Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluation of Different Antenna Positions for Joint Radar-Communication at 77 GHz

The development of millimeter wave systems is driven by the strong trend towards the new communication generations and especially by the emerging joint radar and communication design approach. Safety critical applications like platooning or intersection assistance will significantly benefit from the combination of sensing and communication. This combination requires that both operate on the same frequency bands either in a co-design, a cooperation or even in a coexistence, using the same hardware. However, each technology has its own requirements. While radar performs a channel measurement and thus, needs a wide field of view, communication aims to minimize the interference for other not addressed receivers (e. g. in a platoon) by a focused antenna design. The proposed work analyses the effect of antenna positioning and antenna characteristics (omnidirectional and focused) for a typical automotive scene by evaluating both the communication and sensing performance. Especially, the positions at the front bumper – typical for radar sensors– and at the left mirror convinced regarding the vehicular communication as well as the sensing behaviour.
Heart Watch: Dynamical Systems Based Real Time Data Driven ECG Synthesis

Electrocardiogram (ECG) is an important health monitoring signal that is used in various medical diagnosis, especially identifying potential possibility of heart attacks and strokes. Moreover, many patients are in remote places and in many countries the patients to doctors ration is very poor which calls for a miniature hardware that remotely captures ECG and transmits data to the doctors. However, the exact reproduction of ECG requires high bit rate and thus requires transmitting a compressed set of parameters. Further, sending large volumes of annotated raw data to train diagnostic models also compromises the patients privacy. We design and present a system that generates synthetic ECG signals from clinical data in real-time using a highly minimized set of parameters. The system comprises a nonlinear dynamical model whose parameters are trained in real-time to synthesize a signal which matches clinical data with high accuracy. The parameters of the trained system are then transmitted in each cycle of the ECG wave to reconstruct the original signal using the same model at the medical practitioners’ location. The parameter learning problem is highly complicated as one needs to solve a nonlinear, non-convex dynamic optimization problem, which usually only converges to local optima. To address this issue, we propose a novel two-stage algorithm that automatically chooses an initial set of parameters in the vicinity of the global optimum and then performs stochastic gradient descent iterations. We perform experiments to demonstrate the accuracy and real-time performance of the system. We show that on average our system processes clinical data of one second in 0.68s on a microcontroller, with an RMSE error of 0.0038 the average, and 17 parameters per ECG cycle. Our system is also easy to implement, requires minimal storage i.e. only one ECG cycle at any given time, and does not depend on offline training, unlike existing methods.
Utilizing Multi-Agent Deep Reinforcement Learning For Flexible Job Shop Scheduling Under Sustainable Viewpoints

Current trends place great demands on the flexibility and sustainability of modern production facilities. The optimisation of these Flexible Job Shop Scheduling Problems (FJSSP) under multiple objective variables, such as the makespan or the consumed energy, is a great challenge for today's planning systems due to the constantly changing constraints. In this paper, we present a method for multi-criteria dynamic planning of production facilities under both common and sustainable target variables, based on a Multi-Agent Reinforcement Learning (MARL) procedure. This is experimentally applied to a planning problem in a series of trials and compared with common methods. Finally, the results and further research questions are presented.
Thermal Modelling of a Liquid Cooled Traction Machine with 8-layer Hairpin Windings

This paper describes the conjugate thermal analysis performed on an traction electric motor for electric vehicle (EV). The machine considered adopts hairpin windings and the cooling is achieved by combining an external water jacket and internal oil spray on the end-windings which is delivered by high pressure nozzles. The thermal characterization was performed in two stages; in the first stage the thermal performance of the original design was investigated whilst in the second stage some solutions were proposed to enhance the cooling effectiveness. Due to the heavy computational cost of the analyses, performed by the means of Computational Fluid Dynamics (CFD), fluid flow and conduction heat transfer were investigated separately. The results produced have shown that despite the good overall cooling capabilities of the spray, some issues may arise when the motor operates at low speed-high torque condition as the number pin layers increases. In this scenario droplets might struggle to penetrate through the conductors, leading to localized hotspots. It is also found that minor adjustments of the position of the nozzles may considerably improve the heat transfer rate. The realistic 3D representation of the conductors has allowed to further improve the accuracy of the analyses carried out.
A Lyapunov-based Real Time Energy Management System for Smart IoT Homes

