This document discusses a study on building demand forecasting for a satellite town. It outlines objectives to forecast building demand for a satellite town using literature on forecasting techniques like artificial neural networks and regression analysis. It describes factors that influence building demand forecasting and satellite town selection like population, area, buildings, accessibility and infrastructure availability. The document provides population and area statistics of Erode district and city in India. It also outlines a project schedule and references related literature.
Development of a Geographic Information Systems Road Network Database for Eme...inventionjournals
This document describes the development of a Geographic Information Systems (GIS) road network database for emergency response in Oyo Town, Nigeria. The objectives were to design a database, acquire spatial data, create the database, and conduct spatial analyses. Road centerlines and attributes were collected using GPS and digitizing satellite imagery. The database was created in ArcGIS and allows queries for alternative routes in emergencies and locating the nearest facilities. Network analyses can find the best routes and directions to sites like hospitals. The proposed system would help emergency agencies conduct more effective responses by providing digital and printed maps of the road network.
The document discusses the importance of service science and provides 10 reasons why service science matters more than ever. It notes that there is an opportunity to shift professionals' thinking from a goods-dominant logic to a service-dominant logic. The document also references several articles and provides summaries of key concepts from service science like goods-dominant logic, service-dominant logic, and actor-to-actor interactions between organizations.
The document discusses 7 steps for developing successful satellite towns to address issues of urban development and high land prices:
1) Satellite towns should allocate significantly more space per dwelling than cities, with minimum standards for different land uses.
2) Space should be evenly distributed across localities with minimum allotments for roads, commercial, parks, schools and other facilities.
3) Locations should be far from cities on marginal land to keep land prices 10 times lower than in cities.
4) Laws and taxes should be relaxed to promote development.
5) High quality schools, hospitals and shops should be established in satellite towns.
6) Employers should provide or help fund housing and commuting for employees.
7)
Applications of Artificial Neural Networks in Civil EngineeringPramey Zode
An artificial brain-like network based on certain mathematical algorithms developed using a numerical computing environment is called as an ‘Artificial Neural Network (ANN)’. Many civil engineering problems which need understanding of physical processes are found to be time consuming and inaccurate to evaluate using conventional approaches. In this regard, many ANNs have been seen as a reliable and practical alternative to solve such problems. Literature review reveals that ANNs have already being used in solving numerous civil engineering problems. This study explains some cases where ANNs have been used and its future scope is also discussed.
FCA resilience.io Platform:
Resource Economic Human Ecosystem
Modelling Platform Prototype
Foster Mensah
Centre for Remote Sensing and Geographic Information Services (CERSGIS)
University of Ghana
Rachael Kemp, Future Earth Ltd
Stephen Passmore, The Ecological Sequestration Trust
Koen H. van Dam and Harry Triantafyllidis
Department of Chemical Engineering
Imperial College London, UK
6 August 2015
Geospatial Open Data and Urban Growth Modelling for Evidence-based Decision M...Piyush Yadav
This document provides an overview of using geospatial data and urban growth modeling for evidence-based decision making in smart cities. It discusses using satellite imagery and classification techniques to model urban growth over time. A hidden Markov model is proposed that incorporates temporal factors like GDP and interest rates to better predict land use and land cover changes. A case study of modeling urban growth in Pune, India from 2001-2014 is presented using Landsat satellite imagery and temporal data on economic and population indicators.
This document discusses spatial computing and its potential applications for utility GIS. It begins by providing context on the evolution of spatial computing technologies like digital twins and sensor webs. It then discusses several emerging ideas for spatial computing in utilities, such as using digital twins to model urban energy systems, integrating predictive models across domains, and enabling geo-enabled edge computing. Finally, it considers the technology evolution required to realize these opportunities through standards, interoperability, and integrating emerging techniques like semantics and artificial intelligence.
Development of a Geographic Information Systems Road Network Database for Eme...inventionjournals
This document describes the development of a Geographic Information Systems (GIS) road network database for emergency response in Oyo Town, Nigeria. The objectives were to design a database, acquire spatial data, create the database, and conduct spatial analyses. Road centerlines and attributes were collected using GPS and digitizing satellite imagery. The database was created in ArcGIS and allows queries for alternative routes in emergencies and locating the nearest facilities. Network analyses can find the best routes and directions to sites like hospitals. The proposed system would help emergency agencies conduct more effective responses by providing digital and printed maps of the road network.
The document discusses the importance of service science and provides 10 reasons why service science matters more than ever. It notes that there is an opportunity to shift professionals' thinking from a goods-dominant logic to a service-dominant logic. The document also references several articles and provides summaries of key concepts from service science like goods-dominant logic, service-dominant logic, and actor-to-actor interactions between organizations.
The document discusses 7 steps for developing successful satellite towns to address issues of urban development and high land prices:
1) Satellite towns should allocate significantly more space per dwelling than cities, with minimum standards for different land uses.
