Redundancy Problem Research Articles

User‐optimized data transmission scheduling based on edge community service in opportunistic social network

In order to improve transmission performance, opportunistic social networks are widely used in 5G mobile network communication for network congestion. However, transferring large amounts of data in an instant will lead to data redundancy problems, which will affect the transmission quality. The authors propose a method called user-optimized data transmission scheduling based on edge community service in opportunistic social networks (ECSUO). This method constructs the edge community service model by introducing mobile edge computing into the opportunistic social network. Then several scheduling factors are set to coordinate the dynamic priority assessment. The simulation results show that ECSUO performs better than other classical opportunistic complex network algorithms in a variety of transmission quality evaluation indexes, and this method also has good applicability when applied to different transmission models.

The legacy of Gerald L. Gottlieb in human movement neuroscience.

In this paper, we review the legacy of Gerald (Gerry) Gottlieb in various fields related to the neural control of human movement. His studies on the myotatic (stretch) reflex and postmyotatic responses to ankle joint perturbations paved the way for current explorations of long-loop reflexes and their role in the control of movement. The dual-strategy hypothesis introduced order into a large body of literature on the triphasic muscle activation patterns seen over a variety of voluntary movements in healthy persons. The dual-strategy hypothesis continues to be important for understanding the performance of subjects with disordered motor control. The principle of linear synergy (covariance of joint torques) was an attempt to solve one of the notorious problems of motor redundancy, which remains an important topic in the field. Gerry's attitude toward the equilibrium-point hypothesis varied between rejection and using it to explore patterns of hypothetical control variables and movement variability. The discovery of reciprocal excitation in healthy neonates fostered other studies of changes in spinal cord physiology as motor skills develop. In addition, studies of people with spasticity and the effects of treatment with intrathecal baclofen were crucial in demonstrating the possibility of unmasking voluntary movements after suppression of the hyperreflexia of spasticity. Gerry Gottlieb contributed a significant body of knowledge that formed a solid foundation from which to study a variety of neurological diseases and their treatments, and a more comprehensive and parsimonious foundation to describe the neural control of human movement.

Mine-Microseismic-Signal Recognition Based on LMD–PNN Method

The effective recognition of microseismic signal is related to the accuracy of mine-dynamic-disaster precursor-information processing, which is a difficult method of microseismic-data processing. A mine-microseismic-signal-identification method based on LMD energy entropy and the probabilistic neural network (PNN) is proposed. First, the Local-Mean-Decomposition (LMD) method is used to decompose the mine microseismic signal. Considering the problem of vector redundancy, combined with the correlation-coefficient method, the energy entropy of the effective product-function component (PF) is extracted as the feature vector of mine-microseismic-signal classification. Furthermore, the probabilistic neural network (PNN) is used for learning and training, and the blasting-vibration signal and the coal–rock-mass-rupture signal are effectively identified. The test results show that the recognition accuracy of the PNN is up to 90%, the calculation time and classification effect of the PNN are better, and the recognition accuracy is increased by 15% and 7.5%, respectively, compared with the traditional PBNN and GRNN. This method can accurately and effectively identify the microseismic signals of mines and has good generalization performance.

Design and Optimization of Motion Training System Assisted by Human Posture Estimation Algorithm

With the rapid development of computer technology and electronic information technology, the sports training system no longer depends on the traditional algorithm for operation support, and various advanced posture algorithms are emerging. At the same time, it also further optimizes the intelligence and accuracy of the sports training algorithm. As an advanced algorithm combined with virtual reality technology, human posture estimation algorithm plays an obvious role in optimizing the effect of sports training. This paper will design a motion training system based on the optimized and improved human posture trajectory algorithm, use the depth image correlation theory to solve the problem of non-Gaussian noise crosstalk in the depth image of the traditional human posture algorithm in principle, improve the accurate feature extraction of the depth image by the algorithm, and solve the problem of human feature redundancy, so as to further improve the accuracy of the establishment of a single human model; on the problem of multi-person posture estimation algorithm, this paper proposes a high-resolution multi-person posture high-precision network model and adds the focus mechanism. Based on this, this paper realizes the high-precision and high-speed modeling of multi-person posture, so as to provide an accurate model for the multi-person function of sports training system and improve the efficiency of the algorithm. In the experimental part, this paper takes tennis as a typical case to design the sports training system and experiments based on the system designed in this paper. The experimental results show that the system under the proposed algorithm has obvious advantages in accuracy and training effect.

