Miracle-dz/UniFAR-FTUs huggingface dataset:
High-Speed Scooping: An Implementation through Stiffness Control and Direct-Drive Actuation

This study presents the technique of robotic high-speed scooping: rapidly picking an object lying on a support surface by making contact with the object's open top face and the bottom face that is hidden in contact with the support surface. Essential to high-speed scooping is thus to make suitable dynamic, impactful interaction happen among the robot, object, and environment under errors and uncertainties. We propose a solution to this challenge based on stiffness control, an approach for indirect force control using the robot that is arranged to behave like a desired mechanical system. An implementation of the solution is then presented using a custom-built two-fingered direct-drive gripper. Our experiments verify that high-speed scooping operation is achievable, with the duration of dynamic interaction less than 0.3 s, and effective to various scooping situations featuring objects durable and fragile.
Disturbance Propagation Stability in Droop-Controlled Microgrids

The disturbance response of the angle dynamics for a droop-controlled islanded microgrid is characterized. Specifically, a notion of propagation stability is defined, which is concerned with spatial attenuation vs amplification of input-output responses in the network in a ℋ∞ or ℋ2 sense. Criteria for propagation stability are developed, phrased in terms of the microgrid’s inverter control parameters. The input frequency range over which the network is susceptible to amplification is also characterized, in the case that the criteria are not met. Based on the formal analysis, the design of resilient controls that trade off coherence and disturbance propagation goals is briefly conceptualized. Finally, the propagation stability analysis is illustrated using a 15-bus example microgrid network.
Adaptive Neural Attitude and Altitude Control of a Quadrotor UAV with External Disturbances via Sliding Mode Control

Attitude and altitude control of a quadrotor UAV is designed under time-varying external disturbances using an adaptive sliding mode control (ASMC) integrated with a neural network (NN) algorithm. The dynamic model of the quadrotor is computed considering exterior disturbances and parametric variations. A neural network algorithm combined with adaptive sliding mode control is employed to alleviate the chattering effect, and hence improve system performance. The gain parameters of sliding surfaces are accommodated with a backpropagation algorithm. The external disturbance estimation is obtained by using a disturbance observer (DO) which is incorporated with an adaptive sliding mode control. The stability of the proposed control technique is validated using the Lyapunov theory. The performance of the proposed adaptive sliding mode control using a neural network has been assessed and analyzed with other control methods using numerical simulation.
Reinforcement Learning based Approximate Optimal Control of Nonlinear Systems using Carleman Linearization

We develop a policy iteration-based model-free reinforcement learning (RL) control for nonlinear systems with single input. First, Carleman linearization, a commonly used linearization technique in the Hilbert space, is applied to express the nonlinear system as an infinite-dimensional Carleman state-space model, followed by derivation of an online state-feedback RL controller using state and input data in this infinite-dimensional space. Next, the practicality of using any finite-order truncation of this controller, and the corresponding closed-loop stability of the nonlinear plant is established. Results are validated using two numerical examples, where we show how our proposed method provides solutions close to the optimal control resulting from the model-based Carleman controllers. We also compare our controller to alternative data-driven methods, showing its advantage in terms of shorter learning time.
A Tactile-enabled Hybrid Rigid-Soft Continuum Manipulator for Forceful Enveloping Grasps via Scale Invariant Design

This work presents a novel hybrid rigid-soft continuum manipulator, which integrates high-resolution tactile sensing in a form factor that is forceful, compliant, inherently safe, and easily controllable. We utilize a hybrid approach motivated by scale-invariant principles to fuse the rigid and soft design domains while addressing their respective challenges. We use Euler-Bernoulli beam theory and geometric inference to design and develop a novel variant of folded flexure hinge (FFH) compliant mechanism, the variable area moment of inertia folded flexure hinge (VAFFH), which deforms logarithmically along its length and thus yields first-order scale-invariant grasp behavior. Finally, we characterize the forcefulness of the manipulator and demonstrate its compliance, adaptability, and tactile sensing capabilities in selected tasks.
ChPOS: A Contactless and Continuous Method for Estimation of Heart Rate from Face

