Miracle-dz/UniFAR-FTUs huggingface dataset:
3D Human Motion Data Compression Based on Computer Vision

The motivation of this research is to address the issue of large storage and transmission space occupied by 3D human motion data, as well as the demand for large amounts of motion data in fields such as virtual reality and animation production. Our goal is to develop efficient 3D human motion data compression algorithms to reduce storage space and transmission bandwidth, while ensuring data quality and realism. The importance of 3D human motion data compression is mainly reflected in the fields of virtual reality and animation production. Virtual reality technology relies on accurate and realistic motion data, which often occupies a significant amount of space. Through effective compression algorithms, the storage space of motion data can be greatly reduced, thereby reducing hardware requirements and transmission costs, and improving the smoothness and real-time experience of virtual reality. In animation production, a large amount of motion data needs to be accurately captured and processed, and compression algorithms can optimize the storage and processing efficiency of the data, thereby improving the efficiency and quality of animation production. Through this research, we will delve into the technology and methods of 3D human motion data compression, and strive to provide a more efficient solution for compressing 3D human motion data with the help of advanced compression algorithms. This will bring many benefits to fields such as virtual reality and animation production, including cost reduction, performance improvement, and real-time capability, promoting innovation and development. In conclusion, this research will provide strong support and promotion for the compression of 3D human motion data and its application in virtual reality and animation production.
A Novel Predictive Modelling Approach Towards a Spatiotemporal Traffic Safety Index

Traffic safety is now a major concern due to the increase in roadways, traffic, and pedestrians. An index quantifying the traffic safety of a location can help transportation authorities take necessary measures to enhance traffic safety by enforcing Intelligent Transportation System Warrants. All the existing approaches adopt descriptive, inferential, or exploratory data analysis-based statistical techniques to calculate a traffic safety index based on different spatial features of a geographic location. In this paper, we propose a novel predictive modeling approach to calculate a spatiotemporal traffic safety index incorporating both spatial and temporal features based on the National Collision Database Canada from year 1999 – 2019. We conduct Pearson chi-square feature selection and apply feature encoding with categorical embedding. We built a deep learning model based on heterogeneous spatiotemporal features to predict traffic accident severity in terms of human injury and fatality and use it as an indicator for a spatiotemporal traffic safety index. The results show that the proposed methodology can effectively generate a spatiotemporal traffic safety index based on accident severity with an accuracy of 96.26% and surpasses the state-of-the-art approach that has an accuracy of 83%.
Image classification of electronic components based on Vision Transformer

Image recognition of electronic components is an important application field, which can be applied to the detection, identification and sequencing of electronic components. In order to solve the problem of electronic component recognition, an image recognition algorithm based on Vision Transformer (ViT) is proposed. Traditional image recognition algorithms of electronic components usually use convolutional neural networks, but there are some problems such as long training time and the need to master the global information. During the experiment, transfer learning method and data-enhanced image preprocessing method are used to improve the performance and robustness of the classification network, and the classification accuracy and recognition speed of Vision Transformer and traditional convolutional neural network in the classification task of electronic components are tested and compared. The test results show that compared with traditional convolutional neural networks, Vision Transformer's self-attention mechanism is more efficient for feature extraction, and the highest recognition accuracy in electronic component image classification tasks can reach 98.4%. In the classification task of small and medium-sized data sets, the Vision Transformer network self-attention structure can achieve the same recognition accuracy as the lightweight convolutional neural network ResNet-50, while significantly improving the training speed and classification speed.
Analysis of a New Method for Reduction of Cogging Torque in a Direct-Drive In-wheel Flux Switching Motor

Electric motors in electric vehicle and other industrial applications have always been studied in terms of torque density, efficiency and torque characteristics in various literature. Flux Switching motors could be an appropriate option for electric vehicles, especially for direct drive applications. In direct drive application, simplicity, depreciation and entire system efficiency could be improved by eliminating gearbox. In this application, torque features are highly significant because in addition to average torque and torque ripple, it has some secondary effects such as vibration and noise, which is particularly significant. A new method is presented in this paper to reduce torque ripple and then, by 2D FEM simulation the electromagnetics torque feature of the proposed technique is investigated and compared with the conventional model. It is proved that by employment of the presented method, torque ripple is reduced to a desired degree, meanwhile; other magnetic features are maintained to an acceptable level.
Sound Event Classification Based on Fusion of Multiple Pre-trained Models

