Miracle-dz/UniFAR-FTUs huggingface dataset:
Full-Pose Trajectory Tracking of Overactuated Multi-Rotor Aerial Vehicles With Limited Actuation Abilities

This letter presents a novel optimization-based full-pose trajectory tracking method to control overactuated multi-rotor aerial vehicles with limited actuation abilities. The proposed method allocates feasible control inputs to track a reference trajectory, while ensuring the tracking of the reference position, and while tracking the closest feasible attitude. The optimization simultaneously searches for a feasible trajectory and corresponding feasible control inputs from the infinite possible solutions, while ensuring smooth control inputs. The proposed real-time algorithm is tested in extensive simulation on multiple platforms with fixed and actuated propellers. The simulation experiments show the ability of the proposed approach to exploit the complex set of feasible forces and moments of overactuated platforms while allocating smooth feasible control inputs.
Use of computer graphics methods for efficient stray light analysis in optical design

The problems of using stray light visualization for the effective analysis and design of complex optical systems are considered. Examples of real applications are given where the use of the light propagation criterion in conjunction with the visual representation of the ray path makes it possible to effectively analyze complex optical design problems. The suggested solution allows not only to visualize source of the stray light in the optical system but alto to render the image on the detector taking into account diffuse scattering on all illuminated surfaces.
Multi-objective Optimization of Electric-gas-thermal Integrated Energy System Based on Non-dominant Sorting Genetic Algorithm

With the development of the times, human society has put forward higher requirements on how to dispatch energy. In order to consider economic factor and environment factor, this paper proposed a dispatching plan with multi-objections promotion based on integrated energy system (IES). This system is composed of three parts, energy input part, energy transform part and energy output part. Based on the system above, economic objective function and environmental objective function are constructed. And then, we use non-dominant sorting genetic algorithm (NSGA-II) with elite strategy to calculate the pareto front of objection functions. As a result, economic factors and environmental factors are considered at the same time, energy utilization efficiency is the highest. And the rationality of the model is verified by case.
TGNet: Learning 3D Shape from Sparse and Incomplete Point Cloud

Learning accurate 3D shapes from sparse and incomplete point clouds is challenging and meaningful, on account that the point clouds with low resolution always lack representative and informative details. This paper presents a novel deep auto-encoder called TGNet, which is formulated based on a tree-based generative adversarial network (GAN), to address self-supervised learning tasks on the point cloud with low sparsity. On the encoder side, we employ a PointNet-based framework to intensively capture the global representations. To better infer the spatial information in latent space, we propose a spectral graph learning module in with due consideration to graph topology. Further, we present a new loss that combines Wasserstein metric and multi-resolution Chamfer distance to better estimate global 3D geometry and structural details. The proposed TGNet achieves state-of-the-art performance for various point cloud learning tasks. Qualitative and quantitative evaluations demonstrate the novelty of the proposed model.
Fabric fault and Extra thread Detection Using Convolutional Neural Network

A planar substance made of textile fibers is called fabric. The main reason why defective fabrics are produced is loom malfunctions. A specialized computer vision system called a fabric inspection system is used to find fabric flaws to ensure product quality. In this paper we classify the defect by using Convolutional Neural Network. Utilizing a special type of class-based ensemble convolutional neural network architecture, the defect recognition system is built. The experiment is carried out using several textile fiber kinds. There is four layers in CNN to classify the defect that is Convolution, Relu, Pooling, Fully Connected layer. We tested several well-known CNN architectures, such as Inception, ResNet, VGG, MobileNet, DenseNet, and Xception to classify the defect. Finally, we demonstrate the result by classification and how accurately the defect identified.
Store and Restore Delay Reduction Techniques of Non-volatile SRAM cells

The incorporation of memristor with static random access memory (SRAM) cell, known as non-volatile SRAM (nvSRAM) cell, not only introduces non-volatile feature in it but also enhances stability and reduces power consumption. With the development in technology, this has become a mainstream development focus. During the analysis of nvSRAM cell, the store and restore delays become a major concern as the longer time duration leads to memory failure. Limited work is done to address this problem, which increases complexity during non-volatile operation. In this work, different techniques are introduced to reduce the store and restore delays. These delay reduction techniques deal with the control signals required to perform non-volatile operation. The maximum reduction of 19.41% is achieved in the store delay, while the restore delay is reduced by 21.74%. The SPICE simulations are carried out using 32nm PTM CMOS model at Vdd=1.0V.
Internal wave generation by convection in water. Part 2. Numerical simulations

