It is anticipated that massive amounts of energy will be transferred long distances via High-Voltage Direct Current (HVDC) links in the future and the prospect of having meshed HVDC grids is envisioned, for example the European super grid. Such a power system would benefit greatly if HVDC circuit breakers could reliably clear faults within the HVDC network. Different ways to break large direct currents have been proposed throughout the years and one distinguished concept is based on generating an artificial zero-crossing of the current and pass it through a mechanical interrupter as it opens. This concept is implemented in the Voltage-Source Converter Assisted Resonant Current (VARC) circuit breakers developed at Scibreak which require auxiliary power from an off-line supply unit to energize their electronic equipment. This thesis continuous and builds on research previously carried out at Scibreak on a special Auxiliary Power Supply (APS) concept for 525 kV HVDC applications. In essence, it is a unique modularized high-frequency transformer whose power transfer and voltage withstand capabilities are the cornerstones of its design. The APS must supply an adequate amount of power to drive the VARC circuit breakers with preferably high efficiency while also fulfilling the considerable insulation demands of HVDC grids. A feasibility study of this APS concept was carried out by building a parametric 3D model in the Finite Element Analysis (FEA) software Ansys Maxwell which includes all parts that affect both its magnetic and electrical properties. The initial model reproduced experimental results from a magnetic APS prototype and was then used to explore a plethora of different geometries and materials with regards to its magnetic and dielectric designs. Specific design candidates were selected for more in-depth analysis and experimental work. All obtained results together with knowledge of commercially available materials show that the APS holds great promise to meet the necessary design criteria for its HVDC applications. Its dielectric design is well suited to continuously handle an operating voltage of 525 kV DC, meet the required impulse voltage levels of the grid and properly shield the magnetic structure. It is expected to have a long life time where the design criterion was always 30 years in this work. Moreover, its magnetic design is anticipated to supply a few kW of active power with efficiencies between 80 to 95 percent and manage a continuous operating time of 5 min. Both design aspects are interchangeable to a decent extent in order to cope with one another and produce a compromised design. / Det förväntas att enorma mängder energi kommer överföras långa sträckor med Högspänd Likström (HVDC) i framtiden och blivande HVDC-nät håller på att föreställas, till exempel det europeiska superelnätet. Ett sådant kraftsystem skulle ha stor nytta av HVDC-brytare som tillförlitligt kan bryta felströmmar inom HVDC-nätet. Olika sätt att bryta stora likströmmar har föreslagits med åren och ett distinkt koncept bygger på att generera en artificiell nollgenomgång av strömmen och föra den genom en mekanisk brytarkammare när den öppnas. Detta koncept är implementerat i de så kallade VARC-strömbrytarna som utvecklas hos Scibreak, vilka kräver hjälpmatning av effekt från en extern försörjningsenhet för att driva deras elektroniska utrustning. Denna avhandling fortsätter och bygger vidare på forskning som tidigare utförts hos Scibreak på ett speciellt Hjälpmatningskoncept (APS) för 525 kV HVDC-tillämpningar. I huvudsak är det en unik modulariserad högfrekvenstransformator vars kraftöverföring och spänningståligheter är huvudfokusen i dess design. Hjälpmatningen måste kunna leverera en tillräcklig mängd effekt för att driva VARC-brytarna med företrädesvis hög verkningsgrad, samtidigt som den ska uppfylla de betydande isolationskraven för HVDC-nät. En genomförbarhetsstudie av detta APS-koncept gjordes genom att bygga en parametrisk 3D modell i Finita Elementmetodsprogramvaran (FEM) Ansys Maxwell som inkluderar alla delar som påverkar både dess magnetiska och elektriska egenskaper. Den initiala modellen reproducerade experimentella resultat från en magnetisk APS-prototyp och användes sedan för att utforska en uppsjö av olika geometrier och material med avseende på dess magnetiska och dielektriska konstruktioner. Specifika designkandidater valdes ut för mer djupgående analys och experimentiellt arbete. Alla erhållna resultat tillsammans med kunskap om kommersiellt tillgängliga material visar att APS:n har stora möjligheter att uppfylla alla nödvändiga kriterier för sina HVDC-tillämpningar. Dess dielektriska design är väl lämpad för att kontinuerligt hantera en driftspänning på 525 kV DC, möta de erforderliga stötspänningståligheterna i nätet och ordentligt skydda den magnetiska strukturen. Den förväntas ha en lång livstid där designkriteriet alltid var 30 år i detta arbete. Dessutom förväntas dess magnetiska design leverera några kW aktiv effekt med verkningsgrader mellan 80 till 95 procent och klara en kontinuerlig drifttid på 5 min. Båda designaspekterna kan justeras i en någorlunda utsträckning för möta de krav som ställs från varandra och producera en kompromissad design.

