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Reasoning over the Air: A Reasoning-based Implicit Semantic-Aware Communication Framework

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arXiv 2023年

作者： Xiao, Yong Liao, Yiwei Li, Yingyu Shi, Guangming Poor, H. Vincent Saad, Walid Debbah, Mérouane Bennis, Mehdi The School of Electronic Information and Communications The Huazhong University of Science and Technology Wuhan430074 China The Peng Cheng Laboratory Guangdong Shenzhen518055 China Guangdong Guangzhou510555 China The School of Mech. Eng. and Elect. Inform. The China University of Geosciences Wuhan430074 China The School of Artificial Intelligence The Xidian University Shaanxi Xi’an710071 China The Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States The Department of Electrical and Computer Engineering Virginia Tech BlacksburgVA24061 United States Cyber Security Systems and Applied AI Research Center Lebanese American University Beirut Lebanon Khalifa University of Science and Technology P O Box 127788 Abu Dhabi United Arab Emirates CentraleSupelec University Paris-Saclay Gif-sur-Yvette91192 France The University of Oulu Oulu90014 Finland

—Semantic-aware communication is a novel paradigm that draws inspiration from human communication focusing on the delivery of the meaning of messages. It has attracted significant interest recently due to its potential to improve the efficiency and reliability of communication and enhance users’ quality-of-experience (QoE). Most existing works focus on transmitting and delivering the explicit semantic meaning that can be directly identified from the source signal. This paper investigates the implicit semantic-aware communication in which the hidden information, e.g., hidden relations, concepts and implicit reasoning mechanisms of users, that cannot be directly observed from the source signal must be recognized and interpreted by the intended users. To this end, a novel implicit semantic-aware communication (iSAC) architecture is proposed for representing, communicating, and interpreting the implicit semantic meaning between source and destination users. A graph-inspired structure is first developed to represent the complete semantics, including both explicit and implicit, of a message. A projection-based semantic encoder is then proposed to convert the high-dimensional graphical representation of explicit semantics into a low-dimensional semantic constellation space for efficient physical channel transmission. To enable the destination user to learn and imitate the implicit semantic reasoning process of source user, a generative adversarial imitation learning-based solution, called G-RML, is proposed. Different from existing communication solutions, the source user in G-RML does not focus only on sending as much of the useful messages as possible;but, instead, it tries to guide the destination user to learn a reasoning mechanism to map any observed explicit semantics to the corresponding implicit semantics that are most relevant to the semantic meaning. By applying G-RML, we prove that the destination user can accurately imitate the reasoning process of the source u

关键词： Semantics

Infrared Optical Spectroscopy of Molten Fluorides: Methods, Electronic and Vibrational Data, Structural Interpretation, and Relevance to Radiative Heat Transfer

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arXiv 2022年

作者： Derdeyn, Will B. Mastromarino, Sara Gakhar, Ruchi Anderson, Mark H. Kats, Mikhail A. Scarlat, Raluca O. Department of Engineering Physics University of Wisconsin-Madison MadisonWI53706 United States Department of Nuclear Engineering University of California BerkeleyCA94720 United States Pyrochemistry and Molten Salt Systems Department Idaho National Laboratory Idaho FallsID83415 United States Department of Mechanical Engineering University of Wisconsin-Madison MadisonWI53706 United States Department of Electrical and Computer Engineering University of Wisconsin-Madison MadisonWI53706 United States Department of Materials Science and Engineering University of Wisconsin-Madison MadisonWI53706 United States Department of Physics University of Wisconsin-Madison MadisonWI53706 United States

To help address the need for predicting radiative heat transfer (RHT) behavior of molten salts, we conducted a comprehensive review of methods and data from infrared optical spectroscopic measurements on molten fluoride salts. Transmittance, reflectance, and trans-reflectance experimental methods are discussed, along with the corresponding data reduction methodology and the limitations of each technique. Optical spectroscopy is a convenient indirect probe for changes in structural parameters with temperature and composition. Electronic and vibrational absorption data for transition-metal, lanthanide, and actinide solutes and vibrational absorption data for alkali and alkaline earth fluoride solvents are compiled, and the corresponding structural interpretation is discussed and compared with other experimental and theoretical work. We find that solvent and solute vibrational absorption can be significant in the mid-infrared, resulting in near-infrared edges of significance to RHT. Extrapolation and averaging of existing edge data leads to estimated gray absorption coefficient values at 700 C of 493 m-1 for FLiBe and 148 m-1 for FLiNaK, both within the range of 1 - 6000 m-1 identified to be of engineering relevance for radiative heat transfer analysis. Copyright © 2022, The Authors. All rights reserved.

关键词： Radiative transfer

A parallel Education Based Intelligent Tutoring systems Framework

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A Parallel Education Based Intelligent Tutoring Systems Fram...