Daniel Lecoanet1,2†, Michael Le Bars3,4, Keaton J. Burns5,2, Geoffrey M. Vasil6, Benjamin P. Brown7,2, Eliot Quataert1 and Jeffrey S. Oishi8,9,2 Department of Astrophysics and Theoretical Astrophysics Center, University of California, Berkeley, CA 94720, USA Kavli Institute for Theoretical Physics, University of California, Santa Barbara, CA 93106, USA CNRS, Aix-Marseille Universite, Ecole Centrale Marseille, IRPHE, Marseille, 13013, France Department of Earth, Planetary, and Space Sciences, University of California, Los Angeles, California 90095, USA Department of Physics, Massachusetts Institute of Technology, Cambridge, MA 02139 USA School of Mathematics & Statistics, University of Sydney NSW 2006, Australia Laboratory for Atmospheric and Space Physics and Department of Astrophysical & Planetary Sciences, University of Colorado, Boulder, CO 80309 Department of Physics, Farmingdale State College, Farmingdale, NY 11735 Department of Astrophysics, American Museum of Natural History, New York, NY 10024
Low-Voltage Distribution Network Topology Identification Method Based on Segmented Current Features and CNN-LSTM Deep Learning

The topology of low-voltage distribution network is of great significance for line loss analysis, fault detection, power theft warning and demand response. To address the current prob-lem of missing topology of low-voltage distribution network or the discrepancy between user information and actual, a topology identification method of low-voltage distribution network based on segmented current features and CNN-LSTM deep learning is proposed. Firstly, we analyze the low-voltage distribution network architecture and the similar mechanism of current time series, secondly, we propose a CNN-LSTM based topology identification method, which combines the current data segmentation to gen-erate a data set, combines the spatial feature extraction ability of Convolution Neural Network(CNN) and the temporal feature extraction ability of Long Short-Term Memory(LSTM) to form a CNN-LSTM hybrid neural network model, and uses the neural network to classify the samples to get the topology relationship. Finally, based on the actual measurement data in the low-voltage distribution network, the results demonstrate that the method can quickly and accurately identify the topological relationships, and the accuracy of topological identification reaches 100%.
FEEP: Functional ECO Synthesis with Efficient Patch Minimization

Functional engineering change order (ECO) has been an essential process in modern complex integrated circuit design. Finding a high-quality circuit patch efficiently has long been a challenge. This paper proposes FEEP, an automatic and efficient synthesis-based functional ECO method. Structural pruning and stratified searching techniques are proposed to minimize search space without extra logical equivalence checks. Moreover, we propose a machine-learning-based two-stage patch size predictor that assists in predicting patch quality. Experimental results show that our algorithm can efficiently search and produce high-quality patches under various test cases.
Optimized Mutation of Grey-box Fuzzing: A Deep RL-based Approach

As a vulnerability discovery technique, fuzzing has been widely used in the field of software test in the past years. Traditional fuzzing has several drawbacks, including poor efficiency, low code coverage, and a high dependence on expert experience. By introducing the deep reinforcement learning technique, one can train the mutator of the fuzzer to move in a desired direction, such as maximizing code coverage or finding more code paths. This paper proposes a reinforcement learning-based fuzzing method to enhance the code coverage and explore potential code vulnerabilities. First, the concept of the input field is introduced to the seed file, reducing invalid operations by marking whether each byte of the seed file is a valid byte. Then, we optimize mutation by modeling the grey-box fuzzing as a reinforcement learning problem and training mutator's behavior on test cases. By observing the rewards caused by mutating with a specific set of actions performed on an initial program input, the fuzzing agent learns a policy that can next generate new higher-reward inputs. Finally, experimental results show that the proposed deep reinforcement learning-based fuzzing method outperforms the baseline random fuzzing algorithms.
Solitary wave run-up: wave breaking and bore propagation

ABSTRACT The run-up features of breaking solitary waves over a plane sloping beach are described using both high-speed images and a systematic data analysis. Experiments were conducted in a smooth, rectangular and prismatic channel. The main hydraulic parameters investigated include relative wave height, stillwater depth, and channel bottom inclination. The various types of wave breaking are discussed, including the plunging, spilling, and surging breaker types in addition to the amplitude evolution due to wave shoaling. Furthermore, the generation and propagation of bores is examined. A detailed data analysis, based on hydraulic similitude, allows for deriving mathematical expressions that are considered to represent the observed wave features. The research includes film-like image series describing the wave run-up features, allowing for a description of the flow complexities observed and the beauties associated with wave run-up. This research excludes a numerical analysis, given the complexities due to turbulence generation and air entrainment.
Short Review on Machine Learning Optimization Methods in Surface Mounted Electronics Assembly Technologies

