• Journal of the Korea Convergence Society
• /
• v.13 no.1
• /
• pp.109-118
• /
• 2022

The purpose of this study is to identify trends in the application of artificial intelligence by analyzing upper extremity movement assessment and artificial intelligence convergence research using a systematic literature review method. The research was conducted using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Among the 380 articles searched in three databases, 8 articles were finally selected according to the selection and exclusion criteria. For the evaluation of upper extremity movement, motion performance evaluation, FMA, and ARAT were used. For quantification, data were extracted using various tools, and upper extremity movement classification, recovery prognosis prediction, and evaluation tool score were predicted using artificial intelligence. This study is meaningful in that it systematically reviewed studies that objectively evaluated upper extremity movement using artificial intelligence and identified the direction in which artificial intelligence is being applied. Based on this, the introduction of artificial intelligence technology in the assessment of upper extremity movements is expected to help objectively identify the intervention effect and the patient's recovery.

• Tunnel and Underground Space
• /
• v.21 no.3
• /
• pp.151-163
• /
• 2011

The LCM test is one of the most powerful and reliable methods for designing the disc cutter and for predicting the TBM (Tunnel Boring Machine) performance. It has an advantage to predict the actual load on disc cutter from the laboratory test on the real-size large rock samples, however, it also has a disadvantage to transport and/or prepare the large rock samples and to need an extra cost for experiment. Moreover it is not easy to execute the test for jointed rock mass, and sometimes the design model estimated from the test can not be applied to the real design of disc cutter. In order to break this critical point, lots of numerical studies have been performed. PFC2D can simulate crack propagation and rock fragmentation effectively, because it is useful in particle flow analysis. Consequently, in this study, the PFC2D has been adopted for numerical analysis on cutting power of disc cutter according to the different angle of joint, the different direction of joint, and the different space of joint with jointed rock mass models. From the numerical analyses, it was concluded that the bigger cutting power of disc cutter was needed for reverse cutting direction to joint rather than for forward direction, and the cutting power of disc cutter was increased with decreasing the dip angle of joint and decreasing the space of joints in reverse cutting direction. The more precise numerical model for disc cutter can be developed from comparison between the numerical results and LCM test results, and the resonable guideline is expected for prediction of TBM performance and disc cutter.

• Journal of the Korean Wood Science and Technology
• /
• v.43 no.6
• /
• pp.701-713
• /
• 2015

A model was designed to identify wood species between Pinus densiflora for. erecta Uyeki and Pinus densiflora Sieb. et Zucc. using the near-infrared (NIR) spectroscopy in combination with principal component analysis (PCA) and partial least square discriminant analysis (PLS-DA). In the PCA using all of the spectra, Pinus densiflora for. erecta Uyeki and Pinus densiflora Sieb. et Zucc. could not be classified. In the PCA using the spectrum that has been measured in sapwood, however, Pinus densiflora for. erecta Uyeki and Pinus densiflora Sieb. et Zucc. could be identified. In particular, it was clearly classified by sapwood in radial section. And more, these two species could be perfectly identified using PLS-DA prediction model. The best performance in species identification was obtained when the second derivative spectra was used; the prediction accuracy was 100%. For prediction model, the $R_p{^2}$ value was 0.86 and the RMSEP was 0.38 in second derivative spectra. It was verified that the model designed by NIR spectroscopy with PLS-DA is suitable for species identification between Pinus densiflora for. erecta Uyeki and Pinus densiflora Sieb. et Zucc.

• Tunnel and Underground Space
• /
• v.23 no.6
• /
• pp.480-492
• /
• 2013

After the introduction of geothermal power generation technology based on engineering reservoir creation that can be applied on non-volcanic region, industrial need for studies on the efficient and economic execution of costly deep-depth drilling work becomes manifest increasingly. However, since it is very difficult to predict duration and cost of boring work with acceptable reliability because of many uncertain events during the execution, efficient and organized work management for drilling is not easily achievable. Especially, the round trip that discretely occurs because of the abrasion of bit takes more time as the depth goes deeper and it has a great impact on the work performance. Therefore, a technology that can simulate the occurrence timing and depth of round trip in advance and therefore optimize them is essentially required. This study divided the abrasion state of bit into eight steps for simulation cases and developed a forecast algorithm, i.e., TOSA which can analyze the depth and timing of round trip occurrence. A methodology that can divide a unit section for simulation has been suggested; while the Bourgoyne and Young model has been used for the forecast of drilling rates and bit abrasion extent by section. Lastly, the designed algorithm has been systemized for the convenience of the user.

• Journal of Korean Society of Transportation
• /
• v.27 no.4
• /
• pp.155-166
• /
• 2009

Road design safety assessment by existing tools and methods have normally been examined by expert judgements using design documents and on-site inspections. The existing methods, however, have two main problems such as insufficiency of objectiveness and inability to measure effects of accident countermeasures. This paper studies ways to develop a road safety assessment system through reviewing the IHSDM developed in USA. The crash prediction module of IHSDM calculate accident frequency and rate of roadway segments using accident prediction models and accident modification factors for safety evaluation. The methodology of evaluation and development of accident modification factors somewhat overcome the problems of the existing methods. In spite of these advantages, IHSDM could not relevantly reflect characteristics of domestic rural roadways since it overestimate the number of accidents and rate of korean rural roadways. Especially, IHSDM may not evaluate or consider land use patterns of Korean roadways, and futhermore, original environment on base conditions used to develop IHSDM may not be different from ours. The user requirements being developed for a road safety assessment system for Korean roadways include enhanced flexibility and diversity of data input-output processes.

