Miracle-dz/UniFAR-FTUs huggingface dataset:
A Multiphase Series Connected Converter for High Voltage High Power Dc-dc Applications

Modular multilevel series topologies such as parallel hybrid converter (PHC) are gaining popularity in high power applications owing to fewer sub-modules (SMs), low energy storage and switch requirements. However, the existing PHC requires bulky dc link inductor due to the presence of large voltage ripples at the dc link. Moreover, the voltage handling capability of the PHC is limited because of the use of series-connected switches in its H-bridges. This paper presents a series-stacked multi-phase topology suitable for high voltage high power dc-dc conversion applications. The multiphase structure employed in the proposed converter helps in increasing power level, improving modularity and reliability, and minimizing the dc filtering requirement. A multi-phase front-to-front simulation model developed in PSCAD/EMTDC affirms the performance of the proposed converter. The working principle of the proposed topology is also verified by a developed single-stage hardware prototype.
Android Application Prototype to Study Text Reading Speed and Comprehension

The paper examines a prototype Android application to investigate text reading speed and comprehension. The two types of reading - informative reading and reading with comprehension, the different metrics for measuring reading speed, as well as the factors affecting this speed are examined. Based on the literature analysis and analysis of existing applications, the functional requirements for such applications have been determined. The development and basic steps in working with the prototype are described. The results of two tests are shown and corresponding conclusions are drawn.
Reinforcement Learning Based Optimal Energy Management of A Microgrid

Modern day power systems are characterized by bi-directional power flows, e.g. vehicle-to-grid (V2G) and peer-to-peer (P2P) energy sharing systems. However, the energy scheduling is pivotal to ensure the optimal resource sharing and utilization in these interconnected systems. Renewable energy based distributed generators (DGs) have stochastic outputs and the customer typically produces excess energy comparing to the load. The imbalance between generation and load introduces complexities in the power systems as number of decision variables can be substantially increased for scheduling algorithms. There is a need of an intelligent energy management system (EMS) where we can deal with the stochastic nature of the microgrids (MGs). Recently, the reinforcement learning (RL) based techniques gain a significant interest in MG's scheduling problems. In this paper, we propose a RL based generic environment and explored two model free algorithm for scheduling problem where the RL agent can incorporate a set of constraints and energy balance rules across the system. Proximal Policy Optimization (PPO) as on-policy and Deep Deterministic Policy optimization (DDPO) as off-policy are two RL algorithms used to explore and analyze the system's performance and stability. Both the algorithms showed a great performance as both achieved the system stability with a speedy processing time.
Deep Q-learning Sampling Based on Advantages

Deep Q-learning (DQN) has shown recent success on a wide range of complicated sequential decision-making issues, especially in the classic control area. However, in most DQN training, the sampling policies, particularly the $\epsilon$-greedy policy and its derivation rules, produce noise erroneously due to the unsteadiness and unreliability of Q values utilized for searching for optimum actions. We begin with a fundamental hypothesis: complete advantages-based exploitation may be employed to help in successful exploration, and the gap between advantages on outstanding and poor actions tends to extend during the early phases of training before stabilizing. We then present a novel DQN architecture with a superior sampling policy that is totally and directly based on advantage values. This new DQN is named A-sampling DQN, and it requires minor code changes based on the original DQN codes. In most cases, A-sampling DQN and its derivatives outperformed the traditional DQN method.
Enhancement of electric-field-induced change of magnetic anisotropy by interface engineering of MgO magnetic tunnel junctions