2020 IEEE International Conference on Networking, Sensing and Control, ICNSC 2020

作者： Jing, Sifeng Tang, Ying Liu, Xiwei Gong, Xiaoyan Cui, Wei Liang, Joleen State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Institute of Smart Education Systems Qingdao Academy of Intelligent Industries Qingdao266044 China Department of Electrical and Computer Engineering Rowan University GlassboroNJ08028 United States Squirrel Ai Learning Shanghai200030 China

ISBN: (纸本)9781728168531

although online education has improved efficiency of learners' access to high-quality educational resources, realtime interaction between instructors and learners have not yet been achieved in online personalized learning. Intelligent Tutoring systems (ITS) provides a feasible way to realize realtime personalized learning guidance and resource recommendations by applying AI to capture and analyze online learners' characteristics and behaviors. In this paper, reviews and trends of ITS are discussed and three challenges of ITS research development are pointed out: learner model, guidance mechanism and human-computer interaction mechanism. In order to address these issues, parallel intelligence theory is introduced and a parallel intelligence education based ITS framework is proposed. © 2020 IEEE.

关键词： computer aided instruction

Advances in resonator-based Kerr frequency combs with high conversion efficiencies

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npj Nanophotonics

npj Nanophotonics 2024年第1期1卷 1-21页

作者： Xucheng Zhang Chunxue Wang Zhibo Cheng Congyu Hu Xingchen Ji Yikai Su John Hopcroft Center for Computer Science School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai 200240 China State Key Laboratory of Advanced Optical Communication Systems and Networks Department of Electronic Engineering Shanghai Jiao Tong University Shanghai 200240 China

Recent developments in resonator-based Kerr frequency combs promise excellent applications in a wide range of fields such as biosensing, spectroscopy, optical communications, light detection and ranging (LiDAR), frequency synthesis, astronomical detection, and quantum optics. A key figure of merit (FOM) for Kerr frequency combs is the pump-to-comb conversion efficiency, which is critical for applications requiring sufficient comb power and low power consumption. In this review, we first discuss the limited conversion efficiency of dissipative Kerr soliton in an anomalous dispersion microresonator based on its underlying physical characteristics. And then, we summarize the recent advances in Kerr frequency combs with high conversion efficiencies in both anomalous and normal dispersion regimes. We classify them according to various soliton states, excitation methods as well as novel material platforms. The final section of the paper presents an overview of current progress and glances at potential directions for future research.

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K-9 Artificial Intelligence Education in Qingdao: Issues, Challenges and Suggestions

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K-9 Artificial Intelligence Education in Qingdao: Issues, Ch...

2020 IEEE International Conference on Networking, Sensing and Control, ICNSC 2020

作者： Gong, Xiaoyan Tang, Ying Liu, Xiwei Jing, Sifeng Cui, Wei Liang, Joleen Wang, Fei-Yue State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100084 China Institute of Smart Education Systems Qingdao Academy of Intelligent Industries Qingdao266044 China Department of Electrical and Computer Engineering Rowan University GlassboroNJ08028 United States Squirrel Ai Learning Shanghai200030 China

ISBN: (纸本)9781728168531

Nowadays, AI education from Kindergarten to 9th grade(K-9) is in full swing in China, but many challenges exist, such as fragmented AI curricula, ineffective teaching tools, and uneven educational resources in urban and rural areas, etc. So this paper presents an in-depth study of the current status of K-9 AI education in Qingdao area through questionnaires, expert discussions, and field visits to sort out existing problems and then put forward corresponding advice and suggestions. Collected data is then analyzed from the perspectives of government, schools, teachers, students and parents, respectively. Results show that AI-related educational activities have been carried out in Qingdao area to improve students' AI literacy. But efforts are still needed to make such education more systematic, standardized, and personalized. Finally the paper made a list of recommendations corresponding to these needed efforts. © 2020 IEEE.

关键词： Curricula

Model-Free Primal-Dual Methods for Network Optimization with Application to Real-Time Optimal Power Flow

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Model-Free Primal-Dual Methods for Network Optimization with...

American Control Conference

作者： Yue Chen Andrey Bernstein Adithya Devraj Sean Meyn National Renewable Energy Laboratory Power Systems Engineering Center Golden CO USA Department of Electrical and Computer Engineering University of Florida Gainesville FL USA

ISBN: (数字)9781538682661

ISBN: (纸本)9781538682678

This paper examines the problem of real-time optimization of networked systems and develops online algorithms that steer the system towards the optimal trajectory without explicit knowledge of the system model. The problem is modeled as a dynamic optimization problem with time-varying performance objectives and engineering constraints. The design of the algorithms leverages the online zero-order primal-dual projected-gradient method. In particular, the primal step that involves the gradient of the objective function (and hence requires a networked systems model) is replaced by its zero-order approximation with two function evaluations using a deterministic perturbation signal. The evaluations are performed using the measurements of the system output, hence giving rise to a feedback interconnection, with the optimization algorithm serving as a feedback controller. The paper provides some insights on the stability and tracking properties of this interconnection. Finally, the paper applies this methodology to a real-time optimal power flow problem in power systems, and shows its efficacy on the IEEE 37-node distribution test feeder for reference power tracking and voltage regulation.

