• Journal of Korean Society for Quality Management
• /
• v.38 no.4
• /
• pp.521-530
• /
• 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
• /
• v.19 no.7
• /
• pp.1705-1710
• /
• 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
• /
• v.14 no.2
• /
• pp.87-96
• /
• 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
• /
• 1996.10a
• /
• pp.18-18
• /
• 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
• /
• v.2
• /
• pp.610-614
• /
• 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
• /
• 2010.06a
• /
• pp.298-302
• /
• 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.

• International Journal of Knowledge Content Development & Technology
• /
• v.14 no.3
• /
• pp.77-91
• /
• 2024

This article aims to comprehensively analyze the crucial role played by data analytics and business intelligence (BI) strategies in propelling digital transformation within diverse industries. Through an extensive literature review and examination of real-world case studies, the study employs a systematic analysis of scholarly works and industry reports. This approach provides a panoramic view of how organizations utilize data-driven insights for competitive advantages, improved customer experiences, and fostering innovation. The findings underscore the pivotal significance of data analytics and BI strategies in influencing strategic decision-making, enhancing operational efficiency, and ensuring long-term sustainability across various industries. The study stands out in its originality by offering a unique synthesis of insights derived from scholarly works and real-world case studies, contributing to a holistic understanding of the transformative impact of data analytics and BI on contemporary business practices. While the study provides valuable insights, limitations include the scope of available literature and case studies. The implications call for further research to explore emerging trends and evolving challenges in the dynamic landscape of data analytics and BI. The practical implications highlight the tangible benefits organizations can derive from integrating data analytics and BI strategies, emphasizing their role in shaping strategic decisions and fostering operational efficiency. In a broader context, the study delves into the social implications of the symbiotic relationship between data analytics, BI, and digital transformation. It explores how these strategies impact broader societal and economic aspects, influencing innovation and sustainability.

• The Korean Journal of Applied Statistics
• /
• v.28 no.6
• /
• pp.1103-1111
• /
• 2015

This paper presents a new prediction method based on relative error incorporated with a penalized regression. The proposed method consists of fully data-driven procedures that is fast, simple, and easy to implement. An example of real data analysis and some simulation results were given to prove that the proposed approach works in practice.

• Communications for Statistical Applications and Methods
• /
• v.13 no.2
• /
• pp.233-241
• /
• 2006

Classical principal component analysis (PCA) can be formulated as finding the linear subspace that best accommodates multidimensional data points in the sense that the sum of squared residual distances is minimized. As alternatives to such LS (least squares) fitting approach, we produce LMS (least median of squares) and LTS (least trimmed squares)-type PCA by minimizing the median of squared residual distances and the trimmed sum of squares, in a similar fashion to Rousseeuw (1984)'s alternative approaches to LS linear regression. Proposed methods adopt the data-driven optimization algorithm of Croux and Ruiz-Gazen (1996, 2005) that is conceptually simple and computationally practical. Numerical examples are given.

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
• /
• 2019.08a
• /
• pp.118-119
• /
• 2019