• Structural Engineering and Mechanics
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• v.84 no.3
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• pp.323-335
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• 2022

The main idea of the framework is to seamlessly combine a reasonably accurate and fast surrogate model with the importance sampling strategy. Developing a surrogate model for predicting structures' dynamic responses is challenging because it involves high-dimensional inputs and outputs. For this purpose, a novel surrogate model based on cutting-edge deep learning architectures specialized for capturing temporal relationships within time-series data, namely Long-Short term memory layer and Transformer layer, is designed. After being properly trained, the surrogate model could be utilized in place of the finite element method to evaluate structures' responses without requiring any specialized software. On the other hand, the importance sampling is adopted to reduce the number of calculations required when computing the failure probability by drawing more relevant samples near critical areas. Thanks to the portability of the trained surrogate model, one can integrate the latter with the Importance sampling in a straightforward fashion, forming an efficient framework called TTIS, which represents double advantages: less number of calculations is needed, and the computational time of each calculation is significantly reduced. The proposed approach's applicability and efficiency are demonstrated through three examples with increasing complexity, involving a 1D beam, a 2D frame, and a 3D building structure. The results show that compared to the conventional Monte Carlo simulation, the proposed method can provide highly similar reliability results with a reduction of up to four orders of magnitudes in time complexity.

• Journal of IKEEE
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• v.27 no.4
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• pp.439-449
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• 2023

In an era marked by the increasing use of drones and the growing demand for indoor surveillance, the development of a robust application for detecting and tracking both drones and humans within indoor spaces becomes imperative. This study presents an innovative application that uses FMCW radar to detect human and drone motions from the cloud point. At the outset, the DBSCAN (Density-based Spatial Clustering of Applications with Noise) algorithm is utilized to categorize cloud points into distinct groups, each representing the objects present in the tracking area. Notably, this algorithm demonstrates remarkable efficiency, particularly in clustering drone point clouds, achieving an impressive accuracy of up to 92.8%. Subsequently, the clusters are discerned and classified into either humans or drones by employing a deep learning model. A trio of models, including Deep Neural Network (DNN), Residual Network (ResNet), and Long Short-Term Memory (LSTM), are applied, and the outcomes reveal that the ResNet model achieves the highest accuracy. It attains an impressive 98.62% accuracy for identifying drone clusters and a noteworthy 96.75% accuracy for human clusters.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.10
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• pp.2701-2717
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• 2023

In this paper, a hybrid fuzzy-based method is suggested for determining India's best system for power generation. This suggested approach was created using a fuzzy-based combination of the Giza Pyramids Construction (GPC) and Recalling-Enhanced Recurrent Neural Network (RERNN). GPC is a meta-heuristic algorithm that deals with solutions for many groups of problems, whereas RERNN has selective memory properties. The evaluation of the current load requirements and production profile information system is the main objective of the suggested method. The Central Electricity Authority database, the Indian National Load Dispatch Centre, regional load dispatching centers, and annual reports of India were some of the sources used to compile the data regarding profiles of electricity loads, capacity factors, power plant generation, and transmission limits. The RERNN approach makes advantage of the ability to analyze the ideal power generation from energy data, however the optimization of RERNN factor necessitates the employment of a GPC technique. The proposed method was tested using MATLAB, and the findings indicate that it is effective in terms of accuracy, feasibility, and computing efficiency. The suggested hybrid system outperformed conventional models, achieving the top result of 93% accuracy with a shorter computation time of 6814 seconds.

• International Journal of Advanced Culture Technology
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• v.11 no.4
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• pp.393-405
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• 2023

Accurate prediction of wind speed and power is vital for enhancing the efficiency of wind energy systems. Numerous solutions have been implemented to date, demonstrating their potential to improve forecasting. Among these, deep learning is perceived as a revolutionary approach in the field. However, despite their effectiveness, the noise present in the collected data remains a significant challenge. This noise has the potential to diminish the performance of these algorithms, leading to inaccurate predictions. In response to this, this study explores a novel feature engineering approach. This approach involves altering the data input shape in both Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) and Autoregressive models for various forecasting horizons. The results reveal substantial enhancements in model resilience against noise resulting from step increases in data. The approach could achieve an impressive 83% accuracy in predicting unseen data up to the 24th steps. Furthermore, this method consistently provides high accuracy for short, mid, and long-term forecasts, outperforming the performance of individual models. These findings pave the way for further research on noise reduction strategies at different forecasting horizons through shape-wise feature engineering.