Distributed event-triggered formation control for multi-AUV system via asynchronous periodic sampling control approach

Aiming at the problems of high communication pressure and information redundancy of the multi-AUV system in the marine environment, this paper adopts a distributed event-triggered formation control method via asynchronous periodic sampling control approach, which realizes the discrete leaderless multi-AUV distributed event-triggered formation control. Compared with the common synchronous periodic sampling mechanism, the asynchronous periodic sampling method is more suitable for the discreteness and practicability of the multi-AUV operation process. As a relatively novel multi-agent control method, the key to the event-triggered mechanism is how to accurately determine the time when communications occur and when to update the controller. Besides, this article fully considers the position/velocity states under the two-layer independent communication topology suitable for AUV, the theory of row random matrix is used to analyze the convergence of system, and the sufficient conditions for stability are obtained. Finally, simulation experiments prove that the formation under the event-triggered mechanism will converge stably without Zeno behavior.

Perceived Integrity of Distributed Streaming Media Based on AWTC-TT Algorithm Optimization

With the development of economy, more and more attention has been paid to the monitoring system, which provides a reliable and powerful guarantee for people’s daily life, property security, and national security. The intelligent video surveillance introduces computer vision-related technologies into traditional video surveillance and realizes the analysis and understanding of video data without artificial dependence to obtain valuable target information in the perceived video data. On this basis, functions such as abnormal event monitoring and real-time alarm are realized. Distributed streaming media monitoring has changed the manual-based monitoring and content analysis modes of traditional monitoring, but the high-complexity calculations such as motion estimation and motion compensation in the encoding process increase the burden of monitoring and sensing equipment. Especially with the development of wireless multimedia technology, the traditional video coding has been unable to meet the requirements of monitoring and sensing equipment in the monitoring system based on wireless technology. This paper proposes an adaptive weighted tensor completion algorithm to complete the repair of streaming media data perceived by ordinary sensing devices. In the proposed algorithm, considering the unbalanced information distribution and data redundancy problems that may exist in the data, the tensor data is adjusted according to the approximate solution algorithm to obtain tensor data that only retains important information and the information distribution is more balanced and reasonable. In the iterative solution process, in order to better map the impact of each dimension of data in the repair process, an adaptive weighting mechanism is proposed according to the data characteristics to obtain the corresponding weight value of each dimension of data. Finally, the proposed approximate tensor solving algorithm and adaptive weighting mechanism are applied to a simple low-rank tensor completeness algorithm based on tensor columns to form the algorithm of this paper, and it is used to repair perceptual streaming media data with data missing problems. The experimental results show that the algorithm in this paper can improve the perceived streaming media data quality by 3% based on the known data information and maintain an advantage of 2% in average processing time. It avoids the replacement of sensing equipment and also provides data quality assurance for subsequent sensing streaming media content analysis. It has certain research significance for the development of monitoring system with artificial intelligence management for target perception and tracking.

