Miracle-dz/UniFAR-FTUs huggingface dataset:
Reactive power optimization of AC/DC parallel operation system with VSC-MTDC

With the development of large scale wind power integrated to the grid and the wide applications of VSC-MTDC, the traditional reactive power optimization methods can not satisfy the current parallel AC/DC system‘s operation with the integration of intermittent wind power. Based on scenarios analysis, a reactive power optimization method applied to parallel AC/DC system‘s operation is proposed considering the wind farms and VSC-MTDC. Firstly, the historical data of the wind farm output is used to fit the multi-scene probability of wind power output. Secondly, by considering the multi-scenario combination of wind farms, the objective function of minimizing the expected value of the integrated power loss under multiple scenarios is established. The reactive power optimization model with VSC-MTDC and wind farm under multi-scenario is also proposed considering the parallel AC/DC system operational constraints, which takes the system-level control strategy into account. The global optimal solution solver IPOPT is used to calculate optimal reactive power under two kinds of operations separately considering master-slave control mode and the voltage droop control mode. Finally, the case study based on Nan‘ao power grid, Guangdong, China is used to assess the effectiveness of the proposed method by comparing with two optimized solutions under the master-slave control mode and droop control mode in single scenario operation.
Localizing Turn to Turn Short Circuit in HEV’s Machine Using Thermal Finite Element Model

Hybrid electric vehicles (HEVs) are becoming widely spread due to their advantages when talking about power consumption and pollution free environment. The main concern of HEVs is their reliability and availability; hence, the ability to detect malfunction at early stage or before it occurs is a must. To assure their proper operation, most researches in the field of fault detection, head toward prognosis being the best alternative among the available maintenance strategies and actions. In this paper, the thermal finite element model will be used to detect and localize the presence of turn to turn short circuit at its early stage. A suggestion for localizing temperature sensors, in the electrical machine, will be presented.
Robust Generalized Point Cloud Registration with Expectation Maximization Considering Anisotropic Positional Uncertainties

Alignment of two point clouds is an essential problem in medical robotics and computer-assisted surgery. In this paper, we first formally formulate the generalized point cloud registration problem in a probabilistic manner. Specifically, not only positional but also the orientational information are incorporated into registration. Notably, the positional error is assumed to obey a multivariate Gaussian distribution to accommodate anisotropic cases. Expectation conditional maximization framework is utilized to solve the problem. In E-step, the correspondence probabilities between points in two generalized point clouds are computed. In M -step, the constrained optimization problem with respect to the transformation matrix is re-formulated as an unconstrained one. Extensive experiments are conducted to compare the proposed algorithm with the state-of-the-art registration methods. The experimental results demonstrate the algorithm's robustness to noise and outliers, fast convergence speed.
State space and graphical models for estimating networks dynamics

Aanvankelijk werd de sequencing van het genoom werd door velen leken gezien alsof alles over de genetische werkelijkheid bekend geworden was. Echter, sequencing stelde ons alleen maar voor aan de spelers van een zeer complex genomisch spel, waarvan we de regels nu pas beginnen te begrijpen. Een manier om dit spel te begrijpen is door het uitvinden van hoe de spelers met elkaar omgaan: genomische netwerk reconstructie is een aantrekkelijk paradigma van genomische wetenschap, maar inhomogene monsters en complexe tijdsdynamiek betekent dat de regels van de genomische interactie spel verschillend kunnen zijn voor verschillende subgroepen of zelfs veranderen in de tijd. Dit proefschrift ontwikkelt nieuwe methoden om te gaan met deze twee scenario's.
Efficient Pose Estimation from Single RGB-D Image via Hough Forest with Auto-Context

We propose a high efficient learning approach to estimating 6D (Degree of Freedom) pose of the textured or texture-less objects for grasping purposes in a cluttered environment where the objects might be partially occluded. The method comprises three main steps. Given a single RGB-D image, we first deploy appropriate features and the random forest to deduce the object class probability and cast votes for the 6D pose in Hough space by joint regression and classification framework, adopting reservoir sampling and summarizing the pose distribution by clustering. Next, we integrate the auto-context into cascaded Hough forests to improve the efficiency of learning. Extensive experiments on various public datasets and robotic grasps indicate that our method presents some improvements over the state-of-art and reveals the capability for estimating poses in practical applications efficiently.
The Deformable Quad-Rotor Enabled and Wasp-Pedal-Carrying Inspired Aerial Gripper