• Advances in environmental research
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• v.12 no.2
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• pp.77-93
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• 2023

The water can be contaminated by natural sources or by industrial effluents. One such contaminant is fluoride. Fluoride contamination in the water environment due to natural and artificial activities has been recognized as one of the major problems worldwide. Among the commonly used treatment technologies applied for fluoride removal, the adsorption technique has been explored widely and offers a highly efficient simple and low-cost process for fluoride removal from water. This review paper the recent developments in fluoride removal from surface water by adsorption methods. Studies on fluoride removal from aqueous solutions using various carbon materials are reviewed. Various adsorbents with high fluoride removal capacity have been developed, however, there is still an urgent need to transfer the removal process to an industrial scale. Regeneration studies need to be performed to more extent to recover the adsorbent in field conditions, enhancing the economic feasibility of the process. Based on the review, technical strategies of the adsorption method including the Nano-surface effect, structural memory effect, anti-competitive adsorption and ionic sieve effect can be proposed. The design of adsorbents through these strategies can greatly improve the removal efficiency of fluoride in water and guide the development of new efficient methods for fluoride removal in the future. This paper describes brief discussions on various low-cost adsorbents used for the effective removal of fluoride from water.

• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1107-1118
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• 2021

Climate change brought on by global warming increased the frequency of flood and drought on the Korean Peninsula, along with the casualties and physical damage resulting therefrom. Preparation and response to these water disasters requires national-level planning for water resource management. In addition, watershed-level management of water resources requires flow duration curves (FDC) derived from continuous data based on long-term observations. Traditionally, in water resource studies, physical rainfall-runoff models are widely used to generate duration curves. However, a number of recent studies explored the use of data-based deep learning techniques for runoff prediction. Physical models produce hydraulically and hydrologically reliable results. However, these models require a high level of understanding and may also take longer to operate. On the other hand, data-based deep-learning techniques offer the benefit if less input data requirement and shorter operation time. However, the relationship between input and output data is processed in a black box, making it impossible to consider hydraulic and hydrological characteristics. This study chose one from each category. For the physical model, this study calculated long-term data without missing data using parameter calibration of the Soil Water Assessment Tool (SWAT), a physical model tested for its applicability in Korea and other countries. The data was used as training data for the Long Short-Term Memory (LSTM) data-based deep learning technique. An anlysis of the time-series data fond that, during the calibration period (2017-18), the Nash-Sutcliffe Efficiency (NSE) and the determinanation coefficient for fit comparison were high at 0.04 and 0.03, respectively, indicating that the SWAT results are superior to the LSTM results. In addition, the annual time-series data from the models were sorted in the descending order, and the resulting flow duration curves were compared with the duration curves based on the observed flow, and the NSE for the SWAT and the LSTM models were 0.95 and 0.91, respectively, and the determination coefficients were 0.96 and 0.92, respectively. The findings indicate that both models yield good performance. Even though the LSTM requires improved simulation accuracy in the low flow sections, the LSTM appears to be widely applicable to calculating flow duration curves for large basins that require longer time for model development and operation due to vast data input, and non-measured basins with insufficient input data.

• 한국정보디스플레이학회:학술대회논문집
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• 2008.10a
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• pp.993-994
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• 2008

Electrochromic (EC) devices are capable of reversibly changing their optical properties upon charge injection and extraction induced by the external voltage. The characteristics of the EC device, such as low power consumption, high coloration efficiency, and memory effects under open circuit status, make them suitable for use in a variety of applications including smart windows and electronic papers. Coloration due to reduction or oxidation of redox chromophores can be used for EC devices (e-paper), but the switching time is slow (second level). Recently, with increasing demand for the low cost, lightweight flat panel display with paper-like readability (electronic paper), an EC display technology based on dye-modified $TiO_2$ nanoparticle electrode was developed. A well known organic dye molecule, viologen, was adsorbed on the surface of a mesoporous $TiO_2$ nanoparticle film to form the EC electrode. On the other hand, ZnO is a wide bandgap II-VI semiconductor which has been applied in many fields such as UV lasers, field effect transistors and transparent conductors. The bandgap of the bulk ZnO is about 3.37 eV, which is close to that of the $TiO_2$ (3.4 eV). As a traditional transparent conductor, ZnO has excellent electron transport properties, even in ZnO nanoparticle films. In the past few years, one-dimension (1D) nanostructures of ZnO have attracted extensive research interest. In particular, 1D ZnO nanowires renders much better electron transportation capability by providing a direct conduction path for electron transport and greatly reducing the number of grain boundaries. These unique advantages make ZnO nanowires a promising matrix electrode for EC dye molecule loading. ZnO nanowires grow vertically from the substrate and form a dense array (Fig. 1). The ZnO nanowires show regular hexagonal cross section and the average diameter of the ZnO nanowires is about 100 nm. The cross-section image of the ZnO nanowires array (Fig. 1) indicates that the length of the ZnO nanowires is about $6\;{\mu}m$. From one on/off cycle of the ZnO EC cell (Fig. 2). We can see that, the switching time of a ZnO nanowire electrode EC cell with an active area of $1\;{\times}\;1\;cm^2$ is 170 ms and 142 ms for coloration and bleaching, respectively. The coloration and bleaching time is faster compared to the $TiO_2$ mesoporous EC devices with both coloration and bleaching time of about 250 ms for a device with an active area of $2.5\;cm^2$. With further optimization, it is possible that the response time can reach ten(s) of millisecond, i.e. capable of displaying video. Fig. 3 shows a prototype with two different transmittance states. It can be seen that good contrast was obtained. The retention was at least a few hours for these prototypes. Being an oxide, ZnO is oxidation resistant, i.e. it is more durable for field emission cathode. ZnO nanotetropods were also applied to realize the first prototype triode field emission device, making use of scattered surface-conduction electrons for field emission (Fig. 4). The device has a high efficiency (field emitted electron to total electron ratio) of about 60%. With this high efficiency, we were able to fabricate some prototype displays (Fig. 5 showing some alphanumerical symbols). ZnO tetrapods have four legs, which guarantees that there is one leg always pointing upward, even using screen printing method to fabricate the cathode.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.36 no.4
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• pp.287-298
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• 2000

