Global optimization of stiff dynamical systems

We present a deterministic global optimization method for nonlinear programming formulations constrained by stiff systems of ordinary differential equation (ODE) initial value problems (IVPs). The examples arise from dynamic optimization problems exhibiting both fast and slow transient phenomena commonly encountered in model-based systems engineering applications. The proposed approach utilizes unconditionally stable implicit integration methods to reformulate the ODE-constrained problem into a nonconvex nonlinear program (NLP) with implicit functions embedded. This problem is then solved to global optimality in finite time using a spatial branch-and-bound framework utilizing convex/concave relaxations of implicit functions constructed by a method which fully exploits problem sparsity. The algorithms were implemented in the Julia programming language within the EAGO.jl package and demonstrated on five illustrative examples with varying complexity relevant in process systems engineering. The developed methods enable the guaranteed global solution of dynamic optimization problems with stiff ODE–IVPs embedded.     

Influence of Microstructure and Surface Activation of Dual‐Phase Membrane Ce0.8Gd0.2O2−δ–FeCo2O4 on Oxygen Permeation

Dual‐phase oxygen transport membranes are fast‐growing research interest for application in oxyfuel combustion process. One such potential candidate is CGO‐FCO (60 wt% Ce_0.8Gd_0.2O_2?δ–40 wt% FeCo₂O₄) identified to provide good oxygen permeation flux with substantial stability in harsh atmosphere. Dense CGO‐FCO membranes of 1 mm thickness were fabricated by sintering dry pellets pressed from powders synthesized by one‐pot method (modified Pechini process) at 1200°C for 10 h. Microstructure analysis indicates presence of a third orthorhombic perovskite phase in the sintered composite. It was also identified that the spinel phase tends to form an oxygen deficient phase at the grain boundary of spinel and CGO phases. Surface exchange limitation of the membranes was overcome by La_0.6Sr_0.4Co_0.2Fe_0.8O_3?δ (LSCF) porous layer coating over the composite. The oxygen permeation flux of the CGO‐FCO screen printed with a porous layer of 10 μm thick LSCF is 0.11 mL/cm² per minute at 850°C with argon as sweep and air as feed gas at the rates of 50 and 250 mL/min.     

Reinforcement learning to adjust parametrized motor primitives to new situations

Humans manage to adapt learned movements very quickly to new situations by generalizing learned behaviors from similar situations. In contrast, robots currently often need to re-learn the complete movement. In this paper, we propose a method that learns to generalize parametrized motor plans by adapting a small set of global parameters, called meta-parameters. We employ reinforcement learning to learn the required meta-parameters to deal with the current situation, described by states. We introduce an appropriate reinforcement learning algorithm based on a kernelized version of the reward-weighted regression. To show its feasibility, we evaluate this algorithm on a toy example and compare it to several previous approaches. Subsequently, we apply the approach to three robot tasks, i.e., the generalization of throwing movements in darts, of hitting movements in table tennis, and of throwing balls where the tasks are learned on several different real physical robots, i.e., a Barrett WAM, a BioRob, the JST-ICORP/SARCOS CBi and a Kuka KR?6.     

Bringing law and order to IEEE 802.11 networks—A case for DiscoSec

To improve the tarnished reputation of WLAN security, the new IEEE 802.11i standard provides means for mutual user authentication and assures confidentiality of user data. However, the IEEE 802.11 link-layer is still highly vulnerable to a plethora of simple, yet effective attacks which further jeopardize the already fragile security of wireless communications.Some of these vulnerabilities are related to limited hardware capabilities of access points and their abuse may result in serious degradation of control over the wireless connection, which, especially in the case of broadcast communication, allows for client hijacking attacks. Although these issues are known and their impact is expected to be less prevalent on modern equipment, this work demonstrates the opposite. In our experimental analysis, we tested frequently used access points, and by forcing them to operate on their performance limits, we identified significant operational anomalies and demonstrated their impact on security by implementing a novel version of the Man-In-The-Middle attack, to which we refer as the Muzzle attack.Secondly, this work describes DiscoSec, a solution for “patching” WLANs against a variety of such link-layer attacks. DiscoSec provides DoS-resilient key exchange, an efficient frame authentication, and a performance-oriented implementation. By means of extensive real-world measurements DiscoSec is evaluated, showing that even on very resource-limited devices the network throughput is decreased by only 22% compared to the throughput without any authentication, and by 6% on more performance-capable hardware. To demonstrate its effectiveness, DiscoSec is available as an open-source IEEE 802.11 device driver utilizing well-established cryptographic primitives provided by the Linux Crypto API and OpenSSL library.     

