• Smart Structures and Systems
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• v.24 no.5
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• pp.669-681
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• 2019

Bridge collapses may deliver a huge impact on our society in a very negative way. Out of many reasons why bridges collapse, poor maintenance is becoming a main contributing factor to many recent collapses. Furthermore, the aging of bridges is able to make the situation much worse. In order to prevent this unwanted event, it is indispensable to conduct continuous bridge monitoring and timely maintenance. Visual inspection is the most widely used method, but it is heavily dependent on the experience of the inspectors. It is also time-consuming, labor-intensive, costly, disruptive, and even unsafe for the inspectors. In order to address its limitations, in recent years increasing interests have been paid to the use of unmanned aerial vehicles (UAVs), which is expected to make the inspection process safer, faster and more cost-effective. In addition, it can cover the area where it is too hard to reach by inspectors. However, this strategy is still in a primitive stage because there are many things to be addressed for real implementation. In this paper, a typical procedure of bridge inspection using UAVs consisting of three phases (i.e., pre-inspection, inspection, and post-inspection phases) and the detailed tasks by phase are described. Also, three major challenges, which are related to a UAV's flight, image data acquisition, and damage identification, respectively, are identified from a practical perspective (e.g., localization of a UAV under the bridge, high-quality image capture, etc.) and their possible solutions are discussed by examining recently developed or currently developing techniques such as the graph-based localization algorithm, and the image quality assessment and enhancement strategy. In particular, deep learning based algorithms such as R-CNN and Mask R-CNN for classifying, localizing and quantifying several damage types (e.g., cracks, corrosion, spalling, efflorescence, etc.) in an automatic manner are discussed. This strategy is based on a huge amount of image data obtained from unmanned inspection equipment consisting of the UAV and imaging devices (vision and IR cameras).

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.49 no.1
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• pp.21-30
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• 2021

Mid-course trajectory of the missiles equipped with seeker should be designed to detect target within FOV of seeker and to maximize the maneuverability at the point of transition to terminal guidance phase. Because the trajectory optimization problems are generally hard to obtain the analytic solutions due to its own nonlinearity with several constraints, the various numerical methods have been presented so far. In this paper, mid-course trajectory optimization problem for boost-glide missiles is calculated by using SOCP (Second-Order Cone Programming) which is one of convex optimization methods. At first, control variable augmentation scheme with a control constraint is suggested to reduce state variables of missile dynamics. And it is reformulated using a normalized time approach to cope with a free final time problem and boost time problem. Then, partial linearization and lossless convexification are used to convexify dynamic equation and control constraint, respectively. Finally, the results of the proposed method are compared with those of state-of-the-art nonlinear optimization method for verification.

• Journal of the Korean Physical Society
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• v.73 no.11
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• pp.1703-1707
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• 2018

Ferromagnetic ${\tau}-phase$ $Mn_{54}Al_{46}C_{2.44}$ particles were synthesized, and its composites with commercial $Sm_2Fe_{17}N_3$ and synthesized $Fe_{65}Co_{35}$ powders were fabricated. Smaller grain size than the single domain size of the $Mn_{54}Al_{46}C_{2.44}$ without obvious grain boundaries and secondary phases is the origin for the low intrinsic coercivity. It was confirmed that the magnetic properties of the $Mn_{54}Al_{46}C_{2.44}$ can be enhanced by magnetic exchange coupling with the hard magnetic $Sm_2Fe_{17}N_3$ and soft magnetic $Fe_{65}Co_{35}$. The high degrees of the exchange coupling were verified by calculating first derivative curves. Thermo-magnetic stabilities of the composites from 100 to 400 K were measured and compared. It was demonstrated that the $Mn_{54}Al_{46}C_{2.44}$ based composites containing $Sm_2Fe_{17}N_3$ and $Fe_{65}Co_{35}$ could be promising candidates for future permanent magnetic materials with the proper control of purity, magnetic properties, etc.

