Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving Robot Proficiency Self-Assessment via Meta-Assessment

Proficiency self-assessment (PSA), which is the ability to estimate how likely one can complete a task, is a beneficial property for autonomous robots. Prior work developed the assumption-alignment tracking (AAT) method for PSA, which estimates the probability that a robot will successfully complete a task. This letter refers to the prediction made by AAT as the first-level assessment (FLA), and further proposes a second-level assessment (SLA) that determines whether the FLA prediction is correct. The probability that the FLA prediction is correct is conditioned on four features: 1) the mean distance from a test sample to its nearest neighbors in the training set; 2) the predicted probability of success made by the FLA; 3) the ratio between the robot's current performance and its performance standard; and 4) the percentage of the task the robot has already completed. The SLA model is trained on the four features using a Random Forest algorithm. It is evaluated by two metrics: discriminability, measured by the area under the ROC curve, and calibration, measured using expected calibration error. On a simulated navigation task and a manipulation task by a Sawyer robot, results demonstrate that the SLA model not only calibrates the FLA model as well as existing calibration methods (Platt calibration and isotonic regression), but also produces very high discriminability even if the FLA model's original discriminability is much lower. Results also indicate the usefulness of each of the four features used by the SLA model.
Deep Neural Network Learning for Time Series Cutting Forces Prediction

The measurement and prediction of machining cutting forces is the basis of optimizing machining processes parameters, evaluating machining accuracy and quality, and establishing machining system digital model. Traditional cutting forces computing approaches could be classified into analytical model method based on mechanics and material property analysis, empirical formula method based on cutting experiment, and numerical method represented by finite element method. These models could not directly obtain sequential cutting forces prediction data These models could not directly obtain sequential cutting forces prediction considering the cutting process parameters, cutting tool and workpiece materials, cutting tool geometry characteristics, cutting tool wear and so on. The objective of this paper is to use deep learning algorithm to predict cutting forces with temporal characteristics. The cutting force training database were obtained by screening and interpolation for different experiment groups. Taking the cutting parameters and tool geometry as input features and the cutting force vectors as target output, the deep neural network node learning model of cutting forces for each spindle rotation angle was established. The node training models were obtained by fitting the training database. After the training of each node model, a complete set of cutting forces prediction model is obtained by integrated all the node training models sequentially.
The IFLA Trend Report: Looking Beyond the Walls in Library Planning

ABSTRACT Strategic planning requires us to stop and take a closer look at our organizations and consider the impact that changes in the profession are causing, influenced by the society around us. These considerations lead us past our library walls, past our institutional walls and into a more speculative world of changes in the technological society. Scholars and pundits have provided us with many scenarios for the future, including a world of solely electronic resources and hyper-connected users. But how closely does this match reality and what are the trends in our larger society that will affect how we work in the future. This article provides an overview of several reports on the students and technologies that we work with and closely explores the IFLA Trend Report. The report was developed after discussions with individuals representing many non-academic, non-library organizations and provides us with a view of the larger trends forecast for the information society. The report is envisioned as a living document that invites further comment and wider distribution to the library world and beyond.
Supplemental Control of Hydraulic Systems with Adaptive/Approximate Dynamic Programming

For reinforcement learning based tracking control which utilizes neural network approximations, the critical issue is how to search the solution quickly from the huge spaces to enable system states follow specific reference trajectories. We therefore develop backstepping scheme guided reinforcement learning supplemental control for hydraulic systems in this paper. In this algorithm, backstepping feedback control is designed firstly to ensure the roughly tracking result, then adaptive/approximate dynamic programming (ADP) is synthesized to compensate unknown system dynamic in backstepping framework. This proposed control is data driven, not model based, and only reference output trajectory is needed to fulfill the tracking task. By using Lyapunov approach, the stability analysis is accomplished, which shows that the proposed algorithm can guarantee uniformly ultimately boundedness result.
Side-Scan Sonar Image Segmentation Based on Improved Deeplabv3 Plus

Sonar image segmentation is critical for improving the recognition and detection of underwater targets, particularly in complex environments. Although deep learning has shown promising results in image segmentation using convolutional neural networks (CNNs), obtaining an accurate boundary of the target remains a challenge as a result of the partial loss of details during down-sampling. In this paper, a novel improved model is proposed that incorporates an edge detection structure into the CNN architecture. The edge features are aggregated with class-specific semantic features extracted from deep networks through a channel attention mechanism, which further refines the fine segmentation of object boundaries. The experimental results demonstrate that our improved Deeplabv3+ algorithm achieves better performance in side-scan sonar image segmentation compared to other methods, highlighting its potential for improving the recognition and detection of underwater targets.
GPROF V7 and beyond: assessment of current and potential future versions of the GPROF passive microwave precipitation retrievals against ground radar measurements over the continental US and the Pacific Ocean

