• Journal of Menopausal Medicine
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• 제24권3호
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• pp.183-187
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• 2018

Objectives: The present mini review aimed to summarize the existing knowledge regarding the beneficial and adverse effects of raloxifene in menopausal women. Methods: This study is a review of relevant publications about the effects of raloxifene on sleep disorder, depression, venous thromboembolism, the plasma concentration of lipoprotein, breast cancer, and cognitive function among menopausal women. Results: Raloxifene showed no significant effect on depression and sleep disorder. Verbal memory improved with administration of 60 mg/day of raloxifene while a mild cognitive impairment risk reduction by 33% was observed with administration of 120 mg/day of raloxifene. Raloxifene was associated with a 50% decrease in the need for prolapse surgery. The result of a meta-analysis showed a significant decline in the plasma concentration of lipoprotein in the raloxifene group compared to placebo (standardized mean difference, -0.43; 10 trials). A network meta-analysis showed that raloxifene significantly decreased the risk of breast cancer (relative risk, 0.572; 95% confidence interval, 0.327-0.881; P = 0.01). In terms of adverse effects of raloxifene, the odds ratio (OR) was observed to be 1.54 (P = 0.006), indicating 54% increase in the risk of deep vein thrombosis (DVT) while the OR for pulmonary embolism (PE) was 1.05, suggesting a 91% increase in the risk of PE alone (P = 0.03). Conclusions: Raloxifene had no significant effect on depression and sleep disorder but decreased the concentration of lipoprotein. Raloxifene administration was associated with an increased risk of DVT and PE and a decreased risk of breast cancer and pelvic organ prolapse in postmenopausal women.

• Journal of Advanced Navigation Technology
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• 제23권5호
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• pp.473-480
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• 2019

The smart home is a technology that can monitor and control by connecting everything to a communication network in various fields such as home appliances, energy consumers, and security devices. The Smart home is developing not only automatic control but also learning situation and user's taste and providing the result accordingly. This paper proposes a model that can provide a comfortable indoor environment control service for the user's characteristics by detecting the user's behavior as well as the automatic remote control service. The whole system consists of ESP 8266 with sensor and Wi-Fi, Firebase as a real-time database, and a smartphone application. This model is divided into functions such as learning mode when the home appliance is operated, learning control through learning results, and automatic ventilation using indoor and outdoor sensor values. The study used moving averages for temperature and humidity in the control of home appliances such as air conditioners, humidifiers and air purifiers. This system can provide higher quality service by analyzing and predicting user's characteristics through various machine learning and deep learning.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 한국해양정보통신학회 2009년도 추계학술대회
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• pp.613-616
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• 2009

Information present condition is using situation board by manual processing that is consisted of ship arrangement plan and letterpress and magnet etc. in Pyeongtaekhang's deep-sea fishing vessel company. Study that mark open sea far from land ship information of underway 37 ships that is accepted in every time in internet web application environment that is based on Ubiquitous Network in PC that is linked to internet. 3 through practical use of RIA of Flash technology base compose Digital Dash-Board in width grid structure only and do ship sailing addition that is operating in 6 oceans and latitude, hardness indication as well as various informations to do visual display do. Emphasized in dynamic Web Application construction because can heighten the convenience to operator and user, and take advantage of real time data.

• Journal of Broadcast Engineering
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• 제24권5호
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• pp.735-742
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• 2019

In this paper, we propose a method of extracting speech features for phoneme recognition based on spikegram. The Fourier-transform-based features are widely used in phoneme recognition, but they are not extracted in a biologically plausible way and cannot have high temporal resolution due to the frame-based operation. For better phoneme recognition, therefore, it is desirable to have a new method of extracting speech features, which analyzes speech signal in high temporal resolution following the model of human auditory system. In this paper, we analyze speech signal based on a spikegram that models feature extraction and transmission in auditory system, and then propose a method of feature extraction from the spikegram for phoneme recognition. We evaluate the performance of proposed features by using a DNN-based phoneme recognizer and confirm that the proposed features provide better performance than the Fourier-transform-based features for short-length phonemes. From this result, we can verify the feasibility of new speech features extracted based on auditory model for phoneme recognition.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• 제14권5호
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• pp.430-437
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• 2021

In this paper, we propose a robust face recognition method against soft errors with a deep convolutional generative adversarial network(DCGAN) based compensation method by a cross-layer approach. When soft-errors occur in block data of JPEG files, these blocks can be decoded inappropriately. In previous results, these blocks have been replaced using a mean face, thereby improving recognition ratio to a certain degree. This paper uses a DCGAN-based compensation approach to extend the previous results. When soft errors are detected in an embedded system layer using parity bit checkers, they are compensated in the application layer using compensated block data by a DCGAN-based compensation method. Regarding soft errors and block data loss in facial images, a DCGAN architecture is redesigned to compensate for the block data loss. Simulation results show that the proposed method effectively compensates for performance degradation due to soft errors.

