• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.4C
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• pp.163-174
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• 2009

Mechanical device using the load cell or strain gage sensor can be influenced by tempearute changes because temperature change can cause a shift in the load cell or straing gage output at zero loading. In this paper, micro cone penetrometers with 1~7mm in diameter, are developed by using an optical fiber sensor (FBG: Fiber Bragg Grating) to compensate the continous temperature change during cone penetration test. Note the temperature compensated method using optical fiber sensor which has hair-size in diameter, and is not affected by environmental conditions because the measured data is the wavelength shifting of the light instead of the intensity of the electric voltage. Temperature effect test shows that the output voltage of strain gage changes and increases with an increase in the temperature. A developed FBG cone penetrometer, however, achieves excellent temperature compensation during penetration, and produces continuous change of underground temperature. In addition, the temperature compensated FBG cone shows the excellent sensitivity and detects the interface of the layered soils with higher resolution. This study demonstrates that the fiber optic sensor renders the possibility of the ultra small size cone and the new fiber optic cone may produce more reliable temperature compensated tip resistance.

• The Journal of the Convergence on Culture Technology
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• v.9 no.5
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• pp.867-874
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• 2023

Recently, many problems are occurring in the bee ecosystem due to rapid climate change. The decline in the bee population and changes in the flowering period are having a huge impact on the harvest of bee-keepers. Since it is impossible to continuously observe the beehives in the hive with the naked eye, most people rely on knowledge based on experience about the state of the hive.Therefore, interest is focused on smart beekeeping incorporating IoT technology. In particular, with regard to swarming, which is one of the most important parts of beekeeping, we know empirically that the swarming time can be determined by the sound of the queen bee, but there is no way to systematically analyze this with data.You may think that it can be done by simply recording the sound of the queen bee and analyzing it, but it does not solve various problems such as various noise issues around the hive and the inability to continuously record.In this study, we developed a system that records queen bee sounds in a real-time cloud system and analyzes sound patterns.After receiving real-time analog sound from the hive through multiple channels and converting it to digital, a sound pattern that was continuously output in the queen bee sound frequency band was discovered. By accessing the cloud system, you can monitor sounds around the hive, temperature/humidity inside the hive, weight, and internal movement data.The system developed in this study made it possible to analyze the sound patterns of the queen bee and learn about the situation inside the hive. Through this, it will be possible to predict the swarming period of bees or provide information to control the swarming period.

• Journal of Korea Foundry Society
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• v.43 no.6
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• pp.279-285
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• 2023

In order to predict the mechanical properties of ductile cast iron heat treated in an intercritical temperature range, samples machined from cast iron with a tensile strength of 450 MPa were heat-treated at various intercritical temperatures and air-cooled, after which a microstructural analysis and Brinell hardness test were conducted. As the heat treatment temperature was increased in the intercritical temperature range, the ferrite fraction in the ductile cast iron decreased and the pearlite fraction increased, whereas the nodularity and nodule count did not change considerably from the corresponding values in the as-cast condition. The Brinell hardness values of the heat-treated ductile cast iron increased gradually as the heat treatment temperature was increased. Based on the measured alloy composition, the fraction of each stable phase and the hardness model from the literature, the hardness of the ductile cast iron heat treated in the intercritical temperature range was calculated, showing values very similar to the measured hardness data. In order to check whether it is possible to predict the hardness of heat-treated ductile cast iron by using the phase fraction obtained from thermodynamic calculations, the volumes of graphite, ferrite, and austenite in the alloy were calculated for each temperature condition. Those volume fractions were then converted into areas of each phase for hardness prediction of the heat-treated ductile cast iron. The hardness values of the cast iron samples based on thermodynamic calculations and on the hardness prediction model were similar within an error range up to 27 compared to the measured hardness data.

• Health Policy and Management
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• v.34 no.1
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• pp.68-77
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• 2024

Background: Depressive disorders can be categorized into daily depression and clinical depression. The experience of depressive disorder can increase health care utilization due to decreased treatment compliance and somatization. On the other hand, the clinical depression group may also experience social prejudice associated with the illness, which can limit their access to health care utilization. In terms of the significance of health care utilization as a factor in individual and social issues, this study aims to compare the health care utilization of the clinical depression group with that of the non-depressed group and the daily depression group. Methods: The analysis utilized the inverse probability of treatment weighting based on the generalized propensity score. Results: As a result of the analysis, clinical depression and daily depression were higher among women, low-income groups, individuals with low education levels, and so forth. The clinical depression group was also higher among individuals who were not economically active, did not have private health insurance, or had multiple chronic diseases. The number of outpatient department visits in the depression group was significantly higher than in the non-depressed group. In addition, the number of outpatient department visits for the clinical depression group was significantly higher than that for the daily depression group. Outpatient medical expenses were higher in the depression group than in the non-depressed group, and there was no significant difference between the clinical depression group and the daily depression group. Conclusion: Health care utilization was higher in the depression group than the non-depressed group, it was also higher in the clinical depression group than the daily depression group.

