-
Dynamic response analysis for bridges subjected to moving vehicle loads by using the analytical dynamic stiffness method Comput. Struct. (IF 4.7) Pub Date : 2023-12-05 Xiang Liu, Shitan Tao, Xueyi Zhao, Xiao Liu, Zhaoming Lu, Feiyang Liu
This paper presents a highly efficient and accurate analytical method for the dynamic analysis of subjected to moving loads. Bridges with complex cross section and considering damping are modelled as plate built-up structures by the dynamic stiffness method with few degrees of freedom. Different moving loads with time-varying amplitudes travelling at different speeds can be modelled semi-analytically:
-
Towards defining industry 5.0 vision with intelligent and softwarized wireless network architectures and services: A survey J. Netw. Comput. Appl. (IF 8.7) Pub Date : 2023-12-06 Shah Zeb, Aamir Mahmood, Sunder Ali Khowaja, Kapal Dev, Syed Ali Hassan, Mikael Gidlund, Paolo Bellavista
Industry 5.0 vision, a step toward the next industrial revolution and enhancement to Industry 4.0, conceives the new goals of resilient, sustainable, and human-centric approaches in diverse emerging applications such as factories-of-the-future and digital society. The vision seeks to leverage human intelligence and creativity in nexus with intelligent, efficient, and reliable cognitive collaborating
-
Surgical immunization strategies against lateral movement in Active Directory environments J. Netw. Comput. Appl. (IF 8.7) Pub Date : 2023-12-06 David Herranz-Oliveros, Ivan Marsa-Maestre, Jose Manuel Gimenez-Guzman, Marino Tejedor-Romero, Enrique de la Hoz
Lateral movement, in which a cyber attacker progresses through an enterprise network in order to compromise its most valuable assets, is a key stage of any intrusion nowadays. Therefore, being able to mitigate lateral movement, be it by slowing down attacker progress or by limiting its reach, is a top priority for enterprise cyber-defence. Due to the inherent complexity of enterprise networks, it is
-
Effective quantum volume, fidelity and computational cost of noisy quantum processing experiments Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2023-12-04 K. Kechedzhi, S.V. Isakov, S. Mandrà, B. Villalonga, X. Mi, S. Boixo, V. Smelyanskiy
Today’s experimental noisy quantum processors can compete with and surpass all known algorithms on state-of-the-art supercomputers for the computational benchmark task of Random Circuit Sampling (Boixo et al., 2018, Arute et al., 2019, Wu et al., 2021, Zhu et al., 2022, Morvan et al., 2023). Additionally, a circuit-based quantum simulation of quantum information scrambling (Mi et al., 2021), which
-
Health data security sharing method based on hybrid blockchain Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2023-12-04 Taochun Wang, Qingshan Wu, Jian Chen, Fulong Chen, Dong Xie, Huimin Shen
With the rapid development of Internet of Things(IoT) technology, it is more and more common to use IoT devices to obtain health data. Health data sharing is a crucial component of health data utilization, yet it encounters serious privacy and security challenges. How to solve the problem of data security is the key factor to encourage users to participate, and existing data sharing methods generally
-
Enhanced multi-scale networks for semantic segmentation Complex Intell. Syst. (IF 5.8) Pub Date : 2023-12-04 Tianping Li, Zhaotong Cui, Yu Han, Guanxing Li, Meng Li, Dongmei Wei
-
Zeroth- and first-order difference discrimination for unsupervised domain adaptation Complex Intell. Syst. (IF 5.8) Pub Date : 2023-12-05 Jie Wang, Xing Chen, Xiao-Lei Zhang
-
A flocking control algorithm of multi-agent systems based on cohesion of the potential function Complex Intell. Syst. (IF 5.8) Pub Date : 2023-12-06 Chenyang Li, Yonghui Yang, Guanjie Jiang, Xue-Bo Chen
-
DUAL-C: Building a “soft error efficient” on-the-fly compression mechanism for raw video data at edge devices Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2023-12-04 Xiaohui Wei, Xiaonan Wang, Hengshan Yue, Nan Jiang, Jianpeng Zhao, Meikang Qiu
Nowadays, various edge applications play an increasingly important role in our life, in which video data accounts for a dominant portion of the data traffic. Although standard compression methods, such as H.264 and MPEG4, can considerably reduce the wireless bandwidth for transmission energy savings of edge devices, the large volume of raw video streaming data is still buffered in edge device video
