Miracle-dz/UniFAR-FTUs huggingface dataset:
Determining dissipativity for nonlinear systems from noisy data using Taylor polynomial approximation

In the literature of data-driven dissipativity verification, many approaches are restricted to linear systems or require knowledge on the basis functions of the nonlinear system dynamics. To overcome these limitations, this work proposes based on Taylor approximation a novel polynomial representation of nonlinear systems which can be learned from noise-corrupted measurements. Due to the polynomial characterization and the inclusion of the approximation error into the analysis, we can determine dissipativity properties for nonlinear dynamical systems from noisy data with rigorous guarantees, without explicitly identifying a model, and using computationally tractable sum of squares optimization.
Blood Management System based on Blockchain Approach: A Research Solution in Vietnam

— More and more new health care solutions are born based on the development of science and technology. The subjects who benefit the most, in this case, are not only patients (i.e., shorter healing time, faster recovery) but also medical staff, e.g., doctors/nurses (i.e., easy monitoring of the patient’s recovery process, proposing new treatment). However, there are still products that have not found an alternative: blood and blood products. Regardless of how science and technology can affect all aspects of patient treatment as well as medical care, blood still plays an important role in the treatment method. In addition to the above, blood and blood products may only be obtained from volunteers (i.e., blood donors). The preservation process is also very difficult, and no medical facility has enough facilities to pre- serve them. Therefore, the current process of blood preservation and transportation is done manually and contains many potential risks (e.g., data loss, personal information collection). In addition to the above barriers, developing countries (including Vietnam) also face many difficulties due to limited facilities. It is for this reason that this paper aims at a Blockchain-based technology solution for efficient management and distribution of blood from blood products. On the one hand, the paper contributes to the limitations in the information management process of storing and transporting blood and its products in the traditional database being applied in medical facilities in the cities and provinces in the Mekong Delta (the West-South of Vietnam). On the other hand, the article offers technology-based solutions to increase transparency and reduce the fear of centralized data storage (i.e., security and privacy issues). We also implement a proof-of-concept to evaluate the feasibility of the proposed approach.
Surface morphology engineering of metal oxide-transition metal dichalcogenide heterojunction

ABSTRACT A tremendous effort has been made to develop 2D materials-based FETs for electronic applications due to their atomically thin structures. Typically, the electrical performance of the device can vary with the surface roughness and thickness of the channel layer. Therefore, a two-step surface engineering process is demonstrated to tailor the surface roughness and thickness of MoSe2 multilayers involving exposure of O2 plasma followed by dipping in (NH4)2S(aq) solution. The O2 plasma treatment generated an amorphous MoOx layer to form a MoOx/MoSe2 heterojunction, and the (NH4)2S(aq) treatment tailored the surface roughness of the heterojunction. The ON/OFF current ratio of MoSe2 FET is about 1.1 × 105 and 5.7 × 104 for bare and chemically etched MoSe2, respectively. The surface roughness of the chemically treated MoSe2 is higher than that of the bare, 4.2 ± 0.5 nm against 3.6 ± 0.5 nm. Conversely, a 1-hour exposure of the multilayer MoOx/MoSe2 heterostructure with the (NH4)2S(aq) solution removed the amorphous oxide layer and scaled down the thickness of MoSe2 from ~92.2 nm to ~38.9 nm. The preliminary study shows that this simple two-step strategy can obtain a higher surface-area-to-volume ratio and thickness engineering with acceptable variation in electrical properties.
Driver Assistance Eco-driving and Transmission Control with Deep Reinforcement Learning

With the growing need to reduce energy consumption and greenhouse gas emissions, Eco-driving strategies provide a significant opportunity for additional fuel savings on top of other technological solutions being pursued in the transportation sector. In this paper, a model-free deep reinforcement learning (RL) control agent is proposed for active Eco-driving assistance that trades-off fuel consumption against other driver-accommodation objectives, and learns optimal traction torque and transmission shifting policies from experience. The training scheme for the proposed RL agent uses an off-policy actor-critic architecture that iteratively does policy evaluation with a multi-step return and policy improvement with the maximum posteriori policy optimization algorithm for hybrid action spaces. The proposed Eco-driving RL agent is implemented on a commercial vehicle in car following traffic. It shows superior performance in minimizing fuel consumption compared to a baseline controller that has full knowledge of fuel-efficiency tables.
A digital generation in scrutiny. Children’s ideas about the internet and the online information (Explorando una generación digital. Las ideas de los niños sobre internet y la información online)

