Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust Point Cloud Classification With Permutohedral Lattice-based Representation

Deep learning models have greatly improved the accuracy of point cloud classification. Nevertheless, existing deep learning models are vulnerable to data corruptions such as Gaussian noise and outliers which are inevitable in real-world point cloud collection. To address this challenge, we develop a novel point cloud classification model that is robust to data corruptions, with permutohedral lattice-based representation and density-improved hierarchical feature extraction. Specifically, raw noisy point clouds are firstly projected into a regular per-mutohedral lattice space to obtain the quantized representations. Subsequently, we propose to leverage the local density to improve the farthest point sampling (FPS) and spectral graph convolution for robust hierarchical feature extraction, inspired by that the local point density implicitly reveals the reliability and semantic information of each point. The local density can be efficiently calculated from the permutohedral lattice-based representation. Extensive experiments on the benchmark dataset (i.e., ModelNet-C) verify the robustness of the proposed model. In comparison to the state-of-the-art methods, the proposed model achieves superior performance on the corrupted dataset while maintaining competitive classification accuracy on the clean dataset.
Probabilistic Data-driven Nodal Voltage Forecasting considering Active Distribution Networks

Voltage regulation is a crucial ancillary service provided by transmission system operators to ensure safe grid operation and acceptable power quality. The decommissioning of conventional generators in combination with ongoing installations of controllable Distributed Energy Resources (DERs) and cabling projects in Distribution Networks (DNs) make transmission-level voltage regulation more challenging. The impact of DNs on the transmission-level voltage is not negligible, since they also host generating units and their behaviour has become less predictable. In this paper, we present a methodology to forecast the nodal DN power injections based on machine learning algorithms considering uncertainties. We consider the historical inter-correlations between nodal data by applying copula theory, and we create scenarios that are used in load flow calculations to create a probabilistic nodal voltage forecast for DN nodes. We demonstrate the performance of the proposed method on the Swiss transmission network and highlight its usefulness as a decision-making tool for grid studies.
Pricing Research on Spatial Crowdsourcing Tasks Under Incompletely Uncertain Scene Information

Task pricing is an important step for crowd-sourcing platforms to solve profit-driven task allocation and maximize profits. Most of the existing researches only carry out algorithm design on the premise of fully determining the scene information. However, due to the interference of many factors in the real scene, information such as workers and task costs in the scene is usually not completely uncertain. To solve the above problems, a spatial crowdsourcing task pricing algorithm is proposed. Firstly, the algorithm uses the proposed dimension-up gray wolf algorithm and support vector regression(DU_GWO-SVR) to predict the task price, and then sets the price based on the obtained price. In order to solve the instability of average price and matching number caused by dynamic supply and demand, an adjustment mechanism is designed to stabilize the average price of tasks. The experiment uses a real data set-the New York taxi data set, and compares it with the classic greedy algorithm and binary matching algorithm. The experimental results show that the matching rate of the proposed algorithm is above 75% when the scene information is not completely uncertain.
Towards Safe Model-Free Building Energy Management using Masked Reinforcement Learning

Autonomous control of building energy resources including HVAC and battery storage systems has the potential to optimize operations and achieve objectives such as cost minimization. Existing approaches either require an explicit mathematical model of the building, or resort to simple rule-based controls (RBC) which may be sub-optimal. Model-free reinforcement learning (RL) is a promising method to overcome these limitations - however, it often requires a large number of interactions with the real environment before learning a functional policy. In this work, we investigate ’Action Masking’, a technique to improve the learning efficiency of RL algorithms while respecting safety rules during the learning phase. Our solution achieves a cost reduction of 6% compared to a baseline rule-based controller, and also outperforms a popular transfer learning strategy. This suggests that model-free RL approaches are feasible and practical for problems in this domain
Discrete wave equation upscaling

S U M M A R Y We present homogenization technique for the uniformly discretized wave equation, based on the derivation of an effective equation for the low-wavenumber component of the solution. The method produces a down-sampled, effective medium, thus making the solution of the effective equation less computationally expensive. Advantages of the method include its conceptual simplicity and ease of implementation, the applicability to any uniformly discretized wave equation in 1-D, 2-D or 3-D, and the absence of any constraints on the medium properties. We illustrate our method with a numerical example of wave propagation through a 1-D multiscale medium and demonstrate the accurate reproduction of the original wavefield for sufficiently low frequencies.
A Localization Optimization Algorithm with Improved Feature Tracking Mechanism

