• Proceedings of the Korean Institute of Building Construction Conference
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• 2019.11a
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• pp.155-156
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• 2019

Recently, the use of selective crushed aggregates is increasing due to the supply and demand shortage of aggregates. In the case of selective crushed aggregates, aggregates are produced using soil, rocks, etc., mainly generated at construction sites as raw materials. As a result, the quality of the raw material may not be uniform and may contain a large amount of soil. In the case of using such a bad aggregate shortens the life of the structure, there is a fear that adversely affect the overall performance, such as the strength and durability of the concrete. Therefore, this study analyzes the effect of aggregate soil on mortar in the low-strength mortar and ultimately proposes the regulation value of clay content in the soil content of crushed aggregates such as crushed aggregates.

• Korean Journal of Remote Sensing
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• v.37 no.1
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• pp.111-122
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• 2021

Building footprint extraction is an active topic in the domain of remote sensing, since buildings are a fundamental unit of urban areas. Deep convolutional neural networks successfully perform footprint extraction from optical satellite images. However, semantic segmentation produces coarse results in the output, such as blurred and rounded boundaries, which are caused by the use of convolutional layers with large receptive fields and pooling layers. The objective of this study is to generate visually enhanced building objects by directly extracting the vertices of individual buildings by combining instance segmentation and keypoint detection. The target keypoints in building extraction are defined as points of interest based on the local image gradient direction, that is, the vertices of a building polygon. The proposed framework follows a two-stage, top-down approach that is divided into object detection and keypoint estimation. Keypoints between instances are distinguished by merging the rough segmentation masks and the local features of regions of interest. A building polygon is created by grouping the predicted keypoints through a simple geometric method. Our model achieved an F1-score of 0.650 with an mIoU of 62.6 for building footprint extraction using the OpenCitesAI dataset. The results demonstrated that the proposed framework using keypoint estimation exhibited better segmentation performance when compared with Mask R-CNN in terms of both qualitative and quantitative results.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.7
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• pp.375-381
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• 2021

Underpricing signifies that IPO share prices do not reflect the fundamental value of the listed company. Corporate governance plays an essential role in IPOs where the board of directors, the independent board of directors, and the board of supervisors are significant elements of accurate share pricing. The study investigates the underpricing phenomena and short-term performance of the IPO companies during the listing process in the Ho Chi Minh Stock Exchange (HOSE). The work outcomes illustrate the role of the corporate organizational structure in the period of the IPO process that may attract potential investors. The hypothesis testing is conducted with a multiple regression model including 100 observations from enterprises doing IPO listed on HOSE. The study results generate signals for the investors and regulators that the board of directors holds a strong negative influence on the underpricing process. Secondly, the level of the independent board of directors and stock exchange in itself has no significant impact on the underpricing process. Underpricing is one of the many anomalies of the stock exchanges that provide wrong signals for the market participants. Identifying stock prices that reflect their intrinsic value is an ongoing debate among scholars, investors, and other market participants.

• Corrosion Science and Technology
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• v.21 no.1
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• pp.1-8
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• 2022

The use of cathodic protection for metals can be achieved by sacrificial anode CP or impressed current CP, or a combination of both. Cathodic protection is a highly effective anti-corrosion technique for submerged metals or metals in soil. But because the non-immersion atmospheric automobile environment is a high resistance environment, it is limited by fundamental cathodic protection. However, the application of cathodic protection to automobiles is attractive because of the possibility of maintaining corrosion resistance while using lower-cost materials. A commercially available product for automobiles that uses both sacrificial anode CP and impressed current CP was tested in a periodic salt spray environment to investigate the performance of the devices. Experimental results show that the metal to be protected has different anti-corrosion effects depending on the distance from the anode of the device, but it is effective for the entire 120 cm long specimen exposed with one anode. The cathodic protection is effective because the conductive tape attached to the anode of the structure to be protected acts as a constant electrolyte in wet and dry conditions. The results show that the entire standard passenger car can be protected by cathodic protection with 4 anodes.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.10
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• pp.3373-3389
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• 2022

Exploration of mobile robot without prior data about environments is a fundamental problem during the SLAM processes. In this work, we propose improved version of previous Rmap algorithm by modifying its Exploration submodule. Despite the previous Rmap's performance which significantly reduces the overhead of the grid map, its exploration module costs a lot because of its rectangle following algorithm. To prevent that, we propose a new Rmap+ algorithm for autonomous path planning of mobile robot to explore an unknown environment. The algorithm bases on paired frontiers. To navigate and extend an exploration area of mobile robot, the Rmap+ utilizes the inner and outer frontiers. In each exploration round, the mobile robot using the sensor range determines the frontiers. Then robot periodically changes the range of sensor and generates inner pairs of frontiers. After calculating the length of each frontiers' and its corresponding pairs, the Rmap+ selects the goal point to navigate the robot. The experimental results represent efficiency and applicability on exploration time and distance, i.e., to complete the whole exploration, the path distance decreased from 15% to 69%, as well as the robot decreased the time consumption from 12% to 86% than previous algorithms.

