• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.2B
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• pp.97-109
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• 2012

In this paper, we propose a modulation scheme for a network-coded bi-directional relaying (NBR) system over an asymmetric channel, which means that the qualities of the relay channel (the link between the BS and RS) and access channel (the link between the RS and MS) are not identical. The proposed scheme employs a dual constellation in such a way that the RS broadcasts the network-coded symbols modulated by two different constellations to the MS and BS over two consecutive transmission intervals. We derive an upper bound on the average bit error rate (BER) of the proposed scheme, and compare it with the hybrid constellation-based modulation scheme proposed for the asymmetric bi-directional link. Furthermore, we investigate the channel utilization of the existing bi-directional relaying schemes as well as the NBR system with the proposed dual constellation diversity-based modulation (DCD). From our simulation results, we show that the DCD gives better average BER performance about 3.5~4dB when $E_b/N_0$ is equal to $10^{-2}$, while maintaining the same spectral efficiency as the existing NBR schemes over the asymmetric bi-directional relaying channel.

• Proceedings of the Korean Vacuum Society Conference
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• 2012.08a
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• pp.184-184
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• 2012

Recently, InGaN/GaN multi-quantum well grown on GaN pyramid structures have attracted much attention due to their hybrid characteristics of quantum well, quantum wire, and quantum dot. This gives us broad band emission which will be useful for phosphor-free white light emitting diode. On the other hand, by using quantum dot emission on top of the pyramid, site selective single photon source could be realized. However, these structures still have several limitations for the single photon source. For instance, the quantum efficiency of quantum dot emission should be improved further. As detection systems have limited numerical aperture, collection efficiency is also important issue. It has been known that micro-cavities can be utilized to modify the radiative decay rate and to control the radiation pattern of quantum dot. Researchers have also been interested in nano-cavities using localized surface plasmon. Although the plasmonic cavities have small quality factor due to high loss of metal, it could have small mode volume because plasmonic wavelength is much smaller than the wavelength in the dielectric cavities. In this work, we used localized surface plasmon to improve efficiency of InGaN qunatum dot as a single photon emitter. We could easily get the localized surface plasmon mode after deposit the metal thin film because lnGaN/GaN multi quantum well has the pyramidal geometry. With numerical simulation (i.e., Finite Difference Time Domain method), we observed highly enhanced decay rate and modified radiation pattern. To confirm these localized surface plasmon effect experimentally, we deposited metal thin films on InGaN/GaN pyramid structures using e-beam deposition. Then, photoluminescence and time-resolved photoluminescence were carried out to measure the improvement of radiative decay rate (Purcell factor). By carrying out cathodoluminescence (CL) experiments, spatial-resolved CL images could also be obtained. As we mentioned before, collection efficiency is also important issue to make an efficient single photon emitter. To confirm the radiation pattern of quantum dot, Fourier optics system was used to capture the angular property of emission. We believe that highly focused localized surface plasmon around site-selective InGaN quantum dot could be a feasible single photon emitter.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.7B
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• pp.679-689
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• 2006

Delivering multicast stream is one of the cost-saving approach in the large scale VOD environment. Because implementing VCR-like interactions for user's convenience in the multicast streaming system involves complex problems, we need the proper solutions for them. In this paper, we propose a hybrid scheme which uses the general P2P and the patching scheme with the Collaborative Interaction Streaming Scheme(CISS). CISS provides jumping functionability to the appropriate multicast session after VCR-like interaction in the environment in which multiple peers transmit VCR-like interaction streams to the VCR-like functionability request node to reduce the loads generated by frequent join or departure of peers at the multicast tree during providing VCR-like functionability. Therefore, with the proposed scheme we can distribute network traffic and reduce control overhead and latency. And to evaluate the performance of proposed scheme we compare it in the aspect of the performance of streaming delivery topology, control overhead and streaming quality with P2Cast[10] and DSL[11]. The simulation result shows that proposed P2Patching reduces about 30% of process overhead and enhances about $25{\sim}30%$ of streaming quality compared with DSL.

• The KIPS Transactions:PartB
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• v.12B no.3 s.99
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• pp.349-356
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• 2005

Artificial Organism-used application areas are expanding at a break-neck speed with a view to getting things done in a dynamic and Informal environment. A use of general programming or traditional hi methods as the representation of Artificial Organism behavior knowledge in these areas can cause problems related to frequent modifications and bad response in an unpredictable situation. Strategies aimed at solving these problems in a machine-learning fashion includes Genetic Programming and Evolving Neural Networks. But the learning method of Artificial-Organism is not good yet, and can't represent life in the environment. With this in mind, this research is designed to come up with a new behavior evolution model. The model represents behavior knowledge with Classification Rules and Enhanced Backpropation Neural Networks and discriminate the denomination. To evaluate the model, the researcher applied it to problems with the competition of Artificial-Organism in the Simulator and compared with other system. The survey shows that the model prevails in terms of the speed and Qualify of learning. The model is characterized by the simultaneous learning of classification rules and neural networks represented on chromosomes with the help of Genetic Algorithm and the consolidation of learning ability caused by the hybrid processing of the classification rules and Enhanced Backpropagation Neural Network.

