• Science of Emotion and Sensibility
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• v.24 no.2
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• pp.81-92
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• 2021

In this study, we investigated the effects of five EMS lumbar back pressure belts produced on an anatomical basis on lumbar spine stabilization. Five core muscles were selected, including the urinal, vertebral column, endotracheal, external abdominal, and large back muscles, and patterns were designed using a conductive fabric considering the appropriate muscle shape and pain-causing points. We experimented with four motions to examine the effects of different EMS abdominal compression belts on lumbar spine stabilization. Five healthy men in their 20s were selected. The selection conditions include no back pain history for the past three months, no restricted movements through pre-inspection, and the muscular strength of the body should belong to the normal grade. Using SLR, the sequence of experimental actions was chosen from the following but not limited to left-hand, body-hand, and back-line forces. Resting between movements lasted for 2 min, and the experiments were conducted after wearing the EMS abdominal pressure belt. Electrical stimulation was applied for 10 min to increase blood flow and muscle activation. The statistics of the experimental results were analyzed for specific differences by conducting the Wilcoxon and Friedman tests with nonparametric tests. The ranking results of each pattern were successfully assessed in the order of 5, 4, 3, 1, 2 for the five patterns, and we could identify slightly more significant results for experimental behavior associated with each muscle movement. Patterns produced based on anatomy showed differentiated effects when electric stimulation was applied to each muscle in different shapes, which could improve the stabilization of the lumbar spine in everyday life or training to the public. Based on these results, subsequent research would focus on developing smart healthcare clothing that is practical in daily life by employing different anatomical mechanisms, depending on the back pain, to utilize trunk-type tights.

• Smart Structures and Systems
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• v.14 no.1
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• pp.39-54
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• 2014

Node layout optimization of structural wireless systems is investigated as a means to prolong the network lifetime without, if possible, compromising information quality of the measurement data. The trade-off between these antagonistic objectives is studied within a multi-objective layout optimization framework. A Genetic Algorithm is adopted to obtain a set of Pareto-optimal solutions from which the end user can select the final layout. The information quality of the measurement data collected from a heterogeneous WSN is quantified from the placement quality indicators of strain and acceleration sensors. The network lifetime or equivalently the network energy consumption is estimated through WSN simulation that provides realistic results by capturing the dynamics of the wireless communication protocols. A layout optimization study of a monitoring system on the Great Belt Bridge is conducted to evaluate the proposed approach. The placement quality of strain gauges and accelerometers is obtained as a ratio of the Modal Clarity Index and Mode Shape Expansion values that are computed from a Finite Element model of the monitored bridge. To estimate the energy consumption of the WSN platform in a realistic scenario, we use a discrete-event simulator with stochastic communication models. Finally, we compare the optimization results with those obtained in a previous work where the network energy consumption is obtained via deterministic communication models.

• Smart Structures and Systems
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• v.29 no.6
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• pp.757-766
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• 2022

To train deep learning algorithms, a sufficient number of data are required. However, in most engineering systems, the acquisition of fault data is difficult or sometimes not feasible, while normal data are secured. The dearth of data is one of the major challenges to developing deep learning models, and fault diagnosis in particular cannot be made in the absence of fault data. With this context, this paper proposes an anomaly detection methodology for rotating machines using only normal data with self-labeling. Since only normal data are used for anomaly detection, a self-labeling method is used to generate a new labeled dataset. The overall procedure includes the following three steps: (1) transformation of normal data to self-labeled data based on a pretext task, (2) training the convolutional neural networks (CNN), and (3) anomaly detection using defined anomaly score based on the softmax output of the trained CNN. The softmax value of the abnormal sample shows different behavior from the normal softmax values. To verify the proposed method, four case studies were conducted, on the Case Western Reserve University (CWRU) bearing dataset, IEEE PHM 2012 data challenge dataset, PHMAP 2021 data challenge dataset, and laboratory bearing testbed; and the results were compared to those of existing machine learning and deep learning methods. The results showed that the proposed algorithm could detect faults in the bearing testbed and compressor with over 99.7% accuracy. In particular, it was possible to detect not only bearing faults but also structural faults such as unbalance and belt looseness with very high accuracy. Compared with the existing GAN, the autoencoder-based anomaly detection algorithm, the proposed method showed high anomaly detection performance.

• Journal of the Korea Society of Computer and Information
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• v.24 no.12
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• pp.51-57
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• 2019

In this paper, we analyzed the performance of IoT based smart wearable mine detection device. There are various mine detection methods currently used by the military. Still, in the general field, mine detection is performed by visual detection, probe detection, detector detection, and other detection methods. The detection method by the detector is using a GPR sensor on the detector, which is possible to detect metals, but it is difficult to identify non-metals. It is hard to distinguish whether the area where the detection was performed or not. Also, there is a problem that a lot of human resources and time are wasted, and if the user does not move the sensor at a constant speed or moves too fast, it is difficult to detect landmines accurately. Therefore, we studied the smart wearable mine detection device composed of human body antenna, main microprocessor, smart glasses, body-mounted LCD monitor, wireless data transmission, belt type power supply, black box camera, which is to improve the problem of the error of mine detection using unidirectional ultrasonic sensing signal. Based on the results of this study, we will conduct an experiment to confirm the possibility of detecting underground mines based on the Internet of Things (IoT). This paper consists of an introduction, experimental environment composition, simulation analysis, and conclusion. Introduction introduces the research contents such as mines, mine detectors, and research progress. It consists of large anti-personnel mine, M16A1 fragmented anti-mine, M15 and M19 antitank mines, plastic bottles similar to mines and aluminum cans. Simulation analysis is conducted by using MATLAB to analyze the mine detection device implementation performance, generating and transmitting IoT signals, and analyzing each received signal to verify the detection performance of landmines. Then we will measure the performance through the simulation of IoT-based mine detection algorithm so that we will prove the possibility of IoT-based detection landmine.

