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Modelling and Optimizing Motherboard Functional Testing in Laptop Manufacturing

Journal of Systems Science & Complexity 2024年第6期37卷 2406-2423页

作者： BAI Peng KANG Yu WANG Kangcheng ZHAO Yunbo DONG Shaojie Department of Automation University of Science and Technology of ChinaHefei 230026China Department of Automation&Institute of Advanced Technology University of Science and Technology of ChinaHefei 230026China Institute of Artificial Intelligence Hefei Comprehensive National Science CenterHefei 230088China Institute of Advanced Technology University of Science and Technology of ChinaHefei 230026China

Functional testing is key to fulfill quality control in laptop manufacturing and is of great economic ***,due to the unavailability of practical data,mathematical model and systematic perspective,it has barely been touched from the academic community to *** the first time,this work provides technical understanding of the key principles of functional testing,mathematically models the general framework,elucidates existing testing strategy under the proposed framework and model,and finally proposes a specified optimization strategy which outperforms existing *** work lays the model foundation for the further optimization of functional testing,and can be regarded as a good example of how a systematic approach can solve practical industrial challenges.

关键词： Laptop manufacturing modelling motherboard functional testing optimization

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Physics-informed deep Koopman operator for Lagrangian dynamic systems

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Science China(Information Sciences) 2024年第9期67卷 146-163页

作者： Xuefeng WANG Yang CAO Shaofeng CHEN Yu KANG Department of Automation University of Science and Technology of China State Key Laboratory of Fire Science University of Science and Technology of China Institute of Advanced Technology University of Science and Technology of China

Accurate mechanical system models are crucial for safe and stable control. Unlike linear systems,Lagrangian systems are highly nonlinear and difficult to optimize because of their unknown system *** research thus used deep neural networks to generate linear models of original systems by mapping nonlinear dynamic systems into a linear space with a Koopman observable function encoder. The controller then relies on the Koopman linear model. However, without physical information constraints, ensuring control consistency between the original nonlinear system and the Koopman system is tough, as the learning process of the Koopman observation function is unsupervised. This paper thus proposes a two-stage learning algorithm that uses structural subnetworks to build a physics-informed network topology to simultaneously learn the Koopman observable functions and the system energy representation. In the Koopman matrix learning session, a quadratic-constrained optimization problem is solved to ensure that the Koopman representation satisfies the energy difference matching hard constraint. The proposed energy-preserving deep Lagrangian Koopman(EPDLK) framework effectively represents the dynamics of the Lagrangian system while ensuring control consistency. The effectiveness of EPDLK is compared with those of various Koopman observable function construction methods in multistep prediction and trajectory tracking tasks. EPDLK achieves better control consistency by guaranteeing energy difference matching, which facilitates the application of the control law generated on the Koopman system directly to the original nonlinear Lagrangian system.

关键词： Koopman operator Lagrangian physics-informed nonlinear model predictive control (MPC)

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Decentralized Federated Learning Algorithm Under Adversary Eavesdropping

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IEEE/CAA Journal of Automatica Sinica 2025年第2期12卷 448-456页

作者： Lei Xu Danya Xu Xinlei Yi Chao Deng Tianyou Chai Tao Yang the State Key Laboratory of Synthetical Automation for Process Industries Northeastern University the Department of Mechanical Engineering University of Victoria Shanghai Institute of Intelligent Science and Technology Tongji University IEEE the Institute of Advanced Technology Nanjing University of Posts and Telecommunications

In this paper, we study the decentralized federated learning problem, which involves the collaborative training of a global model among multiple devices while ensuring data *** classical federated learning, the communication channel between the devices poses a potential risk of compromising private information. To reduce the risk of adversary eavesdropping in the communication channel, we propose TRADE(transmit difference weight) concept. This concept replaces the decentralized federated learning algorithm's transmitted weight parameters with differential weight parameters, enhancing the privacy data against eavesdropping. Subsequently, by integrating the TRADE concept with the primal-dual stochastic gradient descent(SGD)algorithm, we propose a decentralized TRADE primal-dual SGD algorithm. We demonstrate that our proposed algorithm's convergence properties are the same as those of the primal-dual SGD algorithm while providing enhanced privacy protection. We validate the algorithm's performance on fault diagnosis task using the Case Western Reserve University dataset, and image classification tasks using the CIFAR-10 and CIFAR-100 datasets,revealing model accuracy comparable to centralized federated learning. Additionally, the experiments confirm the algorithm's privacy protection capability.

