A document by Jakob Svennevik Notland . Click on the document to view its contents.

Job-shop scheduling is an important but difficult problem arising in low-volume high-variety manufacturing. It is usually solved at the beginning of each shift with strict computational time requirements. To obtain near-optimal solutions with quantifiable quality within strict time limits, a direction is to formulate them in an Integer Linear Programming (ILP) form so as to take advantages of widely available ILP methods such as Branch-and-Cut (B&C). Nevertheless, computational requirements for ILP methods on existing ILP formulations are high. In this paper, a novel ILP formulation for minimizing total weighted tardiness is presented. The new formulation has much fewer decision variables and constraints, and is proven to be tighter as compared to our previous formulation. For fast resolution of large problems, our recent decomposition-and-coordination method “Surrogate Absolute-Value Lagrangian Relaxation” (SAVLR) is enhanced by using a 3-segment piecewise linear penalty function, which more accurately approximates a quadratic penalty function as compared to an absolute-value function. Testing results demonstrate that our new formulation drastically reduces the computational requirements of B&C as compared to our previous formulation. For large problems where B&C has difficulties, near-optimal solutions are efficiently obtained by using the enhanced SAVLR under the new formulation.

In this work, a novel three-phase grid inverter topology with split DC-link and LC-filter is discussed. A proportionalintegral (PI) state-feedback current and voltage controller is designed based on linear quadratic regulator (LQR) theory. The midpoint current ripple is suppressed by introducing the filter capacitor voltage as control target. Moreover, a 3-dimensional Space Vector Modulation (3D-SVM) for this topology is implemented, which allows for larger voltage amplitudes compared to conventional Pulse Width Modulation (PWM). Realistic simulation results show that the combination of PI state-feedback controller and 3D-SVM achieves faster and more accurate closedloop dynamics with less oscillations and better decoupling under varying and unbalanced grid conditions.

This paper introduces a method to generate a new efficient sparse convolutional module as a substitute for regular convolution layer.In contrast to previous group convolution modules, this module firstly expand the input features to avoid information loss.By a orthogonalization pruning algorithm, only the linearly independent convolution kernels are conserved.This novel orthogonalization pruning algorithm can significantly reduce the redundant information contained in the features by eliminating the kernels' relevance.The reconfigured and fine-tuned module has a competitive accuracy with less computation resources cost comparing to regular convolution.This approach could be implemented in any modern CNN model. The source codes are available at \url{https://github.com/wangjincheng722/OrthSCB

This work describes the hardware implementation of a connected component labelling (CCL) module in reprogammable logic. The main novelty of the design is the “full”, i.e. without any simplifications, support of a 4 pixel per clock format (4 ppc) and real-time processing of a 4K/UltraHD video stream (3840 x 2160 pixels) at 60 frames per second. To achieve this, a special labelling method was designed and a functionality that stops the input data stream in order to process pixel groups which require writing more than one merger into the equivalence table. The proposed module was verified in simulation and in hardware on the Xilinx Zynq Ultrascale+ MPSoC chip on the ZCU104 evaluation board.

In this paper, Matlab-Simulink and LabView models are constructed for a new nonlinear dynamic system of equations in an eight-dimensional (8D) phase space. For fixed parameters of the 8D dynamical system, the spectrum of Lyapunov exponents and the Kaplan-York dimension are calculated. The presence of two positive Lyapunov exponents demonstrates the hyperchaotic behavior of the 8D dynamical system. The fractional Kaplan-York dimension indicates the fractal structure of strange attractors. We have shown that an adaptive controller is used to stabilize the novel 8D chaotic system with unknown system parameters. An active control method is derived to achieve global chaotic synchronization of two identical novel 8D chaotic systems with unknown system parameters. Based on the results obtained in Matlab-Simulink and LabView models, a chaotic signal generator for the 8D chaotic system is implemented in the Multisim environment. The results of chaotic behavior simulation in the Multisim environment show similar behavior when comparing simulation results in Matlab-Simulink and LabView models.

A stochastic compact model for resistive switching devices is presented. The motivation is twofold: first, introducing variability in a natural way, and second, accounting for the discrete jumps of conductance observed during set and reset transitions. The model is based on an event generation rate, and it is an "on-the-fly" procedure because events are randomly generated as the simulation proceeds in time. For the generation of events, we assume a mixed non-homogeneous Poisson process. Before considering resistive switching, we deal with the generation of successive breakdown events in metalinsulator-semiconductor structures. This confirms the validity of the approach by comparing with experimental data in which discrete events are evident. To deal with resistive switching, we transform a previous compact model into a stochastic model. Comparison with experiments in TiN/Ti/HfO2/W devices show the validity of the approach. Current-voltage loops and potentiationdepression transients in pulsed experiments are captured with a single set of parameters. Moreover, the model is an adequate framework to deal with both cycle-to-cycle and device-to-device variability.

