Miracle-dz/UniFAR-FTUs huggingface dataset:
An Efficient and Accurate Solution to Camera Pose Estimation Problem from Point and Line Correspondences Based on Null Space Analysis

In this paper, we propose an accurate and simultaneously efficient solution to perspective-n-point-and-line (PnPL) problem by null space analysis. Although many PnPL-like methods have been proposed, it is hard to obtain the optimal solution considering both calculation efficiency and accuracy at the same time. Based on the remarkable EOPnP method, the proposed algorithm integrates linear-expressed line constraints with original point constraints, leading to a new solution named EOPnPL. For line error measurement, instead of using the algebraic error built with reprojected endpoints and image lines, we adopt line error function containing the distance from the reprojected midpoint of the model line segment to the image line and giving it a weight 4 times as that of the endpoints. A system of linear homogeneous equations only involving the rotation are derived, containing point and line constraints. Minima are obtained by a null space analysis according to rotation constraints and then the solution is selected by reprojection errors before iterative refinement. Experimental results show that the proposed method showcases great performances in both simulations and real-data experiment on VGG dataset. Great advantages are presented by EOPnPL compared to PnL methods when using only lines. Among the state-of-the-arts methods, the proposed method stands out with high accuracy under ordinary condition and comparable results in planar condition with the speed close to the fastest.
Online Tuning Weighting Factor for A Predictive Torque Control of Induction Motor Drive

This paper proposes an online method to tune the weighting factor of a Finite State Predictive Torque Control (FS-PTC) of an induction motor (IM) drive. The main advantages of this method are its simplicity, not requiring complex calculations, and excellent performance on both flux and torque responses at all speed conditions. The basic idea of this method is to use a Proportional (P)-based controller to tune the weighting factor according to the stator flux error. A simulation using MATLAB/ Simulink is conducted to test the effectiveness of the controller over a wide speed range. The identification algorithm is elaborated in the paper to identify the best value for the proportional gain, kp of the P-controller. The proposed method is compared with the conventional PTC and Direct Torque Control (DTC) techniques, and the simulation results are thoroughly discussed and analyzed. The simulation results showed that the proposed method produced dynamic weighting factors at all speed conditions and provided the best torque control while maintaining the flux ripple within a reasonable value.
Effect of Nano Filler Concentration on Leakage Current of Hybrid Silicone Rubber Composite Insulators

Electrical insulation materials are indispensable to the electrical power system's continual and reliable operation. Over both indoor and outdoor applications, electrical insulation materials like silicon rubber and epoxy have been employed extensively for the past 25 years. To improve dielectric performance and reliability, researchers working in the field of electrical insulation are concentrating on hybrid composite insulation materials. A typical hybrid composite insulation material combines nanoparticles and base materials. The insulation characteristics of these materials are further improved by adding nano and microparticles. This study investigates silicone rubber composite incorporated with nano and microparticles with different percentages. The ultimate stress was improved by 29.88% in the hybrid sample with 6% nano- and 20% micro-silica, which also had a maximum breakdown strength of 26.6 kV mm1 and 50% weight loss at the highest temperature of 575 °C. The main objective is the effect of filler concentration on leakage current (LC) of hybrid silicone rubber composite insulators.
Can we reach human expert programming performance? A tactile manipulation case study in learning time and task performance

Reaching human-level performance in tactile manipulation is one of the grand challenges in nowadays robotics research. Over the past decade significant progress in both skill control and learning was made. However, the achievable execution speed still falls behind the human ability, without clearly understanding whether the specific shortcomings are mainly in the control, skill learning, or motion planning layer. For gaining a better understanding of this complex problem, we draw an experimental side-by-side comparative case study. First, given a task program for a challenging benchmarking task, the goal is to objectify the achievable task performance from a human expert programmer against autonomously learning these assembly behaviors with a state-of-the-art skill learning framework. Second, we compare the manually tuned and learned robot skills to the performance of an adult human solving the task manually. For the former, it could be shown that despite longer learning duration, the task execution speed of the machine learning-based solution is equivalent to the one programmed by the human expert. For the latter, the identified performance gap remained significantly larger, where only for some specific isolated skills the system was able to reach comparable or even faster than human execution speeds. The overall analysis gave also useful hints where in particular manipulation policies and arm-hand coordination still need significant improvements in the future.
Integration in a convex linear topological space

