Miracle-dz/UniFAR-FTUs huggingface dataset:
Hardware Implementation of a Sliding Detection Algorithm for Robotic Hands Using Force Sensors

In this work, a sliding detection algorithm was proposed for a previously developed robotic hand through the utilization of force sensors. Previous works have designed several hardware architectures to filter sensor data, implement dynamic control, and machine learning models for controlling the robotic fingers using a single Field Programmable Gate Arrays (FPGA) chip. In this regard, the slip detection algorithm was developed to comprise three stages, each with a low computational cost, including a moving average filter, a first-order derivative, and a peak detection algorithm. A reference model was constructed and validated through an experimental protocol employing daily use objects to create a database. Subsequently, the slip detection algorithm was mapped onto hardware utilizing previously developed floating-point arithmetic IP cores and implemented using a Zynq 7020 device. The FPGA implementation was characterized in terms of resource occupation, power consumption, execution time, and numerical error with the reference model.
SAFD: A Statistical Analysis-Based Feature Point Descriptor

Describing and matching feature points is one of the most basic tasks in many computer vision tasks. For example, in tasks such as structure from motion, simultaneous localization and mapping, and image stitching, the quality of feature point matching directly determines the accuracy of the vision task, which in turn affects the availability of specific applications such as VR, MR, autonomous driving, and robot navigation. The current mainstream feature point method has the problem that the number of mismatches is relatively large, which will cause a lot of interference to computer vision tasks based on feature point matching. Traditional feature point descriptors only use the feature point and very little information in its neighborhood, so mismatches are prone to occur. The main contribution of this paper is to propose a feature point descriptor and a corresponding matching method. The core idea is to use multiple dimensions of information to describe a feature point, and then integrate the differences in each dimension to evaluate the similarity between the two feature points, and then improve the matching rate of feature points. The experimental results show that the feature description and matching algorithm proposed in this paper is superior to the popular ORB algorithm in terms of matching rate.
Autonomous Navigation of Wheel Loaders using Task Decomposition and Reinforcement Learning

The short-loading cycle is a repetitive task performed in high quantities making it a good candidate for automation. Expert operators perform this task to upkeep high productivity while minimizing the environmental impact of using energy to propel the wheel loader. The need to balance productivity and environmental performance is essential for the sub-task of navigating the wheel loader between the pile of material and a dump truck receiving the material. This task is further complicated by behaviours of the wheel loader such as wheel slip depending on the tire-to-surface friction that is hard to model. Such uncertainties motivate the use of data-driven and adaptable approaches like reinforcement learning to automate navigation. In this paper, we examine the possibility to use reinforcement learning for the navigation sub-task. We focus on the process of developing a solution to the complete sub-task by decomposing it into two distinct steps and training two different agents to perform them separately. These steps are reversing from the pile and approaching the dump truck. The agents are trained in a simulation environment in which the wheel loader is modelled. Our results indicate that task decomposition can be helpful in performing the navigation compared to training a single agent for the entire sub-task. We present unsuccessful experiments using a single agent for the entire sub-task to illustrate difficulties associated with such an approach. A video of the results is available online11Video available at https://youtu.be/IZbgvHvSltI.
COVID-19 Trend Prediction Using CLS-Net Hybrid Model

The break out of Corona Virus Disease 2019 has caused great danger and effect for people's health and life. Therefore, predicting the trend of the epidemic is crucial for politicians and medical conductors go for make correct policy ahead of time. The SEIR model is now a more mature epidemic prediction model, but the dynamic prediction of the outbreak cannot be achieved because in this model, the infection rate of virus can be calculated mathematically. Based on this problem, this thesis proposes a CLS-Net model to predict the trend of global epidemic of Corona Virus Disease 2019 under the influence of human mobility. The model extracts features from a large amount of population migration data by CNN model, and then predicts the transmission rate of New Coronavirus in real time with the excellent time-series learning ability of LSTM. Finally, the live updated virus infection rate apply into the SEIR model to realize the dynamic prediction of epidemic development trend. To validate the approach proposed in paper, the statistical data of neocrown pneumonia from various countries around the world were collected for experiments. Take Mean Absolute Error (MAE), R2(R-Squared) and Root Mean Square Error (RMSE) as the evaluation indexes of the model in this paper. The experimental results show that the model proposed in this paper has better performance compared with the classical infectious disease prediction model.
Oxygen Isotopic Composition of Multiple Species of Planktonic Foraminifera: Recorders of the Modern Photic Zone Temperature Gradient

Abundances of 12 species of planktonic foraminifera collected in two plankton tows from the east tropical Atlantic are compared to the chlorophyll content and the temperature of the sea water from which they were collected. As expected from previous work in the tropics, all dominant tropical species occur in greatest abundance within the photic zone. Many species occur in greatest abundances in the seasonal thermocline in association with the maximum chlorophyll concentration, while a few algal symbiont-bearing species occur in greatest abundance in the mixed layer. The δ18O measurements of planktonic foraminifera shells from core top sediment samples confirm the vertical stratification within the photic zone that is suggested by the relationship between hydrography and abundances found in the plankton tows and found in the statistical study by Ravelo et al. [1990]. Comparison between the measured δ18O values of planktonic foraminifera with the predicted δ18O profiles of the overlying water column at three core locations indicate that species abundances in the sediment record the seasonally integrated conditions of the photic zone and suggests that the abundance of a species in the sediment depends on whether the preferred ecological conditions of that species may be found within the photic zone of the overlying water column sometime during the year. Species which calcify below the photic zone have only trace relative abundances. Finally, it appears that the total range of δ18O values of the dominant species approximates the predicted annual δ18O of calcite range in the upper 80 m of the water column.
A Semi-Supervised Learning Approach for Fault Detection and Diagnosis in Complex Mechanical Systems

The integration of artificial intelligence in mechanical fault detection and diagnosis (FDD) helps to increase reliability, reduce costs, and improve the overall performance of mechanical systems in Industry 4.0 applications. Most interesting industrial applications nowadays come from dynamic environments where data are generated continuously over time and where the labeled data are scarce and expensive. Therefore, semi-supervised learning (SSL) can be particularly useful in FDD because faults may be rare or difficult to identify, and may not be fully represented in the labeled data. By using a combination of labeled and unlabeled data, SSL can help to identify these rare or difficult-to-detect faults, leading to more effective FDD. In this paper, graph-based SSL relying on label propagation is combined with conventional classification algorithms to detect potential failures in complex mechanical systems. Experimental results on realistic pneumatic and hydraulic systems from the related literature show that the proposed method can effectively enlarge the labeled datasets and interestingly identify different types of non-nominal conditions with higher accuracy compared to baseline methodologies.
Optimized Walking Assistance of the Soft Exoskeleton with Twisted String Actuators*

Soft exoskeleton robots have exhibited promising potential in walking assistance with comfortable wearing experience. However, traditional cable-driven exosuits cannot provide adequate driving force to motivate the entire human leg, especially for hemiplegic patients with little movement capability. Also, the human-exosuit coupling dynamics is difficult to be modeled due to the suit-like structure and the varying human performance, and accordingly, accurate control and efficient assistance cannot be guaranteed. In this article, twisted string actuators (TSAs) are developed and equipped with the exosuit to provide powerful actuation and variable assistance intensity. Besides, the human motion intention is estimated based on skin surface electromyography (EMG) signals. A mirror adaptive impedance control is proposed, where the control torques and stiffnesses of the TSAs are regulated based on the performance of the impaired limb and the motion reference of the healthy limb. A linear quadratic regulator (LQR) is formulated to minimize the movement trajectory tracking errors and the human physical effort. An integral reinforcement learning algorithm is adopted to solve the given LQR problem to optimize the impedance parameters with little information of the human and robot models. The proposed robotic system is validated through experiments to perform its effectiveness and superiority.
RF-Enhanced Pavement Markings for Mobile Robot Lane Detection

The ability to detect and keep in lanes is crucial for the safe operation of autonomous mobile robots in construction sites and their coordination with humans in autonomous ports or logistic centers. While computer vision-based lane detection algorithms perform well under normal conditions, their performance may degrade under low visibility conditions and in adverse weather. Since robots are not constrained by human perception limits, this paper proposes a radio frequency (RF) pavement marking system that builds on Radio Frequency Identification (RFID), a short-range communication technology, to provide lane-detection assistance. We present not only the hardware designs for the RFID systems on both vehicles and roads but also a filtering algorithm to mitigate the noise in the backscattered RF signals for lane detection. Experimental results show that the information on lane keeping provided by the RF pavement markings aligns with the visual channel when mobile robots move at a speed of less than 40 miles per hour.
A Point Cloud Registration Algorithm Based on Planar Projection Contour

This paper proposes a novel registration algorithm based on the planar projection contour descriptor (PPC). The construction process of the PPC is as follows. Firstly, a local coordinate system is established based on the query point and its neighboring points' information. Secondly, the three-dimensional information of the neighboring points is projected onto a three-coordinate plane to obtain two-dimensional information. Then, the projection point set is fitted into a convex hull contour, which is encoded as the local feature of the plane. Finally, the local features of the three planes are concatenated in an orderly fashion to form PPC. In addition, the PPC-based point cloud registration algorithm was tested and compared with classical algorithms using the Stanford dataset. The results showed that PPC can accurately identify the feature matching relationship between the source and target point clouds, leading to precise registration. This demonstrates the superiority of the algorithm.
Short-Term Heat Demand Prediction Using Deep Learning for Decentralized Power-to-Heat Solutions

