Miracle-dz/UniFAR-FTUs huggingface dataset:
Gear Stress Analysis and Discrete Optimization for Wheel-Legged Rover

The wheel-legged rover with a double-half-revolution mechanism uses two tandem planetary gear trains to drive. Its virtual prototype model was built in COSMOS Motion software, and the mechanical data of each gears of the wheel-legged rover were obtained by dynamic simulation under the typical working conditions. On this basis, the finite element analysis model of the wheel-leg planetary gear was established and its stress analysis was done. The discrete optimization mathematical model was built with the optimization target of minimizing the rover’s wheel-leg planetary gear volume and the constraint condition of keeping the Mises stress applied on the dedendum under the admissible Mises stress. Based on ANSYS Parametric Design Language (APDL), the optimization design was done for the above gear based on discrete optimization method. The result shows that the optimized gear not only meets the strength requirement, but also its weight is 58% lower than the original, which will provide a new effective method for optimizing the wheel-leg structure of the rover.
AAV‐mediated expression of galactose‐1‐phosphate uridyltransferase corrects defects of galactose metabolism in classic galactosemia patient fibroblasts

Classic galactosemia (CG) is a rare disorder of autosomal recessive inheritance. It is caused predominantly by point mutations as well as deletions in the gene encoding the enzyme galactose‐1‐phosphate uridyltransferase (GALT). The majority of the more than 350 mutations identified in the GALT gene cause a significant reduction in GALT enzyme activity resulting in the toxic buildup of galactose metabolites that in turn is associated with cellular stress and injury. Consequently, developing a therapeutic strategy that reverses both the oxidative and ER stress in CG cells may be helpful in combating this disease. Recombinant adeno‐associated virus (AAV)‐mediated gene therapy to restore GALT activity offers the potential to address the unmet medical needs of galactosemia patients. Here, utilizing fibroblasts derived from CG patients we demonstrated that AAV‐mediated augmentation of GALT protein and activity resulted in the prevention of ER and oxidative stress. We also demonstrate that these CG patient fibroblasts exhibit reduced CD109 and TGFβRII protein levels and that these effectors of cellular homeostasis could be restored following AAV‐mediated expression of GALT. Finally, we show initial in vivo proof‐of‐concept restoration of galactose metabolism in a GALT knockout mouse model following treatment with AAV‐GALT.
A comparative study of different color spaces for foreground and shadow detection for traffic monitoring system

Segmenting foreground objects of interest in real time is an important step in many applications of video surveillance, vehicle tracking and traffic monitoring. Background subtraction is a method very often used to segment moving objects in image sequences. In this paper we present a fundamental unbiased study of different color spaces for detecting foreground objects and their shadows in image sequences. Different color spaces show different efficiency in detection of foreground objects and their shadows. This empirical study was done with the motivation of determining, which color space is best for foreground segmentation and shadow detection. This study of the quality of foreground and shadow detection as a function of color space is unique kind and especially relevant to color image sequences. Our study includes five color spaces "RGB", "XYZ". "YC/sub r/C/sub b/", "HSV" and the normalized "rgb". We use an empirically substantiated model of shadows formulate the detection scheme for each color space. We use a statistical technique to model the background pixels. The results are compared in terms of true detection, misses and false detection of pixels and also detection of the moving foreground objects as blobs. The results show that "YC/sub r/C/sub b/" is the best color space for optimal foreground and shadow detection.
Assessment of the importance of increasing temperature and decreasing soil moisture on global ecosystem productivity using solar‐induced chlorophyll fluorescence

