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Conference Record of the IEEE Industry Applications Society Annual Meeting (IAS)

作者： M. Budzisz Z. Nowacki Institute of Automatic Control Technical University of Lodz Poland

The problem of instability areas of the general purpose drive system with PWM inverter and an induction motor was investigated. The first task of detection of instability (or excessive angular velocity oscillations with a poor damping factor) was solved in the drive system without velocity feedback and without velocity signal. The fuzzy inference based algorithm uses for processing only one signal from power circuit-inverter supply current signal, and two other signals from the control circuit of the inverter. It activates the adequate reference frequency correcting procedure. The improvement of stability is achieved only by the influence on the reference frequency signal.

关键词： inference algorithms Signal processing Pulse width modulation inverters Circuits Frequency Induction motors Angular velocity Damping Feedback Fuzzy control

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On designing approximate inference algorithms for multiply sectioned Bayesian networks

On designing approximate inference algorithms for multiply s...

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IEEE International Conference on Granular Computing (GRC)

作者： Karen H. Jin Dan Wu Libing Wu School of Computer Science University of Windsor Windsor Canada School of Computer Wuhan University of China Wuhan China

An increasing number of applications require cooperative agents to reason about the state of an distributed uncertainty domain. However, inference process of such system could become overly slow for practical applications, and there has been significant interest in developing faster approximation techniques. In this paper, we focus on the existing MSBN models for cooperative reasoning in multi-agent environments. We show that, while the MSBNs provide a framework for exact inference, existing algorithms are usually not feasible in larger problem domains. Therefore, we investigate the issues related to the design of efficient inference algorithm for the MSBN model. We then propose a suite of algorithms for approximate multi-agent probabilistic reasoning in MSBNs. Our approach includes an MSBN subnet calibration process and distributed stochastic sampling on MSBN LJFs.

关键词： Algorithm design and analysis inference algorithms Bayesian methods Sampling methods Stochastic processes Calibration Tree graphs Probability distribution Clustering algorithms Distributed computing

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Grammar inference algorithms and applications in software engineering

Grammar inference algorithms and applications in software en...

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XXII International Symposium on Information, Communication and Automation Technologies, ICAT

作者： Marjan Mernik Dejan Hrnčič Barrett R. Bryant Alan P. Sprague Jeff Gray Qichao Liu Faizan Javed Faculty of Electrical Engineering and Computer Science University of Maribor Maribor Slovenia Department of Computer and Information Sciences University of Alabama Birmingham Birmingham USA

Many problems exist whose solutions take the form of patterns that may be expressed using grammars (e.g., speech recognition, text processing, genetic sequencing). Construction of these grammars is usually carried out by computer scientists working with domain experts. In the case when there is a lack of domain experts, grammar inference can be applied. In this paper, two grammar inference algorithms are briefly described and their application to software engineering is presented.

关键词： Software algorithms inference algorithms Application software Software engineering DSL Learning automata Merging Evolutionary computation Genetic programming Machine learning algorithms

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DISTRIBUTED inference AND FUSION algorithms FOR REAL-TIME SUPERVISORY CONTROLLER POSITIONS

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IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS 1987年第2期17卷 230-239页

作者： SILVERMAN, BG Institute for Artificial Intelligence George Washington University Washington D.C. DC USA

The failure of traditional expert system algorithms and mathematics in real-time tasks is described, and an alternative inference engine design more appropriate to real-time problem solving is explored. The approach is generic, but to facilitate its description an example is used involving human supervisory operators of a semiautomated telecommunication network. Each human's real-time process control tasks are first decomposed into several types of subtasks under cases of certain, uncertain, and conflicting information. Two divergent calculi are seen to be required for these various tasks: 1) the "situational calculus" for real-time deterministic control, and 2) the "calculus of uncertainty" for fusion of conflicting information from diverse knowledge sources and for propagating uncertainty through the inference net to reach conclusions that can be executed by the situational calculus. The distributed architecture ramifications of a dual-calculus appoach are explained, and the fusion technique is elaborated.

关键词： inference algorithms Calculus Telecommunication control Real time systems Humans Uncertainty Expert systems Mathematics Engines Algorithm design and analysis

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Application of fuzzy inference and genetic algorithms to VLSI floorplanning design

Application of fuzzy inference and genetic algorithms to VLS...

