• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.5
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• pp.538-549
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• 2019

This study suggests a new platform in which VI can communicate with their guardians who serve as a system administrator optimizing personal characteristics. In terms of interaction between platform components, this study can contribute to enhancing stability of walking by giving substantial information to VI through server processing and setups of guardians with real-time interaction to VI through server processing and setups of guardians with real-time interaction. Therefore, developing smart phone applications can facilitate both portability and cost effectiveness, by the users' downloading the application and registration. In terms of guardians' cost, this system is cost-effective because they can access to the administration system through personal computers or smart phones, not through specialized system devices. This system is web-based, which automatically communicates with the surroundings of, such as provides the guardians with the necessary information or approaches to the danger through IoT devices, which results in enhancing safety. Moreover, it is possible for guardians to utilize the personalized platform since the system not only senses personal characteristics but also sends a number of information to VI. a new platform makes it possible for guardians to provide electronic support and interaction with the application with the application users, which contributes to systematic and safe walk for VI.

• Journal of Digital Convergence
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• v.17 no.1
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• pp.239-247
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• 2019

An accurate prediction of emotion is a very important issue for the sake of patient-centered medical device development and emotion-related psychology fields. Although there have been many studies on emotion prediction, no studies have applied the heart rate variability and neuro-fuzzy approach to emotion prediction. We propose ANFEP(Adaptive Neuro Fuzzy System for Emotion Prediction) HRV. The ANFEP bases its core functions on an ANFIS(Adaptive Neuro-Fuzzy Inference System) which integrates neural networks with fuzzy systems as a vehicle for training predictive models. To prove the proposed model, 50 participants were invited to join the experiment and Heart rate variability was obtained and used to input the ANFEP model. The ANFEP model with STDRR and RMSSD as inputs and two membership functions per input variable showed the best results. The result out of applying the ANFEP to the HRV metrics proved to be significantly robust when compared with benchmarking methods like linear regression, support vector regression, neural network, and random forest. The results show that reliable prediction of emotion is possible with less input and it is necessary to develop a more accurate and reliable emotion recognition system.

• Journal of Industrial Convergence
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• v.16 no.1
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• pp.21-26
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• 2018

The Seaborne Trade Volume accounts for 97% of the total. This means that the port operation management system can improve port efficiency, reducing operating costs, and the manager who manages all operations at the port needs to check and respond quickly when delays of work and equipment support is needed. Based on the real-time location information confirmation of yard automation equipment used the existing system GPS, the real-time location information confirmation system is a GPS system of the tablet, rather than a port operation system that monitors location information for the entered information, depending on the completion of the task or the start of the task. Network configurations also reduce container processing delays by using commercial LTE services that do not have shading due to containers in the yard also reduce container processing delays. Trough introduction of smart devices using Android or IOS and container processing scheduling utilizing artificial intelligence, we will build a minimum delay system with Smart Device usage of container processing applications and optimization of container processing schedule. The adoption of smart devices and the minimization of container processing delays utilizing artificial intelligence are expected to improve the quality of port services by confirming the processing containers in real time to consumers who are container information demanders.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.13 no.6
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• pp.523-529
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• 2020

Power distribution management system is essential for the efficient management and operation of power distribution networks. The power distribution system is a system that manages the distribution network based on IT, and has been evolving along with the development of the power industry. The current power distribution system is designed to operate at a relatively low network transmission speed based on the independent operation of the main equipment. However, due to distributed resources such as photovoltaic or energy storage devices, which are rapidly increasing in popularity in recent years, the operation of future distribution environments is becoming more complex, and various information needs to be collected in real time. In this study, the requirements of the next-generation power distribution system were derived to overcome the limitations of the existing power distribution system, and based on this, the communication network system and performance requirements for the distribution system were defined. In order to verify the performance of the designed system, a software-based terminal device simulator was developed because it takes excessive time and cost to introduce a large-scale system such as a power distribution system. Using the simulator, a test environment similar to the actual operation was established, and the number of terminal devices was increased up to 1,000. The proposed system was shown to satisfy the requirements to support the functions of the next-generation power distribution system, recording less than 10 % of the communication network bandwidth.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.6
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• pp.573-581
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• 2020

Using CCTV images and weather parameters, a method for estimating PM (Particulate Matter) index was proposed, and an experiment was conducted. For CCTV images, we proposed a method of estimating the PM index by applying a deep learning technique based on a CNN (Convolutional Neural Network) with ROI(Region Of Interest) image including a specific spot and an full area image. In addition, after combining the predicted result values by deep learning with the two weather parameters of humidity and wind speed, a post-processing experiment was also conducted to calculate the modified PM index using the learned regression model. As a result of the experiment, the estimated value of the PM index from the CCTV image was R2(R-Squared) 0.58~0.89, and the result of learning the ROI image and the full area image with the measuring device was the best. The result of post-processing using weather parameters did not always show improvement in accuracy in all cases in the experimental area.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.6
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• pp.1087-1101
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• 2020

Various security devices are used to protect internal networks and valuable information from rapidly evolving cyber attacks. Firewall, which is the most commonly used security device, tries to prevent malicious attacks based on a text-based filtering rule (i.e., access control policy), by allowing or blocking access to communicate between inside and outside environments. However, in order to protect a valuable internal network from large networks, it has no choice but to increase the number of access control policy. Moreover, the text-based policy requires time-consuming and labor cost to analyze various types of vulnerabilities in firewall. To solve these problems, this paper proposes a 3D-based hierarchical visualization method, for intuitive analysis and management of access control policy. In particular, by providing a drill-down user interface through hierarchical architecture, Can support the access policy analysis for not only comprehensive understanding of large-scale networks, but also sophisticated investigation of anomalies. Finally, we implement the proposed system architecture's to verify the practicality and validity of the hierarchical visualization methodology, and then attempt to identify the applicability of firewall data analysis in the real-world network environment.

