• Journal of the Korean Society of Manufacturing Process Engineers
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• v.19 no.12
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• pp.80-86
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• 2020

With the exception of welding activities, it is forbidden to use electricity in shipyards, owing to safety concerns such as the possibility of fire, explosions, and short circuits. In this paper, an automatic chamfering machine using pneumatics is proposed for use in such environments. Customers specify their requirements and the machine derives the corresponding theoretical design conditions. The proposed machine was used to perform 3D modeling, and its suitability and performance were confirmed via cutting experiments of the manufactured device. Two types of sensors may be used in this system: contact and non-contact. In the case of the contact type, an end-stop switch that can recognize the end of the material is installed, and when the machine reaches the end of the material, the end-stop switch is operated to cut off the air pressure. In the non-contact type, four sensors were used: photonic, ultrasonic, metal detection, and encoder. The use of the four sensors was repeated 30 times, and the average error determined. Thus, the optimum sensor was identified.

• Korean Journal of Metals and Materials
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• v.47 no.1
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• pp.7-12
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• 2009

The detailed quantitative microstructural data on the cracking of coarse constituent particles in 7075 (T651) series wrought Al-alloys have been studied using the utility of a novel digital image processing technique, where the particle cracks are generated due to monotonic loading. The microstructural parameters such as number density, volume fraction, size distribution, first nearest neighbor distribution, and two-point correlation function have been quantitatively characterized using the developed technique and such data are very useful to verify and study the theoretical models for the damage evolution and fracture of Al-alloys. The data suggests useful relationships for damage modeling such as a linear relationship between particle cracking and strain exists for the uniaxial tensile loading condition, where the larger particles crack preferentially.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.11
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• pp.4395-4412
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• 2020

The multisource image fusion has become an active topic in the last few years owing to its higher segmentation rate. To enhance the accuracy of multimodal pig-body feature segmentation, a multisource image fusion method was employed. Nevertheless, the conventional multisource image fusion methods can not extract superior contrast and abundant details of fused image. To superior segment shape feature and detect temperature feature, a new multisource image fusion method was presented and entitled as NSST-GF-IPCNN. Firstly, the multisource images were resolved into a range of multiscale and multidirectional subbands by Nonsubsampled Shearlet Transform (NSST). Then, to superior describe fine-scale texture and edge information, even-symmetrical Gabor filter and Improved Pulse Coupled Neural Network (IPCNN) were used to fuse low and high-frequency subbands, respectively. Next, the fused coefficients were reconstructed into a fusion image using inverse NSST. Finally, the shape feature was extracted using automatic threshold algorithm and optimized using morphological operation. Nevertheless, the highest temperature of pig-body was gained in view of segmentation results. Experiments revealed that the presented fusion algorithm was able to realize 2.102-4.066% higher average accuracy rate than the traditional algorithms and also enhanced efficiency.

• Journal of Platform Technology
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• v.8 no.1
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• pp.3-9
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• 2020

In Korea, 63% of the nation's land is made up of forests, and the average temperature of the earth has been increasing. Forest service has been operating a proactive control system for preventing the spread of forest pests such as Pine wilt disease. but there were some hurdles in timely control due to weather, topography and manpower management difficulties. In this paper, we propose a model for building fast, accurate and efficient control system by categorizing the damage and dead wood automatically based on the images acquired using small unmanned aerial vehicles based on information and communication technology. In particular, the proposed model establishes an effective response system for government affairs through cooperation in the private sector. It can also create new jobs in the unmanned aerial vehicle business and service industries.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.6
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• pp.1023-1030
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• 2020

Wearable devices, such as a smart watch and a wrist band, store owner's private information in the devices so that security in a high level is required. Applications developed by third parties in Tizen request for an access to designated services through the desktop bus (D-Bus). The D-Bus verifies application's privileges to grant the request for an access. We developed a fuzzing tool, so-called DAN (the D-bus ANalyzer), to detect errors in implementations for privilege verifications and access controls within Tizen's system services. The DAN has found a number of vulnerable services which granted accesses to unauthorized applications. We built a proof-of-concept application based on those findings to demonstrate a bypass in the privilege examination.