2) Space should be evenly distributed across localities with minimum allotments for roads, commercial, parks, schools and other facilities.
3) Locations should be far from cities on marginal land to keep land prices 10 times lower than in cities.
4) Laws and taxes should be relaxed to promote development.
5) High quality schools, hospitals and shops should be established in satellite towns.
6) Employers should provide or help fund housing and commuting for employees.
7)
Applications of Artificial Neural Networks in Civil EngineeringPramey Zode
An artificial brain-like network based on certain mathematical algorithms developed using a numerical computing environment is called as an ‘Artificial Neural Network (ANN)’. Many civil engineering problems which need understanding of physical processes are found to be time consuming and inaccurate to evaluate using conventional approaches. In this regard, many ANNs have been seen as a reliable and practical alternative to solve such problems. Literature review reveals that ANNs have already being used in solving numerous civil engineering problems. This study explains some cases where ANNs have been used and its future scope is also discussed.
FCA resilience.io Platform:
Resource Economic Human Ecosystem
Modelling Platform Prototype
Foster Mensah
Centre for Remote Sensing and Geographic Information Services (CERSGIS)
University of Ghana
Rachael Kemp, Future Earth Ltd
Stephen Passmore, The Ecological Sequestration Trust
Koen H. van Dam and Harry Triantafyllidis
Department of Chemical Engineering
Imperial College London, UK
6 August 2015
Geospatial Open Data and Urban Growth Modelling for Evidence-based Decision M...Piyush Yadav
This document provides an overview of using geospatial data and urban growth modeling for evidence-based decision making in smart cities. It discusses using satellite imagery and classification techniques to model urban growth over time. A hidden Markov model is proposed that incorporates temporal factors like GDP and interest rates to better predict land use and land cover changes. A case study of modeling urban growth in Pune, India from 2001-2014 is presented using Landsat satellite imagery and temporal data on economic and population indicators.
This document discusses spatial computing and its potential applications for utility GIS. It begins by providing context on the evolution of spatial computing technologies like digital twins and sensor webs. It then discusses several emerging ideas for spatial computing in utilities, such as using digital twins to model urban energy systems, integrating predictive models across domains, and enabling geo-enabled edge computing. Finally, it considers the technology evolution required to realize these opportunities through standards, interoperability, and integrating emerging techniques like semantics and artificial intelligence.
APPLICABILITY OF BIG DATA TECHNIQUES TOSMART CITIES DEPLOYMENTSNexgen Technology
This document proposes applying big data techniques to smart cities to better utilize vast amounts of data from various sensors. It presents a general IoT architecture with four layers and describes two scenarios: 1) Using data from campus sensors to improve energy efficiency and comfort in smart buildings. This achieved 23% average energy savings per month. 2) Analyzing transportation card data to detect travel patterns and improve planning for public tram services. The ongoing work aims to incorporate human behavior data to further optimize smart city services.
AI & IoT in the development of smart citiesRaunak Mundada
Smart cities utilize information and communication technologies to improve economic and social well-being while reducing environmental impact. Internet of things (IoT) technologies allow cities to become smart through applications like smart grids, waste management, traffic management, and load forecasting. Artificial intelligence and deep learning techniques can help with load forecasting and optimizing these smart city applications through analyzing real-time sensor data from areas like energy use, transportation, and infrastructure monitoring. The document provides examples of how cities like Barcelona, London, and Singapore are successfully implementing IoT and AI strategies to address challenges from urbanization and improve services.
Zpryme Report on Modeling and SimulationPaula Smith
The document discusses modeling and simulation (M&S) for smart grid infrastructure. It notes that M&S is being used to study the complex interactions between power systems and communication networks in a smart grid context. Several challenges of smart grid simulation are outlined, including combining power system and communication network simulation and different time models used. Solutions discussed include co-simulation, where separate power system and communication network simulators are connected, and integrated simulation, where components are simulated together within one environment. Government agencies and laboratories leading M&S research are also outlined.
[Keynote] predictive technologies and the prediction of technology - Bob Will...PAPIs.io
I will examine predictive technologies in the light of the history of technology and its prediction. No matter how shiny and new, a new technology is still a technology, and there are general patterns that seem to recur. We can learn from those patterns if we pay attention to them.
In particular I will look at the challenge of predicting the impact of new technologies, talk about how they evolve, and the role that modularity, standards and interoperability play in their evolution.
I will talk more specifically about some of the particular challenges of making APIs and interfaces for predictive technologies such as machine learning, and speculate on the prospects for making machine learning a service, and more of a mature engineering discipline. In passing I will briefly demonstrate some recent machine learning work from NICTA.
This document discusses using data-based approaches for urban energy modeling. It describes the InSMART project which aims to establish a methodology for sustainable urban energy planning applied to four European cities including Nottingham. The project employs GIS databases, building energy simulations, transport modeling, and TIMES-MARKAL energy system modeling. It discusses constructing a residential building stock model for Nottingham including challenges around data gaps, quality and consistency issues in key geospatial datasets.