Rysslands invasion i Ukraina ledde till att Vladimir Putin och hans påstådda medhjälpare hamnade i rampljuset av globala nyhetsorganisationer. Oligarken Roman Abramovitj som var ägaren för fotbollsklubben Chelsea FC, ansågs att vara en av Putins medhjälpare. Det här gjorde att brittisk journalistik började att granska honom. Syftet med undersökning var att ta reda på hur dagstidningen The Guardian framställer Roman Abramovitj. För att genomföra undersökningen användes ett induktivt tillvägagångssätt med metoden tematisk analys för att kunna tolka och tematisera The Guardians innehåll. Genom detta arbetssätt lyckades vi att upptäcka hur The Guardian skildrade Roman Abramovitj. Studien fann att Abramovitjs namn gav The Guardian möjlighet till att kritisera olika samhällsaktörer. Undersökningen fann även att Roman Abramovitj framställdes på ett negativt sätt av The Guardian. / Russia’s invasion of Ukraine thrust Vladimir Putin and his alleged associates into the spotlight by global news organizations. The oligarch Roman Abramovich who was the owner of the football club Chelsea FC, was considered to be one of Putins allies. This caused british journalism to begin to scrutinize him. The aim of the study was to find out how The Guardian depicts Roman Abramovich. Our method was an inductive approach with thematic analysis to interpret and thematize The Guardians content. With this approach, we managed to discover how The Guardian depicted Roman Abramovich. The results showed that Abramovich’s name gave The Guardian an opportunity to criticize different social stakeholders. The study also found that Roman Abramovich was depicted negatively by The Guardian.

This study explores the internal and external views of entrepreneurship among students from various fields of study, including medicine, law, business, engineering and humanities. It investigates the likelihood of students engaging in entrepreneurial activities within their respective fields and identifies target groups with less positive perceptions and lower inclinations towards entrepreneurship. Through a combination of qualitative and quantitative research methods, including a literature review and an interview with an entrepreneur from the medical field, we found that students in medicine show the least interest in entrepreneurship, while students in business show the highest, with other fields of study falling in between. These insights highlight the need for comprehensive entrepreneurship education and courses tailored to the specific fields of study to foster an entrepreneurial mindset among students by exposing them to its possibilities. By promoting innovation and entrepreneurial thinking, such initiatives hold significant potential for creating valuable opportunities within different academic domains.

<p dir="ltr">Our ability to understand language and perform reasoning crucially relies on a robust system of semantic cognition (G. L. Murphy, 2002; Rogers & McClelland, 2004; Rips et al., 2012; Lake & Murphy, 2021): processes that allow us to learn, update, and produce inferences about everyday concepts (e.g., cat, chair), properties (e.g., has fur, can be sat on), categories (e.g., mammals, furniture), and relations (e.g., is-a, taller-than). Meanwhile, recent progress in the field of natural language processing (NLP) has led to the development of language models (LMs): sophisticated neural networks that are trained to predict words in context (Devlin et al., 2019; Radford et al., 2019; Brown et al., 2020), and as a result build representations that encode the knowledge present in the statistics of their training environment. These models have achieved impressive levels of performance on a range of tasks that require sophisticated semantic knowledge (e.g. question answering and natural language inference), often even reaching human parity. To what extent do LMs capture the nuances of human conceptual knowledge and reasoning? Centering around this broad question, this dissertation uses core ideas in human semantic cognition as guiding principles and lays down the groundwork to establish effective evaluation and improvement of conceptual understanding in LMs. In particular, I build on prior work that focuses on characterizing what semantic knowledge is made available in the behavior and representations of LMs, and extend it by additionally proposing tests that focus on functional consequences of acquiring basic semantic knowledge.<br><br>I primarily focus on inductive generalization (Hayes & Heit, 2018)—the unique ability of humans to rely on acquired conceptual knowledge to project or generalize novel information—as a context within which we can analyze LMs’ encoding of conceptual knowledge. I do this, since the literature surrounding inductive generalization contains a variety of empirical regularities that map to specific conceptual abstractions and shed light on how humans store, organize and use conceptual knowledge. Before explicitly analyzing LMs for these empirical regularities, I test them on two other contexts, which also feature the role of inductive generalization. First I test the extent to which LMs demonstrate typicality effects—a robust finding in human categorization literature where certain members of a category are considered to be more central to the category than are others. Specifically, I test the behavior 19 different LMs on two