• Smart Structures and Systems
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• v.31 no.4
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• pp.383-392
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• 2023

Post-earthquake building condition assessment is crucial for subsequent rescue and remediation and can be automated by emerging computer vision and deep learning technologies. This study is based on an endeavour for the 2nd International Competition of Structural Health Monitoring (IC-SHM 2021). The task package includes five image segmentation objectives - defects (crack/spall/rebar exposure), structural component, and damage state. The structural component and damage state tasks are identified as the priority that can form actionable decisions. A multi-task Convolutional Neural Network (CNN) is proposed to conduct the two major tasks simultaneously. The rest 3 sub-tasks (spall/crack/rebar exposure) were incorporated as auxiliary tasks. By synchronously learning defect information (spall/crack/rebar exposure), the multi-task CNN model outperforms the counterpart single-task models in recognizing structural components and estimating damage states. Particularly, the pixel-level damage state estimation witnesses a mIoU (mean intersection over union) improvement from 0.5855 to 0.6374. For the defect detection tasks, rebar exposure is omitted due to the extremely biased sample distribution. The segmentations of crack and spall are automated by single-task U-Net but with extra efforts to resample the provided data. The segmentation of small objects (spall and crack) benefits from the resampling method, with a substantial IoU increment of nearly 10%.

• East Asian Journal of Business Economics (EAJBE)
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• v.11 no.4
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• pp.11-18
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• 2023

Purpose - By filling the existing research hole and supplying a whole evaluation, this test wants to offer actionable insights for stakeholders navigating the intersection of sustainability and financial prosperity. Ultimately, this study contributes to the evolving speak on ESG, fostering a deeper comprehension of its implications for fostering sustainable economic increase. Research design, data, and methodology - Based on the numerous prior literature, the current study adopts a rigorous and systematic approach to discover the connection between Environmental, Social, and Governance (ESG) performance and its effect on a financial boom. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method is the guiding framework for systematically accumulating and analyzing earlier research studies. Result: The finding of this study indicates that using ESG-pushed innovation, practitioners can force technological advancements inside their respective industries. By combining sustainability with research and improvement tasks, corporations can be leaders in selling economic boom through current, green solutions. Conclusion - In summary, this study concludes that embracing those findings in this study allows practitioners and managers to enhance their organization's easy regular, well-known traditional regular standard overall performance and undoubtedly contribute to a broader financial boom via leveraging the transformative strength of ESG necessities.

• Journal of the Korean Association of Geographic Information Studies
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• v.9 no.4
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• pp.165-175
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• 2006

The discussion about capacity development (CD) has been spotlighted as significant drivers for sustainable development in recent years. Multi-dimensional natures of capacities would lead to various definitions of CD in international institutes and organizations. CD is perceived as an endogeneous process to improve actionable learning and knowledge, but most of core capacities still remain abstract notion and might be unreliable in sustainable development (SD). The paper first explicates international perspectives of CD in association with SD. An agent-based model is especially proposed to portray more details of CD. It illuminates the role of assets (or capitals, resources) in agents to impact on ingredients of CDs that are drivers or enablers for improvement of SD. A definition of sustainable capacity development is firstly articulated in international society and its conceptual framework is also creatively designed to assist concerned international organizations. The paper concludes by proposing practical spatial asset mapping linking to agent-based organizational capacity as a tool for measuring sustainable capacity development.

• Journal of the Ergonomics Society of Korea
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• v.25 no.3
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• pp.49-60
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• 2006

Technological encroachment provides human operators with flood of information that must be analyzed to understand the environment and make judgments that lead to strategic actions. Further, the environment is not static and therefore uncertain, changing its aspect dynamically. Complexity accompanied with its dynamics imposes substantial difficulty to human operators' task. Criticality of having situational understanding becomes more important than ever. Situationalunderstanding requires the human operators possessing tacit knowledge in order for them to make the sense out of the situation while interacting with information from many heterogeneous sources, the notion of sensemaking. Sensemaking refers to the process of developing mental framework to assemble pieces of information representing different aspects of the environment that can be used to develop one's own actionable knowledge to implement their judgments in the uncertain environment. Therefore, judgment process and performance is a key component of sensemaking process. Among many judgment and decision making models, the lens model with its extension can be utilized to partially describe the judgmental aspect of sensemaking. One of the lens model parameters, unmodeled knowledge, can be a corresponding quantitative measure for the tacit knowledge that plays an important role in sensemaking. In this paper, a comprehensive literature for sensemaking is provided to formally define the notion of sensemaking in the military domain. Also, it is proposed that there is a crucial link between the sensemaking and human judgment process and performance from the lens model perspective. Potential implications for experimental framework are also proposed.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.46 no.10
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• pp.1-7
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• 2009

