• The KIPS Transactions:PartB
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• v.10B no.2
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• pp.163-168
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• 2003

In transmitting compressed video bit-stream over Internet, packet loss causes error propagation in both spatial and temporal domain, which in turn leads to severe degradation in image quality. In this paper, a new approach for the recovery of lost or erroneous Motion Vector(MV)s by classifying the movements of neighboring blocks by their homogeneity is proposed. MVs of neighboring blocks are classified according to the direction of MVs and a representative value for each class is determined to obtain the candidate MV set. By computing the distortion of the candidates, a MV with the minimum distortion is selected. Experimental results show that the proposed algorithm exhibits better performance in many cases than existing methods.

• Journal of Electrical Engineering and Technology
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• v.10 no.2
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• pp.670-675
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• 2015

Medical imaging is one of the most powerful tools for gaining information about internal organs and tissues. It is a challenging task to develop sophisticated image analysis methods in order to improve the accuracy of diagnosis. The objective of this paper is to develop a Computer Aided Diagnostics (CAD) scheme for Brain Tumour detection from Magnetic Resonance Image (MRI) using active contour models and to investigate with several approaches for improving CAD performances. The problem in clinical medicine is the automatic detection of brain Tumours with maximum accuracy and in less time. This work involves the following steps: i) Segmentation performed by Fuzzy Clustering with Level Set Method (FCMLSM) and performance is compared with snake models based on Balloon force and Gradient Vector Force (GVF), Distance Regularized Level Set Method (DRLSE). ii) Feature extraction done by Shape and Texture based features. iii) Brain Tumour detection performed by various tree classifiers. Based on investigation FCMLSM is well suited segmentation method and Random Forest is the most optimum classifier for this problem. This method gives accuracy of 97% and with minimum classification error. The time taken to detect Tumour is approximately 2 mins for an examination (30 slices).

• Journal of the Korean Society of Industry Convergence
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• v.23 no.6_2
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• pp.979-987
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• 2020

This study used the Deep Learning models used in previous studies, we selected the basic model. The selected model was selected as ZFNet among ZFNet, Googlenet and ResNet, and the object was detected using a ZFNet based FRCNN. In order to reduce the detection error rate of FRCNN, location of four types of objects detected inside the image was designed by SVM classifier and location-based filtering was applied. As simulation results, it showed similar performance to the lane marking classification method with conventional 경계 detection, with an average accuracy of about 88.8%. In addition, studies using the Linear-parabolic Model showed a processing speed of 165.65ms with a minimum resolution of 600 × 800, but in this study, the resolution was treated at about 33ms with an input resolution image of 1280 × 960, so it was possible to classify lane marking at a faster rate than the previous study by CNN-based End to End method.

• International Journal of Computer Science & Network Security
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• v.21 no.1
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• pp.97-106
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• 2021

Social networking platforms have become a smart way for people to interact and meet on internet. It provides a way to keep in touch with friends, families, colleagues, business partners, and many more. Among the various social networking sites, Twitter is one of the fastest-growing sites where users can read the news, share ideas, discuss issues etc. Due to its vast popularity, the accounts of legitimate users are vulnerable to the large number of threats. Spam and Malware are some of the most affecting threats found on Twitter. Therefore, in order to enjoy seamless services it is required to secure Twitter against malicious users by fixing them in advance. Various researches have used many Machine Learning (ML) based approaches to detect spammers on Twitter. This research aims to devise a secure system based on Hybrid Similarity Cosine and Soft Cosine measured in combination with Genetic Algorithm (GA) and Artificial Neural Network (ANN) to secure Twitter network against spammers. The similarity among tweets is determined using Cosine with Soft Cosine which has been applied on the Twitter dataset. GA has been utilized to enhance training with minimum training error by selecting the best suitable features according to the designed fitness function. The tweets have been classified as spammer and non-spammer based on ANN structure along with the voting rule. The True Positive Rate (TPR), False Positive Rate (FPR) and Classification Accuracy are considered as the evaluation parameter to evaluate the performance of system designed in this research. The simulation results reveals that our proposed model outperform the existing state-of-arts.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.2
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• pp.39-57
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• 2018

The purpose of this study was to construct an artificial intelligence model that learns and estimates the relationship between vehicle queue length and link travel time in urban areas. The vehicle queue length estimation model is modeled by three models. First of all, classify whether vehicle queue is a link overflow and estimate the vehicle queue length in the link overflow and non-overflow situations. Deep learning model is implemented as Tensorflow. All models are based DNN structure, and network structure which shows minimum error after learning and testing is selected by diversifying hidden layer and node number. The accuracy of the vehicle queue link overflow classification model was 98%, and the error of the vehicle queue estimation model in case of non-overflow and overflow situation was less than 15% and less than 5%, respectively. The average error per link was about 12%. Compared with the detecting data-based method, the error was reduced by about 39%.