Remaining useful life prediction for rolling bearings using multi-layer grid search and LSTM

Estimating the remaining useful life (RUL) of equipment is critical for ensuring the safe operation of machinery and reducing maintenance losses. For the existing RUL prediction, the problem of data redundancy and initial prediction time dramatically affects the prediction results. Therefore, this paper proposes a long short-term memory network (LSTM) RUL prediction algorithm that is based on multi-layer grid search (MLGS) optimization. This method integrates feature data and optimizes network parameters to ensure accuracy and effectively predict the non-stationary degradation of the bearing. Firstly, this paper uses a data fusion method to extract low dimensional feature vectors of running data, and the multi-feature fusion is performed to obtain the principal component index. Then, the MLGS is used to optimize the network overparameters. The effect of the initial measurement time on prediction results can be reduced, and the calculation speed can be improved. Finally, the IMS data set of NASA is used for verification and comparative testing. The test results show that the proposed RUL prediction algorithm can effectively reduce the influence of the initial prediction time on the prediction accuracy compared with other prediction methods.

Multi-scale redistribution feature pyramid for object detection

Many feature pyramid models now use simple contextual feature aggregation, which does not make full use of the semantic information of multi-scale features. Therefore, Multi-scale Redistribution Feature Pyramid Network (MRFPN) is proposed. In order to strengthen feature fusion and solve the two problems of feature redundancy and high abstraction, modified-BiFPN is designed. The features output by the modified-BiFPN module are semantically balanced through the balanced feature map, so as to alleviate the semantic differences between multi-scales. Then a new channel attention module is proposed, which realizes the multi-scale association of the feature information fused to the balanced feature map. Finally, a new feature pyramid is formed through the residual edge for prediction. MRFPN have been evaluated on PASCAL VOC 2012 dataset and MS COCO dataset, which has higher detection accuracy compared with other state-of-the-art detectors.

Application of image technology to simulate optimal frequency of automatic collection of volumetric soil water content data

Volumetric soil water content (VSWC) monitoring is an important aspect of environmental monitoring of farmland. Accurate and real-time determination of the VSWC is important for crop drought stress diagnosis and smart irrigation. Image technology is commonly used in agricultural information technology. Based on image technology, we simulated the optimal frequency of the sensors to automatically collect VSWC data, thereby solving the problems of data redundancy and data analysis difficulties in real-time monitoring. In this study, a cotton field under mulched drip irrigation in 2018 was utilised as the research subject, 5TE sensors were arranged on the soil profile of the cotton field using the "grid method", and Voxler and Surfer software was used to model the VSWC data and draw contour maps. Image processing technology (image greyscale and image similarity comparison) was employed to determine the image algorithm suitable for contour map pre-processing and the best time period for VSWC monitoring. These results indicated that the contrast-limited adaptive histogram equalisation (CLAHE) greyscale algorithm is a suitable pre-processing algorithm for processing contour maps using image processing technology, and the best 5TE sensor data monitoring time period is every 10 h. This conclusion provides a theoretical reference for VSWC monitoring and water management in production.

Prediction of cement specific surface area in the cement mill grinding process: A method combining dual-frequency principal component analysis and extreme gradient boosting

The cement specific surface area is an important indicator of cement quality. The accurate prediction of the cement specific surface area aims to guide operators to control the cement grinding process to improve product quality while reducing system energy consumption. However, due to the complexity of the cement grinding process, the process variables have coupling, time-varying delay, nonlinear characteristics, and different sampling frequency. Herein, we proposed the specific surface area prediction model, which combined dual-frequency principal component analysis and extreme gradient boosting (DF-PCA-XGB). In order to solve the problem of difficulty in modeling due to different sampling intervals of related data, this paper analyzes the low-frequency sampling data and high-frequency sampling data under multiple working conditions, and establishes prediction models respectively. Aiming at the data redundancy problem of high-frequency and low-frequency variable data in the introduced time window, a method based on the combination of principal component analysis (PCA) and extreme gradient boosting (XGB) cross-validation is proposed to reduce data redundancy while retaining most of the characteristics of the data. The final specific surface area prediction results were obtained by weighting the high-frequency data model and the low-frequency data model. The simulation results showed that the prediction method in this paper can improve the prediction accuracy of the specific surface area of the finished cement product under multiple working conditions with high stability and has promising application in the cement manufacturing process.

