• International Journal of Computer Science & Network Security
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• 제21권8호
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• pp.196-202
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• 2021

DNA sequencing provides fundamental data in genomics, bioinformatics, biology and many other research areas. With the emergent evolution in DNA sequencing technology, a massive amount of genomic data is produced every day, mainly DNA sequences, craving for more storage and bandwidth. Unfortunately, managing, analyzing and specifically storing these large amounts of data become a major scientific challenge for bioinformatics. Those large volumes of data also require a fast transmission, effective storage, superior functionality and provision of quick access to any record. Data storage costs have a considerable proportion of total cost in the formation and analysis of DNA sequences. In particular, there is a need of highly control of disk storage capacity of DNA sequences but the standard compression techniques unsuccessful to compress these sequences. Several specialized techniques were introduced for this purpose. Therefore, to overcome all these above challenges, lossless compression techniques have become necessary. In this paper, it is described a new DNA compression mechanism of pattern matching extended Compression algorithm that read the input sequence as segments and find the matching pattern and store it in a permanent or temporary table based on number of bases. The remaining unmatched sequence is been converted into the binary form and then it is been grouped into binary bits i.e. of seven bits and gain these bits are been converted into an ASCII form. Finally, the proposed algorithm dynamically calculates the compression ratio. Thus the results show that pattern matching extended Compression algorithm outperforms cutting-edge compressors and proves its efficiency in terms of compression ratio regardless of the file size of the data.

• 한국기계가공학회지
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• 제20권6호
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• pp.10-16
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• 2021

Drilling is a crucial process that takes up a significant amount of weight during machining operations. In addition, drill tip-type tools and related operations have been developed for manufacturing industries to achieve economic efficiency. In this study, SM45C carbon steel, widely used for machine structures, was utilized as the working material after quenching and tempering. Insert-tip types of carbide tools, such as TiN and TiAlN, were used as tool materials. Drilling conditions such as the spindle revolution, feed rate, step of cut, and tool diameter were used to measure roughness, roundness, and straightness using the orthogonal array table statistical method. The surface roughness, roundness, and straightness characteristics based on the conditions were analyzed using ANOVA. The results showed that the spindle speed and feed rate were the main factors influencing carbide insert-tip drilling under the same conditions as the experimental conditions.

• 한국정밀공학회지
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• 제11권5호
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• pp.42-55
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• 1994

Presently, abrasive processing is on eof several methods for cutting and grinding brittle materials, and high quality in dimensional accuracy and surface roughness are often required as a structural components, therefore most of them has to be ground. In manufacturing of tungsten-carbide components, grinding by diamond wheel is usually adopted in order to provide configurational and dimensional accuracy to the components. The present study proposes the experi- mental research of optimum condition to the high quality surface grinding of the WC-Co material using diamond abrasive wheel in order to minimize the damage on the ground surface and to pursue the precise dimension by conventional grinding machine. Brief investigation is carried out to decrease the dressing is constant, theoretical grinding effect such as machining precision is changed according to the speed of workpiece. Accordingly, normal and tangential grinding forces, which are Fn, Ft were analyzed for the machining processes of WC-Co material to obtain optimum grinding conditions, 3-point bending test is carried out to check machining damage on the ground surface layer, which is one of sintered brittle materials.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권7호
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• pp.1794-1806
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• 2023

This study presents a method for capturing photographs of users as input and converting them into 2D character animation sprites using a generative adversarial network-based artificial intelligence network. Traditionally, 2D character animations have been created by manually creating an entire sequence of sprite images, which incurs high development costs. To address this issue, this study proposes a technique that combines motion videos and sample 2D images. In the 2D sprite generation process that uses the proposed technique, a sequence of images is extracted from real-life images captured by the user, and these are combined with character images from within the game. Our research aims to leverage cutting-edge deep learning-based image manipulation techniques, such as the GAN-based motion transfer network (impersonator) and background noise removal (U² -Net), to generate a sequence of animation sprites from a single image. The proposed technique enables the creation of diverse animations and motions just one image. By utilizing these advancements, we focus on enhancing productivity in the game and animation industry through improved efficiency and streamlined production processes. By employing state-of-the-art techniques, our research enables the generation of 2D sprite images with various motions, offering significant potential for boosting productivity and creativity in the industry.

• Structural Engineering and Mechanics
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• 제84권3호
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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.

• 한국인터넷방송통신학회논문지
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• 제23권5호
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• pp.77-82
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• 2023

스마트홈 이전에 주목받았던 인텔리전스홈과 홈오토메이션은 첨단 기술들을 주택에 적용하는 데 초점을 맞추어 사용자들에게 불편성을 안겨주었고, 실질적인 효율성은 부족하여 대중화에 실패했다. 하지만, 현재 4차 산업 혁명과 더불어 빅데이터, 인공지능, 사물 인터넷과 관련된 기술들을 활용한 다양한 서비스가 증가하고 있으며, 다양한 기술들을 기반으로 가정 내에서 제품들을 조작 및 관리하며, 자동화하는 스마트홈 서비스 구축률이 점차 증가하고 있다. 본 논문에서는 이런 동향 추세에 맞춰 MQTT 서버, Django 웹 프레임워크, WIFI 통신 모듈을 활용하여 제품을 연결하고, 조작 및 관리할 수 있는 프로그램 앱을 구현했다.