AR-based serious game framework for post-stroke rehabilitation

Stroke is considered one of the main causes of death around the world. Survivors often suffer different kinds of disabilities in terms of their cognitive and motor capabilities, and are therefore unable to perform their day-to-day activities. To regain some of their cognitive as well as motor abilities, they require rehabilitation. To this end, we present a serious game framework based on augmented reality technology that may motivate the patients’ involvement in the rehabilitation exercise. Additionally, we analyze the requirements for such a framework and describe the concept and implementation of the proposed approach. Furthermore, we designed a wireless vibrotactile output device that is attached to a tangible object. The tangible object that is connected to the framework can give haptic as well as audio-visual feedback to the patient in a more motivating and entertaining environment for rehabilitation exercises. The suitability and utility of the proposed framework was evaluated with real stroke patients and compared against the performance of a healthy control group, thus facilitating occupational therapists in assessing a patient’s progress. Our evaluations show that the serious games with vibrotactile feedback are well accepted by patients.     

A framework for developing measurement systems and its industrial evaluation

As in every engineering discipline, metrics play an important role in software development, with the difference that almost all software projects need the customization of metrics used. In other engineering disciplines, the notion of a measurement system (i.e. a tool used to collect, calculate, and report quantitative data) is well known and defined, whereas it is not as widely used in software engineering. In this paper we present a framework for developing custom measurement systems and its industrial evaluation in a software development unit within Ericsson. The results include the framework for designing measurement systems and its evaluation in real life projects at the company. The results show that with the help of ISO/IEC standards, measurement systems can be effectively used in software industry and that the presented framework improves the way of working with metrics. This paper contributes with the presentation of how automation of metrics collection and processing can be successfully introduced into a large organization and shows the benefits of it: increased efficiency of metrics collection, increased adoption of metrics in the organization, independence from individuals and standardized nomenclature for metrics in the organization.     

Probabilistic knowledge representation using the principle of maximum entropy and Gröbner basis theory

An often used methodology for reasoning with probabilistic conditional knowledge bases is provided by the principle of maximum entropy (so-called MaxEnt principle) that realises an idea of least amount of assumed information and thus of being as unbiased as possible. In this paper we exploit the fact that MaxEnt distributions can be computed by solving nonlinear equation systems that reflect the conditional logical structure of these distributions. We apply the theory of Gröbner bases that is well known from computational algebra to the polynomial system which is associated with a MaxEnt distribution, in order to obtain results for reasoning with maximum entropy. We develop a three-phase compilation scheme extracting from a knowledge base consisting of probabilistic conditionals the information which is crucial for MaxEnt reasoning and transforming it to a Gröbner basis. Based on this transformation, a necessary condition for knowledge bases to be consistent is derived. Furthermore, approaches to answering MaxEnt queries are presented by demonstrating how inferring the MaxEnt probability of a single conditional from a given knowledge base is possible. Finally, we discuss computational methods to establish general MaxEnt inference rules.     

Design for Timing Predictability

A large part of safety-critical embedded systems has to satisfy hard real-time constraints. These need sound methods and tools to derive run-time guarantees that are not only reliable but also precise. The achievable precision highly depends on characteristics of the target architecture, the implementation methods and system layers of the software. Trends in hardware and software design run contrary to predictability. This article describes threats to timing predictability of systems, and proposes design principles that support timing predictability. The ultimate goal is to design performant systems with sharp upper and lower bounds on execution times.     

Predicting weekly defect inflow in large software projects based on project planning and test status

Defects discovered during the testing phase in software projects need to be removed before the software is shipped to the customers. The removal of defects can constitute a significant amount of effort in a project and project managers are faced with a decision whether to continue development or shift some resources to cope with defect removal. The goal of this research is to improve the practice of project management by providing a method for predicting the number of defects reported into the defect database in the project. In this paper we present a method for predicting the number of defects reported into the defect database in a large software project on a weekly basis. The method is based on using project progress data, in particular the information about the test progress, to predict defect inflow in the next three coming weeks. The results show that the prediction accuracy of our models is up to 72% (mean magnitude of relative error for predictions of 1 week in advance is 28%) when used in ongoing large software projects. The method is intended to support project managers in more accurate adjusting resources in the project, since they are notified in advance about the potentially large effort needed to correct defects.