Avionics 738 Adaptive Filtering at Air University PAC Campus by Dr. Bilal A. Siddiqui in Spring 2018. This lecture deals with introduction to Kalman Filtering. Based n Optimal State Estimation by Dan Simon.
This document discusses using the Kalman filter for object tracking. It begins by introducing the Kalman filter as a linear discrete-time system and describes its process and measurement equations. It then discusses using the Kalman filter to optimally estimate parameters and extend it to model non-linear systems using a Taylor series approximation. The document describes using the basic and extended Kalman filters for object tracking by initializing the object position and iteratively predicting and correcting its state. It also discusses combining the Kalman filter with mean shift for object tracking and using an adaptive Kalman filter to handle occlusions.
This document provides an overview of Kalman filtering and Kalman filters. It discusses how Kalman filtering is used for optimal filtering and state estimation of time-varying dynamic systems observed through noisy measurements. It describes the prediction and update steps of the Kalman filter, which provides a recursive solution for optimally estimating the state of linear dynamic systems from a series of noisy measurements over time. It also discusses extensions of the Kalman filter, such as the extended Kalman filter (EKF) and unscented Kalman filter (UKF), which can be applied to nonlinear systems.
A KALMAN FILTERING TUTORIAL FOR UNDERGRADUATE STUDENTSIJCSES Journal
This paper presents a tutorial on Kalman filtering that is designed for instruction to undergraduate
students. The idea behind this work is that undergraduate students do not have much of the statistical and
theoretical background necessary to fully understand the existing research papers and textbooks on this
topic. Instead, this work offers an introductory experience for students which takes a more practical usage
perspective on the topic, rather than the statistical derivation. Students reading this paper should be able
to understand how to apply Kalman filtering tools to mathematical problems without requiring a deep
theoretical understanding of statistical theory.
Kalman filter - Applications in Image processingRavi Teja
The document discusses the Kalman filter, an algorithm used to estimate the state of a linear dynamic system from a series of noisy measurements. It describes how the Kalman filter uses a predictor-corrector approach with time update and measurement update equations to estimate the true state. The filter is applied to image processing to reduce noise by modeling the image as an autoregressive process and using the Kalman filter estimates. Extensions to nonlinear and complex systems using the extended and complex Kalman filters are also covered.
Seminar On Kalman Filter And Its ApplicationsBarnali Dey
The document discusses Kalman filters and their applications. It provides an overview of Kalman filters, explaining that they are used to estimate unknown system states from measurements that contain errors. It describes the basic algorithmic steps of Kalman filters, including prediction to project the state ahead and correction to incorporate new measurements. Finally, it gives examples of applications, such as for channel estimation in direct sequence spread spectrum communication systems.
The document discusses different types of flip-flops including RS NAND and NOR flip-flops, and covers the basics of sequential logic circuits. It defines level-triggered and edge-triggered clock inputs for flip-flops and compares asynchronous and synchronous clocked flip-flops. The timing diagrams show how positive and negative edge triggering determines when the output of a flip-flop changes state in response to clock pulses and input signals.
This document discusses the application of the Kalman filter to estimate variables for unmanned aerial vehicles (UAVs). The Kalman filter combines predictions with measurements to estimate variables that cannot be directly measured, like altitude and velocity. It was shown to accurately track the simulated state of a UAV over 10 time steps, keeping the error between estimated and actual values small. Graphs demonstrate how the Kalman filter estimate closely follows the actual simulated state. The Kalman filter is thus a useful tool for state estimation in applications like UAV navigation.
Avionics 738 Adaptive Filtering at Air University PAC Campus by Dr. Bilal A. Siddiqui in Spring 2018. This lecture deals with introduction to Kalman Filtering. Based n Optimal State Estimation by Dan Simon.
This document discusses using the Kalman filter for object tracking. It begins by introducing the Kalman filter as a linear discrete-time system and describes its process and measurement equations. It then discusses using the Kalman filter to optimally estimate parameters and extend it to model non-linear systems using a Taylor series approximation. The document describes using the basic and extended Kalman filters for object tracking by initializing the object position and iteratively predicting and correcting its state. It also discusses combining the Kalman filter with mean shift for object tracking and using an adaptive Kalman filter to handle occlusions.
This document provides an overview of Kalman filtering and Kalman filters. It discusses how Kalman filtering is used for optimal filtering and state estimation of time-varying dynamic systems observed through noisy measurements. It describes the prediction and update steps of the Kalman filter, which provides a recursive solution for optimally estimating the state of linear dynamic systems from a series of noisy measurements over time. It also discusses extensions of the Kalman filter, such as the extended Kalman filter (EKF) and unscented Kalman filter (UKF), which can be applied to nonlinear systems.
