• Journal of Internet Computing and Services
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• v.13 no.1
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• pp.75-81
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• 2012

Previous RFID systems exclusively manage the tags and readers for each company in individual manner. Thus, RFID system manager should understand and design specifications such as tag events, data format, and etc, based on individual companies. But it is very difficult to know all statements. To resolve theses problems, there has been conceptual research about policy-based RFID service management model that is not restrained from standards of typical RFID systems, including EPCglobal standard, and ISO/IEC standard. However, previous proposed service management model only aimed event management without including device management. Therefore, in this paper, we propose extended device management policy model for giving shape to the proposed policy-based RFID service management model. If the proposing device management policy model is used for device management, we can integrate control management for heterogeneous middleware, diverse RFID devices, and applications for each company. Moreover, we show that the RFID device management policy is translated and processed as an example using the proposing policy model in real-time RFID system.

• Proceedings of the KSRS Conference
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• v.1
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• pp.21-24
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• 2006

Processes controlling the interannual variation of mixed layer temperature (MLT) averaged over the NINO3 domain ($150-90^{\circ}W$, $5^{\circ}N-5^{\circ}S$) are studied using an ocean data assimilation product that covers the period of 1993 to 2003. Advective tendencies are estimated here as the temperature fluxes through the domain's boundaries, with the boundary temperature referenced to the domain-averaged temperature to remove the dependence on temperature scale. The overall balance is such that surface heat flux opposes the MLT change but horizontal advection and subsurface processes assist the change. The zonal advective tendency is caused primarily by large-scale advection of warm-pool water through the western boundary of the domain. The meridional advective tendency is contributed mostly by Ekman current advecting large-scale temperature anomalies though the southern boundary of the domain. Unlike many previous studies, we explicitly evaluate the subsurface processes that consist of vertical mixing and entrainment. In particular, a rigorous method to estimate entrainment allows an exact budget closure. The vertical mixing across the mixed layer (ML) base has a contribution in phase with the MLT change. The entrainment tendency due to temporal change in ML depth is negligible comparing to other subsurface processes. The entrainment tendency by vertical advection across the ML base is dominated by large-scale changes in wind-driven upwelling and temperature of upwelling water. Tropical instability waves (TIWs) result in smaller-scale vertical advection that warms the domain during La Ni? cooling events. When the advective tendencies are evaluated by spatially averaging the conventional local advective tendencies of temperature, the apparent effects of currents with spatial scales smaller than the domain (such as TIWs) become very important as they redistribute heat within the NINO3 domain. However, such internal redistribution of heat does not represent external processes that control the domain-averaged MLT.

• Proceedings of the Korean Vacuum Society Conference
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• 2014.02a
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• pp.207.1-207.1
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• 2014

High-mobility and two dimensional conduction at the interface between two band insulators, LaAlO3 (LAO) and SrTiO3 (STO), have attracted considerable research interest for both applications and fundamental understanding. Several groups have reported the photoconductivity of LAO/STO, which give us lots of potential development of optoelectronic applications using the oxide interface. Recently, a giant photo response of Pd nano particles/LAO/STO is observed in UV illumination compared with LAO/STO sample. These phenomena have been suggested that the correlation between the interface and the surface states significantly affect local charge modification and resulting electrical transport. Water and gas adsorption/desorption can alter the band alignment and surface workfunction. Therefore, characterizing and manipulating the electric charges in these materials (electrons and ions) are crucial for investigating the physics of metal oxide. Proposed mechanism do not well explain the experimental data in various ambient and there has been no quantitative work to confirm these mechanism. Here, we have investigated UV photo response in various ambient by performing transport and Kelvin probe force microscopy measurements simultaneously. We found that Pd nano particles on LAO can form Schottky contact, it cause interface carrier density and characteristics of persistence photo conductance depending on gas environment. Our studies will help to improve our understanding on the intriguing physical properties providing an important role in many enhanced light sensing and gas sensing applications as a catalytic material in different kinds of metal oxide systems.

• Korean Management Science Review
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• v.31 no.1
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• pp.1-15
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• 2014

As the scarcity of natural resources has become apparent, the recycling industry has emerged as a promising one for its growth potential. Yet, the recycling industry still remains undeveloped and inefficient for various reasons. In this study, we focus on firms' recycling processes to understand the current status of recycling firms' value creation activities. With respect to the adopted recycling processes, we empirically investigate the differences in firm characteristics and firm performance. We use the data from Keco (Korea Environment Cooperation) which annually conducts a survey of recycling firms in Korea. We exclusively consider the whole group of plastic recycling industry in order to control for a possible bias in firm performance, stemming from the heterogeneity in processing and recycling of materials other than plastics. We review the descriptive statistics from the sample firms and conduct a series of hierarchical regression analyses. The results show that most of the firms in this industry adopt physical transformation processes with a low-level technology. These firms with physical transformation processes are smaller in size and produce entry level items which do not secure higher margins. The results indicate that the recycling industry largely comprises low value added firms which lack economies of scale and resources for R&D. For the stable growth of the industry, recycling firms must create sustainable values through implementation of technology-driven processes and improvement in product quality. In addition, the government should help build a reliable reverse logistic network, lower the entry barriers, and provide necessary funding for the SMEs in the recycling industry.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.35 no.4
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• pp.356-365
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• 2002

One of the most important factors that cause eutrophication is nutrient materials containing nitrogen and phosphorus which stem from excreation of terrestial sources and release from sediment. Therefore, to improve water quality, the reduction of these nutrients loads should be indispensible. At this study, the three-dimensional numerical hydrodynamic and ecosystem model, which was developed by Institute for Resources and Environment of Japan, were applied to analyze the processes affecting the eutrophication. The residual currents, which were obtained by integrating the simulated tidal currents over 1 tidal cycle, showed the presence of a typical counterclockwise eddies between Gyewha and Garyuk island. Density driven currents were generated westward at surface and eastward at the bottom in Saemankeum area where the fresh waters are flowing into, The ecosystem model was calibrated with the data surveyed in the field of the study area in annual average. The simulated results were fairly good coincided with the observed values within relative error of $30\%$. The simulations of DIN and DIP concentrations were performed using ecosystem model under the conditions of $40\~100\%$ pollution load reductions from pollution sources. In study area, concentration of DIN and DIP were reduced to $59\%$ and $28\%$ in case of the $80\%$ reduction of the input loads from fresh water respectively. But pollution loads from sediment had hardly affected DIN and DIP concentration, The $95\%$ input load abatement is necessary to meet the DIN and DIP concentration of second grade of ocean water quality criteria.