Network Intrusion Detection Model Based on CNN and GRU

A network intrusion detection model that fuses a convolutional neural network and a gated recurrent unit is proposed to address the problems associated with the low accuracy of existing intrusion detection models for the multiple classification of intrusions and low accuracy of class imbalance data detection. In this model, a hybrid sampling algorithm combining Adaptive Synthetic Sampling (ADASYN) and Repeated Edited nearest neighbors (RENN) is used for sample processing to solve the problem of positive and negative sample imbalance in the original dataset. The feature selection is carried out by combining Random Forest algorithm and Pearson correlation analysis to solve the problem of feature redundancy. Then, the spatial features are extracted by using a convolutional neural network, and further extracted by fusing Averagepooling and Maxpooling, using attention mechanism to assign different weights to the features, thus reducing the overhead and improving the model performance. At the same time, a Gated Recurrent Unit (GRU) is used to extract the long-distance dependent information features to achieve comprehensive and effective feature learning. Finally, a softmax function is used for classification. The proposed intrusion detection model is evaluated based on the UNSW_NB15, NSL-KDD, and CIC-IDS2017 datasets, and the experimental results show that the classification accuracy reaches 86.25%, 99.69%, 99.65%, which are 1.95%, 0.47% and 0.12% higher than that of the same type of CNN-GRU, and can solve the problems of low classification accuracy and class imbalance well.

A pattern-first pipeline approach for entity and relation extraction

Entity-relation extraction is the task of extracting entities and their semantic relations from a piece of unstructured text. In recent studies, Machine Reading Comprehension (MRC) based methods have been applied to this task and achieved significant results. As a pipelined approach, these methods always extract head entities first, and then identify related tail entities by enumerating each relationship. These entity-first methods will lead to the entity redundancy problem. They also suffer from the error propagation issue, which is an inherent issue of the multi-step inference process. Moreover, most existing MRC-based models, which use tagging-based methods for entity recognition, could not deal with overlapping entities. To address these, we propose Patti, a Pattern-First Pipeline Approach for Entity and Relation Extraction. Firstly, Patti leverages a novel MRC-based pattern classifier to identify relation patterns. Next, a span-based method was introduced to extract entities under the guidance of questions parameterized by the patterns yield in the first step. Finally, to alleviate the error propagation issue, Patti employs an additional MRC-based classifier to remove falsely extracted candidate entity-relation triples. Experiment results show that our approach significantly outperforms the entity-first baseline models on CoNLL04 and ACE05 datasets.

Adaptive Cost Volume Fusion Network for Multi-Modal Depth Estimation in Changing Environments

In this letter, we propose an adaptive cost volume fusion algorithm for multi-modal depth estimation in changing environments. Our method takes measurements from multi-modal sensors to exploit their complementary characteristics and generates depth cues from each modality in the form of adaptive cost volumes using deep neural networks. The proposed adaptive cost volume considers sensor configurations and computational costs to resolve an imbalanced and redundant depth bases problem of conventional cost volumes. We further extend its role to a generalized depth representation and propose a geometry-aware cost fusion algorithm. Our unified and geometrically consistent depth representation leads to an accurate and efficient multi-modal sensor fusion, which is crucial for robustness to changing environments. To validate the proposed framework, we introduce a new multi-modal depth in changing environments (MMDCE) dataset. The dataset was collected by our own vehicular system with RGB, NIR, and LiDAR sensors in changing environments. Experimental results demonstrate that our method is robust, accurate, and reliable in changing environments. Our codes and dataset are available at our project page.

PCXRNet: Pneumonia Diagnosis From Chest X-Ray Images Using Condense Attention Block and Multiconvolution Attention Block.

