Miracle-dz/UniFAR-FTUs huggingface dataset:
Parametric Research on Proportion Characteristics of SUV Body Rear End Design

In order to explore the development trend of SUV body rear end design, a parametric research of SUV rear-view proportion characteristics is conducted on the basis of image processing. Firstly, the proportion characteristics of SUV body rear end design were extracted and transformed into two major parameters, namely height and area. Then the (relative) slope difference and linear goodness of fit of the data were used to compare the significance of trends and the validity of the analysis. It can be found that the proportion of different SUV series has a common development trend in terms of the rear window and rear bumper. But the characteristics of headlights and the license plate are greatly affected by the differences in brands and models. In the SUV body design of the future, the relative area of the rear window can be appropriately decreased, while the relative height of the lower line of the rear window and the relative area of the rear bumper can be increased. Additionally, taillights can be designed with greater freedom, but the layout of the license plate should not be too low, thus preserving the harmony of the body rear end design.
Horizontal structure of hemispheric forecast error correlations for geopotential and temperature

Abstract A detailed study of hemispheric forecast-error (f.e.) statistics for the operational Canadian large-scale forecast model was made in preparation for reparameterization of the correlation function used in the data assimilation step of the forecast cycle. The choice of an appropriate function type for geopotential height and temperature f.e. lag-correlation representations was a major concern. A number of possible functional representations, both isotropic and anisotropic, were considered, and the goodness-of-fit of the various candidate functions to correlations computed from observed f.e. data were compared. The following special form of the second-order autoregressive function was adopted as the function of choice: (1 + c|s|)e−c|s|, where |s| is geographic location separation. An examination of f.e. correlation structure on a regional basis revealed large differences from region to region. Latitudinal, pressure-level, and seasonal dependencies of the statistical structure of constant pressure-su.
Spectrum sensing statistics based-GLRT algorithm in cognitive radio

In Cognitive Radio, because secondary users are completely unknown to the primary users, a statistics algorithm about spectrum sensing is constructed in the paper, which overcomes the shortcoming that the characteristics of the primary users signal and channel are completely unknown to secondary users. The statistic, based on generalized likelihood ratio test (GLRT), is only calculated by means of the received signal. The unknown parameters of the channel are also obtained based on maximum likelihood estimates (MLE) by making use of the sampling signal covariance matrix. Theoretical analysis shows that we can compute the detection probability of primary users with the covariance matrix eigenvalue and the simulation proves that the statistics has the characteristics of the simple calculation and practicability.
Combination theory and equilibrium evaporation

This paper is an analysis of equilibrium evaporation and its role in the energy balance of a terrestrial surface, as described by combination theory. Three themes are covered: first, a brief historical review identifies multiple definitions of the concept of equilibrium evaporation. Second, these are formalized by developing the basic principles of combination theory with minimum approximation. Several measures are utilized to do this: linearization is avoided, radiative and storage coupling are incorporated systematically, and actual and linearized saturation deficits are distinguished. The formalism is used to analyse several algebraically defined states and limits for the surface energy balance. Third, the thermodynamic foundation of equilibrium evaporation is analysed by studying surface‐atmosphere feedbacks in arbitrary closed and open evaporating systems. It is shown that under steady energy supply any closed evaporating system evolves towards a quasi‐steady state in which the Bowen ratio takes the equilibrium value 1/εv, where εv is the ratio of the latent‐ and sensible‐heat contents of saturated air with temperature, evaluated at the volume‐averaged temperature in the closed system. This applies whether the system is well‐mixed or imperfectly mixed, and whatever the internal distribution of surface fluxes and surface and aerodynamic resistances. In contrast, open systems cannot reach such an equilibrium. This evolutionary definition of equilibrium evaporation differs from an alternative algebraic definition, the fully decoupled limit. The differences between the two definitions are identified, and the evolutionary definition is shown to be more fundamental. Thus, the correct temperature for evaluating ε in determining equilibrium evaporation is the volume‐averaged temperature in a closed region, which in the case of a convective boundary layer is well approximated by the mixed‐layer temperature.
An Evaluation of Cybersecurity Risks of DLMS/COSEM Smart Meter Using Fuzzing Testing

Since smart meters are IoT devices that can share energy usage information to users and third-party service providers, any vulnerability of smart meters that can be exploited by hackers is a threat to the reliability of smart grids. The purpose of this paper is to explore the possibilities of DoS attacks to smart meters. Our research focuses on the High-Level Data Link Control layer of DLMS/cosEM smart meters. The testing cases are generated by fuzzing testing methods according to the HDLC specifications. The CTT 3.1 is used as the client sending requests to the smart meter server. The responses of the server are then recorded and analyzed for potential vulnerabilities. The results show that the fuzzing test cases on the buffer overflow/underflow category generate most of the abnormal responses.
High power and high bandwidth CMOS driver for hybrid interconnected GaN microdisplay