Electric-field-induced modification of magnetic anisotropy is studied using tunnel magnetoresistance of the Co40Fe40B20/ MgO/ Co40Fe40B20 and Co40Fe40B20/ Hf (0.08 nm)/ MgO/ Co40Fe40B20 magnetic tunnel junctions. In both systems, the interfacial perpendicular magnetic anisotropy is increased with increasing electron density at the MgO interface. A quantitative comparison between the two systems reveals that the change of magnetic anisotropy energy with electric field is significantly enhanced in Co40Fe40B20/ Hf/ MgO/ Co40Fe40B20 compared to Co40Fe40B20/ MgO/ Co40Fe40B20. The sub-monolayer Hf insertion at the Co40Fe40B20/MgO interface turns out to be critical to the enhanced electric field control of the magnetic anisotropy, indicating the interface sensitive nature of the effect.
Adaptive Fuzzy Control for State Constrained Pure-Feedback Nonlinear Systems with Input Nonlinearity

This paper focuses on the problem of adaptive fuzzy backstepping control for a class of pure-feedback nonlinear systems with time-varying state constraints, input saturation and dead-zone, and unmatched external disturbances. The adaptive fuzzy models are employed to online approximate some uncertain smooth functions. Unlike many neighboring works, this paper carefully solves the open problem of the feasibility of the virtual control laws in the barrier Lyapunov function-based control design framework. By using disturbance observers in a backstepping framework, the estimation errors due to the use of a priori bounded virtual control law are appropriately compensated and the “explosion of complexity” is practically avoided. The proposed controller ensures the convergence of the output tracking error to an arbitrarily tiny neighborhood of the origin, boundedness of all closed-loop signals and no-violation of the predefined state constraints. A numerical example is provided to validate the effectiveness of the proposed method.
Analysis of Dual Phase-shifted Full-bridge Converter with Modular Asymmetry

This paper presents an analysis effort on a dual-PSFB converter in IPOP configuration, examining its functional and behavioural aspects under nonideal conditions in the form of parametric asymmetries,viz., duty-ratio, transformer turns-ratio, filter and leakage inductances. The transition mechanism of individual modules into CCM and/or DCM modes as well as their power sharing characteristics amidst module asymmetry are investigated by leveraging per-unitized analytical expressions. The impact of multiple simultaneous asymmetries in the converter under open- and closed-loop settings are examined. Collectively, these analytical examinations not only uncover the parametric sensitivity on performance of the converter modules but also offer a theoretical basis to dual-PSFB operation under practical conditions. The converter analysis and expressions are validated through simulations as well as experimental verification on a laboratory dual-PSFB hardware.
Analysis and Solution for Resonance of Grid Side Current at Zero-Crossing Point of GaN-Based CRM Totem-Pole PFC

The GaN-based critical conduction mode (CRM) totem-pole power factor correction (PFC) converter is a promising candidate for high-efficiency rectifier. When the EMI filter is connected, the grid side current will have low-frequency resonance at the zero-crossing point, which will worsen the THD of the current. This paper analyzes the causes of this problem, and puts forward a solution based on controlling the average value of inductor current. The proposed solution is only realized from the control, which will not increase the hardware cost and is easy to implement. A prototype is built to verify the effectiveness of the scheme.
Mathematical Modeling and Analysis of the Dictionary Attack Mechanism in IoT Malware Mirai

This paper proposes a model of the dictionary attack mechanism. This is a mathematical model that divides the infection mechanism into two parts, dictionary, and procedure, and considers the number of trials as the time axis. The proposed model revealed the risks and characteristics of dictionary attacks in Mirai. Our analysis result shows that the mechanism cuts its losses against devices that cannot be penetrated by reducing the number of infection attempts to the same device. In addition, it was confirmed that the infection mechanism of Mirai has a trade-off relationship between the number of infections and the success rate of infection.
Ubiquitous Multimodal Seizure Management Using Emerging Consumer Technologies

Seizures are a significant public health concern which impact the quality of life for millions of individuals worldwide. Although dedicated consumer-grade technologies for assisting in seizure management exist, they are unsuitable for detecting all types of seizures due to their strong heterogeneity in presentation. This manuscript proposes a novel multimodal seizure management solution using emerging wearable computing technology. By fusing signals with known efficacy for seizure detection, including eye movement and motion signals, this approach offers a modular solution for supporting seizure sufferers with no additional hardware requirements. The system also offers additional value-added applications beyond seizure detection, including automated data logging and programmable notifications for caregivers.
A Deep Temporal Graph Convolutional Neural Network for Network Traffic Forecasting

