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Nu, mm rm im Modelled the preemptive scheduling problem as timed automata Verification that no deadline is missed in Uppaal Analysing task budgets iteratively Collaboration: Uppaal course Hackathon RTOS Bernhard Aichevnig Verified Dependability","previews":{"preview":{"ref":"535ab98c-7c32-4b32-abd8-f128807bae65","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/90a56dfa-5eed-43db-80f1-ea006f67c9f6/925e0948-cec5-49e7-83f3-e7f77bd1e856/presentation_535ab98c_7c32_4b32_abd8_f128807bae65.jpg"}}},{"index":15,"time":3065470,"duration":131230,"relevance":-1,"hit":false,"text":"DEPENDABLE THlNGS TU max. lie- er uhlelneireval Mugs, ng mmm Publication Highlights conference: Gigerl, Hadzic, Primas, Mangard, Bloem: Coco: CoeDesign and CoeVerification of Masked Software Implementations on CPUs. USENIX Security Symposium, 2021. Bartocci, Bloem, Maderbacher, Manjunath, Nickovic: Adaptive Testing for Specification Coverage in CPS Models, ADHS, 2021. IFAC Young Author Award. Martins Gomes, BK. Aichernig, Baunach: Formal Modeling Approach for Portable Loerevel OS Functionality. SEFM, 2020. Aichernig, Bloem, Ebrahimi, Horn, Pernkopf, Roth, Rupp, Tappler, Tranninger. Learning Behavior Model of Hybrid Systems Through Combining Model- Based Testing and Machine Learning. IFIP ICTSS, 2019. Pferscher, Aichernig: Stateful Black- Box Pferscher, Aichernig, Tappler. From Fuzzing of Bluetooth Devices Using Automata Passive to Active: Learning Timed Automata Learning. NASA Formal Methods, 2022. Efficiently. NASA Formal Methods, 2020. Bernhard Aichemig Verified Dependability 11","previews":{"preview":{"ref":"535ab98c-7c32-4b32-abd8-f128807bae65","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/90a56dfa-5eed-43db-80f1-ea006f67c9f6/78384a48-f921-43b9-b596-d0dfbc379dbf/presentation_535ab98c_7c32_4b32_abd8_f128807bae65.jpg"}}},{"index":16,"time":3196700,"duration":291970,"relevance":-1,"hit":false,"text":"Graz- DEPENDABLE THlNGS TU WWW mm Follow Up: Dependable Embedded Systems Lab TU Graz- DES Lab Dependable bedded Svstei is Cooperation of TU Graz and Silicon Austria Labs SAL since 2020 key researchers postdocs PhD students SAL researchers Core from LEAD project: Aichernig, Baunach, Bloem, Mangard Research Topics foundational research ZeroeBug Software: design and runtime verification Al Meets Formal Methods: automata learning shielding Robust Machine Learning: spiking neural networks, optimization Bernhard Aichemig Verified Dependability","previews":{"preview":{"ref":"535ab98c-7c32-4b32-abd8-f128807bae65","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/90a56dfa-5eed-43db-80f1-ea006f67c9f6/a6282924-c956-458f-ae1c-03fa3955ec00/presentation_535ab98c_7c32_4b32_abd8_f128807bae65.jpg"}}},{"index":17,"time":3488670,"duration":106460,"relevance":-1,"hit":false,"text":"DEPENDABLE THl NGS VWH Wren Mathematical attacks Bruteforce, known- plaintext attack, chosen- ciphertext Alice attack, differential linear Mathematical description cryptanalysis, of encryption scheme: Physical attacks Key message Side- channel attacks, fault ciphertext attacks Exploit physical access to the Enc device DeC Side- channel Information: leaked by the device unintentionally","previews":{"preview":{"ref":"535ab98c-7c32-4b32-abd8-f128807bae65","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/90a56dfa-5eed-43db-80f1-ea006f67c9f6/97866510-2d24-4612-9f72-8cf63e2394e3/presentation_535ab98c_7c32_4b32_abd8_f128807bae65.jpg"}}},{"index":18,"time":3595130,"duration":112000,"relevance":-1,"hit":false,"text":"DEPENDABLE THl NGS Dp ll Wren Power Analysis Attacks Power consumption of CMOS circuit depends on: Whatoperations are performed Break the Which data is processed dependency! Masking Split sensitive value into random shares Observation of up to shares does not reveal any information about sensitive value Algorithmic countermeasure based on assumptions about hardware","previews":{"preview":{"ref":"535ab98c-7c32-4b32-abd8-f128807bae65","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/90a56dfa-5eed-43db-80f1-ea006f67c9f6/1afd23f8-d335-4911-9d49-b4730592ddca/presentation_535ab98c_7c32_4b32_abd8_f128807bae65.jpg"}}},{"index":19,"time":3707130,"duration":326770,"relevance":-1,"hit":false,"text":"DEPENDABLE THl NGS DtpWil Wren Co- Design Case Studies RISC Ibex core 32- bit, single- issue scalar stage pipeline Problematic components: register file, computation units, data memory RISC- SweRV core 32- bit, superscalar, pipeline stages Problematic components: pipelines, memory management components Secure software masking requires: Modified hardware CPU Modified software software constraints","previews":{"preview":{"ref":"535ab98c-7c32-4b32-abd8-f128807bae65","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/90a56dfa-5eed-43db-80f1-ea006f67c9f6/56ec54fa-3ee9-496c-9510-400e07bd51e0/presentation_535ab98c_7c32_4b32_abd8_f128807bae65.jpg"}}},{"index":20,"time":4033900,"duration":127000,"relevance":-1,"hit":false,"text":"DEPENDABLE THl NGS VWH rgwc, Conclusion Secure masked implementations for loT devices: Require both Alice and Bob Bob can use formal tools like Coco to check security of implementation in practice Important factors: platform, implementation","previews":{"preview":{"ref":"535ab98c-7c32-4b32-abd8-f128807bae65","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/90a56dfa-5eed-43db-80f1-ea006f67c9f6/bf27fd4d-ad4d-4cdf-bafa-b4aff5885284/presentation_535ab98c_7c32_4b32_abd8_f128807bae65.jpg"}}},{"index":21,"time":4160900,"duration":307800,"relevance":-1,"hit":false,"text":"DEPENDABLE TH NGS Viririvrn imihuziwvniv inxgmmm Incremental Verification","previews":{"preview":{"ref":"535ab98c-7c32-4b32-abd8-f128807bae65","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/90a56dfa-5eed-43db-80f1-ea006f67c9f6/a5678314-dd1c-4ee9-a349-3e23712625b2/presentation_535ab98c_7c32_4b32_abd8_f128807bae65.jpg"}}},{"index":22,"time":4468700,"duration":119265,"relevance":-1,"hit":false,"text":"DEPENDABLE TH NGS mm mmhm mm Spielberg Racing Track","previews":{"preview":{"ref":"535ab98c-7c32-4b32-abd8-f128807bae65","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/90a56dfa-5eed-43db-80f1-ea006f67c9f6/0010b312-ab31-4315-ae4d-f6769dae0f9d/presentation_535ab98c_7c32_4b32_abd8_f128807bae65.jpg"}}},{"index":23,"time":4587965,"duration":112065,"relevance":-1,"hit":false,"text":"DEPENDABLE THlNGS wwi vlrlrlirii latflumuiiAlv tEiwimim uis Summary Ensure safety of an unverifiable controller Hybrid systems and realtime specifications Incremental verification with proofs on demand","previews":{"preview":{"ref":"535ab98c-7c32-4b32-abd8-f128807bae65","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/90a56dfa-5eed-43db-80f1-ea006f67c9f6/7c1ae3dc-d399-4841-965b-d39aca931c1a/presentation_535ab98c_7c32_4b32_abd8_f128807bae65.jpg"}}},{"index":24,"time":4700030,"duration":349740,"relevance":-1,"hit":false,"text":"DEPENDABLE THlNGS TU Gran urn- 9mm, an WT ArJrzst. mm MQTT Case Study Results out of MQTT brokers showed inconsistencies to the MQTT specification Found issues: Acceptance of invalid characters