On the development of methodology for planning and cost modeling of a wide ar...IJCNCJournal
The most important stages in designing a
computer
network
in a
wider geographical area include:
definition of requirements, topological description
,
identification and calculation of relevant parameters
(
i
.
e
.
traffic matrix
)
, determining the shortest path between nodes, quantification of the effect of various
levels
of technical and technological development of urban areas involved, the cost of technology
,
and the
cost of services. The
se
parameters differ for WAN networks in different regions
–
their calculation depends
directly
on
the data “
i
n the field
”
: number of inhabitants, distance between populated areas,
network
traffic
density
,
as well as
available
bandwidth
. The
main
reason for identification and evaluation of these
parameters
is
to develop a model that could
meet the
constraints
im
posed by poten
tial beneficiaries.
In this
paper
,
we develop a methodology for planning and cost
-
modeling of a wide area network
and
validate it
in
a case study,
under the
supposition
that
behavioral interactions of individuals and groups play a significant
role and have
to be taken into consideration
by employing either simple or composite indicators of
socioeconomic status
.
IRJET- Estimation of Water Level Variations in Dams Based on Rainfall Dat...IRJET Journal
This document presents a study that uses an artificial neural network (ANN) to estimate water level variations in dams based on rainfall data. Specifically, it develops ANN models to forecast daily water levels for the Sukhi Reservoir project in India. The study collects water level, inflow, and release data over many years to train ANN models. It compares the performance of different ANN architectures - cascade, Elman, and feedforward backpropagation networks. The results show that the feedforward backpropagation network achieves the best performance with low errors and high correlation between predicted and actual water levels. The ANN models provide an effective method for timely water level forecasting to aid water management and disaster control.
Scenarios - approaches for exploring urban futures Ian Miles
This document summarizes a presentation on developing scenarios for urban futures. It discusses different types of scenario analysis including departures, destinations, and success scenarios. Departures analyze the consequences of uncertain events, destinations examine how futures could be realized given drivers, and success scenarios envision a desirable future. Methods for developing scenarios like expert groups, surveys, and workshops are presented. The document concludes by summarizing a scenario planning workshop for Greater Manchester that identified drivers, current issues, visions of success, and potential actions across sectors like environment, economy and governance.
The document summarizes research on identifying viable renewable energy technologies for rural electrification in Africa using geographic information systems. Key findings include maps of solar, wind and hydro resources across Africa. Analysis was conducted to determine least-cost electricity options from photovoltaic, diesel or hybrid mini-grids. The research aims to support sustainable energy planning and accelerate rural electrification through an online decision support tool and innovative GIS techniques.
big data analytics in mobile cellular networkshubham patil
This document proposes applying big data analytics to improve mobile cellular networks. It presents an architectural framework that collects big data from mobile networks, including signaling data, traffic data, location data, and radio waveforms. The data is analyzed using platforms like Apache Hadoop. Analytics can optimize network operations and enhance the subscriber experience through applications like identifying coverage issues and facilitating location-based services. Open challenges remain in fully leveraging big data to advance cellular networks.
Brno-IESS 20240206 v10 service science ai.pptxISSIP
It my pleasure to be with you all today – thanks to my host for the opportunity to speak with you all today.
Host: Leonard Walletzky <qwalletz@fi.muni.cz> (https://www.linkedin.com/in/leonardwalletzky/) +420 549 49 7690
Google Scholar: https://scholar.google.com/citations?user=aUvbsmwAAAAJ&hl=cs
Katrina Motkova (https://www.linkedin.com/in/kateřina-moťková-mba-a964a3175/en/?originalSubdomain=cz)
Speaker: Jim Spohrer <spohrer@gmail.com> (https://www.linkedin.com/in/spohrer/) +1-408-829-3112
1. The document discusses geo-informatics and its use for disaster risk reduction and sustainable development through digital platforms like Digital Earth and Digital Asia.
2. Key applications mentioned include public participatory GIS, adaptation for climate change, monitoring glacial lakes, and early warning systems using sensor networks.
3. The Graduate School of Media and Governance at Keio University conducts research related to global innovation systems, security, and emerging crises through its Global Security Research Center.
[20240318_LabSeminar_Huy]GSTNet: Global Spatial-Temporal Network for Traffic ...thanhdowork
GSTNet is a deep learning model for traffic flow prediction that incorporates spatial and temporal information. It contains multi-resolution temporal and global correlated spatial modules. The temporal module captures short and long-term patterns, while the spatial module considers both local and non-local correlations between locations. In experiments on Beijing transportation data, GSTNet achieved more accurate predictions compared to other methods and was able to capture both short and long-term dependencies in traffic flow.
