• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.3
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• pp.978-983
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• 2010

Acoustic target classification in wireless sensor network is important research at environmental surveillance, invasion surveillance, multiple target separation. General sensor node signal processing methods concentrated on received signal energy based target detection and received raw signal compression. The former is not suited to target classification because of almost every target information are lost except target energy. The latter bring down life-time of sensor node owing to high computational complexity and transmission energy. In this paper, we introduce an feature extraction algorithm for acoustic target classification in wireless sensor network which has time and frequency information. The proposed method extracts time information and de-noised target classification information using wavelet decomposition step. This method reduces communication energy by 28% of original signal and computational complexity.

• Journal of Internet Computing and Services
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• v.24 no.2
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• pp.11-18
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• 2023

Recently, in the field of Speech Emotion Recognition (SER), many studies have been conducted to improve accuracy using voice features and modeling. In addition to modeling studies to improve the accuracy of existing voice emotion recognition, various studies using voice features are being conducted. This paper, voice files are separated by time interval in a time series method, focusing on the fact that voice emotions are related to time flow. After voice file separation, we propose a model for classifying emotions of speech data by extracting speech features Mel, Chroma, zero-crossing rate (ZCR), root mean square (RMS), and mel-frequency cepstrum coefficients (MFCC) and applying them to a recurrent neural network model used for sequential data processing. As proposed method, voice features were extracted from all files using 'librosa' library and applied to neural network models. The experimental method compared and analyzed the performance of models of recurrent neural network (RNN), long short-term memory (LSTM) and gated recurrent unit (GRU) using the Interactive emotional dyadic motion capture Interactive Emotional Dyadic Motion Capture (IEMOCAP) english dataset.

• Convergence Security Journal
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• v.22 no.2
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• pp.59-66
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• 2022

Recently, smart home technology has been developed with a great response due to the convenience of home automation. Smart home technology provides various services by connecting various Internet of Things (IoT) and sensors to a home network through wired/wireless networks. In addition, the smart home service easily and conveniently controls lighting, energy, environment, and door cameras through a wall pad. However, while it has become a social issue due to the recent hacking accident of wall pads, personal information leakage and privacy infringement are expected. Accordingly, it is necessary to prepare preventive and countermeasures against security vulnerability factors of wall pads. Therefore, this study expects that it can be used as basic data for future smart home application and response technology development by examining the weak causes and countermeasures related to wall pads.

• IEIE Transactions on Smart Processing and Computing
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• v.3 no.5
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• pp.251-258
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• 2014

This paper proposes an efficient token flow design methodology for a decoder in the MPEG Reconfigurable Media Coding (RMC) framework. The MPEG RMC framework facilitates a decoder to be configured with a set of modules called functional units (FUs) that are connected by tokens. Such a modular design philosophy of the MPEG RMC framework enables the reusability and reconfigurability of FUs. One drawback of the MPEG RMC framework is that the decoder performance can be affected by increasing the token transmissions between FUs. The proposed method improves the design of the FU network in the RMC framework toward real-time decoder implementation. In the proposed method, the merging of FU, the separation of token flow, and the merging of token transactions are applied to minimize the token traffic between FUs. The experimental results of the MPEG-4 SP decoder show that the proposed method reduces the total decoding time by up to 77 percent compared to the design of the RMC simulation model.

• The KIPS Transactions:PartC
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• v.12C no.1 s.97
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• pp.77-82
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• 2005

Protection of user service becomes increasingly important since even very short interruption of service due to link or node failure will cause huge data loss and incur tremendous restoration cost in high speed network environment. Thus fast and efficient protection and restoration is one of the most important issues to be addressed. Protection methods have been proposed to provide efficiency and stability in optical networks. In this paper, an original network is separated into several domains using Hamiltonian cycle. and link protection is performed on the cycles of the domains. We have shown that protection path length can be decreased up to $57{\%}$ with marginal increase of backup capacity. Our proposed method can provide high-speed protection with marginal increase of protection capacity.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.6
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• pp.2735-2751
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• 2020

