• International conference on construction engineering and project management
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• 2022.06a
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• pp.361-369
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• 2022

Unsafe actions and behaviors of workers cause most accidents at construction sites. Nowadays, occupational safety is a top priority at construction sites. However, this problem often requires money and effort from investors or construction owners. Therefore, decreasing the accidents rates of workers and saving monitoring costs for contractors is necessary at construction sites. This study proposes an unsafe behavior detection method based on a skeleton model to classify three common unsafe behaviors on the scaffold: climbing, jumping, and running. First, the OpenPose method is used to obtain the workers' key points. Second, all skeleton datasets are aggregated from the temporary size. Third, the key point dataset becomes the input of the action classification model. The method is effective, with an accuracy rate of 89.6% precision and 90.5% recall of unsafe actions correctly detected in the experiment.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.9
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• pp.4355-4374
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• 2018

Crowd escape event detection has become one of the hottest problems in intelligent surveillance filed. When the 'escape event' occurs, pedestrians will escape in a disordered way with different velocities and directions. Based on these characteristics, this paper proposes a Direction-Collectiveness Model to detect escape event in crowd scenes. First, we extract a set of trajectories from video sequences by using generalized Kanade-Lucas-Tomasi key point tracker (gKLT). Second, a Direction-Collectiveness Model is built based on the randomness of velocity and orientation calculated from the trajectories to express the movement of the crowd. This model can describe the movement of the crowd adequately. To obtain a generalized crowd escape event detector, we adopt an adaptive threshold according to the Direction-Collectiveness index. Experiments conducted on two widely used datasets demonstrate that the proposed model can detect the escape events more effectively from dense crowd.

• Earthquakes and Structures
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• v.23 no.4
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• pp.363-371
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• 2022

Traditional metal dampers have disadvantages such as a higher yield point and inadequate adjustability. The experimental results show that the low yield point steel has superior energy dissipation hysteretic capacity and can be applied to seismic structures. To overcome these deficiencies, a novel compound metal damper comprising both low yield point steel plates and common steel plates is presented. The optimization objectives, including "maximum rigidity" and "full stress state", are proposed to obtain the optimal edge shape of a compound metal damper. The numerical results show that the optimized composite metal damper has the advantages such as full hysteresis curve, uniform stress distribution, more sufficient energy consumption, and it can adjust the yield strength of the damper according to the engineering requirements. In view of the mechanical characteristics of the compound metal damper, the equivalent model of eccentric cross bracing is established, and the approximate analytical solution of the yield strength and the yield displacement is proposed. A nonlinear simulation analysis is carried out for the overall aseismic capacity of three-layer-frame structures with a compound metal damper. It is verified that a compound metal damper has better energy dissipation capacity and superior seismic performance, especially for a damper with double-objective optimized shape.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.8
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• pp.3568-3584
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• 2016

In mobile opportunistic networks (MONs), human-carried mobile devices such as PDAs and smartphones, with the capability of short range wireless communications, could form various intermittent contacts due to the mobility of humans, and then could use the contact opportunity to communicate with each other. The dynamic changes of the network topology are closely related to the human mobility patterns. In this paper, we propose a social motivation-aware mobility model for MONs, which explains the basic laws of human mobility from the psychological point of view. We analyze and model social motivations of human mobility mainly in terms of expectancy value theory and affiliation motivation. Furthermore, we introduce a new concept of geographic functional cells, which not only incorporates the influence of geographical constraints on human mobility but also simplifies the complicated configuration of simulation areas. Lastly, we validate our model by simulating three real scenarios and comparing it with reality traces and other synthetic traces. The simulation results show that our model has a better match in the performance evaluation when applying social-based forwarding protocols like BUBBULE.

• Nuclear Engineering and Technology
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• v.36 no.3
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• pp.229-236
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• 2004

Radiation hardening is a multiscale phenomenon involving various processes over a wide range of time and length. We present a multiscale model for estimating the amount of radiation hardening in pressure vessel steel in the environment of a light water reactor. The model comprises two main parts: molecular dynamics (MD) simulation and a point defect cluster (PDC) model. The MD simulation was used to investigate the primary damage caused by displacement cascades. The PDC model mathematically formulates interactions between point defects and their clusters, which explains the evolution of microstructures. We then used a dislocation barrier model to calculate the hardening due to the PDCs. The key input for this multiscale model is a neutron spectrum at the inner surface of reactor pressure vessel steel of the Younggwang Nuclear Power Plant No.5. A combined calculation from the MD simulation and the PDC model provides a convenient tool for estimating the amount of radiation hardening.