Federated learning (FL) has recently emerged as a novel technique for training shared machine learning models in a distributed fashion while preserving data privacy. However, the application of FL in wireless networks poses a unique challenge on the mobile users (MUs)’ battery lifetime. In this paper, we aim to apply reconfigurable intelligent surface (RIS)-aided wireless power transfer to facilitate sustainable FL-based wireless networks. Our objective is to minimize the total transmit power of participating MUs by jointly optimizing the transmission time, power control, and the RIS’s phase shifts. Numerical results demonstrate that the total transmit power is minimized while satisfying the requirements of both minimum harvested energy and transmission data rate.

We propose an energy-based framework to analyze the statics and dynamics of a ferroelectric negative capacitance-hybrid Microelectromechanical System (MEMS) actuator. A mapping function that relates the charge on the ferroelectric to displacement of the movable electrode, is used to obtain the Hamiltonian of the hybrid actuator in terms of displacement. We then use graphical energy-displacement and phase portrait plots to analyze static pull-in, dynamic pull-in and pull-out phenomena of the hybrid actuator. Using these, we illustrate the low-voltage operation of the hybrid actuator to static and step inputs, as compared to the standalone MEMS actuator. The results obtained are in agreement with the analytical predictions and numerical simulations. The proposed framework enables straightforward inclusion of adhesion between the contacting surfaces, modeled using van der Waals force. We show that the pull-in voltage is not affected, while the pull-out voltage is reduced due to adhesion. The proposed framework provides a physics-based tool to design and analyze negative capacitance based low-voltage MEMS actuators.

Small-scale ocean fronts play a significant role in absorbing the excess heat and CO2 generated by climate change, yet their dynamics are not well understood. Existing in-situ and remote sensing measurements of the ocean have inadequate spatial and temporal coverage to map small-scale ocean fronts globally. Additionally, conventional algorithms to generate ocean front maps are computationally intensive and require data with long lead times. We propose machine learning (ML) models to detect temperature and chlorophyll ocean fronts from unprocessed and radiometrically uncorrected satellite im- agery by transfer learning from existing models for edge detection. We use two separate datasets: one based on conventional approaches to ocean front detection, and a second based on human annotated ground truth1. The deep learning front detection approach significantly reduces the resources and overall lead times needed for detecting ocean fronts. The deep learning models are developed with resource-constrained edge compute platforms like CubeSats in mind, as such platforms can address the spatial and temporal coverage challenges. The highest performing models achieve accuracies of 96% and make predictions in milliseconds using unoptimized desktop CPUs and using less than 100 MB of storage; these capabilities are well- suited for CubeSat deployment.

This paper presents different methods used to allow a number of radar sensors mounted in a room to derive their relative spatial position as well as the floor plan of the room itself by relying on measurements of the distance of a person (walking around the perimeter of the room) from each sensor, at various time instants.

The increase in extreme weather events induced  by climate change, and their impact on power systems, has  created a need for tools that can assess and improve system resilience. To meet this need, the R&D team of the Energy  System Consulting segment of Tractebel Engineering GmbH has developed a novel tool called reXplan. ReXplan is a Python  library for resilient electrical system planning under extreme  hazard events, such as windstorms, earthquakes, floods,  wildfire, etc. It is designed to help power system operators and  planners make better-informed decisions to create more  resilient and secure power grids. This paper provides an overview of reXplan’s main features,  architecture, methodology of analysis and metrics. ReXplan is capable of modeling both spatiotemporal  extreme events and electrical power systems. It leverages technologies and techniques such as Julia/JuMP package and sequential Monte Carlo analysis with multivariate stratified  sampling to achieve high accuracy in the results while reducing  computational load. By quantifying resiliency metrics,  comparing different planning strategies, and validating technical solutions, reXplan can help reduce the risks of severe  outages in the grid. The software can be easily integrated into common data  science environments and is available as a Python library. For  computationally intensive tasks, such as optimal power flow,  reXplan is exploiting the fast speed of Julia programming  language, using PowerModels.jl as backend.

The increasing penetration of renewable resources into the power network through grid-following converters has increased the risks of oscillation instability. In such a power system, it is challenging for assessing the small-signal stability due to the complex interaction among converters interconnected through the power network. Moreover, the assessment complexity is further increased in a heterogeneous multi-converter system, where the interconnected converters have different control configurations or parameters from different manufacturers. To tackle the challenges, this paper proposes a method for the small-signal stability analysis of a heterogeneous multi-converter system. To this end, it is first theoretically proved that the small-signal stability of a heterogeneous system can be characterized by an equivalent homogeneous one, where all interconnected converters have the same control configurations and parameters. This can reduce the complexity of the small-signal stability assessment of the original heterogeneous system by decoupling the equivalent homogeneous system into a set of subsystems. To further reduce the assessment complexity, it is derived that the small-signal stability and stability margin of the heterogeneous system can be estimated based on the smallest eigenvalue of a weighted Laplacian matrix of the power network. Based on the analysis results, a scalable method is developed for assessing the small-signal stability of heterogeneous multi-converter systems. The efficacy of the proposed method is validated by performing both modal analysis and time-domain simulations on two heterogeneous multiple-converter systems with different network sizes and converter numbers.

