• Proceeding of EDISON Challenge
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• 2014.03a
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• pp.63-74
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• 2014

Predicting protein loop structures is an important modeling problem since protein loops are often involved in diverse biological functions by participating in enzyme active sites, ligand binding sites, etc. However, loop structure prediction is difficult even when structures of homologous proteins are known due to large sequence and structure variability among loops of homologous proteins. Therefore, an ab initio approach is necessary to solve loop modeling problems. One of the difficulties in the development of ab initio loop modeling method is to derive an accurate scoring function that closely approximates the true free energy function. In particular, entropy as well as energy contribution have to be considered adequately for loops because loops tend to be flexible compared to other parts of protein. In this study, the contribution of conformational entropy is considered in scoring loop conformations by employing "colony energy" which was previously proposed to estimate the free energy for an ensemble of conformations. Loop conformations were generated by using two EDISON_Chem programs GalaxyFill and GalaxySC, and colony energy was designed for this sampling by tuning relevant parameters. On a test set of 40 loops, the accuracy of predicted loop structure improved on average by scoring with the colony energy compared to scoring by energy alone. In addition, high correlation between colony energy and deviation from the native structure suggested that more extensive sampling can further improve the prediction accuracy. In another test on 6 ligand-binding loops that show conformational changes by ligand binding, both ligand-free and ligand-bound states could be identified by using colony energy when no information on the ligand-bound conformation is used.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.9
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• pp.3496-3514
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• 2015

One of the challenging tasks in Mobile Ad hoc Network is to discover precise optimal routing solution due to the infrastructure-less dynamic behavior of wireless mobile nodes. Ant Colony Optimization, a swarm Intelligence technique, inspired by the foraging behaviour of ants in colonies was used in the past research works to compute the optimal path. In this paper, we propose a Recurrent Ant Colony Optimization (RECACO) that executes the actual Ant Colony Optimization iteratively based on recurrent value in order to obtain an optimal path convergence. Each iteration involves three steps: Pheromone tracking, Pheromone renewal and Node selection based on the residual energy in the mobile nodes. The novelty of our approach is the inclusion of new pheromone updating strategy in both online step-by-step pheromone renewal mode and online delayed pheromone renewal mode with the use of newly proposed metric named ELD (Energy Load Delay) based on energy, Load balancing and end-to-end delay metrics to measure the performance. RECACO is implemented using network simulator NS2.34. The implementation results show that the proposed algorithm outperforms the existing algorithms like AODV, ACO, LBE-ARAMA in terms of Energy, Delay, Packet Delivery Ratio and Network life time.

• International Journal of Computer Science & Network Security
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• v.21 no.11
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• pp.312-320
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• 2021

Increase in computational cost and exhaustive search can lead to more complexity and computational energy. Thus, there is need for effective and efficient scheme to reduce the complexity to achieve optimal energy utilization. This will improve the energy efficiency and enhance the proficiency in terms of the resources needed to achieve convergence. This paper primarily focuses on the development of hybrid swarm intelligence scheme for reducing the computational complexity in binary optimization. In order to reduce the complexity, both artificial bee colony (ABC) and particle swarm optimization (PSO) have been employed to effectively minimize the exhaustive search and increase convergence. First, a new approach using ABC and PSO has been proposed and developed to solve the binary optimization problem. Second, the scout for good quality food sources is accomplished through the deployment of PSO in order to optimally search and explore the best source. Extensive experimental simulations conducted have demonstrate that the proposed scheme outperforms the ABC approaches for reducing complexity and energy consumption in terms of convergence, search and error minimization performance measures.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.2
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• pp.410-415
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• 2007

Recent advances in sensor network technology have open up challenges for its effective routing. Routing protocol receives most of the attention because routing protocols might differ depending on the application and network architecture. In the rapidly changing environment and dynamic nature of network formation efficient routing and energy consumption are very crucial. Sensor networks differ from the traditional networks in terms of energy consumption. Thus, data-centric technologies should be used to perform routing to yield an energy-efficient dissemination. By exploiting the advantages of both ant colony optimization techniques in network routing and the ability of data centric muting to organize data for delivery, our approach will cover features for building an efficient autonomous sensor network.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.6
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• pp.2504-2526
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• 2016