Coronavirus disease2019 (COVID-19)has become a global pandemic. Many recognition approaches based on convolutional neural networks have been proposed for COVID-19 chest X-ray images. However, only a few of them make good use of the potential inter- and intra-relationships of feature maps. Considering the limitation mentioned above, this paper proposes an attention-based convolutional neural network, called PCXRNet, for diagnosis of pneumonia using chest X-ray images. To utilize the information from the channels of the feature maps, we added a novel condense attention module (CDSE) that comprised of two steps: condensation step and squeeze-excitation step. Unlike traditional channel attention modules, CDSE first downsamples the feature map channel by channel to condense the information, followed by the squeeze-excitation step, in which the channel weights are calculated. To make the model pay more attention to informative spatial parts in every feature map, we proposed a multi-convolution spatial attention module (MCSA). It reduces the number of parameters and introduces more nonlinearity. The CDSE and MCSA complement each other in series to tackle the problem of redundancy in feature maps and provide useful information from and between feature maps. We used the ChestXRay2017 dataset to explore the internal structure of PCXRNet, and the proposed network was applied to COVID-19 diagnosis. As a result, the network achieves an accuracy of 94.619%, recall of 94.753%, precision of 95.286%, and F1-score of 94.996% on the COVID-19 dataset.

A Prediction Model Analysis of Behavior Recognition Based on Genetic Algorithm and Neural Network.

With the extensive application of virtual technology and simulation algorithm, motion behavior recognition is widely used in various fields. The original neural network algorithm cannot solve the problem of data redundancy in behavior recognition, and the global search ability is weak. Based on the above reasons, this paper proposes an algorithm based on genetic algorithm and neural network to build a prediction model of behavior recognition. Firstly, genetic algorithm is used to cluster the redundant data, so that the data are in fragment order, and then it is used to reduce the data redundancy of different behaviors and weaken the influence of dimension on behavior recognition. Then, the genetic algorithm clusters the data to form subgenetic particles with different dimensions and carries out coevolution and optimal location sharing for subgenetic particles with different dimensions. Through simulation test, the algorithm constructed in this paper is better than genetic algorithm and neural network algorithm in terms of calculation accuracy and convergence speed. Finally, the prediction model is constructed by setting the initial value and threshold to predict the behavior recognition, and the results show that the accuracy of the model constructed in this paper is improved in the analysis of behavior recognition.

Problems of improving the legislative activity

The article, based on the outline of existing shortcomings in the legislative activity of the Verkhovna Rada of Ukraine, examines the problematic issues of improving the legislative activity of the Verkhovna Rada of Ukraine, substantiates the need to continue parliamentary reform in general communication strategy, strategy for building the human resources of the parliament. First of all, attention is paid to the problem of redundancy of legislative regulation of public relations in the field of business, economy in general, construction, land legislation, labor relations and others and measures aimed at overcoming this negative phenomenon. It is proposed to specify the provisions of the Regulations on the requirement for developers to comply with the minimum adequacy of legislative intervention in public relations, to formalize the requirements for parliamentary competence, simplification and the need to generalize legal regulation, minimize parliamentary interference in existing legal relations, its possible alternatives from the new law, proximity and understanding to citizens, the need for state control, the legitimacy of restrictions on the rights and freedoms of citizens and the possible minimization of conflicts of interest.The issue of shortcomings in the political coordination of bills is considered, given the weak consensus nature of the Ukrainian parliament, the corresponding need to resolve the status of a parliamentary minority. The need to increase the role of committees in the legislative process, improve the mechanisms of interaction of the main committee on the bill with other committees, increase the transparency of parliamentary committees and parliament in general through the creation and better operation of websites of committees, publication of conclusions on bills submitted by the government. scientific institutions, public organizations at the request of relevant parliamentary committees.Proposals are made to increase the systemic nature of the draft law and to amend the current legislation accordingly, including planning on the basis of the optimal ratio of existing short-term, medium-term and long-term draft law plans for three, five years and more.

