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    ๋ณ‘๋ ฌ ๋ฐ ๋ถ„์‚ฐ ์ž„๋ฒ ๋””๋“œ ์‹œ์Šคํ…œ์„ ์œ„ํ•œ ๋ชจ๋ธ ๊ธฐ๋ฐ˜ ์ฝ”๋“œ ์ƒ์„ฑ ํ”„๋ ˆ์ž„์›Œํฌ

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :๊ณต๊ณผ๋Œ€ํ•™ ์ปดํ“จํ„ฐ๊ณตํ•™๋ถ€,2020. 2. ํ•˜์ˆœํšŒ.์†Œํ”„ํŠธ์›จ์–ด ์„ค๊ณ„ ์ƒ์‚ฐ์„ฑ ๋ฐ ์œ ์ง€๋ณด์ˆ˜์„ฑ์„ ํ–ฅ์ƒ์‹œํ‚ค๊ธฐ ์œ„ํ•ด ๋‹ค์–‘ํ•œ ์†Œํ”„ํŠธ์›จ์–ด ๊ฐœ๋ฐœ ๋ฐฉ๋ฒ•๋ก ์ด ์ œ์•ˆ๋˜์—ˆ์ง€๋งŒ, ๋Œ€๋ถ€๋ถ„์˜ ์—ฐ๊ตฌ๋Š” ์‘์šฉ ์†Œํ”„ํŠธ์›จ์–ด๋ฅผ ํ•˜๋‚˜์˜ ํ”„๋กœ์„ธ์„œ์—์„œ ๋™์ž‘์‹œํ‚ค๋Š” ๋ฐ์— ์ดˆ์ ์„ ๋งž์ถ”๊ณ  ์žˆ๋‹ค. ๋˜ํ•œ, ์ž„๋ฒ ๋””๋“œ ์‹œ์Šคํ…œ์„ ๊ฐœ๋ฐœํ•˜๋Š” ๋ฐ์— ํ•„์š”ํ•œ ์ง€์—ฐ์ด๋‚˜ ์ž์› ์š”๊ตฌ ์‚ฌํ•ญ์— ๋Œ€ํ•œ ๋น„๊ธฐ๋Šฅ์  ์š”๊ตฌ ์‚ฌํ•ญ์„ ๊ณ ๋ คํ•˜์ง€ ์•Š๊ณ  ์žˆ๊ธฐ ๋•Œ๋ฌธ์— ์ผ๋ฐ˜์ ์ธ ์†Œํ”„ํŠธ์›จ์–ด ๊ฐœ๋ฐœ ๋ฐฉ๋ฒ•๋ก ์„ ์ž„๋ฒ ๋””๋“œ ์†Œํ”„ํŠธ์›จ์–ด๋ฅผ ๊ฐœ๋ฐœํ•˜๋Š” ๋ฐ์— ์ ์šฉํ•˜๋Š” ๊ฒƒ์€ ์ ํ•ฉํ•˜์ง€ ์•Š๋‹ค. ์ด ๋…ผ๋ฌธ์—์„œ๋Š” ๋ณ‘๋ ฌ ๋ฐ ๋ถ„์‚ฐ ์ž„๋ฒ ๋””๋“œ ์‹œ์Šคํ…œ์„ ๋Œ€์ƒ์œผ๋กœ ํ•˜๋Š” ์†Œํ”„ํŠธ์›จ์–ด๋ฅผ ๋ชจ๋ธ๋กœ ํ‘œํ˜„ํ•˜๊ณ , ์ด๋ฅผ ์†Œํ”„ํŠธ์›จ์–ด ๋ถ„์„์ด๋‚˜ ๊ฐœ๋ฐœ์— ํ™œ์šฉํ•˜๋Š” ๊ฐœ๋ฐœ ๋ฐฉ๋ฒ•๋ก ์„ ์†Œ๊ฐœํ•œ๋‹ค. ์šฐ๋ฆฌ์˜ ๋ชจ๋ธ์—์„œ ์‘์šฉ ์†Œํ”„ํŠธ์›จ์–ด๋Š” ๊ณ„์ธต์ ์œผ๋กœ ํ‘œํ˜„ํ•  ์ˆ˜ ์žˆ๋Š” ์—ฌ๋Ÿฌ ๊ฐœ์˜ ํƒœ์Šคํฌ๋กœ ์ด๋ฃจ์–ด์ ธ ์žˆ์œผ๋ฉฐ, ํ•˜๋“œ์›จ์–ด ํ”Œ๋žซํผ๊ณผ ๋…๋ฆฝ์ ์œผ๋กœ ๋ช…์„ธํ•œ๋‹ค. ํƒœ์Šคํฌ ๊ฐ„์˜ ํ†ต์‹  ๋ฐ ๋™๊ธฐํ™”๋Š” ๋ชจ๋ธ์ด ์ •์˜ํ•œ ๊ทœ์•ฝ์ด ์ •ํ•ด์ ธ ์žˆ๊ณ , ์ด๋Ÿฌํ•œ ๊ทœ์•ฝ์„ ํ†ตํ•ด ์‹ค์ œ ํ”„๋กœ๊ทธ๋žจ์„ ์‹คํ–‰ํ•˜๊ธฐ ์ „์— ์†Œํ”„ํŠธ์›จ์–ด ์—๋Ÿฌ๋ฅผ ์ •์  ๋ถ„์„์„ ํ†ตํ•ด ํ™•์ธํ•  ์ˆ˜ ์žˆ๊ณ , ์ด๋Š” ์‘์šฉ์˜ ๊ฒ€์ฆ ๋ณต์žก๋„๋ฅผ ์ค„์ด๋Š” ๋ฐ์— ๊ธฐ์—ฌํ•œ๋‹ค. ์ง€์ •ํ•œ ํ•˜๋“œ์›จ์–ด ํ”Œ๋žซํผ์—์„œ ๋™์ž‘ํ•˜๋Š” ํ”„๋กœ๊ทธ๋žจ์€ ํƒœ์Šคํฌ๋“ค์„ ํ”„๋กœ์„ธ์„œ์— ๋งคํ•‘ํ•œ ์ดํ›„์— ์ž๋™์ ์œผ๋กœ ํ•ฉ์„ฑํ•  ์ˆ˜ ์žˆ๋‹ค. ์œ„์˜ ๋ชจ๋ธ ๊ธฐ๋ฐ˜ ์†Œํ”„ํŠธ์›จ์–ด ๊ฐœ๋ฐœ ๋ฐฉ๋ฒ•๋ก ์—์„œ ์‚ฌ์šฉํ•˜๋Š” ํ”„๋กœ๊ทธ๋žจ ํ•ฉ์„ฑ๊ธฐ๋ฅผ ๋ณธ ๋…ผ๋ฌธ์—์„œ ์ œ์•ˆํ•˜์˜€๋Š”๋ฐ, ๋ช…์„ธํ•œ ํ”Œ๋žซํผ ์š”๊ตฌ ์‚ฌํ•ญ์„ ๋ฐ”ํƒ•์œผ๋กœ ๋ณ‘๋ ฌ ๋ฐ ๋ถ„์‚ฐ ์ž„๋ฒ ๋””๋“œ ์‹œ์Šคํ…œ์„์—์„œ ๋™์ž‘ํ•˜๋Š” ์ฝ”๋“œ๋ฅผ ์ƒ์„ฑํ•œ๋‹ค. ์—ฌ๋Ÿฌ ๊ฐœ์˜ ์ •ํ˜•์  ๋ชจ๋ธ๋“ค์„ ๊ณ„์ธต์ ์œผ๋กœ ํ‘œํ˜„ํ•˜์—ฌ ์‘์šฉ์˜ ๋™์  ํ–‰ํƒœ๋ฅผ ๋‚˜ํƒ€๊ณ , ํ•ฉ์„ฑ๊ธฐ๋Š” ์—ฌ๋Ÿฌ ๋ชจ๋ธ๋กœ ๊ตฌ์„ฑ๋œ ๊ณ„์ธต์ ์ธ ๋ชจ๋ธ๋กœ๋ถ€ํ„ฐ ๋ณ‘๋ ฌ์„ฑ์„ ๊ณ ๋ คํ•˜์—ฌ ํƒœ์Šคํฌ๋ฅผ ์‹คํ–‰ํ•  ์ˆ˜ ์žˆ๋‹ค. ๋˜ํ•œ, ํ”„๋กœ๊ทธ๋žจ ํ•ฉ์„ฑ๊ธฐ์—์„œ ๋‹ค์–‘ํ•œ ํ”Œ๋žซํผ์ด๋‚˜ ๋„คํŠธ์›Œํฌ๋ฅผ ์ง€์›ํ•  ์ˆ˜ ์žˆ๋„๋ก ์ฝ”๋“œ๋ฅผ ๊ด€๋ฆฌํ•˜๋Š” ๋ฐฉ๋ฒ•๋„ ๋ณด์—ฌ์ฃผ๊ณ  ์žˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ ์ œ์‹œํ•˜๋Š” ์†Œํ”„ํŠธ์›จ์–ด ๊ฐœ๋ฐœ ๋ฐฉ๋ฒ•๋ก ์€ 6๊ฐœ์˜ ํ•˜๋“œ์›จ์–ด ํ”Œ๋žซํผ๊ณผ 3 ์ข…๋ฅ˜์˜ ๋„คํŠธ์›Œํฌ๋กœ ๊ตฌ์„ฑ๋˜์–ด ์žˆ๋Š” ์‹ค์ œ ๊ฐ์‹œ ์†Œํ”„ํŠธ์›จ์–ด ์‹œ์Šคํ…œ ์‘์šฉ ์˜ˆ์ œ์™€ ์ด์ข… ๋ฉ€ํ‹ฐ ํ”„๋กœ์„ธ์„œ๋ฅผ ํ™œ์šฉํ•˜๋Š” ์›๊ฒฉ ๋”ฅ ๋Ÿฌ๋‹ ์˜ˆ์ œ๋ฅผ ์ˆ˜ํ–‰ํ•˜์—ฌ ๊ฐœ๋ฐœ ๋ฐฉ๋ฒ•๋ก ์˜ ์ ์šฉ ๊ฐ€๋Šฅ์„ฑ์„ ์‹œํ—˜ํ•˜์˜€๋‹ค. ๋˜ํ•œ, ํ”„๋กœ๊ทธ๋žจ ํ•ฉ์„ฑ๊ธฐ๊ฐ€ ์ƒˆ๋กœ์šด ํ”Œ๋žซํผ์ด๋‚˜ ๋„คํŠธ์›Œํฌ๋ฅผ ์ง€์›ํ•˜๊ธฐ ์œ„ํ•ด ํ•„์š”๋กœ ํ•˜๋Š” ๊ฐœ๋ฐœ ๋น„์šฉ๋„ ์‹ค์ œ ์ธก์ • ๋ฐ ์˜ˆ์ธกํ•˜์—ฌ ์ƒ๋Œ€์ ์œผ๋กœ ์ ์€ ๋…ธ๋ ฅ์œผ๋กœ ์ƒˆ๋กœ์šด ํ”Œ๋žซํผ์„ ์ง€์›ํ•  ์ˆ˜ ์žˆ์Œ์„ ํ™•์ธํ•˜์˜€๋‹ค. ๋งŽ์€ ์ž„๋ฒ ๋””๋“œ ์‹œ์Šคํ…œ์—์„œ ์˜ˆ์ƒ์น˜ ๋ชปํ•œ ํ•˜๋“œ์›จ์–ด ์—๋Ÿฌ์— ๋Œ€ํ•ด ๊ฒฐํ•จ์„ ๊ฐ๋‚ดํ•˜๋Š” ๊ฒƒ์„ ํ•„์š”๋กœ ํ•˜๊ธฐ ๋•Œ๋ฌธ์— ๊ฒฐํ•จ ๊ฐ๋‚ด์— ๋Œ€ํ•œ ์ฝ”๋“œ๋ฅผ ์ž๋™์œผ๋กœ ์ƒ์„ฑํ•˜๋Š” ์—ฐ๊ตฌ๋„ ์ง„ํ–‰ํ•˜์˜€๋‹ค. ๋ณธ ๊ธฐ๋ฒ•์—์„œ ๊ฒฐํ•จ ๊ฐ๋‚ด ์„ค์ •์— ๋”ฐ๋ผ ํƒœ์Šคํฌ ๊ทธ๋ž˜ํ”„๋ฅผ ์ˆ˜์ •ํ•˜๋Š” ๋ฐฉ์‹์„ ํ™œ์šฉํ•˜์˜€์œผ๋ฉฐ, ๊ฒฐํ•จ ๊ฐ๋‚ด์˜ ๋น„๊ธฐ๋Šฅ์  ์š”๊ตฌ ์‚ฌํ•ญ์„ ์‘์šฉ ๊ฐœ๋ฐœ์ž๊ฐ€ ์‰ฝ๊ฒŒ ์ ์šฉํ•  ์ˆ˜ ์žˆ๋„๋ก ํ•˜์˜€๋‹ค. ๋˜ํ•œ, ๊ฒฐํ•จ ๊ฐ๋‚ด ์ง€์›ํ•˜๋Š” ๊ฒƒ๊ณผ ๊ด€๋ จํ•˜์—ฌ ์‹ค์ œ ์ˆ˜๋™์œผ๋กœ ๊ตฌํ˜„ํ–ˆ์„ ๊ฒฝ์šฐ์™€ ๋น„๊ตํ•˜์˜€๊ณ , ๊ฒฐํ•จ ์ฃผ์ž… ๋„๊ตฌ๋ฅผ ์ด์šฉํ•˜์—ฌ ๊ฒฐํ•จ ๋ฐœ์ƒ ์‹œ๋‚˜๋ฆฌ์˜ค๋ฅผ ์žฌํ˜„ํ•˜๊ฑฐ๋‚˜, ์ž„์˜๋กœ ๊ฒฐํ•จ์„ ์ฃผ์ž…ํ•˜๋Š” ์‹คํ—˜์„ ์ˆ˜ํ–‰ํ•˜์˜€๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ ๊ฒฐํ•จ ๊ฐ๋‚ด๋ฅผ ์‹คํ—˜ํ•  ๋•Œ์— ํ™œ์šฉํ•œ ๊ฒฐํ•จ ์ฃผ์ž… ๋„๊ตฌ๋Š” ๋ณธ ๋…ผ๋ฌธ์˜ ๋˜ ๋‹ค๋ฅธ ๊ธฐ์—ฌ ์‚ฌํ•ญ ์ค‘ ํ•˜๋‚˜๋กœ ๋ฆฌ๋ˆ…์Šค ํ™˜๊ฒฝ์œผ๋กœ ๋Œ€์ƒ์œผ๋กœ ์‘์šฉ ์˜์—ญ ๋ฐ ์ปค๋„ ์˜์—ญ์— ๊ฒฐํ•จ์„ ์ฃผ์ž…ํ•˜๋Š” ๋„๊ตฌ๋ฅผ ๊ฐœ๋ฐœํ•˜์˜€๋‹ค. ์‹œ์Šคํ…œ์˜ ๊ฒฌ๊ณ ์„ฑ์„ ๊ฒ€์ฆํ•˜๊ธฐ ์œ„ํ•ด ๊ฒฐํ•จ์„ ์ฃผ์ž…ํ•˜์—ฌ ๊ฒฐํ•จ ์‹œ๋‚˜๋ฆฌ์˜ค๋ฅผ ์žฌํ˜„ํ•˜๋Š” ๊ฒƒ์€ ๋„๋ฆฌ ์‚ฌ์šฉ๋˜๋Š” ๋ฐฉ๋ฒ•์œผ๋กœ, ๋ณธ ๋…ผ๋ฌธ์—์„œ ๊ฐœ๋ฐœ๋œ ๊ฒฐํ•จ ์ฃผ์ž… ๋„๊ตฌ๋Š” ์‹œ์Šคํ…œ์ด ๋™์ž‘ํ•˜๋Š” ๋„์ค‘์— ์žฌํ˜„ ๊ฐ€๋Šฅํ•œ ๊ฒฐํ•จ์„ ์ฃผ์ž…ํ•  ์ˆ˜ ์žˆ๋Š” ๋„๊ตฌ์ด๋‹ค. ์ปค๋„ ์˜์—ญ์—์„œ์˜ ๊ฒฐํ•จ ์ฃผ์ž…์„ ์œ„ํ•ด ๋‘ ์ข…๋ฅ˜์˜ ๊ฒฐํ•จ ์ฃผ์ž… ๋ฐฉ๋ฒ•์„ ์ œ๊ณตํ•˜๋ฉฐ, ํ•˜๋‚˜๋Š” ์ปค๋„ GNU ๋””๋ฒ„๊ฑฐ๋ฅผ ์ด์šฉํ•œ ๋ฐฉ๋ฒ•์ด๊ณ , ๋‹ค๋ฅธ ํ•˜๋‚˜๋Š” ARM ํ•˜๋“œ์›จ์–ด ๋ธŒ๋ ˆ์ดํฌํฌ์ธํŠธ๋ฅผ ํ™œ์šฉํ•œ ๋ฐฉ๋ฒ•์ด๋‹ค. ์‘์šฉ ์˜์—ญ์—์„œ ๊ฒฐํ•จ์„ ์ฃผ์ž…ํ•˜๊ธฐ ์œ„ํ•ด GDB ๊ธฐ๋ฐ˜ ๊ฒฐํ•จ ์ฃผ์ž… ๋ฐฉ๋ฒ•์„ ์ด์šฉํ•˜์—ฌ ๋™์ผ ์‹œ์Šคํ…œ ํ˜น์€ ์›๊ฒฉ ์‹œ์Šคํ…œ์˜ ์‘์šฉ์— ๊ฒฐํ•จ์„ ์ฃผ์ž…ํ•  ์ˆ˜ ์žˆ๋‹ค. ๊ฒฐํ•จ ์ฃผ์ž… ๋„๊ตฌ์— ๋Œ€ํ•œ ์‹คํ—˜์€ ODROID-XU4 ๋ณด๋“œ์—์„œ ์ง„ํ–‰ํ•˜์˜€๋‹ค.While various software development methodologies have been proposed to increase the design productivity and maintainability of software, they usually focus on the development of application software running on a single processing element, without concern about the non-functional requirements of an embedded system such as latency and resource requirements. In this thesis, we present a model-based software development method for parallel and distributed embedded systems. An application is specified as a set of tasks that follow a set of given rules for communication and