• Transactions of the Korean Society of Automotive Engineers
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• v.11 no.2
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• pp.148-156
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• 2003

Many car manufacturers have frequently adopted an aggressive inflator and a lower threshold speed for airbag deployment in order to meet an injury requirement for unbolted occupant at high speed crash test. Consequently, today's occupant safety restraint system has a weakness due to an airbag induced injury at low speed crash event. This paper proposes a new crash algorithm to improve the weakness by suppressing airbag deployment at low speed crash event in case of belted condition. The proposed algorithm consists of two major blocks-crash severity algorithm and deployment logic block. The first block decides crash severity with two levels by means of velocity and crash energy calculation from acceleration signal. The second block implemented by simple AND/OR logic combines the crash severity level and seat belt status information to generate firing commands for airbag and belt pretensioner. Furthermore, it can be extended to adopt additional sensor information from passenger presence detection sensor and safing sensor. A simulation using real crash data for a 1,800cc passenger vehicle has been conducted to verify the performance of proposed algorithm.

• Journal of the Korea Institute of Military Science and Technology
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• v.18 no.5
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• pp.538-547
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• 2015

The goal of this study is to develop an algorithm to propose optimal deployment of detection sensor nodes in the target area, based on a performance surface, which represents detection performance of active and passive acoustic sonar systems. The performance surface of the active detection system is calculated from the azimuthal average of maximum detection ranges, which is estimated with a transmission loss and a reverberation level predicted using ray-based theories. The performance surface of the passive system is calculated using the transmission loss model based on a parabolic equation. The optimization of deployment configurations is then performed by a hybrid method of a virtual force algorithm and a particle swarm optimization. Finally, the effectiveness of deployment configurations is analyzed and discussed with the simulation results obtained using the algorithm proposed in this paper.

• Journal of IKEEE
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• v.16 no.4
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• pp.316-321
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• 2012

In this paper, we study the energy-efficient deployment strategy for micro base station (BS) in wireless cellular systems. Firstly, we formulate a general problem pertaining to total energy consumption minimization with the requirement of area spectral efficiency (ASE). We start from an observation about the correlation between the area covered by an additional micro BS and the increment of ASE. Under such an observation, we propose an efficient greedy micro BS deployment algorithm. Simulations show that the proposed deployment algorithm can deploy micro BSs with a slight performance reduction comparing with the optimal solution.

• Journal of Korean Institute of Industrial Engineers
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• v.37 no.3
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• pp.198-208
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• 2011

At a shipyard, the efficient handling of blocks is one of the most important factors in the shipbuilding process. We consider the problem of deployment planning of blocks from storage yards. As some information of block arrangement should be considered to handle the problem, we adopt the block arrangement based on the coordinates and sizes of each block at a storage yard. Deployment planning for a block involves deciding upon its transportation route from the storage yard and searching for blocks that would obstruct its transportation along this route. A tabu search algorithm for deploying several blocks is developed to minimize the number of obstructive blocks deployed together from the storage yards at a shipyard. The results of computational experiments show that the developed algorithm is very useful in the deployment planning of multiple blocks from the storage yards.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.4
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• pp.1351-1372
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• 2022

A self-organizing multiple unmanned aerial vehicles (multi-UAVs) deployment based on virtual forces has a difficulty in ensuring the quality-of-experience (QoE) of users because of the difference between the assumed center for users in a hotspot and an actual center for users in the hotspot. This discrepancy is aggravated in a non-uniform and mobile user distribution. To address this problem, we propose a new density based virtual force (D-VF) multi-UAVs deployment algorithm which employs a mean opinion score (MOS) as a metric of QoE. Because MOS is based on signal-to-noise ratio (SNR), a sum of users' MOS is a good metric not only to secure a wide service area but to enhance the link quality between multi-UAVs and users. The proposed algorithm improves users' QoE by combining virtual forces with a random search force for the exploration of finding multi-UAVs' positions which maximize the sum of users' MOS. In simulation results, the proposed deployment algorithm shows the convergence of the multi-UAVs into the position of maximizing MOS. Therefore, the proposed algorithm outperforms the conventional virtual force-based deployment scheme in terms of QoE for non-uniform user distribution scenarios.