One broker accepts prohibited character and forwards it Acceptance of system- internal characters Brokers allow clients to publish 84 subscribe to system- internal topics Inconsistency on number of received publications Brokers merge equal publications Andrea Pferscher: Learning Based Fuzzing of loT Protocols","previews":{"preview":{"ref":"535ab98c-7c32-4b32-abd8-f128807bae65","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/90a56dfa-5eed-43db-80f1-ea006f67c9f6/7bd18138-b7a4-48fd-b65f-72c4ccef6294/presentation_535ab98c_7c32_4b32_abd8_f128807bae65.jpg"}}},{"index":25,"time":5049770,"duration":242350,"relevance":-1,"hit":false,"text":"Sims mmm miss mmauemrgszmysmgga Whom piazvaluvull ru Page aziwiwie FD DEPENDABLE THlNGS TU Gran mm is mm BLE Case Study Results Fingerprinting: Learned models showed behavioral differences between all BLE devices out of devices crashed on fuzzed input Learned models showed inconsistencies to BLE specification Fuzzing revealed unexplored behavior NEW Learned model of BLE device in Tesla Model Andrea Pferscher. Learningr ased Fuzzing of loT Protocols","previews":{"preview":{"ref":"535ab98c-7c32-4b32-abd8-f128807bae65","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/90a56dfa-5eed-43db-80f1-ea006f67c9f6/3c9c693c-a037-4cb2-8ff4-bc2c395ca184/presentation_535ab98c_7c32_4b32_abd8_f128807bae65.jpg"}}},{"index":26,"time":5292120,"duration":126380,"relevance":-1,"hit":false,"text":"DEPENDABLE THINGS nu, Mm- llwu Wu Formal methods for verifying non- functional properties in RTOS Modelling RTOS as timed automata is comprehensive method to verify any nonfunctional requirement. Model checking tool UPPAAL used for model creation and verification Modeled Aspects: PPVVL mas Schedulability sartus PowerConsumption Slices Aliart Interrupt Handling Schedulability Au SAn Schelpel. el al 2022 sznos An 05 Architecture lor Sustainable Embedded Sys1ems in 2022 Fmehjahrstre en FG as Gesellschal fur Inlovmalik Anton Szikia 22 us 2022","previews":{"preview":{"ref":"535ab98c-7c32-4b32-abd8-f128807bae65","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/90a56dfa-5eed-43db-80f1-ea006f67c9f6/8d279cbc-3c65-40c2-9634-46c70ff86f1e/presentation_535ab98c_7c32_4b32_abd8_f128807bae65.jpg"}}},{"index":27,"time":5418500,"duration":103730,"relevance":-1,"hit":false,"text":"DEPENDABLE THINGS mm in MM. am NFR Slice Schedulability art Abstraction from Source Code Templates representing Tasks, Scheduler, Kernel, Syscalls and the Application layer Verification goals: No Task deadline violations forall not TaskRealTime DLViolatlon No Task stan ation Taskstates id Waitinq 77 PrioritySchedu ertemplale Taskstates id Ready Taskstates id Ready 77 SWWam EW Taskstates id Runninq Balis15 Ribeim el al 2022 Verifying Liveness and Ream- lune oi osaaseu Embedded Sollware ln 25th Eummlcm Conference on Digital siem Design BSD EUROMlCRO Anton Salkla 22 us 2022","previews":{"preview":{"ref":"535ab98c-7c32-4b32-abd8-f128807bae65","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/90a56dfa-5eed-43db-80f1-ea006f67c9f6/b0998031-36e7-4eb4-83bd-6c34d8ceef16/presentation_535ab98c_7c32_4b32_abd8_f128807bae65.jpg"}}},{"index":28,"time":5522230,"duration":139240,"relevance":-1,"hit":false,"text":"DEPENDABLE THINGS My my in MN is NFR Slice Power Consumption gmmw Modelling Power as Cost Hardware model In addition Goals: So ware Layer Power Consumption analy5 Power Peak Detection Verification: Power Peak analysis not PiPeak max: temp. Anion Szikia 22 us 2022","previews":{"preview":{"ref":"535ab98c-7c32-4b32-abd8-f128807bae65","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/90a56dfa-5eed-43db-80f1-ea006f67c9f6/79c993d0-ab2a-417a-b07f-c8c9e3a6637f/presentation_535ab98c_7c32_4b32_abd8_f128807bae65.jpg"}}},{"index":29,"time":5661470,"duration":151030,"relevance":-1,"hit":false,"text":"DEPENDABLE THINGS Mill Wu Slicing: Model NFR unification concept Schedulability Power Consumption Interrupt Handling More NFRs Compatible Models for different NFRs designed for the same architecture are referred to as slices. Such slices can be integrated to compose unified and complete system model. Anton Szikia 22 us 2022","previews":{"preview":{"ref":"535ab98c-7c32-4b32-abd8-f128807bae65","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/90a56dfa-5eed-43db-80f1-ea006f67c9f6/652e5c03-626a-4693-8b7c-e6ecf934fb40/presentation_535ab98c_7c32_4b32_abd8_f128807bae65.jpg"}}},{"index":30,"time":5812500,"duration":162500,"relevance":-1,"hit":false,"text":"DEPENDABLE THINGS me mm hei my MW. Model to Code NFR: Schedulability Algorithm for optimized schedulable execution budgets for Rate Monotonic Scheduler An iterative method of verification to obtain optimum execution budget values for system design Anton Szikia 22 09 2022","previews":{"preview":{"ref":"535ab98c-7c32-4b32-abd8-f128807bae65","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/90a56dfa-5eed-43db-80f1-ea006f67c9f6/c7777bbb-b35e-4322-946b-752c4b650105/presentation_535ab98c_7c32_4b32_abd8_f128807bae65.jpg"}}},{"index":31,"time":5975000,"duration":528670,"relevance":-1,"hit":false,"text":"Assessing safety and robustness of reinforcement learning an Enforcing Safety Safety: Can weirv ed deep reinforrement ieaming again nun Mn: nun ms 57 in Rnhustness: H011 ow it when nmng mm mndomiy WW chosen Fr mi Manage pinyiiigSuperMminEmu nn Ar nm Dang vans mm aiang rcfucncc rate manic vans win ingn variabiiity in in mcm Martin Tappler Modei Learning for Dependable Mazhine Learning","previews":{"preview":{"ref":"535ab98c-7c32-4b32-abd8-f128807bae65","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/90a56dfa-5eed-43db-80f1-ea006f67c9f6/680ae30c-54d1-4c61-bc55-f167a6899240/presentation_535ab98c_7c32_4b32_abd8_f128807bae65.jpg"}}},{"index":32,"time":6503670,"duration":261845,"relevance":-1,"hit":false,"text":"DEPENDABLE THING- 11:!- Multi- Agent systems will be ubiquitous How can we improve and guarantee the dependability of multi agent systems","previews":{"preview":{"ref":"535ab98c-7c32-4b32-abd8-f128807bae65","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/90a56dfa-5eed-43db-80f1-ea006f67c9f6/7cf4e838-b985-4d98-8e81-f56fa265ad75/presentation_535ab98c_7c32_4b32_abd8_f128807bae65.jpg"}}},{"index":33,"time":6765515,"duration":359950,"relevance":-1,"hit":false,"text":"DEPENDABLE THING- The Beauty and the Beast Dependable Multi Agent Systems","previews":{"preview":{"ref":"535ab98c-7c32-4b32-abd8-f128807bae65","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/90a56dfa-5eed-43db-80f1-ea006f67c9f6/770909c2-67f7-468d-b6a1-0dfbbe39fac6/presentation_535ab98c_7c32_4b32_abd8_f128807bae65.jpg"}}},{"index":34,"time":7125465,"duration":180620,"relevance":-1,"hit":false,"text":"DEPENDABLE THINGS TU am- in lllHKHHAK lfl Research Areas Diagnosis Networked Control modelebased fault detection communication network modeling probabilistic reasoning communication control coedeSign planning execution monitoring fault tolerant control over different hierarchical levels low level control, high level plan execution,","previews":{"preview":{"ref":"535ab98c-7c32-4b32-abd8-f128807bae65","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/90a56dfa-5eed-43db-80f1-ea006f67c9f6/ed7c72e9-6aeb-4f23-8142-e80f42a2be61/presentation_535ab98c_7c32_4b32_abd8_f128807bae65.jpg"}}},{"index":35,"time":7306085,"duration":151515,"relevance":-1,"hit":false,"text":"DEPENDABieE THVIVNG 161!