• Journal of Electrical Engineering and Technology
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• v.10 no.6
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• pp.2211-2220
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• 2015

This paper analyzes multiple active management (AM) techniques of active distribution network (ADN), and proposes an optimal coordination model of intermittent distributed generation (IDG) accommodation considering the timing characteristic of load and IDG. The objective of the model is to maximize the daily amount of IDG accommodation under the uncertainties of IDG and load. Various active management techniques such as IDG curtailment, on-load tap changer (OLTC) tap adjusting, voltage regulator (VR) tap adjusting, shunt capacitors compensation and so on are fully considered. Genetic algorithm and Primal-Dual Interior Point Method (PDIPM) is used for the model solving. Point estimate method is used to simulate the uncertainties. Different scenarios are selected for the IDG accommodation capability investigation under different active management schemes. Finally a modified IEEE 123 case is used to testify the proposed accommodation model, the results show that the active management can largely increase the IDG accommodation and penetration.

• International Journal of Automotive Technology
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• v.7 no.1
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• pp.91-99
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• 2006

The diesel Auxiliary Power Unit (APU) for vehicle applications is a complex nonlinear system. For the purpose of this paper presents a dynamic average model of the whole system in an entirely physical way, which accounts for the non-ideal behavior of the diode rectifier, the nonlinear phenomena of generator-rectifier set in an elegant way, and also the dynamics of the dc load and diesel engine. Simulation results show the accuracy of the model. Based on the average model, a simple PI control scheme is proposed for the multivariable system, which includes the steps of model linearization, separate PI controller design with robust tuning rules, stability verification of the overall system by considering it as an uncertain one. Finally it is tested on a detailed switching model and good performances are shown for both set-point following and disturbance rejection.

• Journal of Electrical Engineering and Technology
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• v.12 no.3
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• pp.989-995
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• 2017

As is known to all that the output of wind power generation has a character of randomness and volatility because of the influence of natural environment conditions. At present, the research of wind power prediction mainly focuses on point forecasting, which can hardly describe its uncertainty, leading to the fact that its application in practice is low. In this paper, a wind power range prediction model based on the multiple output property of BP neural network is built, and the optimization criterion considering the information of predicted intervals is proposed. Then, improved Particle Swarm Optimization (PSO) algorithm is used to optimize the model. The simulation results of a practical example show that the proposed wind power range prediction model can effectively forecast the output power interval, and provide power grid dispatcher with decision.

• ETRI Journal
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• v.37 no.4
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• pp.696-706
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• 2015

This paper presents an achievable secure videoconferencing system based on quantum key encryption in which key management can be directly applied and embedded in a server/client videoconferencing model using, for example, OpenMeeting. A secure key management methodology is proposed to ensure both a trusted quantum network and a secure videoconferencing system. The proposed methodology presents architecture on how to share secret keys between key management servers and distant parties in a secure domain without transmitting any secrets over insecure channels. The advantages of the proposed secure key management methodology overcome the limitations of quantum point-to-point key sharing by simultaneously distributing keys to multiple users; thus, it makes quantum cryptography a more practical and secure solution. The time required for the encryption and decryption may cause a few seconds delay in video transmission, but this proposed method protects against adversary attacks.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.8
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• pp.1996-2015
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• 2023

High-definition (HD) maps can provide precise road information that enables an autonomous driving system to effectively navigate a vehicle. Recent research has focused on leveraging semantic segmentation to achieve automatic annotation of HD maps. However, the existing methods suffer from low recognition accuracy in automatic driving scenarios, leading to inefficient annotation processes. In this paper, we propose a novel semantic segmentation method for automatic HD map annotation. Our approach introduces a new encoder, known as the convolutional transformer hybrid encoder, to enhance the model's feature extraction capabilities. Additionally, we propose a multi-level fusion module that enables the model to aggregate different levels of detail and semantic information. Furthermore, we present a novel decoupled boundary joint decoder to improve the model's ability to handle the boundary between categories. To evaluate our method, we conducted experiments using the Bird's Eye View point cloud images dataset and Cityscapes dataset. Comparative analysis against stateof-the-art methods demonstrates that our model achieves the highest performance. Specifically, our model achieves an mIoU of 56.26%, surpassing the results of SegFormer with an mIoU of 1.47%. This innovative promises to significantly enhance the efficiency of HD map automatic annotation.