• Journal of Electrical Engineering and Technology
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• v.6 no.5
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• pp.693-696
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• 2011

In this work, an exponentially tapered microstrip antenna was implemented using a resistive loading technique in order to suppress the internal reflections. The inductive loading was realized by introducing slits on the antenna to improve radiation efficiency. Compared with a resistive-loaded antenna, the proposed antenna had an average improvement of about 6.2% in radiation efficiency within the range of 2-10.5 GHz. In addition, the highest peak of the radiated short pulse from the proposed antenna became 45% greater than that of an antenna with resistive loading only.

• Proceedings of the KWS Conference
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• 2001.10a
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• pp.121-124
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• 2001

The main characteristic of the Electron Beam Welding technique is its high energy density which produces thin and deep welds with very little distortion. High accelerated electrons, focused in a beam of 0.5 ∼ 2mm diameter, produce narrow welds with deep penetration. The result is a small HAZ as well as a low and uniform distortion which is predictible within very narrow limits. But the small diameter of the EB increases the requirements for the equipment control system for centering the beam on the welding joint in order to avoid any lack of fusion. Therefore, in this paper, we introduce the system developed at our company and the quality of welding zone, the detail function of system.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.20 no.4
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• pp.1309-1319
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• 1996

The research for the classification of the natural defects in welding zone is performd using the neuro-pattern recognition technology. The signal pattern recognition package including the user's defined function is developed to perform the digital signal processing, feature extraction, feature selection and classifier selection, The neural network classifier and the statistical classifiers such as the linear discriminant function classifier and the empirical Bayesian calssifier are compared and discussed. The neuro-pattern recognition technique is applied to the classificaiton of such natural defects as root crack, incomplete penetration, lack of fusion, slag inclusion, porosity, etc. If appropriately learned, the neural network classifier is concluded to be better than the statistical classifiers in the classification of the natural welding defects.

• Journal of IKEEE
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• v.23 no.2
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• pp.710-715
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• 2019

We consider parameter estimation in distributed systems in which measurements at local nodes are quantized in a non-regular manner, where multiple codewords are mapped into a single local measurement. For the system with non-regular quantization, to ensure a perfect independent encoding at local nodes, a local measurement can be encoded into a set of a great number of codewords which are transmitted to a fusion node where estimation is conducted with enormous computational cost due to the large cardinality of the sets. In this paper, we propose an efficient estimation technique that can handle the non-regular quantized data by efficiently finding the feasible combination of codewords without searching all of the possible combinations. We conduct experiments to show that the proposed estimation performs well with respect to previous novel techniques with a reasonable complexity.

• International journal of advanced smart convergence
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• v.7 no.4
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• pp.75-83
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• 2018

Predictive IoT Sensor Algorithm is a technique of data science that helps computers learn from existing data to predict future behaviors, outcomes, and trends. This algorithm is a cloud predictive analytics service that makes it possible to quickly create and deploy predictive models as analytics solutions. Sensors and computers collect and analyze data. Using the time series prediction algorithm helps to predict future temperature. The application of this IoT in industrial environments like power plants and factories will allow organizations to process much larger data sets much faster and precisely. This rich source of sensor data can be networked, gathered and analyzed by super smart software which will help to detect problems, work more productively. Using predictive IoT technology - sensors and real-time monitoring - can help organizations exactly where and when equipment needs to be adjusted, replaced or how to act in a given situation.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.11a
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• pp.292-294
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• 2022

본 논문은 3 차원 이미징 기술과 컴퓨터 그래픽스 기반의 시뮬레이션 분야에서 매우 성공적인 두 기술의 융합을 기반으로 진행한 연구를 제안한다. 먼저 3 차원 디스플레이 시스템에 재생할 집적 영상 이미지를 생성하는 방법에 대해 설명한다. 이는 3 차원 포인트 클라우드에서 가상 핀홀 배열로 입사각을 역투영하는 계산방식을 통해 해당 이미지를 생성한다. 우리는 재생되는 3 차원 영상의 초점면을 자유롭게 선택하는 방법에 대해서도 설명한다. 또한, 복수의 관찰자에게 동시에 다양한 시점 정보를 기반으로 몰입감 넘치는 3 차원 영상을 제공하는 3 차원 디스플레이 시스템을 소개하고, 다양한 실험결과를 기반으로 결론을 제시한다.

