• Health Policy and Management
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• v.24 no.4
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• pp.380-388
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• 2014

Background: The volume of surgery has been examined as a major source of variation in outcome after surgery. This study investigated the direct effect of surgery volume to in hospitals mortality and the moderating effect of structural complexity-the level of diversity and sophistication of technology a hospital applied in patient care-to the volume outcome relationship. Methods: Discharge summary data of 11,827 cancer patients who underwent surgery and were discharged during a month period in 2010 and 2011 were analyzed. The analytic model included the independent variables such as surgery volume of a hospital, structural complexity measured by the number of diagnosis a hospital examined, and their interaction term. This study used a hierarchical logistic regression model to test for an association between hospital complexity and mortality rates and to test for the moderating effect in the volume outcome relationship. Results: As structural complexity increased the probability of in-hospital mortality after cancer surgery reduced. The interaction term between surgery volume and structural complexity was also statistically significant. The interaction effect was the strongest among the patients group who had surgery in low volume hospitals. Conclusion: The structural complexity and volume of surgery should be considered simultaneously in studying volume outcome relationship and in developing policies that aim to reduce mortality after cancer surgery.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.48 no.11
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• pp.17-21
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• 2011

In this paper, we propose a pilot symbol assisted low complexity LS channel estimation method for OFDM in fast time varying channels. The proposed method shows low complexity characteristics in terms of memory space and processing time compared with conventional BEM channel model LS estimation methods.

• Journal of the Semiconductor & Display Technology
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• v.21 no.3
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• pp.7-11
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• 2022

Object detection technology is one of the main research topics in the field of computer vision and has established itself as an essential base technology for implementing various vision systems. Recent DNN (Deep Neural Networks)-based algorithms achieve much higher recognition accuracy than traditional algorithms. However, it is well-known that the DNN model inference operation requires a relatively high computational power. In this paper, we analyze the inference time complexity of the state-of-the-art object detection architecture Yolov7 in various environments. Specifically, we compare and analyze the time complexity of four types of the Yolov7 model, YOLOv7-tiny, YOLOv7, YOLOv7-X, and YOLOv7-E6 when performing inference operations using CPU and GPU. Furthermore, we analyze the time complexity variation when inferring the same models using the Pytorch framework and the Onnxruntime engine.

• The Journal of Korea Robotics Society
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• v.5 no.1
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• pp.70-76
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• 2010

This paper addresses a design issue of "model complexity and performance trade-off" in the application of bandwidth extension (BWE) methods to the intra-frame predictivevector quantization problem of wideband speech. It discusses model-based linear and non-linear prediction methods and presents a comparative study of them in terms of prediction gain. Through experimentation, the general trend of saturation in performance (with the increase in model complexity) is observed. However, specifically, it is also observed that there is no significant difference between HMM and GMM-based BWE functions.

• Journal of the military operations research society of Korea
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• v.27 no.1
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• pp.10-27
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• 2001

This paper deals with the application of PRICE model in estimating the proper acquisition cost for weapon budgeting phase. The PRICE(Parametric Review of Information for Costing and Evaluation) Hardware model is a computerized method for deriving cost estimates of electronic and mechanical hardware assemblies and systems. The model can be used in obtaining not only initial cost estimates in conceptual phase, but also detailed cost estimates in budgeting phase depending on available historical and empirical data. We analyzed first step cost estimate parameters and derived cost equations using PRICe output dta. Using weight and complexity, We can find cost variation. Sensitivity analysis shows that cost increases exponentially as complexity increases exponentially as complexity increases. We estimated KAAV\`s (Korea Amphibious Assault Vehicle) production cost using the PRICE model and compare with engineering cost estimates which is based on actual production data submitted by the production company. The result shows that tow estimates are close within $\pm2%$ differences.

• International Journal of Computer Science & Network Security
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• v.21 no.6
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• pp.54-60
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• 2021

Monte-Carlo GO is a computer GO program that is sufficiently competent without using knowledge expressions of IGO. Although it is computationally intensive, the computational complexity can be reduced by properly pruning the IGO game tree. Here, I achieve this by using a potential model based on the knowledge expressions of IGO. The potential model treats GO stones as potentials. A specific potential distribution on the GO board results from a unique arrangement of stones on the board. Pruning using the potential model categorizes legal moves into effective and ineffective moves in accordance with the potential threshold. Here, certain pruning strategies based on potentials and potential gradients are experimentally evaluated. For different-sized boards, including an official-sized board, the effects of pruning strategies are evaluated in terms of their robustness. I successfully demonstrate pruning using a potential model to reduce the computational complexity of GO as well as the robustness of this effect across different-sized boards.

• The Journal of the Acoustical Society of Korea
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• v.33 no.3
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• pp.216-224
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• 2014

In this paper, we propose a new method to drastically reduce computational complexity in Gaussian Mixture Model (GMM)-based Speaker Identification (SI). Generally, GMM-based SI systems have very high computational complexity proportional to the length of the test utterance, the number of enrolled speakers, and the GMM size. These make the SI systems difficult to be used in various real applications in spite of their broad applicability. Thus, a trade-off between computational complexity and identification accuracy is considered as a primary issue for practical applications. In order to reduce computational complexity sharply with a little loss of accuracy, we introduce a method based on the Universal Background Model (UBM) clustering approach and then we show that it can be used successfully in real-time applications. In experiments with the proposed algorithm, we obtained a speed-up factor of 6 with a negligible loss of accuracy.

• The Transactions of the Korea Information Processing Society
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• v.6 no.1
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• pp.90-95
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• 1999

A software growth model, a mathematical model describing the growth behavior of a software system during the evolution process, enables us to predict the future system size and incremental erfort required to meet the planned system size. This paper first introduces a software growth model defined with respect to the cumulative incremental effort. It was assumed that the incremental growth of a software system is proportional to the incremental effort and function of the system size is suggested as a system complexity and then applied to real data for its validation. such a system complexity additionally provides us with a measure for complexity comparison. since the measure is independent of the system size, it is useful for comparing complexities of software systems of different size.

• Journal of Convergence for Information Technology
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• v.8 no.5
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• pp.101-106
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• 2018

Software are being studied to register and manage assets. And Methods for evaluating software systems have been based on subjective evaluation criteria. We propose an evaluation model for evaluating complex assets obtained from the complexity measurement of the preceding asset management system. We used scales to measure and provide logical complexity to measure the complexity of our architectural assets. And we used a method to evaluate whether it expresses attribute value of architecture asset. We have also built an evaluation model criterion for evaluating the usability of the asset data based on the ISO/IEC 25010 quality model characteristics of the SQuaRE Series. When the designers design the asset as a composite asset, the optional evaluation of the negative property that weights are assigned according to the characteristics of each asset is applied to secure the flexibility of the evaluation model.

• Management Science and Financial Engineering
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• v.4 no.2
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• pp.1-13
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• 1998

The Shortest Path Problem in Time-dependent Networks, where the travel time of each link depends on the time interval, is not realistic since the model and its solution violate the Non-passing Property (NPP:often referred to as FIFO) of real phenomena. Furthermore, solving the problem needs much more computational and memory complexity than the general shortest path problem. A new model for Time-dependent Networks where the flow speeds of each link depend on time interval, is suggested. The model is more realistic since its solution maintains the NPP. Solving the problem needs just a little more computational complexity, and the same memory complexity, as the general shortest path problem. A solution algorithm modified from Dijkstra's label setting algorithm is presented. We extend this model to the problem of Minimum Expected Time Path in Time-dependent Stochastic Networks where flow speeds of each link change statistically on each time interval. A solution method using the Kth-shortest Path algorithm is presented.