• Korean Journal of Applied Biomechanics
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• v.31 no.1
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• pp.72-78
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• 2021

Objective: The purpose of this study was to investigate relations and effectiveness about mountain climbling exercise with different level of support surfaces by analyzing heart rate and EMG data. A total of 10 male college students with no musculoskeltal disorder were recruited for this study. Method: The biomechanical analysis was performed using heart rate monitor (Polar V800, Polar Electro Oy, Finland), step-box, exercise mat, and EMG device (QEMG8, Laxtha Inc. Korea, sampling frequency = 1,024 Hz, gain = 1,000, input impedance > 1012 Ω, CMRR > 100 dB). In this research, step-box were used to create different surface levels on the upper body (flat surface, 10% of subject's height, 20% of subject's height, and 30% of subject's hight). Based on these different conditions, data was collected by performing mountain climbing exercise during 30 seconds. Subjects were given 5 minutes of break to prevent muscular fatigue after each exercise. For each dependent variable, a one-way analysis of variance with repeated measures was conducted to find significant differences and Bonferroni post-hoc test was performed. Results: The results of this study showed that exercise intensity was reduced statistically as increased surface level on the upper body. Muscle activity of the upper rectus abdominis and biceps femoris for 30% of surface level was significantly higher than the corresponding values for flat surface. However, the opposite was found in the rectus femoris. In general, muscle activity of the lower rectus abdominis, erector spinae, external oblique abdominis, and gluteus maximus increased when surface level increased, but the differences were not significant. Conclusion: As a result, the increase in surface level of the body would change muscle activity of the upper body, indicating that different surface level of the upper body may cause significant effect on particular muscles to be more active during mountain climbing exercise. Based on results of this study, it is suggested to set up an appropriate surface level to target particular muscle to expect an effective training. It is also important to set adequate surface levels to create an effective training condition for preventing exercise injuries.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.34 no.1
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• pp.43-50
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• 2021

A Leg Mating Unit (LMU) is a device utilized during the float-over installation of offshore structures that include hyperelastic pads and mating part. The hyperelastic pads absorb the loads, whereas the mating part works as guidance between topside and supporting structures during the mating sequence of float-over installation. In this study, the design optimization of an LMU for the float-over installation of floating-type offshore structures is conducted to enhance the performance and to satisfy the requirements defined by classification society regulations. The initial dimensions of the LMU are referred to the dimensions of those used in fixed-type float-over installation because only the location and the number of LMUs are known. The two-parameter Mooney-Rivlin model is adopted to describe the hyperelastic pads under given material parameters. Geometric variables, such as the thickness, height, and width of members, as well as configuration variables, such as the angle and number of members, are defined as design variables and are parameterized. A sampling-based design sensitivity analysis based on latin hypercube sampling method is performed to filter the important design variables. The design optimization problem is formulated to minimize the total mass of the LMU under maximum von Mises stress and reaction force constraints.

• Journal of Broadcast Engineering
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• v.27 no.3
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• pp.318-331
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• 2022

With the recent development of hardware computing devices and software based frameworks, machine tasks using deep learning networks are expected to be utilized in various industrial fields and personal IoT devices. However, in order to overcome the limitations of high cost device for utilizing the deep learning network and that the user may not receive the results requested when only the machine task results are transmitted from the server, Collaborative Intelligence (CI) proposed the transmission of feature maps as a solution. In this paper, an efficient compression method for feature maps with vast data sizes to support the CI paradigm was analyzed and presented through experiments. This method increases redundancy by applying feature map reordering to improve compression efficiency in traditional video codecs, and proposes a feature map method that improves compression efficiency and maintains the performance of machine tasks by simultaneously utilizing image compression format and video compression format. As a result of the experiment, the proposed method shows 14.29% gain in BD-rate of BPP and mAP compared to the feature compression anchor of MPEG-VCM.

• Journal of Navigation and Port Research
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• v.46 no.6
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• pp.562-569
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• 2022

The alarm monitoring technology applied to existing operating ships manages data items such as temperature and pressure with AMS (Alarm Monitoring System) and provides an alarm to the crew should these sensing data exceed the normal level range. In addition, the maintenance of existing ships follows the Planned Maintenance System (PMS). whereby the sensing data measured from the equipment is monitored and if it surpasses the set range, maintenance is performed through an alarm, or the corresponding part is replaced in advance after being used for a certain period of time regardless of whether the target device has a malfunction or not. To secure the reliability and operational safety of ship engine operation, it is necessary to enable advanced diagnosis and prediction based on real-time condition monitoring data. To do so, comprehensive measurement of actual ship data, creation of a database, and implementation of a condition diagnosis monitoring system for condition-based predictive maintenance of auxiliary equipment and piping must take place. Furthermore, the system should enable management of auxiliary equipment and piping status information based on a responsive web, and be optimized for screen and resolution so that it can be accessed and used by various mobile devices such as smartphones as well as for viewing on a PC on board. This update cost is low, and the management method is easy. In this paper, we propose CBM (Condition Based Management) technology, for autonomous ships. This core technology is used to identify abnormal phenomena through state diagnosis and monitoring of pumps and purifiers among ship auxiliary equipment, and seawater and steam pipes among pipes. It is intended to provide performance diagnosis and failure prediction of ship auxiliary equipment and piping for convergence analysis, and to support preventive maintenance decision-making.