The results of this paper center around the definition of an integration process for multi-valued set functions which are defined over a c-field 5D? and whose values lie in a convex linear topological space £. As such they represent a substantial generalization of the basic results contained in a paper by A. Kolmogoroff [7](1), who considered the case in which £ is the real numbers. On the other hand, the method of defining the integral is a generalization of that used by R. S. Phillips(2) [12, p. 118], although Phillips considered integration only with respect to a positive numerical measure function and restricted the integral to be single-valued. The importance of the Phillips definition lies in the fact that it relieves one of the necessity of considering infinite sums. Throughout the paper is emphasized a type of convergence for sets in a linear topological space which is analogous to the Hausdorff notion of convergence for sets in a metric space [5, §28]. G. B. Price has made a similar use of the Hausdorff convergence for sets [13, Parts II, V]. The contents of the paper are divided into four parts. Part I (§§1-3) contains a short discussion of convex linear topological spaces, a definition of the notion of unconditional summability, which plays a central role in the definition of the integral, and two theorems on additive set functions. Part II (§§4-9) contains the general theory of the Vand S U-integrals. The TJ-integral is multi-valued and is defined for multi-valued set functions F(a). The S TJ-integral is the single-valued specialization of the TJ-integral. Definitions and basic properties of these integrals account for §§4, 5. Section 6 contains a discussion of a generalization of the Kolmogoroff [7] notion of differential equivalence applied to the U-integral, and §7 contains a proof that the transform of an integrable function by a general type of linear transformation is integrable. In §8 it is shown that the definition of the S U-integral can be weakened in case 3E is complete in a certain sense. Section 9 contains a convergence theorem for the S TJ-integral which involves a generalization of the notion of approximate convergence to functions F(a) of the type considered here. The approximate convergence is relative to a positive numerical measure function m(cr) whose only relation to the integral lies in the condition that
Ancient vicariance is reinforced by adaptive divergence in the southern beech: Contributions from geogenomics

Geogenomics seeks to understand geological processes linked to lineage divergence. However, the mechanisms that conserve ancient signals in spite of gene flow are still unclear. In the southern beech, the deep lineage divergence produced by vicariant events is associated with ancient marine transgressions. We hereby evaluate the hypothesis that this divergence is maintained by diversifying selection.
Utilization of Five-Distinct Dataset to Diagnose and Predict Heart Disease: A Machine Learning Approach

The heart is a finely-tuned instrument that serves the whole body. Heart disease is the most common serious illness affecting human nature. Early diagnosis of heart disease is the most important for reducing mortality and minimizing heart disease-related complications. Manual identification of heart disease is a very monotonous task or perhaps impossible at times. Machine learning algorithms can be used to detect risk characteristics, which might lead to more accurate heart disease prediction. In this article, we present a model that is based on machine learning with the goal of improving the accuracy of the prediction of heart diseases. To make our applied model successful, efficient data collection, data preprocessing techniques have been performed efficiently. The dataset consists of various medical issues relating to a heart disease. To handle missing data, the transformation method has been applied. For the classification purposes, six machine learning (ML) classifiers have applied. We have also calculated seven different performance evaluation metrics to evaluate each classifier’s performance. Our presented model has obtained higher accuracy for the Random Forest classifier which is 95.61%.
Implementation of Machine Learning for Analysis of D/L Method

This article examines the Duckworth- Lewis- Stern (DLS) method for One Day International (ODI) cricket matches. The accuracy of the DLS approach is compared to that of several supervised learning algorithms in order to forecast results. The result of a cricket match is predicted during the second inning. To improve its prognostication, the Duckworth-Lewis (D/L) formula used in the paper was also modified. The last phase is the creation of an Unpredictability Index, which ranks various cricket-playing countries according to how unpredictable they are during an ODI match
Data-Driven Kinematic Control Scheme for Cable-Driven Parallel Robots Allowing Collisions

Cable-Driven Parallel Robots (CDPRs) have been proposed for a variety of applications such as material handling, rehabilitation, and instrumentation. However, the collision-free constraint of CDPRs limits the workspace of CDPRs and the feasible position of anchor points. To address the collision-free constraint of CDPRs, a data-driven kinematic control scheme is developed for CDPRs, enabling a CDPR to control its pose even if suffering collisions between a cable and the base or the end-effector. To deal with the collisions, the data-driven kinematic control scheme utilizes a motion model obtained based on data samples of the motion of the CDPR, rather than the Jacobian matrix of the CDPR, to map a control law in the task space to the time derivative of the length of cables in the joint space. To evaluate the effectiveness of the developed data-driven kinematic control scheme, experiments of controlling a suspended CDPR with two cables allowing collisions are conducted.
SESR: Self-Ensembling Sim-to-Real Instance Segmentation for Auto-Store Bin Picking