ABSTRACT The internet has become a key environment for children’s learning and leisure at an increasing early age. Yet, little is known about what children understand about the internet. We conducted semi-structured interviews with 111 children aged five to nine to assess their notions about the internet, the authorship of online content and the trustworthiness they attribute to it. Considering the socio-cognitive advances throughout these ages, we expected a significant improvement in children’s concept of the internet. However, the results showed modest age differences, and only in some basic notions (e.g., internet functions). Misconceptions about the internet and naïve ideas about the reliability of its content were present at all ages. Only a very few older children envisaged the risk of finding malicious information online and the need to consult other sources in case of doubt. We discuss the necessity to address children’s misconceptions at least from the beginning of primary school, when most of them are cognitively ready to understand many of the issues addressed in this study, provided they receive guided instruction.
Modeling and Simulation of Soft Robots Driven by Embedded Artificial Muscles: an Example using Twisted-and-Coiled Actuators

Soft robots have been intensively investigated for manipulation and locomotion in recent years. However, the current state of soft robotics has significant design and development work but lags in modeling and control due to the difficulty in modeling them. In this paper, we present a physics-based analytical framework to model soft robots driven by Twisted-and-Coiled Actuators (TCAs), an artificial muscle that can be arranged in arbitrary shapes in the soft body of a soft robot to achieve programmable motions. The framework can model 1) the complicated routes of multiple TCAs in a soft body and 2) the coupling effect between the soft body and the TCAs during their actuation process. When not actuated, a TCA in the soft body is an antagonistic elastic element that restrains the magnitude of the motion and increases the stiffness of the robot. By stacking several modules together, we simulate the sequential motion of a soft robotic arm with three-dimensional bending, twisting, and grasping motion. The presented modeling and simulation approach will facilitate the design, optimization, and control of soft robots driven by TCAs or other types of artificial muscles.
Learning from Demonstrations under Stochastic Temporal Logic Constraints

We address the problem of learning from demonstrations when the learner must satisfy safety and/or performance requirements expressed as Stochastic Temporal Logic (StTL) specifications. We extend the maximum causal entropy inverse reinforcement learning framework to account for StTL constraints and show how to encode them via a minimal set of mixed-integer linear constraints. Our method is based on a cut-and-generate algorithm that iterates between two phases: in the cut phase, we use cutting hyperplanes to approximate the feasible region of the non-linear constraint that encodes atomic predicates and in the generate phase, we propagate these hyperplanes through the schematics to generate constraints for arbitrary formulas. Our algorithmic contributions are validated in different environments and specifications.
Sensor Fusion Observer Design and Experimental Validation for an Underwater Kite

Underwater energy-harvesting kites possess the capacity, under appropriate control algorithms, to deliver more than an order of magnitude additional power per unit mass than stationary devices. However, in order to perform path-following control, it is essential to maintain accurate estimates of the kite’s position and velocity. Obtaining these measurements is complicated by: (i) operation in GPS-denied environments, and (ii) tether curvature, which renders straight-line approximated, line angle sensor-based measurements inaccurate. In this paper, we present a nonlinear closed-loop observer that fuses line angle sensor and IMU measurements using a dynamic tether model to maintain an estimate of an underwater kite’s states. The observer design has been experimentally validated on a 1/10scale kite model, using a customized tow testing system deployed in the NC State Aquatic Center.
F-PCNet: A New Fast Object Detection Method Based on Point Cloud Only*

Although deep learning methods have greatly improved the accuracy of the object detection tasks, it is still challenging to balance the efficiency and accuracy of the algorithms under circumstances of point clouds only. In this paper, an anchor-free one-stage deep neural network, F-PCNet, is proposed to realize real-time detection based on point clouds on an autonomous driving platform while maintaining high accuracy. The proposed network takes the bird’s eye view of point clouds collected by LiDAR as input, and outputs the category and 2D bounding box of each detected object. The backbone of F-PCNet is composed of residual network modules of different sizes which effectively reduce the impact of learning degradation. The anchor-free detection head enables F-PCNet to achieve high levels of accuracy and efficiency. Experimental results show that F-PCNet achieves high detection accuracy in a short time consumption and is suitable for real-time detecting scenarios.
Dynamic response and emergency measures under failure conditions of sCO2 Brayton cycle

The sCO2 Brayton cycle has gained interest because of its flexibility and ability to provide higher thermomechanical conversion efficiency. Studies on failure conditions and corresponding emergency measures are of great significance to ensure the safety of the system, while there are limited studies related to the sCO2 Brayton cycle because the test circuit has not been applied in practice at present. In this study, we have developed a dynamic model of the CO2 Brayton cycle, and carefully validated its components against experimental data. Preliminary safety assessment and dynamic response under loss of cooling water (LOCW), loss of heat source (LOHS), and pipeline leakage have been analyzed. Emergency strategies are proposed to maintain the safe operation under failure conditions. The primary objective of this study is to reveal the dynamic performance and emergency measures under failure conditions of the sCO2 Brayton cycle. The results demonstrate that the system net power decreases at rates close to 10%/s and less than 0.1%/s under LOCW and LOHS, respectively. The system responds more dramatically under LOCW compared with LOHS. The system needs emergency measures under LOCW, while it has sufficient time to realize a normal slow shutdown under LOHS. The leakage rate is at least five times higher in the high‐pressure pipeline compared with the low‐pressure pipeline for the same area of the leakage hole. Under pipeline leakage, the heat source should be cut‐off slowly, and the rotation speeds of the main compressor and recompressor should be reduced simultaneously to protect the system.
Estimating Drill String Friction with Model-Based and Data-Driven Methods

