• Journal of Powder Materials
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• v.30 no.3
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• pp.203-209
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• 2023

Aluminum alloy-based additive manufacturing (AM) has emerged as a popular manufacturing process for the fabrication of complex parts in the automotive and aerospace industries. The addition of an inoculant to aluminum alloy powder has been demonstrated to effectively reduce cracking by promoting the formation of equiaxed grains. However, the optimization of the AM process parameters remains challenging owing to their variability. In this study, the response surface methodology (RSM) was used to predict the crack density of AM-processed Al alloy samples. RSM was performed by setting the process parameters and equiaxed grain ratio, which influence crack propagation, as independent variables and designating crack density as a response variable. The RSM-based quadratic polynomial models for crack-density prediction were found to be highly accurate. The relationship among the process parameters, crack density, and equiaxed grain fraction was also investigated using RSM. The findings of this study highlight the efficacy of RSM as a reliable approach for optimizing the properties of AM-processed parts with limited experimental data. These results can contribute to the development of robust AM processing strategies for the fabrication of high-quality Al alloy components for various applications.

• Journal of Radiation Protection and Research
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• v.47 no.4
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• pp.181-194
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• 2022

Internal dosimetry is a discipline which brings together a set of knowledge, tools and procedures for calculating the dose received after incorporation of radionuclides into the body. Several steps are necessary to calculate the committed effective dose (CED) for workers or members of the public. Each step uses the best available knowledge in the field of radionuclide biokinetics, energy deposition in organs and tissues, the efficiency of radiation to cause a stochastic effect, or in the contributions of individual organs and tissues to overall detriment from radiation. In all these fields, knowledge is abundant and supported by many works initiated several decades ago. That makes the CED a very robust quantity, representing exposure for reference persons in reference situation of exposure and to be used for optimization and assessment of compliance with dose limits. However, the CED suffers from certain limitations, accepted by the International Commission on Radiological Protection (ICRP) for reasons of simplification. Some of its limitations deserve to be overcome and the ICRP is continuously working on this. Beyond the efforts to make the CED an even more reliable and precise tool, there is an increasing demand for personalized dosimetry, particularly in the medical field. To respond to this demand, currently available tools in dosimetry can be adjusted. However, this would require coupling these efforts with a better assessment of the individual risk, which would then have to consider the physiology of the persons concerned but also their lifestyle and medical history. Dosimetry and risk assessment are closely linked and can only be developed in parallel. This paper presents the state of the art of internal dosimetry knowledge and the limitations to be overcome both to make the CED more precise and to develop other dosimetric quantities, which would make it possible to better approximate the individual dose.

• The Journal of the Acoustical Society of Korea
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• v.25 no.6
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• pp.269-276
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• 2006

In this paper, we propose an emotion recognition system that can discriminate human emotional state into neutral or anger from the speech captured by a cellular-phone in real time. In general. the speech through the mobile network contains environment noise and network noise, thus it can causes serious System performance degradation due to the distortion in emotional features of the query speech. In order to minimize the effect of these noise and so improve the system performance, we adopt a simple MA (Moving Average) filter which has relatively simple structure and low computational complexity, to alleviate the distortion in the emotional feature vector. Then a SFS (Sequential Forward Selection) feature optimization method is implemented to further improve and stabilize the system performance. Two pattern recognition method such as k-NN and SVM is compared for emotional state classification. The experimental results indicate that the proposed method provides very stable and successful emotional classification performance such as 86.5%. so that it will be very useful in application areas such as customer call-center.