- lntegrating Diagnosis Recovery necessity for dependability of systems the root cause needs to be identified with some guarantees dealing with diverse symptoms and faults Recovery pushes the system back to into nominal state fault- tolerant control behaviors vs. dedicated repair activities safe hm unaeeemahie behavior degraded behavior fault 90;; recovery","previews":{"preview":{"ref":"535ab98c-7c32-4b32-abd8-f128807bae65","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/90a56dfa-5eed-43db-80f1-ea006f67c9f6/f49e2d30-3b6f-4db5-8130-edce488e58f8/presentation_535ab98c_7c32_4b32_abd8_f128807bae65.jpg"}}},{"index":36,"time":7457600,"duration":205200,"relevance":-1,"hit":false,"text":"DEPENDABLE THINGS 11:!- uriw my iii- Wm mm. in xiii um Model- based Diagnosis FDI mics disturbances Fl stem measurcmcuts Uhlilll ii ii m1: iiipiiis prediction 37 faults digital twin behavioral model causal model","previews":{"preview":{"ref":"535ab98c-7c32-4b32-abd8-f128807bae65","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/90a56dfa-5eed-43db-80f1-ea006f67c9f6/795f3da3-0010-410c-9789-2d57540314a0/presentation_535ab98c_7c32_4b32_abd8_f128807bae65.jpg"}}},{"index":37,"time":7662800,"duration":133800,"relevance":-1,"hit":false,"text":"um. DEPENDA BFEVTHING TU Synthesis of Research Done Markus Tranninger: observertor dynamic non- linear systems estimation guarantees basis for residuals Hadi Gharibdoust modelling communication networks stability analysis of networked control basis for recovery on various levels Stalin Munoz Gutierez Christian Knoll planning execution monitoring new methods for distributed diagnosis using discrete models probabilistic reasoning basis for hierarchical diagnosis guarantees for error margins causal reasoning for diagnOSis","previews":{"preview":{"ref":"535ab98c-7c32-4b32-abd8-f128807bae65","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/90a56dfa-5eed-43db-80f1-ea006f67c9f6/e8d214d0-79b8-41c6-af98-1d82c68b87af/presentation_535ab98c_7c32_4b32_abd8_f128807bae65.jpg"}}},{"index":38,"time":7796600,"duration":276230,"relevance":-1,"hit":false,"text":"DEPENDAFLE TH JGS 1529. Summary addressing the dependability of multi- agent systems Improvement, scalability and guarantees combining different disciplines and methods control theory and FDI symbolic AI and model- based diagnosis probabilistic inference communication theory dependability improvement in sub- parts still open issues integration in an integrated hybrid hierarchical approach it considering communication behavior in higher- level control","previews":{"preview":{"ref":"535ab98c-7c32-4b32-abd8-f128807bae65","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/90a56dfa-5eed-43db-80f1-ea006f67c9f6/26a49259-55a5-4b48-a177-07047b57a3b9/presentation_535ab98c_7c32_4b32_abd8_f128807bae65.jpg"}}},{"index":39,"time":8072830,"duration":408070,"relevance":-1,"hit":false,"text":"How accurate are the estimation results Patch potential models3 Connection between number of solutions and model parameters Guarantees when global minimum is optimal Contribution of individual edges Provable conditions for convexity of the energy landscape Chris ial Knoll and Franz Parnkopl Eoliet Progagatlon Accurate Maroinals or Accurate Panltlon FunctlumWherE is the Dlllerencs Journal at Sta l5tlcel Mechanlcs Tneory and Expevlment, 2u2i Harald Lelsenbefgeri Franz Pernkopl ario Chrlsllal Knoll leli lg tno Bethe Approxnnation How Structural Modlllcetluns in Graph improve Eellel Propagatloll onceedlngs at uni, 2022","previews":{"preview":{"ref":"535ab98c-7c32-4b32-abd8-f128807bae65","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/90a56dfa-5eed-43db-80f1-ea006f67c9f6/12927762-ce9b-44b3-a8ef-d4cd6fb13025/presentation_535ab98c_7c32_4b32_abd8_f128807bae65.jpg"}}},{"index":40,"time":8480900,"duration":99730,"relevance":-1,"hit":false,"text":"How can we improve the performance Many knobs to tune Reduce effect of strong couplings in loops Mode Flewaility Scheduling5 Optimwzaticn of Potentials Graph Optim lamcn Hybrid Models Mode Assumptions Christian Kna chhae Ram Sebastian Tscma schek and Franz Psmknpv Message scheduling memods for behef pmpagauan Proceedmgs olECML PKDD. 2015","previews":{"preview":{"ref":"535ab98c-7c32-4b32-abd8-f128807bae65","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/90a56dfa-5eed-43db-80f1-ea006f67c9f6/823bd7a5-591c-4480-b8ff-5f3e5bb77b77/presentation_535ab98c_7c32_4b32_abd8_f128807bae65.jpg"}}},{"index":41,"time":8580630,"duration":294470,"relevance":-1,"hit":false,"text":"DEEENEEEEE Introduction Fault: deviation of system from the usual acceptable, standard behavior Causes: wear, ageing, environmental ill unnco, Consequences: economic losses, environmental damage, serious hazards for humans. Markus nnnmgm msmm of Aummwion and Conn- am Unnrrsin irrhnolngv, Amnin markm Ennmgmmugm an 22 JE ZUQ2","previews":{"preview":{"ref":"535ab98c-7c32-4b32-abd8-f128807bae65","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/90a56dfa-5eed-43db-80f1-ea006f67c9f6/c1cf10eb-6d3d-4a9b-8b28-ded76edad0d6/presentation_535ab98c_7c32_4b32_abd8_f128807bae65.jpg"}}},{"index":42,"time":8875100,"duration":91500,"relevance":-1,"hit":false,"text":"PEPFFDEBLF Model- Based Approach to Fault Detection faults disturbances fault onset time system measurements inputs residual 70:. 10 17: 30 Time! prediction Mt trade off: sensitivity vs. robustness di git 2L1 twin Mar mnningm Inammc anummnnnn and ommL am Unwmm of irr hnnlogv Amma mm kmn nnnmgmi mgmzmi 22 122","previews":{"preview":{"ref":"535ab98c-7c32-4b32-abd8-f128807bae65","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/90a56dfa-5eed-43db-80f1-ea006f67c9f6/2c3dc764-0981-41f7-91e4-65f51a2a142f/presentation_535ab98c_7c32_4b32_abd8_f128807bae65.jpg"}}},{"index":43,"time":8966600,"duration":111100,"relevance":-1,"hit":false,"text":"DEEENEAEEE Model- Based Approach to Fault Detection mm inlmlmmu Models are typically available from an early design stage WW WW cg. Via tools like Matlab Simulink, Modulica, hudumm um Application to real problems not that simple Complex models Iionliiiean lookup tables uii ertai11,... we want fault isolation, not only detection structural analysis We need techniques that, support the design of diagnosis solutions for complex dynamical systems Markus Il nnnmgm lnsnnnr Aummmion and Conn- am limmsm lrr hnolngv, Amnin mm- km Enmngmmugm an 22 22","previews":{"preview":{"ref":"535ab98c-7c32-4b32-abd8-f128807bae65","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/90a56dfa-5eed-43db-80f1-ea006f67c9f6/9da50c9e-038b-495e-806f-680e2f3ca192/presentation_535ab98c_7c32_4b32_abd8_f128807bae65.jpg"}}},{"index":44,"time":9077700,"duration":200330,"relevance":-1,"hit":false,"text":"DEEENEEEEE FDI Example: Mercator Wheel robot Kinetic vehicle dynamics model double track model rontlod by