contexts where typicality effects modulate human behavior: 1) verification of sentences expressing taxonomic category membership, and 2) projecting novel properties from individual category members to the entire category. In both tests, LMs achieved positive but modest correlations with human typicality ratings, suggesting that they can to a non-trivial extent capture subtle differences between category members. Next, I propose a new benchmark to test the robustness of LMs in attributing properties to everyday concepts, and in making inductive leaps to endow properties to novel concepts. On testing 31 different LMs for these capacities, I find that while they can correctly attribute properties to everyday concepts and even predict the properties of novel concepts in simple settings, they struggle to do so robustly. Combined with the analyses of typicality effects, these results suggest that the ability of LMs to demonstrate impressive conceptual knowledge and reasoning behavior can be explained by their sensitivities to shallow predictive cues. When these cues are carefully controlled for, LMs show critical failures in demonstrating robust conceptual understanding. Finally, I develop a framework that can allow us to characterize the extent to which the distributed representations learned by LMs can encode principles and abstractions that characterize inductive behavior of humans. This framework operationalizes inductive generalization as the behavior of an LM after its representations have been partially exposed (via gradient-based learning) to novel conceptual information. To simulate this behavior, the framework uses LMs that are endowed with human-elicited property knowledge, by training them to evaluate the truth of sentences attributing properties to concepts. I apply this framework to test four different LMs on 13 different inductive phenomena documented for humans (Osherson et al., 1990; Heit & Rubinstein, 1994). Results from these analyses suggest that building representations from word distributions can successfully allow the encoding of many abstract principles that can guide inductive behavior in the models—principles such as sensitivity to conceptual similarity, hierarchical organization of categories, reasoning about category coverage, and sample size. At the same time, the tested models also systematically failed at demonstrating certain phenomena, showcasing their inability to demonstrate pragmatic reasoning, preference to rely on shallow statistical cues, and lack of context sensitivity with respect to high-level intuitive theories.</p>

Link prediction is a crucial task in many downstream applications of graph machine learning. Graph Neural Networks (GNNs) are a prominent approach for transductive link prediction, where the aim is to predict missing links or connections only within the existing nodes of a given graph. However, many real-life applications require inductive link prediction for the newly-coming nodes with no connections to the original graph. Thus, recent approaches have adopted a Multilayer Perceptron (MLP) for inductive link prediction based solely on node features. In this work, we show that incorporating both connectivity structure and features for the new nodes provides better model expressiveness. To bring such expressiveness to inductive link prediction, we propose LEAP, an encoder that features LEArnable toPology augmentation of the original graph and enables message passing with the newly-coming nodes. To the best of our knowledge, this is the first attempt to provide structural contexts for the newly-coming nodes via learnable augmentation under inductive settings. Conducting extensive experiments on four real- world homogeneous graphs demonstrates that LEAP significantly surpasses the state-of-the-art methods in terms of AUC and average precision. The improvements over homogeneous graphs are up to 22% and 17%, respectively. The code and datasets are available on GitHub*. / Att förutsäga länkar är en viktig uppgift i många efterföljande tillämpningar av maskininlärning av grafer. Graph Neural Networks (GNNs) är en framträdande metod för transduktiv länkförutsägelse, där målet är att förutsäga saknade länkar eller förbindelser endast inom de befintliga noderna i en given graf. I många verkliga tillämpningar krävs dock induktiv länkförutsägelse för nytillkomna noder utan kopplingar till den ursprungliga grafen. Därför har man på senare tid antagit en Multilayer Perceptron (MLP) för induktiv länkförutsägelse som enbart bygger på nodens egenskaper. I det här arbetet visar vi att om man införlivar både anslutningsstruktur och egenskaper för de nya noderna får man en bättre modelluttryck. För att ge induktiv länkförutsägelse en sådan uttrycksfullhet föreslår vi LEAP, en kodare som innehåller LEArnable toPology augmentation av den ursprungliga grafen och möjliggör meddelandeöverföring med de nytillkomna noderna. Såvitt vi vet är detta det första försöket att tillhandahålla strukturella sammanhang för de nytillkomna noderna genom en inlärningsbar ökning i induktiva inställningar. Omfattande experiment på fyra homogena grafer i den verkliga världen visar att LEAP avsevärt överträffar "state-of-the-art" metoderna när det gäller AUC och genomsnittlig precision. Förbättringarna jämfört med homogena grafer är upp till 22% och 17%. Koden och datamängderna finns tillgängliga på Github*.