The Radio Access Network (RAN) architecture evolves with different generations of mobile communication technologies and forms an indispensable component of the mobile network architecture. The main component of the RAN infrastructure is the base station, which includes a Radio Frequency unit and a baseband unit. The RAN is a collection of base stations connected to the core network to provide coverage through one or more radio access technologies. The advancement towards cloudnative networks has led to centralizing the baseband processing of radio signals. There is a trade-off between the advantages of RAN centralization (energy efficiency, power cost reduction, and the cost of the fronthaul) and the complexity of carrying traffic between the data processing unit and distributed antennas. 5G networks hold high potential for adopting the centralized architecture to reduce maintenance costs while reducing deployment costs and improving resilience, reliability, and coordination. Incorporating the concept of virtualization and centralized RAN architecture enables to meet the overall requirements for both the customer and Mobile Network Operator. Functional splitting is one of the key enablers for 5G networks. It supports Centralized RAN, virtualized Radio Access Network, and the recent Open Radio Access Networks. This survey provides a comprehensive tutorial on the paradigms of the RAN architecture evolution, its key features, and implementation challenges. It provides a thorough review of the 3rd Generation Partnership Project functional splitting complemented by associated challenges and potential solutions. The survey also presents an overview of the fronthaul and its requirements and possible solutions for implementation, algorithms, and required tools whilst providinga vision of the evaluation beyond 5G second phase.

Manuscript submitted to the IEEE Transations in Power Systems in October 2022. Title: Coordination of Frequency Reserves in an Isolated Industrial Grid Equipped with Energy Storage and Dominated by Constant Power Loads Abstract: This paper examines the use of interconnected synchronous system requirements for frequency containment reserves (FCR) on isolated industrial grids that use turbogenerators as main source of energy, have high penetration of wind energy, are equipped with energy storage, and have a high level of constant power loads coupled by power electronic converters. Leveraging on the recent Nordic requirements for reserves in islanded operation (FCRI), we propose an expansion that allows prioritizing among various reserve providers under different isolated grid conditions. The study case of a complex, isolated industrial grid is selected to test this approach. The stability of this grid is evaluated via eigenvalues and participation factors considering the detrimental effects of constant power loads. It is demonstrated that, by prioritizing the reserve allocation to the faster converter-interfaced storage devices and loads, the overall stability is increased in addition to allowing the turbogenerators to operate at a more constant load. The results are supported by computer simulations of a rotating mass model, of the complex isolated grid in PowerFactory, and by laboratory power-hardware-in-the-loop tests. The computer simulation models developed for this paper are made publicly available for reproducibility purposes.

Swarm robotics is a very emerging field of research that studies the collective behaviour of multi-robot systems. It is based on applying various methodologies to a group of several robots to perform tasks unreachable to individual robots. Due to the flexibility of Swarm Intelligence algorithms, Optimization techniques are suitable to be implemented in robotic swarms to deal with real-world complex multi-disciplinary problems. This paper explores various swarm optimization algorithms for robotic swarms like PSO, GPSO, CSO, ACO and GSO. An in-depth survey is presented for all the said algorithms and the projected future of swarm optimization is discussed

Human Activity Recognition (HAR) an important area of research in the light of enormous applications that it provides, such as health monitoring, sports, entertainment, efficient human computer interface, child care, education and many more. Use of Computer Vision for Human Activity Recognition has many limitations. The use of inertial sensors which include accelerometer and gyroscopic sensors for HAR is becoming the norm these days considering their benefits over traditional Computer Vision techniques. In this paper we have proposed a 1-dimensional Convolutions Neural Network which is inspired by two state-of-the art architectures proposed for image classifications; namely Inception Net and Dense Net. We have evaluated its performance on two different publicly available datasets for HAR. Precision, Recall, F1-measure and accuracies are reported.

Active second-harmonic (2ω) filters have become an integral technology for single-phase power converters in high power density designs. The series-stacked buffer (SSB) has emerged as an attractive topology among the existing solutions due to its low power, high efficiency, and compact design. However, one of the challenges in adopting SSB lies in its control, where conventionally hysteresis current control has been adopted. This results in a wide variation in the switching frequency, making the digital control implementation and filter design complex. On the other hand, fixed-frequency pulse-width modulation (PWM) control necessitates developing a model for the systematic controller design to ensure stability and desired filtering performance. The assumption of SSB modelled as a second-harmonic current source, independent of the dc bus circuit components, fails to capture the dc bus loading effect on the SSB. In this work, a dynamic model of SSB is eveloped where the main dc bus and its passive elements are taken into account. The derived model gives a systematic procedure for the controller design while ensuring the desired 2ω filtering. The stability limits and parameter sensitivities of the model are studied analytically and in simulations. The analytical model is validated, and the proposed controller performance and stability limit are verified experimentally on a hardware prototype.