• Smart Media Journal
• /
• v.7 no.4
• /
• pp.79-89
• /
• 2018

In recent years, falls among elderly people have gained serious attention as a major cause of injuries. Falls often lead to fatal consequences due to lack of prompt response and rescue. Therefore, a more accurate fall detection system and an effective feature extraction technique are required to prevent and reduce the risk of such incidents. In this paper, we proposed an efficient feature extraction technique based on multiple sliding windows and validated it through a series of experiments using supervised learning algorithms. The experiments were conducted using the public datasets obtained from tri-axial accelerometers. The results depicted that extraction of the feature from adjacent sliding windows led to high accuracy in supervised machine learning-based fall detection. Also, the experiments conducted in this study suggested that the best accuracy can be achieved by keeping the window size as small as 2 seconds. With the kNN classifier and dataset from wearable sensors, the experiments achieved accuracy rates of 94%.

• Journal of the Semiconductor & Display Technology
• /
• v.17 no.4
• /
• pp.91-96
• /
• 2018

In this study temperature distribution and gas flow inside a planetary type reactor in which a number of satellites on a spinning susceptor were rotating were analyzed using numerical simulation. Effects of flow rates on gas flow and temperature distribution were investigated in order to obtain design parameters. The commercial computational fluid dynamics software CFD-ACE+ was used in this study. The multiple-frame-of-reference was used to solve continuity, momentum and energy conservation equations which governed the transport phenomena inside the reactor. Kinetic theory was used to describe the physical properties of gas mixture. Effects of the rotation speed of the satellites was clearly seen when the inlet flow rate was small. Thickness of the boundary layer affected by the satellites rotation became very thin as the flow rate increased. The temperature field was little affected by the incoming flow rate of precursors.

• Proceedings of the Korean Institute of Building Construction Conference
• /
• 2021.05a
• /
• pp.40-41
• /
• 2021

The construction industry has the highest occupational accidents/injuries among all industries. Korean government installed surveillance camera systems at construction sites to reduce occupational accident rates. Construction safety managers are monitoring potential hazards at the sites through surveillance system; however, the human capability of monitoring surveillance system with their own eyes has critical issues. Therefore, this study proposed to build a deep learning-based safety monitoring system that can obtain information on the recognition, location, identification of workers and heavy equipment in the construction sites by applying multiple-object tracking with instance segmentation. To evaluate the system's performance, we utilized the MS COCO and MOT challenge metrics. These results present that it is optimal for efficiently automating monitoring surveillance system task at construction sites.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.14 no.4
• /
• pp.1689-1703
• /
• 2020

When data is transmitted over an unreliable channel, the error of the data packet may result in serious degradation. The multiple description coding (MDC) can solve this problem and save transmission costs. In this paper, we propose a deep multiple description coding network (MDCN) to realize efficient image compression. Firstly, our network framework is based on convolutional auto-encoder (CAE), which include multiple description encoder network (MDEN) and multiple description decoder network (MDDN). Secondly, in order to obtain high-quality reconstructed images at low bit rates, the encoding network and decoding network are integrated into an end-to-end compression framework. Thirdly, the multiple description decoder network includes side decoder network and central decoder network. When the decoder receives only one of the two multiple description code streams, side decoder network is used to obtain side reconstructed image of acceptable quality. When two descriptions are received, the high quality reconstructed image is obtained. In addition, instead of quantization with additive uniform noise, and SSIM loss and distance loss combine to train multiple description encoder networks to ensure that they can share structural information. Experimental results show that the proposed framework performs better than traditional multiple description coding methods.

• The KIPS Transactions:PartC
• /
• v.11C no.5
• /
• pp.671-676
• /
• 2004

In this paper, we propose ultra-fast multimedia optical subscriber access network using optical frequency domain CDMA method in order to access the multimedia with multiple data rates. We numerically analyze the effects of spectral power distortion in the light source for the optical CDMA system modified PN codes and FBG(Fiber Bragg Grating)s. In the proposed multiple-rate multimedia access optical networks, the performances such as BER(Bit Error Rate) are the same for all data regardless of data rates in the case of the same number of simultaneous ONU. In the proposed ultra-fast multimedia optical subscriber access network, the performances for all data are much more improved than those in the conventional system.

• Journal of Preventive Medicine and Public Health
• /
• v.26 no.3 s.43
• /
• pp.412-429
• /
• 1993

For the purpose of promotion of mental health in the rural elderly, the author surveyed 558 elderlies aged 60 years or more, and assessed the prevalence rates of depression and cognitive impairment by using self-rating depression scale of Zung (SDS) and the Korean version of mini-mental state examination (MMSEK). Also the association between depression or cognitive function and socio-environmental factors were investigated. The major findings were as follows ; 1. The prevalence rates of severe depression and cognitive impairment were 20.9% and 14.9% in all the elderly of both sexes, respectively. 2. The rates of depression and cognitive impairment increased with increasing age in both sex groups. The mean scores of SDS increased and the mean scores of MMSEK decreased significantly among them (p<0.01). 3. Those being female, widows or widowers, and those having low levels of physical activity, showed significantly high the mean scores of depression and had significantly low the mean scores of cognitive impairment (p<0.01). 4. The depression scores relating to decreased libido, confusion, psychomotor retardation, hopelessness and indecisiveness were relatively high in both sexes. 5. All the items of mini-mental state examination were significantly correlated with depression. 6. In stepwise multiple regression analysis on depression, MMSEK, level of physical activity, chronic disease, marital status and family income were selected as highly correlated variables, and the $R^2$-value for these variables was 33.7%. 7. In stepwise multiple regression analysis on cognitive function, level of physical activity, age, depression, sex and marital status were selected as highly correlated variables, and the $R^2$-value for these variables was 62.6%. The depression and cognitive impairment of the elderly were positively correlated with nearly all sociodemographic variables.