Aiming at the traditional SLAM algorithm to extract the matching time consuming, which makes it difficult to run in real time, this paper proposes a fast SLAM algorithm with improved tracking method. The algorithm introduces the tracking decision of fusion of feature and optical flow, filters the input frames first, classifies different frames into feature frames and optical flow frames, extracts feature points only in feature frames, and tracks the points in feature frames in optical flow frames, which avoids time-consuming matching and improves the real-time performance of the algorithm; in the time-sharing asynchronous tracking session, feature tracking and bidirectional optical flow tracking are performed for the classified feature frames and optical flow frames respectively. The test results in the publicly available TUM dataset show that the algorithm of this paper has better advantages in real-time performance and adaptability to the environment.
Uncertainty-Quantified, Robust Deep Learning for Network Intrusion Detection

Cyber threats are moving beyond human comprehension and reaction capability in a rapidly evolving world. Deep learning models for network intrusion detection are becoming evermore crucial in processing network traffic to filter benign content from malicious activity. However, novel attacks such as zero-days are becoming more frequent, demonstrating the need for robust deep learning models to flag attacks while providing predictive certainty guarantees. Therefore, detecting out-of-distribution (OOD) inputs at inference time is crucial to address the rapidly changing environment while keeping up with evolving cyber threats. We develop multi-class deep learning models for network intrusion detection, comparing deterministic with Bayesian neural networks estimated using Hamiltonian Monte Carlo. We also propose new uncertainty quantification scoring measures for performance evaluation to evaluate certainty in predictions. During our experimentation, our best performing proposed Bayesian deep learning model detected 89.1% and 86.9% of the OOD packets at the 5% and 0.1% significance levels, respectively.
A Robust Direct Position Determination Method Based on Block Sparse Bayesian Learning in the Presence of Unknown Mutual Coupling

In this paper, a block sparse Bayesian learning (BSBL) based robust direct position determination (DPD) method is proposed for locating multiple emitters in the presence of unknown mutual coupling in distributed sensor arrays. This method initiates by conducting a matrix transformation on the actual steering vector, effectively segregating the position parameter from the mutual coupling coefficient. Besides, the BSBL algorithm is designed to iteratively update the hyperparameters. Ultimately, the position of the radio emitter is estimated based on the mean value of the posterior distribution derived from the reconstruction process. At the same time, a mutual coupling coefficient estimator is provided based on the principle of subspace, which can achieve more accurate localization by compensating the mutual coupling coefficient in the sparse dictionary. Simulation results verify that the developed BSBL scheme outperforms the state-of-the-art solution in terms of localization accuracy and robustness to unknown mutual coupling.
Queue Time Prediction Methodology in Semiconductor Fab

This paper presents a methodology for predicting queue times in semiconductor fabrication, where numerous complex and costly pieces of equipment are utilized. Queue time, occurring between continuous single or multi-processes, is a crucial factor affecting the quality of wafers, which can significantly impact costs. While most semiconductor fabrications use queue time limits as a key dispatching factor, some wafers may still be scrapped or reworked. By predicting queue times, we can reduce unnecessary waste by blocking or re-dispatching wafers. Two approximations are proposed and compared based on accuracy and prediction time: a machine learning model trained using experimental results and a multi-resolution simulation model with varying fidelity levels. The simulation model is validated using the SMAT2022 data set.
A Real-time Drowsiness Tracking Framework Using the Single Shot with Mediapipe

In the realm of automotive safety, the pivotal challenge of mitigating drowsy driving, especially prevalent during nighttime journeys, assumes critical importance. This endeavor is dedicated to the construction of an advanced Driver Drowsiness Detection system, harnessing two cornerstone algorithms: UYOLOv5, distinguished for its exceptional facial tracking precision, with an impressive 0.993 mAP@0.5, and the MediaPipe library, renowned for its meticulous eye movement detection capabilities. The fusion of these algorithms establishes a real-time drowsiness assessment framework, with a particular focus on its efficacy in the demanding context of nighttime driving. Through the seamless integration of UYOLOv5 and the MediaPipe library, the objective is to provide a comprehensive solution that transcends mere observation, equipped to proactively initiate safety measures upon the identification of signs of driver fatigue. This initiative is geared toward significantly enhancing nighttime road safety, addressing a matter of paramount concern.
A Sleeve Device using Electrical Impedance for Coaching Jump Shots in Basketball