VVllOOl actuation and steering nsors: To be selected for good diagnosis gi velocit eleration. wheel speed. steering, angle. Possible faults: faults in all sensors faults in all electrical drives and steering actuators at tires Clinngu in tire radius mnnlngm lnalimmv anummnnnnmirl nlm nl. mm Hnwm of lrr lmnlogv Amma mm kmn nnnmgmi mgmzml","previews":{"preview":{"ref":"535ab98c-7c32-4b32-abd8-f128807bae65","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/90a56dfa-5eed-43db-80f1-ea006f67c9f6/b3babd90-d535-4f79-869e-beaf1f408276/presentation_535ab98c_7c32_4b32_abd8_f128807bae65.jpg"}}},{"index":45,"time":9278030,"duration":119240,"relevance":-1,"hit":false,"text":"DE PE DAB LE INJ ERNET TH NGS PACKET COMMUNICATION OUTCOME discrete stochastic process","previews":{"preview":{"ref":"535ab98c-7c32-4b32-abd8-f128807bae65","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/90a56dfa-5eed-43db-80f1-ea006f67c9f6/cc219631-8a8b-4f2e-9d31-a14874826d5f/presentation_535ab98c_7c32_4b32_abd8_f128807bae65.jpg"}}},{"index":46,"time":9397270,"duration":146260,"relevance":-1,"hit":false,"text":"DEPENDABLE THINGS mm in MM. PROCESS DEFINITION Discrete Stochastic Process: if the nth packet is received when the nth packet is lost Non- numeric to numeric data type LR iii pe Pe Pe Ree mrn","previews":{"preview":{"ref":"535ab98c-7c32-4b32-abd8-f128807bae65","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/90a56dfa-5eed-43db-80f1-ea006f67c9f6/07cfea63-8166-4d21-9634-4dcc333e8bdb/presentation_535ab98c_7c32_4b32_abd8_f128807bae65.jpg"}}},{"index":47,"time":9543530,"duration":258940,"relevance":-1,"hit":false,"text":"DEPENDABLE THINGS Mm my, mq. wmm. sMm mu SPECTRUM Frobenius Norm Fouriar Yranslorm Aulu- cov: ne: as sampling lrequancy F5 64 Hz Fvocess Samp so so we PM 59 Indepemem 34 as 148 A0 732 34 as as 40 42 44 normahze VrEEluenCv sampunu lreuuency","previews":{"preview":{"ref":"535ab98c-7c32-4b32-abd8-f128807bae65","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/90a56dfa-5eed-43db-80f1-ea006f67c9f6/1ea30b0f-2b80-4bf4-b76a-afcc2b7ddc52/presentation_535ab98c_7c32_4b32_abd8_f128807bae65.jpg"}}},{"index":48,"time":9802470,"duration":131760,"relevance":-1,"hit":false,"text":"DEPENDABLE THINGS Us mm hei my MW. CONCLUSION Stochastic dependence of on pe pe Iz Insignificant time- varying painNise dependence bit Pe gt Pe Spectral Analysis 1st 2nd moments verification when locally stationary PainNise independent model low- frequency components","previews":{"preview":{"ref":"535ab98c-7c32-4b32-abd8-f128807bae65","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/90a56dfa-5eed-43db-80f1-ea006f67c9f6/edb337ba-1d93-470f-8d04-efc1358d340f/presentation_535ab98c_7c32_4b32_abd8_f128807bae65.jpg"}}},{"index":49,"time":9934230,"duration":101570,"relevance":-1,"hit":false,"text":"DEPENDABLE THlNGS 1513. cw, MW KW Dependable robots in industrial logistics scenarios. The RCLL use case. How can we engineer dependable robots with high levels of decisional autonomy Ul dWiger Gerald Steinbauev 2019i Ambuslsnd faring the RoboCup Logistic: lvaguc rhallrngv no 4337499, um 10 007 97873703072754070 40","previews":{"preview":{"ref":"535ab98c-7c32-4b32-abd8-f128807bae65","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/90a56dfa-5eed-43db-80f1-ea006f67c9f6/8656cfa9-9684-4a39-828d-dc0258ee87d4/presentation_535ab98c_7c32_4b32_abd8_f128807bae65.jpg"}}},{"index":50,"time":10035800,"duration":109100,"relevance":-1,"hit":false,"text":"DEPENDABLE THlNGS 1513. mm mm. In this talk Fault tolerant plan Veri able robot execution behaviors History based diagnosis Behavior trees Belief repair Towards veri cation, monitoring and synthesis fault- tolerant control on different hierarchical levels low level control, high level plan execution, focus on modularity and scalability","previews":{"preview":{"ref":"535ab98c-7c32-4b32-abd8-f128807bae65","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/90a56dfa-5eed-43db-80f1-ea006f67c9f6/1ba35c4e-fcc1-40d5-9719-da98aebc9164/presentation_535ab98c_7c32_4b32_abd8_f128807bae65.jpg"}}},{"index":51,"time":10144900,"duration":284450,"relevance":-1,"hit":false,"text":"DEPENDABLE THWGS 10 1513!- by 11- 11 1v 131111121111 Emu Wm Fault- tolerant plan execution Planning models Gaunt- DX DX r1 r2 rm Y2 or mm Fauna prevem cxpecmd ums 55 of mums g3 g6 mum Fault teleram g1 Fault modes for action gel execullon g7 VI 148 Dwagnosws emab g9 DOW Wm gIO mml IX 32 21 18 12 Enabhng know edgerbased repairafter dmgnusu; outperforms strategy based on Icpkummg only. FM PM Marco De aonom 5mm Munoz Gunerrez Serum mmmepwmner 2021 Dxaqnnsxs umuaen we; mthe RCLL 1413101114. Maine enable Diagnosis. Knowledge base. In 32 mienvanona Workshupon Pumme omagnoss Wm, my mm nmcmmpmadg sucsmvrzuzL CU Dxrluzl Doom 15 pa! Model- based diagnosis based on plan execution history is key to repair the belief of the robot","previews":{"preview":{"ref":"535ab98c-7c32-4b32-abd8-f128807bae65","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/90a56dfa-5eed-43db-80f1-ea006f67c9f6/b79ea0c1-416f-4e6f-a7ca-16d75e554c41/presentation_535ab98c_7c32_4b32_abd8_f128807bae65.jpg"}}},{"index":52,"time":10429350,"duration":112700,"relevance":-1,"hit":false,"text":"DEPENDABLE THWGS 10 1513!- 111111 111- 11111131111111111112. 11111 111 111 Fault- tolerant plan execution Planning models Game DX DX r1 r2 mt Y2 or mw Fauhs preve expected ciiucts at 55 of am g3 g6 WNW Fault telerant g1, Fault modes for action get execution g7 VI 148 D1at111051s emabie g9 bci1ei1cpa1i, gm mml IX 32 21 18 12 Enabimg knowiedgerbased repairafter 11119110315; outperforms strategy based on ttzpian1111ig only. Marco De aonom mm 11111102 Gutierrez serum mmmepmgner 12021 mam; mm mthe RCLL H1sto1y Modes enable Diagn0515_ Knowledge base. In 32 interiianowai Workshup on Pundpie oiD1aqnos1s 11 wtvv1h5. n11 ammwp nntmvimpinada 511 51677izuzL Dxrluzl Doom 15 pa! Model- based diagnosis based on plan execution history is key to repair the belief of the robot","previews":{"preview":{"ref":"535ab98c-7c32-4b32-abd8-f128807bae65","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/90a56dfa-5eed-43db-80f1-ea006f67c9f6/919d89ba-005b-41bb-9b62-87dba17abd59/presentation_535ab98c_7c32_4b32_abd8_f128807bae65.jpg"}}},{"index":53,"time":10542050,"duration":160084,"relevance":-1,"hit":false,"text":"DEPENDABLE THiNGS 21 1513!- tw ii ii iaiiiiiiziiim tiiiiiii- iii. Deplendable behavior Behavior trees implement reactive dependable behaviors in the new architecture of GRIPS. Behaviors are currently handcrafted. Dependability requires verifying correctness. We are currently working on the analyse of behavior trees. Challenges include: No standard specification for behavior trees, rich diversity of types, no formal specification. Model- driven engineering is only affordable if it can make make use of automation at design and construction time. Current work towards verification, run- time monitoring and synthesis. Behavior for moving to waypoint. Behavior trees are expressive and transparent yet powerful representations used for implementing complex high- level control","previews":{"preview":{"ref":"535ab98c-7c32-4b32-abd8-f128807bae65","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/90a56dfa-5eed-43db-80f1-ea006f67c9f6/3736bd9b-9cab-4d61-8704-c1808eb47346/presentation_535ab98c_7c32_4b32_abd8_f128807bae65.jpg"}}}]}}}}</article></body></html>

