• KIPS Transactions on Software and Data Engineering
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• v.12 no.3
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• pp.125-132
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• 2023

In this study, we compared the performance of machine learning models for predicting Vapor Pressure Deficits (VPD) in greenhouses that affect pore function and photosynthesis as well as plant growth due to nutrient absorption of plants. For VPD prediction, the correlation between the environmental elements in and outside the greenhouse and the temporal elements of the time series data was confirmed, and how the highly correlated elements affect VPD was confirmed. Before analyzing the performance of the prediction model, the amount and interval of analysis time series data (1 day, 3 days, 7 days) and interval (20 minutes, 1 hour) were checked to adjust the amount and interval of data. Finally, four machine learning prediction models (XGB Regressor, LGBM Regressor, Random Forest Regressor, etc.) were applied to compare the prediction performance by model. As a result of the prediction of the model, when data of 1 day at 20 minute intervals were used, the highest prediction performance was 0.008 for MAE and 0.011 for RMSE in LGBM. In addition, it was confirmed that the factor that most influences VPD prediction after 20 minutes was VPD (VPD_y__71) from the past 20 minutes rather than environmental factors. Using the results of this study, it is possible to increase crop productivity through VPD prediction, condensation of greenhouses, and prevention of disease occurrence. In the future, it can be used not only in predicting environmental data of greenhouses, but also in various fields such as production prediction and smart farm control models.

• Journal of Information Technology Applications and Management
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• v.20 no.1
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• pp.43-51
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• 2013

Human being does work to live out and they have their private life because human has sociality. Both work and life are important to live out but they are on the trade-off relationship. Because keeping the balance between work and life is too hard, it has been interested by academic and practical areas. Definition of Work-life balance here is that balance or imbalance arising between work and life has no negative impact on their daily life. Above all, Work-life balance is important because it is strongly related to identity. Recently, the introduction of the mobile office system has emerged as a way to solve the problem of work-life balance. It is based on the teleworking which was formerly generated. Teleworking is to perform the work in the employee's home or office space set aside without going into the workplace. Concept of the mobile office system here is not only using portable devices during work for convenience but also the system which is designed for the performance. Thanks to the diffusion of smart devices(smart phone, tablet pc), mobile office system has been spread. Although the importance of mobile office systems is emerging, there are few researches about it. Even they mostly focus on the standpoint of performance of mobile office system. However, Quality of life is as important as the performance. As a part of Quality of Life field, Work-life balance is the closest to employee's quality of life. So this study aims to examine the effect of the use of mobile office systems on work-life balance. To do so, we try to find factors effecting Work-life balance from existing studies and then set a research model. We set the use of mobile office systems as independent variables which are divided by use of function, use by location and use by situation. There are four dependent variables - sense of self command, sense of balance, solving work problem, solving life problem. We collected data from employees who are using mobile office systems on their job. 215 people were participated in the survey and we used multiple regression analysis to verify our research model. Results show that every independent variable has no impact on solving work problem while they have slight impact on the other dependent variables. Especially use on the business trip has significant effect on dependent variables. It means that there is a possibility use of mobile office system could control the employee's quality of life and system should be evolved until it covers even critical tasks. Also, support for mobile office system -education, encouragement-should be provided. By mobile office system is maturing, future research would be done.

• Information Systems Review
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• v.18 no.2
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• pp.151-171
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• 2016

With the widespread adoption of mobile devices, such as smart phones and smart pads, as well as the rapid growth of mobile technologies, consumer shopping patterns are changing. This study investigates key factors of consumer purchasing intention in a mobile shopping mall context by incorporating trust belief into the theory of planned behavior. We posit perceived usefulness, perceived enjoyment, perceived ease of use, and trust belief as antecedents of behavioral attitude toward mobile shopping malls. Moreover, social influence and security are identified as key enablers of trust belief on mobile shopping malls. Data collected from 154 consumers with purchasing experience in mobile shopping malls are empirically tested against a research model using partial least squares. Analysis results show that behavioral attitude and perceived behavioral control significantly influence purchasing intention. Moreover, this study reveals the significant effects of perceived usefulness and perceived enjoyment on behavioral attitude. Trust belief indirectly influences purchasing intention through behavioral attitude and is significantly affected by social influence. Understanding consumer purchasing decision-making processes in mobile shopping malls can help service providers to develop effective marketing and operation strategies and campaigns.

