Miracle-dz/UniFAR-FTUs huggingface dataset:
Properties of Several Performance Indicators for Global Multi-Objective Simulation Optimization

We discuss the challenges in constructing and analyzing performance indicators for multi-objective simulation optimization (MOSO), and we examine properties of several performance indicators for assessing algorithms designed to solve MOSO problems to global optimality. Our main contribution lies in the definition and analysis of a modified coverage error; the modification to the coverage error enables us to obtain an upper bound that is the sum of deterministic and stochastic error terms. Then, we analyze each error term separately to obtain an overall upper bound on the modified coverage error that is a function of the dispersion of the visited points in the compact feasible set and the sampling error of the objective function values at the visited points. The upper bound provides a foundation for future mathematical analyses that characterize the rate of decay of the modified coverage error.
Optimal Coplanar Sensor Placement Based on AOA Measurements

Optimizing sensor-target geometry is an effective way to improve target localization accuracy. When localization in three-dimensional (3D) space, the geometry between the sensors lying in the same horizontal plane and the target is constrained. Based on the angle-of-arrival (AOA) localization model, this letter studies the optimal geometry of the sensors relative to the target under the coplanar constraint. We choose the A- and D-optimality criteria as objective functions. Among them, the correlation between the elevation angle and the distance, the coupling between the azimuth angle and the elevation angle, and the measurement noise of the azimuth and elevation angle all affect the optimal geometric configuration. Under different measurement noise conditions, the optimal geometric conclusions of any number of AOA sensors relative to the target are obtained through theoretical analysis. The results of the simulation experiment and theoretical analysis are consistent.
NadGPT: Semi-Supervised Network Anomaly Detection via Auto-Regressive Auxiliary Prediction

We present NadGPT, a transformer-based semi-supervised framework for network anomaly detection. It is known that transformer models are good at modeling long sequence data such as network traffic; however, without sufficient ground-truth labels, transformer models tend to suffer from over-fitting thus leading to inferior performance. Inspired by the recent success of GPT models in natural language processing (NLP), we propose a new auxiliary self-supervised task plugged to the backbone transformer, which enables GPT-like auto-regressive training on network traffic sequence without using ground-truth labels. Experiments demonstrate the proposed method greatly reduces the requirements of labels in network anomaly detection. For example, on ISCX 2012 dataset, given only 0.05% training labels our semi-supervised approach obtains nontrivial 81.7% (2-class) and 64.9% (5-class) Fl-scores on the validation set, which is far better than the supervised counterparts using the same training data. We hope our research could inspire more label-efficient methods in network traffic analysis.
FPGA-Based Hardware-in-the-Loop Real-Time Simulation Implementation

Hardware-in-the-Loop (HIL) simulators have become an essential tool in the automotive industry as they allow comprehensive testing and validation of electronic control units (ECUs), Although HIL simulations are widely used in the vehicle, aerospace and other industries that rely on electronic control systems, they can also be quite challenging to develop. One of the main challenges of HIL simulators is creating a virtual environment that accurately replicates the behavior of the real-world system. Artificial intelligence (AI) algorithms can be trained on extensive real-world datasets to create more accurate virtual environments of complex systems for HIL simulations. Moreover, latency is a crucial factor in producing a reliable virtual environment for ECU in HIL simulations. FPGA (Field Programmable Gate Array) can help to minimize latency in HIL simulations by providing high-performance computing resources. This paper aims to address these challenges by presenting a machine learning-based HIL simulator design on FPGA. The proposed architecture uses FPGA to accelerate the computations of a temporal convolutional neural network (TCN). The paper also describes how increasing the number of input channels can improve the performance of the TCN model on an FPGA.
Homeostatic Plasticity in a Leaky Integrate and Fire Neuron Using Tunable Leak