Unsupervised Generative Adversarial Network with Background Enhancement and Irredundant Pooling for Hyperspectral Anomaly Detection

Lately, generative adversarial networks (GAN)-based methods have drawn extensive attention and achieved a promising performance in the field of hyperspectral anomaly detection (HAD) owing to GAN’s powerful data generation capability. However, without considering the background spatial features, most of these methods can not obtain a GAN with a strong background generation ability. Besides, they fail to address the hyperspectral image (HSI) redundant information disturbance problem in the anomaly detection part. To solve these issues, the unsupervised generative adversarial network with background spatial feature enhancement and irredundant pooling (BEGAIP) is proposed for HAD. To make better use of features, spatial and spectral features union extraction idea is also applied to the proposed model. To be specific, in spatial branch, a new background spatial feature enhancement way is proposed to get a data set containing relatively pure background information to train GAN and reconstruct a more vivid background image. In a spectral branch, irredundant pooling (IP) is invented to remove redundant information, which can also enhance the background spectral feature. Finally, the features obtained from the spectral and spatial branch are combined for HAD. The experimental results conducted on several HSI data sets display that the model proposed acquire a better performance than other relevant algorithms.

Calibration of Collaborative Robots Based on Position Information and Local Product of Exponentials

To improve the positioning accuracy of collaborative robots, a novel modeling and calibration method for collaborative robots is proposed based on position information and modified local product of exponentials (LPoE). The kinematic error model is derived from the kinematic model through differential transformation. To solve the problem of the high redundancy and complexity of the error model that is difficult to identify, the errors of the kinematic parameters are all transferred to the initial position and posture matrix of the local coordinate system, which simplifies the error model and improves the speed and accuracy of the identification calculation. However, the simplified error model still requires posture data which are very difficult to acquire in practice. For this reason, the position error is separated from the kinematic model, and an error model based on position data is established. Then, a kinematic calibration method of the collaborative robot based on position data is proposed, which simplifies the measurement process and improves the efficiency of calibration. The effectiveness of the method is verified by simulations and experiments on a six degree-of-freedom collaborative robot.

Designing of fault-tolerant computer system structures using residue number systems

AbstractThis article discusses computing systems that operate in residue number systems (RNSs). The main direction of improving computer systems (CSs) is increasing the speed of implementation of arithmetic operations and the reliability of their functioning. Encoding data in RNS solves the problem of optimal redundancy, i.e., the creation of such computing systems provides maximum reliability with restrictions on weight and size characteristics. This article proposes new structures of fault-tolerant CSs operating in RNS in the case of the application with an active fault-tolerant method. The use of the active fault-tolerant method (dynamic redundancy) in the RNSs provides higher reliability. In addition, with an increase in the digits of CSs, the efficiency of using the proposed structures increases.

Broadcast Encryption Scheme for V2I Communication in VANETs

Information dissemination in vehicular ad hoc networks (VANETs) is inseparable from the interaction between vehicles and infrastructure. The trust authority (TA) often plays a pivotal role in VANETs and requires interaction with multiple vehicles. However, when the TA sends the same message to multiple vehicles, there are many redundancies, as it needs to negotiate with each vehicle and send them different ciphertexts. This greatly reduces the work efficiency of the TA. To the best of our knowledge, there is no research on the problem of redundancy that occurs when the same message is sent to multiple vehicles in VANETs. The proposed scheme adopts identity-based broadcast encryption (IBBE) technology, which is a secure data-sharing scheme suitable for the vehicle-to-infrastructure communication mode, in VANETs for the first time. Thus, with only one encryption, the TA can generate a fixed-length ciphertext for a group of vehicles. When there are new vehicles that subsequently request a service, the TA can assign encryption tasks to the proxy server. In terms of security, our scheme meets the particular requirements of VANETs. The encryption overhead of the sender and the length of the ciphertext were comparatively analyzed. The results demonstrated that the performance of the scheme improved significantly. Thus, our scheme can prevent redundancies and effectively improve the work efficiency of TA.