Robust Discrete-time Control for Uncertain time-delay Systems with Nonlinearities Under Hölder Continuity

Xin Wang,Xin Zuo,Jianwei Liu,Huaqing Liang,Qi Pan 제어로봇시스템학회 2016 제어로봇시스템학회 국제학술대회 논문집 Vol.2016 No.10

This paper is concerned with the robust discrete-time control for uncertain time-delay systems with nonlinearities and external noises. The parameter uncertainties enter into all the system matrices. The time-varying delay is unknown with given lower and upper bounds. The stochastic nonlinearities are described by satisfying a class of α Hölder condition. The problem addressed is the analysis and design of a stable controller such that, for all the parameter uncertainties, time-delay, stochastic nonlinearities and external noises, the resulting closed-loop system is exponentially stabile. The controller gain can be got by linear matrix inequalities (LMIs). Numerical examples are given to illustrate the effectiveness and verify the proposed theoretical results.

Distributed Optimization over General Directed Networks with Random Sleep Scheme

Zheng Wang,Lifeng Zheng,Huaqing Li 제어·로봇·시스템학회 2020 International Journal of Control, Automation, and Vol.18 No.10

Distributed optimization aims at optimizing a global objective function which is described by a sum of local objective functions through local information processing and sharing. This paper studies the problem of distributed optimization over a network in which underlying graph is generally directed strongly connected. Most existing distributed algorithms require each agent to observe the gradient of the local objective function per iteration, which leads to heavy computational cost. A computation-efficient distributed optimization algorithm incorporating a random sleep scheme is proposed by incorporating a rescaling gradient technique to address the unbalancedness of the directed graph. The implementation of the proposed algorithm allows agents not only locally allocates the weights on the received information, but also independently decides whether to execute gradient observation at each iteration. Theoretical analysis verifies that the proposed algorithm is able to seek the optimal solution with probability one. Simulations are shown to demonstrate the effectiveness of the proposed algorithm, show correctnessof the theoretical analysis, and investigate the tradeoffs between convergence performance and computation cost.

A sequential fuzzy diagnosis method for rotating machinery using ant colony optimization and possibility theory

Hao Sun,Ke Li,Huaqing Wang,Xueliang Ping,Yi Cao 대한기계학회 2014 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.28 No.4

This study proposes a novel intelligent fault diagnosis method for rotating machinery using ant colony optimization (ACO) and possibilitytheory. The non-dimensional symptom parameters (NSPs) in the frequency domain are defined to reflect the features of the vibrationsignals measured in each state. A sensitive evaluation method for selecting good symptom parameters using principal componentanalysis (PCA) is proposed for detecting and distinguishing faults in rotating machinery. By using ACO clustering algorithm, the synthesizingsymptom parameters (SSP) for condition diagnosis are obtained. A fuzzy diagnosis method using sequential inference and possibilitytheory is also proposed, by which the conditions of the machinery can be identified sequentially. Lastly, the proposed method iscompared with a conventional neural networks (NN) method. Practical examples of diagnosis for a V-belt driving equipment used in acentrifugal fan are provided to verify the effectiveness of the proposed method. The results verify that the faults that often occur in V-beltdriving equipment, such as a pulley defect state, a belt defect state and a belt looseness state, are effectively identified by the proposedmethod, while these faults are difficult to detect using conventional NN.

Systemic Family Therapy of Comorbidity of Anxiety and Depression with Epilepsy in Adolescents

Jing Li1,Xuefeng Wang,Huaqing Meng,Kebin Zeng,Fengying Quan,Fang Liu 대한신경정신의학회 2016 PSYCHIATRY INVESTIGATION Vol.13 No.3

ObjectiveaaThe aim of this study was to find if systemic family therapy (SFT) does work in anxiety and depression with epilepsy in adolescents (ADAE). Methodsaa104 adolescents with epilepsy, aged 13–20 years old, were included from December 2009 to December 2010, the enrolled patients were with anxiety [Hamilton Anxiety Scale (HAMA) score ≥14 points] or depression [Hamilton Depression Scale (HAMD) score ≥20 points]. The patients were randomly divided into the control group (n=52) treated with antiepileptic drugs (AED) and the intervention group (n=52) undergone Systemic Family Therapy (SFT) as well as AED. The AED improvements, anxiety and depression scores, Social Support Rating Scale (SSRS), Family Assessment Device (FAD) and scale of systemic family dynamics (SSFD) were observed after 3-month treatment. ResultsaaThe frequencies of epileptic seizures in intervention group was decreased much more significantly than the control group (4.22±3.54 times/month vs. 6.20±5.86 times/month, p=0.04); and the scores of anxiety (9.52±6.28 points vs. 13.48±8.47 points, p=0.01) and depression (13.86±9.17 points vs. 18.89±8.73 points, p=0.02) were significantly decreased than the control group; meanwhile, the family dynamics and family functions were significantly improved, and the social support was also increased (p<0.05). ConclusionaaSFT combined with AEDs had better efficacies than AEDs alone, not only the frequency of epileptic seizures was decreased, but also the patients’ anxiety and depression were improved, and the family dynamics, family functions and social support were improved.

