• International Journal of Computer Science & Network Security
• /
• v.22 no.9
• /
• pp.175-182
• /
• 2022

In our daily life, we come across different types of information, for example in the format of multimedia and text. We all need different types of information for our common routines as watching/reading the news, listening to the radio, and watching different types of videos. However, sometimes we could run into problems when a certain type of information is required. For example, someone is listening to the radio and wants to listen to jazz, and unfortunately, all the radio channels play pop music mixed with advertisements. The listener gets stuck with pop music and gives up searching for jazz. So, the above example can be solved with an automatic audio classification system. Deep Learning (DL) models could make human life easy by using audio classifications, but it is expensive and difficult to deploy such models at edge devices like nano BLE sense raspberry pi, because these models require huge computational power like graphics processing unit (G.P.U), to solve the problem, we proposed DL model. In our proposed work, we had gone for a low complexity model for Audio Event Detection (AED), we extracted Mel-spectrograms of dimension 128×431×1 from audio signals and applied normalization. A total of 3 data augmentation methods were applied as follows: frequency masking, time masking, and mixup. In addition, we designed Convolutional Neural Network (CNN) with spatial dropout, batch normalization, and separable 2D inspired by VGGnet [1]. In addition, we reduced the model size by using model quantization of float16 to the trained model. Experiments were conducted on the updated dataset provided by the Detection and Classification of Acoustic Events and Scenes (DCASE) 2020 challenge. We confirm that our model achieved a val_loss of 0.33 and an accuracy of 90.34% within the 132.50KB model size.

• Journal of Biomedical Engineering Research
• /
• v.44 no.2
• /
• pp.147-156
• /
• 2023

Efforts to employ smart home sensors to monitor the indoor activities of elderly single residents have been made to assess the feasibility of a safe and healthy lifestyle. However, the bathroom remains an area of blind spot. In this study, we have developed and evaluated a new edge computer device that can automatically detect water usage activities in the bathroom and record the activity log on a cloud server. Three kinds of sound as flushing, showering, and washing using wash basin generated during water usage were recorded and cut into 1-second scenes. These sound clips were then converted into a 2-dimensional image using MEL-spectrogram. Sound data augmentation techniques were adopted to obtain better learning effect from smaller number of data sets. These techniques, some of which are applied in time domain and others in frequency domain, increased the number of training data set by 30 times. A deep learning model, called CRNN, combining Convolutional Neural Network and Recurrent Neural Network was employed. The edge device was implemented using Raspberry Pi 4 and was equipped with a condenser microphone and amplifier to run the pre-trained model in real-time. The detected activities were recorded as text-based activity logs on a Firebase server. Performance was evaluated in two bathrooms for the three water usage activities, resulting in an accuracy of 96.1% and 88.2%, and F1 Score of 96.1% and 87.8%, respectively. Most of the classification errors were observed in the water sound from washing. In conclusion, this system demonstrates the potential for use in recording the activities as a lifelog of elderly single residents to a cloud server over the long-term.

• Imaging Science in Dentistry
• /
• v.39 no.3
• /
• pp.149-156
• /
• 2009

Purpose : The purpose of this study was to develop a stent-based image guided surgery system and to apply it to oral and maxillofacial surgeries for anatomically complex sites. Materials and Methods : We devised a patient-specific stent for patient-to-image registration and navigation. Three-dimensional positions of the reference probe and the tool probe were tracked by an optical camera system and the relative position of the handpiece drill tip to the reference probe was monitored continuously on the monitor of a PC. Using 8 landmarks for measuring accuracy, the spatial discrepancy between CT image coordinate and physical coordinate was calculated for testing the normality. Results : The accuracy over 8 anatomical landmarks showed an overall mean of $0.56{\pm}0.16\;mm$. The developed system was applied to a surgery for a vertical alveolar bone augmentation in right mandibular posterior area and possible interior alveolar nerve injury case of an impacted third molar. The developed system provided continuous monitoring of invisible anatomical structures during operation and 3D information for operation sites. The clinical challenge showed sufficient accuracy and availability of anatomically complex operation sites. Conclusion : The developed system showed sufficient accuracy and availability in oral and maxillofacial surgeries for anatomically complex sites.

