• The KIPS Transactions:PartA
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• v.19A no.1
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• pp.69-72
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• 2012

Logic programming based on classical or linear logic has traditionally lacked devices for expressing sequential tasks and sequential iterative tasks. Expressing sequential goal tasks has been addressed by a recent proposal of sequential goals of the form $G_1{\cap}G_2$ which is based on the game semantics of Japaridze. This paper proposes sequential iterative goal formulas of the form ${\cap}_x^LG$ where $G$ is a goal, $x$ is a variable, and $L$ is a list. ${\cap}_x^L$ is called a sequential bounded quantier. These goals allow us to specify the following task: sequentially iterate $G$ with $x$ ranging over all the elements of $L$.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.50 no.11
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• pp.771-781
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• 2022

Sequential convex programming based performance analysis of the designed UAV is performed. The nonlinear optimization problems generated by aerodynamics are approximated to socond order program by discretization and convexification. To improve the performance of the algorithm, the solution of the relaxed problem is used as the initial trajectory. Dive trajectory optimization problem is analyzed through iterative solution procedure of approximated problem. Finally, the maximum final velocity according to the performance of the actuator model was compared.

• Proceedings of the KSME Conference
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• 2003.04a
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• pp.1441-1446
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• 2003

The design optimization of the plate heat exchanger with staggered pin arrays for a fixed volume is performed numerically. The flow and thermal fields are assumed to be a streamwise-periodic flow and heat transfer with constant wall temperature and they are solved by using the finite volume method. The optimization is carried out by using the sequential linear programming (SLP) method and the weighting method is used for solving the multi-objective problem. The results show that the optimal design variables for the weighting coefficient of 0.5 are as follows; S=6.497mm, P=5.496mm, $D_1=0.689mm$, and $D_2=2.396mm$. The Pareto optimal solutions are also presented.

• Journal of The Korean Association of Information Education
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• v.22 no.3
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• pp.297-305
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• 2018

Physical computing is the concept of expanding computing to humans, environments, and objects. It draws attention as a programming learning medium based on physical outputs in integration of hardware and software. This study developed a programming learning model based on interactive prototyping using the characteristics of physical computing with a high degree of technical freedom and analyzed its learning effect in an experiment. To examine the effect of the experimental treatment, this researcher divided fifty nine 5th-grade elementary students into an experimental group and into a control group. the interactive prototyping programming learning model was applied to the experimental group, and a linear sequential programming learning model was applied to the control group. Information Science Creative Personality Test was conducted before and after the experimental treatment. Analysis of Covariance was conducted with the pre-test scores of the two groups. As a result, it was proved that there was the effect of learning at the significance level of .05. It indicates that the physical computing based interactive prototyping programming learning model is applicable to the programming learning for 5th-grade elementary students.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.3B
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• pp.183-198
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• 2006

We define the incomplete medium sharing system as a multi-channel shared medium communication system where constraints are imposed to the set of channels that may be allocated to some transmitter-receiver node pairs. To derive a centralized MAC scheme of a incomplete medium sharing system, we address the problem of optimal channel allocation The optimal channel allocation problem is then translated into a max-flow problem in a multi-commodity flow graph, and it is shown that the optimal solution can then be obtained by solving a linear programming problem. In addition, two suboptimal channel allocation schemes are proposed to bring down the computational complexity to a practical/feasible level; (1) one is a modified iSLIP channel allocation scheme, (2) the other is sequential channel allocation scheme. From the results of a extensive set of numerical experiments, it is found that the suboptimal schemes evaluate channel utilization close to that of the optimal schemes while requiring much less amount of computation than the optimal scheme. In particular, the sequential channel allocation scheme is shown to achieve higher channel utilization with less computational complexity than . the modified iSLIP channel allocation scheme.