The emergence of Software Defined Networking (SDN) overcomes the limitations of traditional networking architectures. There are some advantages in SDN which are centralized global network view, programmability, and separation of the data plane and control plane. Due to the limitation of data plane storage capacity in SDN, it is necessary to process the redundancy rules of switch. In this paper, we propose a method for active detection and processing of redundant rules. We use the result generated by the customized probe package to detect redundant rules. And by checking the forwarding behavior of probe packets in the data plane, the redundancy rules are further processed. Furthermore, in order to quickly check the dynamic networks, we propose an incremental algorithms for rapidly evolve the network strategies. We conduct simulation experiments on Matlab to verify the feasibility of the algorithm. The influence of some parameters on the result are discussed.

• Advances in materials Research
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• v.1 no.4
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• pp.245-268
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• 2012

Results of isothermal torsional oscillation tests are reported on melts of linear low density polyethylene and isotactic polypropylene. Prior to rheological tests, specimens were annealed at various temperatures ranging from $T_a$ = 180 to $310^{\circ}C$ for various amounts of time (from 30 to 120 min). Thermal treatment induced degradation of the melts and caused pronounced decreases in their molecular weights. With reference to the concept of transient networks, constitutive equations are developed for the viscoelastic response of polymer melts. A melt is treated as an equivalent network of strands bridged by junctions (entanglements and physical cross-links). The time-dependent response of the network is modelled as separation of active strands from and merging of dangling strands with temporary nodes. The stress-strain relations involve three adjustable parameters (the instantaneous shear modulus, the average activation energy for detachment of active strands, and the standard deviation of activation energies) that are determined by matching the dependencies of storage and loss moduli on frequency of oscillations. Good agreement is demonstrated between the experimental data and the results of numerical simulation. The study focuses on the effect of molecular weight of polymer melts on the material constants in the constitutive equations.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.31 no.3
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• pp.50-57
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• 2003

In the paper, the design and evaluation of a helicopter trajectory tracking controller are presented. The control algorithm is implemented using the feedback linearization technique and the two time-scale separation architecture. In addition, and on-line adaptive architecture that employs a neural network compensating the model inversion error caused by the deficiency of full knowledge of helicopter dynamic is applied to augment the attitude control system. Trajectory tracking performance of the control system in evaluated using modified TMAN simulation program representing as Apache helicopter. It is show that the on-line neural network in an adaptive control architecture is very effective in dealing with the performance depreciation problem of the trajectory tracking control caused by insufficient information of dynamics.

• Transactions of the Korean Society of Automotive Engineers
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• v.21 no.1
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• pp.128-136
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• 2013

This study presents the development of Gateway Control Module using SAE J1939 protocol for the commercial vehicles. Presently, the load rate of CAN bus is increased by the single network composition and addition of new ECUs for development of intelligent vehicles. Because the embedded system of the integrated network control function has the errors of the CAN bus caused by the increase of ECU, it is needed for development of commercial vehicles. Also, this study presents the development of smart functions that can diagnosis CAN bus errors, fault diagnosis of ECU and basic function that arbitrates CAN bus between ECUs of commercial vehicle. GCM was designed for 4channel separation about Gateway function as solution of load rate decrease and smart functions. HILS(Hardware in the loop simulation)system that can achieve simulation about CAN Messages of all systems on vehicle was applied to evaluate performance and verification of all functions and performance. The load rate on CAN bus was decreased at using functions what was delivery, block and process of GCM. Through this, it was enabled to organize systematic architecture for gateway.

• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.3
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• pp.270-279
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• 2000

This paper describes the on-chip learning algorithm of neural networks using the stochastic pulse arithmetic. Stochastic pulse arithmetic is the computation using the numbers represented by the probability of 1' and 0's occurrences in a random pulse stream. This stochastic arithmetic has the merits when applied to neural network ; reduction of the area of the implemented hardware and getting a global solution escaping from local minima by virtue of the stochastic characteristics. And in this study, the on-chip learning algorithm is derived from the backpropagation algorithm for effective hardware implementation. We simulate the nonlinear separation problem of the some character patterns to verify the proposed learning algorithm. We also had good results after applying this algorithm to recognize printed and handwritten numbers.