• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.1
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• pp.211-232
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• 2024

In this paper, we consider the resource allocation and offloading decisions of device-to-device (D2D) cooperative UAV-assisted mobile edge computing (MEC) system, where the device with task request is served by unmanned aerial vehicle (UAV) equipped with MEC server and D2D device with idle resources. On the one hand, to ensure the fairness of time-delay sensitive devices, when UAV computing resources are relatively sufficient, an optimization model is established to minimize the maximum delay of device computing tasks. The original non-convex objective problem is decomposed into two subproblems, and the suboptimal solution of the optimization problem is obtained by alternate iteration of two subproblems. On the other hand, when the device only needs to complete the task within a tolerable delay, we consider the offloading priorities of task to minimize UAV computing resources. Then we build the model of joint offloading decision and power allocation optimization. Through theoretical analysis based on KKT conditions, we elicit the relationship between the amount of computing task data and the optimal resource allocation. The simulation results show that the D2D cooperation scheme proposed in this paper is effective in reducing the completion delay of computing tasks and saving UAV computing resources.

• Journal of KIISE:Databases
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• v.35 no.3
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• pp.272-285
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• 2008

In this paper, we concentrate on data allocation methods for multiple broadcast channels. When the server broadcasts data, the important issue is to let mobile clients access requested data rapidly. Previous works first sorted data by their access probabilities and allocate the sorted data to the multiple channels by partitioning them into multiple channels. However, they do not reflect the difference of access probabilities among data allocated in the same channel. This paper proposes ZGMD allocation method. ZGMD allocates data item on multiple channels so that the difference of access probability in the same channel is maximized. ZGMD allocates sorted data to each channels and applies Broadcast Disk in each channel. ZGMD requires a proper indexing scheme for the performance improvement. This is because in ZGMD method each channel got allocated both hot and cold data. As a result, the sequential search heuristic does not allow the mobile client to access hot data items quickly. The proposed index scheme is based on using dedicated index channels in order to search the data channel where the requested data is. We show that our method achieve the near-optimal performance in terms of the average access time and significantly outperforms the existing methods.

• Journal of KIISE:Databases
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• v.33 no.1
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• pp.86-101
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• 2006

The bandwidth of channel and the power of the mobile devices are limited on a wireless environment. In this case, data broadcast has become an excellent technique for efficient data dissemination. A significant amount of researches have been done on generating an efficient broadcast program of a set of data items with different access frequencies over multiple wireless broadcast channels as well as single wireless broadcast channel. In this paper, an efficient data allocation method over multiple wireless broadcasting channels is explored. In the traditional approaches, a set of data items are partitioned into a number of channel based on their access probabilities. However, these approaches ignore a variation of access probabilities of data items allocated in each channel. In practice, it is difficult to have many broadcast channels and thus each channel need to broadcast many data items. Therefore, if a set of data items broadcast in each channel have different repetition frequencies based on their access frequencies, it will give much better performance than the traditional approaches. In this paper, we propose an adaptive data allocation technique based on data access probabilities over multiple broadcast channels. Our proposed technique allows the adaptation of repetition frequency of each data item within each channel by taking its access probabilities into at count.

• International journal of advanced smart convergence
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• v.6 no.1
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• pp.39-43
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• 2017

As the industry grows, the amount of data grows exponentially, and data analysis using these serves as a predictable solution. As data size increases and processing speed increases, it has begun to be applied to new fields by combining artificial intelligence technology as well as simple big data analysis. In this paper, we propose a method to quickly apply a machine learning based algorithm through efficient resource allocation. The proposed algorithm allocates memory for each attribute. Learning Distinct of Attribute and allocating the right memory. In order to compare the performance of the proposed algorithm, we compared it with the existing K-means algorithm. As a result of measuring the execution time, the speed was improved.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.41 no.10
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• pp.61-67
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• 2004

The next generation cellular wireless communication systems require high data rate transmissions and large system capacities. In order to meet these requirements, multiple antennas can be used at the base and mobile stations, forming MIMO(Multiple Input Multiple Output) channels. This paper proposes a MIMO SDMA(Space Division Multiple Access) technique with dynamic slot allocation which allows the transmitter to efficiently transmit parallel data streams to each of multiple receivers. The proposed technique can increase system capacities significantly by transmitting a larger number of data streams than conventional MIMO techniques while minimizing the performance degradation due to the beamforming dimension reduction.

