• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.3
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• pp.16-22
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• 2019

Off-line programming and robot calibration through simulation are essential when setting up a robot in a robot automation production line. In this study, we developed a new robot calibration method to match the CAD data of the production line with the measurement data on the site using 3D scanner. The proposed method calibrates the robot using 3D point cloud data through Iterative Closest Point algorithm. Registration is performed in three steps. First, vertices connected by three planes are extracted from CAD data as feature points for registration. Three planes are reconstructed from the scan point data located around the extracted feature points to generate corresponding feature points. Finally, the transformation matrix is calculated by minimizing the distance between the feature points extracted through the ICP algorithm. As a result of applying the software to the automobile welding robot installation, the proposed method can calibrate the required accuracy to within 1.5mm and effectively shorten the set-up time, which took 5 hours per robot unit, to within 40 minutes. By using the developed system, it is possible to shorten the OLP working time of the car body assembly line, shorten the precision teaching time of the robot, improve the quality of the produced product and minimize the defect rate.

• Geomechanics and Engineering
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• v.17 no.4
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• pp.333-342
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• 2019

Geological dynamic hazards during coal mining can be caused by the failure of a composite system consisting of roof rock and coal layers, subject to different loading rates due to different advancing velocities in the working face. In this paper, the uniaxial compression test simulations on the composite rock-coal layers were performed using $PFC^{2D}$ software and especially the effects of loading rate on the stress-strain behavior, strength characteristics and crack nucleation, propagation and coalescence in a composite layer were analyzed. In addition, considering the composite layer, the mechanisms for the advanced bore decompression in coal to prevent the geological dynamic hazards at a rapid advancing velocity of working face were explored. The uniaxial compressive strength and peak strain are found to increase with the increase of loading rate. After post-peak point, the stress-strain curve shows a steep stepped drop at a low loading rate, while the stress-strain curve exhibits a slowly progressive decrease at a high loading rate. The cracking mainly occurs within coal, and no apparent cracking is observed for rock. While at a high loading rate, the rock near the bedding plane is damaged by rapid crack propagation in coal. The cracking pattern is not a single shear zone, but exhibits as two simultaneously propagating shear zones in a "X" shape. Following this, the coal breaks into many pieces and the fragment size and number increase with loading rate. Whereas a low loading rate promotes the development of tensile crack, the failure pattern shows a V-shaped hybrid shear and tensile failure. The shear failure becomes dominant with an increasing loading rate. Meanwhile, with the increase of loading rate, the width of the main shear failure zone increases. Moreover, the advanced bore decompression changes the physical property and energy accumulation conditions of the composite layer, which increases the strain energy dissipation, and the occurrence possibility of geological dynamic hazards is reduced at a rapid advancing velocity of working face.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.9
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• pp.541-550
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• 2019

An emergency generator is key equipment for fire-fighting to supply power to fire-fighting facilities, which protect property and people in cases of fire accidents. A rated load test for emergency generators must be carried out by connecting an emergency load to the generator in accordance with related regulations. However, a no-load test has been performed for emergency generators in general since serious problems can occur when the main power is cut off, including the damage of customer devices and shut down of critical loads. Therefore, this paper proposes a load test method for an emergency generator using energy storage system (ESS) without the interruption of main power. The emergency power system was also modeled based on PSCAD/EMTDC software, and a 200-kW scale ESS load test device was implemented. The simulation and test results show that the load test method is useful and practical for an emergency power supply system.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.30 no.3
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• pp.223-228
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• 2019

This study analyzes the propagation of the path losses between Jeju-do and Jin-do transceivers located in the coastal areas of Korea using the Advanced Refractive Prediction System(AREPS) simulation software based on the actual coastal weather database. The simulated data is used to construct a duct map according to the altitude and thickness of the trap. The duct map is then divided into several regions depending on the altitude parameters of Tx and Rx, which can be used to effectively estimate the abnormal wave propagation characteristics due to duct occurrence in the Jeju-do coastal area. To validate the proposed duct map, two representative atmospheric index samples of the weather database in May 2018 are selected, and the simulated path losses using these atmospheric indices are compared with the measured data. The simulated path losses for abnormal conditions at the Rx point at Jeju-do are 167.7 dB and 192.3 dB, respectively, which are in good agreement with the measured data of 164.4 dB and 194.9 dB, respectively.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.8
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• pp.287-300
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• 2021

Collaborative Cyber-Physical Systems (CCPS) are those systems that contain tightly coupled physical and cyber components, massively interconnected subsystems, and collaborate to achieve a common goal. The safety of a single Cyber-Physical System (CPS) can be achieved by following the safety standards such as ISO 26262 and IEC 61508 or by applying hazard analysis techniques. However, due to the complex, highly interconnected, heterogeneous, and collaborative nature of CCPS, a fault in one CPS's components can trigger many other faults in other collaborating CPSs. Therefore, a safety assurance technique based on fault criticality analysis would require to ensure safety in CCPS. This paper presents a Fault Criticality Matrix (FCM) implemented in our tool called CPSTracer, which contains several data such as identified fault, fault criticality, safety guard, etc. The proposed FCM is based on composite hazard analysis and content-based relationships among the hazard analysis artifacts, and ensures that the safety guard controls the identified faults at design time; thus, we can effectively manage and control the fault at the design phase to ensure the safe development of CPSs. To justify our approach, we introduce a case study on the Platooning system (a collaborative CPS). We perform the criticality analysis of the Platooning system using FCM in our developed tool. After the detailed fault criticality analysis, we investigate the results to check the appropriateness and effectiveness with two research questions. Also, by performing simulation for the Platooning, we showed that the rate of collision of the Platooning system without using FCM was quite high as compared to the rate of collisions of the system after analyzing the fault criticality using FCM.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.6
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• pp.19-26
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• 2021