• Korean Journal of Remote Sensing
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• v.38 no.1
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• pp.45-56
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• 2022

The Ieodo Ocean Research Station (IORS) lies between the exclusive economic zone (EEZ) boundaries of Korea, Japan, and China. The geographical positioning of the IORS makes it ideal for monitoring ships in the area. In this study, we introduce ship monitoring results by Automatic Identification System (AIS) and the Broadband 3G^TM radar, which has been developed for use in small ships using the Frequency Modulated Continuous Wave (FMCW) technique. AIS and FMCW radar data were collected at IORS from November 23th to 30th, 2013. The acquired FMCW radar data was converted to 2-D binary image format over pre-processing, including the internal and external noise filtering. The ship positions detected by FMCW radar images were passed into a tracking algorithm. We then compared the detection and tracking results from FMCW radar with AIS information and found that they were relatively well matched. Tracking performance is especially good when ships are across from each other. The results also show good monitoring capability for small fishing ships, even those not equipped with AIS or with a dysfunctional AIS.

• Imaging Science in Dentistry
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• v.52 no.1
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• pp.27-32
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• 2022

Purpose: The aim of this study was to compare mastoid air cell volumes in patients with or without a pneumatized articular tubercle (PAT) on cone-beam computed tomography (CBCT) images. Materials and Methods: The CBCT images of 224 patients were retrospectively analyzed for the presence of PAT. The Digital Imaging and Communications in Medicine data of 30 patients with PAT and 30 individuals without PAT were transferred to 3D Doctor Software. Mastoid air cell volumes were measured using semi-automatic segmentation on axial sections. Data were analyzed using SPSS version 20.0. Results: The patients with PAT and those without PAT had a mean mastoid volume of 6.31±2.86 cm³ and 3.25±1.99 cm³, respectively. There were statistically significant differences in mastoid air cell volumes between patients with and without PAT regardless of sex and mastoid air cell side (P<0.05). Conclusion: The detection of PAT on routine dental radiographic examinations might be a potential prognostic factor that could be used to detect extensive pneumatization in the temporal bone. Clinicians should be aware that there may be widespread pneumatization of mastoid air cells in patients in whom PAT is detected. Advanced imaging should be performed in these cases, and possible complications due to surgical interventions should be considered.

• Journal of Korea Society of Industrial Information Systems
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• v.26 no.6
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• pp.11-16
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• 2021

In this paper, a quality inspection system was developed to identify the defective assembly of connectors used in automobile wiring. For waterproof connectors, an internal seal must be inserted for waterproofing. However, there are cases where it is omitted or double-inserted during production. An automatic inspection jig was designed using photosensors and touch switches to classify good and bad connector components. In the case of the existing visual inspection, 6,400 connectors were inspected when 5 people inspected for 8 hours. However, when using the inspection jig developed under the same conditions, 20,000 pieces were inspected. In other words, the productivity is greatly improved compared to the conventional visual inspection.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.30 no.2
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• pp.34-43
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• 2022

In Efforts to prevent CFIT accidents so far, have been emphasizing various education measures to minimize the occurrence of human errors, as well as enforcement measures. However, current engineering measures remain in a system (TAWS) that gives warnings before colliding with ground or obstacles, and even actual automatic avoidance maneuvers are not implemented, which has limitations that cannot prevent accidents caused by human error. Currently, various attempts are being made to apply machine learning-based artificial intelligence agent technologies to the aviation safety field. In this paper, we propose a deep reinforcement learning-based artificial intelligence agent that can recognize CFIT situations and control aircraft to avoid them in the simulation environment. It also describes the composition of the learning environment, process, and results, and finally the experimental results using the learned agent. In the future, if the results of this study are expanded to learn the horizontal and vertical terrain radar detection information and camera image information of radar in addition to the terrain database, it is expected that it will become an agent capable of performing more robust CFIT autonomous avoidance.

• Wind and Structures
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• v.34 no.6
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• pp.511-523
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• 2022

Detecting the icing on wind turbine blades built-in cold regions with conventional methods is always a very laborious, expensive and very difficult task. Regarding this issue, the use of smart systems has recently come to the agenda. It is quite possible to eliminate this issue by using the deep learning method, which is one of these methods. In this study, an application has been implemented that can detect icing on wind turbine blades images with visualization techniques based on deep learning using images. Pre-trained models of Resnet-50, VGG-16, VGG-19 and Inception-V3, which are well-known deep learning approaches, are used to classify objects automatically. Grad-CAM, Grad-CAM++, and Score-CAM visualization techniques were considered depending on the deep learning methods used to predict the location of icing regions on the wind turbine blades accurately. It was clearly shown that the best visualization technique for localization is Score-CAM. Finally, visualization performance analyses in various cases which are close-up and remote photos of a wind turbine, density of icing and light were carried out using Score-CAM for Resnet-50. As a result, it is understood that these methods can detect icing occurring on the wind turbine with acceptable high accuracy.