synchronization in a hierarchical fashion, independently of the hardware platform. Having such rules enables us to perform static analysis to check some software errors at compile time to reduce the verification difficulty. Platform-specific program is synthesized automatically after mapping of tasks onto processing elements is determined. The program synthesizer is also proposed to generate codes which satisfies platform requirements for parallel and distributed embedded systems. As multiple models which can express dynamic behaviors can be depicted hierarchically, the synthesizer supports to manage multiple task graphs with a different hierarchy to run tasks with parallelism. Also, the synthesizer shows methods of managing codes for heterogeneous platforms and generating various communication methods. The viability of the proposed software development method is verified with a real-life surveillance application that runs on six processing elements with three remote communication methods, and remote deep learning example is conducted to use heterogeneous multiprocessing components on distributed systems. Also, supporting a new platform and network requires a small effort by measuring and estimating development costs. Since tolerance to unexpected errors is a required feature of many embedded systems, we also support an automatic fault-tolerant code generation. Fault tolerance can be applied by modifying the task graph based on the selected fault tolerance configurations, so the non-functional requirement of fault tolerance can be easily adopted by an application developer. To compare the effort of supporting fault tolerance, manual implementation of fault tolerance is performed. Also, the fault tolerance method is tested with the fault injection tool to emulate fault scenarios and inject faults randomly. Our fault injection tool, which has used for testing our fault-tolerance method, is another work of this thesis. Emulating fault scenarios by intentionally injecting faults is commonly used to test and verify the robustness of a system. To emulate faults on an embedded system, we present a run-time fault injection framework that can inject a fault on both a kernel and application layer of Linux-based systems. For injecting faults on a kernel layer, two complementary fault injection techniques are used. One is based on Kernel GNU Debugger, and the other is using a hardware breakpoint supported by the ARM architecture. For application-level fault injection, the GDB-based fault injection method is used to inject a fault on