Miracle-dz/UniFAR-FTUs huggingface dataset:
Modeling and Analysis of Multi-time Scale Transient Characteristics of a Series Hybrid Power System

In this paper, modeling and analysis of multi-time scale transient characteristics of a series hybrid power system are presented. The electromechanical coupling system composed of diesel engine, generator and composite power has many different transient time scales, which brings difficulties to the optimization of simulation efficiency. Therefore, implicit trapezoid integral algorithm is applied to analysis transient characteristic of the hybrid power system, and adaptive variable-step algorithm is used for dynamic process. Not only to meet the simulation accuracy and minimize simulation time, but also to reflect the interaction between electrical and mechanical process, a switch flow-graph model (SFGM) is introduced. Finally, the effectiveness and superiority of the proposed method is verified through simulation results.
Identification of DNA Methylation Signatures for Diagnosis of Lung Adenocarcinoma

Lung adenocarcinoma is the leading cause of death among men and women with cancer worldwide. Here, we performed an analysis of Illumina HumanMethylation450K data from TCGA to identify DNA methylation markers for lung adenocarcinoma diagnosis. We examined the DNA methylation landscape of lung adenocarcinoma and investigated the relationship between DNA methylation and clinical features. We then extracted differentially methylated cytosines in CpG island promoter regions, and then adopted machine learning techniques to determine the final methylation markers. As a result, we identified three methylation subtypes of lung adenocarcinoma, and found that the methylation status was not significantly related with the prognosis of lung adenocarcinoma. We finally identified two novel lung adenocarcinoma methylation markers including cg14823851 (TBX4) and cg07792478 (MIR124-2) with the AUCs of 100%, 100%, 98.3%, and 100% on support vector machine, logistic regression, decision tree, and random forest, respectively. Overall, our study demonstrates the potential use of methylation markers in lung adenocarcinoma diagnosis and may boost the development of new epigenetic therapies.
Efficiency Optimization of Phase-Shift Full-Bridge through Adaptive Dead-Time and Burst-Mode at Very Light Load

In the traditional phase-shift full-bridge converter, the dead-time is calculated under a pre-set load condition, which leads to more body diodes conduction loss of the switches when the load is heavy and the loss of ZVS when the load is light. Therefore, an adaptive dead-time strategy of efficiency improvement for PSFB converter is proposed in this paper, which can adaptively adjust dead-time based on the relationship between the load current and dead-time. By the proposed strategy, the efficiency of the PSFB converter can be improved at both light and heavy load conditions without additional power devices. In addition, the efficiency can be significantly improved by burst-mode at very light load.
Bio-molecular Circuit Design with Electronic Circuit Software and Cytomorphic Chips

We have previously described a technique for rigorously converting arbitrary biological circuits to electronic circuit schematics that represent them exactly and quantitatively [1], [5], [12]. The technique enables us to simulate and model an experimental microbial synthetic microbial amplifier with electronic circuits in Cadence, a widely used integrated-circuit design tool for very-large-scale silicon chips [2]. Our model is in excellent accord with measured biological data for both the closed-loop and open-loop operation of the biological operational amplifier. In addition, because chemical reaction flux and electronic transistor current obey the same Boltzmann laws of thermodynamics, such analog circuit schematics can be emulated rapidly in custom integrated circuit cytomorphic silicon chip hardware [4]–[9] including sophisticated nonlinear, stochastic, non-modular, and dynamical effects. We show that we can rapidly simulate and fit experimental biological data from our synthetic microbial operational amplifier with cytomorphic chips. Since cytomorphic chips are an example of digitally programmable analog chips [10], [11], they are easily amenable to electronic evolution, parameter exploration, and machine learning. The use of industry-standard circuit software and the rapid emulation on digitally programmable cytomorphic silicon chips suggests that biological design of synthetic circuits can be automated onto electronic platforms in the future.
Extension of measurement range of dew-point potentiometer and evaporation method

