Redundancy Problem Research Articles

A Parallelly Implemented Hybrid Multi-Objective Efficient Persuasion of Coverage and Redundancy Programming Model for Internet of Things in 5G Networks using Hadoop

In 5G networks, the demand for IoT devices is increasing due to their applications. With the development and widespread adoption of 5G networks, the Internet of Things (IoT) coverage issue will collide with the issue of enormous nodes. In this paper, a parallell y implemented Hybridised Mayfly and Rat Swarm Optimizer algorithm utilising Hadoop is proposed for optimising the IoT coverage and node redundancy in IoT with massive nodes, which automatically lengthens the IoT's lifecycle. Initially, parallel operation d ivides the IoT coverage problem involving massive nodes into numerous smaller problems in order to reduce the problem's scope, which are then solved using parallel Hadoop. Using the flight behaviour and mating process of mayflies, we optimise the coverage problem here. Rats' pursuing and attacking behaviours are employed to optimise the redundancy problem. Then, select the non critical nodes from the critical nodes in an optimal manner. Lastly, parallel operation effectively resolves the IoT's coverage issu e through massive nodes by strategically extending the IoT's lifespan. Using the NS2 application, the proposed method is simulated. Computation Time, Energy efficiency, Lifespan, Lifetime, and Remaining Nodes are analysed as performance metrics. The propos ed MOP Hyb MFRS IoT 5GN method achieves lower computation times of 98.38%, 92.34%, and 97.45%, higher lifetime of 89.34%, 83.12%, and 88.96%, and lower remaining time as 91.25%, 79.90%, and 92.88% compared with existing methods such as parallel genetic alg orithm spread the lifespan of internet of things on 5G networks (MPGA IoT 5GN)

Distributed Consensus Hierarchical Optimization and Control Method for Integrated Energy System Based on Event-Triggered Mechanism

For integrated energy systems (IES) composed of a set of energy hubs (EHs), a consensus control method is usually adopted to achieve accurate sharing of electrical and thermal composite energies. To solve the communication redundancy problem of the consensus control method, a hierarchical optimization and distributed control scheme based on a dynamic event-triggered mechanism of EHs is proposed to realize stable operation of IES. An economic optimization strategy based on equal increment principle is improved to minimize the operation costs of IES in the second layer. Due to consensus control being integrated into the supply-demand power deviation calculations of EHs, the desired electrical and thermal power trajectories are accurately determined. To improve dynamic response performances in the presence of uncertain disturbances, an event-triggered communication mechanism is designed in the primary layer. The triggering threshold can be adjusted dynamically according to changes of electrical and thermal power outputs, and the redundant communication requirement in the electrical branches is reduced. Considering the coupling characteristics of IES energy networks, a consensus control method is promoted to synchronously track the desired electric and thermal power trajectories of EHs, and the goal of accurate power sharing is achieved. The frequency and pipeline pressure fluctuations are also limited within the allowable range. The economic optimization and coordinated operation of electrical and thermal composite energies in IES are guaranteed by the proposed hierarchical control structure. Additionally, only information from neighboring EHs at the event-triggered time is involved, so the computation simplicity and control performance can be obtained simultaneously. The hardware-in-loop experimental results are conducted to demonstrate the effectiveness of the proposed control strategy.

FTSC: Fault-tolerant scheduling and control co-design for distributed real-time system

Ensuring control stability is essential for safety-critical systems. Task redundancy is one commonly employed technique to enhance control performance in applications afflicted by intermittent faults. While increasing the number of replicas can improve control performance, it also introduces challenges in terms of scheduling and mapping. Prior research primarily focuses on controller design to enhance control performance, with limited consideration given to fault tolerance and scheduling in a combined manner. In this paper, we propose a co-design scheme of Fault-Tolerant Scheduling and Control (FTSC), which aims to provide stable control performance along with feasible mapping and scheduling solutions. We present a comprehensive formulation that encompasses mapping, scheduling, and fault tolerance, optimizing control performance into a Mixed Integer Linear Programming (MILP) framework. The problem of task redundancy can either be addressed through a heuristic algorithm efficiently or be solved in a MILP-based approach with a better solution. A case study is conducted to demonstrate the effectiveness of our proposed scheme, highlighting its ability to reduce system control costs while meeting real-time, reliability, and schedulability constraints.

