• Smart Structures and Systems
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• v.32 no.2
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• pp.83-99
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• 2023

The temperature prediction approaches of three important locations in an operational longitudinal slab track-bridge structure by using three typical neural network methods based on the field measuring platform of four meteorological factors and internal temperature. The measurement experiment of four meteorological factors (e.g., ambient temperature, solar radiation, wind speed, and humidity) temperature in the three locations of the longitudinal slab and base plate of three important locations (e.g., mid-span, beam end, and Wide-Narrow Joint) were conducted, and then their characteristics were analyzed, respectively. Furthermore, temperature prediction effects of three locations under five various meteorological conditions are tested by using three neural network methods, respectively, including the Artificial Neural Network (ANN), the Long Short-Term Memory (LSTM), and the Convolutional Neural Network (CNN). More importantly, the predicted effects of solar radiation in four meteorological factors could be identified with three indicators (e.g., Root Means Square Error, Mean Absolute Error, Correlation Coefficient of R2). In addition, the LSTM method shows the best performance, while the CNN method has the best prediction effect by only considering a single meteorological factor.

• Journal of Korean Vacuum Science & Technology
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• v.6 no.1
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• pp.12-15
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• 2002

The physical properties, including magneto-optical and transport ones, of Ni$_2$MnG$_2$ alloy in the martensitic and austenitic states were investigated. The dependence of the temperature coefficient of resistivity on temperature shows kinks at the structural and ferro-para magnetic transitions. Electron-magnon and electron-phonon scattering are analyzed to be the dominant scattering mechanisms of the Ni$_2$MnG$_2$ alloy in the martensitic and austenitic states, respectively. The experimental real parts of the off-diagonal components of the dielectric function present two sharp peaks, one at 1.9 eV and the other at 3.2 eV, and a broad shoulder at 3.5 eV, all are identified by the band-structure calculations. These peak positions are coincident with those in the corresponding optical-conductivity spectrum, which is thought to originate from the single-spin state in Ni$_2$MnG$_2$ alloy.

• Korean Journal of Biological Psychiatry
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• v.30 no.1
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• pp.24-30
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• 2023

Objectives Alzheimer's disease (AD) is the most common form of dementia in older adults, damaging the brain and resulting in impaired memory, thinking, and behavior. The identification of differentially expressed genes and related pathways among affected brain regions can provide more information on the mechanisms of AD. The aim of our study was to identify differentially expressed genes associated with AD and combined biomarkers among them to improve AD risk prediction accuracy. Methods Machine learning methods were used to compare the performance of the identified combined biomarkers. In this study, three publicly available gene expression datasets from the hippocampal brain region were used. Results We detected 31 significant common genes from two different microarray datasets using the limma package. Some of them belonged to 11 biological pathways. Combined biomarkers were identified in two microarray datasets and were evaluated in a different dataset. The performance of the predictive models using the combined biomarkers was superior to those of models using a single gene. When two genes were combined, the most predictive gene set in the evaluation dataset was ATR and PRKCB when linear discriminant analysis was applied. Conclusions Combined biomarkers showed good performance in predicting the risk of AD. The constructed predictive nomogram using combined biomarkers could easily be used by clinicians to identify high-risk individuals so that more efficient trials could be designed to reduce the incidence of AD.

• ETRI Journal
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• v.45 no.4
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• pp.650-665
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• 2023

A novel smart metering technique capable of anomaly detection was proposed for real-time home power management system. Smart meter data generated in real-time were obtained from 900 households of single apartments. To detect outliers and missing values in smart meter data, a deep learning model, the autoencoder, consisting of a graph convolutional network and bidirectional long short-term memory network, was applied to the smart metering technique. Power management based on the smart metering technique was executed by multi-objective optimization in the presence of a battery storage system and an electric vehicle. The results of the power management employing the proposed smart metering technique indicate a reduction in electricity cost and amount of power supplied by the grid compared to the results of power management without anomaly detection.

