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Robustness Study and Superior Method Development and Validation for Analytical Assay Method of Atropine Sulfate in Pharmaceutical Ophthalmic Solution
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作者 Md. Nazmus Sakib Chowdhury Sreekanta Nath Dalal +4 位作者 Md. Ariful Islam Md. Anwar Hossain Pranab Kumar Das Shakawat Hossain Parajit Das 《American Journal of Analytical Chemistry》 CAS 2024年第5期151-164,共14页
Background: The robustness is a measurement of an analytical chemical method and its ability to contain unaffected by little with deliberate variation of analytical chemical method parameters. The analytical chemical ... Background: The robustness is a measurement of an analytical chemical method and its ability to contain unaffected by little with deliberate variation of analytical chemical method parameters. The analytical chemical method variation parameters are based on pH variability of buffer solution of mobile phase, organic ratio composition changes, stationary phase (column) manufacture, brand name and lot number variation;flow rate variation and temperature variation of chromatographic system. The analytical chemical method for assay of Atropine Sulfate conducted for robustness evaluation. The typical variation considered for mobile phase organic ratio change, change of pH, change of temperature, change of flow rate, change of column etc. Purpose: The aim of this study is to develop a cost effective, short run time and robust analytical chemical method for the assay quantification of Atropine in Pharmaceutical Ophthalmic Solution. This will help to make analytical decisions quickly for research and development scientists as well as will help with quality control product release for patient consumption. This analytical method will help to meet the market demand through quick quality control test of Atropine Ophthalmic Solution and it is very easy for maintaining (GDP) good documentation practices within the shortest period of time. Method: HPLC method has been selected for developing superior method to Compendial method. Both the compendial HPLC method and developed HPLC method was run into the same HPLC system to prove the superiority of developed method. Sensitivity, precision, reproducibility, accuracy parameters were considered for superiority of method. Mobile phase ratio change, pH of buffer solution, change of stationary phase temperature, change of flow rate and change of column were taken into consideration for robustness study of the developed method. Results: The limit of quantitation (LOQ) of developed method was much low than the compendial method. The % RSD for the six sample assay of developed method was 0.4% where the % RSD of the compendial method was 1.2%. The reproducibility between two analysts was 100.4% for developed method on the contrary the compendial method was 98.4%. 展开更多
关键词 robustness Method Validation HPLC Compendial Method Method Development GDP LOQ
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Image Hiding with High Robustness Based on Dynamic Region Attention in the Wavelet Domain
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作者 Zengxiang Li Yongchong Wu +3 位作者 Alanoud Al Mazroa Donghua Jiang Jianhua Wu Xishun Zhu 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第10期847-869,共23页
Hidden capacity,concealment,security,and robustness are essential indicators of hiding algorithms.Currently,hiding algorithms tend to focus on algorithmic capacity,concealment,and security but often overlook the robus... Hidden capacity,concealment,security,and robustness are essential indicators of hiding algorithms.Currently,hiding algorithms tend to focus on algorithmic capacity,concealment,and security but often overlook the robustness of the algorithms.In practical applications,the container can suffer from damage caused by noise,cropping,and other attacks during transmission,resulting in challenging or even impossible complete recovery of the secret image.An image hiding algorithm based on dynamic region attention in the multi-scale wavelet domain is proposed to address this issue and enhance the robustness of hiding algorithms.In this proposed algorithm,a secret image of size 256×256 is first decomposed using an eight-level Haar wavelet transform.The wavelet transform generates one coefficient in the approximation component and twenty-four detail bands,which are then embedded into the carrier image via a hiding network.During the recovery process,the container image is divided into four non-overlapping parts,each employed to reconstruct a low-resolution secret image.These lowresolution secret images are combined using densemodules to obtain a high-quality secret image.The experimental results showed that even under destructive attacks on the container image,the proposed algorithm is successful in recovering a high-quality secret image,indicating that the algorithm exhibits a high degree of robustness against various attacks.The proposed algorithm effectively addresses the robustness issue by incorporating both spatial and channel attention mechanisms in the multi-scale wavelet domain,making it suitable for practical applications.In conclusion,the image hiding algorithm introduced in this study offers significant improvements in robustness compared to existing algorithms.Its ability to recover high-quality secret images even in the presence of destructive attacksmakes it an attractive option for various applications.Further research and experimentation can explore the algorithm’s performance under different scenarios and expand its potential applications. 展开更多
