• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.4
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• pp.211-221
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• 2012

The development of wireless sensor networks (WSNs) is progressed slowly due to limited resources, but it is in progress to the development of the latest IP-based IP-WSN by the development of hardware and power management technology. IPv6 over Low power WPAN (6LoWPAN) is capable of IPv6-built low-power devices. In these IP-based WSNs, existing IP-based techniques which was impossible in WSNs becomes possible. 6LoWPAN is based on the IEEE 802.15.4 sensor networks and is a IPv6-supported technology. Host-based mobility management scheme in IP-WSNs are not suitable due to the additional signaling, network-based mobility management scheme is more suitable. In this paper, we propose an enhanced PMIPv6-based route optimization scheme which consider multi-6LoWPAN network environments. All SLMA (Sensor Local Mobility Anchor) of the 6LoWPAN domain are connected with the SPIG (Sensor Proxy Internetworking Gateway) and performs distributed mobility control for the 6LoWPAN-based inter-domain operations. All information of SLMA in 6LoWPAN domain is maintained by SMAG (Sensor Mobile Access Gateway), and then is performed the route optimization quickly. The status information of the route optimization from SPIG is stored to SLMA and it is supported without additional signaling.

• Journal of the Korea Convergence Society
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• v.11 no.9
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• pp.1-6
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• 2020

In this study, we propose an association inference model based on data bias optimization for road hazard detection. This is a mining model based on association analysis to collect user's personal characteristics and surrounding environment data and provide traffic accident prevention services. This creates transaction data composed of various context variables. Based on the generated information, a meaningful correlation of variables in each transaction is derived through correlation pattern analysis. Considering the bias of classified categorical data, pruning is performed with optimized support and reliability values. Based on the extracted high-level association rules, a risk detection model for personal characteristics and driving road conditions is provided to users. This enables traffic services that overcome the data bias problem and prevent potential road accidents by considering the association between data. In the performance evaluation, the proposed method is excellently evaluated as 0.778 in accuracy and 0.743 in the Kappa coefficient.

• Nuclear Engineering and Technology
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• v.55 no.11
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• pp.4274-4281
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• 2023

Blanket is of vital importance for engineering application of the fusion reactor. Nuclear heat deposition in materials is the main heat source in blanket structure. In this paper, the three-dimensional method for thermal-hydraulics/neutronics coupling analysis is developed and applied for the full-scale module of the helium-cooled ceramic breeder tritium breeding blanket (HCCB TBB) designed for China Fusion Engineering Test Reactor (CFETR). The explicit coupling scheme is used to support data transfer for coupling analysis based on cell-to-cell mapping method. The coupling algorithm is realized by the user-defined function compiled in Fluent. The three-dimensional model is established, and then the coupling analysis is performed using the paralleled Coupling Analysis of Thermal-hydraulics and Neutronics Interface Code (CATNIC). The results reveal the relatively small influence of the coupling analysis compared to the traditional method using the radial fitting function of internal heat source. However, the coupling analysis method is quite important considering the nonuniform distribution of the neutron wall loading (NWL) along the poloidal direction. Finally, the structure optimization of the blanket is carried out using the coupling method to satisfy the thermal requirement of all materials. The nonlinear effect between thermal-hydraulics and neutronics is found during the blanket structure optimization, and the tritium production performance is slightly reduced after optimization. Such an adverse effect should be thoroughly evaluated in the future work.

• Journal of Korean Tunnelling and Underground Space Association
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• v.24 no.6
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• pp.617-628
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• 2022

A ground support pattern should be designed by properly integrating various support materials in accordance with the rock mass grade when constructing a tunnel, and a technical decision must be made in this process by professionals with vast construction experiences. However, designing supports at the early stage of tunnel design, such as feasibility study or basic design, may be very challenging due to the short timeline, insufficient budget, and deficiency of field data. Meanwhile, the design of the support pattern can be performed more quickly and reliably by utilizing the machine learning technique and the accumulated design data with the rapid increase in tunnel construction in South Korea. Therefore, in this study, the design data and ground exploration data of 48 road tunnels in South Korea were inspected, and data about 19 items, including eight input items (rock type, resistivity, depth, tunnel length, safety index by tunnel length, safety index by rick index, tunnel type, tunnel area) and 11 output items (rock mass grade, two items for shotcrete, three items for rock bolt, three items for steel support, two items for concrete lining), were collected to automatically determine the rock mass class and the support pattern. Three machine learning models (S1, A1, A2) were developed using two machine learning algorithms (SVR, ANN) and organized data. As a result, the A2 model, which applied different loss functions according to the output data format, showed the best performance. This study confirms the potential of support pattern design using machine learning, and it is expected that it will be able to improve the design model by continuously using the model in the actual design, compensating for its shortcomings, and improving its usability.

• KIPS Transactions on Computer and Communication Systems
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• v.4 no.9
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• pp.315-320
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• 2015

In cloud computing, server virtualization supports one or more virtual machines loaded on multiple operating systems on a single physical host server. Migration of a VM is moving the VM running on a source host to another physical machine called target host. A VM live migration is essential to support task performance optimization, energy efficiency and energy saving, fault tolerance and load balancing. In this paper, we propose open source based adaptive VM live migration technique. For this, we design VM monitoring module to decide VM live migration and open source based full-virtualization hypervisor.