Coupling power and heat sectors will contribute to the integration of renewable energy but also promotes decarbonization of heat sector. Better heat demand forecasting is required for reducing the costs and increasing the energy efficiency. The traditional forecasting methods faces difficulties due to the irregularities in data, computational costs and generalizing the output. Application of machine learning based data-driven techniques provides better accuracy in handling data with nonlinear characteristics. This paper proposes the application of Long Short Term Memory (LSTM) based model to forecast the heat energy consumption of a pool of residential consumers considering the real data set. The prediction accuracy is improved by using wavelet transform for data preprocessing and the proposed model gives a better performance.
IoT-Enabled MRO Intralogistics Management: A Case Study in Hong Kong

Aviation maintenance, repair and overhaul intralogistics (MRO intralogistics) is an essential process to ensure the availability and safety of aircraft. This paper proposes an IoT-enabled MRO intralogistics management design to enhance the traceability and visibility of resources and operations. First, the study analyses and establishes the MRO intralogistics process model for well-organising the process. Second, this study proposes a domain model based on Domain-driven Design to simplify IoT-enabled MRO intralogistics management. Third, the work presents the data model and the mechanisms for resource and operation management in MRO intralogistics, showing the simple design to cope with the complex MRO intralogistics. Finally, this study conducts a case study of the proposed IoT-enabled solution in one of the largest MRO organisations to show its benefits.
Remote Teleoperated Micromanipulation System for Electrophysiology

Electrophysiology plays a crucial role in characterizing the electrical properties of biological cells and tissues. The electrophysiological signals have enabled research across multiple disciplines, including the diagnosis of neurological disorders, the development of brain-computer interfaces, and the study of interactions within the metaverse. However, conducting electrophysiological measurements at the cellular level demands meticulous micromanipulation, which currently relies on on-site, labor-intensive, and expertise-driven procedures. In this paper, we have proposed a remote teleoperated micromanipulation system to implement cellular electrophysiological measurements accurately and reliably. A data glove has been developed to enable the operation of a remote automated patch clamp system via TCP/IP connections. The glove collected hand velocities, roll status, and open/close status. Data processing algorithms have been developed to convert raw data into control commands for driving electrophysiological micromanipulation. Using our previous predictive model of seal condition, the engaging process has been automated to further enhance system performance. In the development, HEK 293 cells were employed to obtain whole cell voltage clamp electrophysiological recordings for the validation of the system. Consequently, users can now perform remote and precise micromanipulation of patch clamp recordings, overcoming the limitations of previous approaches that relied on skilled professionals and had low success rates. This study provides valuable insights for the advancement of remote electrophysiological micromanipulation. The teleoperated micromanipulation system has the potential to significantly enhance cellular-level measurements and their applications.
Validating an Automated Asynchronous Synthesis Environment with a Challenging Design: RISC-V

The coupling of synchronous digital design techniques with electronic design automation tools and support systems ensured several decades of steadfast evolution of electronics based on semiconductor devices, where humankind now depends on products derived from this evolution. As technology nodes advance towards semiconductor physical limits, the obtainment of better circuits faces challenges such as increased variability. Addressing this and other challenges requires new and/or alternative devices and/or tools, and/or design techniques. Asynchronous quasi-delay insensitive design potentially ensures automatic or streamlined adaptability to several variations plaguing digital design. The Pulsar environment proposes using commercial design automation tools and specific software to enable the high-level design of complex circuits and their automated semi-custom implementation. Several small to medium circuits such as multiply and accumulate pipelined operators, first-in-first-out modules and others helped develop and validate Pulsar's basic functionality. The main contribution of this paper is to demonstrate the feasibility of using an evolved version of Pulsar to design and automatically implement more complex asynchronous quasi-delay insensitive circuits, such as processors, starting from RTL-like descriptions. Results include 174 post-synthesis/post-layout RISC-V processor simulations, originating from 29 synthesis processes, for cycle-times varying from 10ns to 3ns, in 0.25ns steps.
A Demand Response Scheme Based on Electric Vehicle Aggregation Model