Structural models based on spectral graph theory for land-based digital communication networks

Problem statement. For the organization of digital communication networks in the range of meter and decimeter waves, as well as when providing communications based on them in hard-to-reach areas, problems arise related to maintaining the stability and continuity of information exchange between the switching nodes of the ground infrastructure. The existing approaches to solving these problems are based on the use of means of retransmission using radio relay stations and individual flight-lifting equipment. However, with limited deployment time, in emergency situations and when performing special tasks, these approaches are unacceptable for a number of objective reasons. Therefore, the search for ways to improve digital communication networks in the range of meter and decimeter waves is relevant and has important scientific and practical significance for ensuring the stability and continuity of information exchange by ground switching nodes in a given area. Purpose. To develop a structurally stable model of digital communication networks in the range of meter and decimeter waves based on a group of unmanned aerial vehicles, ensuring the organization of continuous information exchange of ground switching nodes in a given area for the required period of time. Results. Based on the results of the research, practical recommendations are formulated for their use to solve the problems of organization, restoration and redundancy, as well as the operational build-up of digital communication networks in the range of meter and decimeter waves for a given time interval in unfavorable operating conditions. The prospects for further research in the organization of the structural contour of the complex multi-circuit adaptation of digital communication networks for various purposes are determined. Practical significance. The results can be used by specialists in the field of satellite radio navigation in the development of local navigation systems.

Comparative studies of organic contaminant removal in different calcium sulfite-enhanced oxidant/Fe(II) systems: Kinetics, mechanisms, and differentiated degradation pathways

The application of S(IV) for the regeneration of Fe(II) has been widely investigated. As the common S(IV) sources, sodium sulfite (Na2SO3) and sodium bisulfite (NaHSO3) are soluble in the solution, resulting in excessive SO32− concentration and redundant radical scavenging problems. In this research, calcium sulfite (CaSO3) was applied as the substitution for the enhancement of different oxidant/Fe(II) systems. The advantages of CaSO3 could be summarized as follows: (1) it could sustainedly supplement SO32− for Fe(II) regeneration, preventing radical scavenging and unnecessary reagent waste; (2) the cost and toxicity of CaSO3 were extremely lower than that of other S(IV) sources; (3) the concentration of reactive species increased in the presence of CaSO3; and (4) after the reaction, SO42− would form CaSO4 precipitate, which would not increase the burden of SO42− in the solution. In the participation of CaSO3, the removal of trichloroethylene (TCE) and other organic contaminants were significantly promoted and different enhanced systems had high tolerance on complex solution conditions. The major reactive species in different systems were determined through qualitative and quantitative analyses. Eventually, the dechlorination and mineralization of TCE were measured and the differentiated degradation pathways in different CaSO3-enhanced oxidants/Fe(II) systems were elucidated.

Practical adaptive robust tracking control of the dual-valve parallel electro-hydraulic servo system with reduced-order model

To meet the increasing demand for trajectory tracking accuracy and high-efficiency requirements in modern mobile machinery, this paper proposes a practical adaptive robust control method based on the dual-valve parallel electro-hydraulic servo system. Existing tracking control strategies for such servo systems rely on a backstepping method and assume that full states are known, which is stringent in practice. To cope with the problem, we reduce the mathematical model order of the studied system using singular perturbation theory in this paper, and only the position feedback is required for the control implementation. Then, to achieve high accuracy tracking control, a direct adaptive robust control scheme combing with a dynamic flow allocation layer is adopted to synthesize the controller. With this method, parameter uncertainties and load disturbance are rejected, and valve characteristics are considered in the dynamic flow allocation layer to solve the flow redundancy problem, respectively. Convergence of the simplified system tracking results is proved theoretically. Extensive co-simulations and experimental tests are carried out to illustrate the effectiveness of the proposed strategy, both tracking precision and flow distribution can be achieved efficiently by simple parameter adjustments in the field.

