• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.17-26
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• 2022

Sensor Nodes play a major role to monitor and sense the variations in physical space in various real-time application scenarios. These nodes are powered by limited battery resources and replacing those resource is highly tedious task along with this it increases implementation cost. Thus, maintaining a good network lifespan is amongst the utmost important challenge in this field of WSN. Currently, energy efficient routing techniques are considered as promising solution to prolong the network lifespan where multi-hop communications are performed by identifying the most energy efficient path. However, the existing scheme suffer from performance related issues. To solve the issues of existing techniques, a novel hybrid technique by merging particle swarm optimization and game theory model is presented. The PSO helps to obtain the efficient number of cluster and Cluster Head selection whereas game theory aids in finding the best optimized path from source to destination by utilizing a path selection probability approach. This probability is obtained by using conditional probability to compute payoff for agents. When compared to current strategies, the experimental study demonstrates that the proposed GTPSO strategy outperforms them.

• Journal of Photoscience
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• v.10 no.2
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• pp.199-202
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• 2003

The relationship between the morphology of nanostructured TiO$_2$ films and the photo-injected electron transport has been investigated using intensity-modulated photocurrent spectroscopy (IMPS). For this purpose, three different TiO$_2$ films with 5 ${\mu}{\textrm}{m}$ thickness are prepared: The rutile TiO$_2$ film with 500 nm-sized cluster-like spherical bundles composed of the individual needles (Tl), the rutile TiO$_2$ film made up of non-oriented, homogeneously distributed rod-shaped particles having a dimension of approximately 20${\times}$80 nm (T2), and the anatase TiO$_2$ film with 20 nm-sized spherically shaped particles (T3). Cross sectional scanning electron micrographs show that all of the TiO$_2$films have a quite different particle packing density: poorly packed Tl film, loosely packed T2 film and densely packed T3 film. The electron transport is found to be significantly influenced by film morphology. The effective electron diffusion coefficient D$_{eff}$ derived from the IMPS time constant is an order of magnitude lower for T2 than for T3, but the D$_{eff}$ for the Tl sample is much lower than T2. These differences in the rate of electron transport are ascribed to differences in the extent of interparticle connectivity associated with the particle packing density.ity.

• Korean Institute of Interior Design Journal
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• v.24 no.2
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• pp.67-77
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• 2015

This study is to contemplate an ultimate goal and new methodology the architecture and space design community should pursue forward by analyzing concepts in Kengo Kuma's idea of "Anti-object" and examining his design methods and characteristics. To this end, I reviewed space design methods and features in his book of "Anti-Object" and his architectures built around in 2000. The result is as in the followings. (1) Contact is an essential concept of "Anti-object" to connect and integrate divided materials and consciousness with time and space. (2) Elimination is a meaningful way to reverse "cohesiveness" of agglomerated cluster which is a form of object and reconstruct it into the form of passive and acceptive "Anti-object". This idea is realized through overlap of material property and removal of massing. (3) Minimization is a concept of "Anti-object" to set the temporality free from constraints of materials. Three-dimensional transparent faces and lines or patterns of porous materials can be used to remove static and coercive volume. (4) A particle is a "reflector of its environment." It rebuilds one-way or disconnected communication between human and architecture into an interactive one. Kengo Kuma materializes this "particle" by exploring positional relation with physical paths, precise details and measurements.

• ETRI Journal
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• v.33 no.6
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• pp.857-863
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• 2011

Due to uncertainties in target motion and randomness of deployed sensor nodes, the problem of imbalance of energy consumption arises from sensor scheduling. This paper presents an energy-efficient adaptive sensor scheduling for a target monitoring algorithm in a local monitoring region of wireless sensor networks. Owing to excessive scheduling of an individual node, one node with a high value generated by a decision function is preferentially selected as a tasking node to balance the local energy consumption of a dynamic clustering, and the node with the highest value is chosen as the cluster head. Others with lower ones are in reserve. In addition, an optimization problem is derived to satisfy the problem of sensor scheduling subject to the joint detection probability for tasking sensors. Particles of the target in particle filter algorithm are resampled for a higher tracking accuracy. Simulation results show this algorithm can improve the required tracking accuracy, and nodes are efficiently scheduled. Hence, there is a 41.67% savings in energy consumption.

• Journal of Information Processing Systems
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• v.13 no.4
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• pp.1000-1013
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• 2017

Clustering is a NP-hard problem that is used to find the relationship between patterns in a given set of patterns. It is an unsupervised technique that is applied to obtain the optimal cluster centers, especially in partitioned based clustering algorithms. On the other hand, cat swarm optimization (CSO) is a new meta-heuristic algorithm that has been applied to solve various optimization problems and it provides better results in comparison to other similar types of algorithms. However, this algorithm suffers from diversity and local optima problems. To overcome these problems, we are proposing an improved version of the CSO algorithm by using opposition-based learning and the Cauchy mutation operator. We applied the opposition-based learning method to enhance the diversity of the CSO algorithm and we used the Cauchy mutation operator to prevent the CSO algorithm from trapping in local optima. The performance of our proposed algorithm was tested with several artificial and real datasets and compared with existing methods like K-means, particle swarm optimization, and CSO. The experimental results show the applicability of our proposed method.

