• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.10a
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• pp.549-551
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• 2016

In this paper, With the development of modern science and technology are Sheds to stay indoors rather than outdoors space it increased significantly compared to the past. And a wide variety of research about outdoor air quality until recently, efforts are underway but the issue of air quality in the room is the fact that all considered relatively lightly. As the contamination of the room air is polluted, unlike the natural environment, a large outdoor air dilution rate, the dilution rate is very low, once the contaminated air continuously circulating exerts a very bad influence on the health of people staying in the room. In this study, movement characteristics of the person living in a room, the air measuring device for the study of the active indoor environmental control system reflects the life form to measure the quality of the measured air in real time for transmitting the information to the user of the smart devices, alarm bot It was implemented and operational applications.

• Smart Structures and Systems
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• v.29 no.1
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• pp.77-91
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• 2022

Various monitoring systems have been implemented in civil infrastructure to ensure structural safety and integrity. In long-term monitoring, these systems generate a large amount of data, where anomalies are not unusual and can pose unique challenges for structural health monitoring applications, such as system identification and damage detection. Therefore, developing efficient techniques is quite essential to recognize the anomalies in monitoring data. In this study, several machine learning techniques are explored and implemented to detect and classify various types of data anomalies. A field dataset, which consists of one month long acceleration data obtained from a long-span cable-stayed bridge in China, is employed to examine the machine learning techniques for automated data anomaly detection. These techniques include the statistic-based pattern recognition network, spectrogram-based convolutional neural network, image-based time history convolutional neural network, image-based time-frequency hybrid convolution neural network (GoogLeNet), and proposed ensemble neural network model. The ensemble model deliberately combines different machine learning models to enhance anomaly classification performance. The results show that all these techniques can successfully detect and classify six types of data anomalies (i.e., missing, minor, outlier, square, trend, drift). Moreover, both image-based time history convolutional neural network and GoogLeNet are further investigated for the capability of autonomous online anomaly classification and found to effectively classify anomalies with decent performance. As seen in comparison with accuracy, the proposed ensemble neural network model outperforms the other three machine learning techniques. This study also evaluates the proposed ensemble neural network model to a blind test dataset. As found in the results, this ensemble model is effective for data anomaly detection and applicable for the signal characteristics changing over time.

• International journal of advanced smart convergence
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• v.11 no.1
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• pp.111-116
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• 2022

After COVID-19, the number of people with sleep disorders around the world is increasing. In particular, in the flow of the 4th industrial revolution, the differentiation of types and characteristics of the sleep industry is accelerating. Therefore, in this study, the characteristics of each type of sleep-related industry were reclassified from an industrial point of view, and based on this, an attempt was made to review the classification system that can help companies develop sleep products and improve related national systems. Based on the 10th standard industry classification, we compared input cost, value, and usability and analyzed common characteristics, treatments, and preventive effects based on this. A comprehensive taxonomy using matrix analysis was reviewed. As a result, in terms of cost (A), the most common sleeping products are general mattresses and general bedding. It is an IOT device (auxiliary device), and the value aspect (B, B/D) included sleep cafe, bedding rental and management service, and sleep consulting. In terms of utility (A/B), a total of 6 product groups including sleep aids (health functional foods) belong to this category, and in terms of treatment (A/C), a total of 3 product groups including sleep clinics (medical services) belong to this category. As for the product group (A/D) with both properties, it was found that non-insurance sleep treatment medical devices, sleep-related over-the-counter drugs, and some sleep monitoring applications belong to this category. Ultimately, it was found that the sleep industry classification enables the most active product development and composition according to the relative relationship between cost and utility, and treatment and utility. appeared to be necessary.

