• Smart Structures and Systems
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• v.29 no.1
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• pp.93-103
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• 2022

With the wider availability of sensor technology through easily affordable sensor devices, several Structural Health Monitoring (SHM) systems are deployed to monitor vital civil infrastructure. The continuous monitoring provides valuable information about the health of the structure that can help provide a decision support system for retrofits and other structural modifications. However, when the sensors are exposed to harsh environmental conditions, the data measured by the SHM systems tend to be affected by multiple anomalies caused by faulty or broken sensors. Given a deluge of high-dimensional data collected continuously over time, research into using machine learning methods to detect anomalies are a topic of great interest to the SHM community. This paper contributes to this effort by proposing a relatively new time series representation named "Shapelet Transform" in combination with a Random Forest classifier to autonomously identify anomalies in SHM data. The shapelet transform is a unique time series representation based solely on the shape of the time series data. Considering the individual characteristics unique to every anomaly, the application of this transform yields a new shape-based feature representation that can be combined with any standard machine learning algorithm to detect anomalous data with no manual intervention. For the present study, the anomaly detection framework consists of three steps: identifying unique shapes from anomalous data, using these shapes to transform the SHM data into a local-shape space and training machine learning algorithms on this transformed data to identify anomalies. The efficacy of this method is demonstrated by the identification of anomalies in acceleration data from an SHM system installed on a long-span bridge in China. The results show that multiple data anomalies in SHM data can be automatically detected with high accuracy using the proposed method.

• International Journal of Computer Science & Network Security
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• v.23 no.2
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• pp.203-209
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• 2023

The paper brings across data that is utilized in the Bring Your Own Device (BYOD) status collected between February and April of 2021 across Saudi Arabia. The data set was collected using questionnaires established through online mechanisms for the respondents. In the questionnaire, personal details included five questions while seven questions addressed the working model of personal mobile devices. Six questions addressed the awareness of employees bring your own device awareness for employees comprised seven questions and two questions addressed the benefits of business achievements. In the identification of suitable respondents for the research, two approaches were applied. The research demanded that the respondents be Saudi Arabian nationals and have attained 18 years. Snowball and purposive techniques were applied in the collection of information from a wide area of Saudi Arabia while employing social media approaches that include the use of WhatsApp and emails in the collection of data. The approach ensured the collection of data from 857 respondents used in the identification of the status as well as issues across the BYOD environment and accompanying solutions. The data was also used in the provision of awareness in the community through short-term courses, cyber security training and awareness programs. The results of the research are therefore applicable to the context of the Saudi Arabian country that is currently facing issues in dealing with the application of personal devices in the work environment.

• Journal of Internet of Things and Convergence
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• v.9 no.1
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• pp.41-47
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• 2023

Today, due to the increase in global population growth, the international community is discussing solving the food problem. The aquaculture industry is emerging as an alternative to solving the food problem. For the innovative growth of the aquaculture industry, smart fish farms that combine the fourth industrial technology are recently being distributed, and full-cycle digitalization is being promoted. Water quality sensors, which are important in the aquaculture industry, are electrochemical portable sensors that check water quality individually and intermittently, making it impossible to analyze and manage water quality in real time. Recently, optically-based monitoring sensors have been developed and applied, but the reliability of monitoring data cannot be guaranteed because the state information of the water quality sensor is unknown. Therefore, this paper proposes an algorithm representing self-diagnosis status such as Failure, Out of Specification, Maintenance Required, and Check Function based on monitoring data collected by water quality sensors to ensure data reliability.

• The Journal of Engineering Geology
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• v.33 no.3
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• pp.489-502
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• 2023

Urban floods pose significant challenges to cities worldwide, driven by the interplay between urbanization and climate change. This review examines recent studies of urban floods to understand their causes, impacts, and potential mitigation strategies. Urbanization, with its increase in impermeable surfaces and altered drainage patterns, disrupts natural water flow, exacerbating surface runoff during intense rainfall events. The impacts of urban floods are far-reaching, affecting lives, infrastructure, the economy, and the environment. Loss of life, property damage, disruptions to critical services, and environmental consequences underscore the urgency of effective urban flood management. To mitigate urban floods, integrated flood management strategies are crucial. Sustainable urban planning, green infrastructure, and improved drainage systems play pivotal roles in reducing flood vulnerabilities. Early warning systems, emergency response planning, and community engagement are essential components of flood preparedness and resilience. Looking to the future, climate change projections indicate increased flood risks, necessitating resilience and adaptation measures. Advances in research, data collection, and modeling techniques will enable more accurate flood predictions, thus guiding decision-making. In conclusion, urban flooding demands urgent attention and comprehensive strategies to protect lives, infrastructure, and the economy.

• Journal of The Korean Society of Agricultural Engineers
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• v.65 no.2
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• pp.35-45
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• 2023

The DOR (Drainage outlet raising) in the paddy field has been suggested as one of the most important best management practices for the TMDL (Total maximum daily load) management in the technical guidelines by the NIER (National institute of environmental research). However, this method is underestimated and is not well adopted by local governments for the TMDL. The purpose of this study is to evaluate the unit load reduction equation according to the application of DOR in order to expand this equation. The original equation in the guideline was derived using the HSPF (Hydrological Simulation Program-Fortran) model for 1 year in Changnyeong. We analyzed the reduction effect of the original equation application by collecting additional long-term monitoring data from the Buan, Icheon, Iksan, and Jeonju. When comparing the reduction loads between the original equation and monitoring results, the evaluation results of the original equation were 11% of the monitoring analysis results, which was underestimated. This means that the original equation needs to be improved. For assessing the equation, the HSPF Paddy-RCH model was established according to the NI ER guideline and evaluated for applicability. The performance results of the model showed a reasonable range by the statistical criteria. Modified equations 1 and 2 were proposed based on the monitoring and modeling results. Modified equation 1 was the method of modifying the original equation's main factors, and modified equation 2 was the method of applying the non-point pollution reduction efficiency according to the rainfall class using the long-term modeling results. At the level of 58.6~64.6% of monitoring data, the difference between them could be further reduced compared to the original equation. The suggested approach will be more reasonable and practicable for decision-makers and will contribute to the TMDL management plans.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.2D
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• pp.259-267
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• 2008