IRJET- Integration of Solar Electricity Into National Grid: Case Study of...IRJET Journal
This document discusses integrating solar electricity into Nigeria's national grid. It begins with an abstract that outlines the study's purpose of exploring solar electricity integration and addressing Nigeria's energy problems. The introduction provides background on Nigeria's energy challenges and potential for solar power.
The methodology section explains that this is a survey research study covering Nigeria's 36 states. It describes the population and sample, as well as the questionnaire used to collect data on solar electricity application, transmission success, and accessibility/availability.
The results section presents findings from the questionnaire. It found that solar technologies can be deployed in urban and rural areas, but are currently more accessible to wealthy urban residents. It also found that solar electricity can meet consumer needs by expanding energy
Electronics and Robotics - Ajith AmarasekaraSTS FORUM 2016
The document discusses opportunities and challenges in the electronics industry due to paradigm shifts driven by new technologies. It notes that mechanical systems are increasingly being replaced by intelligent electronic systems, enabling autonomous operation and adaptability. However, it also notes that working with manufacturers to understand requirements, long development times, and needing skills in areas like data analytics, signal processing, machine learning and control theory present challenges. The opportunities lie in developing solutions that leverage Sri Lanka's existing strengths and addressing niche application areas.
Elementary & Auxiliary Strategies Imparting Smartness to a cityAntara Nandy
The buzz word smart-city has gained momentum in the recent few months owing to the nation-wide programs launched by the Indian government. According to the sources, a smart city is defined as a city that provides all the modern facilities to ease the lifestyle of the people. Further, it must ensure the safety of the environment and conserve energy and other natural resources. This paper presents a comprehensive report on the elements and strategies that need to be implemented for a city to be considered as a smart city. It contains a report on the various futuristic plans and measures that the Indian government has formulated to turn the concept of smart cities into reality. The paper also intends to describe the roles and responsibilities of the various stakeholders in the actualization of the smart cities.
This document discusses smart cities and KT Corporation's smart city strategy. It begins with definitions of traditional urban ICT, U-City, and smart city concepts. It then outlines KT's vision for smart cities and its partnership with Cisco to provide total ICT services through all phases of smart space development. KT aims to export its smart city expertise and has established a public-private company called Incheon U-City to implement its first smart city project in South Korea.
Predictive geospatial analytics using principal component regression IJECEIAES
Nowadays, exponential growth in geospatial or spatial data all over the globe, geospatial data analytics is absolutely deserved to pay attention in manipulating voluminous amount of geodata in various forms increasing with high velocity. In addition, dimensionality reduction has been playing a key role in high-dimensional big data sets including spatial data sets which are continuously growing not only in observations but also in features or dimensions. In this paper, predictive analytics on geospatial big data using Principal Component Regression (PCR), traditional Multiple Linear Regression (MLR) model improved with Principal Component Analysis (PCA), is implemented on distributed, parallel big data processing platform. The main objective of the system is to improve the predictive power of MLR model combined with PCA which reduces insignificant and irrelevant variables or dimensions of that model. Moreover, it is contributed to present how data mining and machine learning approaches can be efficiently utilized in predictive geospatial data analytics. For experimentation, OpenStreetMap (OSM) data is applied to develop a one-way road prediction for city Yangon, Myanmar. Experimental results show that hybrid approach of PCA and MLR can be efficiently utilized not only in road prediction using OSM data but also in improvement of traditional MLR model.
বাংলাদেশের অর্থনৈতিক সমীক্ষা ২০২৪ [Bangladesh Economic Review 2024 Bangla.pdf] কম্পিউটার , ট্যাব ও স্মার্ট ফোন ভার্সন সহ সম্পূর্ণ বাংলা ই-বুক বা pdf বই " সুচিপত্র ...বুকমার্ক মেনু 🔖 ও হাইপার লিংক মেনু 📝👆 যুক্ত ..
আমাদের সবার জন্য খুব খুব গুরুত্বপূর্ণ একটি বই ..বিসিএস, ব্যাংক, ইউনিভার্সিটি ভর্তি ও যে কোন প্রতিযোগিতা মূলক পরীক্ষার জন্য এর খুব ইম্পরট্যান্ট একটি বিষয় ...তাছাড়া বাংলাদেশের সাম্প্রতিক যে কোন ডাটা বা তথ্য এই বইতে পাবেন ...
তাই একজন নাগরিক হিসাবে এই তথ্য গুলো আপনার জানা প্রয়োজন ...।
বিসিএস ও ব্যাংক এর লিখিত পরীক্ষা ...+এছাড়া মাধ্যমিক ও উচ্চমাধ্যমিকের স্টুডেন্টদের জন্য অনেক কাজে আসবে ...