Recent years have seen an increase in interest in remote photoplethysmography (rPPG), as it is a fast, inexpensive, and convenient method for contactless estimation of a person’s heart rate from facial videos and has potential in cardiac monitoring. Compared to traditional photoplethysmography (PPG), rPPG offers ease of access for disadvantaged and vulnerable members of the population, as this method saves cost and time by reducing frequent visits to the hospital. However, there are currently limitations to using rPPG in practice due to issues with consistent response skin colour, subject movement, and lighting artefacts. In this work we develop a new framework, ChPOS, by combining two traditional algorithms, CHROM and plane orthogonal to skin (POS). We modified the POS algorithm by incorporating the additional feature of the chrominance colour signal and changing the projection axis, to improve the accuracy of heart rate detection on subjects with darker skin complexion. The performance of our model is validated on two publicly available datasets, UBFC-RPPG and PURE. We compare the approach with state-of-the-art algorithms and the results show that our algorithm outperforms state of art models in the estimation of heart rate, with a mean absolute error (MAE) of 5.71 and root mean squared error (RMSE) of 7.27 on the UBFC-RPPG database and MAE of 5.39 and RMSE of 6.61 on the PURE database.
Scene-level Point Cloud Colorization with Semantics-and-geometry-aware Networks

In robotic applications, we often obtain tons of 3D point cloud data without color information, and it is difficult to visualize point clouds in a meaningful and colorful way. Can we colorize 3D point clouds for better visualization? Existing deep learning-based colorization methods usually only take simple 3D objects as input, and their performance for complex scenes with multiple objects is limited. To this end, this paper proposes a novel semantics-and-geometry-aware colorization network, termed SGNet, for vivid scene-level point cloud colorization. Specifically, we propose a novel pipeline that explores geometric and semantic cues from point clouds containing only coordinates for color prediction. We also design two novel losses, including a colorfulness metric loss and a pairwise consistency loss, to constrain model training for genuine colorization. To the best of our knowledge, our work is the first to generate realistic colors for point clouds of large-scale indoor scenes. Extensive experiments on the widely used ScanNet benchmarks demonstrate that the proposed method achieves state-of-the-art performance on point cloud colorization.
Getting Air: Modelling and Control of a Hybrid Pneumatic-Electric Legged Robot

With their combination of power and compliance, pneumatic actuators have great potential for enabling dynamic and agile behaviors in legged robots, but their complex dynam-ics impose control challenges that have hindered widespread use. In this paper, we describe the development of a tractable model and characterization procedure of an off-the-shelf double acting pneumatic cylinder controlled by on/off solenoid valves for use in trajectory optimization. With this we are able to generate motions which incorporate both the body and actuator dynamics of our robot Kemba: a novel quadrupedal robot prototype with a combination of electric and pneumatic actu-ators. We demonstrate both a 0.5 m jump and land maneuver, and a maximal 1 m jump, approximately 2.2 times its leg length, on the physical hardware with the proposed model and approach. The hardware matches the desired trajectory with a maximum height error of only 5 cm without any feedback on the pneumatic joints, demonstrating the utility of the model in high-level motion generation, and capability of the physical robot.
A Hybrid Model Based on LSTM for Water Prediction Algorithm

Focusing on the challenge of predicting water levels under highly nonlinear and non-smooth characteristics, we propose a data reconstruction blend prediction model based on Long Short-Term memory (LSTM) networks. The model utilizes a completely ensemble empirical model decomposition with adaptive noise to decompose the time series, and then applies the Temporal Convolutional Network (TCN) and LSTM structures as water prediction models. Through analysis of the predicted results from a pumping plant’s pumping port data, we demonstrate the superiority of our model over traditional approaches.
Error Bounds for Native Space Embedding Observers with Operator-Valued Kernels

