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Actuator and sensor fault isolation in a class of nonlinear dynamical systems

Journal of Automation and Intelligence 2024年第2期3卷 57-72页

作者： Hamed Tirandaz Christodoulos Keliris Marios M.Polycarpou KIOS Research Center for Intelligent Systems and Networks and the Department of Electrical and Computer EngineeringUniversity of CyprusNicosia 1678Cyprus

Fault isolation in dynamical systems is a challenging task due to modeling uncertainty and measurement noise,interactive effects of multiple faults and fault *** paper proposes a unified approach for isolation of multiple actuator or sensor faults in a class of nonlinear uncertain dynamical *** and sensor fault isolation are accomplished in two independent modules,that monitor the system and are able to isolate the potential faulty actuator(s)or sensor(s).For the sensor fault isolation(SFI)case,a module is designed which monitors the system and utilizes an adaptive isolation threshold on the output residuals computed via a nonlinear estimation scheme that allows the isolation of single/multiple faulty sensor(s).For the actuator fault isolation(AFI)case,a second module is designed,which utilizes a learning-based scheme for adaptive approximation of faulty actuator(s)and,based on a reasoning decision logic and suitably designed AFI thresholds,the faulty actuator(s)set can be *** effectiveness of the proposed fault isolation approach developed in this paper is demonstrated through a simulation example.

关键词： Actuator and sensor fault isolation Adaptive approximation Observer-based fault diagnosis Reasoning-based decision logic

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Reducing the Computational Cost of Transformers for Person Re-identification

Reducing the Computational Cost of Transformers for Person R...

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2023 IEEE International Conference on robotics and Biomimetics, ROBIO 2023

作者： Wang, Wen Lin, Zheyuan Ji, Shanshan Li, Te Gu, Jason Wan, Minhong Zhang, Chunlong Research Center for Intelligent Robotics Zhejiang Lab Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou311100 China Dalhousie University Department of Electrical and Computer Engineering Canada

ISBN: (纸本)9798350325706

Transformer-based visual technologies have witnessed remarkable progress in recent years, and person re-identification (ReID) is one of the active research areas that adopts transformers to improve the performance. However, a major challenge of applying transformers to ReID is the high computational cost, which hinders the real-time deployment of such methods. To address this issue, this paper proposes two simple yet effective techniques to reduce the computation of transformers for ReID. The first technique is to eliminate the invalid patches that do not contain any person information, thereby reducing the number of tokens fed into the transformer. Considering that computational complexity is quadratic with respect to input tokens, the second technique partitions the image into multiple windows, applies separate transformers to each window, and merges class tokens from each window, which can reduce the complexity of the self-attention mechanism. By combining these two techniques, our proposed method reduces the SOTA baseline model by 12.2% FLOPs, while slightly improving the rank-1 accuracy and only sacrificing 1.1% mAP on DukeMTMC-ReID dataset. © 2023 IEEE.

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Gradient Optimizer Algorithm with Hybrid Deep Learning Based Failure Detection and Classification in the Industrial Environment

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computer Modeling in engineering & Sciences 2024年第2期138卷 1341-1364页

作者： Mohamed Zarouan Ibrahim M.Mehedi Shaikh Abdul Latif Md.Masud Rana Department of Electrical and Computer Engineering(ECE) King Abdulaziz UniversityJeddah21589Saudi Arabia Center of Excellence in Intelligent Engineering Systems(CEIES) King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Nuclear Engineering King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Electrical and Electronic Engineering Rajshahi University of Engineering and TechnologyRajshahiBangladesh

Failure detection is an essential task in industrial systems for preventing costly downtime and ensuring the seamlessoperation of the system. Current industrial processes are getting smarter with the emergence of Industry ***, various modernized industrial processes have been equipped with quite a few sensors to collectprocess-based data to find faults arising or prevailing in processes along with monitoring the status of *** diagnosis of rotating machines serves a main role in the engineering field and industrial production. Dueto the disadvantages of existing fault, diagnosis approaches, which greatly depend on professional experienceand human knowledge, intellectual fault diagnosis based on deep learning (DL) has attracted the researcher’sinterest. DL reaches the desired fault classification and automatic feature learning. Therefore, this article designs a Gradient Optimizer Algorithm with Hybrid Deep Learning-based Failure Detection and Classification (GOAHDLFDC)in the industrial environment. The presented GOAHDL-FDC technique initially applies continuous wavelettransform (CWT) for preprocessing the actual vibrational signals of the rotating machinery. Next, the residualnetwork (ResNet18) model was exploited for the extraction of features from the vibration signals which are thenfed into theHDLmodel for automated fault detection. Finally, theGOA-based hyperparameter tuning is performedtoadjust the parameter valuesof theHDLmodel *** experimental result analysis of the GOAHDL-FD Calgorithm takes place using a series of simulations and the experimentation outcomes highlight the better resultsof the GOAHDL-FDC technique under different aspects.

