• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.131-145
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• 2020

In line with the trend of industrial innovation, IoT technology utilized in a variety of fields is emerging as a key element in creation of new business models and the provision of user-friendly services through the combination of big data. The accumulated data from devices with the Internet-of-Things (IoT) is being used in many ways to build a convenience-based smart system as it can provide customized intelligent systems through user environment and pattern analysis. Recently, it has been applied to innovation in the public domain and has been using it for smart city and smart transportation, such as solving traffic and crime problems using CCTV. In particular, it is necessary to comprehensively consider the easiness of securing real-time service data and the stability of security when planning underground services or establishing movement amount control information system to enhance citizens' or commuters' convenience in circumstances with the congestion of public transportation such as subways, urban railways, etc. However, previous studies that utilize image data have limitations in reducing the performance of object detection under private issue and abnormal conditions. The IoT device-based sensor data used in this study is free from private issue because it does not require identification for individuals, and can be effectively utilized to build intelligent public services for unspecified people. Especially, sensor data stored by the IoT device need not be identified to an individual, and can be effectively utilized for constructing intelligent public services for many and unspecified people as data free form private issue. We utilize the IoT-based infrared sensor devices for an intelligent pedestrian tracking system in metro service which many people use on a daily basis and temperature data measured by sensors are therein transmitted in real time. The experimental environment for collecting data detected in real time from sensors was established for the equally-spaced midpoints of 4×4 upper parts in the ceiling of subway entrances where the actual movement amount of passengers is high, and it measured the temperature change for objects entering and leaving the detection spots. The measured data have gone through a preprocessing in which the reference values for 16 different areas are set and the difference values between the temperatures in 16 distinct areas and their reference values per unit of time are calculated. This corresponds to the methodology that maximizes movement within the detection area. In addition, the size of the data was increased by 10 times in order to more sensitively reflect the difference in temperature by area. For example, if the temperature data collected from the sensor at a given time were 28.5℃, the data analysis was conducted by changing the value to 285. As above, the data collected from sensors have the characteristics of time series data and image data with 4×4 resolution. Reflecting the characteristics of the measured, preprocessed data, we finally propose a hybrid algorithm that combines CNN in superior performance for image classification and LSTM, especially suitable for analyzing time series data, as referred to CNN-LSTM (Convolutional Neural Network-Long Short Term Memory). In the study, the CNN-LSTM algorithm is used to predict the number of passing persons in one of 4×4 detection areas. We verified the validation of the proposed model by taking performance comparison with other artificial intelligence algorithms such as Multi-Layer Perceptron (MLP), Long Short Term Memory (LSTM) and RNN-LSTM (Recurrent Neural Network-Long Short Term Memory). As a result of the experiment, proposed CNN-LSTM hybrid model compared to MLP, LSTM and RNN-LSTM has the best predictive performance. By utilizing the proposed devices and models, it is expected various metro services will be provided with no illegal issue about the personal information such as real-time monitoring of public transport facilities and emergency situation response services on the basis of congestion. However, the data have been collected by selecting one side of the entrances as the subject of analysis, and the data collected for a short period of time have been applied to the prediction. There exists the limitation that the verification of application in other environments needs to be carried out. In the future, it is expected that more reliability will be provided for the proposed model if experimental data is sufficiently collected in various environments or if learning data is further configured by measuring data in other sensors.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.163-177
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• 2019