There are numerous methods for measuring soil water retention curve (SWRC). With tensiometer device it is possible to measure suction up to 85 kPa, but above this point measurements are not possible due to cavitation of water. To measure higher suctions other methods should be used. One of very popular devices is dew-point potentiometer WP4-T (Decagon Devices), which enables suction measurements above 1000 kPa. If high capacity tensiometers from HYPROP evaporation method device and dewpoint potentiometer are used for suction measurements, the measurements are only possible for low suction range between 0 and approximately 100 kPa and for high suction range between 1000 and 300000 kPa. Between 100 and 1000 kPa suction could be measured by other methods or some extension of these two methods should be used. This paper presents SWRC measurements with HYPROP and WP4-T devices for different soils, the extension of suction range for both devices and comparison between measured suctions in the extended measurement range. RÉSUMÉ : Il y a de nombreuses méthodes pour mesurer la courbe de rétention d'eau de sol (SWRC). Il est possible de mesurer la succion jusqu'à 85 kPa avec le dispositif de tensiomètre, mais au-dessus de ce point les mesures ne sont pas possibles en raison de cavitation d'eau. Pour mesurer des succions plus élevées d'autres méthodes devraient être utilisées et l’un des dispositifs très connus est le potentiomètre de point de rosée WP4-T (les appareils Décagone), qui permet des mesures de succion au-dessus de 1000 kPa. Si des tensiomètres haute capacité de l’instrument HYPROP, la méthode d'évaporation et le potentiomètre de point de rosée sont utilisés pour des mesures de succion, les mesures sont seulement possibles pour la gamme de succion basse entre 0 et environ 100 kPa et pour la gamme de succion élevée entre 1000-300000 kPa. Entre 100 et 1000 kPa la succion pourrait être mesurée par d'autres méthodes ou une certaine extension de ces deux méthodes devrait être utilisée. Cet article présente des mesures SWRC avec des dispositifs HYPROP et WP4-T pour des sols différents, l'extension d’une gamme de succion pour les deux dispositifs et la comparaison entre des succions mesurées dans la gamme de mesure prolongée.
Impact of Process Variability and Single Event Transient on FinFET Technology

The evolution of integrated circuits has made them more susceptible to the radiation effects, besides increasing the manufacturing process variability. Traditionally, complex gates are adopted to reduce area, delay and power consumption. However, they can introduce challenges related to a robustness that might be avoided with more regular and basic cells. This extended abstract presents my current research works. Firstly, the different works performed in my research are described. Then, a short version methodology and the main results are presented.
MU-MIMO-OFDM with Analog Beamforming and Spreading Codes for User Separation

This paper presents a multiuser (MU)-multiple-input multiple-output (MIMO)-orthogonal frequency division multiplexing (OFDM) scheme that realizes signal separation among mobile terminals by spreading codes on top of analog beamforming with analog-digital hybrid massive MIMO. Multiuser MIMO scheme suffers from interference among terminals caused by inaccurate information of channel responses. The channel state information (CSI) of the terminals are outdated owing to a Doppler shift if there is a time lag between channel estimation and CSI feedback. Thus, this paper proposes an MU-MIMO-OFDM scheme with analog-digital hybrid massive MIMO and orthogonal spreading codes that separates signals for mobile terminals. Numerical results obtained through computer simulation show that on WINNER II Umi NLOS channel the proposed scheme improves bit error rate (BER) performance by 1.0dB at a bit error rate of 10−3 in comparison with the conventional MU-MIMO.
A DSP48-Based Reconfigurable 2-D Convolver on FPGA

Two-dimensional (2-D) convolution is a widely used operator exploited in image processing and computer vision. Many schemes of the FPGA-based 2-D convolver are focused on the buffer scheme and computational complexity. This paper optimizes the convolution unit from a unique perspective – underlying resource utilization, and presents a novel reconfigurable 2-D convolver. The purpose of this structure is to probe into the internal structure and resources of DSP blocks in FPGA and give full play to their computing power to complete convolution. This 2-D convolver can greatly reduce the consumption of logic resources without sacrificing the equivalent performance of convolution. The experiment results also demonstrate that this structure presents considerable adaptability to different sizes of images and kernels, making it suitable for various FPGA implementations.
Resilience for Consensus-based Distributed Algorithms in Hostile Environment†