关键词： Fault detection Industry 4.0 gradient optimizer algorithm deep learning rotating machineries artificial intelligence

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Modeling and Parameter Identification for Human-robot Coupled systems in Powered Lower Limb Prostheses. 5

Modeling and Parameter Identification for Human-robot Couple...

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5th World Robot Conference Symposium on Advanced robotics and Automation, WRC SARA 2023

作者： Huang, Yongshan Wang, Xin Liang, Dingkun Xiong, Jiaming Xie, Anhuan Gu, Jason Research Center for Intelligent Robotics Zhejiang Lab Hangzhou311100 China Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou311100 China Dalhousie University Department of Electrical and Computer Engineering HalifaxNS Canada

ISBN: (纸本)9798350307320

Good trajectory tracking results are the basis for the implementation of other upper-level algorithms in powered lower limb prostheses, which rely on dynamics modeling and good parameter identification. However, powered lower limb prosthesis are human-robot coupled systems with human-inthe-loop, and it has been a challenge to model them as well as to perform parameter identification. This paper proposes a modeling method that can realize the decoupling of human-machine, and by constructing the linearized models of human-machine coupled system and prosthetic subsystem about the physical parameter sets respectively, and finally obtaining the relationship of their corresponding physical parameter sets, thus the method of indirectly identifying the parameters of human-machine coupled system through the prosthetic subsystem can be realized. The experimental results verify the feasibility and effectiveness of the method proposed in this paper. And the experimental results also shows that prosthetic controller should take advantage of dynamic of hip motion in order to make the wearer more comfortable and fluid when swinging the leg, and will also make the prosthesis more energy efficient. © 2023 IEEE.

关键词： Energy efficiency

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A novel deep learning approach for automated grading of knee osteoarthritis severity

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Multimedia Tools and Applications 2024年 1-20页

作者： Kaur, PrabSimran Kohli, Guneet Singh Bedi, Jatin Wasly, Saud Department of Computer Science and Engineering Thapar Institute of Engineering and Technology Punjab Patiala India Electrical and Computer Engineering Department King Abdulaziz University Jeddah22254 Saudi Arabia Center of Excellence in Intelligent Engineering Systems King Abdulaziz University Jeddah22254 Saudi Arabia

Knee Osteoarthritis (OA) is a prevalent musculoskeletal disorder that affects the knee joint that causes pain, stiffness, and reduced mobility. It is also known as "Degenerative Joint Disease" and is caused by the degeneration of cartilage in the knee joint, leading to bone-on-bone contact and further damage. Knee OA is prevalent in the population, affecting around 22% to 39% of people in India, and there is currently no treatment available that can halt the progression of the disease. Therefore, early diagnosis and management of symptoms are essential to reduce its impact on an individual’s quality of life. To address this issue, have introduced a framework that leverages ConvNeXt architecture, a modernization of ResNets (ResNet-50) architecture towards Hierarchical Transformers (Swin Transformers), to provide accurate identification and classification of knee osteoarthritis. The classification of knee osteoarthritis was done using the Kellgren and Lawrence (KL) graded X-ray images. These images of the damaged knees are preprocessed and augmented, creating a scaled, enhanced, and varied version of the features, thus making the data fitter and more significant for classification. The performance estimation of the proposed strategy is conducted on the Osteoarthritis Initiative (OAI), a research project focused on knee osteoarthritis that works in partnership with NIH and other private industries to develop a public domain dataset that can facilitate research and evaluation. It involves training the prepared data using various hyper-tuned versions of ConvNeXt. The different fine-tuned results of the ConvNeXt models on each KL Grade are evaluated against the other state-of-the-art models and vision transformers. The comparative assessment of widely used performance measures shows that the proposed approach outperforms the conventional models by generating the highest score for all the KL grades. Lastly, an approach is employed to statistically confirm the validity of t

关键词： Joints (anatomy)

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Integrated Direct/Indirect Adaptive Robust Control for electrical Driven Injection Machine Mold Closing with Accurate Parameter Estimations 16th

Integrated Direct/Indirect Adaptive Robust Control for Elec...

