Miracle-dz/UniFAR-FTUs huggingface dataset:
Pose-Aware Disentangled Multiscale Transformer for Pose Guided Person Image Generation

Pose Guided Person Image Generation (PGPIG) is a task that involves generating an image of a person in a target pose, given an image in a source pose, source pose information and target pose information. Many of the existing PGPIG techniques need extra pose-related data or tasks, which cause the limitations on their applicability. In addition CNNs are used as the feature extractor which do not have long-range dependency. However, CNNs can only extract features from neighboring pixels and cannot consider image consistency. This paper introduces a PGPIG network that solves these challenges by incorporating modules that use Axial Transformers with wide receptive fields. The proposed approach disentangles the PGPIG task into two subtasks: “rough pose transformation” and “detailed texture generation. ” In “rough pose transformation,” lower-resolution feature maps is processed by Axial Transformer-based blocks. These blocks employ an Encoder-Decoder structure, which allows the network to use the pose information well and improves the stability and performance of the training. The latter subtask employs a CNN network with Adaptive Instance Normalization. Experimental results show the competitive performance of the proposed method compared to existing methods. The proposed method achieves lowest LPIPS in Deep Fashion dataset and FID in Market-1501 dataset. Remarkably, despite the great results obtained, the number of parameters of the proposed network is significantly less in contrast to existing methods.
End-to-End Trainable Deep Neural Network for Radar Interference Detection and Mitigation

The automotive radar sensor is an essential sensor for intelligent transportation systems. However, the increasing number of such systems leads to mutual interference between sensors. Radar interference can be a severe disturbance, where targets within the victim radar are not perceived anymore if no countermeasures are taken. To prevent fatal injuries caused by this disturbance, several classical and deep-learning based methods to detect and mitigate radar interference have been proposed. We present an end-to-end trainable deep neural network for radar interference detection and mitigation. We evaluate different radar interference detection and mitigation methods on a large scale urban driving dataset using simulated radar interference. To our knowledge, this is the first end-to-end trainable architecture for interference detection and mitigation in the time domain. We show that our method achieves state-of-the-art results in interference detection and mitigation. Code for our method, reference methods, simulation, and the evaluation is available at https://github.com/KIT-MRT/ridam.
Data-driven Motion-force Control for Acceleration Minimization of Robots

It is tricky to accurately solve the redundancy solutions of redundant robots with uncertain-structure information. Besides, position-force control as a challenging technical problem is significant for redundant robots with impact forces generated by the end-effector especially. Noteworthily, without considering posture maintenance, the end-effector of redundant robots may experience jittery movements and potentially fail in accurately tracking the target. A data-driven motion-force scheme, considering constraints including position-force control, posture maintaining, and physical joint limits, solved by neural dynamics in the acceleration level, is proposed for redundant robots with unknown structure information. Comparisons and simulation experiments are supplied to substantiate the availability and superiority of the proposed date-driven motion-force scheme.
Development of Novel Conductive Grease to Mitigate PWM Inverter-Induced Bearing Currents

Common mode voltages generated by high frequency, high dv/dt switching of inverters cause shaft voltages and bearing currents in motor-drive systems. When the shaft voltage magnitude exceeds the dielectric insulating capability of the bearing lubricant, a damaging electric discharge machining (EDM) current pulses through the bearing, leading to eventual bearing failure. The integration of wide band gap switching devices such as silicon carbide (SiC) in the motor drive exacerbates shaft voltage and EDM bearing current application issues. the This paper presents a novel conductive bearing grease that was developed to eliminate EDM damage and extend bearing life in SiC inverter driven motors. Shaft voltages and bearing current discharge energies associated with the novel grease are examined along with the measured conductive grease bearing capacitance and the grease consistency. The shaft voltages, bearing currents, and bearing current discharge energies present in the SiC inverter driven induction motor with conductive grease bearings are characterized, the rheological properties of the novel conductive grease are examined and compared to that of conventional bearing grease, and the lifetime of the conductive grease bearings are predicted.
Comparative Study of Nonlinear Black-Box Modeling for Power Electronics Converters

With the increasing penetration level of renewable sources and power electronics loads in modern power systems, accurate and computationally efficient models are needed. Black-box model (BBM) could be a useful method in such systems. However, not very extensive research efforts have been made for power electronics BBM so far, and existing works mostly focus on steady-state operation, neglecting the important transient behaviors such as load transients, voltage transients, and faults. This paper presents a comparative study of three commonly used nonlinear BBM approaches for transient behaviors of power electronics converters. Comparison methods are proposed, and the evaluations are conducted under different transients using a grid-connected single-phase photovoltaic inverter. The findings of this study provide valuable references for further feasibility investigations on implementing BBMs in large-scale power electronics-rich power systems.
Unified Power Quality Conditioner (UPQC) Based on Two Three-leg Convertes with Standby Operation Mode and Improved Voltage Swell Compensation