• Geotechnical Engineering
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• v.10 no.3
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• pp.79-96
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• 1994

A tunnel design expert system entitled NESTED is developed using the artificial neural network. The expert system includes three neural network computer models designed for the stability assessment of underground openings and the estimation of correlation between the RMR and Q systems. The expert system consists of the three models and the computerized rock mass classification programs that could be driven under the same user interface. As the structure of the neural network, a multi -layer neural network which adopts an or ror back-propagation learning algorithm is used. To set up its knowledge base from the prior case histories, an engineering database which can control the incomplete and erroneous information by learning process is developed. A series of experiments comparing the results of the neural network with the actual field observations have demonstrated the inferring capabilities of the neural network to identify the possible failure modes and the support timing. The neural network expert system thus complements the incomplete geological data and provides suitable support recommendations for preliminary design of tunnels in rock masses.

• Journal of IKEEE
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• v.21 no.4
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• pp.331-337
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• 2017

In this paper, we present the design of a 20MHz bandwidth 3rd-order continuous-time low-pass sigma-delta modulator with low-noise and low-power consumption. The bandwidth of the system is sufficient to accommodate LTE and other wireless network standards. The 3rd-order low-pass filter with feed-forward architecture achieves the low-power consumption as well as the low complexity. The system uses 3bit flash quantizer to provide fast data conversion. The current-steering DAC achieves low-power and improved sensitivity without additional circuitries. Cross-coupled transistors are adopted to reduce the current glitches. The proposed system achieves a peak SNDR of 65.9dB with 20MHz bandwidth and power consumption of 32.65mW. The in-band IM3 is simulated to be 69dBc with 600mVp-p two tone input tones. The circuit is designed in a 0.18-um CMOS technology and is driven by 500MHz sampling rate signal.

• The Journal of Natural Sciences
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• v.11 no.1
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• pp.89-94
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• 1999

ALG-2 (apoptosis linked gene-2) is a 22 kDa calcium-binding protein necessary for apoptosis induced by various stimuli in lymphocyte. The transactivation of human ALG-2 was assessed in yeast as a fusion protein with the DNA binding domains (DBDs) of LexA. The C-terminal of hALG-2 (93-191 amino acid) exhibited transacitivation of the reporter gene, LacZ, whereas the full-length hALG-2 (1-91 amino acid) and its N-terminal (1-98 amino acid) did not. The transactivation of LacZ reporter was driven more strongly (more than 2.7-fold increase) by the C-terminus of hALG-2 than by the B42, as a positive control for transactivation. Hence, our data suggested a possible regulatory role of the N-termini of hALG-2 upon transactivation.

• Nuclear Engineering and Technology
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• v.53 no.1
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• pp.148-163
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• 2021

Timely fault identification is important for safe and reliable operation of the electric valve system. Many research works have utilized different data-driven approach for fault diagnosis in complex systems. However, they do not consider specific characteristics of critical control components such as electric valves. This work presents an integrated shallow-deep fault diagnostic model, developed based on signals extracted from DN50 electric valve. First, the local optimal issue of particle swarm optimization algorithm is solved by optimizing the weight search capability, the particle speed, and position update strategy. Then, to develop a shallow diagnostic model, the modified particle swarm algorithm is combined with support vector machine to form a hybrid improved particle swarm-support vector machine (IPs-SVM). To decouple the influence of the background noise, the wavelet packet transform method is used to reconstruct the vibration signal. Thereafter, the IPs-SVM is used to classify phase imbalance and damaged valve faults, and the performance was evaluated against other models developed using the conventional SVM and particle swarm optimized SVM. Secondly, three different deep belief network (DBN) models are developed, using different acoustic signal structures: raw signal, wavelet transformed signal and time-series (sequential) signal. The models are developed to estimate internal leakage sizes in the electric valve. The predictive performance of the DBN and the evaluation results of the proposed IPs-SVM are also presented in this paper.

• Smart Media Journal
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• v.11 no.9
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• pp.9-20
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• 2022

Deep reinforcement learning(RL) is an end-to-end data-driven control method that is widely used in the autonomous driving domain. However, conventional RL approaches have difficulties in applying it to autonomous driving tasks due to problems such as inefficiency, instability, and uncertainty. These issues play an important role in the autonomous driving domain. Although recent studies have attempted to solve these problems, they are computationally expensive and rely on special assumptions. In this paper, we propose a new algorithm MCDT that considers inefficiency, instability, and uncertainty by introducing a method called uncertainty sequence modeling to autonomous driving domain. The sequence modeling method, which views reinforcement learning as a decision making generation problem to obtain high rewards, avoids the disadvantages of exiting studies and guarantees efficiency, stability and also considers safety by integrating uncertainty estimation techniques. The proposed method was tested in the OpenAI Gym CarRacing environment, and the experimental results show that the MCDT algorithm provides efficient, stable and safe performance compared to the existing reinforcement learning method.