• Biomolecules & Therapeutics
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• v.30 no.1
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• pp.98-116
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• 2022

The small GTPase RhoA has been studied extensively for its role in actin dynamics. In this study, multiple bioinformatics tools were applied cooperatively to the microarray dataset GSE64714 to explore previously unidentified functions of RhoA. Comparative gene expression analysis revealed 545 differentially expressed genes in RhoA-null cells versus controls. Gene set enrichment analysis (GSEA) was conducted with three gene set collections: (1) the hallmark, (2) the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, and (3) the Gene Ontology Biological Process. GSEA results showed that RhoA is related strongly to diverse pathways: cell cycle/growth, DNA repair, metabolism, keratinization, response to fungus, and vesicular transport. These functions were verified by heatmap analysis, KEGG pathway diagramming, and direct acyclic graphing. The use of multiple gene set collections restricted the leakage of information extracted. However, gene sets from individual collections are heterogenous in gene element composition, number, and the contextual meaning embraced in names. Indeed, there was a limit to deriving functions with high accuracy and reliability simply from gene set names. The comparison of multiple gene set collections showed that although the gene sets had similar names, the gene elements were extremely heterogeneous. Thus, the type of collection chosen and the analytical context influence the interpretation of GSEA results. Nonetheless, the analyses of multiple collections made it possible to derive robust and consistent function identifications. This study confirmed several well-described roles of RhoA and revealed less explored functions, suggesting future research directions.

• Steel and Composite Structures
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• v.44 no.1
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• pp.119-139
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• 2022

This paper aims to use the artificial intelligence approach to develop a new model for predicting the ultimate axial strength of the circular concrete-filled steel tubular (CFST) stub columns. For this, the results of 314 experimentally tested circular CFST stub columns were employed in the generation of the design model. Since the influence of the column diameter, steel tube thickness, concrete compressive strength, steel tube yield strength, and column length on the ultimate axial strengths of columns were investigated in these experimental studies, here, in the development of the design model, these variables were taken into account as input parameters. The model was developed using the backpropagation algorithm named Bayesian Regularization. The accuracy, reliability, and consistency of the developed model were evaluated statistically, and also the design formulae given in the codes (EC4, ACI, AS, AIJ, and AISC) and the previous empirical formulations proposed by other researchers were used for the validation and comparison purposes. Based on this evaluation, it can be expressed that the developed design model has a strong and reliable prediction performance with a considerably high coefficient of determination (R-squared) value of 0.9994 and a low average percent error of 4.61. Besides, the sensitivity of the developed model was also monitored in terms of dimensional properties of columns and mechanical characteristics of materials. As a consequence, it can be stated that for the design of the ultimate axial capacity of the circular CFST stub columns, a novel artificial intelligence-based design model with a good and robust prediction performance was proposed herein.

• The Journal of Korea Robotics Society
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• v.17 no.4
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• pp.484-492
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• 2022

This paper presents a simple and robust under-actuated robotic finger mechanism that enables self-adaptive grip, fingertip pinch, and caging grasp functions. In order to perform daily activities using hands, the fingers should be able to perform adaptive gripping and pinching motion, and the caging grasp function is required to realize natural gripping motions and improve grip reliability. However, general commercial prosthetic hands cannot implement all three functions because they use under-actuation mechanism and simple mechanical structure to achieve light-weight and high robustness characteristic. In this paper, new mechanism is proposed that maintains structural simplicity and implements all the three finger functions with simple one degree-of-freedom control through a combination of a four-bar linkage mechanism and a wire-driven mechanism. The basic structure and operating principle of the proposed finger mechanism were explained, and simulation and experiments using the prototype were conducted to verify the gripping performance of the proposed finger mechanism.

• International conference on construction engineering and project management
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• 2009.05a
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• pp.1509-1514
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• 2009

CII BM&M(Benchmarking & Metrics) in USA and DTI(Department of Trade and Industry) in UK have built up systems that enable performance measuring and made the results of performance measurement comparable between projects to utilize them as benchmarking tools. By comparing the results of performance measurement, it is possible to grasp the success level of project management and to establish the direction of management. However, construction projects are much diversified and even those projects with the same work type have different attributes. Therefore, simply comparing the results of project performance measurement without considering the characteristics of projects is not justifiable and affects the reliability of the benchmarking results. Therefore, to solve this problem, this study presents a methodology that makes it possible to compare the individual construction projects considering various characteristics. The benefits and importance of project characteristics to overall project performance will be quantitatively expressed and they will be reflected on the results of performance management. By maximally converting multiple projects with different characteristics into the same projects through a new methodology to convert different projects into the same level utilizing such utility-bases and comparing the performances of those projects, project performance results can be utilized in project management as a tool for more accurate decision making and as a robust benchmarking tool.

• Korean Journal of Artificial Intelligence
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• v.11 no.4
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• pp.29-34
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• 2023

The flow from developing a machine learning model to deploying it in a production environment suffers challenges. Efficient and reliable deployment is critical for realizing the true value of machine learning models. Bridging this gap between development and publication has become a pivotal concern in the machine learning community. FastAPI, a modern and fast web framework for building APIs with Python, has gained substantial popularity for its speed, ease of use, and asynchronous capabilities. This paper focused on leveraging FastAPI for deploying machine learning models, addressing the potentials associated with integration, scalability, and performance in a production setting. In this work, we explored the seamless integration of machine learning models into FastAPI applications, enabling real-time predictions and showing a possibility of scaling up for a more diverse range of use cases. We discussed the intricacies of integrating popular machine learning frameworks with FastAPI, ensuring smooth interactions between data processing, model inference, and API responses. This study focused on elucidating the integration of machine learning models into production environments using FastAPI, exploring its capabilities, features, and best practices. We delved into the potential of FastAPI in providing a robust and efficient solution for deploying machine learning systems, handling real-time predictions, managing input/output data, and ensuring optimal performance and reliability.

