• International Journal of Computer Science & Network Security
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• v.21 no.11
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• pp.345-353
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• 2021

Cloud computing represent a new era of computing that's forms through the combination of service-oriented architecture (SOA), Internet and grid computing with virtualization technology. Virtualization is a concept through which every cloud is enable to provide on-demand services to the users. Most IT service provider adopt cloud based services for their users to meet the high demand of computation, as it is most flexible, reliable and scalable technology. Energy based performance tradeoff become the main challenge in cloud computing, as its acceptance and popularity increases day by day. Cloud data centers required a huge amount of power supply to the virtualization of servers for maintain on- demand high computing. High power demand increase the energy cost of service providers as well as it also harm the environment through the emission of CO₂. An optimization of cloud computing based on energy-performance tradeoff is required to obtain the balance between energy saving and QoS (quality of services) policies of cloud. A study about power usage of resources in cloud data centers based on workload assign to them, says that an idle server consume near about 50% of its peak utilization power [1]. Therefore, more number of underutilized servers in any cloud data center is responsible to reduce the energy performance tradeoff. To handle this issue, a lots of research proposed as energy efficient algorithms for minimize the consumption of energy and also maintain the SLA (service level agreement) at a satisfactory level. VM (virtual machine) consolidation is one such technique that ensured about the balance of energy based SLA. In the scope of this paper, we explore reinforcement with fuzzy logic (RFL) for VM consolidation to achieve energy based SLA. In this proposed RFL based active VM consolidation, the primary objective is to manage physical server (PS) nodes in order to avoid over-utilized and under-utilized, and to optimize the placement of VMs. A dynamic threshold (based on RFL) is proposed for over-utilized PS detection. For over-utilized PS, a VM selection policy based on fuzzy logic is proposed, which selects VM for migration to maintain the balance of SLA. Additionally, it incorporate VM placement policy through categorization of non-overutilized servers as- balanced, under-utilized and critical. CloudSim toolkit is used to simulate the proposed work on real-world work load traces of CoMon Project define by PlanetLab. Simulation results shows that the proposed policies is most energy efficient compared to others in terms of reduction in both electricity usage and SLA violation.

• Korean Journal of Clinical Laboratory Science
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• v.55 no.1
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• pp.16-28
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• 2023

Lung adenocarcinoma accounts for about 40% of all lung cancers. With the recent development of gene profiling technology, studies on mutations in oncogenes and tumor suppressor genes, which are important for the development and growth of tumors, have been actively conducted. Companion diagnosis using next-generation sequencing helps improve survival with targeted therapy. In this study, formalin-fixed paraffin-embedded tissues of non-small cell lung cancer patients were subjected to hematoxylin and eosin staining for detecting genetic mutations that induce lung adenocarcinoma in Koreans. Immunohistochemical staining was also performed to accurately classify lung adenocarcinoma tissues. Based on the results, next-generation sequencing was applied to analyze the types and patterns of genetic mutations, and the association with smoking was established as the most representative cause of lung cancer. Results of next-generation sequencing analysis confirmed the single nucleotide variations, copy number variations, and gene rearrangements. In order to validate the reliability of next-generation sequencing, we additionally performed the existing genetic testing methods (polymerase chain reaction-epidermal growth factor receptor, immunohistochemistry-anaplastic lymphoma kinase (D5F3), and fluorescence in situ hybridiation-receptor tyrosine kinase 1 tests) to confirm the concordance rates with the next-generation sequencing test results. This study demonstrates that next-generation sequencing of lung adenocarcinoma patients simultaneously identifies mutation.

• International conference on construction engineering and project management
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• 2009.05a
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• pp.30-31
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• 2009

Early detection of schedule delay in field construction activities is vital to project management. It provides the opportunity to initiate remedial actions and increases the chance of controlling such overruns or minimizing their impacts. This entails project managers to design, implement, and maintain a systematic approach for progress monitoring to promptly identify, process and communicate discrepancies between actual and as-planned performances as early as possible. Despite importance, systematic implementation of progress monitoring is challenging: (1) Current progress monitoring is time-consuming as it needs extensive as-planned and as-built data collection; (2) The excessive amount of work required to be performed may cause human-errors and reduce the quality of manually collected data and since only an approximate visual inspection is usually performed, makes the collected data subjective; (3) Existing methods of progress monitoring are also non-systematic and may also create a time-lag between the time progress is reported and the time progress is actually accomplished; (4) Progress reports are visually complex, and do not reflect spatial aspects of construction; and (5) Current reporting methods increase the time required to describe and explain progress in coordination meetings and in turn could delay the decision making process. In summary, with current methods, it may be not be easy to understand the progress situation clearly and quickly. To overcome such inefficiencies, this research focuses on exploring application of unsorted daily progress photograph logs - available on any construction site - as well as IFC-based 4D models for progress monitoring. Our approach is based on computing, from the images themselves, the photographer's locations and orientations, along with a sparse 3D geometric representation of the as-built scene using daily progress photographs and superimposition of the reconstructed scene over the as-planned 4D model. Within such an environment, progress photographs are registered in the virtual as-planned environment, allowing a large unstructured collection of daily construction images to be interactively explored. In addition, sparse reconstructed scenes superimposed over 4D models allow site images to be geo-registered with the as-planned components and consequently, a location-based image processing technique to be implemented and progress data to be extracted automatically. The result of progress comparison study between as-planned and as-built performances can subsequently be visualized in the D4AR - 4D Augmented Reality - environment using a traffic light metaphor. In such an environment, project participants would be able to: 1) use the 4D as-planned model as a baseline for progress monitoring, compare it to daily construction photographs and study workspace logistics; 2) interactively and remotely explore registered construction photographs in a 3D environment; 3) analyze registered images and quantify as-built progress; 4) measure discrepancies between as-planned and as-built performances; and 5) visually represent progress discrepancies through superimposition of 4D as-planned models over progress photographs, make control decisions and effectively communicate those with project participants. We present our preliminary results on two ongoing construction projects and discuss implementation, perceived benefits and future potential enhancement of this new technology in construction, in all fronts of automatic data collection, processing and communication.

