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Influence of Organizational and Project Practices on Design Error Costs
Love, Peter E. D.,Lopez, Robert,Kim, Jeong Tai,Kim, Mi Jeong American Society of Civil Engineers 2014 Journal of performance of constructed facilities Vol.28 No.2
The organizational and project-related practices adopted by design firms can influence the nature and ability of people to perform their tasks. In recognition of such influences, a structured survey questionnaire was used to determine the key factors contributing to design error costs in 139 Australian construction projects. Using stepwise multiple regressions, the significant organizational and project-related variables influencing design error costs are determined. The analysis revealed that the mean design error costs for the sample projects were 14.2% of the original contract value. Significant organizational and project factors influencing design error included inadequate training for employees and unrealistic design and documentation schedules required by clients. From the findings, key strategies for reducing design errors that are attributable to organization and project-related practices are identified.
Love Allen Chijioke Ahakonye,Cosmas Ifeanyi Nwakanma,이재민,김동성 한국통신학회 2024 韓國通信學會論文誌 Vol.49 No.2
In Industrial Internet of Things (IIoT) environments, the reliability and adaptability of machine learning models are crucial for accurate decision-making. This paper introduces the Characteristic Stability Index (CSI) to monitor and ensure the stability of models in the context of heterogeneous IIoT sensor data. The CSI quantifies the variations in feature importance rankings, enabling the early detection of data drift and shifts. The experimentation results validate the performance of the decision tree algorithm to provide actionable insights, facilitating domain experts’ adaptability and enhancing decision-making while minimizing operational risks and costs in the choice of intrusion detection systems model.
Love Allen Chijioke Ahakonye,Cosmas Ifeanyi Nwakanma,Jae Min Lee,Dong-Seong Kim 한국통신학회 2021 한국통신학회 학술대회논문집 Vol.2021 No.11
A dependable Smart Factory (SF) Supervisory Control and Data Acquisition (SCADA) network consolidates the security features of Artificial Intelligence (AI) and Information Technology (IT) trustworthiness by exploiting Machine Learning (ML) capabilities in attack detection. This study proposes to improve the efficiency of the SF SCADA network through a reliable ML detection technique. Specifically, Hyperparameter optimization of trees was lead for various optimizers for improving ML reliability. The Grid Search Optimizer improved the model by the combined advantage of training time and prediction speed. Hence, reliable for the improvement of ML in SF SCADA attack detection.
Love, J.S.,Morava, B. Council on Tall Building and Urban Habitat Korea 2021 International journal of high-rise buildings Vol.10 No.2
Dynamic vibration absorbers (DVAs) in the form of tuned sloshing dampers (TSDs) and tuned mass dampers (TMDs) are commonly used to reduce the wind-induced motion of high-rise buildings. Full-scale performance of structure-DVA systems must be evaluated during the DVA commissioning process using structural monitoring data. While the random decrement technique (RDT) is sometimes employed to evaluate the DVA performance, it is shown to have no theoretical justification for application to structure-DVA systems, and to produce erroneous results. Subsequently, several practical methods with a sound theoretical basis are presented and illustrated using simulated and real-world data. By monitoring the responses of the structure and DVA simultaneously, it is possible to directly measure the effective damping of the system or perform system identification from which the DVA performance can be evaluated.
Applied Computational Tools for Crop Genome Research
Love Christopher G,Batley Jacqueline,Edwards David The Korean Society of Plant Biotechnology 2003 Plant molecular biology and biotechnology research Vol.5 No.4
A major goal of agricultural biotechnology is the discovery of genes or genetic loci which are associated with characteristics beneficial to crop production. This knowledge of genetic loci may then be applied to improve crop breeding. Agriculturally important genes may also benefit crop production through transgenic technologies. Recent years have seen an application of high throughput technologies to agricultural biotechnology leading to the production of large amounts of genomic data. The challenge today is the effective structuring of this data to permit researchers to search, filter and importantly, make robust associations within a wide variety of datasets. At the Plant Biotechnology Centre, Primary Industries Research Victoria in Melbourne, Australia, we have developed a series of tools and computational pipelines to assist in the processing and structuring of genomic data to aid its application to agricultural biotechnology resear-ch. These tools include a sequence database, ASTRA, for the processing and annotation of expressed sequence tag data. Tools have also been developed for the discovery of simple sequence repeat (SSR) and single nucleotide polymorphism (SNP) molecular markers from large sequence datasets. Application of these tools to Brassica research has assisted in the production of genetic and comparative physical maps as well as candidate gene discovery for a range of agronomically important traits.
( Love Kumar Dhandole ),( Mahadik Mahadeo Abasaheb ),김수경,조민,류정호,장점석 한국공업화학회 2016 한국공업화학회 연구논문 초록집 Vol.2016 No.1
Transition metal oxides loaded acid treated TiO<sub>2</sub> nanorods (NRs) were successfully prepared by chemical treatment and wet impregnation methods. The catalysts were characterized by XRD, TEM, XPS, FT-IR and UV-DRS. The photocatalytic activities of as-prepared, acid treated, metal oxide loaded and metal oxide loaded acid treated NRs were compared and dye degradation efficiency were determined from kinetics of the degradation of Orange (II) dye. Cobalt oxide 1w% loaded on 1.0 M acid treated TiO<sub>2</sub> NRs exhibited the higher photocatalytic Orange (II) degradation efficiency 98.57% (within 120 min) than as-prepared and metal oxide loaded samples. The synergistic effect of cobalt oxide on acid treated TiO<sub>2</sub> NRs over dye degradation is considered as fine dispersion of metal oxides on the OH rich surface of TiO<sub>2</sub>. The mechanism of enhanced photocatalytic activity and photoelectrochemical analysis of photocatalyst also studied. <sup>**</sup>This work was supported by the BK21 plus program.
Anomaly Detection of Malicious Energy Usage in Smart Factories using Deep Neural Network
Love Allen Chijioke Ahakonye,Cosmas Ifeanyi Nwakanma,Jae Min Lee,Dong-Seong Kim 한국통신학회 2021 한국통신학회 학술대회논문집 Vol.2021 No.6
In Smart Factory, an extensive volume of data is generated daily by Advanced Metering Infrastructures (AMI) and Smart Sensors. One such data is the amount of energy usage and the need to keep track of normal and abnormal energy usage in the smart factory. This allows energy producers to uncover abnormal power consumption as well as realizing distinct malicious energy usage. Recognition of abnormal conducts is essential to predict the unusual occurrence and to enhance energy productivity. This work proposes the Long Short-Term Memory (LSTM) Network to accurately recognize malicious energy usage in a smart factory. The proposed system is implemented using Python on Google collaborate with Tanh activation function. The performance of the proposed scheme showed 99.92%, 99.98%, 99.92%, and 99.85% for accuracy, precision, F1-Score, and recall respectively.