In this paper, in an effort to implement an unsupervised learning algorithm for silicon neurons, we present a mixed-signal Leaky Integrate-And-Fire (LIF) neuron with two different integrated homeostasis circuits using programmable leak. The homeostasis mechanism is realized by controlling the charge accumulation rate on the neuron integrator by varying the leakage rate using external signals. The proposed homeostasis circuits have been simulated using a 65nm CMOS process and their performances have been compared with existing homeostasis implementations. Results show that our designs achieve 12.8%-18.1 % power improvements and 25.1 %-48.2% area improvements over similar prior implementation. Also, power consumption can be reduced in the circuits by adjusting the leakage through bias currents.
Modeling and Control of PV and Multiple Batteries based DC Microgrid

This article explores the integration of photovoltaic (PV) systems and multiple Battery Energy Storage Systems (BESSs) through DC-DC converters connected to a common DC bus. It presents an averaged nonlinear model of the converter system, which incorporates a PV system and two distinct BESSs. The model is linearized and validated around a specified operating point. A comprehensive control scheme is discussed that encompasses (i) An outer voltage controller which maintains a constant DC bus voltage, (ii) A battery reference current generation block, and (iii) Inner current controllers which regulate the battery currents or inductor currents to their reference values calculated based on the State of Charges (SOCs). The reference current generation scheme ensures that batteries with a higher SOC discharge at a higher rate and charge at a lower rate, while the opposite is true for batteries with lower SOC. Simulations are conducted in MATLAB/Simulink under varying irradiance and load conditions.
Design and Implementation of Intuitive Human Robot Interface System by DDPG with HER and RCA

This paper presents an intuitive human-robot interface system (iHRIS), where the deep deterministic policy gradient (DDPG) with hindsight experience replay (HER) training method is proposed to accelerate model training. The system consists of a motion capture system and a motion learning network. The motion capture system includes an RGB-D camera and an operating glove. The position of the human operator's hand is estimated by Openpose using the RGB-D images, while the hand's posture is determined by the information captured by the glove. An inertial measurement unit (IMU) and a microprocessor are equipped on the glove. The IMU data is calibrated using the Recursive Least Squares (RLS) method and computed using Madgwick's algorithm. Based on the observed position and posture of the human operator's hand, a motion can be generated. This motion is then trained using the DDPG network with the Reverse Curriculum Generation (RCG) method. The network has an Actor-Critic structure and a replay experience buffer, which makes it more feasible and helps avoid overfitting. Furthermore, HER is integrated into the network to enhance convergence speed and performance. Finally, the experiments demonstrate that the proposed iHRIS enables real-time imitation of the human operator by the robot, and the imitated motion can be learned by the DDPG network.
Rotor Offset Angle Estimation and Field Oriented Control of Synchronous Reluctance Motor

The paper proposes method to precisely estimate the rotor offset angle arising due to improper or faulty mounting of the encoder wherein the encoder index slit and stator axis are not aligned for a Synchronous Reluctance Motor (SyRM) along with the implementation of Field Oriented Control (FOC) of the same. To calculate the offset angle between the stator axis and index pulse of the encoder, a DC excitation on the stator windings, resulting in a net flux directed along the required phase is provided. This leads to the alignment of the rotor with the established flux, traversing an angle, which when compared with the index pulse provides the offset angle of the rotor. Thereafter, Field Oriented Control of the motor has been explained in rated field operation. Experimental results of tests conducted on a 3.7 kilo-Watt laboratory prototype have been used to validate the proposed scheme.
Investigation of Stability Issue of IPM Starter/Generator for More Electric Aircraft