This paper proposes a single-phase unified power quality conditioner (UPQC) based on two three-leg AC-DC-AC converters. The converter A (main converter) is responsible for the operation under grid voltage sag and nominal conditions. In these scenarios, the converter B (auxiliary converter) remains in standby mode. When a voltage swell ocurrs the converter B starts operating in conjunction with converter A. For any operating scenarios of the grid voltage, the proposed system provides a high power factor on the grid side, keeping the load supplied with a compensated sinusoidal voltage. The system model and the system control, including the PWM techniques and the strategy to regulate dc-link voltage of converter B, whether during standby mode or in grid voltage swell scenarios, is presented. Additionally, simulations and experimental results are presented to demonstrate the viability of the proposed 6L-UPQC.
Experimental Evaluation of Power Losses in High-Power Transformer

This paper presents a simple, reliable, cost-effective, time-efficient strategy for testing a hundreds-of-kilowatts, hundreds-of-amperes transformer under rated operating conditions by phase-shifted and frequency control without the need for an expensive high-power high-current power supply and without any load. This rapid engineering approach can determine whether a designed transformer will perform its role in a real-world system, or whether the next design iteration is required in just one day. It provides an efficient method to differentiate transformer core and winding losses from other losses in the test system by measuring only the high accuracy average DC value on the DC bus side. The transformer thermal performance can also be evaluated since it can run continuously under rated conditions. The effectiveness of this proposed testing strategy is verified by a comprehensive evaluation of multiple 200 kW / 30 kHz transformers, requiring only a 5 kW power supply without any load.
A Simplified Fixed Switching Frequency Model Predictive Control for an AFPM Motor Drive in a Distributed Electric Aircraft Propulsion System

The growing focus on aircraft electrification has led to a great emphasis on distributed electric propulsion (DEP). This paper proposes a simplified fixed switching frequency model predictive control (SFSF-MPC) for an axial flux permanent magnet (AFPM) motor drive fed by a three-level T-type inverter in a DEP system. This inverter has multiple performance benefits for DEP applications due to providing more voltage vectors; however, its use in the conventional finite control set MPC (FCS-MPC) leads to an increased computational burden and hinders using higher sampling frequencies. The proposed SFSF-MPC aims to reduce the computational burden in the conventional two-step FCS-MPC by offering a set of candidate voltage vectors, which are determined based on the spatial position and magnitude of the reference voltage vector. Only the voltage vectors close to the reference voltage vector participate in optimizing the cost function. Meantime, a switching frequency regulation strategy is applied based on the gate signal edge detection to maintain a fixed switching frequency. Counting the gate signal edges can predict the switching frequency and optimize it within the cost function. Moreover, the neutral point potential is balanced by equalizing the voltages of the DC-link capacitors based on the compensated stator currents. The performance of the proposed SFSF-MPC is evaluated in the cruise mode of an electric aircraft mission profile.
Torque Control of PMSMs Suffering from PM Edge Demagnetization

The combination of high stator currents and rotor temperatures in PMSMs can lead to PM edge demagnetization (ED). This will produce a torque derating as well as a displacement of the rotor magnetic dq- axes. Under these circumstances, optimized torque trajectories, e.g., maximum-torque-per-ampere (MTPA), maximum-torque-per-voltage (MTPV) or maximum-torque-per-flux (MTPF), can be altered. This paper analyzes the effects of edge demagnetization on torque production capability and control performance. The use of a search coil (SC) is proposed for on-line edge demagnetization detection and control reconfiguration 1.
Creation of an Automated Cultivation System Containing Phyto-illumination of the UV and Visible Ranges of the Spectrum

The paper considers the process of creating an automated hydroponic installation with LED lighting in the UV and visible ranges of the spectrum for the germination of agricultural crops. The system implements remote monitoring of microclimate parameters, which include humidity and air temperature, oxygen and carbon dioxide indicators, lighting for effective plant growth. Maintenance of the required operating modes of the installation is automated.
Joint Expansion Planning of Offshore Wind Farms and Transmission Lines Considering N - 1 Contigency