THE STRUCTURE OF THE COMPUTER SYSTEM IN THE RESIDUAL CLASSES

The subject of the article is the formulation and solution of the inverse problem of optimal redundancy in the system of residual classes (RNS) based on the use of the dynamic programming method. The solution of this problem makes it possible to improve the reliability of the operation of computer systems and components (CSC) in the RNS. The purpose of the article is to increase the reliability of the functioning of CSC, which are built on the basis of the use of RNS, without reducing the speed of calculations, as well as to calculate and compare the reliability, in terms of the probability of failure-free operation, of CSC in RNS and a tripled computing system that operates in a positional binary number system (PNS). Tasks: to analyze the influence of the number system used on the reliability of the CSC, taking into account the primary and secondary redundancy; to synthesize a computing system in RNS for a l-byte bit grid based on the use of the passive fault tolerance method (constant structural redundancy); formulate and solve the inverse problem of optimal redundancy in RNS based on the use of the dynamic programming method; to check the correctness of the results obtained, calculate the conditional amount of computer system equipment in the residual classes; evaluate the efficiency of using RNS to improve reliability when building a redundant CSC in relation to a redundant CSC in the PNS. Research methods: methods of analysis and synthesis of computer systems, number theory, coding theory in RNS, reliability theory. The following results are obtained. The paper shows that the use of PNS as a number system does not allow a radical increase in the performance and reliability of CSC. In this regard, the article developed the concept of using RNS as a number system for constructing a CSC. Based on this, the inverse problem of optimal redundancy in RNS is formulated and solved. Conclusions. As shown by the results of calculations and comparative analysis, the use of RNS provides a higher reliability of the CSC than the majority three-channel computing system in the PNS. The obtained research results can be used for the synthesis of fault-tolerant computer structures in RNS.

Optimal reconstruction of constrained janbu method with ADP and non-integral safety factor

Notwithstanding the rich research on the slicing method, it is still impossible to accomplish the limit equilibrium analysis of all phases before the evaluation of the safety factor. Bearing this dilemma in view, in this paper, the Janbu method is optimized and reformulated by introducing adaptive dynamic programming (ADP). The fractional average safety factor is no longer used; instead, a sequence of safety factors is used. The critical slip state is chosen as the equilibrium point, the nonlinearity of safety coefficients of adjacent blocks is approximated by the neural network, and the remaining variables and constraints of the system are added to assist in transforming the original redundant safety coefficient evaluation-finding optimal solution problem into a Janbu-qualified cost function. The action network and critic network of ADP accomplish the boundary trajectory solving and safety evaluation, respectively. The validity of the method is verified when the optimized and reorganized framework is applied to solve the critical slip surface of a clay slope.

Adaptive cascade enhancement broad learning system combined with stacked correlation information autoencoder for soft sensor modeling of industrial process

In the actual soft sensor project, the complex industrial process leads to a large number of monitoring variables that lead to obvious problems of high dimension and data redundancy. To solve these problems, an adaptive cascade enhancement broad learning system combined with stacked correlation information autoencoder, referred to as SCIAE-ACEBLS, is proposed in this study. The latter is based on the broad learning system (BLS) and it includes two parts: feature node and enhancement node. In the feature node part, the correlation coefficient and the dominant variable are introduced into the stacked autoencoder (SAE), and all the hidden nodes are used as feature nodes. Both the correlation coefficient and the dominant variable are introduced to ensure that the information of feature nodes not only include the relevant information of the dominant variable in the auxiliary variables, but also in the dominant variable, and thus all the information related to dominant variables can be effectively used. In the enhancement node part, an adaptive cascade enhancement node algorithm is used to reduce the redundancy of effective information and solve the problems of node information redundancy and node number uncertainty caused by the randomness of the node parameters in BLS. Finally, two industrial examples show that the proposed model is effective and outperforms existing methods.

