• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.8
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• pp.3090-3102
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• 2015

The accuracy of an augmented reality (AR) application is highly dependent on the resolution of the object's image and the device's computational processing capability. Naturally, a mobile smart device equipped with a high-resolution camera becomes the best platform for portable AR services. AR applications require significant energy consumption and very fast response time, which are big burdens to the smart device. However, there are very few ways to overcome these burdens. Computation offloading via mobile cloud computing has the potential to provide energy savings and enhance the performance of applications executed on smart devices. Therefore, in this paper, adaptive mobile computation offloading of mobile AR applications is considered in order to determine optimal offloading points that satisfy the required quality of experience (QoE) while consuming minimum energy of the smart device. AR feature extraction based on SURF algorithm is partitioned into sub-stages in order to determine the optimal AR cloud computational offloading point based on conditions of the smart device, wireless and wired networks, and AR service cloud servers. Tradeoffs in energy savings and processing time are explored also taking network congestion and server load conditions into account.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.13 no.1
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• pp.63-73
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• 1988

In this paper, an algorithm is proposed to evaluate the source-to-terminal reliability, the probability that a source node can communicate with a terminal node, in a probabilistic derected graph. By using Satyanaratana's factoring $theorem^{(7)}$, the original graph can be partitioned into two reduced graphs obtained by contracting and deleting the edge connected to the source node in the probabilistic directed graph. The edge removal proposed in this paper and the general series-parallel reduction can then be applied to the reduced graph. This edge reduction can be applied recursively to the reduced graphs until a source node can be connected to a terminal node by one edge. A computer program which can be applied to evaluating the source-to-terminal reliability in a complex and large network has also been developed.

• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.1990-1992
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• 2001

In this paper, a NeuroFuzzy controller (NFC) with enhanced packet reservation multiple access (PRMA) protocol for QoS-guaranteed multimedia communication systems is proposed. The enhanced PRMA protocol adopts mini-slot technique for reducing contention cost, and these minislot are futher partitioned into multiple MAC regions for access requests coming from users with their respective QoS (quality-of-service) requirements. And NFC is designed to properly determine the MAC regions and access probability for enhancing the PRMA efficiency under QoS constraint. It mainly contains voice traffic estimator including the slot information estimator with recurrent neural networks (RNNs) using real-time recurrent learning (RTRL), and fuzzy logic controller with Mandani- and Sugeno-type of fuzzy rules. Simulation results show that the enhanced PRMA protocol with NFC can guarantee QoS requirements for all traffic loads and further achieves higher system utilization and less non real-time packet delay, compared to previously studied PRMA, IPRMA, SIR, HAR, and F2RAC.

• Journal of Korean Institute of Industrial Engineers
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• v.42 no.3
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• pp.182-197
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• 2016

One of the distinctive characteristics of industrial engineering (IE) is its multidisciplinarity. This paper explores the multidisciplinary nature of IE using journal citation network analysis. Using the relatedness indexes of IE journals obtained from journal citation report (JCR), we firstly construct the IE network only composed of 26 IE journals. The resulting IE network is partitioned into three sub-networks: management engineering, manufacturing/quality, and ergonomics. We then propose the IE convergence network which includes 81 related journals in other disciplines as well as 26 IE journals. Scrutinizing the IE convergence network reveals that IE has a high degree of interactions with seven disciplines : Operations Research and Management Science, Statistics and Probability, Manufacturing Engineering, Computer Science, Engineering Design, Business Management, Human Factors and Ergonomics. We investigate the contributions of the related disciplines to IE as well as contributions of IE to the related disciplines. The role of IE journals in exchanging knowledge with related disciplines is also identified by brokerage analysis. It is shown that visualizing and analyzing the IE convergence network can provide an excellent overview of the multidisciplinary structure of IE, which can help IE researchers easily grasp the state-of-the art of IE research.

• Geomechanics and Engineering
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• v.39 no.1
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• pp.27-41
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• 2024

Accurately estimating the performance of tunnel boring machines (TBMs) is crucial for mitigating the substantial financial risks and complexities associated with tunnel construction. Machine learning (ML) techniques have emerged as powerful tools for predicting non-linear time series data. In this research, six advanced meta-heuristic optimization algorithms based on long short-term memory (LSTM) networks were developed to predict TBM penetration rate (TBM-PR). The study utilized 1125 datasets, partitioned into 20% for testing, 70% for training, and 10% for validation, incorporating six key input parameters influencing TBM-PR. The performances of these LSTM-based models were rigorously compared using a suite of statistical evaluation metrics. The results underscored the profound impact of optimization algorithms on prediction accuracy. Among the models tested, the LSTM optimized by the particle swarm optimization (PSO) algorithm emerged as the most robust predictor of TBM-PR. Sensitivity analysis further revealed that the orientation of discontinuities, specifically the alpha angle (α), exerted the greatest influence on the model's predictions. This research is significant in that it addresses critical concerns of TBM manufacturers and operators, offering a reliable predictive tool adaptable to varying geological conditions.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.38 no.1
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• pp.79-85
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• 2001