Wireless sensors are always deployed in brutal environments, but as we know, the nodes are powered only by non-replaceable batteries with limited energy. Sending, receiving and transporting information require the supply of energy. The essential problem of wireless sensor network (WSN) is to save energy consumption and prolong network lifetime. This paper presents a new communication protocol for WSN called Dynamical Threshold Control Algorithm with three-parameter Particle Swarm Optimization and Ant Colony Optimization based on residual energy (DPA). We first use the state of WSN to partition the region adaptively. Moreover, a three-parameter of particle swarm optimization (PSO) algorithm is proposed and a new fitness function is obtained. The optimal path among the CHs and Base Station (BS) is obtained by the ant colony optimization (ACO) algorithm based on residual energy. Dynamical threshold control algorithm (DTCA) is introduced when we re-select the CHs. Compared to the results obtained by using APSO, ANT and I-LEACH protocols, our DPA protocol tremendously prolongs the lifecycle of network. We observe 48.3%, 43.0%, and 24.9% more percentages of rounds respectively performed by DPA over APSO, ANT and I-LEACH.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.42 no.1
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• pp.87-94
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• 2019

A wireless sensor network is emerging technology and intelligent wireless communication paradigm that is dynamically aware of its surrounding environment. It is also able to respond to it in order to achieve reliable and efficient communication. The dynamical cognition capability and environmental adaptability rely on organizing dynamical networks effectively. However, optimally clustering the cognitive wireless sensor networks is an NP-complete problem. The objective of this paper is to develop an optimal sensor network design for maximizing the performance. This proposed Ranking Artificial Bee Colony (RABC) is developed based on Artificial Bee Colony (ABC) with ranking strategy. The ranking strategy can make the much better solutions by combining the best solutions so far and add these solutions in the solution population when applying ABC. RABC is designed to adapt to topological changes to any network graph in a time. We can minimize the total energy dissipation of sensors to prolong the lifetime of a network to balance the energy consumption of all nodes with robust optimal solution. Simulation results show that the performance of our proposed RABC is better than those of previous methods (LEACH, LEACH-C, and etc.) in wireless sensor networks. Our proposed method is the best for the 100 node-network example when the Sink node is centrally located.

• Bioinformatics and Biosystems
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• v.1 no.2
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• pp.103-108
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• 2006

Computational prediction of protein-ligand binding has been widely used as a tool to discover lead compounds fur new drugs. Prediction accuracy is determined in part by the scoring function used in docking calculations. Diverse scoring functions are available, and these can be classified into force-field based, empirical, and knowledge-based functions depending upon the basic assumptions made in development. Among these, force-field based functions consider physical interactions the most in detail. However, the force-field based functions have the drawback of not including the entropic effect while considering only the energy contribution such as dispersion or electrostatic forces. In this article, a method to take into account of the entropic effect using the colony energy is suggested when force-field based scoring functions is used by extracting conformational information obtained from the pre-existing docking program. An improved result for decoy discrimination is illustrated when the method is applied to the DOCK scoring function, and this implies that more accurate docking calculation is possible.

• Korean Management Science Review
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• v.26 no.3
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• pp.55-65
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• 2009

The objective of this paper is to propose an ant colony optimization (ACO) for clustering design in wireless sensor network problem. This proposed ACO approach is designed to deal with the dynamics of the sensor nodes which can be adaptable to topological changes to any network graph in a time. Long communication distances between sensors and a sink in a sensor network can greatly consume the energy of sensors and reduce the lifetime of a network. We can greatly minimize the total communication distance while minimizing the number of cluster heads using proposed ACO. Simulation results show that our proposed method is very efficient to find the best solutions comparing to the optimal solution using CPLEX in 100, 200, and 400 node sensor networks.

• Mycobiology
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• v.38 no.2
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• pp.118-121
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• 2010

Single ascospore isolates of Cordyceps bassiana were observed for their colony pigmentation on Sabouraud Dextrose agar plus Yeast Extract (SDAY) plates and were inoculated in a brown rice medium for production of fruiting bodies. Colony pigmentation did not show any relationship with perithecial stromata formation. The isolates were also grown on opposite sides of SDAY agar plates and were observed for vegetative compatibility. Neither vegetative compatibility nor perithecial stromata could be found to be related to each other. It was concluded that fertile fruiting body production was independent of colony pigmentation and vegetative compatibility. Synnemata formation was found to be more common than perithecial stromata formation. This might be due to its highly conidiogenous anamorphic stage, i.e., Beauveria bassiana.

• Proceedings of the Korean Society of Veterinary Pathology Conference
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• 2000.09a
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• pp.21-21
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• 2000

We performed this study to determine the effect of Bu-Zhong-Yi-Qi-Tang, as a prescription of traditional Oriental medicine, and its major ingredients on jejunal crypt survival, endogenous spleen colony formation, and apoptosis in jejunal crypt cells of mice irradiated with high and low dose of r-radiation. Bu-Zhong-Yi-Qi-Tang administration before irradiation protected the jejunal crypts (p<0.0001), increased the formation of endogenous spleen colony (p<0.05) and reduced the frequency of radiation-induced apoptosis (p<0.05). (omitted)