Multi-Cycle at Speed Test

In this research, we focus on the development of an algorithm that is used to generate a minimal number of patterns for path delay test of integrated circuits using the multi-cycle at-speed test. We test the circuits in functional mode, where multiple functional cycles follow after the test pattern scan-in operation. This approach increases the delay correlation between the scan and functional test, due to more functionally realistic power supply noise. We use multiple at-speed cycles to compact K-longest paths per gate tests, which reduces the number of scan patterns. After a path is generated, we try to place each path in the first pattern in the pattern pool. If the path does not fit due to conflicts, we attempt to place it in later functional cycles. This compaction approach retains the greedy nature of the original dynamic compaction algorithm where it will stop if the path fits into a pattern. If the path is not able to compact in any of the functional cycles of patterns in the pool, we generate a new pattern. In this method, each path delay test is compared to at-speed patterns in the pool. The challenge is that the at-speed delay test in a given at-speed cycle must have its necessary value assignments set up in previous (preamble) cycles, and have the captured results propagated to a scan cell in the later (coda) cycles. For instance, if we consider three at-speed (capture) cycles after the scan-in operation, and if we need to place a fault in the first capture cycle, then we must generate it with two propagation cycles. In this case, we consider these propagation cycles as coda cycles, so the algorithm attempts to select the most observable path through them. Likewise, if we are placing the path test in the second capture cycle, then we need one preamble cycle and one coda cycle, and if we are placing the path test in the third capture cycle, we require two preamble cycles with no coda cycles

Extending Conditional Simple Temporal Networks with Partially Shrinkable Uncertainty

The proper handling of temporal constraints is crucial in many domains. As a particular challenge, temporal constraints must be also handled when different specific situations happen (conditional constraints) and when some event occurrences can be only observed at run time (contingent constraints). In this paper we introduce Conditional Simple Temporal Networks with Partially Shrinkable Uncertainty (CSTNPSUs), in which contingent constraints are made more flexible (guarded constraints) and they are also specified as conditional constraints. It turns out that guarded constraints require the ability to reason on both kinds of constraints in a seamless way. In particular, we discuss CSTNPSU features through a motivating example and, then, we introduce the concept of controllability for such networks and the related sound checking algorithm

Missions and Vehicle Concepts for Modern, Propelled, Lighter-Than-Air Vehicles

The results of studies conducted over the last 15 years to assess missions and vehicle concepts for modern, propelled, lighter-than-air vehicles (airships) were surveyed. Rigid and non-rigid airship concepts are considered. The use of airships for ocean patrol and surveillance is discussed along with vertical heavy lift airships. Military and civilian needs for high altitude platforms are addressed. Around 1970 a resurgence of interest about lighter-than-air vehicles (airships) occurred in both the public at large and in certain isolated elements of the aerospace industry. Such renewals of airship enthusiasm are not new and have, in fact, occurred regularly since the days of the Hindenburg and other large rigid airships. However, the interest that developed in the early 1970's has been particularly strong and self-sustaining for a number of good reasons. The first is the rapid increase in fuel prices over the last decade and the common belief (usually true) that airships are the most fuel efficient means of air transportation. Second, a number of new mission needs have arisen, particularly in surveillance and patrol and in vertical heavy-lift, which would seem to be well-suited to airship capabilities. The third reason is the recent proposal of many new and innovative airship concepts. Finally, there is the prospect of adapting to airships the tremendous amount of new aeronautical technology which has been developed in the past few decades thereby obtaining dramatic new airship capabilities. The primary purpose of this volume is to survey the results of studies, conducted over the last 15 years, to assess missions and vehicle concepts for modern propelled lighter-than-air vehicles