GaN technology have been a revolutionary development in the light emitting sources field. Their high efficiency, high bandwidth, high lifetime and high integration capabilities has opened a brand-new research field. Furthermore, the integration of hybrid microdisplays based on GaN-on-Si allowed the development of high efficiency visible light communication devices and new branch of image applications among others. In this work, we present an in-pixel driving circuit with switching capabilities up to 1MHz and LED bias current up to 120µA. The pixel driver is designed to be integrated in a 512x512 microdisplay of 1411 ppi with 10kfps working capabilities. The in-pixel driver allows to eliminate the leakage problems of the conventional drivers for hybrid interconnexion, besides having high brightness and high-speed capabilities.
Personalized Driver Car-Following Model — Considering Human's Limited Perception Ability and Risk Assessment Characteristics

Understanding and modeling driver behavior is crucial to the design, testing and evaluation of intelligent driving systems. In this study, a personalized car-following model with considering human's limited perception ability and risk assessment characteristics is proposed to enhance the accuracy and the scope of application of driver model. This paper presents a modular personalized driver following model framework to describe car-following behavior. In the perception module, human's limited perception ability and risk assessment characteristic are modeled based on normal distribution and fuzzy logic. In the decision module, we proposed a personalized spacing strategy and a speed-distance index car-following model. The simulation result indicated that the accuracy of the proposed model can meet the requirements. In addition, since the parameters of the model can be flexibly adjusted and have clear physical significance, the proposed model can describe personalized car-following behavior.
Educational software-as-a-service based on JupyterHub and nbgrader running on Kubernetes

We present our cloud-based software-as-a-service (SaaS) solution for educational purposes deployed within the Engineering faculity of our university. Its foundation is the open source Jupyter Notebook project with a web application providing an online environment for various programming purposes. These Jupyter Notebooks have evolved into the JupyterHub project. JupyterHub enables multi-user capabilities and allows students and researchers to have their individual Notebook environments hosted on an online platform. This paper presents our experiences with JupyterHub together with nbgrader, which allows the creation and grading of assignments. This powerful educational SaaS solution has been validated for multiple programming related courses, such as Python, C, C# and Java, throughout the past two academic years. Feedback from students and academic personnel is positive. JupyterHub is currently actively developed for deployment on the popular cloud-based Kubernetes container orchestrator platform, providing nearly infinite cloud-based scaling potential. This open source platform-as-a-service (PaaS) solution can be consumed from various public cloud providers, or it can be set up on-premise. We introduce the means to do the latter on a data center within the university.
Stencil Printing of Low-Cost Carbon-Based Stretchable Strain Sensors

The rapidly growing field of stretchable sensors has recently produced a myriad of sensing devices. However, these are often realized using expensive materials and complex manufacturing techniques. This work demonstrates stretchable stencil printed strain sensors, fabricated using either graphite or carbon black as a conductive filler in a polydimethylsiloxane (PDMS) matrix, that was also used as the device substrate. The strain sensors demonstrated highly linear responses and sensitivities (R2 = 0.95 and R2 = 0.98, and gauge factors of 2.77 and 1.50, respectively) that are comparable to other published sensors manufactured using similar or more complex processes.
Fretting and Electrical Contact Resistance Characteristics of Carbon Nanoparticle-Coated Cu Electrical Contacts

Fretting wear is a phenomenon that can severely affect the duty life and efficiency of electrical contacts. To reduce its effect, the contacting sites are lubricated; however, these are usually detrimental to the electrical characteristics of the system. Therefore, in this work we propose carbon nanoparticle coatings. Carbon black, graphite flakes, graphene oxide, and carbon nanotube coatings were produced via electrophoretic deposition over copper surfaces. These coatings have the potential to act as solid lubricants – thus minimizing the damage incurred on the connector by fretting – while also having a minor effect on the conductivity of the substrate. To evaluate the lubricity of the coatings and their impact on the electrical conductivity of the system, fretting tests and electrical contact resistance measurements were carried out. The coatings and the fretting marks were morphologically and chemically characterized by electron microscopy and spectroscopy. Furthermore, the worn area of the fretting marks was evaluated using confocal laser scanning microscopy. The worn and oxidized areas of the coated samples were smaller than those of the copper reference after 50,000 fretting cycles – excluding the graphite coatings. For prolonged fretting cycles the resistance of all coated samples fell below that of the uncoated reference, highlighting their advantage.
Electrothermal analysis of temperature-limited loads for domestic induction heating applications