To contribute to flexible loads participating in the demand response in high renewable energy permeability distribution network, a clustering method based on electric vehicles (EVs) aggregation for virtual power plant (VPP) is proposed in this paper. The proposed clustering method is based on charging characteristics parameters from multiple load profiles of electric vehicles. For the VPP bidding strategy, aggregation model based on clustering method, economic scheduling model considering EVs aggregation, inelastic loads, wind power resource, photovoltaic resource, distributed generators and energy storage devices. In this paper, a linearized VPP model for multi-resources participating in day-ahead scheduling under active distribution network is proposed. The presented model is a linear mixed-integer programming and solved the optimization solver. The proposed aggregation method can increase the economics for VPP and promote the consumption of renewable energy of distribution network by applying the model to the IEEE 33-bus feeder.
Improved Predictive Torque Control with Unidirectional Voltage Vector Selection of PMSM Fed by Three-Level Neutral-Point-Clamped Inverter

This paper proposes an improved predictive toque control with unidirectional voltage vector selection method for a three-level neutral-point-clamped inverter (3L-NPCI) fed permanent magnet synchronous motor. The proposed algorithm can restrict the candidate voltage vectors (VVs) within $60^{0}$ of the space voltage diagram (SVD), which are the nearest to the flux trajectory for each $60^{\mathrm{o}}$ flux sector. After the subdivision of the SVD and flux trajectory, the proposed method can keep VVs in one direction during the prediction process which can result in significant torque/flux reduction. Therefore, the proposed algorithm achieves superior performance to the conventional method which requires enumerating all 19 voltage vectors while significantly reducing the computational burden. The proposed FCS-PTC of PMSM has also other advantages, such as improved capacitance voltages balancing, small computation time due to the reduced number of admissible voltage vectors considered in the cost function, and easy implementation. The effectiveness of the proposed method is verified through simulation and experimental results.
A Study on Single Phase On-Board Charger Topologies for Light Electric Vehicle

In the case of electric mobility, the effectiveness and quality of the charging process are crucial. Significant customer repercussions will occur if electricity is not maintained. The on-board chargers use single-stage and two-stage charging technologies. Light Electric Vehicles (LEV) chargers should be economical, light in weight, with minimal losses, enhanced power-factor, and minimized distortion. The front end of the converters uses a variety of power electronic circuits with power factor adjustment techniques. In this article, various converter topologies utilized in vehicle charging solutions are explored, and a comparison is made between these converters.
Leafony Board IoT and Its Development for Engineering Education

Leafony Board is a Micro device that works using the ATmega 328P Microcontroller. Leafony in the future is essential for developing the Micro Sensor world and the Internet of Things and Low Power sensor Node devices. Leafony has 14 digital input and output pins; six can be used as PWM outputs, six input pins, 8 MHz oscillators, and a reset function. Leafony board is very flexible; it can use the same USB port as the USB port to charge our cellphones. The installation process is straightforward by stacking the shield parts according to their functions, starting from the power supply, MCU, sensors, USB Port, or ICSP; each function is different and supports each other; Leafony board is appropriate to be used for engineering education at University level, so it can be done continue development plans towards a larger project. Students can use the Arduino Integrated Development Environment (IDE) by choosing a board compatible with the Leafony Board, namely the Arduino Pro Mini Board and the 3.3 volts, 8 MHz ATmega 328p Processor. The hope of this research on the Leafony board is for the future development of the advanced Internet of Things.
Evaluation of the Effectiveness of Forest Restoration and Check‐Dams to Reduce Catchment Sediment Yield

Reforestations and check‐dams are two commonly used measures to reduce soil erosion rates and sediment export from highly eroding catchments. Here, we evaluated the impact of the construction of 94 check‐dams and land use changes (caused by agricultural abandonment and reforestations) on sediment yield in the Upper Taibilla catchment (320 km2, SE Spain) from 1956 to 2000. We combined land use change analysis, field surveys, and application of the WaTEM‐SEDEM erosion and sediment yield prediction model for nine scenarios combining land use maps (1956, 1987, and 2000) and different numbers of check‐dams throughout the catchment. Land use changes alone reduced sediment yield up to 14%, but in combination with check‐dams, the reduction in sediment yield reached 44 ± 6%. Sediment yield reduction was higher in smaller sub‐catchments, with, on average, a higher transport capacity than larger catchments, illustrating the scale dependency of human impacts on sediment fluxes and the buffer capacity of larger catchments. From an economical perspective, the construction of check‐dams was estimated to be more expensive than reforestation programs in the studied catchment, while adding more check‐dams did not always result in a proportional reduction of sediment yield. This indicates that optimizing check‐dam distribution relative to land use patterns is crucial to decrease catchment sediment yield. Check‐dams have a large and instantaneous impact on sediment yield over a restricted time period, while reforestations have important sustained effects at a lower economic cost. These contrasting effects require a careful evaluation for optimal effective catchment management. Copyright © 2014 John Wiley & Sons, Ltd.