The accurate assessment of the global gross primary productivity (GPP) of vegetation is the key to estimating the global carbon cycle. Temperature (Ts) and soil moisture (SM) are essential for vegetation growth. It is acknowledged that the global Ts has shown an increasing trend, yet SM has shown a decreasing trend. However, the importance of SM and Ts changes on the productivity of global ecosystems remains unclear, as SM and Ts are strongly coupled through soil‐atmosphere interactions. Using solar‐induced chlorophyll fluorescence (SIF) as a proxy for GPP and by decoupling SM and Ts changes, our investigation shows Ts plays a more important role in SIF in 60% of the vegetation areas. Overall, increased Ts promotes SIF by mitigating the resistance from SM’s reduction. However, the importance of SM and Ts varies, given different vegetation types. The results show that in the humid zone, the variation of Ts plays a more important role in SIF, but in the arid and semi‐arid zones, the variation of SM plays a more important role; in the semi‐humid zone, the disparity in the importance of SM and Ts is difficult to unravel. In addition, our results suggest that SIF is very sensitive to aridity gradients in arid and semi‐arid ecosystems. By decoupling the intertwined SM‐Ts impact on SIF, our study provides essential evidence that benefits future investigation on the factors the influence ecosystem productivity at regional or global scales.
An imported case of vaccine-derived poliovirus type 2, Spain in the context of the ongoing polio Public Health Emergency of International Concern, September 2021

The monthly retrospective search for unreported acute flaccid paralysis (AFP) cases conducted as a complementary component of the Spanish AFP surveillance system identified a case of AFP in a child admitted in Spain from Senegal during August 2021. Vaccine-derived poliovirus 2 was identified in the stool in September 2021. We present public health implications and response undertaken within the framework of the National Action Plan for Polio Eradication and the Public Health Emergency of International Concern.
Grid disturbance response of wind turbines equipped with induction generator and doubly-fed induction generator

This panel paper presents measured and simulated grid disturbance responses of fixed-speed wind turbines equipped with induction generator and variable-speed wind turbines equipped with double-fed induction generator. The measured response of the fixed-speed turbine agrees very well with simulations, while the response of a double-fed induction generator cannot be assessed without detailed knowledge of the control system.
Genome-Wide Association Study of Root Mealiness and Other Texture-Associated Traits in Cassava

Cassava breeders have made significant progress in developing new genotypes with improved agronomic characteristics such as improved root yield and resistance against biotic and abiotic stresses. However, these new and improved cassava (Manihot esculenta Crantz) varieties in cultivation in Nigeria have undergone little or no improvement in their culinary qualities; hence, there is a paucity of genetic information regarding the texture of boiled cassava, particularly with respect to its mealiness, the principal sensory quality attribute of boiled cassava roots. The current study aimed at identifying genomic regions and polymorphisms associated with natural variation for root mealiness and other texture-related attributes of boiled cassava roots, which includes fibre, adhesiveness (ADH), taste, aroma, colour, and firmness. We performed a genome-wide association (GWAS) analysis using phenotypic data from a panel of 142 accessions obtained from the National Root Crops Research Institute (NRCRI), Umudike, Nigeria, and a set of 59,792 high-quality single nucleotide polymorphisms (SNPs) distributed across the cassava genome. Through genome-wide association mapping, we identified 80 SNPs that were significantly associated with root mealiness, fibre, adhesiveness, taste, aroma, colour and firmness on chromosomes 1, 4, 5, 6, 10, 13, 17 and 18. We also identified relevant candidate genes that are co-located with peak SNPs linked to these traits in M. esculenta. A survey of the cassava reference genome v6.1 positioned the SNPs on chromosome 13 in the vicinity of Manes.13G026900, a gene recognized as being responsible for cell adhesion and for the mealiness or crispness of vegetables and fruits, and also known to play an important role in cooked potato texture. This study provides the first insights into understanding the underlying genetic basis of boiled cassava root texture. After validation, the markers and candidate genes identified in this novel work could provide important genomic resources for use in marker-assisted selection (MAS) and genomic selection (GS) to accelerate genetic improvement of root mealiness and other culinary qualities in cassava breeding programmes in West Africa, especially in Nigeria, where the consumption of boiled and pounded cassava is low.
Scene Descriptor Expressing Ambiguity in Information Recovery Based on Incomplete Partial Observation

