TASKers: A Whole-System Generator for Benchmarking Real-Time-System Analyses

Implementation-based benchmarking of timing and schedulability analyses requires system code that can be executed on real hardware and has defined properties, for example, known worst-case execution times (WCETs) of tasks. Traditional approaches for creating benchmarks with such characteristics often result in implementations that do not resemble real-world systems, either due to work only being simulated by means of busy waiting, or because tasks have no control-flow dependencies between each other. In this paper, we address this problem with TASKers, a generator that constructs realistic benchmark systems with predefined properties. To achieve this, TASKers composes patterns of real-world programs to generate tasks that produce known outputs and exhibit preconfigured WCETs when being executed with certain inputs. Using this knowledge during the generation process, TASKers is able to specifically introduce inter-task control-flow dependencies by mapping the output of one task to the input of another

Reactive power control in photovoltaic systems through (explainable) artificial intelligence

Across the world, efforts to support the energy transition and halt climate change have resulted in significant growth of the number of renewable distributed generators (DGs) installed over the last decade, among which photovoltaic (PV) systems are the fastest growing technology. However, high PV penetration in the electricity grid is known to lead to numerous operational problems such as voltage fluctuations and line congestions, which could be eased by utilizing the reactive power capability of PV systems. To this end, we propose to use artificial neural network (ANN) to predict optimal reactive power dispatch in PV systems by learning approximate input–output mappings from AC optimal power flow (ACOPF) solutions in either a centralized or a decentralized manner. In the case of decentralized control, we leverage Shapley Additive Explanations (SHAP), an explainable artificial intelligence (XAI) technique, to identify non-local grid state measurements which significantly influence the optimal dispatch of each individual system. Both centralized and decentralized ANN-based controllers are evaluated through a case study based on the CIGRE medium-voltage distribution grid and compared to baseline control strategies. Results show that both ANN-based controllers exhibit superior performance, hindering voltage problems and line congestions which are encountered with baseline strategies while recording an energy saving of 0.44% compared to fixed power factor control. By leveraging ANN and SHAP, the proposed decentralized controllers for reactive power control are able to achieve ACOPF-level performance while promoting data privacy and reducing computational burden

Whole-System WCEC Analysis for Energy-Constrained Real-Time Systems (Artifact)

Although internal devices (e.g., memory, timers) and external devices (e.g., sensors, transceivers) significantly contribute to the energy consumption of an embedded real-time system, their impact on the worst-case response energy consumption (WCRE) of tasks is usually not adequately taken into account. Most WCRE analysis techniques only focus on the processor and neglect the energy consumption of other hardware units that are temporarily activated and deactivated in the system. To solve the problem of system-wide energy-consumption analysis, we present SysWCEC, an approach that addresses these problems by enabling static WCRE analysis for entire real-time systems, including internal as well as external devices. For this purpose, SysWCEC introduces a novel abstraction, the power-state--transition graph, which contains information about the worst-case energy consumption of all possible execution paths. To construct the graph, SysWCEC decomposes the analyzed real-time system into blocks during which the set of active devices in the system does not change and is consequently able to precisely handle devices being dynamically activated or deactivated. In this artifact evaluation, which accompanies our related conference paper, we present easy to reproduce WCRE analyses with the SysWCEC framework using several benchmarks. The artifact comprises the generation of the power-state--transition graph from a given benchmark system and the formulation of an integer linear program whose solution eventually yields safe WCRE bounds

Whole-System Worst-Case Energy-Consumption Analysis for Energy-Constrained Real-Time Systems

Although internal devices (e.g., memory, timers) and external devices (e.g., transceivers, sensors) significantly contribute to the energy consumption of an embedded real-time system, their impact on the worst-case response energy consumption (WCRE) of tasks is usually not adequately taken into account. Most WCRE analysis techniques, for example, only focus on the processor and therefore do not consider the energy consumption of other hardware units. Apart from that, the typical approach for dealing with devices is to assume that all of them are always activated, which leads to high WCRE overestimations in the general case where a system switches off the devices that are currently not needed in order to minimize energy consumption. In this paper, we present SysWCEC, an approach that addresses these problems by enabling static WCRE analysis for entire real-time systems, including internal as well as external devices. For this purpose, SysWCEC introduces a novel abstraction, the power-state-transition graph, which contains information about the worst-case energy consumption of all possible execution paths. To construct the graph, SysWCEC decomposes the analyzed real-time system into blocks during which the set of active devices in the system does not change and is consequently able to precisely handle devices being dynamically activated or deactivated

Explainable artificial intelligence for photovoltaic fault detection: A comparison of instruments

