• Journal of Korean Institute of Industrial Engineers
• /
• v.39 no.4
• /
• pp.253-259
• /
• 2013

Investment scenarios in the transportation network design problem usually contain installation or expansion of multi-mode transportation links. When one applies the mode choice analysis and traffic assignment sequentially for each investment scenario, it is possible that the travel impedance used in the mode choice analysis is different from the user equilibrium cost of the traffic assignment step. Therefore, to estimate the travel impedance and mode choice accurately, one needs to develop a combined model for the mode choice and traffic assignment. In this paper, we derive the inverse demand and the excess demand functions for the multi-mode multinomial logit mode choice function and develop a combined model for the multi-mode variable demand traffic assignment problem. Using data from the regional O/D and network data provided by the KTDB, we compared the performance of the partial linearization algorithm with the Frank-Wolfe algorithm applied to the excess demand model and with the sequential heuristic procedures.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2001.11b
• /
• pp.725-730
• /
• 2001

This paper presents a vibration analysis method for constrained mechanical systems driven by constant generalized speeds. Equilibrium positions are obtained first and vibration analysis are performed around the positions. The method developed in this paper employs partial velocity matrix to obtain a minimum number of differential equations. To verify the accuracy of the proposed algorithm, linear vibration analyses of two numerical examples are performed and the results are compared with results from a commercial program or previous literature.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2022.10a
• /
• pp.591-593
• /
• 2022

This paper proposes a partial offloading system for minimizing application service processing latency in an FEC (Fog/Edge Computing) environment, and it analyzes the offloading effect of the proposed system against local-only and edge-server-only processing based on network load. A partial offloading algorithm based on reconstruction linearization of multi-branch structures is included in the proposed system, as is an optimal collaboration algorithm between mobile devices and edge servers [1,2]. The experiment was conducted by applying layer scheduling to a logical CNN model with a DAG topology. When compared to local or edge-only executions, experimental results show that the proposed system always provides efficient task processing strategies and processing latency.

• Journal of Korean Society of Transportation
• /
• v.6 no.2
• /
• pp.7-20
• /
• 1988

Recent developments in combining transportation planning models and input-output approaches, together with inclusion of intensity of land uses, have made it possible to construct realistic comprehensive urban and regional activity models. These modes form the basis for a rigorous approach to studying the interactions among urban activities. However, efficient computational solution methods for implementing such comprehensive models are still not available. In this paper an efficient solution method for the urban activity model is developed by combining Evans' partial linearization technique with Powell's hybrid method. The solution algorithm is applied to a small but realistic urban area with a detailed transportation network.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.26 no.10
• /
• pp.1551-1558
• /
• 2022

We propose a two-tier cooperative computing system comprised of a mobile device and an edge server for partial offloading of multi-branch structures in Fog/Edge Computing environments in this paper. The proposed system includes an algorithm for splitting up application service processing by using reconstructive linearization techniques for multi-branch structures, as well as an optimal collaboration algorithm based on partial offloading between mobile device and edge server. Furthermore, we formulate computation offloading and CNN layer scheduling as latency minimization problems and simulate the effectiveness of the proposed system. As a result of the experiment, the proposed algorithm is suitable for both DAG and chain topology, adapts well to different network conditions, and provides efficient task processing strategies and processing time when compared to local or edge-only executions. Furthermore, the proposed system can be used to conduct research on the optimization of the model for the optimal execution of application services on mobile devices and the efficient distribution of edge resource workloads.

• Journal of Power Electronics
• /
• v.13 no.6
• /
• pp.955-963
• /
• 2013

To acquire a performed and practical solution that is free from chattering, this study proposes the use of an adaptive super-twisting algorithm to drive a single-phase induction motor. Partial feedback linearization is applied before using a super-twisting algorithm to control the speed and stator currents. The load torque is considered an unknown but bounded disturbance. Therefore, a time-varying switching gain that does not require prior knowledge of the disturbance boundary is proposed. A simple sliding surface is formulated as the difference between the real and desired trajectories obtained from the indirect rotor flux oriented control strategy. To illustrate the effectiveness of the proposed control structure, an experimental setup around a digital signal processor (dS1104) is developed and several tests are performed.

• International Journal of Aeronautical and Space Sciences
• /
• v.11 no.4
• /
• pp.326-337
• /
• 2010

This work addresses problems regarding trajectory planning for unmanned aerial vehicle sensors. Such sensors are used for taking measurements of large nonlinear systems. The sensor investigations presented here entails methods for improving estimations and predictions of large nonlinear systems. Thoroughly understanding the global system state typically requires probabilistic state estimation. Thus, in order to meet this requirement, the goal is to find trajectories such that the measurements along each trajectory minimize the expected error of the predicted state of the system. The considerable nonlinearity of the dynamics governing these systems necessitates the use of computationally costly Monte-Carlo estimation techniques, which are needed to update the state distribution over time. This computational burden renders planning to be infeasible since the search process must calculate the covariance of the posterior state estimate for each candidate path. To resolve this challenge, this work proposes to replace the computationally intensive numerical prediction process with an approximate covariance dynamics model learned using a nonlinear time-series regression. The use of autoregressive time-series featuring a regularized least squares algorithm facilitates the learning of accurate and efficient parametric models. The learned covariance dynamics are demonstrated to outperform other approximation strategies, such as linearization and partial ensemble propagation, when used for trajectory optimization, in terms of accuracy and speed, with examples of simplified weather forecasting.