Machine learning (ML) is increasingly adopted to support the production process of surface-mounted electronics assembly technology (SMT). In the literature, extensive research was carried out to enhance automated optical inspection (AOI) by applying ML algorithms, like artificial neural networks. However, predicting production defects based on data from the sensors built into the production equipment is still not that widespread. Furthermore, applying ML-based approaches may allow the optimization of production control by fine-tuning process parameters of stencil printing or component placement, for example. Many research papers were surveyed to identify the strong and weak points of current approaches in this area. A clear overview was given to aid the proper classification of recent results and the applicability of ML-based methods in SMT. Our results contain not only the review of multiple approaches, but a comparison of the numerical results, the complexity of the experimental environment and the extent of applicability are also included.
Research on Traffic Signal Light Recognition Method in Complex Scene

Aiming at the problem of poor real-time performance and low recognition rate of traffic light recognition methods in complex scenes, a traffic light recognition method for complex scenes is studied. Firstly, the bilateral median filter is used to suppress the noise in the complex background, and the image with low illumination is enhanced; Then, the improved Scharr operator is used for edge segmentation, and the interference region is removed by mathematical morphology processing and setting double threshold, and the accurate location of signal lamp is realized by region analysis algorithm; Finally, according to the color histogram of Cr component in YCbCr color space, the threshold range for judging color is set, and according to the shape characteristics of signal lamps, the discrimination conditions of shape or number is set. Finally, the accurate recognition of traffic signal lamps is realized. The experimental comparative analysis shows that for the traffic light recognition of complex scenes such as cloudy, rainy, snowy and foggy days, the average recognition accuracy of this method is 98.8%, and the average running time of the algorithm is 0.8s. The recognition rate is greatly improved, and the traffic light can be recognized quickly and accurately. Therefore, this method has good robustness and real-time performance.
Uncovering Archaeological Sites in Airborne LiDAR Data With Data-Centric Artificial Intelligence

Mapping potential archaeological sites using remote sensing and artificial intelligence can be an efficient tool to assist archaeologists during project planning and fieldwork. This paper explores the use of airborne LiDAR data and data-centric artificial intelligence for identifying potential burial mounds. The challenge of exploring the landscape and mapping new archaeological sites, coupled with the difficulty of identifying them through visual analysis of remote sensing data, results in the recurring issue of insufficient annotations. Additionally, the top-down nature of LiDAR data hinders artificial intelligence in its search, as the morphology of archaeological sites blends with the morphology of natural and artificial shapes, leading to a frequent occurrence of false positives. To address this problem, a novel data-centric artificial intelligence approach is proposed, exploring the available data and tools. The LiDAR data is pre-processed into a dataset of 2D digital elevation images, and the known burial mounds are annotated. This dataset is augmented with a copy-paste object embedding based on Location-Based Ranking. This technique uses the Land-Use and Occupation Charter to segment the regions of interest, where burial mounds can be pasted. YOLOv5 is trained on the resulting dataset to propose new burial mounds. These proposals go through a post-processing step, directly using the 3D data acquired by the LiDAR to verify if its 3D shape is similar to the annotated sites. This approach drastically reduced false positives, attaining a 72.53% positive rate, relevant for the ground-truthing phase where archaeologists visit the coordinates of proposed burial mounds to confirm their existence.
A Control-Based Model of IPMSM with Hysteresis Current Control PWM

This paper presents an electric circuit model of an IPMSM which is based on the 90-degree torque angle vector control method. The electrical and mechanical characteristic equations of an IPMSM are modeled with one electric circuit. The model is simple yet accurate. It is tested for speed control with the hysteresis current control pulse width modulation (HCC PWM) technique. This method of controlling the current is one of the most popular methods due to its simplicity and ease of implementation. The HCC PWM is well explained in this paper using op-amps and SR flip flops. The motor model and its control are simulated in Power Simulation environment and show good and fast performance in tracking the commanded speed and load torque.
Time Series Data Augmentation Classifier for Industrial Process Imbalanced Fault Diagnosis

