• Speech Sciences
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• v.10 no.2
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• pp.85-95
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• 2003

In speech signal processing, it is very important to detect the pitch exactly in speech recognition, synthesis and analysis. If we exactly pitch detect in speech signal, in the analysis, we can use the pitch to obtain properly the vocal tract parameter. It can be used to easily change or to maintain the naturalness and intelligibility of quality in speech synthesis and to eliminate the personality for speaker-independence in speech recognition. In this paper, we proposed a new pitch detection algorithm. First, positive center clipping is process by using the incline of speech in order to emphasize pitch period with a glottal component of removed vocal tract characteristic in time domain. And rough formant envelope is computed through peak-fitting spectrum of original speech signal infrequence domain. Using the roughed formant envelope, obtain the smoothed formant envelope through calculate the linear interpolation. As well get the flattened harmonics waveform with the algebra difference between spectrum of original speech signal and smoothed formant envelope. Inverse fast fourier transform (IFFT) compute this flattened harmonics. After all, we obtain Residual signal which is removed vocal tract element. The performance was compared with LPC and Cepstrum, ACF. Owing to this algorithm, we have obtained the pitch information improved the accuracy of pitch detection and gross error rate is reduced in voice speech region and in transition region of changing the phoneme.

• Science of Emotion and Sensibility
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• v.17 no.1
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• pp.53-62
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• 2014

Despite the huge potential of the practical application of emotion recognition technologies, the enhancement of the technologies still remains a challenge mainly due to the difficulty of recognizing emotion. Although not perfect, human emotions can be recognized through human images and sounds. Emotion recognition technologies have been researched by extensive studies that include image-based recognition studies, sound-based studies, and both image and sound-based studies. Studies on emotion recognition through facial expression detection are especially effective as emotions are primarily expressed in human face. However, differences in user environment and their familiarity with the technologies may cause significant disparities and errors. In order to enhance the accuracy of real-time emotion recognition, it is crucial to note a mechanism of understanding and analyzing users' personality traits that contribute to the improvement of emotion recognition. This study focuses on analyzing users' personality traits and its application in the emotion recognition system to reduce errors in emotion recognition through facial expression detection and improve the accuracy of the results. In particular, the study offers a practical solution to users with subtle facial expressions or low degree of emotion expression by providing an enhanced emotion recognition function.

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.07a
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• pp.529-532
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• 2023

본 논문은 소셜 네트워크 이용자의 텍스트 데이터를 대상으로, 트랜스포머 계열의 언어모델을 전이학습해 이용자의 MBTI 성격 유형을 분류한 국내 첫 연구이다. Kaggle MBTI Dataset을 대상으로 RoBERTa Distill, DeBERTa-V3 등의 사전 학습모델로 전이학습을 해, MBTI E/I, N/S, T/F, J/P 네 유형에 대한 분류의 평균 정확도는 87.9181, 평균 F-1 Score는 87.58를 도출했다. 해외 연구의 State-of-the-art보다 네 유형에 대한 F1-Score 표준편차를 50.1% 낮춰, 유형별 더 고른 분류 성과를 보였다. 또, Twitter, Reddit과 같은 글로벌 소셜 네트워크 서비스의 텍스트 데이터를 추가로 분류, 트랜스포머 기반의 MBTI 분류 방법론을 확장했다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.4
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• pp.1224-1248
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• 2022

In various sensor network applications, such as climate observation organizations, sensor nodes need to collect information from time to time and pass it on to the recipient of information through multiple bounces. According to field tests, this information corresponds to most of the energy use of the sensor hub. Decreasing the measurement of information transmission in sensor networks becomes an important issue.Compression sensing (CS) can reduce the amount of information delivered to the network and reduce traffic load. However, the total number of classification of information delivered using pure CS is still enormous. The hybrid technique for utilizing CS was proposed to diminish the quantity of transmissions in sensor networks.Further the energy productivity is a test task for the sensor nodes. However, in previous studies, a clustering approach using hybrid CS for a sensor network and an explanatory model was used to investigate the relationship between beam size and number of transmissions of hybrid CS technology. It uses efficient data integration techniques for large networks, but leads to clone attacks or attacks. Here, a new algorithm called SBEA (Snowball Endurance Algorithm) was proposed and tested with a bow. Thus, you can extend the battery life of your WSN by running effective copy detection. Often, multiple nodes, called observers, are selected to verify the reliability of the nodes within the network. Personal data from the source centre (e.g. personality and geographical data) is provided to the observer at the optional witness stage. The trust and reputation system is used to find the reliability of data aggregation across the cluster head and cluster nodes. It is also possible to obtain a mechanism to perform sleep and standby procedures to improve the life of the sensor node. The sniffers have been implemented to monitor the energy of the sensor nodes periodically in the sink. The proposed algorithm SBEA (Snowball Endurance Algorithm) is a combination of ERCD protocol and a combined mobility and routing algorithm that can identify the cluster head and adjacent cluster head nodes.This algorithm is used to yield the network life time and the performance of the sensor nodes can be increased.

