A comprehensive experimental evaluation of orthogonal polynomial expanded random vector functional link neural networks for regression

Vuković, Najdan; Petrović, Milica; Miljković, Zoran

doi:10.1016/j.asoc.2017.10.010

Cited by 93 publications

(54 citation statements)

References 35 publications

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“…To statistically assess performance of the algorithms, series of statistical tests are conducted, following the examples from [16,17]. We have performed a multiple pairwise comparison of algorithms, between DNN and RF model, as well as comparison of both models with the KN model, and the results are summarized below in Table 4 for t-test.…”

Section: Human Motion Recognition Test Results and Statistical Analysismentioning

confidence: 99%

Human activity recognition based on machine learning classification of smartwatch accelerometer dataset

Radivojević¹,

Mirkov²,

Maletić³

2021

FME Transactions

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This paper presents two Machine Learning models that classify time series data given from smartwatch accelerometer of observed subjects. For the purpose of classification we use Deep Neural Network and Random Forest classifier algorithms. The comparison of both models shows that they have similar performance with regard to recognition of subject's activities that are used in the test group of the dataset. Training accuracy reaches approximately 95% and 100% for Deep Learning and Random Forest model respectively. Since the validation and recognition, reached about 81% and 75% respectively, a tendency for improving accuracy as a function of number of participants is considered. The influence of data sample precision to the accuracy of the models is examined since the input data could be given from various wearable devices.

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Section: Human Motion Recognition Test Results and Statistical Analysismentioning

confidence: 99%

Human activity recognition based on machine learning classification of smartwatch accelerometer dataset

Radivojević¹,

Mirkov²,

Maletić³

2021

FME Transactions

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“…However, it is not possible to conduct comprehensive tests for multiple factors belonging to a factor set because employing such a large number of tests is impractical. Therefore, an orthogonal test is adopted when designing the driving simulation test [33,34].…”

Section: Testing Methodsmentioning

confidence: 99%

Investigation of esthetic evaluation and its influencing factors for a tunnel portal based on dynamic vision

Wei

et al. 2020

PLoS ONE

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With the development of modern cities, roads, and landscapes, it is becoming increasingly important for infrastructure such as tunnels to provide an esthetically pleasing experience. In this respect, it is necessary to conduct studies that consider the esthetic design of tunnel portals using esthetics research. Regarding the esthetic evaluation of tunnel portals, this paper fully considers the dynamic visual effect from the driver's perspective. This study combines the use of Blender, SpeedTree Modeler Cinema, Adobe Photoshop CS6, and other software for secondary development. These programs are connected to the driving simulation platform Euro Truck Simulator 2 (which is equipped with a driving simulator) to construct a set of driving simulation tests that enable the esthetic evaluation of a tunnel portal. The Banlun Tunnel on the Funing-Longliu Expressway in Yunnan Province, China, is used as a case study, and four impact factors that vary significantly in esthetic design are included: the linearity, color, greening and texture of the portal. Using an orthogonal experimental design, the influence of the esthetic degree was simulated and evaluated, and the order of sensitivity to esthetic factors of a headwall tunnel portal was sequentially determined as follows: the portal texture exerts the maximum impact on the beauty degree of the headwall portal, followed by the portal greening and the portal color, while the portal linearity exerts the minimum impact. The results show that the developed driving simulation test system can be used to determine the sensitivity of esthetic factors for a tunnel portal and obtain an optimal collocation of esthetic factors on different levels; hence, it provides feedback for use in designing the optimum esthetic tunnel portal. This test system can be used as a reference when conducting future evaluations and studies on tunnel portal esthetics.

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“…Because there is no hidden layer, the training computational complexity of the algorithm is reduced significantly. Therefore, FLANN is often applied to the modeling of chemical process, nonlinear system, statistical regression problems, etc. The structure of the traditional FLANN algorithm is shown in Figure .…”

Section: Methodsmentioning

confidence: 99%

Energy efficiency optimization of ethylene production process with respect to a novel FLPEM‐based material‐product nexus

2019

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Summary Energy efficiency optimization and energy management can bring multiple economic benefits for the complicated petrochemical industries. However, the influence of the production scale and materials, and the uncertain data have a direct impact on the production prediction and energy efficiency optimization of ethylene production process, which is ignored by the conventional optimization strategies. To achieve energy management and reduce energy consumption considering the production technology, total energy, and material flows, a multiobjective energy efficiency optimization scheme for the ethylene production process with respect to material‐product nexus is proposed. Specifically, a total‐factor material‐product nexus model of the ethylene production is established by a novel functional link prediction error method–integrated algorithm, providing system‐level material equilibrium constraints for the energy efficiency optimization scheme. Then an optimization model with two mutually complementary energy efficiency indicators is established and implemented by the multiobjective particle swarm optimization integrating functional link prediction error method. The proposed energy efficiency optimization scheme is applied into a practical ethylene plant, and the optimization results demonstrate that energy efficiency of this plant is increased by 10.30%, synthetical energy consumption is decreased by 13.05%, and energy cost is decreased by 10.42%.

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A comprehensive experimental evaluation of orthogonal polynomial expanded random vector functional link neural networks for regression

Cited by 93 publications

References 35 publications

Human activity recognition based on machine learning classification of smartwatch accelerometer dataset

Human activity recognition based on machine learning classification of smartwatch accelerometer dataset

Investigation of esthetic evaluation and its influencing factors for a tunnel portal based on dynamic vision

Energy efficiency optimization of ethylene production process with respect to a novel FLPEM‐based material‐product nexus

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