The lattice structure is attracting attention from industry because of its excellent strength and stiffness, ultra-lightweight, and energy absorption capability. Despite these advantages, widespread commercialization is limited by the difficult manufacturing processes for complex shapes. Additive manufacturing is attracting attention as an optimal technology for manufacturing lattice structures as a technology capable of fabricating complex geometric shapes. In this study, a unit cell was formed using a three-dimensional coordinate method. The relative density relational equation according to the boundary box size and strut radius of the unit cell was derived. Simple cubic (SC), body-centered cubic (BCC), and face-centered cubic (FCC) with a controlled relative density were designed using modeling software. The accuracy of the equations for calculating the relative density proposed in this study secured 98.3%, 98.6%, and 96.2% reliability in SC, BCC, and FCC, respectively. A simulation of the lattice structure revealed an increase in compressive yield load with increasing relative density under the same cell arrangement condition. The compressive yield load decreased in the order of SC, BCC, and FCC under the same arrangement conditions. Finally, structural optimization for the compressive load of a 20 mm × 20 mm × 20 mm structure was possible by configuring the SC unit cells in a 3 × 3 × 3 array.

• Journal of Internet Computing and Services
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• v.23 no.3
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• pp.13-20
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• 2022

IEEE 802.11ay Wi-Fi is the next generation wireless technology and operates in mmWave band. It supports the MU-MIMO (Multiple User Multiple Input Multiple Output) transmission in which an AP (Access Point) can transmit multiple data streams simultaneously to multiple STAs (Stations). To this end, the AP should perform MU-MIMO beamforming training with the STAs. For efficient MU-MIMO beamforming training, it is important for the AP to estimate signal strength measured at each STA at which multiple beams are used simultaneously. Therefore, in the paper, we propose a deep learning-based link quality estimation scheme. Our proposed scheme estimates the signal strength with high accuracy by utilizing a deep learning model pre-trained for a certain indoor or outdoor propagation scenario. Specifically, to estimate the signal strength of the multiple concurrent beams, our scheme uses the signal strengths of the respective single beams, which can be obtained without additional signaling overhead, as the input of the deep learning model. For performance evaluation, we utilized a Q-D (Quasi-Deterministic) Channel Realization open source software and extensive channel measurement campaigns were conducted with NIST (National Institute of Standards and Technology) to implement the millimeter wave (mmWave) channel. Our simulation results demonstrate that our proposed scheme outperforms comparison schemes in terms of the accuracy of the signal strength estimation.

• The Journal of the Convergence on Culture Technology
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• v.8 no.2
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• pp.367-372
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• 2022

With the recent advent of the New Normal era, realistic technologies and non-contact technologies are receiving social attention. However, the hair styling field focuses on the direction of the hair itself, individual movements, and modeling, focusing on hair simulation. In order to create an improved practice environment and demand of the times, this study proposed a non-contact hair styling VR system. In the theoretical review, we studied the existing cases of hair cut research. Existing haircut-related research tend to be mainly focused on force-based feedback. Research on the interactive haircut work in the virtual environment as addressed in this paper has not been done yet. VR controllers capable of finger tracking the movements necessary for beauty enable selection, cutting, and rotation of beauty tools, and built a non-contact collaboration environment. As a result, we conducted two experiments for interactive hair cutting in VR. First, it is a haircut operation for synchronization using finger tracking and holding hook animation. We made position correction for accurate motion. Second, it is a real-time interactive cutting operation in a multi-user virtual collaboration environment. This made it possible for instructors and learners to communicate with each other through VR HMD built-in microphones and Photon Voice in non-contact situations.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.11
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• pp.449-456
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• 2021

An intrusion detection system is a technology that detects abnormal behaviors that violate security, and detects abnormal operations and prevents system attacks. Existing intrusion detection systems have been designed using statistical analysis or anomaly detection techniques for traffic patterns, but modern systems generate a variety of traffic different from existing systems due to rapidly growing technologies, so the existing methods have limitations. In order to overcome this limitation, study on intrusion detection methods applying various machine learning techniques is being actively conducted. In this study, a comparative study was conducted on data preprocessing techniques that can improve the accuracy of anomaly detection using NGIDS-DS (Next Generation IDS Database) generated by simulation equipment for traffic in various network environments. Padding and sliding window were used as data preprocessing, and an oversampling technique with Adversarial Auto-Encoder (AAE) was applied to solve the problem of imbalance between the normal data rate and the abnormal data rate. In addition, the performance improvement of detection accuracy was confirmed by using Skip-gram among the Word2Vec techniques that can extract feature vectors of preprocessed sequence data. PCA-SVM and GRU were used as models for comparative experiments, and the experimental results showed better performance when sliding window, skip-gram, AAE, and GRU were applied.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.9
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• pp.407-418
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• 2023

The Multi-Object Goal Visual Navigation(MultiOn) is a visual navigation task in which an agent must visit to multiple object goals in an unknown indoor environment in a given order. Existing models for the MultiOn task suffer from the limitation that they cannot utilize an integrated view of multimodal context because use only a unimodal context map. To overcome this limitation, in this paper, we propose a novel deep neural network-based agent model for MultiOn task. The proposed model, MCFMO, uses a multimodal context map, containing visual appearance features, semantic features of environmental objects, and goal object features. Moreover, the proposed model effectively fuses these three heterogeneous features into a global multimodal context map by using a point-wise convolutional neural network module. Lastly, the proposed model adopts an auxiliary task learning module to predict the observation status, goal direction and the goal distance, which can guide to learn the navigational policy efficiently. Conducting various quantitative and qualitative experiments using the Habitat-Matterport3D simulation environment and scene dataset, we demonstrate the superiority of the proposed model.