• Geomechanics and Engineering
• /
• 제34권6호
• /
• pp.697-726
• /
• 2023

Brittleness as an important property of rock plays a crucial role both in the failure process of intact rock and rock mass response to excavation in engineering geological and geotechnical projects. Generally, rock brittleness indices are calculated from the mechanical properties of rocks such as uniaxial compressive strength, tensile strength and modulus of elasticity. These properties are generally determined from complicated, expensive and time-consuming tests in laboratory. For this reason, in the present research, an attempt has been made to predict the rock brittleness indices from simple, inexpensive, and quick laboratory test results namely dry unit weight, porosity, slake-durability index, P-wave velocity, Schmidt rebound hardness, and point load strength index using multiple linear regression, exponential regression, support vector machine (SVM) with various kernels, generating fuzzy inference system, and regression tree ensemble (RTE) with boosting framework. So, this could be considered as an innovation for the present research. For this purpose, the number of 39 rock samples including five igneous, twenty-six sedimentary, and eight metamorphic were collected from different regions of Iran. Mineralogical, physical and mechanical properties as well as five well known rock brittleness indices (i.e., B₁, B₂, B₃, B₄, and B₅) were measured for the selected rock samples before application of the above-mentioned machine learning techniques. The performance of the developed models was evaluated based on several statistical metrics such as mean square error, relative absolute error, root relative absolute error, determination coefficients, variance account for, mean absolute percentage error and standard deviation of the error. The comparison of the obtained results revealed that among the studied methods, SVM is the most suitable one for predicting B₁, B₂ and B₅, while RTE predicts B₃ and B₄ better than other methods.

• 한국산학기술학회논문지
• /
• 제15권7호
• /
• pp.4105-4110
• /
• 2014

앙상블에서 베이스라는 악기의 역할은 화음의 토대를 만들어주는 것이다. 우선은 그 기본 임무에 충실해야 한다. 그러나 그 역할에만 집중하다보면 다소 밋밋하고 안정적이기만 한 연주가 되기 때문에 보다 재미있는 연주를 위해서라면 다양한 아이디어를 반영해야 할 필요가 있다. 그런 의미에서 볼 때, Rutger Gunnarsson은 연주곡 위주의 음악이 아닌 대중음악에 이러한 사례를 적용한 예를 ABBA라는 대 그룹에서의 연주를 통해 뚜렷하게 보여주었다. 화려한 테크닉이나 고난이도의 기교에 의존하지 않고 기본기에 충실한 연주만으로도 이처럼 다양한 느낌을 낼 수 있다는 것에 주목해야 한다.

• 한국의류산업학회지
• /
• 제16권1호
• /
• pp.13-25
• /
• 2014

The purpose of this study is to investigate the concept and characteristics of 'grotesque,' examine the aesthetic characteristics of grotesque reflected in arts and dress, and modern fashion. The findings are as follows: 1) Grotesque indicates unnatural, unpleasant, and exaggerated that it upsets or shocks person. The characteristics of grotesque include terror, abnormality, unreality, amusement, disgust. 2) The grotesque art represented terror, abnormality, unreality, amusement, disgust by disordered form, nonnatural things, evil world, unorthodox methods, unrealistic image, strange dreamland. 3) The grotesque dress represented terror, abnormality, unreality, amusement, disgust by exaggerated silhouette, exaggerated adornment, excessive decoration, incroyables, using exaggerated silhouette, crinoline silhouette, bustle silhouette, surrealist style, extraordinary materials, glam rock style, unique silhouette, cyber look. 3) Terror was implied in the punk look suits of Junya Watanabe, and exaggerated outers of Viktor & Rolf. Abnormality was shown in the atypical suit of John Galliano, Junya Watanabe's dress decorated with the extreme ruff, Thom Browne's suit of abnormal proportion. Unreality was reflected in the architectural dress of Gareth Pugh, Mermaid dress of Giles, the surreal suit of Jean-Charles de Castelbajac. Amusement was represented in the amusing suit of Gareth Pugh, John Galliano's dress of sexual perversion. Disgust was reflected in the decadent dress of Thierry Mugler, Undercove's suit, and the ensemble of shocking details.

