Miracle-dz/UniFAR-FTUs huggingface dataset:
Model Predictive Control of Current Source Inverter for PMSM Drive

The main content of this paper is about the current source inverter device of permanent magnet synchronous motor (PMSM). Based on the method of finite-control-set model predictive control (FCS-MPC), a control strategy is proposed that can realize the torque and speed control of three-phase PMSM as long as the output capacitor voltage is controlled. Compared with the traditional control method, this method omits the adjustment of the weight coefficient and has better dynamic performance. In addition, the capacitor voltage control also has a good inhibitory effect on the resonance generated by the filter capacitor and the motor inductance. In this paper, the proposed control strategy is effectively verified by the simulation model.
Challenges of Testing an Evolving Cancer Registration Support System in Practice

The Cancer Registry of Norway (CRN) is a public body responsible for capturing and curating cancer patient data histories to provide a unified access to research data and statistics for doctors, patients, and policymakers. For this purpose, CRN develops and operates a complex, constantly-evolving, and socio-technical software system. Recently, machine learning (ML) algorithms have been introduced into this system to augment the manual decisions made by humans with automated decision support from learned models. To ensure that the system is correct and robust and cancer patients' data are properly handled and do not violate privacy concerns, automated testing solutions are being developed. In this paper, we share the challenges that we identified when developing automated testing solutions at CRN. Such testing potentially impacts the quality of cancer data for years to come, which is also used by the system's stakeholders to make critical decisions. The challenges identified are not specific to CRN but are also valid in the context of other healthcare registries. We also provide some details on initial solutions that we are investigating to solve the identified challenges.
DocMine: A Software Documentation-Related Dataset of 950 GitHub Repositories

Software documentation is one of the critical aspects of a software project, that could support multiple tasks throughout the software development life-cycle. There is extensive research on understanding issues and challenges with existing documentation, which is typically available as readme files. In projects that support collaborative development, such as those on GitHub, other software artifacts such as commits, pull requests and issues, apart from the conventional readme files, wikis and source code comments, also contain useful information, that supports in understanding, using, extending and maintaining the project. However, we are not aware of any dataset that explicitly focuses on documentation-related information in multiple software artifacts such as readme files, commits and pull requests across a repository. To address this concern and to facilitate further research in software documentation, we present DocMine, as a dataset of documentation-related information, extracted from around 1.35M software artifacts in 950 GitHub repositories, spanning across four different programming languages. The dataset along with its documentation is made available in CSV and . sql formats at - https://doi.org/10.5281/zenodo.5195084.
An Improved Model-Free Predictive Power Control for Three-phase Grid-connected Inverter Based on Hybrid SVM Under Unbalanced Grids

Model based predictive power control based on space vector modulation (SVM-MBPPC) is a popular control method for three-phase grid-connected inverter with the advantage of quick response and excellent performance. However, the excellent control performance of the conventional SVMMBPPC mainly depends on the accuracy of system parameters. To overcome the poor robustness of SVM-MBPPC, some scholars propose model-free predictive power control based on SVM (SVM-MFPPC). In spite of the strong robustness of the SVMMFPPC, the steady-state performance of conventional SVMMBPPC with accurate parameters is greater than the SVMMFPPC. This paper proposes an improved MFPPC based on hybrid SVM (HSVM-MFPPC), which has equal robustness to conventional SVM-MFPPC and achieves better steady-state performance. The proposed HSVM-MFPPC is also extended to unbalanced grid condition by adding an compensation power value. The simulated results confirm that the proposed HSVMMFPPC inherits strong robustness and can reduce the current total harmonic distortion (THD) by 30.6% than conventional SVM-MFPPC when the average modulation index is high enough.
Test Scenario Generation for Autonomous Driving Systems with Reinforcement Learning