• Clinical and Experimental Reproductive Medicine
• /
• v.25 no.2
• /
• pp.217-231
• /
• 1998

The effectiveness of intrauterine insemination (IUI) combined with controlled ovanan hyperstimulation (COH) in the treatment of infertility with various etiologies was compared in a total of 152 cycles. Patients received a maximum of three IUI cycles for the treatment. Severe male ($<2\times10^6$ motile sperm) or age factor (> 39 y) patients were excluded in this study. Pregnancy was classified as clinical if a gestational sac was seen on ultrasound. The overall clinical pregnancy rate was 7.9% per cycle (12/152) and 9.7% per patient (12/124). The pregnancy rates were 0% in unstimulated natural (0/18), 7.5% in CC (3/40), 8.2% in CC+hMG (4/49), 5.9% in GnRH-a ultrashort (1/17), 5.9% in GnRH-a long (1/17) and 27.3% in dual suppression cycles (3/11), respectively. The pregnancy rate was higher in dual suppression cycle than other stimulated cycles, but this was not significant. The multiple pregnancy rates were 25.0% (2 twins and 1 triplet). No patient developed ovarian hyperstimulation. Abortion rates were 66.7% in CC (2/3) and 100% in ultrashort cycles (1/1). The livebirth rate was 5.9% per cycle (9/152) and 7.3% per patient (9/124). There were no differences in age, duration of infertility, follicle size, total ampules of gonadotropins and days of stimulation between pregnant and non-pregnant groups. However, significant(P<0.05) differences were observed in the level of estradiol $(E_2)$ on the day of hCG injection ($3,266.6{\pm}214.2$ vs $2,202.7{\pm}139.4$ pg/ml) and total motile sperm count ($212.1{\pm}63.4$ vs $105.1{\pm}9.9\times10^6$) between pregnant group and non-pregnant group. These results suggest that IUI combined with successful ovarian stimulation tends to improve the chance of pregnancy as compared to IUI without COH and a total motile sperm count may be considered predictive of the success for pregnancy.

• The KIPS Transactions:PartD
• /
• v.10D no.6
• /
• pp.927-934
• /
• 2003

Database query optimates the selectivety of a query to find the most efficient access plan. Multi-dimensional selectivity estimation technique is required for a query with multiple attributes because the attributes are not independent each other. Histogram is practically used in most commercial database products because it approximates data distributions with small overhead and small error rates. However, histogram is inadequate for a query with multiple attributes because it incurs high storage overhead and high error rates. In this paper, we propose a novel method for multi-dimentional selectivity estimation. Compressed information from a large number of small-sized histogram buckets is maintained using the discrete cosine transform. This enables low error rates and low storage overheads even in high dimensions. Extensive experimental results show adventages of the proposed approach.

• Journal of Institute of Control, Robotics and Systems
• /
• v.22 no.4
• /
• pp.293-300
• /
• 2016

The Voice recognition is one of convenient methods to communicate between human and robots. This study proposes a speech recognition method using speech recognizers based on Hidden Markov Model (HMM) with a combination of techniques to enhance a biped robot control. In the past, Artificial Neural Networks (ANN) and Dynamic Time Wrapping (DTW) were used, however, currently they are less commonly applied to speech recognition systems. This Research confirms that the HMM, an accepted high-performance technique, can be successfully employed to model speech signals. High recognition accuracy can be obtained by using HMMs. Apart from speech modeling techniques, multiple feature extraction methods have been studied to find speech stresses caused by emotions and the environment to improve speech recognition rates. The procedure consisted of 2 parts: one is recognizing robot commands using multiple HMM recognizers, and the other is sending recognized commands to control a robot. In this paper, a practical voice recognition system which can recognize a lot of task commands is proposed. The proposed system consists of a general purpose microprocessor and a useful voice recognition processor which can recognize a limited number of voice patterns. By simulation and experiment, it was illustrated the reliability of voice recognition rates for application of the manufacturing process.

• Journal of The Korean Society of Agricultural Engineers
• /
• v.63 no.2
• /
• pp.51-60
• /
• 2021

Effective nonpoint source (NPS) pollution management requires frequent water quality monitoring, which is, however, often costly to be implemented in practice. Statistical techniques and machine learning methods allow us to identify and focus on fundamental environmental variables that have close relationships with NPS pollutants of interest. This study developed surrogate models to predict the concentrations of suspended sediment (SS) and total phosphorus (T-P) from turbidity and runoff discharge rates using multiple linear regression (MLR) and random forest (RF) methods. The RF models provided acceptable performance in predicting SS and T-P, especially when runoff discharge rates were high. The RF models outperformed the MLR models in all the cases. Such finding highlights the potential of RF techniques and models as a tool to identify fundamental environmental variables that are measured in relatively inexpensive ways or freely available but still able to provide information required to quantify the concentrations of NP S pollutants. The analysis of relative importance rates showed that the temporal variations of SS and T-P concentrations could be more effectively explained by that of turbidity than runoff discharge rate. This study demonstrated that the advanced statistical techniques such as machine learning could help to improve the efficiency of NPS pollutants monitoring.