The amount of force and the timing of force application are necessary training skills in basketball free-throw practice. Coaches cannot readily assess the shooter's motion and provide instructions because the throwing motion is performed in a short time, and because the applied force cannot be directly observed by a second person. In this work we propose a wearable device that measures the wearer's force control during throwing based on electrical impedance and acceleration measurement of the forearm, and provides feedback such that the wearer can adjust their motion appropriately. Temporal features of the electrical impedance and acceleration measured during throwing are determined experimentally to differentiate between experts and beginners. Using these features, we propose a training system for force control using the proposed device and verify its effectiveness in free-throw training.
Performance Analysis of Model Predictive Control With Variable Weights in Optimization as Applied to PMSM

PMSMs (Permanent Magnet Synchronous Motors) have the major advantage of higher efficiency than induction machines. This is due to the use of permanent magnets that reduce the losses associated with the generation of magnetic fields and rotor windings. They also offer high power density and torque, which makes them suitable for applications requiring high performance and precision. Also, they are reliable and require less maintenance. PMSMs are being used increasingly in industries like automotive, aerospace, robotics, etc. Although PI control, Sliding Mode Control (SMC), and Intelligent Control are popular control algorithms for PMSMs. However, control vector optimization is lacking in these algorithms. Model Predictive Control (MPC) is the solution for this. MPC was primarily created for slower applications like process control and it can now be easily applied to electric drives due to the development of fast processors and quick-switching technology. The algorithm, which always uses an explicit model, predicts the system output for a limited number of future sampling intervals or future time horizons. Then it defines a cost function based on the estimated future errors. This cost function is minimized so as to minimize the error and a control input. The performance of an MPC-based drive depends on the weight matrices used in the optimization process. It has been found that MPC-based control not only takes care of errors and cost-effectiveness but also increases the robustness and stability of the drive. This paper presents a comparison of the performance of the MPC algorithm applied to PMSM. By selecting various optimizing weights in the cost function, the drive is tested for various operating conditions. Extensive simulations are carried out on MATLAB/SIMULINK platform to study the effects.
Autonomous Vision-based Robotic Grasping of Household Objects: A Practical Case Study

Autonomous robotic grasping has emerged as a valuable capability for automating everyday household tasks like table setting (bossing), cleaning, and meal preparation. Still, accurately detecting and manipulating diverse everyday objects in unstructured home environments remains a significant challenge. In this study, an integrated robotic system combining YOLOv8s-seg deep learning for object detection and segmentation with computer vision techniques to determine optimal grasping points is proposed. The accuracy of this model has also been compared with other models through their mAP0.5, mAP.05-. 95, precision and recall. A custom dataset of over 1000 images containing common household objects was created, with manual annotation and augmentation to train the YOLOv8s-seg segmentation model. The approach processes input images to understand desired arrangements and identify target objects and their locations for grasping. The detected objects are localized using camera-to-robot calibration and aligned with estimated orientations from computer vision techniques. Extensive tests have proven that representative household objects in cluttered arrangements have a grasping success rate of over 90% using a 3DOF Delta Parallel robot with a 2-finger gripper guided entirely by visual perception. By overcoming real-world challenges like transparent and reflective materials, the perception-guided robots provide precise and efficient robotic grasping in unstructured home environments. This integrated approach combines robust perception and localization, providing promising evidence for reliable automation of repetitive household organization tasks1.1A video of real-time grasp performance is uploaded as supplementary material.
Life and death in a corn desert oasis: reproduction, mortality, genetic diversity, and viability of Illinois' last Eastern Massasauga population

Demographic and environmental stochasticity can lead to a loss of genetic diversity, and are particularly damaging in those species that persist as small isolated populations with elevated probabilities of extirpation. Thus, monitoring genetic diversity is a critical component of adaptive management plans. The Eastern Massasauga (Sistrurus catenatus) is declining throughout its native range, and is thus a species of high conservation priority. In Illinois, only a single population remains of a once widespread distribution. I documented genetic diversity in this population at three time periods (2002, 2007, and 2012), and assessed levels of heterozygosity, allelic diversity, inbreeding (FIS), and effective population size (Ne). Neither heterozygosity nor levels of inbreeding differed significantly among time periods. I identified 21 private alleles (of 144 total alleles), some of which may have been lost from the population given our estimated detection probability of 88%. Effective population sizes (Ne) were numerically small, but high relative to the census population size (Nc). While the small population size and a lack of connectivity are troubling, there was no significant decline in genetic diversity over the 10-year study. Multiple paternity of litters and possible kin recognition, coupled with a preference for an historically patchy habitat, may mitigate the loss of genetic diversity in the species, and promote their persistence in the fragmented habitats of the Anthropocene. Nevertheless, Ne < 50 is predicted to increase inbreeding and elevate the extinction risk of the population. Therefore, continued genetic monitoring is recommended, and recovery measures
Minimizing Human-Exoskeleton Interaction Force through PILCO: A Probabilistic Learning Control Approach