• Korean Journal of Metals and Materials
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• v.46 no.12
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• pp.788-799
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• 2008

This study is concerned with the effects of Mo, Cr, and V addition on tensile and Charpy impact properties of API X80 linepipe steels. Four kinds of steels were processed by varying Mo, Cr, and V additions, and their microstructures and tensile and Charpy impact properties were investigated. Since the addition of Mo and V promoted to form fine acicular ferrite and granular bainite, while prohibiting the coarsening of granular bainite, it increased the strength and upper shelf energy, and decreased the energy transition temperature. The Cr addition promoted the formation of coarse granular bainite and secondary phases such as martensite-austenite constituents, thereby leading to the increased effective grain size, energy transition temperature, and strength and to the decreased upper shelf energy. The steel containing 0.3wt.% Mo and 0.06wt.% V without Cr had the highest upper shelf energy and the lowest energy transition temperature because its microstructure was composed of fine acicular ferrite and granular bainite, together with a small amount of hard secondary phases, while its tensile properties maintained excellent.

• Korean Journal of Materials Research
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• v.32 no.1
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• pp.44-50
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• 2022

Transition metal carbides (TMCs) are used to process difficult-to-cut materials due to the trend of requiring superior wear and corrosion properties compared to those of cemented carbides used in the cutting industry. In this study, TMC (TiC, TaC, Mo₂C, and NbC)-based cermets were consolidated by spark plasma sintering at 1,300 ℃ (60 ℃min) with a pressure of 60 MPa with Co addition. The sintering behavior of TMCs depended exponentially on the function of the sintering exponent. The Mo₂C-6Co cermet was fully densified, with a relative density of 100.0 %. The Co-binder penetrated the hard phase (carbides) by dissolving and re-precipitating, which completely densified the material. The mechanical properties of the TMCs were determined according to their grain size and elastic modulus: TiC-6Co showed the highest hardness of 1,872.9 MPa, while NbC-6Co showed the highest fracture toughness of 10.6 MPa^*m^1/2. The strengthened grain boundaries due to high interfacial energy could cause a high elastic modules; therefore, TiC-6Co showed a value of 452 ± 12 GPa.

• Korean Journal of Head & Neck Oncology
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• v.38 no.2
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• pp.23-27
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• 2022

Systemic lupus erythematosus(SLE) is a multisystemic disorder of autoimmune etiology. SLE can occur commonly in young women, and the early symptoms include fever, myalgia, arthralgia, weight loss, lymphadenopathy and these nonspecific symptoms develop into skin rash, splenomegaly, serositis and encephalopathy. Diagnosis of SLE requires clinical and serologic criteria, and treatment choices are hydroxyquinolone and NSAIDs for mild disease, corticosteroids and immunosuppressant for severe disease. In lupus patient, the prevalence of lymphadenopathy is 12~59%. Although lymphadenopathy is common finding in SLE, it is hard to distinguish in early phase of SLE. A 38-year-old woman visited our hospital for cervical lymphadenopathy with polyarthritis and malaise. Multiple cervical lymph nodes enlargement was found on Neck CT, and serologic laboratory test including ANA, antiphospholipid antibody, and anti-dsDNA was positive. For excluding lymphoma, PET-CT and excisional biopsy were performed. The patient finally diagnosed with SLE, and got regular follow-up without complication.

• Advances in nano research
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• v.12 no.2
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• pp.185-195
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• 2022

Deep learning is another field of artificial intelligence (AI) utilized for computer aided diagnosis (CAD) and image processing in scientific research. Considering numerous mechanical repetitive tasks, reading image slices need time and improper with geographical limits, so the counting of image information is hard due to its strong subjectivity that raise the error ratio in misdiagnosis. Regarding the highest mortality rate of Lung cancer, there is a need for biopsy for determining its class for additional treatment. Deep learning has recently given strong tools in diagnose of lung cancer and making therapeutic regimen. However, identifying the pathological lung cancer's class by CT images in beginning phase because of the absence of powerful AI models and public training data set is difficult. Convolutional Neural Network (CNN) was proposed with its essential function in recognizing the pathological CT images. 472 patients subjected to staging FDG-PET/CT were selected in 2 months prior to surgery or biopsy. CNN was developed and showed the accuracy of 87%, 69%, and 69% in training, validation, and test sets, respectively, for T1-T2 and T3-T4 lung cancer classification. Subsequently, CNN (or deep learning) could improve the CT images' data set, indicating that the application of classifiers is adequate to accomplish better exactness in distinguishing pathological CT images that performs better than few deep learning models, such as ResNet-34, Alex Net, and Dense Net with or without Soft max weights.