• Journal of Korea Water Resources Association
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• v.51 no.10
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• pp.839-852
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• 2018

The purpose of this study is to predict monthly agricultural reservoir storage by developing weather data-based Multiple Linear Regression Model (MLRM) with precipitation, maximum temperature, minimum temperature, average temperature, and average wind speed. Using Naïve-Bayes classification, total 1,559 nationwide reservoirs were classified into 30 clusters based on geomorphological specification (effective storage volume, irrigation area, watershed area, latitude, longitude and frequency of drought). For each cluster, the monthly MLRM was derived using 13 years (2002~2014) meteorological data by KMA (Korea Meteorological Administration) and reservoir storage rate data by KRC (Korea Rural Community). The MLRM for reservoir storage rate showed the determination coefficient ($R^2$) of 0.76, Nash-Sutcliffe efficiency (NSE) of 0.73, and root mean square error (RMSE) of 8.33% respectively. The MLRM was evaluated for 2 years (2015~2016) using 3 months weather forecast data of GloSea5 (GS5) by KMA. The Reservoir Drought Index (RDI) that was represented by present and normal year reservoir storage rate showed that the ROC (Receiver Operating Characteristics) average hit rate was 0.80 using observed data and 0.73 using GS5 data in the MLRM. Using the results of this study, future reservoir storage rates can be predicted and used as decision-making data on stable future agricultural water supply.

• The Journal of the Acoustical Society of Korea
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• v.22 no.2
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• pp.145-152
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• 2003

In this paper, assuming that the score of speech utterance is the product of HMM log likelihood and HMM weight, we propose a new method that HMM weights are adapted iteratively like the general MCE training. The proposed method adjusts HMM weights for better performance using delta coefficient defined in terms of misclassification measure. Therefore, the parameter estimation and the Viterbi algorithms of conventional 1:.um can be easily applied to the proposed model by constraining the sum of HMM weights to the number of HMMs in an HMM set. Comparing with the general segmental MCE training approach, computing time decreases by reducing the number of parameters to estimate and avoiding gradient calculation through the optimal state sequence. To evaluate the performance of HMM-based speech recognizer by weighting HMM likelihood, we perform Korean isolated digit recognition experiments. The experimental results show better performance than the MCE algorithm with state weighting.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.31 no.1 s.256
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• pp.26-35
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• 2007

The vehicle of urban transit is a complex system that consists of various electric, electronic, and mechanical equipments, and the maintenance cost of this complex and large-scale system generally occupies sixty percent of the LCC (Life Cycle Cost). For reasonable establishing of maintenance strategies, safety security and cost limitation must be considered at the same time. The concept of system reliability has been introduced and optimized as the key of reasonable maintenance strategies. For optimization, three preceding studies were accomplished; standardizing a maintenance classification, constructing RBD (Reliability Block Diagram) of VVVF (Variable Voltage Variable Frequency) urban transit, and developing a web based reliability evaluation system. Historical maintenance data in terms of reliability index can be derived from the web based reliability evaluation system. In this paper, we propose applying inverse problem analysis method and hybrid neuro-genetic algorithm to system reliability optimization for using historical maintenance data in database of web based system. Feed-forward multi-layer neural networks trained by back propagation are used to find out the relationship between several component reliability (input) and system reliability (output) of structural system. The inverse problem can be formulated by using neural network. One of the neural network training algorithms, the back propagation algorithm, can attain stable and quick convergence during training process. Genetic algorithm is used to find the minimum square error.

• Journal of KIISE:Computer Systems and Theory
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• v.35 no.7
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• pp.340-353
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• 2008

A computational color palette extraction model is introduced to describe paint brush objectively and efficiently. In this model, a color palette is defined as a minimum set of colors in which a painting can be displayed within error allowance and extracted by the two step processing of color grouping and major color extraction. The color grouping controls the resolution of colors adaptively and produces a basic color set of given painting images. The final palette is obtained from the basic color set by applying weighted k-means clustering algorithm. The extracted palettes from several famous painters are displayed in a 3-D color space to show the distinctive palette styles using RGB and CIE LAB color models individually. And the two experiments of painter classification and color transform of photographic image has been done to check the performance of the proposed method. The results shows the possibility that the proposed palette model can be a computational color analysis metric to describe the paint brush, and can be a color transform tool for computer graphics.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.2
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• pp.520-541
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• 2023

In CRNs, SS is of utmost significance. Every CR user generates a sensing report during the training phase beneath various circumstances, and depending on a collective process, either communicates or remains silent. In the training stage, the fusion centre combines the local judgments made by CR users by a majority vote, and then returns a final conclusion to every CR user. Enough data regarding the environment, including the activity of PU and every CR's response to that activity, is acquired and sensing classes are created during the training stage. Every CR user compares their most recent sensing report to the previous sensing classes during the classification stage, and distance vectors are generated. The posterior probability of every sensing class is derived on the basis of quantitative data, and the sensing report is then classified as either signifying the presence or absence of PU. The ISVM technique is utilized to compute the quantitative variables necessary to compute the posterior probability. Here, the iterations of SVM are tuned by novel GO-PSA by combining GOA and PSO. Novel GO-PSA is developed since it overcomes the problem of computational complexity, returns minimum error, and also saves time when compared with various state-of-the-art algorithms. The dependability of every CR user is taken into consideration as these local choices are then integrated at the fusion centre utilizing an innovative decision combination technique. Depending on the collective choice, the CR users will then communicate or remain silent.