This paper derives new rates of convergence for observers for a class of uncertain systems governed by non-linear ODEs. We assume that the generally nonlinear function appearing in the ODEs that represents the unknown dynamics is an element of a vector-valued reproducing kernel Hilbert space ℍ (RKHS) that is induced by an operator-valued kernel. The vector-valued RKHS embedding method described in the paper yields a nonparametric adaptive estimator that takes the form of distributed parameter system (DPS). The original ODEs in ℝd are thus embedded in a product space ℝd × ℍ in which state and functional uncertainty estimates evolve. We first discuss the well-posedness of the DPS formulation, and subsequently describe an approximation scheme in finitedimensional subspaces based on certain types of history dependent bases. We derive sufficient conditions that ensure the consistency of the finite-dimensional approximation scheme, and further derive rates of convergence in some particular cases when the samples that define the scattered bases are dense in some sufficiently regular and invariant subset of the observation space. A numerical example is given to illustrate the qualitative behavior of implementations of the theoretical results derived in the paper.
Research on Sensorless Control Technology of Brushless DC Motor with Delta Winding for Cooling Fan in Electric Reactor

The cooling fan of the electric reactor is based on the brushless DC motor with delta winding and the commutation point of sensorless drive is 60° after the back electromotive force (EMF) passes through zero point. In this paper, the mechanism of back EMF zero-crossing detection method for delta winding is deduced and the starting problem at low speed and the commutation error caused by diode freewheeling at high speed and large current are analyzed. On this basis, combining I/f constant current up-frequency starting and the back EMF zero-crossing detection method and performing the commutation angle compensation, a full-speed range sensorless control strategy is proposed. Based on this strategy, an electric reactor cooling fan with rated speed of 4750(r/min) and rated power of 800W under low voltage and large current conditions is verified by simulations and experiments. The results show that the sensorless control strategy proposed can ensure the stable operation of the cooling fan of the electric reactor in the full-speed range, which has good feasibility and robustness.
Robust Trajectory Tracking Control for Unmanned Aerial Vehicle with Actuator Faults

In this paper, an extended state observer (ESO)-based sliding mode tracking control method is designed for the quadrotor unmanned aerial vehicle (UAV) with external disturbances and actuator faults. Firstly, the nonlinear model of the quadrotor UAV is established. Then the ESO is constructed to tackle the unknown differentiable disturbances and the sliding mode control technique is combined with adaptive estimation to address the unknown nondifferentiable actuator faults, respectively. Finally, a robust fault-tolerant tracking control method is proposed to make sure that all closed-loop system errors are uniformly ultimate bounded via Lyapunov stability analysis, and the efficiency of the proposed approach is confirmed by the numerical simulation.
Spatio-Temporal Variable Structure Graph Neural Network for EEG Data Classification

This paper proposes a strategy for improving the correct diagnosis of epilepsy based on electroencephalogram (EEG) using a spatio-temporal variable structure graph convolutional neural network. Specifically, this method is called the variable-structure graph convolutional neural network (VGCRN), which is derived by combining spatial information and noise removal through variable structured graph convolutional neural network and temporal information through recurrent neural network. Despite the potential benefits of EEG for diagnosing and monitoring neurological conditions, the low signal-to-noise ratio often hinders timely and accurate diagnosis in many clinical cases. Previous research on EEG data classification has mainly focused on extracting features from the time or frequency domain, disregarding the spatial features among electrodes. EEG can be viewed as a structured time series, consisting of multivariate time series data with prior information provided by the spatial location of electrodes on the patient’s scalp. Spatial information is just as crucial as time or frequency-domain information, but introducing unconstrained spatial features in topological map structures can result in noise and the aggregation of irrelevant information by nodes. The proposed method in this paper can better leverage the spatial and intrinsic temporal information of brain waves while reducing noise, thus enhancing the robustness and accuracy of the model.
Detection and Prediction of Terrorist Activities and Threatening Events in Twitter - A Survey