关键词 Image hiding robustness wavelet transform dynamic region attention
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Physics-Constrained Robustness Enhancement for Tree Ensembles Applied in Smart Grid
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作者 Zhibo Yang Xiaohan Huang +2 位作者 Bingdong Wang Bin Hu Zhenyong Zhang 《Computers, Materials & Continua》 SCIE EI 2024年第8期3001-3019,共19页
With the widespread use of machine learning(ML)technology,the operational efficiency and responsiveness of power grids have been significantly enhanced,allowing smart grids to achieve high levels of automation and int... With the widespread use of machine learning(ML)technology,the operational efficiency and responsiveness of power grids have been significantly enhanced,allowing smart grids to achieve high levels of automation and intelligence.However,tree ensemble models commonly used in smart grids are vulnerable to adversarial attacks,making it urgent to enhance their robustness.To address this,we propose a robustness enhancement method that incorporates physical constraints into the node-splitting decisions of tree ensembles.Our algorithm improves robustness by developing a dataset of adversarial examples that comply with physical laws,ensuring training data accurately reflects possible attack scenarios while adhering to physical rules.In our experiments,the proposed method increased robustness against adversarial attacks by 100%when applied to real grid data under physical constraints.These results highlight the advantages of our method in maintaining efficient and secure operation of smart grids under adversarial conditions. 展开更多
关键词 Tree ensemble robustness enhancement adversarial attack smart grid
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Dynamic Hypergraph Modeling and Robustness Analysis for SIoT
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作者 Yue Wan Nan Jiang Ziyu Liu 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第9期3017-3034,共18页
The Social Internet of Things(SIoT)integrates the Internet of Things(IoT)and social networks,taking into account the social attributes of objects and diversifying the relationship between humans and objects,which over... The Social Internet of Things(SIoT)integrates the Internet of Things(IoT)and social networks,taking into account the social attributes of objects and diversifying the relationship between humans and objects,which overcomes the limitations of the IoT’s focus on associations between objects.Artificial Intelligence(AI)technology is rapidly evolving.It is critical to build trustworthy and transparent systems,especially with system security issues coming to the surface.This paper emphasizes the social attributes of objects and uses hypergraphs to model the diverse entities and relationships in SIoT,aiming to build an SIoT hypergraph generation model to explore the complex interactions between entities in the context of intelligent SIoT.Current hypergraph generation models impose too many constraints and fail to capture more details of real hypernetworks.In contrast,this paper proposes a hypergraph generation model that evolves dynamically over time,where only the number of nodes is fixed.It combines node wandering with a forest fire model and uses two different methods to control the size of the hyperedges.As new nodes are added,the model can promptly reflect changes in entities and relationships within SIoT.Experimental results exhibit that our model can effectively replicate the topological structure of real-world hypernetworks.We also evaluate the vulnerability of the hypergraph under different attack strategies,which provides theoretical support for building a more robust intelligent SIoT hypergraph model and lays the foundation for building safer and more reliable systems in the future. 展开更多
关键词 Large-scale artificial intelligence Social Internet of Things hypernetwork robustness analysis
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Data-driven diagnosis of high temperature PEM fuel cells based on the electrochemical impedance spectroscopy: Robustness improvement and evaluation
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作者 Dan Yu Xingjun Li +2 位作者 Samuel Simon Araya Simon Lennart Sahlin Vincenzo Liso 《Journal of Energy Chemistry》 SCIE EI CAS CSCD 2024年第9期544-558,共15页
