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检索条件"主题词=Inference Algorithms"

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Poster: Using Consensus to Automatically Infer Post-conditions

Poster: Using Consensus to Automatically Infer Post-conditio...

引用

International Conference on Software Engineering - Companion, ICSE, Companion

作者： Jingyi Su Mohd Arafat Robert Dyer Bowling Green State University Bowling Green Ohio

Formal behavioral specifications help ensure the correctness of programs. Writing such specifications by hand however is time-consuming and requires substantial expertise. Previous studies have shown how to use a notion of consensus to automatically infer preconditions for APIs by using a large set of projects. In this work, we propose a similar idea of consensus to automatically infer post-conditions for popular APIs. We propose two new algorithms for mining potential post-conditions from API client code. The first algorithm looks for guarded post-conditions that test the value returned from the API and throws an exception. The second algorithm looks for values flowing from the API to another API with already known preconditions, which recommends them as post-conditions of the first API.

关键词： inference algorithms Software engineering Target tracking Writing Specification languages Collective intelligence Libraries

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Fuzzy logic extends a general purpose instruction set

Fuzzy logic extends a general purpose instruction set

引用

Northcon

作者： J.M. Sibigtroth Motorola Inc. Austin TX USA

By extending the general purpose instruction set of the CPU12 to include four new fuzzy logic instructions, the 16-bit MC68HC12 gains more than an order of magnitude improvement in execution speed for a fuzzy inference program compared to the 8-bit MCS8HC11. Coding is also dramatically simplified by reducing a 250 byte program to about 50 bytes. At the same time, re-use of existing logic in the CPU allows these additions without increasing the die size (and thus cost) of the MCU. The general purpose instruction set provides the means for programming non-fuzzy portions of the embedded control application as well as large portions of the inference program itself. This makes it possible for the fuzzy instructions to be focused on the portions of the fuzzy algorithm where they can produce the most benefits. Any of the specialized fuzzy instructions can be replaced by an alternate algorithm coded with traditional general purpose instructions. This provides a great deal of flexibility not found in hardware approaches.

关键词： Fuzzy sets Fuzzy logic inference algorithms Application software Hardware Workstations Fuzzy control Table lookup Control systems Software performance

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Online tuning scheme for generalized predictive controller via simulation-optimization

Online tuning scheme for generalized predictive controller v...

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： Shaoyuan Li Guoning Du Institute of Automation Shanghai JiaoTong University Shanghai China

From the process control point of view, it is difficult to find out the optimal parameters for the control system based on the single quadratic performance index, which is used in the standard predictive control algorithm. The fuzzy decision-making function is investigated in this paper. First, M control actions are obtained by an unconstrained predictive control algorithm, and fuzzy goals and fuzzy constraints can then be calculated and the global satisfactory degree is obtained by fuzzy inference. Moreover, the weighting coefficient /spl lambda/ in the cost function is tuned using simulation-optimization according to the fuzzy criteria.

关键词： Fuzzy control Predictive control Prediction algorithms inference algorithms Process control Optimal control Control systems Performance analysis Decision making Cost function

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Transformer-based Multi-Target Tracking with Bayesian Perspective

Transformer-based Multi-Target Tracking with Bayesian Perspe...

引用

International Conference on Information Fusion

作者： Xinwei Wei Yiru Lin Linao Zhang Zhiyuan Zou Jianwei Wei Wei Yi School of Information and Communication Engineering University of Electronic Science and Technology of China

ISBN: (数字)9781737749769

ISBN: (纸本)9798350371420

The Bayesian inference has a two-step recursion structure, i.e., prediction and updating, which can be viewed as a dynamic reasoning process. Based on this elegant structure, various multi-target tracking (MTT) algorithms have been invented and successfully applied in many areas. On the other hand, Bayesian inference MTT algorithms are model-based methods that rely on models’ accuracy and first-order Markov assumption. In recent years, the MTT algorithms based on deep learning have received much attention due to their model-free property and the ability to learn from data, although they have issues such as over-fitting, generalization, etc. In this work, we propose a Transformer-based multi-target tracker whose architecture mimics the Bayesian inference, referred to as the Bayesian inference-based Transformer (BAIT) for MTT. To deal with the model mismatch issues, BAIT uses neural networks instead of the pre-assumed motion and observation models while retaining the excellent architecture of Bayesian inference. BAIT can recursively complete accurate predictions and updates via Transformer by refining the estimation of target states in a Bayesian inference-like manner. Thus, BAIT can be viewed as a combination of model-based and data-based methods. The simulation results show that, because of combining the advantages of Bayesian architecture with intelligent data association structure, BAIT is competitive in simple scenarios and achieves superior performance when the data association task becomes complicated.