We have seen rapid development of autonomous driving systems (ADSs) in recent years. These systems place high requirements on safety and reliability for their mass adoption, and ADS testing is one of the crucial approaches to ensure the success of ADSs. To this end, this paper presents RLTester, a novel ADS testing approach, which adopts reinforcement learning (RL) to learn critical environment configurations (i.e., test scenarios) of the operating environment of ADSs that could reveal their unsafe behaviors. To generate diverse and critical test scenarios, we defined 142 environment configuration actions, and adopted the Time- To-Collision metric to construct the reward function. Our evaluation shows that RLTester discovered a total of 256 collisions, of which 192 are unique collisions, and took on average 11.59 seconds for each collision. Further, RLTester is effective in generating more diverse test scenarios compared to a state-of-the art approach, DeepCollision.
Wave propagation in channels and cracks with elastic walls

In this paper wave propagation in the uniform and nonuniform elastic channels filled with fluid or gas is investigated. The weak discontinuities theory approach is proposed to determine the criteria of gradient catastrophe phenomena to occur for the cases of uniform and nonuniform channels. The results of the paper can be applied to studying of the nonlinear wave propagation phenomena in elastic pores, cracks and channels filled with fluid or gas.
Staggered Grid for Maxwell’s Equations in 3-D Anisotropic Media

The standard staggered grid for Maxwell’s equations is awkward for anisotropic media because the different components of the electric and magnetic fields are located at different nodes. There is, however, a natural alternative that places all components of the electric field at each node of one grid and all components of the magnetic field at each node of a staggered grid. This staggering allows a conservative finite-difference approximation for Maxwell’s equations with arbitrary 3-D tensor electrical conductivity, magnetic permeability, and dielectric permittivity. An example of the time-domain solution using spectral Lanczos decomposition is considered.
Model Free Predictive Current Control for Voltage Source Inverter using Luenberger Observer

The model predictive control (MPC) methods for voltage source inverter (VSI) shows highly dependence on system parameters, and the performance degrades dramatically when the parameter mismatch happens. In contrast, model free predictive current control (MFPCC) can solve this problem with only the information of the measurement data. However, since the form of the conventional MFPCC for VSI consisting current difference term calculated by two adjacent sampling periods, the noise suppression ability of the conventional MFPCC method is incompetent. Therefore, this article proposes an improved MFPCC method, where an ultra-local model of VSI is established to replace the conventional MPC methods’ mathematical model, and a Luenberger observer is adopted to estimate the disturbance term in ultra-local model to improve the regulation performance. In this way, the current difference term in traditional MFPCC can be avoided. Therefore, the proposed MFPCC method shows superior noise suppression performance capability compared to the conventional methods. Finally, the effectiveness of the proposed MFPCC method is verified through a series of simulation results.
HasBugs - Handpicked Haskell Bugs

We present HasBugs, an extensible and manually-curated dataset of real-world 25 Haskell Bugs from 6 open source repositories. We provide a faulty, tested, and fixed version of each bug in our dataset with reproduction packages, description, and bug context. For technical users, the dataset is meant to either help researchers adapt techniques from other programming languages to Haskell or to provide a human-verified gold standard for tools evaluation and enable future reproducibility. We also see applicability for qualitative research, e.g., by analysis of bug lifecycles and comparison to other languages. We provide a companion website for easy access and overview under https://ciselab.github. io/HasBugs/.
Multi-Target Tracking Resources Allocation Using Multi-Agent Modeling and Auction Algorithm

This paper proposes a multi-agent technique for modeling and simulating multiple objects and multiple sensor tracking for drones. The goal is to achieve efficient resource allocation based on different preset cost functions that take into account several local and global attributes. The target dynamics, the sensor selection, and the measurements are simulated by way of an agent-based modeling framework called MESA. The results show an optimum resource allocation with an information-based cost function that is optimized by an Auction Algorithm.
Ultrashort Baseline Underwater Acoustic Positioning System Based on Timestamp