MCFF-MTDDI: multi-channel feature fusion for multi-typed drug-drug interaction prediction.

Adverse drug-drug interactions (DDIs) have become an increasingly serious problem in the medical and health system. Recently, the effective application of deep learning and biomedical knowledge graphs (KGs) have improved the DDI prediction performance of computational models. However, the problems of feature redundancy and KG noise also arise, bringing new challenges for researchers. To overcome these challenges, we proposed a Multi-Channel Feature Fusion model for multi-typed DDI prediction (MCFF-MTDDI). Specifically, we first extracted drug chemical structure features, drug pairs' extra label features, and KG features of drugs. Then, these different features were effectively fused by a multi-channel feature fusion module. Finally, multi-typed DDIs were predicted through the fully connected neural network. To our knowledge, we are the first to integrate the extra label information into KG-based multi-typed DDI prediction; besides, we innovatively proposed a novel KG feature learning method and a State Encoder to obtain target drug pairs' KG-based features which contained more abundant and more key drug-related KG information with less noise; furthermore, a Gated Recurrent Unit-based multi-channel feature fusion module was proposed in an innovative way to yield more comprehensive feature information about drug pairs, effectively alleviating the problem of feature redundancy. We experimented with four datasets in the multi-class and the multi-label prediction tasks to comprehensively evaluate the performance of MCFF-MTDDI for predicting interactions of known-known drugs, known-new drugs and new-new drugs. In addition, we further conducted ablation studies and case studies. All the results fully demonstrated the effectiveness of MCFF-MTDDI.

Goal-Directed Tactile Exploration for Body Model Learning Through Self-Touch on a Humanoid Robot

An early integration of tactile sensing into motor coordination is the norm in animals, but still a challenge for robots. Tactile exploration through touches on the body gives rise to first body models and bootstraps further development such as reaching competence. Reaching to one’s own body requires connections of the tactile and motor space only. Still, the problems of high dimensionality and motor redundancy persist. Through an embodied computational model for the learning of self-touch on a simulated humanoid robot with artificial sensitive skin, we demonstrate that this task can be achieved (i) effectively and (ii) efficiently at scale by employing the computational frameworks for the learning of internal models for reaching: intrinsic motivation and goal babbling. We relate our results to infant studies on spontaneous body exploration as well as reaching to vibrotactile targets on the body. We analyze the reaching configurations of one infant followed weekly between 4 and 18 months of age and derive further requirements for the computational model: accounting for (iii) continuous rather than sporadic touch and (iv) consistent redundancy resolution. Results show the general success of the learning models in the touch domain, but also point out limitations in achieving fully continuous touch.

Ability-aware knowledge distillation for resource-constrained embedded devices

Deep Neural Network (DNN) models have notably improved the efficiency of machine learning tasks. However, their high storage and computational costs restrict their deployment on resource-limited embedded devices. Knowledge distillation (KD) has emerged as a promising approach for compressing DNN models. However, two challenges in KD, namely the capacity gap problem and the time-consuming redundancy problem, have hindered its performance and efficiency in compression. To alleviate these challenges, this paper proposes a novel framework, called Ability-Aware Knowledge Distillation (AAKD). AAKD introduces a knowledge sample selection strategy and an adaptive teacher switching strategy based on the dynamic awareness of the student’s ability. This enables the framework to automatically select suitable knowledge samples and teacher networks according to the increasing representation ability of students. Extensive experiments on different datasets and models have demonstrated that AAKD can enhance the performance of compact student models, significantly improve the efficiency of distillation, and lead to higher compression rates.