-
Enhanced abnormal data detection hybrid strategy based on heuristic and stochastic approaches for efficient patients rehabilitation Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2023-12-02 Murad Ali Khan, Naeem Iqbal, Harun Jamil, Faiza Qayyum, Jong-Hyun Jang, Salabat Khan, Jae-Chul Kim, Do-Hyeun Kim
Over the last few years, substantial research has been conducted towards developing efficient abnormal detection techniques while considering efficiency, accuracy, high-dimensional data, distributed environments, and others. Researchers increasingly deal with “abnormalities” in clinical patient data to derive relevant clinical knowledge for making informed decisions. However, data collection for clinically
-
Geometric deep learning for statics-aware grid shells Comput. Struct. (IF 4.7) Pub Date : 2023-12-01 Andrea Favilli, Francesco Laccone, Paolo Cignoni, Luigi Malomo, Daniela Giorgi
This paper introduces a novel method for shape optimization and form-finding of free-form, triangular grid shells, based on geometric deep learning. We define an architecture which consumes a 3D mesh representing the initial design of a free-form grid shell, and outputs vertex displacements to get an optimized grid shell that minimizes structural compliance, while preserving design intent. The main
-
A novel surrogate-based crack identification method for cantilever beam based on the change of natural frequencies Comput. Struct. (IF 4.7) Pub Date : 2023-12-02 Long Zhang, Wenlin Liao, Juntao Fan
A novel crack identification method is presented in this paper for cantilever beam-type structures within the scope of vibration-based damage identification. Natural frequencies are more easily to be measured yet are weakly sensitive to damage in comparison to other dynamic parameters such as mode shape and damping ratio. Therefore, this paper develops a surrogate-based crack identification method
-
Securing the Industrial Internet of Things against ransomware attacks: A comprehensive analysis of the emerging threat landscape and detection mechanisms J. Netw. Comput. Appl. (IF 8.7) Pub Date : 2023-12-04 Muna Al-Hawawreh, Mamoun Alazab, Mohamed Amine Ferrag, M. Shamim Hossain
Due to the complexity and diversity of Industrial Internet of Things (IIoT) systems, which include heterogeneous devices, legacy and new connectivity protocols and systems, and distributed networks, sophisticated attacks like ransomware will likely target these systems in the near future. Researchers have focused on studying and addressing ransomware attacks against various platforms in recent years
-
Overcoming cold start and sensor bias: A deep learning-based framework for IoT-enabled monitoring applications J. Netw. Comput. Appl. (IF 8.7) Pub Date : 2023-12-03 Mohammed Shurrab, Dunia Mahboobeh, Rabeb Mizouni, Shakti Singh, Hadi Otrok
Target localization is an essential aspect of smart environmental monitoring, which has gained prominence thanks to the Internet of Things (IoT) paradigm. Localization is the process of determining the location of an unknown target within an area of interest (AoI) based on data gathered by IoT sensors. Existing target localization works assume full knowledge about the sensors without considering the
-
P2 random walk: self-supervised anomaly detection with pixel-point random walk Complex Intell. Syst. (IF 5.8) Pub Date : 2023-12-02 Liujie Hua, Qianqian Qi, Jun Long
-
A Trustworthy Security Model for IIoT Attacks on Industrial Robots Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2023-12-02 Lianpeng Li, Xu Zhao, Junfang Fan, Fuchao Liu, Ning Liu, Hui Zhao
The security of industrial Internet of Things (IIOT) has recently attracted significant attention. As typical IIoT systems, industrial robots are suffering from lots of threats involving control, communication, and computing, which are difficult to detect IIoT attacks accurately in real-time due to resource constraints. How to efficiently and accurately identify IoT attacks on industrial robots is
-
Transplantation and optimization of molecular dynamics simulation on MT-3000 Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2023-12-02 Jianjiang Li, Hongyaoxing Gu, Jing Zhao, Lin Qiao, Chunye Gong, Gang Zheng