The paper presents the development of a novel deformable quad-rotor enabled aerial gripper. The mechanism of our deformable quad-rotor is based on simultaneous expansion or contraction of the quad-rotor body, which is generated by controlling a rigid elements based morphing structure (REMS). Such deformation results in a highly deformable quad-rotor that can not only perform morphological adaptation in response to environmental changes and obstacles, but also improve the flight performance by contracting to facilitate the agility/maneuverability or by expanding to enhance the stability. Meanwhile, inspired by the wasp grasping behavior, such controllable expansion and contraction from the REMS ingeniously enable a new function of aerial gripper. In this paper, we start to detail the mechanism and design of the REMS based deformable quad-rotor, then present the quad-rotor deformation enabled aerial gripper design, its dynamics modeling, the grasping function and analysis. The simulation was conducted in order to graphically show the elicited aerodynamic flow situation during expansion or contraction of the quad-rotor with and without carrying payload. Experiments were further implemented to validate the grasping function of the gripper and the flight performance of the quad-rotor. Finally, two case studies on the new aerial gripper were performed. All results demonstrate the excellent performance of the deformable quad-rotor enabled aerial gripper, that is, it has the advantages of both flight maneuverability and grasping capability during performing tasks.
Vision-based Target Tracking for a Skid-steer Vehicle using Guided Policy Search with Field-of-view Constraint

This paper describes a vision-based target tracking method for a skid-steer vehicle. With the development of deep reinforcement learning, many researchers have tried to generate an end-to-end policy to control the mobile robot from a raw pixel image data. However, the action in most research only concerns high-level decisions such as go straight, turn left and right. High-level decisions alone are not sufficient to precisely control platforms such as a skid-steer vehicle due to the lack of steering mechanism. Thus, unlike existing work, we aim to control the motor command for the wheels directly. To this end, we employ guided policy search (GPS) based on the general kinematic slip model for the skid-type robot. Furthermore, to prohibit the target from getting out of the camera field of view (FOV) in the training phase, we update local policy optimization with a FOV constraint and perform a pre-training to make the initial policy more efficient. Our method allows the skid-type robot to automatically acquire the vision-based tracking policy while local policies satisfy the FOV constraint during the training phase. We evaluate our method through both simulation and experiment with a skid-steer mobile robot. Finally, we test the performance of learned policy with a moving target in a new environment.
3D Convolutional Network Based Foreground Feature Fusion

with explosion of videos, action recognition has become an important research subject. This paper makes a special effort to investigate and study 3D Convolutional Network. Focused on the problem of ConvNet dependence on multiple large scale dataset, we propose a 3D ConvNet structure which incorporate the original 3D-ConvNet features and foreground 3D-ConvNet features fused by static object and motion detection. Our architecture is trained and evaluated on the standard video actions benchmarks of UCF-101 and HMDB-51, experimental results demonstrate that with merely 50% pixels utilization, foreground ConvNet achieves satisfying performance as same as origin. With feature fusion, we achieve 83.7% accuracy on UCF-101 exceeding original ConvNet.
A Review of Automated Detection Methods for Cyberbullying

As we see the cyberspace evolve we also see a directly proportional growth of the people using the cyberspace for communication. As a result, the misuse of the cyberspace has given rise to negative issues such as cyberbullying, which is a form of harassing other people using information technology in a deliberate and continual manner. The detection and prevention of cyberbullying becomes critical for safe and health social media platforms. In this paper, a review of the cyberbullying content in Internet, the categories of cyberbullying, data sources containing cyberbullying data for research, and machine learning techniques for cyberbullying detection are overviewed. The main challenges of the cyberbullying detection are demonstrated, including the lack of multimedia content-based detection and availability of public accessible dataset. Suggestions are provided as the conclusion of the overview.
Tools for the Development of Macroeconomic Models of the Fuel and Energy Complex

The article describes the software tools used for macroeconomic modeling of the Fuel and Energy Complex. These software tools allow you to generate and modify the relevant macroeconomic models. Its architecture and functional components provide a flexible interaction between the user and the computer simulation system. The software package includes two autonomous subsystems - CREATOR and DIGGER. The CREATOR system is used to create models and change their formal structure. The DIGGER system is intended for the organization of input and editing of data, conducting direct calculations on models, launching optimization procedures. Direct calculations realize the simulation mode of model user. They determine the values of the model indicators according to formulas of the static and dynamic modes. The optimization mode may be used only for multilinear indicators. It is divided into static and dynamic. In the static optimization mode (optimization of one current time circle) non-multilinear indicators are excluded from the optimization task. Dynamic non-multilinear indicators are excluded for the dynamic optimization mode. A direct calculation performed after the optimization procedure completes, it shows whether these indicators satisfy the boundary conditions specified for them. The macroeconomic problems related to the fuel and energy complex that these tools helps to solve online are listed. The capabilities of the described tools are illustrated with a significant example.
Stabilize an Unsupervised Feature Learning for LiDAR-based Place Recognition

