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    Cities in American federalism: evidence on state-local government conflict from a survey of mayors

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    Previous scholarship on American federalism has largely focused on the national government's increasingly conflictual relationship with the states. While some studies have explored the rise of mandates at the state level, there has been comparatively less attention on stateโ€“local relationships. Using a new survey of mayors, we explore variations in local government attitudes towards their state governments. We find some evidence that, regardless of partisanship, mayors in more conservative states are unhappy about state funding andโ€”especiallyโ€”regulations. More strikingly, we also uncover a partisan mismatch in which Democratic mayors provide especially negative ratings of their stateโ€™s funding andโ€”even more stronglyโ€”regulations. These findings have important implications for stateโ€“local relations as cities continue to become more Democratic and Republicans increasingly dominate state-level contests

    ์ด๋™๋ธ”๋ก ๋ฐ ์ž”๋ฅ˜ํŽธ์ฐจ ์ œ๊ฑฐ ๋ชจ๋ธ์˜ˆ์ธก์ œ์–ด ๊ธฐ๋ฒ•์˜ ์ตœ์ ์„ฑ ํ–ฅ์ƒ

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :๊ณต๊ณผ๋Œ€ํ•™ ํ™”ํ•™์ƒ๋ฌผ๊ณตํ•™๋ถ€,2020. 2. ์ด์ข…๋ฏผ.Model predictive control (MPC) is a receding horizon control which derives finite-horizon optimal solution for current state on-line by solving an optimal control problem. MPC has had a tremendous impact on both industrial and control research areas. There are several outstanding issues in MPC. MPC has to solve the optimization problem within a sampling period so that the reduction of on-line computational complexity is a one of the main research subject in MPC. Another major issue is model-plant mismatch due to the model based predictive approach so that offset-free tracking schemes by compensating model-plant mismatch or unmeasured disturbance has been developed. In this thesis, we focused on the optimality performance of move blocking which fixes the decision variables over arbitrary time intervals to reduce computational load for on-line optimization in MPC and disturbance estimator approach based offset-free MPC which is the most standardly used method to accomplish offset-free tracking in MPC. We improve the optimality performance of move blocked MPC in two ways. The first scheme provides a