• International conference on construction engineering and project management
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• 2009.05a
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• pp.732-737
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• 2009

Due to various project delivery methods and the complexity of construction projects in the construction industry, developing the framework of construction management for critical, highly complex projects in the construction industry has become problematic. Currently, a set of construction manuals play a pivotal role in planning and managing construction projects as subcontractors try to complete their scope of work according to the instructions of a general contractor. It is challenging for general contractors to write a construction management procedure manual to cover various types of project delivery methods and construction projects. In construction, the construction procedure manuals describe specific actions to be taken through the project. In reality a few contactors own such manuals and their construction schedules include more construction operation activities. Thus, it is hard to estimate the workload and productivity of construction managers because the manual and the schedule do not present the amount of management efforts required to complete a project. This paper proposes a framework to present construction management tasks according to project delivery methods which can be applied to various construction projects. Actions for management tasks were mapped and were integrated with construction activities throughout the project life cycle. The framework can then be used to give specific instructions to project participants, collect management actions, and replicate management actions throughout the project life cycle. The framework can also be can used to visualize complete construction project to analyze and manage construction management activities in each phase of a project in order to enhance productivity and efficiency. The studies of existing construction manuals were carried out to identify construction managers' responsibilities. An artificial intelligence program, CLIPS (C-Language Integrated Production System) was used to search for appropriate actions for impending tasks from a set of predefined actions to be performed for a given situation. The framework would significantly help construction managers to understand interrelations among management tasks or actions within a project. Furthermore, the framework can be embedded into Building Information Modeling (BIM) or Facility Management Systems (FMS) so that designers and constructors would execute constructability review before construction begins.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.339-343
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• 2015

To rapidly recover ceased functionality of a facility after a catastrophic seismic event, critical decisions on facility repair works are made within a limited period of time. However, prolonged damage assessment of facilities, due to massive damage in the surrounding region and the complicated damage judgment procedures, may impede restoration planning. To assist reliable structural damage estimation without a deep knowledge and rapid interactive analysis among facility damage and restoration operations during the approximate restoration project planning phase, we developed a prototype of distributed facility restoration simulations through the use of high-level architecture (HLA) (IEEE 1516). The simulation prototype, in which three different simulations (including a seismic data retrieval technique, a structural response simulator, and a restoration simulation module) interact with each other, enables immediate damage estimation by promptly detecting earthquake intensity and the restoration operation analysis according to estimated damage. By conducting case simulations and experiments, research outcomes provide key insights into post-disaster restoration planning, including the extent to which facility damage varies according to disaster severity, facility location, and structures. Additional insights arise regarding the extent to which different facility damage patterns impact a project's performance, especially when facility damage is hard to estimate by observation. In particular, an understanding of required type and amount of repair activities (e.g., demolition works, structural reinforcement, frame installation, or finishing works) is expected to support project managers in approximate work scheduling or resource procurement plans.

• The Journal of the Korea Contents Association
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• v.9 no.5
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• pp.61-75
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• 2009

Generally, the development of the database application includes the requirement analysis phase of creating ERD (Entity Relationship Diagram) and process models, coding, and testing. From the above phases, the analysis phase is not most formalized. It is usually hard task because (1) customers don't know the details of the desired system; (2) developers can't with ease understand the business logic of the customers; (3) the outcomes of the analysis, which are ERD and process models, are not easy to understand to the customers. This paper propose that the executional forms, which are better to understand the systems, should be presented to the customers instead of the ERD. These forms should accept the data input so that customers can review the various aspects of the outcome models. The developers should be able to instantly implement the business logic and also should be able to visually demonstrate the logic in order to get the details of it. For this goal, the customer supplied business logic should be able to be implemented by the references between forms, actions, constraints from the perspective of the data flow. The customers try to execute the forms implementing the business logic and review their supplied logic find new necessary business logic of their own. Iterating these processes for the requirement analysis would result in the success of the analysis which is sufficiently detailed without conflicts.