Redundant Space Research Articles

Coordinated base disturbance and Cartesian stiffness optimization of a cable-driven redundant space robot

Coordinated base disturbance and Cartesian stiffness optimization of a cable-driven redundant space robot

Trajectory control in redundant space robot systems: a comparative evaluation of various float mechanisms

Trajectory control in redundant space robot systems: a comparative evaluation of various float mechanisms

Accelerating one-shot neural architecture search via constructing a sparse search space

Neural Architecture Search (NAS) has garnered significant attention for its ability to automatically design high-quality deep neural networks (DNNs) tailored to various hardware platforms. The major challenge for NAS is the time-consuming network estimation process required to select optimal networks from a large pool of candidates. Rather than training each candidate from scratch, recent one-shot NAS methods accelerate the estimation process by only training a supernet and sampling sub-networks from it, inheriting partial network architectures and weights. Despite significant acceleration, the supernet training with a large search space (i.e., the number of candidate sub-networks) still requires thousands of GPU hours to support high-quality sub-network sampling. In this work, we propose SparseNAS, an approach for one-shot NAS acceleration by reducing the redundancy of the search space. We observe that many sub-networks in the space are underperforming, with significant performance disparity to high-performance sub-networks. Crucially, this disparity can be observed early in the beginning of the supernet training. Therefore, we train an early predictor to learn this disparity and filter out high-quality networks in advance. Then, the supernet training will be conducted in this space sub-space. Compared to the state-of-the-art one-shot NAS, our SparseNAS reports a 3.1× training speedup with comparable network performance on the ImageNet dataset. Compared to the state-of-the-art acceleration method, SparseNAS reports a maximum of 1.5% higher Top-1 accuracy and 28% training cost reduction with a 7× bigger search space. Extensive experiment results demonstrated that SparseNAS achieves better trade-offs between efficiency and performance than state-of-the-art one-shot NAS.

Killing them softly: China’s counterspace developments and force posture in space

ABSTRACT China is developing counterspace capabilities which may threaten US space systems. At the same time, its increasing space dependence encourages restraint in warfare. We argue that these conflicting trends have affected China’s counterspace posture in two main ways. First, China is bolstering space deterrence through offensive weapons, as well as more resilient and redundant space systems. Second, China prioritizes soft-kill counterspace capabilities that can be used earlier in a conflict with lower escalation risks. Our findings highlight how first-strike stability in space differs from robust to tenuous depending on means and targets, with soft-kill attacks being harder to deter.

AI-generated text detection and classification based on BERT deep learning algorithm

With the rapid development and wide application of deep learning technology, AI-generated text detection plays an increasingly important role in various fields. In this study, we developed an efficient AI-generated text detection model based on the BERT algorithm, which provides new ideas and methods for solving related problems. In the data preprocessing stage, a series of steps were taken to process the text, including operations such as converting to lowercase, word splitting, removing stop words, stemming extraction, removing digits, and eliminating redundant spaces, to ensure data quality and accuracy. By dividing the dataset into a training set and a test set in the ratio of 60% and 40%, and observing the changes in the accuracy and loss values during the training process, we found that the model performed well during the training process. The accuracy increases steadily from the initial 94.78% to 99.72%, while the loss value decreases from 0.261 to 0.021 and converges gradually, which indicates that the BERT model is able to detect AI-generated text with high accuracy and the prediction results are gradually approaching the real classification results. Further analysis of the results of the training and test sets reveals that in terms of loss value, the average loss of the training set is 0.0565, while the average loss of the test set is 0.0917, showing a slightly higher loss value. As for the accuracy, the average accuracy of the training set reaches 98.1%, while the average accuracy of the test set is 97.71%, which is not much different from each other, indicating that the model has good generalisation ability. In conclusion, the AI-generated text detection model based on the BERT algorithm proposed in this study shows high accuracy and stability in experiments, providing an effective solution for related fields. In the future, the model performance can be further optimised and its potential for application in a wider range of fields can be explored to promote the development and application of AI technology in the field of text detection.

High-capacity multi-MSB predictive reversible data hiding in encrypted domain for triangular mesh models

Reversible data hiding in encrypted domain (RDH-ED) is widely used in sensitive fields such as privacy protection and copyright authentication. However, the embedding capacity of existing methods is generally low due to the insufficient use of model topology. In order to improve the embedding capacity, this paper proposes a high-capacity multi-MSB predictive reversible data hiding in encrypted domain (MMPRDH-ED). Firstly, the 3D model is subdivided by triangular mesh subdivision (TMS) algorithm, and its vertices are divided into reference set and embedded set. Then, in order to make full use of the redundant space of embedded vertices, Multi-MSB prediction (MMP) and Multi-layer Embedding Strategy (MLES) are used to improve the capacity. Finally, stream encryption technology is used to encrypt the model and data to ensure data security. The experimental results show that compared with the existing methods, the embedding capacity of MMPRDH-ED is increased by 53 %, which has higher advantages.

