• International Journal of Computer Science & Network Security
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• v.22 no.10
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• pp.237-245
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• 2022

The prominence of IoTs (Internet of Things) and exponential advancement of computer networks has resulted in massive essential applications. Recognizing various cyber-attacks or anomalies in networks and establishing effective intrusion recognition systems are becoming increasingly vital to current security. MLTs (Machine Learning Techniques) can be developed for such data-driven intelligent recognition systems. Researchers have employed a TFDNNs (Tensor Flow Deep Neural Networks) and DCNNs (Deep Convolution Neural Networks) to recognize pirated software and malwares efficiently. However, tuning the amount of neurons in multiple layers with activation functions leads to learning error rates, degrading classifier's reliability. HTFDNNs ( Hybrid tensor flow DNNs) and MRNs (Modified Residual Networks) or Resnet CNNs were presented to recognize software piracy and malwares. This study proposes HTFDNNs to identify stolen software starting with plagiarized source codes. This work uses Tokens and weights for filtering noises while focusing on token's for identifying source code thefts. DLTs (Deep learning techniques) are then used to detect plagiarized sources. Data from Google Code Jam is used for finding software piracy. MRNs visualize colour images for identifying harms in networks using IoTs. Malware samples of Maling dataset is used for tests in this work.

• Database Research
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• v.34 no.3
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• pp.137-147
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• 2018

A key to a well-performing human activity recognition (HAR) system through machine learning technique is the availability of a substantial amount of labeled data. Collecting sufficient labeled data is an expensive and time-consuming task. To build a HAR system in a new environment (i.e., the target domain) with very limited labeled data, it is unfavorable to naively exploit the data or trained classifier model from the existing environment (i.e., the source domain) as it is due to the domain difference. While traditional machine learning approaches are unable to address such distribution mismatch, transfer learning approach leverages the utilization of knowledge from existing well-established source domains that help to build an accurate classifier in the target domain. In this work, we propose a transfer learning approach to create an accurate HAR classifier with very limited data through the multitask neural network. The classifier loss function minimization for source and target domain are treated as two different tasks. The knowledge transfer is performed by simultaneously minimizing the loss function of both tasks using a single neural network model. Furthermore, we utilize the unlabeled data in an unsupervised manner to help the model training. The experiment result shows that the proposed work consistently outperforms existing approaches.

• Journal of the East Asian Society of Dietary Life
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• v.14 no.1
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• pp.54-63
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• 2004

This study was accomplished to investigate the recognition and the using practices about food-nutrition labeling system of 20's consumers. Two hundred and fifty-four man and women were examined using the questionnaire. The results were as follows. 1. The degree of recognition of subjects about food-nutrition labeling system was low (43.3％) and the using practices were even lower (18.1％). 2. The items considered as important were in the order of shelf life, manufactured date and cost. 3. The confidence score to the food-nutrition labeling system of consumers was 3.2 and was higher in the females compared to males. 4. The preferred method of food-nutrition labeling was in the order of picture and graphic type method, table type method and descriptive type method. 5. The expected effects of the current food-nutrition labeling system were easy to select foods for the prevention of the adult diseases and diet therapy. From these results, we might propose the conclusion as follows: Food-nutrition labeling system might be a good source of nutrition information and the consumers'demand for disease-prevention and dietary purpose. Therefore, the agencies concerned should make an every effort for the successful implementation of food-nutrition labeling system.

• Journal of Korea Society of Digital Industry and Information Management
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• v.11 no.3
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• pp.1-9
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• 2015

In this paper, we implemented the computer vision platform design with MEAN Stack through Raspberry PI 2 model which is an open source platform. we experimented the face recognition, temperature and humidity sensor data logging with WiFi communication under Raspberry Pi 2 model. Especially we directly made the shape of platform with 3D printing design. In this paper, we used the face recognition algorithm with OpenCV software through haarcascade feature extraction machine learning algorithm, and extended the functionality of wireless communication function ability with Bluetooth technology for the purpose of making Android Mobile devices interface. And therefore we implemented the functions of the vision platform for identifying the face recognition characteristics of scanning with PI camera with gathering the temperature and humidity sensor data under IoT environment. and made the vision platform with 3D printing technology. Especially we used MongoDB for developing the performance of vision platform because the MongoDB is more akin to working with objects in a programming language than what we know of as a database. Afterwards, we would enhance the performance of vision platform for clouding functionalities.