The rapid development of offshore wind farms (OWFs) has been witnessed all over the world for its excellent application prospects. To ensure the accommodation of wind power and the maintenance of operational security, it may need to implement transmission expansion and flexibility reformation of thermal power units. This research paper presents an optimization approach that aims to jointly optimize the flexibility reformation of thermal power units, integration sites for OWFs, and transmission expansion. The planning model focuses on minimizing the total investment cost while improving the wind power utilization and satisfying N-1 security. To solve the problem, an iterative algorithm is employed, which involves iteratively solving mixed-integer linear programming problems with adjusted constraints. By conducting a case study using the IEEE-30 bus system, we validate the effectiveness of proposed method.
Fast and Accurate Alignment and Bonding System Based on Advanced Packaging Technology

In the semiconductor packaging production line, with the increasing integration of chips, the size and pitch of solder joints are decreasing, which puts high demands on the alignment process. Especially with the emergence of chiplet and die-to-wafer technology, high-precision and high-speed alignment technology has become popular. In this paper, based on some image processing schemes, we propose a machine vision algorithm for realizing high-precision and high-speed flip chip bonding, and a flip chip bonding system is then constructed. Finally, the idea for the realization of the die-to-wafer bonding system is discussed.
Threshold Voltage Extraction Method for Low-Voltage SiC MOSFETs at High-Temperature

This paper demonstrates the threshold voltage extraction method of silicon carbide (SiC) n-channel MOSFETs over temperatures. The presence of high-density traps at the SiC/SiO2 interface affects the change of threshold voltage and the channel region mobility of SiC device. To design complex integrated circuits (ICs) in a SiC process and meet extensive application requirements, accurate measurement of threshold voltage with temperature is of paramount importance. The linear extrapolation method cannot be implemented for SiC because of the difference in the transconductance curve compared to silicon (Si). The extracted threshold voltages obtained with the second derivative method are also not consistent with the increasing temperature. A slope-based approach on the logarithmic transfer curve has been proposed here to extract the threshold voltage. Multiple dies have been measured after examining different 4H-SiC CMOS wafers fabricated at Fraunhofer IISB. The 1 µm technology node is a triple well process, with a single poly and a single metal layer. Characterization results for NMOS devices with different lengths and widths are shown for temperatures 25°C, 150°C, and 300°C. The transfer characteristics of the n-channel SiC MOSFETs are measured utilizing a Keysight B1500A Semiconductor Device Parameter Analyzer. The findings of this work can be used in further investigation of the threshold voltage drift of SiC CMOS devices and the design of ICs capable of operating in harsh environments.
Control Strategy of Hybrid PV/ FC/ Battery System For A Malaysian Household Application

The negative impact of fossil fuels has increased the importance of renewable energy, however due to its intermittency, second energy such as fuel cell (FC) is required in many applications. This paper proposes a control strategy of a hybrid PV/ FC/ Battery system for a Malaysian household application by using a combination of PI and switching controllers. Solar radiation and temperature data are based on location in Johor Bahru, Malaysia and the simulation is done using MATLAB Simulink. In this study, the FC is designed as a backup system that operates only when there is no solar radiation and the state of charge (SOC) of the battery is lower than 30%. To evaluate the performance of the proposed control strategy, three different states of SOC have been studied: 30%, 45%, and 100%. In this analysis, the efficiency of the maximum power point tracking (MPPT), boost converter, and bidirectional converter is also being investigated. The result shows that the individual efficiencies of MPPT, PV boost, FC boost and bidirectional converters are 90.38%, 92.33%, 91.35%, and 94.35%, respectively. It can be concluded that the FC works well as a backup generator with overall system efficiency is 71.92%.
Automatic Schizophrenia Detection from Scalp EEG Using CNN with Wide Convolutional Kernel and ELM

Detection of schizophrenia is of great significance in clinical practice. In conventional machine learning methods, manual feature extraction is required, which is a hard and time-consuming task. This paper proposes a hybrid model consisting of a convolutional neural network (CNN) with a wide convolution kernel and an extreme learning machine (ELM). By changing the size of the convolution kernel to form a suitable wide convolution kernel, and automatically extracting and optimizing the features of the scalp electroencephalogram (EEG), the performance of the model is effectively improved and accurate detection of schizophrenia is achieved. The input of this model is the original multi-channel EEG picture after short-time Fourier transform, and the output are two types of classification labels for schizophrenia and healthy people. In the end, the detection accuracy of schizophrenia reached 95.59%. This model has a powerful advantage in detecting schizophrenia.
Airborne LiDAR-based Wind Deflection Check for Power Transmission Lines