关键词： Optimization Real-time systems Trajectory Approximation algorithms Perturbation methods Power system stability Time-varying systems

A Learning Convolutional Neural Network Approach for Network Robustness Prediction

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arXiv 2022年

作者： Lou, Yang Wu, Ruizi Li, Junli Wang, Lin Li, Xiang Chen, Guanrong The Department of Computing and Decision Sciences Lingnan University Hong Kong The Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China The College of Computer Science Sichuan Normal University Chengdu610066 China The Department of Automation Shanghai Jiao Tong University Shanghai200240 China The Institute of Complex Networks and Intelligent Systems Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai201210 China The Department of Control Science and Engineering Tongji University Shanghai200240 China The Department of Electrical Engineering City University of Hong Kong Hong Kong

Network robustness is critical for various societal and industrial networks again malicious attacks. In particular, connectivity robustness and controllability robustness reflect how well a networked system can maintain its connectedness and controllability against destructive attacks, which can be quantified by a sequence of values that record the remaining connectivity and controllability of the network after a sequence of node- or edge-removal attacks. Traditionally, robustness is determined by attack simulations, which are computationally very time-consuming or even practically infeasible. In this paper, an improved method for network robustness prediction is developed based on learning feature representation using convolutional neural network (LFR-CNN). In this scheme, higher-dimensional network data are compressed to lower-dimensional representations, and then passed to a CNN to perform robustness prediction. Extensive experimental studies on both synthetic and real-world networks, both directed and undirected, demonstrate that 1) the proposed LFR-CNN performs better than other two state-of-the-art prediction methods, with significantly lower prediction errors;2) LFR-CNN is insensitive to the variation of the network size, which significantly extends its applicability;3) although LFR-CNN needs more time to perform feature learning, it can achieve accurate prediction faster than attack simulations;4) LFR-CNN not only can accurately predict network robustness, but also provides a good indicator for connectivity robustness, better than the classical spectral measures. © 2022, CC BY.

关键词： Complex networks

Deep learning-aided model predictive control of wind farms for AGC considering the dynamic wake effect

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arXiv 2020年

作者： Chen, Kaixuan Lin, Jin Qiu, Yiwei Liu, Feng Song, Yonghua State Key Laboratory of Control and Simulation of Power Systems and Generation Equipment Department of Electrical Engineering Tsinghua University Beijing100087 China Department of Electrical and Computer Engineering University of Macau 999078 China

To provide automatic generation control (AGC) service, wind farms (WFs) are required to control their operation dynamically to track the time-varying power reference. Wake effects impose significant aerodynamic interactions among turbines, which remarkably influence the WF dynamic power production. The nonlinear and high-dimensional nature of dynamic wake model, however, brings extremely high computation complexity and obscure the design of WF controllers. This paper overcomes the control difficulty brought by the dynamic wake model by proposing a novel control-oriented reduced order WF model and a deep-learning-aided model predictive control (MPC) method. Leveraging recent advances in computational fluid dynamics (CFD) to provide high-fidelity data that simulates WF dynamic wake flows, two novel deep neural network (DNN) architectures are specially designed to learn a dynamic WF reduced-order model (ROM) that can capture the dominant flow dynamics. Then, a novel MPC framework is constructed that explicitly incorporates the obtained WF ROM to coordinate different turbines while considering dynamic wake interactions. The proposed WF ROM and the control method are evaluated in a widely-accepted high-dimensional dynamic WF simulator whose accuracy has been validated by realistic measurement data. A 9-turbine WF case and a larger 25-turbine WF case are studied. By reducing WF model states by many orders of magnitude, the computational burden of the control method is reduced greatly. Besides, through the proposed method, the range of AGC signals that can be tracked by the WF in the dynamic operation is extended compared with the existing greedy controller. Copyright © 2020, The Authors. All rights reserved.

关键词： Model predictive control

Environmental Sensitivity Evaluation of Neural Networks in Unmanned Vehicle Perception Module

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Environmental Sensitivity Evaluation of Neural Networks in U...

IEEE Conference on Wireless Communications and Networking

作者： Yuru Li Dongliang Duan Chen Chen Xiang Cheng Liuqing Yang State Key Laboratory of Advanced Optical Communication Systems and Networks Peking University Beijing China Department of Electrical and Computer Engineering University of Wyoming Laramie WY USA Department of Electrical and Computer Engineering Colorado State University Fort Collins CO USA

ISBN: (数字)9781728131061

ISBN: (纸本)9781728131078

For autonomous driving of unmanned vehicles in intelligent transportation systems, multi-vehicle cooperative perception supported by vehicular networks can greatly improve the accuracy and reliability of the perception decisions. Currently, the perception decisions for a single vehicle are mostly provided by neural networks. Therefore, in order to fuse the perception decisions from multiple vehicles, the credibility of the neural network outputs needs to be studied. Among various factors, the environment is one of the most important affecting vehicles' perception decisions. In this paper, we propose a new evaluation criteria for the neural networks used in the perception module of unmanned vehicles. This criterion is termed as Environmental Sensitivity (ES), indicates the sensitivity of the network to environmental changes. We design an algorithm to quantitatively measure the ES value of different perception networks based on the extracted features. Experimental results show that our algorithm can well capture the sensitivity of the network in different environments and the ES values will be helpful to the subsequent decision fusion process.

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