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Annual Conference of Industrial Electronics Society

作者： K. Eguchi O. Yamashiro H. Kawamoto N. Tsuji S. Yamane K. Oshima Research & Development Group Hitachi and Limited Chiyoda Tokyo Japan System Memory Business Division Hitachi and Limited Chiyoda Tokyo Japan Department of Environmental Science & Human Engineering Graduate School of Science & Engineering Saitama University Urawa Saitama Japan

VLSI floorplanning design automation based on soft computing is discussed. Authors have proposed the fusion of fuzzy inference and genetic algorithms for the automation of VLSI floorplanning design. In this paper, the expansion of inference is presented. The fuzzy rules are used to infer the initial position of the on-blocks based on analysis of the accumulated knowledge of the expert design engineer. Only the dominant combinations of place and block are inferred. In the authors' previous work (1998, 1999), blocks deemed suitable candidates for placement at the center, relative to the four corners and side of the chip, are inferred. In addition to those inference, such blocks that are relatively appropriate to be placed along with the four perimeters (edges) of active area of the chip. These inferences are then reflected in the initial population of the genetic algorithms. The rest of the block placement phase is entrusted to the genetic algorithms. Experimental software to implement the proposed approach was developed. The results of the experiments showed a level and quality of placement close to that of the expert design engineer.

关键词： Genetic algorithms Very large scale integration Algorithm design and analysis Design engineering Design automation Tiles Humans Genetic engineering Knowledge engineering inference algorithms

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SBL algorithms for the Multiple Measurement Vector Problem: New Modeling and inference Methods

SBL Algorithms for the Multiple Measurement Vector Problem: ...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Vinay Kanakeri Florian Meyer Bhaskar D. Rao Department of Electrical and Computer Engineering University of California San Diego

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

This paper introduces new and practically relevant non-Gaussian priors for the Sparse Bayesian Learning (SBL) framework applied to the Multiple Measurement Vector (MMV) problem. We extend the Gaussian Scale Mixture (GSM) framework to model prior distributions for row vectors, exploring the use of shared and different hyperparameters across different measurements. We propose Expectation Maximization (EM) based algorithms to estimate the parameters of the prior density along with the hyperparameters. To promote sparsity more effectively in a non-Gaussian setting, we show the importance of incorporating learning of the parameters of the mixing density. Such an approach effectively utilizes the common support notion in the MMV problem and promotes sparsity without explicitly imposing a sparsity-promoting prior, indicating the methods’ robustness to model mismatches. Numerical simulations are provided to compare the proposed approaches with the existing SBL algorithm for the MMV problem.

关键词： GSM Signal processing algorithms Signal processing Numerical simulation Vectors inference algorithms Robustness Numerical models Bayes methods Speech processing

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Analysis of Short Video Platform algorithms Based on Causal inference: The Formation and Decoupling of the Information Cocoon Effect

Analysis of Short Video Platform Algorithms Based on Causal ...

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Cybersecurity, Internet of Things and Soft Computing (CITSC), Asia-Europe Conference on

作者： Jingya Cheng School of Culture and Communication Guangdong Business and Technology University Zhaoqing China

ISBN: (数字)9798331504205

ISBN: (纸本)9798331504212

Short video platforms push content to users through recommendation algorithms, which greatly improves user experience, but also triggers the information cocoon effect. This paper explores the impact of the recommendation algorithm of short video platforms on the information cocoon effect based on the theory of causal inference, and proposes algorithm design and model simulation methods to decouple the relationship between the information cocoon effect and content diversity. First, this paper constructs a causal inference model to identify the formation mechanism of the information cocoon effect in the recommendation algorithm. Then, by introducing a diversity constraint factor, adjustments are made in the recommendation algorithm to increase the probability that users receive different types of content, thereby reducing the generation of the information cocoon effect. The model simulation results show that without affecting the user experience, the optimized recommendation algorithm improves the information diversity index by 15.7 % , while the intensity of the information cocoon effect is reduced by 22.4%. This study shows that the optimization of the recommendation algorithm based on causal inference can effectively alleviate the information cocoon effect in the short video platform, improve the breadth of user information acquisition, and has high practical value.

关键词： Simulation inference algorithms User experience Indexes Internet of Things Computer security Optimization

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An Attack-Defense Game-Based Reinforcement Learning Privacy-Preserving Method Against inference Attack in Double Auction Market

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2025年 22卷 9075-9089页

作者： Li, Donghe Hu, Chunlin Yang, Qingyu Ma, Yuhao Zhang, Feiye An, Dou Xi An Jiao Tong Univ Sch Automat Sci & Engn Xian 710049 Peoples R China Xi An Jiao Tong Univ MOE Key Lab Intelligent Networks & Network Secur Xian 710049 Peoples R China Xi An Jiao Tong Univ SKLMSE Lab Xian 710049 Peoples R China Xi An Jiao Tong Univ Sch Automat Sci & Engn Xian 710049 Peoples R China