Network traffic forecasting is the key issue of network resource management. Network traffic forecasting accuracy, timing, is directly related to the efficiency and performance of the network management. However, the existing traffic forecasting models have relatively shallow models and cannot obtain network traffic data temporal and spatial deep characteristics. Therefore, we propose a deep temporal dropout graph convolutional neural network (TDGCN) architecture for network traffic prediction. TDGCN uses a gated linear unit (GLU) to obtain the temporal convolution feature of network traffic. To obtain spatial structural features, we propose a novel dropout graph generation method and develop a deep graph convolutional network model based on a dropout graph. The increase of network depth will reduce the training efficiency of the model, and even fail to converge. We design a novel deep batch normalization(DBN) method. Zero mean and unit normal form are added to the weight matrix (filter) of neural network. TDGCN can obtain deep features of the network topology spatial structure. Finally, TDGCN is verified on the dataset of UKERNA. In comparison with traditional methods, our method has better performance.
Thermal Management of the Passenger Compartment in Electric Vehicles

Thermal management of the passenger compartment is an important issue for electric vehicles. This paper summarizes current heating and cooling techniques proposed for the car cabin of electric vehicles, which are categorized as the vapor compression refrigeration with electric heating system, the heat pump system and other systems. The advantages and challenges of each system are discussed in detail. Application samples are also provided at the end for better understanding of air conditioning thermal management techniques used in the current market.
Tracking Power Photovoltaic System with Robust Polynomial Output Feedback Control Strategy

This paper proposes a new robust stabilization conditions for Maximum Power Point Tracking (MPPT) control of photovoltaic (PV) systems based on polynomial fuzzy systems. In this scheme, an output feedback controller is exploited to reduce the number of needed sensors. The stabilization conditions are formulated in terms of sum-of-squares (SOS) conditions, which can be solved efficiently by using semidefinite program-ming (SDP) tool. The efficiency of the proposed controller is demonstrated through the simulation results.
Development of Internet of Things System for Environment Control in Niam Hom (Strobilanthesnivea Craib) House

This research aims to 1) Study the use of IoT technology to measure soil moisture and air humidity and control water spraying and brightness values in Niam Hom Houses, and 2) Develop systems and tools for users to monitor and record the house's temperature, soil moisture, air humidity, and brightness values. The development tool uses Arduino MEGA and NodeMCU ESP8266 to connect the sensors to obtain data from a specific environment. Design and control the measurement circuit system in the farmhouse with a size of 4 x 6 meters using black shading nets of 50% and 70%. The IoT system helps to control soil moisture, and the air humidity is good, making onion trees grow well. Good yield and different physiology in black shading net 50% in combination with chemical fertilizer application.
Investigation of Tribocorrosion Properties of Titanium Implant used in Orthopedics

A titanium implant is a mechanical system that undergoes tribocorrosion at the interface between the implant and the abutment alloy, where material degradation is common. When various titanium implant materials, which are used to fix the bones broken by trauma or to replace a joint or bones, such as osteoporosis, are exposed to a simulated body fluid (SBF), the effects of wear and corrosion phenomena in these titanium implant materials are observed to be determined quantitatively. For the samples extracted from Titanium-Ti6Al4V screws belonging to two different companies (A and B), very important parameters for tribocorrosion such as wear rate, depth of wear and corrosion rate were determined experimentally in the laboratory environment. The results were examined how suitable titanium is to resist material loss in body implants due to both wear and corrosion. In addition, it was determined that the wear rates obtained from the sample belonging to company B were more stable than those of company A, and the corrosion rates (0.0047 mm/year) obtained from the sample of company A were lower than those of company B.
A High-Accuracy Power Loss Model of SiC MOSFETs in Current Source Inverter Considering Current Commutation and Parasitic Parameters