• International Journal of Automotive Technology
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• v.7 no.2
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• pp.179-185
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• 2006

Airbag deployment has been responsible for huge death, incidental injuries and broken bones due to low crash severity and wrong deployment decision. This misfortune has led the authorities and the industries to pursue uniquely designed airbags incorporating crash-sensing technologies. This paper provides a thorough discussion underlying crash sensing algorithm approaches for the subject matter. Unfortunately, most algorithms used for crash sensing still have some problems. They either deploy at low severity or fail to trigger the airbag on time. In this work, the crash-sensing algorithm is studied by analyzing the data obtained from the variables such as (i) change of velocity, (ii) speed of the vehicle and (iii) acceleration. The change of velocity is used to detect crash while speed of the vehicle provides relevant information for deployment decision. This paper also demonstrates crash severity with respect to the changing speed of the vehicle. Crash sensing simulations were carried out using Simulink, Stateflow, SimMechanics and Virtual Reality toolboxes. These toolboxes are also used to validate the results obtained from the simulated experiments of crash sensing, airbag deployment decision and its crash severity detection of the proposed system.

• Journal of IKEEE
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• v.22 no.3
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• pp.658-663
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• 2018

In this paper, we consider node deployment algorithms for the sequence-based localization (SBL) which is recently employed for in-door positioning systems, Whereas previous node selection or deployment algorithms seek to place nodes at centrold of the region where more targets are likely to be found, we observe that the boundaries dividing such regions can be good locations for the nodes in SBL systems. Motivated by this observation, we propose an efficient node deployment algorithm that determines the boundaries by using the well-known K-means algorithm and find the potential node locations based on the bi-section method for low-complexity design. We demonstrate through experiments that the proposed algorithm achieves significant localization performance over random node allocation with a substantially reduced complexity as compared with a full search.

• Journal of Advanced Marine Engineering and Technology
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• v.29 no.4
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• pp.369-376
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• 2005

In recent years, AGVs(Automated Guided Vehicles) have received growing attention as a subsystem of the integrated container operating system which enables unmanned control. improvement of job reliability, accuracy and productivity. Therefore, a number of works have been done to enhance the performance AGVs. In this paper. job deployment and a dynamic routing control system composed of supervisor, traffic controller. motion controller and routing table are discussed. A simple job deployment scheme and an efficient dynamic routing algorithm incorporating with the deadlock prediction and avoidance algorithm are investigated.

• International Journal of Contents
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• v.16 no.4
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• pp.26-38
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• 2020

Determining the optimal levels of the technical attributes (TAs) of a product to achieve a high level of customer satisfaction is the main activity in the planning process for quality function deployment (QFD). In real applications, the number of customer requirements for developing a single product is quite large, and the number of converted TAs is also high so the size of the house of quality (HoQ) becomes huge. Furthermore, the TA levels are often discrete instead of continuous and the product market can be divided into several market segments corresponding to the number of HoQ, which also unacceptably increases the size of the QFD optimization problem and the time spent on making decisions. This paper proposed a genetic algorithm (GA) solution approach to finding the optimum set of TAs in QFD in the above situation. A numerical example is provided for illustrating the proposed approach. To assess the computational performance of the GA, tests were performed on problems of various sizes using a fractional factorial design.

• Transactions of the Korean Society of Automotive Engineers
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• v.8 no.6
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• pp.182-193
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• 2000

Several metrics have been used in crash discrimination algorithms in order to have timely air bag deployment during all frontal crash modes. However, it is still challengine to have timely air bag deployment especially during the oblique, the pole and the underride crash mode. Therefore, in this paper a new crash discrimination algorithm was proposed, using the absolute value of the deceleration change multiplied by the velocity change as a metric, and processing the metric as a function of the velocity change. The new algorithm was applied for all frontal crash modes of a minivan and a sports utility vehicle, and it resulted in timely air bag deployment for all frontal crash modes including the oblique, the pole and the underride crash mode. Moreover, it was proposed that an accelerometer be installed at each side of the rails, rockers or pillars to assess the crash severity of each side and to deploy the frontal air bags at different time especially during an asymmetric crash such as an oblique and an offset crash. As an example, the deceleration pulses measured at the left and right B-pillar·rocker locations were processed through the new algorithm, and faster time-to-fires were obtained for the air bag at the struck side for the air bag at the other side.