• Journal of Digital Convergence
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• v.17 no.3
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• pp.353-360
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• 2019

The purpose of this study was to investigate the relationship between job stress and coping style in college students. Participants were 314 students in a college. Data were collected using a self administered questionnaire. The survey was conducted from May 02, 2018 to May 28, 2018. There were statistically significant correlations between personality stress, family environmental stress, academic stress, school environment stress and emotion - centered coping style among sub - variables of job stress, Job anxiety stress was significantly correlated with social support seeking and emotion - centered coping style. Since college students' emotional stress coping style is related to depressive emotional and physical health problems, it is necessary to provide a psychological treatment program for early detection and coping with psychological support services, and a mixed service such as education, lecture, and camp. In addition, it is thought that strategic action skill training (plan, method, and technology) is needed to change from emotion - centered coping style to problem - solving style.

• Mood & Emotion
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• v.10 no.1
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• pp.13-21
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• 2012

Suicide is a complex behavior associated with various neurobiological and psychosocial factors. It is considered that genetic polymorphism combined with environmental stress such as child-adolescent trauma make differences in neurobiological systems, which cause psychiatric disorders or pessimistic personality, impulse-aggressive behaviors, lack of judgment, and finally result in suicidal behavior. Much progress in the neurobiology of suicide has been made over the several decades. There seems to be a hereditary disposition to suicide independent of psychiatric disorder. The changes in neurotransmitters, neurohormones, neurotrophic factors, cytokines, lipid metabolisms related with their genetic polymorphism can contribute to disturbance of signal transductions and neuronal circuits vulnerable to suicide. It is likely that the main factors are dysfunctions of serotonin (5-HT) and hypothalamus-pituitary-adrenal (HPA) axis. Our understanding about the neurobiology of suicide is still limited. However, clinical practice could be assisted by neurobiological findings capable of making the detection of risk populations with higher sensitivity and the development of new treatment interventions. The settlement of biological markers in suicidal behaviors and their relationships is required.

• Journal of Life Science
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• v.25 no.8
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• pp.861-869
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• 2015

One of the domesticated species; the dog has been selectively bred for various aims by human. The dog has many breeds, which are artificially selected for specific behaviors and morphologies. Dogs contribute their life to human as working dogs for guide, rescue, detection or etc. Working dogs requires good personality, such as gentleness, robustness and patience for performing their special duty. Many studies have concentrated on finding genetic marker for selecting the high-quality working dog. In this study, we confirmed quantitative expression patterns of eight genes (ABAT; 4-Aminobutyrate Aminotransferase, PLCB1; Phospholipase C, Beta 1, SLC10A4; Solute Carrier Family 10, Member 4, WNT1; Wingless-Type MMTV Integration Site Family, Member 1, BARX2; BarH-Like Homeobox 2, NEUROD6; Neuronal Differentiation 6, SEPT9; Septin 9 and TBR1; T-Box, Brain, 1) among brains tissues from four dog breeds (Beagle, Sapsaree, Shepherd and Jindo), because these genes were expressed and have functions in brain mostly. Specially, BARX2, SEPT9, SLC10A4, TBR1 and WNT1 genes were highly expressed in Beagle and Jindo, and Sapsaree and German Shepherd were vice versa. The biological significance of total genes was estimated by database for annotation, visualization and integrated discovery (DAVID) to determine a different gene ontology (GO) class. In these analyses, we suppose to these eight genes could provide influential information for brain development, and intelligence of organisms. Taken together, these results could provide clues to discover biomarker related to functional traits in brain, and beneficial for selecting superior working dogs.

• Clinical and Experimental Pediatrics
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• v.46 no.6
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• pp.566-571
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• 2003

Purpose : Leukoencephalopathy(LE) is one of the most serious complications in children with hematologic malignancies during the course of treatment. Early recognition is important to reduce the impact and sequelae from LE. We therefore investigated the clinical features of LE following central nervous system(CNS) prophylaxis in children with hematologic malignancies and evaluated the significance of regular check-ups of brain MRI. Methods : We retrospectively reviewed children with hematologic malignancies who had CNS prophylaxis including intrathecal(IT) methotrexate(MTX) and/or cranial irradiation at the Department of Pediatrics, Kyungpook National University Hospital from Oct. 1995 to May 2002. Fifteen cases of acute leukemia and one case of lymphoma who experienced LE following CNS prophylaxis were included in the study. Clinical data were analyzed from the medical records and brain MRIs were reviewed by neuroradiologists. Results : The ages ranged from 1 to 13 years(median age=5.2 years), and the male to female ratio was 3 : 1. The time interval from the beginning of chemotherapy to the time of diagnosis of LE ranged from 2 to 17 months. They all had IT MTX two to 15 times and ten underwent cranial irradiation(1,800 rads). At the time of diagnosis, ten of them had neuropsychiatric symptoms including seizures, personality changes, headache, etc. After the change of treatment modality, four cases showed significant improvement on follow-up MRIs, six cases had no significant changes and two had worsening of LE. Four patients died of infection and bone marrow relapse. Conclusion : CNS prophylaxis with IT therapy and cranial irradiation may cause leukoencephalopathy during the course of treatment. As a result, regular brain MRI check-up is recommended for the early detection and reducing the incidence of LE, along with changes in the treatment modality.