• Information Systems Review
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• v.4 no.2
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• pp.223-236
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• 2002

In an agent-based system, each agent has its own decision making capability and competes, cooperates, and communicates each other with an agent interface. Virtual manufacturing has characteristics as a typical application area of agent-based system: complex and time-bound decision making and negotiation. The time-boundness influences the choice of decision making models and design of protocols for internal and external negotiation. In this paper, we provide a case study which suggests a time-bound framework for external and internal negotiation between the agents for virtual manufacturing environment. We illustrate decision making model selection strategy and the system architecture of the agent-based system for the complex and time-bound environment.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.6
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• pp.1706-1727
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• 2023

Under Water Sensor Networks (UWSN) has gained attraction among various communities for its potential applications like acoustic monitoring, 3D mapping, tsunami detection, oil spill monitoring, and target tracking. Unlike terrestrial sensor networks, it performs an acoustic mode of communication to carry out collaborative tasks. Typically, surface sink nodes are deployed for aggregating acoustic phenomena collected from the underwater sensors through the multi-hop path. In this context, UWSN is constrained by factors such as lower bandwidth, high propagation delay, and limited battery power. Also, the vulnerabilities to compromise the aquatic environment are in growing numbers. The paper proposes an Energy-Efficient standalone Intrusion Detection System (EEIDS) to entail the acoustic environment against malicious attacks and improve the network lifetime. In EEIDS, attributes such as node ID, residual energy, and depth value are verified for forwarding the data packets in a secured path and stabilizing the nodes' energy levels. Initially, for each node, three agents are modeled to perform the assigned responsibilities. For instance, ID agent verifies the node's authentication of the node, EN agent checks for the residual energy of the node, and D agent substantiates the depth value of each node. Next, the classification of normal and malevolent nodes is performed by determining the score for each node. Furthermore, the proposed system utilizes the sheep-flock heredity algorithm to validate the input attributes using the optimized probability values stored in the training dataset. This assists in finding out the best-fit motes in the UWSN. Significantly, the proposed system detects and isolates the malicious nodes with tampered credentials and nodes with lower residual energy in minimal time. The parameters such as the time taken for malicious node detection, network lifetime, energy consumption, and delivery ratio are investigated using simulation tools. Comparison results show that the proposed EEIDS outperforms the existing acoustic security systems.

• The KIPS Transactions:PartA
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• v.10A no.5
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• pp.571-578
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• 2003

The appearnace of web technology has accelerated the development of the multimedia technology, the computer communication technology and the multimedia application contents. Researches on WBI(Web-based Instruction) system have combined the technology of the digital library and LOD,. REcently WBI(Web-based Instruction) model which is based on web has been proposed in the part of the new activity model of teaching-learning. As the demand of the customized coursewares from the lwarners is increased, the needs of the efficient and automated education agents in the web-based instruction are recognized. In this paper we propose a system monitors learner's behaviors constantly, evaluates them, and calculates his accomplishment. And the system offers suitable course to learner applying this accomplishment degree to agent's schedules. Therefore, the learner achieves an active and complete learning from the repeated and suitable course semantic-based retrieval.

• Proceedings of the Korean Vacuum Society Conference
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• 2014.02a
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• pp.382.2-382.2
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• 2014

We explain a method to fabricate multi-layered transparent conductive films (TCF) using graphene oxide (GO), copper powder and polyurethane (PU) solution. The flexible graphene nanosheets (GNSs) serve as nanoscale connection between conductive copper nanoparticles (CuNps) and PU nanofibers, resulting in a highly flexible TCF. To fabricate conductive films with high transmittance, polyurethane (PU) nanofibers were used for a conductive network consisting of CuNps and GNSs (CuNps-GNSs). In this experiment, copper powder and graphene oxides were mixed in deionized water with the ultrasonication for 2 h. NaBH4 solution is used as a reduction agents of CuNps and GNSs (CuNps-GNSs) under a nitrogen atmosphere in the oil bath at 100% for 24 h to mixed. The purified and dispersed CuNp-GNS were obtained in deionized water, and diluted to a 10wt.% based on the contents of GNSs. Polyurethane (PU) nanofibers on a PET substrate were formed by electrospinning method. PET slides coated with the PU nanofibers were immersed into CuNp-GNS solution for several second, rinsed briefly in deionized water, and dried to obtain self-assembled CuNp-GNS/PU films. The morphology of the multi-layered films were characterized with a field emission scanning electron microscope (FE-SEM, Hitachi S-4700) and atomic force microscope (AFM, PSIA XE-100). The electrical property was analysed by the I-V measurement system and the optical property was measured by the UV/VIS spectroscopy.

• The KIPS Transactions:PartB
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• v.13B no.6 s.109
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• pp.615-624
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• 2006

The objective of this paper is to implement parallel multi-layer ANN(Artificial Neural Network) simulator based on the mobile agent system which is executed in parallel in the virtual parallel distributed computing environment. The Multi-Layer Neural Network is classified by training session, training data layer, node, md weight in the parallelization-level. In this study, We have developed and evaluated the simulator with which it is feasible to parallel the ANN in the training session and training data parallelization because these have relatively few network traffic. In this results, we have verified that the performance of parallelization is high about 3.3 times in the training session and training data. The great significance of this paper is that the performance of ANN's execution on virtual parallel computer is similar to that of ANN's execution on existing super-computer. Therefore, we think that the virtual parallel computer can be considerably helpful in developing the neural network because it decreases the training time which needs extra-time.