Consensus-based distributed algorithms have been the key to many problems arising in multi-agent systems including reinforcement learning [1], [2], formation control [3], [4], task allocation [5]and so on. Byconsensushere is meant that all agents in the network reach an agreement regarding a certain quantity of interest [6], [7]. Bydistributedhere is meant that the whole multi-agent system achieve global objectives by only local coordination among nearby neighbors [8]. On one hand, the absence of central controllers in multi-agent systems make them inherently robust against individual agent/link failures. On the other hand, the high dependence of the whole system on local coordination also raises a significant concern that algorithms for multi-agent networks may be crashed down in the presence of even one malicious agent [9]. This has motivated us to develop methodologies to achieveresiliencein order to guarantee nice performance for consensus-based distributed algorithms especially in hostile environment. One challenge along this direction comes from the fact that each agent is usually with locally available information, which makes it very difficult to identify or isolate malicious agents [10]. The authors of [11]–[13]have achieved significant progress by showing that given $N$adversarial nodes under Byzantine attacks, there exists a strategy for normal agents to achieve consensus if the network connectivity is $2 N+1. $These results are usually computationally expensive, assume the network topology to be all-to-all networks, or require normal agents to be aware of non-local information. Most recently the authors of [14], [15]have investigated consensus-based distributed optimizations under adversarial agents. They have introduced a local filtering mechanism which allows each agent to discard the most extreme values in their neighborhood at each step. This is not directly applicable to consensus-based distributed computation algorithms [16]–[19], in which extreme values may come from the local constraints instead of malicious agents. Thus in this talk we will present a new approach developed in [9], which achieves automated resilience without the identification of malicious agents for consensus-based distributed algorithms based on intersection of convex hulls [20].
Novel “Concentrated Solar Energy Soldering (CSES) Technique” - a Feasibility Study

Concentrated solar energy (CSE) is being used for material processing. In this work, CSE is applied for soldering and named as the “Concentrated Solar Energy Soldering (CSES) technique”. A Fresnel lens along with a two-axis sun tracker to concentrate the solar energy required for soldering is used. Cu substrates are soldered with CSES technique using SAC 305 & SACX 0307 lead-free solders. Feasibility study along with the initial results are discussed. CSES technique, a green technology is found to be encouraging. The CSES technique can be used to solder on-site electronics components used in nuclear, space, automobiles, computer hardware, electronic circuits used in process equipment's, etc. Specifically, in the remote areas CSES will be useful to solder various components, circuits, etc.
Barrier-Lyapunov-Function-Based Backstepping Control for PMSM Servo System with Full State Constraints

This paper presents the output constraint problem of permanent magnet synchronous motor servo system. Although the ordinary backstepping control can ensure the stability of the system, it cannot guarantee the safety of the system or meet certain special conditions. This paper proposes to introduce the barrier Lyapunov function (BLF) in the servo system backstepping controller. It not only solves the constraint problem, but also improves the tracking and fast performance of system. The effectiveness and correctness of the method are proved by experiment.
Zero-Voltage-Switching Current-Source-Inverter Motor Drives Based on Silicon Carbide Devices

Silicon carbide (SiC) devices have been widely discussed for their desirable properties including low on resistance, high switching frequency and high junction temperature. In this paper, a zero-voltage-switching (ZVS) scheme is proposed for SiC based current-source-inverter (CSI) fed motor drives for high reliability and high efficiency. Compared to conventional CSI fed motor drive, resonant circuit is proposed, which can help realize zero-voltage-switching of devices in CSI and mitigate electromagnetic interference (EMI) caused by SiC devices with high-frequency switching. The simulations and experiments are given to verify the validity of the proposed scheme.
A Method of Open Circuit Fault Diagnosis for Five-phase Permanent Magnet Synchronous Motor Based on Wavelet Analysis