A KALMAN FILTERING TUTORIAL FOR UNDERGRADUATE STUDENTSIJCSES Journal
This paper presents a tutorial on Kalman filtering that is designed for instruction to undergraduate
students. The idea behind this work is that undergraduate students do not have much of the statistical and
theoretical background necessary to fully understand the existing research papers and textbooks on this
topic. Instead, this work offers an introductory experience for students which takes a more practical usage
perspective on the topic, rather than the statistical derivation. Students reading this paper should be able
to understand how to apply Kalman filtering tools to mathematical problems without requiring a deep
theoretical understanding of statistical theory.
Kalman filter - Applications in Image processingRavi Teja
The document discusses the Kalman filter, an algorithm used to estimate the state of a linear dynamic system from a series of noisy measurements. It describes how the Kalman filter uses a predictor-corrector approach with time update and measurement update equations to estimate the true state. The filter is applied to image processing to reduce noise by modeling the image as an autoregressive process and using the Kalman filter estimates. Extensions to nonlinear and complex systems using the extended and complex Kalman filters are also covered.
Seminar On Kalman Filter And Its ApplicationsBarnali Dey
The document discusses Kalman filters and their applications. It provides an overview of Kalman filters, explaining that they are used to estimate unknown system states from measurements that contain errors. It describes the basic algorithmic steps of Kalman filters, including prediction to project the state ahead and correction to incorporate new measurements. Finally, it gives examples of applications, such as for channel estimation in direct sequence spread spectrum communication systems.
The document discusses different types of flip-flops including RS NAND and NOR flip-flops, and covers the basics of sequential logic circuits. It defines level-triggered and edge-triggered clock inputs for flip-flops and compares asynchronous and synchronous clocked flip-flops. The timing diagrams show how positive and negative edge triggering determines when the output of a flip-flop changes state in response to clock pulses and input signals.
This document discusses the application of the Kalman filter to estimate variables for unmanned aerial vehicles (UAVs). The Kalman filter combines predictions with measurements to estimate variables that cannot be directly measured, like altitude and velocity. It was shown to accurately track the simulated state of a UAV over 10 time steps, keeping the error between estimated and actual values small. Graphs demonstrate how the Kalman filter estimate closely follows the actual simulated state. The Kalman filter is thus a useful tool for state estimation in applications like UAV navigation.
This document provides an agenda and procedures for conducting cluster optimization tests. It describes drive routes that cover all sectors of each base station in the cluster. Key tests include FTP uplink/downlink calls, VoLTE calls, and checks of coverage, mobility, accessibility and voice quality. The objectives are to validate RF design, handover performance, retainability, and identify worst areas for improvement through two drive tests and analysis of call logs and KPIs. Attendees should be RF and drive test engineers familiar with the XCAL tool and SCFT procedures.
The document summarizes the theory of finite automata and formal languages. It defines a deterministic finite automaton (DFA) as a 5-tuple consisting of a finite set of states, an input alphabet, transition function, initial state, and set of final/accepting states. It provides examples of DFAs and their transition graphs for strings over the alphabet {a,b}. It also defines the extended transition function that maps state-input pairs to the next state.
The document discusses the extended Kalman filter (EKF), which extends the standard Kalman filter to nonlinear systems through linearization. The EKF linearizes the system equations at each time step by taking the derivative of the nonlinear functions around the current state estimate. This results in an approximate linear system that can then be processed using the standard Kalman filter equations. The key steps of the EKF algorithm are to 1) compute the linearized system matrices using derivatives, 2) use these in a first-order Taylor approximation to linearize the system equations, and 3) apply the standard Kalman filter equations to this approximate linear system to recursively estimate the state.
This presentation introduces pushdown automata (PDA). A PDA is a nondeterministic finite state automaton that has an additional stack. It can perform epsilon transitions and manipulate symbols on the stack. PDAs have states, an initial stack symbol, and transitions that can pop, push or replace symbols on the stack. The presentation provides an example of a nondeterministic PDA and traces its execution on an input string to demonstrate how it works. A string is accepted if the PDA reaches a final state after consuming all input, regardless of the stack contents.
The document describes pushdown automata (PDA) which are analogous to context-free languages in the same way that finite automata are analogous to regular languages. A PDA has states, input symbols, stack symbols, transition functions, an initial state, initial stack symbol, and accepting states. The transition function specifies state transitions based on the current state, input symbol, and top of stack symbol and can modify the stack. The document provides examples of PDAs for languages of the form wwr and balanced parentheses and discusses how PDAs work by changing their instantaneous descriptions as the input is processed and stack is modified.
Coursera Machine Learning by Andrew NG 강의를 들으면서, 궁금했던 내용을 중심으로 정리.
내가 궁금했던건, 데이터를 분류하는 Decision boundary를 만들때...
- 왜 가중치(W)와 decision boundary가 직교해야 하는지?