superior base sequence by linearly interpolating complementary base sequences, and the second scheme provides a proper time-varying blocking structure with semi-explicit approach. Moreover, we improve the optimality performance of offset-free MPC by exploiting learned model-plant mismatch compensating signal from estimated disturbance data. With the proposed schemes, we efficiently improve the optimality performance while guaranteeing the recursive feasibility and closed-loop stability.๋ชจ๋ธ์˜ˆ์ธก์ œ์–ด๋Š” ํ˜„์žฌ ์‹œ์Šคํ…œ ์ƒํƒœ์— ๋Œ€ํ•œ ์œ ํ•œ ๊ตฌ๊ฐ„ ์ตœ์ ํ•ด๋ฅผ ๋„์ถœํ•˜๋Š” ์˜จ๋ผ์ธ ์ด๋™ ๊ตฌ๊ฐ„ ์ œ์–ด ๋ฐฉ์‹์ด๋‹ค. ๋ชจ๋ธ์˜ˆ์ธก์ œ์–ด๋Š” ํ”ผ๋“œ๋ฐฑ์„ ํ†ตํ•œ ๊ณต์ • ๋™ํŠน์„ฑ๊ณผ ์ œ์•ฝ ์กฐ๊ฑด์„ ํšจ๊ณผ์ ์œผ๋กœ ๋ฐ˜์˜ํ•˜๋Š” ์žฅ์ ์œผ๋กœ ์ธํ•ด ์‚ฐ์—… ๋ฐ ์ œ์–ด ์—ฐ๊ตฌ ๋ถ„์•ผ์— ํฐ ์˜ํ–ฅ์„ ๋ฏธ์ณค๋‹ค. ์ด๋Ÿฌํ•œ ๋ชจ๋ธ์˜ˆ์ธก์ œ์–ด์—๋Š” ๋ช‡ ๊ฐ€์ง€ ํ•ด๊ฒฐ๋˜์–ด์•ผ ํ•  ๋ฌธ์ œ๊ฐ€ ์žˆ๋‹ค. ๋ชจ๋ธ์˜ˆ์ธก์ œ์–ด์—์„œ๋Š” ์ƒ˜ํ”Œ๋ง ๊ธฐ๊ฐ„ ๋‚ด์— ์ตœ์ ํ™” ๋ฌธ์ œ๋ฅผ ํ’€์–ด๋‚ด์•ผ ํ•˜๊ธฐ ๋•Œ๋ฌธ์—, ์˜จ๋ผ์ธ ๊ณ„์‚ฐ ๋ณต์žก์„ฑ์˜ ๊ฐ์†Œ๊ฐ€ ์ฃผ์š” ์—ฐ๊ตฌ ์ฃผ์ œ ์ค‘ ํ•˜๋‚˜๋กœ ํ™œ๋ฐœํžˆ ์—ฐ๊ตฌ๋˜๊ณ  ์žˆ๋‹ค. ๋˜ ๋‹ค๋ฅธ ์ฃผ์š” ๋ฌธ์ œ๋Š” ๋ชจ๋ธ์— ๊ธฐ๋ฐ˜ํ•œ ์˜ˆ์ธก์„ ์ด์šฉํ•˜๋Š” ์ ‘๊ทผ ๋ฐฉ์‹์œผ๋กœ ์ธํ•ด ๋ชจ๋ธ-ํ”Œ๋žœํŠธ ๋ถˆ์ผ์น˜๋กœ ์ธํ•œ ์˜ค์ฐจ๋ฅผ ํ•ด๊ฒฐํ•ด์•ผ ํ•œ๋‹ค๋Š” ์ ์ด๋ฉฐ, ๋ชจ๋ธ ํ”Œ๋žœํŠธ ๋ถˆ์ผ์น˜ ๋˜๋Š” ์ธก์ •๋˜์ง€ ์•Š์€ ์™ธ๋ž€์„ ๋ณด์ƒํ•˜์—ฌ ์ž”๋ฅ˜ํŽธ์ฐจ ์—†์ด ์ฐธ์กฐ์‹ ํ˜ธ๋ฅผ ์ถ”์ ํ•˜๋Š” ์—ฐ๊ตฌ๊ฐ€ ํ™œ๋ฐœํžˆ ์ด๋ฃจ์–ด์ง€๊ณ  ์žˆ๋‹ค. ์ด ๋…ผ๋ฌธ์—์„œ๋Š” ๋ชจ๋ธ์˜ˆ์ธก์ œ์–ด์—์„œ์˜ ์˜จ๋ผ์ธ ์ตœ์ ํ™”๋ฅผ ์œ„ํ•œ ๊ณ„์‚ฐ ๋ถ€ํ•˜๋ฅผ ์ค„์ด๊ธฐ ์œ„ํ•ด ์ž„์˜์˜ ์‹œ๊ฐ„ ๊ฐ„๊ฒฉ์— ๊ฑธ์ณ ๊ฒฐ์ • ๋ณ€์ˆ˜๋ฅผ ๊ณ ์ •์‹œํ‚ค๋Š” ์ด๋™ ๋ธ”๋ก ์ „๋žต์˜ ์ตœ์ ์„ฑ ํ–ฅ์ƒ์— ์ค‘์ ์„ ๋‘์—ˆ์œผ๋ฉฐ, ๋˜ํ•œ ์ž”๋ฅ˜ํŽธ์ฐจ๋ฅผ ์ œ๊ฑฐํ•˜๊ธฐ ์œ„ํ•ด ๊ฐ€์žฅ ํ‘œ์ค€์ ์œผ๋กœ ์‚ฌ์šฉ๋˜๋Š” ์™ธ๋ž€ ์ถ”์ •๊ธฐ๋ฅผ ์ด์šฉํ•œ ์ž”๋ฅ˜ํŽธ์ฐจ-์ œ๊ฑฐ ๋ชจ๋ธ์˜ˆ์ธก์ œ์–ด ๊ธฐ๋ฒ•์˜ ์ตœ์ ์„ฑ ํ–ฅ์ƒ์— ์ค‘์ ์„ ๋‘์—ˆ๋‹ค. ์ด ๋…ผ๋ฌธ์—์„œ๋Š” ์ด๋™ ๋ธ”๋ก ๋ชจ๋ธ์˜ˆ์ธก์ œ์–ด์˜ ์ตœ์  ์„ฑ๋Šฅ์„ ํ–ฅ์ƒ์‹œํ‚ค๊ธฐ ์œ„ํ•œ ๋‘ ๊ฐ€์ง€ ์ „๋žต์„ ์ œ์‹œํ•œ๋‹ค. ์ฒซ ๋ฒˆ์งธ ์ „๋žต์€ ์ด๋™ ๋ธ”๋ก ์ „๋žต์—์„œ ์ผ๋ฐ˜์ ์œผ๋กœ ๊ณ ์ •๋œ ์ฑ„๋กœ ์‚ฌ์šฉ๋˜๋Š” ๊ธฐ๋ฐ˜ ์‹œํ€€์Šค๋ฅผ ์ƒํ˜ธ ๋ณด์™„์ ์ธ ๋‘ ๊ธฐ๋ฐ˜ ์‹œํ€€์Šค์˜ ์„ ํ˜• ๋ณด๊ฐ„์œผ๋กœ ๋Œ€์ฒดํ•จ์œผ๋กœ์จ ๋ณด๋‹ค ์šฐ์ˆ˜ํ•œ ๊ธฐ๋ฐ˜ ์‹œํ€€์Šค๋ฅผ ์ œ๊ณตํ•˜๋ฉฐ, ๋‘ ๋ฒˆ์งธ ์ „๋žต์€ ์ค€-๋ช…์‹œ์  ์ ‘๊ทผ๋ฒ•์„ ํ™œ์šฉํ•˜์—ฌ ํ˜„์žฌ ์‹œ์Šคํ…œ ์ƒํƒœ์— ์ ์ ˆํ•œ ์‹œ๋ณ€ ๋ธ”๋ก ๊ตฌ์กฐ๋ฅผ ์˜จ๋ผ์ธ์—์„œ ์ œ๊ณตํ•œ๋‹ค. ๋˜ํ•œ, ์ž”๋ฅ˜ํŽธ์ฐจ-์ œ๊ฑฐ ๋ชจ๋ธ์˜ˆ์ธก์ œ์–ด ๊ธฐ๋ฒ•์˜ ์ตœ์  ์„ฑ๋Šฅ์„ ํ–ฅ์ƒ์‹œํ‚ค๊ธฐ ์œ„ํ•ด ์ถ”์ • ์™ธ๋ž€ ๋ฐ์ดํ„ฐ๋กœ๋ถ€ํ„ฐ ํ•™์Šต๋œ ๋ชจ๋ธ-ํ”Œ๋žœํŠธ ๋ถˆ์ผ์น˜ ๋ณด์ƒ ์‹ ํ˜ธ๋ฅผ ์˜จ๋ผ์ธ์—์„œ ์ด์šฉํ•˜๋Š” ์ „๋žต์„ ์ œ์•ˆํ•˜์˜€๋‹ค. ์ œ์•ˆ๋œ ์„ธ ๊ฐ€์ง€ ๊ธฐ๋ฒ•์„ ํ†ตํ•ด ๋ชจ๋ธ์˜ˆ์ธก์ œ์–ด์˜ ๋ฐ˜๋ณต์  ์‹คํ˜„๊ฐ€๋Šฅ์„ฑ๊ณผ ํ์‡„-๋ฃจํ”„ ์•ˆ์ •์„ฑ์„ ๋ณด์žฅํ•˜๋ฉด์„œ ์ตœ์  ์„ฑ๋Šฅ์„ ํšจ์œจ์ ์œผ๋กœ ๊ฐœ์„  ํ•˜์˜€๋‹ค.1. Introduction 1 2. Move-blocked model predictive control with linear interpolation of base sequences 5 2.1 Introduction 5 2.2 Preliminaries 9 2.2.1 MPC formulation 9 2.2.2 Move blocking 12 2.2.3 Move blocked MPC (MBMPC) 15 2.3 Move blocking schemes 16 2.3.1 Previous solution based offset blocking 17 2.3.2 LQR solution based offset blocking 18 2.4 Interpolated solution based move blocking 20 2.4.1 Interpolated solution based MBMPC 20 2.4.2 QP formulation 26 2.5 Numerical examples 29 2.5.1 Example 1 (Feasible region) 30 2.5.2 Example 2 (Performance in regulation problem) 33 2.5.3 Example 3 (Performance in tracking problem) 36 3. Move-blocked model predictive control with time-varying blocking structure by semi-explicit approach 43 3.1 Introduction 43 3.2 Problem formulation 46 3.3 Move blocked MPC 48 3.3.1 Move blocking scheme 48 3.3.2 Implementation of move blocking 51 3.4 Semi-explicit approach for move blocked MPC 53 3.4.1 Off-line generation of critical region 56 3.4.2 On-line MPC scheme with critical region search 60 3.4.3 Property of semi-explicit move blocked MPC 62 3.5 Numerical examples 70 3.5.1 Example 1 (Regulation problem) 71 3.5.2 Example 2 (Tracking problem) 77 4. Model-plant mismatch learning offset-free model predictive control 83 4.1 Introduction 83 4.2 Offset-free MPC: Disturbance estimator approach 86 4.2.1 Preliminaries 86 4.2.2 Disturbance estimator and controller design 87 4.2.3 Offset-free tracking condition 89 4.3 Model-plant mismatch learning offset-free MPC 91 4.3.1 Model-plant mismatch learning 92 4.3.2 Application of learned model-plant mismatch 97 4.3.3 Robust asymptotic stability of model-plant mismatch learning offset-free MPC 102 4.4 Numerical example 117 4.4.1 System with random set-point 120 4.4.2 Transformed system 125 4.4.3 System with multiple random set-points 128 5. Concluding remarks 134 5.1 Move-blocked model predictive control with linear interpolation of base sequences 134 5.2 Move-blocked model predictive control with time-varying blocking structure by semi-explicit approach 135 5.3 Model-plant mismatch learning offset-free model predictive control 136 5.4 Conclusions 138 5.5 Future work 139 Bibliography 145Docto