Abstract. The Goddard Profiling Algorithm (GPROF) is used operationally for the retrieval of surface precipitation and hydrometeor profiles from the passive microwave (PMW) observations of the Global Precipitation Measurement (GPM) mission. Recent updates have led to GPROF V7, which has entered operational use in May 2022. In parallel, development is underway to improve the retrieval by transitioning to a neural-network-based algorithm called GPROF-NN. This study validates retrievals of liquid precipitation over snow-free and non-mountainous surfaces from GPROF V7 and multiple configurations of GPROF-NN against ground-based radar measurements over the conterminous United States (CONUS) and the tropical Pacific. GPROF retrievals from the GPM Microwave Imager (GMI) are validated over several years, and their ability to reproduce regional precipitation characteristics and effective resolution is assessed. Moreover, the retrieval accuracy for several other sensors of the constellation is evaluated. The validation of GPROF V7 indicates that the retrieval produces reliable estimates of liquid precipitation over the CONUS. During all four assessed years, annual mean precipitation is within 8 % of gauge-corrected radar measurements. Although biases of up to 25 % are observed over sub-regions of the CONUS and the tropical Pacific, the retrieval reliably reproduces each region's diurnal and seasonal precipitation characteristics. The effective resolution of GPROF V7 is found to be 51 km over the CONUS and 18 km over the tropical Pacific. GPROF V7 also produces robust precipitation estimates for the other sensors of the GPM constellation. The evaluation further shows that the GPROF-NN retrievals have the potential to significantly improve the GPM PMW precipitation retrievals. GPROF-NN 1D, the most basic neural network implementation of GPROF, improves the mean-squared error, mean absolute error, correlation and symmetric mean absolute percentage error of instantaneous precipitation estimates by about 20 % for GPROF GMI while the effective resolution is improved to 31 km over land and 15 km over oceans. The two GPROF-NN retrievals that are based on convolutional neural networks can further improve the accuracy up to the level of the combined radar–radiometer retrievals from the GPM core observatory. However, these retrievals are found to overfit on the viewing geometry at the center of the swath, reducing their overall accuracy to that of GPROF-NN 1D. For the other sensors of the constellation, the GPROF-NN retrievals produce larger biases than GPROF V7 and only GPROF-NN 3D achieves consistent improvements compared to GPROF V7 in terms of the other assessed error metrics. This points to shortcomings in the hydrometeor profiles or radiative transfer simulations used to generate the training data for the other sensors of the GPM constellation as a critical limitation for improving GPM PMW retrievals.
Hierarchical Backstepping Fast Terminal Sliding Mode Control Strategy With GPIO For Double Pendulum Cranes

In the field of cranes, the double pendulum effects may occur when the payload is large, and the hook mass is a non-negligible factor. Under these effects, the dynamic model of the system will be more complex, which brings many difficulties to the design of the controller. To achieve trolley positioning, suppress double pendulum effects, and improve anti-disturbances ability, this article presents a hierarchical backstepping fast terminal sliding mode control strategy for the double pendulum cranes. First, to design the observers and controller, the double pendulum crane system is divided into three subsystems. A generalized proportional integral observer (GPIO) is designed for each subsystem, which can estimate potential model uncertainties and unknown external disturbances of the system. According to the estimates of the GPIO, we design a first layer fast terminal sliding surface for each subsystem, and equivalent control law is designed using the idea of backstepping. Meanwhile, using the advantages of backstepping control makes the structure of the system more clear and brings convenience to the design of the Lyapunov function. Based on the first layer of sliding surfaces, we design a second layer sliding surface. Then, an ultimate control law is designed to ensure the stability of the entire system. In the end, the effectiveness of this control strategy is validated through simulation results on MATLAB/Simulink.
Parking Slot Detection for Autonomous Parking System with Template Matching

In this paper, a parking slot detection algorithm based on a bird's eye view is proposed. A density-based spatial clustering of applications with noise (DBSCAN) clustering algorithm and template matching algorithm are fused to detect parking slots in a bird's eye view. Progressive probabilistic Hough line detection and an improved DBSCAN clustering algorithm is developed to locate the sidelines of parking slots. Then, template matching is provided to locate and classify the “T shape“ and “L shape“ marking points more accurately. Finally, the marking points and sidelines of parking slots are integrated to complete the parking slot detection. The recall rate and precision rate of experimental results are 74.4% and 92.0%.
Cooperative Adaptive Tracking Control of Random Surface Vessel Multi-Agent Systems

This paper investigates the cooperative adaptive tracking control of random surface vessel multi-agent systems based on network graph structure. Firstly, we establish the model of random surface vessel multi-agent systems with three free degree. Secondly, a new cooperative adaptive tracking controller is constructed such that each follower's output can track the leader's output as close as possible in the mean square sense based on the communication topology graph of random surface vessel multi-agent systems. Finally, the effectiveness of the proposed control scheme is verified by a simulation example.
Coronary artery stenosis detection algorithm based on improved yolov5