• Journal of the Korea Convergence Society
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• 제12권10호
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• pp.9-17
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• 2021

With the advancement of big data analysis, artificial intelligence, machine learning, etc., data analytics technology has developed to help with optimal decision-making. However, in certain areas, the lack of data restricts the use of these techniques. For example, real estate related data often have a long release cycle because of its recent release or being a non-liquid asset. In order to overcome these limitations, we studied the scalability of the existing time series through the TimeGAN model. A total of 45 time series related to weekly real estate data were collected within the period of 2012 to 2021, and a total of 15 final time series were selected by considering the correlation between the time series. As a result of data expansion through the TimeGAN model for the 15 time series, it was found that the statistical distribution between the real data and the extended data was similar through the PCA and t-SNE visualization algorithms.

• Journal of IKEEE
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• 제25권3호
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• pp.571-577
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• 2021

This paper describes ISO/TR 4804, an international standard to describe how to design and verify autonomous cars to ensure safety and cybersecurity. Goals of ISO/TR 4804 are (1) positive risk balance and (2) avoidance of unreasonable risk. It also 12 principles of safety and cybersecurity to achieve these goals. In the design procedures, it describes (1) 13 capabilities to achieve these safety and cybersecurity principles, (2) hardware and software elements to achieve these capabilities, and (3) a generic logical architecture to combine these elements. In the verification procedures, it describes (1) 5 challenges to ensure safety and cybersecurity, (2) test goals, platforms, and solutions to achieve these challenges, (3) simulation and field operation methods, and (4) verification methods for hardware and software elements. Especially, it regards deep neural network as a software component and it describe design and verification methods of autonomous cars.

• The Korean Journal of Applied Statistics
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• 제34권6호
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• pp.979-993
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• 2021

Acoustic scene classification (ASC) categorizes an audio file based on the environment in which it has been recorded. This has long been studied in the detection and classification of acoustic scenes and events (DCASE). In this study, we considered the problem that ASC faces in real-world applications that the model used should have low-complexity. We compared several models that apply light-weight techniques. First, a base CNN model was proposed using log mel-spectrogram, deltas, and delta-deltas features. Second, depthwise separable convolution, linear bottleneck inverted residual block was applied to the convolutional layer, and Quantization was applied to the models to develop a low-complexity model. The model considering low-complexity was similar or slightly inferior to the performance of the base model, but the model size was significantly reduced from 503 KB to 42.76 KB.

• Journal of the Korea Convergence Society
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• 제12권7호
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• pp.45-51
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• 2021

In accordance with the rapid non-face-to-face environment and mobile first strategy, the explosive increase and creation of many structured/unstructured data every year demands new decision making and services using big data in all fields. However, there have been few reference cases of using the Hadoop Ecosystem, which uses the rapidly increasing big data every year to collect and load big data into a standard platform that can be applied in a practical environment, and then store and process well-established big data in a relational database. Therefore, in this study, after collecting unstructured data searched by keywords from social network services based on Hadoop 2.0 through three virtual machine servers in the Spring Framework environment, the collected unstructured data is loaded into Hadoop Distributed File System and HBase based on the loaded unstructured data, it was designed and implemented to store standardized big data in a relational database using a morpheme analyzer. In the future, research on clustering and classification and analysis using machine learning using Hive or Mahout for deep data analysis should be continued.

• Journal of Korean Neurosurgical Society
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• 제62권3호
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• pp.353-360
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• 2019

Epilepsy surgery that eliminates the epileptogenic focus or disconnects the epileptic network has the potential to significantly improve seizure control in patients with medically intractable epilepsy. Magnetic resonance-guided laser interstitial thermal therapy (MRgLITT) has been an established option for epilepsy surgery since the US Food and Drug Administration cleared the use of MRgLITT in neurosurgery in 2007. MRgLITT is an ablative stereotactic procedure utilizing heat that is converted from laser energy, and the temperature of the tissue is monitored in real-time by MR thermography. Real-time quantitative thermal monitoring enables titration of laser energy for cellular injury, and it also estimates the extent of tissue damage. MRgLITT is applicable for lesion ablation in cases that the epileptogenic foci are localized and/or deep-seated such as in the mesial temporal lobe epilepsy and hypothalamic hamartoma. Seizure-free outcomes after MRgLITT are comparable to those of open surgery in well-selected patients such as those with mesial temporal sclerosis. Particularly in patients with hypothalamic hamartoma. In addition, MRgLITT can also be applied to ablate multiple discrete lesions of focal cortical dysplasia and tuberous sclerosis complex without the need for multiple craniotomies, as well as disconnection surgery such as corpus callosotomy. Careful planning of the target, the optimal trajectory of the laser probe, and the appropriate parameters for energy delivery are paramount to improve the seizure outcome and to reduce the complication caused by the thermal damage to the surrounding critical structures.