• Journal of Family Resource Management and Policy Review
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• v.12 no.2
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• pp.73-93
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• 2008

The purpose of this study was to analyze the effects of workers' human resource development and their recognition of human resource on-the-job satisfaction. A sample of 4,727 workers that was selected from Korea Labor Panel Data was analyzed by t-test and multiple regression, and was tested by causal effects among related variables. The major findings were as follows: First, the workers' recognition of their job performance level vs. educational attainment was affected by their annual income, job status, educational attainment, gender, and experiences of human resource development. Second, the workers' job satisfaction was affected by gender, age, educational attainment, health status, job status, annual income, experiences of human resource development, recognition of their job performance level vs. educational attainment, and recognition for their job availability. Third, the factors that had a causal effect on workers' job satisfaction were educational attainment, gender, age, health status, annual income, and experiences of human resource development. Above all, workers' educational attainment had a strong direct effect on job satisfaction, and annual income had a strong indirect effect on it. From these findings, it can be concluded that workers' effort and trial for development and investment of human resource played an important role in increasing job satisfaction.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.54 no.3
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• pp.156-165
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• 2005

Human being recognizes the physical world by integrating a great variety of sensory inputs, the information acquired by their own action, and their knowledge of the world using hierarchically parallel-distributed mechanism. In this paper, authors propose the sensor fusion system that can recognize multiple 3D objects from 2D projection images and tactile informations. The proposed system focuses on improving recognition performance of 3D objects. Unlike the conventional object recognition system that uses image sensor alone, the proposed method uses tactual sensors in addition to visual sensor. Neural network is used to fuse the two sensory signals. Tactual signals are obtained from the reaction force of the pressure sensors at the fingertips when unknown objects are grasped by four-fingered robot hand. The experiment evaluates the recognition rate and the number of learning iterations of various objects. The merits of the proposed systems are not only the high performance of the learning ability but also the reliability of the system with tactual information for recognizing various objects even though the visual sensory signals get defects. The experimental results show that the proposed system can improve recognition rate and reduce teeming time. These results verify the effectiveness of the proposed sensor fusion system as recognition scheme for 3D objects.

• 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.

• Proceedings of the IEEK Conference
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• 2002.06d
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• pp.377-380
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• 2002

In this paper, speaker and context independent emotion recognition using speech signal is studied. For this purpose, a corpus of emotional speech data recorded and classified according to the emotion using the subjective evaluation were used to make statical feature vectors such as average, standard deviation and maximum value of pitch and energy and to evaluate the performance of the conventional pattern matching algorithms. The vector quantization based emotion recognition system is proposed for speaker and context independent emotion recognition. Experimental results showed that vector quantization based emotion recognizer using MFCC parameters showed better performance than that using the Pitch and energy Parameters.

• Industry Promotion Research
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• v.9 no.2
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• pp.1-12
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• 2024

This study examines the role of Environment, Social, and Governance (ESG) activity recognition on job satisfaction, prosocial activities, and job performance among customer representatives working in call center environments. After gathering data from 264 call center workers in major South Korean insurance companies, the analysis w as performed using SmartPLS 4.0. This study's findings reveal that employee recognition of ESG activities significantly enhanced job satisfaction. The impact of ESG activity recognition on prosocial behavior was positive but relatively weak. Job satisfaction influences both prosocial behavior and the job performance of employees. Finally, prosocial behavior positively influences job performance. The most significant finding is that employees' recognition of companies' ESG management practices serves as a job resource. This recognition enhances employees' attitudes, behavior, and performance, signaling the potential benefits of informing employees about corporations' ethical behaviors.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.5
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• pp.586-591
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• 2008

This paper studied the emotion recognition system combined with robust speech recognition system in order to improve the performance of emotion recognition system. For this purpose, the effect of emotional variation on the speech recognition system and robust feature parameters of speech recognition system were studied using speech database containing various emotions. Final emotion recognition is processed using the input utterance and its emotional model according to the result of speech recognition. In the experiment, robust speech recognition system is HMM based speaker independent word recognizer using RASTA mel-cepstral coefficient and its derivatives and cepstral mean subtraction(CMS) as a signal bias removal. Experimental results showed that emotion recognizer combined with speech recognition system showed better performance than emotion recognizer alone.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 1999.11a
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• pp.132-135
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• 1999

In this work, two approaches were proposed for the recognition of partial discharge patterns. The first approach was neural network with backpropagation algorithm, and the second approach was angle calculation between two operator vectors. PD signal were detected using three electrode systems; IEC(b), needle-plane and CIGRE method II electrode system. Both of neural network and angle comparison method showed good recognition performance for the patte군 similar to the trained patterns. And the number of operators to be used had a great influence on the recognition performance to the untrained patterns.

• Journal of Biomedical Engineering Research
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• v.31 no.6
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• pp.417-426
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• 2010

We present a speech processing strategy incorporating instantaneous frequency (IF) encoding for the enhancement of melody recognition performance of cochlear implants. For the IF extraction from incoming sound, we propose the use of a Teager energy operator (TEO), which is advantageous for its lower computational load. From time-frequency analysis, we verified that the TEO-based method provides proper IF encoding of input sound, which is crucial for melody recognition. Similar benefit could be obtained also from the use of a Hilbert transform (HT), but much higher computational cost was required. The melody recognition performance of the proposed speech processing strategy was compared with those of a conventional strategy using envelope extraction, and the HT-based IF encoding. Hearing tests on normal subjects were performed using acoustic simulation and a musical contour identification task. Insignificant difference in melody recognition performance was observed between the TEO-based and HT-based IF encodings, and both were superior to the conventional strategy. However, the TEO-based strategy was advantageous considering that it was approximately 35% faster than the HT-based strategy.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.7C
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• pp.538-543
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• 2012

The performance of the speech recognition engine is one of the most critical elements of the in-vehicle speech recognition interface. The objective of this paper is to develop an automated platform for running performance tests on the in-vehicle speech recognition engine. The developed platform comprise of main program, agent program, database management module, and statistical analysis module. A simulation environment for performance tests which mimics the real driving situations was constructed, and it was tested by applying pre-recorded driving noises and a speaker's voice as inputs. As a result, the validity of the results from the speech recognition tests was proved. The users will be able to perform the performance tests for the in-vehicle speech recognition engine effectively through the proposed platform.

• Journal of Information Processing Systems
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• v.16 no.1
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• pp.224-237
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• 2020

Recognition of banknote serial number is one of the important functions for intelligent banknote counter implementation and can be used for various purposes. However, the previous character recognition method is limited to use due to the font type of the banknote serial number, the variation problem by the solid status, and the recognition speed issue. In this paper, we propose an aspect ratio based character region segmentation and a convolutional neural network (CNN) based banknote serial number recognition method. In order to detect the character region, the character area is determined based on the aspect ratio of each character in the serial number candidate area after the banknote area detection and de-skewing process is performed. Then, we designed and compared four types of CNN models and determined the best model for serial number recognition. Experimental results showed that the recognition accuracy of each character was 99.85%. In addition, it was confirmed that the recognition performance is improved as a result of performing data augmentation. The banknote used in the experiment is Indian rupee, which is badly soiled and the font of characters is unusual, therefore it can be regarded to have good performance. Recognition speed was also enough to run in real time on a device that counts 800 banknotes per minute.