• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.10
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• pp.4873-4888
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• 2018

A joint blind parameter estimation algorithm based on minimum channel stability function aimed at the non-cooperative high-order modulated paired carrier multiple access (PCMA) signals is proposed. The method, which uses hierarchical search to estimate time delay, amplitude and frequency offset and the estimation of phase offset, including finite ambiguity, is presented simultaneously based on the derivation of the channel stability function. In this work, the structure of hierarchical iterative processing is used to enhance the performance of the algorithm, and the improved algorithm is used to reduce complexity. Compared with existing data-aided algorithms, this algorithm does not require a priori information. Therefore, it has significant advantage in solving the problem of blind parameter estimation of non-cooperative high-order modulated PCMA signals. Simulation results show the performance of the proposed algorithm is similar to the modified Cramer-Rao bound (MCRB) when the signal-to-noise ratio is larger than 16 dB. The simulation results also verify the practicality of the proposed algorithm.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.3
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• pp.1111-1130
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• 2016

Cooperative communications can significantly improve the wireless transmission performance with the help of relay nodes. In cooperative communication networks, relay selection and power allocation are two key issues. In this paper, we propose a relay selection and power allocation scheme RS-PA-PSACO (Relay Selection-Power Allocation-Particle Swarm Ant Colony Optimization) based on PSACO (Particle Swarm Ant Colony Optimization) algorithm. This scheme can effectively reduce the computational complexity and select the optimal relay nodes. As one of the swarm intelligence algorithms, PSACO which combined both PSO (Particle Swarm Optimization) and ACO (Ant Colony Optimization) algorithms is effective to solve non-linear optimization problems through a fast global search at a low cost. The proposed RS-PA-PSACO algorithm can simultaneously obtain the optimal solutions of relay selection and power allocation to minimize the SER (Symbol Error Rate) with a fixed total power constraint both in AF (Amplify and Forward) and DF (Decode and Forward) modes. Simulation results show that the proposed scheme improves the system performance significantly both in reliability and power efficiency at a low complexity.

• Journal of The Korean Association of Information Education
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• v.4 no.1
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• pp.24-31
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• 2000

This paper is a study on the design and implementation of cooperative monitoring agent using mobile agent for educational portal site. Generally educational portal sites have many addresses of teachers homepage. Therefore, portal site has a very difficult task with maintaining a consistent address of site and administration of portal is impossible while to be on the search for all dead sites. In order to overcome this problem, we designed and implemented a mutual cooperative monitoring agent in order to filter off dead site using mobile agent. The monitoring agent applies at Korean educational portal site (KEPS) for elementary student and makes an experiment efficient. Finally a cooperative mobile agent is compared with a stationary monitoring agent.

• The Journal of Information Systems
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• v.8 no.1
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• pp.27-44
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• 1999

Case-Based Reasoning(CBR) offers a new approach for developing knowledge based systems. CBR has several research issues which can be divided into two categories : (1) static issues and (2) dynamic issues. The static issues are related to case representation scheme and case data model, that is, focus on casebase which is a repository of cases. The dynamic issues, on the other hand, are related to case retrieval procedure and problem solving process, i.e. case adaptation phase. This research is forcused on retrieval procedure Traditional query processing accepts precisely specified queries and only provides exact answers, thus requiring users to fully understand the problem domain and the casebase schema, but returning limited or even null information if the exact answer is not available. To remedy such a restriction, extending the classical notion of query answering to approximate query answering(AQA) has been explored. AQA can be achieved by neighborhood query answering or associative query answering. In this paper, neighborhood query answering technique is used for AQA. To reinforce the CBR process, a new retrieval procedure(cooperative CBR) using neighborhood query answering is proposed. An neighborhood query answering relaxes a query scope to enlarge the search range, or relaxes an answer scope to include additional information. Computer Aided Process Planning(CAPP) is selected as cooperative CBR application domain for test. CAPP is an essential key for achieving CIM. It is the bridge between CAD and CAM and translates the design information into manufacturing instructions. As a result of the test, it is approved that the problem solving ability of cooperative CBR is improved by relaxation technique.

• Journal of Intelligence and Information Systems
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• v.12 no.4
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• pp.61-72
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• 2006

Ubiquitous computing technologies become mature enough to be applied in acceptable ubiquitous services. In particular, in u-shopping area, personalized recommender systems which automatically collect the nomadic user-related context data and then provide them with products or shops in a flexible manner. However, legacy cooperative queries and context-aware queries so far do not come up with dynamically changing situations and ambiguous query commands, respectively. Hence, The purpose of this paper is to propose a personalized context-aware cooperative query that supports a multi-level data abstraction hierarchy and conceptual distance metric among node instances, while considering the user's context data. To show the feasibility of the methodology proposed in this paper, we have implemented a prototype system, CACO, in the area of site search in a large-scale shopping mall.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38C no.12
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• pp.1207-1215
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• 2013

This paper proposes the location estimation techniques of distributed targets with the multi-sensor data perceived through IR sensors of the military robots in consideration of obstacles. In order to match up targets with measured azimuths, to add to the depth-first search (DFS) algorithms in free-obstacle environment, we suggest the expanded DFS (EDS) algorithm including bypass path search, partial path search, middle level ending, and the supplementation of decision metric. After matching up targets with azimuths, we estimate the coordinate of each target by obtaining the intersection point of the azimuths with the least square error (LSE) algorithm. The experimental results show the error rate of estimated location, mean number of calculating nodes, and mean distance between real coordinates and estimated coordinates of the proposed algorithms.

