• Computers and Concrete
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• v.27 no.2
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• pp.99-109
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• 2021

Experimental and discrete element methods were used to investigate the effects of echelon non-persistent joint on the failure behaviour of joint's bridge area under uniaxial compressive test. Concrete samples with dimension of 150 mm×100 mm×50 mm were prepared. Uniaxial compressive strength and tensile strength of concrete were 14 MPa and 1MPa, respectivly. Within the specimen, three echelon non-persistent notches were provided. These joints were distributed on the three diagonal plane. the angle of diagonal plane related to horizontal axis were 15°, 30° and 45°. The angle of joints related to diagonal plane were 30°, 45°, 60°. Totally, 9 different configuration systems were prepared for non-persistent joint. In these configurations, the length of joints were taken as 2 cm. Similar to those for joints configuration systems in the experimental tests, 9 models with different echelon non-persistent joint were prepared in numerical model. The axial load was applied to the model by rate of 0.05 mm/min. the results show that the failure process was mostly governed by both of the non-persistent joint angle and diagonal plane angle. The compressive strengths of the specimens were related to the fracture pattern and failure mechanism of the discontinuities. It was shown that the shear behaviour of discontinuities is related to the number of the induced tensile cracks which are increased by increasing the joint angle. The strength of samples increase by increasing both of the joint angle and diagonal plane angle. The failure pattern and failure strength are similar in both methods i.e. the experimental testing and the numerical simulation methods.

• Journal of Korea Society of Industrial Information Systems
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• v.15 no.5
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• pp.179-185
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• 2010

In a sensitivity analysis, an uncertainty importance measure is often used to assess how much uncertainty of an output is attributable to the uncertainty of an input, and thus, to identify those inputs whose uncertainties need to be reduced to effectively reduce the uncertainty of output. A function is called monotonic if the output is either increasing or decreasing with respect to any of the inputs. In this paper, for a monotonic function, we propose a method for evaluating the measure which assesses the expected percentage reduction in the variance of output due to ascertaining the value of input. The proposed method can be applied to the case that the output is expressed as linear and nonlinear monotonic functions of inputs, and that the input follows symmetric and asymmetric distributions. In addition, the proposed method provides a stable uncertainty importance of each input by discretizing the distribution of input to the discrete distribution. However, the proposed method is computationally demanding since it is based on Monte Carlo simulation.

• Journal of Korea Society of Industrial Information Systems
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• v.28 no.5
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• pp.67-75
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• 2023

This paper deals with the operation of air taxis, which is one of the latest innovative technologies aimed at solving the issue of traffic congestion in cities. A key challenge for the successful introduction of the technology and efficient operation is a vertiport location problem. This paper employs a discrete choice model to calculate choice probabilities of transportation modes for each route, taking into account factors such as cost and travel time associated with different modes. Based on this probability, a mathematical formulation to maximize the utilization rate for air taxi is proposed. However, the proposed model is NP-hard, effective and efficient solution methodology is required. Compared to previous studies that simply proposed the optimization models, this study presents a solution methodology using the cross-entropy algorithm and confirms the effectiveness and efficiency of the algorith through numerical experiments. In addition to the academic excellence of the algorithm, it suggests that decision-making that considers actual data and air taxi utilization plans can increase the practial usability.

• Journal of the Semiconductor & Display Technology
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• v.22 no.3
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• pp.112-118
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• 2023

As technology advances, the need for enhanced preparedness against cyber-attacks becomes an increasingly critical problem. Therefore, it is imperative to consider various circumstances and to prepare for cyber-attack strategic technology. This paper proposes a method to solve network security problems by applying reinforcement learning to cyber-security. In general, traditional static cyber-security methods have difficulty effectively responding to modern dynamic attack patterns. To address this, we implement cyber-attack scenarios such as 'Tiny Alpha' and 'Small Alpha' and evaluate the performance of various reinforcement learning methods using Network Attack Simulator, which is a cyber-attack simulation environment based on the gymnasium (formerly Open AI gym) interface. In addition, we experimented with different RL algorithms such as value-based methods (Q-Learning, Deep-Q-Network, and Double Deep-Q-Network) and policy-based methods (Actor-Critic). As a result, we observed that value-based methods with discrete action spaces consistently outperformed policy-based methods with continuous action spaces, demonstrating a performance difference ranging from a minimum of 20.9% to a maximum of 53.2%. This result shows that the scheme not only suggests opportunities for enhancing cybersecurity strategies, but also indicates potential applications in cyber-security education and system validation across a large number of domains such as military, government, and corporate sectors.