One of the most important factors in the development of a home appliance is the safety of the users. For this reason, the temperature control and the protection against human errors, such as the abandonment of the cookware while cooking, are always of interest in induction heating applications. This paper presents a solution for self-limiting the temperature in cooking vessels that prevents its overheating, protecting the system and preventing food from burning. It is proposed the manufacture of cookware with materials whose electromagnetic properties change at a preset temperature within the range of interest in cooking, these changes cause the power transmitted to the load to drop considerably providing self-protection capabilities against overheating. This solution does not require additional sensors or communication with the induction cooktop, making it a simple and economical option. The performance of the proposed solution has been analyzed using a multiphysics tool, which includes electromagnetic and thermal submodels, and allows to obtain the temperature distribution and the changes in the system as the vessel heats up. Finally, an experimental verification of the model and the solution has also been presented.
MEMS Self-Packaged Capacitive Absolute Pressure and Force Sensors for High-Temperature Application

This paper presents shielded capacitive absolute pressure and force sensors for high-temperature applications with low-temperature drift. Design and flexibility of the novel manufacturing process allow adjustments to the sensor type, dimensions, measuring range and temperature ranges. The electrodes are contacted through the developed innovative through-silicon vias (TSVs), which together with the eutectic bonding offer a new approach for the realization of a hermetically-sealed measuring cell. The sensors are self-packaged and fabricated entirely from monocrystalline silicon, which is particularly suitable due to its highly reproducible elasticity properties. The measurements show a nonlinearity error of the inverse capacitance of 0.003 % full scale (FS) and high thermal stability of only - 0.0005 % FS / K at 220 °C without additional mechanical or electrical compensations.
Identifying Plasmon-Exciton Coupling in Au Nanoislands Coated with Thin Films of J-Aggregates

The coupling between plasmons and excitons gives rise to a hybrid quasiparticle known as a plexciton. Plexcitons have vast sensing applications in photodetection, refractive index sensing, gas sensing, probing hot carriers, creation and detection of entangled states, etc. Plexcitonic coupling can exist in the weak coupling, intermediate coupling and strong coupling regimes. Of these regimes, strongly coupled plexcitonic states are the hardest to create and observe, and yet are the most desirable for high fidelity sensing applications. Herein, we report the observation of Rabi splitting in cyanine dye J-aggregates on gold nanoislands, which indicates strong plasmon-exciton coupling.
Novel Gate Electrode Design for Flexible Planar Electrolyte-Gated Field-Effect Transistor-Based Sensors for Real-Time Ammonium Detection

In this work, the comparison between two different gate electrode layouts (square-shaped and U-shaped, designed with the same area) for flexible and planar electrolyte-gated field-effect transistor-based sensors for ammonium detection is reported. Spray-deposited semiconducting carbon nanotubes were employed as active material, while the functionalization was achieved by means of an ion-selective membrane, based on the nonactin ionophore. The devices fabricated with the two designs were compared in terms of the response towards different concentrations of ammonium. Both showed excellent linear detection for ammonium in the physiological range of interest from 0.1 to 10 mM. The devices with the square-shaped gate achieved average sensitivity of 1.270 µA/decade (relative standard deviation 70.55%), while the devices with the novel U-shaped gate showed an improved sensitivity of 2.669 µA/decade (relative standard deviation 31.46%).
Robust Artificial NN-based Tracking Control Implementation of Grid-Connected AC-DC Rectifier for DC Microgrids Performance Enhancement

This paper introduces the control and operation of a grid-connected converter with an energy storage system. A complete mathematical model was presented for the developed converter and its control system. The system under study was a small microgrid comprising an AC grid that is feeding a DC load through a converter. The converter was connected to the AC grid through an R-L filter. The classical linear controllers have limitations due to their slow transient performance and low robustness against parameter variations and load disturbances. In this paper, machine-learned controllers were used to dealing with those drawbacks of the traditional controller. First, a study for conventional nested loop Proportional Integral (PI) was introduced for both outer and inner loops PI-PI controller. A Data-Driven Online Learning (DDOL) controller was then proposed. This controller was a Proportional Integral Neural Network (PI-NN) that enhanced the system performance in terms of dynamic and steady-state responses. A comparison between the normal traditional PI-PI controller and the proposed DDOL ones was made under different operating scenarios. The converter control was tested under various operational conditions, and its dynamic and steady-state behavior was analyzed. The model was done through a MATLAB Simulink to check the normal operation of the network in a grid-connected mode under different load disturbances and AC input voltage. Then, the system was designed, fabricated, and implemented in a hardware environment in our testbed, and the test results were verified. The results show that the intelligent controller would achieve better performance in both dynamic and steady-state responses.
Comparing the Prediction Accuracy with Passenger Time Series of Taipei MRT under Different Deep RNNs