Power loss estimation of power electronic devices is important to the efficiency optimization of motor drives used in many applications. However, most existing simplified power loss models of silicon carbide (SiC) MOSFETs are not sufficiently accurate due to their neglect of parasitic parameters in the current commutation loop. In addition, the loss model of the voltage source inverter (VSI) cannot be directly applied to the current source inverter (CSI) because of differences between their commutation loops. First, the commutation processes for VSIs and CSIs are compared. The voltage and current trajectories of SiC MOSFETs in the switching transition of a CSI-based motor drive system are analyzed in detail. Based on these results, the conduction and switching losses of SiC MOSFETs in CSIs are modeled considering the current commutation details. In addition, the proposed analytical model includes parasitic inductances and capacitances in the current commutation loop. Experimental results have verified that the proposed power loss model delivers higher accuracy loss predictions than the conventional loss model.
Efficient Bio-Robotic Estimation of Visual Dynamic Complexity

Visual dynamic complexity is ubiquitous, hidden attribute of the visual world that every motion-sensitive vision system is faced with. However, it is implicit and intractable which has never been quantitatively described due to difficulty in defending temporal features correlated to spatial image complexity. Learning from biological visual processing, we propose a novel bio-robotic approach to estimate visual dynamic complexity, effectively and efficiently, which can be used as a new metric for assessing dynamic vision systems implemented in robots. Here we apply a bio-inspired neural network model to quantitatively estimate such complexity associated with spatial-temporal frequency of moving visual scene. The model is implemented in an autonomous micro-mobile robot navigating freely in an arena encompassed by visual walls displaying moving scenes. The response of the embedded visual module can make reasonable prediction on surrounding dynamic complexity since it can be mapped monotonically to varying moving frequencies of visual scene. The experiments demonstrate this “predictor” is effective against different visual scenarios that can be established as a new metric for assessing visual systems. To prove its viability, we utilise it to investigate the performance boundary of a collision detection visual system in changing environment with increasing dynamic complexity.
Sea Clutter Data Simulation based on VI CFAR Algorithm

VI (Variability Index) technology firstly judges the uniformity of clutter background, then automatically selects a relatively uniform reference sliding window to estimate the background clutter power, which has better detection performance in the multi-target background and clutter edge background. This paper aims at the problems of low detection performance of CA-CFAR detector in the multi-target background, high false alarm spike in clutter edge background, and high false alarm spike in OS-CF AR detector in clutter edge background, by applying ML VI-CFAR detector and OSVI-CFAR detector to the Weibull clutter background, the detection performance of the detector in the background of multiple targets and the background of clutter edge is improved.
Deep Learning Based Multi-Classifier System for Crack Segmentation in Civil Structure Images

In civil structures, cracks are one of the initial signs of structure deterioration. Therefore, cracks identification is an essential task for structures maintenance. In this framework, automatic inspection of structures is a suitable replacement to the manual approaches. These automatic methods are mainly based on computer vision techniques which has had a growing interest in last decades. Crack segmentation is similar to edge detection problems; thus, it could be solved by edge detection methods. In this paper, cracks in civil structures such as concretes and pavements are segmented by a Convolutional Neural Network (CNN) based multi-classifier system which applies pixel-wise segmentation on images of cracks. The dataset we have used made of 537 three channel images with manual annotation maps. The final results claims that the proposed method with F-score of 86.8, has good performance which is superior compared to holistically nested networks like HED and DeepCrack.
A Method to Compensate for the Distortion of the Output Voltage of an H-Bridge Inverter Under Sinusoidal Unipolar PWM