No description available.

A joining implication is a restricted form of an implication where it is explicitly specified which attributesmay occur in the premise and in the conclusion, respectively. A technique for sound and complete axiomatization of joining implications valid in a given formal context is provided. In particular, a canonical base for the joining implications valid in a given formal context is proposed, which enjoys the property of being of minimal cardinality among all such bases. Background knowledge in form of a set of valid joining implications can be incorporated. Furthermore, an application to inductive learning in a Horn description logic is proposed, that is, a procedure for sound and complete axiomatization of Horn-M concept inclusions from a given interpretation is developed. A complexity analysis shows that this procedure runs in deterministic exponential time.

The work for this thesis is in line with the field of Instrumentation for Particle Accelerators, so called Beam Diagnostics. It is presented the development of a series of electro-mechanical devices called Inductive Pick-Ups (IPU) for Beam Position Monitoring (BPM). A full set of 17 BPM units (16 + 1 spare), named BPS units, were built and installed into the Test Beam Line (TBL), an electron beam decelerator, of the 3rd CLIC Test Facility (CTF3) at CERN ¿European Organization for the Nuclear Research¿. The CTF3, built at CERN by an international collaboration, was meant to demonstrate the technical feasibility of the key concepts for CLIC ¿Compact Linear Collider¿ as a future linear collider based on the novel two-beam acceleration scheme, and in order to achieve the next energy frontier for a lepton collider in theMulti-TeV scale. Modern particle accelerators and in particular future colliders like CLIC requires an extreme alignment and stabilization of the beam in order to enhance its quality, which rely heavily on a beam based alignment techniques. Here the BPMs, like the BPS-IPU, play an important role providing the beam position with precision and high resolution, besides a beam current measurement in the case of the BPS, along the beam lines. The BPS project carried out at IFIC was mainly developed in two phases: prototyping and series production and test for the TBL. In the first project phase two fully functional BPS prototypes were constructed, focusing in this thesis work on the electronic design of the BPS on-board PCBs (Printed Circuit Boards) which are based on transformers for the current sensing and beam position measurement. Furthermore, it is described the monitor mechanical design with emphasis on all the parts directly involved in its electromagnetic functioning, as a result of the coupling of the EM fields generated by the beam with those parts. For that, it was studied its operational parameters, according the TBL specifications, and it was also simulated a new circuital model reproducing the BPS monitor frequency response for its operational bandwidth (1kHz-100MHz). These prototypes were initially tested in the laboratories of the BI-PI section¿Beam Instrumentation - Position and Intensity¿ at CERN. In the second project phase the BPS monitor series, which were built based on the experience acquired during the prototyping phase, the work was focused on the realization of the characterization tests to measure the main operational parameters of each series monitor, for which it was designed and constructed two test benches with different purposes and frequency regions. The first one is designed to work in the low frequency region, between 1kHz-100MHz, in the time scale of the electron beam pulse with a repetition period of 1s and an approximate duration of 140ns. This kind of test setups called Wire Test-bench are commonly used in the accelerators instrumentation field in order to determine the characteristic parameters of a BPM (or pick-up) like its linearity and precision in the position measurement, and also its frequency response (bandwidth). This is done by emulating a low current intensity beam with a stretched wire carrying a current signals which can be precisely positioned with respect the device under test. This test bench was specifically made for the BPS monitor and conceived to perform the measurement data acquisition in an automated way, managing the measurement equipment and the wire positioning motors controller from a PC workstation. Each one of the BPS monitors series were characterized by using this system at the IFIC labs, and the test results and analysis are presented in this work. On the other hand, the high frequency tests, above the X band in the microwave spectrum and at the time scale of the micro-bunch pulses with a bunching period of 83ps (12GHz) inside a long 140ns pulse, were performed in order to