关键词： Accuracy Tracking Heuristic algorithms Simulation Neural networks Transformers Prediction algorithms inference algorithms Bayes methods Resource management

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Edge detection techniques using fuzzy thresholding

Edge detection techniques using fuzzy thresholding

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World Congress on Information and Communication Technologies (WICT)

作者： Mehul Thakkar Hitesh Shah A. C. R. E. Indian Institute of Technology New Vallabh Vidyanagar Gujarat India G. H. Patel College of Engineering and Technology Vallabh Vidyanagar Gujarat India

Edge detection is forefront of image processing algorithms. There are problems associated with different edge detectors. Thresholding is critical problem in edge detection. The paper represents three different methods of fuzzy based thresholding and resulted adaptive threshold is used in edge detection algorithm. Simulation results shows fuzzy thresholding based edge detection gives better results than conventional methods.

关键词： Image edge detection Detectors Brightness Fuzzy reasoning Fuzzy systems Histograms inference algorithms

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Optimizing Multiple Consumer-specific Objectives in End-to-End Ensemble Machine Learning Serving

Optimizing Multiple Consumer-specific Objectives in End-to-E...

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IEEE International Conference on Utility and Cloud Computing (UCC)

作者： Minh-Tri Nguyen Hong-Linh Truong Paolo Arcaini Fuyuki Ishikawa Department of Computer Science Aalto University Espoo Finland National Institute of Informatics Tokyo Japan

ISBN: (数字)9798350367201

ISBN: (纸本)9798350367218

Optimizing the quality of machine learning (ML) services for individual consumers with specific objectives is crucial for improving consumer satisfaction. In this context, end-to-end ensemble ML serving (EEMLS) faces many challenges in selecting and deploying ensembles of ML models on diverse resources across the edge-cloud continuum. This paper provides a method for evaluating the runtime performance of inference services via consumer-defined metrics. We enable ML consumers to define high-level metrics and consider consumer satisfaction in estimating service costs. Moreover, we introduce a time-efficient ensemble selection algorithm to optimize the EEMLS with intricate trade-offs between service quality and costs. Our intensive experiments demonstrate that the algorithm can be executed periodically despite the extensive search space, enabling dedicated optimization for individual consumers in dynamic contexts.

关键词： Costs Machine learning algorithms Runtime Heuristic algorithms Customer satisfaction Machine learning inference algorithms Resource management Reliability Optimization

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Special Session: Estimation and Optimization of DNNs for Embedded Platforms

Special Session: Estimation and Optimization of DNNs for Emb...

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International Conference on Hardware/Software Codesign and System Synthesis (CODES)

作者： Axel Jantsch Song Han Lin Meng Oliver Bringmann Haotian Tang Shang Yang Hengyi Li Matthias Wess Martin Lechner Christian Doppler Laboratory for Embedded Machine Learning TU Wien Vienna Austria Department of EECS Massachusetts Institute of Technology Cambridge MA USA Department of ECE Ritsumeikan University Kusatsu Shiga Japan Department of Computer Science University of Tübingen Tübingen Germany

ISBN: (数字)9798350356397

ISBN: (纸本)9798350356403

Several state of the art estimation and optimization techniques for CNNs and LLMs on embedded devices are summarized. For LLMs an Activation-aware Weight Quantization and on-the-fly dequantization techniques is presented. For CNNs various pruning algorithms and an integrated optimization and implementation flow is discussed. To estimate inference latency of CNNs on specific hardware platforms, three different techniques are reviewed: A mixed analytic-stochastic model, an analytic model based on step-wise linear functions, and a method that uses a detailed architecture description of the hardware.

关键词： Analytical models Quantization (signal) Estimation Hardware inference algorithms Optimization

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An Intelligent Switching Controller for A Plug-in Hybrid Electric Vehicle- A comparative analysis

An Intelligent Switching Controller for A Plug-in Hybrid Ele...