• International journal of advanced smart convergence
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• v.12 no.4
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• pp.111-118
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• 2023

In the ever-changing landscape of finance, the fusion of artificial intelligence (AI)and pair trading strategies has captured the interest of investors and institutions alike. In the context of supervised machine learning, crafting precise and accurate labels is crucial, as it remains a top priority to empower AI models to surpass traditional pair trading methods. However, prevailing labeling techniques in the financial sector predominantly concentrate on individual assets, posing a challenge in aligning with pair trading strategies. To address this issue, we propose an inventive approach that melds the Triple Barrier Labeling technique with pair trading, optimizing the resultant labels through genetic algorithms. Rigorous backtesting on cryptocurrency datasets illustrates that our proposed labeling method excels over traditional pair trading methods and corresponding buy-and-hold strategies in both profitability and risk control. This pioneering method offers a novel perspective on trading strategies and risk management within the financial domain, laying a robust groundwork for further enhancing the precision and reliability of pair trading strategies utilizing AI models.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1283-1297
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• 2023

Mid-wave infrared (MWIR) imagery, due to its ability to capture the temperature of land cover and objects, serves as a crucial data source in various fields including environmental monitoring and defense. The KOMPSAT-3A satellite acquires MWIR imagery with high spatial resolution compared to other satellites. However, the limited spatial resolution of MWIR imagery, in comparison to electro-optical (EO) imagery, constrains the optimal utilization of the KOMPSAT-3A data. This study aims to create a highly visible MWIR fusion image by leveraging the edge information from the KOMPSAT-3A panchromatic (PAN) image. Preprocessing is implemented to mitigate the relative geometric errors between the PAN and MWIR images. Subsequently, we employ a pre-trained pixel difference network (PiDiNet), a deep learning-based edge information extraction technique, to extract the boundaries of objects from the preprocessed PAN images. The MWIR fusion imagery is then generated by emphasizing the brightness value corresponding to the edge information of the PAN image. To evaluate the proposed method, the MWIR fusion images were generated in three different sites. As a result, the boundaries of terrain and objects in the MWIR fusion images were emphasized to provide detailed thermal information of the interest area. Especially, the MWIR fusion image provided the thermal information of objects such as airplanes and ships which are hard to detect in the original MWIR images. This study demonstrated that the proposed method could generate a single image that combines visible details from an EO image and thermal information from an MWIR image, which contributes to increasing the usage of MWIR imagery.

• Korean Journal of Materials Research
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• v.22 no.4
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• pp.211-214
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• 2012

Optimal conditions for HA plasma spray-coating on Ti6Al4V alloy were investigated in order to obtain enhanced bone-bonding ability with Ti6Al4V alloy. The properties of plasma spray coated film were analyzed by SEM, XRD, surface roughness measurement, and adhesion strength test because the film's transformed phase and crystallinity were known to be influential to bone-bonding ability withTi6Al4V alloy. The films were formed by a plasma spray coating technique with various combinations of plasma power, spray distance, and auxiliary He gas pressure. The film properties were analyzed in order to determine the optimal spray coating parameters with which we will able to achieve enhanced bone-bonding ability with Ti6Al4V alloy. The most influential coating parameter was found to be the plasma spray distance to the specimen from the spray gun nozzle. Additionally, it was observed that a relatively higher film crystallinity can be obtained with lower auxiliary gas pressure. Moderate adhesion strength can be achievable at minimal plasma power. That is, adhesion strength is minimally dependent on the plasma power. The combination of shorter spray distance, lower auxiliary gas pressure, and moderate spray power can be recommended as the optimal spray conditions. In this study, optimal plasma spray coated films were formed with spray distance of 70 mm, plasma current of 800 A, and auxiliary gas pressure of 60 psi.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.16
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• pp.7343-7350
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• 2015

Genetic changes associated with acute lymphoblastic leukemia (ALL) provide very important diagnostic and prognostic information with a direct impact on patient management. Detection of chromosome abnormalities by conventional cytogenetics combined with fluorescence in situ hybridization (FISH) play a very significant role in assessing risk stratification. Identification of specific chromosome abnormalities has led to the recognition of genetic subgroups based on reciprocal translocations, deletions and modal number in B or T-cell ALL. In the last twelve years 102 newly diagnosed childhood/adult ALL bone marrow samples were analysed for chromosomal abnormalities with conventional G-banding, and FISH (selected cases) using specific probes in our hospital. G-banded karyotype analysis found clonal numerical and/or structural chromosomal aberrations in 74.2% of cases. Patients with pseudodiploidy represented the most frequent group (38.7%) followed by high hyperdiploidy group (12.9%), low hyperdiploidy group (9.7%), hypodiploidy (<46) group (9.7%) and high hypertriploidy group (3.2%). The highest observed numerical chromosomal alteration was high hyperdiploidy (12.9%) with abnormal karyotypes while abnormal 12p (7.5%) was the highest observed structural abnormality followed by t(12;21)(p13.3;q22) resulting in ETV6/RUNX1 fusion (5.4%) and t(9;22)(q34.1;q11.2) resulting in BCR/ABL1 fusion (4.3%). Interestingly, we identified 16 cases with rare and complex structural aberrations. Application of the FISH technique produced major improvements in the sensitivity and accuracy of cytogenetic analysis with ALL patients. In conclusion it confirmed heterogeneity of ALL by identifying various recurrent chromosomal aberrations along with non-specific rearrangements and their association with specific immunophenotypes. This study pool is representative of paediatric/adult ALL patients in Oman.