• Journal of Korean Society of Transportation
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• v.23 no.3 s.81
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• pp.35-47
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• 2005

This paper is to review a literature concerning Bus Transit Route Network Design(BTRNDP), to describe a future study direction for a systematic application for the BTRNDP. Since a bus transit uses a fixed route, schedule, stop, therefore an approach methodology is different from that of auto network design problem. An approach methodology for BTRNDP is classified by 8 categories: manual & guideline, market analysis, system analytic model. heuristic model. hybrid model. experienced-based model. simulation-based model. mathematical optimization model. In most previous BTRNDP, objective function is to minimize user and operator costs, and constraints on the total operator cost, fleet size and service frequency are common to several previous approach. Transit trip assignment mostly use multi-path trip assignment. Since the search for optimal solution from a large search space of BTRNDP made up by all possible solutions, the mixed combinatorial problem are usually NP-hard. Therefore, previous researches for the BTRNDP use a sequential design process, which is composed of several design steps as follows: the generation of a candidate route set, the route analysis and evaluation process, the selection process of a optimal route set Future study will focus on a development of detailed OD trip table based on bus stop, systematic transit route network evaluation model. updated transit trip assignment technique and advanced solution search algorithm for BTRNDP.

• Geomechanics and Engineering
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• v.17 no.4
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• pp.333-342
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• 2019

Geological dynamic hazards during coal mining can be caused by the failure of a composite system consisting of roof rock and coal layers, subject to different loading rates due to different advancing velocities in the working face. In this paper, the uniaxial compression test simulations on the composite rock-coal layers were performed using $PFC^{2D}$ software and especially the effects of loading rate on the stress-strain behavior, strength characteristics and crack nucleation, propagation and coalescence in a composite layer were analyzed. In addition, considering the composite layer, the mechanisms for the advanced bore decompression in coal to prevent the geological dynamic hazards at a rapid advancing velocity of working face were explored. The uniaxial compressive strength and peak strain are found to increase with the increase of loading rate. After post-peak point, the stress-strain curve shows a steep stepped drop at a low loading rate, while the stress-strain curve exhibits a slowly progressive decrease at a high loading rate. The cracking mainly occurs within coal, and no apparent cracking is observed for rock. While at a high loading rate, the rock near the bedding plane is damaged by rapid crack propagation in coal. The cracking pattern is not a single shear zone, but exhibits as two simultaneously propagating shear zones in a "X" shape. Following this, the coal breaks into many pieces and the fragment size and number increase with loading rate. Whereas a low loading rate promotes the development of tensile crack, the failure pattern shows a V-shaped hybrid shear and tensile failure. The shear failure becomes dominant with an increasing loading rate. Meanwhile, with the increase of loading rate, the width of the main shear failure zone increases. Moreover, the advanced bore decompression changes the physical property and energy accumulation conditions of the composite layer, which increases the strain energy dissipation, and the occurrence possibility of geological dynamic hazards is reduced at a rapid advancing velocity of working face.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.2
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• pp.377-384
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• 2022

Vortex-induced vibration (VIV) occurs owing to the vortex generated from the back side of the appendages of ships and submarines during operation. Recently, the importance of high-order modes (HOMs) vibration and fatigue failure has become increasingly emphasized by increasing the speed of ships and the size of structures. In addition, predicting the vibration of HOMs is significantly necessary as the VIV becomes stronger in the fast flow speed condition than in the low flow speed condition. This study introduces a methodology according to HOMs hybrid Fluid Structure Interaction (FSI) for predicting the HOMs VIV on the hydrofoils. The HOMs FSI system is verified by comparing the VIV results from the FSI simulation with the experimental results. Finally, the effectiveness of the HOMs FSI is determined by applying the maximum von-Mises stress obtained from the VIV on the hydrofoil to the S-N curve released from Det Norske Veritas (DNV). VIV results from the HOMs FSI include the lock-in characteristics as well as a significant increase of more than 10 times compared with that of low-order modes (LOMs) FSI. In the future works, advanced studies will be required for improving cantilever boundary conditions and the shape of hydrofoils.