In this paper, we propose a new technology mapping algorithm for CPLD consider area under time constraint(TMFCPLD). This technology mapping algorithm detect feedbacks from boolean networks, then variables that have feedback are replaced to temporary variables. Creating the temporary variables transform sequential circuit to combinational circuit. The transformed circuits are represented to DAG. After traversing all nodes in DAG, the nodes that have output edges more than two are replicated and reconstructed to fanout free tree. This method is for reason to reduce area and improve total run time of circuits by TEMPLA proposed previously. Using time constraints and delay time of device, the number of graph partitionable multi-level is decided. Initial cost of each node are the number of OR-terms that it have. Among mappable clusters, clusters of which the number of multi-level is least is selected, and the graph is partitioned. Several nodes in partitioned clusters are merged by collapsing, and are fitted to the number of OR-terms in a given CLB by bin packing. Proposed algorithm have been applied to MCNC logic synthesis benchmark circuits, and have reduced the number of CLBs by 62.2% than those of DDMAP. And reduced the number of CLBs by 17.6% than those of TEMPLA, and reduced the number of CLBs by 4.7% than those of TMCPLD. This results will give much efficiency to technology mapping for CPLDs.

• Journal of KIISE:Computer Systems and Theory
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• v.30 no.3_4
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• pp.130-137
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• 2003

This paper presents the problem of partitioning the Single-Stage Shuffle-Exchange Network(SSEN). An algorithm, named SSEN_to_PSEN, is devised to transform an SSEN into a Partitionable Shuffle-Exchange Network (PSEN). The proposed algorithm presents that the SSEN can be partitioned into independent sub-networks without additional links for N $\leq$ 8. Additional links are needed in order to partition an SSEN, but only when N $\geq$ 16. The running time of the algorithm SSEN_to_PSEN is $\theta$(NlogN). By comparing with a hypercube network, the PSEN is less expensive than a hypercube network even when some additional links are added. By partitioning, a large PSEN in a massively parallel machine can compute various problems for multiple users simultaneously, thereby the processing efficiency of the machine is improved.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.40 no.4
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• pp.266-274
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• 2003

Distance Fourier descriptors of local target boundary and feature information fusion using multiple MLPs (Multilayer perceptrons) are proposed. They are used to identify nonoccluded and partially occluded targets in natural FLIR (forward-looking infrared) images. After segmenting a target, radial Fourier descriptors as global shape features are defined from the target boundary. A target boundary is partitioned into four local boundaries to extract local shape features. In a local boundary, a distance function is defined from boundary points and a line between two extreme points. Distance Fourier descriptors as local shape features are defined by using distance function. One global feature vector and four local feature vectors are used as input data for multiple MLPs to determine final identification result of the target. In the experiments, we show that the proposed method is superior to the traditional feature sets with respect to the identification performance.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.2
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• pp.75-82
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• 2017

In this paper, we consider social network analysis that focuses on community detection. Social networks embed community structure characteristics, i.e., a society can be partitioned into many social groups of individuals, with dense intra-group connections and much sparser inter-group connections. Exploring the community structure allows predicting as well as understanding individual's behaviors and interactions between people. In this paper, based on the interaction information extracted from a real-life Bluetooth contacts, we aim to reveal the social groups in a society of mobile carriers. Focusing on estimating the closeness of relationships between network entities through different similarity measurement methods, we introduce the clustering scheme to determine the underlying social structure. To evaluate our community detection method, we present the evaluation mechanism based on the basic properties of friendship.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.11 no.4
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• pp.91-97
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• 2011

In this paper we present an approach to the structure identification based on genetic algorithm and to the parameter identification by hybrid learning method in neuro-fuzzy-genetic hybrid system in order to predicate the Mackey-Glass Chaotic time series. In this scheme the basic idea consists of two steps. One is the construction of a fuzzy rule base for the partitioned input space via genetic algorithm, the other is the corresponding parameters of the fuzzy control rules adapted by the backpropagation algorithm. In an attempt to test the performance the proposed system, three patterns, x(t-3), x(t-6) and x(t-9), was prepared according to time interval. It was through lots of simulation proved that the initial small error of learning owed to the good structural identification via genetic algorithm. The performance was showed in Table 2.