a remote application. The viability of the proposed fault injection tool is proved by real-life experiments with an ODROID-XU4 system.Chapter 1 Introduction 1 1.1 Motivation 1 1.2 Contribution 6 1.3 Dissertation Organization 8 Chapter 2 Background 9 2.1 HOPES: Hope of Parallel Embedded Software 9 2.1.1 Software Development Procedure 9 2.1.2 Components of HOPES 12 2.2 Universal Execution Model 13 2.2.1 Task Graph Specification 13 2.2.2 Dataflow specification of an Application 15 2.2.3 Task Code Specification and Generic APIs 21 2.2.4 Meta-data Specification 23 Chapter 3 Program Synthesis for Parallel and Distributed Embedded Systems 24 3.1 Motivational Example 24 3.2 Program Synthesis Overview 26 3.3 Program Synthesis from Hierarchically-mixed Models 30 3.4 Platform Code Synthesis 33 3.5 Communication Code Synthesis 36 3.6 Experiments 40 3.6.1 Development Cost of Supporting New Platforms and Networks 40 3.6.2 Program Synthesis for the Surveillance System Example 44 3.6.3 Remote GPU-accelerated Deep Learning Example 46 3.7 Document Generation 48 3.8 Related Works 49 Chapter 4 Model Transformation for Fault-tolerant Code Synthesis 56 4.1 Fault-tolerant Code Synthesis Techniques 56 4.2 Applying Fault Tolerance Techniques in HOPES 61 4.3 Experiments 62 4.3.1 Development Cost of Applying Fault Tolerance 62 4.3.2 Fault Tolerance Experiments 62 4.4 Random Fault Injection Experiments 65 4.5 Related Works 68 Chapter 5 Fault Injection Framework for Linux-based Embedded Systems 70 5.1 Background 70 5.1.1 Fault Injection Techniques 70 5.1.2 Kernel GNU Debugger 71 5.1.3 ARM Hardware Breakpoint 72 5.2 Fault Injection Framework 74 5.2.1 Overview 74 5.2.2 Architecture 75 5.2.3 Fault Injection Techniques 79 5.2.4 Implementation 83 5.3 Experiments 90 5.3.1 Experiment Setup 90 5.3.2 Performance Comparison of Two Fault Injection Methods 90 5.3.3 Bit-flip Fault Experiments 92 5.3.4 eMMC Controller Fault Experiments 94 Chapter 6 Conclusion 97 Bibliography 99 ์š” ์•ฝ 108Docto

    Real-time multi-domain optimization controller for multi-motor electric vehicles using automotive-suitable methods and heterogeneous embedded platforms

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    Los capรญtulos 2,3 y 7 estรกn sujetos a confidencialidad por el autor. 