APPLICABILITY OF BIG DATA TECHNIQUES TOSMART CITIES DEPLOYMENTSNexgen Technology
This document proposes applying big data techniques to smart cities to better utilize vast amounts of data from various sensors. It presents a general IoT architecture with four layers and describes two scenarios: 1) Using data from campus sensors to improve energy efficiency and comfort in smart buildings. This achieved 23% average energy savings per month. 2) Analyzing transportation card data to detect travel patterns and improve planning for public tram services. The ongoing work aims to incorporate human behavior data to further optimize smart city services.
AI & IoT in the development of smart citiesRaunak Mundada
Smart cities utilize information and communication technologies to improve economic and social well-being while reducing environmental impact. Internet of things (IoT) technologies allow cities to become smart through applications like smart grids, waste management, traffic management, and load forecasting. Artificial intelligence and deep learning techniques can help with load forecasting and optimizing these smart city applications through analyzing real-time sensor data from areas like energy use, transportation, and infrastructure monitoring. The document provides examples of how cities like Barcelona, London, and Singapore are successfully implementing IoT and AI strategies to address challenges from urbanization and improve services.
Zpryme Report on Modeling and SimulationPaula Smith
The document discusses modeling and simulation (M&S) for smart grid infrastructure. It notes that M&S is being used to study the complex interactions between power systems and communication networks in a smart grid context. Several challenges of smart grid simulation are outlined, including combining power system and communication network simulation and different time models used. Solutions discussed include co-simulation, where separate power system and communication network simulators are connected, and integrated simulation, where components are simulated together within one environment. Government agencies and laboratories leading M&S research are also outlined.
[Keynote] predictive technologies and the prediction of technology - Bob Will...PAPIs.io
I will examine predictive technologies in the light of the history of technology and its prediction. No matter how shiny and new, a new technology is still a technology, and there are general patterns that seem to recur. We can learn from those patterns if we pay attention to them.
In particular I will look at the challenge of predicting the impact of new technologies, talk about how they evolve, and the role that modularity, standards and interoperability play in their evolution.
I will talk more specifically about some of the particular challenges of making APIs and interfaces for predictive technologies such as machine learning, and speculate on the prospects for making machine learning a service, and more of a mature engineering discipline. In passing I will briefly demonstrate some recent machine learning work from NICTA.
This document discusses using data-based approaches for urban energy modeling. It describes the InSMART project which aims to establish a methodology for sustainable urban energy planning applied to four European cities including Nottingham. The project employs GIS databases, building energy simulations, transport modeling, and TIMES-MARKAL energy system modeling. It discusses constructing a residential building stock model for Nottingham including challenges around data gaps, quality and consistency issues in key geospatial datasets.
On the development of methodology for planning and cost modeling of a wide ar...IJCNCJournal
The most important stages in designing a
computer
network
in a
wider geographical area include:
definition of requirements, topological description
,
identification and calculation of relevant parameters
(
i
.
e
.
traffic matrix
)
, determining the shortest path between nodes, quantification of the effect of various
levels
of technical and technological development of urban areas involved, the cost of technology
,
and the
cost of services. The
se
parameters differ for WAN networks in different regions
–
their calculation depends
directly
on
the data “
i
n the field
”
: number of inhabitants, distance between populated areas,
network
traffic
density
,
as well as
available
bandwidth
. The
main
reason for identification and evaluation of these
parameters
is
to develop a model that could
meet the
constraints
im
posed by poten
tial beneficiaries.
In this
paper
,
we develop a methodology for planning and cost
-
modeling of a wide area network
and
validate it
in
a case study,
under the
supposition
that
behavioral interactions of individuals and groups play a significant
role and have
to be taken into consideration
by employing either simple or composite indicators of
socioeconomic status
.
IRJET- Estimation of Water Level Variations in Dams Based on Rainfall Dat...IRJET Journal
This document presents a study that uses an artificial neural network (ANN) to estimate water level variations in dams based on rainfall data. Specifically, it develops ANN models to forecast daily water levels for the Sukhi Reservoir project in India. The study collects water level, inflow, and release data over many years to train ANN models. It compares the performance of different ANN architectures - cascade, Elman, and feedforward backpropagation networks. The results show that the feedforward backpropagation network achieves the best performance with low errors and high correlation between predicted and actual water levels. The ANN models provide an effective method for timely water level forecasting to aid water management and disaster control.
Scenarios - approaches for exploring urban futures Ian Miles
This document summarizes a presentation on developing scenarios for urban futures. It discusses different types of scenario analysis including departures, destinations, and success scenarios. Departures analyze the consequences of uncertain events, destinations examine how futures could be realized given drivers, and success scenarios envision a desirable future. Methods for developing scenarios like expert groups, surveys, and workshops are presented. The document concludes by summarizing a scenario planning workshop for Greater Manchester that identified drivers, current issues, visions of success, and potential actions across sectors like environment, economy and governance.
