• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.8
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• pp.1925-1936
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• 2010

In this paper, multi-legged robot was designed and produced using stable walking pattern algorithm. The robot had embedded camera and wireless communication function and it is possible to recognize both hand posture and obstacles. The algorithm decided moving paths, and recognized and avoided obstacles through Hough Transform using Edge Detection of inputed image from image sensor. The robot can be controlled by hand posture using Mahalanobis Distance and average value of skin's color pixel, which is previously learned in order to decide the destination. The developed system has shown obstacle detection rate of 96% and hand posture recognition rate of 94%.

• Journal of KIISE:Software and Applications
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• v.34 no.7
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• pp.585-594
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• 2007

The back propagation algorithm took a long time to learn the input patterns and was difficult to train the additional or repeated learning patterns. So Aleksander proposed the binary neural network which could overcome the disadvantages of BP Network. But it had the limitation of repeated learning and was impossible to extract a generalized pattern. In this paper, we proposed a dynamic 3 dimensional Neuro System which was consisted of a learning network which was based on weightless neural network and a feedback module which could accumulate the characteristic. The proposed system was enable to train additional and repeated patterns. Also it could be produced a generalized pattern by putting a proper threshold into each learning-net's discriminator which was resulted from learning procedures. And then we reused the generalized pattern to elevate the recognition rate. In the last processing step to decide right category, we used maximum response detector. We experimented using the MNIST database of NIST and got 99.3% of right recognition rate for training data.

• Journal of Korea Multimedia Society
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• v.16 no.3
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• pp.302-308
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• 2013

In this research, we have explored personal authentication system through multimodal biometrics for mobile computing environment. We have selected face and speaker recognition for the implementation of multimodal biometrics system. For face recognition part, we detect the face with Modified Census Transform (MCT). Detected face is pre-processed through eye detection module based on k-means algorithm. Then we recognize the face with Principal Component Analysis (PCA) algorithm. For speaker recognition part, we extract features using the end-point of voice and the Mel Frequency Cepstral Coefficient (MFCC). Then we verify the speaker through Dynamic Time Warping (DTW) algorithm. Our proposed multimodal biometrics system shows improved verification rate through combining two different biometrics described above. We implement our proposed system based on Android environment using Galaxy S hoppin. Proposed system presents reduced false acceptance ratio (FAR) of 1.8% which shows improvement from single biometrics system using the face and the voice (presents 4.6% and 6.7% respectively).

• KIPS Transactions on Software and Data Engineering
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• v.2 no.3
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• pp.199-204
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• 2013

An implementation method of real-time numeral recognizer based on gesture is presented in this paper for various information devices. The proposed algorithm steadily captures the motion of a hand on 3D open space with the Kinect sensor. The captured hand motion is simplified with PCA, in order to preserve the trace consistency and to minimize the trace variations due to noises and size changes. In addition, we also propose a new HMM using both the gradient and the positional features of the simplified hand stroke. As the result, the proposed algorithm has robust characteristics to the variations of the size and speed of hand motion. The recognition rate is increased up to 30%, because of this combined model. Experimental results showed that the proposed algorithm gives a high recognition rate about 98%.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.3
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• pp.195-198
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• 2013

Many libraries utilize RFID tags for checking in and out of books. However, the recognition rate of this automatic process may depend on the orientation of antennas and RFID tags. Therefore we need supplemental systems to improve the recognition rate. The proposed algorithm sets up the ROI of the book existing area from the input image and then performs Canny edge detection algorithm to extract edges of books. Finally Hough line transform algorithm allows to detect the number of books from the extracted edges. To evaluate the performance of the proposed method, we applied our method to 350 book images under various circumstances. We then analyzed the performance of proposed method from results using recognition and mismatch ratio. The experimental result gave us 97.1% accuracy in book counting.

