Miracle-dz/UniFAR-FTUs huggingface dataset:
A Robust In-Motion Alignment Method for OD-Aided SINS Based on Magnitude Matching

Initial alignment is one of the key technologies of strapdown inertial navigation system (SINS), which will directly affect the accuracy of subsequent navigation. In this paper, the odometer (OD) is used to implement the in-motion alignment process. The OD measurement often produces outliers due to faults or disturbances, which will affect the accuracy of alignment and positioning. Thus, we propose a robust in-motion alignment method based on magnitude matching. First, by using the velocity provided by OD and the specific force and angular velocity provided by SINS, an observation vector and a reference vector in the inertial frame are constructed. Then, an OD outlier detection method is designed based on the magnitude matching of the mentioned vectors. When an outlier is detected, we predict the velocity at the current moment based on historical velocity data and replace the outlier. Finally, the simulation is constructed to demonstrate the effectiveness of the proposed method. The alignment results show that this method can achieve reasonable attitude results, the interference of outliers in OD measurement can be greatly restrained.
Model-Free Fault-Tolerant Control for Formation Flying with Derivative Couplings

This paper addresses the robust adaptive fault-tolerant control (FTC) problem for formation flying systems with actuator faults and unknown dynamics. Since it is challenging to determine the precise dynamics for the formation flying multi-agent systems in the presence of unknown actuator defects and disturbances, a data-based policy algorithm using real system data instead of the system model is developed. By leveraging the spectral decomposition approach, the solution of spacecraft flying control with coupled communication links is obtained. We demonstrate that the formation flying system is asymptotically stable and all signals in the closed-loop systems are bounded. Finally, the efficiency of the proposed model-free fault-tolerant controller is confirmed by the simulation results.
Integrating Exact Simulation into Sweeping for Datapath Combinational Equivalence Checking

In the application of IC design for microprocessors, there are often demands for optimizing the implementation of datapath circuits, on which various arithmetic operations are performed. Combinational equivalence checking (CEC) plays an essential role in ensuring the correctness of design optimization. The most prevalent CEC algorithms are based on SAT sweeping, which utilizes SAT to prove the equivalence of the internal node pairs in topological order, and the equivalent nodes are merged. Datapath circuits usually contain equivalent pairs for which the transitive fan-in cones are small but have a high XOR chain density, and proving such node pairs is very difficult for SAT solvers. An exact probability-based simulation (EPS) is suitable for verifying such pairs, while this method is not suitable for pairs with many primary inputs due to the memory cost. We first reduce the memory cost of EPS and integrate it to improve the SAT sweeping method. Considering the complementary abilities of SAT and EPS, we design an engine selection heuristic to dynamically choose SAT or EPS in the sweeping process, according to XOR chain density. Our method is further improved by reducing unnecessary engine calls by detecting regularity. Experiments on a benchmark suite from industrial datapath circuits show that our method is much faster than the state-of-the-art CEC tool namely ABC ‘&cec’ on nearly all instances, and is more than 100× faster on 30% of the instances, 1000× faster on 12% of the instances.
Adaptive prescribed-time control for stochastic nonlinear systems

This paper studies the adaptive state-feedback prescribed-time control problems for a class of stochastic nonlinear systems with uncertain control coefficients. Firstly, an adaptive prescribed-time stability theorem is proposed. Then, based on this theorem, a smooth adaptive state-feedback controller is designed, which can guarantee that the system has an almost surely unique strong solution on the prescribed interval for any initial conditions and the equilibrium at the origin of the plant is prescribed-time mean-square stable. Finally, a concrete example is provided to validate the efficiency of the proposed design schemes.
OGIL : An Improved ORB Algorithm Based on GMS, Information Entropy and LC

The ORB feature extraction and matching algorithm is in commonly used. It is based on single-channel grayscale images for feature point extraction and matching, which results in a huge loss of information to cause inaccurate feature extraction. To solve this problem, we propose OGIL (Improved ORB algorithm based on GMS, information entropy and LC), the OGIL algorithm based on the ORB algorithm, which clearly outperforms the original algorithm. The OGIL understands RGB images as a sequential superposition of three independent layers and uses the information entropy metric of RGB images to determine the gray shift weights based on the information entropy of the three layers, which fuses into a grayscale image that most effectively retains the information contained in the image. Secondly, the LC (Luminance Contrast) saliency detection algorithm based on gray value is introduced to filter out redundant information, extract feature points quickly, and speed up the operation speed. To figure out the problem of mismatching in ORB feature extraction, the OGIL brings in a GMS (Grid-based Motion Statistics) algorithm. The comparison results on the dataset images show that the OGIL algorithm is significantly better.
A Multi-dimensional Evaluation Criterion Scheme for Autonomous Vehicles: Behavioral Performance