• ETRI Journal
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• v.45 no.6
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• pp.1079-1089
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• 2023

Stroke is one of the leading causes of long-term disability worldwide, placing huge burdens on individuals and society. Further, automatic human activity recognition is a challenging task that is vital to the future of healthcare and physical therapy. Using a baseline long short-term memory recurrent neural network, this study provides a novel dataset of stretching, upward stretching, flinging motions, hand-to-mouth movements, swiping gestures, and pouring motions for improved model training and testing of stroke-affected patients. A MATLAB application is used to output textual and audible prediction results. A wearable sensor with a triaxial accelerometer is used to collect preprocessed real-time data. The model is trained with features extracted from the actual patient to recognize new actions, and the recognition accuracy provided by multiple datasets is compared based on the same baseline model. When training and testing using the new dataset, the baseline model shows recognition accuracy that is 11% higher than the Activity Daily Living dataset, 22% higher than the Activity Recognition Single Chest-Mounted Accelerometer dataset, and 10% higher than another real-world dataset.

• International journal of advanced smart convergence
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• v.13 no.1
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• pp.37-47
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• 2024

This study presents the results of designing a system that determines the location of a person in an indoor environment based on a single IMU sensor attached to the tip of a person's shoe in an area where GPS signals are inaccessible. By adjusting for human footfall, it is possible to accurately determine human location and trajectory by correcting errors originating from the Inertial Measurement Unit (IMU) combined with advanced machine learning algorithms. Although there are various techniques to identify stepping, our study successfully recognized stepping with 98.7% accuracy using an artificial intelligence model known as Long Short-Term Memory (LSTM). Drawing upon the enhancements in our methodology, this article demonstrates a novel technique for generating a 200-meter trajectory, achieving a level of precision marked by a 2.1% error margin. Indoor pedestrian navigation systems, relying on inertial measurement units attached to the feet, have shown encouraging outcomes.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.46 no.6
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• pp.6-15
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• 2009

In this paper, a new 4-way search window is designed for the high-performance 2D PE architecture in H.264 Motion Estimation(ME) to improve the memory bandwidth. While existing 2D PE architectures reuse the overlapped data of adjacent search windows scanned in 1 or 3-way, the new window utilizes the overlapped data of adjacent search windows as well as adjacent multiple scanning (window) paths to enhance the reusage of retrieved search window data. In order to scan adjacent windows and multiple paths instead of single raster and zigzag scanning of adjacent windows, bidirectional row and column window scanning results in the 4-way(up. down, left, right) search window. The proposed 4-way search window could improve the reuse of overlapped window data to reduce the redundancy access factor by 3.1, though the 1/3-way search window redundantly requires $7.7{\sim}11$ times of data retrieval. Thus, the new 4-way search window scheme enhances the memory bandwidth by $70{\sim}58%$ compared with 1/3-way search window. The 2D PE architecture in H.264 ME for 4-way search window consists of $16{\times}16$ pe array. computing the absolute difference between current and reference frames, and $5{\times}16$ reusage array, storing the overlapped data of adjacent search windows and multiple scanning paths. The reference data could be loaded upward and downward into the new 2D PE depending on scanning direction, and the reusage array is combined with the pe array rotating left as well as right to utilize the overlapped data of adjacent multiple scan paths. In experiments, the new implementation of 4-way search window on Magnachip 0.18um could deal with the HD($1280{\times}720$) video of 1 reference frame, $48{\times}48$ search area and $16{\times}16$ macroblock by 30fps at 149.25MHz.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.89-106
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• 2022