关键词： Adversary eavesdropping decentralized federated learning privacy protection stochastic gradient descent

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Modelling and Optimizing Motherboard Functional Testing in Laptop Manufacturing

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Journal of Systems Science & Complexity 2024年

作者： KANG Yu BAI Peng WANG Kangcheng ZHAO Yunbo DONG Shaojie Department of Automation & Institute of Advanced Technology University of Science and Technology of China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Department of Automation University of Science and Technology of China Institute of Advanced Technology University of Science and Technology of China

Functional testing is key to fulfill quality control in laptop manufacturing and is of great economic value. However, due to the unavailability of practical data, mathematical model and systematic perspective, it has barely been touched from the academic community to date. For the first time,this work provides technical understanding of the key principles of functional testing, mathematically models the general framework, elucidates existing testing strategy under the proposed framework and model, and finally proposes a specified optimization strategy which outperforms existing *** work lays the model foundation for the further optimization of functional testing, and can be regarded as a good example of how a systematic approach can solve practical industrial challenges.

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Multivariate Time-Series Modeling and Forecasting With Parallelized Convolution and Decomposed Sparse-Transformer

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第10期5卷 5232-5243页

作者： Ma, Shusen Zhao, Yun-Bo Kang, Yu Bai, Peng USTC Institute of Advanced Technology Shushan Anhui Hefei230031 China USTC Institute of Advanced Technology Department of Automation Shushan Anhui Hefei230031 China Hefei Comprehensive National Science Center Institute of Artificial Intelligence Shushan District Anhui Hefei230031 China USTC Department of Automation Shushan Anhui Hefei230031 China

Many real-world scenarios require accurate predictions of time series, especially in the case of long sequence time-series forecasting (LSTF), such as predicting traffic flow and electricity consumption. However, existing time-series prediction models encounter certain limitations. First, they struggle with mapping the multidimensional information present in each time step to high dimensions, resulting in information coupling and increased prediction difficulty. Second, these models fail to effectively decompose the intertwined temporal patterns within the time series, which hinders their ability to learn more predictable features. To overcome these challenges, we propose a novel end-to-end LSTF model with parallelized convolution and decomposed sparse-Transformer (PCDformer). PCDformer achieves the decoupling of input sequences by parallelizing the convolutional layers, enabling the simultaneous processing of different variables within the input sequence. To decompose distinct temporal patterns, PCDformer incorporates a temporal decomposition module within the encoder–decoder structure, effectively separating the input sequence into predictable seasonal and trend components. Additionally, to capture the correlation between variables and mitigate the impact of irrelevant information, PCDformer utilizes a sparse self-attention mechanism. Extensive experimentation conducted on five diverse datasets demonstrates the superior performance of PCDformer in LSTF tasks compared to existing approaches, particularly outperforming encoder–decoder-based models. © 2020 IEEE.

关键词： Convolution

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Traded Control of Human-Machine Systems for Sequential Decision-Making Based on Reinforcement Learning

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2022年第4期3卷 553-566页

作者： Zhang, Qianqian Kang, Yu Zhao, Yun-Bo Li, Pengfei You, Shiyi University of Science and Technology of China Department of Automation Institute of Advanced Technology Hefei230027 China

Sequential decision-making (SDM) is a common type of decision-making problem with sequential and multistage characteristics. Among them, the learning and updating of policy are the main challenges in solving SDM problems. Unlike previous machine autonomy driven by artificial intelligence alone, we improve the control performance of SDM tasks by combining human intelligence and machine intelligence. Specifically, this article presents a paradigm of a human-machine traded control systems based on reinforcement learning methods to optimize the solution process of sequential decision problems. By designing the idea of autonomous boundary and credibility assessment, we enable humans and machines at the decision-making level of the systems to collaborate more effectively. And the arbitration in the human-machine traded control systems introduces the Bayesian neural network and the dropout mechanism to consider the uncertainty and security constraints. Finally, experiments involving machine traded control, human traded control were implemented. The preliminary experimental results of this article show that our traded control method improves decision-making performance and verifies the effectiveness for SDM problems. © 2020 IEEE.

关键词： Reinforcement learning

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Semi-adaptive spectrally normalized identifier based model uncertainty online compensation for racing drones

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Science China Technological Sciences 2025年

作者： Jian DI Kun LI Yu KANG Qi DONG Shaofeng CHEN Wenjun LYU Institute of Advanced Technology University of Science and Technology of China Department of Automation University of Science and Technology of China China Academy of Electronics and Information Technology

Accurate dynamic modeling of racing drones, characterized by high speed and maneuverability, is challenging due to model uncertainty stemming from personalized modifications and frequent in-flight collisions. Although deep neural networkbased methods have shown some effectiveness, they struggle with online adaptability as the system and environment change,and they present difficulties in analysis. To address these challenges, we propose a novel semi-adaptive spectrally normalized neural network(SASNNet) to characterize model uncertainty. SASNNet learns long-term features representing inherent operational dynamics through offline training, while online training enables it to capture short-term features reflecting system changes,enhancing its adaptability. Additionally, spectral normalization is integrated into the training process to improve SASNNet's Lipschitz stability, and an adaptive parameter update rule is designed to accelerate the model response. Building on this uncertainty characterization approach, we develop a control compensation method for trajectory tracking in racing drones. We validate the proposed method through physics-engine-based simulations, with results demonstrating high modeling accuracy, enhanced adaptability, and fast response speed.