For the fault diagnosis of Multiphase Permanent Magnet Synchronous Motor (PMSM), this paper tends to improve the robustness and accuracy of the diagnosis system and a wavelet-based diagnosis method is proposed based on third harmonic current components has been presented to three open circuit faults of Five-phase PMSM under various speeds and loads. The proposed method utilizes wavelet transform to extract current vector features of the third harmonic space for fault diagnosis, and the fault diagnosis is carried out by Support Vector Machine (SVM) after data dimensionality reduction. The validity of proposed method is verified by Matlab/Simulink and the average accuracy of this diagnosis method is up to 97.19%.
Power Electronic Traction Transformer Based on No Phase-locked Loop Control

MW-class Power Electronic Traction Transformer (PETT) has excellent prospects in 25kV traction network which adopts Cascaded H-Bridge Rectifier (CHBR) and LLC resonant DC/DC converter. However, the electrical transient problems caused by harsh condition of traction network bring higher requirements on the control strategy. Hence, a topology based on no Phase-locked Loop (no-PLL) control is proposed in this paper. By constructing a new control mathematical model, which is not limited by the voltage phase angle of the PLL, the unity power factor operation under various operating conditions can be realized quickly and effectively. Besides, Line side converter (LSC) adopts phase-locked loopless voltage equalization control and quasi-PR control. It works well in the presence of harsh conditions such as traction network voltage sag, frequency mutation and phase fluctuations. Galvanically isolation adopts resonant converter, realizing soft-switching to reduce switching loss. Simulation results show that the PETT under proposed control not only achieves the energy transfer in 25kV traction network, but also works well under the common traction network harsh condition.
Optimization of Electromagnetic Performance of Single Phase Line-Start Permanent Magnet Synchronous Motor Based on the Finite Element Method

The single-phase motors are widely used in the field of household appliances. Compared with three-phase motors, they are small in size and directly connected to 220V single-phase civilian power supplies. The single phase line- start permanent magnet synchronous motor (SPLSPMSM) inherits the advantages of single-phase induction motor and effectively overcomes its disadvantage of low efficiency [1]. Due to the asymmetry of the stator phases, a lot of research is still needed to improve working performance. In this paper, the mathematical model in steady-state operation of SPLSPMSM is built, and the inductance parameters are analytically calculated. Based on the finite element method (FEM), the influences of parameters such as phase turns, capacitance of auxiliary phase and permanent magnet thickness are analyzed for both interior and surface-mounted SPLSPMSM.
Discriminative Marginalized Least-Squares Regression for Hyperspectral Image Classification

Least-squares regression (LSR)-based classifiers are effective in multiclassification tasks. However, most existing methods use limited projections, resulting in loss of much discriminant information; furthermore, they focus only on exactly fitting samples to target matrix while ignoring overfitting issue. To solve these drawbacks, discriminative marginalized LSR (DMLSR) is proposed to learn a more discriminative projection matrix with consideration of class separability and data-reconstruction ability simultaneously. In the proposed framework, an intraclass compactness graph is employed to avoid the overfitting problem and enhance class separability, and a data-reconstruction constraint is imposed to preserve discriminant information on limited projections. Experimental results on several hyperspectral data sets demonstrate that the proposed method significantly outperforms some state-of-the-art classifiers.
A Comparison Study on Time Series Forecasting Given Smart Grid Load Uncertainties