Nowadays PMIS (Project Management Information System) based on web is widely applied to communication, collaboration and community in construction jobsite for various organization such as subcontractors, general contractor, engineering company, supervisors and client. However, individual PMIS in jobsite is prone to be troublesome issue for integration and exchange of information regarding project management in corporate and may result in conflict of data application. The main reason should be lack of information strategy plan such as company master plan for information. This research shows a critical success factor that a big construction company of Korea establishes procedure and method for information strategy plan. In addition, It is to suggest how to be implemented PMIS architecture and execute business process innovation in corporate level. This research expects contribution that many construction companies reduce mistakes to be implemented management information system and to optimize system in corporate level.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.967-974
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• 2022

It is no news that Nigeria's infrastructure challenge is enormous. In the global ranking, Nigeria ranked low in quantity and quality of its infrastructural provision which has a great impact on the ease of business transaction. Low investments in transportation have brought about the current infrastructural deficit. Recently, the Nigerian government has made effort to address at least to some extent the infrastructural deficit through Public-Private Partnership, but this has not yielded the desired result. Moreover, the sustainability issues relating to railway projects such as, emissions, noise pollution, ecosystem, and other environmental issues calls for urgent attention. Hence, this necessitated consideration on sustainability appraisal for the Chinese rail project in Nigeria. This study reviews sustainability of railway projects built by the Chinese firm in Nigeria with particular emphasis on the environmental and social impact of these projects. The study further identified issues and challenges in project implementation with a particular focus on civil dialogue and community engagements. A detailed literature search was conducted on railway projects and infrastructure by systematically reviewing selected published articles.The analysis of the selected articles identified sustainability issues and potential for improvement of Chinese railway projects and how they contribute to or inhibit competitiveness in the Nigerian railway market. From the literature searched, some of the projects constructed by Chinese firm revealed that there is economic and social impact of railway projects delivered by the Chinese firm in terms of capacity development and knowledge transfer potentiality. For instance, in the just concluded Lagos-Ibadan railway projects, the study gathered that the project brought about 5000 jobs and local staff were trained by the Chinese company, this will boost man power and local content capability. Also, it will significantly improve Nigeria's infrastructure and boost its economic development. The study suggests that Nigerian government should ensure and provide an enabling environment that is conducive for investment on the continent. Peace, improved security, and decent governance are the best conditions for sustainable transportation growth.

• Journal of Practical Engineering Education
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• v.15 no.3
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• pp.761-770
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• 2023

This study was conducted to identify factors affecting depression in the elderly using three-year Community Health Survey data from 2020, when COVID-19 was declared an epidemic. Differences in depression according to general characteristics, health behavior, subjective health level, and medical use among 220,921 elderly were analyzed using complex samples t-test and ANOVA, and multiple regression analysis was performed to identify factors affecting depression it was carried out. As a result of the study, the level of depression among elderly women was found to be high, with an average of 1.21±0.01 for elderly men and 1.74±0.02 for elderly women, and there was a difference in generation type, with depression being higher in the first generation for elderly men and the third generation for elderly women. Variables that had a great influence on depression were the experience of depression and perceived stress.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.10
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• pp.2768-2787
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• 2023

As an important research direction of the application of computer science in the medical field, the automatic generation technology of radiology report has attracted wide attention in the academic community. Because the proportion of normal regions in radiology images is much larger than that of abnormal regions, words describing diseases are often masked by other words, resulting in significant feature loss during the calculation process, which affects the quality of generated reports. In addition, the huge difference between visual features and semantic features causes traditional multi-modal fusion method to fail to generate long narrative structures consisting of multiple sentences, which are required for medical reports. To address these challenges, we propose a multi-feature optimization Transformer (MFOT) for generating radiology reports. In detail, a multi-dimensional mapping attention (MDMA) module is designed to encode the visual grid features from different dimensions to reduce the loss of primary features in the encoding process; a feature pre-fusion (FP) module is constructed to enhance the interaction ability between multi-modal features, so as to generate a reasonably structured radiology report; a detail enhanced attention (DEA) module is proposed to enhance the extraction and utilization of key features and reduce the loss of key features. In conclusion, we evaluate the performance of our proposed model against prevailing mainstream models by utilizing widely-recognized radiology report datasets, namely IU X-Ray and MIMIC-CXR. The experimental outcomes demonstrate that our model achieves SOTA performance on both datasets, compared with the base model, the average improvement of six key indicators is 19.9% and 18.0% respectively. These findings substantiate the efficacy of our model in the domain of automated radiology report generation.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.5
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• pp.47-53
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• 2023

With the advent of the 4th Industrial Revolution, diverse and a large amount of data has been accumulated now. The agricultural community has also collected environmental data that affects the growth of crops in smart farms or open fields with sensors. Environmental data has different features depending on where and when they are measured. Studies have been conducted using collected agricultural data to predict growth and yield with statistics and artificial intelligence. The results of these studies vary greatly depending on the data on which they are based. So, studies to enhance data quality have also been continuously conducted for performance improvement. A lot of data is required for high performance, but if there are outlier or missing values in the data, it can greatly affect the results even if the amount is sufficient. So, adjustment of outlier and missing values is essential in the data preprocessing. Therefore, this paper integrates data collected from actual farms and proposes a adjustment system for outlier and missing values based on it.