Autonomous driving (AD) is an enhancement or replacement for human drivers, while AD has brought a huge challenge to automotive testing and evaluation (T&E) technologies. As AD replaces drivers’ perception, decision-making and control, the T&E procedure for autonomous vehicles (AVs) changes from independent T&E of human-vehicle binary to integrated T&E of human-vehicle systems. Complex weather, traffic and electromagnet environments under open-road testing make a quantitative T&E of AVs very difficult. To solve this problem, in this paper, we propose a multi-dimensional evaluation criterion scheme for AVs, in which behavioral performance is the highlight. Specifically, The criterion framework of behavioral performance consists of five dimensions: safety, efficiency, comfort, intelligence and reliability. Then, a scenario-based evaluation approach is realized by the formal approach, i.e., linear temporal logic (LTL). The proposed evaluation criterion scheme is verified by a typical scenario in China GB and implemented by simulations based on the SCANeR studio software.
Real-time analysis scheme of football matches based on face recognition and multiple object tracking

The real-time analysis of football matches can bring more event information and wonderful visual experience to the audience. However, most of the existing game analysis is offline processing, and it is difficult to achieve real-time performance. This paper proposes a real-time analysis scheme for football matches based on face recognition and multiple object tracking. First, we classify the live broadcast data of the game, and then process the different scene data separately. Finally, the processed player game information is rendered into the video. For close-up images of game videos, we utilize face detection, face recognition, and optimized head tracking to determine player information. For distant shots, we propose a multi-object tracking algorithm based on player features to determine player associations in consecutive frames. This scheme has excellent performance in actual use. It can improve the user experience of watching games and is scalable.
CEEMDAN and Informer Based Model for Aero-Engine Parameter Prediction

Predicting the parameter values of an aero-engine is beneficial to detect abnormal states in advance. Most existing methods directly predict the original parameters without adopting mode decomposition. This paper proposes a mode decomposition based fusion parameter prediction model, which can capture the transformation law of the raw signal and enjoy improved prediction accuracy. To this end, we utilize complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to decompose the raw time series data (N1r) to obtain multiple distinct mode components and residual signals. The complexity of each component is evaluated by fuzzy entropy, and the components with similar complexity are fused to reduce the input dimension of the prediction part. Informer is used to predict the fusion components, and the summation method is used to fuse each component to obtain the predicted value of N1r. Finally, the efficiency of the fusion model and the original Informer are verified over an engine’s flight log data. The results show that the proposed mode decomposition based fusion parameter prediction model has better performance than Informer.
A Two-Stage Transformer-Based Point Cloud Upsampling

Point cloud upsampling is used to densify the sparse set of points collected by 3D sensors, which is widely used in the field of robotics. In this paper, a two-stage method is used to generate upsampling point cloud. In the first stage, the rough upsampling point cloud is generated, and in the second stage, the rough point cloud is refined to obtain high-quality point cloud. The generation of high quality point cloud heavily relies on feature extractors. In the first stage, we introduce a transformer model to improve the feature extraction effect, which helps to fully extract the features of different location points. A Refinement Unit is proposed in the second stage to improve the rough upsampling point cloud generated in the first stage. The Refinement Unit is based on another transformer model in which the relative position coding function improves the coordinates of the deviation points generated in the first stage. We evaluate our approach on synthetic data sets. Experimental results show that the performance of this method is better than other methods.
Optimal Configuration Scheme of Multi-Sensor Perception for Autonomous Vehicles based on Solid-State LiDAR*

Environmental perception is the first and most critical part of realizing autonomous driving. Solid-state LiDAR, as an important sensor in the perception layer of autonomous vehicles, gives the vehicles the ability to perceive beyond human eyes and guards the safety of people’s travel. This paper proposes an optimal multi-sensor configuration method for autonomous vehicles based on solid-state LiDAR, camera, millimeter wave radar, and ultrasonic radar. The range of interests (RoIs) is divided into six zones and they are further classified into the core RoI and the extended RoI based on importance. The RoIs discretization mechanism is designed according to the functions and sensing range of various sensors. Considering the cost, coverage, and redundancy, a mixed integer programming model is built to determine the optimal number of sensors and placement positions. The optimal configuration scheme is obtained by using the IBM ILOG CPLEX solver. A model of a well-known vehicle brand is chosen to verify the effectiveness and feasibility of the proposed method. The solid-state LiDAR-based multi-sensor perception solution can meet the needs of environment perception for autonomous vehicles, and its coverage and redundancy are better than other control schemes, but its cost is relatively high due to the high cost of the solid-state LiDAR. The configuration scheme proposed in this paper will provide effective support for the design of an autonomous vehicle sensing solution.
EffiSyn: Efficient Logic Synthesis with Dynamic Scoring and Pruning

