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Event-based optimization with random packet dropping

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Science China(Information Sciences) 2020年第11期63卷 175-194页

作者： Qing-Shan JIA Jing-Xian TANG Zhenning LANG center for intelligent and networked systems Department of AutomationTsinghua University NetEase Inc.

Event-based optimization(EBO) provides a general framework for policy optimization in many discrete event dynamic systems where decision making is triggered by events that represent state transitions with common features. Because the number of events can be defined by the user and usually increases linearly with respect to the system scale, EBO has good potential to address large-scale problems where the state space grows exponentially. However, in many practical systems, sensors are geographically distributed and connected to a central controller through imperfect communication channels. Therefore, events observed by the sensors may not reach the controller. Optimization methods of event-based policies in these cases have not yet been identified. In this paper, we consider this important problem and make three major contributions. First, we formulate a mathematical EBO model in which the communication between sensors and controllers is subject to random packet dropping. Second, we show that this EBO model can be converted to another EBO model with perfect communication. Then, the performance difference equation and the performance derivative equation for event-based policies are straightforward to develop. One gradient-based policy iteration algorithm is developed for problems where the state transition probabilities are explicitly known, while another for problems where they are unknown. Third, the performance of the algorithms and the impact of the packet dropping probability on policy performance are numerically demonstrated on a single-zone occupant level estimation problem in buildings.

关键词： event-based optimization packet dropping discrete event dynamic systems

Geometric approach to robust quantum control for time-varying noises

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Science China(Physics,Mechanics & Astronomy) 2024年第9期67卷 197-198页

作者： Rebing Wu center for intelligent and networked systems Department of AutomationTsinghua UniversityBeijing 100084China

Quantum technologies are expected to revolutionze modern information processing *** all quntum technologies demand high-precision control of quantum ***,maintaining the high-precision against noises is rather difficult[1].This requirement calls for robust control pulse design that is insensitive to *** practical implementation,the control pulses need to be smoothly shaped and be able to tackle parametric uncertainties or time-varying noises with complicated frequency characteristics.

关键词： quantum smoothly shaped

Using knowledge inference to suppress the lamp disturbance for fire detection

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Journal of Safety Science and Resilience 2021年第3期2卷 124-130页

作者： Kailai Sun Qianchuan Zhao Xinwei Wang center for intelligent and networked systems Department of AutomationBNRistTsinghua UniversityBeijingChina

Fire detection in buildings is crucial for people’s lives and *** temperature and smoke sen-sors have many disadvantages:the limited cover range;detection delays;the difficulty in distinguishing smoke and ***,research on convolutional neural networks(CNN)for fire image detection has become a hot ***,existing fire classification and object detection methods are often interfered with by flash-lights,red objects and the high-brightness background,resulting in a high false alarm ***,light and lamps often exist in *** address this issue,this paper focuses on introducing scene prior knowledge and causal inference mechanisms to suppress the lamp ***,we train the YoloV3 network to detect and recognize ***,to reduce the dataset bias,we mask the lamp regions with the pro-posed Local Grabcut segmentation ***,compared with direct fire classification methods,our proposed methods reduce about 34.6%false alarm rate based on InceptionV4 *** experimental results verify the effectiveness among different CNN architectures(Resnet101,Firenet,Densenet121).The code is online at https://***/kailaisun/fire-detection-without-lamp.

关键词： fire detection lamp prior knowledge causal inference convolutional neural networks

Fork-Join program response time on multiprocessors with exchangeable join

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Journal of Zhejiang University-Science A(Applied Physics & Engineering) 2006年第6期7卷 927-936页

作者： WANG Yong-cai ZHAO Qian-chuan ZHENG Da-zhong center for intelligent and networked systems Department of Automation Tsinghua University Beijing 100084 China

The Fork-Join program consisting of K parallel tasks is a useful model for a large number of computing applications. When the parallel processor has multi-channels, later tasks may finish execution earlier than their earlier tasks and may join with tasks from other programs. This phenomenon is called exchangeable join (EJ), which introduces correlation to the task’s service time. In this work, we investigate the response time of multiprocessor systems with EJ with a new approach. We analyze two aspects of this kind of systems: exchangeable join (EJ) and the capacity constraint (CC). We prove that the system response time can be effectively reduced by EJ, while the reduced amount is constrained by the capacity of the multiprocessor. An upper bound model is constructed based on this analysis and a quick estimation algorithm is proposed. The approximation formula is verified by extensive simulation results, which show that the relative error of approximation is less than 5%.

关键词： Exchangeable join First come first served (FCFS) Fork-Join Multiprocessor Response time

Subgradient-based feedback neural networks for non-differentiable convex optimization problems

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Science in China(Series F) 2006年第4期49卷 421-435页

作者： LI Guocheng SONG Shiji WU Cheng center of intelligent and networked systems Department of Automation Tsinghua University Beijing100084 China

This paper developed the dynamic feedback neural network model to solve the convex nonlinear programming problem proposed by Leung et al. and introduced subgradient-based dynamic feedback neural networks to solve non-differentiable convex optimization problems. For unconstrained non-differentiable convex optimization problem, on the assumption that the objective function is convex coercive, we proved that with arbitrarily given initial value, the trajectory of the feedback neural network constructed by a projection subgradient converges to an asymptotically stable equilibrium point which is also an optimal solution of the primal unconstrained problem. For constrained non-differentiable convex optimization problem, on the assumption that the objective function is convex coercive and the constraint functions are convex also, the energy functions sequence and corresponding dynamic feedback subneural network models based on a projection subgradient are successively constructed respectively, the convergence theorem is then obtained and the stopping condition is given. Furthermore, the effective algorithms are designed and some simulation experiments are illustrated.

关键词： projection subgradient non-differentiable convex optimization convergence feedback neural network.