• Proceedings of the Korean Society of Computer Information Conference
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• 2024.01a
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• pp.55-58
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• 2024

최근 각 기업의 AI 면접시스템 도입이 증가하고 있으며, AI 면접에 대한 실효성 논란 또한 많은 상황이다. 본 논문에서는 AI 면접 과정에서 지원자를 평가하는 방식을 시각, 음성, 자연어처리 3영역에서 구현함으로써, 면접 지원자를 다방면으로 분석 방법론의 적절성에 대해 평가하고자 한다. 첫째, 시각적 측면에서, 면접 지원자의 감정을 인식하기 위해, 합성곱 신경망(CNN) 기법을 활용해, 지원자 얼굴에서 6가지 감정을 인식했으며, 지원자가 카메라를 응시하고 있는지를 시계열로 도출하였다. 이를 통해 지원자가 면접에 임하는 태도와 특히 얼굴에서 드러나는 감정을 분석하는 데 주력했다. 둘째, 시각적 효과만으로 면접자의 태도를 파악하는 데 한계가 있기 때문에, 지원자 음성을 주파수로 환산해 특성을 추출하고, Bidirectional LSTM을 활용해 훈련해 지원자 음성에 따른 6가지 감정을 추출했다. 셋째, 지원자의 발언 내용과 관련해 맥락적 의미를 파악해 지원자의 상태를 파악하기 위해, 음성을 STT(Speech-to-Text) 기법을 이용하여 텍스트로 변환하고, 사용 단어의 빈도를 분석하여 지원자의 언어 습관을 파악했다. 이와 함께, 지원자의 발언 내용에 대한 감정 분석을 위해 KoBERT 모델을 적용했으며, 지원자의 성격, 태도, 직무에 대한 이해도를 파악하기 위해 객관적인 평가지표를 제작하여 적용했다. 논문의 분석 결과 AI 면접의 다면적 평가시스템의 적절성과 관련해, 시각화 부분에서는 상당 부분 정확도가 객관적으로 입증되었다고 판단된다. 음성에서 감정분석 분야는 면접자가 제한된 시간에 모든 유형의 감정을 드러내지 않고, 또 유사한 톤의 말이 진행되다 보니 특정 감정을 나타내는 주파수가 다소 집중되는 현상이 나타났다. 마지막으로 자연어처리 영역은 면접자의 발언에서 나오는 말투, 특정 단어의 빈도수를 넘어, 전체적인 맥락과 느낌을 이해할 수 있는 자연어처리 분석모델의 필요성이 더욱 커졌음을 판단했다.

• The Transactions of the Korea Information Processing Society
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• v.13 no.6
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• pp.284-290
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• 2024

Speech emotion recognition (SER) is a technique that is used to analyze the speaker's voice patterns, including vibration, intensity, and tone, to determine their emotional state. There has been an increase in interest in artificial intelligence (AI) techniques, which are now widely used in medicine, education, industry, and the military. Nevertheless, existing researchers have attained impressive results by utilizing acted-out speech from skilled actors in a controlled environment for various scenarios. In particular, there is a mismatch between acted and spontaneous speech since acted speech includes more explicit emotional expressions than spontaneous speech. For this reason, spontaneous speech-emotion recognition remains a challenging task. This paper aims to conduct emotion recognition and improve performance using spontaneous speech data. To this end, we implement deep learning-based speech emotion recognition using the VGG (Visual Geometry Group) after converting 1-dimensional audio signals into a 2-dimensional spectrogram image. The experimental evaluations are performed on the Korean spontaneous emotional speech database from AI-Hub, consisting of 7 emotions, i.e., joy, love, anger, fear, sadness, surprise, and neutral. As a result, we achieved an average accuracy of 83.5% and 73.0% for adults and young people using a time-frequency 2-dimension spectrogram, respectively. In conclusion, our findings demonstrated that the suggested framework outperformed current state-of-the-art techniques for spontaneous speech and showed a promising performance despite the difficulty in quantifying spontaneous speech emotional expression.