Utilizing machine learning techniques for data-driven diagnosis of high temperature PEM fuel cells is beneficial and meaningful to the system durability. Nevertheless, ensuring the robustness of diagnosis remains a cr... Utilizing machine learning techniques for data-driven diagnosis of high temperature PEM fuel cells is beneficial and meaningful to the system durability. Nevertheless, ensuring the robustness of diagnosis remains a critical and challenging task in real application. To enhance the robustness of diagnosis and achieve a more thorough evaluation of diagnostic performance, a robust diagnostic procedure based on electrochemical impedance spectroscopy (EIS) and a new method for evaluation of the diagnosis robustness was proposed and investigated in this work. To improve the diagnosis robustness: (1) the degradation mechanism of different faults in the high temperature PEM fuel cell was first analyzed via the distribution of relaxation time of EIS to determine the equivalent circuit model (ECM) with better interpretability, simplicity and accuracy;(2) the feature extraction was implemented on the identified parameters of the ECM and extra attention was paid to distinguishing between the long-term normal degradation and other faults;(3) a Siamese Network was adopted to get features with higher robustness in a new embedding. The diagnosis was conducted using 6 classic classification algorithms—support vector machine (SVM), K-nearest neighbor (KNN), logistic regression (LR), decision tree (DT), random forest (RF), and Naive Bayes employing a dataset comprising a total of 1935 collected EIS. To evaluate the robustness of trained models: (1) different levels of errors were added to the features for performance evaluation;(2) a robustness coefficient (Roubust_C) was defined for a quantified and explicit evaluation of the diagnosis robustness. The diagnostic models employing the proposed feature extraction method can not only achieve the higher performance of around 100% but also higher robustness for diagnosis models. Despite the initial performance being similar, the KNN demonstrated a superior robustness after feature selection and re-embedding by triplet-loss method, which suggests the necessity of robustness evaluation for the machine learning models and the effectiveness of the defined robustness coefficient. This work hopes to give new insights to the robust diagnosis of high temperature PEM fuel cells and more comprehensive performance evaluation of the data-driven method for diagnostic application. 展开更多
关键词 PEM fuel cell Data-driven diagnosis robustness improvement and evaluation Electrochemical impedance spectroscopy
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Robust Beamforming Under Channel Prediction Errors for Time-Varying MIMO System 被引量:1
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作者 ZHU Yuting LI Zeng ZHANG Hongtao 《ZTE Communications》 2023年第3期77-85,共9页
The accuracy of acquired channel state information(CSI)for beamforming design is essential for achievable performance in multiple-input multiple-output(MIMO)systems.However,in a high-speed moving scene with time-divis... The accuracy of acquired channel state information(CSI)for beamforming design is essential for achievable performance in multiple-input multiple-output(MIMO)systems.However,in a high-speed moving scene with time-division duplex(TDD)mode,the acquired CSI depending on the channel reciprocity is inevitably outdated,leading to outdated beamforming design and then performance degradation.In this paper,a robust beamforming design under channel prediction errors is proposed for a time-varying MIMO system to combat the degradation further,based on the channel prediction technique.Specifically,the statistical characteristics of historical channel prediction errors are exploited and modeled.Moreover,to deal with random error terms,deterministic equivalents are adopted to further explore potential beamforming gain through the statistical information and ultimately derive the robust design aiming at maximizing weighted sum-rate performance.Simulation results show that the proposed beamforming design can maintain outperformance during the downlink transmission time even when channels vary fast,compared with the traditional beamforming design. 展开更多
关键词 time-varying channels time-division duplex robust beamforming channel prediction errors weighted sum-rate maximization
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Adversarial Attack-Based Robustness Evaluation for Trustworthy AI
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作者 Eungyu Lee Yongsoo Lee Taejin Lee 《Computer Systems Science & Engineering》 SCIE EI 2023年第11期1919-1935,共17页
Artificial Intelligence(AI)technology has been extensively researched in various fields,including the field of malware detection.AI models must be trustworthy to introduce AI systems into critical decisionmaking and r... Artificial Intelligence(AI)technology has been extensively researched in various fields,including the field of malware detection.AI models must be trustworthy to introduce AI systems into critical decisionmaking and resource protection roles.The problem of robustness to adversarial attacks is a significant barrier to trustworthy AI.Although various adversarial attack and defense methods are actively being studied,there is a lack of research on robustness evaluation metrics that serve as standards for determining whether AI models are safe and reliable against adversarial attacks.An AI model’s robustness level cannot be evaluated by traditional evaluation indicators such as accuracy and recall.Additional evaluation indicators are necessary to evaluate the robustness of AI models against adversarial attacks.In this paper,a Sophisticated Adversarial Robustness Score(SARS)is proposed for AI model robustness evaluation.SARS uses various factors in addition to the ratio of perturbated features and the size of perturbation to evaluate robustness accurately