Computing Optimal Designs of multiresponse Experiments reduces to Second-Order Cone Programming

Elfving's Theorem is a major result in the theory of optimal experimental design, which gives a geometrical characterization of $c-$optimality. In this paper, we extend this theorem to the case of multiresponse experiments, and we show that when the number of experiments is finite, $c-,A-,T-$ and $D-$optimal design of multiresponse experiments can be computed by Second-Order Cone Programming (SOCP). Moreover, our SOCP approach can deal with design problems in which the variable is subject to several linear constraints. We give two proofs of this generalization of Elfving's theorem. One is based on Lagrangian dualization techniques and relies on the fact that the semidefinite programming (SDP) formulation of the multiresponse $c-$optimal design always has a solution which is a matrix of rank $1$. Therefore, the complexity of this problem fades. We also investigate a \emph{model robust} generalization of $c-$optimality, for which an Elfving-type theorem was established by Dette (1993). We show with the same Lagrangian approach that these model robust designs can be computed efficiently by minimizing a geometric mean under some norm constraints. Moreover, we show that the optimality conditions of this geometric programming problem yield an extension of Dette's theorem to the case of multiresponse experiments. When the number of unknown parameters is small, or when the number of linear functions of the parameters to be estimated is small, we show by numerical examples that our approach can be between 10 and 1000 times faster than the classic, state-of-the-art algorithms

Grid-Connected Renewable Energy Sources

The use of renewable energy sources (RESs) is a need of global society. This editorial, and its associated Special Issue “Grid-Connected Renewable Energy Sources”, offers a compilation of some of the recent advances in the analysis of current power systems that are composed after the high penetration of distributed generation (DG) with different RESs. The focus is on both new control configurations and on novel methodologies for the optimal placement and sizing of DG. The eleven accepted papers certainly provide a good contribution to control deployments and methodologies for the allocation and sizing of DG

Moderating Effect of Supply Chain Dynamic Capabilities on the Relationship of Sustainable Supply Chain Management Practices and Organizational Sustainable Performance: A Study on the Restaurant Industry in Indonesia

The sustainable supply chain management (SSCM) is a fairly new topic that has become a superior attention for the researchers recently. The current study is investigated empirically moderating effect of Supply chain dynamic capabilities (SCDC) on the relationship of SSCM practices and organizational sustainable performance (OSP) indicators namely; “economic performance, environmental performance, social performance” in the restaurant industry of Indonesia. For this purpose, data was collected from the 210 supply chain managers by using the simple random sampling technique which yield a 78% response rate. For data analysis Smart PLS 3 software and PLS Structural Equation Modeling (SEM) approach was employed. The SEM analysis has shown, SSCM practices has a significant association with the OSP indicators. Moreover, the findings of the current study also shown that SCDC is significantly moderates on the relationship of SSCM practices and OSP in the restaurant industry of Indonesia. This shows that SCDC is considered to be an important contribution of the study. The current research also contributes a body of knowledge in the way of theoretical and practical implications. The study limitations and future directions are also discussed at last of the study

Pattern synthesis of narrowband conformal arrays using iterative second-order cone programming

A new design method is proposed for the power or shaped beam pattern synthesis problem of narrowband conformal arrays, where only the magnitude response is specified. The proposed method iteratively linearizes the non-convex power pattern function to obtain a convex subproblem in the design variables, which can be solved optimally using second-order cone programming (SOCP). In addition, a wide variety of magnitude constraints such as non-convex lower bound magnitude constraints can be incorporated. An efficient technique for determining a reasonably good initial guess to the problem is also proposed to further improve the reliability of the method. Computer simulations show that the initial guesses so obtained converge to satisfactory solutions while satisfying various prescribed magnitude constraints. Design results show that the performance of the proposed method is comparable to the optimal solution previously obtained for uniform linear arrays with isotropic elements. Moreover, we show by means of examples that the proposed method is also applicable to general non-convex power pattern synthesis problems involving arbitrary array geometries, arbitrary polarization characteristics and mutual coupling effect. © 2010 IEEE.published_or_final_versio

Analyse und Erweiterung eines fehler-toleranten NoC für SRAM-basierte FPGAs in Weltraumapplikationen