Although audio event classification is a task that involves recognizing event categories in audio signals, it is well-known that a single model may possess limited generalization ability, making it unable to accurately predict audio events. Therefore, this paper proposes a non-mean ensemble method. Specifically, this approach concatenates multiple classification models in the channel dimension and employs non-mean modules to generate learnable parameters, which enables confidence operations to be carried out on each model. By doing so, the approach does not rely on mean information from model outputs, but instead adapts weight adjustments based on learnable parameters, thereby enhancing its adaptability to different classification models and improving the flexibility of the prediction model. Furthermore, our test results on the official dataset of the 2023 DCASE Task4 demonstrate that the proposed method is effective. The best F1 score achieved by a single pre-trained model is 87.6%, with a corresponding mAP of 93.4%. After applying the non-mean ensemble method, the F1 score and mAP are respectively improved to 88.4% and 93.5%.
An Investigation into Student Performance Prediction using Regularized Logistic Regression

The problem of university dropout poses a significant challenge to education systems worldwide, affecting administrators, teachers, and students. Early identification and intervention strategies are crucial for addressing this issue. In addition, advances in machine learning have paved the way for more accurate predictions of student performance. This paper investigates the use of regularization techniques, specifically Lasso (L1) and Ridge (L2) regularization, within logistic regression models to improve the accuracy of performance prediction. This research’s dataset was obtained from the Binus Online Learning platform at Bina Nusantara University, with a focus on the Information System study program between 2020 and 2021. The results reveal that logistic regression with regularization achieves a high level of accuracy, recall, and precision in predicting student performance. The findings contribute to the development of an early warning system to identify at-risk students, enabling timely intervention and support.
Evaluation of Mechanical Fault’s Severity in Power Transformers by using Maximum Points of FRA Window Curves

Frequency Response Analysis has emerged as a crucial tool for diagnosing mechanical faults in power transformers, enabling early detection of defects and hence minimizing transformer downtime. This study presents an advanced approach combining Frequency response analysis and the window calculation technique to enhance the accuracy of fault’s severity estimation, a crucial aspect in maintaining the reliability of power transformers. In this method, the interpretation algorithm divides the transfer function into small frequency windows. Then, each window is interpreted by a numerical index and the interpretation results form window curves. Finally, the approach scrutinizes the monotonic behavior of maximum point amplitudes in the window curves of frequency response analysis with selected indices. The investigation aims to identify the numerical index that exhibits the best monotonicity correlated with fault severity across different mechanical fault types including axial displacement, radial deformation, and disk space variation. Experimental validation across various test transformer mechanical faults in inductive inter-winding measurement test mode underscores the effectiveness of this novel technique.
DPC-SLAM: Discrete Plane Constrained VSLAM for Intelligent Vehicle in Road Environment

Simultaneous Localization and Mapping (SLAM) plays a significant role in providing accurate localization for intelligent vehicles. However, the traditional six-degree-of-freedom motion pose estimation assumption does not align with the actual vehicle motion, leading to pose estimation errors. To address this problem, we propose a stereo-visual SLAM algorithm specifically designed for road scenes to incorporate road constraints into the system. Our method leverages road constraints to eliminate errors caused by the six-degree-of-freedom motion assumption. Firstly, we represent the road as a set of discrete planes, simplifying the complex road surface into discrete local planes, and establishing the constraint relationship between camera poses and local planes. Then, we propose a tightly-coupled SLAM system that optimizes camera poses, map points, and discrete planes simultaneously. Finally, we evaluate the proposed system on the KITTI dataset and our self-collected dataset. Experimental results demonstrate that our method significantly improves the accuracy and stability of camera pose estimation.
An End-to-End Collaborative Learning Approach for Connected Autonomous Vehicles in Occluded Scenarios