This research aims to enhance the interaction between lower limb exoskeleton robots and human users, focusing on the swing phase of normal gait. The primary challenge is modeling the dynamics of the system and interactions, considering uncertainties and measurement noise. A control approach “Probabilistic Inference for Learning Control” (PILCO) initially introduced in a different context, has shown promise in optimizing control policies efficiently. To address the aforementioned challenge, this study adopts the principles of PILCO to the domain of lower limb exoskeletons. Within this framework, Gaussian processes (GPs) will serve as the foundation for extracting probabilistic models from data. These models will be integrated with reinforcement learning techniques to facilitate optimal control by minimization of interaction forces. Reducing interaction forces, not only enhances user comfort but also enables the robot to accurately follow the user’s movements. Our results show that the proposed control framework predicted the dynamic model of the robot, taking into account the measurement noise and disturbances, with an acceptable error. The root mean square error (RMSE) of identified model through GPs, respectively for hip, knee and ankle joints were 0.9864, 0.9027, and 1.4613. Meanwhile the proposed algorithm effectively converges the interaction disturbance to near-zero levels, which is considerable in comparison with other deterministic model-based control.
Few-Shot Fine-Grained Classification of Histological Images

Histological image classification plays a crucial role in cancer diagnosis. However, the acquisition of well-labeled histological images is prohibitively expensive, and obtaining rare abnormal samples is challenging. Therefore, applying few-shot learning methods to histological image classification tasks holds significant clinical value. Nevertheless, existing research predom-inantly relies on coarse-grained image classification approaches based on natural image datasets, which struggle to address the fine-grained challenges encountered in histological image classification, such as intra-class diversity and inter-class similarity. To tackle this issue, this study proposes a novel few-shot fine-grained classification method for histological images, named “Category-Aware Feature Map Reconstruction Network. ” This method employs channel weights to localize the differences between inter-class and intra-class regions, composed of intra-class channel weights and inter-class channel weights, collectively referred to as category-aware weights. Specifically, intra-class channel weights indicate the matching degree of salient regions within the support set of a particular class, while inter-class channel weights represent the degree of containing distinct information between classes. The category-aware weights are utilized to transform the support feature maps and query feature maps, generating feature maps that capture differentiating details between categories. Finally, the distance between the transformed query feature map and support feature map is calculated to achieve probabilistic predictions for the categories. On a histological few-shot dataset, this method achieves an accuracy of 90.23% using ResNet-12 as the feature extractor, surpassing the baseline model by 5.24% and outperforming other few-shot methods by at least 10% in the 5-way 10-shot experimental setting. The proposed method exhibits exceptional performance on histological image few-shot datasets, playing a vital role in more accurate and pathologist-independent cancer diagnosis.
Grasp the Graph (GtG): A Super Light Graph-RL Framework for Robotic Grasping

This paper introduces Grasp the Graph, an innovative framework addressing challenges posed by processing point clouds with conventional neural networks for robotic grasping. Such challenges often lead to the conversion of point clouds to multi-view images or voxel grids, requiring large networks with millions of parameters. Grasp the Graph formulates robotic grasping as a one-step reinforcement learning problem and leverages graph representation and graph neural networks to efficiently process geometric relationships in point clouds. The lightweight design with only 67k parameters facilitates swift training and strong generalization to novel objects, offering the capability to propose multiple valid grasping candidates. Empirical testing demonstrates the effectiveness of the proposed approach, achieving a 90.4% success rate on test objects while being trained with only 10 objects. Even with a three-camera setup during training, reducing the cameras during evaluation minimally impacts performance, resulting in less than a 3% decrease.1
Modeling of Photovoltaic Systems for Self-Consumption

The paper presents an approach for modeling of operation and performance of photovoltaic systems intended for electricity production for self-consumption. With the recent development of technologies for solar electricity generation the usage of PV systems for self-consumption increases rapidly. The models developed by the authors give the possibility for accurate calculation of powers and energies in PV systems for self-consumption. Different configurations are studied: with and without connection to the public electrical grid, with and without energy storage, with and without limitation of energy export. The work reveals that setting an accurate hourly load profile is of utmost importance for the precision of the calculations and the results obtained. The models also give the possibility for optimization of: the battery usage strategy, the load profile (to match the generation from the PV panels), the battery capacity as well as the PV system as a whole.