Place recognition is one of the major challenges for the LiDAR-based effective localization and mapping task. Traditional methods are usually relying on geometry matching to achieve place recognition, where a global geometry map need to be restored. In this paper, we accomplish the place recognition task based on an end-to-end feature learning framework with the LiDAR inputs. This method consists of two core modules, a dynamic octree mapping module that generates local 2D maps with the consideration of the robot's motion; and an unsupervised place feature learning module which is an improved adversarial feature learning network with additional assistance for the long-term place recognition requirement. More specially, in place feature learning, we present an additional Generative Adversarial Network with a designed Conditional Entropy Reduction module to stabilize the feature learning process in an unsupervised manner. We evaluate the proposed method on the Kitti dataset and North Campus Long-Term LiDAR dataset. Experimental results show that the proposed method outperforms state-of-the-art in place recognition tasks under long-term applications. What's more, the feature size and inference efficiency in the proposed method are applicable in real-time performance on practical robotic platforms.
An Origami-Inspired Flexible Pneumatic Actuator

This paper presents a new actuator designed to produce forces under short stroke displacements. Two variants of the prototype have been manufactured using Multi-Material Additive Manufacturing process, based on a flexible origami-inspired architecture. The structure consists of an airtight chamber constituted by rigid plates combined with flexible hinges and surfaces in order to allow the generation of motion. We propose several insights on integration issues such as limited material resistance and maximum range of motion. Both versions of the prototype are then tested to assess their performances for single strokes and cyclic loading.
$M^3\text{Fusion}$: A Deep Learning Architecture for Multiscale Multimodal Multitemporal Satellite Data Fusion

Modern Earth Observation systems provide remote sensing data at different temporal and spatial resolutions. Among all the available spatial mission, today the Sentinel-2 program supplies high temporal (every five days) and high spatial resolution (HSR) (10 m) images that can be useful to monitor land cover dynamics. On the other hand, very HSR (VHSR) imagery is still essential to figure out land cover mapping characterized by fine spatial patterns. Understanding how to jointly leverage these complementary sources in an efficient way when dealing with land cover mapping is a current challenge in remote sensing. With the aim of providing land cover mapping through the fusion of multitemporal HSR and VHSR satellite images, we propose a suitable end-to-end deep learning framework, namely $M^3\text{Fusion}$, which is able to simultaneously leverage the temporal knowledge contained in time series data as well as the fine spatial information available in VHSR images. Experiments carried out on the Reunion Island study area confirm the quality of our proposal considering both quantitative and qualitative aspects.
Automatic Electronic Organ Reduction System Based on Melody Clustering Considering Melodic and Instrumental Characteristics

To maintain the artistic side of music, it is important that many people play it. However, musical arrangement is difficult and time-consuming for amateurs, so even good music is not arranged for instruments that relatively few people play. In this study, we propose an arrangement system that can easily generate scores for various musical instruments, with the aim of helping to keep music alive. Here, we target electronic organ arrangements that can represent a full score. Since such scores include a large number of instrumental parts, we summarize the melodies that play the same role using four features (pitch, harmony, note length, and timbre of the instrument). Next, we select a melody cluster for each part, considering the connections between the melodies and the characteristics of electronic organs. Finally, we adjust the score to make it easier to play on an electronic organ. We then conduct experiments to confirm whether the system can create arrangements that maintain the original piece's overall impression. Here, we compare our new arrangements of four pieces with the existing electronic organ scores. The results showed that the proposed arrangement system can create an electronic organ score from the full score.
Simulations of Nonthermal Electrical Discharges in Air Over Solid Insulating Barrier

The propagation of streamer discharges in air in strongly divergent electric fields and their interaction with solid dielectric barrier inserted between the energized electrodes are analyzed by means of computer simulations. The model used is based on a hydrodynamic approximation of the transport of charge carriers in both media (electrons and ions in air, and electrons and holes in solid) coupled with Poisson’s equation for the electric field and accounts for charge injection and accumulation on gas–solid interfaces. The model is solved numerically utilizing customized finite-element formulation employing streamline upwind Petrov–Galerking method for stabilizing solution of drift–diffusion equations for fluxes of charged species. The results of the simulations performed for conditions of earlier reported experimental studies are presented and discussed. It is shown that streamer discharges may develop in different modes depending upon particular conditions and are capable of propagating along solid surfaces in the direction transverse to the applied field. In the latter case, their progress is fully controlled by space and surface charges accumulated within discharge plasma and on gas–solid interfaces. The developed calculation method creates a base for considering more complicated and practically oriented tasks.
Appearance-Based Along-Route Localization for Planetary Missions