• The Journal of Korean Academy of Orthopedic Manual Physical Therapy
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• v.28 no.1
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• pp.71-80
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• 2022

Background: This study investigated the effect of robot-assisted arm training on muscle activity of arm and weight bearing in stroke patients. Methods: The study subjects were selected 20 stroke patients who met the selection criteria. 10 people in the robot-assisted arm training group and 10 people in the task-oriented arm training group were randomly assigned. The experimental group performed robot-assisted arm training, and the control group performed task-oriented arm training for 6 weeks, 5 days a week, 30 minutes a day. The measurement tools included surface electromyography and smart insole system. Data were analyzed using independent sample t-test and the paired sample t-test. Results: Comparing the muscle activity of arm within the group, the experimental group and the control group showed significant differences in muscle activity in the biceps brachii, triceps brachii, anterior deltoid, upper trapezius, middle trapezius, and lower trapezius. Comparing the muscle activity of arms between the groups, the experimental group showed significant difference in all muscle activity of arm compared to the control group. Comparing the weight bearing within the groups, the experimental group showed significant difference in the affected side and non-affected side weight bearings and there were significant differences in anterior and posterior weight bearing. The control group showed significant difference only in the non-affected side weight bearing. Comparing the weight bearings between groups, the experimental group showed significant difference in the affected side and non-affected side weight bearings compared to the control group. Conclusion: This study confirmed that robot-assisted arm training applied to stroke patients for 6 weeks significantly improved muscle activity of arm and weight bearing. Based on these results, it is considered that robot-assisted arm training can be a useful treatment in clinical practice to improve the kinematic variables in chronic stroke patients.

• International journal of advanced smart convergence
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• v.12 no.4
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• pp.434-442
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• 2023

The relationship between acute kidney injury (AKI) prediction and nephrotoxic drugs, or drugs that adversely affect kidney function, is one that has yet to be explored in the critical care setting. One contributing factor to this gap in research is the limited investigation of drug modalities in the intensive care unit (ICU) context, due to the challenges of processing prescription data into the corresponding drug representations and a lack in the comprehensive understanding of these drug representations. This study addresses this gap by proposing a novel approach that leverages patient prescription data as a modality to improve existing models for AKI prediction. We base our research on Electronic Health Record (EHR) data, extracting the relevant patient prescription information and converting it into the selected drug representation for our research, the extended-connectivity fingerprint (ECFP). Furthermore, we adopt a unique multimodal approach, developing machine learning models and 1D Convolutional Neural Networks (CNN) applied to clinical drug representations, establishing a procedure which has not been used by any previous studies predicting AKI. The findings showcase a notable improvement in AKI prediction through the integration of drug embeddings and other patient cohort features. By using drug features represented as ECFP molecular fingerprints along with common cohort features such as demographics and lab test values, we achieved a considerable improvement in model performance for the AKI prediction task over the baseline model which does not include the drug representations as features, indicating that our distinct approach enhances existing baseline techniques and highlights the relevance of drug data in predicting AKI in the ICU setting.

• Membrane and Water Treatment
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• v.14 no.4
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• pp.163-173
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• 2023

The presence of high amounts of organic and inorganic contaminants in textile wastewater is a major environmental concern. Therefore, the treatment of textile wastewater is an urgent issue to save the aquatic environment. The disposal of large quantities of untreated textile wastewater into inland water bodies can cause serious water pollution. In this study, synthetic dye wastewater samples were prepared using orange dye in the laboratory. The synthetic samples were then treated by a batch adsorption process using the prepared activated carbon (AC) from date pits. The wastewater parameters studied were the pH, total dissolved solids (TDS), total suspended solids (TSS), electrical conductivity (EC) and salinity. The activated adsorption process showed that the maximum removal efficiencies of electric conductivity (EC), salinity, TDS and TSS were 65%, 92%, 89% and 90%, respectively. The removal efficiencies were proportional to the increase in contact time (30-120 min) and AC adsorbent dose (1, 3 and 5 g/L). The adsorption profile indicates that 5 g/L of adsorbent delivers better results for TDS, EC, TSS and salinity at contact time of 120 min. The adsorption characteristics are better suited to the pseudo-second-order kinetic model than to the pseudo-first-order kinetic model. The Langmuir and Freundlich isotherms were well suited for describing the adsorption or contact behavior of EC and TSS within the studied system.

