6,347 research outputs found
Kajian terhadap ketahanan hentaman ke atas konkrit berbusa yang diperkuat dengan serat kelapa sawit
Konkrit berbusa merupakan sejenis konkrit ringan yang mempunyai kebolehkerjaan yang
baik dan tidak memerlukan pengetaran untuk proses pemadatan. Umum mengenali
konkrit berbusa sebagai bahan binaan yang mempunyai sifat kekuatan yang rendah dan
lemah terutama apabila bahan binaan ini dikenakan tenaga hentaman yang tinggi.
Namun begitu, konkrit berbusa merupakan bahan yang berpotensi untuk dijadikan
sebagai bahan binaan yang berkonsepkan futuristik. Binaan futuristik adalah binaan yang
bercirikan ringan, ekonomi, mudah dari segi kerja pembinaan dan yang paling penting
adalah mesra alam. Dalam kajian ini, konkrit berbusa ditambah serat buangan pokok
kelapa sawit untuk untuk meningkatkan sifat kekuatan atau rapuh. Serat kelapa sawit juga
berfungsi mempertingkatkan ketahanan hentaman terutamanya aspek nilai penyerapan
tenaga hentaman dan nilai tenaga hentaman. Kandungan peratusan serat kelapa sawit
yang digunakan adalah 10%, 20% dan 30% dengan dua ketumpatan konkrit berbusa iaitu
1000kg/m3
dan 1400kg/m3
. Untuk menentukan nilai penyerapan tenaga hentaman dan
nilai tenaga hentaman, ujikaji Indentasi dan ujikaji hentaman dilakukan ke atas sampel�sampel yang telah diawet selama 28 hari. Luas bawah graf tegasan-terikan yang
diperolehi daripada ujikaji Indentasi merupakan nilai penyerapan tenaga hentaman bagi
sampel konkrit berbusa. Untuk ujikaji hentaman, keputusan ujikaji dinilai berdasarkan
nilai tenaga hentaman untuk meretakkan sampel yang diperolehi daripada mesin ujikaji
dynatup. Secara keseluruhannya, hasil dapatan utama bagi kedua-dua ujikaji
menunjukkan sampel yang mengandungi peratusan serat kelapa sawit sebanyak 20%
mempunyai nilai penyerapan tenaga hentaman dan nilai tenaga hentaman yang tinggi.
Serapan tenaga maksimum adalah sebanyak 4.517MJ/m3
untuk ketumpatan 1400kg/m3
.
Ini menunjukkan ketumpatan 1400kg/m3
berupaya menyerap tenaga lebih baik
berbanding ketumpatan 1000kg/m3
. Manakala untuk nilai tenaga hentaman maksimum
adalah sebanyak 27.229J untuk ketumpatan 1400kg/m3
. Hasil dapatan tersebut menunjukkan ketumpatan 1400kg/m3
dengan peratusan serat sebanyak 20% berupaya
mengalas tenaga hentaman yang lebih banyak sebelum sampel retak. Kesimpulannya,
peningkatan ketumpatan konkrit berbusa dan pertambahan serat buangan kelapa sawit ke
dalam konkrit berbusa dapat meningkatkan ciri ketahanan hentaman konkrit berbusa
khususnya aspek nilai penyerapan tenaga hentaman dan nilai tenaga hentaman
Generative Adversarial Networks for Financial Trading Strategies Fine-Tuning and Combination
Systematic trading strategies are algorithmic procedures that allocate assets
aiming to optimize a certain performance criterion. To obtain an edge in a
highly competitive environment, the analyst needs to proper fine-tune its
strategy, or discover how to combine weak signals in novel alpha creating
manners. Both aspects, namely fine-tuning and combination, have been
extensively researched using several methods, but emerging techniques such as
Generative Adversarial Networks can have an impact into such aspects.
Therefore, our work proposes the use of Conditional Generative Adversarial
Networks (cGANs) for trading strategies calibration and aggregation. To this
purpose, we provide a full methodology on: (i) the training and selection of a
cGAN for time series data; (ii) how each sample is used for strategies
calibration; and (iii) how all generated samples can be used for ensemble
modelling. To provide evidence that our approach is well grounded, we have
designed an experiment with multiple trading strategies, encompassing 579
assets. We compared cGAN with an ensemble scheme and model validation methods,
both suited for time series. Our results suggest that cGANs are a suitable
alternative for strategies calibration and combination, providing
outperformance when the traditional techniques fail to generate any alpha
Review of Face Detection Systems Based Artificial Neural Networks Algorithms
Face detection is one of the most relevant applications of image processing
and biometric systems. Artificial neural networks (ANN) have been used in the
field of image processing and pattern recognition. There is lack of literature
surveys which give overview about the studies and researches related to the
using of ANN in face detection. Therefore, this research includes a general
review of face detection studies and systems which based on different ANN
approaches and algorithms. The strengths and limitations of these literature
studies and systems were included also.Comment: 16 pages, 12 figures, 1 table, IJMA Journa
Bibliometric Mapping of the Computational Intelligence Field
In this paper, a bibliometric study of the computational intelligence field is presented. Bibliometric maps showing the associations between the main concepts in the field are provided for the periods 1996–2000 and 2001–2005. Both the current structure of the field and the evolution of the field over the last decade are analyzed. In addition, a number of emerging areas in the field are identified. It turns out that computational intelligence can best be seen as a field that is structured around four important types of problems, namely control problems, classification problems, regression problems, and optimization problems. Within the computational intelligence field, the neural networks and fuzzy systems subfields are fairly intertwined, whereas the evolutionary computation subfield has a relatively independent position.neural networks;bibliometric mapping;fuzzy systems;bibliometrics;computational intelligence;evolutionary computation
Metaheuristic design of feedforward neural networks: a review of two decades of research
Over the past two decades, the feedforward neural network (FNN) optimization has been a key interest among the researchers and practitioners of multiple disciplines. The FNN optimization is often viewed from the various perspectives: the optimization of weights, network architecture, activation nodes, learning parameters, learning environment, etc. Researchers adopted such different viewpoints mainly to improve the FNN's generalization ability. The gradient-descent algorithm such as backpropagation has been widely applied to optimize the FNNs. Its success is evident from the FNN's application to numerous real-world problems. However, due to the limitations of the gradient-based optimization methods, the metaheuristic algorithms including the evolutionary algorithms, swarm intelligence, etc., are still being widely explored by the researchers aiming to obtain generalized FNN for a given problem. This article attempts to summarize a broad spectrum of FNN optimization methodologies including conventional and metaheuristic approaches. This article also tries to connect various research directions emerged out of the FNN optimization practices, such as evolving neural network (NN), cooperative coevolution NN, complex-valued NN, deep learning, extreme learning machine, quantum NN, etc. Additionally, it provides interesting research challenges for future research to cope-up with the present information processing era
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