Cardiovascular disease caused by coronary artery stenosis has high morbidity and mortality, and has become an important factor threatening human life and health. Therefore, early diagnosis of coronary artery stenosis is particularly critical. In actual medical diagnosis, manual detection of vascular stenosis is inefficient. Screening vascular stenosis by deep learning algorithm is one of the effective methods. However, in the field of medical image processing, it is difficult to obtain publicly available coronary artery CTA image datasets due to the protection of patient privacy. To this end, this paper establishes a available coronary artery CTA dataset and annotates it under the guidance of doctors. Coronary artery edge features affect the judgment of stenosis. In view of the problem that YOLOv5 is easy to ignore the details of vessel edge contours in CTA images, the MFFN module is used to replace the SPP module in the YOLOv5 feature extraction layer to achieve deep and shallow feature fusion; the Neck layer adds PAM-ECA-N attention force mechanism to improve the target detection effect. Experiments show that the improved YOLOv5-MFFN-PAM-ECA-N model has increased by 2.92% on F1-score and 1.64% on mAP@. 5, which proves that this improvement can improve model performance.
Defect Finding for Power-Related Failure Due to Internal Circuitry Issue

Nowadays Very Large-Scale Integration (VLSI) technology is moving to sub-90 nm nodes, and power signal problems still become one of the most common issues. Due to the geometry shrinking and increase in circuit complexity, the failure mechanism becomes more complex. Hence, different methodologies and techniques were implemented to successfully localize the defect. In this paper, all of the discussed cases are related to the faulty internal signal. Hence, the typical methodologies and techniques are less effective to localize the defect. Thus, the combination of EOFM/EOP and micro-probing on internal signal traces enhanced fault localization of complex power leakage failure where the defect resided in the internal power management circuitry instead of causing direct power supply abnormality. Finally, physical FA revealed the cause of the failure. The defect found is usually caused by the imperfection of the metal line either open metal or shorted to another metal line.
Design of High-Isolation Yagi—Uda Antennas for Military Full-Duplex Radios at 300 MHz

Military full-duplex radios enable multiple novel applications including simultaneous jamming and reconnaissance at the same frequency band. However, the high power levels typical to military radios induce very demanding requirements for antenna isolation, analog RF cancellation and digital baseband processing to enable receiver operation with high dynamic range. This paper presents a proposed design of a novel dual Yagi—Uda antenna system for simultaneous jamming and reconnaissance with simulated isolation level of over 80 dB, 60 dB and 50 dB for bandwidths of 1 MHz, 5 MHz and 15 MHz, respectively, targeting the 300 MHz center frequency within the NATO Band I. The high isolation is obtained by placing a decoupling element between two closely placed collinear Yagi—Uda antennas for improving the isolation over 50 dB. The interception performance and jamming effectiveness of the whole transceiver system are studied without and with the decoupling element that yields significant improvement in interception range and increase in maximum supported jamming power. This paper was originally presented at the NATO Science and Technology Organization Symposium (ICMCIS) organized by the Information Systems Technology (IST) Panel, IST-200RSY — the ICMCIS, held in Skopje, North Macedonia, 16–17 May 2023.
Finger Vein Image Quality Assessment Based on Stochastic Embedding Robustness

Finger vein image quality assessment is used to evaluate the quality of images, namely to evaluate the applicability of the finger vein images to the recognition system. The work of quality assessment will largely affects the performance of the finger vein recognition system. In previous work, people usually design quality indicators manually, construct quality functions or perform supervised learning based on visual inspection, which has limitations of subjectivity. In order to solve these problems, this paper proposes a finger vein image quality assessment algorithm based on Stochastic Embedding Robustness (SER), which is called SER algorithm in this paper. The algorithm generates random subnetworks from the deployed finger vein recognition system, and indirectly evaluates the image quality by calculating the variation of different subnetworks. The simulation results show that, SER algorithm can effectively filter out low-quality images and improve the performance of the recognition system. Compared with other methods, it is more efficient and stable.
Two-Stage Multi-Camera Constrain Mapping Pipeline for Large-Scale 3D Reconstruction