in the evaluation process.This evaluation indicator reflects aspects that are difficult to evaluate using traditional evaluation indicators.Moreover,the level of robustness can be evaluated by considering the difficulty of generating adversarial samples through adversarial attacks.This paper proposed using SARS,calculated based on adversarial attacks,to identify data groups with robustness vulnerability and improve robustness through adversarial training.Through SARS,it is possible to evaluate the level of robustness,which can help developers identify areas for improvement.To validate the proposed method,experiments were conducted using a malware dataset.Through adversarial training,it was confirmed that SARS increased by 70.59%,and the recall reduction rate improved by 64.96%.Through SARS,it is possible to evaluate whether an AI model is vulnerable to adversarial attacks and to identify vulnerable data types.In addition,it is expected that improved models can be achieved by improving resistance to adversarial attacks via methods such as adversarial training. 展开更多
关键词 AI robustness adversarial attack adversarial robustness robustness indicator trustworthy AI
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Robust Information Hiding Based on Neural Style Transfer with Artificial Intelligence 被引量:1
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作者 Xiong Zhang Minqing Zhang +3 位作者 Xu AnWang Wen Jiang Chao Jiang Pan Yang 《Computers, Materials & Continua》 SCIE EI 2024年第5期1925-1938,共14页
This paper proposes an artificial intelligence-based robust information hiding algorithm to address the issue of confidential information being susceptible to noise attacks during transmission.The algorithm we designe... This paper proposes an artificial intelligence-based robust information hiding algorithm to address the issue of confidential information being susceptible to noise attacks during transmission.The algorithm we designed aims to mitigate the impact of various noise attacks on the integrity of secret information during transmission.The method we propose involves encoding secret images into stylized encrypted images and applies adversarial transfer to both the style and content features of the original and embedded data.This process effectively enhances the concealment and imperceptibility of confidential information,thereby improving the security of such information during transmission and reducing security risks.Furthermore,we have designed a specialized attack layer to simulate real-world attacks and common noise scenarios encountered in practical environments.Through adversarial training,the algorithm is strengthened to enhance its resilience against attacks and overall robustness,ensuring better protection against potential threats.Experimental results demonstrate that our proposed algorithm successfully enhances the concealment and unknowability of secret information while maintaining embedding capacity.Additionally,it ensures the quality and fidelity of the stego image.The method we propose not only improves the security and robustness of information hiding technology but also holds practical application value in protecting sensitive data and ensuring the invisibility of confidential information. 展开更多
关键词 Information hiding neural style transfer robustness
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Finite-time Prescribed Performance Time-Varying Formation Control for Second-Order Multi-Agent Systems With Non-Strict Feedback Based on a Neural Network Observer 被引量:1
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作者 Chi Ma Dianbiao Dong 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2024年第4期1039-1050,共12页
This paper studies the problem of time-varying formation control with finite-time prescribed performance for nonstrict feedback second-order multi-agent systems with unmeasured states and unknown nonlinearities.To eli... This paper studies the problem of time-varying formation control with finite-time prescribed performance for nonstrict feedback second-order multi-agent systems with unmeasured states and unknown nonlinearities.To eliminate nonlinearities,neural networks are applied to approximate the inherent dynamics of the system.In addition,due to the limitations of the actual working conditions,each follower agent can only obtain the locally measurable partial state information of the leader agent.To address this problem,a neural network state observer based on the leader state information is designed.Then,a finite-time prescribed performance adaptive output feedback control strategy is proposed by restricting the sliding mode surface to a prescribed region,which ensures that the closed-loop system has practical finite-time stability and that formation errors of the multi-agent systems converge to the prescribed performance bound in finite time.Finally,a numerical simulation is provided to demonstrate the practicality and effectiveness of the developed algorithm. 展开更多
关键词 Finite-time control multi-agent systems neural network prescribed performance control time-varying formation control
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Influence of High-Robustness Polycarboxylate Superplasticizer on the Performances of Concrete Incorporating Fly Ash and Manufactured Sand