Estimation of the behavior of long dynamic systems with limited sensing remains an open question. In this paper, we consider the rotational motion of a deep drilling system and compare three algorithms to estimate the friction factors along the drillstring and thus provide an estimate of bottomhole rotational velocity. These friction terms characterize the interaction between the drill pipe and the wellbore walls (Coulomb source terms) within the curving wellbore. This information is essential to design the next generation of stick-slip mitigation controllers, to develop real-time wellbore monitoring tools, and to enable effective toolface control for directional drilling. We propose two model-based algorithms (an adaptive observer and a recursive dynamics framework) and a machine learning-based algorithm to estimate friction parameters, all of them presenting advantages and drawbacks. The performances of the two model-based estimators are finally compared with the data-driven neural network.
Viewport History as a Heuristic for Quality Enhancement and Quality Variation Control in Viewport-Aware Tile-Based 360-Degree Video Streaming

Despite the growing popularity of Virtual Reality (VR), 360-degree videos are often regarded as challenging to stream due to their large bandwidth requirement. As a solution, the 360-degree video content is spatially divided into tiles, and the quality level for each tile is selected based on the user’s network environment and viewport information. To determine the high quality tiles, viewport prediction and viewport history methods are used to estimate the user’s viewport. However, due to the unpredictability of user head movements, generating accurate viewport estimates are difficult, which can severely degrade the Quality of Experience (QoE) for the user. In this paper, to sustain high user QoE, we detail a novel tile quality selection algorithm that employs viewport prediction, viewport history, viewport extensions, and a viewport tile count limit. In addition, we also include comparison analysis on six 360-degree videos that vary in content pace. Based from simulations, using viewport history as a heuristic for tile quality selection demonstrated a significant increase in the perceived quality while suppressing quality variation inside the viewport and across segments compared to eight reference methods; and secondly, 360-degree videos slow in content pace tended to result in lower viewport prediction accuracy, QoE performance, and weaker viewport history trends compared to 360-degree videos fast in content pace.
Trade-offs in Sampled-data Control: a DC-DC Buck Converter Example

DC-DC power converters are often used with closed-loop control in order to be robust to signal and parametric uncertainty. Most modern control systems implement the controller digitally because of implementation and redesign flexibility. Provided the switching frequency is sufficiently high, traditional digital control methods yield acceptable designs; however, a high switching frequency is associated with higher power loss. With a view to developing more efficient power converters in the future, this paper quantifies how much we can reduce the switching frequency and still obtain acceptable performance from traditional digital control design methods and provides an alternative (based on modern "lifting" techniques) when the switching frequency is so small that the traditional methods do not yield an acceptable controller.
Quasi-LPV Control Design for a Class of Underactuated Mechanical Systems

In this work, we present a quasi-linear parameter varying (quasi-LPV) control design approach for a class of underactuated mechanical systems in cascaded normal form. By incorporating linear system theory, robust control techniques, and linear matrix inequality (LMI) tools, a trajectory-based approach is employed to prove semi-global asymptotic stability of the closed-loop systems. The proposed controller can be applied to a wide class of mechanical systems with various degrees of underactuation. The simple structure of the controller facilitates its straightforward implementation for a variety of applications when compared to existing approaches. The controller is implemented for the translational oscillator with a rotational actuator (TORA) system and an underactuated quadrotor model to illustrate its broad applicability and effectiveness.
LIAD: A Method for Extending the Effective Time of 3-D TLC NAND Flash Hard Decision

Triple-level cell NAND flash memory is widely used today due to its higher storage density and capacity. However, with the increase in the storage density, lower reliability results in more read times for flash memory and significantly reduces the read performance. In order to avoid unnecessary read operations, this article proposes a hard decision–soft decoding method called location information-assisted decoding (LIAD) method, which determines the additional information required for decoding by mutual information, and then transmits the required information to correct the log-likelihood ratio (LLR). Different from the conventional LLR correction algorithm, this method does not require additional read operations and correct data. Only using sensing results, our method can reduce uncorrectable error bit rate (UBER) by up to 99%, and the system read latency under SSDsim (Hu et al. 2011) simulation can be reduced by up to 53%.
A Smoke and Flame Detection Method Using an Improved YOLOv5 Algorithm