- margin은 어떻게 계산하는지?
- margin은 어떻게 최대화 할 수 있는지?
- 실제로 margin을 최대화 하는 과정의 수식은 어떤지?
- 비선형 decision boundary를 찾기 위해서 어떻게 kernel을 이용하는지?...
http://blog.naver.com/freepsw/221032379891
This presentation speak's about Kalman Filter. In the presentation arithmetic behind Kalman filter is defined. The presentation is concluded with application example of Kalman filter.
The document discusses Kalman filters and their applications in tracking and data prediction. It provides an overview of the basic Kalman filter, which works optimally for linear models. It then describes the extended Kalman filter (EKF) which uses Taylor series linearization to apply the Kalman filter to nonlinear systems. Finally, it introduces the unscented Kalman filter (UKF) which uses the unscented transform for better linearization compared to the EKF when nonlinearities are large.
TEMS tools are used at various stages of radio network design, rollout, operation and improvement. During the design and rollout phase, TEMS is used for network integration testing, initial tuning, and GPRS performance verification. In the operation and improvement phase, it is used for traditional optimization and network feature optimization. TEMS allows measurement of key performance indicators, analysis of issues like low signal strength, interference, handover problems and call setup failures. It helps identify root causes and evaluate potential solutions.
Lecture Notes on Adaptive Signal Processing-1.pdfVishalPusadkar1
Adaptive filters are time-variant, nonlinear, and stochastic systems that perform data-driven approximation to minimize an objective function. The chapter discusses adaptive filter applications like system identification, inverse modeling, linear prediction, and noise cancellation. It also covers stochastic signal models, optimum linear filtering techniques like Wiener filtering, and solutions to the Wiener-Hopf equations. Numerical techniques like steepest descent are discussed for minimizing the mean square error function in adaptive filters. Stability and convergence analysis is presented for the steepest descent approach.
This document discusses optimization of networks using drive testing and TEMS software. It provides information on:
1) How drive testing and TEMS can analyze network performance from a subscriber perspective by recording measurement data.
2) The types of information displayed in TEMS windows including cell identity, signal strength, quality, and timing advance measurements.
3) How to use the TEMS software including default tabs, maps, recording properties, and report generation.
Application of adaptive linear equalizerSayahnarahul
This document discusses various applications of adaptive linear equalizers including: system identification, linear prediction, inverse modeling, jammer suppression, adaptive notch filtering, noise cancellation, echo cancellation in voice/data communications, fetal monitoring, ocular artifact removal from EEGs, and noise cancellation in AC electrical measurements. Adaptive linear equalizers are used across many domains including telecommunications, radar, sonar, video/audio processing, and noise cancellation to adapt filter coefficients over time to compensate for changes in systems and optimize signal recovery/interference rejection.
The document discusses pushdown automata (PDA). It defines a PDA as a 7-tuple that includes a set of states, input alphabet, stack alphabet, initial/start state, starting stack symbol, set of final/accepting states, and a transition function. PDAs operate on an input tape with a stack, and can accept languages that finite automata cannot, such as anbn. The document provides examples of designing PDAs for specific languages and converting between context-free grammars and PDAs.
This document provides an overview of adaptive filters, including:
- Adaptive filters have coefficients that are adjusted based on input data to optimize performance, unlike fixed filters.
- The LMS algorithm is commonly used to adjust coefficients to minimize the mean square error between the filter output and a desired signal.
- Key applications of adaptive filters include noise cancellation, system identification, channel equalization, and echo cancellation.
The document discusses optimization of 3G radio networks, focusing on the RF Optimization phase. It describes the various stages of network optimization including single site verification, RF optimization of clusters of sites, parameter optimization testing, and ongoing reference route testing and analysis. The RF Optimization process involves preparing clusters and drive routes, analyzing data to identify issues, determining solutions such as antenna adjustments, implementing changes, and retesting. Analysis approaches discussed include examining cell dominance, coverage, interference, uplink coverage, pilot pollution, neighbor lists, soft handover performance, and drop calls.
This document discusses the current state and issues surrounding public sector data in Korea. It defines open government data as data produced by government entities that can be freely used, reused and redistributed. While Korea has built various data through e-government initiatives, most data is not truly "open" as it cannot be freely reused or used for commercial purposes without restrictions. The document also outlines some international and domestic open data portals that have been established to increase access to government data.
This document provides an agenda and procedures for conducting cluster optimization tests. It describes drive routes that cover all sectors of each base station in the cluster. Key tests include FTP uplink/downlink calls, VoLTE calls, and checks of coverage, mobility, accessibility and voice quality. The objectives are to validate RF design, handover performance, retainability, and identify worst areas for improvement through two drive tests and analysis of call logs and KPIs. Attendees should be RF and drive test engineers familiar with the XCAL tool and SCFT procedures.