Data Processing Units for scientific space mission need to process ever higher volumes of data and perform ever complex calculations. But the performance of available space-qualified general purpose processors is just in the lower three digit megahertz range, which is already insufficient for some applications. As an alternative, suitable processing steps can be implemented in hardware on a space-qualified SRAM-based FPGA. However, suitable devices are susceptible against space radiation. At the Institute for Communication and Network Engineering a fault-tolerant, network-based communication architecture was developed, which enables the construction of processing chains on the basis of different processing modules within suitable SRAM-based FPGAs and allows the exchange of single processing modules during runtime, too. The communication architecture and its protocol shall isolate non SEU mitigated or just partial SEU mitigated modules affected by radiation-induced faults to prohibit the propagation of errors within the remaining System-on-Chip. In the context of an ESA study, this communication architecture was extended with further components and implemented in a representative hardware platform. Based on the acquired experiences during the study, this work analyses the actual fault-tolerance characteristics as well as weak points of this initial implementation. At appropriate locations, the communication architecture was extended with mechanisms for fault-detection and fault-differentiation as well as with a hardware-based monitoring solution. Both, the former measures and the extension of the employed hardware-platform with selective fault-injection capabilities for the emulation of radiation-induced faults within critical areas of a non SEU mitigated processing module, are used to evaluate the effects of radiation-induced faults within the communication architecture. By means of the gathered results, further measures to increase fast detection and isolation of faulty nodes are developed, selectively implemented and verified. In particular, the ability of the communication architecture to isolate network nodes without SEU mitigation could be significantly improved.Instrumentenrechner für wissenschaftliche Weltraummissionen müssen ein immer höheres Datenvolumen verarbeiten und immer komplexere Berechnungen ausführen. Die Performanz von verfügbaren qualifizierten Universalprozessoren liegt aber lediglich im unteren dreistelligen Megahertz-Bereich, was für einige Anwendungen bereits nicht mehr ausreicht. Als Alternative bietet sich die Implementierung von entsprechend geeigneten Datenverarbeitungsschritten in Hardware auf einem qualifizierten SRAM-basierten FPGA an. Geeignete Bausteine sind jedoch empfindlich gegenüber der Strahlungsumgebung im Weltraum. Am Institut für Datentechnik und Kommunikationsnetze wurde eine fehlertolerante netzwerk-basierte Kommunikationsarchitektur entwickelt, die innerhalb eines geeigneten SRAM-basierten FPGAs Datenverarbeitungsmodule miteinander nach Bedarf zu Verarbeitungsketten verbindet, sowie den Austausch von einzelnen Modulen im Betrieb ermöglicht. Nicht oder nur partiell SEU mitigierte Module sollen bei strahlungsbedingten Fehlern im Modul durch das Protokoll und die Fehlererkennungsmechanismen der Kommunikationsarchitektur isoliert werden, um ein Ausbreiten des Fehlers im restlichen System-on-Chip zu verhindern. Im Kontext einer ESA Studie wurde diese Kommunikationsarchitektur um Komponenten erweitert und auf einer repräsentativen Hardwareplattform umgesetzt. Basierend auf den gesammelten Erfahrungen aus der Studie, wird in dieser Arbeit eine Analyse der tatsächlichen Fehlertoleranz-Eigenschaften sowie der Schwachstellen dieser ursprünglichen Implementierung durchgeführt. Die Kommunikationsarchitektur wurde an geeigneten Stellen um Fehlerdetektierungs- und Fehlerunterscheidungsmöglichkeiten erweitert, sowie um eine hardwarebasierte Überwachung ergänzt. Sowohl diese Maßnahmen, als auch die Erweiterung der Hardwareplattform um gezielte Fehlerinjektions-Möglichkeiten zum Emulieren von strahlungsinduzierten Fehlern in kritischen Komponenten eines nicht SEU mitigierten Prozessierungsmoduls werden genutzt, um die tatsächlichen auftretenden Effekte in der Kommunikationsarchitektur zu evaluieren. Anhand der Ergebnisse werden weitere Verbesserungsmaßnahmen speziell zur schnellen Detektierung und Isolation von fehlerhaften Knoten erarbeitet, selektiv implementiert und verifiziert. Insbesondere die Fähigkeit, fehlerhafte, nicht SEU mitigierte Netzwerkknoten innerhalb der Kommunikationsarchitektur zu isolieren, konnte dabei deutlich verbessert werden

Communication for Teams of Networked Robots

There are a large class of problems, from search and rescue to environmental monitoring, that can benefit from teams of mobile robots in environments where there is no existing infrastructure for inter-agent communication. We seek to address the problems necessary for a team of small, low-power, low-cost robots to deploy in such a way that they can dynamically provide their own multi-hop communication network. To do so, we formulate a situational awareness problem statement that specifies both the physical task and end-to-end communication rates that must be maintained. In pursuit of a solution to this problem, we address topics ranging from the modeling of point-to-point wireless communication to mobility control for connectivity maintenance. Since our focus is on developing solutions to these problems that can be experimentally verified, we also detail the design and implantation of a decentralized testbed for multi-robot research. Experiments on this testbed allow us to determine data-driven models for point-to-point wireless channel prediction, test relative signal-strength-based localization methods, and to verify that our algorithms for mobility control maintain the desired instantaneous rates when routing through the wireless network. The tools we develop are integral to the fielding of teams of robots with robust wireless network capabilities

Evolutionary Computation, Optimization and Learning Algorithms for Data Science

A large number of engineering, science and computational problems have yet to be solved in a computationally efficient way. One of the emerging challenges is how evolving technologies grow towards autonomy and intelligent decision making. This leads to collection of large amounts of data from various sensing and measurement technologies, e.g., cameras, smart phones, health sensors, smart electricity meters, and environment sensors. Hence, it is imperative to develop efficient algorithms for generation, analysis, classification, and illustration of data. Meanwhile, data is structured purposefully through different representations, such as large-scale networks and graphs. We focus on data science as a crucial area, specifically focusing on a curse of dimensionality (CoD) which is due to the large amount of generated/sensed/collected data. This motivates researchers to think about optimization and to apply nature-inspired algorithms, such as evolutionary algorithms (EAs) to solve optimization problems. Although these algorithms look un-deterministic, they are robust enough to reach an optimal solution. Researchers do not adopt evolutionary algorithms unless they face a problem which is suffering from placement in local optimal solution, rather than global optimal solution. In this chapter, we first develop a clear and formal definition of the CoD problem, next we focus on feature extraction techniques and categories, then we provide a general overview of meta-heuristic algorithms, its terminology, and desirable properties of evolutionary algorithms