Molecular dynamics (MD) simulation is a research method to simulate the change of particle systems with the help of the powerful calculating power of computers. As the number of atoms increases and the duration of the simulation system extends, current mainstream software for MD simulations has become increasingly strained. This is largely due to a surge in computing time, attributed to the architecture
-
Bidirectional utilization of blockchain and privacy computing: Issues, progress, and challenges J. Netw. Comput. Appl. (IF 8.7) Pub Date : 2023-12-01 Hongwei Zhang, Wei Fan, Jinsong Wang
With the rapid development of information technology and the increasing popularity of personalized services, the flow of users’ personal information in the Internet is inevitable between different platforms and applications, which greatly affects the trust relationship in the digital society. In recent years, an increasing number of researchers have been dedicated to exploring the applications of blockchain
-
Network shortcut in data plane of service mesh with eBPF J. Netw. Comput. Appl. (IF 8.7) Pub Date : 2023-11-30 Wanqi Yang, Pengfei Chen, Guangba Yu, Haibin Zhang, Huxing Zhang
In recent years, the adoption of the service mesh as a dedicated infrastructure layer to support cloud-native systems has gained significant popularity. Service meshes involve the incorporation of proxies to handle communication between microservices, thereby speeding up the development and deployment of microservice applications. However, the use of service meshes also increases the request latency
-
PUDT: Plummeting Uncertainties in Digital Twins for Aerospace Applications using Deep Learning Algorithms Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2023-11-30 Shitharth Selvarajan, Hariprasath Manoharan, Achyut Shankar, Alaa O. Khadidos, Adil O. Khadidos, Antonino galletta
Identifying objects in aircraft monitoring systems poses significant challenges due to the presence of extreme loading conditions. Despite the presence of several sensor units, the transmission of precise data to multiple data units is hindered by an increase in time intervals. Therefore, the suggested methodology is specifically developed for the purpose of generating digital replicas for aeronautical
-
Using FlowVisor and evolutionary algorithms to improve the switch migration in SDN J. Netw. Comput. Appl. (IF 8.7) Pub Date : 2023-11-30 Ali El Kamel
The switch migration (SM) issue has been widely addressed in distributed Software Defined Networks (SDN). Most of researches have focused on achieving SM with balanced loads among controllers but little efforts are devoted to equilibrate the service offered to switches when limited resources in control plane are experienced. This paper deals with the problem of dynamic switch migration in multi-controller
-
UniPreCIS: A data preprocessing solution for collocated services on shared IoT Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2023-11-28 Anirban Das, Navlika Singh, Suchetana Chakraborty
Next-generation smart city applications, attributed to the power of the Internet of Things (IoT) and Cyber-Physical Systems (CPS), significantly rely on sensing data quality. With an exponential increase in intelligent applications for urban development and enterprises offering sensing-as-a-service these days, it is imperative that a shared sensing infrastructure could thwart the better utilization
-
Sliding-Mode Control for Perturbed MIMO Systems With Time-Synchronized Convergence. IEEE Trans. Cybern. (IF 11.8) Pub Date : 2023-11-30 Wanyue Jiang,Shuzhi Sam Ge,Qinglei Hu,Dongyu Li
This article introduces a novel approach called terminal sliding-mode control for achieving time-synchronized convergence in multi-input-multi-output (MIMO) systems under disturbances. To enhance controller design, the systems are categorized into two groups: 1) input-dimension-dominant and 2) state-dimension-dominant, based on signal dimensions and their potential for achieving thorough time-synchronized
-
TRACE: Transformer-based continuous tracking framework using IoT and MCS J. Netw. Comput. Appl. (IF 8.7) Pub Date : 2023-11-27 Shahmir Khan Mohammed, Shakti Singh, Rabeb Mizouni, Hadi Otrok
Target tracking, a critical application in the Internet of Things (IoT) and Mobile Crowd Sensing (MCS) domains, is a complex task that involves the continuous estimation of the positions of an object by using efficient and accurate algorithms. Some potential applications of target tracking include surveillance systems, asset tracking, wildlife monitoring, and cross-border security. The existing target