Automatic summarization model based on clustering algorithm

Extractive document summary is usually seen as a sequence labeling task, which the summary is formulated by sentences from the original document. However, the selected sentences usually are high redundancy in semantic space, so that the composed summary are high semantic redundancy. To alleviate this problem, we propose a model to reduce the semantic redundancy of summary by introducing the cluster algorithm to select difference sentences in semantic space and we improve the base BERT to score sentences. We evaluate our model and perform significance testing using ROUGE on the CNN/DailyMail datasets compare with six baselines, which include two traditional methods and four state-of-art deep learning model. The results validate the effectiveness of our approach, which leverages K-means algorithm to produce more accurate and less repeat sentences in semantic summaries.

A data-driven LSTMSCBLS model for soft sensor of industrial process

Abstract In the chemical industry, data-driven soft sensor modeling plays a crucial role in efficiently monitoring product quality and status. Industrial data in these applications typically exhibit significant temporal characteristics, meaning that current information is influenced by data from previous periods. Effectively extracting and utilizing these temporal features is essential for achieving accurate soft sensor modeling in complex chemical scenarios. To address this challenge, this study proposes a data-driven Broad Learning System (BLS) model, which combines Long Short-Term Memory (LSTM) networks with an adaptive algorithm known as the Stochastic Configuration Algorithm (SC), referred to as LSTMSCBLS. The model operates in two stages: temporal feature extraction and final prediction. In the temporal feature extraction stage, the integration of the LSTM network with a feature attention mechanism allows for efficient extraction of temporal features from high-dimensional time-series data. In the final prediction stage, the SC is integrated into the BLS, effectively mitigating issues related to node space redundancy and the determination of the number of nodes. The effectiveness and superiority of the proposed model are demonstrated through two industrial case studies involving a debutanizer column and a sulfur recovery unit.

Anisotropic regularization for sparsely sampled and noise-robust Fourier ptychography

Fourier ptychography (FP) is a powerful computational imaging technique that provides super-resolution and quantitative phase imaging capabilities by scanning samples in Fourier space with angle-varying illuminations. However, the image reconstruction in FP is inherently ill-posed, particularly when the measurements are noisy and have insufficient data redundancy in the Fourier space. To improve FP reconstruction in high-throughput imaging scenarios, we propose a regularized FP reconstruction algorithm utilizing anisotropic total variation (TV) and Tikhonov regularizations for the object and pupil functions, respectively. To solve this regularized FP problem, we formulate a reconstruction algorithm using the alternating direction method of multipliers and show that our approach successfully recovers high-quality images with sparsely sampled and/or noisy measurements. The results are quantitatively and qualitatively compared against various FP reconstruction algorithms to analyze the effect of regularization under harsh imaging conditions. In particular, we demonstrate the effectiveness of our method on the real experimental FP microscopy images, where the TV regularizer effectively suppresses the measurement noise while maintaining the edge information in the biological specimen and helps retrieve the correct amplitude and phase images even under insufficient sampling.

Mobility-aware proactive video caching based on asynchronous federated learning in mobile edge computing systems

With the rapid advance of the Internet of Things (IoT) and the surging user-centric smart devices, video services such as short videos, real-time video streaming, and Virtual Reality (VR) gaming are emerging. Traditional cloud-centric caching is no longer able to satisfy the low-latency needs of mobile user groups. Predicting the most popular content in advance and caching it on edge devices such as small base stations (SBS) is a promising solution to alleviate network congestion and supply better Quality of Service (QoS) for mobile users. However, frequently changing popular content, limited edge caching resources, privacy of user data, and user mobility pose significant challenges to edge video caching. To tackle these challenges, we propose an efficient asynchronous federated learning (AFL)-based mobility-aware proactive video caching scheme (AF-MPVC), which significantly improves the overall network performance while preventing the risk of user data leakage. First, we model the proactive video caching problem in mobile edge computing systems and consider the impact of user mobility on AFL, representing the problem as an autoencoder (AE) model loss minimization problem. Then, a proactive video caching algorithm is proposed based on a filtering model, which uses the hidden features obtained from the trained AE model to compute the user similarity to find the popular videos that are most likely to be accessed by users and combine them with the most frequently requested videos. Meanwhile, we take into account the short duration of high mobility users staying on the current SBS and cache some videos to neighboring edge servers to have more redundant space to store more less popular videos. Finally, experiments on three real-world datasets demonstrate that the presented caching scheme outperforms other baseline caching schemes with respect to cache hit rate and latency reduction.

