• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.27 no.1
• /
• pp.27-33
• /
• 2023

Demands for eco-friendly food materials are increasing rapidly because of increased interest in well-being and health care, deterioration of air quality due to fine dust, and various soil and water pollution. Aquaponics is a system that can solve various problems such as economic activities, environmental problems, and safe food provision of the elderly population. However, techniques for deriving the optimal growth environment should be preceded. In this paper, we intend to design an intelligent plant growth measurement system that considers the characteristics of existing aquaponics. In particular, we would like to propose a module configuration plan for learning data and judgment systems when providing a uniform growth environment, focusing on designing systems suitable for production sites that do not have high-performance processing resources among intelligent aquaponics production management modules. It is believed that the proposed system can effectively perform deep learning with small analysis resources.

• KIPS Transactions on Computer and Communication Systems
• /
• v.12 no.2
• /
• pp.61-68
• /
• 2023

Demand for "pet plants" and "planteriers" is increasing due to the increase in COVID-19 and fine dust. In this paper, Smart pots for planterior should be small in size while providing the functions for cultivation without any problems. They should provide a user interface for long-range control for user's convenience. We implemented smart pots by incorporating IoT into pots. In response to the growing number of iPhone users, we developed an iOS app for user interface and UX/UI design. By communicating with the smartphone app and a home pot server over the Internet, users can check and control the state of the pot anytime, anywhere. The server and the pot module were separated to reduce the size of the pot itself. By locating a water bottle at the bottom of the pot, we expect that it is suitable for a "planterier" because it adopts a circulating structure in which drainage flows down to the water bottle as it is.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.17 no.3
• /
• pp.473-482
• /
• 2022

Wearing a mask is also necessary to limit the risk of infection in today's era of COVID-19 and wearing a helmet is inevitable for the safety of personnel who works in a dangerous working environment such as construction sites. This paper proposes an effective deep learning model, HelmetMask-Net, to classify both Helmet and Mask. The proposed HelmetMask-Net is based on CNN which consists of data processing, convolution layers, max pooling layers and fully connected layers with four output classifications, and 4 classes for Helmet, Mask, Helmet & Mask, and no Helmet & no Mask are classified. The proposed HelmatMask-Net has been chosen with 2 convolutional layers and AdaGrad optimizer by various simulations for accuracy, optimizer and the number of hyperparameters. Simulation results show the accuracy of 99% and the best performance compared to other models. The results of this paper would enhance the safety of personnel in this era of COVID-19.

• KIPS Transactions on Computer and Communication Systems
• /
• v.11 no.2
• /
• pp.59-66
• /
• 2022

The Joint Coastal Guard System is composed of a maritime surveillance system and a anti-coastal infiltration system, and is a system in which the Navy is mainly responsible for the maritime and the Army is responsible for the coast. We analyzed the operations effectiveness of the joint coastal guard system, in which various weapon systems of the army and navy are operated in a complex way, to the extent to which successful operation is possible against small targets. The operations effectiveness analysis was conducted by defining the operations effectiveness by operation type, configuring the simulation environment using METT-T elements, establishing the assumptions of the simulation scenario, conducting the simulation and analyzing the simulation results by weather condition. The simulation tools used were NORAM and EADSIM. As a result of the operations effectiveness analysis, the joint coastal guard system currently in operation showed a significant difference in operational success depending on the size of the target and weather conditions. This research can be used as useful data for establishing an effective joint coastal guard system and conducting systematic guard operations.

• The Journal of the Convergence on Culture Technology
• /
• v.8 no.6
• /
• pp.487-492
• /
• 2022

Universities are looking for various methods to enhance educational competence level suitable for the rapidly changing social environment. This study suggests a method to promote academic and educational achievements by reducing drop-out rate from their majors through implementation of pre-survey of satisfaction that revised and complemented survey items. To supplement the CQI method implemented after a general satisfaction survey, a pre-survey of satisfaction was carried out. To consolidate students' competences, this study made prediction and analysis of data with more importance possible using the Random Forest of the machine learning technique that can be applied to AI Medici platform, whose design is underway. By pre-processing the pre-survey of satisfaction, the students information enrolled in classes were defined as an explanatory variable, and they were classified, and a model was created and learning was conducted. For the experimental environment, the algorithms and sklearn library related in Jupyter notebook 3.7.7, Python 3.7 were used together. This study carried out a comparative analysis of change in educational satisfaction survey, carried out after classes, and trends in the drop-out students by reflecting the results of the suggested method in the classes.

