Designing reliable cyber-physical systems overview associated to the special session at FDL’16

CPS, that consist of a cyber part – a computing system – and a physical part – the system in the physical environment – as well as the respective interfaces between those parts, are omnipresent in our daily lives. The application in the physical environment drives the overall requirements that must be respected when designing the computing system. Here, reliability is a core aspect where some of the most pressing design challenges are: • monitoring failures throughout the computing system, • determining the impact of failures on the application constraints, and • ensuring correctness of the computing system with respect to application-driven requirements rooted in the physical environment. This paper provides an overview of techniques discussed in the special session to tackle these challenges throughout the stack of layers of the computing system while tightly coupling the design methodology to the physical requirements.</p

Synthesizing Adaptive Test Strategies from Temporal Logic Specifications

Constructing good test cases is difficult and time-consuming, especially if the system under test is still under development and its exact behavior is not yet fixed. We propose a new approach to compute test strategies for reactive systems from a given temporal logic specification using formal methods. The computed strategies are guaranteed to reveal certain simple faults in every realization of the specification and for every behavior of the uncontrollable part of the system's environment. The proposed approach supports different assumptions on occurrences of faults (ranging from a single transient fault to a persistent fault) and by default aims at unveiling the weakest one. Based on well-established hypotheses from fault-based testing, we argue that such tests are also sensitive for more complex bugs. Since the specification may not define the system behavior completely, we use reactive synthesis algorithms with partial information. The computed strategies are adaptive test strategies that react to behavior at runtime. We work out the underlying theory of adaptive test strategy synthesis and present experiments for a safety-critical component of a real-world satellite system. We demonstrate that our approach can be applied to industrial specifications and that the synthesized test strategies are capable of detecting bugs that are hard to detect with random testing

Profile: Board of regents names Mark Yudof as next University president

In these pages: Conversation with...Roger McCannon talks about The Center for Small Towns; Meet a member of UMM\u27s extended family; Cornerstone...did you guess correctly UMM\u27s original 13?https://digitalcommons.morris.umn.edu/profile/1057/thumbnail.jp

On multiobjective optimization from the nonsmooth perspective

Practical applications usually have multiobjective nature rather than having only one objective to optimize. A multiobjective problem cannot be solved with a single-objective solver as such. On the other hand, optimization of only one objective may lead to an arbitrary bad solutions with respect to other objectives. Therefore, special techniques for multiobjective optimization are vital. In addition to multiobjective nature, many real-life problems have nonsmooth (i.e. not continuously differentiable) structure. Unfortunately, many smooth (i.e. continuously differentiable) methods adopt gradient-based information which cannot be used for nonsmooth problems. Since both of these characteristics are relevant for applications, we focus here on nonsmooth multiobjective optimization. As a research topic, nonsmooth multiobjective optimization has gained only limited attraction while the fields of nonsmooth single-objective and smooth multiobjective optimization distinctively have attained greater interest. This dissertation covers parts of nonsmooth multiobjective optimization in terms of theory, methodology and application. Bundle methods are widely considered as effective and reliable solvers for single-objective nonsmooth optimization. Therefore, we investigate the use of the bundle idea in the multiobjective framework with three different methods. The first one generalizes the single-objective proximal bundle method for the nonconvex multiobjective constrained problem. The second method adopts the ideas from the classical steepest descent method into the convex unconstrained multiobjective case. The third method is designed for multiobjective problems with constraints where both the objectives and constraints can be represented as a difference of convex (DC) functions. Beside the bundle idea, all three methods are descent, meaning that they produce better values for each objective at each iteration. Furthermore, all of them utilize the improvement function either directly or indirectly. A notable fact is that none of these methods use scalarization in the traditional sense. With the scalarization we refer to the techniques transforming a multiobjective problem into the single-objective one. As the scalarization plays an important role in multiobjective optimization, we present one special family of achievement scalarizing functions as a representative of this category. In general, the achievement scalarizing functions suit well in the interactive framework. Thus, we propose the interactive method using our special family of achievement scalarizing functions. In addition, this method utilizes the above mentioned descent methods as tools to illustrate the range of optimal solutions. Finally, this interactive method is used to solve the practical case studies of the scheduling the final disposal of the spent nuclear fuel in Finland.Käytännön optimointisovellukset ovat usein luonteeltaan ennemmin moni- kuin yksitavoitteisia. Erityisesti monitavoitteisille tehtäville suunnitellut menetelmät ovat tarpeen, sillä monitavoitteista optimointitehtävää ei sellaisenaan pysty ratkaisemaan yksitavoitteisilla menetelmillä eikä vain yhden tavoitteen optimointi välttämättä tuota mielekästä ratkaisua muiden tavoitteiden suhteen. Monitavoitteisuuden lisäksi useat käytännön tehtävät ovat myös epäsileitä siten, etteivät niissä esiintyvät kohde- ja rajoitefunktiot välttämättä ole kaikkialla jatkuvasti differentioituvia. Kuitenkin monet optimointimenetelmät hyödyntävät gradienttiin pohjautuvaa tietoa, jota ei epäsileille funktioille ole saatavissa. Näiden molempien ominaisuuksien ollessa keskeisiä sovelluksia ajatellen, keskitytään tässä työssä epäsileään monitavoiteoptimointiin. Tutkimusalana epäsileä monitavoiteoptimointi on saanut vain vähän huomiota osakseen, vaikka sekä sileä monitavoiteoptimointi että yksitavoitteinen epäsileä optimointi erikseen ovat aktiivisia tutkimusaloja. Tässä työssä epäsileää monitavoiteoptimointia on käsitelty niin teorian, menetelmien kuin käytännön sovelluksien kannalta. Kimppumenetelmiä pidetään yleisesti tehokkaina ja luotettavina menetelminä epäsileän optimointitehtävän ratkaisemiseen ja siksi tätä ajatusta hyödynnetään myös tässä väitöskirjassa kolmessa eri menetelmässä. Ensimmäinen näistä yleistää yksitavoitteisen proksimaalisen kimppumenetelmän epäkonveksille monitavoitteiselle rajoitteiselle tehtävälle sopivaksi. Toinen menetelmä hyödyntää klassisen nopeimman laskeutumisen menetelmän ideaa konveksille rajoitteettomalle tehtävälle. Kolmas menetelmä on suunniteltu erityisesti monitavoitteisille rajoitteisille tehtäville, joiden kohde- ja rajoitefunktiot voidaan ilmaista kahden konveksin funktion erotuksena. Kimppuajatuksen lisäksi kaikki kolme menetelmää ovat laskevia eli ne tuottavat joka kierroksella paremman arvon jokaiselle tavoitteelle. Yhteistä on myös se, että nämä kaikki hyödyntävät parannusfunktiota joko suoraan sellaisenaan tai epäsuorasti. Huomattavaa on, ettei yksikään näistä menetelmistä hyödynnä skalarisointia perinteisessä merkityksessään. Skalarisoinnilla viitataan menetelmiin, joissa usean tavoitteen tehtävä on muutettu sopivaksi yksitavoitteiseksi tehtäväksi. Monitavoiteoptimointimenetelmien joukossa skalarisoinnilla on vankka jalansija. Esimerkkinä skalarisoinnista tässä työssä esitellään yksi saavuttavien skalarisointifunktioiden perhe. Yleisesti saavuttavat skalarisointifunktiot soveltuvat hyvin interaktiivisten menetelmien rakennuspalikoiksi. Täten kuvaillaan myös esiteltyä skalarisointifunktioiden perhettä hyödyntävä interaktiivinen menetelmä, joka lisäksi hyödyntää laskevia menetelmiä optimaalisten ratkaisujen havainnollistamisen apuna. Lopuksi tätä interaktiivista menetelmää käytetään aikatauluttamaan käytetyn ydinpolttoaineen loppusijoitusta Suomessa