• Journal of KIISE:Software and Applications
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• v.32 no.5
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• pp.396-405
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• 2005

Since sentences are the basic propositional units of text, their themes would be helpful for various tasks that require knowledge about the semantic content of text. Despite the importance of determining the theme of a sentence, however, few studies have investigated the problem of automatically assigning the theme to a sentence. Therefore, we propose a sentence theme allocation scheme based on the head-driven patterns of sentences in encyclopedia. In a serious of experiments using Dusan Dong-A encyclopedia, the proposed method outperformed the baseline of the theme allocation performance. The head-driven pattern 4, which is reconfigured based on the predicate, showed superior performance in the theme allocation with the average F-score of $98.96\%$ for the training data, and $88.57\%$ for the test data.

• Journal of Korea Water Resources Association
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• v.42 no.3
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• pp.259-270
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• 2009

The applicability of the developed distributed rainfall runoff model using a multi-directional flow allocation algorithm and a real-time updating algorithm was evaluated. The rainfall runoff processes were simulated for the events of the Andong dam basin and the Namgang dam basin using raingauge network data and weather radar rainfall data, respectively. Model parameters of the basins were estimated using previous storm event then those parameters were applied to a current storm event. The physical propriety of the multi-directional flow allocation algorithm for flow routing was validated by presenting the result of flow grouping for the Andong dam basin. Results demonstrated that the developed model has efficiency of simulation time with maintaining accuracy by applying the multi-directional flow allocation algorithm and it can obtain more accurate results by applying the real-time updating algorithm. In this study, we demonstrated the applicability of a distributed rainfall runoff model for the advanced basin-wide flood management.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.3
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• pp.1325-1344
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• 2019

Recently, to satisfy mobile users' increasing data transmission requirement, energy efficiency (EE) resource allocation in distributed antenna systems (DASs) has become a hot topic. In this paper, we aim to maximize EE in DASs subject to constraints of the minimum data rate requirement and the maximum transmission power of distributed antenna units (DAUs) with different density distributions. Virtual cell is defined as DAUs selected by the same user equipment (UE) and the size of virtual cells is dependent on the number of subcarriers and the transmission power. Specifically, the selection rule of DAUs is depended on different scenarios. We develop two scenarios based on the density of DAUs, namely, the sparse scenario and the dense scenario. In the sparse scenario, each DAU can only be selected by one UE to avoid co-channel interference. In order to make the original non-convex optimization problem tractable, we transform it into an equivalent fractional programming and solve by the following two sub-problems: optimal subcarrier allocation to find suitable DAUs; optimal power allocation for each subcarrier. Moreover, in the dense scenario, we consider UEs could access the same channel and generate co-channel interference. The optimization problem could be transformed into a convex form based on interference upper bound and fractional programming. In addition, an energy-efficient DAU selection scheme based on the large scale fading is developed to maximize EE. Finally, simulation results demonstrate the effectiveness of the proposed algorithm for both sparse and dense scenarios.

• Journal of Navigation and Port Research
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• v.44 no.6
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• pp.501-508
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• 2020

His paper looks more closely at the Republic of Korea's (ROK) Navy submarine berth-allocation strategies, with the study's results ultimately resulting in the proposition of an integer programming model. Current submarine berth-allocation planning problems include the need for more minimal berth-shifting and general support service failures, as a lack of efficient submarine berth-allocation often leads to submarines unable to receive the support service they need due to the inadequacy of their assigned berths. Currently, the ROK Navy allocates berths by only taking available reserve resources and the full-capacity limits of support services into account. This paper expands upon this strategy, and proposes a model which would allow for submarine berth allocation planning to also take submarine support service scheduling into account, allowing for more efficient and timely submarine servicing. This proposed model in turn minimizes berth shifting, support service failures, and allows for full coordination with the submarine support service request schedule. The construction of this model was carried out through data gathered on the ROK Navy's fleets and forces, allowing for a more nuanced analysis of existing issues with submarine berth-allocation planning.

• Journal of Korea Multimedia Society
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• v.14 no.9
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• pp.1165-1174
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• 2011

In data broadcasting systems, servers continuously disseminate data items through broadcast channels, and mobile client only needs to wait for the data of interest to present on a broadcast channel. However, because broadcast channels are shared by a large set of data items, the expected delay of receiving a desired data item may increase. This paper explores the issue of designing proper data allocation on multiple broadcast channels to minimize the average expected delay time of all data items, and proposes a new data allocation scheme named two level bin-packing(TLBP). This paper first introduces the theoretical lower-bound of the average expected delay, and determines the bin capacity based on this value. TLBP partitions all data items into a number of groups using bin-packing algorithm and allocates each group of data items on an individual channel. By employing bin-packing algorithm in two step, TLBP can reflect a variation of access probabilities among data items allocated on the same channel to the broadcast schedule, and thus enhance the performance. Simulation is performed to compare the performance of TLBP with three existing approaches. The simulation results show that TLBP outperforms others in terms of the average expected delay time at a reasonable execution overhead.