• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.59 no.8
• /
• pp.1489-1496
• /
• 2010

A feed forward differential architecture of analog PRML decoder is investigated to implement on analog parallel processing circuits. The conventional PRML decoder performs the trellis processing with the implementation of single stage in digital and its repeated use. The analog parallel processing-based PRML comes from the idea that the decoding of PRML is done mainly with the information of the first several number of stages. Shortening the trellis processing stages but implementing it with analog parallel circuits, several benefits including higher speed, no memory requirement and no A/D converter requirement are obtained. Most of the conventional analog parallel processing-based PRML decoders are differential architecture with the feedback of the previous decoded data. The architecture used in this paper is without feedback, where error metric accumulation is allowed to start from all the states of the decoding stage, which enables to be decoded without feedback. The circuit of the proposed architecture is simpler than that of the conventional analog parallel processing structure with the similar decoding performance. Characteristics of the feed forward differential architecture are investigated through various simulation studies.

• International Journal of Naval Architecture and Ocean Engineering
• /
• v.12 no.1
• /
• pp.768-783
• /
• 2020

Typically, a Dynamic Positioning System (DPS) uses a PID feed-back system, and it often adopts a wind feed-forward system because of its easier implementation than a feed-forward system based on current or wave. But, because a ship's drifting motion is caused by wind, current, and wave drift loads, all three environmental loads should be considered. In this study, a motion predictive control for the PID feedback system of the DPS is proposed, which considers the three environmental loads by utilizing predicted drifted ship positions in the future since it contains information about the three environmental loads from the moment to the future. The prediction accuracy for the future drifted ship position is ensured by adopting deep learning algorithms and a replay buffer. Finally, it is shown that the proposed motion predictive system results in better station-keeping performance than the wind feed-forward system.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.3 no.3
• /
• pp.309-330
• /
• 2009

In this paper, we introduce the architecture of Genetic Algorithm(GA) based Feed-forward Polynomial Neural Networks(PNNs) and discuss a comprehensive design methodology. A conventional PNN consists of Polynomial Neurons, or nodes, located in several layers through a network growth process. In order to generate structurally optimized PNNs, a GA-based design procedure for each layer of the PNN leads to the selection of preferred nodes(PNs) with optimal parameters available within the PNN. To evaluate the performance of the GA-based PNN, experiments are done on a model by applying Medical Imaging System(MIS) data to a multi-variable software process. A comparative analysis shows that the proposed GA-based PNN is modeled with higher accuracy and more superb predictive capability than previously presented intelligent models.

• Journal of Digital Contents Society
• /
• v.8 no.3
• /
• pp.393-401
• /
• 2007

P2P systems have gained a lot of research interests and popularity over the years and have the capability to unleash and distribute awesome amounts of computing power, storage and bandwidths currently languishing - often underutilized - within corporate enterprises and every Internet connected home in the world. Since there is no central control over resources or devices and no before hand information about the resources or devices, device discovery remains a substantial problem in P2P environment. In this paper, we cover some of the current solutions to this problem and then propose our feed forward neural network (FFNN) based solution for device discovery in mobile P2P environment. We implements feed forward neural network (FFNN) trained with back propagation (BP) algorithm for device discovery and show, how large computation task can be distributed among such devices using agent technology. It also shows the possibility to use our architecture in home networking where devices have less storage capacity.

• Journal of the Institute of Electronics and Information Engineers
• /
• v.51 no.10
• /
• pp.57-63
• /
• 2014

This paper proposes an architecture of $3^{rd}$ order SDM(Sigma-Delta Modulator) with delayed feed-forward path in order to reduce the power consumption and area. The proposed SDM improve the architecture of conventional $3^{rd}$ order SDM which consists of two integrators. The proposed architecture can increase the coefficient values of first stage doubly by inserting the delayed feed-forward path. Accordingly, compared with the conventional architecture, the capacitor value($C_I$) of first integrator is reduced by half. Thus, because the load capacitance of first integrator became the half of original value, the output current of first op-amp is reduced as 51% and the capacitance area of first integrator is reduced as 48%. Therefore, the proposed method can optimize the power and the area. The proposed architecture in this paper is simulated under conditions which are supply voltage of 1.8V, input signal 1Vpp/1KHz, signal bandwidth of 24KHz and sampling frequency of 2.8224MHz in the 0.18um CMOS process. The simulation results are SNR(Signal to Noise Ratio) of 88.9dB and ENOB(Effective Number of Bits) of 14-bits. The total power consumption of the proposed SDM is $180{\mu}W$.

