• Nuclear Engineering and Technology
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• v.54 no.9
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• pp.3293-3298
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• 2022

Removing radioactive contaminated metal materials is a vital task during the decommissioning of nuclear power plants to reduce the cost of the post-dismantling process. The laser decontamination technique has been recognized as a key tool for a successful dismantling process as it enables a remote operation in radioactive facilities. It also minimizes exposure of workers to hazardous materials and reduces secondary waste, increasing the environmental friendless of the post-dismantling processing. In this work, we present a thorough and efficient laser decontamination approach using a single-mode continuous-wave (CW) laser. We subjected stainless steels to a surface-removal process that repetitively exposes the laser to a confined region of ~75 ㎛ at a high scanning rate of 10 m/s. We evaluate the decontamination performance by measuring the removal depth with a 3D scanning microscope and further investigate optimal removal conditions given practical parameters such as the laser power and scan properties. We successfully removed the metal surface to a depth of more than 40 ㎛ with laser power of 300 W and ten scans, showing the potential to achieve an extremely high DF more than 1000 by simply increasing the number of scans and the laser power for the decontamination of primary circuits.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.1
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• pp.334-349
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• 2022

The effectiveness of Wi-Fi networks is greatly influenced by the optimization of contention window (CW) parameters. Unfortunately, the conventional approach employed by IEEE 802.11 wireless networks is not scalable enough to sustain consistent performance for the increasing number of stations. Yet, it is still the default when accessing channels for single-users of 802.11 transmissions. Recently, there has been a spike in attempts to enhance network performance using a machine learning (ML) technique known as reinforcement learning (RL). Its advantage is interacting with the surrounding environment and making decisions based on its own experience. Deep RL (DRL) uses deep neural networks (DNN) to deal with more complex environments (such as continuous state spaces or actions spaces) and to get optimum rewards. As a result, we present a new approach of CW control mechanism, which is termed as contention window threshold (CW_Threshold). It uses the DRL principle to define the threshold value and learn optimal settings under various network scenarios. We demonstrate our proposed method, known as a smart exponential-threshold-linear backoff algorithm with a deep Q-learning network (SETL-DQN). The simulation results show that our proposed SETL-DQN algorithm can effectively improve the throughput and reduce the collision rates.

• Journal of the Korean Society of Industry Convergence
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• v.25 no.5
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• pp.861-868
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• 2022

The purpose of this study is to design an effective atmospheric environment system through the design of the dust collection in the air shot room being operated in a domestic shipyard. The ventilation system in the current air shot room mostly uses a dust collecting filter to filter internal particles and releases them in the atmosphere. A conventional design was made too much. In order to prevent an error and draw an optimal design, Computational fluid dynamics (CFD) tried to be applied only to air shot room. In the advanced design technique, computer simulation was conducted to secure basic design data. In order to find the basic design of the ventilation system and the flow field in the air shot room at propeller mold workplace of a shipyard, the CFD was conducted. In the case of Model-1 as a conventional workplace, where air flows in the inlet due to the subatmospheric pressure generated by inhalation of an air blower and flows out to the outlet, a discharge flow rate was somewhat low, and there was the holdup zone in the room. In the case of Model-2 as an improved model, the ventilation system was improved in the Push-Pull type, and the holdup of the internal flow field was improved.

• Current Optics and Photonics
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• v.6 no.5
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• pp.479-488
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• 2022

To account for the internal thermal effects of solid-state lasers, a method using a back propagation (BP) neural network integrated with a particle swarm optimization (PSO) algorithm is developed, which is a new wavefront distortion correction technique. In particular, by using a slab laser model, a series of fiber pumped sources are employed to form a controlled array to pump the gain medium, allowing the internal temperature field of the gain medium to be designed by altering the power of each pump source. Furthermore, the BP artificial neural network is employed to construct a nonlinear mapping relationship between the power matrix of the pump array and the thermally induced wavefront aberration. Lastly, the suppression of thermally induced wavefront distortion can be achieved by changing the power matrix of the pump array and obtaining the optimal pump light intensity distribution combined using the PSO algorithm. The minimal beam quality β can be obtained by optimally distributing the pumping light. Compared with the method of designing uniform pumping light into the gain medium, the theoretically computed single pass beam quality β value is optimized from 5.34 to 1.28. In this numerical analysis, experiments are conducted to validate the relationship between the thermally generated wavefront and certain pumping light distributions.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.11
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• pp.4122-4144
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• 2021

In this fast-paced technological world, the Internet of Things is a ground breaking technology which finds an immense role in the present electronic world which includes different embedded sensors, devices and most other things which are connected to the Internet. The IoT devices are designed in a way that it helps to collect various forms of data from varied sources and transmit them in digitalized form. In modern era of IoT technology data security is a trending issue which greatly affects the confidentiality of important information. Keeping the issue in mind a novel light encryption strategy known as LCB is designed for IoT devices for optimal security. LCB exploits the benefits of Feistel structure and the architectural benefits of substitution permutation network both to give more security. Moreover, this newly designed technique is tested on (Virtex-7) XC7VX330T FPGA board and it takes much little area of 224 GE (Gate Equivalent) and is extremely fast with very less combinational path delay of 0.877 ns. An in-depth screening confirms the proposed work to promise more security to counter cryptographic attacks. Lastly the Avalanche Effect (AE) of LCB showed as 63.125% and 63.875% when key and plaintext (PT) are taken into consideration respectively.