• 콘크리트학회논문집
• /
• 제15권6호
• /
• pp.924-929
• /
• 2003

A majority of infrastructures has been deteriorated over time. Therefore, it is very important to verify the quality of construction, and the level of structural deterioration in existing structures, to ensure their safety and functionality. Many researchers have studied non-destructive testing (NDT) methods to identify structural problems in existing structures. The impact echo technique is one of the widely used NDT techniques. The impact echo technique has several inherent problems, including the difficulties in P-wave velocity evaluation due to inhomogeneous concrete properties, deterioration of evaluation accuracy where multiple reflection boundaries exist, and the influence of the receiver location in evaluating the thickness of the tested structures. Therefore, the objective of this paper is to propose an enhanced impact echo technique that can reduce the aforementioned problems and develop a Virtual Instrument for the application via a thickness evaluation technique which has same technical background to find deterioration in concrete structures. In the proposed impact echo technique, transfer function from dual channel system analysis is used, and coherence is improved to achieve reliable data. Also an averaged signal -ensemble- is used to achieve more reliable results. From the analysis of transfer function, the thickness is effectively identified.

• Smart Structures and Systems
• /
• 제26권2호
• /
• pp.241-251
• /
• 2020

Due to the undeniable importance of approximating the concrete compressive strength (CSC) in civil engineering, this paper focuses on presenting four novel optimizations of multi-layer perceptron (MLP) neural network, namely artificial bee colony (ABC-MLP), grasshopper optimization algorithm (GOA-MLP), shuffled frog leaping algorithm (SFLA-MLP), and salp swarm algorithm (SSA-MLP) for predicting this crucial parameter. The used dataset consists of 103 rows of information concerning seven influential parameters (cement, slag, water, fly ash, superplasticizer, fine aggregate, and coarse aggregate). In this work, the best-fitted complexity of each ensemble is determined by a population-based sensitivity analysis. The GOA distinguished its self by the least complexity (population size = 50) and emerged as the second time-effective optimizer. Referring to the prediction results, all tested algorithms are able to construct reliable networks. However, the SSA (Correlation = 0.9652 and Error = 1.3939) and GOA (Correlation = 0.9629 and Error = 1.3922) performed more accurately than ABC (Correlation = 0.7060 and Error = 4.0161) and SFLA (Correlation = 0.8890 and Error = 2.5480). Therefore, the SSA-MLP and GOA-MLP can be promising alternatives to laboratorial and traditional CSC evaluative methods.

• International Journal of Aeronautical and Space Sciences
• /
• 제11권4호
• /
• pp.326-337
• /
• 2010

This work addresses problems regarding trajectory planning for unmanned aerial vehicle sensors. Such sensors are used for taking measurements of large nonlinear systems. The sensor investigations presented here entails methods for improving estimations and predictions of large nonlinear systems. Thoroughly understanding the global system state typically requires probabilistic state estimation. Thus, in order to meet this requirement, the goal is to find trajectories such that the measurements along each trajectory minimize the expected error of the predicted state of the system. The considerable nonlinearity of the dynamics governing these systems necessitates the use of computationally costly Monte-Carlo estimation techniques, which are needed to update the state distribution over time. This computational burden renders planning to be infeasible since the search process must calculate the covariance of the posterior state estimate for each candidate path. To resolve this challenge, this work proposes to replace the computationally intensive numerical prediction process with an approximate covariance dynamics model learned using a nonlinear time-series regression. The use of autoregressive time-series featuring a regularized least squares algorithm facilitates the learning of accurate and efficient parametric models. The learned covariance dynamics are demonstrated to outperform other approximation strategies, such as linearization and partial ensemble propagation, when used for trajectory optimization, in terms of accuracy and speed, with examples of simplified weather forecasting.