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End User License Agreement, Nov 2020 https: Www. cisco, com en us about legal cloud and software end_user_license_agreement html Skydrop Sprinkler Controller: This warranty does not cover consumable parts, including batteries, unless damage is due to defects in materials or workmanship of the Product, or software even if packaged or sold with the product Marketing with https www skydrop com warranty APP Bernhard Aichemig Verified Dependability 12","previews":{"preview":{"ref":"535ab98c-7c32-4b32-abd8-f128807bae65","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/90a56dfa-5eed-43db-80f1-ea006f67c9f6/6069052f-0334-4618-982d-57555e3106ea/presentation_535ab98c_7c32_4b32_abd8_f128807bae65.jpg"}}},{"index":10,"time":2320370,"duration":133400,"relevance":-1,"hit":false,"text":"DEPENDABLE THlNGS lug- 9mm an i13 ArJKDl mm Verification and More Specification 84 Design Formal Methods Verification Bernhard Aichemig Falsi cation Verified Dependability Logic and models Refinement Compositionality Model based testing Model learning Fuzzing Theorem proving Model checking Shielding TU Graz-","previews":{"preview":{"ref":"535ab98c-7c32-4b32-abd8-f128807bae65","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/90a56dfa-5eed-43db-80f1-ea006f67c9f6/c23556f7-db20-432f-884f-2be832d06aba/presentation_535ab98c_7c32_4b32_abd8_f128807bae65.jpg"}}},{"index":11,"time":2453770,"duration":103600,"relevance":-1,"hit":false,"text":"DEPENDABLE TH NGS TU Graz- ne- emmr an WT ArJKDLL mm Automata Models and Model Learning vahdate verify In. 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Nu, mm rm im Modelled the preemptive scheduling problem as timed automata Verification that no deadline is missed in Uppaal Analysing task budgets iteratively Collaboration: Uppaal course Hackathon RTOS Bernhard Aichevnig Verified Dependability","previews":{"preview":{"ref":"535ab98c-7c32-4b32-abd8-f128807bae65","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/90a56dfa-5eed-43db-80f1-ea006f67c9f6/925e0948-cec5-49e7-83f3-e7f77bd1e856/presentation_535ab98c_7c32_4b32_abd8_f128807bae65.jpg"}}},{"index":15,"time":3065470,"duration":131230,"relevance":-1,"hit":false,"text":"DEPENDABLE THlNGS TU max. lie- er uhlelneireval Mugs, ng mmm Publication Highlights conference: Gigerl, Hadzic, Primas, Mangard, Bloem: Coco: CoeDesign and CoeVerification of Masked Software Implementations on CPUs. USENIX Security Symposium, 2021. Bartocci, Bloem, Maderbacher, Manjunath, Nickovic: Adaptive Testing for Specification Coverage in CPS Models, ADHS, 2021. IFAC Young Author Award. Martins Gomes, BK. Aichernig, Baunach: Formal Modeling Approach for Portable Loerevel OS Functionality. SEFM, 2020. Aichernig, Bloem, Ebrahimi, Horn, Pernkopf, Roth, Rupp, Tappler, Tranninger. Learning Behavior Model of Hybrid Systems Through Combining Model- Based Testing and Machine Learning. IFIP ICTSS, 2019. Pferscher, Aichernig: Stateful Black- Box Pferscher, Aichernig, Tappler. From Fuzzing of Bluetooth Devices Using Automata Passive to Active: Learning Timed Automata Learning. NASA Formal Methods, 2022. Efficiently. NASA Formal Methods, 2020. 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Masking Split sensitive value into random shares Observation of up to shares does not reveal any information about sensitive value Algorithmic countermeasure based on assumptions about hardware","previews":{"preview":{"ref":"535ab98c-7c32-4b32-abd8-f128807bae65","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/90a56dfa-5eed-43db-80f1-ea006f67c9f6/1afd23f8-d335-4911-9d49-b4730592ddca/presentation_535ab98c_7c32_4b32_abd8_f128807bae65.jpg"}}},{"index":19,"time":3707130,"duration":326770,"relevance":-1,"hit":false,"text":"DEPENDABLE THl NGS DtpWil Wren Co- Design Case Studies RISC Ibex core 32- bit, single- issue scalar stage pipeline Problematic components: register file, computation units, data memory RISC- SweRV core 32- bit, superscalar, pipeline stages Problematic components: pipelines, memory management components Secure software masking requires: Modified hardware CPU Modified software software constraints","previews":{"preview":{"ref":"535ab98c-7c32-4b32-abd8-f128807bae65","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/90a56dfa-5eed-43db-80f1-ea006f67c9f6/56ec54fa-3ee9-496c-9510-400e07bd51e0/presentation_535ab98c_7c32_4b32_abd8_f128807bae65.jpg"}}},{"index":20,"time":4033900,"duration":127000,"relevance":-1,"hit":false,"text":"DEPENDABLE THl NGS VWH rgwc, Conclusion Secure masked implementations for loT devices: Require both Alice and Bob Bob can use formal tools like Coco to check security of implementation in practice Important factors: platform, implementation","previews":{"preview":{"ref":"535ab98c-7c32-4b32-abd8-f128807bae65","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/90a56dfa-5eed-43db-80f1-ea006f67c9f6/bf27fd4d-ad4d-4cdf-bafa-b4aff5885284/presentation_535ab98c_7c32_4b32_abd8_f128807bae65.jpg"}}},{"index":21,"time":4160900,"duration":307800,"relevance":-1,"hit":false,"text":"DEPENDABLE TH NGS Viririvrn imihuziwvniv inxgmmm Incremental Verification","previews":{"preview":{"ref":"535ab98c-7c32-4b32-abd8-f128807bae65","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/90a56dfa-5eed-43db-80f1-ea006f67c9f6/a5678314-dd1c-4ee9-a349-3e23712625b2/presentation_535ab98c_7c32_4b32_abd8_f128807bae65.jpg"}}},{"index":22,"time":4468700,"duration":119265,"relevance":-1,"hit":false,"text":"DEPENDABLE TH NGS mm mmhm mm Spielberg Racing Track","previews":{"preview":{"ref":"535ab98c-7c32-4b32-abd8-f128807bae65","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/90a56dfa-5eed-43db-80f1-ea006f67c9f6/0010b312-ab31-4315-ae4d-f6769dae0f9d/presentation_535ab98c_7c32_4b32_abd8_f128807bae65.jpg"}}},{"index":23,"time":4587965,"duration":112065,"relevance":-1,"hit":false,"text":"DEPENDABLE THlNGS wwi vlrlrlirii latflumuiiAlv tEiwimim uis Summary Ensure safety of an unverifiable controller Hybrid systems and realtime specifications Incremental verification with proofs on demand","previews":{"preview":{"ref":"535ab98c-7c32-4b32-abd8-f128807bae65","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/90a56dfa-5eed-43db-80f1-ea006f67c9f6/7c1ae3dc-d399-4841-965b-d39aca931c1a/presentation_535ab98c_7c32_4b32_abd8_f128807bae65.jpg"}}},{"index":24,"time":4700030,"duration":349740,"relevance":-1,"hit":false,"text":"DEPENDABLE THlNGS TU Gran urn- 9mm, an WT ArJrzst. mm MQTT Case Study Results out of MQTT brokers showed inconsistencies to the MQTT specification Found issues: Acceptance of invalid characters One broker accepts prohibited character and forwards it Acceptance of system- internal characters Brokers allow clients to publish 84 subscribe to system- internal topics Inconsistency on number of received publications Brokers merge equal publications Andrea