• The Journal of the Acoustical Society of Korea
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• v.21 no.4
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• pp.387-393
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• 2002

The enhancement technique of noise signal using mixture HFM (Midden Filter Model) are proposed. Given the parameters of the clean signal and noise, noisy signal is modeled by a linear state-space model with Markov switching parameters. Estimation of state vector is required for estimating original signal. The estimation procedure is based on mixture interacting multiple model (MIMM) and the estimator of speech is given by the weighted sum of parallel Kalman filters operating interactively. Simulation results showed that the proposed method offers performance gains relative to the previous results with slightly increased complexity.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.3B
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• pp.171-182
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• 2003

In this paper, we propose an efficient and simple fair queueing algorithm, called Minimum Possible Virtual Start Time Fair Queueing (MPSFQ), which has O(1) complexity for the virtual time computation while it has good delay and fairness properties. The key idea of MPSFQ is that it has an easy system virtual time recalibration method while it follows a rate-proportional property. MPSFQ algorithm recalibrates system virtual time to the minimum possible virtual start time of all backlogged sessions. We will show our algorithm has good delay and fairness properties by analysis.

• Proceedings of the KIEE Conference
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• 1993.07a
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• pp.254-257
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• 1993

This paper presents a new method to implement Hebbian learning method on artificial neural network. In hebbian learning algorithm, complexity in terms of multiplications is high. To save the chip area, we consider a new learning circuit. By calculating similarity, or correlation between $X_i$ and $O_i$, large portion of circuits commonly used in conventional neural networks is not necessary for this new hebbian learning circuit named COR. The output signals of COR is applied to weight storage capacitors for direct control the voltages of the capacitors. The weighted sum, ${\Sigma}W_{ij}O_j$, is realized by multipliers, whose output currents are summed up in one line which goes to learning circuit or output circuit. The drain current of the multiplier can produce positive or negative synaptic weights. The pass transistor selects eight learning mode or recall mode. The layout of an learnable six-neuron fully connected Hopfield neural network is designed, and is simulated using PSPICE. The network memorizes, and retrieves the patterns correctly under the existence of minor noises.

• Journal of Korean Port Research
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• v.14 no.4
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• pp.451-461
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• 2000

The design and maintenance of navigation channel and water facilities of an harbor which is located at the mouth of river or at the estuary area are difficult due to the complexity of estuarial water and sediment circulation. Effects of deepening navigable waterways, of changing coastline configurations, or of discharging dredged material to the open sea are necessary to be investigated and predicted in terms of water quality and possible physical changes to the coastal environment. A borad analysis of the transport mechanism in the estuary area was made in terms of sediment property, falling velocity, concentration and flow characteristics. In order to simulate the transport processes, a two-dimensional finite element model is developed, which includes erosion, transport and deposition mechanism of suspended sediments. Galerkin’s weighted residual method is used to solve the transient convection-diffusion equation. The fluid domain is subdivided into a series of triangular elements in which a quadratic approximation is made for suspended sediment concentration. Model could deal with a continuous aggregation by stipulating the settling velocity of the flocs in each element. The model provides suspended sediment concentration, bed shear stress, erosion versus deposition rate and bed profile at the given time step.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.7
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• pp.2339-2355
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• 2021

In this paper, a joint probabilistic data association algorithm based on loss function (LJPDA) is proposed so that the computation load and accuracy of the multi-target tracking algorithm can be guaranteed simultaneously. Firstly, data association is divided in to three cases based on the relationship among validation gates and the number of measurements in the overlapping area for validation gates. Also the contribution coefficient is employed for evaluating the contribution of a measurement to a target, and the loss function, which reflects the cost of the new proposed data association algorithm, is defined. Moreover, the equation set of optimal contribution coefficient is given by minimizing the loss function, and the optimal contribution coefficient can be attained by using the Newton-Raphson method. In this way, the weighted value of each target can be achieved, and the data association among measurements and tracks can be realized. Finally, we compare performances of LJPDA proposed and joint probabilistic data association (JPDA) algorithm via numerical simulations, and much attention is paid on real-time performance and estimation error. Theoretical analysis and experimental results reveal that the LJPDA algorithm proposed exhibits small estimation error and low computation complexity.

• Journal of Korea Multimedia Society
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• v.24 no.5
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• pp.667-675
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• 2021

The treatments for symptoms of Alzheimer's disease are being provided and for the early diagnosis several researches are undergoing. In this regard, by using T1-weighted images several classification techniques had been proposed to distinguish among AD, MCI, and Healthy Control (HC) patients. In this paper, we also used some traditional Machine Learning (ML) approaches in order to diagnose the AD. This paper consists of an improvised feature selection method which is used to reduce the model complexity which accounted an issue while utilizing the ML approaches. In our presented work, combination of subcortical and cortical features of 308 subjects of ADNI dataset has been used to diagnose AD using structural magnetic resonance (sMRI) images. Three classification experiments were performed: binary classification. i.e., AD vs eMCI, AD vs lMCI, and AD vs HC. Proposed Feature Selection method consist of a combination of Principal Component Analysis and Recursive Feature Elimination method that has been used to reduce the dimension size and selection of best features simultaneously. Experiment on the dataset demonstrated that SVM is best suited for the AD vs lMCI, AD vs HC, and AD vs eMCI classification with the accuracy of 95.83%, 97.83%, and 97.87% respectively.