    Move blocking strategies in receding horizon control

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    Abstract โ€” In order to deal with the computational burden of optimal control, it is common practice to reduce the degrees of freedom by fixing the input or its derivatives to be constant over several time-steps. This policy is referred to as โ€œmove blockingโ€. This paper will address two issues. First, a survey of various move blocking strategies is presented and the shortcomings of these blocking policies, such as the lack of stability and constraint satisfaction guarantees, will be illustrated. Second, a novel move blocking scheme, โ€œMoving Window Blockingโ€ (MWB), will be presented. In MWB, the blocking strategy is time-dependent such that the scheme yields stability and feasibility guarantees for the closed-loop system. Finally, the results of a large case-study are presented that illustrate the advantages and drawbacks of the various control strategies discussed in this paper

    The ethics of forgetting in an age of pervasive computing

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    In this paper, we examine the potential of pervasive computing to create widespread sousveillance, that will complement surveillance, through the development of lifelogs; socio-spatial archives that document every action, every event, every conversation, and every material expression of an individualโ€™s life. Examining lifelog projects and artistic critiques of sousveillance we detail the projected mechanics of life-logging and explore their potential implications. We suggest, given that lifelogs have the potential to convert exterior generated oligopticons to an interior panopticon, that an ethics of forgetting needs to be developed and built into the development of life-logging technologies. Rather than seeing forgetting as a weakness or a fallibility we argue that it is an emancipatory process that will free pervasive computing from burdensome and pernicious disciplinary effects

    On the Interface Between Operations and Human Resources Management

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    Operations management (OM) and human resources management (HRM) have historically been very separate fields. In practice, operations managers and human resource managers interact primarily on administrative issues regarding payroll and other matters. In academia, the two subjects are studied by separate communities of scholars publishing in disjoint sets of journals, drawing on mostly separate disciplinary foundations. Yet, operations and human resources are intimately related at a fundamental level. Operations are the context that often explains or moderates the effects of human resource activities such as pay, training, communications and staffing. Human responses to operations management systems often explain variations or anomalies that would otherwise be treated as randomness or error variance in traditional operations research models. In this paper, we probe the interface between operations and human resources by examining how human considerations affect classical OM results and how operational considerations affect classical HRM results. We then propose a unifying framework for identifying new research opportunities at the intersection of the two fields

    IoT Security Vulnerabilities and Predictive Signal Jamming Attack Analysis in LoRaWAN

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    Internet of Things (IoT) gains popularity in recent times due to its flexibility, usability, diverse applicability and ease of deployment. However, the issues related to security is less explored. The IoT devices are light weight in nature and have low computation power, low battery life and low memory. As incorporating security features are resource expensive, IoT devices are often found to be less protected and in recent times, more IoT devices have been routinely attacked due to high profile security flaws. This paper aims to explore the security vulnerabilities of IoT devices particularly that use Low Power Wide Area Networks (LPWANs). In this work, LoRaWAN based IoT security vulnerabilities are scrutinised and loopholes are identified. An attack was designed and simulated with the use of a predictive model of the device data generation. The paper demonstrated that by predicting the data generation model, jamming attack can be carried out to block devices from sending data successfully. This research will aid in the continual development of any necessary countermeasures and mitigations for LoRaWAN and LPWAN functionality of IoT networks in general
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