• Journal of the Korea institute for structural maintenance and inspection
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• v.16 no.1
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• pp.120-130
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• 2012

As long span and complex bridges are constructed often recently, safety estimation became a big issue. Various types of measuring instruments are installed in case of long span bridge. New wireless technologies for long span bridges such as sending information through a gateway at the field or sending it through cables by signal processing the sensing data are applied these days. However, The case of occurred accidents related to bridge in the world have been reported that serious accidents occur due to lack of real-time proactive, intelligent action based on recognition accidents. To solve this problem in this study, the idea of "communication among things", which is the basic method of RFID/USN technology, is applied to the bridge monitoring system. A sensor node module for USN based intelligent bridge system in which sensor are utilized on the bridge and communicates interactively to prevent accidents when it captures the alert signals and urgent events, sends RF wireless signal to the nearest traffic signal to block the traffic and prevent massive accidents, is designed and tested by performing TinyOS based middleware design and sensor test free Space trans-receiving distance.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.8 no.5
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• pp.407-416
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• 2018

The development of modern technology has rapidly grown the field of wearable devices. Wearable equipments should satisfy low power consumption and small/lightweight because of characteristics of body wearing. In this paper, an overview of wearable equipments is explained, and wearable device market is investigated. In addition, we investigate developed technology of wearable components, which is divided into component and communication technology. Meanwhile, a smart HSE system is required to meet the demand of the society for the serious industrial accident. To address this issue, we propose an interactive method between the wearable component and the HSE system, which are expected to be effective in safety management. As a detailed case study, a risk scenario is made with risk factors in welding workshop, and then we propose an interactive method between a wearable component and an HSE system that can reduce the risk. This proposed method is useful to achieve high level of worker's safety.

• Korean Chemical Engineering Research
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• v.51 no.2
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• pp.221-225
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• 2013

Sustainability, in general, means the protection of environmental resources and economic prosperity, with the consideration of the social, economic and environmental effect, as well as human health and the enhancement of life. Profound consideration about sustainability has to handle the overall cycle of feedstock, resource extraction, transportation and production in addition to the environmental effect. Sustainable development of the chemical industries should be carried out complementarily by strengthening the chemical process safety of the industries. In this respect, chemical process safety can be called an opportunity to enhance the compatibility internationally. Changing new paradigm in chemical process safety is formed from the overall life cycle considering basic design of existing systems and production processes. To improve the chemical process safety, the integrated smart system is necessary, comprising various chemical safety database and knowledge base and improved methods of quantitative risk analysis, including management system. This paper discussed the necessity of overall life cycle in chemical process safety and proposed new technology to improve the sustainability. To develop the sustainable industries in process systems engineering, three S, which include Safety, Stability and Security, will have to be combined appropriate.

• Journal of the Korea Society of Computer and Information
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• v.16 no.12
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• pp.227-234
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• 2011

Many technologies for wireless internet are increasing as more and more laptop computers, net books, smart phone and other terminals, which provide wireless network, are created. IEEE 802.11 is computer wireless network technology that used in small area, called wireless LAN or Wi-Fi. IEEE 802.11 is a set of standards for implementing wireless local area network (WLAN) computer communication in the 2.4, 3.6 and 5 GHz frequency bands. They are created and maintained by the IEEE LAN/MAN Standards Committee (IEEE 802). AP (Access Point) is installed at cafes and public places providing wireless environment. It is more convenient to use wireless internet, however, It can be seen easily around us and possible to communicate with AP. IEEE 802.11 has many vulnerability, such as packet manipulation and information disclosure, so we should pay more attention when using IEEE 802.11. Therefore this paper developing monitoring system which can find out AP and Stations that connect with it, and capturing AP's information to find out vulnerability. This paper suggests monitoring system which traffic analysis in wireless environment.