• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.6
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• pp.2168-2187
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• 2021

The smart grid replaces the traditional power structure with information inventiveness that contributes to a new physical structure. In such a field, malicious information injection can potentially lead to extreme results. Incorrect, FDI attacks will never be identified by typical residual techniques for false data identification. Most of the work on the detection of FDI attacks is based on the linearized power system model DC and does not detect attacks from the AC model. Also, the overwhelming majority of current FDIA recognition approaches focus on FDIA, whilst significant injection location data cannot be achieved. Building on the continuous developments in deep learning, we propose a Deep Learning based Locational Detection technique to continuously recognize the specific areas of FDIA. In the development area solver gap happiness is a False Data Detector (FDD) that incorporates a Convolutional Neural Network (CNN). The FDD is established enough to catch the fake information. As a multi-label classifier, the following CNN is utilized to evaluate the irregularity and cooccurrence dependency of power flow calculations due to the possible attacks. There are no earlier statistical assumptions in the architecture proposed, as they are "model-free." It is also "cost-accommodating" since it does not alter the current FDD framework and it is only several microseconds on a household computer during the identification procedure. We have shown that ANN-MLP, SVM-RBF, and CNN can conduct locational detection under different noise and attack circumstances through broad experience in IEEE 14, 30, 57, and 118 bus systems. Moreover, the multi-name classification method used successfully improves the precision of the present identification.

• 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.

• Korean Journal of Ecology and Environment
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• v.55 no.4
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• pp.259-273
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• 2022

Interactions between species in a community are very complex, and they are visualized and analyzed through a food web in simple way. Food web is a network of species connected by trophic links showing energy flow from prey to predator. Various models were developed to characterize the food web in ecosystems. In this study, we classified food web models to static models such as Ecopath and dynamic models such as AQUATOX. We presented characteristics of several different types of food web models in each category, and reviewed their applications used in aquatic ecosystems. Finally, we presented issues to be considered to develop food web models.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.6B
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• pp.551-560
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• 2009

This paper describes a newly developed pollutant transport model named ARPTM which was designed to simulate the transport and characteristics of pollutant materials after an accidental spill in upstream of river system up to a given position in the downstream. In particular, the ARPTM incorporated ADCP data to compute longitudinal dispersion coefficient and advection velocity which are necessary to apply one-dimensional advection-dispersion equation. ARPTM was built on top of the geographic information system platforms to take advantage of the technology's capabilities to track geo-referenced processes and visualize the simulated results in conjunction with associated geographic layers such as digital maps. The ARPTM computes travel distance, time, and concentration of the pollutant cloud in the given flow path from the river network, after quickly finding path between the spill of the pollutant material and any concerned points in the downstream. ARPTM is closely connected with a recently developed GIS-based Arc River database that stores inputs and outputs of ARPTM. ARPTM thereby assembles measurements, modeling, and cyberinfrastructure components to create a useful cyber-tool for determining and visualizing the dynamics of the clouds of pollutants while dispersing in space and time. ARPTM is expected to be potentially used for building warning system for the transport of pollutant materials in a large basin.

• IMMUNE NETWORK
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• v.20 no.5
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• pp.40.1-40.15
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• 2020

The protein encoded by the Gene Associated with Retinoid-Interferon-Induced Mortality-19 (GRIM-19) is located in the mitochondrial inner membrane and is homologous to the NADH dehydrogenase 1-alpha subcomplex subunit 13 of the electron transport chain. Multiple sclerosis (MS) is a demyelinating disease that damages the brain and spinal cord. Although both the cause and mechanism of MS progression remain unclear, it is accepted that an immune disorder is involved. We explored whether GRIM-19 ameliorated MS by increasing the levels of inflammatory cytokines and immune cells; we used a mouse model of experimental autoimmune encephalomyelitis (EAE) to this end. Six-to-eight-week-old male C57BL/6, IFNγ-knockout (KO), and GRIM-19 transgenic mice were used; EAE was induced in all strains. A GRIM-19 overexpression vector (GRIM19 OVN) was electrophoretically injected intravenously. The levels of Th1 and Th17 cells were measured via flow cytometry, immunofluorescence, and immunohistochemical analysis. IL-17A and IFNγ expression levels were assessed via ELISA and quantitative PCR. IL-17A expression decreased and IFNγ expression increased in EAE mice that received injections of the GRIM-19 OVN. GRIM19 transgenic mice expressed more IFNγ than did wild-type mice; this inhibited EAE development. However, the effect of GRIM-19 overexpression on the EAE of IFNγ-KO mice did not differ from that of the empty vector. GRIM-19 expression was therapeutic for EAE mice, elevating the IFNγ level. GRIM-19 regulated the Th17/Treg cell balance.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.39 no.6
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• pp.743-750
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• 2019

Most of the water supply facilities that use rivers as sources do not have monitoring facilities such as precipitation and stream flow measurement, and there is no judgment standard for drought response such as water intake control in river flow during dry season. In addition, it was confirmed that local government officials, who deal with actual drought work, have limitations in applying the drought index (SPI, PDSI, etc.) and diffusion models that have been proposed so far in advance. Therefore, in this study, the drought prediction system was constructed to determine the number of water-intake available days using SWAT (Soil and Water Assessment Tool) and the water supply network from the intake source to the beneficiary area, suggesting the drought spreading time and space.

