Miracle-dz/UniFAR-FTUs huggingface dataset:
Discharge and Deposition Characteristics of High-Power Impulse Magnetron Sputtering Using Various Target Materials

The discharge and deposition characteristics of high-power impulse magnetron sputtering using various target materials with different sputtering yields (Cu, Cr, Ti, and C) were analyzed by a plasma global model. The experimental discharge voltages and currents of various target materials were used as the input parameters of the model. The analysis reveals that the ionization fraction of the sputtered species decreases as increasing the sputtering yield, due to the electron temperature was reduced through the cooling effect of sputtered species. However, as the sputtering yield increases, the plasma density under a given discharge power density is increased and the self-sputtering runaway can be fully developed, resulting in a higher fraction of ion density in the form of metal ions in the ion deposition flux. For some high-sputtering yield materials, such as Cu and Cr, this fraction can be up to 95%.
Web Authentication Security Using Image Steganography and AES Encryption

In this paper we present Web based highly secure Authentication Security system that uses Image Steganography and 128-bit Advanced Encryption Standard algorithm. Our developed web authentication system encrypts users passwords with an AES encryption that takes octillion years to decrypt and is coupled with face identification photograph which is used as a stegoimage to hide the encrypted password for more robust security. When put to test our Web Authentication Security systems shows positive results which outstand advanced steganalysis attacks such as the chi-squared attack and neighbourhood histogram. We recommend this security for prospective web applications of the future that will be handling sensitive user information.
Evaluation of Dynamic Influence of Harmonics of AC System on HVDC Commutation Failure

Currently, most of the works done on the popular problem of commutation failure (CF) in HVDC systems are based on analyzing the fundamental voltage and lack the consideration of the influence of harmonics. This paper uses the method called commutation voltage-time area to analyze the mechanism of the commutation process. We concluded that the commutation area is mainly affected by the harmonic voltage amplitude. Based on the qualitative research of harmonic-related parameters affecting CF, this paper proposes evaluation indexes of CFI through theoretical derivation, and we concluded that the real-time variation of hth-degrees harmonics ratio (HRUh) and CFI can be used to dynamically evaluate the influence of harmonics on CF. By PSCAD/EMTDC simulation analysis and index calculation, we identified voltage distortion caused by harmonic as an important cause for the CF of single-phase asymmetric fault and the subsequent CF of three-phase symmetric fault, and the 3rd, 2nd non-characteristic harmonics produced by the corresponding faults have the greatest influence on CF. Because of CFI has the same variation trend with the total harmonic distortion (THDu), $THD_{\mathrm {u}}$ can be used for risk estimation of the CF by AC system faults. Moreover, we found that the harmonics generated by asymmetry fault are more likely to cause CF, and the voltage distortion during the fault recovery is an important cause of the power fluctuation during the recovery of the CF. These research results have important theoretical guiding significance for taking corresponding protection and control measures in engineering and reducing the probability of CF.
Robotic Grasping Using Proximity Sensors for Detecting both Target Object and Support Surface

The robustness of the positioning and posturing of robot hands relative to target object and support surface is an important issue for autonomous grasping. For example, to perform a grasping action such as picking up thin objects from a table top, the position and posture of the hand must be controlled to keep adequate relative posture and distance to the support surface besides those between the hand and the target object. Because slight errors in the posture and position are enough to cause grasping failure, the positioning and posturing of the hand must be precise enough, specially when the hand is close to the target object and support surface. To improve the robustness of robotic grasping, in this paper we present a method by grasping control based on the relative posture and position between hand and support surface besides those between hand and target object, using proximity sensors. Proximity sensors are newly installed on fingernails besides on the fingertips. As the fingernail sensor, an integration of Time-of-Flight (TOF) sensor and photo-reflector is designed to realize long range detection, as well as with precise and high-speed detection regardless of the reflectance of support surfaces when approaching the support surface. By the sensors, the hand can approach the object and support surface coarsely first, and then can be controlled fast and precisely to realize adequate grasping motion along the support surface but without contact with the support face. The method has been implemented to a manipulator system, and successful grasping experiments have demonstrated the effectiveness of the proposed method.
Design and Implementation of Programmable Drawing Automata based on Cam Mechanisms for Representing Spatial Trajectory