The document summarizes the theory of finite automata and formal languages. It defines a deterministic finite automaton (DFA) as a 5-tuple consisting of a finite set of states, an input alphabet, transition function, initial state, and set of final/accepting states. It provides examples of DFAs and their transition graphs for strings over the alphabet {a,b}. It also defines the extended transition function that maps state-input pairs to the next state.
The document discusses the extended Kalman filter (EKF), which extends the standard Kalman filter to nonlinear systems through linearization. The EKF linearizes the system equations at each time step by taking the derivative of the nonlinear functions around the current state estimate. This results in an approximate linear system that can then be processed using the standard Kalman filter equations. The key steps of the EKF algorithm are to 1) compute the linearized system matrices using derivatives, 2) use these in a first-order Taylor approximation to linearize the system equations, and 3) apply the standard Kalman filter equations to this approximate linear system to recursively estimate the state.
This presentation introduces pushdown automata (PDA). A PDA is a nondeterministic finite state automaton that has an additional stack. It can perform epsilon transitions and manipulate symbols on the stack. PDAs have states, an initial stack symbol, and transitions that can pop, push or replace symbols on the stack. The presentation provides an example of a nondeterministic PDA and traces its execution on an input string to demonstrate how it works. A string is accepted if the PDA reaches a final state after consuming all input, regardless of the stack contents.
The document describes pushdown automata (PDA) which are analogous to context-free languages in the same way that finite automata are analogous to regular languages. A PDA has states, input symbols, stack symbols, transition functions, an initial state, initial stack symbol, and accepting states. The transition function specifies state transitions based on the current state, input symbol, and top of stack symbol and can modify the stack. The document provides examples of PDAs for languages of the form wwr and balanced parentheses and discusses how PDAs work by changing their instantaneous descriptions as the input is processed and stack is modified.
Coursera Machine Learning by Andrew NG 강의를 들으면서, 궁금했던 내용을 중심으로 정리.
내가 궁금했던건, 데이터를 분류하는 Decision boundary를 만들때...
- 왜 가중치(W)와 decision boundary가 직교해야 하는지?
- margin은 어떻게 계산하는지?
- margin은 어떻게 최대화 할 수 있는지?
- 실제로 margin을 최대화 하는 과정의 수식은 어떤지?
- 비선형 decision boundary를 찾기 위해서 어떻게 kernel을 이용하는지?...
http://blog.naver.com/freepsw/221032379891
This presentation speak's about Kalman Filter. In the presentation arithmetic behind Kalman filter is defined. The presentation is concluded with application example of Kalman filter.
The document discusses Kalman filters and their applications in tracking and data prediction. It provides an overview of the basic Kalman filter, which works optimally for linear models. It then describes the extended Kalman filter (EKF) which uses Taylor series linearization to apply the Kalman filter to nonlinear systems. Finally, it introduces the unscented Kalman filter (UKF) which uses the unscented transform for better linearization compared to the EKF when nonlinearities are large.
TEMS tools are used at various stages of radio network design, rollout, operation and improvement. During the design and rollout phase, TEMS is used for network integration testing, initial tuning, and GPRS performance verification. In the operation and improvement phase, it is used for traditional optimization and network feature optimization. TEMS allows measurement of key performance indicators, analysis of issues like low signal strength, interference, handover problems and call setup failures. It helps identify root causes and evaluate potential solutions.
Lecture Notes on Adaptive Signal Processing-1.pdfVishalPusadkar1
Adaptive filters are time-variant, nonlinear, and stochastic systems that perform data-driven approximation to minimize an objective function. The chapter discusses adaptive filter applications like system identification, inverse modeling, linear prediction, and noise cancellation. It also covers stochastic signal models, optimum linear filtering techniques like Wiener filtering, and solutions to the Wiener-Hopf equations. Numerical techniques like steepest descent are discussed for minimizing the mean square error function in adaptive filters. Stability and convergence analysis is presented for the steepest descent approach.
This document discusses optimization of networks using drive testing and TEMS software. It provides information on:
1) How drive testing and TEMS can analyze network performance from a subscriber perspective by recording measurement data.
2) The types of information displayed in TEMS windows including cell identity, signal strength, quality, and timing advance measurements.
3) How to use the TEMS software including default tabs, maps, recording properties, and report generation.
Application of adaptive linear equalizerSayahnarahul
This document discusses various applications of adaptive linear equalizers including: system identification, linear prediction, inverse modeling, jammer suppression, adaptive notch filtering, noise cancellation, echo cancellation in voice/data communications, fetal monitoring, ocular artifact removal from EEGs, and noise cancellation in AC electrical measurements. Adaptive linear equalizers are used across many domains including telecommunications, radar, sonar, video/audio processing, and noise cancellation to adapt filter coefficients over time to compensate for changes in systems and optimize signal recovery/interference rejection.