• Journal of the Korean Society of Industry Convergence
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• v.26 no.5
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• pp.867-874
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• 2023

In this paper, a wireless communication-based sensor data monitoring device with an explosion-proof (Exd IIC) case was implemented to enable installation at explosion-risk industrial sites such as plants. In existing industrial plant sites, most of the temperature sensors and vibration and impact sensors are wired up to several kilometers, which takes a lot of time and money to bury long pipes and cables. In addition, there are not many cases where some wireless devices have been applied to actual plant industry sites due to communication quality problems. Therefore, in order to solve this problem, zigbee mesh wireless communication was applied to provide high reliability wireless communication quality to industrial plant sites, and the time and cost incurred in new or additional installation of sensors could be greatly reduced. In particular, in the event of loss or error of some wireless communication devices, the communication network is automatically bypassed or recovered to enable real-time data monitoring.

• The Journal of Korea Robotics Society
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• v.19 no.2
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• pp.149-158
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• 2024

This paper presents a robust autonomous navigation and reconnaissance system for tracked robots, designed to handle complex multi-floor indoor environments with stairs. We introduce a localization algorithm that adjusts scan matching parameters to robustly estimate positions and create maps in environments with scarce features, such as narrow rooms and staircases. Our system also features a path planning algorithm that calculates distance costs from surrounding obstacles, integrated with a specialized PID controller tuned to the robot's differential kinematics for collision-free navigation in confined spaces. The perception module leverages multi-image fusion and camera-LiDAR fusion to accurately detect and map the 3D positions of objects around the robot in real time. Through practical tests in real settings, we have verified that our system performs reliably. Based on this reliability, we expect that our research team's autonomous reconnaissance system will be practically utilized in actual disaster situations and environments that are difficult for humans to access, thereby making a significant contribution.

• Journal of Distribution Science
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• v.22 no.8
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• pp.65-76
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• 2024

Purpose: The study aims to recognize the relationship between big data analytics capabilities, big data analytics process, and perceived business performance from supply chain management to total customer satisfaction. Research design, data and methodology: The study followed a quantitative approach with a descriptive design. The data was collected from leading e-commerce companies in India using a structured questionnaire, and the data was coded and decoded using MS Excel, SPSS, and R language. It was further tested using Cronbach's alpha, KMO, and Bartlett's test for reliability and internal consistency. Results: The results showed that the big data analytics process acts as a robust mediator between big data analytics capabilities and perceived business performance. The 'direct, indirect and total effect of the model' and 'PLS-SEM model' showed that the big data analytics process directly impacts business performance. Conclusions: A complete indirect relationship exists between big data analytics capabilities and perceived business performance through the big data analytics process. The research contributesto e-commerce companies' understanding of the importance of big data analytics capabilities and processes.

• The Korean Journal of Food & Health Convergence
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• v.10 no.4
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• pp.9-19
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• 2024

This qualitative study explores the impact of online food delivery platform attributes on customer satisfaction and repurchase intentions. Employing a phenomenological approach, we conducted in-depth interviews and focus group discussions with 15 participants to gain rich insights into user experiences. Thematic analysis revealed key factors influencing satisfaction and loyalty: service quality dimensions (efficiency, reliability, fulfillment, privacy), expectation disconfirmation, perceived usefulness and ease of use, multi-level customer value, relationship quality, electronic word-of-mouth, value co-creation, and phased loyalty formation. Our findings extend customer behavior theory in digital platforms, offering a comprehensive framework for understanding the complex mechanisms underlying user satisfaction and repurchase decisions. The study provides valuable implications for platform operators, highlighting the importance of exceeding customer expectations, enhancing user experience, building trust, leveraging user-generated content, and fostering co-creation processes. Methodologically, we demonstrate the efficacy of qualitative approaches in uncovering nuanced insights in digital service contexts. While acknowledging limitations in generalizability, this research establishes a solid foundation for future investigations into the rapidly evolving domain of online food delivery services. The integrated theoretical approach offers a robust model for analyzing customer behavior in emerging digital service environments, contributing significantly to both academic understanding and practical application in the field of digital service provision and platform management.

• The Journal of the Acoustical Society of Korea
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• v.21 no.6
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• pp.552-558
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• 2002

Recently, based on the human speech recognition (HSR) model of Fletcher, the multi-band speech recognition has been intensively studied by many researchers. As a new automatic speech recognition (ASR) technique, the multi-band speech recognition splits the frequency domain into several sub-bands and recognizes each sub-band independently. The likelihood scores of sub-bands are weighted according to reliabilities of sub-bands and re-combined to make a final decision. This approach is known to be robust under noisy environments. When the noise is stationary a sub-band SNR can be estimated using the noise information in non-speech interval. However, if the noise is non-stationary it is not feasible to obtain the sub-band SNR. This paper proposes the inverse sub-band distance (ISD) weighting, where a distance of each sub-band is calculated by a stochastic matching of input feature vectors and hidden Markov models. The inverse distance is used as a sub-band weight. Experiments on 1500∼1800㎐ band-limited white noise and classical guitar sound revealed that the proposed method could represent the sub-band reliability effectively and improve the performance under both stationary and non-stationary band-limited noise environments.