• Journal of the Korean Institute of Intelligent Systems
• /
• v.23 no.3
• /
• pp.226-231
• /
• 2013

In The matching algorithm for automatic packaging of drugs is essential to determine whether the canister can exactly refill the suitable medicine. In this paper, we propose a hierarchical neural network with the upper and lower layers which can perform real-time processing and classification of many types of medicine bottles to prevent accidental medicine disaster. A few number of low-dimensional feature vector are extracted from the label images presenting medicine-bottle information. By using the extracted feature vectors, the lower layer of MLP(Multi-layer Perceptron) neural networks is learned. Then, the output of the learned middle layer of the MLP is used as the input to the upper layer of the MLP learning. The proposed hierarchical neural network shows good classification performance and real- time operation in the test of up to 30 degrees rotated to the left and right images of 100 different medicine bottles.

• Journal of KIISE:Software and Applications
• /
• v.37 no.10
• /
• pp.723-735
• /
• 2010

Evaluating the level of achievement and providing the knowledge which is appropriate at the evaluated level have great influence in studying of the human beings. This shows the importance of the order of training and the training order should be considered in machine learning. In this research, to assess the influence of the order of training, we propose a method of controlling the order of training samples utilizing the experience of supervisor in the training of MLP. The supervisor finds out the current state of MLP using teaching experience and student evaluation, and then selects the most instructive sample for MLP in that state. We use CRF to represent and utilize the experience of supervisor. While the proposed method is similar to active learning in selecting samples, it is basically different in that selection is not to reduce the number of samples to be used but to assist the learning progress. The result from classification problem shows that the method is usually effective in terms of time taken in training in contrast to random selection.

• The Korean Journal of Pharmacology
• /
• v.31 no.3
• /
• pp.333-344
• /
• 1995

The effects of adenosine and $N^6-cyclopentyladenosine$ (CPA) on superoxide production, myeloperoxidase release and $Ca^{2+}$ mobilization stimulated by fMLP in neutrophils were investigated. The effects were also observed on the stimulatory actions of C5a and PMA and the responses in lipopolysaccharide-primed neutrophils. In addition, the involvement of cAMP in the inhibitory action of adenosine was examined. The fMLP-stimulated neutrophil respiratory burst, degranulation and intracellular $Ca^{2+}$ mobilization may be regulated by activation of adenosine receptors. Adenosine may not affect the stimulated neutrophil responses due to activation of protein kinase C. fMLP-stimulated respiratory burst in lipopolysaccharide-primed neutrophils may be less sensitive to adenosine, compared with nonprimed cells. The inhibitory effect of theophylline in the presence of adenosine on neutrophil responses appears to be ascribed to accumulation of intracellular cAMP.

• The Korean Journal of Physiology and Pharmacology
• /
• v.18 no.6
• /
• pp.497-502
• /
• 2014

Extracellular nicotinamide adenine dinucleotide (NAD) cleaving activity of a particular cell type determines the rate of the degradation of extracellular NAD with formation of metabolites in the vicinity of the plasma membrane, which has important physiological consequences. It is yet to be elucidated whether intact human neutrophils have any extracellular NAD cleaving activity. In this study, with a simple fluorometric assay utilizing $1,N^6$-ethenoadenine dinucleotide (etheno-NAD) as the substrate, we have shown that intact peripheral human neutrophils have scant extracellular etheno-NAD cleaving activity, which is much less than that of mouse bone marrow neutrophils, mouse peripheral neutrophils, human monocytes and lymphocytes. With high performance liquid chromatography (HPLC), we have identified that ADP-ribose (ADPR) is the major extracellular metabolite of NAD degradation by intact human neutrophils. The scant extracellular etheno-NAD cleaving activity is decreased further by N-formyl-methionine-leucine-phenylalanine (fMLP), a chemoattractant for neutrophils. The fMLP-mediated decrease in the extracellular etheno-NAD cleaving activity is reversed by WRW4, a potent FPRL1 antagonist. These findings show that a much less extracellular etheno-NAD cleaving activity of intact human neutrophils compared to other immune cell types is down-regulated by fMLP via a low affinity fMLP receptor FPRL1.

• Smart Media Journal
• /
• v.7 no.1
• /
• pp.31-36
• /
• 2018

In this paper, we propose automatic screen on / off method of OST-HMD screen using gaze depth estimation technique. The proposed method uses MLP (Multi-layer Perceptron) to learn the user's gaze information and the corresponding distance of the object, and inputs the gaze information to estimate the distance. In the learning phase, eye-related features obtained using a wearable eye-tracker. These features are then entered into the Multi-layer Perceptron (MLP) for learning and model generation. In the inference step, eye - related features obtained from the eye tracker in real time input to the MLP to obtain the estimated depth value. Finally, we use the results of this calculation to determine whether to turn the display of the HMD on or off. A prototype was implemented and experiments were conducted to evaluate the feasibility of the proposed method.

