• Journal of Power Electronics
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• v.19 no.2
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• pp.487-496
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• 2019

For a solid-state transformer (SST), some factors, such as signal delay, switching loss and differences in the system parameters, lead to unbalanced DC-link voltages among the cascaded H-bridges (CHB). With a control method implemented in the ${\alpha}{\beta}$ frame, the DC-link voltages are balanced, and the reactive power is equally distributed among all of the H-bridges. Based on the ${\alpha}{\beta}$ frame control, the system can achieve independent active current and reactive current control. In addition, the control method of the high-voltage stage is easy to implement without decoupling or a phase-locked loop. Furthermore, the method can eliminate additional current delays during transients and get the dynamic response rapidly without an imaginary current component. In order to carry out the controller design, the vector refactoring relations that are used to balance DC-link voltages are derived. Different strategies are discussed and simulated under the unbalanced load condition. Finally, a three-cell CHB rectifier is constructed to conduct further research, and the steady and transient experimental results verify the effectiveness and correctness of the proposed method.

• International journal of advanced smart convergence
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• v.13 no.2
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• pp.48-60
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• 2024

With the exponential growth of satellite data utilization, machine learning has become pivotal in enhancing innovation and cybersecurity in satellite systems. This paper investigates the role of machine learning techniques in identifying and mitigating vulnerabilities and code smells within satellite software. We explore satellite system architecture and survey applications like vulnerability analysis, source code refactoring, and security flaw detection, emphasizing feature extraction methodologies such as Abstract Syntax Trees (AST) and Control Flow Graphs (CFG). We present practical examples of feature extraction and training models using machine learning techniques like Random Forests, Support Vector Machines, and Gradient Boosting. Additionally, we review open-access satellite datasets and address prevalent code smells through systematic refactoring solutions. By integrating continuous code review and refactoring into satellite software development, this research aims to improve maintainability, scalability, and cybersecurity, providing novel insights for the advancement of satellite software development and security. The value of this paper lies in its focus on addressing the identification of vulnerabilities and resolution of code smells in satellite software. In terms of the authors' contributions, we detail methods for applying machine learning to identify potential vulnerabilities and code smells in satellite software. Furthermore, the study presents techniques for feature extraction and model training, utilizing Abstract Syntax Trees (AST) and Control Flow Graphs (CFG) to extract relevant features for machine learning training. Regarding the results, we discuss the analysis of vulnerabilities, the identification of code smells, maintenance, and security enhancement through practical examples. This underscores the significant improvement in the maintainability and scalability of satellite software through continuous code review and refactoring.

• Journal of Microbiology and Biotechnology
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• v.29 no.12
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• pp.1931-1937
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• 2019

The heterologous expression of the Streptomyces natural product (NP) biosynthetic gene cluster (BGC) has become an attractive strategy for the activation, titer improvement, and refactoring of valuable and cryptic NP BGCs. Previously, a Streptomyces artificial chromosomal vector system, pSBAC, was applied successfully to the precise cloning of large-sized polyketide BGCs, including immunosuppressant tautomycetin and antibiotic pikromycin, which led to stable and comparable production in several heterologous hosts. To further validate the pSBAC system as a generally applicable heterologous expression system, the daptomycin BGC of S. roseosporus was cloned and expressed heterologously in a model Streptomyces cell factory. A 65-kb daptomycin BGC, which belongs to a non-ribosomal polypeptide synthetase (NRPS) family, was cloned precisely into the pSBAC which resulted in 28.9 mg/l of daptomycin and its derivatives in S. coelicolor M511(a daptomycin non-producing heterologous host). These results suggest that a pSBAC-driven heterologous expression strategy is an ideal approach for producing low and inconsistent Streptomyces NRPS-family NPs, such as daptomycin, which are produced low and inconsistent in native host.

• The KIPS Transactions:PartB
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• v.13B no.5 s.108
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• pp.547-552
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• 2006

There have been various cohesion measurements studied considering reference relation among attributes and methods in a class. Generally, these cohesion measurement are camed out in one class. If the range of reference relation considered are extended from one class to two classes, we could find out the reference relation between two classes. Tn this paper, we proposed a neural network to determine the method location. Neural network is effective to predict output value from input data not to be included in training and generalize after training input and output pattern repeatedly. Learning vector is generated with 30-dimensional input vector and one target binary values of method location in a constraint that there are two classes which have less than or equal to 5 attributes and methods The result of the proposed neural network is about 95% in cross-validation and 88% in testing.