• Journal of Korea Society of Industrial Information Systems
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• v.28 no.1
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• pp.27-34
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• 2023

The optimal assignment problem between agents and tasks is known as one of the representative problems of combinatorial optimization and an NP-hard problem. This paper covers multi agent-multi task assignment problems with uncertain completion probability. The completion probabilities are generally uncertain due to endogenous (agent or task) or exogenous factors in the system. Assignment decisions without considering uncertainty can be ineffective in a real situation that has volatility. To consider uncertain completion probability mathematically, a mathematical formulation with stochastic programming is illustrated. We also present an algorithm by using the sample average approximation method to solve the problem efficiently. The algorithm can obtain an assignment decision and the upper and lower bounds of the assignment problem. Through numerical experiments, we present the optimality gap and the variance of the gap to confirm the performances of the results. This shows the excellence and robustness of the assignment decisions obtained by the algorithm in the problem with uncertainty.

• Proceedings of the Korean Society for Language and Information Conference
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• 2002.02a
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• pp.2-27
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• 2002

As pact of a long-ranged project that aims at establishing database-theoretic semantics as a model of computational semantics, this presentation focuses on the development of a syntactic component for processing strings of words or sentences to construct semantic data structures. For design arid modeling purposes, the present treatment will be restricted to the analysis of some problematic constructions of Korean involving semi-free word order, conjunction arid temporal anchoring, and adnominal modification and antecedent binding. The present work heavily relies on Hausser's (1999, 2000) SLIM theory for language that is based on surface compositionality, time-linearity arid two other conditions on natural language processing. Time-linear syntax for natural language has been shown to be conceptually simple and computationally efficient. The associated semantics is complex, however, because it must deal with situated language involving interactive multi-agents. Nevertheless, by processing input word strings in a time-linear mode, the syntax cart incrementally construct the necessary semantic structures for relevant queries and valid inferences. The fragment of Korean syntax will be implemented in Malaga, a C-type implementation language that was enriched for both programming and debugging purposes arid that was particluarly made suitable for implementing in Left-Associative Grammar. This presentation will show how the system of syntactic rules with constraining subrules processes Korean sentences in a step-by-step time-linear manner to incrementally construct semantic data structures that mainly specify relations with their argument, temporal, and binding structures.

• Analytical Science and Technology
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• v.20 no.6
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• pp.524-528
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• 2007

Variations of the twenty one metal components (Mg, Al, V, Cr, Co, Sr, Ba, Na, K Mn, Fe, Cu, Zn, As, Hg, Pb, Ca, P, Mo, Cd, Sb) were analyzed in human hair sample by inductively coupled plasma mass spectrometry (ICP-MS). The effect of bleach and permanent wave manipulation on the elemental composition of hair were investigated. It was found that the composition of hair varied with hair bleach and permanent wave. Hair sample was collected from male in the age of thirties. Hair sample (0.05 g) was added to the Teflon digestion bomb together with 1.5 mL of nitric acid and an appropriate amount of In as an internal standard. The sample was then decomposed in the microwave digestion system. In normal hair, the contents of V, Cr, Mn, Fe, Co, Cu, Zn, As, Mo, Cd, Sb and P were increased in permanent wave hair, and Mg, Al, V, Co, Sr, Ba, Na and K were increased in bleached hair. But Mg, Al, Sr, Ba, Hg, Pb, Na, K, and Ca contents were decreased with permanent wave hair, Mn, Fe, Cu, Zn, As, Hg, Pb and Ca contents were decreased with bleached hair.

• IMMUNE NETWORK
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• v.22 no.1
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• pp.5.1-5.22
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• 2022

The approval of immunotherapies such as checkpoint inhibitors (CPIs), adoptive cell therapies and cancer vaccines has revolutionized the way cancer treatment is approached. While immunotherapies have improved clinical outcome in a variety of tumor types, some cancers have proven harder to combat using single agents, underscoring the need for multi-targeted immunotherapy approaches. Efficacy of CPIs and cancer vaccines requires patients to have a competent immune system with adequate cell numbers while the efficacy of adoptive cellular therapy is limited by the expansion and persistence of cells after infusion. A promising strategy to overcome these challenges is combination treatment with common gamma-chain cytokines. Gamma-chain cytokines play a critical role in the survival, proliferation, differentiation and function of multiple immune cell types, including CD8 T-cells and NK cells, which are at the center of the anti-tumor response. While the short halflife of recombinant cytokines initially limited their application in the clinic, advancements in protein engineering have led to the development of several next-generation drug candidates with dramatically increased half-life and bioactivity. When combining these cytokines with other immunotherapies, strong evidence of synergy has been observed in preclinical and clinical cancer settings. This promising data has led to the initiation of 70 ongoing clinical trials including IL-2, IL-7, IL-15 and IL-21. This review summarizes the recent advancements of common gamma-chain cytokines and their potential as a cancer immunotherapy.

• The Journal of Korean Association of Computer Education
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• v.7 no.6
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• pp.117-128
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• 2004

Compared with traditional face-to-face instruction, online learning causes learners to experience more severe feeling of isolation and results in higher dropout rate. This is due to the lack of interaction, sense of belonging, membership, interdependency, cooperation among members and social environment that enables persistence in online learning. Therefore, it is very important for grouping e-learning community to lower the dropout rate and eliminate feeling of isolation. In this paper, the research has been done on the inclination test list to be applied for grouping the desirable learning community. And on the basis of this research, the grouping system for e-learning community(GSE) based on intelligent multi agents for an inclination test using homogeneous and heterogeneous items has been developed. GSE system has such properties that construct a personalized user profile by an agent, and then make groupings according to users' inclination. When this system was evaluated, about 88% of learners were satisfied, and they wanted the group not to be disorganized but to be maintained.