• Journal of KIBIM
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• v.12 no.4
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• pp.61-69
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• 2022

Earthmoving operations account for approximately 25% of construction cost, generally executed prior to the construction of buildings and structures with heavy equipment. For the successful completion of earthwork projects, it is crucial to constantly monitor earthwork equipment (e.g., trucks), estimate productivity, and optimize the construction process and equipment on a construction site. Traditional methods however require time-consuming and painstaking tasks for the manual observations of the ongoing field operations. This study proposed the use of a GPS sensor embedded in a smartphone for the tracking and visualization of equipment locations, which are in turn used for the estimation and simulation of cycle times and production rates of ongoing earthwork. This approach is implemented into a digital platform enabling real-time data collection and simulation, particularly in a 2D (e.g., maps) or 3D (e.g., point clouds) virtual environment where the spatial and temporal flows of trucks are visualized. In the case study, the digital platform is applied for an earthmoving operation at the site development work of commercial factories. The results demonstrate that the production rates of various equipment usage scenarios (e.g., the different numbers of trucks) can be estimated through simulation, and then, the optimal number of tucks for the equipment fleet can be determined, thus supporting the practical potential of real-time sensing and simulation for onsite equipment management.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.7
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• pp.2257-2285
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• 2022

Cyber-physical systems (CPS) have been growing exponentially due to improved cloud-datacenter infrastructure-as-a-service (CDIaaS). Incremental expandability (scalability), Quality of Service (QoS) performance, and reliability are currently the automation focus on healthy Tier 4 CDIaaS. However, stable QoS is yet to be fully addressed in Cyber-physical data centers (CP-DCS). Also, balanced agility and flexibility for the application workloads need urgent attention. There is a need for a resilient and fault-tolerance scheme in terms of CPS routing service including Pod cluster reliability analytics that meets QoS requirements. Motivated by these concerns, our contributions are fourfold. First, a Distributed Non-Recursive Cloud Model (DNRCM) is proposed to support cyber-physical workloads for remote lab activities. Second, an efficient QoS stability model with Routh-Hurwitz criteria is established. Third, an evaluation of the CDIaaS DCN topology is validated for handling large-scale, traffic workloads. Network Function Virtualization (NFV) with Floodlight SDN controllers was adopted for the implementation of DNRCM with embedded rule-base in Open vSwitch engines. Fourth, QoS evaluation is carried out experimentally. Considering the non-recursive queuing delays with SDN isolation (logical), a lower queuing delay (19.65%) is observed. Without logical isolation, the average queuing delay is 80.34%. Without logical resource isolation, the fault tolerance yields 33.55%, while with logical isolation, it yields 66.44%. In terms of throughput, DNRCM, recursive BCube, and DCell offered 38.30%, 36.37%, and 25.53% respectively. Similarly, the DNRCM had an improved incremental scalability profile of 40.00%, while BCube and Recursive DCell had 33.33%, and 26.67% respectively. In terms of service availability, the DNRCM offered 52.10% compared with recursive BCube and DCell which yielded 34.72% and 13.18% respectively. The average delays obtained for DNRCM, recursive BCube, and DCell are 32.81%, 33.44%, and 33.75% respectively. Finally, workload utilization for DNRCM, recursive BCube, and DCell yielded 50.28%, 27.93%, and 21.79% respectively.

• International Journal of Computer Science & Network Security
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• v.23 no.1
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• pp.153-163
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• 2023