In recent years, time series forecasting has been one of the hot research topics in prediction. This paper is based on the passenger time series data of Taipei MRT to make a prediction. By comparing the prediction accuracy with different deep RNN models; namely, LSTM (long short -term memory), GRU (Gated Recurrent Unit), TCN (Temporal Convolutional Network). Besides, LSTM and GRU are both modified RNN (Recurrent Neural Network) variants. TCN uses multi-layer dilated convolution and input padding, etc. techniques. This paper used three models of the deep neural networks to compare the prediction accuracy on medium-sized time series dataset Taipei MRT.
The double structure of W3-IRS 5 as determined from high-resolution spatial scans

Slit scans over the infrared source W3-IRS 5, from 4.8 to 12.7 microns are interpreted in terms of two sources of luminosity separated by 0.9 arcsec in declination. The data, as a function of wavelength, show that the two sources must have remarkably similar properties.
Modeling the Thermal and Power Control Subsystem in HPC Processors

In the last decade, high-performance multi-core processors have become pervasive. Processors with high cores count, multi-die SoC configuration with multiple heterogeneous units, and accelerators are commonplace today, while tailored and specific operating points are required for efficient execution of applications. In this scenario, an advanced and configurable Power Controller System (PCS) is essential to meet power and thermal constraints, as it is not effective to rely on simple reactive control policies with static conservative margins on the operating points. This paper provides a mathematical model of an HPC processor and the different control approaches implemented in state-of-the-art PCS solutions. In particular, we are comparing the performance of a custom cascade model-based control algorithm that favors cores executing more demanding workloads with the IBM Power9 control algorithm used as a reference design. The results show an average increase in the number of retired instructions of 3.46 %, with a peak of 6.45 % increase for the cores executing more demanding instructions. - Modeling, Simulation, Nonlinear systems
Comparison of Cascaded and Flatness-Based Control of a Pneumatically-Driven Rotary Joint

When it comes to robot joints, pneumatic actuators are an interesting alternative to classic electric actuators. They are inherently compliant due to the compressibility of air, which is an advantageous safety feature for human-machine collaboration. In this work, we consider a recently developed pneumatic swivel drive, to be employed as a robot rotary joint. This joint exhibits strong nonlinearities, which makes it challenging to design a trajectory tracking controller. We construct the nonlinear multiple input multiple output model of the joint's kinetics and pressure dynamics. Based thereon, two model-based nonlinear control concepts are presented. The first one is a cascaded controller consisting of an outer loop with a joint angle controller plus disturbance observer and an inner pressure control loop. For the second concept, differential flatness of the system is shown and a flatness-based controller is derived, which combines both kinetics and pressure dynamics in one central controller. Experimental results demonstrate the trajectory tracking performance of both controllers. Based on these results, the differences between the two control strategies are analyzed. This provides the foundation for the control of more complex pneumatically-driven robot manipulators.
NFL Prediction using Committees of Artificial Neural Networks

This paper analyzes the ability of a neural network model to predict the outcome of NFL games. This model uses only readily available statistics, such as passing yards, rushing yards, fumbles lost, and scoring. A key component of this model is the use of statistical differentials to compare teams. For example, the offensive passing yards gained by one team are compared to the defensive passing yards allowed by an opposing team to create a data set of expected values for a given matchup. By using principal component analysis and derivative based analysis, we determined which statistics influence our model the most. We assessed the performance of the model by comparing its performance to that of published prediction algorithms and the Las Vegas oddsmakers over multiple seasons. Two novel aspects of this work include the use of multiple committees of machines for prediction and the use of our model to simulate virtual round-robin tournaments to establish an objective ranking of the teams.
A Hybrid Model for Early Prediction of Stroke Disease

In the healthcare industry, the application of artificial intelligence technology has resulted as an incredible shift in terms of early diagnosis and identification of fatal diseases like cancer, heart attack, stroke etc. Out of which stroke is the second most deadly disease following coronary artery disease. A stroke ensues when blood artery in the brain aneurysm leaks and bleeds, that can cause brain damage or even death. The aim of this paper is to propose an efficient three layer hybrid model for early prediction of stroke disease. The proposed system assisted in tuning the hyperparameters using a grid search approach. Various machine learning classifiers Decision tree, Random Forest, Support vector machine, K-nearest neighbour and XgBoost were applied on stroke prediction dataset. It has been analyzed that after class balancing and hyperparameter tuning of classifiers, the SVM classifier outperforms with 98.45 accuracy which is 2.5 percent more than recent research works.