• KIPS Transactions on Software and Data Engineering
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• v.9 no.1
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• pp.17-24
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• 2020

The traveling salesman problem is an algorithmic problem tasked with finding the shortest route that a salesman visits, visiting each city and returning to the started city. Due to the exponential time complexity of TSP, it's hard to implement on cases like amusement park or delivery. Also, TSP is hard to meet user's demand that is associated with multi-dimensional attributes like travel time, interests, waiting time because it uses only one attribute - distance between nodes. This paper proposed Top-n Skyline-Multi Dimension TSP to resolve formerly adverted problems. The proposed algorithm finds the shortest route faster than the existing method by decreasing the number of operations, selecting multi-dimensional nodes according to the dominance of skyline. In the simulation, we compared computation time of dynamic programming algorithm to the proposed a TS-MDT algorithm, and it showed that TS-MDT was faster than dynamic programming algorithm.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.10
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• pp.479-486
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• 2017

Recently, GPGPU has been able to increase the degradation of computer performance, and it is now possible to run physically based real-time simulations on PCs that require high computational complexity. Physical calculations applied in physics simulation can be performed by parallel processing, and can be efficiently performed using parallel computation using Compute shader recently supported by OpenGL 4.3 and Unity 4.0. In this paper, we measure and compare the number of performance in real - time physics simulation in OpenGL running on various platforms and Unity, a content creation tool supporting various platforms. Particle simulation experiments show that particle simulation using Unity performs faster than 136.04%. It is expected that it will be able to select better development tools for future multi - platform support.

• The Journal of the Korea Contents Association
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• v.9 no.9
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• pp.1-8
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• 2009

H.264/AVC video coding standard uses the rate distortion optimization method which determines the best coding mode for macroblock(MB) to improve coding efficiency. Whereas RDO selects the best coding mode, it causes the heavy computational burden comparing with previous standards. To reduce the complexity, in this paper, a fast intra prediction mode decision algorithm using directional gradients is proposed. The proposed algorithm is composed of 2-path structure. In the first path, $16{\times}16$ intra prediction mode is determined using directional gradients. In the second path, 3 modes instead of 9 modes are chosen for RDO to decide the best mode for $4{\times}4$ block. Finally, the two modes determined in the two-path decision process are compared to decide the final block mode. Experimental results show that the computation time of the proposed method is decreased to about 77% of the exhaustive mode decision method with negligible quality loss.

• Korean Journal of Remote Sensing
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• v.25 no.6
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• pp.547-557
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• 2009

LIDAR (LIght Detection And Ranging) is an active remote sensing technology which provides 3D coordinates of the Earth's surface by performing range measurements from the sensor. Early small footprint LIDAR systems recorded multiple discrete returns from the back-scattered energy. Recent advances in LIDAR hardware now make it possible to record full digital waveforms of the returned energy. LIDAR waveform decomposition involves separating the return waveform into a mixture of components which are then used to characterize the original data. The most common statistical mixture model used for this process is the Gaussian mixture. Waveform decomposition plays an important role in LIDAR waveform processing, since the resulting components are expected to represent reflection surfaces within waveform footprints. Hence the decomposition results ultimately affect the interpretation of LIDAR waveform data. Computational requirements in the waveform decomposition process result from two factors; (1) estimation of the number of components in a mixture and the resulting parameter estimates, which are inter-related and cannot be solved separately, and (2) parameter optimization does not have a closed form solution, and thus needs to be solved iteratively. The current state-of-the-art airborne LIDAR system acquires more than 50,000 waveforms per second, so decomposing the enormous number of waveforms is challenging using traditional single processor architecture. To tackle this issue, four parallel LIDAR waveform decomposition algorithms with different work load balancing schemes - (1) no weighting, (2) a decomposition results-based linear weighting, (3) a decomposition results-based squared weighting, and (4) a decomposition time-based linear weighting - were developed and tested with varying number of processors (8-256). The results were compared in terms of efficiency. Overall, the decomposition time-based linear weighting work load balancing approach yielded the best performance among four approaches.