The trade-off between energy generation and its consumption is usually a difficult task for electricity power grids, since the data obtained from this system tied to uncertainty occasioned by seasonality and natural environment disturbances. Therefore, efforts have been made on the construction of Smart Grids, i.e. intelligent energy networks, which combine Computational Intelligence with the electricity power grids to improve the balance between energy generation and its consumption. For that, Smart Grids controllers have to be aware of future loads, and the prediction of this data must be very accurate to provide an efficient decision support. Since Smart Grid's energy consumption data varies over time, following a time series distribution, we present a comparison study over different time series forecasting in order to evaluate which one would achieve better accuracy in energy distribution. Our investigation was carried out using the Smart Grid of Ontario, Canada. The algorithms used on this experiments were the Adaptive Fuzzy Neural Network (ANFIS), Recurrent Neural Networks (RNN), Support Vector Regression (SVR), Random Forest and SARIMAX. The ANFIS algorithm had outperformed the other approaches, delivering more accurate results.
Magnetic Circuit Model and Performance Analysis of a V-Type Hybrid Permanent-Magnet Variable- Flux Machine

The permanent-magnet variable-flux machine (PM-VFM) is a promising candidate in the application of extended-speed operation. In this paper, a V-type hybrid PMVFM which utilizes AlNiCo magnet to enable the magnetization state regulation and the NdFeB magnet to enhance the power density is investigated. The magnetic circuit models (MCMs) of the machine under positive magnetization state and negative magnetization state are developed and solved iteratively. The calculated results of the air-gap flux density, open-circuit leakage flux factor, and the operating points of the AlNiCo magnet and the NdFeB magnet are obtained and compared with the simulated results of finite element analysis (FEA). The selection rules of dimensional parameters of hybrid PMs are investigated. To reduce the negative influence of the q-axis armature magnetic field on the electromagnetic performance of PM-VFMs, the effects of the q-axis flux barrier on the air-gap flux density, flux variation range (FVR) and torque characteristics are investigated. The mechanical strength of the rotor with q-axis flux barrier is verified.
Torque Analysis of the Hysteresis Motor with Over-excitation Using Play Model

The short-duration over-excitation of the hysteresis motor running at synchronous speed reduces the stator current and increases the pull-out torque at the same time. This paper presents new hysteresis torque analysis using B-H loop in the rotor hysteresis ring of the hysteresis motor with over-excitation at an arbitrary load, where the time-stepping finite element method (FEM) using play model is used. The behavior of the operating point on B-H loops before and after the over-excitation is clear from simulation results using the play model. The calculated value of the hysteresis torque obtained from the B-H loop area versus space agreed well with the measured one.
Voxgraph: Globally Consistent, Volumetric Mapping Using Signed Distance Function Submaps

Globally consistent dense maps are a key requirement for long-term robot navigation in complex environments. While previous works have addressed the challenges of dense mapping and global consistency, most require more computational resources than may be available on-board small robots. We propose a framework that creates globally consistent volumetric maps on a CPU and is lightweight enough to run on computationally constrained platforms. Our approach represents the environment as a collection of overlapping signed distance function (SDF) submaps and maintains global consistency by computing an optimal alignment of the submap collection. By exploiting the underlying SDF representation, we generate correspondence-free constraints between submap pairs that are computationally efficient enough to optimize the global problem each time a new submap is added. We deploy the proposed system on a hexacopter micro aerial vehicle (MAV) with an Intel i7-8650 U CPU in two realistic scenarios: mapping a large-scale area using a 3D LiDAR and mapping an industrial space using an RGB-D camera. In the large-scale outdoor experiments, the system optimizes a 120 × 80 m map in less than 4 s and produces absolute trajectory RMSEs of less than 1 m over 400 m trajectories. Our complete system, called voxgraph, is available as open source.11https://github.com/ethz-asl/voxgraph.
Secure and Privacy Preserving Pattern Matching in Distributed Cloud-based Data Storage

Given two strings: pattern $p$ of length $m$ and text $t$ of length $n$. The string matching problem is to find all (or some) occurrences of the pattern $p$ in the text $t$. We introduce a new simple data structure, called index arrays, and design fast privacy-preserving matching algorithm for string matching. The motivation behind introducing index arrays is determined by the need for pattern matching on distributed cloud-based datasets with semi-trusted cloud providers. It is intended to use encrypted index arrays both to improve performance and protect confidentiality and privacy of user data.