Auction mechanism, as a fair and efficient resource allocation method, has been widely used in varieties trading scenarios, such as advertising, crowdsensoring and spectrum. However, in addition to obtaining higher profits and satisfaction, the privacy concerns have attracted researchers' attention. In this paper, we mainly study the privacy preserving issue in the double auction market against the indirect inference attack. Most of the existing works apply differential privacy theory to defend against the inference attack, but there exists two problems. First', indistinguishability' of differential privacy (DP) cannot prevent the disclosure of continuous valuations in the auction market. Second, the privacy-utility trade-off (PUT) in differential privacy deployment has not been resolved. To this end, we proposed an attack-defense game-based reinforcement learning privacy-preserving method to provide practically privacy protection in double auction. First, the auctioneer acts as defender, adds noise to the bidders' valuations, and then acts as adversary to launch inference attack. After that the auctioneer uses the attack results and auction results as a reference to guide the next deployment. The above process can be regarded as a Markov Decision Process (MDP). The state is the valuations of each bidders under the current steps. The action is the noise added to each bidders. The reward is composed of privacy, utility and training speed, in which attack success rate and social welfare are taken as measures of privacy and utility, a delay penalty term is used to reduce the training time. Utilizing the deep deterministic policy gradient (DDPG) algorithm, we establish an actor-critic network to solve the problem of MDP. Finally, we conducted extensive evaluations to verify the performance of our proposed method. The results show that compared with other existing DP-based double auction privacy preserving mechanisms, our method can achieve better results in both privacy

关键词： Privacy Protection Differential privacy Noise Games Reinforcement learning Cost accounting Automation Training inference algorithms Double auction inference attack differential privacy privacy-utility trade-off reinforcement learning

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Joint Optimization of Device Placement and Model Partitioning for Cooperative DNN inference in Heterogeneous Edge Computing

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2025年第1期24卷 210-226页

作者： Dai, Penglin Han, Biao Li, Ke Xu, Xincao Xing, Huanlai Liu, Kai Southwest Jiaotong Univ Sch Comp & Artificial Intelligence Chengdu 611756 Peoples R China Univ Elect Sci & Technol China Shenzhen Inst Adv Study Chengdu 610051 Peoples R China Chongqing Univ Coll Comp Sci Chongqing 400040 Peoples R China

EdgeAI represents a compelling approach for deploying DNN models at network edge through model partitioning. However, most existing partitioning strategies have primarily concentrated on homogeneous environments, neglecting the effect of device placement and their inapplicability to heterogeneous settings. Moreover, these strategies often rely on either data parallelism or model parallelism, each presenting its own limitations, including data synchronization and communication overhead. This paper aims at enhancing inference performance through a pipeline system of devices through leveraging both parallel and sequential relationships among them. Accordingly, the problem of Multi-Device Cooperative DNN inference is formulated by optimizing both device placement and model partitioning, taking into account the unique characteristics of heterogeneous edge resources and DNN models, with the goal of maximizing throughput. To this end, we propose an evolutionary device placement technique to determine the pipeline stage of devices by enhancing a variant of particle swarm optimization. Subsequently, an adaptive model partitioning strategy is developed by combining intra-layer and inter-layer model partitioning based on dynamic programming and the input-output mapping of DNN layers, respectively, to accommodate edge resource limitations. Finally, we construct a simulation model and a prototype, and the extensive results demonstrate that our proposed algorithm outperforms current state-of-the-art algorithms.

关键词： Computational modeling Artificial neural networks Parallel processing Data models Adaptation models Partitioning algorithms inference algorithms Multi-device cooperative inference heterogeneous edge computing DNN Model Partitioning device placement

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Distributed Collaborative inference System in Next-Generation Networks and Communication

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IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING 2025年第2期11卷 923-932页

作者： Zhang, Chuan Zheng, Xixi Tao, Xiaolong Hu, Chenfei Zhang, Weiting Zhu, Liehuang Beijing Inst Technol Sch Cyberspace Sci & Technol Beijing 100081 Peoples R China Minist Educ China Key Lab Cyberspace Secur Zhengzhou 450001 Peoples R China Beijing Jiaotong Univ Sch Elect & Informat Engn Beijing 100044 Peoples R China

With the rapid advancement of artificial intelligence, generative artificial intelligence (GAI) has taken a leading role in transforming data processing methods. However, the high computational demands of GAI present challenges for devices with limited resources. As we move towards the sixth generation of mobile networks (6G), the higher data rates of 6G create a need for more efficient data processing in GAI. Traditional GAI, however, shows its limitations in meeting these demands. To address these challenges, we introduce a multi-level collaborative inference system designed for next-generation networks and communication. Our proposed system features a deployment strategy that assigns models of varying sizes to devices at different network layers. Then, we design a task offloading strategy to optimise both efficiency and latency. Furthermore, a modified early exit mechanism is implemented to enhance the inference process for single models. Experimental results demonstrate that our system effectively reduces inference latency while maintaining high-quality output. Specifically, compared to existing work, our system can reduce inference time by up to 17% without sacrificing the inference accuracy.

关键词： Computational modeling Accuracy Transformers inference algorithms Collaboration Next generation networking Adaptation models 6G mobile communication Servers Probabilistic logic Generative artificial intelligence early exit collaborative inference next-generation networks and communication

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