In recent studies, the widespread of deep learning has made many kinds of large-scale image datasets available and it has enabled to improve the performance of image-based 3-D scene reconstruction. Several studies estimate whole 3-D scenes including occluded or unseen parts consistent with the obtained partial observations by integrating prior knowledge from training datasets with them, under no camera parameters nor image landmark correspondence are known. Although they generate “discrete” scene instances, they cannot represent and treat their “ambiguity” at all.This paper proposes a novel deep-learning-based framework that can directly represent and treat the ambiguity of scene reconstructions. We introduce a neural network which encodes a target scene as a descriptor. The network takes partial observations as input and outputs a parametric set of the scene descriptors containing all scenes consistent with given observations. The input observations may be “incomplete” in the sense that they do not have enough pieces of information to uniquely determine the whole scene due to neither geometry in-formation nor landmark correspondences available (ill-defined cases). The network is trained based on the dataset of the complete 3-D scenes and possible partial observations so that it can predict the unseen parts from incomplete observations. The paper introduces the method to induce such a descriptor space into the encoder/decoder architecture by employing novel definitions of loss functions measuring “validity”, “consistency” and “reproducibility”. When the series of partial and incom-plete observations for the same 3-D scene is obtained, the reconstruction ambiguity is explicitly treated by parametrically integrating the descriptor set for each observation into one descriptor set.
Towards Robust Visual Diver Detection Onboard Autonomous Underwater Robots: Assessing the Effects of Models and Data1

Deep neural networks are the leading solution to the object detection problem. However, challenges arise when applying these networks to the kind of real-time, first-person video data that a robotic platform must process: specifically, detections may not be consistent from frame to frame, and objects may frequently appear at viewpoints that are particularly challenging for the model, resulting in inaccurate detections. In this paper, we present our approach for addressing these challenges for our particular vision problem: diver detection onboard autonomous underwater vehicles (AUVs). We begin by producing and releasing a dataset of approximately 105,000 annotated images of divers sourced from videos in order to address the challenge of learning a wide variety of object rotations and translations. This is one of the largest and most varied diver detection datasets ever created, and we compare models trained and tested on both our dataset and a previous dataset to demonstrate that our dataset improves the state-of-the-art in diver detection. Then, in order to choose an object detection model that produces detections that are consistent from frame to frame, we evaluate several state-of-the-art object detection models on the temporal stability of their detections in addition to the typical accuracy and efficiency metrics, mean average precision (mAP) and frames per second. Importantly, our results showed that models with the highest mAP do not also have the highest temporal stability.
Evaluation of Long-term LiDAR Place Recognition

We compare a state-of-the-art deep image retrieval and a deep place recognition method for place recognition using LiDAR data. Place recognition aims to detect previously visited locations and thus provides an important tool for navigation, mapping, and localisation. Experimental comparisons are conducted using challenging outdoor and indoor datasets, Oxford Radar RobotCar and COLD, in the "long-term" setting where the test conditions differ substantially from the training and gallery data. Based on our results the image retrieval methods using LiDAR depth images can achieve accurate localization (the single best match recall 80%) within 5.00 m in urban outdoors. In office indoors the comparable accuracy is 50 cm but is more sensitive to changes in the environment.
Planning and implementation of large synchronously switched shunt capacitor banks in the Entergy system