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16th International Conference on intelligent robotics and Applications, ICIRA 2023

作者： Liao, Jianfeng Zhu, Shiqiang Meng, Qiwei Song, Wei Gu, Jason Research Center for Intelligent Robotics Zhejiang Engineering Research Center for Intelligent Robotics Zhejiang Lab Hangzhou Hangzhou China Zhejiang University Hangzhou China Department of Electrical and Computer Engineering Dalhousie University Halifax Canada

ISBN: (纸本)9789819964949

In this paper, the control issue of the mold closing system of Injection machine is studied. In actual application, there are two major control problems needed to be addressed in controller design: to ensure the quality of products, the precision of mold needs to be guaranteed;the operation parameters of the machines are willing to be recorded for analysis, such as health monitoring and prognosis. However, the existing controllers usually rely on the servo motor, where the dynamics of the mold closing system is not taken into consideration, that may lead to degraded tracking performances. Moreover, the uncertain model parameters are usually ignored or simply treated as disturbances. Instead in this paper, the nonlinear dynamic model of mold closing system is first proposed including parameter uncertainties and disturbances for controller design. Subsequently, an integrated direct/indirect adaptive robust control law(DIARC) is proposed that not only achieves excellent tracking performance but also attenuates the parameter uncertainties with accurate parameter learning. Comparative experiments are carried out to verify our proposed approach and the results demonstrate the effectiveness of the integrated direct/indirect adaptive robust control. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Robust control

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Decision-Making in Robotic Grasping with Large Language Models 16th

Decision-Making in Robotic Grasping with Large Language Mo...

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16th International Conference on intelligent robotics and Applications, ICIRA 2023

作者： Liao, Jianfeng Zhang, Haoyang Qian, Haofu Meng, Qiwei Sun, Yinan Sun, Yao Song, Wei Zhu, Shiqiang Gu, Jason Research Center for Intelligent Robotics Zhejiang Lab Hangzhou China Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou China Zhejiang University Hangzhou China Department of Electrical and Computer Engineering Dalhousie University Halifax Canada

ISBN: (纸本)9789819964949

Recent advances in large language models have highlighted their potential to encode massive amounts of semantic knowledge for long-term autonomous decision-making, positioning them as a promising solution for powering the cognitive capabilities of future home-assistant robots. However, while large language models can provide high-level decision, there is still no unified paradigm for integrating them with robots’ perception and low-level action. In this paper, we propose a framework centered around a large language model, integrated with visual perception and motion planning modules, to investigate the robotic grasping task. Unlike traditional methods that only focus on generating stable grasps, our proposed approach can handle personalized user instructions and perform tasks more effectively in home scenarios. Our approach integrates existing state-of-the-art models in a simple and effective way, without requiring any fine-tuning, which makes it low-cost and easy to deploy. Experiments on a physical robot system demonstrate the feasibility of our approach. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Decision making

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Vision-Based Categorical Object Pose Estimation and Manipulation 16th

Vision-Based Categorical Object Pose Estimation and Manipul...

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16th International Conference on intelligent robotics and Applications, ICIRA 2023

作者： Meng, Qiwei Liao, Jianfeng Jun, Shao Xu, Nuo Xu, Zeming Sun, Yinan Sun, Yao Zhu, Shiqiang Gu, Jason Song, Wei Center for Intelligent Robotics Zhejiang Lab Hangzhou China Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou China School of Mechanical Engineering Zhejiang University Hangzhou China Department of Electrical and Computer Engineering Dalhousie University Halifax Canada

ISBN: (纸本)9789819964826

Object manipulation and environment interaction are of great significance for intelligent robots, especially service robots working under unstructured household and office scenarios. This paper proposes a novel approach for categorical unseen object grasping and manipulation. Different from recently popular end-to-end reinforcement learning methods, we develop models for geometric primitive abstraction of target objects, and accordingly estimate their pose as well as generate task-orientated grasp points. Such design emphasizes visual perception in guiding robotic manipulation, thereby enhancing model interpretability and reliability during implementation. In addition, we also conduct object grasping experiments both under simulation and real-world settings, which further verify the effectiveness and superiority of our method. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： intelligent robots

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Explainability Spectrum Analysis of Weather Sequences in Short-Term Load Forecasting 14th

Explainability Spectrum Analysis of Weather Sequences in Sho...

