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- Lv, Zhihan, Dr. 1984-, et al.
(författare)
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Secure Deep Learning in Defense in Deep-Learning-as-a-Service Computing Systems in Digital Twins
- 2024
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Ingår i: IEEE Transactions on Computers. - : IEEE. - 0018-9340 .- 1557-9956. ; 73:3, s. 656-668
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Tidskriftsartikel (refereegranskat)abstract
- While Digital Twins (DTs) bring convenience to city managers, they also generate new challenges to city network security. Currently, cyberspace security becomes increasingly complicated. Intrusion detection and Deep Learning (DL) are combined with shunning security threats in service computing systems and improving network defense capabilities. DTs can be applied to network security. People's understanding of cyberspace security can be improved using DTs to digitally define, model, and display the network environment and security status. The intrusion detection data are optimized based on DL technology, and a network intrusion detection algorithm integrated with Deep Neural Network (DNN) model is proposed. In the cloud service system, a trust model based on Keyed-Hashing-based Self-Synchronization (KHSS) is introduced. This model predicts the security state and detects attacks according to existing malicious attacks, ensuring the network security defense system's regular operation. Finally, simulation experiments verify the Deep Belief Networks (DBN) model's feasibility and the cloud trust model. The DBN algorithm proposed improves the correct detection rate of unknown samples by 4.05% compared with the Support Vector Machine (SVM) algorithm. From the 20,100 pieces of data in the test dataset, the number of correct attacks detected by the DBN algorithm exceeds those by the SVM algorithm by 818. DBN algorithm requires a short detection time while ensuring optimal detection accuracy. The KHSS+DBN model predicts cloud security states, and the results are the same as the actual states, with an error of only 1%similar to 2%.
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- Cao, Bin, et al.
(författare)
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Mobility-Aware Multiobjective Task Offloading for Vehicular Edge Computing in Digital Twin Environment
- 2023
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Ingår i: IEEE Journal on Selected Areas in Communications. - : Institute of Electrical and Electronics Engineers (IEEE). - 0733-8716 .- 1558-0008. ; 41:10, s. 3046-3055
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Tidskriftsartikel (refereegranskat)abstract
- In vehicular edge computing (VEC), vehicle users (VUs) can offload their computation-intensive tasks to edge server (ES) that provides additional computation resources. Due to the edge server being closer to VUs, the propagation delay between the ESs and the VUs is lower compared to cloud computing. Applying digital twin to VEC allows for low-cost trial in task offloading. In real-word, the mobility of VUs cannot be ignored and the downlink delay in receiving process results from ES is related to the mobility of VUs. Therefore, a five-objective optimization model including downlink delay, computation delay, energy consumption, load balancing, and user satisfaction of the VUs is constructed. To solve the above model, an improved CMA-ES algorithm based on the guiding point (GP-CMA-ES) is proposed. When the number of VUs increases, the dimension of variables also increases. Therefore, a convergence-related variable grouping strategy based on the relationship detection between variables and objectives is proposed. The performance of algorithm GP-CMA-ES is compared with five algorithms in the digital twin environment.
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- Cao, Bin, et al.
(författare)
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Multiobjective Evolution of the Explainable Fuzzy Rough Neural Network With Gene Expression Programming
- 2022
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Ingår i: IEEE transactions on fuzzy systems. - : Institute of Electrical and Electronics Engineers (IEEE). - 1063-6706 .- 1941-0034. ; 30:10, s. 4190-4200
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Tidskriftsartikel (refereegranskat)abstract
- The fuzzy logic-based neural network usually forms fuzzy rules via multiplying the input membership degrees, which lacks expressiveness and flexibility. In this article, a novel neural network model is designed by integrating the gene expression programming into the interval type-2 fuzzy rough neural network, aiming to generate fuzzy rules with more expressiveness utilizing various logical operators. The network training is regarded as a multiobjective optimization problem through simultaneously considering network precision, explainability, and generalization. Specifically, the network complexity can be minimized to generate concise and few fuzzy rules for improving the network explainability. Inspired by the extreme learning machine and the broad learning system, an enhanced distributed parallel multiobjective evolutionary algorithm is proposed. This evolutionary algorithm can flexibly explore the forms of fuzzy rules, and the weight refinement of the final layer can significantly improve precision and convergence by solving the pseudoinverse. Experimental results show that the proposed multiobjective evolutionary network framework is superior in both effectiveness and explainability.
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- Cao, Bin, et al.