The existing conventional ultra-short baseline system uses half of the propagation time delay as the processing parameter for positioning solution, but the hydroacoustic transponder will move a certain distance when interconnecting with the underwater operation device and the hull where the ultra-short baseline base array is located, which will have some influence on the positioning of the ultra-short baseline system. In order to reduce the positioning error of the ultrashort baseline system, this paper adopts broadband spread spectrum signals and modulated signals carrying time stamps for hydroacoustic positioning, and adopts the method of obtaining time stamp information and combining with atomic clock chips to obtain time information when receiving and transmitting acoustic signals to estimate the slant distance between the underwater operating unit and the water platform and estimate the one-sided propagation time delay, so that the water platform can obtain more accurate data, which has been It is proved that this method can reduce the hydroacoustic positioning error.
Model-free Optimal Containment of Heterogeneous Multiagent Systems

This paper discussed optimal containment problem of heterogeneous multiagent systems using reinforcement learning technique, in which the leaders’ controls are nonzero. Distributed adaptive observers are presented to estimate the leaders’ dynamics and construct the state trajectories insider the convex hull of the leaders. To achieve the system’s optimality, we define a discounted cost function for each follower. We develop a reinforcement learning algorithm to address the ARE. Then we give a simulation to verify the effectiveness of the algorithm.
Regularity of global attractors for reaction-diffusion systems with no more than quadratic growth

We consider reaction-diffusion systems in a three-dimensional bounded domain under standard dissipativity conditions and quadratic growth conditions. No smoothness or monotonicity conditions are assumed. We prove that every weak solution is regular and use this fact to show that the global attractor of the corresponding multi-valued semiflow is compact in the space \begin{document}$(H_{0}^{1} (Ω))^{N}$\end{document} .
UWB Radar-Based Pet Monitoring on Daily Basis in an Unconstrained Living Environment

Pets in the developed countries, specifically dogs are dealt with special care. Increase in the pet market has led to several smart solutions for the dog vital signs and movement measurement. Amongst these solutions, radar sensors provide a non-contact and non-invasive vital sign measurement setup. Unlike the existing studies, we propose a strategy to measure dogs vital sign in clinical as well as home-environment on daily basis. Particularly for an unconstrained home environment case, we propose a strategy to measure vital signs along with two other parameters which are presence and movement. Daily monitoring for a long duration of time can be used quantify the health and wellbeing of pets. In the clinical environment mean absolute error of 3.70 between radar and references sensors is achieved for HR measurement.
Travel Route Planning in Smart Cities

Travel route planning techniques for motor and electric vehicles are designed to find the optimal path from the start to the destination point on a roadmap. As traffic conditions can change very quickly during the journey, it is necessary to immediately update all changes in real time. For example, traffic congestion may occur in certain parts of the route due to an increased number of vehicles. The shortest path generally may not be in all cases the best path, as many conditions can affect the length of the journey. If we want to make our cities smart, the transportation system also has to be smart. In this paper, a practically implemented system for vehicle route planning in urban areas is presented, which works in real time and takes a large number of parameters of travel into account. Therefore, this system provides an estimation of travel time that is much more accurate than the standard auto router. In this paper, this estimation of travel time is practically proven.
Stimuli Generation for IC Design Verification using Reinforcement Learning with an Actor-Critic Model

With Integrated Circuit (IC) designs becoming larger and more complex, there is a growing risk of errors in the Register-Transfer Layer (RTL) implementation. Stimuli generation to achieve high coverage in functional verification is paramount for finding these errors and preventing them from ending up in the final design. Several custom methods have been proposed for stimuli generation to reduce functional testing duration of RTL designs, while more flexible or generic methods could reduce verification time significantly by supporting larger range of RTL designs. This paper proposes a novel flexible stimuli generation technique by using reinforcement learning with an Actor-Critic model. Our benchmarking results showed that the proposed method achieves a higher coverage than baseline solution for a diverse range of RTL designs, making it a valuable addition to test automation tool-flow.
Relating fuzzy autoepistemic logic and Łukasiewicz KD45 modal logic