As the aerospace industry keeps moving towards greener and more eco-friendly solutions, the traditional hydraulic, pneumatic, and mechanical sub-systems are replaced with advanced electrical counterparts. Such more electric configuration offers improved efficiency, reduced emissions, less fuel consumption, and increased reliability. Due to the high speed, high power density and high torque density requirement from the aircraft application, Interior Permanent Magnet Starters/Generators (IPM S/G) are preferred over other machine configurations. However, significant challenges exist due to the voltage stability issue caused by constant power loads (CPLs), which may result in DC bus oscillations and effect the aircraft’s power quality and subsystem performance. This paper analyses the said stability problem in on-board DC microgrids (DCMG) for More Electric Aircraft (MEA) applications. A IPM S/G controlled by the rotor flux oriented vector control feeding power to CPLs is implemented in MATLAB/Simulink to investigate the voltage stability issues and concluding remarks are obtained.
ADC-Less 3D-NAND Compute-in-Memory Architecture Using Margin Propagation

Compute In Memory (CIM) has gained significant attention in recent years due to its potential to overcome the memory bottleneck in Von-Neumann computing architectures. While most CIM architectures use non-volatile memory elements in a NOR-based configuration, NAND-based configuration, and in particular, 3D-NAND flash memories are attractive because of their potential in achieving ultra-high memory density and ultra-low cost per bit storage. Unfortunately, the standard multiply-and-accumulate (MAC) CIM-paradigm can not be directly applied to NAND-flash memories. In this paper, we report a NAND-Flash-based CIM architecture by combining the conventional 3D-NAND flash with a Margin-Propagation (MP) based approximate computing technique. We show its application for implementing matrix-vector multipliers (MVMs) that do not require analog-to-digital converters (ADCs) for read-out. Using simulation results we show that this approach has the potential to provide a 100 x improvement in compute density, read speed, and computation efficiency compared to the current state-of-the-art.
Review on Detection and Classification of Diabetic Retinopathy using Fundus Images

Diabetic Retinopathy (DR) is a prevalent outcome of diabetic mellitus. It leads to lesions forming on the retina, impairing eyesight. Most likely, blindness can be avoided if the DR condition is discovered at an initial stage. Although DR is a non-reversible condition, early identification and intervention can greatly lower the risk of vision loss. Fundus images are used to manually detect DR, which is a laborious and error-prone procedure. Today in assessing and categorizing medical images, deep learning has emerged as the most efficient method, even surpassing human performance, common image processing methods, and other computer-aided detection systems. For this study, the most recent approaches of deep learning using fundus images to classify and detect DR have been reviewed. The freely accessible DR Datasets consisting of fundus images and performance metrics have also been discussed. We concluded with some important current and future trends of DR. Further, several open problems and limitations of DR that need further study are discussed.
On the Discretization of Fractional-Order Laplacian Operators

This work introduces two new discrete-time generating functions to discretize the fractional-order Laplacian integro-differential operators denoted by $s^{\pm\alpha}$. The indirect discretization method is used for this purpose. The order of such functions is first or second-order ones, where both are stable and of nonminimum phase. Furthermore, as $\alpha\rightarrow 1$, both functions reduce to the well-known bilinear transformation. It will be shown via numerical example that the proposed functions are robust to the sampling rate, which becomes a test point for any new discrete-time generating function to be used for fractional-order system analysis and control. The stability property of the new functions makes them perfect candidates that can be used to fabricate practical fractional-order PID controllers. The main points of this work are verified via numerical simulations.
Enhanced Facial Expression Recognition Based on Facial Action Unit Intensity and Region

Facial expression recognition (FER) is an attractive research area with important applications, such as in human-computer interaction, where FER can facilitate better collaboration between intelligent machines and humans. Facial Action Units (AUs) can describe facial changes in more detail, and Facial Action Unit Intensities (AUIs) reflect the intensity level of facial behavioral expression, and they can help enhance the performance of ambiguous expressions. In this paper, we propose a FER method based on AUs and use a effective Convolutional Neural Network (CNN) called AUs-Enhanced Expressions Representation Network (AU-EERN). Specifically, first we define the center position of the region of interest of each AU through face landmarks. Second, we attach AUIs to facial expressions in the form of Gaussian heatmaps and crop AU regions of interest (RoI) based on each AU center point. Finally, we fuse the obtained AUI-enhanced expression features with the deep features of the AU RoI region via a convolutional neural network for further expression recognition. Our proposed method is evaluated on the widely available FER databases RaFD and Oulu-CASIA. Experimental results and comparisons show that our model achieves better performance than several other popular methods.
Virtual Temperature Sensor in Battery Thermal Management System Using LSTM-DNN