145 p.In this Thesis, an elaborate control solution combining Machine Learning and Soft Computing techniques has been developed, targeting a chal lenging vehicle dynamics application aiming to optimize the torque distribution across the wheels with four independent electric motors.The technological context that has motivated this research brings together potential -and challenges- from multiple dom ains: new automotive powertrain topologies with increased degrees of freedom and controllability, which can be approached with innovative Machine Learning algorithm concepts, being implementable by exploiting the computational capacity of modern heterogeneous embedded platforms and automated toolchains. The complex relations among these three domains that enable the potential for great enhancements, do contrast with the fourth domain in this context: challenging constraints brought by industrial aspects and safe ty regulations. The innovative control architecture that has been conce ived combines Neural Networks as Virtual Sensor for unmeasurable forces , with a multi-objective optimization function driven by Fuzzy Logic , which defines priorities basing on the real -time driving situation. The fundamental principle is to enhance vehicle dynamics by implementing a Torque Vectoring controller that prevents wheel slip using the inputs provided by the Neural Network. Complementary optimization objectives are effici ency, thermal stress and smoothness. Safety -critical concerns are addressed through architectural and functional measures.Two main phases can be identified across the activities and milestones achieved in this work. In a first phase, a baseline Torque Vectoring controller was implemented on an embedded platform and -benefiting from a seamless transition using Hardware-in -the -Loop - it was integrated into a real Motor -in -Wheel vehicle for race track tests. Having validated the concept, framework, methodology and models, a second simulation-based phase proceeds to develop the more sophisticated controller, targeting a more capable vehicle, leading to the final solution of this work. Besides, this concept was further evolved to support a joint research work which lead to outstanding FPGA and GPU based embedded implementations of Neural Networks. Ultimately, the different building blocks that compose this work have shown results that have met or exceeded the expectations, both on technical and conceptual level. The highly non-linear multi-variable (and multi-objective) control problem was tackled. Neural Network estimations are accurate, performance metrics in general -and vehicle dynamics and efficiency in particular- are clearly improved, Fuzzy Logic and optimization behave as expected, and efficient embedded implementation is shown to be viable. Consequently, the proposed control concept -and