• Journal of the Korea Computer Graphics Society
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• v.8 no.4
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• pp.1-7
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• 2002

This paper presents a new approach for modeling human limbs shape from 3D scan data. Based on the cylindrical structure of limbs, the overall shape is approximated with a set of ellipsoids through ellipsoid fitting and interpolation of fit-ellipsoids. Then, the smooth domain surface representing the coarse shape is generated as the envelope surface of ellipsoidal sweep, and the fine details are reconstructed by constructing parametric displacement function on the domain surface. For fast calculation, the envelope surface is approximated with ellipse sweep surface, and points on the reconstructed surface are mapped onto the corresponding ellipsoid. We demonstrate the effectiveness of our approach for skeleton-driven body deformation.

• Smart Structures and Systems
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• v.29 no.1
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• pp.181-193
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• 2022

Data-driven structural health monitoring (SHM) of civil infrastructure can be used to continuously assess the state of a structure, allowing preemptive safety measures to be carried out. Long-term monitoring of large-scale civil infrastructure often involves data-collection using a network of numerous sensors of various types. Malfunctioning sensors in the network are common, which can disrupt the condition assessment and even lead to false-negative indications of damage. The overwhelming size of the data collected renders manual approaches to ensure data quality intractable. The task of detecting and classifying an anomaly in the raw data is non-trivial. We propose an approach to automate this task, improving upon the previously developed technique of image-based pre-processing on one-dimensional (1D) data by enriching the features of the neural network input data with multiple channels. In particular, feature engineering is employed to convert the measured time histories into a 3-channel image comprised of (i) the time history, (ii) the spectrogram, and (iii) the probability density function representation of the signal. To demonstrate this approach, a CNN model is designed and trained on a dataset consisting of acceleration records of sensors installed on a long-span bridge, with the goal of fault detection and classification. The effect of imbalance in anomaly patterns observed is studied to better account for unseen test cases. The proposed framework achieves high overall accuracy and recall even when tested on an unseen dataset that is much larger than the samples used for training, offering a viable solution for implementation on full-scale structures where limited labeled-training data is available.

• Journal of Korean Society for Quality Management
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• v.51 no.4
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• pp.515-530
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• 2023

Purpose : This research aims to develop a data-driven inspection policy for radio stations utilizing the KS Q ISO 2859-1 sampling method, addressing potential regulatory relaxations and impending management challenges. Methods : Using radio station inspection big data from the past six years, we established a simulation model to evaluate the current policy. A new inspection sampling policy framework was designed based on the KS Q ISO 2859-1 method. The study compares the performance of the current and proposed inspection systems, offering insights for an improved inspection strategy. Results : This study introduced a simulation model for inspection system based on the KS Q ISO 2859-1 sampling method. Through various experimental designs, key performance indicators such as non-detection rate and sample proportion were derived, providing foundational data for the new inspection policy. Conclusion : Using big data from radio station inspections, we evaluated current inspection systems and quantitatively compared a new system across diverse scenarios. Our simulation model effectively verified the feasibility and efficiency of the proposed framework. For practical implementation, essential factors such as lot size, inspection cycle, and AQL standards need precise definition and consideration. Enhancing radio station inspections requires a policy-driven approach that factors in socio-economic impacts and solicits feedback from industry participants. Future study should also explore various perspectives related to legislative, institutional, and operational aspects of inspection organizations.

• Journal of the Korea Convergence Society
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• v.12 no.1
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• pp.133-141
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• 2021

This research suggests a design process that effectively complements the human-centered design through an objective data-driven approach. The subjective human-centered design process can often lack objectivity and can be supplemented by the data-driven approaches to effectively discover hidden user needs. This research combines the data mining analysis with co-design process and verifies its applicability through the case study on the smart heating jacket. In the data mining process, the clustering can group the users which is the basis for selecting the target groups and the decision tree analysis primarily identifies the important user perception attributes and values. The broad point of view based on the data analysis is modified through the co-design process which is the deeper human-centered design process by using the developed workbook. In the co-design process, the journey maps, needs and pain points, ideas, values for the target user groups are identified and finalized. They can become the basis for starting new product development.

• Journal of Korean Society for Quality Management
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• v.38 no.4
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• pp.521-530
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• 2010

Global regulatory pressures relating climate change and environmental responsibility are asking companies to find out the best way for sustaining their continuous business growths. It could be known that inadequate management for environmental issues are bad for business, negatively affecting brand image, causing unnecessary losses and costs for environmental preservation. For this reason, environmentally conscious green business growth has been recognized as an essential requirement for a company to stay in business. Many companies are looking for green business opportunities of improving their environmental and financial results, and struggling with how green fits into their business. In this paper, the Green Six Sigma, an environmentally conscious Six Sigma methodology, is presented as a way to find solutions for green growths. The Six Sigma is known as a disciplined, data-driven approach and methodology for achieving world-class performance in any process from manufacturing to transactional. In chronological order, the Six Sigma has been evolved from Motorola's quality-oriented methodology to GE's cost-oriented lean approach, and is being evolved and developed as an environment-oriented green growth approach. There is no doubt that the Green Six Sigma, as an engine of green growth, is a power tool for achieving competitive business performance and reducing the impact on the environment.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.7
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• pp.1705-1710
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• 2015