• Journal of Navigation and Port Research
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• v.47 no.6
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• pp.410-418
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• 2023

Recently, advancements and commercialization in the field of maritime autonomous surface ships (MASS) has rapidly progressed. Concurrently, studies are also underway to develop methods for automatically surveying the condition of various on-board equipment remotely to ensure the navigational safety of MASS. One key issue that has gained prominence is the method to obtain values from analog gauges installed in various equipment through image processing. This approach has the advantage of enabling the non-contact detection of gauge values without modifying or changing already installed or planned equipment, eliminating the need for type approval changes from shipping classifications. The objective of this study was to identify a dynamically changing indicator needle within noisy images of analog gauges. The needle object must be identified because its position significantly affects the accurate reading of gauge values. An analog pressure gauge attached to an emergency fire pump model was used for image capture to identify the needle object. The acquired images were pre-processed through Gaussian filtering, thresholding, and morphological operations. The needle object was then identified through Hough Transform. The experimental results confirmed that the center and object of the indicator needle could be identified in images of noisy analog gauges. The findings suggest that the image processing method applied in this study can be utilized for shape identification in analog gauges installed on ships. This study is expected to be applicable as an image processing method for the automatic remote survey of MASS.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.1
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• pp.89-95
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• 2024

Nowadays, the competitiveness of a company depends on the ability of all organizational members to share and utilize the organizational knowledge accumulated by the organization. As if to prove this, the world is now focusing on ChetGPT service using generative AI technology based on LLM (Large Language Model). However, it is still difficult to apply the ChetGPT service to work because there are many hallucinogenic problems. To solve this problem, sLLM (Lightweight Large Language Model) technology is being proposed as an alternative. In order to construct sLLM, corporate data is essential. Corporate data is the organization's ERP data and the company's office document knowledge data preserved by the organization. ERP Data can be used by directly connecting to sLLM, but office documents are stored in file format and must be converted to data format to be used by connecting to sLLM. In addition, there are too many technical limitations to utilize office documents stored in file format as organizational knowledge information. This study proposes a method of storing office documents in DB format rather than file format, allowing companies to utilize already accumulated office documents as an organizational knowledge system, and providing office documents in data form to the company's SLLM. We aim to contribute to improving corporate competitiveness by combining AI technology.

• Sim-seong Yeon-gu
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• v.37 no.2
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• pp.49-76
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• 2022

This study is to illuminate the problem of shadows and evil appearing in contemporary events in the midst of a period of upheaval through Inferno of Divine Comedy. First, the concept of shadow and evil were briefly summarized in analytical psychology and discussed the importance of considering the concepts with the empirical aspect of relativity and ethics in the field of psychotherapy. The 14th century, the age of Dante, was the embryonic period of the Renaissance beyond the end of the Middle Ages. It was when Dante, who was writing Divine Comedy, had to take off his persona forcibly and live in exile. In a nutshell, it was a transition period for both the individual and the collective. The dark forest is a nigredo, darkness and chaos we face in this transition, but it can be a place of transformation and rebirth. The three beasts (leopard, lion, and she-wolf) encountered in the forest can be considered as the instinctual images that Dante ignored and alienated, which the medieval Christian world had suppressed and eliminated. Especially at the collective level, as destructive instincts, ferocious beasts roam throughout society when a crisis breaks dominant laws of values. The three beasts of Inferno appear as phenomena of shadows and evil. The aspect of leopard was explored Cerberus and Chiaco(pig) as a symbol of greed, and the lion, Farinata as the form of violent passion. The aspect of the she-wolf was examined as Geryon, a deceitful monster with a beast nature hidden behind goodness, the giants in the frozen lake of Cocytus, and Branca D'Oria, who betrayed and murdered the country and family. Inferno reveals the "state" of being trapped when one yields to the evil hidden within oneself and falls into prey.

• Journal of the Korea Society of Computer and Information
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• v.29 no.4
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• pp.115-123
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• 2024

In this paper, we propose a vibration notification system that combines navigation information and wearable bands to ensure safe driving for the transportation vulnerable. This system transmits navigation driving information to a linked application, converts it into a vibration signal, and provides notifications through a wearable band. Existing navigation systems focus on providing route guidance and location information, so the driver's concentration is dispersed, and safety and convenience are deteriorated, especially for those with mobility impairments, due to standard vision and delayed recognition of stimuli, resulting in an increasingly high traffic accident rate. To solve this problem, navigation driving information is converted into vibration signals through a linked application, and vibration notifications for events, left turns, right turns, and speeding are provided through a wearable band to ensure driver safety and convenience. In the future, we will use cameras and vehicle sensors to increase awareness of safety inside and outside the vehicle by adding a function that provides notifications with vibration and LED when the vehicle approaches or recognizes an object, and we will continue to conduct research to build a safer driving environment. plan.