A model for visual evacuation of people considering the effects of gravity

This paper improves the visual change-based personnel evacuation model by considering the evacuees’ gravity. Specifically, first, the new model incorporates the gravity formula in the model’s mechanic part to consider the influence of gravity. Second, the new model involves rules for determining the visual range of personnel moving in the stairwell. Third, the proposed model investigates the influence of the angle and width of the stairwell, the number of people, and other factors during personnel evacuation under the influence of gravity. The model is developed in Python and is compared with actual results, revealing that the proposed model is more realistic considering the evacuation time compared to current models. Indeed, under a fixed number of people, when the stairwell angle is less than 34°, the evacuation time decreases as the angle increases, and when the stairwell angle exceeds 34°, the evacuation time is almost unchanged. Additionally, under a fixed number of evacuees, the evacuation time decreases as the width of the stairwell increases, and due to stairwell width space redundancy, the evacuation time tends to stabilize. The results of the new model research provide reference for the design of building safety evacuation, thereby improving the safety of buildings.

Synchronous planning of initial and target positions of redundant space manipulator based on deterministic configuration tree

Planning the operating position of redundant space manipulators (RSMs) can effectively improve the performance of manipulators while improving the quality of on-orbit servicing (OOS) is of great significance. For RSM, OOS with synchronous planning of initial and target positions (ITPs) has many combinations of positions or configurations, making it difficult to apply existing methods directly. To address this problem, this study proposes a method for synchronous planning ITPs of RSMs based on a deterministic configuration tree (DCT). First, the planning area is divided into grids, with the global performance analyzed utilizing self-motion manifolds. Then, a DCT is constructed based on the analysis results of global performance. Finally, a position planning strategy of pre-planning, rewiring, and regrowth is proposed to achieve synchronized planning of the ITP of the RSM on the DCT. This method is conducted alternately in operational space and joint space. The gridding and regrowth process in operational space avoids the considerable calculation required by gridding in high-dimensional joint space and improves the efficiency of position planning. In joint space pre-planning and rewiring, the ITP is optimized with the constraint of motion planning results while position planning is realized. The simulation experiment results of the 3-degree-of-freedom (DOF) planar RSM, 4-DOF RSM, and 7-DOF RSM indicate that the proposed method reduces time consumption by 34.81%, 18.15%, and 16.26% compared to conventional methods. Furthermore, energy consumption is reduced by 30.68%, 16.93%, and 15.64%, respectively. A typical ITPs synchronous planning task is also simulated, and the results also show that this planning method can effectively optimize the ITPs and reduce energy consumption.

IB-CBB: an improved spatial index considering intersection based on clipped bounding boxes

ABSTRACT Efficiently querying multiple spatial datasets is a challenging task in geoscience. The majority of spatial processing techniques use minimum bounding box (MBB) to approximate neighbouring spatial objects and and place them adjacent in the spatial index. However, due to the existence of redundant space in MBB of these methods, this problem can significantly reduce the query efficiency. In this paper, we propose a novel two-stage adaptive method of clipping the bounding box in spatial query, called IB-CBB (Intersection Based Clipped Bounding Boxes). The first stage employs a clipped bounding box, which records the redundant spatial spaces within the bounding box of the spatial index by calculating the clip points. As a result, the computational complexity of indexed child nodes in the query process is reduced. The second stage optimizes the above query algorithm by judging the intersection of the query box and the MBB of index node, significantly reducing the query time. Experiments demonstrate that IB-CBB outperforms the baseline method in terms of reducing the computational time.

LBO-MPAM: Ladybug Beetle Optimization-based multilayer perceptron attention module for segmenting the skin lesion and automatic localization