the surrounding solutions and enablers- have proven their qualities in what respects to functionality, performance, implementability and industry suitability.The most relevant contributions to be highlighted are firstly each of the algorithms and functions that are implemented in the controller solutions and , ultimately, the whole control concept itself with the architectural approaches it involves. Besides multiple enablers which are exploitable for future work have been provided, as well as an illustrative insight into the intricacies of a vivid technological context, showcasing how they can be harmonized. Furthermore, multiple international activities in both academic and professional contexts -which have provided enrichment as well as acknowledgement, for this work-, have led to several publications, two high-impact journal papers and collateral work products of diverse nature

    Model-based systems engineering for life-sciences instrumentation development

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    Nextโ€generation genome sequencing machines and Pointโ€ofโ€Care (PoC) in vitro diagnostics devices are precursors of an emerging class of Cyberโ€Physical Systems (CPS), one that harnesses biomolecularโ€scale mechanisms to enable novel "wetโ€technology" applications in medicine, biotechnology, and environmental science. Although many such applications exist, testifying the importance of innovative lifeโ€sciences instrumentation, recent events have highlighted the difficulties that designing organizations face in their attempt to guarantee safety, reliability, and performance of this special class of CPS. New regulations and increasing competition pressure innovators to rethink their design and engineering practices, and to better address the above challenges. The pace of innovation will be determined by how organizations manage to ensure the satisfaction of aforementioned constraints while also streamlining product development, maintaining high costโ€efficiency and shortening timeโ€toโ€market. Modelโ€Based Systems Engineering provides a valuable framework for addressing these challenges. In this paper, we demonstrate that existing and readily available modelโ€based development frameworks can be adopted early in the lifeโ€sciences instrumentation design process. Such frameworks are specifically helpful in describing and characterizing CPS including elements of a biological nature both at the architectural and performance level. We present the SysML model of a smartphoneโ€based PoC diagnostics system designed