(författare)
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Multiobjective Image Compression based on Tensor Decomposition
- 2023
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Ingår i: 2023 8th International Conference on Cloud Computing and Big Data Analytics, ICCCBDA. - : IEEE. - 9781665455336 - 9781665455343 ; , s. 545-550
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Konferensbidrag (refereegranskat)abstract
- Most of the traditional image compression methods vectorize the data contained in the image and then compress it. However, this approach does not take into account the high-dimensional information inside the image. To solve this problem, this paper regards the color image as a third-order tensor, and proposes a method of color image compression based on Tucker decomposition and multiobjective optimization. The tensor size compression ratio and Hu invariant moment similarity are proposed to measure the image compression quality. And to more comprehensively consider the sensitivity of human visual system to different visual signals, the five-objective optimization model of image compression is constructed. The five-objective optimization model includes: the above two indexes, information content weighted structure similarity index, color image feature similarity and information fidelity criterion. In addition, an angle-aware opposition-based learning strategy is proposed to improve the reference vector guided selection strategy of RVEA*. In the experiments, this method could effectively solve the problem of color image compression.
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- Chen, Ke-Ling, et al.
(författare)
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Effects of Tocilizumab on Experimental Severe Acute Pancreatitis and Associated Acute Lung Injury
- 2016
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Ingår i: Critical Care Medicine. - : LIPPINCOTT WILLIAMS & WILKINS. - 0090-3493 .- 1530-0293. ; 44:8, s. E664-E677
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Tidskriftsartikel (refereegranskat)abstract
- Objective: To examine the therapeutic effects of tocilizumab, an antibody against interleukin-6 receptor, on experimental severe acute pancreatitis and associated acute lung injury. The optimal dose of tocilizumab and the activation of interleukin-6 inflammatory signaling were also investigated. Design: Randomized experiment. Setting: Research laboratory at a university hospital. Subject: Experimental severe acute pancreatitis in rats. Interventions: Severe acute pancreatitis was induced by retrograde injection of sodium taurocholate (50 mg/kg) into the biliopancreatic duct. In dose-study, rats were administered with different doses of tocilizumab (1, 2, 4, 8, and 16 mg/kg) through the tail vein after severe acute pancreatitis induction. In safety-study, rats without severe acute pancreatitis induction were treated with high doses of tocilizumab (8, 16, 32, and 64 mg/kg). Serum and tissue samples of rats in time-study were collected for biomolecular and histologic evaluations at different time points (2, 6, 12, 18, and 24 hr). Measurements and Main Results: 1) Under the administration of tocilizumab, histopathological scores of pancreas and lung were decreased, and severity parameters related to severe acute pancreatitis and associated lung injury, including serum amylase, C-reactive protein, lung surfactant protein level, and myeloperoxidase activity, were all significant alleviated in rat models. 2) Dose-study demonstrated that 2 mg/kg tocilizumab was the optimal treatment dose. 3) Basing on multi-organ pathologic evaluation, physiological and biochemical data, no adverse effect and toxicity of tocilizumab were observed in safety-study. 4) Pancreatic nuclear factor-kappa B and signal transducer and activator of transcription 3 were deactivated, and the serum chemokine (C-X-C motif) ligand 1 was down-regulated after tocilizumab administration. Conclusions: Our study demonstrated tocilizumab, as a marketed drug commonly used for immune-mediated diseases, was safe and effective for the treatment of experimental severe acute pancreatitis and associated acute lung injury. Our findings provide experimental evidences for potential clinical application of tocilizumab in severe acute pancreatitis and associated complications.
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- Cui, Shaohua, et al.
(författare)
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Adaptive Collision-Free Trajectory Tracking Control for String Stable Bidirectional Platoons
- 2023
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Ingår i: IEEE Transactions on Intelligent Transportation Systems. - 1524-9050 .- 1558-0016. ; 24:11, s. 12141-12153
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Tidskriftsartikel (refereegranskat)abstract
- Autonomous vehicle (AV) platoons, especially those with the bidirectional communication topology, have significant practical value, as they not only increase link capacity and reduce vehicle energy consumption, but also reduce the consumption of communication resources. Small gaps between AVs in a platoon easily lead to emergency braking or even collisions between consecutive AVs. This paper applies barrier Lyapunov functions to collision avoidance between AVs in a bidirectional platoon during trajectory tracking. Based on backstepping technique, an adaptive collision-free platoon trajectory tracking control algorithm is developed to distributedly design control laws for each AV in the platoon. The control algorithm does not need to introduce additional car-following models to simulate AV driving, and only needs to integrate the position trajectories of consecutive AVs to avoid inter-vehicle collisions. Two sign functions are introduced into the control laws of each AV to ensure strong string stability for bidirectional AV platoons. Moreover, uncertainties and external disturbances in vehicle motion are effectively compensated by introducing adaptation laws. Strong string stability is rigorously proved. CarSIM-based comparison simulations verify the effectiveness of the proposed control algorithm in avoiding inter-vehicle collisions, compensating for uncertainties in vehicle motion, and suppressing the amplification of spacing errors along the platoon.
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