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作者 Panpan Cao Xiulin Huang +1 位作者 Shenxu Bao Jin Yang 《Fluid Dynamics & Materials Processing》 EI 2023年第8期2041-2051,共11页
Using ethylene glycol monovinyl polyoxyethylene ether,2-acrylamido-2-methylpropane sulfonic acid(AMPS)and acrylic acid as the main synthetic monomers,a high robustness polycarboxylate superplasticizer was prepared.The... Using ethylene glycol monovinyl polyoxyethylene ether,2-acrylamido-2-methylpropane sulfonic acid(AMPS)and acrylic acid as the main synthetic monomers,a high robustness polycarboxylate superplasticizer was prepared.The effects of initial temperature,ratio of acid to ether,amount of chain transfer agent,and synthesis process on the properties of the superplasticizer were studied.The molecular structure was characterized by GPC(Gel Permeation Chromatography)and IR(Infrared Spectrometer).As shown by the results,when the initial reaction temperature is 15℃,the ratio of acid to ether is 3.4:1 and the acrylic acid pre-neutralization is 15%,The AMPS substitution is 10%,the amount of chain transfer agent is 8%,and the performance of the synthesized superplasticizer is the best.Compared with commercially available ordinary polycarboxylate superplasticizer in C30 concrete prepared with manufactured sand and fly ash,the bleeding rate decreases by 52%,T50 decreases by 1.2 s,and the slump time decreases by 1.1 s.In C60 concrete prepared with fly ash and river sand,the bleeding rate decreases by 46%,T50 decreases by 0.8 s,and the slump time decreases by 3.2 s. 展开更多
关键词 Polycarboxylate superplasticizer EPEG robustness WORKABILITY
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Research on the model of high robustness computational optical imaging system
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作者 苏云 席特立 邵晓鹏 《Chinese Physics B》 SCIE EI CAS CSCD 2023年第2期264-272,共9页
Computational optical imaging is an interdisciplinary subject integrating optics, mathematics, and information technology. It introduces information processing into optical imaging and combines it with intelligent com... Computational optical imaging is an interdisciplinary subject integrating optics, mathematics, and information technology. It introduces information processing into optical imaging and combines it with intelligent computing, subverting the imaging mechanism of traditional optical imaging which only relies on orderly information transmission. To meet the high-precision requirements of traditional optical imaging for optical processing and adjustment, as well as to solve its problems of being sensitive to gravity and temperature in use, we establish an optical imaging system model from the perspective of computational optical imaging and studies how to design and solve the imaging consistency problem of optical system under the influence of gravity, thermal effect, stress, and other external environment to build a high robustness optical system. The results show that the high robustness interval of the optical system exists and can effectively reduce the sensitivity of the optical system to the disturbance of each link, thus realizing the high robustness of optical imaging. 展开更多
关键词 computational optical imaging high robustness sensitivity
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Robustness of community networks against cascading failures with heterogeneous redistribution strategies
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作者 宋波 吴惠明 +3 位作者 宋玉蓉 蒋国平 夏玲玲 王旭 《Chinese Physics B》 SCIE EI CAS CSCD 2023年第9期611-618,共8页
Network robustness is one of the core contents of complex network security research.This paper focuses on the robustness of community networks with respect to cascading failures,considering the nodes influence and com... Network robustness is one of the core contents of complex network security research.This paper focuses on the robustness of community networks with respect to cascading failures,considering the nodes influence and community heterogeneity.A novel node influence ranking method,community-based Clustering-LeaderRank(CCL)algorithm,is first proposed to identify influential nodes in community networks.Simulation results show that the CCL method can effectively identify the influence of nodes.Based on node influence,a new cascading failure model with heterogeneous redistribution strategy is proposed to describe and analyze node fault propagation in community networks.Analytical and numerical simulation results on cascading failure show that the community attribute has an important influence on the cascading failure process.The network robustness against cascading failures increases when the load is more distributed to neighbors of the same community instead of different communities.When the initial load distribution and the load redistribution strategy based on the node influence are the same,the network shows better robustness against node failure. 展开更多
关键词 community networks cascading failure model network robustness nodes influence identification
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Doping-enhanced robustness of anomaly-related magnetoresistance in WTe_(2±α)flakes
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作者 孟建超 陈鑫祥 +6 位作者 邵婷娜 刘明睿 姜伟民 张子涛 熊昌民 窦瑞芬 聂家财 《Chinese Physics B》 SCIE EI CAS CSCD 2023年第4期634-638,共5页