Nowadays there are many studies and there's huge development about RFID and WSN which have great developmental potential to many kinds of applications. In particular, the healthcare field is expected to could be securing international competitive power in u-Healthcare and combined medical treatment industry and service. More and more real time application apply RFID and WSN technology to identify, data collect and locate objects. Wide deployment of RFID and WSN will generate an unprecedented volume of primitive data in a short time. Duplication and redundancy of primitive data will affect real time performance of application. Thus, emerging applications must filter primitive data and correlate them for complex pattern detection and transform them to events that provide meaningful, actionable information to end application. In this paper, we design a complex event processing system. This system will process RFID and WSN primitive data and event and perform data transformation. Integrate RFID and WSN system had applied each now in medical treatment through this study and efficient data transmission and management forecast that is possible.

• Journal of Intelligence and Information Systems
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• v.29 no.4
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• pp.325-345
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• 2023

In the rapidly evolving domain of e-commerce, our study presents a cohesive approach to enhance customer satisfaction prediction from online reviews, aligning methodological innovation with practical insights. We integrate the RFE-SHAP feature selection with LDA topic modeling to streamline predictive analytics in e-commerce. This integration facilitates the identification of key features-specifically, narrowing down from an initial set of 28 to an optimal subset of 14 features for the Random Forest algorithm. Our approach strategically mitigates the common issue of overfitting in models with an excess of features, leading to an improved accuracy rate of 84% in our Random Forest model. Central to our analysis is the understanding that certain aspects in review content, such as quality, fit, and durability, play a pivotal role in influencing customer satisfaction, especially in the clothing sector. We delve into explaining how each of these selected features impacts customer satisfaction, providing a comprehensive view of the elements most appreciated by customers. Our research makes significant contributions in two key areas. First, it enhances predictive modeling within the realm of e-commerce analytics by introducing a streamlined, feature-centric approach. This refinement in methodology not only bolsters the accuracy of customer satisfaction predictions but also sets a new standard for handling feature selection in predictive models. Second, the study provides actionable insights for e-commerce platforms, especially those in the clothing sector. By highlighting which aspects of customer reviews-like quality, fit, and durability-most influence satisfaction, we offer a strategic direction for businesses to tailor their products and services.

• The Journal of the Korea Contents Association
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• v.16 no.5
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• pp.124-136
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• 2016

Because of continuous outbreaks of disasters and emergencies, the importance of government crisis management has been increased. This study analyzed the Korean government's response messages during the 2015 MERS outbreak, which was an emergency issue that showed a great ripple effect. According to the three diffusion phases of MERS, the semantic network analysis of 134 press releases on the central and local governments' official web sites of MERS was conducted. The results showed that during the early stage of MERS, the central government misperceived the crisis situation, and as a result, specific and enough information was not provided promptly regarding a list of hospitals with known MERS exposure and prevention method. During MERS diffusion and decline stages, Seoul and Gyeonggi-do provided more specific and actionable messages than the central government. This study was meaningful in that it analyzed and evaluated crisis communication messages during an outbreak of the infectious disease. The findings of this study provide useful implications for government officials in their crisis management and communication strategy during emergency risk situations.

• The KIPS Transactions:PartD
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• v.17D no.1
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• pp.1-8
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• 2010

Association rule mining techniques enable us to acquire knowledge concerning sales patterns among individual items from voluminous transactional data. Certainly, one of the major purposes of association rule mining is utilizing the acquired knowledge to provide marketing strategies such as catalogue design, cross-selling and shop allocation. However, this requires too much time and high cost to only extract the actionable and profitable knowledge from tremendous numbers of discovered patterns. In currently available literature, a number of interest measures have been devised to accelerate and systematize the process of pattern evaluation. Unfortunately, most of such measures, including support and confidence, are prone to yielding impractical results because they are calculated only from the sales frequencies of items. For instance, traditional measures cannot differentiate between the purchases in a small basket and those in a large shopping cart. Therefore, some adjustment should be made to the size of market baskets because there is a strong possibility that mutually irrelevant items could appear together in a large shopping cart. Contrary to the previous approaches, we attempted to consider market basket's size in calculating interest measures. Because the devised measure assigns different weights to individual purchases according to their basket sizes, we expect that the measure can minimize distortion of results caused by accidental patterns. Additionally, we performed intensive computer simulations under various environments, and we performed real case analyses to analyze the correctness and consistency of the devised measure.