• International Journal of Naval Architecture and Ocean Engineering
• /
• v.13 no.1
• /
• pp.641-649
• /
• 2021

Fuel oil consumption (FOC) must be minimized to determine the economic route of a ship; hence, the ship power must be predicted prior to route planning. For this purpose, a numerical method using test results of a model has been widely used. However, predicting ship power using this method is challenging owing to the uncertainty of the model test. An onboard test should be conducted to solve this problem; however, it requires considerable resources and time. Therefore, in this study, a deep feed-forward neural network (DFN) is used to predict ship power using deep learning methods that involve data pattern recognition. To use data in the DFN, the input data and a label (output of prediction) should be configured. In this study, the input data are configured using ocean environmental data (wave height, wave period, wave direction, wind speed, wind direction, and sea surface temperature) and the ship's operational data (draft, speed, and heading). The ship power is selected as the label. In addition, various treatments have been used to improve the prediction accuracy. First, ocean environmental data related to wind and waves are preprocessed using values relative to the ship's velocity. Second, the structure of the DFN is changed based on the characteristics of the input data. Third, the prediction accuracy is analyzed using a combination comprising five hyperparameters (number of hidden layers, number of hidden nodes, learning rate, dropout, and gradient optimizer). Finally, k-means clustering is performed to analyze the effect of the sea state and ship operational status by categorizing it into several models. The performances of various prediction models are compared and analyzed using the DFN in this study.

• Proceedings of the IEEK Conference
• /
• 2004.08c
• /
• pp.671-674
• /
• 2004

We propose accurate classification method of EEG signals during mental tasks. In the experimental task, the tasks of subjects show 3 major measurements; there are mathematical tasks, color decision tasks, and Chinese phrase tasks. The classifier implemented for this work is a feed-forward neural network that trained with the error back-propagation algorithm. The new BCI system is proposed by using neural network. In this system, tr e architecture of the neural network is composed of three layers with a feed-forward network, which implements the error back propagation-learning algorithm. By applying this algorithm to 4 subjects, we achieved $95{\%}$ classification rates. The results for BCI mathematical task experiments show performance better than those of the Chinese phrase tasks. The selection time of each task depends on the mental task of subjects. We expect that the proposed detection method can be a basic technology for brain-computer interface by combining with left/right hand movement or yes/no discrimination methods.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2005.11a
• /
• pp.483-487
• /
• 2005

We introduce a new architecture of hetero-hybridized feed-forward neural networks composed of fuzzy set-based polynomial neural networks (FSPNN) and polynomial neural networks (PM) that are based on a genetically optimized multi-layer perceptron and develop their comprehensive design methodology involving mechanisms of genetic optimization and Information Granulation. The construction of Information Granulation based HFSPNN (IG-HFSPNN) exploits fundamental technologies of Computational Intelligence(Cl), namely fuzzy sets, neural networks, and genetic algorithms(GAs) and Information Granulation. The architecture of the resulting genetically optimized Information Granulation based HFSPNN (namely IG-gHFSPNN) results from a synergistic usage of the hybrid system generated by combining new fuzzy set based polynomial neurons (FPNs)-based Fuzzy Neural Networks(PM) with polynomial neurons (PNs)-based Polynomial Neural Networks(PM). The design of the conventional genetically optimized HFPNN exploits the extended Group Method of Data Handling(GMDH) with some essential parameters of the network being tuned by using Genetie Algorithms throughout the overall development process. However, the new proposed IG-HFSPNN adopts a new method called as Information Granulation to deal with Information Granules which are included in the real system, and a new type of fuzzy polynomial neuron called as fuzzy set based polynomial neuron. The performance of the IG-gHFPNN is quantified through experimentation.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.13 no.3
• /
• pp.1434-1449
• /
• 2019

Video object segmentation is a significant task in computer vision, but its performance is not very satisfactory. A method of video object segmentation using weakly temporal information is presented in this paper. Motivated by the phenomenon in reality that the motion of the object is a continuous and smooth process and the appearance of the object does not change much between adjacent frames in the video sequences, we use a feed-forward architecture with motion estimation to predict the mask of the current frame. We extend an additional mask channel for the previous frame segmentation result. The mask of the previous frame is treated as the input of the expanded channel after processing, and then we extract the temporal feature of the object and fuse it with other feature maps to generate the final mask. In addition, we introduce multi-mask guidance to improve the stability of the model. Moreover, we enhance segmentation performance by further training with the masks already obtained. Experiments show that our method achieves competitive results on DAVIS-2016 on single object segmentation compared to some state-of-the-art algorithms.

• ETRI Journal
• /
• v.29 no.6
• /
• pp.835-837
• /
• 2007

A multi-bit sigma-delta modulator architecture is described for low-distortion performance and a high-speed operation. The proposed architecture uses both a delayed code and a delayed differential code of analog-to-digital converter in the feedback path, thereby suppressing signal components in the integrators and relaxing the timing requirement of the analog-to-digital converter and the scrambler logic. Implemented by a 0.13 ${\mu}m$ CMOS process, the sigma-delta modulator achieves high linearity. The measured spurious-free dynamic range is 89.1 dB for -6 dBFS input signal.