• Journal of the Korean Institute of Telematics and Electronics C
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• v.35C no.11
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• pp.21-30
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• 1998

In this paper, a digital current control system using a FPGA(Field Programmable Gate Array) was implemented, and the system was applied to an induction motor widely used as an industrial driving machine. The FPGA designed by VHDL(VHSIC Hardware Description Language) consists of a PWM(Pulse Width Modulation) generation block, a PWM protection block, a speed measuring block, a watch dog timer block, an interrupt control block, a decoder logic block, a wait control block and digital input and output blocks respectively. Dedicated clock inputs on the FPGA were used for high-speed execution, and an up-down counter and a latch block were designed in parallel, in order that the triangle wave could be operated at 40 MHz clock. When triangle wave is compared with many registers respectively, gate delay occurs from excessive fan-outs. To reduce the delay, two triangle wave registers were implemented in parallel. Amplitude and frequency of the triangle wave, and dead time of PWM could be changed by software. This FPGA was synthesized by pASIC 2SpDE and Synplify-Lite synthesis tool of Quick Logic company. The final simulation for worst cases was successfully performed under a Verilog HDL simulation environment. And the FPGA programmed for an 84 pin PLCC package was applied to digital current control system for 3-phase induction motor. The digital current control system of the 3 phase induction motor was configured using the DSP(TMS320C31-40 MHz), FPGA, A/D converter and Hall CT etc., and experimental results showed the effectiveness of the digital current control system.

• Journal of KIISE
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• v.43 no.10
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• pp.1115-1123
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• 2016

Assumption-based truth maintenance system (ATMS) is a tool that maintains the reasoning process of inference engine. It also supports non-monotonic reasoning based on dependency-directed backtracking. Bookkeeping all the reasoning processes allows it to quickly check and retract beliefs and efficiently provide solutions for problems with large search space. However, the amount of data has been exponentially grown recently, making it impossible to use a single machine for solving large-scale problems. The maintaining process for solving such problems can lead to high computation cost due to large memory overhead. To overcome this drawback, this paper presents an approach towards incrementally maintaining the reasoning process of inference engine on cluster using Spark. It maintains data dependencies such as assumption, label, environment and justification on a cluster of machines in parallel and efficiently updates changes in a large amount of inferred datasets. We deployed the proposed ATMS on a cluster with 5 machines, conducted OWL/RDFS reasoning over University benchmark data (LUBM) and evaluated our system in terms of its performance and functionalities such as assertion, explanation and retraction. In our experiments, the proposed system performed the operations in a reasonably short period of time for over 80GB inferred LUBM2000 dataset.