The document discusses pushdown automata (PDA). It defines a PDA as a 7-tuple that includes a set of states, input alphabet, stack alphabet, initial/start state, starting stack symbol, set of final/accepting states, and a transition function. PDAs operate on an input tape with a stack, and can accept languages that finite automata cannot, such as anbn. The document provides examples of designing PDAs for specific languages and converting between context-free grammars and PDAs.
This document provides an overview of adaptive filters, including:
- Adaptive filters have coefficients that are adjusted based on input data to optimize performance, unlike fixed filters.
- The LMS algorithm is commonly used to adjust coefficients to minimize the mean square error between the filter output and a desired signal.
- Key applications of adaptive filters include noise cancellation, system identification, channel equalization, and echo cancellation.
The document discusses optimization of 3G radio networks, focusing on the RF Optimization phase. It describes the various stages of network optimization including single site verification, RF optimization of clusters of sites, parameter optimization testing, and ongoing reference route testing and analysis. The RF Optimization process involves preparing clusters and drive routes, analyzing data to identify issues, determining solutions such as antenna adjustments, implementing changes, and retesting. Analysis approaches discussed include examining cell dominance, coverage, interference, uplink coverage, pilot pollution, neighbor lists, soft handover performance, and drop calls.
This document discusses the current state and issues surrounding public sector data in Korea. It defines open government data as data produced by government entities that can be freely used, reused and redistributed. While Korea has built various data through e-government initiatives, most data is not truly "open" as it cannot be freely reused or used for commercial purposes without restrictions. The document also outlines some international and domestic open data portals that have been established to increase access to government data.
The Reign of Enterprise Mobility Begins - Q4 e zine- week 41Pamela Vega
Mobility-as-a-Service (MaaS) is a new platform that combines cloud and mobile technologies. It allows enterprises to deploy, manage, and monitor mobile devices and users in a scalable and secure way without having the infrastructure themselves. MaaS shifts responsibility for the mobile infrastructure from enterprises to service providers. It boosts productivity while enhancing scalability, reliability, and security for enterprises. MaaS empowers enterprises to meet the demands of an increasingly mobile workforce in a cost-effective manner.
'공공정보의 개방과 API'가 의미하는 바와 정책적 함의가 무엇인지에 대하여, 동국대학교 최고위과정 중 '빅데이터와 공공정보'라는 주제로 강장묵 교수(고려대)의 강의 교안입니다.
특강형식을 빌었으나, 본 강의는 2015년 3월에 있었던 경찰본청의 '공공정보 공유' 등에 대한 3일 연속 강의의 내용을 재사용하였음을 밝힙니다.
인용을 달고 PPT를 활용하시기 바랍니다.
공공정책 기획 방법으로서 서비스디자인 활용에 관한 연구 - 디자인학회 추계학술대회 발표자료(20141101. 윤성원, 지민정, 김광순)한국디자인진흥원 공공서비스디자인PD
공공정책 기획 방법으로서 서비스디자인 활용에 관한 연구
A study on service design as a public policy planning method.
공공서비스디자인 사용설명서 개발 결과를 중심으로
2014.11.1. 디자인학회 추계 학술대회
윤성원 한국디자인진흥원 서비스디지털융합팀 팀장
지민정 안전행정부 정부3.0 브랜드과제 발굴·홍보단 사무관
김광순 디맨드 대표
공공서비스디자인은 공공정책 및 공공서비스를 구상하고 전달하는 과정 전반에 디자인적 사고를 적용함으로써 수요자의 행동변화를 효과적으로 유도하여 정책 목표를 달성할 수 있도록 하는 방법을 의미한다.
선진국에서는 2000년대 초반부터 공공영역에서 서비스디자인이 교통, 의료, 치안 등 사회 현안을 해결하는 방법으로 활용되면서 다양한 성과를 거두며 학계와 업계 전방위적으로 입지를 확립해가고 있다. 공공영역에서 그간 디자인이 활용되지 않았던 범위에서 활용되면서 영역이 확장되고 있는 점도 주목할 만하지만, 프로세스 상 ‘정책집행’ 단계에서 활용되던 디자인이 보다 앞 단계인 ‘정책기획’의 방법으로써 활용되는 동향에 특히 주목할 필요가 있다. 프로세스의 앞단계에서 개입하게 된다는 것은 디자인이 주어진 문제의 해결자로서의 기존 역할을 넘어 문제를 규정하는 문제정의자로서 보다 근본적인 역할을 할 수 있는 기회가 주어지는 것을 의미하기 때문이다. 우리나라에서도 공공정책의 기획방법으로 디자인이 적용되는 서비스디자인 적용 사례가 나타나고 있다. 박근혜 정부는 국민 중심의, 개인의 행복에 맞춰진 정부를 의미하는 정부3.0을 국정 운영 기조로 정한 바 있다. 국민 중심 정부가 되기 위해서는 정책의 구상과 실행 절차 전반에 수요자인 국민이 무엇을 원하는지 제대로 파악하여 정책으로 설계할 수 있는 방법이 필요하다. 한국디자인진흥원은 공공서비스를 수요자 욕구 중심으로 기획할 수 있게 하는 서비스디자인 방법을 안행부에 소개하면서 협력체계를 갖추고 2014년 5월부터 7월까지 19개 과제에 대해 서비스디자인 전문가와 함께 서비스디자인 방법을 적용했으며 그것의 성과를 확인할 수 있었다.
