• Journal of the Korea Computer Graphics Society
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• v.27 no.3
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• pp.65-74
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• 2021

3D reconstruction means that reconstructing the 3D shape of the object in an image and a video. We proposed a progressive occupancy network architecture that can recover not only the overall shape of the object but also the local details. Unlike the original occupancy network, which uses a feature vector embedding information of the whole image, we extract and utilize the different levels of image features depending on the receptive field size. We also propose a novel network architecture that applies the image features sequentially to the decoder blocks in the decoder and improves the quality of the reconstructed 3D shape progressively. In addition, we design a novel decoder block structure that combines the different levels of image features properly and uses them for updating the input point feature. We trained our progressive occupancy network with ShapeNet. We compare its representation power with two prior methods, including prior occupancy network(ONet) and the recent work(DISN) that used different levels of image features like ours. From the perspective of evaluation metrics, our network shows better performance than ONet for all the metrics, and it achieved a little better or a compatible score with DISN. For visualization results, we found that our method successfully reconstructs the local details that ONet misses. Also, compare with DISN that fails to reconstruct the thin parts or occluded parts of the object, our progressive occupancy network successfully catches the parts. These results validate the usefulness of the proposed network architecture.

• Journal of the Korean Recycled Construction Resources Institute
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• v.9 no.3
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• pp.303-310
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• 2021

Cracks in bridges are important factors that indicate the condition of bridges and should be monitored periodically. However, a visual inspection conducted by a human expert has problems in cost, time, and reliability. Therefore, in recent years, researches to apply a deep learning model are started to be conducted. Deep learning requires sufficient data on the situations to be predicted, but bridge crack data is relatively difficult to obtain. In particular, it is difficult to collect a large amount of crack data in a specific situation because the shape of bridge cracks may vary depending on the bridge's design, location, and construction method. This study developed a crack detection model that generates and trains insufficient crack data through a Generative Adversarial Network. GAN successfully generated data statistically similar to the given crack data, and accordingly, crack detection was possible with about 3% higher accuracy when using the generated image than when the generated image was not used. This approach is expected to effectively improve the performance of the detection model as it is applied when crack detection on bridges is required, though there is not enough data, also when there is relatively little or much data f or one class.

• Composites Research
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• v.34 no.1
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• pp.8-15
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• 2021

In the shipbuilding and marine industry, as a technology for reducing the weight of parts to reduce energy and improve operational efficiency of ships is required, a method of applying fibers-reinforced composites which is high-strength lightweight materials, as part materials can be considered. In this study, the possibility of applying fibers-reinforced composites to the pipe support clamps was evaluated to reduce the weight of LNGC. The fibers-reinforced composites were manufactured using carbon fibers and glass fibers as reinforcing fibers. Through the computer simulation program, the properties of the reinforcing materials and the matrix materials of the composites were inversely calculated, and the performance prediction was performed according to the change in the properties of each fiber lamination pattern. In addition, the structural analysis of the clamps according to the thickness of the composites was performed through the finite element analysis program. As a result of the study, it was confirmed that attention is needed in selecting the thickness when applying the fibers-reinforced composites of the clamp for weight reduction. It is considered that it will be easy to change the shape of the structure and change the structure for weight reduction in future supplementary design.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.6
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• pp.1115-1130
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• 2020

Modern vehicles are equipped with a number of ECUs(Electronic Control Units), and ECUs can control vehicles efficiently by communicating each other through CAN(Controller Area Network). However, CAN bus is known to be vulnerable to cyber attacks because of the lack of message authentication and message encryption, and access control. To find these security issues related to vehicle hacking, CAN Fuzzing methods, that analyze the vulnerabilities of ECUs, have been studied. In the existing CAN Fuzzing methods, fuzzing inputs are randomly generated without considering the structure of CAN messages transmitted by ECUs, which results in the non-negligible fuzzing time. In addition, the existing fuzzing solutions have limitations in how to monitor fuzzing results. To deal with the limitations of CAN Fuzzing, in this paper, we propose a Non-Random CAN Fuzzing, which consider the structure of CAN messages and systematically generates fuzzing input values that can cause malfunctions to ECUs. The proposed Non-Random CAN Fuzzing takes less time than the existing CAN Fuzzing solutions, so it can quickly find CAN messages related to malfunctions of ECUs that could be originated from SW implementation errors or CAN DBC(Database CAN) design errors. We evaluated the performance of Non-Random CAN Fuzzing by conducting an experiment in a real vehicle, and proved that the proposed method can find CAN messages related to malfunctions faster than the existing fuzzing solutions.