-
Continuous agile cyber–physical systems architectures based on digital twins Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2023-11-28 Alexander Vodyaho, Nataly Zhukova, Radhakrishnan Delhibabu, Alexey Subbotin
Modern cyber–physical systems by the most part are large-scale multilevel distributed heterogeneous systems consisting of elements of different physical nature, built using Internet of Things platforms and are characterized by a high level of structural and behavioral (architectural) variability. Managing such systems and keeping them in working condition throughout their lifetime is a difficult task
-
MuSelect Chain: trusted decentralized mutual selection through blockchain Complex Intell. Syst. (IF 5.8) Pub Date : 2023-11-27 Xiaohu Shi, Ying Chang, Zhongqi Fu, Yu Zhang, Deyin Ma, Yi Yang
-
Pseudo-partial-derivative information-driven adaptive fault-tolerant tracking control for discrete-time systems Complex Intell. Syst. (IF 5.8) Pub Date : 2023-11-29 Yuan Wang, Zhenbin Du, Yanming Wu
-
Directional maximum length scale control in density-based topology optimization Comput. Struct. (IF 4.7) Pub Date : 2023-11-28 Longlong Song, Tong Gao, Jie Wang, Weihong Zhang
In this paper, directional maximum length scale control for both the solid and void phases is proposed for density-based topology optimization with a lower computational cost. The method introduces porosity and material rate in the locally searched domain to achieve the length scale control for the solid and void phases, respectively. To enable directional length scale control, local rectangle and
-
Hydromechanical tensile strength modelling at particle size level for non cohesive granular materials Comput. Struct. (IF 4.7) Pub Date : 2023-11-28 Hiram Arroyo, Eduardo Rojas, Jatziri Y. Moreno-Martínez, Otoniel Palacios, Arturo Galván
The aim of this paper is to propose a robust yet simple model to predict the variation of the tensile strength of granular materials with the degree of saturation. Because capillary phenomenon and air–water surface tension govern interparticle bonding in granular materials, they are explicitly taken into consideration. These vary with the degree of saturation as with all geotechnical structures that
-
Input Design for Active Fault Detection: Reconciling System Control Objectives. IEEE Trans. Cybern. (IF 11.8) Pub Date : 2023-11-29 Fangfei Cao,Fanlin Jia,Xiao He
Active fault detection (AFD) is the newest frontier in the field of fault detection and has drawn increasing amounts of research attention. AFD technology can enhance fault detection performance by injecting a predesigned auxiliary input signal for a specific fault. In most existing studies, system control objectives are not fully considered in the auxiliary input design of AFD. This article investigates
-
A Knapsack-based Metaheuristic for Edge Server Placement in 5G networks with heterogeneous edge capacities Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2023-11-28 Vaibhav Tiwari, Chandrasen Pandey, Abisek Dahal, Diptendu Sinha Roy, Ugo Fiore
-
A multi-hierarchy particle swarm optimization-based algorithm for cloud workflow scheduling Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2023-11-25 Chang Lu, Jie Zhu, Haiping Huang, Yuzhong Sun
In this paper, we consider the market-driven workflow scheduling problem on heterogeneous cloud resources with deadline constraints. The transmission delay between dependent tasks on different virtual machines (VMs) is considered. The objective is to minimize the total monetary cost, which is computed based on the on-demand price structure. Inspired by the heuristic and meta-heuristic algorithms, a
-
An online bi-objective scheduling algorithm for service provisioning in cloud computing J. Netw. Comput. Appl. (IF 8.7) Pub Date : 2023-11-28 Yuxiao Qi, Li Pan, Shijun Liu
To reduce costs and risks, service providers can purchase Infrastructure-as-a-Service (IaaS) instances from public clouds to deploy service applications and deliver job-execution services to users. However, it is challenging to make optimal instance purchasing and job scheduling decisions due to diverse instance billing methods and random service demands. In order to minimize time average service costs
-
A novel algorithm to model concrete based on geometrical properties of aggregate and its application Comput. Struct. (IF 4.7) Pub Date : 2023-11-24 Pramod Kumar Gupta, Chandrabhan Singh