This study is situated in the context of intelligent transport systems, where in-vehicle devices assist drivers to avoid accidents and therefore improve road safety. The vehicles present in a given area form an ad' hoc network of vehicles called vehicular ad' hoc network. In this type of network, the nodes are mobile vehicles and the messages exchanged are messages to warn about obstacles that may hinder the correct driving. Node mobilities make it impossible for inter-node communication to be end-to-end. Recognizing this characteristic has led to delay-tolerant vehicular networks. Embedded devices have small buffers (memory) to hold messages that a node needs to transmit when no other node is within its visibility range for transmission. The performance of a vehicular delay-tolerant network is closely tied to the successful management of the nodes' transit buffer. In this paper, we propose a message transit buffer management model for nodes in vehicular delay tolerant networks. This model consists in setting up, on the one hand, a policy of dropping messages from the buffer when the buffer is full and must receive a new message. This drop policy is based on the concept of intermediate node to destination, queues and priority class of service. It is also based on the properties of the message (size, weight, number of hops, number of replications, remaining time-to-live, etc.). On the other hand, the model defines the policy for selecting the message to be transmitted. The proposed model was evaluated with the ONE opportunistic network simulator based on a 4000m x 4000m area of downtown Bouaké in Côte d'Ivoire. The map data were imported using the Open Street Map tool. The results obtained show that our model improves the delivery ratio of security alert messages, reduces their delivery delay and network overload compared to the existing model. This improvement in communication within a network of vehicles can contribute to the improvement of road safety.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.04a
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• pp.346-349
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• 2011

In this paper, we design a perceptually invisible dot pattern layout and its recognition scheme, and we apply the recognition scheme into a smart self learning aid for interactive learning aid. To increase maximum information capacity and also increase robustness to the noises, we design a ECC (error correcting code) based dot pattern with directional vector indicator. To make a smart self-learning aid, we embed the micro dot pattern (20 information bit + 15 ECC bits + 9 layout information bit) using K ink (CMYK) and extract the dot pattern using IR (infrared) LED and IR filter based camera, which is embedded in the smart pen. The reason we use K ink is that K ink is a carbon based ink in nature, and carbon is easily recognized with IR even without light. After acquiring IR camera images for the dot patterns, we perform layout adjustment using the 9 layout information bit, and extract 20 information bits from 35 data bits which is composed of 20 information bits and 15 ECC bits. To embed and extract information bits, we use topology based dot pattern recognition scheme which is robust to geometric distortion which is very usual in camera based recognition scheme. Topology based pattern recognition traces next information bit symbols using topological distance measurement from the pivot information bit. We implemented and experimented with sample patterns, and it shows that we can achieve almost 99% recognition for our embedding patterns.

• Journal of the Korean Geotechnical Society
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• v.22 no.3
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• pp.37-43
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• 2006

An analytical solution method capable of determining the geometric configuration and developed tensile forces of mooring lines associated with fixed plate/pile or drag anchors has been developed. The solution method, satisfying complete equilibrium conditions, is capable of analyzing multi-segmented mooring lines that can consist of either chains, cables, or synthetic wires embedded in layered seafloor soils. The solution method utilizes a systematic iterative search method based on specific boundary conditions. This paper describes the principles associated with the development of the solution for the mooring line analysis. Comparisons of predictions with results from a series of field tests of mooring lines on various types of drag anchors are also described. Comparisons include the tension in anchor, the length of mooring line on the bottom, and the angle of mooring line at the water surface buoy. The results indicate that the analytical solution method is capable of predicting the behavior of mooring lines with high degree of accuracy.

• Journal of the Korean Geotechnical Society
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• v.25 no.9
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• pp.5-17
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• 2009

The microcomputer program PileNSF (Pile Negative Skin Friction) is developed by the authors in a graphical user interface (GUI) environment using $MATLAB^{(R)}$ for predicting the bearing capacity of a pile embedded in a consolidating ground by surcharge loading. The proposed method extends the one-dimensional soil-pile model based on the nonlinear load transfer method in OpenSees to perform an advanced one-dimensional consolidation settlement analysis based on finite strain. The developed program has significant features of incorporating Mikasa's finite strain consolidation theory that accounts for reduction in the thickness of the clay layer as well as the change of the soil-pile interface length during the progress of consolidation. In addition, the consolidating situation of the ground by surcharge filling after the time of pile installation can also be considered in the analysis. The program analysis by the presented method has been verified and validated with several case studies of long-term test on single piles subjected to negative skin friction. Predicted results of negative skin friction (downdrag and dragload) as a result of long from consolidation settlement are shown to be in good agreement with measured and observed case data.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.7 no.4
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• pp.623-630
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• 2017

