• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.1
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• pp.42-48
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• 2006

In this paper a joint detection method for other cell interference cancellation is proposed in the next generation hybrid TD-CDMA mobile communication systems. A joint detection technique, a most characteristic feature of hybrid TD-CDMA mobile communication systems. retrieves users' data in the same time slot simultaneously with the elimination of multiple user interference. Previously a two stage joint detection method was proposed to cancel other cell interference as well as multiple user interference in the target cell. However the previous scheme does not have concrete ways to recognize other cell users who give major interference to the target cell. Thus all users in neighbor other cells has to be jointly detected and it causes huge complexity of the two stage joint detection. In this paper a method is proposed to perform two stage joint detection according to users' interference with the target cell. Performances of the proposed scheme are investigated through simulations and compared to the previous method the proposed method has no performance degradation and also lower the complexity of two stage joint detection significantly.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.1
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• pp.135-140
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• 2020

Recently, due to the rapid diffusion of intelligent systems and IoT technologies, power saving techniques in real-time embedded systems has become important. In this paper, we propose P-GA (Parallel Genetic Algorithm), a scheduling algorithm aims at reducing the power consumption of real-time systems in multi-core hybrid memory environments. P-GA improves the Proportional-Fairness (PF) algorithm devised for multi-core environments by combining the dynamic voltage/frequency scaling of the processor with the nonvolatile memory technologies. Specifically, P-GA applies genetic algorithms for optimizing the voltage and frequency modes of processors and the memory types, thereby minimizing the power consumptions of the task set. Simulation experiments show that the power consumption of P-GA is reduced by 2.85 times compared to the conventional schemes.

• Proceedings of the Materials Research Society of Korea Conference
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• 2003.11a
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• pp.135-135
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• 2003

The fast evolution in the fold of optical communication systems demands powerful optical information treatment. These functions can be performed by integrated optical systems. A key component of such systems is erbium doped waveguide amplifier(EDWA). The intra 4f radiative transition of Er at 1.5 $\mu\textrm{m}$ is particularly interesting because this wavelength is standard in optical telecommunications. The fabrication of waveguide amplifier for integrated optics using sol-gel process has received an increasing attention. Potential advantage of lower cost by less capital equipment and easy processing makes this process an attractive alternatives to conventional technologies like flame hydrolysis deposition, ion exchange and chemical vapor deposition, etc. In addition, sol-gel process has been found to be extremely suitable for the control of composition and refractive index related directly with optical properties. The main drawback of such an amplifier with respect to the EDWA is the need for a much higher Er3+ concentration to compensate for the smaller interaction length. However, the high doping of Er might be resulted in the non-radiative relaxation by clustering of Er ions End co-operative upconversion. In order to solve this problem, we investigate the possibility of avoiding short Er-Er distances by encapsulation of Er3+ ions in hosts such as organic-inorganic hybrid materials. For inorganic-organic hybrid sols, methacryloxypropyltrimethoxysilane (MPTS), zirconyl chloride octahydrate and erbium(III) chloride hexahydrate were used as starting materials, followed by conventional sol-gel process. It was observed by TEM that nano sols having core/shell toplology were formed, depending on the mole ratio of Zr/Er. The surface roughness for the coatings on Si substrate was investigated by AFM as a function of Zr/Er ratio. The local environment and vibrational Properties of Er3+ ions were studied using Near-IR, FT-IR, and UV/Vis spectroscopy. Nano hybrid coatings derived from polymer and Er doped encapsulation Eave the good luminescence at 1.55$\mu\textrm{m}$.

• Journal of Intelligence and Information Systems
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• v.10 no.2
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• pp.133-145
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• 2004

Combining multiple classifiers to obtain improved performance over the individual classifier has been a widely used technique. The task of constructing a multiple classifier system(MCS) contains two different issues : how to generate a diverse set of base-level classifiers and how to combine their predictions. In this paper, we review the characteristics of the existing multiple classifier systems: bagging, boosting, and stacking. And then we propose new MCSs: stacked bagging, stacked boosting, bagged stacking, and boasted stacking. These MCSs are a sort of hybrid MCSs that combine advantageous characteristics of the existing ones. In order to evaluate the performance of the proposed schemes, we conducted experiments with nine different real-world datasets from UCI KDD archive. The result of experiments showed the superiority of our hybrid MCSs, especially bagged stacking and boosted stacking, over the existing ones.

• The Journal of Korean Association of Computer Education
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• v.8 no.5
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• pp.129-137
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• 2005

Criticality prediction models that identify fault-prone spots using system design specifications play an important role in reducing development costs of large systems such as telecommunication systems. Many criticality prediction models using complexity metrics have been suggested. But most of them need training data set for model training. And they are classification models that can only classify design entities into fault-prone group and non fault-prone group. To solve this problem, this paper builds a new prediction model, HMM, using two styled hybrid metrics. HMM has strong point that it does not need training data and it enables comparison between design entities by criticality. HMM is implemented and compared with a well-known prediction model, BackPropagation neural network Model(BPM), considering internal characteristics and accuracy of prediction.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.4 no.4
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• pp.575-594
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• 2010