Gated recurrent unit-enhanced deep convolutional neural network for real-time industrial process fault diagnosis

When deep learning-based models are employed for the fault diagnosis of chemical processes, problems of poor calculation accuracy and efficiency often occur in the scenarios of high-dimensional, nonlinear, and time-varying data, which affects the overall robustness of the fault diagnosis model. This study proposes a novel Gated Recurrent Unit (GRU) - enhanced deep convolutional neural network (EDCNN) model for the improved fault detection and diagnosis of chemical processes. In the GRU-EDCNN model, a maximum smooth function (MSF) is developed and presented to replace the classical activation function for better matching the input data format, which can significantly improve the calculation accuracy and the overall efficiency of DCNN models. Moreover, the convolutional layers in DCNN are optimized by a decentralized convolutional structure, thereby reducing the problem of parameter redundancy. The issue of gradient disappearance is tackled by the embedment of GRU, whose ability to control time series features and gateway information can ensure that no overfitting occurs in the GRU-EDCNN training processes. In two case studies, the GRU-EDCNN model was applied to the benchmark Tennessee Eastman (TE) process and an acid gas absorption process, and the corresponding data of fault diagnosis rate (FDR), false positive rate (FPR), and fault diagnosis time (FDT) of the GRU-EDCNN model were analyzed and compared with those of the other deep learning-based models, verifying the effectiveness of the GRU-EDCNN model for fault diagnosis in both simulated and real-time industrial processes.

Fuzzified imperfect repair redundant machine repair problems

Fuzzified imperfect repair redundant machine repair problems

An improved LSTM-based model for identifying high working intensity load segments of the tractor load spectrum

In order to solve the problems of serious redundancy of existing load spectrum data, unclear identification of load segments corresponding to high working intensity, and failure of load spectrum interception to meet the requirements of heavy load conditions, this paper proposes an MHA-ConvLSTM (Multi-head attention Convolutional LSTM) network model for identifying high working intensity load segments of the tractor load spectrum. The deep learning model integrates a multi-head attention mechanism and ConvLSTM network as the core, keeping the temporal order of continuously changing load sequences as the basic principle, deeply mining the local features contained within the small range load of dynamically changing load, and strengthening the matching relationship of the intrinsic features in long distance and large data volume load. This research selects rotary tillage as the verification condition, builds the multi-sensor test system to carry out the working load test, and takes the tractor rotary tillage load spectrum data as the validation object. The analysis shows that the accuracy and F1-score of the MHA-ConvLSTM model reach 97.69% and 97.83%, respectively, and the operation time is only 0.5289 s, which is 15.82% faster than LSTM. In addition, the model in this paper was used to identify 399 load segments with high workload and high work intensity, and 388 load segments were successfully verified with an error rate of less than 3%. This paper provides a new technical solution for applying the agricultural equipment load spectrum.

A KLMS Dual Control Chart Based on Dynamic Nearest Neighbor Kernel Space

Traditional projection dynamic monitoring methods focus on simultaneously decoupling the correlations among the process variables and the autocorrelations of the variables, which leads to a mixing problem of the two correlations. The mixing problem decreases the ability to model the dynamic correlations, thus decreasing the detection rates (DRs) of some faults. Considering that the projection matrix may cause the mixing of the two correlations, this paper proposes a dynamic monitoring method based on directly monitoring original variables. This paper firstly adopts the kernel least mean squares (KLMS) method to establish a univariate dynamic model, then adopts the univariate dynamic model and the Dual Control Chart (DCC) to build the multivariate direct monitoring method, which is named the KL2C method (KL represents KLMS. and 2 C represents DCC). Then, the Dynamic Nearest Neighbor Kernel Space (DNNKS) is proposed to overcome the redundant dimensions problem of the KL2C method, which is named the DKL2C method (D represents DNNKS). Furthermore, based on the Joint Control Chart (JCC), this paper puts forward a dynamic monitoring method of two sequences, which both considers the advantages of the projection dynamic monitoring method and the direct dynamic monitoring method together. Finally, this paper utilizes the Tennessee Eastman (TE) process and the continuously stirred tank reactor (CSTR) process to verify the effectiveness of the proposed methods.