In this paper, a novel algorithm is developed to generate the geometrical model of coarse aggregate based on its physical properties. The size, elongation index, and flakiness index are considered while developing the algorithm. The developed geometrical model of aggregate is further used for generating the finite element (FE) meso-model of concrete. Three distinct phases are considered in the FE model
-
Data management in Ethereum DApps: A cost and performance analysis Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2023-11-27 Periklis Kostamis, Andreas Sendros, Pavlos S. Efraimidis
The cost of interacting with a blockchain and the time required to search and retrieve information from it are important factors in the design of Decentralized Applications (DApps). The corresponding approaches for data management in Ethereum vary from pure on-chain solutions to hybrid architectures. In this work, we thoroughly examine a wide range of data management methods, including unconventional
-
Multi-classification deep learning models for detection of ulcerative colitis, polyps, and dyed-lifted polyps using wireless capsule endoscopy images Complex Intell. Syst. (IF 5.8) Pub Date : 2023-11-24 Hassaan Malik, Ahmad Naeem, Abolghasem Sadeghi-Niaraki, Rizwan Ali Naqvi, Seung-Won Lee
-
Data-driven characterization of viscoelastic materials using time-harmonic hydroacoustic measurements Comput. Struct. (IF 4.7) Pub Date : 2023-11-22 Laura Río-Martín, A. Prieto
Any numerical procedure in mechanics requires choosing an appropriate model for the constitutive law of the material under consideration. The most common assumptions regarding linear wave propagation in a viscoelastic material are the standard linear solid model, (generalized) Maxwell, Kelvin-Voigt models or the most recent fractional derivative models. Usually, once the frequency-dependent constitutive
-
An efficient flattened index structure with lazy restructuring and hotness awareness Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2023-11-25 Kunpeng Zhang, Edwin Hsing-Mean Sha, Qingfeng Zhuge, Rui Xu
Hotspot issue is ubiquitous in the in-memory key-value stores, which seriously affects its performance. The efficiency of the index structure is the key to the performance of the in-memory key-value store, and the access of hotspot data must be through the search of the index structure. However, the existing index structures do not consider the hotspot issue, they treat and manage all the data equally
-
A scalable modified deep reinforcement learning algorithm for serverless IoT microservice composition infrastructure in fog layer Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2023-11-25 Mina Emami Khansari, Saeed Sharifian
Nowadays many modern and Artificial Intelligence (AI) enabled Internet of Things (IoT) applications consist of chains connecting microservices distributed across the fog and cloud layers to achieve a certain functionality. These microservices should be assigned to the available processing instances placed on the things, fog and cloud layers while considering the resource utilization, Quality of Service
-
FedBnR: Mitigating federated learning Non-IID problem by breaking the skewed task and reconstructing representation Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2023-11-22 Chao Wang, Hui Xia, Shuo Xu, Hao Chi, Rui Zhang, Chunqiang Hu
Federated Learning (FL), as a novel distributed machine learning paradigm, offers infinite possibilities for collaborative use of decentralized data among distributed entities. However, the potential data heterogeneity in distributed entities poses a great challenge to deploying FL for real-world practical applications. Inspired by the observed phenomenon of data heterogeneity simulation, we propose
-
Performance analysis of parallel composite service-based applications in clouds Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2023-11-22 Xiulin Li, Li Pan, Wei Song, Shijun Liu, Xiangxu Meng
When processing composite service application jobs containing parallel tasks, service providers can optimize their quality of services (QoS) based on refined parallelism settings and resource allocation schemes by leveraging analytical models. However, building such analytical models is particularly challenging due to the fact that an accurate model is required to capture the dependence among sequential
-