• Journal of Drive and Control
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• v.20 no.4
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• pp.115-122
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• 2023

This study utilized a discrete element method (DEM) simulation, as one of the virtual field trials, to predict the impact of tillage depth on the rotary blade shaft during rotavator tilling. The virtual field for the simulation was generated according to soil properties observed in an actual field. Following the generation of particles for the virtual field, a sequence of calibration steps followed to align the mechanical properties more closely with those of real soil. Calibration was conducted with a focus on bulk density and shear torque, resulting in calibration errors of just 0.02% for bulk density and 0.52% for shear torque. The prediction of the load on a rotary tiller's blade shaft involved a three-pronged approach, considering shaft torque, draft force, and vertical force. In terms of shaft torque, the values exhibited significant increases of 42.34% and 36.91% for every 5-centimeter increment in tillage depth. Similarly, the vertical force saw substantial growth by 40.41% and 36.08% for every 5-centimeter increment. In contrast, the variation in draft force based on tillage depth was comparatively lower at 18.49% and 0.96%, indicating that the effect of tillage depth on draft force was less pronounced than its impact on shaft torque and vertical force. From a perspective of agricultural machinery research, this study provides valuable insights into the DEM soil modeling process, accounting for changes in soil properties with varying tillage depths. These findings are expected to be instrumental in future agricultural machinery design studies.

• The Journal of the Convergence on Culture Technology
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• v.8 no.1
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• pp.441-452
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• 2022

Training using metacognition in a learning environment is one of the topics that have been continuously studied since the 1990s. Metacognition can be broadly divided into declarative metacognitive knowledge and procedural metacognitive knowledge (metacognitive skills). Accordingly, metacognitive training has also been studied focusing on one of the two metacognitive knowledge. The purpose of this study was to examine the role of metacognitive skills training in the context of mathematical problem solving. Specifically, the learner performed the prediction of problem difficulty, estimation of problem solving time, and prediction of accuracy in the context of a test in which problems of various difficulty levels were mixed within a set, and this was repeated 5 times over a total of 5 weeks. As a result of the analysis, we found that there was a significant difference in all three predictive indicators after training than before training, and we revealed that training can help learners in problem-solving strategies. In addition, we analyzed whether there was a difference between the experiment group and control group in the degree of test anxiety and math achievement. As a result, we found that learners in the experiment group showed less emotional and relationship anxiety at 5 weeks. This effect through metacognitive skill training is expected to help learners improve learning strategies needed for test situations.

• Journal of Korea Water Resources Association
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• v.57 no.3
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• pp.151-164
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• 2024

In water treatment plants supplying potable water, the management of chlorine concentration in water treatment processes involving pre-chlorination or intermediate chlorination requires process control. To address this, research has been conducted on water quality prediction techniques utilizing AI technology. This study developed an AI-based predictive model for automating the process control of chlorine disinfection, targeting the prediction of residual chlorine concentration downstream of sedimentation basins in water treatment processes. The AI-based model, which learns from past water quality observation data to predict future water quality, offers a simpler and more efficient approach compared to complex physicochemical and biological water quality models. The model was tested by predicting the residual chlorine concentration downstream of the sedimentation basins at Plant, using multiple regression models and AI-based models like Random Forest and LSTM, and the results were compared. For optimal prediction of residual chlorine concentration, the input-output structure of the AI model included the residual chlorine concentration upstream of the sedimentation basin, turbidity, pH, water temperature, electrical conductivity, inflow of raw water, alkalinity, NH₃, etc. as independent variables, and the desired residual chlorine concentration of the effluent from the sedimentation basin as the dependent variable. The independent variables were selected from observable data at the water treatment plant, which are influential on the residual chlorine concentration downstream of the sedimentation basin. The analysis showed that, for Plant, the model based on Random Forest had the lowest error compared to multiple regression models, neural network models, model trees, and other Random Forest models. The optimal predicted residual chlorine concentration downstream of the sedimentation basin presented in this study is expected to enable real-time control of chlorine dosing in previous treatment stages, thereby enhancing water treatment efficiency and reducing chemical costs.

