Human performance modeling for manufacturing based on an improved KNN algorithm

Li, Ni; Kong, Haipeng; Ma, Yuanyuan; Gong, Guanghong; Huai, Wenqing

doi:10.1007/s00170-016-8418-6

Cited by 33 publications

(18 citation statements)

References 41 publications

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“…Since the range of each predictor is significantly different and the test results might rely on the values of a few predictors, they are preprocessed using a normalization [26]. We compute the upper and lower bound of each predictor, and the process for the used normalization is represented as Equation 16, Equation (17) and Equation (18).…”

Section: Prediction Model Of the Rockfall Runout Range Based On Oumentioning

confidence: 99%

“…Although the WKNN and DWKNN algorithms perform well in comparison with the traditional KNN approach, the sensitivity of the classification performance to the choices of the neighborhood size k still exits. It is also noticed that the exponential of some distance, which is chosen as the weighting scheme, exhibits better classification accuracy and lower variance [17]. Inspired by the effectiveness of the exponential of some distance for classification, we believe that this approach should be a better choice as the weighting scheme.…”

Section: Introductionmentioning

confidence: 97%

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Analysis of Factors Influencing Rockfall Runout Distance and Prediction Model Based on an Improved KNN Algorithm

et al. 2019

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The prediction method plays crucial roles in the accurate prediction of rockfall runout range which could improve the protection of endangered residential areas and infrastructure. Recently, the Knearest neighbor (KNN) algorithm, one of many machine learning techniques, showed good performance in pattern classification. Therefore, the aim of this study was to use the K-nearest neighbor (KNN) algorithm to predict the rockfall runout range which is classified into different subintervals according to the distance from the slope toe. First, we proposed the prediction model of the rockfall runout range based on our improved KNN algorithm which could better offer robustness against different choices of the neighborhood size k, and it is the first work of applying our improved KNN algorithm to rockfall runout range prediction. Second, the shaking table tests of rockfall runout models were conducted for simulating the rockfall process, and the influence laws of factors-including types of an earthquake, peak ground acceleration, vibration frequency, slope angle, slope height, and block mass and block shape-on rockfall distance are investigated. Finally, there is a discussion of the performance of our proposed prediction model based on our improved KNN algorithm in the prediction of rockfall runout range. The extensive experimental results for rockfall runout range prediction demonstrate the effectiveness of our proposed prediction model. INDEX TERMS Improved KNN algorithm, rockfall runout range, earthquake, shaking table test.

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Section: Prediction Model Of the Rockfall Runout Range Based On Oumentioning

confidence: 99%

Section: Introductionmentioning

confidence: 97%

Analysis of Factors Influencing Rockfall Runout Distance and Prediction Model Based on an Improved KNN Algorithm

et al. 2019

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“…In this paper, we attempt to find the optimal behavior of an operator for a given situation during a battle. However, defining a behavior is a challenging task due to the complexity human behaviors [26]. Therefore, we chosen six behaviors based on the military documents such as the rules of engagement, and in order to mimic human behaviors, we define them as a form of probability, called intention.…”

Section: Behavior Optimization For Uvsmentioning

confidence: 99%

“…The two major novelties this study has with existing studies are: First, we attempt to perform a prior study on the optimization of human behaviors in the military domain. It is a well-known fact that finding the optimal behavior of a human is a difficult task as the behaviors are complex and hard to define [26], whereas defining combat behaviors of UVs is relatively viable since the actions are already defined through military documents such as the rules of engagement. Second, in a more practical view, we sought to contribute to the development of a key technology that can build an agent acting optimally in a battle as UVs are started to be used in a real-world situation with the advancement of military technologies.…”

Section: Introductionmentioning

confidence: 99%

A Behavior Optimization Method for Unmanned Combat Aerial Vehicles Using Matrix Factorization

et al. 2020

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One of the fundamental technologies for unmanned combat aerial vehicles and combat simulators is behavior optimization, which finds a behavior that maximizes the probability of winning a battle. With the advent of military science, combat logs became available, allowing machine learning algorithms to be used for the behavior optimization. Due to implicit attributes such as the experience of an operator that are not explicitly presented in log data, existing methods for behavior optimization have limitations in performance improvement. Furthermore, specific behaviors occur with low frequency, resulting in a dataset with imbalanced and empty values. Therefore, we apply a matrix factorization (MF) method, which is one of latent factor models and known for sophisticated imputation of empty values, to the behavior optimization problem of unmanned combat aerial vehicles. A situation-behavior matrix, whose elements are ratings indicating the optimality of behaviors in situations, is defined to implement the MF based method. Experiments for performance comparison were conducted on combat logs, in which the proposed method yielded satisfactory results. INDEX TERMS behavior optimization, unmanned vehicle, matrix factorization, reinforcement learning, situation-behavior matrix ABBREVIATIONS AM Advantage matrix. FOV Field of view. GA Genetic algorithm. LOS Line of sight. MF Matrix factorization. nDCG Normalized discounted cumulative gain. RL Reinforcement learning. SB Situation-behavior. UV Unmanned vehicle.

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“…Gobert et al [ 4 ] developed an interactive environment to evaluate students’ scientific research capability via data mining technology. Li et al [ 5 ] proposed an improved KNN algorithm to deal with human performance prediction in a manufacturing system. This method utilized a distance calculation formula based on entropy, a classification rule, and a quantitative description way of human performance.…”

Section: Introductionmentioning

confidence: 99%

Convolutional Recurrent Neural Networks with a Self-Attention Mechanism for Personnel Performance Prediction

Xue

Feng

Gao

et al. 2019

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Personnel performance is important for the high-technology industry to ensure its core competitive advantages are present. Therefore, predicting personnel performance is an important research area in human resource management (HRM). In this paper, to improve prediction performance, we propose a novel framework for personnel performance prediction to help decision-makers to forecast future personnel performance and recruit the best suitable talents. Firstly, a hybrid convolutional recurrent neural network (CRNN) model based on self-attention mechanism is presented, which can automatically learn discriminative features and capture global contextual information from personnel performance data. Moreover, we treat the prediction problem as a classification task. Then, the k-nearest neighbor (KNN) classifier was used to predict personnel performance. The proposed framework is applied to a real case of personnel performance prediction. The experimental results demonstrate that the presented approach achieves significant performance improvement for personnel performance compared to existing methods.

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Human performance modeling for manufacturing based on an improved KNN algorithm

Cited by 33 publications

References 41 publications

Analysis of Factors Influencing Rockfall Runout Distance and Prediction Model Based on an Improved KNN Algorithm

Analysis of Factors Influencing Rockfall Runout Distance and Prediction Model Based on an Improved KNN Algorithm

A Behavior Optimization Method for Unmanned Combat Aerial Vehicles Using Matrix Factorization

Convolutional Recurrent Neural Networks with a Self-Attention Mechanism for Personnel Performance Prediction

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