The Western Part of the Entergy system is one of the fastest growing in the system with a high concentration of industrial loads. The power into the area is imported primarily over Richard-Webre and Mt. Olive-Hartburg 500 kV lines. The loss of the Richard-Webre line coupled with an outage of the largest generator in the area (Nelson 6 unit) leads to a reactive power deficiency in the area resulting in a voltage collapse. Amongst the various solutions for the voltage collapse problem, the installation of large shunt capacitor banks was deemed to be one of the most cost-effective option and one that had the least implementation time. Therefore, 140 MVAr capacitor banks were recommended for the Nelson 230 kV and Carlyss 138 kV substations. These capacitor banks were the largest ever installed on the Entergy system. Due to the extremely sensitive nature of loads in the area and potential voltage magnification problems, it was decided to use synchronous closing breakers to switch these capacitor banks. Transient studies were performed to determine the impact of overvoltages introduced during switching of the capacitor banks. This paper focuses on factors that influenced bank sizing and location, results of the switching studies, choice of switching device, installation and operating experience.
Model Predictive Control of a Wind Turbine

The increase in size, prize and power production of modern wind turbines continue to improve the overall economy of their installation and maintenance. A suitable place to install these mega wind turbines is on the sea as their is a more stable wind. These water based wind farms are confined to reefs near land where the construction of the foundations doesnt become to expensive and problematic. It has been suggested to build floating wind turbines instead and thus enabling previous unsuited locations to become potential wind farms. This thesis investigates control of both wind turbines mounted on solid foundations and their floating counter parts. The wind turbine operates over a wide wind speed range and the control objectives changes over that range. It has been investigated how to identify and switch between these different modes of operation. The turbulent nature of the wind causes the control of the gigantic structures to react within fractions of a second. Such rapid response should cross certain limits otherwise the fatigue of the actuator systems is greatly accelerated leading to uneconomic operation of the wind turbine. Model predictive control have been investigated as a method to keep within these constraints.
Dynamics Computation of a Hybrid Multi-link Humanoid Robot Integrating Rigid and Soft Bodies

This study presents dynamics computation and control of a hybrid multi-link system that integrates rigid- and soft-bodies. It is a challenging problem to install a softness in a robot system, which is an important factor in human body. Softness achieved by human muscles and ligaments contributes to dynamic motion. Flexibility of a sports prosthetic leg allows a handicapped person to run. However, traditional algorithms of dynamics computation for a robot system or human skeletal model only consider a rigid-body multi-link system. Recent progress in soft robotics such as piecewise constant strain (PCS) model provides the way to compute dynamics of soft deformation with a low computational cost. We construct a hybrid multi-link system integrating rigid-body and the PCS model. For controlling a humanoid robot with soft links, we implement a dynamics computation with a floating-base and derive the center-of-gravity Jacobian matrix of the hybrid link system. Moreover, we demonstrate a forward dynamics simulation of a humanoid robot with prosthetic legs.
Analysis of rotor slot harmonics of multiphase induction motor based on detection coil

In this paper, utilizing the cogging effect caused by the slotting of the rotor of a multiphase induction motor and its modulation effect on the air gap magnetic field, one or more detection coils are embedded in the stator windings of the motor to detect the rotor slot harmonics (RSH) in the air gap magnetic field. The induction voltage of corresponding frequency is generated inside the detection coil, and then the speed-sensorless speed estimation of the induction motor can be realized. Based on the analysis of the main characteristics of the rotor slot harmonics of a multiphase induction motor, the goal is to improve the signal-to-noise ratio(SNR) of the rotor slot harmonics, taking into account the convenience of detecting coil layout. And verified by finite element simulation. The result proves that this scheme has certain practical value for the speed-sensorless speed estimation of the multiphase induction motor.
High Power-Density High-Efficiency Electric Drive Design with Halbach-Rotor PMSM and WBG-Based High-Frequency Inverter

This paper presents the design of an electric drive for light-vehicle applications, using wide-band-gap semiconductor technologies and advanced machine concepts to maximize the power density of the whole system. The high-frequency output characteristic of inverter using the wide-band-gap semiconductor technologies can effectively reduce the current harmonics of the motor. That gives the motor design more freedom to use the new structure to improve its power density from a perspective of the thermal and electromagnetic performances. Meanwhile, a series of studies on the new rotor structure with Halbach permanent magnets are carried out to suppress the torque ripples at full-range speed during the motor design. Furthermore, since the EV motor needs to operate under variable operating conditions, systematic matching is considered to improve the overall performance following the usual working region of the light electric vehicles.
Cascaded PI Controller based Wind Turbine Generator System for Battery Charging Applications