This paper presents the design and implementation of a preliminary version of a programmable drawing automaton (PDA-0) that draws a user-specified 3D trajectory. PDA-0 is strongly inspired by Jaquet Droz's a programmable drawing automaton built in the 1770s using 6,000 moving parts, which was hand-coded. PDA-0 consists of RSSR (Revolute-Spherical-S-R) linkage and cam mechanisms with three interchangeable cams. Interchangeable cams make PDA-0 programmable because a user-specified 2D/3D trajectory is encoded into the set of three cams. The user programs PDA-0 by specifying a trajectory via a GUI or 3D animation. Subsequently, the compiler estimates a 3D trajectory mathematically from the user-specified 2D/3D trajectory and calculates the shape of the three cams, i.e., a code for PDA-0 by solving kinematic constraints. Finally, PDA-0 with the 3D-printed cams executes the code to draw the user-specified trajectory. The current PDA-0 with three cams demonstrates drawing simple trajectories such as letters and symbols.
Comparison of Dynamic Models for Non-Contact Micromanipulation Based on Dielectrophoretic Actuation

Several approaches are proposed in the literature to calculate the drag force, the electric field and the induced dielectrophoretic force. This paper analyzes the performances of various models for closed loop control of dielectrophoretic systems in comparison with experiments. This article compares their performance in terms of accuracy, computation time, and memory consumption. Four classical approaches are available to calculate the electric field. Their performances are analyzed in the paper. We have shown that combining the dipolar model of dielectrophoresis force with an anisotropic drag force (integrating the wall-effect) provides an interesting ratio precision/computation time. This paper provides an original comparison of several models described in literature whose performances have been compared with experiments.
Smooth Point-to-Point Trajectory Planning in $SE$ (3)with Self-Collision and Joint Constraints Avoidance

In this paper we introduce a novel point-to-point trajectory planner for serial robotic structures that combines the ability to avoid self-collisions and to respect motion constraints, while complying with the requirement of being $C^{4}$ continuous. The latter property makes our approach also suited for 4th order dynamics flexible joint robots, which gained significant practical relevance recently. In particular, we address the problem of generating a smooth, kinematically nearly time-optimal $SE$ (3)trajectory while simultaneously avoiding potential collisions of the robot end-effector with its base as well as respecting the Cartesian unreachable states induced by the joint limits of the proximal kinematic structure.
Novel Two-Fold Data Aggregation and MAC Scheduling to Support Energy Efficient Routing in Wireless Sensor Network

Network lifetime maximization is the primary challenge in the wireless sensor network (WSN) due to its resource limitations. E2R2 routing algorithm uses a hierarchical WSN model to minimize energy consumption in the WSN. However, it increases complexity, time consumption, and energy consumption due to the hierarchical model. To resolve these problems, this paper proposes a novel ring partitioned based MAC (RP-MAC) protocol for the energy-efficient WSN with a mobile sink node. Energy efficiency is achieved by the following phases: clustering phase, MAC scheduling phase, data aggregation phase, and routing phase. Clustering phase is initiated by a weighted Voronoi diagram (WVD) algorithm by assigning a weight value for each node. Energy consumption due to idle listening is minimized by enabling novel RP-MAC scheduling in each cluster. Involvement of RP-MAC protocol also achieves collision-free data transmission in the network. A two-fold data aggregation (TFDA) scheme is proposed for the data aggregation phase to minimize energy consumption by reducing the number of transmissions. Routing phase supports both intra-cluster routing and inter-cluster routing. For intra-cluster routing, a hybrid chicken swarm optimization algorithm is proposed. For inter-cluster routing, position-based routing tree is constructed based on the sink node’s position. Our proposed RP-MAC protocol minimizes energy consumption in all possible ways for improving the network lifetime. Extensive simulation in ns-3 shows that the RP-MAC protocol achieves promising results in the following performance metrics: the number of dead nodes, average energy consumption, network lifetime, and throughput.
A Method for Robot Motor Fatigue Management in Physical Interaction and Human-Robot Collaboration Tasks

Collaborative robots are often designed with limited power and force capacity, with the aim to provide affordable solutions and ensure human safety in case of accidental collisions and impacts. If a task requires a power beyond this capacity, or is performed repeatedly over long periods, such limits may be exceeded, which can cause inevitable robot damage and contribute to the lost productivity. In such cases, where hardware solutions and improvements are not applicable, effective software frameworks can prolong robot productivity and lifetime. To this end, in this paper we propose a novel technique for the monitoring and management of robot fatigue in repetitive or high-effort task execution scenarios. The robot fatigue is estimated by the measured temperature of motors in the joints. The proposed fatigue management system is composed of two-stage reaction process that is triggered by different levels of the estimated fatigue. The first stage exploits the kinematic redundancy of robot structure in attempt to minimise the load in the specific joints that under fatigue by reconfiguration in the joint space through the null space of the Cartesian task production. If the first stage is not successful in reducing the fatigue, the second stage is activated that gradually reduces the forces of hybrid controller. At that point, the human co-worker can temporarily take over the task execution until the robot will be recovered from the excessive fatigue. To validate the proposed approach we conducted experiments on KUKA Lightweight Robot performing two interaction tasks: autonomous surface wiping and collaborative human-robot surface polishing.
ETA-based analysis of small signal stability considering WADC with multiple time delays

