• Journal of the Computational Structural Engineering Institute of Korea
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• v.32 no.3
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• pp.141-147
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• 2019

In this paper, a systematic beam splitter design for multi-beam forming is proposed. The objective of this research is to a design beam splitter that splits and focuses scattering microwaves into intense beams in multiple directions. It is difficult to split multi-beam to non-specific directions with theoretical approaches. Therefore, instead of using transformation optics(TO), which is a widely used process for controlling electromagnetic wave propagation, we used a systematic design process called the phase field design method to obtain an optimal topological structure of beam splitter. The objective function is to maximize the norm of electric field of the target areas of each direction. To avoid island structure and obtain the structure in one body, volume constraint is added to the optimization problem by using augmented Lagrangian. Target frequency is set to X-band 10GHz. The optimal beam splitter performed well in multi-beam forming and the transported electric energy of target areas improved. A frequency dependency test was conducted in the X-band to determine effective frequency range.

• Journal of KIISE:Software and Applications
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• v.35 no.4
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• pp.207-218
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• 2008

With the growth of the Internet, multimedia streaming becomes an important means to deliver video contents over the Internet and the amount of the streaming multimedia contents is also getting increased. However, it becomes difficult to guarantee the quality of service in real-time over the IP network environment with instantaneously varying bandwidth. In this paper, we propose an optimal adaptation framework for streaming contents over the Internet in the sense that the perceptual quality of the multi-angie content with multiple visual objects is maximized given the constraints such as available bandwidth and transcoding cost. In the multi-angle video service framework, the user can select his/her preferred alternate views among the given multiple video streams captured at different view angles for a same event. This enhanced experience often entails streaming problems in real-time over the network, such as instantaneous bandwidth changes in the Internet. In order to cope with this problem, we assume that multi-angle video contents are encoded at different bitrates and the appropriate video streams are then selected or transcoded for delivery to meet such bandwidth constraints. For the user selective consumption of the various bitstreams in the multi-angle video service, the bitstream in each angle can be encoded in various bitrate, and the user can select a sub-bitrstream in the given bitrstreams or transcode the corresponding content in order to deliver the optimally adapted video contents to the instantaneously changing network condition. Therefore, we define the transcoding cost which means the time taken for transcoding the video stream and formulate a unified optimization framework which maximizes the perceptual quality of the multiple video objects in the given constraints such as the transcoding cost and the network bandwidth. Finally, we present plenty of the experimental results to show the effectiveness of the proposed method.

• The KIPS Transactions:PartD
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• v.9D no.4
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• pp.555-560
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• 2002

Subsequence matching, which consists of index searching and post-processing steps, is an operation that finds those subsequences whose changing patterns are similar to that of a given query sequence from a time-series database. This paper discusses optimization of post-processing for subsequence matching. The common problem occurred in post-processing of previous methods is to compare the candidate subsequence with the query sequence for discarding false alarms whenever each candidate subsequence appears during index searching. This makes a sequence containing candidate subsequences to be accessed multiple times from disk, and also have a candidate subsequence to be compared with the query sequence multiple times. These redundancies cause the performance of subsequence matching to degrade seriously. In this paper, we propose a new optimal method for resolving the problem. The proposed method stores ail the candidate subsequences returned by index searching into a binary search tree, and performs post-processing in a batch fashion after finishing the index searching. By this method, we are able to completely eliminate the redundancies mentioned above. For verifying the performance improvement effect of the proposed method, we perform extensive experiments using a real-life stock data set. The results reveal that the proposed method achieves 55 times to 156 times speedup over the previous methods.

• KIPS Transactions on Computer and Communication Systems
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• v.10 no.4
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• pp.117-122
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• 2021

In recent years, human damage and loss of money due to various disasters such as typhoons, earthquakes, forest fires, landslides, and wars are steadily occurring, and a lot of manpower and funds are required to prevent and recover them. In this paper, we designed and developed a disaster drone system based on artificial intelligence in order to monitor these various disaster situations in advance and to quickly recognize and respond to disaster occurrence. In this study, multiple disaster drones are used in areas where it is difficult for humans to monitor, and each drone performs an efficient search with an optimal path by applying a deep learning-based optimal path algorithm. In addition, in order to solve the problem of insufficient battery capacity, which is a fundamental problem of drones, the optimal route of each drone is determined using Ant Colony Optimization (ACO) technology. In order to implement the proposed system, it was applied to a forest fire situation among various disaster situations, and a forest fire map was created based on the transmitted data, and a forest fire map was visually shown to the fire fighters dispatched by a drone equipped with a beam projector. In the proposed system, multiple drones can detect a disaster situation in a short time by simultaneously performing optimal path search and object recognition. Based on this research, it can be used to build disaster drone infrastructure, search for victims (sea, mountain, jungle), self-extinguishing fire using drones, and security drones.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.1
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• pp.97-104
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• 2016