CPLR-SFS: Contrastive Prompt Learning to Reduce Redundancy for Scientific Faceted Summarization

Scientific faceted summarization is a task to generate four summaries for a scientific article from different facets, including Purpose, Method, Findings, and Value. Existing works usually generate summary independently for each facet using pre-training or prompt-training paradigms. However, these works tend to produce duplicate content among different facets of the same scientific article, because they do not consider the relation among the aforementioned four facets. To solve the redundancy problem, we propose a novel Contrastive Prompt Learning method to Reduce redundancy for Scientific Faceted Summarization, named CPLR-SFS, to generate concise and less-overlapping faceted summaries. Specifically, CPLR-SFS receives the facet-specific prompt to guide the generation and utilizes the faceted contrastive loss for better distinguishing different faceted summaries in semantic space. Extensive experiments on the FacetSum dataset demonstrate that the proposed model can generate better faceted summaries than the state-of-the-art baselines with less redundancy.

Диффузность признаковой и статальной семантики предикатива

The aim of the study is to determine the mechanisms of the semantic-grammatical drift of predicatives in the context of the problem of grammatical redundancy of language units. The paper considers the parameters of the grammatical redundancy of the predicative as a hybrid language class formed of units with different grammatical characteristics. In particular, the paper considers the mechanisms of the mutually directed drift of the attributive and static semantics of predicatives both within the traditionally distinguished “category of state” and beyond it. In addition, models of semantic modifications (interpretative and other types) are described, as well as some types of syntactic modifications in the analysed language process. The scientific novelty of the study lies in the formation of a systematic approach to solving the problem of the semantic-grammatical status of the predicative as a class of words in the context of the processes of interlevel interaction in language. As a result of the study, the models of the semantic-grammatical drift within the considered language class have been described. These models are considered both in the context of lexical semantics and within the framework of the language structure analysis from the point of view of redundancy/insufficiency in grammatical semantics.

Deep learning based spatio‐temporal hand gesture recognition system in complex environment

AbstractGesture recognition nowadays has grabbed the attention of researchers as they represent human behaviour in multiple practical ways. Amongst a variety of gestures available, hand gestures play an essential role in the field of human‐computer interaction when recognised efficiently in complex and dynamic environments. In this paper, we propose a dynamic hand gesture recognition system to recognise hand gestures appearing in different indoor and outdoor environments. Hand detection and tracking uses a two‐level system resulting in the formation of gesture trajectory in challenging conditions in which existing detection and tracking algorithms could not do so. A set of 45 features is provided as input to the various classification techniques. The redundancy problem has been reduced by selecting a set of optimum features using the analysis of variance method, which ranks the list of features. An incremental feature selection technique calculates recognition accuracy by selecting features according to rankings. This system provides an accuracy of 96.32% when used with machine learning and 97.5% when used with deep learning techniques. Recognition accuracy is calculated for various environments, including an extra hand, multiple persons in the video frame, and outdoor environment. All machine‐learning classifiers are combined using classifier combination to calculate the accuracy according to the majority‐voting rule. Based on the experimental results, it has been observed that deep learning provides better results compared to machine learning.

Wrist redundancy management during pointing tasks remains stable over time and in presence of a visuomotor perturbation

Pointing at a screen using wrist and forearm movements is a kinematically redundant task, and the Central Nervous System seems to manage this redundancy by using a simplifying strategy, named Donders’ Law for the wrist. In this work we investigated (1) whether this simplifying approach is stable over time and (2) whether a visuomotor perturbation provided in the task space influences the strategy used to solve the redundancy problem. We conducted two experiments asking participants to perform the same pointing task in four different days (first experiment), and providing a visual perturbation, i.e. a visuomotor rotation to the controlled cursor (second experiment), while recording their wrist and forearm rotations. Results showed that the participant-specific wrist redundancy management (described by the Donders’ surfaces) (1) neither changes over time (2) nor varies when a visuomotor perturbation is provided in the task space.