A genetically optimized neural classifier applied to numerical pile integrity tests considering concrete piles

Protopapadakis, Eftychios; Schauer, Marco; Pierri, Erika; Doulamis, Anastasios; Stavroulakis, Georgios Ε.; Böhrnsen, Jens-U.; Langer, Sabine

doi:10.1016/j.compstruc.2015.08.005

Cited by 38 publications

(14 citation statements)

References 37 publications

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“…Morovvat [21] examined the "Layer Air Conditioning System" in conjunction with the Hydronic Radiation Cooler, comprehensively in terms of thermal comfort, air quality, and energy consumption, and as part of his findings, he confirmed the conclusions of Tian et al [20]. The combination of genetic algorithms with neural networks has been utilized, and provided prominent results in many real life applications [22][23][24]. A genetic algorithm is used by the strategy to solve the on-line optimization problem of multiple parameters [25].…”

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confidence: 74%

Air Condition’s PID Controller Fine-Tuning Using Artificial Neural Networks and Genetic Algorithms

2018

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In this paper, a Proportional-Integral-Derivative (PID) controller is fine-tuned through the use of artificial neural networks and evolutionary algorithms. In particular, PID's coefficients are adjusted on line using a multi-layer. In this paper, we used a feed forward multi-layer perceptron. There was one hidden layer, activation functions were sigmoid functions and weights of network were optimized using a genetic algorithm. The data for validation was derived from a desired results of system. In this paper, we used genetic algorithm, which is one type of evolutionary algorithm. The proposed methodology was evaluated against other well-known techniques of PID parameter tuning.In Section 2, we describe the system model and related works. In Section 3, we provided the proposed methodology. Finally, in Section 4, we present the simulation results, and we provide a general conclusion in Section 5. A complete list of references is also provided. Related WorkThe Earth system model always displays many of changes in the earth's climate. International agreements such as the Kyoto Protocol and the Copenhagen Accord emphasize the reduction of carbon dioxide propagation as one of the main ways to deal with risk of climate change [12]. The suggestions made by the Chinese Government's Electric and Mechanical Services Organization (25.5 • C), EU expansion and Innovation of China (26 • C), and the Ministry of Economy and Knowledge Republic of Korea (26-28 • C) are related to the thermal environment and its optimization [13]. It is also possible to refer to the suggestion of the Carrier 1 software (25-26.2 • C) in relation to the internal temperature [14]. The result of this research is logical and presents an acceptable increase in the average temperature of the air in summer. On the other hand, one of the most important human environmental requirements is the provision of thermal comfort conditions. The most common definition of thermal comfort has been defined by the ASHRAE Standard 55, which defines thermal comfort is that condition of mind that expresses satisfaction with the thermal environment [15].Furthermore, researchers have concluded that humans spend between 80 and 90 percent of their lives in an indoor environment [16]. Hence, indoor air quality like thermal comfort, has a direct impact on the health, productivity, and morale of humans [17]. An increase in the average indoor air temperature in summer can improve the energy consumption of air conditioning systems and consequently is a solution against undesirable climate changes [18]. However, this action may disturb the residents' thermal comfort conditions, especially in the summer. Therefore, optimizing energy consumption in air conditioning systems is a useful action to maintain thermal comfort conditions [19]. Among the most important studies in this area is the research that has been undertaken by Tian et al. [20].By assessing the mean local air age, Tian et al. showed the average of the people's vote indicator, relative to the thermal conditions of the envir...

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confidence: 74%

Air Condition’s PID Controller Fine-Tuning Using Artificial Neural Networks and Genetic Algorithms

2018

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“…The adopted stacked autoencoder approach consists of three layers or four layers, depending on the PCA outcome. The loss function was the well-known mean square error [43] with L2 and sparsity regularizers [44]. …”

Section: Resultsmentioning

confidence: 99%

Stacked Autoencoders for Outlier Detection in Over-the-Horizon Radar Signals

Protopapadakis

Voulodimos

Doulamis

et al. 2017

Computational Intelligence and Neuroscience

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Detection of outliers in radar signals is a considerable challenge in maritime surveillance applications. High-Frequency Surface-Wave (HFSW) radars have attracted significant interest as potential tools for long-range target identification and outlier detection at over-the-horizon (OTH) distances. However, a number of disadvantages, such as their low spatial resolution and presence of clutter, have a negative impact on their accuracy. In this paper, we explore the applicability of deep learning techniques for detecting deviations from the norm in behavioral patterns of vessels (outliers) as they are tracked from an OTH radar. The proposed methodology exploits the nonlinear mapping capabilities of deep stacked autoencoders in combination with density-based clustering. A comparative experimental evaluation of the approach shows promising results in terms of the proposed methodology's performance.

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“…In real life, there are several fields of SSL testing, assuming that there is data availability. The work of [4] evaluates the foundation piles structural condition using graph-based approaches. A scalable graph-based approach was utilized in [5] for the initialization of a maritime surveillance system.…”

Section: Introductionmentioning

confidence: 99%

On the Impact of Labeled Sample Selection in Semisupervised Learning for Complex Visual Recognition Tasks

2018

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One of the most important aspects in semisupervised learning is training set creation among a limited amount of labeled data in such a way as to maximize the representational capability and efficacy of the learning framework. In this paper, we scrutinize the effectiveness of different labeled sample selection approaches for training set creation, to be used in semisupervised learning approaches for complex visual pattern recognition problems. We propose and explore a variety of combinatory sampling approaches that are based on sparse representative instances selection (SMRS), OPTICS algorithm, k-means clustering algorithm, and random selection. These approaches are explored in the context of four semisupervised learning techniques, i.e., graph-based approaches (harmonic functions and anchor graph), low-density separation, and smoothness-based multiple regressors, and evaluated in two real-world challenging computer vision applications: image-based concrete defect recognition on tunnel surfaces and video-based activity recognition for industrial workflow monitoring.

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A genetically optimized neural classifier applied to numerical pile integrity tests considering concrete piles

Cited by 38 publications

References 37 publications

Air Condition’s PID Controller Fine-Tuning Using Artificial Neural Networks and Genetic Algorithms

Air Condition’s PID Controller Fine-Tuning Using Artificial Neural Networks and Genetic Algorithms

Stacked Autoencoders for Outlier Detection in Over-the-Horizon Radar Signals

On the Impact of Labeled Sample Selection in Semisupervised Learning for Complex Visual Recognition Tasks

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