• Journal of Information Processing Systems
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• v.15 no.4
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• pp.986-1016
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• 2019

Mobile user interface pattern (MUIP) is a kind of structured representation of interaction design knowledge. Several studies have suggested that MUIPs are a proven solution for recurring mobile interface design problems. To facilitate MUIP selection, an effective clustering method is required to discover hidden knowledge of pattern data set. In this paper, we employ the semi-supervised kernel fuzzy c-means clustering (SSKFCM) method to cluster MUIP data. In order to improve the performance of clustering, clustering parameters are optimized by utilizing the global optimization capability of particle swarm optimization (PSO) algorithm. Since the PSO algorithm is easily trapped in local optima, a novel PSO algorithm is presented in this paper. It combines an improved intuitionistic fuzzy entropy measure and a new population search strategy to enhance the population search capability and accelerate the convergence speed. Experimental results show the effectiveness and superiority of the proposed clustering method.

• Journal of the Semiconductor & Display Technology
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• v.21 no.3
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• pp.45-49
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• 2022

The fourth industrial revolution, internet of things, and the expansion of online web services have increased an exponential growth and deployment in the number of cloud data centers (CDC). The cloud is emerging as new paradigm for delivering the Internet-based computing services. Due to the dynamic and non-linear workload and availability of the resources is a critical problem for efficient workload and resource management. In this paper, we propose the particle swarm optimization (PSO) based gated recurrent unit (GRU) neural network for efficient prediction the future value of the CPU and memory usage in the cloud data centers. We investigate the hyper-parameters of the GRU for better model to effectively predict the cloud resources. We use the Google Cluster traces to evaluate the aforementioned PSO-GRU prediction. The experimental shows the effectiveness of the proposed algorithm.

• Publications of The Korean Astronomical Society
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• v.7 no.1
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• pp.89-96
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• 1992

Recent spectroscopic observations indicate concentration of dark masses in the nuclei of nearby galaxies. This has been usually interpreted as the presence of massive black holes in these nuclei. Alternative explanations such as the dark cluster composed of low mass stars (brown dwarfs) or dark stellar remnants are possible provided that these systems can be stably maintained for the age of galaxies. For the case of low mass star cluster, mass of individual stars can grow to that of conventional stars in collision time scale. The requirement of collision time scale being shorter than the Hubble time gives the minimum cluster size. For typical conditions of M31 or M32, the half-mass radii of dark clusters can be as small as 0.1 arcsecond. For the case of clusters composed of stellar remnants, core-collapse and post-collapse expansion are required to take place in longer than Hubble time. Simple estimates reveal that the size of these clusters also can be small enough that no contradiction with observational data exists for the clusters made of white dwarfs or neutron stars. We then considered the possible outcomes of interactions between the black hole and the surrounding stellar system. Under typical conditions of M31 or M32, tidal disruption will occur every $10^3$ to $10^4$ years. We present a simple scenario for the evolution of stellar debris based on basic principles. While the accretion of stellar material could produce large amount of radiation so that the mass-to-light ratio can become too small compared to observational values it is too early to rule out the black hole model because the black hole can consume most of the stellar debris in time scale much shorter than mean time between two successive tidal disruptions. Finally we outline recent effort to simulate the process of tidal disruption and subsequent evolution of the stellar debris numerically using Smoothed Particle Hydrodynamics technique.

• Proceedings of the KSME Conference
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• 2007.05b
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• pp.2879-2884
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• 2007

Dynamic responses of electrorheological (ER) fluids in steady pressure flow to stepwise electric field excitations are investigated experimentally. The transient periods under various applied electric fields and flow velocities were determined from the pressure behavior of the ER fluid in the flow channel with two parallel-plate electrodes. The pressure response times were exponentially decreased with the increase of the flow velocity, but increased with the increase of the applied electric field strength. In order to investigate the cluster structure formation of the ER particles, it was verified using the flow visualization technique that the transient response of ER fluids in the flow mode is assigned to the densification process in the competition of the electric field-induced particle attractive interaction forces and the hydrodynamic forces, unlike that in the shear mode determined by the aggregation process.

• Bulletin of the Korean Chemical Society
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• v.26 no.11
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• pp.1723-1727
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• 2005

A generalized fractional diffusion equation (FDE) is presented, which describes the time-evolution of the spatial distribution of a particle performing continuous time random walk (CTRW) on a fractal lattice. For a case corresponding to the CTRW with waiting time distribution that behaves as $\psi(t) \sim (t) ^{-(\alpha+1)}$, the FDE is solved to give analytic expressions for the Green’s function and the mean squared displacement (MSD). In agreement with the previous work of Blumen et al. [Phys. Rev. Lett. 1984, 53, 1301], the time-dependence of MSD is found to be given as < $r^2(t)$ > ~ $t ^{2\alpha/dw}$, where $d_w$ is the walk dimension of the given fractal. A Monte-Carlo simulation is also performed to evaluate the range of applicability of the proposed FDE.