Faults in photovoltaic arrays are known to cause severe energy losses. Data-driven models based on machine learning have been developed to automatically detect and diagnose such faults. A majority of the models proposed in the literature are based on artificial neural networks, which unfortunately represent black-boxes, hindering user interpretation of the models’ results. Since the energy sector is a critical infrastructure, the security of energy supply could be threatened by the deployment of such models. This study implements explainable artificial intelligence (XAI) techniques to extract explanations from a multi-layer perceptron (MLP) model for photovoltaic fault detection, with the aim of shedding some light on the behavior of XAI techniques in this context. Three techniques were implemented: Shapley Additive Explanations (SHAP), Anchors and Diverse Counterfactual Explanations (DiCE), each representing a distinct class of local explainability techniques used to explain predictions. For a model with 99.11% accuracy, results show that SHAP explanations are largely in line with domain knowledge, demonstrating their usefulness to generate valuable insights on model behavior which could potentially increase user trust in the model. Compared to Anchors and DiCE, SHAP demonstrated a higher degree of stability and consistency

Ligaments of the Lisfranc joint in MRI: 3D-SPACE (sampling perfection with application optimized contrasts using different flip-angle evolution) sequence compared to three orthogonal proton-density fat-saturated (PD fs) sequences

Purpose: To compare the detection rate and visibility of the ligaments in the Lisfranc joint with a single 3D (−SPACE) MR sequence and three orthogonal PD fat-saturated sequences. Materials and methods: Thirty-one asymptomatic feet and 15 patients with posttraumatic pain in the Lisfranc joint were evaluated with a 3D-SPACE-sequence (0.5mm section thickness, acquisition time 10:22min, secondary reformations) and three orthogonal PD fs sequences (2mm section thickness, 9:20min). The Lisfranc-ligament, the dorsal and plantar tarsometatarsal ligaments (TMT), the dorsal, interosseous, and plantar intermetatarsal ligaments (IMT) (24 ligaments for each foot) were assessed. Results: In asymptomatic feet, 692 ligaments were detected with the SPACE sequence, thereof 90.6% exhibited normal signal, and most (96.9%) were completely visible on one single image. A total of 659 ligaments were detected with the PD fs sequence, thereof 86.6% yielded normal signal, and 28.5% were completely visible on one single image. In patients, 327 ligaments were detected with SPACE, thereof 50.6% appeared completely visible with high signal. On PD fs, 308 ligaments were detected, 42.2% of the ligaments had high signals. Conclusions: The ligaments of the Lisfranc joint are better detected with a single 3D-SPACE sequence and secondary reformations than with three orthogonal PD fs sequence

Compactness and convergence rates in the combinatorial integral approximation decomposition

The combinatorial integral approximation decomposition splits the optimization of a discrete-valued control into two steps: solving a continuous relaxation of the discrete control problem, and computing a discrete-valued approximation of the relaxed control. Different algorithms exist for the second step to construct piecewise constant discrete-valued approximants that are defined on given decompositions of the domain. It is known that the resulting discrete controls can be constructed such that they converge to a relaxed control in the weak* topology of L∞ if the grid constant of this decomposition is driven to zero. We exploit this insight to formulate a general approximation result for optimization problems, which feature discrete and distributed optimization variables, and which are governed by a compact control-to-state operator. We analyze the topology induced by the grid refinements and prove convergence rates of the control vectors for two problem classes. We use a reconstruction problem from signal processing to demonstrate both the applicability of the method outside the scope of differential equations, the predominant case in the literature, and the effectiveness of the approach

Formal Specification and Verification of JDK’s Identity Hash Map Implementation

Hash maps are a common and important data structure in efficient algorithm implementations. Despite their wide-spread use, real-world implementations are not regularly verified. In this paper, we present the first case study of the \IHM class in the Java JDK. We specified its behavior using the Java Modeling Language (JML) and proved correctness for the main insertion and lookup methods with \key, a semi-interactive theorem prover for JML-annotated Java programs. Furthermore, we report how unit testing and bounded model checking can be leveraged to find a suitable specification more quickly. We also investigated where the bottlenecks in the verification of hash maps lie for \key by comparing required automatic proof effort for different hash map implementations and draw conclusions for the choice of hash map implementations regarding their verifiability

Sideband pump-probe technique resolves nonlinear modulation response of PbS/CdS quantum dots on a silicon nitride waveguide

For possible applications of colloidal nanocrystals in optoelectronics and nanophotonics, it is of high interest to study their response at low excitation intensity with high repetition rates, as switching energies in the pJ/bit to sub-pJ/bit range are targeted. We develop a sensitive pump-probe method to study the carrier dynamics in colloidal PbS/CdS quantum dots deposited on a silicon nitride waveguide after excitation by laser pulses with an average energy of few pJ/pulse. We combine an amplitude modulation of the pump pulse with phase-sensitive heterodyne detection. This approach permits to use co-linearly propagating co-polarized pulses. The method allows resolving transmission changes of the order of 10(-5) and phase changes of arcseconds. We find a modulation on a sub-nanosecond time scale caused by Auger processes and biexciton decay in the quantum dots. With ground state lifetimes exceeding 1 mu s, these processes become important for possible realizations of opto-electronic switching and modulation based on colloidal quantum dots emitting in the telecommunication wavelength regime