Logic synthesis tools synthesize circuit structures to optimize specific targets given reasonable constraints and runtime using a set of well-defined operators. The efficiency of these operators is critical to achieving better runtime and optimization convergence. However, most synthesis operators are designed heuristically with fixed and sub-optimal traversal orders of nodes, cuts, and candidate subgraphs that are independent of circuit structures and functionalities. This leads to redundant computation and loss of optimization opportunities. Due to the spatial structure similarity, sub-circuits already synthesized in the same circuit contain meaningful information to guide more efficient synthesis for unvisited sub-circuits. Historical evaluation and gain features can learn from conflicts and be utilized to predict synthesis gain and prune invalid sub-circuits. Instead of using high-weight feature extraction and models, we utilize efficient prediction models with lightweight structural and functional features to reduce overhead. We thus propose a generalizable and dynamic scoring and pruning framework EffiSyn to accelerate logic synthesis operators while maintaining synthesis effectiveness. For example, we further improve the highly optimized operator drw by scoring and pruning invalid cuts and precomputed subgraphs. Extensive experiments on 20 public and industrial circuits validate that EffiSyn can accelerate drw by about 35% with negligible effectiveness loss or even with effectiveness improvement. Experiments over diverse circuits and synthesis sequences also validate the generalization of the proposed framework.
Accuracy-Preserving Reduction of Sparsified Reduced Power Grids with A Multilevel Node Aggregation Scheme

Analyzing large power grids directly is computationally expensive. To tackle this issue, various power grid reduction methods, which aim to reduce original power grid to a smaller one while preserving voltage responses at port nodes as much as possible, have been proposed. In the past decade, node elimination based methods incorporated with graph sparsification have shown good performance in the reduction of modern large-scale power grids. The resulting sparsified reduced power grids enable more efficient transient analysis than simulating original power grids directly, with tolerable simulation accuracy. However, due to the large number of ports, the efficiency gained from these elimination-plus-sparsification methods can be still limited. In this work, we propose an effective node aggregation scheme to further reduce the sparsified reduced grids generated by elimination-plus-sparsification methods without increasing simulation errors. We first propose an accuracy-aware multilevel node aggregation framework with an effective scheme to determine aggregation levels automatically, which ensures simulation accuracy does not deteriorate. Then, an aggregation error induced spectral node similarity metric is proposed based on rigorous proof. Experimental results show that the proposed method can reduce the sparsified reduced grids to much smaller sizes with simulation accuracy even improved. It enables a further 3.0X speedup of transient simulation on average. Compared with the transient analysis of original power grids, the proposed method achieves an average 12.4X speedup with average voltage error below 1 mV and maximum error below 6 mV for all test cases with 1.8 V supply voltage. Finally, we demonstrate the advantages of the proposed method over other power grid reduction methods [1], [2] in terms of accuracy, model size and time for reduction.
Combined Binocular Vision and IMU Navigation Algorithm Based on Nonlinear Optimization

To address the problem that multiple interfering factors join the optimization process and lead to large errors in the optimization results, a nonlinear optimization algorithm based on the Cauchy robust kernel function is proposed to directly reduce the weight of interfering factors in the optimization solution model, and the fusion of binocular camera and IMU data is performed by the Dog-Leg algorithm. Simulation results show that the algorithm has significant advantages in localization and navigation accuracy in complex environments by comparing with ROVIO and VINS-Mono in two aspects of algorithm accuracy and algorithm performance, as well as reducing the time complexity and space complexity of the algorithm and improving the robustness of the algorithm.
Conservation of biodiversity: a useful paradigm for forest ecosystem management

The coniferous forests of the Western Hemlock Zone of western Oregon and western Washington are remarkable in the longevity and stature of their trees, long intervals between stand-replacing events, capacity to produce timber, diversity of life forms and species, and controversy over their management. The controversy is hardly new (Overton and Hunt 1974). But the current battle among those primarily interested in short-term commodity extraction, those interested in long-term support of rural communities through sustainable forestry, and those primarily interested in wildlife and recreation is unprecedented in its ecological, social, and economic impacts. We need a better approach to management of forest lands not reserved as natural areas, wildernesses, and parks. If we are to avoid past failures in conservation of natural resources (Ludwig et al. 1993), we must avoid the simplistic trap of viewing issues as timber versus wildlife as or commodity versus intangible values. We must seek reconciliation among the competitors who would use the forests. Much of forest management over the past 50 years was based on commodity-oriented objectives and a less than adequate scientific evaluation of alternative management possibilities (Miller and Seidel 1990, Curtis 1995, Curtis and Carey 1996). Objectives often were narrowly focused, and nontimber values were not considered integral to management. Recent appreciation of other aspects of forest ecosystems has led to changes in management in the Pacific Northwest that, by and large, have been based on hypotheses unsupported by controlled experimenttation, carefully formulated theoretical constructs (management pathways), or even rigorous retrospective studies. Rather, these approaches have been based on individual professional experience, personal and philosophical attitudes, and the social
Efficient Sampling and Grouping Acceleration for Point Cloud Deep Learning via Single Coordinate Comparison