• Journal of Korean Society of Transportation
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• v.22 no.3 s.74
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• pp.145-157
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• 2004

The cycle length design model of the Korean traffic responsive signal control systems is devised to vary a cycle length as a response to changes in traffic demand in real time by utilizing parameters specified by a system operator and such field information as degrees of saturation of through phases. Since no explicit guideline is provided to a system operator, the system tends to include ambiguity in terms of the system optimization. In addition, the cycle lengths produced by the existing model have yet been verified if they are comparable to the ones minimizing delay. This paper presents the studies conducted (1) to find shortcomings embedded in the existing model by comparing the cycle lengths produced by the model against the ones minimizing delay and (2) to propose a new direction to design a cycle length minimizing delay and excluding such operator oriented parameters. It was found from the study that the cycle lengths from the existing model fail to minimize delay and promote intersection operational conditions to be unsatisfied when traffic volume is low, due to the feature of the changed target operational volume-to-capacity ratio embedded in the model. The 64 different neural network based cycle length design models were developed based on simulation data surrogating field data. The CORSIM optimal cycle lengths minimizing delay were found through the COST software developed for the study. COST searches for the CORSIM optimal cycle length minimizing delay with a heuristic searching method, a hybrid genetic algorithm. Among 64 models, the best one producing cycle lengths close enough to the optimal was selected through statistical tests. It was found from the verification test that the best model designs a cycle length as similar pattern to the ones minimizing delay. The cycle lengths from the proposed model are comparable to the ones from TRANSYT-7F.

• Journal of Intelligence and Information Systems
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• v.18 no.4
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• pp.43-57
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• 2012

To enhance the competitive advantage in a constantly changing business environment, an enterprise management must make the right decision in many business activities based on both internal and external information. Thus, providing accurate information plays a prominent role in management's decision making. Intuitively, historical data can provide a feasible estimate through the forecasting models. Therefore, if the service department can estimate the service quantity for the next period, the service department can then effectively control the inventory of service related resources such as human, parts, and other facilities. In addition, the production department can make load map for improving its product quality. Therefore, obtaining an accurate service forecast most likely appears to be critical to manufacturing companies. Numerous investigations addressing this problem have generally employed statistical methods, such as regression or autoregressive and moving average simulation. However, these methods are only efficient for data with are seasonal or cyclical. If the data are influenced by the special characteristics of product, they are not feasible. In our research, we propose a forecasting framework that predicts service demand of manufacturing organization by combining Case-based reasoning (CBR) and leveraging an unsupervised artificial neural network based clustering analysis (i.e., Self-Organizing Maps; SOM). We believe that this is one of the first attempts at applying unsupervised artificial neural network-based machine-learning techniques in the service forecasting domain. Our proposed approach has several appealing features : (1) We applied CBR and SOM in a new forecasting domain such as service demand forecasting. (2) We proposed our combined approach between CBR and SOM in order to overcome limitations of traditional statistical forecasting methods and We have developed a service forecasting tool based on the proposed approach using an unsupervised artificial neural network and Case-based reasoning. In this research, we conducted an empirical study on a real digital TV manufacturer (i.e., Company A). In addition, we have empirically evaluated the proposed approach and tool using real sales and service related data from digital TV manufacturer. In our empirical experiments, we intend to explore the performance of our proposed service forecasting framework when compared to the performances predicted by other two service forecasting methods; one is traditional CBR based forecasting model and the other is the existing service forecasting model used by Company A. We ran each service forecasting 144 times; each time, input data were randomly sampled for each service forecasting framework. To evaluate accuracy of forecasting results, we used Mean Absolute Percentage Error (MAPE) as primary performance measure in our experiments. We conducted one-way ANOVA test with the 144 measurements of MAPE for three different service forecasting approaches. For example, the F-ratio of MAPE for three different service forecasting approaches is 67.25 and the p-value is 0.000. This means that the difference between the MAPE of the three different service forecasting approaches is significant at the level of 0.000. Since there is a significant difference among the different service forecasting approaches, we conducted Tukey's HSD post hoc test to determine exactly which means of MAPE are significantly different from which other ones. In terms of MAPE, Tukey's HSD post hoc test grouped the three different service forecasting approaches into three different subsets in the following order: our proposed approach > traditional CBR-based service forecasting approach > the existing forecasting approach used by Company A. Consequently, our empirical experiments show that our proposed approach outperformed the traditional CBR based forecasting model and the existing service forecasting model used by Company A. The rest of this paper is organized as follows. Section 2 provides some research background information such as summary of CBR and SOM. Section 3 presents a hybrid service forecasting framework based on Case-based Reasoning and Self-Organizing Maps, while the empirical evaluation results are summarized in Section 4. Conclusion and future research directions are finally discussed in Section 5.