Pferscher: Learning Based Fuzzing of loT Protocols","previews":{"preview":{"ref":"535ab98c-7c32-4b32-abd8-f128807bae65","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/90a56dfa-5eed-43db-80f1-ea006f67c9f6/7bd18138-b7a4-48fd-b65f-72c4ccef6294/presentation_535ab98c_7c32_4b32_abd8_f128807bae65.jpg"}}},{"index":25,"time":5049770,"duration":242350,"relevance":-1,"hit":false,"text":"Sims mmm miss mmauemrgszmysmgga Whom piazvaluvull ru Page aziwiwie FD DEPENDABLE THlNGS TU Gran mm is mm BLE Case Study Results Fingerprinting: Learned models showed behavioral differences between all BLE devices out of devices crashed on fuzzed input Learned models showed inconsistencies to BLE specification Fuzzing revealed unexplored behavior NEW Learned model of BLE device in Tesla Model Andrea Pferscher. Learningr ased Fuzzing of loT Protocols","previews":{"preview":{"ref":"535ab98c-7c32-4b32-abd8-f128807bae65","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/90a56dfa-5eed-43db-80f1-ea006f67c9f6/3c9c693c-a037-4cb2-8ff4-bc2c395ca184/presentation_535ab98c_7c32_4b32_abd8_f128807bae65.jpg"}}},{"index":26,"time":5292120,"duration":126380,"relevance":-1,"hit":false,"text":"DEPENDABLE THINGS nu, Mm- llwu Wu Formal methods for verifying non- functional properties in RTOS Modelling RTOS as timed automata is comprehensive method to verify any nonfunctional requirement. Model checking tool UPPAAL used for model creation and verification Modeled Aspects: PPVVL mas Schedulability sartus PowerConsumption Slices Aliart Interrupt Handling Schedulability Au SAn Schelpel. el al 2022 sznos An 05 Architecture lor Sustainable Embedded Sys1ems in 2022 Fmehjahrstre en FG as Gesellschal fur Inlovmalik Anton Szikia 22 us 2022","previews":{"preview":{"ref":"535ab98c-7c32-4b32-abd8-f128807bae65","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/90a56dfa-5eed-43db-80f1-ea006f67c9f6/8d279cbc-3c65-40c2-9634-46c70ff86f1e/presentation_535ab98c_7c32_4b32_abd8_f128807bae65.jpg"}}},{"index":27,"time":5418500,"duration":103730,"relevance":-1,"hit":false,"text":"DEPENDABLE THINGS mm in MM. am NFR Slice Schedulability art Abstraction from Source Code Templates representing Tasks, Scheduler, Kernel, Syscalls and the Application layer Verification goals: No Task deadline violations forall not TaskRealTime DLViolatlon No Task stan ation Taskstates id Waitinq 77 PrioritySchedu ertemplale Taskstates id Ready Taskstates id Ready 77 SWWam EW Taskstates id Runninq Balis15 Ribeim el al 2022 Verifying Liveness and Ream- lune oi osaaseu Embedded Sollware ln 25th Eummlcm Conference on Digital siem Design BSD EUROMlCRO Anton Salkla 22 us 2022","previews":{"preview":{"ref":"535ab98c-7c32-4b32-abd8-f128807bae65","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/90a56dfa-5eed-43db-80f1-ea006f67c9f6/b0998031-36e7-4eb4-83bd-6c34d8ceef16/presentation_535ab98c_7c32_4b32_abd8_f128807bae65.jpg"}}},{"index":28,"time":5522230,"duration":139240,"relevance":-1,"hit":false,"text":"DEPENDABLE THINGS My my in MN is NFR Slice Power Consumption gmmw Modelling Power as Cost Hardware model In addition Goals: So ware Layer Power Consumption analy5 Power Peak Detection Verification: Power Peak analysis not PiPeak max: temp. Anion Szikia 22 us 2022","previews":{"preview":{"ref":"535ab98c-7c32-4b32-abd8-f128807bae65","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/90a56dfa-5eed-43db-80f1-ea006f67c9f6/79c993d0-ab2a-417a-b07f-c8c9e3a6637f/presentation_535ab98c_7c32_4b32_abd8_f128807bae65.jpg"}}},{"index":29,"time":5661470,"duration":151030,"relevance":-1,"hit":false,"text":"DEPENDABLE THINGS Mill Wu Slicing: Model NFR unification concept Schedulability Power Consumption Interrupt Handling More NFRs Compatible Models for different NFRs designed for the same architecture are referred to as slices. Such slices can be integrated to compose unified and complete system model. Anton Szikia 22 us 2022","previews":{"preview":{"ref":"535ab98c-7c32-4b32-abd8-f128807bae65","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/90a56dfa-5eed-43db-80f1-ea006f67c9f6/652e5c03-626a-4693-8b7c-e6ecf934fb40/presentation_535ab98c_7c32_4b32_abd8_f128807bae65.jpg"}}},{"index":30,"time":5812500,"duration":162500,"relevance":-1,"hit":false,"text":"DEPENDABLE THINGS me mm hei my MW. Model to Code NFR: Schedulability Algorithm for optimized schedulable execution budgets for Rate Monotonic Scheduler An iterative method of verification to obtain optimum execution budget values for system design Anton Szikia 22 09 2022","previews":{"preview":{"ref":"535ab98c-7c32-4b32-abd8-f128807bae65","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/90a56dfa-5eed-43db-80f1-ea006f67c9f6/c7777bbb-b35e-4322-946b-752c4b650105/presentation_535ab98c_7c32_4b32_abd8_f128807bae65.jpg"}}},{"index":31,"time":5975000,"duration":528670,"relevance":-1,"hit":false,"text":"Assessing safety and robustness of reinforcement learning an Enforcing Safety Safety: Can weirv ed deep reinforrement ieaming again nun Mn: nun ms 57 in Rnhustness: H011 ow it when nmng mm mndomiy WW chosen Fr mi Manage pinyiiigSuperMminEmu nn Ar nm Dang vans mm aiang rcfucncc rate manic vans win ingn variabiiity in in mcm Martin Tappler Modei Learning for Dependable Mazhine Learning","previews":{"preview":{"ref":"535ab98c-7c32-4b32-abd8-f128807bae65","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/90a56dfa-5eed-43db-80f1-ea006f67c9f6/680ae30c-54d1-4c61-bc55-f167a6899240/presentation_535ab98c_7c32_4b32_abd8_f128807bae65.jpg"}}},{"index":32,"time":6503670,"duration":261845,"relevance":-1,"hit":false,"text":"DEPENDABLE THING- 11:!- Multi- Agent systems will be ubiquitous How can we improve and guarantee the dependability of multi agent systems","previews":{"preview":{"ref":"535ab98c-7c32-4b32-abd8-f128807bae65","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/90a56dfa-5eed-43db-80f1-ea006f67c9f6/7cf4e838-b985-4d98-8e81-f56fa265ad75/presentation_535ab98c_7c32_4b32_abd8_f128807bae65.jpg"}}},{"index":33,"time":6765515,"duration":359950,"relevance":-1,"hit":false,"text":"DEPENDABLE THING- The Beauty and the Beast Dependable Multi Agent Systems","previews":{"preview":{"ref":"535ab98c-7c32-4b32-abd8-f128807bae65","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/90a56dfa-5eed-43db-80f1-ea006f67c9f6/770909c2-67f7-468d-b6a1-0dfbbe39fac6/presentation_535ab98c_7c32_4b32_abd8_f128807bae65.jpg"}}},{"index":34,"time":7125465,"duration":180620,"relevance":-1,"hit":false,"text":"DEPENDABLE THINGS TU am- in lllHKHHAK lfl Research Areas Diagnosis Networked Control modelebased fault detection communication network modeling probabilistic reasoning communication control coedeSign planning execution monitoring fault tolerant control over different hierarchical levels low level control, high level plan execution,","previews":{"preview":{"ref":"535ab98c-7c32-4b32-abd8-f128807bae65","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/90a56dfa-5eed-43db-80f1-ea006f67c9f6/ed7c72e9-6aeb-4f23-8142-e80f42a2be61/presentation_535ab98c_7c32_4b32_abd8_f128807bae65.jpg"}}},{"index":35,"time":7306085,"duration":151515,"relevance":-1,"hit":false,"text":"DEPENDABieE THVIVNG 161!