Instance segmentation is an important task for supporting robotic grasping in auto-store scenarios. Accurate segmentation usually relies on the quantity and quality of available annotated training data. However, it requires tremendous cost to obtain these labels. In this work, without requiring any human annotations on real data, our proposed self-ensembling sim-to-real network, namely SESR, is able to generate precise instance masks for a wide variety of supermarket goods. We design our SESR with a teacher model and a student model trained with a self-ensembling strategy. We adopt different levels of consistency to bridge the sim-to-real gap and boost the model generalization ability. Also, we compile an auto-store bin-picking dataset covering various goods. Extensive experiments on both unseen scenarios and unseen objects validate the effectiveness and superiority of our method over others, and the robot arm demonstrations further show that our segmentation results can support real-time auto-store bin picking.
Disturbance Observer-Based Robust Control of A Flexible Joint Manipulator in The Presence of Unknown Disturbance

This study proposed a State feedback controller (SFC) with a Disturbance observer (DO) for a flexible joint robotic arm under disturbances. The dynamic model of flexible joint robotic arms has vibrations. Due to this system response changes while the movement of a robotic arm, which results in the position of the tip of a link is not set in a proper place. since there is a need to control the vibration of a robotic arm. The design of a traditional controller is capable only of state estimation and evaluations of internal disturbance. Since there is a need for a robust controller and this is achieved by designing the SFC + DO. Which determines the state vector, uncertainties like modeling error, parameter variation, external disturbances, etc. The proposed approach proves closed-loop stability. Simulation results show the efficacy of SFC + DO in the performance of position tracking, estimating states, and uncertainties.
Deliberative Democracy with Robot Swarms

Decision-making among groups of humans can benefit from open discussion and inclusion of a diversity of opinions, promoting deliberative democracy. In this work, we test whether a swarm of robots can help facilitate decision-making by visually representing the diversity of opinions. We used a swarm of robots we built, called MOSAIX, that consists of 4-inch touchscreens-on-wheels robots called Tiles. The robots acted as physical avatars for opinions, helping them travel and mix together. We recruited 46 participants split into groups of 7 and 8 to test whether the robot movement had an impact on the decision-making process versus using the robots stationary in the participants' hands akin to smartphones. Furthermore, we wanted to test whether the participants felt comfortable expressing their opinion through the robots. Results show the participants indeed felt comfortable using the robots, and user engagement increased with the movement of the robots. The difference between the participants' first and last opinions also increased with the movement of the robots. We believe that robot swarms have not been used before to facilitate decision-making among a group of people. Therefore, our contribution is in testing the possibility of how and whether using a moving robot swarm helps humans reach a decision.
Model Learning and Predictive Control for Autonomous Obstacle Reduction via Bulldozing

We investigate how employing model learning methods in concert with model predictive control (MPC) can be used to automate obstacle reduction to mitigate risks to Combat Engineers operating construction equipment in an active battlefield. We focus on the task of earthen berm removal using a bladed vehicle. We introduce a novel data-driven formulation for earthmoving dynamics that enables prediction of the vehicle and detailed terrain state over a one second horizon. In a simulation environment, we first record demonstrations from a human operator and then train two different earthmoving models to produce predictions of the high-dimensional state using under six minutes of data. Optimization over the learned model is performed to select an action sequence, constrained to a 2D space of template action trajectories. Simple recovery controllers are implemented to improve controller performance when the model predictions degrade. This system yields near human-level performance on a berm removal task, indicating that model learning and predictive control is a promising data-efficient approach to autonomous earthmoving.
ReINView: Re-interpreting Views for Multi-view 3D Object Recognition

Multi-view-based 3D object recognition is important in robot-environment interaction. However, recent methods simply extract features from each view via convolutional neural networks (CNNs) and then fuse these features together to make predictions. These methods ignore the inherent ambiguities of each view caused due to 3D-2D projection. To address this problem, we propose a novel deep framework for multi-view-based 3D object recognition. Instead of fusing the multi-view features directly, we design a re-interpretation module (ReINView) to eliminate the ambiguities at each view. To achieve this, ReINView re-interprets view features patch by patch by using their context from nearby views, considering that local patches are generally co-visible at nearby viewpoints. Since contour shapes are essential for 3D object recognition as well, ReINView further performs view-level re-interpretation, in which we use all the views as context sources since the target contours to be re-interpreted are globally observable. The re-interpreted multi-view features can better reflect the 3D global and local structures of the object. Experiments on both ModelNet40 and ModelNet10 show that the proposed model outperforms state-of-the-art methods in 3D object recognition.
Identification of Critical Molecular Factors and Side Effects Underlying the Response to Thalicthuberine in Prostate Cancer: A Systems Biology Approach