Smart homes are an integral component in developing smart cities. This paper studies the problem of real-time energy management of controllable loads for Internet of Things (IoT) connected homes with renewable energy generators and energy storage devices. By exploiting the delay tolerance of elastic loads, we develop a joint real-time energy storage control and load management system, aiming to minimize the long term time-averaged energy consumption cost without reducing energy consumption. This residential energy management problem is formulated as a constrained stochastic programming problem. A Lyapunov-based control algorithm is designed to decompose the long-term optimization problem into per-slot sub-problems and provide an asymptotically online optimal solution that is able to quickly adapt to the system dynamics without requiring any statistics of time-varying load demand and stochastic renewable generation. The proposed online control algorithm jointly optimizes energy consumption, load scheduling, and energy charging/discharging actions while satisfying the time-varying energy consumption preference of the user in each time slot. It is demonstrated through numerical simulations that the low-complexity online control algorithm ensures the load demand of the user served with a lower delay at a relatively low cost.
Robust embedded MPC with reduced-precision arithmetic for cost-optimized implementations

The use of reduced-precision formats is a valuable strategy to improve performance and reduce costs in embedded computing. In case of embedded model predictive control (MPC), utilizing reduced-precision numbers to speed-up underlying optimization algorithms can help to extend the application scope of MPC. In this paper we show how the improved spectral properties of linear systems inside interior point-proximal method of multipliers (IP-PMM) combined with the application of online regularization and instability correction mechanisms, can prevent embedded MPC controllers from failure when reduced-precision arithmetic units are used. Thus, the proposed approach can also contribute to designing efficient domain-specific processors for embedded MPC using custom floating-point formats. To our knowledge this is the first time an IP-PMM algorithm is applied to solve quadratic programming (QP) problems in MPC.
A Data-Driven Method for Paint Curing Quality Control in Automotive Industry

In the literature, there are different quality control methods for the paint curing process. In the automotive industry, direct temperature measurement and process simulation are the common methods. In the direct temperature measurement method, a thermograph is created with the data coming from the sensors placed on a sample vehicle while it passes through the curing process. The curing quality is determined considering the average temperature and the time elapsed above the threshold temperature value in the thermograph. Another method is simulating the paint curing process. However, these common approaches have some disadvantages. Due to the instrumentation of the sensors in the thermograph evaluation, only a certain number of vehicles can be evaluated in terms of curing quality. In addition, the differences between the locations where the sensors are placed complicate the quality analysis. In the process simulation method, it is very difficult to develop simulation models and validity of the simulation model may drastically reduce over time in the face of the variable conditions of the curing process (e.g. thermal leaks, process aging etc.). This manuscript presents a set of data-driven analytical methods of paint curing quality control.
Comparison and Modeling of Multi-Frequency Wideband Channels at Sub-6 GHz Bands

Developing multi-frequency cooperation algorithms in wireless communication systems has been a topic of interest since the ever-growing complexity and network density prompt the current system to work at multiple frequencies. In this paper, firstly a wideband multiple-input and multiple-output (MIMO) channel model capable of reflecting characteristics in multi-frequency systems is presented, in which channel impulse response is given, and then large-scale and small-scale channel model parameters are investigated for their frequency dependence. In order to gain deeper insight into the behaviors of channels with different sub-6 GHz bands, a simulation setup is established, which compares multiple channel properties at widely available frequencies, namely 2.4, 3.5, and 5 GHz. Statistical properties such as time auto correlation function (ACF), and frequency correlation function (FCF) of the same frequency, spatial correlation coefficients between different frequencies, as well as instantaneous delay power spectrum density (PSD)) are among the metrics to be compared. Analysis of the comparisons offers insightful discoveries about channel characteristics. After justifying the presented multi-frequency channel model with data from measurement results, it is concluded that the model can be used to develop and evaluate multi-frequency communication protocols and systems.
Consistency Validation Method for Java Fine-grained Lock Refactoring

Many existing refactoring tools reduce the possibility of lock conflicts and improve the concurrency of system by reducing lock granularity and narrowing the scope of locked objects. However, such refactorings can lead to changes in concurrent program behavior, introduce concurrency errors, and often even produce code that does not compile or can be compiled but has changed semantics. To address the problem that coarse-grained locking to fine-grained locking refactoring leads to changes in concurrent program behavior, a refactoring consistency validation method for fine-grained locking is proposed. Firstly, the types of behavioral changes caused by the existing refactoring engine are analyzed in terms of thread interactions. Secondly, the relevant consistency checking rules are summarized according to the types. Finally, with the help of various program analysis techniques such as call graph analysis, alias analysis and side-effect analysis, the corresponding checking algorithms are designed according to the consistency checking rules to check the consistency of the program before and after refactoring. We implement an automatic validation tool as the Eclipse plug-in. Our approach is verified by ten open-source projects including HSQLDB,Xalan and Cassandra,etc. A total of 1,483 refactoring methods were tested, and 60 inconsistent synchronization behaviors were found, which improved the robustness of refactoring in terms of data dependence and execution order.
Adaptive Flux Weakening Controller for Dual Three-Phase PMSM Drives in Vector Space Decomposition