Modelling & analysis of hybrid dynamic systems using a bond graph approach

Hybrid models are those containing continuous and discontinuous behaviour. In constructing dynamic systems models, it is frequently desirable to abstract rapidly changing, highly nonlinear behaviour to a discontinuity. Bond graphs lend themselves to systems modelling by being multi-disciplinary and reflecting the physics of the system. One advantage is that they can produce a mathematical model in a form that simulates quickly and efficiently. Hybrid bond graphs are a logical development which could further improve speed and efficiency. A range of hybrid bond graph forms have been proposed which are suitable for either simulation or further analysis, but not both. None have reached common usage. A Hybrid bond graph method is proposed here which is suitable for simulation as well as providing engineering insight through analysis. This new method features a distinction between structural and parametric switching. The controlled junction is used for the former, and gives rise to dynamic causality. A controlled element is developed for the latter. Dynamic causality is unconstrained so as to aid insight, and a new notation is proposed. The junction structure matrix for the hybrid bond graph features Boolean terms to reflect the controlled junctions in the graph structure. This hybrid JSM is used to generate a mixed-Boolean state equation. When storage elements are in dynamic causality, the resulting system equation is implicit. The focus of this thesis is the exploitation of the model. The implicit form enables application of matrix-rank criteria from control theory, and control properties can be seen in the structure and causal assignment. An impulsive mode may occur when storage elements are in dynamic causality, but otherwise there are no energy losses associated with commutation because this method dictates the way discontinuities are abstracted. The main contribution is therefore a Hybrid Bond Graph which reflects the physics of commutating systems and offers engineering insight through the choice of controlled elements and dynamic causality. It generates a unique, implicit, mixed-Boolean system equation, describing all modes of operation. This form is suitable for both simulation and analysis

Attributes of Big Data Analytics for Data-Driven Decision Making in Cyber-Physical Power Systems

Big data analytics is a virtually new term in power system terminology. This concept delves into the way a massive volume of data is acquired, processed, analyzed to extract insight from available data. In particular, big data analytics alludes to applications of artificial intelligence, machine learning techniques, data mining techniques, time-series forecasting methods. Decision-makers in power systems have been long plagued by incapability and weakness of classical methods in dealing with large-scale real practical cases due to the existence of thousands or millions of variables, being time-consuming, the requirement of a high computation burden, divergence of results, unjustifiable errors, and poor accuracy of the model. Big data analytics is an ongoing topic, which pinpoints how to extract insights from these large data sets. The extant article has enumerated the applications of big data analytics in future power systems through several layers from grid-scale to local-scale. Big data analytics has many applications in the areas of smart grid implementation, electricity markets, execution of collaborative operation schemes, enhancement of microgrid operation autonomy, management of electric vehicle operations in smart grids, active distribution network control, district hub system management, multi-agent energy systems, electricity theft detection, stability and security assessment by PMUs, and better exploitation of renewable energy sources. The employment of big data analytics entails some prerequisites, such as the proliferation of IoT-enabled devices, easily-accessible cloud space, blockchain, etc. This paper has comprehensively conducted an extensive review of the applications of big data analytics along with the prevailing challenges and solutions

A Survey on Aerial Swarm Robotics

The use of aerial swarms to solve real-world problems has been increasing steadily, accompanied by falling prices and improving performance of communication, sensing, and processing hardware. The commoditization of hardware has reduced unit costs, thereby lowering the barriers to entry to the field of aerial swarm robotics. A key enabling technology for swarms is the family of algorithms that allow the individual members of the swarm to communicate and allocate tasks amongst themselves, plan their trajectories, and coordinate their flight in such a way that the overall objectives of the swarm are achieved efficiently. These algorithms, often organized in a hierarchical fashion, endow the swarm with autonomy at every level, and the role of a human operator can be reduced, in principle, to interactions at a higher level without direct intervention. This technology depends on the clever and innovative application of theoretical tools from control and estimation. This paper reviews the state of the art of these theoretical tools, specifically focusing on how they have been developed for, and applied to, aerial swarms. Aerial swarms differ from swarms of ground-based vehicles in two respects: they operate in a three-dimensional space and the dynamics of individual vehicles adds an extra layer of complexity. We review dynamic modeling and conditions for stability and controllability that are essential in order to achieve cooperative flight and distributed sensing. The main sections of this paper focus on major results covering trajectory generation, task allocation, adversarial control, distributed sensing, monitoring, and mapping. Wherever possible, we indicate how the physics and subsystem technologies of aerial robots are brought to bear on these individual areas