Tailoring Light–Matter Interactions in Overcoupled Resonator for Biomolecule Recognition and Detection

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Nano-Micro Letters 2025年第1期17卷 262-280页

作者： Dongxiao Li Hong Zhou Zhihao Ren Cheng Xu Chengkuo Lee Department of Electrical and Computer Engineering National University of SingaporeSingapore 117583Singapore center for intelligent Sensors and MEMS(CISM) National University of SingaporeSingapore 117608Singapore

Plasmonic nanoantennas provide unique opportunities for precise control of light–matter coupling in surface-enhanced infrared absorption(SEIRA)spectroscopy,but most of the resonant systems realized so far suffer from the obstacles of low sensitivity,narrow bandwidth,and asymmetric Fano resonance ***,we demonstrated an overcoupled resonator with a high plasmon-molecule coupling coefficient(μ)(OC-Hμresonator)by precisely controlling the radiation loss channel,the resonator-oscillator coupling channel,and the frequency detuning *** observed a strong dependence of the sensing performance on the coupling state,and demonstrated that OC-Hμresonator has excellent sensing properties of ultra-sensitive(7.25%nm^(−1)),ultra-broadband(3–10μm),and immune asymmetric Fano *** characteristics represent a breakthrough in SEIRA technology and lay the foundation for specific recognition of biomolecules,trace detection,and protein secondary structure analysis using a single array(array size is 100×100μm^(2)).In addition,with the assistance of machine learning,mixture classification,concentration prediction and spectral reconstruction were achieved with the highest accuracy of 100%.Finally,we demonstrated the potential of OC-Hμresonator for SARS-CoV-2 *** findings will promote the wider application of SEIRA technology,while providing new ideas for other enhanced spectroscopy technologies,quantum photonics and studying light–matter interactions.

关键词： Plasmonic nanoantennas Light-matter interaction Surface-enhanced infrared absorption Overcoupled Biosensing

Common quantitative characteristics of music melodies——pursuing the constrained entropy maximization casually in composition

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Science China(Information Sciences) 2022年第7期65卷 240-242页

作者： Nan NAN Xiaohong GUAN Yixin WANG Youtian DU MOE KLINNS Lab Faculty of Electronic and Information Engineering Xi'an Jiaotong University center for intelligent and networked systems Tsinghua University

Music with a beautiful melody has been appreciated by humankind for thousands of years. Almost all music is generally tonal, e.g., Western music originated from ancient Greece and developed for more than 300 years from Baroque to classical, romantic, and modern music. Tonal music has a tonal center with a melody named tonic, and it is based on a specific pitch series called mode scale and harmonic progressions. On the contrary,

关键词： Power Law music melody variations complementary communitive distribution entropy functional optimization

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An Online Exploratory Maximum Likelihood Estimation Approach to Adaptive Kalman Filtering

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IEEE/CAA Journal of Automatica Sinica 2025年第1期 228-254页

作者： Jiajun Cheng Haonan Chen Zhirui Xue Yulong Huang Yonggang Zhang the College of intelligent systems Science and Engineering Harbin Engineering University the Engineering Research center of Navigation Instruments Ministry of Education the College of intelligent systems Science and Engineering the College of Future Technology Harbin Engineering University IEEE

Over the past few decades, numerous adaptive Kalman filters(AKFs) have been proposed. However, achieving online estimation with both high estimation accuracy and fast convergence speed is challenging, especially when both the process noise and measurement noise covariance matrices are relatively inaccurate. Maximum likelihood estimation(MLE) possesses the potential to achieve this goal, since its theoretical accuracy is guaranteed by asymptotic optimality and the convergence speed is fast due to weak dependence on accurate state ***, the maximum likelihood cost function is so intricate that the existing MLE methods can only simply ignore all historical measurement information to achieve online estimation,which cannot adequately realize the potential of MLE. In order to design online MLE-based AKFs with high estimation accuracy and fast convergence speed, an online exploratory MLE approach is proposed, based on which a mini-batch coordinate descent noise covariance matrix estimation framework is developed. In this framework, the maximum likelihood cost function is simplified for online estimation with fewer and simpler terms which are selected in a mini-batch and calculated with a backtracking method. This maximum likelihood cost function is sidestepped and solved by exploring possible estimated noise covariance matrices adaptively while the historical measurement information is adequately utilized. Furthermore, four specific algorithms are derived under this framework to meet different practical requirements in terms of convergence speed, estimation accuracy,and calculation load. Abundant simulations and experiments are carried out to verify the validity and superiority of the proposed algorithms as compared with existing state-of-the-art AKFs.

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On large action space in EV charging scheduling optimization

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Science China(Information Sciences) 2022年第2期65卷 45-60页

作者： Zhaoyu JIANG Qing-Shan JIA Xiaohong GUAN center for intelligent and networked systems Beijing National Research Center for Information Science and Technology Department of Automation Tsinghua University MOE KLINNS Lab Xi'an Jiaotong University

In order to reduce the air pollution by traditional fossil fuel and save the charging cost, much attention has been paid to the charging scheduling of electric vehicles(EVs) in the uncertain supplement of wind power and solar power. Owing to the randomness in the renewable power generation and the EV charging demand, simulation-based policy improvement(SBPI) has been well adopted for making charging decision. However, it is challenging to explore the large action space which grows exponentially with respect to the system scale. We consider this important problem in this paper and make the following ***, we explore the structural property of the problem and develop an urgency index to rank the ***, we apply three methods to search in the action space. Third, we numerically demonstrate the performance of the urgency index and the search methods, and compare the SBPI methods with the CPLEXbased method. It shows that SBPI improves the base policies in all these cases.

关键词： EV simulation-based policy improvement large action space sampling

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