• Journal of Korean Society of Disaster and Security
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• v.15 no.4
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• pp.11-20
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• 2022

The general non-destructive inspection method for anchor bolts in Korea applies visual inspection and hammering inspection, but it is difficult to check corrosion or fatigue cracks of anchor bolts in the part included in the foundation or in the part where the nut and base plate are installed. In reality, objective investigation is difficult because inspection is affected by the surrounding environment and individual differences, so it is necessary to develop non-destructive inspection technology that can quantitatively estimate these defects. Inspection of the anchor bolts of domestic road facilities is carried out by visual inspection, and since the importance of anchor bolts such as bridge bearings and fall prevention facilities is high, the life span of bridges is extended through preventive maintenance by developing non-destructive testing technology along with existing inspection methods. Through the development of this technology, non-destructive testing of anchor bolts is performed and as a technology capable of preemptive/active maintenance of anchor bolts for road facilities, practical use is urgently needed. In this paper, the possibility of detecting defects in anchor bolts such as corrosion and cracks and reliability were experimentally verified by applying the ultrasonic test among non-destructive inspection techniques. When the technology development is completed, it is expected that it will be possible to realize preemptive/active maintenance of anchor bolts by securing source technology for improving inspection reliability.

• Journal of the Korean Orthopaedic Association
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• v.54 no.6
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• pp.509-518
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• 2019

Purpose: Lymph node metastasis is a very important prognostic factor for all skin cancers and some sarcomas. A sentinel lymph node (SLN) biopsy is the most useful technique for identifying SLNs. Recently, a new generation of diagnostic tools, such as single photon emission computed tomography/computed tomography (SPECT/CT) and positron emission tomography/CT (PET/CT) enabled the detection of SLNs. This study compared the efficacy of PET/CT for detecting lymph node metastases with a SLN biopsy in a single medical center. Materials and Methods: From 2008 to 2018, 72 skin cancers of sarcoma patients diagnosed with some lymph node involvement in a whole body PET/CT reading were assessed. Patients suspected of lymph node metastasis were sent to biopsy and those suspected to be reactive lesions were observed. The analysis was performed retrospectively using the medical records, clinical information, PET/CT readings, and pathology results. Results: The age of patients ranged from 14 to 88 years and the mean follow-up period was 2.4 years. Twenty-two patients were suspected of a lymph node metastasis and confirmed. The sensitivity, specificity, positive predictive value and negative predictive value of PET/CT images in sarcoma and non-sarcoma tumors were increased significantly when the expert's findings were considered together. Conclusion: PET/CT is effective in detecting lymph node metastases.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.7
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• pp.1951-1975
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• 2023

Recent advances in Cognitive Radio Networks (CRN) have elevated them to the status of a critical instrument for overcoming spectrum limits and achieving severe future wireless communication requirements. Collaborative spectrum sensing is presented for efficient channel selection because spectrum sensing is an essential part of CRNs. This study presents an innovative cooperative spectrum sensing (CSS) model that is built on the Firefly Algorithm (FA), as well as machine learning artificial neural networks (ANN). This system makes use of user grouping strategies to improve detection performance dramatically while lowering collaboration costs. Cooperative sensing wasn't used until after cognitive radio users had been correctly identified using energy data samples and an ANN model. Cooperative sensing strategies produce a user base that is either secure, requires less effort, or is faultless. The suggested method's purpose is to choose the best transmission channel. Clustering is utilized by the suggested ANN-FA model to reduce spectrum sensing inaccuracy. The transmission channel that has the highest weight is chosen by employing the method that has been provided for computing channel weight. The proposed ANN-FA model computes channel weight based on three sets of input parameters: PU utilization, CR count, and channel capacity. Using an improved evolutionary algorithm, the key principles of the ANN-FA scheme are optimized to boost the overall efficiency of the CRN channel selection technique. This study proposes the Artificial Neural Network with Firefly Algorithm (ANN-FA) for cognitive radio networks to overcome the obstacles. This proposed work focuses primarily on sensing the optimal secondary user channel and reducing the spectrum handoff delay in wireless networks. Several benchmark functions are utilized We analyze the efficacy of this innovative strategy by evaluating its performance. The performance of ANN-FA is 22.72 percent more robust and effective than that of the other metaheuristic algorithm, according to experimental findings. The proposed ANN-FA model is simulated using the NS2 simulator, The results are evaluated in terms of average interference ratio, spectrum opportunity utilization, three metrics are measured: packet delivery ratio (PDR), end-to-end delay, and end-to-average throughput for a variety of different CRs found in the network.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.1A
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• pp.61-73
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• 2009