• The Transactions of the Korean Institute of Power Electronics
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• v.27 no.6
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• pp.471-480
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• 2022

Inductive power transfer (IPT) is an attractive power transmission solution that is already used in many applications. In the IPT system, optimal coil design is essential to achieve high power efficiency, but the effective design method is yet to be investigated. The inductance formula and finite element method (FEM) are popular means to link the coil geometric parameters and circuit parameters; however, the former lacks generality and accuracy, and the latter consumes much computation time. This study proposes a novel coil design method to achieve speed and generality without much loss of accuracy. By introducing one-turn permeance simulation in each FEM phase combined with curve fitting and optimization by MATLAB in the efficiency calculation phase, the iteration number of FEM can be considerably reduced, and the generality can be retained. The proposed method is verified through a 100 W IPT system experiment.

• Wind and Structures
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• v.35 no.3
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• pp.157-175
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• 2022

This paper highlights the minimization of drag and lift coefficient of different types both side setback tall buildings by the multi-objective optimization technique. The present study employed 48 number both-side setback models for simulation purposes. This study adopted three variables to find the two objective functions. Setback height and setback distances from the top of building models are considered variables. The setback distances are considered between 10-40% and setback heights are within 6-72% from the top of the models. Another variable is wind angles, which are considered from 0° to 90° at 15° intervals according to the symmetry of the building models. Drag and lift coefficients according to the different wind angles are employed as the objective functions. Therefore 336 number population data are used for each objective function. Optimum models are compared with computational simulation and found good agreements of drag and lift coefficient. The design wind angle variation of the optimum models is considered for drag and lift study on the main square model. The drag and lift data of the square model are compared with the optimum models and found the optimized models are minimizing the 45-65% drag and 25-60% lift compared to the initial square model.

• Geomechanics and Engineering
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• v.28 no.1
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• pp.1-10
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• 2022

In Korea, accidents related to the collapse of deteriorated aging reservoirs occur every year. The grouting method is generally applied to reinforce an aging reservoir. However, when using this method, different reinforcing effects appear depending on the ground conditions. Thus, new construction methods and materials capable of providing consistent reinforcing effects are required. In this study, the direct shear test (DST), model test, and simulation analysis were performed to evaluate the impact of surface stabilizers, generally used to reinforce roads, rivers, and slopes of roads, applied using surface curtain walls on aging reservoirs. The DST results indicate that when the surface stabilizer was mixed with in-situ soil, the increase in cohesion was the highest at a mixing ratio of 9%. No changes in the friction angle were evident; therefore, 9% was determined to be the optimal mixing ratio. In addition, the model test and simulation analysis showed that when 9% of the surface stabilizer was mixed and applied to the aging reservoir, the seepage quantity of water and the saturated area were reduced by approximately 42% and 73%, respectively. Moreover, the comprehensive analysis of results showed that the grouting method could be completely replaced by surface stabilizers applied through surface curtain walls because the technique could secure stability by decreasing the seepage in the aging reservoir.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.6
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• pp.1035-1043
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• 2022

With the spread of the Internet of Things (IoT), cloud computing, and big data, the need for high-speed encryption for applications is emerging. GPU optimization can be used to validate cryptographic analysis results or reduced versions theoretically obtained by the GPU in a reasonable time. In this paper, PIPO lightweight encryption implemented in various environments was implemented on GPU. Optimally implemented considering the brute force attack on PIPO. In particular, the optimization implementation applying the bit slicing technique and the GPU elements were used as much as possible. As a result, the implementation of the proposed method showed a throughput of about 19.5 billion per second in the RTX 3060 environment, achieving a throughput of about 122 times higher than that of the previous study.

• The Journal of Korea Robotics Society
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• v.17 no.3
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• pp.264-272
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• 2022

Human-robot cooperative tasks are increasingly required in our daily life with the development of robotics and artificial intelligence technology. Interactive reinforcement learning strategies suggest that robots learn task by receiving feedback from an experienced human trainer during a training process. However, most of the previous studies on Interactive reinforcement learning have required an extra feedback input device such as a mouse or keyboard in addition to robot itself, and the scenario where a robot can interactively learn a task with human have been also limited to virtual environment. To solve these limitations, this paper studies training strategies of robot that learn table balancing tasks interactively using deep reinforcement learning with human's facial expression feedback. In the proposed system, the robot learns a cooperative table balancing task using Deep Q-Network (DQN), which is a deep reinforcement learning technique, with human facial emotion expression feedback. As a result of the experiment, the proposed system achieved a high optimal policy convergence rate of up to 83.3% in training and successful assumption rate of up to 91.6% in testing, showing improved performance compared to the model without human facial expression feedback.