We propose an appearance-based along-route localization algorithm that relies on robust place recognition by matching image sequences instead of individual frames. Our approach extends state of the art place recognition framework SeqSLAM in several aspects to realize real-time localization along routes for autonomous navigation. First, our method is online in that we only rely on the recently observed image frames. Second, we provide a homing mechanism based on rotations computed from frame matches. And third, we use a more flexible mechanism to search for matching locations, not restricting the search to straight lines in the cost matrix as in SeqSLAM, but allowing for a wide variety of route traversal conditions such as varying velocities or rotational and translational viewpoint differences. We investigate different image preprocessing steps as well as image similarity metrics wrt. their influence on illumination and viewpoint invariance for a more robust place recognition. On a new challenging dataset, recorded in real world experiments with a planetary rover, in the course of a Moon-analogue mission on Sicily's Mount Etna, we show the feasibility of our direct, sequence-based approach to along-route localization.
The Magician's Doubts: Nabokov and the Risks of Fiction

As a child in Russia, Vladimir Nabokov enjoyed conjuring: "I loved doing simple tricks--turning water into wine, that kind of thing". In this engrossing book Michael Wood explores the blend of arrogance and mischief that makes Nabokov such a fascinating and elusive master of fiction". Michael Wood's study of Nabokov is a stunningly brilliant analysis of how, as he says, the sly idiot, the haughty mandarin, and the great, doubting magician get along together in works like Lolita and Ada. In his own beautifully supple and attractive prose, [Wood] renders his insights with grace and wisdom. The Magician's Doubts is, quite simply, a wonderful book, not to be passed up". --Edward Said"This is a fine example of an endangered species: the full- length book of literary criticism dedicated to the appreciation and interpretation of a single author, addressed to the general reader. Reading The Magician's Doubts, we re-experience and recover . pleasures of Nabokov's texts we may have forgotten or overlooked". --David Lodge, The New York Times Book Review"Wood's book is so thronged with pleasures, so acute in its insights, so replete with clear thoughts limpidly expressed, that one could.write a review of it consisting entirely of quotations from the text. [It] offers us an entirely new set of insights into the work of a modern master". --John Banville, The New York Review of Books
On the Suitability of Ionospheric Gradient Estimation Techniques for IRNSS Based GBAS Applications

Time-Step method and Station-Pair method are prominent techniques for estimation of spatial gradients. Since GBAS is meant to serve a limited area of about 50km of an airport for aircraft Precision Approach and landing, these two methods were considered for gradient estimation within the GBAS service area. Much of the work on these techniques has been reported for GPS-based GBAS applications. In this paper, the suitability of these methods to IRNSS-based GBAS applications is investigated. It is observed that since IRNSS satellites are either GEO or GSO, the time interval (At), of Time-Step method should be significantly high (30min for GSO), to obtain gradient data for GBAS’ service area. With the Station-Pair method, a dense network of stations, each separated by not more than 1–2 kms is required.
Faster Collision Checks for Car-Like Robot Motion Planning

In this paper, we describe how collision checking for car-like robots can be sped up utilizing system knowledge. Their non-holonomic motion, while being a challenge for motion planning, is utilized here to place discs which are used as an approximation of the robot's shape in a predictive manner. For ease of comparison, we assume the robot to be rectangular, i. e. , we use bounding boxes. Our algorithm is compared to a widely-used baseline and shows similar performance in terms of under- and oversampling while being approximately 20–40 % faster. Another feature of the algorithm is its predictive nature: with the frontal disc, we already check for collisions that would occur with the rear disc in the next sample, assuming near-constant curvature. While this might be conservative in some cases where large steering rates are necessary, in our evaluation even tight corridors could be navigated without negative effects.
Evaluation of Health Condition for IMA Module Based on Mahalanobis Distance

In this paper, the degenerate state data of the Complex Programmable Logic Device (CPLD) functional circuit module of Integrated Modular Avionics (IMA) system is obtained by the method of fault injection. By extracting the characteristics of the time-domain data of the degraded state and using the minimum redundancy maximum relevance (mRMR) method, the related sensitive degradation characteristics are obtained. Then the health assessment of CPLD functional modules is carried out by the single parameter as well as the multi parameter evaluation method based on the calculation of confidence value and Mahalanobis distance. The results of this paper provide a practical and engineering approach to the health evaluation of the IMA system at module level based on health management technology of the IMA system.
Construction of Latch Design with Complete Double Node Upset Tolerant Capability Using C-Element

Due to VLSI downsizing and high integration, the incidence of soft error has increased. The soft error is a temporary event caused by striking of a-rays and high energy neutron radiation. Since the scale of VLSI has become smaller in recent development, it is necessary to consider the occurrence of not only single node upset (SNU) but also double node upset (DNU). The existing High-performance, Low-cost, and DNU Tolerant Latch design (HLDTL) does not completely tolerate DNU. This paper presents a DNU tolerant latch to solve this problem by adding some transistors to the HLDTL latch.