Because of rapid growth of data size, the number of data storage has been increased. When using multiple data storages, a distribute file system is essential to insure the availability of data files. The power consumption is a major problem when using a distributed file system with many data storages. Previous works have aimed at reducing the energy consumption with efficient file block layout by changing some data servers into stand-by mode. The file block migration has not been seriously considered because migration causes large cost. But when we consider addition of a new data server or file, file block migration is needed. This paper formulates the minimization of data block migration as an ILP optimization problem and solves it using branch-and-bound method. Using this technique, we can maximize the number of stand-by data servers with the minimum number of file block movement. However, computation time of branch-and-bound method of an ILP optimization problem increases exponentially as the problem size grows. Therefore this paper also proposes a data block and data server grouping method to solve many small ILP problems.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.5
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• pp.537-543
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• 2006

There have been many recent studies on the nonlinear rainfall-runoff modeling, where the use of neural networks is shown to be quite successful. Due to fundamental limitation of linear structures, employing linear models has often been considered inferior to the neural network approaches in this area. However, we believe that with an appropriate extension, the concept of linear impulse responses can be a viable tool since it enables us to understand underlying dynamics principles better. In this paper, we propose the use of multiple impulse responses for the problem of rainfall-runoff analysis. The proposed method is based on a simple and fixed strategy for switching among multiple linear impulse-response models, each of which satisfies the constraints of non-negativity and uni-modality. The computational analysis performed for a certain Korean hydrometeorologic data set showed that the proposed method can yield very meaningful results.

• The Journal of Bigdata
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• v.4 no.2
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• pp.159-173
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• 2019

Last-Mile delivery optimization plays a key role in the urban supply chain operation, which is the most expensive and time-consuming and most complicated part of the whole delivery process. The urban consolidation center (UCC) is regarded as a significant asset for supporting customer demand in the last-mile delivery service. It is the key benefit of UCC to improve the load balance of vehicles and to reduce the total traveling distance by finding the better route with the well-organized multi-leg vehicle journey in the urban area. This paper presents the model using multiple scenario analysis integrated with mathematical optimization techniques using Geographic Information System (GIS). The model aims to find the best solution for the distribution network consisted of DC and UCC, which is applied to the case of Ulaanbaatar Mongolia. The proposed methodology integrates two sub-models, location-allocation model and vehicle routing problem. The multiple scenarios devised by selecting locations of UCC are compared considering the general performance and delivery patterns together. It has been adopted to make better decisions the quantitative metrics such as the economic value of capital cost, operating cost, and balance of using available resources. The result of this research may help the manager or public authorities who should design the distribution network for the last mile delivery service optimization using UCC within the urban area.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.11 no.5
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• pp.70-77
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• 2012

Dial-a-ride is the most widely available transit service for disabled persons or seniors in the United States and Europe. This paper studies a static dial-a-ride problem considering multiple depots, heterogeneous vehicles, and soft time windows. In this paper, we apply a heuristic based on clustering first-routing second(HCR) to a real-world large dial-a-ride problem from Maryland Transit Administration(MTA). MTA's real operation is compared with the results of developed heuristic for 24 cases. The objective function of the proposed model is to minimize the total cost composed of the service provider's cost and the customers' inconvenience cost. For the comparison, the objective function values of HCR do not include waiting cost, delay cost, and excess ride cost. The objective function values from HCR are better than those from MTA's operation for all cases. This result shows that our heuristic method can make the real operation better and more efficient.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.1
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• pp.21-40
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• 2018

In this paper, we propose the distributed robust beamforming design scheme in cognitive two-way amplify-and-forward (AF) relay networks with imperfect channel state information (CSI). Assuming the CSI errors follow a complex Gaussian distribution, the objective of this paper is to design the robust beamformer which minimizes the total transmit power of the collaborative relays. This design will guarantee the outage probability of signal-to-interference-plus-noise ratio (SINR) beyond a target level at each secondary user (SU), and satisfies the outage probability of interference generated on the primary user (PU) above the predetermined maximum tolerable interference power. Due to the multiple CSI uncertainties in the two-way transmission, the probabilistic constrained optimization problem is intractable and difficult to obtain a closed-form solution. To deal with this, we reformulate the problem to the standard form through a series of matrix transformations. We then accomplish the problem by using the probabilistic approach based on two sorts of Bernstein-type inequalities and the worst-case approach based on S-Procedure. The simulation results indicate that the robust beamforming designs based on the probabilistic method and the worst-case method are both robust to the CSI errors. Meanwhile, the probabilistic method can provide higher feasibility rate and consumes less power.

• The Transactions of the Korea Information Processing Society
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• v.7 no.2
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• pp.426-436
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• 2000

Given a linear arrangement of n base stations which generate multiple types of traffic among themselves, we consider the problem of finding a set of disjoint clusters to cover n base statons so that a cluster is assigned a location server. Our goal is to minimize the total communication cost for the entire network where the cost of intra-cluster communication is usually lower than that of intercluster communication for each type of traffic. The optimization problem is transformed into an equivavalent problem using the concept of relative cost, which generates the difference of communication costs between intracluster and intercluster communications. Using the relative cost matrix, an efficient algorithm of O($mm^2$), where m is the number of clusters in a partition, is designed by dynamic programming. The algorithm also finds all thevalid partitions in the same polynomial time, given the size constraint on a cluster, and the total allowable communication cost for the entire network.