• Proceedings of the KIPE Conference
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• 2008.06a
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• pp.413-415
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• 2008

The charge equalizer design for a series connected battery string is very challenging because it needs to satisfy many requirements such as implementation possibility, equalization speed, equalization efficiency, controller complexity, size and cost issues, voltage and current stress, and so on. Numerous algorithms and circuits were developed to meet the above demands and some interesting results have been obtained through them. However, for a large number of cells, for example, eighty or more batteries, the previous approaches might cause problems. Such problems include long equalization time, high controller complexity, bulky size, high implementation cost, and high voltage and current stress. To overcome these circumstances, this paper proposes a charge equalizer design method based on a battery modularization technique. In this method, the number of cells that we consider in an equalizer design procedure can be effectively reduces; thus, designing a charge equalizer becomes much easier. Furthermore, by applying the previously verified charge equalizers to the intramodule and the outer-module, we can obtain easy design of a charge equalizer and good charge balancing performance. Several examples and experimental results are presented to demonstrate the usefulness of the charge equalizer design method.

• Maxillofacial Plastic and Reconstructive Surgery
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• v.32 no.3
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• pp.276-281
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• 2010

The maxillary posterior area is the most challenging site for the dental implant. After missing of teeth on maxillary posterior area due to periodontal problems, the remaining alveolar ridge is usually very thin because of not only pneumatization of maxillary sinus but also destruction of alveolar bone. The maxillary sinus bone graft procedure is one of the most predictable and successful treatments for the rehabilitation of atrophic and pneumatized endentulous posterior maxilla. But, in case of severe destruction of alveolar bone due to periodontal problems, very long crown length is still remaining problem after successful sinus graft procedures. We performed vertical augmentation of maxillary posterior alveolar ridge using mandibular ramal block bone graft with simultaneous sinus graft. After this procedures, we could get more favorable crown-implant ratio of final prosthodontic appliance and more satisfactory results on biomechanics. This is a preliminary report of the vertical augmentation of maxillary posterior alveolar ridge using mandibular ramal block bone graft with simultaneous sinus graft, so requires more long-term follow up and further studies.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.11
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• pp.5419-5435
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• 2017

The construction of panoramic images on smartphones and low-powered devices is a challenging task. In this paper, we propose a new approach for smoothly stitching images on mobile phones in the presence of moving objects in the scene. Our main contributions include handling moving object problems, reducing processing time, and generating rectangular panoramic images. First, unique and robust feature points are extracted using fast ORB method and a feature matching technique is applied to match the extracted feature points. After obtaining good matched feature points, we employ the non-deterministic RANSAC algorithm to discard wrong matches, and the hommography transformation matrix parameters are estimated with the algorithm. Afterward, we determine precise overlap regions of neighboring images and calculate their absolute differences. Then, thresholding operation and noise removal filtering are applied to create a mask of possible moving object regions. Sequentially, an optimal seam is estimated using dynamic programming algorithm, and a combination of linear blending with the mask information is applied to avoid seam transition and ghosting artifacts. Finally, image-cropping operation is utilized to obtain a rectangular boundary image from the stitched image. Experiments demonstrate that our method is able to produce panoramic images quickly despite the existence of moving objects.

• Korean Journal of Cleft Lip And Palate
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• v.15 no.2
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• pp.83-88
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• 2012

Macroglossia is a relatively uncommon condition that occurs in pediatric patients for several reasons and contributes to variety of functional problems. Most of macroglossia arises from tissue overgrowth and tongue muscle hypertrophy. There are no definite guideline in prenatal management or diagnosis in this conditions. However, macroglossia is often associated with syndrome or congenital disease, prenatal diagnosis is important in early detection. There are difficulty in measurement of tongue size, and standardization. Macroglossia can be risky in some aspects, such as airway obstruction. In this review, the author suggest prenatal ultrasonographic findings of macroglossia, investigate differential diagnosis of conditions associated with macroglossia, and management in clinical situation. Macroglossia, when present, can cause a number of functional and aesthetic problems for individuals. Treatment of this problem is challenging and controversial.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.12
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• pp.1215-1219
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• 2006

Scheduling plays an important role in the process management in terms of providing profit-maximizing operation sequence of multiple orders and estimating completion times of them. In order to takes its full potential, varying conditions should be properly reflected in computing the schedule. The adjustment of scheduling decisions has to be made frequently in response to the occurrence of variations. It is often challenging because their model has to be adjusted and their solutions have to be computed within short time period. This paper employs Model Predictive Control(MPC) principles for updating the process condition in the scheduling model. The solutions of the resulting problems considering variations are computed using parametric programming techniques. The key advantage of the proposed framework is that repetition of solving similar programming problems with decreasing dimensionis avoided and all potential schedules are obtained before the execution of the actual processes. Therefore, the proposed framework contributes to constructing a robust decision-support tool in the face of varying environment. An example is solved to illustrate the potential of the proposed framework with remarks on potential wide applications.