measure the longitudinal impedance of the BPS monitor. This must be low enough in order to minimize the perturbations on the beam produced at crossing the monitor, which affects to its stability during the propagation along the line. For that, it was built the high frequency test bench as a coaxial waveguide structure of 24mm diameter matched at 50¿ and with a bandwidth from 18MHz to 30GHz, which was previously simulated, and having room in the middle to place the BPS as the device under test. This high frequency test bench is able to reproduce the TEM (Transversal Electro-Magnetic) propagative modes corresponding to an ultra-relativistic electron beam of 12GHz bunching frequency, so that the Scattering parameters can be measured to obtain the longitudinal impedance of the BPS in the frequency range of interest. Finally, it is also presented the results of the beam test made in the TBL line, with beam currents from 3.5A to 13A (max. available at the moment of the test). In order to determine the minimum resolution attainable by a BPS monitor in the measurement of the beam position, being the device figure of merit, with a resolution goal of 5¿m at maximum beam current of 28A according to the TBL specifications. / García Garrigós, JJ. (2013). Development of the Beam Position Monitors for the Diagnostics of the Test Beam Line in the CTF3 at CERN [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/34327

[EN] The stupefying success of Artificial Intelligence (AI) for specific problems, from recommender systems to self-driving cars, has not yet been matched with a similar progress in general AI systems, coping with a variety of problems. This dissertation deals with the long-standing problem of creating more general AI systems, through the analysis of their development and the evaluation of their cognitive abilities. Firstly, this thesis contributes with a general-purpose learning system that meets several desirable characteristics in terms of expressiveness, comprehensibility and versatility. The system works with approaches that are inherently general: inductive programming and reinforcement learning. The system does not rely on a fixed library of learning operators, but can be endowed with new ones, so being able to operate in a wide variety of contexts. This flexibility, jointly with its declarative character, makes it possible to use the system as an instrument for better understanding the role (and difficulty) of the constructs that each task requires. The learning process is also overhauled with a new developmental and lifelong approach for knowledge acquisition, consolidation and forgetting, which is necessary when bounded resources (memory and time) are considered. Secondly, this thesis analyses whether the use of intelligence tests for AI evaluation is a much better alternative to most task-oriented evaluation approaches in AI. Accordingly, we make a review of what has been done when AI systems have been confronted against tasks taken from intelligence tests. In this regard, we scrutinise what intelligence tests measure in machines, whether they are useful to evaluate AI systems, whether they are really challenging problems, and whether they are useful to understand (human) intelligence. Finally, the analysis of the concepts of development and incremental learning in AI systems is done at the conceptual level but also through several of these intelligence tests, providing further insight for the understanding and construction of general-purpose developing AI systems. / [ES] El éxito abrumador de la Inteligencia Artificial (IA) en la resolución de tareas específicas (desde sistemas de recomendación hasta vehículos de conducción autónoma) no ha sido aún igualado con un avance similar en sistemas de IA de carácter más general enfocados en la resolución de una mayor variedad de tareas. Esta tesis aborda la creación de sistemas de IA de propósito general así como el análisis y evaluación tanto de su desarrollo como de sus capacidades cognitivas. En primer lugar, esta tesis contribuye con un sistema de aprendizaje de propósito general que reúne distintas ventajas como expresividad, comprensibilidad y versatilidad. El sistema está basado en aproximaciones de carácter inherentemente general: programación inductiva y aprendizaje por refuerzo. Además, dicho sistema se basa en una biblioteca dinámica de operadores de aprendizaje por lo que es capaz de operar en una amplia variedad de contextos. Esta flexibilidad, junto con su carácter declarativo, hace que sea posible utilizar el sistema de forma instrumental con el objetivo de facilitar la comprensión de las distintas construcciones que cada tarea requiere para ser resuelta. Por último, el proceso de aprendizaje también se revisa por medio de un enfoque evolutivo e incremental de