Fault classification is a common problem in industrial fault diagnosis. Usually, classifiers are built assuming an equal amount of data across different classes. However, the amount of normal data and fault data collected from industrial processes is often imbalanced. Intrinsically, the fault classification is also an imbalanced data classification problem. To address this issue, data augmentation methods can be used to effectively generate more data and achieve data balance. While the performance of classification is greatly influenced by the generated data. The quality of the generated data can greatly impact the classification performance. To ensure the stability of the generated data, this paper extends the generation of single data to the generation of time series data using a time variational autoencoder. Using the generated time series data, a new classifier called the Time Series Data Augmentation Classifier (TSDAC) is proposed to solve the imbalanced fault classification problem. After that, the TSDAC is applied to Tennessee Eastman (TE) benchmark process. The results show that the TSDAC is recommended for imbalanced fault classification.
LOWER BODY EXOSKELETON DESIGN AND CONTROL

This research focuses on the development and refinement of a motorized lower-body exoskeleton system for the recovery of astronauts after missions in space. The exoskeleton is designed for strength assistance and rehabilitation of the lower body. It was developed successfully within a budget of only $4000. The exoskeleton system consists of angle encoders, force sensors, distance sensors, spring-loaded supports, and motorized actuators. The exoskeleton supplies a force of 35lbs (155N) per leg, which provides support during locomotion. It is adjustable to be worn over clothes and has been tested with individuals of various body types.
Multi-scale Heterogeneous Graph Attention Network for Prison Term Prediction

Legal judgment prediction (LJP) is one of the most critical tasks in legal artificial intelligence. One of the most important subtasks is the prison term prediction, which aims to predict the term of the penalty based on the documents of the criminal case. Existing methods consider the problem of the prison term prediction either as a pure text classification problem or a pure text regression problem. The drawback of such approaches is that the accuracy of the prediction is largely affected by the imbalanced data which is due to the complicated distribution of the prison terms. Moreover, most existing methods fail to use the prior legal knowledge or fail to have a better representation of it that may enhance the interpretability of the methods. To address these issues, we construct a heterogeneous graph attention network (HGAT) with a multi-scale structure for the prison term prediction. Our method first takes sentencing elements as the input, which can be considered as the summaries of the information of the legal documents by legal experts. We then use the graph network as the basic module to represent the sentencing elements and to obtain the relationships between the groups of the sentencing elements. Most importantly, we propose a multi-scale structure, which essentially separates the prison term prediction problem into a classification problem of the time intervals and a regression problem of the accurate term. Since this structure decomposes the original complicated distribution of the prison terms into simple distributions, it effectively promotes the learning ability of the regression models and consequently improves the overall performance of the prediction. We conduct a series of experiments on the dataset of the crime of fraud in mainland China. The results demonstrate that our model can outperform most of the baselines and generate good robustness. In addition, in the classification problem, we find a good legal interpretation for the labels of the intervals when clustering the prison terms. This may enhance the interpretability of our method in the eyes of legal practitioners.
The Impact of Total Product Purchase Limit on Consumer Purchase Intention in E-business

Currently, limited purchase is widely used in various online promotional activities in e-business. Previous studies have found that limited quantities often enhance consumers’ desire to buy goods. In fact, with the frequent occurrence of promotional activities, when the number of purchase restrictions is large, it may be more able to stimulate consumers’ willingness to purchase. The existing research has not been able to conduct a comparative study on the amount of total purchase restrictions. Therefore, this study introduces perceived sincerity and perceived scarcity to explore the impact mechanism of the total amount of product purchase limit on consumers’ purchase intention. Through online experimental research, we find that: the total amount of product purchase limit has a significant positive impact on consumer purchase intention; Compared to the case where the limited purchase quantity is small, when the limited purchase quantity of a product is large, the consumer’s purchase intention is stronger; Perceived sincerity and perceived scarcity play a mediating role in the impact of total purchase limit on consumers’ purchase intentions. The results of this study enrich the research on online limited sales promotion, and provide reference suggestions for enterprises and businesses to plan promotional activities in e-business.
Indoor Localization of the Radio Devices Based on Channel State Information Using a Restricted Boltzmann Machine

The paper proposes an algorithm for determining the distance between radio devices based on data on channel state information for building systems for indoor localization of the radio devices using a restricted Boltzmann machine (RBM). As input data, Channel State Information (CSI) is used - information about the values of the amplitudes and phases of the signal during the interaction of the transmitter and receiver. The determination of the measure of compliance with the current and reference data is carried out on the basis of the L1 measure. The data used in the work was collected during the practical implementation of the proposed solutions at an industrial enterprise. An error matrix is used to analyze the accuracy of determining the distance between radio devices. The conducted experimental studies have shown the possibility of determining the distance between routers at distances up to 4 meters with an average accuracy of 93.7%.