As smartphones are getting widely used, human activity recognition (HAR) tasks for recognizing personal activities of smartphone users with multimodal data have been actively studied recently. The research area is expanding from the recognition of the simple body movement of an individual user to the recognition of low-level behavior and high-level behavior. However, HAR tasks for recognizing interaction behavior with other people, such as whether the user is accompanying or communicating with someone else, have gotten less attention so far. And previous research for recognizing interaction behavior has usually depended on audio, Bluetooth, and Wi-Fi sensors, which are vulnerable to privacy issues and require much time to collect enough data. Whereas physical sensors including accelerometer, magnetic field and gyroscope sensors are less vulnerable to privacy issues and can collect a large amount of data within a short time. In this paper, a method for detecting accompanying status based on deep learning model by only using multimodal physical sensor data, such as an accelerometer, magnetic field and gyroscope, was proposed. The accompanying status was defined as a redefinition of a part of the user interaction behavior, including whether the user is accompanying with an acquaintance at a close distance and the user is actively communicating with the acquaintance. A framework based on convolutional neural networks (CNN) and long short-term memory (LSTM) recurrent networks for classifying accompanying and conversation was proposed. First, a data preprocessing method which consists of time synchronization of multimodal data from different physical sensors, data normalization and sequence data generation was introduced. We applied the nearest interpolation to synchronize the time of collected data from different sensors. Normalization was performed for each x, y, z axis value of the sensor data, and the sequence data was generated according to the sliding window method. Then, the sequence data became the input for CNN, where feature maps representing local dependencies of the original sequence are extracted. The CNN consisted of 3 convolutional layers and did not have a pooling layer to maintain the temporal information of the sequence data. Next, LSTM recurrent networks received the feature maps, learned long-term dependencies from them and extracted features. The LSTM recurrent networks consisted of two layers, each with 128 cells. Finally, the extracted features were used for classification by softmax classifier. The loss function of the model was cross entropy function and the weights of the model were randomly initialized on a normal distribution with an average of 0 and a standard deviation of 0.1. The model was trained using adaptive moment estimation (ADAM) optimization algorithm and the mini batch size was set to 128. We applied dropout to input values of the LSTM recurrent networks to prevent overfitting. The initial learning rate was set to 0.001, and it decreased exponentially by 0.99 at the end of each epoch training. An Android smartphone application was developed and released to collect data. We collected smartphone data for a total of 18 subjects. Using the data, the model classified accompanying and conversation by 98.74% and 98.83% accuracy each. Both the F1 score and accuracy of the model were higher than the F1 score and accuracy of the majority vote classifier, support vector machine, and deep recurrent neural network. In the future research, we will focus on more rigorous multimodal sensor data synchronization methods that minimize the time stamp differences. In addition, we will further study transfer learning method that enables transfer of trained models tailored to the training data to the evaluation data that follows a different distribution. It is expected that a model capable of exhibiting robust recognition performance against changes in data that is not considered in the model learning stage will be obtained.

• Journal of Bio-Environment Control
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• v.15 no.4
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• pp.296-305
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• 2006

The heterogeneity of crop transpiration is important to clearly understand the microclimate mechanisms and to efficiently handle the water resource in greenhouses. A computational fluid dynamic program (Fluent CFD version 6.2) was developed to study the internal climate and crop transpiration distributions of greenhouses. Additionally, the global solar radiation model and a crop heat exchange model were programmed together. Those models programmed using $C^{++}$ software were connected to the CFD main module using the user define function (UDF) technology. For the developed CFD validity, a field experiment was conducted at a $17{\times}6 m^2$ plastic-covered mechanically ventilated single-span greenhouse located at Pusan in Korea. The CFD internal distributions of air temperature, relative humidity, and air velocity at 1m height were validated against the experimental results. The CFD computed results were in close agreement with the measured distributions of the air temperature, relative humidity, and air velocity along the greenhouse. The averaged errors of their CFD computed results were 2.2%,2.1%, and 7.7%, respectively.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.37 no.1
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• pp.67-76
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• 2024

In this paper, we present a DIP-MLS testing method that combines digital image processing with a rigid body-based MLS differencing approach to measure mechanical variables and analyze the impact of target location and image resolution. This method assesses the displacement of the target attached to the sample through digital image processing and allocates this displacement to the node displacement of the MLS differencing method, which solely employs nodes to calculate mechanical variables such as stress and strain of the studied object. We propose an effective method to measure the displacement of the target's center of gravity using digital image processing. The calculation of mechanical variables through the MLS differencing method, incorporating image-based target displacement, facilitates easy computation of mechanical variables at arbitrary positions without constraints from meshes or grids. This is achieved by acquiring the accurate displacement history of the test specimen and utilizing the displacement of tracking points with low rigidity. The developed testing method was validated by comparing the measurement results of the sensor with those of the DIP-MLS testing method in a three-point bending test of a rubber beam. Additionally, numerical analysis results simulated only by the MLS differencing method were compared, confirming that the developed method accurately reproduces the actual test and shows good agreement with numerical analysis results before significant deformation. Furthermore, we analyzed the effects of boundary points by applying 46 tracking points, including corner points, to the DIP-MLS testing method. This was compared with using only the internal points of the target, determining the optimal image resolution for this testing method. Through this, we demonstrated that the developed method efficiently addresses the limitations of direct experiments or existing mesh-based simulations. It also suggests that digitalization of the experimental-simulation process is achievable to a considerable extent.