We study systematically the negative magnetoresistance(MR)effect in WTe_(2±α)flakes with different thicknesses and doping concentrations.The negative MR is sensitive to the relative orientation between electrica... We study systematically the negative magnetoresistance(MR)effect in WTe_(2±α)flakes with different thicknesses and doping concentrations.The negative MR is sensitive to the relative orientation between electrical-/magnetic-field and crystallographic orientation of WTe_(2±α).The analysis proves that the negative MR originates from chiral anomaly and is anisotropic.Maximum entropy mobility spectrum is used to analyze the electron and hole concentrations in the flake samples.It is found that the negative MR observed in WTe_(2±α)flakes with low doping concentration is small,and the high doping concentration is large.The doping-induced disorder obviously inhibits the positive MR,so the negative MR can be more easily observed.In a word,we introduce disorder to suppress positive MR by doping,and successfully obtain the negative MR in WTe_(2±α)flakes with different thicknesses and doping concentrations,which indicates that the chiral anomaly effect in WTe_(2)is robust. 展开更多
关键词 Weyl semimetal WTe_(2±α)flakes DOPING chiral anomaly robustness
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Robustness optimization for rapid prototyping of functional artifacts based on visualized computing digital twins
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作者 Jinghua Xu Kunqian Liu +5 位作者 Linxuan Wang Hongshuai Guo Jiangtao Zhan Xiaojian Liu Shuyou Zhang Jianrong Tan 《Visual Computing for Industry,Biomedicine,and Art》 EI 2023年第1期33-50,共18页
This study presents a robustness optimization method for rapid prototyping(RP)of functional artifacts based on visualized computing digital twins(VCDT).A generalized multiobjective robustness optimization model for RP... This study presents a robustness optimization method for rapid prototyping(RP)of functional artifacts based on visualized computing digital twins(VCDT).A generalized multiobjective robustness optimization model for RP of scheme design prototype was first built,where thermal,structural,and multidisciplinary knowledge could be integrated for visualization.To implement visualized computing,the membership function of fuzzy decision-making was optimized using a genetic algorithm.Transient thermodynamic,structural statics,and flow field analyses were conducted,especially for glass fiber composite materials,which have the characteristics of high strength,corrosion resistance,temperature resistance,dimensional stability,and electrical insulation.An electrothermal experiment was performed by measuring the temperature and changes in temperature during RP.Infrared thermographs were obtained using thermal field measurements to determine the temperature distribution.A numerical analysis of a lightweight ribbed ergonomic artifact is presented to illustrate the VCDT.Moreover,manufacturability was verified based on a thermal-solid coupled finite element analysis.The physical experiment and practice proved that the proposed VCDT provided a robust design paradigm for a layered RP between the steady balance of electrothermal regulation and manufacturing efficacy under hybrid uncertainties. 展开更多
关键词 robustness optimization design Rapid prototyping Functional artifacts Fuzzy decision-making Infrared thermographs Visualized computing digital twins
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Robustα-Fe_(2)O_(3)/Epoxy Resin Superhydrophobic Coatings for Anti-icing Property
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作者 乔燕明 TAO Xuan +2 位作者 LI Lei 阮敏 鲁礼林 《Journal of Wuhan University of Technology(Materials Science)》 SCIE EI CAS CSCD 2024年第3期621-626,共6页
α-Fe_(2)O_(3)/epoxy resin composite superhydrophobic coating was prepared withα-Fe_(2)O_(3) nanoparticles and epoxy resin by spin coating method.The coating without epoxy resin has higher contact angle(CA)and lower ... α-Fe_(2)O_(3)/epoxy resin composite superhydrophobic coating was prepared withα-Fe_(2)O_(3) nanoparticles and epoxy resin by spin coating method.The coating without epoxy resin has higher contact angle(CA)and lower ice adhesion strength(IAS),but the mechanical properties are poor.Theα-Fe_(2)O_(3)/epoxy resin composite superhydrophobic coating exhibits good mechanical durability.In addition,compared with the bare aluminum substrate,the Ecorr of the composite coating is positive and the Jcorr is lower.The inhibition efficiency of the composite coating is as high as 99.98%in 3.5 wt%NaCl solution.The difference in the microstructure caused by the two preparation methods leads to the changes in mechanical properties and corrosion resistance of composite superhydrophobic coating. 展开更多
关键词 SUPERHYDROPHOBIC ANTI-CORROSION ANTI-ICING robust
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Complementary-Label Adversarial Domain Adaptation Fault Diagnosis Network under Time-Varying Rotational Speed and Weakly-Supervised Conditions
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作者 Siyuan Liu Jinying Huang +2 位作者 Jiancheng Ma Licheng Jing Yuxuan Wang 《Computers, Materials & Continua》 SCIE EI 2024年第4期761-777,共17页