This paper proposes an adaptive voltage regulation (VR) flux-weakening (FW) control technique for dual three-phase (DTP) permanent magnet synchronous machine (PMSM) drives. Firstly, a small-signal model is developed to demonstrate the need of gain adaptation in order to maintain the controller bandwidth. Moreover, the small-signal model serves as a tuning tool for the FW proportional-integral (PI) controller. Then, the proposed adaptive controller is implemented and tested on a 100 kW DTP-PMSM drive. Simulation results demonstrate an improved drive performance at high speeds when benchmarked against non-adaptive controllers. Furthermore, the adaptive controller offers an extended speed range.
Production of Stop-Nasal Sequences by Korean Learners of English

This study investigates how Korean learners of English produce stop-nasal sequences in English words/phrases like Batman and a good neighbor, and explores how their interlanguage phonology can be accounted for within the Optimality Theoretic framework, which characterizes a grammar as a set of violable universal constraints. In Korean, a stop is assimilated to its following nasal and becomes a nasal. The results show that Korean learners of English transfer Korean constraint ranking, which results in nasal-nasal sequences (like those of Korean) replacing stop-nasal sequences in L2. Vowel insertion also occurs; this appears to occur in perception (Park, 2002), and it results from a phonological constraint which rules out stop-nasal sequences at the phonetic level. This insertion gives rise to alternative input representations. Factors such as stress patterns, place of articulation, word boundary, or voicing play a role in stop nasalization and vowel insertion. The variations may be accounted for by some constraints proposed by Davis and Shin (1999) and a constraint proposed in this paper, and by adopting a model of floating constraints (Nagy and Reynolds, 1997) which yield the variable rankings apparent in L2 speech.
Simultaneous Lightwave Information and Power Transfer with Non-orthogonal Multiple Access

This paper proposes a non-orthogonal multiple access (NOMA) based simultaneous lightwave information and power transfer (SLIPT) scheme for downlink visible light communication (VLC) systems. A downlink VLC system with a light emitting diode (LED) transmitter and two users with photo detector (PD) receivers is considered. Also, a receiver circuit with a mode selector is proposed to be employed at each user. The proposed receiver circuit has energy harvesting capability by employing the power splitting (PS) as well as time switching (TS). The near user (NU) harvest energy utilizing PS protocol, whereas the far user (FU) utilized the TS protocol for energy harvesting. The numerical results are presented to illustrate that the user’s data rates and the energy efficiency (EE) of the NOMA based SLIPT system show superior performance compared to the OMA based SLIPT system. Also, the harvested energy of the proposed energy harvesting scheme is within the reach of OMA based SLIPT systems.
A High-Step up and Low-Spikes Inverter based on Two T-type Coupled Inductors

In order to further improve the boost ratio of the traditional impedance source inverter, and to improve the DC bus voltage spikes during switching transient caused by the leakage inductance of the coupled inductor. A new high-step up and low-spikes impendence inverter based two coupled inductors is proposed. By using two T-type coupled inductors to replace the inductance in the classic Z-source inverter, the proposed inverter can flexibly control the turns ratio of the two coupled inductors to obtain a much higher boost ratio. The DC bus voltage spike absorption circuit composed of several passive components effectively recovers the leakage inductance energy and suppresses the DC bus voltage spikes. In order to show the characteristics of the proposed topologies, the operating principles, analysis are shown in this paper. Experimental results verify the feasibility of the proposed inverters.
Analysis and Control Development of IPM Traction Machines with Skewed Rotor using Unskewed Machine Model

The paper presents a simplified analysis and control development method for IPM traction machines with skewed rotor using unskewed machine model. It is shown that the skewed machine characteristics under a given set of dq-axis currents could be obtained from the unskewed machine model using relevant modified dq-axis currents associated with the skewed information. Therefore, under the proposed method, the tested IPM machine model with skewed rotor could be rapidly derived from the unskewed machine model without FEA implementation for each skewed slice. It is demonstrated that in comparison with the FEA-based model, the proposed method could maintain up to 0.2% difference for both stator flux linkage and torque maps over dq-axis currents applied as well as 0.1% efficiency difference map over main torque speed operation. The proposed method is validated by FEA and measurement from a high-speed high-power (8000rpm, 80kW) IPM traction machine with skewed rotor.
Cascaded Kalman Filters for a Sliding Mode Control in a Peltier Structure for an Innovative Manufacturing System

This paper deals with a Peltier cell used in an innovative manufacturing system with a cascaded Kalman filter (KF) structure to estimate states and parameters to be used in a closed loop control law. While disturbances, states, and parameters of the Peltier cell are estimated online using a cascaded Kalman filter structure, a sliding mode control (SMC) strategy is proposed to maintain the cold-side temperature even in the presence of disturbances. One theorem is given that allows the design of a constructive control strategy. Simulation results are shown and with them the effectiveness of the method.
Formation Control of Multi-Robot System with Collision and Obstacle Avoidance by Novel APF