ABSTRACT In recent years, skin cancer has been the most dangerous disease noticed among people worldwide. Skin cancer should be identified earlier to reduce the rate of mortality. Employing dermoscopic images can identify and categorise skin cancer effectively. But, the visual evaluation is a complex procedure to be done in the dermoscopic image. However, Deep learning (DL) is an efficient method for skin cancer detection; however, segmenting the skin lesion and automatic localisation in an earlier stage is complicated. In this paper, a novel Ladybug Beetle Optimization-Double Attention Based Multilevel 1-D CNN (LBO-DAM 1-D CNN) technique is proposed to detect and classify skin cancer. To improve skin lesion type discriminability, the two types of attention modules are introduced. The Ultra-Lightweight Subspace Attention Module (ULSAM) is utilised for classifying the feature maps into different stages to validate the frequency from different image samples. However, the multilayer perceptron attention module (MLPAM) is determined to provide information regarding skin cancer classification and diminish the noise and unwanted data. To minimise data loss, it is then combined with hierarchical complementarity during classification. Second, a modified MLPAM is used to extract significant feature spaces for network learning, select the most important information, and reduce feature space redundancy. The Ladybug Beetle Optimization (LBO) algorithm provides the optimal classification solution by minimising the loss rate of DAM 1-D CNN architecture. The experimentation is conducted on three different datasets such as ISIC2020, HAM10000, and the melanoma detection dataset. The experimental results revealed that the proposed method is compared with different existing methods such as IMFO-KELM, Mask RCNN, M-SVM, DCNN-9, and TL-CNN with different datasets. These methods attained 94.56, 92.65, 90.56, 88.65, and 95.5 for the ISIC2020 dataset but the proposed method enhanced the classification performance by attaining 97.02. Also, the validation is based on metrics, namely, accuracy, precision, sensitivity, and F1-score of 97.03%, 97.05%, 97.58%, and 97.27% for a total of 500 epochs.

Partial Multilabel Learning Using Noise-Tolerant Broad Learning System With Label Enhancement and Dimensionality Reduction.

Partial multilabel learning (PML) addresses the issue of noisy supervision, which contains an overcomplete set of candidate labels for each instance with only a valid subset of training data. Using label enhancement techniques, researchers have computed the probability of a label being ground truth. However, enhancing labels in the noisy label space makes it impossible for the existing partial multilabel label enhancement methods to achieve satisfactory results. Besides, few methods simultaneously involve the ambiguity problem, the feature space's redundancy, and the model's efficiency in PML. To address these issues, this article presents a novel joint partial multilabel framework using broad learning systems (namely BLS-PML) with three innovative mechanisms: 1) a trustworthy label space is reconstructed through a novel label enhancement method to avoid the bias caused by noisy labels; 2) a low-dimensional feature space is obtained by a confidence-based dimensionality reduction method to reduce the effect of redundancy in the feature space; and 3) a noise-tolerant BLS is proposed by adding a dimensionality reduction layer and a trustworthy label layer to deal with PML problem. We evaluated it on six real-world and seven synthetic datasets, using eight state-of-the-art partial multilabel algorithms as baselines and six evaluation metrics. Out of 144 experimental scenarios, our method significantly outperforms the baselines by about 80%, demonstrating its robustness and effectiveness in handling partial multilabel tasks.

Security analysis of a reversible data hiding scheme in encrypted images by redundant space transfer

Redundant space transfer (RST) encryption can effectively enhance the security and embedding capacity of reversible data hiding in encrypted images. To demonstrate the security risks of RST encryption in the cloud environment, according to the known plaintext image and marked encrypted image(MEI) pairs, this paper proposes a known plaintext attack (KPA) based on hamming-weight of bit block. The proposed KPA method includes two stages. The first stage is to find the correct ciphertext image. For possible block sizes, all possible ciphertext images of MEI are reconstructed by deleting the embedded secret data. The Hamming weight image (HWI) of plaintext and all possible ciphertext images is calculated in m×8n domain (m, n is the image size). When the histogram distance between the plaintext HWI and a possible ciphertext HWI is the minimum, the correct ciphertext image is found. The second stage is to estimate the permutation keys. The bit block characteristic matrix (BBCM) is defined by using that the bit values of plaintext and ciphertext remain unchanged in the process of RST encryption. Based on the intersection of BBCM’s index set, the block permutation key is directly estimated. Then, the bit plane permutation key in each image block is further estimated. Experimental results show that for RST encryption, when the block size is not less than 3 × 3, the block permutation and bit plane permutation key can be estimated in less than 3 min by using one pair of plain–ciphertext. The estimation accuracy of block permutation key is more than 50%, and the estimation accuracy of bit plane permutation key is 10% for Qin’s RST algorithm, most of the content information of the ciphertext image is leaked.