• Journal of Power Electronics
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• v.17 no.4
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• pp.1014-1026
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• 2017

The traditional fault diagnosis method for photovoltaic (PV) inverters has a difficult time meeting the requirements of the current complex systems. Its main weakness lies in the study of nonlinear systems. In addition, its diagnosis time is long and its accuracy is low. To solve these problems, a hidden Markov model (HMM) is used that has unique advantages in terms of its training model and its recognition for diagnosing faults. However, the initial value of the HMM has a great influence on the model, and it is possible to achieve a local minimum in the training process. Therefore, a genetic algorithm is used to optimize the initial value and to achieve global optimization. In this paper, the HMM is combined with a genetic algorithm (GHMM) for PV inverter fault diagnosis. First Matlab is used to implement the genetic algorithm and to determine the optimal HMM initial value. Then a Baum-Welch algorithm is used for iterative training. Finally, a Viterbi algorithm is used for fault identification. Experimental results show that the correct PV inverter fault recognition rate by the HMM is about 10% higher than that of traditional methods. Using the GHMM, the correct recognition rate is further increased by approximately 13%, and the diagnosis time is greatly reduced. Therefore, the GHMM is faster and more accurate in diagnosing PV inverter faults.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.5
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• pp.1004-1012
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• 2007

In this paper, we proposed a new car license plate recognition method using an unsupervised ART2 algorithm with HSI color model. The proposed method consists of two main modules; extracting plate area from a vehicle image and recognizing the characters in the plate after that. To extract plate area, hue(H) component of HSI color model is used, and the sub-area containing characters is acquired using modified fuzzy binarization method. Each character is further divided by a 4-directional edge tracking algorithm. To recognize the separated characters, noise-robust ART2 algorithm is employed. When the proposed algorithm is applied to recognize license plate characters, the extraction rate is better than that of existing RGB model and the overall recognition rate is about 97.4%.

• Journal of agriculture & life science
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• v.45 no.6
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• pp.57-63
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• 2011

This study was performed to figure out the recognition on forest certification by consumption propensity and socio-economic characteristics of wood cabinet consumers. The mean score of recognition on SFM and FSC showed comparatively low, 2.25 and 2.20(5-point likert scale), from the analysis result on 88 valid questionnaires of 90. The respondents rate of eco and non eco-friendly group formed 80.7% and 18.2%, there is a significant gap between two groups on total questionnaires. The percentage of respondents recognizing on SFM and FSC was about 31% (eco-friendly), 5.6%(non eco-friendly, SFM) and 2.8%(non eco-friendly, FSC) within each group. It showed that the socio-economic characteristics on eco-friendly respondents were higher than the others about more 1.3 times in the married rate, average age and monthly householding income.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.30 no.2
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• pp.177-180
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• 2019

To improve the accuracy rate of guided weapons, many studies have been conducted on the accurate detection and identification of targets from sea clutter. Because of the variety and complicated characteristics of both sea-clutter and target signals, an active target recognition technique is required. In this study, we propose an algorithm to distinguish clutter and recognize targets by applying a fractal signature(FS) classifier, which is a fractal dimension, and a high-resolution target image(HRTI) classifier, which applies scene matching to an image formed from a scanned image. Simulation results using the algorithm revealed that the HRTI classifier recognized targets 1 and 2 at a 100 % rate, whereas the FS classifier recognized targets 1 and 2 at rates of 90 % and 93 %, respectively.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.10
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• pp.1280-1287
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• 2020

This paper proposes a vision-based food recognition method for automated production of custom cakes. A small camera module mounted on a food art printer recognizes objects' shape and estimates their center points through image processing. Through the perspective transformation, the top-view image is obtained from the original image taken at an oblique position. The line and circular hough transformations are applied to recognize square and circular shapes respectively. In addition, the center of gravity of each figure are accurately detected in units of pixels. The test results show that the shape recognition rate is more than 98.75% under 180 ~ 250 lux of light and the positioning error rate is less than 0.87% under 50 ~ 120 lux. These values sufficiently meet the needs of the corresponding market. In addition, the processing delay is also less than 0.5 seconds per frame, so the proposed algorithm is suitable for commercial purpose.