for detecting a particular molecular marker. By modeling components and behaviors spanning across the biological, physicalโ€nonbiological, and computational domains, we were able to characterize the important systemic relations involved in the specification of our system's Limit of Detection. Our results illustrate the suitability of such an approach and call for further work toward formalisms enabling the formal verification of systems including biomolecular components

    Real-time multi-domain optimization controller for multi-motor electric vehicles using automotive-suitable methods and heterogeneous embedded platforms

    Get PDF
    Los capรญtulos 2,3 y 7 estรกn sujetos a confidencialidad por el autor. 145 p.In this Thesis, an elaborate control solution combining Machine Learning and Soft Computing techniques has been developed, targeting a chal lenging vehicle dynamics application aiming to optimize the torque distribution across the wheels with four independent electric motors.The technological context that has motivated this research brings together potential -and challenges- from multiple dom ains: new automotive powertrain topologies with increased degrees of freedom and controllability, which can be approached with innovative Machine Learning algorithm concepts, being implementable by exploiting the computational capacity of modern heterogeneous embedded platforms and automated toolchains. The complex relations among these three domains that enable the potential for great enhancements, do contrast with the fourth domain in this context: challenging constraints brought by industrial aspects and safe ty regulations. The innovative control architecture that has been conce ived combines Neural Networks as Virtual Sensor for unmeasurable forces , with a multi-objective optimization function driven by Fuzzy Logic , which defines priorities basing on the real -time driving situation. The fundamental principle is to enhance vehicle dynamics by implementing a Torque Vectoring controller that prevents wheel slip using the inputs provided by the Neural Network. Complementary optimization objectives are effici ency, thermal stress and smoothness. Safety -critical concerns are addressed through architectural and functional measures.Two main phases can be identified across the activities and milestones achieved in this work. In a first phase, a baseline Torque Vectoring controller was implemented on an embedded platform and -benefiting from a seamless transition using Hardware-in -the -Loop - it was integrated into