The output voltage of an inverter (output of the LC filter) significantly deviates from the ideal sinusoidal waveshape under sinusoidal unipolar pulse width modulation with a sinusoidal modulating signal. Due to the error between desirable and practical waveshape of the output voltage, the total harmonic distortion deteriorates. To compensate for the distortion, the existing approach requires a lot of computational efforts to correct the pulse width of each gate pulse over an entire fundamental AC period. This paper proposes a simple method to reshape the modulating signal based on the predicted error signal in the rotating frame of reference. The proposed technique is validated experimentally on a DQ-controlled IGBT-based inverter module operating at 500 W, 110 V. The experimental results show an improvement in THD of the output voltage by approximately 5%.
A New Multi-Phase DC-DC Converter with Interleaved TCM Si Phases and High-Frequency Auxiliary SiC Phase

A new hybrid Si/SiC multi-phase interleaved dc-dc converter is proposed in this article, which consists of two interleaved large-capacity Si phases and an auxiliary small-capacity SiC phase. The Si phases operate in Triangle Current Mode (TCM) to process the base power with Zero Voltage Switching (ZVS) capability and lower switching frequency, obtaining significantly reduced switching losses. While the auxiliary SiC phase handles a small part of the total power, and its switching frequency is high and rated current is small, which is mainly used to compensate the current ripple of the Si phases to obtain superior current and voltage waveforms and fixed frequency spectrum. In order to coordinate the three phases with different current modes and switching frequencies, a hybrid-frequency interleaved control strategy is proposed. Finally, we present a 3.1 kW mixed mode Si/SiC multi-phase interleaved dc-dc converter, it exhibits a peak efficiency of 96.8% and a 120kHz effective frequency.
Multi-Object Tracking Algorithm Based on Multi-layer Feature Adaptive Fusion

In recent years, computer vision applications based on deep learning have developed rapidly, and systems such as smart buildings, smart cities and surveillance and security have become key research areas for research scholars. Although scholars at home and abroad have made remarkable achievements in the field of multi-object tracking, in complex scenes with dense targets and indistinguishable target backgrounds, multi-object tracking algorithms still suffer from problems such as target miss detection and false detection, resulting in a large number of disconnected, erroneous and other low-quality target tracking trajectories. To address the above problems, this paper proposes a Multi-Object Tracking Algorithm Based on Multi-layer Feature Adaptive Fusion (MFAF-MOT). This algorithm designs a high-level semantic adaptive weighting module and effectively achieve multi-layer feature fusion. By adaptively weighting deep high-level semantic features and shallow fine-grained features, this algorithm improves the network's ability to express target features and reduce errors such as target miss detection and false detection. The algorithm proposed in this paper achieves competitive results in three different scenarios, reducing problems such as disconnected target trajectories and wrong target trajectories, and improving the precision and stability of the multi-object tracking algorithm.
Short circuit ruggedness of SiC MOSFETs for high reliability applications

The electrification of aerospace is being driven by the need to reduce carbon emissions in order to meet the requirements of Net-Zero by 2050. To support this, the short circuit performance and switching losses of a family of 1200 V SiC MOSFETs has been evaluated under conditions relevant to those found in high reliability applications, such as aerospace. The application in high reliability systems requires both ruggedness under fault conditions in addition to the conventional electrical performance. Fault conditions were simulated using Hard Switching Fault (HSF) tests, where the MOSFET was short circuited across a 270 V DC bus, matching the power system in commercial aircraft. Silicon Carbide MOSFETs with on-state resistances of 60 $\mathbf{m}\Omega, 90\ \mathbf{m}\Omega$ and 140 $\mathrm{m}\Omega$ showed degradation after being exposed to short circuit fault conditions and demonstrated a shift in both VTH and $\int I_{gs}dt$ prior to failure. However, there is no indication of degradation prior to catastrophic failure for devices from the same family with 30 $\mathbf{m}\Omega$ and 45 $\mathrm{m}\Omega$ on-state resistance devices. For these to be incorporated into a high reliability aerospace based power system will require additional protection systems capable of limiting the current flow in the $\mu \mathrm{s}$ regime to avoid catastrophic failure.