• Geomechanics and Engineering
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• 제38권6호
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• pp.633-645
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• 2024

Disc cutters, used as excavation tools for rocks in a Tunnel Boring Machine (TBM), naturally undergo wear during the tunneling process, involving crushing and cutting through the ground, leading to various wear types. When disc cutters reach their wear limits, they must be replaced at the appropriate time to ensure efficient excavation. General disc cutter life prediction models are typically used during the design phase to predict the total required quantity and replacement locations for construction. However, disc cutters are replaced more frequently during tunneling than initially planned. Unpredictable disc cutter replacements can easily diminish tunneling efficiency, and abnormal wear is a common cause during tunneling in complex ground conditions. This study aims to overcome the limitations of existing disc cutter life prediction models by utilizing machine data generated during tunneling to predict disc cutter wear patterns and determine the need for replacements in real-time. Artificial intelligence classification algorithms, including K-nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree (DT), and Stacking, are employed to assess the need for disc cutter replacement. Binary classification models are developed to predict which disc cutters require replacement, while multi-class classification models are fine-tuned to identify three categories: no replacement required, replacement due to normal wear, and replacement due to abnormal wear during tunneling. The performance of these models is thoroughly assessed, demonstrating that the proposed approach effectively manages disc cutter wear and replacements in shield TBM tunnel projects.

• Smart Structures and Systems
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• 제33권5호
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• pp.333-347
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• 2024

There has been an increasing interest in the construction of smart buildings that can actively monitor and react to their surroundings. The capacity of these intelligent structures to precisely predict and respond to deflection is a crucial feature that guarantees both their structural soundness and efficiency. Conventional techniques for determining deflection often depend on intricate mathematical models and computational simulations, which may be time- and resource-consuming. Artificial intelligence (AI) algorithms have become a potent tool for anticipating and controlling deflection in intelligent structures in response to these difficulties. The term "deflection-aware smart structures" in this sense refers to constructions that have AI algorithms installed that continually monitor and analyses deflection data in order to proactively detect any problems and take appropriate action. These structures anticipate deflection across a range of operating circumstances and environmental factors by using cutting-edge AI approaches including deep learning, reinforcement learning, and neural networks. AI systems are able to predict real-time deflection with high accuracy by using data from embedded sensors and actuators. This capability enables the systems to identify intricate patterns and linkages. Intelligent buildings have the potential to self-correct in order to reduce deflection and maximize performance. In conclusion, the development of deflection-aware smart structures is a major stride forward for structural engineering and has enormous potential to enhance the performance, safety, and dependability of designed systems in a variety of industries.

• Nuclear Engineering and Technology
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• 제56권10호
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• pp.4355-4364
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• 2024

Fluid pipelines, commonly utilized in the oil industry, often face efficiency and reliability issues due to sediment buildup causing erosion, corrosion, and pipe wall thinning. Traditional assessment methods involve disruptive measures like cutting or creating holes and temporarily taking pipelines out of service. A non-destructive alternative, Limited-Number-Detector Computed Tomography (LNDCT), proves cost-effective and superior. Our proposed algorithm enhances data acquisition and projections using discrete detectors, employing Co-60 as a gamma-ray source and thallium-doped sodium iodide, NaI(Tl), detectors in an arc configuration. Monte Carlo simulations aligned closely with experimental data. Optimization involved adjusting the detector aperture angle based on a primary-to-scatter ratio of gamma-ray photons. We investigated the utility of various isotopes (Co-60, Cs-137, Am-241, Ir-192) to determine optimal projection signal amplitude. The algorithm generates a large sinogram matrix, and a filtered back-projection algorithm with a Hamming filter maximizes image quality while ensuring acceptable calculation volume and time. Using four phantoms, including pipelines filled to different scales, our study evaluates LNDCT configuration, performance, and validation. The results highlight its potential for efficiently evaluating sediment in pipelines, confirming the correctness and accuracy of our proposed algorithm.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2009년도 학술발표회 초록집
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• pp.2169-2175
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• 2009

This research aims to evaluate the concept of eco-efficient water infrastructure and provides a list of case studies in order to help understand the applicability of eco-efficient water infrastructure to Asia and the Pacific. A set of indicators have been explored to assess eco-efficiency in water infrastructure for the region on a micro and macro scale. The core idea of eco-efficiency, 'more value with less impact (on the environment)', has proven to be applicable in management of water infrastructure. The fundamental elements in eco-efficient water infrastructure should encompass physical infrastructure and non-physical infrastructure, which is more needed particularly in Asian countries. The case studies have demonstrated the applicability of the concept of eco-efficient water infrastructure. The Republic of Korea has provided the case of the eco-friendly approaches to enhance dam management and its innovative solutions how to use water more efficiently through state-of-art technologies. The experiences of Singapore are some of the best evidence to establish eco-efficient water infrastructure, for instance, the NEWater project via application of cutting edge technologies (recycled water) and institutional reform in water tariff systems to conserve water as well as enhance water quality. A list of indicators to assess eco-efficiency in water infrastructure have been discussed, and the research presents a myriad of project cases which are good to represent eco-efficiency in water infrastructure, including multipurpose small dams, customized flood defense systems, eco-efficient ground water use, and eco-efficient desalination plants. The study has presented numerous indicators in five different categories: 1) the status of water availability and infrastructure; 2) production and consumption patterns of freshwater; 3) agricultural products and sources of environmental loads; 4) damages from water-caused natural disaster; and 5) urban water supply and sanitation. There are challenges as well as benefits in such indicators, since the indicators should be applied very carefully in accordance with specific socio-economic, political and policy contexts in different countries in Asia and the Pacific Region. The key to success of establishment of eco-efficient water infrastructure in Asia primarily depends on the extent to which each country is committed to balancing its development of physical as well as non-physical water infrastructure. Particularly, it is imperative for Asian countries to transform its policy focus from physical infrastructure to non-physical infrastructure. Such shift will help lead to implementation of sustainable in Asian countries.