With the focus on three-dimensional (3D) applications, the importance of applying deep learning to point clouds have been growing recently. It is known that mapping operations including sampling and grouping play a critical role in extracting local features in point-based deep learning models. However, the mapping operations often become bottlenecks in terms of computing times due to the repetitive comparison of distances between input points. In this paper, we analyzed the characteristics of distance distribution during sampling and grouping operations, and discovered that substantial portion of the distance comparison does not need exact 3D Euclidean distance using all three coordinates. Based on the observations, we propose a technique called single coordinate comparison which selectively determines the comparison output with 1D-distance only. We also present a hardware architecture with a distance calculator capable of handling both 3D and 1D distance. The experimental results demonstrate the effectiveness of our approach in reducing both time and energy consumption, particularly as the number of points increases.
Finite-time backstepping control for quadrotor UAV with uncertain disturbances

In this article, the finite-time backstepping method is presented for quadrotor UAV with uncertain external disturbances and model parameter uncertainties. First, a finite-time extended state observer (FTESO) is proposed, which can accurately compensate the complex unknown disturbance of the system in finite time and increase the anti-disturbance capability of quadrotor UAV. After that, the finite-time backstepping controller (FTBC) is designed to improve the control accuracy of the system and achieve the stability of attitude angle system in finite time. Finally, the effectiveness of the control methods are verified through theoretical proof and numerical simulation.
An Adversarial Active Sampling-Based Data Augmentation Framework for AI-Assisted Lithography Modeling

Lithography modeling is a crucial problem in chip design to ensure the manufacturability of chip design masks. It requires rigorous simulations of optical and chemical models that are computationally expensive. Recent developments in machine learning have provided alternative solutions in replacing time-consuming lithography simulations with deep neural networks. However, considerable accuracy drop still impede its industrial adoption. Most importantly, the quality and quantity of the training dataset directly affects the model performance. To tackle this problem, we propose a Litho-Aware Data Augmentation (LADA) framework to resolve the limited data dilemma and improve the machine learning model performance. First, we pretrain the neural networks for lithography modeling and a gradient-friendly StyleGAN2 generator. We then perform adversarial active sampling to generate informative and synthetic in-distribution mask designs. These synthetic mask images augment the original limited training dataset used to finetune the lithography model for improved performance. Experimental results demonstrate that LADA can successfully improve the model robustness and exploit the neural network capacity by narrowing the performance gap between the training and testing data instances.
Frequency-Domain Transient Electromigration Analysis Using Circuit Theory

The analysis of transient stress buildup in on-chip interconnects due to electromigration (EM) requires the solution of partial differential equations (PDEs) with appropriate boundary conditions, but prior approaches have been computationally expensive. This paper uses a stress-electrical equivalence to map the solution of the system of PDEs for a general multisegment interconnect to an RC network. For tree structures, this system is solved in linear time in the frequency domain using model order reduction (MOR) techniques. We present two MOR approaches: one that is not guaranteed to provide a stable approximant due to the presence of the mass-conservation equation, but empirically does so for a large fraction of testcases; and another that is guaranteed-stable. To achieve a guaranteed-stable solution, the approach approximates the RC circuit in a Krylov space and captures the impact of mass conservation in the form of a mass conservation excitation. However, the latter is observed to be slightly less accurate than the first approach when it does provide a solution. The method demonstrates excellent accuracy against a commercial numerical solver, and is scalable, solving transient EM analysis problems on large power grid interconnect benchmarks.
A Pavement Cracks detection algorithm based on CCA-YOLOv5s

A pavement crack detection algorithm based on CCA-YOLOv5s is proposed to address the issue of the low efficiency and accuracy of traditional pavement crack detection. Firstly, as the inadequacy of the dataset may affect the detection accuracy, we perform data enhancement in this paper. Secondly, the attention mechanism CA (Coordinate Attention) module is introduced in the YOLOv5s network backbone, which strengthens the ability of the network to extract features. Thirdly, to make the regression more accurate, use the loss function CIOU rather than the loss function GIOU as the bounding box regression loss function. The experimental results show that the Mean Average Precision (mAP) of the CCA-YOLOv5s model on the test dataset has reached 96.5%, and the detection rate is 36.7ms. Compared with YOLOv5s, the mAP is increased by 1.8%, while the detection rate is only decreased by 0.4ms. Therefore, the CCA-YOLOv5s algorithm can effectively detect pavement cracks and provides certain engineering practical significance for pavement quality monitoring and maintenance.