• Mass Spectrometry Letters
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• v.15 no.1
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• pp.40-48
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• 2024

Capacity of the analytical/quantitative evaluation techniques to satisfy both qualitative and quantitative considerations for effective extraction of marigold oleoresins/xanthophylls and their potential as anti-mycotic and antioxidant activity was assessed. Accelerated solvent extraction (ASE), Soxhlet extraction (SE), Supercritical fluid extraction (SCFE), Cold extraction (CE), and ultrasonically assisted extraction (USE) techniques were evaluated for extraction of oleoresin/xanthophyll content from Tagetes erecta (Var. Pusa Narangi Genda) with respect to solvent consumption, extraction time, reproducibility, and yield. Followed by the antifungal and antioxidant activity evaluation. The overall yield of Tagetes oleoresin was higher in ASE (64.5 g/kg) followed by SE (57.3 g/kg), USE (50.7 g/kg), SCFE (45.3 g/kg) and CE (31.6 g/kg). The lutein esters represented more than 80% of the constituents. Further, xanthophyll/ lutein content in oleoresin was found to be quite higher in HPLC (r2 = 0.996) analysis than in the AOAC recommended UV spectrophotometer analysis. The oleoresin exhibited moderate antioxidant activity (DPPH assay) and antifungal activity against three phytopathogenic fungi. Based on the various parameters, the reproducibility of ASE was better (0.3-8.0%) than that of SE (0.5-12.9%), SCFE (0.2-9.4%), USE (0.3-12.4%) and CE (0.8-15.3%). ASE with (RSD 1.6%) is preferred being faster, reproducible, uses less solvent, robust and automation allows sequential extraction of the sample in less time.

• Advances in nano research
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• v.16 no.6
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• pp.623-638
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• 2024

This study investigates the thermal post-buckling behavior of concrete eccentric annular sector plates reinforced with graphene oxide powders (GOPs). Employing the minimum total potential energy principle, the plates' stability and response under thermal loads are analyzed. The Haber-Schaim foundation model is utilized to account for the support conditions, while the transform differential quadrature method (TDQM) is applied to solve the governing differential equations efficiently. The integration of GOPs significantly enhances the mechanical properties and stability of the plates, making them suitable for advanced engineering applications. Numerical results demonstrate the critical thermal loads and post-buckling paths, providing valuable insights into the design and optimization of such reinforced structures. This study presents a machine learning algorithm designed to predict complex engineering phenomena using datasets derived from presented mathematical modeling. By leveraging advanced data analytics and machine learning techniques, the algorithm effectively captures and learns intricate patterns from the mathematical models, providing accurate and efficient predictions. The methodology involves generating comprehensive datasets from mathematical simulations, which are then used to train the machine learning model. The trained model is capable of predicting various engineering outcomes, such as stress, strain, and thermal responses, with high precision. This approach significantly reduces the computational time and resources required for traditional simulations, enabling rapid and reliable analysis. This comprehensive approach offers a robust framework for predicting the thermal post-buckling behavior of reinforced concrete plates, contributing to the development of resilient and efficient structural components in civil engineering.

• KSBB Journal
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• v.14 no.3
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• pp.309-314
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• 1999

In order to produce high concentration of sodium gluconate, optimization of the fermentation conditions, such as glucose concentration, inoculum size, dissolved oxygen concentration and glucose feeding method, was examined. When the glucose concentration was maintained in the range of 30∼50 g/L during the batch fermentation, glucose conversion yield and productivity were 92.2% and 6.0 g/L/hr, respectively. In the case of the low concentration below 30 g/L, the yield decreased by about 25%. As the inoculum size increased above 20%(w/v), lag phase was shortened but the productivity decreased. The dissolved oxygen level of 60∼70% was shown to be the threshold point for 75% of increase in the productivity of sodium gluconate. Finally, optimal glucose feeding rate was determined using various feeding methods such as exponential feeding, feeding based on the average glucose consumption rate and was determined using various feeding methods such as exponential feeding, feeding based on the average glucose consumption rate and on the oxygen uptake rate and etc. Our result shows that glucose feeding, based on the oxygen uptake rate is a very simple, efficient and robust method, especially when oxygen is consumed as a substrate for the bioconversion. Using the above glucose feeding strategy under the optimized condition, 255 g/L of sodium gluconate concentration, 12 g/L/hr of productivity and 95% of glucose conversion yield were achieved with A. niger ACM53.