Recently, the amount of video data collected from smartphones, CCTVs, black boxes, and high-definition cameras has increased rapidly. According to the increasing video data, the requirements for analysis and utilization are increasing. Due to the lack of skilled manpower to analyze videos in many industries, machine learning and artificial intelligence are actively used to assist manpower. In this situation, the demand for various computer vision technologies such as object detection and tracking, action detection, emotion detection, and Re-ID also increased rapidly. However, the object detection and tracking technology has many difficulties that degrade performance, such as re-appearance after the object's departure from the video recording location, and occlusion. Accordingly, action and emotion detection models based on object detection and tracking models also have difficulties in extracting data for each object. In addition, deep learning architectures consist of various models suffer from performance degradation due to bottlenects and lack of optimization. In this study, we propose an video analysis system consists of YOLOv5 based DeepSORT object tracking model, SlowFast based action recognition model, Torchreid based Re-ID model, and AWS Rekognition which is emotion recognition service. Proposed model uses single-linkage hierarchical clustering based Re-ID and some processing method which maximize hardware throughput. It has higher accuracy than the performance of the re-identification model using simple metrics, near real-time processing performance, and prevents tracking failure due to object departure and re-emergence, occlusion, etc. By continuously linking the action and facial emotion detection results of each object to the same object, it is possible to efficiently analyze videos. The re-identification model extracts a feature vector from the bounding box of object image detected by the object tracking model for each frame, and applies the single-linkage hierarchical clustering from the past frame using the extracted feature vectors to identify the same object that failed to track. Through the above process, it is possible to re-track the same object that has failed to tracking in the case of re-appearance or occlusion after leaving the video location. As a result, action and facial emotion detection results of the newly recognized object due to the tracking fails can be linked to those of the object that appeared in the past. On the other hand, as a way to improve processing performance, we introduce Bounding Box Queue by Object and Feature Queue method that can reduce RAM memory requirements while maximizing GPU memory throughput. Also we introduce the IoF(Intersection over Face) algorithm that allows facial emotion recognized through AWS Rekognition to be linked with object tracking information. The academic significance of this study is that the two-stage re-identification model can have real-time performance even in a high-cost environment that performs action and facial emotion detection according to processing techniques without reducing the accuracy by using simple metrics to achieve real-time performance. The practical implication of this study is that in various industrial fields that require action and facial emotion detection but have many difficulties due to the fails in object tracking can analyze videos effectively through proposed model. Proposed model which has high accuracy of retrace and processing performance can be used in various fields such as intelligent monitoring, observation services and behavioral or psychological analysis services where the integration of tracking information and extracted metadata creates greate industrial and business value. In the future, in order to measure the object tracking performance more precisely, there is a need to conduct an experiment using the MOT Challenge dataset, which is data used by many international conferences. We will investigate the problem that the IoF algorithm cannot solve to develop an additional complementary algorithm. In addition, we plan to conduct additional research to apply this model to various fields' dataset related to intelligent video analysis.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.32 no.5
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• pp.481-486
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• 2014

This study aims at enhancing the performance of file-referring octree, suggested by Han(2014), for efficiently querying huge 3D point clouds, acquired by the 3D terrestrial laser scanning. Han's method(2014) has revealed a problem of heavy declining in query speed, when if it was applied on a very long tunnel, which is the lengthy and narrow shaped anisometric structure. Hereupon, the shape of octree has been analyzed of its influence on the query efficiency with the testing method of generating an independent octree in each isometric subdivision of 3D object boundary. This method tested query speed and main memory usage against the conventional single octree method by capturing about 300 million points in a very long tunnel. Finally, the testing method resulted in which twice faster query speed is taking similar size of memory. It is also approved that the conclusive factor influencing the query speed is the destination level, but the query speed can still increase with more proximity to isometric bounding shape of octree. While an excessive unbalance of octree shape along each axis can heavily degrade the query speed, the improvement of octree shape can be more effectively enhancing the query speed than increasement of destination level.

• Journal of Advanced Navigation Technology
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• v.20 no.4
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• pp.321-328
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• 2016

Typical MLC NAND flash devices are considered less reliable than SLC NAND flash devices. Although raw bit error rate (RBER) of MLC flash had been considered approximately 1000times or more higher than that of SLC flash, recent research conducted on Google's data center shows that it is much lower than such expectation. Based on the research, we devised In Drive Data Duplication (IDDD) scheme that efficiently exploit MLC flash's sufficient capacity to improve its data reliability without structural complexity increment using SSD intrinsic firmware layer, and showed the data reliability expectation of MLC flash could be significantly higher than that of SLC flash from measured and calculated error rates. Even though RBER of SLC flash was lower than that of MLC flash in 44 out of 48 cases we studied, applying IDDD scheme, RBER of MLC flash was became lower than that of SLC in all 48 cases and uncorrectable bit error rate (UBER) of MLC flash was became lower than that of SLC flash in 45 out of 48 cases.