An Edge-based Stochastic Proximal Gradient Algorithm for Decentralized Composite Optimization

Ling Zhang,Yu Yan,Zheng Wang,Huaqing Li 제어·로봇·시스템학회 2021 International Journal of Control, Automation, and Vol.19 No.11

This paper investigates decentralized composite optimization problems involving a common non-smooth regularization term over an undirected and connected network. In the same situation, there exist lots of gradientbased proximal distributed methods, but most of them are only sublinearly convergent. The proof of linear convergence for this series of algorithms is extremely difficult. To set up the problem, we presume all networked agents use the same non-smooth regularization term, which is the circumstance for most machine learning to implement based on centralized optimization. For this scenario, most existing proximal-gradient algorithms trend to ignore the cost of gradient evaluations, which results in degraded performance. To tackle this problem, we further set the local cost function to the average of a moderate amount of local cost subfunctions and develop an edge-based stochastic proximal gradient algorithm (SPG-Edge) by employing local unbiased stochastic averaging gradient method. When the non-smooth term does not exist, the proposed algorithm could be extended to some notable primal-dual domain algorithms, such as EXTRA and DIGing. Finally, we provide a simplified proof of linear convergence and conduct numerical experiments to illustrate the validity of theoretical results.

Biomechanical Study of 3 Osteoconductive Materials Applied in Pedicle Augmentation and Revision for Osteoporotic Vertebrae: Allograft Bone Particles, Calcium Phosphate Cement, Demineralized Bone Matrix

Chongyu Jia,Renjie Zhang,Jiaqi Wang,Bo Zhang,Huaqing Zhang,Liang Kang,Luping Zhou,Cailiang Shen 대한척추신경외과학회 2023 Neurospine Vol.20 No.4

Objective: This study assessed biomechanical properties of pedicle screws enhanced or revised with 3 materials. We aimed to compare the efficacy of these materials in pedicle augmentation and revision. Methods: One hundred twenty human cadaveric vertebrae were utilized for in vitro testing. Vertebrae bone density was evaluated. Allograft bone particles (ABP), calcium phosphate cement (CPC), and demineralized bone matrix (DBM) were used to augment or revise pedicle screw. Post the implantation of pedicle screws, parameters such as insertional torque, pullout strength, cycles to failure and failure load were measured using specialized instruments. Results: ABP, CPC, and DBM significantly enhanced biomechanical properties of the screws. CPC augmentation showed superior properties compared to ABP or DBM. ABP-augmented screws had higher cycles to failure and failure loads than DBM-augmented screws, with no difference in pullout strength. CPC-revised screws exhibited similar strength to the original screws, while ABP-revised screws showed comparable cycles to failure and failure loads but lower pullout strength. DBM-revised screws did not match the original screws’ strength. Conclusion: ABP, CPC, and DBM effectively improve pedicle screw stability for pedicle augmentation. CPC demonstrated the highest efficacy, followed by ABP, while DBM was less effective. For pedicle revision, CPC is recommended as the primary choice, with ABP as an alternative. However, using DBM for pedicle revision is not recommended.

Dual-loss CNN: A separability-enhanced network for current-based fault diagnosis of rolling bearings

Dongdong Liu,Lingli Cui,Gang Wang,Jiawei Xiang,Huaqing Wang 국제구조공학회 2024 Smart Structures and Systems, An International Jou Vol.33 No.4

Current-based mechanical fault diagnosis is more convenient and low cost since additional sensors are not required. However, it is still challenging to achieve this goal due to the weak fault information in current signals. In this paper, a dual-loss convolutional neural network (DLCNN) is proposed to implement the intelligent bearing fault diagnosis via current signals. First, a novel similarity loss (SimL) function is developed, which is expected to maximize the intra-class similarity and minimize the inter-class similarity in the model optimization operation. In the loss function, a weight parameter is further introduced to achieve a balance and leverage the performance of SimL function. Second, the DLCNN model is constructed using the presented SimL and the cross-entropy loss. Finally, the two-phase current signals are fused and then fed into the DLCNN to provide more fault information. The proposed DLCNN is tested by experiment data, and the results confirm that the DLCNN achieves higher accuracy compared to the conventional CNN. Meanwhile, the feature visualization presents that the samples of different classes are separated well.