Twitter is one of the prominent social media applications which influence civilians due to its fast information dissemination. Researchers have shown that the majority of the tweets posted involve the current issues prevailing in society. Terrorism is a global threat to the peaceful existence of humanity. Terrorists widely use online social media platforms to communicate and propagate their activities. User-to-user interaction leads a major role in the formation of complex communication in the terrorism domain. This paper aims to discuss the different methods in literature used to identify both single and groups of users who are related to terrorist activities. The study focuses on the different techniques for identifying threatening activities, detecting terrorist attacks and predicting terrorist attacks.
Bayesian inference to characterize Josephson oscillations in a double-well trap

We use quantum trajectories to simulate Josephson oscillations of atomic condensates between the two sides of a double-well potential. In the simulations the atoms in both wells are monitored using oﬀ-resonant light scattering, and the ultimate outcome of our thought experiment is a sequence of photon counts probing the numbers of the atoms in each potential well. We show how to reconstruct the Josephson oscillations from the observed photon counts using Bayesian inference, and study the oscillations quantitatively by averaging the inferred time dependent oscillation amplitude over a large number of realizations. Scaling behaviors that characterize the oscillations are uncovered and related to physics principles such as measurement back-action. It turns out that the scalings hold true for quite small atom numbers, so that in this sense four atoms in a potential well may already make a Bose-Einstein condensate.
Analysing the Safety and Security of a UV-C Disinfection Robot

Safety is paramount for robots used in environments they share with humans. In such scenarios, security is also growing in importance. However, conventional approaches to analysing safety requirements are aimed at identifying hazards only. Security-related aspects such as cyber threats, cyber attacks and vulnerabilities have hardly been integrated into analysis and design methods to date. The methods available so far for the joint analysis of safety and security are based on established methods of safety engineering, where the amount of information is very large and usually stored in text- and table-based documents. This makes it challenging for engineers to systematically assess and maintain safety and security information. Thus, adequate tool support for robot engineers is required to cope with the increased complexity and to manage the safety and security risks. In this paper, we demonstrate that robot's safety and security information can be expressed, stored, analysed and queried in a knowledge graph representation paving the way to automated analysis. More specifically, we apply an integrated, systems-oriented safety and security co-analysis approach, namely STPA-Safesec, to a robot performing disinfection tasks in domestic environments. By querying the resulting graph of safety and security artefacts, we automatically retrieve hazardous scenarios, identify gaps in the analysis and increase our understanding of the overall risks of the robot.
Treating a different kind of patient: curing security weaknesses in digital health systems of the future

Technological innovations are changing the world we live in, but in no sector they can have a greater impact on our lives than in the area of digital healthcare. The potential is immense: from AI-driven drug discovery to pervasive sensing to predict and prevent diseases and health conditions, the promise of technology-enabled medicine is that of better diagnoses, more effective treatments, improved prognoses, and ultimately longer and healthier lives.While technological advances in healthcare are being introduced at an ever increasing pace, they face an existential risk for their eventual adoption and public acceptance: they do not currently embed advanced cyber security mechanism that will be capable of confronting the complex cyber threats landscape of the future. In fact, while advanced cryptographic and security techniques such as secure multi-party computation, federated learning and root of trust are edging closer to deployment in the real world, they have largely not been adopted in the medical and healthcare domain. In this work, we argue that such adoption, enabled by stronger collaboration and deeper mutual knowledge between the healthcare and cyber security research community, is crucial for the success and safety of medical devices and sensors. In particular, we discuss the case of sensors, and their security, as well as the privacy of the data they collect.
Coherent Noise Attenuation by Kurtosis-Guided Adaptive Dictionary Learning Based on Variational Sparse Representation