Wind deflection faults in transmission lines are an undeniable reality that can disrupt the normal operation of power systems. To address this issue and ensure the continuous and reliable functioning of transmission lines, this paper proposes an innovative approach: an airborne LiDAR-based wind deflection check method. By acknowledging the occurrence of wind deflection faults, we aim to develop a solution that effectively mitigates their impact and enhances the overall stability of power systems. The extraction and single-component segmentation of power lines are achieved by using a linear extraction algorithm based on principal components and an improved region growth segmentation algorithm for the filtered. The Euclidean clustering method is used to segment other components to obtain the point cloud data of each type of component monomer. The point cloud data are used to measure the basic attribute information and location information of the components. The experiments use UAVs with airborne LiDAR to select transmission lines in a certain region for data scanning and processing. The experimental results show that the method in this paper can realize fast and effective wind deflection checking of transmission lines, which can be done in a certain region for data scanning and processing. The experimental results show that the method in this paper can realize fast and effective wind deflection checking of transmission lines, which helps to realize reconstruction, localization, and early warning of transmission lines in 3D scenes.
Integrated On-Board Battery Charger for Electric Aircraft Based on Multiphase SPM Synchronous Machine

The transition to electric aircraft has gained worldwide attention as initiatives and legislations are presented in support of zero-emission aviation industry. Due to the high-power and high reliability requirements for electric aircraft propulsion, multiphase electric machines become attractive in such applications, which enables the possibility of having integrated on-board battery charger (IOBC). IOBCs fully leverage the existing powertrain propulsion elements, i.e., the multiphase machine and power inverter, into the charging process, requiring no additional hardware for fast charging. Moreover, zero average torque production and unity power factor operation at the grid side can be simultaneously achieved. Thus, this paper proposes an IOBC concept for electric aircraft, utilizing a multiphase surface-mounted permanent magnet (SPM) synchronous machine. First, an asymmetrical six-phase 36-slot/34-pole SPM machine design is presented based on the magnetic equivalent circuit (MEC) model. Thereafter, the machine is optimized using a multi-objective genetic algorithm (MOGA) optimization approach. Finally, finite element (FE) simulations have been carried out to verify the theoretical findings.
Gamification in the Teaching of Ecology for University Students

Technological resources have enabled the development of new learning options in scientific education. Among these resources, gamification promotes learning through activities such as game elements, use of prizes, flagship rewards, dynamic missions, levels, and scores. Keeping students interested in learning scientific concepts can be a challenge for teachers. Therefore, the objective of the present research was to apply gamification using the Genially platform as a complement to the teaching of the Ecology subject to generate a positively impact on the learning process of students of the Department of Environmental Engineering. In this study, the students used the gamifications and completed the activities and dynamic missions focused on biodiversity, conservation, and sustainability. The research data was collected in an initial test and a final test. The results showed that gamifications for the Ecology subject improved students’ grades up to 62.5% compared to the control group. The use of gamification is expected to be a tool that provides teachers with new alternatives to develop in students’, professional skills such as teamwork, effective communication, research, and innovation in students.
Optimized GRU Based on Satin Bowerbird Algorithm for Lithium-ion Battery State of Health Estimation

Lithium-(Li-ion) battery health status is a key indicator of battery safety performance. Due to the complexity of lithium-ion batteries in practical application scenarios, the development of effective State of Health estimation models and the selection of appropriate model parameters have an important impact on improving their estimation accuracy. In this regard, this study proposes a method for estimating the health status of lithium-ion batteries based on an optimized gated recurrent unit with the satin bowerbird algorithm. First, we selected three highly correlated features with SoH recession based on the battery charge and discharge data. Next, we developed gated recurrent unit for mining the hidden mapping relationships between the selected features and State of Health. Finally, we use the satin bowerbird algorithm to optimize the parameters of the gated recurrent unit to improve the accuracy of its estimation of health status. The experimental results show that the root mean square error of the method proposed in this study is made less than 1%, and the accuracy is improved by 32.74% based on its benchmark model.
Improved Multi-Person Vital Signs Estimation Algorithm in Sitting and Standing Positions using MIMO FMCW Radar

The use of FMCW radars for non-contact monitoring of vital signs has gained an increased attention in the healthcare sector within the past few years. In this paper, we propose an improved contactless vital signs estimation algorithm that is capable of detecting multiple persons, both in sitting and standing positions, and accurately estimating their respiratory and heart rates. The proposed algorithm combines several state-of-the-art radar vital signs estimation approaches, including empirical mode decomposition with principal component analysis, density-based clustering, and Kalman filtering. The proposed algorithm was developed and tested using a market-available FMCW radar in MIMO configuration. The testing results showed that the algorithm can estimate the respiratory and heart rates with an error of 0.7 cycles/minute and 2.3 beats/minute, respectively.