The LIB's performance, safety, and longevity in electric vehicles are significantly affected by temperature. Battery thermal management maintains the batteries’ operation to a safe region to improve their performance and extends their lifetime, especially during fast charging and high power discharge, and in extreme weather conditions. Therefore, by utilizing a surface temperature estimation model, the multitude temperature sensors which typically monitor battery operation temperature can be reduced, thereby reducing its cost and improving its reliability. This paper presents a novel deep learning method based on long-Short term memory (LSTM) and deep neural network (DNN) algorithm for battery surface temperature estimation using cylindrical lithium ion battery. The estimation results are well validated on test battery.
Toeplitz Inverse Eigenvalue Problem (ToIEP) and Random Matrix Theory (RMT) Support for Calculation of the Toeplitz Covariance Matrix Estimate

“Toeplitization” or “redundancy averaging” is the well-known procedure of getting the Toeplitz matrix estimate from the standard sample covariance matrix. Despite the recently proven asymptotic consistency of this estimate (with $N\rightarrow\infty_{2}T\rightarrow \infty,T/N\rightarrow c)$, for the weakly positive definite covariance matrices, redundancy averaging typically leads to the estimates with a number of negative eigenvalues. In this paper, we demonstrate how these estimates may be rectified to meet a sub-optimal condition, using computational tools from ToIEP and RMT methodologies.
ChatGuard: A Profanity Classification Approach for Safer Online Conversations

With every passing day, our world becomes more and more technology driven. Kids are now being introduced to a variety of devices and exposed to the internet from a very young age. The internet is full of information, good or bad. And it has become fundamental for parents to exercise some control and censorship over not only the content that their kids consume but also their interactions over the internet. The aim of this project has been to build to tool that will help in doing just that. It allows to closely monitor the language so that any use of obscene or offensive words can be detected. Even the simple knowledge of what words has been used, can aid parents in initiating a conversation to help their kids understand why certain language is inappropriate or even wrong to guide them as they unlearn any such unhealthy habits and language patterns. The program essentially analyses the text we provide to it and detect profanity or offensive terms. It has been trained against a large dataset and uses machine learning methods to process text and find out if there are any such terms in it.
A Multi-UAV Cooperative Tracking Strategy Based on Deep Reinforcement Learning

This paper proposes a model for cooperative combat among multiple Unmanned Aerial Vehicles (UAVs) in an isomorphic scenario. A significant challenge with multi-agent reinforcement learning algorithms used in such scenarios is the sparse reward problem. To overcome this, a novel reward design strategy based on reward shaping is suggested, which combines discrete targeted reward with continuous procedural reward functions. This strategy aims to guide the agents towards optimal strategy learning. The QMIX algorithm is used to train multiple agents, and a detection capability evaluation indicator is designed to assess the performance of this approach. The effectiveness of this method is verified through simulations conducted on the MaCA platform. This technique offers useful insights into solving the sparse reward problem.
Five Phase Impedance Source VSI with the Capability of Delivering two Loads

The main aim of this paper is to develop a new switching strategy to deliver power to two independent three phase loads operating at different frequencies. In this proposed topology, switched boost and quasi switched boost impedance networks are integrated with five phase inverter to improve the voltage gain profile at two independent loads operation. The shoot through period is inserted in the space vector pulse width modulation technique to implement the proposed strategies. Proposed topologies achieve high voltage gain and control two loads independently without disturbing the conventional inverter operation. In addition, the proposed topologies achieve a wide voltage gain range by controlling the shoot through period and modulation indexes. The Proposed converters are simulated in MATLAB to support the theoretical analysis. Simulation results show the effectiveness of the topologies.
Designing User Interface Elements for Remotely Operated Rubber-Tired Gantry Cranes