• The Journal of Korea Robotics Society
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• v.17 no.1
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• pp.1-7
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• 2022

Robots are widely used in industries and services. Traditional robots have been used to perform repetitive tasks in a fixed environment, and it is very difficult to solve a problem in which the physical interaction of the surrounding environment or other objects is complicated with the existing control method. Reinforcement learning has been actively studied as a method of machine learning to solve such problems, and provides answers to problems that robots have not solved in the conventional way. Studies on the learning of all physical robots are commonly affected by noise. Complex noises, such as control errors of robots, limitations in performance of measurement equipment, and complexity of physical interactions with surrounding environments and objects, can act as factors that degrade learning. A learning method that works well in a virtual environment may not very effective in a real robot. Therefore, this paper proposes a weighted sum method and a linear regression method as an effective and accurate learning method in a noisy environment. In addition, the bottle flipping was trained on a robot and compared with the existing learning method, the validity of the proposed method was verified.

• Smart Media Journal
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• v.12 no.3
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• pp.30-37
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• 2023

Alzheimer's disease (AD) symptoms are being treated by early diagnosis, where we can only slow the symptoms and research is still undergoing. In consideration, using T1-weighted images several classification models are proposed in Machine learning to identify AD. In this paper, we consider the improvised feature selection, to reduce the complexity by using wrapping techniques and Restricted Boltzmann Machine (RBM). This present work used the subcortical and cortical features of 278 subjects from the ADNI dataset to identify AD and sMRI. Multi-class classification is used for the experiment i.e., AD, EMCI, LMCI, HC. The proposed feature selection consists of Forward feature selection, Backward feature selection, and Combined PCA & RBM. Forward and backward feature selection methods use an iterative method starting being no features in the forward feature selection and backward feature selection with all features included in the technique. PCA is used to reduce the dimensions and RBM is used to select the best feature without interpreting the features. We have compared the three models with PCA to analysis. The following experiment shows that combined PCA &RBM, and backward feature selection give the best accuracy with respective classification model RF i.e., 88.65, 88.56% respectively.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.1
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• pp.64-88
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• 2024

Security and reliability are the utmost importance facts in intelligent networked vehicles. Stochastic Petri Net and Z (SPZN) as an excellent formal verification tool for modeling concurrent systems, can effectively handles concurrent operations within a system, establishes relationships among components, and conducts verification and reasoning to ensure the system's safety and reliability in practical applications. However, the application of a system with numerous nodes to Petri Net often leads to the issue of state explosion. To tackle these challenges, a refinement and abstraction method based on SPZN is proposed in this paper. This approach can not only refine and abstract the Stochastic Petri Net but also establish a corresponding relationship with the Z language. In determining the implementation rate of transitions in Stochastic Petri Net, we employ the interval average and weighted average method, which significantly reduces the time and space complexity compared to alternative techniques and is suitable for expert systems at various levels. This reduction facilitates subsequent comprehensive system analysis and module analysis. Furthermore, by analyzing the properties of Markov Chain isomorphism in the case study, recommendations for minimizing system risks in the application of intelligent parking within the intelligent networked vehicle system can be put forward.

• Korean Journal of Radiology
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• v.24 no.11
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• pp.1114-1130
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• 2023

Magnetic resonance neurography (MRN) is increasingly used to visualize peripheral nerves in vivo. However, the implementation and interpretation of MRN in the brachial and lumbosacral plexi are challenging because of the anatomical complexity and technical limitations. The purpose of this article was to review the clinical context of MRN, describe advanced magnetic resonance (MR) techniques for plexus imaging, and list the general categories of utility of MRN with pertinent imaging examples. The selection and optimization of MR sequences are centered on the homogeneous suppression of fat and blood vessels while enhancing the visibility of the plexus and its branches. Standard 2D fast spin-echo sequences are essential to assess morphology and signal intensity of nerves. Moreover, nerve-selective 3D isotropic images allow improved visualization of nerves and multiplanar reconstruction along their course. Diffusion-weighted and diffusion-tensor images offer microscopic and functional insights into peripheral nerves. The interpretation of MRN in the brachial and lumbosacral plexi should be based on a thorough understanding of their anatomy and pathophysiology. Anatomical landmarks assist in identifying brachial and lumbosacral plexus components of interest. Thus, understanding the varying patterns of nerve abnormalities facilitates the interpretation of aberrant findings.