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14th International Conference on Information, Intelligence, systems, and Applications, IISA 2023

作者： Kontogiannis, Dimitrios Bargiotas, Dimitrios Daskalopulu, Aspassia Tsoukalas, Lefteri H. Department of Electrical and Computer Engineering University of Thessaly Volos Greece Center for Intelligent Energy Systems Purdue University West Lafayette United States

ISBN: (纸本)9783031674259

Short-term load forecasting tasks utilize a plethora of weather sequences in order to study meaningful features that describe complex relationships between environmental parameters and the variable of load, as well as the occurrence of impactful events, thus reinforcing the predictive power of estimators. It is evident that the inclusion of those additional features increases the input dimensions and decreases the overall explainability of forecasting models, since not all of the extracted features are equally compatible with the assumptions of the regressors, resulting in models where the justification of predictive potency, the connection between the variance of the target variable and the variance of predictors, as well as the explanation of the computational flow become increasingly difficult due to ambiguity. Therefore, this analysis investigates the representativeness of weather variables and examines the explainability of interpretable short-term load forecasting structures that utilize environmental sequences towards the selection of important feature subsets that maximize the explained variance of load time series as well as the seasonal, trend and residual load components during training. This methodology considers linear regression and prominent tree-based estimators coupled with model-dependent feature importance frameworks utilizing game theoretic, permutative and impurity-based approaches for the derivation of optimal feature sets and feature set combinations in terms of explained variance scores. Additionally, two structural approaches of explainable feature set generation from the joint feature importance scores through the utilization of hierarchical and majority inclusion are compared, their execution behavior towards explainability maximization is examined, and the main observations from each stage of this analysis are discussed. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Weather forecasting

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An Investigation into the Accuracy of Blood Oxygen Saturation Measurement using Smart Watch Devices Among Pediatric Users in Saudi Arabia 3

An Investigation into the Accuracy of Blood Oxygen Saturatio...

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3rd IEEE International Biomedical Instrumentation and Technology Conference, IBITeC 2023

作者： Turki, Ahmad F. King Abdul Aziz University Center of Excellence in Intelligent Engineering Systems Electrical and Computer Engineering Department Jeddah Saudi Arabia

ISBN: (纸本)9798350302424

Advancements in technology have led to the development and increased use of biomedical wearable devices equipped with various biosensors. This study aimed to evaluate the accuracy of blood oxygen saturation measurements in Saudi pediatric users using the Apple Watch Series 6 and Samsung Galaxy Watch 4. These smartwatches, widely available and relatively affordable fitness trackers, offer the capability to measure blood oxygen saturation and facilitate health monitoring. This paper presents preliminary findings from an investigation into the accuracy of the Apple Watch and Samsung Watch in measuring blood oxygen saturation. Data was collected from a sample of one hundred Saudi pediatric users, comprising 43 females and 57 males, with an average age of 13±3.3 years and a BMI of 21.07±6.3 kg/m2. Three measurements of blood oxygen saturation were simultaneously recorded using a hospital-grade pulse oximetry machine, as well as the Apple Watch and the Samsung Watch. Statistical comparisons were conducted using a T-test with a significance level of p=0.05, and the results were visualized using Bland-Altman plots. The T-test results indicated a statistically significant difference between the oximeter readings of the smartwatches and the medical-grade oximeter. Additionally, the calculation of errors revealed discrepancies between the measurements obtained from the smartwatches and the hospital-grade oximeter. It was observed that the errors were generally lower in measurements taken from male subjects compared to female subjects. The Bland-Altman plots, considering data from both the Apple Watch and the Samsung Watch, showed moderate agreement between the smartwatches and the hospital pulse oximetry. These findings suggest that while the Apple Watch and Samsung Watch demonstrate potential for measuring blood oxygen saturation, there are discrepancies compared to the accuracy of the hospital-grade oximeter. Further research and refinement may be necessary to improve the pr

关键词： Watches

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