• Environmental Engineering Research
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• v.24 no.2
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• pp.331-338
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• 2019

Particulate matter (PM) was collected using micro-orifice uniform deposit impactors from a residential (RES) site and an industrial (IND) site in Ulsan, South Korea, in September-October 2014. The PM samples were measured based on their size distributions (11 stages), ranging from $0.06{\mu}m$ to over $18.0{\mu}m$. Nine trace metals (As, Se, Cr, V, Cd, Pb, Ba, Sb, and Zn) associated with PM were analyzed. The PM samples exhibited weak bimodal distributions irrespective of sampling sites and events, and the mean concentrations of total PM (TPM) measured at the IND site ($56.7{\mu}g/m^3$) was higher than that measured at the RES site ($38.2{\mu}g/m^3$). The IND site also showed higher levels of nine trace metals, reflecting the influence of industrial activities and traffic emissions. At both sites, four trace metals (Ba, Zn, V, and Cr) contributed to over 80% of the total concentrations in TPM. The modality of individual trace metals was not strong except for Zn; however, the nine trace metals in $PM_{2.5}$ and $PM_{10}$ accounted for approximately 50% and 90% of the total concentrations in TPM, respectively. This result indicates that the size distributions of PM and trace metals are important to understand how respirable PM affects public health.

• Journal of The Korean Society For Urban Railway
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• v.6 no.4
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• pp.279-286
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• 2018

Recently, a safety control platform to monitor the safety of train operation in real time and prevent accidents and risks through control is under study. In the initial design, DDS communication method supporting distributed network is adopted for real-time processing of large amount of data according to the integration of existing distributed safety data. However, communication between server and console inside the safety control platform is applied to existing TCP socket communication. In the case of TCP socket communication, it is possible to process data for a small system of a safety control test bed by one-to-one communication. However, if the data is expanded all over the country in the future, it becomes difficult to cope with a case where communication traffic occurs due to vast amount of data. In this paper, we propose DDS communication method to support distributed network between server and console of security control platform, and demonstrate TCP socket and DDS method, and compare throughput and speed. As a result, we have found that the scalability and flexibility are improved in case of applying DDS communication to future systems.

• The Journal of the Convergence on Culture Technology
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• v.5 no.3
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• pp.55-60
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• 2019

It is true that Blockchain has been known as a core technology for cryptocurrency like bitcoin (BTC). It is caused by its rapid value rises. Now, one BTC is trading around 10,000 US dollars while it bought just less than one dollar at its first trading in May, 2010. Blockchain makes on-line transactions possible by the safe cryptocurrency swiftly based on P2P network and distributed public ledger while its on-line traffic is rapidly increasing. However, this technology has bigger potential in the fourth industrial revolution era and its application areas will be varied. The evolving intelligent information society needs to make new added value through utilizing, sharing and processing of useful digital information. Obstacles such as hacking and fraud often exist when transactions of digital properties, right transfers, etc. are done through digital network specialized with anonymity. It is expected that blockchain will be a definite solution in this regard. This paper addresses useful development directions and countermeasures for blokchain in the digital economy by analysis of its current status and issues.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.12
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• pp.5744-5764
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• 2018

In Wireless Sensor Networks (WSNs) for Internet of Things (IoT) environment, fault tolerance is a most fundamental issue due to strict energy constraint of sensor node. In this paper, a robust energy saving data dissemination protocol for IoT-WSNs is proposed. Minimized energy consumption and dissemination delay time based on signal strength play an important role in our scheme. The representative dissemination protocol SPIN (Sensor Protocols for Information via Negotiation) overcomes overlapped data problem of the classical Flooding scheme. However, SPIN never considers distance between nodes, thus the issue of dissemination energy consumption is becoming more important problem. In order to minimize the energy consumption, the shortest path between sensors should be considered to disseminate the data through the entire IoT-WSNs. SPMS (Shortest Path Mined SPIN) scheme creates routing tables using Bellman Ford method and forwards data through a multi-hop manner to optimize power consumption and delay time. Due to these properties, it is very hard to avoid heavy traffic when routing information is updated. Additionally, a node failure of SPMS would be caused by frequently using some sensors on the shortest path, thus network lifetime might be shortened quickly. In contrast, our scheme is resilient to these failures because it employs energy aware concept. The dissemination delay time of the proposed protocol without a routing table is similar to that of shortest path-based SPMS. In addition, our protocol does not require routing table, which needs a lot of control packets, thus it prevents excessive control message generation. Finally, the proposed scheme outperforms previous schemes in terms of data transmission success ratio, therefore our protocol could be appropriate for IoT-WSNs environment.