• Proceedings of the Korea Water Resources Association Conference
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• 2010.05a
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• pp.283-287
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• 2010

Stochastic simulation of hydrologic data has been widely developed for several decades. However, despite the several advances made in literature still a number of limitations and problems remain. In the current study, some stochastic simulation approaches tackling some of the existing problems are discussed. The presented models are based on nonparametric techniques such as block bootstrapping, and K-nearest neighbor resampling (KNNR), and kernel density estimate (KDE). Three different types of the presented stochastic simulation models are (1) Pilot Gamma Kernel estimate with KNNR (a single site case) and (2) Enhanced Nonparametric Disaggregation with Genetic Algorithm (a disaggregation case). We applied these models to one of the most challenging and critical river basins in USA, the Colorado River. These models are embedded into the hydrological software package, Pros and cons of the models compared with existing models are presented through basic statistics and drought and storage-related statistics.

• Journal of Communications and Networks
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• v.14 no.5
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• pp.481-486
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• 2012

Accurate and efficient estimation of the number of sources is critical for providing the parameter of targets in problems of array signal processing and blind source separation among other such problems. When conventional estimators work in unfavorable scenarios, e.g., at low signal-to-noise ratio (SNR), with a small number of snapshots, or for sources with a different strength, it is challenging to maintain good performance. In this paper, the detection limit of the minimum description length (MDL) estimator and the signal strength required for reliable detection are first discussed. Though a comparison, we analyze the reason that performances of classical estimators deteriorate completely in unfavorable scenarios. After discussing the limiting distribution of eigenvalues of the sample covariance matrix, we propose a new approach for estimating the number of sources which is based on a sequential hypothesis test. The new estimator performs better in unfavorable scenarios and is consistent in the traditional asymptotic sense. Finally, numerical evaluations indicate that the proposed estimator performs well when compared with other traditional estimators at low SNR and in the finite sample size case, especially when weak signals are superimposed on the strong signals.

• Applied Microscopy
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• v.46 no.4
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• pp.184-187
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• 2016

Neuronal connectivity determines brain function. Therefore, understanding the full map of brain connectivity with functional annotations is one of the most desirable but challenging tasks in science. Current methods to achieve this goal are limited by the resolution of imaging tools and the field of view. Macroscale imaging tools (e.g., magnetic resonance imaging, diffusion tensor images, and positron emission tomography) are suitable for large-volume analysis, and the resolution of these methodologies is being improved by developing hardware and software systems. Microscale tools (e.g., serial electron microscopy and array tomography), on the other hand, are evolving to efficiently stack small volumes to expand the dimension of analysis. The advent of mesoscale tools (e.g., tissue clearing and single plane ilumination microscopy super-resolution imaging) has greatly contributed to filling in the gaps between macroscale and microscale data. To achieve anatomical maps with gene expression and neural connection tags as multimodal information hubs, much work on information analysis and processing is yet required. Once images are obtained, digitized, and cumulated, these large amounts of information should be analyzed with information processing tools. With this in mind, post-imaging processing with the aid of many advanced information processing tools (e.g., artificial intelligence-based image processing) is set to explode in the near future, and with that, anatomic problems will be transformed into informatics problems.

• The Journal of Engineering Geology
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• v.1 no.1
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• pp.137-145
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• 1991

Groundwater flow in fractured rocks generates many challenging problems to scientist and engineers in the projects related to oil and geothermal reservoirs, subsurface contaminations and underground openings. To circumvent these problems, the numerical simulation of groundwater system is used as an established tool in these days. Discrete modelling approach emphasizes geometric parameters, aperture and transport properties of fracture. On the other hand, continuum modelling approach uses the parameters formulated in a way of average hydraulic property. In recent years, the results of field observations from underground opening indicate that groundwater in rock mass flows in a channel form. The channel flow is postulated as the result of the combined effects of geometric pattern and aperture variation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.6
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• pp.3182-3198
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• 2019

Vision-based Human Pose Estimation has been considered as one of challenging research subjects due to problems including confounding background clutter, diversity of human appearances and illumination changes in scenes. To tackle these problems, we propose to use a new multi-stage convolution machine for estimating human pose. To provide better heatmap prediction of body joints, the proposed machine repeatedly produces multiple predictions according to stages with receptive field large enough for learning the long-range spatial relationship. And stages are composed of various modules according to their strategic purposes. Pyramid stacking module and dilation module are used to handle problem of human pose at multiple scales. Their multi-scale information from different receptive fields are fused with concatenation, which can catch more contextual information from different features. And spatial and channel information of a given input are converted to gating factors by squeezing the feature maps to a single numeric value based on its importance in order to give each of the network channels different weights. Compared with other ConvNet-based architectures, we demonstrated that our proposed architecture achieved higher accuracy on experiments using standard benchmarks of LSP and MPII pose datasets.