• Science of Emotion and Sensibility
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• v.13 no.3
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• pp.467-474
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• 2010

Recently, Study of functional clothing for Vital sensing is focused on reducing artifact by human motions, in order to enhance the electrocardiogram(ECG) sensing accuracy. In this study, considering the factors for each element found from the analysis, a 3-lead electrode inside textile embroidered with silver yarn was developed, and draft designs off our types of vital-signal sensing garments, which are 'chest-belt typed' garment, 'cross-typed' garment 'x-typed' garment and 'curved x-typed' garment, were prepared. The draft designs were implemented on a sleeveless male shirt made of an elastic material so that the garment and the electrodes can remain closely attached along the contour of the human body, and the acquired data was sent to the main computer over a wireless network. In order to evaluate the effects caused by body movements and the ECG-sensing capability for each type in static and dynamic states, displacements were measured from one and two dimensional perspectives. ECG measurement evaluation was also performed for Signal-to-noise ratio(SNR) analysis. Applying the experimental results, the draft garment designs were modified and complemented to produce two types of modular approaches 'continuous-attached' and 'insertion-detached' for the ECG-sensing smart clothing.

• Korean Journal of Environment and Ecology
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• v.35 no.2
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• pp.126-134
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• 2021

It is necessary to set a management period by analyzing growth trends for individual species because the time taken for planted trees to become established differs by species. The purpose of this study was to suggest an appropriate management period through the analysis of the annual growth of Pinus densiflora planted in the riverine eco belt. The average annual growth before planting was 0.6cm. The growth after planting showed an increase of 0.3cm in the 1st and 2nd year, 0.5cm in the 3rd and 4th year, and 0.7cm after the 5th year. Since P. densiflora was confirmed to go through poor growth stages in the 1st and 2nd year, a recovery stage in the 3rd and 4th year, and a normal growth stage in the 5th year, management should pay more attention to improve inappropriate environmental conditions until at least the 4th year, unlike the growth of hardwood. Since the period required for activation by species may vary, the management period of each species will need to be set through growth research.

• International journal of advanced smart convergence
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• v.11 no.1
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• pp.19-27
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• 2022

Across the world, 'housing' comprises a significant portion of wealth and assets. For this reason, fluctuations in real estate prices are highly sensitive issues to individual households. In Korea, housing prices have steadily increased over the years, and thus many Koreans view the real estate market as an effective channel for their investments. However, if one purchases a real estate property for the purpose of investing, then there are several risks involved when prices begin to fluctuate. The purpose of this study is to design a real estate price 'return rate' prediction model to help mitigate the risks involved with real estate investments and promote reasonable real estate purchases. Various approaches are explored to develop a model capable of predicting real estate prices based on an understanding of the immovability of the real estate market. This study employs the LSTM method, which is based on artificial intelligence and deep learning, to predict real estate prices and validate the model. LSTM networks are based on recurrent neural networks (RNN) but add cell states (which act as a type of conveyer belt) to the hidden states. LSTM networks are able to obtain cell states and hidden states in a recursive manner. Data on the actual trading prices of apartments in autonomous districts between January 2006 and December 2019 are collected from the Actual Trading Price Disclosure System of the Ministry of Land, Infrastructure and Transport (MOLIT). Additionally, basic data on apartments and commercial buildings are collected from the Public Data Portal and Seoul Metropolitan Government's data portal. The collected actual trading price data are scaled to monthly average trading amounts, and each data entry is pre-processed according to address to produce 168 data entries. An LSTM model for return rate prediction is prepared based on a time series dataset where the training period is set as April 2015~August 2017 (29 months), the validation period is set as September 2017~September 2018 (13 months), and the test period is set as December 2018~December 2019 (13 months). The results of the return rate prediction study are as follows. First, the model achieved a prediction similarity level of almost 76%. After collecting time series data and preparing the final prediction model, it was confirmed that 76% of models could be achieved. All in all, the results demonstrate the reliability of the LSTM-based model for return rate prediction.

• Korean Journal of Agricultural Science
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• v.49 no.4
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• pp.677-696
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• 2022

Development of radish collectors may enhance radish production and promote upland crop mechanization in the Republic of Korea. Theoretical analysis of power is crucial to ensure the optimum design of agricultural machinery. The aim of the present study is to analyze theoretically the power requirement of a tractor-mounted radish collector under development and to propose design guidelines. The important components of the radish collector were belt-type conveyors, three hydraulic motors, and a direct current (DC) winch motor to operate the total radish collecting process. Theoretical equations were used to calculate the hydraulic motor's power, winch motor power, and draft power at loaded and unloaded conditions. A variety of tractors (44 - 74 kW) and different soil characteristics (hard, firm, tilted, and sandy) were considered to investigate the appropriate drawbar power. Variations of the power requirement of the tractor-mounted radish collector were observed due to modifications of the design parameters. The required hydraulic power of the stem cutting conveyor, stem cutting blade, and transfer conveyor of the radish collector were 0.23 and 0.24, 0.18 and 0.19, and 0.19 and 0.22 kW under unloaded and loaded conditions, respectively. The maximum draft power was calculated as 0.89, 1.07, 1.25, and 1.61 kW at a 30° tilted angle for hard, firm, tilted, and sandy soil, respectively. The calculation showed 2.07 kW DC power was required for unfolding or folding the stem-cutting conveyor. A maximum power of 4.78 kW was prescribed for conducting the whole process of the tractor-mounted radish collector. The analysis of power introduced in this study will be helpful to select the appropriate design parameters for the successful development of a tractor-mounted radish collector.