• Advances in environmental research
• /
• v.5 no.3
• /
• pp.153-167
• /
• 2016

We applied multilayer perceptron (MLP) and radial basis function (RBF) neural network in upstream and downstream water quality stations of the Karaj Reservoir in Iran. For both neural networks, inputs were pH, turbidity, temperature, chlorophyll-a, biochemical oxygen demand (BOD) and nitrate, and the output was dissolved oxygen (DO). We used an MLP neural network with two hidden layers, for upstream station 15 and 33 neurons in the first and second layers respectively, and for the downstream station, 16 and 21 neurons in the first and second hidden layer were used which had minimum amount of errors. For learning process 6-fold cross validation were applied to avoid over fitting. The best results acquired from RBF model, in which the mean bias error (MBE) and root mean squared error (RMSE) were 0.063 and 0.10 for the upstream station. The MBE and RSME were 0.0126 and 0.099 for the downstream station. The coefficient of determination ($R^2$) between the observed data and the predicted data for upstream and downstream stations in the MLP was 0.801 and 0.904, respectively, and in the RBF network were 0.962 and 0.97, respectively. The MLP neural network had acceptable results; however, the results of RBF network were more accurate. A sensitivity analysis for the MLP neural network indicated that temperature was the first parameter, pH the second and nitrate was the last factor affecting the prediction of DO concentrations. The results proved the workability and accuracy of the RBF model in the prediction of the DO.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.19 no.5
• /
• pp.713-719
• /
• 2009

In this paper, we develope a water demand forecasting algorithm using AR(Auto-regressive) and MLP(Multi-layer perceptron). To show effectiveness of the proposed method, we analyzed characteristics of time-series data collected in "A" purification plant at Jeon-Buk province during 2007-2008, and then performed the proposed method with various input factors selected through various analyses. As noted in experimental results, the performance of three types model such as multi-regressive, AR(Auto-regressive), and AR+MLP(Auto-regressive + Multi-layer perceptron) show 5.1%, 3.8%, and 3.6% with respect to MAPE(Mean Absolute Percentage Error), respectively. Thus, it is noted that the proposed method can be used to predict short-term water demand for the efficient operation of a water purification plant.

• Structural Engineering and Mechanics
• /
• v.78 no.5
• /
• pp.585-597
• /
• 2021

This paper presents a comparative study of analytical method, finite element method (FEM) and Multilayer Perceptron (MLP) for analysis of a contact problem. The problem consists of a functionally graded (FG) layer resting on a half plane and pressed with distributed load from the top. Firstly, analytical solution of the problem is obtained by using theory of elasticity and integral transform techniques. The problem is reduced a system of integral equation in which the contact pressure are unknown functions. The numerical solution of the integral equation was carried out with Gauss-Jacobi integration formulation. Secondly, finite element model of the problem is constituted using ANSYS software and the two-dimensional analysis of the problem is carried out. The results show that contact areas and the contact stresses obtained from FEM provide boundary conditions of the problem as well as analytical results. Thirdly, the contact problem has been extended based on the MLP. The MLP with three-layer was used to calculate the contact distances. Material properties and loading states were created by giving examples of different values were used at the training and test stages of MLP. Program code was rewritten in C++. As a result, average deviation values such as 0.375 and 1.465 was obtained for FEM and MLP respectively. The contact areas and contact stresses obtained from FEM and MLP are very close to results obtained from analytical method. Finally, this study provides evidence that there is a good agreement between three methods and the stiffness parameters has an important effect on the contact stresses and contact areas.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.26 no.3
• /
• pp.422-429
• /
• 2022

In this paper, the intermediate frequency transmission signal level between the network system-based baseband and RF unit consisting of multi-channel airborne relay devices and a lot of mission devices, which are currently undergoing technology development tasks, is kept constant at the reference signal level. Considering the other party's receiving input range, despite changes in the short-range long-range wireless communication environment, it presents a multi-link protection and MLP-A algorithm that allows signals to be transmitted stably and reliably through signal detection automatic gain control, and experiments and analysis considering short-distance and long-distance wireless environments were performed by designing, manufacturing, and implementing RF units to which MLP-A algorithms were applied, and applying distance calculation equations to the configuration of multiple air movements and verification networks. Through this, it was confirmed that a stable and reliable RF communication system can be operated.

• Geomechanics and Engineering
• /
• v.28 no.4
• /
• pp.385-396
• /
• 2022

The accurate determination of the subgrade reaction modulus (K_s) of soil is an important factor for geotechnical engineers. This study estimated the K_s of soft soil improved with floating deep cement mixing (DCM) columns. A novel prediction model was developed that emphasizes the accuracy of identifying the most significant parameters of K_s. Several multi-layer perceptron (MLP) models that were trained using the Levenberg Marquardt (LM) backpropagation method were developed to estimate K_s. The models were trained using a reliable database containing the results of 36 physical modelling tests. The input parameters were the undrained shear strength of the DCM columns, undrained shear strength of soft soil, area improvement ratio and length-to-diameter ratio of the DCM columns. Grey wolf optimization (GWO) was coupled with the MLPs to improve the performance indices of the MLPs. Sensitivity tests were carried out to determine the importance of the input parameters for prediction of K_s. The results showed that both the MLP-LM and MLP-GWO methods showed high ability to predict K_s. However, it was shown that MLP-GWO (R = 0.9917, MSE = 0.28 (MN/m²/m)) performed better than MLP-LM (R =0.9126, MSE =6.1916 (MN/m²/m)). This proves the greater reliability of the proposed hybrid model of MLP-GWO in approximating the subgrade reaction modulus of soft soil improved with floating DCM columns. The results revealed that the undrained shear strength of the soil was the most effective factor for estimation of K_s.