• 한국실내디자인학회논문집
• /
• 제15권4호
• /
• pp.64-72
• /
• 2006

The purpose of this study is to interpret Korean traditional space as text on the basis of Structuralism. The process of this study is illustrated as follows: At first, this research contains basic concepts and theories of Structuralism and discusses the possibilities of typological approaches in Korean traditional space interpretation. Secondly, As typological structure of traditional space, spacial and visual expressive characteristics in traditional residential space based on traditional thoughts of Koreans formed with inherent consciousness are considered with Hjelmslev's Structuralism and Wonhyo's Whajaeng theory. Finally, This study tries conceptual analysis based on Lacan's Metaphor and Metonymy emerged from unconscious mechanisms about interpretation and induction of the traditional spacial structure for contemporary Application. Structure is a group of elements forming a covariant ensemble. From this point of view, this study validates that texts are understood when methods that bring back to the creative processes and intentions are found by Text Hermeneutics.

• Advances in Energy Research
• /
• 제6권2호
• /
• pp.91-101
• /
• 2019

This paper proposes an efficient data-driven approach to build models for predicting energy consumption in buildings. Data used in this research is collected by installing humidity and temperature sensors at different locations in a building. In addition to this, weather data from nearby weather station is also included in the dataset to study the impact of weather conditions on energy consumption. One of the main emphasize of this research is to make feature selection independent of domain knowledge. Therefore, to extract useful features from data, two different approaches are tested: one is feature selection through principal component analysis and second is relative importance-based feature selection in original domain. The regression model used in this research is gradient boosting regression and its optimal parameters are chosen through a two staged coarse-fine search approach. In order to evaluate the performance of model, different performance evaluation metrics like r2-score and root mean squared error are used. Results have shown that best performance is achieved, when relative importance-based feature selection is used with gradient boosting regressor. Results of proposed technique has also outperformed the results of support vector machines and neural network-based approaches tested on the same dataset.

• KIEAE Journal
• /
• 제17권5호
• /
• pp.77-86
• /
• 2017

Purpose: Because of the growing concern over fossil fuel use and increasing demand for greenhouse gas emission reduction since the 1990s, the building energy analysis field has produced various types of methods, which are being applied more often and broadly than ever. A lot of research products have been actively proposed in the area of the building energy simulation for over 50 years around the world. However, in the last 20 years, there have been only a few research cases where the trend of building energy analysis is examined, estimated or compared. This research aims to investigate a trend of the building energy analysis by focusing on methodology and characteristics of each method. Method: The research papers addressing the building energy analysis are classified into two types of method: engineering analysis and algorithm estimation. Especially, EPG(Energy Performance Gap), which is the limit both for the existing engineering method and the single algorithm-based estimation method, results from comparing data of two different levels- in other words, real time data and simulation data. Result: When one or more ensemble algorithms are used, more accurate estimations of energy consumption and performance are produced, and thereby improving the problem of energy performance gap.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제11권8호
• /
• pp.4043-4060
• /
• 2017

Network Intrusion Detection (NID), an important topic in the field of information security, can be viewed as a pattern recognition problem. The existing pattern recognition methods can achieve a good performance when the number of training samples is large enough. However, modern network attacks are diverse and constantly updated, and the training samples have much smaller size. Furthermore, to improve the learning ability of SVM, the research of kernel functions mainly focus on the selection, construction and improvement of kernel functions. Nonetheless, in practice, there are no theories to solve the problem of the construction of kernel functions perfectly. In this paper, we effectively integrate the advantages of the radial basis function kernel and the polynomial kernel on the notion of the game theory and propose a novel kernel SVM algorithm with game theory for NID, called GTNID-SVM. The basic idea is to exploit the game theory in NID to get a SVM classifier with better learning ability and generalization performance. To the best of our knowledge, GTNID-SVM is the first algorithm that studies ensemble kernel function with game theory in NID. We conduct empirical studies on the DARPA dataset, and the results demonstrate that the proposed approach is feasible and more effective.