• Journal of the Institute of Electronics Engineers of Korea CI
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• v.45 no.1
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• pp.78-85
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• 2008

Most researches for power management have focused on increasing the utilization of system performance by scaling operating frequency or operating voltage. If operating frequency is changed frequently, it reduces the real system performance. To reduce power consumption, alternative approaches use the limited number of operating frequencies or set the smoothing frequencies during execution to increase the system performance, but they are not suitable for real time applications. To reduce power consumption and increase system performance for real time applications, this paper proposes a new power-aware schedule method by allocating operating frequencies and by setting smoothing frequencies. The algorithm predicts so that frequencies with continuous interval are mapped into discrete operating frequencies. The frequency smoothing reduces overheads of systems caused by changing operating frequencies frequently as well as power consumption caused by the frequency mismatch at a wide frequency interval. The simulation results show that the proposed algorithm reduces the power consumption up to 40% at maximum and 15% on average compared to the CC RT-DVS.

• The KIPS Transactions:PartD
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• v.9D no.1
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• pp.43-56
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• 2002

Most of works have been performed on representation of spatiotemporal objects from various points of view. Most of them represent spatiotemporal objects using approaches from GIS, temporal databases, object-oriented databases or data type. Spatiotemporal objects can be classified as objects whose position and shape changes discretely over time, objects whose position changes continuously and objects whose shape changes continuously as well as position. Previous works on spatiotemporal model have focused on only one of them. In this paper, we propose a spatiotemporal model that can represent three types of objects in Euclidean plan. For this purpose, we represent both discrete and continuous moving objects by defining temporal model extended from valid time and by defining relationship between two consecutive versions of objects. The proposed spatiotemporal object model is based on open GIS specification so that it has compatibility with existing spatial data model.

• The KIPS Transactions:PartA
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• v.10A no.2
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• pp.111-122
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• 2003

Military wargame simulation models must support the HLA in order to facilitate interoperability with other simulations, and using parallel simulation engines offer efficiency in reducing system overhead generated by propelling interoperability. However, legacy military simulation model engines process events using sequential event-driven method. This is due to problems generated by parallel processing such as synchronous reference to global data domains. Additionally. using legacy simulation platforms result in insufficient utilization of multiple CPUs even if a multiple CPU system is under use. Therefore, in this paper, we propose conversing the simulation engine to an object model-based parallel simulation engine to ensure military wargame model's improved system processing capability, synchronous reference to global data domains, external simulation time processing, and the sequence of parallel-processed events during a crash recovery. The converted parallel simulation engine is designed and implemented to enable parallel execution on a multiple CPU system (SMP).

• Journal of the Korean Chemical Society
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• v.55 no.2
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• pp.199-203
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• 2011

The title one-dimensional hydrogen-bonded coordination compound $[Cu^{II}(C_{13}H_{17}N_3OBr)(C_8H_5O_4)]{\cdot}2H_2O.CH_3OH$ has been synthesized and characterized by single crystal X-ray diffraction study. The monomeric unit contains a square-planar $Cu^{II}$ centre. The four coordination sites are occupied by a tridentate anionic Schiff base ligand (4-bromo-2-[(2-piperazin-1-yl-ethylimino)-methyl]-phenol) which furnishes an $N_2O$-donor set, with the fourth position being occupied by the oxygen atom of an adjacent terephthalate unit. Two adjacent neutral molecules are linked through intermolecular N-H---O and O-H---N hydrogen bonds and generate a dimeric pair. Each dimeric pair is connected with each other via discrete water and methanol molecules by hydrogen bonding to form a one-dimensional supramolecular network.

• Journal of Digital Contents Society
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• v.19 no.1
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• pp.35-41
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• 2018

The Q-learning algorithm based on reinforcement learning is useful for learning the goal for one behavior at a time, using a combination of discrete states and actions. In order to learn multiple actions, applying a behavior-based architecture and using an appropriate behavior adjustment method can make a robot perform fast and reliable actions. Q-learning is a popular reinforcement learning method, and is used much for robot learning for its characteristics which are simple, convergent and little affected by the training environment (off-policy). In this paper, Q-learning algorithm is applied to a lamp robot to learn multiple behaviors (human recognition, desk object recognition). As the learning rate of Q-learning may affect the performance of the robot at the learning stage of multiple behaviors, we present the optimal multiple behaviors learning model by changing learning rate.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.40 no.1
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• pp.11-19
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• 2003

