Jangwu Jo
Dong A Universit. Korea, Computer Sience, Faculty Member
The ways of present transformer design can be divided into two types: using spreadsheet like Excel and using design software. Our first work is to develop the design software that can be used by small companies for free. We developed... more
The ways of present transformer design can be divided into two types: using spreadsheet like Excel and using design software. Our first work is to develop the design software that can be used by small companies for free. We developed design software which generates as many designs as possible, without aid of expert's knowledge. Our second work is to improve efficiency by parallelizing the repetitive calculations. The performance of our design software is evaluated by comparing the results obtained from different numbers of cores. We show improvement of execution time where execution of 4 cores is about 45% faster than that of 1 core.
Research Interests:
The efficiency of transformers is critical to save electrical energy. There are three types of current transformer design methods. The current methods have problems. In this paper, we described current ways of designing a transformer, and... more
The efficiency of transformers is critical to save electrical energy. There are three types of current transformer design methods. The current methods have problems. In this paper, we described current ways of designing a transformer, and we also explained their weakness. To overcome these weakness, we apply deep learning to designing a transformer. This paper also shows that our model recommend design parameters to satisfy design specification.
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Research Interests:
Many program-analysis techniques, such as data-flow and control-dependence analysis, and software-engineering techniques, such as program slicing and testings, use control flow graph (CFG). For these analyses to be safe and useful, the... more
Many program-analysis techniques, such as data-flow and control-dependence analysis, and software-engineering techniques, such as program slicing and testings, use control flow graph (CFG). For these analyses to be safe and useful, the CFG should incorporate the exception-induced control flow. Failure to account for exception induced control flows in performing analyses can result in incorrect analysis information. In this paper, we propose a method to construct CFG that accounts for exception-induced control flow. We show that normal control flows and exception induced control flows can be safely decoupled, hence these two flows can be computed separately. We propose the analysis that estimates exception-induced control flows, and also propose exception propagation graph that represents exception induced control flows. The control flow graph that accounts for exception-induced control flow can be constructed by merging exception propagation graph onto control flow graph with normal...
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Research Interests:
Research Interests:
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Exception analyses so far cannot provide information on the propagation of thrown exceptions, which is necessary to construct interprocedural control flow graph, visualize ex- ception propagation, and slice exception-related parts of... more
Exception analyses so far cannot provide information on the propagation of thrown exceptions, which is necessary to construct interprocedural control flow graph, visualize ex- ception propagation, and slice exception-related parts of programs. In this paper, we propose a set-based analysis, which estimates exception propagation of Java programs. To formalize exception propagation, we first describes an operational semantics with exception propagation
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Research Interests:
In this paper, we present a new slicing technique named abstract program slicing that allows a decomposition of a program for the set of initial states. We apply abstract inter- pretation to the derivation of slices from existing... more
In this paper, we present a new slicing technique named abstract program slicing that allows a decomposition of a program for the set of initial states. We apply abstract inter- pretation to the derivation of slices from existing programs. Abstract interpretation allows us to yield safe information about the run-time behavior of the program without having to run it for all input data. Thus, we can statically com- pute safe approximations of program slices on the slicing criterion.