• 한국신재생에너지학회:학술대회논문집
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• 2010.06a
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• pp.253-253
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• 2010

Rice straw was pretreated with aqueous ammonia in order to enhance enzyme digestibility. Soaking in ammonia aqueous (SAA) was conducted with 15% ammonia, at $60^{\circ}C$. for 24 h. Optimization of both saccharification conditions and enzyme loading of SAA rice straw was carried out. Especially enzyme loading test was performed using statistical method. Moreover proton beam irradiation (PBI) was also performed to overcome the problem which inhibit the enzyme digestibility at 1-25 kGy doses with 45 MeV of beam energy. Optimal condition for enzymatic saccharification was follows; pH 4.8, $50^{\circ}C$, 60 FPU of enzyme activity, 1:4 ratio of celluase and ${\beta}$-glucosidase. Also, optimal doses of PBI on rice straw and SAA-treated rice straw for efficient sugar recovery were found to be 3 kGy, respectively. When saccharification was performed with optimal condition, glucose conversion yield was 89% of theocratical maximum in 48 h, and 3 kGy of PBI was applied to SAA-treated rice straw, approximately 90% of the theoretical glucose yield was obtained in 12 h. The results of X-ray diffractometry (XRD) support the effect of both SAA and PBI on sugar recovery, and scanning electron microscopy (SEM) images unveiled the physical change of the rice straw surface since rugged rice straw surface was observed.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.716-721
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• 2004

Several Solid Freeform Fabrication Systems(SFFS) are commercialized in a few companies for rapid prototyping. However, they have many technical problems including the limitation of applicable materials. A new method of speedy prototyping is required for the recent manufacturing environments of multi-item and small quantity production. The objectives of this paper include the development of a novel method of SFFS, the ${CAFL}^{VM}$(Computer Aided Fabrication of Lamination for Various Material), and the manufacture of the various material samples for the certification of the proposed system and the creation of new application areas. For these objectives, the technologies for a highly accurate robot path control, the optimization of support structure, CAD modeling, adaptive slicing was implemented. In this paper, we design an algorithm that the cutting path of a laser beam which is controlled with constant speed. The laser beam is tangentially controlled in order to solve the inaccuracy of a 3D model surface. The designed algorithm for constant-speed path control and tangent-cutting control is implemented and experimented in the ${CAFL}^{VM}$ system.

• International Journal of Internet, Broadcasting and Communication
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• v.12 no.1
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• pp.144-150
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• 2020

Reinforcement learning is a technology that can present successful and creative solutions in many areas. This reinforcement learning technology was used to deploy containers from cloud servers to fog servers to help them learn the maximization of rewards due to reduced traffic. Leveraging reinforcement learning is aimed at predicting traffic in the network and optimizing traffic-based fog computing network environment for cloud, fog and clients. The reinforcement learning system collects network traffic data from the fog server and IoT. Reinforcement learning neural networks, which use collected traffic data as input values, can consist of Long Short-Term Memory (LSTM) neural networks in network environments that support fog computing, to learn time series data and to predict optimized traffic. Description of the input and output values of the traffic-based reinforcement learning LSTM neural network, the composition of the node, the activation function and error function of the hidden layer, the overfitting method, and the optimization algorithm.

• Transactions of the Korean Society of Pressure Vessels and Piping
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• v.7 no.2
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• pp.7-13
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• 2011

Turbine journal bearings are designed to support the weight of the rotors on a hydrodynamic oil film and to provide dynamic stability to the rotor system. The life time of journal bearings is infinite theoretically because the journal bearings are separated from the shaft journal by oil film. But poor design, assembly, operation and maintenance can cause problems to the journal bearings. The FMEA(Failure Mode and Effects Analysis) results of the journal bearings show that frequent maintenance of the journal bearings can cause failures and reduction of the bearing life. Therefore, the maintenance periods and history of the journal bearings with the bearing FMEA results are reviewed in order to establish the optimized maintenance period of the journal bearing for the nuclear power plants. Consequently it is necessary to maintain a best condition of lubrication system, reject time-based maintenance and perform the condition-based maintenance of journal bearings in order to maintain optimum condition of the journal bearing.

• Journal of Information Technology Application
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• v.1
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• pp.173-211
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• 1999

The task of classification permeates all walks of life, from business and economics to science and public policy. In this context, nonlinear techniques from artificial intelligence have often proven to be more effective than the methods of classical statistics. The objective of knowledge discovery and data mining is to support decision making through the effective use of information. The automated approach to knowledge discovery is especially useful when dealing with large data sets or complex relationships. For many applications, automated software may find subtle patterns which escape the notice of manual analysis, or whose complexity exceeds the cognitive capabilities of humans. This paper explores the utility of a collaborative learning approach involving integrated models in the preprocessing and postprocessing stages. For instance, a genetic algorithm effects feature-weight optimization in a preprocessing module. Moreover, an inductive tree, artificial neural network (ANN), and k-nearest neighbor (kNN) techniques serve as postprocessing modules. More specifically, the postprocessors act as second0order classifiers which determine the best first-order classifier on a case-by-case basis. In addition to the second-order models, a voting scheme is investigated as a simple, but efficient, postprocessing model. The first-order models consist of statistical and machine learning models such as logistic regression (logit), multivariate discriminant analysis (MDA), ANN, and kNN. The genetic algorithm, inductive decision tree, and voting scheme act as kernel modules for collaborative learning. These ideas are explored against the background of a practical application relating to financial fraud management which exemplifies a binary classification problem.