The document summarizes research on identifying viable renewable energy technologies for rural electrification in Africa using geographic information systems. Key findings include maps of solar, wind and hydro resources across Africa. Analysis was conducted to determine least-cost electricity options from photovoltaic, diesel or hybrid mini-grids. The research aims to support sustainable energy planning and accelerate rural electrification through an online decision support tool and innovative GIS techniques.
big data analytics in mobile cellular networkshubham patil
This document proposes applying big data analytics to improve mobile cellular networks. It presents an architectural framework that collects big data from mobile networks, including signaling data, traffic data, location data, and radio waveforms. The data is analyzed using platforms like Apache Hadoop. Analytics can optimize network operations and enhance the subscriber experience through applications like identifying coverage issues and facilitating location-based services. Open challenges remain in fully leveraging big data to advance cellular networks.
Brno-IESS 20240206 v10 service science ai.pptxISSIP
It my pleasure to be with you all today – thanks to my host for the opportunity to speak with you all today.
Host: Leonard Walletzky <qwalletz@fi.muni.cz> (https://www.linkedin.com/in/leonardwalletzky/) +420 549 49 7690
Google Scholar: https://scholar.google.com/citations?user=aUvbsmwAAAAJ&hl=cs
Katrina Motkova (https://www.linkedin.com/in/kateřina-moťková-mba-a964a3175/en/?originalSubdomain=cz)
Speaker: Jim Spohrer <spohrer@gmail.com> (https://www.linkedin.com/in/spohrer/) +1-408-829-3112
1. The document discusses geo-informatics and its use for disaster risk reduction and sustainable development through digital platforms like Digital Earth and Digital Asia.
2. Key applications mentioned include public participatory GIS, adaptation for climate change, monitoring glacial lakes, and early warning systems using sensor networks.
3. The Graduate School of Media and Governance at Keio University conducts research related to global innovation systems, security, and emerging crises through its Global Security Research Center.
[20240318_LabSeminar_Huy]GSTNet: Global Spatial-Temporal Network for Traffic ...thanhdowork
GSTNet is a deep learning model for traffic flow prediction that incorporates spatial and temporal information. It contains multi-resolution temporal and global correlated spatial modules. The temporal module captures short and long-term patterns, while the spatial module considers both local and non-local correlations between locations. In experiments on Beijing transportation data, GSTNet achieved more accurate predictions compared to other methods and was able to capture both short and long-term dependencies in traffic flow.
IRJET- Integration of Solar Electricity Into National Grid: Case Study of...IRJET Journal
This document discusses integrating solar electricity into Nigeria's national grid. It begins with an abstract that outlines the study's purpose of exploring solar electricity integration and addressing Nigeria's energy problems. The introduction provides background on Nigeria's energy challenges and potential for solar power.
The methodology section explains that this is a survey research study covering Nigeria's 36 states. It describes the population and sample, as well as the questionnaire used to collect data on solar electricity application, transmission success, and accessibility/availability.
The results section presents findings from the questionnaire. It found that solar technologies can be deployed in urban and rural areas, but are currently more accessible to wealthy urban residents. It also found that solar electricity can meet consumer needs by expanding energy
Electronics and Robotics - Ajith AmarasekaraSTS FORUM 2016
The document discusses opportunities and challenges in the electronics industry due to paradigm shifts driven by new technologies. It notes that mechanical systems are increasingly being replaced by intelligent electronic systems, enabling autonomous operation and adaptability. However, it also notes that working with manufacturers to understand requirements, long development times, and needing skills in areas like data analytics, signal processing, machine learning and control theory present challenges. The opportunities lie in developing solutions that leverage Sri Lanka's existing strengths and addressing niche application areas.
Elementary & Auxiliary Strategies Imparting Smartness to a cityAntara Nandy
The buzz word smart-city has gained momentum in the recent few months owing to the nation-wide programs launched by the Indian government. According to the sources, a smart city is defined as a city that provides all the modern facilities to ease the lifestyle of the people. Further, it must ensure the safety of the environment and conserve energy and other natural resources. This paper presents a comprehensive report on the elements and strategies that need to be implemented for a city to be considered as a smart city. It contains a report on the various futuristic plans and measures that the Indian government has formulated to turn the concept of smart cities into reality. The paper also intends to describe the roles and responsibilities of the various stakeholders in the actualization of the smart cities.
This document discusses smart cities and KT Corporation's smart city strategy. It begins with definitions of traditional urban ICT, U-City, and smart city concepts. It then outlines KT's vision for smart cities and its partnership with Cisco to provide total ICT services through all phases of smart space development. KT aims to export its smart city expertise and has established a public-private company called Incheon U-City to implement its first smart city project in South Korea.