Low frequency oscillation occurs frequently in power systems. Wide-area damping controller (WADC) based on Wide-Area Measurement System (WAMS) can effectively suppress inter-area low frequency oscillations. However, with the introduction of global signals, time delays are inevitable. Electromagnetic torque analysis method(ETA) can establish the analytic relation between electromagnetic torque coefficients and control parameters of power component, which is facilitative to analyze small signal stability. Considering that WADC has inherent characteristics of multiple time delays, based on the ETA for equivalent two-machine system, this paper deduces the general mathematics expression among electromagnetic torque coefficients, WADC parameters and time delays. The influence mechanism of time delay on electromagnetic torque coefficients and small signal stability is further analyzed. Based on these analysis, performance of damping control of WADC can be improved, on one hand, time-delay small signal stability region is established under fixed WADC parameters, on the other hand, if small signal stability is not achieved under the given WADC parameters due to set of the time delays, optimization scheme is proposed to carry out parameters coordination according to the measured time delays. Finally, taking two time delays as an example, the paper verifies the effectiveness of the proposed method by comparing the system dynamic performance considering two time delays with the system only considering single time delay and the system without time delay in a two-area four-machine power system.
Multi-modal Image Retrieval for Search-Based Image Annotation with RF

Search-based annotation methods can be used for proposing descriptive keywords to users who need to annotate images e.g. in image stock databases. From the annotation output, users select keywords which they want to assign to the given image. The selected keywords can serve as a relevance feedback for additional annotation refinement. In this paper, we study the possibilities of exploiting the annotation relevance feedback, which is a novel problem that has not been systematically addressed yet. In particular, we focus on the subtask of utilizing the feedback for the retrieval of related annotated images that are subsequently used for mining of candidate keywords. We select three multi-modal search techniques that can be applied to this problem, implement them within a state-of-the-art search-based annotation system, and experimentally evaluate their usefulness for annotation quality improvement.
Algorithm Implementation of Data Acquisition System of Fiber Bragg Grating Demodulator Based on Compressive Sampling

According to the demodulation of high frequency vibration signal in the FBG (Fiber Bragg Grating) demodulator, the sampling frequency of the demodulator is high. And the high frequency sampling circuit designed in the practical application has the problem of high cost and unstable system. Therefore, the compressive sampling method is introduced into the FBG demodulator to replace the traditional Nyquist sampling for the compressive sampling of the reflection spectrum signal of FBG. This method reduces the sampling frequency requirement of data acquisition system by compressing data. In this paper, simulation experiments are used to verify the feasibility of compressive sampling. Through simulation experiments, the signal can still be reconstructed by algorithm when the data compressive ratio is 3.6:1. The MSE (mean-square error) of the reconstructed signal and the original signal is 0.0161, and the RE (relative error) near the peak value of the reconstructed signal is less than 0.009. Compared to directly reducing the sampling frequency, the reconstruction error of compressive sampling is much smaller than that of low-frequency sampling. In addition, through simulation experiments, it was found that compressive sampling can reconstruct signals under certain noise.
Online prediction of threading task failure using Convolutional Neural Networks

Fasteners assembly automation in different industries require flexible systems capable of dealing with faulty situations. Fault detection and isolation (FDI) techniques are used to detect failure and deal with them, avoiding losses on parts, tools or robots. However, FDI usually deals with the faults after or at the moment they occur. Thus, we propose a method that predicts potential failures online, based on the forces and torques signatures captured during the task. We demonstrate the approach experimentally using an industrial robot, equipped with a force-torque sensor and a pneumatic gripper, used to align and thread nuts into bolts. All effort information is fed into a supervised machine learning algorithm, based on a Convolutional Neural Network (CNN) classifier. The network was able to predict and classify the threading task outcomes in 3 groups: mounted, not mounted or jammed. Our approach was able to reduce in 10.9% the threading task execution time when compared to a reference without FDI, but had problem to predict jammed cases. The same experiment was also performed with other two additional learning algorithms, and the results were systematically compared.
Flux Weakening Control for Dual Three-Phase PMSM