Recent research in cross-domain intelligence fault diagnosis of machinery still has some problems,such as relatively ideal speed conditions and sample conditions.In engineering practice,the rotational speed of the mac... Recent research in cross-domain intelligence fault diagnosis of machinery still has some problems,such as relatively ideal speed conditions and sample conditions.In engineering practice,the rotational speed of the machine is often transient and time-varying,which makes the sample annotation increasingly expensive.Meanwhile,the number of samples collected from different health states is often unbalanced.To deal with the above challenges,a complementary-label(CL)adversarial domain adaptation fault diagnosis network(CLADAN)is proposed under time-varying rotational speed and weakly-supervised conditions.In the weakly supervised learning condition,machine prior information is used for sample annotation via cost-friendly complementary label learning.A diagnosticmodel learning strategywith discretized category probabilities is designed to avoidmulti-peak distribution of prediction results.In adversarial training process,we developed virtual adversarial regularization(VAR)strategy,which further enhances the robustness of the model by adding adversarial perturbations in the target domain.Comparative experiments on two case studies validated the superior performance of the proposed method. 展开更多
关键词 time-varying rotational speed weakly-supervised fault diagnosis domain adaptation
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On the Application of Mixed Models of Probability and Convex Set for Time-Variant Reliability Analysis
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作者 Fangyi Li Dachang Zhu Huimin Shi 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第5期1981-1999,共19页
In time-variant reliability problems,there are a lot of uncertain variables from different sources.Therefore,it is important to consider these uncertainties in engineering.In addition,time-variant reliability problems... In time-variant reliability problems,there are a lot of uncertain variables from different sources.Therefore,it is important to consider these uncertainties in engineering.In addition,time-variant reliability problems typically involve a complexmultilevel nested optimization problem,which can result in an enormous amount of computation.To this end,this paper studies the time-variant reliability evaluation of structures with stochastic and bounded uncertainties using a mixed probability and convex set model.In this method,the stochastic process of a limit-state function with mixed uncertain parameters is first discretized and then converted into a timeindependent reliability problem.Further,to solve the double nested optimization problem in hybrid reliability calculation,an efficient iterative scheme is designed in standard uncertainty space to determine the most probable point(MPP).The limit state function is linearized at these points,and an innovative random variable is defined to solve the equivalent static reliability analysis model.The effectiveness of the proposed method is verified by two benchmark numerical examples and a practical engineering problem. 展开更多
关键词 Mixed uncertainty probability model convex model time-variant reliability analysis
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Semi-supervised learning based hybrid beamforming under time-varying propagation environments
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作者 Yin Long Hang Ding Simon Murphy 《Digital Communications and Networks》 SCIE CSCD 2024年第4期1168-1177,共10页
Hybrid precoding is considered as a promising low-cost technique for millimeter wave(mm-wave)massive Multi-Input Multi-Output(MIMO)systems.In this work,referring to the time-varying propagation circumstances,with semi... Hybrid precoding is considered as a promising low-cost technique for millimeter wave(mm-wave)massive Multi-Input Multi-Output(MIMO)systems.In this work,referring to the time-varying propagation circumstances,with semi-supervised Incremental Learning(IL),we propose an online hybrid beamforming scheme.Firstly,given the constraint of constant modulus on analog beamformer and combiner,we propose a new broadnetwork-based structure for the design model of hybrid beamforming.Compared with the existing network structure,the proposed network structure can achieve better transmission performance and lower complexity.Moreover,to enhance the efficiency of IL further,by combining the semi-supervised graph with IL,we propose a hybrid beamforming scheme based on chunk-by-chunk semi-supervised learning,where only few transmissions are required to calculate the label and all other unlabelled transmissions would also be put into a training data chunk.Unlike the existing single-by-single approach where transmissions during the model update are not taken into the consideration of model update,all transmissions,even the ones during the model update,would make contributions to model update in the proposed method.During the model update,the amount of unlabelled transmissions is very large and they also carry some information,the prediction performance can be enhanced to some extent by these unlabelled channel data.Simulation results demonstrate the spectral efficiency of the proposed method outperforms that of the existing single-by-single approach.Besides,we prove the general complexity of the proposed method is lower than that of the existing approach and give the condition under which its absolute complexity outperforms that of the existing approach. 展开更多