In this paper, wc propose an axiomatization for a Kripke-swle possible world semantics relatcd to the fuzzy autoepistemic logic defined in [l], and we also provide an equivalent algebraic semantics. Autoepistemic logic is a formalism of noninonotonic reasoning that was originally intended to modcl an ideally rational agent reflecting upon his own beliefs [7]. Thcse beliefs are sets of sentences in a propositional language augmented by a modal operator U. If p is a formula, then Op, which has to bc intcrpreted as "p is believed", is a formula as wcll. Hcnce, in this language nested modal operators are allowed; it is possible to have beliefs ahout beliefs. Let A be any set of formulas in this language. In [7], a stable expansion of A is defined as a set of forinulas T such that the following fix-poiiit condition holds:
Advanced contacts on 3D nanostructured channels for vertical transport gate-all-around transistors

Gate-all-around (GAA) transistors are anticipated to have a substantial impact in achieving logic scaling in the nanometer technology node range, serving as a substitute for the current FinFET technology which lacks acceptable immunity against short channel effect at such miniaturization. GAA transistors offer several advantages over older transistor designs, such as better performance, lower leakage, and reduced energy consumption. This makes them a more sustainable and environmentally friendly alternative to current architectures. In term of integration, vertical gate-all-around devices offer extreme density capability, surpassing equivalent planar technologies. Nevertheless, the development of vertical technology requires rethinking the entire development chain from design to technology. Here, we will present an overview of this technology with a particular focus on the engineering of 3D nanostructured channels and on the integration of S/D contacts on such vertical channels.
An Efficient Dynamic Sequential Predictive Control of Module Multilevel Power Converters

Modular multilevel converters (MMC) have been widely used in high voltage direct current transmission. Model predictive control (MPC) has become a promising control alternative for MMC due to its good dynamic performance and multi-objective control ability. However, classical MPC requires traversing all switch vectors and causes huge computational burden. Therefore, the sequential optimization MPC is adopted to reduce the computational burden. But it has poor dynamic performance and control effect on suboptimal targets. This paper proposes a new efficient dynamic sequential MPC (DSMPC) method for MMCs. Two cost functions are designed forming a sequential structure, to achieve current tracking and circulating current suppression, respectively. The number of candidate combinations is automatically adjusted based on the operation mode, to ensure dynamic performance during transient state while alleviating the computational burden during steady state operation. The increased number of candidate combinations is related to the degree of control targets, which improves the effect of secondary control targets. Simulation and experimental results confirm that the proposed method reduces computational burden while maintaining superior steady and dynamic state performance.
Full Predictive Control of Permanent Magnet Synchronous Machine Based on Differential-Free Disturbance Position-Torque Observer and Online Parameter Identification

This paper proposes a novel fully predictive control (FPC) for permanent magnet synchronous machine (PMSM), which adopts the techniques of online parameter identification and position sensorless. The FPC comprises a speed outer loop and a current inner loop. Online parameter identification with recursive least squares (RLS-OPI) is used to get PMSM’s resistance, magnetic linkage and inductance. Position sensorless with a derivative-free disturbance observer (DFDO) is used to obtain the speed and torque. The simulation and experimental results show that the proposed control algorithm has a faster response and better dynamic performance in different temperatures.
DaMAT: A Data-driven Mutation Analysis Tool

We present DaMAT, a tool that implements data-driven mutation analysis. In contrast to traditional code-driven mutation analysis tools it mutates (i.e., modifies) the data ex-changed by components instead of the source of the software under test. Such an approach helps ensure that test suites appropriately exercise components interoperability - essential for safety-critical cyber-physical systems. A user-provided fault model drives the mutation process. We have successfully evalu-ated DaMAT on software controlling a microsatellite and a set of libraries used in deployed CubeSats. A demo video of DaMAT is available at https://youtu.be/sSMS2xWCj84
Multi-Step Predictive Control Based on Ant Colony Optimization for Offshore Wind Turbine System