- lntegrating Diagnosis Recovery necessity for dependability of systems the root cause needs to be identified with some guarantees dealing with diverse symptoms and faults Recovery pushes the system back to into nominal state fault- tolerant control behaviors vs. dedicated repair activities safe hm unaeeemahie behavior degraded behavior fault 90;; recovery","previews":{"preview":{"ref":"535ab98c-7c32-4b32-abd8-f128807bae65","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/90a56dfa-5eed-43db-80f1-ea006f67c9f6/f49e2d30-3b6f-4db5-8130-edce488e58f8/presentation_535ab98c_7c32_4b32_abd8_f128807bae65.jpg"}}},{"index":36,"time":7457600,"duration":205200,"relevance":-1,"hit":false,"text":"DEPENDABLE THINGS 11:!- uriw my iii- Wm mm. in xiii um Model- based Diagnosis FDI mics disturbances Fl stem measurcmcuts Uhlilll ii ii m1: iiipiiis prediction 37 faults digital twin behavioral model causal model","previews":{"preview":{"ref":"535ab98c-7c32-4b32-abd8-f128807bae65","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/90a56dfa-5eed-43db-80f1-ea006f67c9f6/795f3da3-0010-410c-9789-2d57540314a0/presentation_535ab98c_7c32_4b32_abd8_f128807bae65.jpg"}}},{"index":37,"time":7662800,"duration":133800,"relevance":-1,"hit":false,"text":"um. DEPENDA BFEVTHING TU Synthesis of Research Done Markus Tranninger: observertor dynamic non- linear systems estimation guarantees basis for residuals Hadi Gharibdoust modelling communication networks stability analysis of networked control basis for recovery on various levels Stalin Munoz Gutierez Christian Knoll planning execution monitoring new methods for distributed diagnosis using discrete models probabilistic reasoning basis for hierarchical diagnosis guarantees for error margins causal reasoning for diagnOSis","previews":{"preview":{"ref":"535ab98c-7c32-4b32-abd8-f128807bae65","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/90a56dfa-5eed-43db-80f1-ea006f67c9f6/e8d214d0-79b8-41c6-af98-1d82c68b87af/presentation_535ab98c_7c32_4b32_abd8_f128807bae65.jpg"}}},{"index":38,"time":7796600,"duration":276230,"relevance":-1,"hit":false,"text":"DEPENDAFLE TH JGS 1529. Summary addressing the dependability of multi- agent systems Improvement, scalability and guarantees combining different disciplines and methods control theory and FDI symbolic AI and model- based diagnosis probabilistic inference communication theory dependability improvement in sub- parts still open issues integration in an integrated hybrid hierarchical approach it considering communication behavior in higher- level control","previews":{"preview":{"ref":"535ab98c-7c32-4b32-abd8-f128807bae65","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/90a56dfa-5eed-43db-80f1-ea006f67c9f6/26a49259-55a5-4b48-a177-07047b57a3b9/presentation_535ab98c_7c32_4b32_abd8_f128807bae65.jpg"}}},{"index":39,"time":8072830,"duration":408070,"relevance":-1,"hit":false,"text":"How accurate are the estimation results Patch potential models3 Connection between number of solutions and model parameters Guarantees when global minimum is optimal Contribution of individual edges Provable conditions for convexity of the energy landscape Chris ial Knoll and Franz Parnkopl Eoliet Progagatlon Accurate Maroinals or Accurate Panltlon FunctlumWherE is the Dlllerencs Journal at Sta l5tlcel Mechanlcs Tneory and Expevlment, 2u2i Harald Lelsenbefgeri Franz Pernkopl ario Chrlsllal Knoll leli lg tno Bethe Approxnnation How Structural Modlllcetluns in Graph improve Eellel Propagatloll onceedlngs at uni, 2022","previews":{"preview":{"ref":"535ab98c-7c32-4b32-abd8-f128807bae65","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/90a56dfa-5eed-43db-80f1-ea006f67c9f6/12927762-ce9b-44b3-a8ef-d4cd6fb13025/presentation_535ab98c_7c32_4b32_abd8_f128807bae65.jpg"}}},{"index":40,"time":8480900,"duration":99730,"relevance":-1,"hit":false,"text":"How can we improve the performance Many knobs to tune Reduce effect of strong couplings in loops Mode Flewaility Scheduling5 Optimwzaticn of Potentials Graph Optim lamcn Hybrid Models Mode Assumptions Christian Kna chhae Ram Sebastian Tscma schek and Franz Psmknpv Message scheduling memods for behef pmpagauan Proceedmgs olECML PKDD. 2015","previews":{"preview":{"ref":"535ab98c-7c32-4b32-abd8-f128807bae65","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/90a56dfa-5eed-43db-80f1-ea006f67c9f6/823bd7a5-591c-4480-b8ff-5f3e5bb77b77/presentation_535ab98c_7c32_4b32_abd8_f128807bae65.jpg"}}},{"index":41,"time":8580630,"duration":294470,"relevance":-1,"hit":false,"text":"DEEENEEEEE Introduction Fault: deviation of system from the usual acceptable, standard behavior Causes: wear, ageing, environmental ill unnco, Consequences: economic losses, environmental damage, serious hazards for humans. Markus nnnmgm msmm of Aummwion and Conn- am Unnrrsin irrhnolngv, Amnin markm Ennmgmmugm an 22 JE ZUQ2","previews":{"preview":{"ref":"535ab98c-7c32-4b32-abd8-f128807bae65","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/90a56dfa-5eed-43db-80f1-ea006f67c9f6/c1cf10eb-6d3d-4a9b-8b28-ded76edad0d6/presentation_535ab98c_7c32_4b32_abd8_f128807bae65.jpg"}}},{"index":42,"time":8875100,"duration":91500,"relevance":-1,"hit":false,"text":"PEPFFDEBLF Model- Based Approach to Fault Detection faults disturbances fault onset time system measurements inputs residual 70:. 10 17: 30 Time! prediction Mt trade off: sensitivity vs. robustness di git 2L1 twin Mar mnningm Inammc anummnnnn and ommL am Unwmm of irr hnnlogv Amma mm kmn nnnmgmi mgmzmi 22 122","previews":{"preview":{"ref":"535ab98c-7c32-4b32-abd8-f128807bae65","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/90a56dfa-5eed-43db-80f1-ea006f67c9f6/2c3dc764-0981-41f7-91e4-65f51a2a142f/presentation_535ab98c_7c32_4b32_abd8_f128807bae65.jpg"}}},{"index":43,"time":8966600,"duration":111100,"relevance":-1,"hit":false,"text":"DEEENEAEEE Model- Based Approach to Fault Detection mm inlmlmmu Models are typically available from an early design stage WW WW cg. Via tools like Matlab Simulink, Modulica, hudumm um Application to real problems not that simple Complex models Iionliiiean lookup tables uii ertai11,... we want fault isolation, not only detection structural analysis We need techniques that, support the design of diagnosis solutions for complex dynamical systems Markus Il nnnmgm lnsnnnr Aummmion and Conn- am limmsm lrr hnolngv, Amnin mm- km Enmngmmugm an 22 22","previews":{"preview":{"ref":"535ab98c-7c32-4b32-abd8-f128807bae65","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/90a56dfa-5eed-43db-80f1-ea006f67c9f6/9da50c9e-038b-495e-806f-680e2f3ca192/presentation_535ab98c_7c32_4b32_abd8_f128807bae65.jpg"}}},{"index":44,"time":9077700,"duration":200330,"relevance":-1,"hit":false,"text":"DEEENEEEEE FDI Example: Mercator Wheel robot Kinetic vehicle dynamics model double track model rontlod by VVllOOl actuation and steering nsors: To be selected for good diagnosis gi velocit eleration. wheel speed. steering, angle. Possible faults: faults in all sensors faults in all electrical drives and steering actuators at tires Clinngu in tire radius mnnlngm