Predictive geospatial analytics using principal component regression IJECEIAES
Nowadays, exponential growth in geospatial or spatial data all over the globe, geospatial data analytics is absolutely deserved to pay attention in manipulating voluminous amount of geodata in various forms increasing with high velocity. In addition, dimensionality reduction has been playing a key role in high-dimensional big data sets including spatial data sets which are continuously growing not only in observations but also in features or dimensions. In this paper, predictive analytics on geospatial big data using Principal Component Regression (PCR), traditional Multiple Linear Regression (MLR) model improved with Principal Component Analysis (PCA), is implemented on distributed, parallel big data processing platform. The main objective of the system is to improve the predictive power of MLR model combined with PCA which reduces insignificant and irrelevant variables or dimensions of that model. Moreover, it is contributed to present how data mining and machine learning approaches can be efficiently utilized in predictive geospatial data analytics. For experimentation, OpenStreetMap (OSM) data is applied to develop a one-way road prediction for city Yangon, Myanmar. Experimental results show that hybrid approach of PCA and MLR can be efficiently utilized not only in road prediction using OSM data but also in improvement of traditional MLR model.
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2nd review
1. A STUDY ON THE BUILDING DEMAND
FORECASTING FOR A SATELLITE
TOWN
NAME OF THE SCHOLAR NAME OF THE GUIDE
T.R.RAGHAVAN Mr.T.PRADEEP
12CMR009 ASST.PROFESSOR
DEPARTMENT OF CIVIL ENGINEERING
2. INTRODUCTION
• The effects of population growth are varied
and vast. While population growth, of any
species, may be beneficial to a certain extent,
there may come a time when the number in the
population exceeds the natural resources
available to sustain it. This is referred to as
overpopulation. The consequences of such an
event are severe and major.
3. SATELLITE TOWN
• A satellite town or satellite city is a concept in
urban planning that refers essentially to smaller
metropolitan areas which are located somewhat
near to, but are mostly independent of larger
metropolitan areas.
• Satellite cities are small or medium-sized cities
near a large metropolis, that:
• predate the metropolis' suburban expansion
• are at least partially independent from that
metropolis economically and socially
4. Continues…
• are physically separated from the metropolis by
rural territory or by a major geographic barrier
such as a large river;
• satellite cities should have their own independent
urbanized area, or equivalent
• have their own bedroom communities
• have a traditional downtown surrounded by
traditional "inner city" neighborhoods
• may or may not be counted as part of the large
metropolis' Combined Statistical Area
7. • S. Saravanan, S. Kannan and C. Thangaraj Department
of Electrical and Electronics Engineering, Kalasalingam
University, India , India’s Electricity Demand Forecast
Using Regression Analysis and Artificial Neural Networks
based on Principal Components, ICTACT Journal on Soft
Computing, july 2012, Volume: 02, ISSUE: 04
• Power System planning starts with Electric load (demand)
forecasting.
• Accurate electricity load forecasting is one of the most
important challenges in managing supply and demand of the
electricity, since the electricity demand is volatile in nature;
• The aim of this study deals with electricity consumption in
India, to forecast future projection of demand for a period of
19 years from 2012 to 2030.
8. Guoqiang Zhang, B. Eddy Patuwo, Michael Y. Hu*,
Forecasting with artificial neural networks: The state of the art,
International Journal of Forecasting 14 (1998) 35 –62
•Interest in using artificial neural networks (ANNs) for forecasting
has led to a tremendous surge in research activities in the past
decade.
•While ANNs provide a great deal of promise, they also embody
much uncertainty. Researchers to date are still not certain about
the effect of key factors on forecasting performance of ANNs.
•This paper presents a state-of-the-art survey of ANN applications
in forecasting. Our purpose is to provide
(1) a synthesis of published research in this area,
(2) insights on ANN modeling issues, and
(3) the future research directions.
9. • Bassam M. AbuAl-Foul, Economics Department,
American University of Sharjah, Forecasting Energy
Demand in Jordan Using Artificial Neural Networks, Topics
in Middle Eastern and African Economies Vol. 14,
September 2012
• The purpose of this study is to forecast energy use in one of
the MENA countries, Jordan using annual data over the
period 1976-2008.
• The methodology used in this study follows the artificial
neural networks analyses.
• We use four independent variables, namely, gross domestic
product, population, exports, and imports to forecast energy
use.
10. Literature collectionLiterature collection
Review of literatureReview of literature
Data CollectionData Collection
Selection of ParametersSelection of Parameters
Population ForecastingPopulation Forecasting
Demand ForecastingDemand Forecasting
METHODOLOGY
11. Analysing the suitable place for Satellite
Town
Analysing the suitable place for Satellite
Town
Justifying the analysisJustifying the analysis
Providing SuggestionsProviding Suggestions
12. SELECTION OF SOFTWARE
• Artificial Neural Network
• Statistical Product and Service Solutions
(SPSS)
13. ARTIFICIAL NEURAL NETWORK
• Neural network software is used to simulate,
research, develop and apply
artificial neural networks,
biological neural networks and in some cases a
wider array of adaptive systems.