In this paper, hybrid health monitoring techniques using acceleration-impedance features are newly proposed to detect two damage-type in steel plate-girder bridges, which are girder's stiffness-loss and support perturbation. The hybrid techniques mainly consists of three sequential phases: 1) to alarm the occurrence of damage in global manner, 2) to classify the alarmed damage into subsystems of the structure, and 3) to estimate the classified damage in detail using methods suitable for the subsystems. In the first phase, the global occurrence of damage is alarmed by monitoring changes in acceleration features. In the second phase, the alarmed damage is classified into subsystems by recognizing patterns of impedance features. In the final phase, the location and the extent of damage are estimated by using modal strain energy-based damage index method and root mean square deviation (RMSD) method. The feasibility of the proposed hybrid technique is evaluated on a laboratory-scaled steel plate-girder bridge model for which hybrid acceleration-impedance signatures were measured for several damage scenarios. Also, the effect of temperature on the accuracy of the impedance-based damage monitoring results are experimentally examined from combined scenarios of support damage cases and temperature changes.

• Journal of the Korea institute for structural maintenance and inspection
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• v.27 no.6
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• pp.30-38
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• 2023

As the number of old buildings subject to safety inspection increases, the burden on designated institutions and management entities that are responsible for safety management is increasing. Accordingly, when selecting buildings subject to safety inspection, appropriate safety inspection standards and appropriate technology are essential. The current safety inspection standards for old buildings give low scores when it is difficult to confirm damage such as cracks in structural members due to finishing materials. This causes the evaluation results to be underestimated regardless of the actual safety status of the structure, resulting in an increase in the number of aging buildings subject to safety inspection. Accordingly, this study proposed a thermal imaging technique, a non-destructive and non-contact inspection, to detect cracks inside finishing materials. A concrete specimen was produced to observe cracks inside the finishing material using a thermal imaging camera, and thermal image data was measured by exciting a heat source on the concrete surface and cracked area. As a result of the measurement, it was confirmed that it was possible to observe cracks inside the finishing material with a width of 0.3mm, 0.5mm, and 0.7mm, but it was difficult to determine the cracks due to uneven temperature distribution due to surface peeling and peeling of the wallpaper. Accordingly, as a result of performing data analysis by deriving the amplitude and phase difference of the thermal image data, clear crack measurement was possible for 0.5mm and 0.7mm cracks. Based on this study, we hope to increase the efficiency of field application and analysis through the development of technology using big data-based deep learning in the diagnosis of internal crack damage in finishing materials.

• Journal of Sensor Science and Technology
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• v.33 no.1
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• pp.40-47
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• 2024

The creation of vertically aligned one-dimensional (1D) nanostructures through the decoration of n-type tin oxide (SnO₂) on p-type chromium oxide (Cr₂O₃) constitutes an effective strategy for enhancing gas sensing performance. These heterostructures are deposited in multiple stages using a glancing angle deposition technique with an electron beam evaporator, resulting in a reduction in the surface porosity of the nanorods as SnO₂ is incorporated. In comparison to Cr₂O₃ films, the bare Cr₂O₃ nanorods exhibits a response 3.3 times greater to 50 ppm H₂S at 300℃, while the SnO₂-decorated Cr₂O₃ nanorods demonstrate an eleven-fold increase in response. Furthermore, when subjected to various gases (CH₄, H₂S, CO₂, H₂), a notable selectivity toward H₂S is observed. This study paves the way for the development of p-type semiconductor sensors with heightened selectivity and sensitivity towards H₂S, thus advancing the prospects of gas sensor technology.

• Tunnel and Underground Space
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• v.34 no.3
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• pp.218-230
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• 2024

Recently, many studies have been conducted for safety management in construction sites by incorporating computer vision. Anchor box parameters are used in state-of-the-art deep learning-based object detection and segmentation, and the optimized parameters are critical in the training process to ensure consistent accuracy. Those parameters are generally tuned by fixing the shape and size by the user's heuristic method, and a single parameter controls the training rate in the model. However, the anchor box parameters are sensitive depending on the type of object and the size of the object, and as the number of training data increases. There is a limit to reflecting all the characteristics of the training data with a single parameter. Therefore, this paper suggests a method of applying multiple parameters optimized through data split to solve the above-mentioned problem. Criteria for efficiently segmenting integrated training data according to object size, number of objects, and shape of objects were established, and the effectiveness of the proposed data split method was verified through a comparative study of conventional scheme and proposed methods.