• Journal of Veterinary Clinics
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• v.13 no.2
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• pp.171-176
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• 1996

In this study, dldctronic identification system of individual dairy cow was developed for autocatization of stoxkvreeding management. To automize the breeding management, it is necessary to obtain and analyze the individual information distinguished from others perferentially. Electronic identification system can distinguish individual livestock from others with electromagnetic wave signal recognition system. Electoronic identification system consists of transmitter transmitting the oscillated signal and receiver set. The transmitted signal from transmitter clung to individual livestock is received from the receiving antenna and the signal in different according to the established value of the register. By distinct signal recieved from the reciever, wi can distinguish the identity of a livestock from others clearly. This system can manage $2^{12}$ individuals with a reciever theoretically. However in order to reduce the errors by analogous signal, this system uses only triple number and can manage 1365 individuals with a reciever practically. This system can be connevtted to Max 232 and microcomputer for the breeding management efficiently.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.10
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• pp.4808-4824
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• 2016

Signal individual reconnaissance and identification is an extremely important research topic in non-cooperative domains such as electronic countermeasures and intelligence reconnaissance. Facing the characteristics of the complexity and changeability of current communication environment, how to realize radiation source signal individual identification under the low SNR conditions is an emphasis of research. A novel emitter individual identification method combined bi-spectrum analysis with wavelet feature is presented in this paper. It makes a feature fusion of bi-spectrum slice characteristics and energy variance characteristics of the secondary wavelet transform coefficient to identify MFSK signals under the low SNR (signal-to-noise ratios) environment. Theoretical analyses and computer simulation results show that the proposed algorithm has good recognition performance with the ability to suppress noise and interference, and reaches the recognition rate of more than 90% when the SNR is -6dB.

• Journal of Industrial Convergence
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• v.17 no.3
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• pp.23-29
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• 2019

Object recognition distinguish objects which are different from each other. But Face recognition distinguishes Identity of Faces with Similar Patterns. Feature extraction algorithm such as LBP, HOG, Gabor is being replaced with Deep Learning. As the technology that identify individual face with machine learning using Deep Learning Technology is developing, The Face Recognition Technology is being used in various field. In particular, the technology can provide individual and detailed service by being used in various offline environments requiring user identification, such as Smart Mirror. Face Recognition Technology can be developed as the technology that authenticate user easily by device like Smart Mirror and provide service authenticated user. In this paper, we present investigation about Face Recognition among various techniques for user authentication and analysis of Python source case of Face recognition and possibility of various service using Face Recognition Technology.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.3
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• pp.266-270
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• 2006

In this paper, we propose the human identification system based on Hidden Markov model using gait. Since each gait cycle consists of a set of continuous motion states and transition across states has probabilistic dependences, individual gait can be modeled using Hidden Markov model. We assume that individual gait consists of N discrete transitions and we propose gait feature representation, Modified Motion Silhouette Image (MMSI) to represent and recognize individual gait. MMSI is defined as a gray-level image and it provides not only spatial information but also temporal information. The experimental results show gait recognition performance of proposed system.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.7
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• pp.1557-1564
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• 2010

In this paper, we propose a maximum entropy-based emotion recognition model using the individual average difference of emotional signal, because an emotional signal pattern depends on each individual. In order to accurately recognize a user's emotion, the proposed model utilizes the difference between the average of the input emotional signals and the average of each emotional state's signals(such as positive emotional signals and negative emotional signals), rather than only the given input signal. With the aim of easily constructing the emotion recognition model without the professional knowledge of the emotion recognition, it utilizes a maximum entropy model, one of the best-performed and well-known machine learning techniques. Considering that it is difficult to obtain enough training data based on the numerical value of emotional signal for machine learning, the proposed model substitutes two simple symbols such as +(positive number)/-(negative number) for every average difference value, and calculates the average of emotional signals per second rather than the total emotion response time(10 seconds).