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Partial Domain Adaptation for Building Borehole Lithology Model Under Weaker Geological Prior

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第12期5卷 6645-6658页

作者： Li, Jing Wang, Jichen Li, Zerui Kang, Yu Lv, Wenjun Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei230088 China University of Science and Technology of China Department of Automation Hefei230026 China Institute of Advanced Technology University of Science and Technology of China Hefei230088 China

Lithology identification plays a pivotal role in stratigraphic characterization and reservoir exploration. The promising field of intelligent logging lithology identification, which employs machine learning algorithms to infer lithology from logging curves, is gaining significant attention. However, models trained on labeled wells currently face challenges in accurately predicting the lithologies of new unlabeled wells due to significant discrepancies in data distribution among different wells caused by the complex sedimentary environment and variations in logging equipment. Additionally, there is no guarantee that newly drilled wells share the same lithology classes as previously explored ones. Therefore, our research aims to leverage source logging and lithology data along with target logging data to train a model capable of directly discerning the lithologies of target wells. The challenges are centered around the disparities in data distribution and the lack of prior knowledge regarding potential lithology classes in the target well. To tackle these concerns, we have made concerted efforts: 1) proposing a novel lithology identification framework, sample transferability weighting based partial domain adaptation (ST-PDA), to effectively address the practical scenario of encountering an unknown label space in target wells;2) designing a sample transferability weighting module to assign higher weights to shared-class samples, thus effectively mitigating the negative transfer caused by unshared-class source samples;3) developing a module, convolutional neural network with integrated channel attention mechanism (CG2CA), to serve as the backbone network for feature extraction;and 4) incorporating a target sample reconstruction module to enhance the feature representation and further facilitating positive transfer. Extensive experiments on 16 real-world wells demonstrated the strong performance of ST-PDA and highlighted the necessity of each component in the framework.

关键词： Lithology

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A Reliable Routing Method for Remote Entanglement Distribution under Limited Resources 26

A Reliable Routing Method for Remote Entanglement Distributi...

引用

26th International Conference on advanced Communications technology, ICACT 2024

作者： Hu, Tianzhu Jiang, Xiaofeng Zhu, Tianze Sun, Xin Chen, Haomin Yang, Jian Institute of Advanced Technology University of Science and Technology of China Hefei China University of Science and Technology of China Department of Automation Hefei China

ISBN: (纸本)9791188428120

Generating and distributing entangled pairs between arbitrary nodes is essential to fully realize the network's capabilities, with the challenges of limited qubit resources, severe decoherence and stochastic physical mechanism. In this paper, we model the service process of quantum repeater nodes based on the concept of queuing theory to help characterize their availability. Further, we propose a link-disjoint multi-path routing algorithm, with repeaters' availability, nodes' qubit capacity, entangled links' fidelity and classical delay taken into consideration. The performance of our scheme has been evaluated with simulated environment and compared with other existing routing schemes. © 2024 Global IT Research institute - GIRI.

关键词： Routing algorithms

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A SLAM Method for Indoor Degradation Environment Based on Lidar/IμUWB Fusion

A SLAM Method for Indoor Degradation Environment Based on Li...

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2024 IEEE International Conference on Control Science and Systems Engineering, ICCSSE 2024

作者： Pan, Tianxiang Song, Jiajia Li, Kun Di, Jian Ji, Haibo Wu, Ke University of Science and Technology of China Department of Automation Hefei China Institute of Advanced Technology University of Science and Technology of China Hefei China

ISBN: (纸本)9798331517199

Currently, 3D Lidar is widely used in indoor SLAM tasks due to its stable and reliable measurement performance. However, it suffers from significant positioning and mapping errors in indoor degraded environments, making it difficult to ensure high global consistency. This issue has long constrained its development. To address these challenges, this paper proposes an indoor mobile robot SLAM algorithm based on multi-sensor information fusion. This method bypasses the need for relying solely on degraded state estimation, thereby overcoming the limitations of a single sensor in real-world environments by leveraging complementary features between sensors to solve the indoor degradation problem. This paper integrates Inertial Measurement Unit (IMU), Ultra-Wideband (UWB) ranging, and Lidar information, and designs a complete SLAM algorithm framework based on nonlinear optimization and an error-state Kalman filter. At the same time, the degradation level of the point cloud perceived by the Lidar is used as a key basis for selecting the UWB residual weight, thereby addressing the problem of indoor Lidar point cloud degradation. Finally, this paper demonstrates the superiority of the algorithm through real-world experiments. © 2024 IEEE.

关键词： Information fusion

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