• Restorative Dentistry and Endodontics
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• v.34 no.5
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• pp.450-459
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• 2009

The aim of this study was to measure the initial dynamic modulus changes of light cured composites using a custom made rheometer. The custom made rheometer consisted of 3 parts: (1) a measurement unit of parallel plates made of glass rods, (2) an oscillating shear strain generator with a DC motor and a crank mechanism, (3) a stress measurement device using an electromagnetic torque sensor. This instrument could measure a maximum torque of 2Ncm, and the switch of the light-curing unit was synchronized with the rheometer. Six commercial composite resins [Z-100 (Z1), Z-250 (Z2), Z-350 (Z3), DenFil (DF), Tetric Ceram (TC), and Clearfil AP-X (CF)] were investigated. A dynamic oscillating shear test was undertaken with the rheometer. A certain volume ($14.2\;mm^3$) of composite was loaded between the parallel plates, which were made of glass rods (3 mm in diameter). An oscillating shear strain with a frequency of 6 Hz and amplitude of 0.00579 rad was applied to the specimen and the resultant stress was measured. Data acquisition started simultaneously with light curing, and the changes in visco-elasticity of composites were recorded for 10 seconds. The measurements were repeated 5 times for each composite at $25{\pm}0.5^{\circ}C$. Complex shear modulus G*, storage shear modulus G', loss shear modulus G" were calculated from the measured strain-stress curves. Time to reach the complex modulus G* of 10 MPa was determined. The G* and time to reach the G* of 10 MPa of composites were analyzed with One-way ANOVA and Tukey's test ($\alpha$ = 0.05). The results were as follows. 1. The custom made rheometer in this study reliably measured the initial visco-elastic modulus changes of composites during 10 seconds of light curing. 2. In all composites, the development of complex shear modulus G* had a latent period for $1{\sim}2$ seconds immediately after the start of light curing, and then increased rapidly during 10 seconds. 3. In all composites, the storage shear modulus G" increased steeper than the loss shear modulus G" during 10 seconds of light curing. 4. The complex shear modulus of Z1 was the highest, followed by CF, Z2, Z3, TC and DF the lowest. 5. Z1 was the fastest and DF was the slowest in the time to reach the complex shear modulus of 10 MPa.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.7
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• pp.1216-1221
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• 2022

Ships tend to be larger to increase the efficiency of cargo transportation. Larger ships lead to increased travel time for ship workers, increased work intensity, and reduced work efficiency. Problems such as increased work intensity are reducing the influx of young people into labor, along with the phenomenon of avoidance of high intensity labor by the younger generation. In addition, the rapid aging of the population and decrease in the young labor force aggravate the labor shortage problem in the maritime industry. To overcome this, the maritime industry has recently introduced technologies such as an intelligent production design platform and a smart production operation management system, and a smart autonomous logistics system in one of these technologies. The smart autonomous logistics system is a technology that delivers various goods using intelligent mobile robots, and enables the robot to drive itself by using sensors such as lidar and camera. Therefore, in this paper, it was checked whether the mobile robot could autonomously drive to the stop sign by detecting the passage way of the ship deck. The autonomous driving was performed by detecting the passage way of the ship deck through the camera mounted on the mobile robot based on the data learned through Nvidia's End-to-end learning. The mobile robot was stopped by checking the stop sign using SSD MobileNetV2. The experiment was repeated five times in which the mobile robot autonomously drives to the stop sign without deviation from the ship deck passage way at a distance of about 70m. As a result of the experiment, it was confirmed that the mobile robot was driven without deviation from passage way. If the smart autonomous logistics system to which this result is applied is used in the marine industry, it is thought that the stability, reduction of labor force, and work efficiency will be improved when workers work.

• Journal of Oral Medicine and Pain
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• v.30 no.4
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• pp.457-464
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• 2005

The purpose of our study was to investigate whether the intrapulpal temperature during cavity preparation of enamel or dentin with Er:YAG laser still remained in range of safety for dental pulp protection when combined with appropriate water flow rate. The effect of different pulse repetition rates at the same pulse energy during ablation was evaluated as well. Caries-free, restoration-free extracted human molar teeth were prepared for the specimen and divided two experimental groups of enamel and dentin. Each group comprised 5 specimens and each of tooth specimens were embedded into a resin block each and measuring probe was placed on the irradiated pulpal walls. For experiments of dentin ablation, enamel layers were prepared to produce dentin specimen with a same dentin thickness of 2 mm. A pulse energy of Er:YAG laser was set to 300 mJ and three different pulse repetition rates of 20 Hz, 15 Hz and 10 Hz were employed. Laser beam was delivered with 3 seconds and less per application over enamel and dentin surfaces constant sized by $3\;mm{\times}2\;mm$ and water spray added during irradiation was a rate of 1.6 ml/min. Temperature change induced by Er:YAG laser irradiation was monitored and recorded While enamel was ablated, there was no significant difference of temperature related to pulse repetition rates(p=0.358) and temperature change at any pulse repetition rate was negligible. Significant statistical difference in temperature changes during cavity preparation in dentin existed among three different pulse groups(p=0.001). While temperature rise was noticeable when the dentinal wall was perforated, actual change of temperature due to Er:YAG laser irradiation was not enough to compromise safety of dental pulp when irradiation was conjugated with appropriate water spray. Conclusively, it can be said that cavity preparation on enamel or dentin with an Er:YAG laser is performed safely without pulp damage if appropriate volume of water is sprayed properly over the irradiated site.