a real Motor -in -Wheel vehicle for race track tests. Having validated the concept, framework, methodology and models, a second simulation-based phase proceeds to develop the more sophisticated controller, targeting a more capable vehicle, leading to the final solution of this work. Besides, this concept was further evolved to support a joint research work which lead to outstanding FPGA and GPU based embedded implementations of Neural Networks. Ultimately, the different building blocks that compose this work have shown results that have met or exceeded the expectations, both on technical and conceptual level. The highly non-linear multi-variable (and multi-objective) control problem was tackled. Neural Network estimations are accurate, performance metrics in general -and vehicle dynamics and efficiency in particular- are clearly improved, Fuzzy Logic and optimization behave as expected, and efficient embedded implementation is shown to be viable. Consequently, the proposed control concept -and the surrounding solutions and enablers- have proven their qualities in what respects to functionality, performance, implementability and industry suitability.The most relevant contributions to be highlighted are firstly each of the algorithms and functions that are implemented in the controller solutions and , ultimately, the whole control concept itself with the architectural approaches it involves. Besides multiple enablers which are exploitable for future work have been provided, as well as an illustrative insight into the intricacies of a vivid technological context, showcasing how they can be harmonized. Furthermore, multiple international activities in both academic and professional contexts -which have provided enrichment as well as acknowledgement, for this work-, have led to several publications, two high-impact journal papers and collateral work products of diverse nature

    XXIII Congreso Argentino de Ciencias de la Computaciรณn - CACIC 2017 : Libro de actas

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    Trabajos presentados en el XXIII Congreso Argentino de Ciencias de la Computaciรณn (CACIC), celebrado en la ciudad de La Plata los dรญas 9 al 13 de octubre de 2017, organizado por la Red de Universidades con Carreras en Informรกtica (RedUNCI) y la Facultad de Informรกtica de la Universidad Nacional de La Plata (UNLP).Red de Universidades con Carreras en Informรกtica (RedUNCI

    Security in Embedded Systems: A Model-Based Approach with Risk Metrics

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