lnalimmv anummnnnnmirl nlm nl. mm Hnwm of lrr lmnlogv Amma mm kmn nnnmgmi mgmzml","previews":{"preview":{"ref":"535ab98c-7c32-4b32-abd8-f128807bae65","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/90a56dfa-5eed-43db-80f1-ea006f67c9f6/b3babd90-d535-4f79-869e-beaf1f408276/presentation_535ab98c_7c32_4b32_abd8_f128807bae65.jpg"}}},{"index":45,"time":9278030,"duration":119240,"relevance":-1,"hit":false,"text":"DE PE DAB LE INJ ERNET TH NGS PACKET COMMUNICATION OUTCOME discrete stochastic process","previews":{"preview":{"ref":"535ab98c-7c32-4b32-abd8-f128807bae65","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/90a56dfa-5eed-43db-80f1-ea006f67c9f6/cc219631-8a8b-4f2e-9d31-a14874826d5f/presentation_535ab98c_7c32_4b32_abd8_f128807bae65.jpg"}}},{"index":46,"time":9397270,"duration":146260,"relevance":-1,"hit":false,"text":"DEPENDABLE THINGS mm in MM. PROCESS DEFINITION Discrete Stochastic Process: if the nth packet is received when the nth packet is lost Non- numeric to numeric data type LR iii pe Pe Pe Ree mrn","previews":{"preview":{"ref":"535ab98c-7c32-4b32-abd8-f128807bae65","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/90a56dfa-5eed-43db-80f1-ea006f67c9f6/07cfea63-8166-4d21-9634-4dcc333e8bdb/presentation_535ab98c_7c32_4b32_abd8_f128807bae65.jpg"}}},{"index":47,"time":9543530,"duration":258940,"relevance":-1,"hit":false,"text":"DEPENDABLE THINGS Mm my, mq. wmm. sMm mu SPECTRUM Frobenius Norm Fouriar Yranslorm Aulu- cov: ne: as sampling lrequancy F5 64 Hz Fvocess Samp so so we PM 59 Indepemem 34 as 148 A0 732 34 as as 40 42 44 normahze VrEEluenCv sampunu lreuuency","previews":{"preview":{"ref":"535ab98c-7c32-4b32-abd8-f128807bae65","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/90a56dfa-5eed-43db-80f1-ea006f67c9f6/1ea30b0f-2b80-4bf4-b76a-afcc2b7ddc52/presentation_535ab98c_7c32_4b32_abd8_f128807bae65.jpg"}}},{"index":48,"time":9802470,"duration":131760,"relevance":-1,"hit":false,"text":"DEPENDABLE THINGS Us mm hei my MW. CONCLUSION Stochastic dependence of on pe pe Iz Insignificant time- varying painNise dependence bit Pe gt Pe Spectral Analysis 1st 2nd moments verification when locally stationary PainNise independent model low- frequency components","previews":{"preview":{"ref":"535ab98c-7c32-4b32-abd8-f128807bae65","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/90a56dfa-5eed-43db-80f1-ea006f67c9f6/edb337ba-1d93-470f-8d04-efc1358d340f/presentation_535ab98c_7c32_4b32_abd8_f128807bae65.jpg"}}},{"index":49,"time":9934230,"duration":101570,"relevance":-1,"hit":false,"text":"DEPENDABLE THlNGS 1513. cw, MW KW Dependable robots in industrial logistics scenarios. The RCLL use case. How can we engineer dependable robots with high levels of decisional autonomy Ul dWiger Gerald Steinbauev 2019i Ambuslsnd faring the RoboCup Logistic: lvaguc rhallrngv no 4337499, um 10 007 97873703072754070 40","previews":{"preview":{"ref":"535ab98c-7c32-4b32-abd8-f128807bae65","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/90a56dfa-5eed-43db-80f1-ea006f67c9f6/8656cfa9-9684-4a39-828d-dc0258ee87d4/presentation_535ab98c_7c32_4b32_abd8_f128807bae65.jpg"}}},{"index":50,"time":10035800,"duration":109100,"relevance":-1,"hit":false,"text":"DEPENDABLE THlNGS 1513. mm mm. In this talk Fault tolerant plan Veri able robot execution behaviors History based diagnosis Behavior trees Belief repair Towards veri cation, monitoring and synthesis fault- tolerant control on different hierarchical levels low level control, high level plan execution, focus on modularity and scalability","previews":{"preview":{"ref":"535ab98c-7c32-4b32-abd8-f128807bae65","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/90a56dfa-5eed-43db-80f1-ea006f67c9f6/1ba35c4e-fcc1-40d5-9719-da98aebc9164/presentation_535ab98c_7c32_4b32_abd8_f128807bae65.jpg"}}},{"index":51,"time":10144900,"duration":284450,"relevance":-1,"hit":false,"text":"DEPENDABLE THWGS 10 1513!- by 11- 11 1v 131111121111 Emu Wm Fault- tolerant plan execution Planning models Gaunt- DX DX r1 r2 rm Y2 or mm Fauna prevem cxpecmd ums 55 of mums g3 g6 mum Fault teleram g1 Fault modes for action gel execullon g7 VI 148 Dwagnosws emab g9 DOW Wm gIO mml IX 32 21 18 12 Enabhng know edgerbased repairafter dmgnusu; outperforms strategy based on Icpkummg only. FM PM Marco De aonom 5mm Munoz Gunerrez Serum mmmepwmner 2021 Dxaqnnsxs umuaen we; mthe RCLL 1413101114. Maine enable Diagnosis. Knowledge base. In 32 mienvanona Workshupon Pumme omagnoss Wm, my mm nmcmmpmadg sucsmvrzuzL CU Dxrluzl Doom 15 pa! Model- based diagnosis based on plan execution history is key to repair the belief of the robot","previews":{"preview":{"ref":"535ab98c-7c32-4b32-abd8-f128807bae65","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/90a56dfa-5eed-43db-80f1-ea006f67c9f6/b79ea0c1-416f-4e6f-a7ca-16d75e554c41/presentation_535ab98c_7c32_4b32_abd8_f128807bae65.jpg"}}},{"index":52,"time":10429350,"duration":112700,"relevance":-1,"hit":false,"text":"DEPENDABLE THWGS 10 1513!- 111111 111- 11111131111111111112. 11111 111 111 Fault- tolerant plan execution Planning models Game DX DX r1 r2 mt Y2 or mw Fauhs preve expected ciiucts at 55 of am g3 g6 WNW Fault telerant g1, Fault modes for action get execution g7 VI 148 D1at111051s emabie g9 bci1ei1cpa1i, gm mml IX 32 21 18 12 Enabimg knowiedgerbased repairafter 11119110315; outperforms strategy based on ttzpian1111ig only. Marco De aonom mm 11111102 Gutierrez serum mmmepmgner 12021 mam; mm mthe RCLL H1sto1y Modes enable Diagn0515_ Knowledge base. In 32 interiianowai Workshup on Pundpie oiD1aqnos1s 11 wtvv1h5. n11 ammwp nntmvimpinada 511 51677izuzL Dxrluzl Doom 15 pa! Model- based diagnosis based on plan execution history is key to repair the belief of the robot","previews":{"preview":{"ref":"535ab98c-7c32-4b32-abd8-f128807bae65","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/90a56dfa-5eed-43db-80f1-ea006f67c9f6/919d89ba-005b-41bb-9b62-87dba17abd59/presentation_535ab98c_7c32_4b32_abd8_f128807bae65.jpg"}}},{"index":53,"time":10542050,"duration":160084,"relevance":-1,"hit":false,"text":"DEPENDABLE THiNGS 21 1513!- tw ii ii iaiiiiiiziiim tiiiiiii- iii. Deplendable behavior Behavior trees implement reactive dependable behaviors in the new architecture of GRIPS. Behaviors are currently handcrafted. Dependability requires verifying correctness. We are currently working on the analyse of behavior trees. Challenges include: No standard specification for behavior trees, rich diversity of types, no formal specification. Model- driven engineering is only affordable if it can make make use of automation at design and construction time. Current work towards verification, run- time monitoring and synthesis. Behavior for moving to waypoint. Behavior trees are expressive and transparent yet powerful representations used for implementing complex high- level control","previews":{"preview":{"ref":"535ab98c-7c32-4b32-abd8-f128807bae65","$":"https://tube.tugraz.at/static/mh_default_org/engage-player/90a56dfa-5eed-43db-80f1-ea006f67c9f6/3736bd9b-9cab-4d61-8704-c1808eb47346/presentation_535ab98c_7c32_4b32_abd8_f128807bae65.jpg"}}}]}}}}</article></body></html>