Automated Machine Learning (AutoML) aims to make machine learning accessible to non-experts by minimizing technical barriers and enabling the creation of high-performance models without extensive programming knowledge...
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ISBN:
(纸本)9798350389814;9798350389807
Automated Machine Learning (AutoML) aims to make machine learning accessible to non-experts by minimizing technical barriers and enabling the creation of high-performance models without extensive programming knowledge. Despite advances, existing AutoML solutions often struggle with complexity and inefficiency in handling intricate tasks. Herein, we present LLM2AutoML, a zero-code AutoML framework that leverages Large Language models (LLMs) to generate high-performance actionable ML models without coding and with explanations. LLM2AutoML enables bidirectional human-machine alignment by interpreting user intentions expressed in natural language, converting them into executable AutoML tasks, and delivering detailed analytical reports that foster user understanding and trust. We propose the template-bounded AutoML method to ensure the LLM-generated code is highly executable, enabling fully end-to-end automation, including intention parsing, model auto-selection, and hyperparameter auto-tuning. Additionally, we incorporate techniques such as adaptive loss functions and configuration recommendations to improve efficiency and performance. Experiments on the SECOM dataset from the semiconductor manufacturing industry demonstrate that LLM2AutoML enhances the automation and usability of AutoML and LLMs, achieves superior performance, and produces high-quality analytical reports. This framework presents a novel approach to advancing the effectiveness and capabilities of both AutoML and LLMs.
The thermal conductivities of CFRP composites in the direction parallel to the fiber and vertical to the fiber and the specific heat of it were investigated by the methods of theoretical models and finite element anal...
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An adaptive fuzzy logic control scheme is developed for tracking a square trajectory by the endpoint of a two-link rigid joint space robot. The control strategy is based on a direct adaptive control scheme in which th...
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An adaptive fuzzy logic control scheme is developed for tracking a square trajectory by the endpoint of a two-link rigid joint space robot. The control strategy is based on a direct adaptive control scheme in which the controller gains are adapted in real-time according to fuzzy logic systems such that the tracking errors between a reference model and the actual robot system outputs are brought to zero.
This paper has developed a three degree-offreedom (3-DOF) haptic device which has adopted parallel structure. Compared with haptic devices adopting serial structure, parallel haptic devices have low inertia, high stif...
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BPM software automation projects require different approaches for effort estimation for they are developed based on business process models rather than traditional requirements analysis outputs. In this empirical rese...
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ISBN:
(纸本)9783642384844;9783642384837
BPM software automation projects require different approaches for effort estimation for they are developed based on business process models rather than traditional requirements analysis outputs. In this empirical research we examine the effect of various measures for BPMN compliant business process models on the effort spent to automate those models. Although different measures are suggested in the literature, only a few studies exist that relate these measures to effort estimation. We propose that different perspectives of business process models need to be considered such as behavioral, organizational, functional and informational to determine the automation effort effectively. The proposed measures include number of activities, number of participating roles, number of outputs from the process and control flow complexity. We examine the effect of these measures on the automation effort and propose a prediction model developed by multiple linear regression analysis. The data were collected from a large IS integration project which cost 300 person-months along a three-year time frame. The results indicate that some of the measures collected have significant effect on the effort spent to develop the BPM automation software. We envision that prediction models developed by using the suggested approach will be useful to make accurate estimates of project effort for BPM intensive software development projects.
Adaptive identification method based on modulating function approach is described in this paper. The real glass melting process is modelled by a linear system, which parameters values can be changed on-line during the...
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Adaptive identification method based on modulating function approach is described in this paper. The real glass melting process is modelled by a linear system, which parameters values can be changed on-line during the identification procedure. The overall model is composed of several submodels. The models, which inputs can be controlled, are approximated by the Strejc transfer function. This approach allows to reduce the number of model parameters. Moreover, PID tuning methods dedicated to the Strejc models can be applied. The developed procedure was applied for data collected from a real glass containers production installation.
Characteristics of several RGB-HSX conversion methods and differences among their space structures were pointed *** of those differences were *** between RGB-HSX conversions and 3D models were put forwarded to constru...
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ISBN:
(纸本)9781457708596
Characteristics of several RGB-HSX conversion methods and differences among their space structures were pointed *** of those differences were *** between RGB-HSX conversions and 3D models were put forwarded to construct ideal *** conclusions have references values for using color model effectively.
This paper presents an iterative learning control strategy for a fed-batch fermentation process using linearised models identified from process operational data. Off-line calculated control policies for batch fermenta...
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ISBN:
(纸本)9781424422869
This paper presents an iterative learning control strategy for a fed-batch fermentation process using linearised models identified from process operational data. Off-line calculated control policies for batch fermentation processes may not be optimal when implemented on the processes due to model plant mismatches and/or the presence of unknown disturbances. In order to overcome the effect of model plant mismatches and unknown disturbances, a batch to batch iterative learning control strategy is developed to modify the control actions for the next batch using the information obtained form current and previous batches. The control policy updating is calculated using a model linearised around a reference batch. In order to cope with process variations and disturbances, the reference batch can be taken as the immediate previous batch. After each batch, the newly obtained process operation data is added to the historical process data base and an updated linearised model is re-identified. Since the control actions during different stages of a batch are usually correlated, it is proposed in this paper that the linearised model can be identified from partial least square regression. The proposed technique has been successfully applied to a simulated fed-batch fermentation process.
In this paper, a new probabilistic path planning algorithm is described. The algorithm has been developed for assembly planning purposes, but however it can also be used in similar scenarios. Most assembly planning al...
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ISBN:
(纸本)9781617387197
In this paper, a new probabilistic path planning algorithm is described. The algorithm has been developed for assembly planning purposes, but however it can also be used in similar scenarios. Most assembly planning algorithms apply the so called assembly-by-disassembly strategy, therewith planning starts from the goal position of parts and tries to remove single parts or group of parts. Such problems are characterized by the appearance of many narrow passages. Thus, we have developed an probabilistic algorithm able to find paths even if many of such passages exist. The idea of our path planner emanates from the particle In each iteration, it propagates new samples, discards bad evaluated samples and assigns higher weights to good examples. The evaluation functions propagates the samples to explore free space as well as to condensate on the border of obstacles, which leads samples to pass narrow passages. We have evaluated our path planning algorithm with different examples and compared it to the well-known RRT and PRMplanners. We could achieve good execution times for realistic industrial assembly tasks.
Rapid prototyping as well as retrofitting and digitization of legacy manufacturing equipment often needs design and application of closed loop controllers. The analysis and modeling for such systems like energy-conver...
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