In this study, we propose a space search algorithm (SSA) and then introduce a hybrid optimization of fuzzy inference systems based on SSA and information granulation (IG). In comparison with "conventional" evolutionary algorithms (such as PSO), SSA leads no.t only to better search performance to find global optimization but is also more computationally effective when dealing with the optimization of the fuzzy models. In the hybrid optimization of fuzzy inference system, SSA is exploited to carry out the parametric optimization of the fuzzy model as well as to realize its structural optimization. IG realized with the aid of C-Means clustering helps determine the initial values of the apex parameters of the membership function of fuzzy model. The overall hybrid identification of fuzzy inference systems comes in the form of two optimization mechanisms: structure identification (such as the number of input variables to be used, a specific subset of input variables, the number of membership functions, and polyno.mial type) and parameter identification (viz. the apexes of membership function). The structure identification is developed by SSA and C-Means while the parameter estimation is realized via SSA and a standard least square method. The evaluation of the performance of the proposed model was carried out by using four representative numerical examples such as No.n-linear function, gas furnace, NO.x emission process data, and Mackey-Glass time series. A comparative study of SSA and PSO demonstrates that SSA leads to improved performance both in terms of the quality of the model and the computing time required. The proposed model is also contrasted with the quality of some "conventional" fuzzy models already encountered in the literature.

• Journal of KIISE
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• v.42 no.2
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• pp.183-189
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• 2015

Recently, computer systems have encountered difficulties in making further progress due to the technical limitations of DRAM based main memory technologies. This has motivated the development of next generation memory technologies that have high density and non-volatility. However, these new memory technologies also have their own intrinsic limitations, making it difficult for them to currently be used as main memory. In order to overcome these problems, we propose a hybrid main memory system, namely HyMN, which utilizes the merits of next generation memory technologies by combining two types of memory: Write-Affable RAM(WAM) and Read-Affable RAM(ReAM). In so doing, we analyze the appropriate WAM size for HyMN, at which we can avoid the performance degradation. Further, we show that the execution time performance of HyMN, which provides an additional benefit of durability against unexpected blackouts, is almost comparable to legacy DRAM systems under normal operations.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.4
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• pp.211-220
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• 2017

The application of the theoretical model to real assembly lines has been one of the biggest challenges for researchers and industrial engineers. There should be some realistic approach to achieve the conflicting objectives on real systems. Therefore, in this paper, a model is developed to synchronize a real system (A discrete event simulation model) with a theoretical model (An optimization model). This synchronization will enable the realistic optimization of systems. A job assignment model of the assembly line is formulated for the evaluation of proposed realistic optimization to achieve multiple conflicting objectives. The objectives, fluctuation in cycle time, throughput, labor cost, energy cost, teamwork and deviation in the skill level of operators have been modeled mathematically. To solve the formulated mathematical model, a multi-objective simulation integrated hybrid genetic algorithm (MO-SHGA) is proposed. In MO-SHGA each individual in each population acts as an input scenario of simulation. Also, it is very difficult to assign weights to the objective function in the traditional multi-objective GA because of pareto fronts. Therefore, we have proposed a probabilistic based linearization and multi-objective to single objective conversion method at population evolution phase. The performance of MO-SHGA is evaluated with the standard multi-objective genetic algorithm (MO-GA) with both deterministic and stochastic data settings. A case study of the goalkeeping gloves assembly line is also presented as a numerical example which is solved using MO-SHGA and MO-GA. The proposed research is useful for the development of synchronized human based assembly lines for real time monitoring, optimization, and control.

• The KIPS Transactions:PartB
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• v.10B no.3
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• pp.297-304
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• 2003

Nowdays, with the explosive growth of the web information, web users Increase requests of systems which collect and analyze web pages that are relevant. The systems which were develop to solve the request were used clustering methods to improve the duality of information. Clustering is defining inter relationship of unordered data and grouping data systematically. The systems using clustering provide the grouped information to the users. So, they understand the information efficiently. We proposed a hybrid clustering method to cluster a large quantity of data efficiently. By that method, We generate initial clusters using COBWEB Algorithm and refine them using Ezioni Algorithm. This paper adds two ideas in prior hybrid clustering method to increment accuracy and efficiency of clusters. Firstly, we propose the clustering method considering weight of attributes of data. Second, we redefine evaluation functions which generate initial clusters to increase efficiency in clustering. Clustering method proposed in this paper processes a large quantity of data and diminish of dependancy on sequence of input of data. So the clusters are useful to make user profiles in high quality. Ultimately, we will show that the proposed clustering method outperforms the pervious clustering method in the aspect of precision and execution speed.

• Journal of Advanced Navigation Technology
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• v.26 no.6
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• pp.448-455
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• 2022

In this study, when establishing a UTM(UAS Traffic Management) system, a corridor must be set to separate the flight distance between unmanned aerial vehicles, and the size of the corridor was calculated in consideration of TSE(Total System Error). The flight data of the straight section and the turning section were collected using a hybrid vertical take-off and landing unmanned aerial vehicle. The flight data were derived from the TSE using the SQSM(Scalar Quantity Summation Method) method, and the impact on the straight and turning sections was analyzed by calculating in detail by NSE(Navigation System Error) and FTE(Flight Technical Error). The corridor size was calculated by referring to the TSE analysis results and PBN (Performance-based Navigation) manual.