Wind turbine generator system is a promising alternative to fossil fuel based sources of energy. In recent decades, this technology has seen rapid developments with respect to efficiency and versatility. This paper proposes a wind turbine-permanent magnet synchronous generator-buck converter system for battery charging applications using MATLAB/Simulink. Cascaded proportional integral (PI) control was used to provide gate signals to the mosfet. Various permutations and combinations have been considered in input wind speed and the battery’s initial state of charge. It is observed that the dynamic response of the system is robust and time to attain steady state stability is desirously less.
ZVT Boost Converter Integrated with Charging and Discharging Topologies

The paper analyses a ZVT based PV connected PWM boost converter topology with an integrated battery charger circuit exhibiting flexible charging and discharging functions in order to ensure that the load demand is being met under varying input conditions. While both the main and auxiliary switches always operate under soft-switching conditions, battery utilizes the circulating energy in the resonant tank circuit in its charging process, thus effectively minimizing the losses. To further improve the overall system efficiency, the system continuously tracks the maximum power point (MPP) of the photovoltaic supply irrespective of variations in irradiance and temperature. The system flexibility further increases as the system offers a wide variation in choice of the battery irrespective of the load voltage. Furthermore, the discharging current of the battery is continuous which greatly reduces the stresses on the battery. The system is simulated and analyzed in PSIM and the results have been presented to validate the analysis and design.
Planning Robotic Manipulation with Tight Environment Constraints

In many real-world manipulation problems, the constraints imposed by the environment on an object are tight. In these cases, most state-of-the-art planners struggle to fit satisfactorily in low dimensional sub-manifolds, while still ensuring geometric and force feasibility. On the other hand, humans are at ease with such situations and indeed exploit constraints to manipulate objects proficiently.To face this challenge, we propose to merge state-of-art randomized grasp planning methods with model-based grasp analysis. We use the partial form-closure analysis framework to find the geometrically feasible motions of the object. Then, to ensure that the desired motions are physically realizable by the robot, we resort to an extension of the force-closure analysis framework accounting also for dynamic friction. We use these instruments to construct a random tree in a simplified planning space containing only object-robot configurations that are reachable through effectively applicable contact forces. The algorithm, validated in simulation and in preliminary experiments with a collaborative robot, features the ability to compute solutions for heavily constrained real-world manipulation problems.
Area Defense and Surveillance on Rectangular Regions Using Control Barrier Functions

A formulation of the area defense and surveillance problem for one intruder and one defense and surveillance robot and its corresponding solution using control barrier functions is presented. The defense robot must follow the intruder as it moves through a rectangular region in the plane, ensuring that the position of the intruder is also within a rectangular region attached to the surveillance robot. The proposed reactive and closed-form control laws depend on the positions of the robots, their maximum speeds, and the size of the rectangular regions. We show the application and effectiveness of our results in experiments with real robots.
Research of a Magnetic-Field Modulated Brushless Dual-Rotor Compound-Structure Machine

Compared with electronic continuously variable transmission (ECVT) based on planet gear, magnetic-field modulated brushless dual-rotor machine (MFM-BDRM) has advantage of low noise and manufacturing difficulty. But MFM-BDRM should operate with the help of a permanent magnet synchronous machine (PMSM), which reduces integration substantially. In this article, a novel magnetic-field modulated brushless dual-rotor compound-structure machine (MFM-BDRCSM) topology with spoke type PMs is proposed, which could integrate a MFM-BDRM and a PMSM. Electromagnetic performance of proposed MFM-BDRCSM, such as flux density and electromagnetic torque, are analyzed. Several structure parameters are changed to realize better performance.