• Journal of IKEEE
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• v.18 no.4
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• pp.552-558
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• 2014

This paper suggests how to design a ZigBee-chip-based communication module to remotely measure radiation level. The suggested communication module consists of two control processors for the chip as generally required to configure a ZigBee system, and one chip module to configure a ZigBee RF device. The ZigBee-chip-based communication module for remote radiation measurement consists of a wireless communication controller; sensor and high-voltage generator; charger and power supply circuit; wired communication part; and RF circuit and antenna. The wireless communication controller is to control wireless communication for ZigBee and to measure radiation level remotely. The sensor and high-voltage generator generates 500 V in two consecutive series to amplify and filter pulses of radiation detected by G-M Tube. The charger and power supply circuit part is to charge lithium-ion battery and supply power to one-chip processors. The wired communication part serves as a RS-485/422 interface to enable USB interface and wired remote communication for interfacing with PC and debugging. RF circuit and antenna applies an RLC passive component for chip antenna to configure BALUN and antenna impedance matching circuit, allowing wireless communication. After configuring the ZigBee-chip-based communication module, tests were conducted to measure radiation level remotely: data were successfully transmitted in 10-meter and 100-meter distances, measuring radiation level in a remote condition. The communication module allows an environment where radiation level can be remotely measured in an economically beneficial way as it not only consumes less electricity but also costs less. By securing linearity of a radiation measuring device and by minimizing the device itself, it is possible to set up an environment where radiation can be measured in a reliable manner, and radiation level is monitored real-time.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.35 no.2
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• pp.161-169
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• 1999

This paper describes the swimming and escaping behavior of amber fish, Seriola aureovittata released in the first bag net of the setnet and observed with telemetry techniques. The setnet used in experiment is composed of a leader, a fish court with a flying net and two bag nets having ramp net. The behavior of the fish attached an ultrasonic depth pinger of 50 KHz is observed using a prototype LBL fish tracking system. The 3-D underwater position ofthe fish is calculated by hyperbolic method with three channels of receiver and the depth of pinger. The results obtained are as follows: 1. The fish released on the sea surface was escaped down to 15 m depth and rised up to near the sea surface during 5 minutes after release. The average swimming speed of the fish during this time was 0.87 m/sec. 2. The swimming speed of the fish is decreased slowly in relation to the time elapsed and the fish showed some escaping behavior forward to the fish court staying 1 to 7 m depth layer near the ramp net. The average speed of the fish during this time was 0.52 m/sec. 3. During 25 minutes after beginning of hauling net, the fish showed a faster swimming speed than before hauling and an escaping behavior repeatedly from the first ramp net to the second one in horizontal. In vertical, the fish moved up and down between the sea surface and 20 m depth. After this time, the fish showed the escaping behavior forward to fish court after come back to the first ramp net in spite of the hauling was continued. It is found that the fish was escaped from the first ramp net to the fish court while the hauling was carried out. The average speed of the fish after beginning of hauling was 0.72 m/sec which increased 38.5 ％ than right before the hauling and showed 0.44 to 0.82 m/see of speed till escaping the first bag net. The average swimming speed during observation was 0.67 m/sec (2.2 times of body length).

• Economic and Environmental Geology
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• v.49 no.4
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• pp.281-290
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• 2016

Coastal areas, used as human utilization areas like leisure space, medical care, ports and power plants, etc., are regions that are continuously changing and interconnected with oceans and land. Regular monitoring of coastal changes is essential at key locations with such volatility. But the survey method of terrestial LiDAR(Light Detection and Ranging) system has much time consuming and many restrictions. For effective monitoring coastal changes, KIOST(Korea Institute of Ocean Science & Technology) has constructed a shipborne mobile LiDAR system. The shipborne mobile LiDAR system, installed in a research vessel, comprised a land based LiDAR(RIEGL LMS-420i), an IMU(MAGUS Inertial+), a RTKGNSS(LEICA GS15 GS25), and a fixed platform. The shipborne mobile LiDAR system is much more effective than a land based LiDAR system in the measuring of fore shore areas without shadow zone. Because the vessel with the shipborne mobile LiDAR system is continuously moved along the shoreline, it is possible to efficiently survey a large area in a relatively short time. We conducted test measurements in the Anmok-Songjung beach around the Gangneung port. Effective monitoring of the changes using the constructed shipborne mobile LiDAR system for seriously eroded coastal areas will be able to contribute to coastal erosion management and response.