关键词 Hybrid beamforming time-varying environments Broad network Semi-supervised learning Online learning
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Gates joint locally connected network for accurate and robust reconstruction in optical molecular tomography
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作者 Minghua Zhao Yahui Xiao +2 位作者 Jiaqi Zhang Xin Cao Lin Wang 《Journal of Innovative Optical Health Sciences》 SCIE EI CSCD 2024年第3期11-22,共12页
Optical molecular tomography(OMT)is a potential pre-clinical molecular imaging technique with applications in a variety of biomedical areas,which can provide non-invasive quantitative three-dimensional(3D)information ... Optical molecular tomography(OMT)is a potential pre-clinical molecular imaging technique with applications in a variety of biomedical areas,which can provide non-invasive quantitative three-dimensional(3D)information regarding tumor distribution in living animals.The construction of optical transmission models and the application of reconstruction algorithms in traditional model-based reconstruction processes have affected the reconstruction results,resulting in problems such as low accuracy,poor robustness,and long-time consumption.Here,a gates joint locally connected network(GLCN)method is proposed by establishing the mapping relationship between the inside source distribution and the photon density on surface directly,thus avoiding the extra time consumption caused by iteration and the reconstruction errors caused by model inaccuracy.Moreover,gates module was composed of the concatenation and multiplication operators of three different gates.It was embedded into the network aiming at remembering input surface photon density over a period and allowing the network to capture neurons connected to the true source selectively by controlling three different gates.To evaluate the performance of the proposed method,numerical simulations were conducted,whose results demonstrated good performance in terms of reconstruction positioning accuracy and robustness. 展开更多
关键词 Optical molecular tomography gates module positioning accuracy robustness
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Time-dependent model for two-phase flow in ultra-high water-cut reservoirs:Time-varying permeability and relative permeability
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作者 Shao-Chun Wang Na Zhang +3 位作者 Zhi-Hao Tang Xue-Fei Zou Qian Sun Wei Liu 《Petroleum Science》 SCIE EI CAS CSCD 2024年第4期2536-2553,共18页
For the ultra-high water-cut reservoirs,after long-term water injection exploitation,the physical properties of the reservoir change and the heterogeneity of the reservoir becomes increasingly severe,which further agg... For the ultra-high water-cut reservoirs,after long-term water injection exploitation,the physical properties of the reservoir change and the heterogeneity of the reservoir becomes increasingly severe,which further aggravates the spatial difference of the flow field.In this study,the displacement experiments were employed to investigate the variations in core permeability,porosity,and relative permeability after a large amount of water injection.A relative permeability endpoint model was proposed by utilizing the alternating conditional expectation(ACE)transformation to describe the variation in relative permeability based on the experimental data.Based on the time dependent models for permeability and relative permeability,the traditional oil-water two-phase model was improved and discretized using the mimetic finite difference method(MFD).The two cases were launched to confirm the validation of the proposed model.The impact of time-varying physical features on reservoir production performance was studied in a real water flooding reservoir.The experimental results indicate that the overall relative permeability curve shifts to the right as water injection increases.This shift corresponds to a transition towards a more hydrophilic wettability and a decrease in residual oil saturation.The endpoint model demonstrates excellent accuracy and can be applied to time-varying simulations of reservoir physics.The impact of variations in permeability and relative permeability on the reservoir production performance yields two distinct outcomes.The time-varying permeability of the reservoir results in intensified water channeling and poor development effects.On the other hand,the time-varying relative permeability enhances the oil phase seepage capacity,facilitating oil displacement.The comprehensive time-varying behavior is the result of the combined influence of these two parameters,which closely resemble the actual conditions observed in oil field exploitation.The time-varying simulation technique of reservoir physical properties proposed in this paper can continuously and stably characterize the dynamic changes of reservoir physical properties during water drive development.This approach ensures the reliability of the simulation results regarding residual oil distribution. 展开更多
关键词 Mimetic finite difference Water flooding reservoir time-varying physical properties Numerical simulation
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