• Proceedings of the Korea Water Resources Association Conference
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• 2016.05a
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• pp.1-1
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• 2016

Many of the world's large ecosystems are severely stressed due to population growth, water quality and quantity problems, vulnerability to flood and drought, and the loss of native species and cultural resources. Consequences of climate change further increase uncertainties about the future. These major societal challenges must be addressed through innovations in governance, policy, and ways of implementing management strategies. Science and engineering play a critical role in helping define possible alternative futures that could be achieved and the possible consequences to economic development, quality of life, and sustainability of ecosystem services. Science has advanced rapidly during the past decade with the emergence of science communities coalescing around 'Grand Challenges' and the maturation of how these communities function has resulted in large interdisciplinary research networks. An example is the River Experiment Center of KICT that engages researchers from throughout Korea and the world. This trend has been complemented by major advances in sensor technologies and data synthesis to accelerate knowledge discovery. These factors combine to allow scientific debate to occur in a more open and transparent manner. The availability of information and improved communication of scientific and engineering issues is raising the level of dialogue at the science-policy interface. However, severe challenges persist since scientific discovery does not occur on the same timeframe as management actions, policy decisions or at the pace sometimes expected by elected officials. Common challenges include the need to make decisions in the face of considerable uncertainty, ensuring research results are actionable and preventing science being used by special interests to delay or obsfucate decisions. These challenges are explored in the context of examples from the United States, including the California Bay-Delta system. California transfers water from the wetter northern part of the state to the drier southern part of the state through the Central Valley Project since 1940 and this was supplemented by the State Water Project in 1973. The scale of these activities is remarkable: approximately two thirds of the population of Californians rely on water from the Delta, these waters also irrigate up to 45% of the fruits & vegetables produced in the US, and about 80% of California's commercial fishery species live in or migrate through the Bay-Delta. This Delta region is a global hotspot for biodiversity that provides habitat for over 700 species, but is also a hotspot for the loss of biodiversity with more than 25 species currently listed by the Endangered Species Act. Understanding the decline of the fragile ecosystem of the Bay-Delta system and the potential consequences to economic growth if water transfers are reduced for the environment, the California State Legislature passed landmark legislation in 2009 (CA Water Code SS 85054) that established "Coequal goals of providing a more reliable water supply for California and protecting, restoring, and enhancing the Delta ecosystem". The legislation also stated that "The coequal goals shall be achieved in a manner that protects and enhances the unique cultural, recreational, natural resource, and agricultural values of the Delta as an evolving place." The challenges of integrating policy, management and scientific research will be described through this and other international examples.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.1-18
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• 2016

Owing to the extensive use of Web media and the development of the IT industry, a large amount of data has been generated, shared, and stored. Nowadays, various types of unstructured data such as image, sound, video, and text are distributed through Web media. Therefore, many attempts have been made in recent years to discover new value through an analysis of these unstructured data. Among these types of unstructured data, text is recognized as the most representative method for users to express and share their opinions on the Web. In this sense, demand for obtaining new insights through text analysis is steadily increasing. Accordingly, text mining is increasingly being used for different purposes in various fields. In particular, issue tracking is being widely studied not only in the academic world but also in industries because it can be used to extract various issues from text such as news, (SocialNetworkServices) to analyze the trends of these issues. Conventionally, issue tracking is used to identify major issues sustained over a long period of time through topic modeling and to analyze the detailed distribution of documents involved in each issue. However, because conventional issue tracking assumes that the content composing each issue does not change throughout the entire tracking period, it cannot represent the dynamic mutation process of detailed issues that can be created, merged, divided, and deleted between these periods. Moreover, because only keywords that appear consistently throughout the entire period can be derived as issue keywords, concrete issue keywords such as "nuclear test" and "separated families" may be concealed by more general issue keywords such as "North Korea" in an analysis over a long period of time. This implies that many meaningful but short-lived issues cannot be discovered by conventional issue tracking. Note that detailed keywords are preferable to general keywords because the former can be clues for providing actionable strategies. To overcome these limitations, we performed an independent analysis on the documents of each detailed period. We generated an issue flow diagram based on the similarity of each issue between two consecutive periods. The issue transition pattern among categories was analyzed by using the category information of each document. In this study, we then applied the proposed methodology to a real case of 53,739 news articles. We derived an issue flow diagram from the articles. We then proposed the following useful application scenarios for the issue flow diagram presented in the experiment section. First, we can identify an issue that actively appears during a certain period and promptly disappears in the next period. Second, the preceding and following issues of a particular issue can be easily discovered from the issue flow diagram. This implies that our methodology can be used to discover the association between inter-period issues. Finally, an interesting pattern of one-way and two-way transitions was discovered by analyzing the transition patterns of issues through category analysis. Thus, we discovered that a pair of mutually similar categories induces two-way transitions. In contrast, one-way transitions can be recognized as an indicator that issues in a certain category tend to be influenced by other issues in another category. For practical application of the proposed methodology, high-quality word and stop word dictionaries need to be constructed. In addition, not only the number of documents but also additional meta-information such as the read counts, written time, and comments of documents should be analyzed. A rigorous performance evaluation or validation of the proposed methodology should be performed in future works.