• Commonly used artificial neural network
simulators include the
Stuttgart Neural Network Simulator (SNNS),
Emergent, JavaNNS, Neural Lab and
NetMaker
14. Continues...
• A neural network (NN), in the case of artificial
neurons called artificial neural network (ANN), is an
interconnected group of natural or artificial neurons
that uses a mathematical or computational model for
information processing based on a connectionistic
approach to computation.
15. STATISTICAL PRODUCT AND
SERVICE SOLUTIONS (SPSS)
• SPSS consists of an integrated series of computer
programs which enable the user to read data from
questionnaire surveys and other sources (e.g. Demand
and administrative records),to manipulate them in
various ways and to produce a wide range of
statistical analyses and reports, together
with documentation.
16. Continues...
• SPSS Statistics is a software package used for
statistical analysis. It is now officially named "IBM
SPSS Statistics". Companion products in the same
family are used for survey authoring and deployment
(IBM SPSS Data Collection), data mining (IBM
SPSS Modeler), text analytics, and collaboration and
deployment (batch and automated scoring services).
17. Continues...
• With SPSS predictive analytics software can
predict with confidence what will happen next so that
can make smarter decisions, solve problems and
improve outcomes.
19. FACTORS INFLUENCING THE
BUILDING DEMAND FORECASTING
• AREA
– Free space available inside the city (private &
public)
– Free space available outside the city (private &
public)
– Area of the proposed satellite towns separately
(Thindal & Solar)
20. Continues...
• POPULATION
– Population of the whole city
– Population of the proposed satellite towns
separately (Thindal & Solar)
– Population of the western side of the district (from
Thindal to Sengapalli)
– Population of the north-eastern side of the district
(from Solar to Sengodampalayam)
21. Continues...
• BUILDINGS
– Number of Residential buildings inside the city
– Number of Governmental buildings
– Capacity of the Governmental building as per the
codal provision & raw data
– Utilisation of the government building (in %)
23. FACTORS INFLUENCING THE
SELECTION OF A SATELLITE
TOWN
• THINDAL & SOLAR
– Accessibility to the city from Thindal & Solar
– Development of the town
– Job opportunities around the town
– Number of peoples approaching the city from
Thindal (western side of the district)
24. Continues...
– Number of peoples approaching the city from
Solar (North-eastern side of the district)
– Availability of water facility (ground water &
other sources)
– Sources and availability of electricity
– Availability of enough Government land to setup a
Satellite Town
25. REFERENCES
• Refense, A.N.; Zapranis, A. and Francis, G. (1994):
“Stock Performance Modelling using Neural
Networks: a comparitive study with regression
models.” Neural Networks, 7, No.2, PP. 375-388
• Zheng, D.X.M., NG, S.T. and Kumaraswamy, M.M.
(2004) Applying a GA-based multiobjective approach
for time-cost optimization. Journal of Construction
Engineering and Management, ASCE, 130(2), 168-
176.
26. Continues…
• Zhang, G. and Hu, M. Y.(1998): “Neural Network
forecasting of the British/US dollar exchange rate.”
Omega, Vol.26, No. 44, PP. 495-506.
• Tse, R.Y.C., Ho, C.W. and Ganesan, S.(1999)
Matching housing supply and demand: an emprical
study of Hong Kong’s market, Construction
Management and Economics, 17(5), 625-634.
27. PROJECT SCHEDULE
July August September October
Weeks 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4
Literature collection & Project
selection
Literature study
Selection of software
Factors influencing the building
demand forecasting (for software
analysis)
Factors influencing the selection
of a Satellite Town (for
questionnaire survey)
Data collection
28. POPULATION OF THE
ERODE DISTRICT
• 1991 - 18,02,900 peoples
• 2001 - 20,16,582 peoples (11.85% incd.)
• 2011 - 22,51,744 peoples (11.66% incd.)
No.of.Males - 10,24,732
No.of.Females - 991850
29. POPULATION OF THE
ERODE CITY
• 2011 - 5,21,776 peoples
• Male - 2,61,470 peoples (82.2%)
• Female - 2,60,306 peoples (72.42%)
• Ratio - 996 Females : 1000 Males
30. AREA OF THE ERODE
DISTRICT
• Area - 2198 sq.miles (5692 sq.kms)
(whole district)
• Area of the Erode City :
– Rural Area - 287 sq.miles (743 sq.kms)
– Urban Area - 3.22 sq.miles (8.34 sq.kms)
31. Continues…
• Area of the North-eastern side of the
district
– From Solar to Sengodampalayam
– 400 sq.miles (1037 sq.kms)
• Area of the Western side of the District
– From Thindal to Sengapalli
– 1798 sq.miles (4655 sq.kms)
32. LAND USAGE
• 83.25% of the ERODE municipal area has
been developed along the road side in all
major roads, in municipal area along Kongan
road near the southern boundary of the local
planning area, mainly the commercial area in
Erode town is concentrated near the junction
of Brough road and archery road and Bazaar
area.