The charge of distance and the change of tack between paired boats were measured by ship's distance measuring system fixed MCS in the main boat and MS in the following boat. The operating depth of the anchovy boat seine was recorded and analysed by self memory temperature/depth sensor in order to compare the relationship between the distance between towing boats and geometry of the anchovy boat seine net. The results are as follow, (1) When distance between paired boat was 5m, the fishing net was spreaded down deeply and unstably in accordance with bag net and flapper may be help to pass out anchovy school. (2) When distance between paired boat was 100m, vertical opening of the net was gradually increased with higher slope of towing depth in the square, bosom and flapper. Therefore, fishing efficiency could be decreased by preventing the entering of anchovy due to unstable shape of the bag net. (3) When distance between paired boat was 200m, the geometry of the anchovy seine was stable condition with the end of bag net was up while flapper was down and it may cause bad effect in fishing efficiency. (4) When distance between paired boat was 300m, the shape from wing net to bag net was gradually slow down and stable enough as well as good shape in bag net and flapper. (5) The ship's distance measuring system could be used for measurement and accurate control of distance between paired boat in accordance of anchovy recordings by fish finder in order to get higher fishing efficiency in anchory boat seine operation.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.2
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• pp.335-342
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• 2016

This paper proposes efficient hardware structure of in-loop filter for a high-performance HEVC (High Efficiency Video Coding) encoder. HEVC uses in-loop filter consisting of deblocking filter and SAO (Sample Adaptive Offset) to improve the picture quality in a reconstructed image due to a quantization error. However, in-loop filter causes an increase in complexity due to the additional encoder and decoder operations. A proposed in-loop filter is implemented as a three-stage pipeline to perform the deblocking filtering and SAO operation with a reduced number of cycles. The proposed deblocking filter is also implemented as a six-stage pipeline to improve efficiency and performs a new filtering order for efficient memory architecture. The proposed SAO processes six pixels parallelly at a time to reduce execution cycles. The proposed in-loop filter encoder architecture is designed by Verilog HDL, and implemented by 131K logic gates in TSMC $0.13{\mu}m$ process. At 164MHz, the proposed in-loop filter encoder can support 4K Ultra HD video encoding at 60fps in real time.

• The KIPS Transactions:PartA
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• v.12A no.6 s.96
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• pp.523-530
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• 2005

When a PCI 2.2 bus master requests data using Memory Read command, a target device may hold PCI bus without data to be transferred for long time because a target device needs time to prepare data infernally. Because the usage efficiency of the PCI bus and the data transfer efficiency are decreased due to this situation, the PCI specification recommends to use the Delayed Transaction mechanism to improve the system performance. But the mechanism cann't fully improve performance because a target device doesn't know the exact size of prefetched data. In the previous work, we propose a new method called Prefetch Request when a bus master intends to read data from the target device. In this paper, we design PCI 2.2 controller and local device that support the proposed method. The designed PCI 2.2 controller has simple local interface and it is used to convert the PCI protocol into the local protocol. So the typical users, who don't know the PCI protocol, can easily design the PCI target device using the proposed PCI controller. We propose the basic behavioral verification, hardware design verification, and random test verification to verify the designed hardware. We also build the test bench and define assembler instructions. And we propose random testing environment, which consist of reference model, random generator ,and compare engine, to efficiently verify corner case. This verification environment is excellent to find error which is not detected by general test vector. Also, the simulation under the proposed test environment shows that the proposed method has the higher data transfer efficiency than the Delayed Transaction about $9\%$.