There have been lots of researches and developments for smart agriculture that enables higher efficiency and productivity by applying ICT into the agriculture field recently. However, there still remain many difficulties in purchasing foreign equipments or overseas expansion due to lack of international standards that are internationally approved. Such necessity leads to the international standardization activities driven by domestic experts especially in ITU-T SG13. This paper presents several smart agriculture services based on the service approach that can effectively describe use cases. The proposed architecture of service provision was applied to the document that has been consented as the ITU-T Recommendation Y.2238.

• IEMEK Journal of Embedded Systems and Applications
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• v.14 no.2
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• pp.87-96
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• 2019

Low power issue is one of the most critical problems in the Internet of Things (IoT), which are powered by battery. To solve this problem, various approaches have been presented so far. In this paper, we propose a method to reduce the power consumption by reducing the numbers of accesses into the flash memory consuming a large amount of power for on-chip software execution. Our approach is based on using cooperative logging structure to distribute the sampling overhead in single sensor node to adjacent nodes in case of rare-event applications. The proposed algorithm to identify event occurrence is newly introduced with negative feedback method by observing difference between past data and recent data coming from the sensor. When an event with need of flash access is determined, the proposed approach only allows access to write the sampled data in flash memory. The proposed event detection algorithm (EDA) result in 30% reduction of power consumption compared to the conventional flash write scheme for all cases of event. The sampled data from the sensor is first traced into the random access memory (RAM), and write access to the flash memory is delayed until the page buffer of the on-chip flash memory controller in the micro controller unit (MCU) is full of the numbers of the traced data, thereby reducing the frequency of accessing flash memory. This technique additionally reduces power consumption by 40% compared to flash-write all data. By sharing the sampling information via LoRa channel, the overhead in sampling data is distributed, to reduce the sampling load on each node, so that the 66% reduction of total power consumption is achieved in several IoT edge nodes by removing the sampling operation of duplicated data.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10a
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• pp.18-18
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• 1996

This paper reviews some of the most prominent and promising areas of chemical process control both in relations to batch and continuous processes. These areas include the modeling, optimization, control and monitoring of chemical processes and entire plants. Most of these areas explicitly utilize a model of the process. For this purpose the types of models used are examined in some detail. These types of models are categorized in knowledge-driven and datadriven classes. In the areas of modeling and optimization, attention is paid to batch reactors using the Tendency Modeling approach. These Tendency models consist of data- and knowledge-driven components and are often called Gray or Hybrid models. In the case of continuous processes, emphasis is placed in the closed-loop identification of a state space model and their use in Model Predictive Control nonlinear processes, such as the Fluidized Catalytic Cracking process. The effective monitoring of multivariate process is examined through the use of statistical charts obtained by the use of Principal Component Analysis (PMC). Static and dynamic charts account for the cross and auto-correlation of the substantial number of variables measured on-line. Centralized and de-centralized chart also aim in isolating the source of process disturbances so that they can be eliminated. Even though significant progress has been made during the last decade, the challenges for the next ten years are substantial. Present progress is strongly influenced by the economical benefits industry is deriving from the use of these advanced techniques. Future progress will be further catalyzed from the harmonious collaboration of University and Industrial researchers.

• Proceedings of the KSRS Conference
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• v.2
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• pp.610-614
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• 2006

In this paper the advanced method of geodesic active contours was developed for the task of building detection from aerial and satellite images. Automatic extraction of man-made structures including buildings, building blocks or roads from remote sensing data is useful for land use mapping, scene understanding, robotic navigation, image retrieval, surveillance, emergency management procedures, cadastral etc. A level set method based on a region-driven segmentation model was implemented with which building boundaries were detected, through this curve propagation technique. The essence of this approach is to optimize the position and the geometric form of the curve by measuring information along that curve, and within the regions that compose the image partition. To this end, one can consider uniform intensities inside objects and the background. Thus, given an initial position of the curve, one can determine global, region-driven functions and provide a statistical description of the inside and outside object area. The calculus of variations and a gradient descent method was used to optimize the variational functional by an iterative steady state process. Experimental results demonstrate the potential of the proposed processing scheme.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2010.06a
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• pp.298-302
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• 2010

This study proposes a differential method to analyze the properties of the topographic surface driven from lidar imagery. Although airborne lidar imagery provides elevation information rapidly, a sequence of extraction processes are needed to acquire semantic information about objects such as terrain, roads, trees, vegetation, and buildings. For the processes, the properties present in a given lidar data need to be analyzed. In order to investigate the geometric characteristics of the surface, this study employs eigenvalues of the Hessian matrix. For experiments, a lidar image containing university campus buildings with the point density of about 1 meter was processed and the results show that the approach is effective to obtain the properties of each land object Surface.