• Journal of the Microelectronics and Packaging Society
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• v.29 no.3
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• pp.1-12
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• 2022

With the rapid development of artificial intelligence technology that gives existing sensors functions similar to human intelligence is drawing attention. Previously, researches were mainly focused on an improvement of fundamental performance indicators as sensors. However, recently, attempts to combine artificial intelligence such as classification and prediction with sensors have been explored. Based on this, intelligent sensor research has been actively reported in almost all kinds of sensing fields such as disease detection, motion detection, and gas sensor. In this paper, we introduce the basic concepts, types, and driving mechanisms of artificial intelligence and review some examples of its use.

• Journal of Multimedia Information System
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• v.8 no.4
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• pp.233-242
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• 2021

Various anatomical MRI imaging biomarkers for Alzheimer's Disease (AD) identification have been recognized so far. Cortical and subcortical volume, hippocampal, amygdala volume, and genetics patterns have been utilized successfully to diagnose AD patients from healthy. These fundamental sMRI bio-measures have been utilized frequently and independently. The entire possibility of anatomical MRI imaging measures for AD diagnosis might thus still to analyze fully. Thus, in this paper, we merge different structural MRI imaging biomarkers to intensify diagnostic classification and analysis of Alzheimer's. For 54 clinically pronounce Alzheimer's patients, 58 cognitively healthy controls, and 99 Mild Cognitive Impairment (MCI); we calculated 1. Cortical and subcortical features, 2. The hippocampal subfield, amygdala nuclei volume using Freesurfer (6.0.0) and 3. Genetics (APoE ε4) biomarkers were obtained from the ADNI database. These three measures were first applied separately and then combined to predict the AD. After feature combination, we utilize the sequential feature selection [SFS (wrapper)] method to select the top-ranked features vectors and feed them into the Multi-Kernel SVM for classification. This diagnostic classification algorithm yields 94.33% of accuracy, 95.40% of sensitivity, 96.50% of specificity with 94.30% of AUC for AD/HC; for AD/MCI propose method obtained 85.58% of accuracy, 95.73% of sensitivity, and 87.30% of specificity along with 91.48% of AUC. Similarly, for HC/MCI, we obtained 89.77% of accuracy, 96.15% of sensitivity, and 87.35% of specificity with 92.55% of AUC. We also presented the performance comparison of the proposed method with KNN classifiers.

• Journal of Auto-vehicle Safety Association
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• v.14 no.4
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• pp.113-119
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• 2022

The structural safety of hydrogen buses is being evaluated for the successful introduction of hydrogen buses. The crash test methodology, for example, side impact test procedure is being discussed for hydrogen bus structure safety with a compressed hydrogen storage system located under the bus floor. Thus this study describes a new experiment method for side impact test with compressed hydrogen storage system independently based on finite element analysis instead of side impact test using full hydrogen bus. A side crash procedure of conceptual compressed hydrogen storage structure was investigated and impact simulations were performed. The finite element models of hydrogen bus, simplified structures, fuel tank system and side impact moving barrier were set up and simulation results reported model performance and result comparison of three different simplified models. Computational results and research discussion proposed the fundamental test framework for safety assessment of the compressed hydrogen storage system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.11
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• pp.3780-3797
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• 2022

The estimation of radio map (RM) is a fundamental and critical task for the network planning and optimization performance of mobile communication. In this paper, a RM estimation method is proposed based on a deep dual learning structure. This method can simultaneously and accurately reconstruct the urban building map (UBM) and estimate the RM of the whole cell by only part of the measured reference signal receiving power (RSRP). Our proposed method implements UBM reconstruction task and RM estimation task by constructing a dual U-Net-based structure, which is named RadioCycle. RadioCycle jointly trains two symmetric generators of the dual structure. Further, to solve the problem of interference negative transfer in generators trained jointly for two different tasks, RadioCycle introduces a dynamic weighted averaging method to dynamically balance the learning rate of these two generators in the joint training. Eventually, the experiments demonstrate that on the UBM reconstruction task, RadioCycle achieves an F1 score of 0.950, and on the RM estimation task, RadioCycle achieves a root mean square error of 0.069. Therefore, RadioCycle can estimate both the RM and the UBM in a cell with measured RSRP for only 20% of the whole cell.

• Journal of IKEEE
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• v.27 no.3
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• pp.220-224
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• 2023

Row hammering is a phenomenon in which bit flips occur in adjacent rows when accessing a particular row continuously, causing data damage, security problems, and poor computing performance. This paper analyzes the cause and response method of row hammering through TCAD simulation in 2ynm DRAM. In the experiment, the row hammering is reproduced while changing the parameters of the trap and the device structure, and the trap density, temperature. It analyzes the relationship with Active Wisdom, etc. As a result, it was confirmed that changes in trap parameters and device structures directly affect ΔV_cap/pulse. This enables a fundamental understanding of low hammering and finding countermeasures, and can contribute to improving the stability and security of DRAM.