• Korea Journal of Hospital Management
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• v.18 no.2
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• pp.57-80
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• 2013

The purpose of this study is to examine the information search behavior of medical consumers visiting a hospital, and investigate the consumption values of medical consumers, classified according to the information search behavior, and characteristics of online health and disease information. This study also tried to identify the factors affecting medical consumers classified according to information search behavior, and gain an extensive understanding of medical consumers'consumption values and online information use. The analysis results of this study are as follows: First, the consumption values of medical consumers were classified into a total of 7 factors, i.e. future-positive value, family-oriented value, sustainablehealth value, rational-progressive value, social-cooperative value, socialachiever value and hedonistic-individual value. Next, the characteristics of medical consumers'online information search behavior were classified into three types of consumer groups, i.e. the limited information-oriented consumer group, the practical information-oriented consumer group and the passionate information-oriented consumer group. Second, the analysis of the differences among the three groups classified according to the characteristics of the information search behavior in terms of consumption values, use of online information sources, utility and the intention to reuse online information showed that all the differences were statistically significant. The passionate information-oriented consumer group showed the highest scores in the sustainable-health value, the social-achiever value and the hedonisticindividual value. Third, the factors affecting medical consumers, classified into three groups according to the characteristics of the information search behavior, were found to be socio-demographic variables like consumers' age and occupation, consumers'consumption values, use of online information sources, and utility of online information. This study tried to understand what values medical consumers have according to the differences in the information search behavior by examining the consumption values of medical consumers according to the information search behavior. The significance of this study lies in the fact that consumption values are instrumental in understanding medical consumers by identifying the fundamental motives and desires of consumers' behavior.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.4
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• pp.2038-2057
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• 2017

Gaussian learning is a new technology in the computational intelligence area. However, this technology weakens the learning ability of a particle swarm and achieves a lack of diversity. Thus, this paper proposes a vector Gaussian learning strategy and presents an effective approach, named particle swarm optimization based on vector Gaussian learning. The experiments show that the algorithm is more close to the optimal solution and the better search efficiency after we use vector Gaussian learning strategy. The strategy adopts vector Gaussian learning to generate the Gaussian solution of a swarm's optimal location, increases the learning ability of the swarm's optimal location, and maintains the diversity of the swarm. The method divides the states into normal and premature states by analyzing the state threshold of the swarm. If the swarm is in the premature category, the algorithm adopts an inertia weight strategy that decreases linearly in addition to vector Gaussian learning; otherwise, it uses a fixed inertia weight strategy. Experiments are conducted on eight well-known benchmark functions to verify the performance of the new approach. The results demonstrate promising performance of the new method in terms of convergence velocity and precision, with an improved ability to escape from a local optimum.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.5A
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• pp.450-456
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• 2011

In this paper, idle channel search schemes based on spectrum sensing are proposed for cognitive radio systems with multiple channels. Specifically, we propose a scheme for determining the order of sensing for multiple channels, for which the probability of each channel being idle is estimated every search interval. By performing sensing in the descending order of the probabilities, the time required for searching idle channels is expected to decrease. In addition, we combine the proposed scheme with a user grouping scheme to further improve the sensing performance. Simulation results show that the user grouping reduces the search time, although it degrades the reliability of detection. The proposed search scheme based on probability estimation of channel idleness is found to reduce the search time significantly as compared to the conventional random search scheme. We apply both the proposed search scheme and user grouping scheme to a cognitive radio system to validate the overall performance.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.1
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• pp.201-209
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• 2014

This paper proposes a genetic algorithm to maximize the connectivity among the mobile nodes for the cooperative communication in ad-hoc networks. In general, as the movement of the mobile nodes in the networks increases, the amount of calculation for finding the solution would be too much increased. To obtain the optimal solution within a reasonable computation time for a high-density network, we propose a genetic algorithm to obtain the optimal solution for maximizing the connectivity. In order to make a search more efficient, we propose some efficient neighborhood generating operations of the genetic algorithm. We evaluate those performances through some experiments in terms of the maximum number of connections and the execution time of the proposed algorithm. The comparison results show that the proposed algorithm outperforms other existing algorithms.