Collaborative navigation becomes essential in situations of occluded scenarios in autonomous driving where independent driving policies are likely to lead to collisions. One promising approach to address this issue is through the use of Vehicle-to-Vehicle (V2V) networks that allow for the sharing of perception information with nearby agents, preventing catastrophic accidents. In this article, we propose a collaborative control method based on a V2V network for sharing compressed LiDAR features and employing Proximal Policy Optimisation to train safe and efficient navigation policies. Unlike previous approaches that rely on expert data (behaviour cloning), our proposed approach learns the multi-agent policies directly from experience in the occluded environment, while effectively meeting bandwidth limitations. The proposed method first prepossesses LiDAR point cloud data to obtain meaningful features through a convolutional neural network and then shares them with nearby CAVs to alert for potentially dangerous situations. To evaluate the proposed method, we developed an occluded intersection gym environment based on the CARLA autonomous driving simulator, allowing real-time data sharing among agents. Our experimental results demonstrate the consistent superiority of our collaborative control method over an independent reinforcement learning method and a cooperative early fusion method.
Landing a Quadrotor on a Ground Vehicle without Exteroceptive Airborne Sensors: A Non-Robocentric Framework and Implementation

This research addresses the problem of a quadrotor UAV landing on a ground vehicle. Yet, unlike most existing literature, we transfer most sensing and computing tasks to the ground vehicle, designing the landing system in a non-robocentric fashion. Such a framework greatly alleviates the payload burden, allowing more resource allocation for the quadrotor UAV. To validate the proposed framework, the implementation starts with relative pose estimation through detection and tracking of LED markers on an aerial vehicle. The 6 DoF orientation and position information is then returned through a PnP-based algorithm. Successively, by considering the visibility and dynamic constraints in the target local frame, the motion planning module computes an optimized landing trajectory, such that the aerial vehicle stays within a safety corridor and performs the landing mission. Through experiments, we demonstrate the applicability of this research work, in which a quadrotor could be guided remotely and landed on a moving ground vehicle smoothly without the support from any airborne exteroceptive sensors and computers.
Cooperative Rerouting to Redistribute the Load of Connected and Automated Vehicles in Urban Networks

In this paper, we propose a novel distributed algorithm for cooperative rerouting of Connected and Automated Vehicles (CAVs) in urban networks, in which each intersection unit manages the portion of the network for which they are in charge by sending updated routes to follow to the CAVs therein, while communicating among each other to be updated on the situation of the (in general non-homogeneous) roads of the network. The proposed approach allows to decompose the problem into subproblems, which are resolved distributively with little information exchange. The problems are constructed to obtain a fair compromise between user equilibrium and system performance. We show the results we obtained on a simulated urban network with CAVs and compared them with a baseline scenario.
Safety Standards for Autonomous Vehicles: Challenges and Way Forward

This paper examines current autonomous vehicle (AV) safety standards and identifies gaps in their scope and depth. It is the first to present a thorough review and critique of such standards. It suggests that they lack the necessary guidance to ensure the safety of AVs, causing a bottleneck in transitioning from AV development to public adoption. Primary challenges of standards are presented, explicitly covering gaps and weaknesses pertaining to AVs' user-system interactions, operational design domains (ODDs), safety testing methods, safety metrics, acceptance criteria, and machine learning pit- falls across the lifecycle of AVs. A way forward to address these challenges is also proposed, emphasising the need to decomplexify the ecosystem of AV standards and promote stake-holder interdependence. The paper concludes by recognising the criticality of legislative and regulatory frameworks for AVs and advocates for cohering safety standards, certification, and regulation.
Fast Super-Resolution Network via Dynamic Path Selection

In the context of edge computing environments, where the storage space and computational resources are constrained, complex super-resolution network models face significant challenges during inference. In this paper, we propose a Fast Super-Resolution Network (FSRN) based on the dynamic path selection mechanism. This method employs a policy network to boost the inference process of super-resolution network models, enabling efficient super-resolution tasks under resource limitations. The primary function of the policy network is to intelligently select appropriate inference paths based on input data and available computational resources. Its key objective is to minimize the degradation in super-resolution quality while reducing the computational burden as much as possible. In view of this objective, we designed a reward function to guide the policy network in discovering the optimal strategies. With the guidance of the policy network, we successfully enhanced the inference speed of super-resolution network models on edge devices while efficiently reducing computational overhead. Extensive experiments verify that the proposed method can significantly reduce the inference time at the cost of slight or even no performance degradation.
A High-Precision Current Sensing Front-End IC Design for Wide-Range Dynamic Current Detection