• Journal of Information Technology Application
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• v.3 no.2
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• pp.67-82
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• 2001

The ILOF project addresses the needs of logistics industrial organizations to reduce information processing time, improve added and residual value of information and reduce processing and transportation costs. It deals with the information supply chain information systems shared by vertical partner as important entity, whose performance and optimization very significantly affects the efficiency and performance of logistics industries. This paper deals with logistics information exchange systems based on supply chain management, focusing on sharing database and processes between partners such as shipper, logistics broker, transportation company, shipping company etc., for smoothing the information flow, enhancing consumer service and reducing communication fee and labour costs. The significance of contribution of this research is the provision of a model for logistics information exchange including entity relationship diagram, data flow diagram and functions which is able to facilitate the formulation of a customer driven supply chain information network, there by enhancing the competitive edge of companies in logistics industries on local and global basis.

• IMMUNE NETWORK
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• v.11 no.1
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• pp.68-78
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• 2011

Background: Graft-versus-host disease (GVHD) is a huddle for success of hematopoietic stem cell transplantation. In this study, effects of irradiation dose on immune kinetics of GVHD were investigated using B6 ${\rightarrow}$ BALB.B system, a mouse model for GVHD after MHC-matched allogeneic transplantation. Methods: BALB.B mice were transplanted with bone marrow and spleen cells from C57BL/6 mice after irradiation with different doses. Leukocytes residing in the peripheral blood and target organs were collected periodically from the GVHD hosts for analysis of chimerism formation and immune kinetics along the GVHD development via flow cytometry. Myeloid cells were tested for production of IL-17 via flow cytometry. Results: Pre-conditioning of BALB.B hosts with 900 cGy and 400 cGy resulted in different chimerism of leukocytes from the blood and affected survival of GVHD hosts. Profiles of leukocytes infiltrating GVHD target organs, rather than profiles of peripheral blood leukocytes (PBLs), were significantly influenced by irradiation dose. Proportions of IL-17 producing cells in the infiltrating $Gr-1^+$ or $Mac-1^+$ cells were higher in the GVHD hosts with high does irradiation than those with low dose irradiation. Conclusion: Pre-conditioning dose affected tissue infiltration of leukocytes and cytokine production by myeloid cells in the target organs.

• Journal of the Korean Society of Industry Convergence
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• v.23 no.5
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• pp.773-787
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• 2020

In the injection molding process, the controlling stability of products quality is a very important factor in terms of productivity. Even when the optimum process conditions for the desired product quality are applied, uncontrollable external factors such as ambient temperature and humidity cause inevitable changes in the state of the melt resin, mold temperature. etc. Therefore, it is very difficult to maintain prodcut quality. In this study, a system that learns the correlation between process variables and product weight through artificial neural networks and predicts process conditions for the target weight was established. Then, when a disturbance occurs in the injection molding process and fluctuations in the weight of the product occur, the stability control of the product quality was performed by ANN predicting a new process condition for the change of weight. In order to artificially generate disturbance in the injection molding process, controllable factors were selected and changed among factors not learned in the ANN model. Initially, injection molding was performed with a polypropylene having a melt flow index of 10 g/10min, and then the resin was replaced with a polypropylene having a melt floiw index of 33 g/10min to apply disturbance. As a result, when the disturbance occurred, the deviation of the weight was -0.57 g, resulting in an error of -1.37%. Using the control method proposed in the study, through a total of 11 control processes, 41.57 g with an error of 0.00% in the range of 0.5% deviation of the target weight was measured, and the weight was stably maintained with 0.15±0.07% error afterwards.

• Journal of Korea Multimedia Society
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• v.14 no.9
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• pp.1152-1164
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• 2011

Stock price is stream data to change continuously. The characteristics of these data, stock trends according to flow of time intervals may differ. therefore, stock price should be continuously prediction when the price is updated. In this paper, we propose the new prediction system that continuously predicts the stock price according to the predefined time intervals for the selected stock item using HTM model. We first present a preprocessor which normalizes the stock data and passes its result to the stream sensor. We next present a stream sensor which efficiently processes the continuous input. In addition, we devise a storage node which stores the prediction results for each level and passes it to next upper level and present the HTM network for prediction using these nodes. We show experimented our system using the actual stock price and shows its performance.