Comprehensive research and analysis on obstacle–singularity–joint limit avoidance of redundant robot

Redundant robots can complete other tasks while performing main task and have excellent motion performance. Although redundant robots have redundancy characteristics, they may still fall into singular configuration or cannot overcome joint limits due to the limitation of their own structure and motion law. Besides, currently, most of the research mainly focus on obstacle avoidance at the end of the robot. So comprehensive study is conducted on avoidance of obstacle, singularity, and joint limit of redundant robot. Firstly, the kinematics model of robot is obtained by D-H matrix method. Space vector distance method is proposed to calculate the position relationship between robot and obstacle, and potential function is constructed. Based on gradient projection method, using the damped generalized inverse and the weighted norm method, the mathematical models of obstacle avoidance, singularity avoidance, and joint limit avoidance are constructed. The simulation results show that the robot can avoid the obstacle in three-dimensional redundant space. At the same time, manipulability and minimum singular value of the robot are always positive. After adding the function of avoiding joint limit, during the whole control process, the motion range of joint angle is reduced and the angular velocity is relatively small, which ensure the motion stability of robot. Namely, the singular configuration and joint limit of robot are avoided. The research is of great significance for practical application of redundant robot.

Latent Low-Rank Representation With Weighted Distance Penalty for Clustering.

Latent low-rank representation (LatLRR) is a critical self-representation technique that improves low-rank representation (LRR) by using observed and unobserved samples. It can simultaneously learn the low-dimensional structure embedded in the data space and capture the salient features. However, LatLRR ignores the local geometry structure and can be affected by the noise and redundancy in the original data space. To solve the above problems, we propose a latent LRR with weighted distance penalty (LLRRWD) for clustering in this article. First, a weighted distance is proposed to enhance the original Euclidean distance by enlarging the distance among the unconnected samples, which can enhance the discriminitation of the distance among the samples. By leveraging on the weighted distance, a weighted distance penalty is introduced to the LatLRR model to enable the method to preserve both the local geometric information and global information, improving discrimination of the learned affinity matrix. Moreover, a weight matrix is imposed on the sparse error norm to reduce the effect of noise and redundancy. Experimental results based on several benchmark databases show the effectiveness of our method in clustering.

A multi-objective robust evolutionary optimization approach applied to the multivariate helical milling process of super duplex steel

This work presents an approach for multivariate modeling and multi-objective robust evolutionary optimization for processes presenting as inputs control and noise variables and as outputs a set of correlated responses regarding process performance and product quality. Factor analysis is applied to obtain a transformed output set that is uncorrelated and retains most part of the information of the original outputs. Models that are robust regarding noise variables relating this transformed output set with the control variables are obtained through robust parameter design. Response models in the function of process and noise variables are obtained through the least squares method. Mean and variance models as a function of process variables are obtained through error propagation. Robust mean square error models are obtained to model the bias and variance of each latent variable. Finally, multi-objective evolutionary optimization is performed. Three evolutionary algorithms were applied and compared through hypervolume. The approach is applied in the helical milling of super duplex stainless steel UNS S32760. Tool overhang length, hole depth, and lubri-cooling flow rate were set as noise factors. Cutting forces were monitored, while roughness and roundness were measured to evaluate process performance and product quality. The three factors, obtained to represent the six outputs, guaranteed dimensionality reduction and minimized the redundancy in output space. The mean square error models obtained to these three factors as a function of control variables minimize both bias and variance regarding noise factors. The AGE-MOEA multi-objective evolutionary algorithm obtained the best performance considering the hypervolume. Some solutions with high trade-off were selected through pseudo-weights to aid the decision-making.

Novel ground microgravity experiment system for a spacecraft-manipulator system based on suspension and air-bearing

With the development of aerospace technology, the on-orbit activities of large spacecraft are increasing day by day, and accordingly the amount of space debris is increasing. Therefore, a large spacecraft-manipulator system is needed to perform on-orbit service, such as, assembling large spacecraft and removing space debris. Before launching, the trajectory optimization and control algorithm of the spacecraft-manipulator system have to be verified on the ground microgravity experiment system since the complex systems are prone to failure. In this study, a ground microgravity experiment system based on suspension and air-bearing is proposed to provide a long-term and stable microgravity environment for the large spacecraft-manipulator system. A suspension gravity compensation system with multiple suspensions is designed for the redundant space manipulator that moves in three dimensions, and a suspension tracking smooth sliding mode control algorithm based on hierarchical control is presented. Then, the coupling-reduction strategy based on equivalent spring-mass is presented to reduce the electromechanical coupling of the suspension gravity compensation system and the space manipulator. For the roll and pitch motion of the large-mass/inertia spacecraft, the target spacecraft simulator with a 2-degree-of-freedom rotating mechanism is designed. Some air-bearings are installed on the base of the chaser spacecraft simulator and target spacecraft simulator, which produce an air gap to enable the frictionless plane motion of the spacecraft. Finally, the target spacecraft simulator transposition experiment was carried out. The results show that the residual torque of the space manipulator is very small, and the ground microgravity experiment system compensates the gravity of the spacecraft-manipulator system with high precision.