This paper presents a high-precision front-end Integrated Circuit (IC) for wide-range dynamic current detection. Traditional current detection circuits usually employ the topology with linear transconductance for current detection, which is limited in its ability to accommodate large signal variations. We propose a multi-range detection method to address this issue. To amplify the input current into a voltage signal, the input current is divided into multiple ranges, and then it is amplified into a voltage signal using corresponding current-to-voltage conversion circuits. The circuit is designed in TSMC 0. 18μm-CMOS process. The power supply voltage is 3.3 V. The results show that the designed circuit is capable of detecting current in a dynamic range from 1nA to 100μA, with a maximum detection error of 0.848%.
Skin Cancer Diagnosis and Detection Using Deep Learning

If early diagnosis and early detection of skin cancer is achieved, many patients can survive. The traditional method for the public has always suffered from problems such as imprecision and biased results. This study therefore presents a method that can be introduced in a comprehensive workflow to build a healthcare system with artificial intelligence. More exactly an intelligent system that can detect and diagnose skin cancer. While most work that use deep learning techniques focuses either on detection or diagnosis skin cancer, we have use these techniques to develope a model that does both tasks at once. Transfer learning was applied on different models of object detection and diagnosis using a dataset from Kaggle with TensorFlow API. The dataset images are a total set of 3297 dermatoscopic images. To train our model, 2637 images were used. And to test it, 660 images were used. VGG16 was used as an object recognition backbone network (for diagnosis) and Yolo as the object detection framework. The evaluation accuracy of the model was more than 83 %, which is promising.
Message Prediction Method and Application Based on V2X Interactive Data Completeness

With the rapid development of V2X (Vehicle-to-Everything) technology, traffic safety under vehicle-connected communication environment has become a research hotspot. This paper focuses on how to reduce the impact of occlusion effect on the data integrity of communication node interaction, and proposes a V2X interactive data integrity evaluation index based on C-V2X (Cellular Vehicle-to-everything) technology. At the same time, an information completion mechanism based on information integrity evaluation, pipelining mechanism, is proposed to complete missing messages. Finally, SUMO and NS3 joint simulation verified the reliability of the evaluation index and the accuracy of the pipeline mechanism.
A Control grid-connected PV system with NPC Multi-level inverter

This paper introduces a control strategy for a distributed generation system utilizing photovoltaics. The system employs a dual-stage configuration consisting of a DC-DC boost converter and a five-level neutral-point-clamped (NPC) voltage source inverter (VSI). In order to control the inverter and the current injected into the grid, the voltage-oriented control (VOC) approach and pulse width modulation (PWM) technology are utilized. The voltage-oriented control approach guarantees effective power monitoring, voltage balancing, NPC inverter functionality, and the injection of clean sinusoidal grid current, even in the face of potential dynamic variations. The effectiveness of the proposed control scheme is demonstrated using MATLAB/Simulink simulations of the electric power distribution system's steady-state and transient responses.
Robust Tunable Trajectory Repairing for Autonomous Vehicles Using Bernstein Basis Polynomials and Path-Speed Decoupling

Adaptation to changing dynamic situations is yet an open problem for automated driving systems that require robust and efficient solutions. Particularly in the context of motion planning algorithms, this problem is typically addressed by re-planning the whole trajectory or repairing the invalid part. The main drawback of all the current approaches is the increased demand for computational resources, a critical safety issue in automated vehicles. Motivated by this, in this paper we propose a novel and efficient method for trajectory repairing utilizing Bernstein basis polynomials and path-speed decoupling. A robustness metric is introduced to tune the driving behavior. Accurate numerical simulations indicate performance figures typically better than $25ms$ for a feasible solution in representative driving scenarios, which was not achievable in other state-of-the-art approaches.
Topology-aware Road Extraction via Multi-task Learning for Autonomous Driving

The road network is essential for autonomous driving vehicles to navigate in intelligent transportation systems. With the success of deep learning, many methods regard road extraction as dense pixel-wise segmentation based on neural networks. However, the predicted fragmented road regions, resulting from frequently occurring occlusions, are challenging to utilize in topology-required autonomous driving applications. In this paper, we formulate road extraction as sparse anchor-points estimation and their connectivity construction problems to enhance the global topology of roads. We design a multi-task architecture within an encoder-decoder framework to simultaneously predict the segmentation, anchor-points and connectivity maps. Besides, we propose a topo-swin block and incorporate multi-level feature maps in the decoder to combine local information with long-distance topological relationships. Furthermore, a graph simplification criterion is designed to make it more efficient for autonomous driving applications. Experiments on the challenging Deepglobe dataset demonstrate that our proposed model outperforms existing road extraction methods by achieving 77.24% in topology-aware metrics.