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Smart Electronics and Communication (ICOSEC), International Conference on

作者： K. Sai Dileep Kumar Reddy M. Lokesh Reddy Diddi Ramakrishna K. Sireesha Department of Electrical and Electronics Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bangalore India

ISBN: (数字)9798331504403

ISBN: (纸本)9798331504410

This research study explores a variety of control strategies that utilise advanced technologies to regulate hybrid electric vehicles (HEVs). A controller that can adaptively switch between different modes has been designed and simulated using the MATLAB/Simulink ${ }^{\circledR}$ platform for Hybrid Electric Vehicles (HEVs).The simulation results of Fuzzy-tuned proportional integral derivative (F-PID) and Genetic Algorithm (GA) tuned PID controllers are systematically evaluated, and a comparative study of fuzzy-PID and GA-PID is presented.

关键词： Simulation Switches Control systems inference algorithms Combustion Hybrid electric vehicles MATLAB Engines Genetic algorithms

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Recognition of shape-changing hand gestures based on switching linear model

Recognition of shape-changing hand gestures based on switchi...

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International Conference on Image Analysis and Processing

作者： Mun Ho Jeong Y. Kuno N. Shimada Y. Shirai Dept. Computer-Controlled Mech. Systems Osaka Univ. Japan Department Information and Computer Science Saitama University Japan Dept. Information and Computer Sciences Saitama Unv. Dept. Computer-Controlled Mech. Systems Osaka Univ.

ISBN: (纸本)076951183X

We present a method to track and recognise shape-changing hand gestures simultaneously. The switching linear model using the active contour model corresponds well to temporal shapes and motions of hands. inference in the switching linear model is computationally intractable and therefore the learning process cannot be performed via the exact EM (expectation maximization) algorithm. However, we present an approximate EM algorithm using a collapsing method in which some Gaussians are merged into a single Gaussian. Tracking is performed through the forward algorithm based on Kalman filtering and the collapsing method. We also present the regularized smoothing, which plays a role in reducing jump changes between the training sequences of state vectors to cope with complex-variable hand shapes. The recognition process is performed by the selection of a model with the maximum likelihood from some learned models while tracking is being performed. Experiments for several shape-changing hand gestures are demonstrated.

关键词： Hidden Markov models Vectors Active contours Active shape model inference algorithms Gaussian approximation Shape measurement Computational modeling Kalman filters Man machine systems

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Virtual Teacher Distillation: Enhancing Performance on Edge Smart Ecosystem Devices

Virtual Teacher Distillation: Enhancing Performance on Edge ...

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Chinese Automation Congress (CAC)

作者： Runjian Li Yupei Zeng Hongyu Ma Binchao Yu Longhua Ma Guanzhong Tian Zhejiang University Hangzhou China Ningbo Lvhe Era Technology Co. Ltd Ningbo China PipeChina Zhejiang Pipeline Network Co. Ltd. Ningbo China NingboTech University Ningbo China Ningbo Innovation Center Zhejiang University Ningbo China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

Knowledge Distillation is a method that essentially facilitates the transfer of knowledge from a larger, sophisticated model (named Teacher), into a smaller, less complex model(named Student). Multi-teacher distillation introduces multiple teachers to collaboratively guide the learning process of the student. Knowledge distillation does not alter the model's architecture; it simply replaces the original weight files to enhance algorithm performance. This approach effectively addresses the computational limitations of edge recognition devices in the field of smart ecosystems. However, current multi-teacher distillation methods require multiple pretrained teachers under the same dataset, which makes it challenging to test the performance of the methods in large scale datasets. We propose ‘Virtual Teachers' as a solution, which introduces Gaussian noise into the features generated one single teacher, creating new features. These then act as ‘Virtual Teachers' in place of the additional teachers used in multi-teacher methods. Virtual Teacher method has two main advantages: it eliminates the need for multiple pretrained teachers and reduces the inference. Furthermore, we design a ‘plug-and-play’ masking tool, called Virtual Teacher-Mask (VT-MASK) and a Direction-Norm Loss. Virtual Teacher-Mask has the capability to negate the impact of incorrect information from the Virtual Teachers. Our experiments on CIFAR-100 confirm that the exclusive replacement of Additional Teachers with Virtual Teacher does not compromise the efficacy of current multi-teacher methods. Furthermore, we expand our testing to larger datasets like ImageNet, demonstrating the generalizability of our method.

关键词： Performance evaluation Costs Computational modeling Image edge detection Gaussian noise Ecosystems Computer architecture inference algorithms Testing Convergence

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