Over the last decade, Augmented reality (AR) has witnessed a significant increase in popularity largely. Consequently, as a central component of these applications, large-scale 3D reconstruction has gained more and more attention. One of the promising solutions is using the camera and the Structure from Motion(SfM) method, especially with the advent of multi-camera devices. However, most of these methods are susceptible to failure in some high-traffic commercial street scenarios due to lots of moving objects and texture-free scenes. Besides, these methods treat each image as a separate source of information, which significantly degrades the performance of the multi-camera acquisition systems now commonly used. Hence, this paper presents a two-stage reconstruction pipeline to obtain an accurate large-scale 3D map in the central business district with multi-camera devices. In the first stage, we enhance the robustness of the incremental SfM method by using the pose prior obtained from the multi-camera visual-inertial system(VINS) and advance the accuracy of the reconstruction result in the second stage by performing the global optimization considering the multi-camera extrinsic constraints. Experiments show that our method yields more complete and accurate maps than its counterpart.
Influence of Ocean Alkalinity Enhancement with Olivine or Steel 1 Slag on a Coastal Plankton Community in Tasmania 2

Ocean alkalinity enhancement (OAE) aims to increase atmospheric CO 2 sequestration in the oceans through the 11 acceleration of chemical rock weathering. This could be achieved by grinding rocks containing alkaline minerals and 12 adding the rock powder to the surface ocean where it dissolves and chemically locks CO 2 in seawater as bicarbonate. 13 However, CO 2 sequestration during dissolution coincides with the release of potentially bio-active chemicals and may 14 induce side effects. Here, we used 53 L microcosms to test how coastal plankton communities from Tasmania respond to 15 OAE with olivine (mainly Mg 2 SiO 4 ) or steel slag (mainly CaO and Ca(OH) 2 ) as alkalinity sources. Three microcosms were 16 left unperturbed and served as a control, three were enriched with olivine powder (1.9 g L -1 ), and three with steel slag 17 powder (0.038 g L -1 ). Phytoplankton and zooplankton community responses as well as some biogeochemical parameters 18 were monitored for 21 days. Olivine and steel slag additions increased total alkalinity by 29 µmol kg -1 and 361 µmol kg -1 19 respectively, which corresponds to a theoretical increase of 0.9 % and 14.8 % of the seawater storage capacity for 20 atmospheric CO 2 . Olivine and steel slag released silicate nutrients into the water column, but steel slag released 21 considerably
Odour and Ammonia Emissions from Livestock Farming

This paper deals with the ammonia volatilization from arable land. Volatilization from liquid pig manure was measured using a modified windtunnel method. Up to 40 % of the total ammonium nitrogen content of the slurry was lost as NH3 during the first 4 days following application. Fifty per cent of the NH3-gas was lost during the first twelve hours and 70 % was volatilized after 24 hours following application. Reduction of the NH3 losses was studied using chemical substances. A reduction of 50 to 90 % was obtained.
Social Insurance, Living Standard and Pension Responsibility

The ‵14th Five-Year Plan for Healthy Aging′ mentioned that China's population aging has deepened and will enter a moderately aging society. With the progress of healthy life and medical technology, there will be more and more elderly people, and the contradiction between delayed retirement and pension responsibility will always exist. In order to deal with the pension responsibility of the elderly, based on the 2018 Chinese General Social Survey (CGSS) data, this paper uses the multivariate mlogit model to analyze the impact of social insurance and living standards on the sense of pension responsibility. The study found that compared with the three parties jointly responsible for pension, residents participating in social insurance are more inclined to pension is the common responsibility of the three parties. Physiologically unhealthy residents are more inclined to pension is the responsibility of children and the government; residents with mental health have a variety of pension responsibility tendencies. At a significant level, self-responsibility is more significant than child responsibility and government responsibility. People with lower-level standard are more inclined to rely on government pension than those with upper-middle-level standard. Residents with children are more inclined to support their children or their own responsibilities. As the number of children increases, the tendency to choose children to support the elderly increases.
Spatial Electric Load Forecasting Using a Geographic Information System: A Case Study of Khon Kaen, Thailand

Electricity is an indispensable element in daily life and economic development. Hence, load forecasting is a vital part of electricity generation and transmission expansion planning. Although most previous studies in the current literature have focused on quantitative load forecasting using regression or machine learning techniques, they fail to consider complex location-oriented problems of load growth. Therefore, this study presents a spatial load forecasting method using a geographic information system (GIS) via ArcGIS software. A case study is conducted in the urban area of Tessaban Nakhon Khon Kaen, Muang District, Khon Kaen Province, Thailand, which has experienced considerable load growth in recent years. This study focuses on two categories of end-users: residential and business consumers. The forecasting procedures are separated into two parts: quantitative forecasting, achieved using regression methods, and spatial forecasting. Spatial forecasting is achieved by evaluating access to an area causing load growth, considering multiple constraints and evaluation criteria for each end-user. The spatial load forecasting calculation uses four technical evaluation scores: consumer preference, physical accessibility, load availability, and access suitability. Load forecasting maps are created and used to represent the spatial resolution of load growth. The results reveal the trend and direction of the increase in power consumption annually, which can be applied to future electricity grid expansion plans.