A micro sensor array with 4 discrete sensors integrated on a microhotplate was developed for identifying the kinds and quantities of explosive gases. The sensor array consisited of four tin oxide-based thin films with the high and broad sensitivity to the tested explosive gases and uniform thermal distribution on the plate. The microhotplate, using silicon substrate with N/O/N membrane, dangling in air by Al bonding wires, and controlling the thickness by chemical mechanical process (CMP), has been designed and fabricated. By employing the sensitivity signal of the sensor array at 40$0^{\circ}C$, we could reliably classily the kinds and quantities of the explosive gases like butan, propane, LPG, and carbon monoxide within the range of threshold limit values (TLVs), employing principal component analysis (PCA).

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.38 no.3
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• pp.282-291
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• 2001

In this paper, an efficient VLSI architecture of Shape Adaptive Discrete Cosine Transform(SADCT) based on systolic array is proposed. Since transform size in SADCT is varied according to the shape of object in each block, it are dropped that both usability of processing elements(PE´s) and throughput rate in time-recursive SADCT structure. To overcome these disadvantages, it is proposed that the architecture based on a systolic way structure which doesn´t need memory. In the proposed architecture, throughput rate is improved by consecutive processing of one-dimensional SADCT without memory and PE´s in the first column are connected to that in the last one for improvement of usability of PE. And input data are put into each column of PE in parallel according to the maximum data number in each rearranged block. The proposed architecture is described by VHDL. Also, its function is evaluated by MentorTM. Even though the hardware complexity is somewhat increased, the throughput rate is improved about twofold.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.6
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• pp.55-62
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• 2005

This paper proposes a method to recognize a face using DCT/LDA where LDA is applied to DCT coefficients of an input face image. In the proposed method, SSS problem of LDA due to less number of training data than the size of feature space can be avoided by expressing an input image in low dimensional space using DCT coefficients. In terms of the recognition rate, both the proposed method and the PCA/LDA method have shown almost equal performance while the training time of the proposed method is much shorter than the other. This is because DCT has the fixed number of basis vectors while the property of energy compaction rate is similar to that of PCA. Although depending on the number of coefficients employed for the recognition, the experimental results show that the performance of the proposed method in terms of recognition rate is very comparable to PCA/LDA method and other DCT/LDA methods, and it can be trained 13,000 times faster than PCA/LDA method.

• Korean Journal of Materials Research
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• v.17 no.5
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• pp.250-255
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• 2007

The alloying effects on the electronic structures of Zinc are investigated using the relativistic $DV-X{\alpha}molecular$ orbital method in order to obtain useful information for alloy design. A new parameter which is the d obital energy level(Md) and the bonder order(Bo) of alloying elements in Zinc was introduced and used for prediction of the mechanical properties. The Md correlated with the atomic radius and the electronegativity of elements. The Bo is a measure of the strength of the covalent bond between M and X atoms. First-principles calculations of electronic structures were performed with a series of models composed of a MZn18 cluster and the electronic states were calculated by the discrete variational- $X{\alpha}method$ by using the program code SCAT. The central Zinc atom(M) in the cluster was replaced by various alloying elements. In this study energy level structures of pure Zinc and alloyed Zinc were calculated. From calculated results of energy level structures in MZn18 cluster, We found Md and Bo values for various elements of Zn. In this work, Md and Bo values correlated to the tensile strength for the Zn. These results will give some guide to design of zinc based alloys for high temperature applications and it is possible the excellent alloys design.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.2
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• pp.179-184
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• 2006

An intuitive understanding of the dynamic pattern generation in asymmetric networks may be considered an essential component in developing models for the dynamic information processing. It has been reported that the neural network with cyclic connections generates multiple limit cycles. The dynamics of discrete time network with cyclic connections has been investigated intensively. However, the dynamics of a cyclic connection neural network in continuous-time has not been well-known due to the considerable complexity involved in its calculation. In this paper, the dynamic behavior of a continuous-time cyclic connection neural network, in which each neuron is connected only to its nearest neurons with binary synaptic weights of ${\pm}1$, has been investigated. Furthermore, the dynamics and stability of the network have been analyzed using a piece-wise linear approximation.