In seismic data denoising, coherent noise is usually challenging to remove because it has similar features with signal. Therefore, attenuating coherent noise is of great importance for seismic data. To obtain a good denoising result, it is necessary to fully consider the features of coherent noise and choose the appropriate denoising methods. Here, we propose a kurtosis-guided adaptive dictionary learning (KGADL) algorithm based on the variational sparse representation model. First, the variational sparse representation is to construct initial dictionary that depends on the seismic data, so that it can accurately contain the features of the seismic data, so as to improve the accuracy of sparse representation. Then, we use the K-singular value decomposition (K-SVD) algorithm to update the dictionary and introduce kurtosis to measure each atom after updating. Atoms with high kurtosis values usually exhibit strong irregularities and are considered noise, while atoms with low kurtosis values have more feature of valid waves and are considered signal. The atoms with low kurtosis values characterizing valid signal are retained to get the special dictionary that adapts to the complexity of the input seismic data, and this dictionary is used to suppress the coherent noise in seismic data. Finally, the denoising performance of the proposed method is illustrated by examples of both synthetic and field seismic data.
KESESUAIAN KONDISI OSEANOGRAFI DALAM MENDUKUNG EKOSISTEM TERUMBU KARANG DI PULAU PARI

Parameter kualitas air laut meliputi suhu, salinitas, pH, dissolved oxygen (DO), dan kekeruhan (turbiditas) merupakan faktor penting yang harus diukur secara kontinu untuk memonitor kondisi laut dan dampaknya bagi biota laut. Karang merupakan salah satu biota laut sangat sensitif terhadap perubahan kualitas air laut, khusunya suhu permukaan laut. Kondisi air laut yang melebihi ambang batas yang dapat ditolerir oleh karang, diduga dapat menghambat laju pertumbuhan maupun proses resiliensinya. Oleh karena itu penelitian ini bertujuan untuk memahami kondisi oseanografi dan kesesuaiannya dalam mendukung ekosistem terumbu karang. Pulau Pari dipilih menjadi area kajian karena wilayah ini memiliki keanekaragaman ekosistem terumbu karang yang cukup tinggi. Dalam penelitian ini dilakukan pengukuran kualitas air laut pada bulan Maret dan Juni 2015 di tiga stasiun pengamatan utama yaitu PR01 (Daerah Perlindungan Biota Laut), PR02 (Goba Pulau Tikus), dan PR03 (Bintang Rama) yang merupakan lokasi ekosistem terumbu karang. Selain itu dilakukan pula pengukuran di 14 stasiun pengamatan untuk mengetahui pola kualitas air secara spasial. Studi ini menghasilkan informasi suhu permukaan laut berada pada rentang 28.5 hingga 29.5 oC; salinitas air laut berada pada rentang 29.0 hingga 30.5 psu; pH berada pada rentang 8.3 hingga 8.43 satuan; turbiditas berada pada rentang 0 hingga 1.4 NTU; dan DO berada pada rentang 4 hingga 5.3 mg/L. Kualitas air laut pada dua bulan tersebut masih sesuai dengan baku mutu air laut khususnya bagi karang, yang ditetapkan oleh Menteri Lingkungan Hidup Republik Indonesia, kecuali untuk parameter DO pada bulan Juni 2015 yang berada di bawah ambang batas minimum.
Application of Hybrid Techniques for Efficient Photovoltaic Systems-An Overview

In the last few decades, the application of photovoltaic (PV) systems grabs significant attention as it is one of the major promising sources of green energy. Modern advancement in PV technologies makes it well effectual and affordable in wide areas such as power generation on-grid/ off-grid system, street illumination, pumping out water in agriculture sector etc. However, the extraction of maximum power from the modules is inevitably needed for the productive utilization of PV systems. Hence, a maximum power point tracking (MPPT) mechanism is essential for optimizing net energy production. This paper encapsulated the concept of different approaches based on the intelligent mechanisms for efficient power extraction in PV systems. An extensive summary in terms of the efficiency and eventuality of the reported hybrid techniques has been discussed in detail in this work.