• The Journal of the Acoustical Society of Korea
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• v.23 no.4E
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• pp.134-139
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• 2004

It is generally known that accurate segmentation is very necessary for both an individual word and continuous utterances in speech recognition. It is also commonly known that techniques are now being developed to classify the voiced and the unvoiced, also classifying the plosives and the fricatives. The method for accurate recognition of the phonemes isn't yet scientifically established. Therefore, in this study we analyze the Korean language, using the classification of 'Hunminjeongeum' and contemporary phonetics, with the frequency band, Mel band and Mel Cepstrum, we extract notable features of the phonemes from Korean speech and segment speech by the unit of the phonemes to normalize them. Finally, through the analysis and verification, we intend to set up Phonemic Segmentation System that will make us able to adapt it to both an individual word and continuous utterances.

• Journal of Biosystems Engineering
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• v.24 no.1
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• pp.75-80
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• 1999

Signal separation and transmission are essential for automatic breeding management of domestic animals. Electronic identification system could transmit the signal of an individual within a defined range to a personal computer by an electromagnetic signal recognition method. Signals for individual recognition were originated by controlling 12 tri-state pins of IC(PT2262) in a transmitter. PT 2262 can generate 4,096 codes. These encoded signals were modulated and transmitted with wireless lines from the transmitter. Then they were demodulated in a receiver, and the signals were transmitted to the micro-processor through an interface and were identified in a PC.

• Journal of Industrial Convergence
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• v.8 no.1
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• pp.1-18
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• 2010

The purpose of this study is to examine the impacts of sports marketing mix factors on brand equities and consumer behaviors. The findings of this study are as follows: first, there are differences in sports marketing mix factors, depending on individual characteristics. Differences are found in gender and living standard with regard to individual characteristics and prices. second, there are differences in brand equities, according to individual characters. third, sports marketing mix factors impact brand equities. fourth, the sports marketing mix factors impact perceive quality, brand recognition, location related to brand image and products. fifth, sports marketing mix factors impact brand equities and consumer behaviors. In regards with brand equities and repeat purchases, sports marketing mix factors impact promotion, products, perceived quality and image. In satisfaction, the sports marketing mix factors impact location, price promotion and product recognition. In the intention of transmission by word of mouth, the factors impact price, products, perceive quality and image.

• International journal of advanced smart convergence
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• v.9 no.1
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• pp.193-201
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• 2020

The main objective of this study is to investigate the impact of additional modalities on the performance of emotion recognition using speech, facial expression and physiological measurements. In order to compare different approaches, we designed a feature-based recognition system as a benchmark which carries out linear supervised classification followed by the leave-one-out cross-validation. For the classification of four emotions, it turned out that bimodal fusion in our experiment improves recognition accuracy of unimodal approach, while the performance of trimodal fusion varies strongly depending on the individual. Furthermore, we experienced extremely high disparity between single class recognition rates, while we could not observe a best performing single modality in our experiment. Based on these observations, we developed a novel fusion method, called parametric decision fusion (PDF), which lies in building emotion-specific classifiers and exploits advantage of a parametrized decision process. By using the PDF scheme we achieved 16% improvement in accuracy of subject-dependent recognition and 10% for subject-independent recognition compared to the best unimodal results.

• Korean Journal of Human Ecology
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• v.20 no.5
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• pp.993-1008
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• 2011

An increase in interests in consumers' social responsibilities, or ethical spending, starts from a recognition that the consumption of an individual does not stop with the individual, but also affects overall society. The recognition of consumers' social responsibilities leads to demands for corporate social responsibility. Therefore, this study analyzed how social responsibility recognition affects consumers' needs for corporate social responsibility using college students. All data was analyzed with the SPSS Windows 18.0 program in terms of frequency, Crobach's ${\alpha}$, factor analyses, paired t-test, one-way ANOVA, and multiple regression. The results are as following: first, the recognition level of consumers' social responsibilities in college students was at an average level and the consumer's needs for corporate social responsibility were higher than usual. Second, the grade level, military experience, and economic status of the college students changed their views on consumers' needs for corporate social responsibility. Groups with higher consumers' social responsibilities had higher consumer demands for corporate social responsibility. Through this, we can see that consumers' social responsibilities affects the consumer's needs for corporate social responsibility.