Regelungstheorie

The workshop “Regelungstheorie” (control theory) covered a broad variety of topics that were either concerned with fundamental mathematical aspects of control or with its strong impact in various fields of engineering

Manipulation of cognitive image properties

Photo retouching is an inherent part of visual content production in the profes- sional world. Although some general gPhoto retouching is an inherent part of visual content production in the professional world. Although some general guides exist, there is no universal principle of how the retouched photo should look, as there is no one strict de nition of beauty. This process is often subjective and laborious. Deep learning techniques have provided tremendous improvements to the image processing domain in recent years. Nowadays we can generate realistic images and edit them. Recent work in this  eld proves, that we can enhance photos by style transfer or guide a Generative Adversarial Network to create more aesthetic images. Less focus was given to modifying arbitrary photos according to the broad notion of aesthetics. Our main question in this thesis is: Is it possible to increase the aesthetics of any photo, with no direct human supervision? We propose a simple technique for  nding a tone curve mapping that increases photo aesthetics. The process is guided by a neural network that was previously trained to assess this property. We optimize the tone curve parameters using gradients backpropagated from the network. The framework does not assume anything specific about aesthetics and can be used with other cognitive properties, such as memorability or emotional valence. We investigate the properties and limitations of the algorithm. We design and run a user study to validate our results. We  nd that participants prefer our enhancements from initial photos in 66.5% of cases. We analyze the probable causes of their decisions. uides exist, there is no universal principle of how the retouched photo should look, as there is no one strict de nition of beauty. This process is often subjective and laborious. Deep learning techniques have provided tremendous improvements to the image processing domain in recent years. Nowadays we can generate realistic images and edit them. Recent work in this  eld proves, that we can enhance photos by style transfer or guide a Generative Adversarial Network to create more aesthetic images. Less focus was given to modifying arbitrary photos according to the broad notion of aesthetics. Our main question in this thesis is: Is it possible to increase the aesthetics of any photo, with no direct human supervision? We propose a simple technique for  nding a tone curve mapping that increases photo aesthetics. The process is guided by a neural network that was previously trained to assess this property. We optimize the tone curve parameters using gradients backpropagated from the network. The framework does not assume anything specific about aesthetics and can be used with other cognitive properties, such as memorability or emotional valence. We investigate the properties and limitations of the algorithm. We design and run a user study to validate our results. We  nd that participants prefer our enhancements from initial photos in 66.5% of cases. We analyze the probable causes of their decisions

Power allocation for optimal synchronization of CDMA and UWB signals based on game theory

This thesis describes a theoretical framework for the design and the analysis of distributed (decentralized) power control algorithms for wireless networks using ultrawideband (UWB) technologies over a frequency-selective and slow-fading channel, focusing of the issue of initial code synchronization. The framework described here is general enough to also encompass the analysis of Code Division Multiple Access (CDMA) systems, seen as a special case of the Impulse-Radio (IR)-UWB technology. To develop this work, we use the tools of game theory that are expedient for deriving scalable, energy-efficient, distributed power control schemes to be applied to a population of battery-operated user terminals in a rich multipath environment. The power control issue is modeled as a noncooperative game in which each transmitter-receiver pair chooses its transmit power and detection threshold pair so as to maximize its own utility, which is defined as the ratio of the probability of signal detection to the transmitted energy per acquisition period (or per bit)