• The KIPS Transactions:PartB
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• v.17B no.2
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• pp.107-114
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• 2010

A guitar, a piano, and a violin are popular musical instruments for User Created Contents(UCC). However the patterns of audio signal generated by a guitar and a piano are too similar to differentiate. The difference between two musical instruments can be found by analyzing the frequency variation per each band near signal peaks. The distribution of probability on the existence of signal peaks based on Cumulative Histogram were applied to musical instrument recognition. Experiments with statistical models of the frequency variation per each band near signal peaks showed the 14% improvement of musical instrument recognition.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.2
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• pp.63-68
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• 2018

Interests in face recognition area have been increasing due to diverse emerging applications. Face recognition algorithm from a two-dimensional source could be challenging in dealing with some circumstances such as face orientation, illuminance degree, face details such as with/without glasses and various expressions, like, smiling or crying. Hopfield Network capabilities have been used specially within the areas of recalling patterns, generalizations, familiarity recognitions and error corrections. Based on those abilities, a specific experimentation is conducted in this paper to apply the Redundant Parallel Hopfield Network on a face recognition problem. This new design has been experimentally confirmed and tested to be robust in any kind of practical situations.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.19 no.10
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• pp.942-946
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• 2006

We simulated insulation defects of stator winding wire on high voltage generator by 5 types. 4 types have one discharge source and other one has multi discharge source by simulation. For accurate decision, measurements used to PRPD pattern to occurred partial discharge source of various types. In this research, when PRPD pattern carried out or analyzed pattern recognition of discharge source, it used to powerful tools. In this result, PRPD Pattern defined to have single discharge source of 4 types by insulation defect. When insulation defect simulated, all the defected winding have not the same result. Errors for a little different can make mistakes from a subtle distinction. The difference between internal and void discharge have magnitude of pulse amplitude of inner discharge bigger than void discharge and have a shape of bisymmetry. But void discharge has a shape of bisymmetry against maximum value on polarity respectively. In cases of slot and surface discharge, we confirmed to show similar results those other researchers. In case of multi-discharge, as a result of we could classify not perfect match with occurred patterns in single discharge eachother. In the future, we will have to recognize and classify with results of multi-discharge.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.2
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• pp.126-132
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• 2012

The technique for estimation of sound source direction is one of the important methods necessary for various engineering fields such as monitoring system, military services and so on. As a new approach for estimation of sound source direction, this paper propose the bio-mimetic localization sensor based on mechanically coupling structure motivated by hearing structure of fly, Ormia Ochracea. This creature is known for its outstanding recognition ability to the sound which has large wavelength compared to its own size. ITTF (Inter-Tympanal Transfer Function) which is the transfer function between displacements of the tympanal membranes on each side has the all inter-tympanal information dependent on sound direction. The peak and notch features of desired ITTF can be generated by using the appropriate mechanical properties. A example of estimation of sound source direction using generated ITTF with monotonically changing notch and peak patterns is shown.

• Journal of the East Asian Society of Dietary Life
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• v.7 no.4
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• pp.475-483
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• 1997

The main purpose of this research is to provide Information regarding the preference and intake of calcium of middle school students in the Daegu area. The results were summarized as follows: The recognition score of calcium of the subjects was male 12.85 and female 12.45, respectively. Also the preference scores of calcium source foods of the subjects were male 3.60 and female 3.49. They preferred ice cream, laver, yoghurt and steamed fish cakes in that order, but they disliked cheese and loach soup. The daily calcium intake was 483.78mg(53.8% of RDA) for males and 545.91mg (68.2% of RDA) for females.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.17 no.9
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• pp.1006-1012
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• 2004

In this paper, neural networks is studied to apply as a PD source classification in XLPE power cable specimen. Two learning schemes are used to classification; BP(Back propagation algorithm), SOM(self organized map - kohonen network). As a PD source, using treeing discharge sources in the specimen, three defected models are made. And these data making use of a computer-aided discharge analyser, statistical and other discharge parameters is calculated to discrimination between different models of discharge sources. And a]so these distribution characteristics are applied to classify PD sources by two scheme of the neural networks. In conclusion, recognition efficiency of BP is superior to SOM.