• Journal of Distribution Science
• /
• v.14 no.4
• /
• pp.67-79
• /
• 2016

Purpose - Over recent years, we have concerned about safety and quality on food products because of delivery complexity. The dependence of foreign food products escalate supply of products. And there are often negligent accident of marine and agricultural products. Therefore, the complexity increases the importance of safety on food and information quality for consumers. In spite of the interest augmentation of various interested parties, there is decrease in reliability and effectiveness, if it would be established without the right directivity. For the study, we tried to examine the first considerations the point of - view in service environment and information quality with accepting and diffusing the Traceability System. Then, we tried to verify the relationships between the factors of TS and the determinants of behavior decision. Next, we made efforts to find the mutual relationship among distributors, producers, consumers and the other prerequisite factors from the point of view in service environment and information quality in order to operate effectively the information perspective and system. Research design, data, and methodology - For the purpose of this study, the samples of consumers were targeted to Traceability System, and 661 people have been investigated. Through theoretical discussion of previous research, nine hypotheses were established, the influence of Continuous User Intention in TS. In order to test the hypotheses, a survey had conducted for 661 consumers as opinion leaders in their 20s-60s as data, and structural equation modeling was used. The difference analysis between Agricultural and Marine Products in TS; SPSS 22.0 and AMOS 22.0 were used for statistical analysis. Results - The major findings from this study were as follow; all factors of information quality excluding completeness and a social-impact had effects on the ease of use; all factors excluding understand ability in information quality and a social-impact had effects on the usefulness; completeness and social-impact had effects on perceived value; the ease of use had effects on usefulness and perceived value; usefulness had effects on perceived value and the intention of continuous use. From the results of different analysis, the CPLT(Completeness) factor has positive effects on Easy of USE and PV(Perceived Value) strongly in agricultural products. On the other hand, Social Duty has positive effects on Easy of Use strongly in marine products. Conclusion - In the age of information overflowing, TS will be a burden for users if it places too much emphasis upon accessibility. To accept and diffuse TS safely, therefore, Information System should be settled first into initial market formation. In addition, if TS elements are considered in conjunction with information factors and user environment, the acceptance and diffusion of TS would make synergy effect, even better. That is, this study contributes to the acceptance and diffusion of Traceability System. Accordingly, information quality will be settled into initial market formation. Also, social-impact element will be considered in conjunction with information quality's factors, and it will make synergy effect.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.19 no.10
• /
• pp.2307-2314
• /
• 2015

This paper focuses on the generation algorithm of BeiDou pseudorange correction (PRC) and simulation based performance verification for design of Differential Global Navigation Satellite System (DGNSS) reference station and integrity monitor (RSIM) in order to prepare for recapitalization of DGNSS. First of all, it discusses the International standard on DGNSS RSIM, based on the interface control document (ICD) for BeiDou, estimates the satellite position using satellite clock offset and user receiver clock offset, and the system time offset between Global Positioning System (GPS) and BeiDou. Using the performance verification platform interfaced with GNSS (GPS/BeiDou) simulator, it calculates the BeiDou pseudorange corrections , compares the results of position accuracy with GPS/DGPS. As the test results, this paper verified to meet the performance of position accuracy for DGNSS RSIM operation required on Radio Technical Commission for Maritime Services (RTCM) standard.

• Trans-
• /
• v.12
• /
• pp.81-106
• /
• 2022

The possibility of metaverse system to be a catalyst for hyper-connected society will be dependent on the speed of connected technological development and its social utilization in the same manner as AI technology. Putting these technical realization processes in brackets, this paper focus on some philosophical-political issues in connection with cognitive-ecological changes in the future cinema which will be influenced by the complexive techno-socio couples of accelerated development of metaverse system. Generally speaking, essence of metaverse system seems to be the degree of immersion by technical accuracy, but is not true. In perspective of cognitive-ecology, flow degree of a picture or photograph is relied not on 'accuracy of representation' but on its message's contextual link-up. In this aspect, real potentiality of metaverse system shall be understood in the context of cognitive-ecological changes of human brain's multi-intelligence networking abilities(intersection of augmentation-simulation and outside-inside) which will be activated in the new structure of natural-social-technological coupling of metaverse system. These cognitive-ecological potentialities have been partially actualized in the cinematic process of tripod mimesis for the longest time, [real contradiction/conflicts (Mimesis-1) -->fictional solutions of cinema (Mimesis-2) --> selective interpretation of spectator's wish fulfillment (Mimesis-3) --> real change (Mimesis-1')]. Therefore metaverse's real potentiality must be considered to be dependent on the possibility of deepening and extending of cinematic circulation between real seperation/problems and ideal connection/solutions. In this context, advanced metaverse system can be compared as a modern technical version of ideal circulation of physics and metaphysics

• International Journal of High-Rise Buildings
• /
• v.12 no.3
• /
• pp.225-234
• /
• 2023