• Structural Engineering and Mechanics
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• v.83 no.3
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• pp.305-326
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• 2022

To realize the recycling utilization of waste concrete and alleviate the shortage of resources, 11 specimens of steel reinforced recycled concrete (SRRC) filled circular steel tube columns were designed and manufactured in this study, and the cyclic loading tests on the specimens of columns were also carried out respectively. The hysteretic curves, skeleton curves and performance indicators of columns were obtained and analysed in detail. Besides, the finite element model of columns was established through OpenSees software, which considered the adverse effect of recycled coarse aggregate (RA) replacement rates and the constraint effect of circular steel tube on internal RAC. The numerical calculation curves of columns are in good agreement with the experimental curves, which shows that the numerical model is relatively reasonable. On this basis, a series of nonlinear parameters analysis on the hysteretic behaviors of columns were also investigated. The results are as follows: When the replacement rates of RA increases from 0 to 100%, the peak loads of columns decreases by 7.78% and the ductility decreases slightly. With the increase of axial compression ratio, the bearing capacity of columns increases first and then decreases, but the ductility of columns decreases rapidly. Increasing the wall thickness of circular steel tube is very profitable to improve the bearing capacity and ductility of columns. When the section steel ratio increases from 5.54% to 9.99%, although the bearing capacity of columns is improved, it has no obvious contribution to improve the ductility of columns. With the decrease of shear span ratio, the bearing capacity of columns increases obviously, but the ductility decreases, and the failure mode of columns develops into brittle shear failure. Therefore, in the engineering design of columns, the situation of small shear span ratio (i.e., short columns) should be avoided as far as possible. Based on this, the calculation model on the skeleton curves of columns was established by the theoretical analysis and fitting method, so as to determine the main characteristic points in the model. The effectiveness of skeleton curve model is verified by comparing with the test skeleton curves.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.1
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• pp.23-34
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• 2022

With the development of network technology, the application area has also been diversified, and protocols for various purposes have been developed and the amount of traffic has exploded. Therefore, it is difficult for the network administrator to meet the stability and security standards of the network with the existing traditional switching and routing methods. Software Defined Networking (SDN) is a new networking paradigm proposed to solve this problem. SDN enables efficient network management by programming network operations. This has the advantage that network administrators can flexibly respond to various types of attacks. In this paper, we design a threat level management module, an attack detection module, a packet statistics module, and a flow rule generator that collects attack information through the controller and switch, which are components of SDN, and detects attacks based on these attributes of SDN. It proposes a method to block denial of service attacks (DoS) of advanced attackers by programming and applying honeypot. In the proposed system, the attack packet can be quickly delivered to the honeypot according to the modifiable flow rule, and the honeypot that received the attack packets analyzed the intelligent attack pattern based on this. According to the analysis results, the attack detection module and the threat level management module are adjusted to respond to intelligent attacks. The performance and feasibility of the proposed system was shown by actually implementing the proposed system, performing intelligent attacks with various attack patterns and attack levels, and checking the attack detection rate compared to the existing system.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.10
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• pp.323-332
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• 2022

