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Ilsang Woo,Areum Lee,Seung Chai Jung,Hyunna Lee,Namkug Kim,조세진,Donghyun Kim,Jungbin Lee,선우준,Dong-Wha Kang 대한영상의학회 2019 Korean Journal of Radiology Vol.20 No.8
Objective: To develop algorithms using convolutional neural networks (CNNs) for automatic segmentation of acute ischemic lesions on diffusion-weighted imaging (DWI) and compare them with conventional algorithms, including a thresholding-based segmentation. Materials and Methods: Between September 2005 and August 2015, 429 patients presenting with acute cerebral ischemia (training:validation:test set = 246:89:94) were retrospectively enrolled in this study, which was performed under Institutional Review Board approval. Ground truth segmentations for acute ischemic lesions on DWI were manually drawn under the consensus of two expert radiologists. CNN algorithms were developed using two-dimensional U-Net with squeeze-and-excitation blocks (U-Net) and a DenseNet with squeeze-and-excitation blocks (DenseNet) with squeeze-and-excitation operations for automatic segmentation of acute ischemic lesions on DWI. The CNN algorithms were compared with conventional algorithms based on DWI and the apparent diffusion coefficient (ADC) signal intensity. The performances of the algorithms were assessed using the Dice index with 5-fold cross-validation. The Dice indices were analyzed according to infarct volumes (< 10 mL, ≥ 10 mL), number of infarcts (≤ 5, 6–10, ≥ 11), and b-value of 1000 (b1000) signal intensities (< 50, 50–100, > 100), time intervals to DWI, and DWI protocols. Results: The CNN algorithms were significantly superior to conventional algorithms (p < 0.001). Dice indices for the CNN algorithms were 0.85 for U-Net and DenseNet and 0.86 for an ensemble of U-Net and DenseNet, while the indices were 0.58 for ADC-b1000 and b1000-ADC and 0.52 for the commercial ADC algorithm. The Dice indices for small and large lesions, respectively, were 0.81 and 0.88 with U-Net, 0.80 and 0.88 with DenseNet, and 0.82 and 0.89 with the ensemble of U-Net and DenseNet. The CNN algorithms showed significant differences in Dice indices according to infarct volumes (p < 0.001). Conclusion: The CNN algorithm for automatic segmentation of acute ischemic lesions on DWI achieved Dice indices greater than or equal to 0.85 and showed superior performance to conventional algorithms.
Mind-Wandering in task-oriented group : An application of The Theory of Interpersonal Behavior
Ilsang Ko 한국경영정보학회 2023 한국경영정보학회 학술대회논문집 Vol.2023 No.06
Mind-wandering consists of the process of thinking task-unrelated thoughts (TUT) during task performance. In a task-oriented group, the subject and the subject matter of team members’ mindwandering episodes are of critical interest to the team's performance. However, many writers suggested that mind-wandering may be more common in some circumstances than others. Understanding the social-psychological factors that precipitate mind-wandering in a technological setting is necessary because mind-wandering influences numerous outcomes connected with taskoriented groups. Therefore, this paper aims to explore whether certain social-psychological factors predict self-reported engagement behavior in a number of TUT in the assessment of attitudes. Specifically, our objectives are to put in place behavioral experience sampling methodologies and probe-caught methodologies for measuring online mind-wandering. We intend to design a set of virtual teams using laboratory experiments followed by survey. Our study aims to add to the body of research on virtual teams and mind-wandering by determining the relation between attitude, social factors, and emotions of team members and self-reported engagement in TUT.
Ko, Ilsang 조선대학교 경영경제연구소 1996 經營經濟硏究 = Management and economics research Vol.19 No.2
In building a system within an object-oriented environment, several factors of problem characteristics should be considered carefully to determine what kind of objects be created, what kind of relationships between objects be developed to reach a parent-child structure, and what attributes and behaviors be defined within each object. These factors include the decomposability of problem domain, its stability, its structuredness, the role of entities, their changeability, their number, their complexity, their possible groups, and the decomposability of processes. The same factors not only impact important OOAD design decisions, but also be used as a tool to assess whether the application system is well-designed or not. Two prototypical models, the City Network System and the Oochnori Gameboard, are developed and programmed in Smalltalk, a well-known object-oriented programming language. These models are compared together in order to explain the impact of problem characteristics on design constructs. The suggested factors contribute to making important design decisions on class requirements, especially on class groupings and hierarchies. This study also suggests dozens of questions to be tested to evaluate the design of the application system.
Uncertainty decomposition in climate-change impact assessments: a Bayesian perspective
Ohn, Ilsang,Seo, Seung Beom,Kim, Seonghyeon,Kim, Young-Oh,Kim, Yongdai The Korean Statistical Society 2020 Communications for statistical applications and me Vol.27 No.1
A climate-impact projection usually consists of several stages, and the uncertainty of the projection is known to be quite large. It is necessary to assess how much each stage contributed to the uncertainty. We call an uncertainty quantification method in which relative contribution of each stage can be evaluated as uncertainty decomposition. We propose a new Bayesian model for uncertainty decomposition in climate change impact assessments. The proposed Bayesian model can incorporate uncertainty of natural variability and utilize data in control period. We provide a simple and efficient Gibbs sampling algorithm using the auxiliary variable technique. We compare the proposed method with other existing uncertainty decomposition methods by analyzing streamflow data for Yongdam Dam basin located at Geum River in South Korea.
Ko, Ilsang 한국경영과학회 1997 한국경영과학회지 Vol.22 No.3
An AI approach with tabu search is designed to solve multi-level knapsack problems. The approach performs intelligent actions with memories of historic data and learning effect. These actions are developed not only by observing the attributes of the optimal solution, the solution space, and its corresponding path to the optimal, but also by applying human intelligence, experience, and intuition with respect to the search strategies. The approach intensifies, or diversifies the search process appropriately in time and space. In order to create a good neighborhood structure, this approach uses two powerful choice rules that emphasize the impact of candidate variables on the current solution with respect to their profit contribution. "Pseudo moves." similar to "aspirations" support these choice rules during the evaluation process. For the purpose of visiting as many relevant points as possible, strategic oscillation between feasible and infeasible solutions around the boundary is applied. To avoid redundant moves, short-term (tabu-lists), intermediate-term (cycle-detection), and long-term (recording frequency and significant solutions for diversification) memories are used. Test results show that among the 45 generated problems (these problems pose significant or insurmountable challenges to exact methods) the approach produces the optimal solutions in 39 cases.