Even at this point in the era of digital transformation, we are still facing many problems in the safety sector that cannot prevent the occurrence or spread of human casualties. When you are in an unexpected emergency, it is often difficult to respond only with human physical ability. Human casualties continue to occur at construction sites, manufacturing plants, and multi-use facilities used by many people in everyday life. If you encounter a situation where normal judgment is impossible in the event of an emergency at a life site where there are still many safety blind spots, it is difficult to cope with the existing manual guidance method. New variable guidance technology, which combines artificial intelligence and digital twin, can make it possible to prevent casualties by processing large amounts of data needed to derive appropriate countermeasures in real time beyond identifying what safety accidents occurred in unexpected crisis situations. When a simple control method that divides and monitors several CCTVs is digitally converted and combined with artificial intelligence and 3D digital twin control technology, intelligence augmentation (IA) effect can be achieved that strengthens the safety decision-making ability required in real time. With the enforcement of the Serious Disaster Enterprise Punishment Act, the importance of distributing a smart location guidance system that urgently solves the decision-making delay that occurs in safety accidents at various industrial sites and strengthens the real-time decision-making ability of field workers and managers is highlighted. The smart location guidance system that combines artificial intelligence and digital twin consists of AIoT HW equipment, wireless communication NW equipment, and intelligent SW platform. The intelligent SW platform consists of Builder that supports digital twin modeling, Watch that meets real-time control based on synchronization between real objects and digital twin models, and Simulator that supports the development and verification of various safety management scenarios using intelligent agents. The smart location guidance system provides on-site monitoring using IoT equipment, CCTV-linked intelligent image analysis, intelligent operating procedures that support workflow modeling to immediately reflect the needs of the site, situational location guidance, and digital twin virtual fencing access control technology. This paper examines the limitations of traditional fixed passive guidance methods, analyzes global technology development trends to overcome them, identifies the digital transformation properties required to switch to intelligent variable smart location guidance methods, explains the characteristics and components of AI-based public facility smart fire safety integrated system (ISFS).

• Journal of the Korea Society of Computer and Information
• /
• v.25 no.12
• /
• pp.63-71
• /
• 2020

In this paper, we propose a fire detection technology using YOLOv3 and EfficientDet, the most reliable artificial intelligence detection algorithm recently, an alert service that simultaneously transmits four kinds of notifications: text, web, app and e-mail, and an AWS system that links fire detection and notification service. There are two types of our highly accurate fire detection algorithms; the fire detection model based on YOLOv3, which operates locally, used more than 2000 fire data and learned through data augmentation, and the EfficientDet, which operates in the cloud, has conducted transfer learning on the pretrained model. Four types of notification services were established using AWS service and FCM service; in the case of the web, app, and mail, notifications were received immediately after notification transmission, and in the case of the text messaging system through the base station, the delay time was fast enough within one second. We proved the accuracy of our fire detection technology through fire detection experiments using the fire video, and we also measured the time of fire detection and notification service to check detecting time and notification time. Our AI fire detection and notification service system in this paper is expected to be more accurate and faster than past fire detection systems, which will greatly help secure golden time in the event of fire accidents.

• Journal of the Korea institute for structural maintenance and inspection
• /
• v.26 no.2
• /
• pp.28-36
• /
• 2022

As port structures are exposed to various extreme external loads such as wind (typhoons), sea waves, or collision with ships; it is important to evaluate the structural safety periodically. To monitor the port structure, especially the rubber fender, a fender segmentation system using a vision sensor and deep learning method has been proposed in this study. For fender segmentation, a new deep learning network that improves the encoder-decoder framework with the receptive field block convolution module inspired by the eccentric function of the human visual system into the DenseNet format has been proposed. In order to train the network, various fender images such as BP, V, cell, cylindrical, and tire-types have been collected, and the images are augmented by applying four augmentation methods such as elastic distortion, horizontal flip, color jitter, and affine transforms. The proposed algorithm has been trained and verified with the collected various types of fender images, and the performance results showed that the system precisely segmented in real time with high IoU rate (84%) and F1 score (90%) in comparison with the conventional segmentation model, VGG16 with U-net. The trained network has been applied to the real images taken at one port in Republic of Korea, and found that the fenders are segmented with high accuracy even with a small dataset.

• Journal of the Korean Society of Marine Environment & Safety
• /
• v.30 no.2
• /
• pp.147-156
• /
• 2024

The sustainable management and enhancement of marine resources are becoming increasingly important issues worldwide. This study was conducted in response to these challenges, focusing on the development and performance comparison of fish detection and classification models as part of a deep learning-based technique for assessing the effectiveness of marine resource enhancement projects initiated by the Korea Fisheries Resources Agency. The aim was to select the optimal model by training various sizes of YOLOv8-Seg models on a fish image dataset and comparing each performance metric. The dataset used for model construction consisted of 36,749 images and label files of 12 different species of fish, with data diversity enhanced through the application of augmentation techniques during training. When training and validating five different YOLOv8-Seg models under identical conditions, the medium-sized YOLOv8m-Seg model showed high learning efficiency and excellent detection and classification performance, with the shortest training time of 13 h and 12 min, an of 0.933, and an inference speed of 9.6 ms. Considering the balance between each performance metric, this was deemed the most efficient model for meeting real-time processing requirements. The use of such real-time fish detection and classification models could enable effective surveys of marine resource enhancement projects, suggesting the need for ongoing performance improvements and further research.