그 결과 안행부는 정부3.0 정책 기획 단계에서 서비스디자인 활용 방법을 안내하는 지침서인 ‘공공서비스디자인 사용설명서’를 제작, 배포하기로 한다....
논문 및 발표자료 : http://cafe.naver.com/usable/3267
고농도의 미세먼지 발생에 대한 관리 정책은 다양하게 제시되고 있으나, 미세먼지 평균 농도에 대한 접근과 일상에서 밀접하게 체감할 수 있는 미세먼지 저감 대책은 부족한 실정이다. 기존 미세먼지 총량 감축 정책에서 실생활권 교통미세먼지 저감으로의 패러다임 전환이 필요하다.
소형화물차의 gps 자료를 기반으로 통행 및 미세먼지 배출 특성을 살펴보고, 시민이 체감할 수 있는 생활 공간에서의 미세먼지 저감 대안으로 소형화물차량의 친환경 전환에 대한 제도 개선 방안을 논의하였다.
A Study on the Future Sustainability of Sejong City, South Korea's Multifunct...Jeongmuk Kang
대중교통지향형개발의 실행을 중심으로 한, 대한민국의 행정중심복합도시 세종시의 미래 지속가능성에 대한 연구
A Study on the Future Sustainability of Sejong, South Korea’s Multifunctional Administrative City, Focusing on Implementation of Transit Oriented Development
Uppsala University
Master of Science in Sustainable Development
강정묵
인류는 천연 자원들을 대규모로 개발하고 소비하며 지구의 환경과 기후를 바꿔가고 있다. 18 세기 후반 증기기관이 발명과 함께 교통의 발달과 그에 따른 도시의 경제 활동 및 인구 증가로 도시들은 급속도로 성장하였다. 현재 도시들은 전 세계 인구의 절반 이상을 수용하고 있으며, 2030년에는 세계 에너지 소비의 73%가 도시에서 소비될 것으로 예상된다. 이러한 도시의 성장과 함께, 도시는 현재의 환경 및 에너지 관련 문제들의 원인으로 대두됨과 동시에 해결책의 근본으로 여겨지고 있다. 다양한 에너지 소비 부문 중에서, 교통 부문에서의 에너지 소비는 전체 에너지 소비의 19%를 차지하며 에너지 관련 이산화탄소 배출량의 23%를 차지하고 있다. 그리고 그 수치는 날로 증가하는 추세에 있다. 세계의 많은 도시들은 도시 교통에서 소비되는 화석 연료와 온난화 가스의 배출량을 줄이기 위해 많은 노력들을 하고 있다. 2050년까지 전 세계 도시 인구의 54%를 수용할 것으로 예측되고 있는 아시아 국가에서는, 주요 도시의 과밀화를 피하고 국토의 균형 발전을 도모하기 위해서 에코시티와 새로운 행정도시 건설과 같은 프로젝트들을 시도하고 있다. 한 편, 남아메리카의 개발 도상국에서는 대중교통지향형도시개발(Transit Oriented Development: TOD)과 간선급행버스체계(Bus Rapid Transit: BRT)를 도입하면서, 개발 도상국에서의 효율적인 도시 교통네트워크 및 지속 가능한 도시 구조 개선을 상대적인 저비용으로 구축할 수 있는 가능성을 보여줬다. 세종시는 2030년 완성을 목표로 대한민국에서 개발 중인 행정기능을 중심으로 한 계획 도시이다. 이 논문의 목적은 세종시 건설의 마스터플랜을 바탕으로, 세종시의 BRT를 기반으로 한 대중교통의 역할과 도시의 미래 지속가능성을 평가하는 것이다. 말레이시아의 행정도시인 푸트라자야(Putrajaya)에서 발견된 문제점들을 바탕으로 세종시가 풀어가야 할 과제들을 제시함과 동시에, 다른 여러 도시에서 연구된 TOD와 관련한 도시 정책들이 도시에 미치는 영향들을 종합하여 세종시 BRT가 도시의 지속가능성에 미치는 영향을 시스템 분석을 통해 제시할 것이다.
