Identification of gas bubble, void detection and porosity estimation are important factors in many liquid metal processes. In steel casting, the importance of flow condition and phase distribution in crucial parts, such as submerged entry nozzle (SEN) and mould raises the needs to observe the phenomena. Cross-section of flow shapes can be visualised using the magnetic induction tomography (MIT) technique. However, the inversion procedure in the image reconstruction has either limited resolution or complex computation degrading its real-time capability. Additionally, in some cases, the actual image may not be essential whereas the void fraction or porosity needs to be estimated. This work proposes an interior void classifier based on multi-frequency mutual induction measurements with eutectic alloy GaInSn as a cold liquid metal model contained in a 3D printed plastic miniature of an SEN. The sensors consist of eight coils arranged in a circle encapsulating the column, providing combinatorial detection on conductive surface and depth. The datasets are induced voltage collections of several non-metallic inclusions (NMI) patterns in liquid metal static test and used to train a machine learning model. The model architectures are a fully connected neural network (FCNN) for 1D; and a convolutional neural network (CNN) for 2D data. The classifier using 1D data has been trained to approximately 86% accuracy on this dataset. CNN classification using multi-dimensional data with more classes produces 96% of test accuracy. Refined with representative flow scenarios, the trained model could be deployed for an intelligent online control system of the liquid metal process.

We present the concept of power HotSpots wherein the bigger satellites in low earth orbits (LEO), having power generation capacity much larger than CubeSats, can transfer their excess energy to CubeSats in need, using optical wireless technology. This provides a business opportunity for larger enterprises having the capability of launching bigger satellites to sell their power to CubeSats. As a proof of concept, this article presents a basic simulation regarding optical wireless power transfer (OWPT) to CubeSats. In addition, we highlight future research challenges in this area to maximize OWPT.

DevOps, a prevalent terminology in organizations today, has evolved as a paradigm that once was SDLC. From the mammoth concept of the “WaterFall Model” to the more recent Agile Framework and the emerging DevOps methodology, the Software Development Life Cycle saw a huge business win in “Time to market”, over several other advantages like risk aversion, flexibility, requirement readjustments, collaboration etc. to name a few. This whitepaper elaborates on the various salient features of DevOps that shapes the way software releases are made today. DevOps is most suited to Cloud and this paper explains the way the two technologies synergize. Service Oriented Architecture is an architectural style that is essential in simplifying the business and this paper explains how DevOps facilitates creation of granular services that ultimately helps in attaining flexibility and agility of development and operations in Cloud.

This paper proposes a novel control design method for high-precision positioning systems. The method aims to eliminate the tracking error caused by measurement quantization present in positioning systems with optical encoders. By employing a combined internal model based feedback and quantized feedforward design, we are able to make the output of the positioning system asymptotically track any input signal with one or more sinusoidal components of known frequencies and a possible constant component. When combined with a micro actuator, the resulting dual-stage positioning system is able to track any continuous periodic signal with a known period. Besides theoretical guarantees, the proposed design is validated experimentally and proved able to achieve asymptotic tracking error below ?1 ?m when subject to a sensor quantization level of 5 ?m.

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.

The exponential stabilization of eigenstates by using switching state feedback strategy for quantum spin-$\frac{1}{2}$ systems is considered in this paper. In order to obtain faster state exponential convergence, we divide the state space into two subspaces, and use two different continuous state feedback controls in the corresponding subspace. The two continuous state feedback controls form the switching state feedback, under which the state convergence is faster than that under continuous state feedback. The exponential convergence and the superiority of switching state feedback are proved in theory and verified in numerical simulations. Besides, the influence of the control parameter on the state convergence rate is also studied.

Formal verification provides strong guarantees of correctness of software, which are especially important in safety or security critical systems. Hoare logic is a widely used formalism for rigorous verification of software against specifications in the form of pre-condition/post-condition assertions. The advancement of semantic parsing techniques and higher computational capabilities enable us to extract semantic content from natural language text as formal logical forms, with increasing accuracy and coverage. This paper proposes a formal framework for Hoare logic-based formal verification of imperative programs using logical forms generated from compositional semantic parsing of natural language assertions. We call our reasoning approach Natural Hoare Logic. This enables formal verification of software directly against safety requirements specified by a domain expert in natural language. We consider both declarative assertions of program invariants and state change as well as imperative assertions that specify commands which alter the program state. We discuss how the reasoning approach can be extended using domain knowledge and a practical approach for guarding against semantic parser errors.