The complex background scenes in traditional fireworks detection methods make flame identification challenging and complicated. This paper focuses on improving the detection efficiency and accuracy of flame disasters. First, the data augmentation strategy and label smoothing are used to preprocess the sample set, which solves the over-fitting problem caused by the insufficient number of samples. Second, we add Convolutional Block Attention Module (CBAM) before each backbone classifier, to compress and re-weight the input features from two independent channel and space dimensions. By focusing on smoke and fire’s feature information, the ability of desired feature extraction is strengthened. Third, the Focal loss function is utilized to enhance the weights of complex samples. Consequently, the imbalance problem about positive and negative samples in single-stage detection, and the high proportion of easy-to-separate samples in the loss function are both resolved. Experimental examples demonstrate that the proposed network is easy to converge and expand, which guarantees detection accuracy and satisfies detection speed requirements.
In-suit monitoring melt pool states in direct energy deposition using ResNet

One critical challenge of directed energy deposition (DED) in additive manufacturing (AM) is the lack of comprehension of the relationship between the part parameters and the formation quality. Components fabricated by the inappropriate manufacturing parameters will be too unreliable to satisfy the strict requirements of industrial applications. To address these issues, the present study established an experiment with a coaxial high-speed charge coupled device (CCD) camera to monitor the 316L deposition process and developed a data-driven model with ResNet101 to identify different melt pool states. We adopted the t-distributed stochastic neighbor embedding clustering algorithm, accuracy rate, and normalized confusion matrix to evaluate the performance of ResNet101. Furthermore, the visualization technique class activation mapping was used to analyze the morphological characteristics of the melt pool formed under different experimental parameters, explained the classification behavior of the ResNet101 model. The result indicated that ResNet101 gains better feature extraction and higher capability to classify different melt pool states with an average accuracy of 99.07%, compared with other CNNs (LeNet, GoogLeNet, AlexNet, ResNet34, and ResNet50) models. This demonstrated the effectiveness of ResNet101 in monitoring the DED process and the potential to reduce fabrication costs in DED.
Point‐of‐interest detection from Weibo data for map updating

Points‐of‐interest (POIs) geographic information system data are increasingly important for supporting map generation and navigation services, although updating their semantic and location information still largely depends on manual labor. In this study, we propose a novel method to automatically detect the changes in POIs from Chinese text and check‐in position data provided by the Chinese social media platform, Weibo. The proposed method includes three steps: (1) POI name recognition; (2) location confirmation; (3) and change detection. First, we propose recognizing a POI's name from Weibo text using the improved conditional random field algorithm. Then, we detect the location of each named POI by integrating the text address with the check‐in position. The changes in the detected POIs are recognized by extracting the status words from Weibo text and a three‐level status word database. To verify the effectiveness of the proposed method, we examine Wuhan as a case and detect the changes in the commercial POI using real‐world Weibo data collected from January to September 2020. Based on the validation of three common map platforms, the data provided and the manual field investigation of 55 random samples, the identification accuracies for newly added POIs, the unchanged POIs, and expired POIs are approximately 100, 95.8, and 91.7%, respectively.
Numerical Calculation of Diffusion Depth and Skin Depth Based on a GEMTIP Model in Electromagnetic Sounding Methods

Electromagnetic (EM) sounding methods in the time and frequency domains are widely used in polymetallic ore exploration and can measure both EM induction and induced polarization (IP) responses simultaneously. Depth inversion is a crucial factor affecting the accuracy of data interpretation. The effect of IP on EM wave propagation has been ignored in traditional studies of diffusion depth and skin depth. This article is based on the generalized effective-medium theory of the IP effect (GEMTIP) model to represent the IP effect. The frequency domain solution based on GEMTIP is obtained. To address the problem whereby the time-domain solution cannot be solved by inverse Laplace transform, rational approximation and linear programming methods are used to simulate the variation of pulse field source with time and distance. We analyze the influences of geometrical parameters and physical properties of rocks and ores with the IP effect on the depth. Finally, we consider the influence of intrinsic chargeability and improve the classic diffusion depth formula. The formulae for estimating the time-domain diffusion depth of the IP effect under certain conditions are given and verified. These methods are of great significance to better understand the relationship between the subsurface dispersive media and the propagation of EM waves.
The Texas Health Resources Clinical Scholars Program: Learning healthcare system workforce development through embedded translational research

Texas Health Resources (THR), a large, nonprofit health care system based in the Dallas‐Fort Worth area, has collaborated with the University of Texas Southwestern Medical Center (UTSW) to develop and operate a unique, integrated approach for Learning Health System (LHS) workforce development. This training model centers on academic health system faculty members conducting later‐stage translational research within a partnering regional care delivery organization.