• Journal of Korean Society of Transportation
• /
• v.20 no.6
• /
• pp.45-56
• /
• 2002

In this research we estimated regional gross fixed capital stock of transport sector, such as road railroad, airport and seaport during 1968-1997 in Korea. We also compared our estimation results with those of Korea and Japan. As basic analytic method, we used the regional allocation method. To estimate regional gross fixed capital stock of transport sector, we used the basic data on national wealth surveys in 1997, regional land price index and regional facilities index in transport sectors. We used the most reasonable data in the process of estimation after reviewing the collected data In order to get the reasonable capital stock by regions. we chose the allocation index which can minimize the difference between the estimated result and the real regional capital stock in the process to allocate the total gross capital into the regions. Compared our results with those of other researches in Korea, estimates in our research project could be said more accurate than those.

• Korean Journal of Environment and Ecology
• /
• v.24 no.2
• /
• pp.101-107
• /
• 2010

This study was carried out to provide basic data for efficient park management by analysing visitors' satisfaction factors and estimated regression model through questionnaire survey method at Taeanhaean National Park in Korea. Performance(satisfaction) variables as 'touting', 'illegal merchant', 'noise', 'indiscreet use' and 'collection of natural plants or animals', and Importance variables as 'littering problem', 'water pollution act', 'careless cooking' and 'exorbitant pay' were relatively high score. It was clarified that the 'souvenir & special product', 'lack of use program' 'lack of public facility', 'lack of information facility', and 'lack of commercial facility' were 'concentrate here' ones by the Importance-Performance analysis. 'Facility management', 'Use management' and 'Resource management' factors were found out by Factor Analysis and the 'Facility management' was the biggest factor accounting for 32.6% of all. In the estimated model by Multiple Regression Analysis, 'lack of employee's guidance or kindness', 'lack of convenience facility', 'noise', 'lack of facilities to stay' and 'charge of user fee, parking fee' were the variables to impact visitors' satisfaction and to need concentrated management. These results were unique characteristics of marine national park and then the different management strategy and policy from mountain national park were necessary.

• Therapeutic Science for Rehabilitation
• /
• v.11 no.4
• /
• pp.23-39
• /
• 2022

Objective : To summarize clinical and demographic variables and machine learning uses for predicting functional outcomes of patients with stroke. Methods : We searched PubMed, CINAHL and Web of Science to identify published articles from 2010 to 2021. The search terms were "machine learning OR data mining AND stroke AND function OR prediction OR/AND rehabilitation". Articles exclusively using brain imaging techniques, deep learning method and articles without available full text were excluded in this study. Results : Nine articles were selected for this study. Support vector machines (19.05%) and random forests (19.05%) were two most frequently used machine learning models. Five articles (55.56%) demonstrated that the impact of patient initial and/or discharge assessment scores such as modified ranking scale (mRS) or functional independence measure (FIM) on stroke patients' functional outcomes was higher than their clinical characteristics. Conclusions : This study showed that patient initial and/or discharge assessment scores such as mRS or FIM could influence their functional outcomes more than their clinical characteristics. Evaluating and reviewing initial and or discharge functional outcomes of patients with stroke might be required to develop the optimal therapeutic interventions to enhance functional outcomes of patients with stroke.

• Smart Media Journal
• /
• v.12 no.11
• /
• pp.57-66
• /
• 2023

In this paper, we present a study on an AI-based system utilizing the CCTV system within city buses to predict the intentions of boarding and alighting passengers, with the aim of preventing accidents. The proposed system employs the YOLOv7 Pose model to detect passengers, while utilizing an LSTM model to predict intentions of tracked passengers. The system can be installed on the bus's CCTV terminals, allowing for real-time visual confirmation of passengers' intentions throughout driving. It also provides alerts to the driver, mitigating potential accidents during passenger transitions. Test results show accuracy rates of 0.81 for analyzing boarding intentions and 0.79 for predicting alighting intentions onboard. To ensure real-time performance, we verified that a minimum of 5 frames per second analysis is achievable in a GPU environment. his algorithm enhance the safety of passenger transitions during bus operations. In the future, with improved hardware specifications and abundant data collection, the system's expansion into various safety-related metrics is promising. This algorithm is anticipated to play a pivotal role in ensuring safety when autonomous driving becomes commercialized. Additionally, its applicability could extend to other modes of public transportation, such as subways and all forms of mass transit, contributing to the overall safety of public transportation systems.

• Applied Chemistry for Engineering
• /
• v.21 no.2
• /
• pp.115-128
• /
• 2010

Recently the use heat sink material grows where the polymer filled with thermal conductive fillers effectively dissipates heat generated from electronic components. Therefore the management of heat is directly related to the lifetime of electronic devices. For the purpose of improving thermal conductivity of composites, fillers with excellent thermaly conductive behavior are commonly used. Polymer composites filled with thermally conductive particles have advantages due to their processibility, cheap price, and durability to the corrosion. This paper aims to review the thermal interface materials and their model equations for predicting the thermal conductivity of polymer composites, and to introduce the commercial thermal conductive fillers and their applications.