• 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.

• Tunnel and Underground Space
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• v.33 no.4
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• pp.281-298
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• 2023

Continuous research efforts are being devoted to unmanned mobile platforms for lunar exploration. There is an ongoing demand for real-time information processing to accurately determine the positioning and mapping of areas of interest on the lunar surface. To apply deep learning processing and analysis techniques to practical rovers, research on software integration and optimization is imperative. In this study, a foundational investigation has been conducted on real-time analysis of virtual lunar base construction site images, aimed at automatically quantifying spatial information of key objects. This study involved transitioning from an existing region-based object recognition algorithm to a boundary box-based algorithm, thus enhancing object recognition accuracy and inference speed. To facilitate extensive data-based object matching training, the Batch Hard Triplet Mining technique was introduced, and research was conducted to optimize both training and inference processes. Furthermore, an improved software system for object recognition and identical object matching was integrated, accompanied by the development of visualization software for the automatic matching of identical objects within input images. Leveraging satellite simulative captured video data for training objects and moving object-captured video data for inference, training and inference for identical object matching were successfully executed. The outcomes of this research suggest the feasibility of implementing 3D spatial information based on continuous-capture video data of mobile platforms and utilizing it for positioning objects within regions of interest. As a result, these findings are expected to contribute to the integration of an automated on-site system for video-based construction monitoring and control of significant target objects within future lunar base construction sites.

• Smart Structures and Systems
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• v.26 no.5
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• pp.575-589
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• 2020

For vibration control of stay cables in cable-stayed bridges, viscous dampers are frequently used, and they are regularly installed between the cable and the bridge deck. In practice, neoprene rubber bushings (or of other types) are also widely installed inside the cable guide pipe, mainly for reducing the bending stresses of the cable near its anchorages. Therefore, it is important to understand the effect of the bushings on the performance of the external damper. Besides, for long cables, external dampers installed at a single position near a cable end can no longer provide enough damping due to the sag effect and the limited installation distance. It is thus of interest to improve cable damping by additionally installing dampers inside the guide pipe. This paper hence studies the combined effects of an external damper and an internal damper (which can also model the bushings) on a stay cable. The internal damper is assumed to be a High Damping Rubber (HDR) damper, and the external damper is considered to be a viscous damper with intrinsic stiffness, and the cable sag is also considered. Both the cases when the two dampers are installed close to one cable end and respectively close to the two cable ends are studied. Asymptotic design formulas are derived for both cases considering that the dampers are close to the cable ends. It is shown that when the two dampers are placed close to different cable ends, their combined damping effects are approximately the sum of their separate contributions, regardless of small cable sag and damper intrinsic stiffness. When the two dampers are installed close to the same end, maximum damping that can be achieved by the external damper is generally degraded, regardless of properties of the HDR damper. Field tests on an existing cable-stayed bridge have further validated the influence of the internal damper on the performance of the external damper. The results suggest that the HDR is optimally placed in the guide pipe of the cable-pylon anchorage when installing viscous dampers at one position is insufficient. When an HDR damper or the bushing has to be installed near the external damper, their combined damping effects need to be evaluated using the presented methods.

• Smart Media Journal
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• v.13 no.3
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• pp.18-26
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• 2024

Road surface monitoring is essential for maintaining road environment safety through managing risk factors like rutting and crack detection. Using autonomous driving-based UGVs with high-performance 2D laser sensors enables more precise measurements. However, the increased energy consumption of these sensors is limited by constrained battery capacity. In this paper, we propose a fusion sensor system for efficient surface monitoring with UGVs. The proposed system combines color information from cameras and depth information from line laser sensors to accurately detect surface displacement. Furthermore, a dynamic sampling algorithm is applied to control the scanning frequency of line laser sensors based on the detection status of monitoring targets using camera sensors, reducing unnecessary energy consumption. A power consumption model of the fusion sensor system analyzes its energy efficiency considering various crack distributions and sensor characteristics in different mission environments. Performance analysis demonstrates that setting the power consumption of the line laser sensor to twice that of the saving state when in the active state increases power consumption efficiency by 13.3% compared to fixed sampling under the condition of λ=10, µ=10.