicates the data to the local BS visualization interface and to IoT/cloud platform for cloud visualization and analytics. For the SN IoT/cloud framework, the sensor nodes communicate the data directly to the local BS visualization interface and IoT/cloud platforms without the need for the IoT gateway. The BS framework was then developed and simulated for a large operational environment ANR monitoring application. The developed BS framework involved static and dynamic nodes with a fuzzy logic-based event classification system, a two-tier network connecting the sensor nodes, a local graphical user interface (GUI) for sensor node data visualization and ThingSpeak cloud visualization and analytics. The simulation results showed concurrent interaction and an effective exchange of data among the system components.

关键词： Robot sensing systems Wireless sensor networks Cloud computing Robots Robot kinematics data visualization Base stations

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Optimal phase swapping in low voltage distribution networks based on smart meter data and optimization heuristics 3rd

Optimal phase swapping in low voltage distribution networks ...

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Proceedings of the 3rd international conference on Harmony Search Algorithm, ICHSA 2017

作者： Mendia, Izaskun Gil-López, Sergio Del Ser, Javier Bordagaray, Ana González Prado, Jesús García Vélez, Manuel TECNALIA Derio48160 Spain University of the Basque Country UPV/EHU Bilbao48013 Spain Bilbao48009 Spain IBERDROLA Distribución Eléctrica S. A. Bilbao48003 Spain

ISBN: (纸本)9789811037276

In this paper a modified version of the Harmony Search algorithm is proposed as a novel tool for phase swapping in Low Voltage Distribution Networks where the objective is to determine to which phase each load should be connected in order to reduce the unbalance when all phases are added into the neutral conductor. Unbalanced loads deteriorate power quality and increase costs of investment and operation. A correct assignment is a direct, effective alternative to prevent voltage peaks and network outages. The main contribution of this paper is the proposal of an optimization model for allocating phases consumers according to their individual consumption in the network of low-voltage distribution considering mono and bi-phase connections using real hourly load patterns, which implies that the computational complexity of the defined combinatorial optimization problem is heavily increased. For this purpose a novel metric function is defined in the proposed scheme. The performance of the HS algorithm has been compared with classical Genetic Algorithm. Presented results show that HS outperforms GA not only on terms of quality but on the convergence rate, reducing the computational complexity of the proposed scheme while provide mono and bi phase connections. © Springer Nature Singapore Pte Ltd. 2017.

关键词： Computational complexity

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IoT-Enabled Automated Radish Care System: intelligent Detection and Response for Watering and Sunlight Needs

IoT-Enabled Automated Radish Care System: Intelligent Detect...

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international conference on Communication and Electronics Systems (ICCES)

作者： Nathaniel R. Macaranas Joseph Andrei A. Mandariaga Carlos Miguel D. Pablo Raymund M. Dioses Joseph Darwin C. Co Mark Anthony S. Mercado College of Information Systems and Technology Management Pamantasan ng Lungsod ng Maynila Manila Philippines

ISBN: (数字)9798350377972

ISBN: (纸本)9798350377989

One of the critical issues, both in households and agriculture, is the ability to take care of radish plants due to their complexity. Traditional techniques, on the other hand, have problems such as inconsistent monitoring, high labor costs, wastage of resources, and improper timing of harvesting. Nowadays, sensor-based monitoring and automated irrigation systems are examples of novel methods that have tried to solve these problems but are mostly poorly integrated and do not respond smartly. In this paper, an intelligent system and an IoT-based system for the automated care of radish vegetables where the system can detect and watering the plants or exposing them to sunlight whenever need arises is presented. Proposed solutions to the challenges are based on modern aggregate sensor technology and automatic control, cloud communication, and predictive analysis. The system smartly incorporates multiple environmental sensors networks, data storage, and analytics in the cloud, and takes advantage of pre-energy for automated operations to ensure proper care of plants with little human input and resource usage. PlanterBot, a mobile app, allows users to control and monitor system activities remotely, integrating machine learning to perform pre-harvest assessments. Evaluation using ISO/IEC 25010 standards showed excellent performance in all targeted Functional Suitability (1.32), Usability (1.31), Reliability (1.40), and Performance Efficiency (1.37). The findings prove that the current device is functional in solving the existing problems while developing the concept of smart farming.

关键词： Temperature sensors Performance evaluation Temperature measurement Cloud computing Moisture Humidity Mobile applications Predictive analytics Usability Monitoring

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Innovative Machine Learning Approaches to Cardiovascular Disease Prediction

Innovative Machine Learning Approaches to Cardiovascular Dis...

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intelligent computing and Sustainable Innovations in Technology (IC-SIT), international conference on

作者： Mithila Mehedi Nourin Nishat Janifer Nahar Muniroopesh Raasetti A S M Shoaib Basharat Ali Md Amran Hossen Pabel Department of Electrical and Computer Engineering Rajshahi University of Engineering and Technology Rajshahi Bangladesh Department of Management Information Systems Lamar University Texas USA Department of Finance Louisiana State University USA Department of Electrical Engineering Lamar University Texas USA Business Analytics and Insights Wright State University Dayton Ohio

ISBN: (数字)9798350369175

ISBN: (纸本)9798350369182

Cardiovascular diseases (CVDs) are a principal factor in health issues and death throughout the world, introducing extensive challenges to healthcare systems and impacting countless lives. This paper explores the application of various machine learning models to predict and monitor CVDs, leveraging data from wearable sensors and smart health devices. We analysed models featuring Logistic Regression, Support Vector Machines (SVM), Decision Trees, Random Forests, Gradient Boosting, KNearest Neighbors, and Voting Classifiers. Our in-depth examination revealed that most models achieved remarkable accuracy, precision, recall, and F1-scores with the Voting Classifier. These models’ high accuracy and straightforward implementation make them suitable for real-world clinical settings, where computational resources may be limited. By enabling early detection and continuous monitoring, these machine learning approaches can personalize healthcare, improve patient outcomes, and reduce healthcare costs. The integration of these advanced technologies promises to enhance cardiovascular disease management, providing timely interventions and better health monitoring, ultimately contributing to reduced incidence and severity of CVDs.

关键词： Support vector machines Logistic regression Accuracy Computational modeling Medical services Predictive models Boosting Cardiovascular diseases Monitoring Random forests

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