Manuscript as submitted for peer review and publication in IEEE Transactions on Plasma Science in March 2021. The paper describes the behavior of C5-fluoroketone and C4-fluoronitrile-based gas mixtures under internal-arc-like-conditions in high voltage gas insulated switchgear.

This paper describes Tiresias, a low-cost, unobtrusive networked radar system designed to monitor vulnerable patients in domestic environments and provide high quality behavioural and health data. Dementia is a disease that affects millions worldwide and progressively degrades an individual’s ability to care for themselves. Eventually most people living with dementia will need to reside in assisted living facilities as they become unable to care for themselves. Understanding the effects dementia has on ability to self-care and extending the length of time people living with dementia can remain living independently are key goals of dementia research and care. The networked radar system proposed in this paper is designed to provide high quality behavioural and health data from domestic environments. This is achieved using multiple radar sensors networked together with their data outputs integrated and processed to produce high confidence measures of position and movement. It is hoped the data produced by this system will both provide insights into how dementia progresses, and also help monitor vulnerable individuals in their own homes, allowing them to remain independent longer than would otherwise be possible.

We have reported a high-performance dual gated carbon nanotube enzyme modified field-effect transistor (CNT-ENFET) for cholesterol detection. To improve the device performance, we have used dual-gate design with high κ dielectric as top gate and low κ dielectric as bottom gate and a nanocomposite of potassium doped carbon nanotube with polypyrrole (K/PPy/CNT) as the biosensing membrane. The device exhibited a good sensitivity (~1.0 V/decade), low response time (~1 s), wide dynamic range (0.1 - 25) mM, wide linear range (2-20 mM), low detection limit (0.11 mM), good stability (7 months) and high selectivity (interference ~1.8 %).

In order to analytically solve the optimal feed- forward torque control (OFTC) problem of induction machines (IMs), the unified theory for synchronous machine introduced in [1] is extended by considering relevant IM nonlinearities and incorporating stator and rotor copper losses. Instead of the well known Maximum Torque per (stator) Current (MTPC) operation strategy, Maximum Torque per (copper) Losses (MTPL Cu ) is realized and extended by the Maximum (rotor) Current (MC r, ext ) strategy due to stator and rotor current limitations. Modeling magnetic saturation and cross-coupling effects leads to a con- strained nonlinear optimization problem which is solved based on the idea of sequential quadratic programming (SQP). The second order Taylor approximations are formulated in implicit form as quadrics. Applying the Lagrangian formalism to the quadratic problem leads to analytical solution for the optimal rotor currents. For a doubly-fed induction machine (DFIM), a decision tree for optimal operation management is presented and the OFTC is validated in simulations for a real nonlinear IM.

As deep neural networks (DNNs) continue their reach into a wide range of application domains, the neural network architecture of DNN models becomes an increasingly sensitive subject, due to either intellectual property protection or risks of adversarial attacks. In observing the large gap between the architectural surfaces exploration and the model integrity study, this paper first presents the formulated schema of the model leakage risks. Then, we propose DeepSniffer, a learning-based model extraction framework, to obtain the complete model architecture information without any prior knowledge of the victim model. It is robust to architectural and system noises introduced by the complex memory hierarchy and diverse run-time system optimizations. Taking GPU platforms as a showcase, DeepSniffer performs model extraction by learning both the architecture-level execution features of kernels and the inter-layer temporal association information introduced by the common practice of DNN design. We demonstrate that DeepSniffer works experimentally in the context of an off-the-shelf Nvidia GPU platform running a variety of DNN models. The extracted models are directly helpful to the attempting of crafting adversarial inputs. The DeepSniffer project has been released in https://github.com/xinghu7788/DeepSniffer.

We report ultrahigh bandwidth applications of Kerr microcombs to optical neural networks and to optical data transmission, at data rates from 44 Terabits/s (Tb/s) to approaching 100 Tb/s. Convolutional neural networks (CNNs) are a powerful category of artificial neural networks that can extract the hierarchical features of raw data to greatly reduce the network complexity and enhance the accuracy for machine learning tasks such as computer vision, speech recognition, playing board games and medical diagnosis [1-7].