결과적으로, 본 연구는 TOD에서 파생된 실행 계획들이 도시의 교통 혼잡
[2012.11] A Study on the Future Sustainability of Sejong CityJeongmuk Kang
This document summarizes a study on the future sustainability of Sejong City, South Korea, focusing on the implementation of Transit-Oriented Development (TOD). The study analyzes Sejong City's master plan and its TOD implementation, particularly the planned Bus Rapid Transit system. TOD is expected to help Sejong City avoid potential urban problems seen in Putrajaya, Malaysia by optimizing the urban transportation system. Mixed land use, improved walking environments, and policies that increase public transit ridership are analyzed as ways TOD could promote environmental, economic and social sustainability in Sejong City. The conclusion is that TOD would be a better development option for cities in developing regions and is necessary for new planned cities to relieve traffic and
[2012.01] Brief introduction of Uppsala universityJeongmuk Kang
Uppsala University is located in Uppsala, Sweden, which is 70km north of Stockholm. It was founded in 1477 and is the oldest center of higher education in Scandinavia. Notable alumni include eight Nobel Laureates. The university has around 200,000 students and offers 30 international master's programs and 300 single-subject courses, with opportunities to study abroad at 500 foreign universities in 50 countries. Uppsala has a population of around 200,000 and is known for its beautiful landscapes, forests, wetlands and cycle lanes, as well as annual festivals like Valborg and winter activities like skiing.
1. Korea has experienced rapid economic growth since the 1960s, with GDP growing at an average annual rate of 7.6% between 1953 and 1995 and per capita income rising from $67 to $10,543.
2. The government implemented five-year economic development plans focused on industrialization, with early plans emphasizing infrastructure and exports and later plans focusing on technology development and rural development.
3. Korea overcame challenges like a lack of capital and domestic market by attracting foreign investment, pursuing OEM manufacturing, and increasing R&D spending to develop its own brands and technology.
3. WHY 생태교통의 배경과 필요성
Portland, Oregon, US
생태교통(EcoMobility)이란?
걷기, 자전거, 비동력 교통수단, 대중교통 그리고 공유자동차를 포함한
이동수단과 이들 이동수단 간의 효율적 연계체계를 말하며, 동시에 이
체계에 친환경성, 사회적 통합성을 실현한 이동을 의미한다.
4. 1
국제사회의 교통·수송 부문에서 온실가스
감축 및 적응 역할 강조
현재
• 수송부문의 화석연료 의존율은 97%
• 교통부문은 전체 에너지 사용과 온실가스 배출에서 각각 20%와 13%를 차지
• 2013 UN기후변화협약에서 ‘높은 잠재력’의 온실가스 감축 부문으로 소개되어 도시와 국가 하위
기관의 역할과 행동이 강조
• 교통환경과 개선 사항을 수치적으로 측정하고, 보고하고, 검증할 수 있는 시스템의 필요성이 대두
2050년까지
• 지구상 자동차 수 33억대(400% 증가)
• 수송부문에서의 이산화탄소 배출량
170% 증가 (OECD국가)
420% 증가 (OECD 이외 국가)
사진: OECD International Transport Forum 2014
5. 2
국내 온실가스 감축목표에서 수송부문의
역할 중요
2020년 국가 온실가스 감축 목표와 부문별 이행 계획
• 수송부문은 2020년까지 배출 전망치(BAU) 대비 34.3%의 가장 높은 비율
• 감축총량은 34.2백만 톤, 산업, 발전, 건물에 이어 네 번째로 높은 감축 목표
그래픽: 환경부
6. 3
천문학적 금액으로 증가하는 대도시의
교통혼잡비용
2014년 한국교통연구원의 발표
• 2015년에는 7대 도시의 교통혼잡비용이 33조
4천억 원에 이를 것으로 예측
• 국내총생산(GDP)의 2.16%에 이르는 비용으로
인천국제공항 3.8개를 건설할 수 있는 정도의 비
용
• 갈수록 심각해지는 대도시의 교통 혼잡과 그에 따
른 천문학적인 비용은 기후변화는 물론 시민들의
삶의 질에 직·간접적인 영향
7. 4 도시에서의 이동성(Mobility) 관리의 중요성
지속가능 교통물류 발전법 [시행 2013.5.22.]
지속가능 교통물류 발전법 시행령 [시행 2014.7.15]
• 각 지방정부는 주민과 관계 전문가의 의견을 들어 10년 단위의
지속가능 지방교통물류 발전계획을 수립하고 연간 시행계획을
수립하여야 한다.
• 지속가능 교통물류체계의 발전을 위하여 교통물류체계의 지속
가능성 관리지표를 설정하여 고시하여야 한다.