The graphical user interfaces (GUIs) for operating heavy machinery, such as cranes, vary significantly even for the same type of machines depending on machine manufacturers or third-party suppliers who develop the GUIs. This situation leads to diverse GUIs, which require operators to train themselves every time they use GUIs from different machine manufacturers or third-party suppliers. Using significantly different GUIs may also increase the risk of human error, since the GUIs may have different rules or mechanisms that operators should follow. To improve the design consistency across different machine manufacturers and third-party suppliers, there is a need for a design system that crane manufacturers and third-party suppliers can use when developing their own GUIs. This paper presents the process of designing user interface elements for operating remote rubber-tired gantry (RTG) cranes, which will be offered as part of OpenCrane Design System.
A Power Method to Convex-Concave Minmax Optimization Problems with Nonlinear Constraints

In this paper, we presents a new power penalty method to convex-concave minimax optimization problems with nonlinear constraints. This method first reformulate the KKT conditions of such optimization problem approximated by a nonlinear equation containing a power penalty term. Then we will show that the solution of the penalty equation converges to that of the mixed complementarity problem at an exponential rate, which is depending on the parameters in the penalty equation, and some numerical results are presented to verify the theoretical results.
Misfit – Impact on Porcelain Fracture and Screw Loosening of Implant-supported FDPs - A Laboratory Pilot Study

Syfte: Syftet med denna laborativa pilotstudie var att undersoka huruvida en misfit mellan ett implantat och en skruvretinerad bro okade risken for sprickor och chip-off frakturer i porslinet, samt risken for uppskruvning av implantatskruvarna. Material och metod: Tjugo stycken implantatstodda skruvretinerade femledsbroar tillverkades i titan med ytporslin. Provkropparna fordelades lika mellan en testgrupp och en kontrollgrupp. I testgruppen skapades en misfit pa 150 μm mellan bron och implantatet vid position 5. Provkropparna genomgick artificiellt aldrande i en cyklisk belastningsmaskin i 100 000 cykler. Synliga sprickor och chip-off frakturer noterades under och efter testet och skruvarna kontrollerades gallande uppskruvning. Resultat: Synliga sprickor i porslinet uppstod signifikant mer frekvent i testgruppen i jamforelse med kontrollgruppen. Nio broar i testgruppen uppvisade sprickor jamfort med en bro i kontrollgruppen. Tre chip-off frakturer uppstod i testgruppen jamfort med noll i kontrollgruppen, skillnaden var inte statistiskt signifikant. Vid kontroll av implantatskruvarna sags inga signifikanta skillnader mellan varken implantatpositioner eller grupper gallande retorque-varde. Inga av skruvarna var losa. Slutsats: Inom denna studies begransningar tyder resultaten pa att narvaron av en misfit mellan implantat och overkonstruktion kan oka risken for sprickbildning och/eller chip-off frakturer. Vidare studier kravs for att bekrafta dessa resultat.
Co-Designing Hardware/Software to Protect Crypto-Chip Side Channel Analysis at Design Time

SCA is an influential method used in physical cryptanalysis. Generally, the assessment of SCA security is carried out post-production in typical industrial procedures. This post-production analysis, however, is prone to errors, costly, and time intensive. Thus, to heighten efficiency, it has been proposed to carry out SCA analysis during design-time, drawing inspiration from the ethos of hardware/software co-design. This paper first introduces the limitations of traditional embedded system software/hardware design and the process of software/hardware co-design and a general SCA leakage model, followed by an overview of the process behind a design-time security analysis. Next, we establish a versatile simulation and analytical framework to evaluate various SCA leakages. In conclusion, we provide a demonstrated experiment that carries out a design data SCA analysis for the crypto-chip integrated having an AES-128 core.