Multi-step predictive control will significantly reduce the switching frequency of a power conversion system at the same current quality, which is suitable for high power offshore wind turbine systems. However, the computational burden increases exponentially with the prediction horizon, making it difficult to apply in practical. In order to solve the above problems, a multi-step predictive control method based on ant colony optimization is proposed. The proposed method maintains good control performance and reduces the computational burden by 90% compared to traditional enumeration strategies. The innovations of the proposed method are: An ant colony optimization framework for back-to-back converter PMSG wind turbine systems with multi-step MPC is constructed, and a pheromone update mechanism is designed to fit the system. The relevant parameters design is also introduced.
Two-Stage Shrinkage Behavior of Multielement-Molecular-Ion-Implantation-Induced Dislocation Loops Revealed by Real-Time TEM Observation

Complementary metal-oxide-semiconductor image sensors (CISs) have important functions in sensing devices in automotive, infrastructural, industrial, and medical applications. One of the most serious technical issues for complementary metal-oxide-semiconductor image sensors (CISs) is dark current originating from pixels. The representative origins of dark current are metal and oxygen impurities, and silicon dioxide (SiO$_{2})/$silicon (Si) interface state defects. Therefore, it is indispensable for high-performance CISs to develop technologies for removal of metal and oxygen impurities from pixels, and reduction of SiO$_{2}/$Si interface state defect density. Recently, we have developed hydrocarbon-molecular-ion $(C_{3} H_{5}$ ion)-implanted epitaxial silicon (Si) wafer as a functional wafer for CISs [1]. In $C_{3} H_{5} -$ion-implanted epitaxial Si wafer, C-interstitial Si precipitates act as gettering sinks [1]. $C_{3} H_{5} -$ion-implanted epitaxial Si wafer can reduce dark current in typical CISs with four-transistor-type pinned photodiode [1] owing to two attractive features of C-interstitial Si precipitates; one is the gettering of metal and oxygen (O) impurities [2], [3], and the another is the trapping of hydrogen (H) [4] and the reduction in SiO$_{2}/$Si interface state defect density by re-release of once-trapped hydrogen [5].
A Resilience Framework for Synapse Weight Errors and Firing Threshold Perturbations in RRAM Spiking Neural Networks

Spiking Neural Networks (SNNs) can be implemented with power-efficient digital as well as analog circuitry. However, in Resistive RAM (RRAM) based SNN accelerators, synapse weights programmed into the crossbar can differ from their ideal values due to defects and programming errors, degrading inference accuracy. In addition, circuit nonidealities within analog spiking neurons that alter the neuron spiking rate (modeled by variations in neuron firing threshold) can degrade SNN inference accuracy when the value of inference time steps (ITSteps) of SNN is set to a critical minimum that maximizes network throughput. We first develop a recursive linearized check to detect synapse weight errors with high sensitivity. This triggers a correction methodology which sets out-of-range synapse values to zero. For correcting the effects of firing threshold variations, we develop a test methodology that calibrates the extent of such variations. This is then used to proportionally increase inference time steps during inference for chips with higher variation. Experiments on a variety of SNNs prove the viability of the proposed resilience methods.
Dependability of Future Edge-AI Processors: Pandora’s Box

This paper addresses one of the directions of the HORIZON EU CONVOLVE project being dependability of smart edge processors based on computation-in-memory and emerging memristor devices such as RRAM. It discusses how how this alternative computing paradigm will change the way we used to do manufacturing test. In addition, it describes how these emerging devices inherently suffering from many non-idealities are calling for new solutions in order to ensure accurate and reliable edge computing. Moreover, the paper also covers the security aspects for future edge processors and shows the challenges and the future directions.