Current Issue : July - September
Volume : 2021
Issue Number : 3
Articles : 5 Articles
To satisfy practical requirements of high real-time accuracy and low computational
complexity of synthetic aperture radar (SAR) image ship small target
detection, this paper proposes a small ship target detection method based on
the improved You Only Look Once Version 3 (YOLOv3). The main contributions
of this study are threefold. First, the feature extraction network of the
original YOLOV3 algorithm is replaced with the VGG16 network convolution
layer. Second, general convolution is transformed into depthwise separable
convolution, thereby reducing the computational cost of the algorithm.
Third, a residual network structure is introduced into the feature extraction
network to reuse the shallow target feature information, which enhances the
detailed features of the target and ensures the improvement in accuracy of
small target detection performance. To evaluate the performance of the proposed
method, many experiments are conducted on public SAR image datasets............................
The chicken swarm optimization (CSO) algorithm is a new swarm intelligence optimization (SIO) algorithm and has been widely
used in many engineering domains. However, there are two apparent problems with the CSO algorithm, i.e., slow convergence
speed and difficult to achieve global optimal solutions. Aiming at attacking these two problems of CSO, in this paper, we propose
an adaptive fuzzy chicken swarm optimization (FCSO) algorithm. The proposed FCSO uses the fuzzy system to adaptively adjust
the number of chickens and random factors of the CSO algorithm and achieves an optimal balance of exploitation and exploration
capabilities of the algorithm. We integrate the cosine function into the FCSO to compute the position update of roosters and
improve the convergence speed. We compare the FCSO with eight commonly used, stateThofThtheThart SIO algorithms in terms of
performance in both lowTh and highThdimensional spaces. We also verify the FCSO algorithm with the nonparametric statistical
Friedman test.Theresults of the experiments on the 30 blackThbox optimization benchmarking (BBOB) functions demonstrate that
our FCSO outperforms the other SIO algorithms in both convergence speed and optimization accuracy. In order to further test the
applicability of the FCSO algorithm, we apply it to four typical engineering problems with constraints on the optimization
processes. The results show that the FCSO achieves better optimization accuracy over the standard CSO algorithm....
Anomaly detection algorithms (ADA) have been widely used as services in many maintenance monitoring platforms. However, there are
numerous algorithms that could be applied to these fast changing stream data. Furthermore, in IoT stream data due to its dynamic
nature, the phenomena of conception drift happened. )erefore, it is a challenging task to choose a suitable anomaly detection service
(ADS) in real time. For accurate online anomalous data detection, this paper developed a service selection method to select and configure
ADS at run-time. Initially, a time-series feature extractor (Tsfresh) and a genetic algorithm-based feature selection method are applied to
swiftly extract dominant features which act as representation for the stream data patterns. Additionally, stream data and various efficient
algorithms are collected as our historical data. A fast classification model based on XGBoost is trained to record stream data features to
detect appropriate ADS dynamically at run-time. )ese methods help to choose suitable service and their respective configuration based
on the patterns of stream data. )e features used to describe and reflect time-series data’s intrinsic characteristics are the main success
factor in our framework. Consequently, experiments are conducted to evaluate the effectiveness of features closed by genetic algorithm.
Experimentations on both artificial and real datasets demonstrate that the accuracy of our proposed method outperforms various
advanced approaches and can choose appropriate service in different scenarios efficiently....
This paper presents research focusing on visualization and pattern recognition based on
computer science. Although deep neural networks demonstrate satisfactory performance regarding
image and voice recognition, as well as pattern analysis and intrusion detection, they exhibit inferior
performance towards adversarial examples. Noise introduction, to some degree, to the original data
could lead adversarial examples to be misclassified by deep neural networks, even though they can
still be deemed as normal by humans. In this paper, a robust diversity adversarial training method
against adversarial attacks was demonstrated. In this approach, the target model is more robust to
unknown adversarial examples, as it trains various adversarial samples. During the experiment,
Tensorflow was employed as our deep learning framework, while MNIST and Fashion-MNIST were
used as experimental datasets..........................
With the rapid development of the social economy, the rapid development of all social circles places higher demands on the
electricity industry. As a fundamental industry supporting the salvation of the national economy, society, and human life, the
electricity industry will face a significant improvement and the restructuring of the network as an important part of the power
system should also be optimised. -is paper first introduces the development history of swarm intelligence algorithm and related
research work at home and abroad. Secondly, it puts forward the importance of particle swarm optimization algorithm for power
system network reconfiguration and expounds the basic principle, essential characteristics, and basic model of the particle swarm
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