Unraveling the MEV enigma: ABI-free detection model using Graph Neural Networks Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2023-11-21 Seongwan Park, Woojin Jeong, Yunyoung Lee, Bumho Son, Huisu Jang, Jaewook Lee
The detection of Maximal Extractable Value (MEV) in blockchain is crucial for enhancing blockchain security, as it enables the evaluation of potential consensus layer risks, the effectiveness of anti-centralization solutions, and the assessment of user exploitation. However, existing MEV detection methods face limitations due to their low recall rate, reliance on pre-registered Application Binary Interfaces
-
Leveraging a visual language for the awareness-based design of interaction requirements in digital twins Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2023-11-21 Rafael Duque, Crescencio Bravo, Santos Bringas, Daniel Postigo
User interfaces for digital twins (DTs) should provide information that allows the user to be aware of the state of the physical entity that is virtualised. Typically, this real entity is a shared space in which various human and artificial agents interact (for instance, in smart cities, various citizens and vehicles interact; in manufacturing, operators and machinery cooperate in the production, etc
-
Fedisp: an incremental subgradient-proximal-based ring-type architecture for decentralized federated learning Complex Intell. Syst. (IF 5.8) Pub Date : 2023-11-24 Jianjun Huang, Zihao Rui, Li Kang
-
A flexible algorithm to offload DAG applications for edge computing J. Netw. Comput. Appl. (IF 8.7) Pub Date : 2023-11-19 Gabriel F.C. de Queiroz, José F. de Rezende, Valmir C. Barbosa
Multi-access Edge Computing (MEC) is an enabling technology to leverage new network applications, such as virtual/augmented reality, by providing faster task processing at the network edge. This is done by deploying servers closer to the end users to run the network applications. These applications are often intensive in terms of task processing, memory usage, and communication; thus mobile devices
-
Blockchain-assisted verifiable certificate-based searchable encryption against untrusted cloud server for industrial internet of things Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2023-11-20 Hang Liu, Yang Ming, Chenhao Wang, Yi Zhao, Songnian Zhang, Rongxing Lu
The Industrial Internet of Things (IIoT) has brought practical application value to many industries, where significant amounts of IIoT data and resources are outsourced to cloud server (CS) via diverse networks for data fusion, monitoring, sharing, and calculation analysis. Considering privacy, there is a need to execute the encryption operation on the data before outsourcing, while how to retrieve
-
Motion estimation and multi-stage association for tracking-by-detection Complex Intell. Syst. (IF 5.8) Pub Date : 2023-11-22 Ye Li, Lei Wu, Yiping Chen, Xinzhong Wang, Guangqiang Yin, Zhiguo Wang
-
Multi-objective structural optimization for the automatic member grouping of truss structures using evolutionary algorithms Comput. Struct. (IF 4.7) Pub Date : 2023-11-22 José Pedro G. Carvalho, Dênis E.C. Vargas, Breno P. Jacob, Beatriz S.L.P. Lima, Patrícia H. Hallak, Afonso C.C. Lemonge
This paper aims to formulate the multi-objective structural optimization problem to find the best member grouping of truss structures. The weight of the structure and the different number of discrete cross-sectional areas are the conflicting objective functions to be minimized simultaneously, generating a Pareto front presenting the non-dominated solutions. Sixteen multi-objective evolutionary algorithms
-
ESS MS-G3D: extension and supplement shift MS-G3D network for the assessment of severe mental retardation Complex Intell. Syst. (IF 5.8) Pub Date : 2023-11-21 Quan Liu, Mincheng Cai, Dujuan Liu, Simeng Ma, Qianhong Zhang, Dan Xiang, Lihua Yao, Zhongchun Liu, Jun Yang
-
Evolutionary auto-design for aircraft engine cycle Complex Intell. Syst. (IF 5.8) Pub Date : 2023-11-22 Xudong Feng, Zhening Liu, Feng Wu, Handing Wang
-
A sequential quadratic programming based strategy for particle swarm optimization on single-objective numerical optimization Complex Intell. Syst. (IF 5.8) Pub Date : 2023-11-22 Libin Hong, Xinmeng Yu, Guofang Tao, Ender Özcan, John Woodward
-
An online surrogate-assisted neighborhood search algorithm based on deep neural network for thermal layout optimization Complex Intell. Syst. (IF 5.8) Pub Date : 2023-11-22 Jiliang Zhao, Handing Wang, Wen Yao, Wei Peng, Zhiqiang Gong