adquisición, consolidación y olvido de conocimiento, necesario cuando se trabaja con recursos limitados (memoria y tiempo). En segundo lugar, esta tesis analiza el uso de tests de inteligencia humana para la evaluación de sistemas de IA y plantea si su uso puede constituir una alternativa válida a los enfoques actuales de evaluación de IA (más orientados a tareas). Para ello se realiza una exhaustiva revisión bibliográfica de aquellos sistemas de IA que han sido utilizados para la resolución de este tipo de problemas. Esto ha permitido analizar qué miden realmente los tests de inteligencia en los sistemas de IA, si son significativos para su evaluación, si realmente constituyen problemas complejos y, por último, si son útiles para entender la inteligencia (humana). Finalmente se analizan los conceptos de desarrollo cognitivo y aprendizaje incremental en sistemas de IA no solo a nivel conceptual, sino también por medio de estos problemas mejorando por tanto la comprensión y construcción de sistemas de propósito general evolutivos. / [CA] L'èxit aclaparant de la Intel·ligència Artificial (IA) en la resolució de tasques específiques (des de sistemes de recomanació fins a vehicles de conducció autònoma) no ha sigut encara igualat amb un avanç similar en sistemes de IA de caràcter més general enfocats en la resolució d'una major varietat de tasques. Aquesta tesi aborda la creació de sistemes de IA de propòsit general així com l'anàlisi i avaluació tant del seu desenvolupament com de les seues capacitats cognitives. En primer lloc, aquesta tesi contribueix amb un sistema d'aprenentatge de propòsit general que reuneix diferents avantatges com ara expressivitat, comprensibilitat i versatilitat. El sistema està basat en aproximacions de caràcter inherentment general: programació inductiva i aprenentatge per reforç. A més, el sistema utilitza una biblioteca dinàmica d'operadors d'aprenentatge pel que és capaç d'operar en una àmplia varietat de contextos. Aquesta flexibilitat, juntament amb el seu caràcter declaratiu, fa que siga possible utilitzar el sistema de forma instrumental amb l'objectiu de facilitar la comprensió de les diferents construccions que cada tasca requereix per a ser resolta. Finalment, el procés d'aprenentatge també és revisat mitjançant un enfocament evolutiu i incremental d'adquisició, consolidació i oblit de coneixement, necessari quan es treballa amb recursos limitats (memòria i temps). En segon lloc, aquesta tesi analitza l'ús de tests d'intel·ligència humana per a l'avaluació de sistemes de IA i planteja si el seu ús pot constituir una alternativa vàlida als enfocaments actuals d'avaluació de IA (més orientats a tasques). Amb aquesta finalitat, es realitza una exhaustiva revisió bibliogràfica d'aquells sistemes de IA que han sigut utilitzats per a la resolució d'aquest tipus de problemes. Açò ha permès analitzar què mesuren realment els tests d'intel·ligència en els sistemes de IA, si són significatius per a la seua avaluació, si realment constitueixen problemes complexos i, finalment, si són útils per a entendre la intel·ligència (humana). Finalment s'analitzen els conceptes de desenvolupament cognitiu i aprenentatge incremental en sistemes de IA no solament a nivell conceptual, sinó també per mitjà d'aquests problemes millorant per tant la comprensió i construcció de sistemes de propòsit general evolutius. / Martínez Plumed, F. (2016). Incremental and developmental perspectives for general-purpose learning systems [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/67269

ABSTRACT The Lateral Double-Diffused Metal-Oxide-Semiconductor Field Effect Transistor (LDMOSFET or LDMOS) has made an enormous impact in the field of power electronics. Its integration, low cost, and power performance have made it the popular choice for power system on chips (SoC's). Over the years, much research has gone into ways of optimizing this crucial power device. Particularly, the safe operating area (SOA) has become a focus of research in order to allow a wide range of various bias schemes. More so, device ruggedness is an important factor in the usability of these devices as there are many circuits in which high current and voltage are present in a device. In this study, a conventional LDMOS is simulated using a 2-D device simulator. Two specific device enhancement techniques are implemented and analyzed, including a p+ bottom layer and an n-adaptive layer. The parasitic BJT of the LDMOS and its effect on SOA is investigated by using meaningful and in depth device cross-section analysis. The ruggedness of these devices are then considered and analyzed by means of an undamped inductive switching test (UIS). The purpose is to realize the relationship and the possible trade-offs between safe operating area enhancement and device ruggedness.