Background: Uncontrolled mitosis of cancer cells and resistance cells to chemotherapy drugs are the challenges of prostate cancer. Thalicthuberine causes a mitotic arrest and a reduction of the effects of drug resistance, resulting in cell death. In this study, we applied bioinformatics and computational biology methods to identify functional pathways and side effects in response to Thalicthuberine in prostate cancer patients. Methods: Microarray data were retrieved from Gene Expression Omnibus (GEO), and protein-protein interactions and gene regulatory networks were constructed, using the Cytoscape software. The critical genes and molecular mechanisms in response to Thalicthuberine and its side effects were identified, using the Cytoscape software and WebGestalt server, respectively. Finally, GEPIA2 was used to predict the relationship between critical genes and prostate cancer. Results: The POLQ, EGR1, CDKN1A, FOS, MDM2, CDC20, CCNB1, and CCNB2 were identified as critical genes in response to this drug. The functional mechanisms of Thalicthuberine include a response to oxygen levels, toxic substances and immobilization stress, cell cycle regulation, regeneration, the p53 signaling pathway, the action of the parathyroid hormone, and the FoxO signaling pathway. Besides, the drug has side effects including muscle cramping, abdominal pains, paresthesia, and metabolic diseases. Conclusion: Our model suggested newly predicted crucial genes, molecular mechanisms, and possible side effects of this drug. However, further studies are required.
Design of a Soft Wearable Passive Fitness Device for Upper Limb Resistance Exercise

An increase in health awareness has fueled the development of fitness equipment or devices nowadays. Most conventional fitness devices have had some issues in space limitation and the high cost of equipment. With the advance in wearable robotics, we proposed a soft passive fitness wearable device for upper limb resistance exercises such as chest press, frontal raise, and chest fly. Users can customize the exercise intensity by adjusting the length of the elastic bands embedded in the wearable device. Moreover, the exer-tainment (Exercise-entertainment) user display interface was designed to motivate the user. Movements of users were estimated using inertial measurement units (IMUs), and haptic feedback was provided through the vibro-stimulation. Furthermore, the effectiveness of the proposed device was evaluated with the Borg scales representing the rating of perceived exertion (RPE) and measuring the surface electromyography (sEMG) of the three muscles located one on the shoulder and two on the chest. Both the Borg 6–20 and CR 10 scales were increased, and the normalized sEMG activities of the upper limb muscles with the activated device had more than double in magnitude compared to that with a bare condition; therefore, the proposed device has a potential effectiveness as for resistance exercise. Overall, this research devotes preliminary evidence on the benefits of the device in promoting the user to work out and contributing to the exercise effects.
Enhancement of surface electrical current on silicon via nanodiamond particles deposited by pulsed spray technique

The deposition of as‐received nanodiamond (ND) particles on silicon substrate was performed by the pulsed spray technique, using a dispersion of 250 nm ND in 1,2‐dichloroethane. A set of samples was sprayed by varying the number of pulses from 1 to 500. The morphology of the samples was characterized and monitored by means of optical, atomic force, and confocal microscopies. At a low number of pulses, sparse diamond particles were observed, whereas at a high number of pulses dense/quasi‐continuous ND layers were formed. The electrical conductivity measurements of surface silicon substrate evidenced a remarkable change for the presence of ND particles. This behavior is also found by theoretical simulations (finite element method). Finally, a comparison between the electrical resistances measured on these samples versus the pulse number and the inverse current density calculated as a function of the number of ND particles, showed a good agreement. The experimental results highlighted an increase of the electrical current by using a number of pulses <100, whereas the simulation results proved the enhancement of current density and its surface rectification by employing a specific number of particles. The current increased by increasing the temperature and during the heating–cooling cycles hysteresis was observed.
Climate sensitivity of a one-dimensional radiative-convective model with cloud feedback

Abstract We illustrate the potential complexity of the feedback between global mean cloud amount and global mean surface temperature when variations of the vertical cloud distribution are included by studying the behavior of a one-dimensional radiative–convective model with two types of cloud variation: 1) variable cloud cover with constant optical thickness and 2) variable optical thickness with constant cloud cover. The variable parameter is calculated assuming a correlation between cloud amount and precipitation or the vertical flux convergence of latent heat. Since the vertical latent heat flux is taken to be a fraction of the total heat flux, modeled by convective adjustment, we examine the sensitivity of the results to two different critical lapse rates, a constant 6.5 K km−1 lapse rate and a temperature-dependent, moist adiabatic lapse rate. The effects of the vertical structure of climate perturbations on the nature of the cloud feedback are examined using two cases: a 2% increase in the solar con.