• Smart Structures and Systems
• /
• v.31 no.4
• /
• pp.351-363
• /
• 2023

Visual structural inspections are an inseparable part of post-earthquake damage assessments. With unmanned aerial vehicles (UAVs) establishing a new frontier in visual inspections, there are major computational challenges in processing the collected massive amounts of high-resolution visual data. We propose twin deep learning models that can provide accurate high-resolution structural components and damage segmentation masks efficiently. The traditional approach to cope with high memory computational demands is to either uniformly downsample the raw images at the price of losing fine local details or cropping smaller parts of the images leading to a loss of global contextual information. Therefore, our twin models comprising Trainable Resizing for high-resolution Segmentation Network (TRS-Net) and DmgFormer approaches the global and local semantics from different perspectives. TRS-Net is a compound, high-resolution segmentation architecture equipped with learnable downsampler and upsampler modules to minimize information loss for optimal performance and efficiency. DmgFormer utilizes a transformer backbone and a convolutional decoder head with skip connections on a grid of crops aiming for high precision learning without downsizing. An augmented inference technique is used to boost performance further and reduce the possible loss of context due to grid cropping. Comprehensive experiments have been performed on the 3D physics-based graphics models (PBGMs) synthetic environments in the QuakeCity dataset. The proposed framework is evaluated using several metrics on three segmentation tasks: component type, component damage state, and global damage (crack, rebar, spalling). The models were developed as part of the 2^nd International Competition for Structural Health Monitoring.

• KIPS Transactions on Computer and Communication Systems
• /
• v.12 no.6
• /
• pp.189-196
• /
• 2023

Today, in the era of the 4th industrial revolution based on the paradigm of hyper-connectivity, super-intelligence, and superconvergence, the remote work environment is becoming central based on technologies such as mobile, cloud, and big data. This remote work environment has been accelerated by the demand for non-face-to-face due to COVID-19. Since the remote work environment can perform various tasks by accessing services and resources anytime and anywhere, it has increased work efficiency, but has caused a problem of incapacitating the traditional boundary-based network security model by making the internal and external boundaries ambiguous. In this paper, we propse a method to improve the limitations of the traditional boundary-oriented security strategy by building a security model centered on core components and their relationships based on the zero trust idea that all actions that occur in the network beyond the concept of the boundary are not trusted.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
• /
• v.7 no.2
• /
• pp.331-339
• /
• 2017

This paper proposes a thread pool management technique of an websocket server that is applicable to embedded systems. WebSocket is a proposed technique for consisting a dynamic web, and is constructed using HTML5 and jQuery. Various studies have been progressing to construct a dynamic web by Apache, Oracle and etc. Previous web service systems require high-capacity, high-performance hardware specifications and are not suitable for embedded systems. The node.js which is consist of HTML5 and jQuery is a typical websocket server which is made by open sources, and is a java script based web application which is composed of a single thread. The node.js has a limitation on the performance for processing a high velocity data on the embedded system. We make up a multi-thread based websoket server which can solve the mentioned problem. The thread pool is managed by a bit register and suitable for embedded systems. To evaluate the performance of the proposed algorithm, we uses JMeter that is a network test tool.

• Imaging Science in Dentistry
• /
• v.53 no.2
• /
• pp.109-115
• /
• 2023

Purpose: The aim of this study was to evaluate changes in the trabecular bone through texture analysis and compare the texture analysis characteristics of different areas in patients with medication-related osteonecrosis of the jaw (MRONJ). Materials and Methods: Cone-beam computed tomographic images of 16 patients diagnosed with MRONJ were used. In sagittal images, 3 regions were chosen: active osteonecrosis(AO); intermediate tissue (IT), which presented a zone of apparently healthy tissue adjacent to the AO area; and healthy bone tissue (HT) (control area). Texture analysis was performed evaluating 7 parameters: secondary angular momentum, contrast, correlation, sum of squares, inverse moment of difference, sum of entropies, and entropy. Data were analyzed using the Kruskal-Wallis test with a significance level of 5%. Results: Comparing the areas of AO, IT, and HT, significant differences (P<0.05) were observed. The IT and AO area images showed higher values for parameters such as contrast, entropy, and secondary angular momentum than the HT area, indicating greater disorder in these tissues. Conclusion: Through texture analysis, changes in the bone pattern could be observed in areas of osteonecrosis. The texture analysis demonstrated that areas visually identified and classified as IT still had necrotic tissue, thereby increasing the accuracy of delimiting the real extension of MRONJ.

• The Journal of the Convergence on Culture Technology
• /
• v.9 no.3
• /
• pp.835-843
• /
• 2023

Given the structure of XML data, path and tree pattern matching algorithms play an important role in XML query processing. To facilitate decisions or relationships between nodes, nodes in an XML tree are typically labeled in a way that can quickly establish an ancestor-descendant on relationship between two nodes. However, these techniques have the disadvantage of re-labeling existing nodes or recalculating certain values if insertion occurs due to sequential updates. Therefore, in current labeling techniques, the cost of updating labels is very high. In this paper, we propose a new labeling technique called Fast XML encoding, which supports the update of order-sensitive XML documents without re-labeling or recalculation. It also controls the length of the label by reusing deleted labels at the same location in the XML tree. The proposed reuse algorithm can reduce the length of the label when all deleted labels are inserted in the same location. The proposed technique in the experimental results can efficiently handle order-sensitive queries and updates.