• 통행량 총량 설정 및 감축계획, 보행교통 개선계획 수립 등
• 지속가능 지방교통물류 발전계획과 시행계획의 사전 기준자료
가 될 수 있는 생태교통 시프트(EcoMobility SHIFT) 프로그
램을 회원 지방정부를 중심으로 우선 도입을 지원하고자 한다.
사진: 대전광역시 갑천 누리길
8. WHAT 생태교통 시프트
사진: 콜롬비아 보고타
생태교통 시프트(EcoMobility SHIFT)는 도시의 현재 교통 역량을
평가하고, 지속적인 향상의 방안을 구축하여, 최종적으로는 그 성과에
대한 검증을 통해 공식인증을 받는 생태교통 역량인증프로그램이다.
9. 1 생태교통 시프트란?
• 유럽연합의 EACI(EU-IEE 프로그램) 재정적 지원을 바
탕으로 생태교통 시프트 프로젝트 시작
• 2013년 5월 공식 발족
• 유럽 6개 도시에서 시범사업 실시: 룬드(스웨덴), 던디
(영국), 오쓰(네덜란드), 턴하우트(벨기에), 부르가스
(불가리아), 미슈콜츠(헝가리)
• 12명의 공식 검증원 활동 중
사진: 브라질 쿠리치바
10. 2 전체 프로세스
1단계 평가를 자체적으로 반복 수행하여 검증 단계까지의 수준까지 도시
교통수준의 역량을 끌어올린다.
11. 3 평가 지표(20개)
구현요소
Enabler
교통체계와 서비스
Transport System and Service
결과와 영향
Result and Impact
① 사용자 수요에 대한 이해
② 대중의 참여
③ 비전, 전략, 리더십
④ 생태교통을 위한 재정 지원
⑤ 인력 및 자원
⑥ 모니터링, 평가, 검토
① 교통계획
② 저속운행/보행 전용지구
③ 교통정보시스템(ITS)
④ 교통관리
⑤ 주차관리
⑥ 보행환경
⑦ 자전거 이용환경
⑧ 대중교통 서비스 범위&속도
⑨ 대중교통의 편리성
⑩ 탄소 저배출 이동수단
① 수송 분담율
② 안전성
③ 온실가스
④ 대기환경
12. 4 국내 시범사업 계획
평가 준비
• 생태교통 지표와 시프트 프로그램에 대한 이해
• 업무 분담 및 외부 전문가 영입 계획
지표측정
• 지표 근거 확보 및 계산
• 테이터 취합 담당자와 확인 및 피드백
성과 평가 1
• 지표 증거자료 공유 및 지표에 대한 점수 책정
• 타당성 확인 및 감정과 약점 토론
계획 및 조치 검토
• 개선 방향과 시행 계획을 위한 전략 미팅
• 자체평가 과정에 대한 최종 보고서 작성
• 공인 검증 신청 여부 결정
성과평가 2 • 생태교통 최종 점수 계산 및 리포트 준비
검증 신청 • 검증 신청서 작성 및 일정 조율
공인검증 / 라벨링 • 공인 검증원의 검증 실시 및 라벨 발급
13. 5 국내 시범사업 일정
8월
1. 준 비
2. 시 행
3. 결과보고
7월 9월 10월 11월 12월
자문회의
자체 평가 실시 및 미팅(1·2·3차)
결과보고
워크숍
정식사업 대상도시 발굴 및 사업 준비
도시 선정 및
개시 회의
계획 및 조
치 검토
14. 6 시프트를 통한 기대효과
• 지방정부의 지속가능교통물류계획 및 이행 안을
수립하는데 있어서 방향성 제시
• 도시교통환경에 대해 지속적으로 모니터링하고
피드백을 받을 수 있는 기준체계 구축
• 도시의 지도자 및 의사결정자들에게 교통을 통
해 시민들 삶의 질적 향상을 도모해야 한다는 인
식 공유를 위한 근거자료
• 교통정책의 우선순위 설정 및 재정 자원의 효과
적인 분배. 추가 재원마련을 위한 근거 자료
• 시민들에게 도시정부가 그들의 이동 수요에 관
심을 갖고 적극적으로 관리하고 있다는 사실을
알린다.
• 주변 도시들에 동기를 부여하고 도시교통 관련
협력을 도모하는 자료가 된다.
사진: 브라질 쿠리치바
15. 참고 유럽의 시범 도시들
프로그램 구축과 함께 시범사업을 수행한 유럽 6개 도시의 사례를 소개합니다. 시프트를 통해 도시의 강점과
약점을 구체적으로 확인할 수 있고, 앞으로 도시가 나아가야 할 방향에 대한 자료를 확보합니다.
지도: Google maps
Dundee
Oss
Miskolc
Burgas
Turnhout
Lund