• Smart Media Journal
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• v.12 no.11
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• pp.36-47
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• 2023

Industrial Control System(ICS), which controls facilities at major industrial sites, is increasingly connected to other systems through networks. With this integration and the development of intelligent attacks that can lead to a single external intrusion as a whole system paralysis, the risk and impact of security on industrial control systems are increasing. As a result, research on how to protect and detect cyber attacks is actively underway, and deep learning models in the form of unsupervised learning have achieved a lot, and many abnormal detection technologies based on deep learning are being introduced. In this study, we emphasize the application of preprocessing methodologies to enhance the anomaly detection performance of deep learning models on time series data. The results demonstrate the effectiveness of a Wavelet Transform (WT)-based noise reduction methodology as a preprocessing technique for deep learning-based anomaly detection. Particularly, by incorporating sensor characteristics through clustering, the differential application of the Dual-Tree Complex Wavelet Transform proves to be the most effective approach in improving the detection performance of cyber attacks.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.12
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• pp.74-79
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• 2017

The fire extinguishing performance of hybrid nozzle systems is improved by injecting an extinguishing agent concentrically into the target site and, in this study, water mist is used as a water curtain to confine the droplets of the agent. In this study, we numerically investigated the effect of the foundation angle and injection pressure on the performance of a hybrid nozzle by evaluating the mean radius of the volume fractions of the agent and water mists. An experiment involving a water mist nozzle was carried out to validate the numerical method and then the droplet behaviors, e.g., stochastic collision, coalescence and breakup, were calculated with 2-way interaction Discrete Particle Modeling (DPM) in the steady state for the hybrid nozzle system. The mean radius of the water mists increased by about 40 %, whereas that of the agent decreased by about 21 %, when the injection pressure was increased from 30 bar to 60 bar. In addition, the mean radius of the agent increased by about 24 % as the foundation angle of the hybrid nozzle head increased from $30^{\circ}$ to $60^{\circ}$. As a result, it can be inferred that the injection angle and pressure are important factors for hybrid water mist designs.

• Journal of Navigation and Port Research
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• v.27 no.1
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• pp.97-102
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• 2003

Wire ropes are used in a myriad of various industrial applications such as elevator, mine hoist, construction machinery, lift, and suspension bridge. Especially, the wire rope of crane is important component to container transfer. If it happens wire rope failures during the operation, it may lead to safety accident, economic loss by productivity decline and so on. To solve this problem, we developed remote wire rope fault detecting system, and this system is consisted of 3 parts that portable fault detecting part, signal processing part and remote monitoring part. All detected signal has external noise or disturbance according to circumstances. So, we applied to discrete wavelet transform to extract a signal from noisy data. It is verified that the detecting system by de-noising has good efficiency for inspecting faults of wire ropes in service. As a result, by developing this system, container terminal could reduce expense because of extension fo wire ropes exchange period and could competitive power. Also, this system is possible to apply in several field such as elevator, lift and so on.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.7
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• pp.22-30
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• 2015

For long-term evolution (LTE) MIMO transmission, codebooks are used to utilize the estimated channel information under the limited feedeback environment, and related study has been actively performed. Existing codebooks include codevectos constructed based on vector quantization (VQ) and discrete Fourier transform (DFT), and the LTE standard specifies codebooks modified from these examples to support up to 8 transmit antennas. As the number of antennas increases and as the spatial channel model is used as a standard environment to evaluate the LTE transmission performance, new beamforming methods as well as codebook designs are needed. In this paper, we implement the 3-dimensional spatial channel model (3D-SCM) to analyze the key statistical characteristics of the generated channel, and present efficient ways of determining corresponding codebooks. In particular, we propose a nonuniform-phase DFT-based codebook to improve the existing uniform-phase DFT-based codebook, and evaluate its performance under the given SCM transmission environment. There exists a strong tendancy in statistical distributions of the phase difference between adjacent antenna elements for the SCM, which can be appropriately exploited in codebook design to produce a performance gain over the existing design.

• Journal of Internet Computing and Services
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• v.22 no.2
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• pp.29-39
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• 2021

As damages to individuals, private sectors, and businesses increase due to newly occurring cyber attacks, the underlying network security problem has emerged as a major problem in computer systems. Therefore, NIDS using machine learning and deep learning is being studied to improve the limitations that occur in the existing Network Intrusion Detection System. In this study, a deep learning-based NIDS model study is conducted using the Convolution Neural Network (CNN) algorithm. For the image classification-based CNN algorithm learning, a discrete algorithm for continuity variables was added in the preprocessing stage used previously, and the predicted variables were expressed in a linear relationship and converted into easy-to-interpret data. Finally, the network packet processed through the above process is mapped to a square matrix structure and converted into a pixel image. For the performance evaluation of the proposed model, NSL-KDD, a representative network packet data, was used, and accuracy, precision, recall, and f1-score were used as performance indicators. As a result of the experiment, the proposed model showed the highest performance with an accuracy of 85%, and the harmonic mean (F1-Score) of the R2L class with a small number of training samples was 71%, showing very good performance compared to other models.

• Knowledge Management Research
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• v.22 no.4
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• pp.27-40
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• 2021

Click-Through Rate (CTR) prediction is a key function that determines the ranking of candidate items in the recommendation system and recommends high-ranking items to reduce customer information overload and achieve profit maximization through sales promotion. The fields of natural language processing and image classification are achieving remarkable growth through the use of deep neural networks. Recently, a transformer model based on an attention mechanism, differentiated from the mainstream models in the fields of natural language processing and image classification, has been proposed to achieve state-of-the-art in this field. In this study, we present a method for improving the performance of a transformer model for CTR prediction. In order to analyze the effect of discrete and categorical CTR data characteristics different from natural language and image data on performance, experiments on embedding regularization and transformer normalization are performed. According to the experimental results, it was confirmed that the prediction performance of the transformer was significantly improved when the L2 generalization was applied in the embedding process for CTR data input processing and when batch normalization was applied instead of layer normalization, which is the default regularization method, to the transformer model.