This research article develops a novel artificial potential field (APF) for a mobile robot swarm of second-order dynamics. The mobile robot swarm follows a leader-follower structure for trajectory tracking and distributed formation control. The paper has mainly two contributions. One is the development of an artificial potential field by exponential terms that can successfully avoid obstacles and collision among the agents. The second is introduction of a rotational field that, combined with the novel APF successfully avoids the local minimum in the robot motion path. The robots can successfully form the desired shape before and after the obstacle avoidance. The results are compared with traditional APF and a very recent method. The numerical simulations show the superiority of the proposed scheme.
SAREF-Compliant Knowledge Discovery for Semantic Energy and Grid Interoperability

Modern cities are becoming “green and sustainable” once effective and optimized energy management of resources, gathering, and integration of data from smart energy and building are executed. The integration of heterogeneous technologies, and devices, require an interoperable solution to describe devices and data exchanged.SAREF is an ontology supported by the ETSI SmartM2M standard to achieve interoperability among IoT projects, architectures, etc. that can be extended to any IoT vertical domains such as smart buildings or energy. SAREF can be used to describe data sent through communication protocols or once that data must be processed (e.g., on the cloud, gateways, devices). ETSI does not provide tools yet that support the SAREF ontology to avoid engineers to develop from scratch. We design a SAREF-compliant sensor dictionary which also overcomes SAREF limitations. The sensor dictionary, applied to energy scenarios, is employed by a reasoner to infer meaningful knowledge from sensor data. Online demonstrators are available. The scenarios are also relevant for the Interconnect European-funded project that comprises 50 partners to design interoperable smart building and grids.
New Fast GPS code-acquisition technique using FFT

A new spread-spectrum code-acquisition technique for the navigation systems Navstar/GPS and Glonass is introduced. This technique uses the FFT to compute the correlation function, thereby eliminating the time-consuming code phase shift process. Comparisons with existing systems show a theoretical reduction in acquisition time of about 2000 times.
Bipolar Degradation monitoring of 4H-SiC MOSFET Power Devices by Electroluminescence Measurements

Expansion and contraction of Shockley type stacking faults (SF) due to the glide of basal plane dislocations are reported for the first time in a commercial 4H-SiC MOSFET bare die rated at 100A/1.2kV. Electroluminescence of the laboratory fabricated power MOSFET body diode is measured to detect the bipolar degradation which provides firm evidence of the presence of a 3C- like regions affected by SF. Accelerated bipolar degradation tests were performed on the device and it was found that the on-resistance which is a key indicator of the degradation level, increased by 15.6 % during the first stress while it decreased by 6.9 % in the second stress. Other related SiC material defects were as well detected. The electroluminescence spectra is an excellent tool for monitoring SiC power devices deterioration related to material defects without performing destructive tests such as SEM.
A Review on Artificial Intelligence Based Strategies for Open-Circuit Switch Fault Detection in Multilevel Inverters

Multi-Level Inverters (MLI) have become of great importance for electrical energy supplement to grids due to its modularity, lower Total Harmonic Distortion (THD) and decreased filter needs. However, although multilevel inverters achieve high voltage levels, increased number of power switches are required which make them more prone to breakdowns and faults. Among active devices failures, Open-Circuit (OC) faults are extensively explored in research studies. Hence, this paper presents a deep overview concerning methods based on Artificial Intelligence (AI) algorithms involved in diagnosis and localization of OC failure in different multilevel inverter architectures. Initially, two major classifications of fault diagnosis methods for OC switch failure are listed and clarified briefly. Then, AI algorithms are discussed thoroughly. Further, certain criteria with several standards are formulated to differentiate between strategies investigated in publications. Then, various implemented techniques in literature are widely overviewed and compared in an original table.
Dynamic Flexible Power Point Tracking in Photovoltaic Power Plants

Frequency support from photovoltaic (PV) power plants is required with new grid codes. To provide frequency support, the PV system needs to regulate the PV power in such a way that a predefined amount power reserve is kept in the PV system. Accordingly, a dynamic flexible power point tracking algorithm (FPPT) for grid-connected PV power plants is introduced in this paper. The proposed algorithm regulates the average PV power by continuously sweeping some portion of the PV curve that includes the given power reference point. The proposed strategy is different from traditional static FPPT techniques as it ensures a predefined amount of power reserve in the PV system. This power reserve provides the flexibility of increasing the power injected into the grid that can be withdrawn with a fast dynamic. Compared to the traditional solutions, in the proposed algorithm, the PV injects a constant power into the grid without any fluctuations. Effectiveness of the proposed technique in providing frequency support in a two-stage grid-connected PV plan is demonstrated by simulation results.