As various services using AI technology are being developed, much attention is being paid to AI service production. Recently, AI technology is acknowledged as one of ICT services, a lot of research is being conducted for general-purpose AI service production. In this paper, I describe the research results in terms of systems for AI service production, focusing on the distribution and production of machine learning models, which are the final steps of general machine learning development procedures. Three different Ubuntu systems were built, and experiments were conducted on the system, using data from 2017 validation COCO dataset in combination of different AI models (RFCN, SSD-Mobilenet) and different communication methods (gRPC, REST) to request and perform AI services through Tensorflow serving. Through various experiments, it was found that the type of AI model has a greater influence on AI service inference time than AI machine communication method, and in the case of object detection AI service, the number and complexity of objects in the image are more affected than the file size of the image to be detected. In addition, it was confirmed that if the AI service is performed remotely rather than locally, even if it is a machine with good performance, it takes more time to infer the AI service than if it is performed locally. Through the results of this study, it is expected that system design suitable for service goals, AI model development, and efficient AI service production will be possible.

• Journal of the Korean GEO-environmental Society
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• v.22 no.6
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• pp.27-32
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• 2021

Due to heavy rainfall and typhoons caused by climate change, it has become common to witness heavy rain that exceeds the design frequency of agricultural reservoirs. This has brought greater attention to the safety of irrigation facilities including agricultural reservoirs. Out of approximately 17,740 reservoirs available in Korea, 83.87% were built before 1970. To ensure the safety of these old reservoirs, their embankments are being repaired and reinforced using various techniques. Among these techniques, using the cement-bentonite cutoff wall makes it possible to construct diaphragm walls with slurry composed of cement and bentonite, while excavation. The advantages of this technique include that it is simple and fast, and ensures the uniformity of cutoff walls by enabling the immediate application of the replacement method to excavation areas; thus excellent performance is guaranteed. However, despite these advantages, the technique is not commonly used in Korea. Thus, this study investigated the changes in strength and permeability by varying the mix ratio of cement and bentonite. As a major experimental results, when the cement of 200 kg/m³ and the bentonite of 60 to 80 kg/m³ is most suitable for the repair and reinforcement of the reservoir embankments.

• Journal of Korea Entertainment Industry Association
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• v.14 no.3
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• pp.431-442
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• 2020

This study examined the effect of the theratainment swiss ball exercise on the upper limb function, pain, and daily activities of a patient with axillary nerve injury. The research duration was from November 5^th, 2019 to February 25^th, 2020. The research subject was a 23-year-old female patient living in the metropolitan city of G in South Korea, and the A-B-A' type of single-subject experimental research design was used. In this study, repeated training was provided to the patient in the form of exercises employing different directions and gradually increased weights. The training increased the structural stability and mobility of the shoulder and was effective for pain relief as it strengthened shoulder function. The training helped the subject improve her posture change adaptability and reaction ability in different environments and ultimately enabled her to increase and maximize her performance of independent daily activities. This study thus demonstrated the positive effect of the Swiss ball exercise on the upper limb function, pain and disability, daily activities of a patient with axillary nerve injury and confirmed the potential of the exercise as an intervention method. Continued investigation to develop and test the effect of the Swiss ball exercise will be required for it to be used professionally as a therapeutic approach by occupational therapists in treating a variety of patients.

• Journal of Aerospace System Engineering
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• v.17 no.1
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• pp.42-50
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• 2023

In this study, hexagonal ferrite powder with chemical formula SrZn_2-xCo_xFe₁₆O₂₇ was synthesized by a solid-state reaction method and its electromagnetic (EM) wave absorption characteristics were evaluated in the frequency range of 0.1-18 GHz with absorber thickness range of 0 - 10 mm. Reflection loss (RL) affecting electromagnetic wave absorption performance was calculated based on the transmission line theory using measured complex permeabilities and permittivities. RL spectra were also directly measured for some samples. They were well matched with calculated results. High-frequency complex permeability characteristics were changed gradually according to the amount of Co substitution (x). The EM wave absorption frequency band could be tuned accordingly. Hexaferrite samples with x = 1.0, 1.25, and 1.5 exhibited remarkable maximum electromagnetic wave absorption performances with minimum RL (RL_min) lowered than -50 dB. They also showed a very broad frequency band (Δf > 10 GHz) in which more than 90% of the EM wave energy absorption occurred (RL ≤ -10 dB).