• Smart Structures and Systems
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• v.24 no.5
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• pp.567-585
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• 2019

In the past two decades, structural health monitoring (SHM) systems have been widely installed on various civil infrastructures for the tracking of the state of their structural health and the detection of structural damage or abnormality, through long-term monitoring of environmental conditions as well as structural loadings and responses. In an SHM system, there are plenty of sensors to acquire a huge number of monitoring data, which can factually reflect the in-service condition of the target structure. In order to bridge the gap between SHM and structural maintenance and management (SMM), it is necessary to employ advanced data processing methods to convert the original multi-source heterogeneous field monitoring data into different types of specific physical indicators in order to make effective decisions regarding inspection, maintenance and management. Conventional approaches to data analysis are confronted with challenges from environmental noise, the volume of measurement data, the complexity of computation, etc., and they severely constrain the pervasive application of SHM technology. In recent years, with the rapid progress of computing hardware and image acquisition equipment, the deep learning-based data processing approach offers a new channel for excavating the massive data from an SHM system, towards autonomous, accurate and robust processing of the monitoring data. Many researchers from the SHM community have made efforts to explore the applications of deep learning-based approaches for structural damage detection and structural condition assessment. This paper gives a review on the deep learning-based SHM of civil infrastructures with the main content, including a brief summary of the history of the development of deep learning, the applications of deep learning-based data processing approaches in the SHM of many kinds of civil infrastructures, and the key challenges and future trends of the strategy of deep learning-based SHM.

• Journal of the Korea Society of Computer and Information
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• v.15 no.1
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• pp.71-81
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• 2010

In a typical web environment, it is difficult to determine the update and re-computation status of WebView content or the transition of WebView processing included in web page. If an update to one of data is performed before a read operation to that, we could get a wrong result due to the incorrect operation and increase a complexity of the problem to process. To solve this problem, lots of researchers have studied and most of these problems at the single user environment is not problems. However, the problems at a distributed environment might be occurred. For this reason, in this paper, we proposed the extended state transition graph and algorithms for each status of WebView for explaining WebView state in the distributed environment and analyze the performance of using the materialized WebView and not. Additionally, also analyze the timing issues in network and effectiveness which follows in size of WebView contents.

• The Journal of the Acoustical Society of Korea
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• v.38 no.1
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• pp.51-55
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• 2019

This paper proposes a speech enhancement method utilizing the feature compensation gain for robust speech recognition performances in noisy environments. In this paper we propose a speech enhancement method utilizing the feature compensation gain which is obtained from the PCGMM (Parallel Combined Gaussian Mixture Model)-based feature compensation method employing variational model composition. The experimental results show that the proposed method significantly outperforms the conventional front-end algorithms and our previous research over various background noise types and SNR (Signal to Noise Ratio) conditions in mismatched ASR (Automatic Speech Recognition) system condition. The computation complexity is significantly reduced by employing the noise model selection technique with maintaining the speech recognition performance at a similar level.

• Korean Journal of Remote Sensing
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• v.35 no.3
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• pp.389-400
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• 2019

Level meters are non-invasively capable of measuring the level of the medium, and a growing variety of level meters are being used in the industry in connection with safety and maintenance. The level meter can be measured according to various kinds of medium such as solid medium such as coal, flour, rice and liquid medium such as water and petroleum. In order to reduce the error depending on the medium, the measurement using the Doppler Effect can compensate the measurement error, However, the number of signal processing steps is increased, the operation speed is further increased, the hardware complexity increases, and a high cost structure is required. In this paper, we propose a method to improve the signal processing operation structure of the remote measurement level meter to reduce the amount of computation and the resource usage of the required FPGA.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.43 no.4
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• pp.23-32
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• 2020

Many small and medium-sized manufacturing companies process various product types to respond different customer orders in a single production line. To improve their productivity, they often apply batch processing while considering various product types, constraints on batch sizes and setups, and due date of each order. This study introduces a batch scheduling heuristic for a production line with multiple product types and different due dates of each order. As the process times vary due to the different batch sizes and product types, a recursive equation is developed based on a flow line model to obtain the upper bound on the completion times with less computational complexity than full computation. The batch scheduling algorithm combines and schedules the orders with same product types into a batch to improve productivity, but within the constraints to match the due dates of the orders. The algorithm incorporates simple and intuitive principles for the purpose of being applied to small and medium companies. To test the algorithm, two case studies are introduced; a high pressure coolant (HPC) manufacturing line and a press process at a plate-type heat exchanger manufacturer. From the case studies, the developed algorithm provides significant improvements in setup frequency and thus convenience of workers and productivity, without violating due dates of each order.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.14 no.1
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• pp.113-119
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• 2004

Exponentiation is widely used in practical applications related with cryptography, and as the discrete log is easily solved in case of a low exponent n, a large exponent n is needed for a more secure system. However. since the time complexity for exponentiation algorithm increases in proportion to the n figure, the development of an exponentiation algorithm that can quickly process the results is becoming a crucial problem. In this paper, we propose a parallel exponentiation algorithm which can reduce the number of rounds with a fixed number of processors, where the field elements are in GF($2^m$), and also analyzed the round bound of the proposed algorithm. The proposed method uses window method which divides the exponent in a particular bit length and make idle processors in window value computation phase to multiply some terms of windows where the values are already computed. By this way. the proposed method has improved round bound.