Control strategies for dual three-phase permanent magnet synchronous motors (PMSMs) mostly focus on the reduction of harmonic currents and torque ripple. But the investigation on the control approach on the dual three-phase PMSM in high-speed region has not been well discussed before. This paper aims to present a new flux weakening control scheme for a dual three-phase PMSM. Actually, the control scheme is expanded from the traditional three-phase PMSM counterparts. First, the limitation of voltage and current in dual three phase PMSMs is discussed. Then, a flux weakening control scheme is designed, which is to utilize the limited cycle to improve the high speed performance of dual three-phase PMSM. Finally, this control scheme is verified through the demonstration. The result proves the feasibility of the proposed method.
Object Recognition Through Active Sensing Using a Multi-Fingered Robot Hand with 3D Tactile Sensors

This paper investigates tactile object recognition with relatively densely distributed force vector measurements and evaluates what kind of tactile information is beneficial for object recognition. The uSkin tactile sensors are embedded in an Allegro Hand, and provide 240 triaxial force vector measurements in total in all fingers. Active object sensing is used to gather time-series training and testing data. A simple feedforward, a recurrent, and a convolutional neural network are used for recognizing objects. Evaluations with different number of employed measurements, static vs. time series data and force vector vs. only normal force vector measurements show that the high-dimensional information provided by the sensors is indeed beneficial. An object recognition rate of up to 95% for 20 objects was achieved.
Gcforest-Based Fault Diagnosis Method For Rolling Bearing

At present, the intelligent diagnosis technology of bearing is mostly dependent on the deep neural network, which has high requirements for computer hardware, especially CPU. It also requires a large enough training data set with labels and a large number of hyperparameters. To solve these problems, a rolling bearing intelligent diagnosis method based on gcforest (multi-Grained Cascade forest) theory is proposed in this paper. First, the data signals collected by the experiment are preprocessed, and stored in the data set in the form of numerical values; then the data in the data set is directly input into the model for training or recognition. Experiments show that this method has good fault recognition accuracy, and it can also achieve satisfactory results by training with small-scale data sets. At the same time, it has few hyperparameters, and the training process has very low requirements for the hardware.
Hyperspectral camera with polarized filter as modern supersensor device for cyber-physical systems

Starting from 2008–2010, the hyperspectral cameras have become very popular supersensor devices capable to record spectral information for every pixel of the observed view field. Those cameras can be applied in numerous application fields where human eye is not able to distinguish between the different spectral signatures. The article describes measurements of natural forest background, dry hay ground, military vehicle and compares reflection of dry, wet and frozen road surfaces. As a special advanced feature, the experiments with linear polarization filter are presented and analyzed. Article offers a simple formula based analytical model of a the pine forest “700 nm red step” and “near infrared plateau”.
Condition Monitoring for Wind Turbine Pitch System Using Multi-parameter Health Indicator

As one of the critical components of wind turbines, pitch system suffers the highest failure rate and downtime. Thus, it is particularly important to improve the reliability and reduce the operation and maintenance (O&M) costs of pitch system. With the development of condition monitoring (CM) technology, the condition based maintenance is often employed to reduce the O&M costs of wind turbines. This paper proposed a data-driven CM approach to identify the abnormal condition of pitch system, using supervisory control and data acquisition (SCADA) data. The result of the proposed approach could be useful for wind farm operators to improve their maintenance strategy. Firstly, the Relief algorithm is used to select the appropriated parameters as the health indicator for evaluating the condition of pitch system. Meanwhile, the correlation between the selected parameters is considered to remove the redundant variables. With the selected parameters as the model output and environment variables as the model input, then the random forest regression based model is established by only using the historical healthy SCADA data. Eventually, the distance calculated based on the residual errors between model output and monitored value is used to identify the abnormal condition of pitch system specifically. Several case studies have been analyzed to validate the feasibility of the proposed method. The results indicate that the proposed method can detect the pitch system faults earlier than the SCADA alarm system. It is applicable for the on-line CM of pitch system because of simplicity and low cost.
Real-Time 3D Magnetometer Calibration for Embedded Systems Based on Ellipsoid Fitting

Magnetometers are massively diffused in a variety of instruments for different applications. However, these sensors are affected by non-idealities, especially bias and hard/soft-iron interference. Even though a number of calibration approaches could be adopted to compensate for magnetic interferences, their implementation on resources-constrained platforms is still problematic due to the highly complex, computationally intensive mathematical operations involved. The present work demonstrates the possibility to develop a fast and efficient 3D magnetometer calibration for real-time embedded systems with limited system resources. A number of techniques and approaches are discussed to mitigate the computational burden. Results confirm that this is achievable by preserving the same level of accuracy reached with more computationally intensive approaches. Results are also promising regarding the adoption of the discussed method on low-power real-life systems in several applications.