This paper surveys the state-of-the-art in korean commercial Internet of Things(IoT) services for deriving the core elements of a curriculum for major courses of IoT using RaspberryPi3. First, we survey the state-of-the-art of IoT researches and commercial services in three korean major telecommunication corporations such as Korean Telecommunications (KT), LGU+ Telecommunication (LGT), and SK Telecommunication(SKT). Second, we consider the components and advantages of the RaspberryPi3 which is popular as a representative educational tool. Concludingly, this paper derives the core elements of curriculum for major courses of IoT using RaspberryPi3 from above both processes. The corresponding elements consist of platforms, hardwares, softwares, and big-data network. Based on the important design elements of the IoT curriculum using Raspberry Pie 3, we taught embedded system course to junior students for one semester. It was successfully completed and more than 90% students were satisfied with its contents and amounts.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.7 no.10
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• pp.935-943
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• 2017

CNN (Convolution neural network), which is used for image classification and speech recognition among neural networks learning based on positive data, has been continuously developed to have a high performance structure to date. There are many difficulties to utilize in an embedded system with limited resources. Therefore, we use GPU (General-Purpose Computing on Graphics Processing Units), which is used for general-purpose operation of GPU to solve the problem because we use pre-learned weights but there are still limitations. Since CNN performs simple and iterative operations, the computation speed varies greatly depending on the thread allocation and utilization method in the Single Instruction Multiple Thread (SIMT) based GPGPU. To solve this problem, there is a thread that needs to be relaxed when performing Convolution and Pooling operations with threads. The remaining threads have increased the operation speed by using the method used in the following feature maps and kernel calculations.

• Restorative Dentistry and Endodontics
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• v.47 no.1
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• pp.12.1-12.11
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• 2022

Objectives: This pilot study aimed to establish the interrelationship between collagen and mast cells in periapical granulomas and periapical cysts. Materials and Methods: An observational cross-sectional study was conducted on the paraffin-embedded tissue sections of 68 specimens (34 periapical granulomas and 34 periapical cysts). The specimens were stained with picrosirius to observe collagen fiber birefringence and anti-tryptase antibody to evaluate the mast cell count immunohistochemically. The mean number and birefringence of collagen fibers, as well as the mean number of mast cells (total, granulated, and degranulated), and the mean inflammatory cell density were calculated. The data obtained were analyzed using the Kruskal Wallis test, Mann Whitney U test, and Spearman correlation test (p < 0.05). Results: The mean number of thick collagen fibers was higher in periapical cysts, while that of thin fibers was higher in granulomas (p = 0.00). Cysts emitted orange-yellow to red birefringence, whereas periapical granulomas had predominantly green fibers (p = 0.00). The mean inflammatory cell density was comparable in all groups (p = 0.129). The number of total, degranulated, and granulated mast cells exhibited significant results (p = 0.00) in both groups. Thick cyst fibers showed significant inverse correlations with inflammation and degranulated mast cells (p = 0.041, 0.04 respectively). Conclusions: Mast cells and inflammatory cells influenced the nature of collagen fiber formation and its birefringence. This finding may assist in the prediction of the nature, pathogenesis, and biological behavior of periapical lesions.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.5
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• pp.237-242
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• 2023

Research on high-performance walking robots is being actively conducted, and quadruped walking robots are receiving a lot of attention due to their excellent mobility and adaptability on uneven terrain, but they are difficult to introduce and utilize due to high cost. In this paper, to increase utilization by applying intelligent functions to a low-cost quadruped robot, we present a method of improving uneven terrain overcoming ability by mounting IMU and reinforcement learning on embedded board and automatically detecting objects using camera and deep learning. The robot consists of the legs of a quadruped mammal, and each leg has three degrees of freedom. We train complex terrain in simulation environments with designed 3D model and apply it to real robot. Through the application of this research method, it was confirmed that there was no significant difference in walking ability between flat and non-flat terrain, and the behavior of performing person detection in real time under limited experimental conditions was confirmed.