-
Sonnet: A control-theoretic approach for resource allocation in cluster management Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2023-11-22 Ruifeng Ma, Yufeng Zhan, Yuanqing Xia, Chuge Wu, Liwen Yang, Runze Gao
Cluster users expect to minimize the resource costs while ensuring target performance for different applications. It is particularly difficult to reach such a goal, because the applications are diverse with dynamic load changes, and interference exists between them. In addition, the performance of the applications depends on heterogeneous resources with different costs. However, existing works either
-
Mobile edge assisted multi-view light field video system: Prototype design and empirical evaluation Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2023-11-22 Yuxuan Pan, Kaiyue Luo, Yunming Liu, Chen Xu, Yu Liu, Lin Zhang
Metaverse is recently envisioned as the main driver for immersive multimedia in future networks. Light field video (LFV), considered an intermediate transition scheme towards the metaverse, is conducive to sensing and reconstructing realistic scenes in the digital twins. However, the research on LFV systems still lacks comprehensive investigation, impeding their practical application. This paper addresses
-
Intelligent mesh generation for crack simulation using graph neural networks Comput. Struct. (IF 4.7) Pub Date : 2023-11-20 Xiao Wang, Qingrui Yue, Xiaogang Liu
Mesh generation for crack simulation is often the rate-limiting step because of the rapid variations in crack shape. The classical meshing paradigm, place-nodes-and-link, relies on predefined rules and fails to generalize various crack shapes. We proposed a graph neural networks-based method for recovering the missing connection information in the crack meshes. The constrained Delaunay triangulation
-
WToE: Learning When to Explore in Multiagent Reinforcement Learning. IEEE Trans. Cybern. (IF 11.8) Pub Date : 2023-11-21 Shaokang Dong,Hangyu Mao,Shangdong Yang,Shengyu Zhu,Wenbin Li,Jianye Hao,Yang Gao
Existing multiagent exploration works focus on how to explore in the fully cooperative task, which is insufficient in the environment with nonstationarity induced by agent interactions. To tackle this issue, we propose When to Explore (WToE), a simple yet effective variational exploration method to learn WToE under nonstationary environments. WToE employs an interaction-oriented adaptive exploration
-
Sampled-Data Model-Free Adaptive Control for Nonlinear Continuous-Time Systems. IEEE Trans. Cybern. (IF 11.8) Pub Date : 2023-11-21 Ronghu Chi,Wenzhi Cui,Na Lin,Zhongsheng Hou,Biao Huang
This work aims at presenting a new sampled-data model-free adaptive control (SDMFAC) for continuous-time systems with the explicit use of sampling period and past input and output (I/O) data to enhance control performance. A sampled-data-based dynamical linearization model (SDDLM) is established to address the unknown nonlinearities and nonaffine structure of the continuous-time system, which all the
-
Robust federated learning with voting and scaling Future Gener. Comput. Syst. (IF 7.5) Pub Date : 2023-11-20 Xiang-Yu Liang, Heng-Ru Zhang, Wei Tang, Fan Min
Federated learning is vulnerable to poisoning attacks due to the inability to verify the authenticity of local data. Existing robust federated learning methods maintain a global model by discarding potentially risky local updates. However, they generally assume that the server knows the number of potentially abnormal clients. In this paper, we propose a robust federated learning method based on voting
-
A damage-informed neural network framework for structural damage identification Comput. Struct. (IF 4.7) Pub Date : 2023-11-18 Hau T. Mai, Seunghye Lee, Joowon Kang, Jaehong Lee
In this work, an effective Damage-Informed Neural Network (DINN) is first developed to pinpoint the position and extent of structural damage. Instead of resolving the damage identification problem by conventional numerical methods, a Deep Neural Network (DNN) is employed to minimize the loss function which is designed by combining multiple damage location assurance criterion and flexibility matrices
-
A novel twin branch network based on mutual training strategy for ship detection in SAR images Complex Intell. Syst. (IF 5.8) Pub Date : 2023-11-17 Yilong Lv, Min Li, Yujie He