This is a preprint of an article published in the Science of Computer Programming. The final peer-reviewed publication is available online at: https://doi.org/10.1016/j.scico.2023.102999 Code smells are structures in code that indicate the presence of maintainability issues. A significant problem with code smells is their ambiguity. They are challenging to define, and software engineers have a different understanding of what a code smell is and which code suffers from code smells. A solution to this problem could be an AI digital assistant that understands code smells and can detect (and perhaps resolve) them. However, it is challenging to develop such an assistant as there are few usable datasets of code smells on which to train and evaluate it. Furthermore, the existing datasets suffer from issues that mostly arise from an unsystematic approach used for their construction. Through this work, we address this issue by developing a procedure for the systematic manual annotation of code smells. We use this procedure to build a dataset of code smells. During this process, we refine the procedure and identify recommendations and pitfalls for its use. The primary contribution is the proposed annotation model and procedure and the annotators’ experience report. The dataset and supporting tool are secondary contributions of our study. Notably, our dataset includes open-source projects written in the C# programming language, while almost all manually annotated datasets contain projects written in Java.

We propose nonlinear beamforming for phased array weather radars (PAWRs). Conventional beamforming is linear in the sense that a backscattered signal arriving from each elevation is reconstructed by a weighted sum of received signals, which can be seen as a linear transform for the received signals. For distributed targets such as raindrops, however, the number of scatterers is significantly large, differently from the case of point targets that are standard targets in array signal processing. Thus, the spatial resolution of the conventional linear beamforming is limited. To improve the spatial resolution, we exploit two characteristics of a periodogram of each backscattered signal from the distributed targets. The periodogram is a series of the powers of the discrete Fourier transform (DFT) coefficients of each backscattered signal and utilized as a nonparametric estimate of the power spectral density. Since each power spectral density is proportional to the Doppler frequency distribution, (i) major components of the periodogram are concentrated in the vicinity of the mean Doppler frequency, and (ii) frequency indices of the major components are similar between adjacent elevations. These are expressed as group-sparsities of the DFT coefficient matrix of the backscattered signals, and we propose to reconstruct the signals through convex optimization exploiting the group-sparsities. We consider two optimization problems. One problem roughly evaluates the group-sparsities and is relatively easy to solve. The other evaluates the group-sparsities more accurately, but requires more time to solve. Both problems are solved with the alternating direction method of multipliers including nonlinear mappings. Simulations using synthetic and real-world PAWR data show that the proposed method dramatically improves the spatial resolution.

This paper presents a technique for designing controllers for rational plants using Laguerre functions. The inputs to the process are the a) the plant transfer function P(s), b) the target transfer function T(s) and c) the desired order of the controller. Future work will extend this process so it is applicable to 1) irrational continuous time plants and 2) to irrational discrete time plants.

Efficient integration of variable renewable energy (VRE) such as wind power into power systems requires methods for power system operation planning that account for VRE uncertainty and variability. This has motivated extensive research into unit commitment (UC) and optimal power flow (OPF) formulations with VRE uncertainty. However, these formulations are often tested using significantly simplified representations of VRE production. We seek to address this issue by providing a model for generating realistic wind power scenarios using real production and forecast data. The scenarios are generated using 5-min production and 30-min forecast data for real wind farms from Australia. The model captures the empirical distribution of the forecast errors and the covariance between different wind farms. The high time resolution of the production data also allows the recreation of the high-frequency (5-min) component of wind power production. The resulting model is openly available, and can be used to generate wind power scenarios for use in formulations for operation planning of power systems (UC/OPF) considering wind uncertainty and intra-hour variability. The scenarios can be tailored according to preferences for, e.g., the number of wind farms and their geographical dispersion.

In this work, we propose a novel extended target tracking algorithm, which is capable of representing a target or a group of targets with multiple ellipses. Each ellipse is modeled by an unknown symmetric positive-definite random matrix. The proposed model requires solving two challenging problems. First, the data association problem between the measurements and the sub-objects. Second, the inference problem that involves non-conjugate priors and likelihoods which needs to be solved within the recursive filtering framework. We utilize the variational Bayes inference method to solve the association problem and to approximate the intractable true posterior. The performance of the proposed solution is demonstrated in simulations and real-data experiments. The results show that our method outperforms the state-of-the-art methods in accuracy with lower computational complexity.