• International Journal of Internet, Broadcasting and Communication
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• v.13 no.4
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• pp.112-120
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• 2021

Recently, the number of consumers using digital online distribution platforms is increasing. This caused the rapid growth of the e-commerce market and increased delivery volume in urban areas. The logistics system, designed ar006Fund the city center to handle the delivery volume, operates a delivery system from the outskirts of the city to the urban area using cargo trucks. This maintains an ecosystem of high-cost and inefficient structures that increase social costs such as road traffic congestion and environmental problems. To solve this problem, research is being conducted worldwide to establish a high-efficiency urban joint logistics system using urban railway facilities and underground space infrastructure existing in existing cities. The joint logistics system begins with linking unmanned delivery automation services that link terminal delivery such as cargo classification and stacking, infrastructure construction that performs cargo transfer function by separating from passengers such as using cargo platform. To this end, it is necessary to apply the device to the vertical and horizontal transportation machine supporting the vertical transfer in the flat space of the joint logistics terminal, which is the base technology for transporting cargo using the transfer robot to the destination designated as a freight-only urban railway vehicle. Therefore, this paper aims to derive holistic viewpoints needs for design requirements for vertical and vertical transportation machines and freight transportation standard containers, which are underground railway logistics transport devices to be constructed by urban logistics ecosystem changes.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.15 no.5
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• pp.3-11
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• 2005

This paper presents a performance analysis model based on an M/M/1 queue and Poisson distribution of input data traffic. The simulation on a pipelined AES system with processing rate of 10 rounds per clock shows $4.0\%$ higher performance than a non-pipelined version consuming 10 clocks per transaction. Physical implementation of pipelined AES with FPGA takes 3.5 times bigger gate counts than the non-pipelined version whereas the pipelined version yields only $3.5\%$ performance enhancement. The proposed analysis model can be used to optimize cost-performance of AES hardware designs.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.40 no.6
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• pp.563-569
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• 2020

Presenting the impact of meteorological disasters departs from the traditional weather forecasting approach for meteorological phenomena. It is important to provide impact forecasts so that precautions against disruption and damage can be taken. Countries such as the United States, the U.K., and France already conduct impact forecasting for heavy rain, heavy snow, and cold weather. This study improves and applies forecasts of the impact of heavy rain among various weather phenomena in accordance with domestic conditions. A total of 33 impact factors for heavy rain were constructed per 1 km grids, and four impact levels (minimal, minor, significant, and severe) were calculated using standard normal distribution. Estimated criteria were used as indicators to estimate heavy rain risk impacts for 6 categories (residential, commercial, utility, community, agriculture, and transport) centered on people, facilities, and traffic.

• Journal of Environmental Science International
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• v.31 no.4
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• pp.329-339
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• 2022

Better understanding the mechanism of black ice occurrence on the road in winter is necessary to reduce the socio-economic damage it causes. In this study, intensive observations of road weather elements and surface information under the influence of synoptic high-pressure patterns (22^nd December, 2020 and 29^th January, and 25^th February, 2021) were carried out using a mobile observation vehicle. We found that temperature and road surface temperature change is significantly influenced by observation time, altitude and structure of the road, surrounding terrain, and traffic volume, especially in tunnels and bridges. In addition, even if the spatial distribution of temperature and road surface temperature for the entire observation route is similar, there is a difference between air and road surface temperatures due to the influence of current weather conditions. The observed road temperature, air temperature and air pressure in Nongong Bridge were significantly different to other fixed road weather observation points.

• Steel and Composite Structures
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• v.41 no.6
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• pp.831-850
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• 2021

Surface subsidence caused by mining subsidence has an impact on neighboring structures and utilities. In other words, subsurface voids created by mining or tunneling activities induce soil movement, exposing buildings to physical and/or functional destruction. Soil-structure is evaluated employing probability distribution laws to account for their uncertainty and complexity to estimate structural vulnerability. In this study, to investigate the displacement field and surface settlement profile caused by mining subsidence, on the basis of a Winklersoil model, analytical equations for the moment-rotation response ofsoil during mining induced ground movements are developed. To define the full static moment-rotation response, an equation for the uplift-yield state is constructed and integrated with equations for the uplift- and yield-only conditions. The constructed model's findings reveal that the inverse of the factor of safety (x) has a considerable influence on the moment-rotation curve. The maximal moment-rotation response of the footing is defined by X = 0:6. Despite the use of Winkler model, the computed moment-rotation response results derived from the literature were analyzed through the ELM-SVM hybrid of Extreme Learning Machine (ELM) and Support Vector Machine (SVM). Also, Monte Carlo simulations are used to apply continuous random parameters to assess the transmission of ground motions to structures. Following the findings of RMSE and R2, the results show that the choice of probabilistic laws of input parameters has a substantial impact on the outcome of analysis performed.

• Journal of Cadastre & Land InformatiX
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• v.51 no.2
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• pp.141-150
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• 2021

Fine dust is a substance that greatly affects human health, and various studies have been conducted in this regard. Due to the human influence of particulate matter, various studies are being conducted to predict particulate matter grade using past data measured in the monitoring network of Seoul city. In this paper, predictive model have focused on particulate matter concentration in May, 2019, Seoul. The air pollutant variables were used to training such as SO₂, CO, NO₂, O₃. The predictive model based on Adaboost, and training model was dividing PM₁₀ and PM_2.5. As a result of the prediction performance comparison through confusion matrix, the Adaboost model was more conformable for predicting the particulate matter concentration grade. Although air pollutant variables have a higher correlation with PM2.5, training model need to train a lot of data and to use additional variables such as traffic volume to predict more effective PM₁₀ and PM_2.5 distribution grade.