• Korean Journal of Optics and Photonics
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• v.20 no.3
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• pp.175-181
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• 2009

Holographic lithography is one of the potential technologies for next generation lithography which can print large areas (6") as well as very fine patterns ($0.35{\mu}m$). Usually, photolithography has been developed with two target purposes. One was for LCD applications which require large areas (over 6") and micro pattern (over $1.5{\mu}m$) exposure. The other was for semiconductor applications which require small areas (1.5") and nano pattern (under $0.2{\mu}m$) exposure. However, holographic lithography can print fine patterns from $0.35{\mu}m$ to $1.5{\mu}m$ keeping the exposure area inside 6". This is one of the great advantages in order to realize high speed fine pattern photolithography. How? It is because holographic lithography is taking holographic optics instead of projection optics. A hologram mask is the key component of holographic optics, which can perform the same function as projection optics. In this paper, Surface-Relief TIR Hologram Mask technology is introduced, and enables more robust hologram masks than those previously reported that were formed in photopolymer recording materials. We describe the important parameters in the fabrication process and their optimization, and we evaluate the patterns printed from the surface-relief TIR hologram masks.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.46 no.5
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• pp.26-35
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• 2009

OFDM (Orthogonal Frequency Division Multiplexing) system is robust to frequency selective fading and narrowband interference in high-speed data communications. However, an OFDM signal consists of a number of independently modulated subcarriers and the superposition of these subcarriers causes a problem that can give a large PAPR(Peak-to-Average Power Ratio). Phase rotation method can reduce the PAPR without nonlinear distortion by multiplying phase weighting factors. But computational complexity of searching phase weighting factors is increased exponentially with the number of subblocks and considered phase factor. Therefore, a new method, which can reduce computational complexity and detect phase weighting factors efficiently, should be developed. In this paper, a modeling process is introduced, which apply metaheuristic algerian in phase rotation method and optimize in PTS (Particle Swarm Optimization) scheme. Proposed algorithm can solve the computational complexity and guarantee to reduce PAPR We analyzed the efficiency of the PAPR reduction through a simulation when we applied the proposed method to telecommunication systems.

• Journal of the Korea Society of Computer and Information
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• v.24 no.12
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• pp.25-33
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• 2019

This paper presents a novel evolutionary framework for optimizing a bio-inspired fully dynamic neurocontroller for the maneuverable flapping flight of a simulated bird-sized ornithopter robot which takes advantage of the morphological computation and mechansensory feedback to improve flight stability. In order to cope with the difficulty of generating robust flapping flight and its maneuver, the wing of robot is modelled as a series of sub-plates joined by passive torsional springs, which implements the simplified version of feathers attached to the forearm skeleton. The neural controller is designed to have a bilaterally symmetric structure which consists of two fully connected neural network modules receiving mirrored sensory inputs from a series of flight navigation sensors as well as feather mechanosensors to let them participate in pattern generation. The synergy of wing compliance and its sensory reflexes gives a possibility that the robot can feel and exploit aerodynamic forces on its wings to potentially contribute to the agility and stability during flight. The evolved robot exhibited target-following flight maneuver using asymmetric wing movements as well as its tail, showing robustness to external aerodynamic disturbances.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2010.05a
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• pp.47-47
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• 2010

Over the past 20 years, constant progress in noise and vibration (NVH) engineering has enabled to constantly advance quality and comfort of operation and use of really any products - from automobiles to aircraft, to all kinds of industrial vehicles and machines - to the extend that for many products, supreme NVH performance has becomes part of its brand image in the market. At the same time, the product innovation agenda in the automotive, aircraft and really many other industries, has been extended very much in recent years by meeting ever more strict environmental regulations. Like in the automotive industry, the drive towards meeting emission and CO2 targets leads to very much accelerated adoption of new powertrain concepts (downsizing of ICE, hybrid-electrical...), and to new vehicle architectures and the application of new materials to reduce weight, which bring new challenges for not only maintaining but further improving NVH performance. This drives for innovation in NVH engineering, so as to succeed in meeting a product brand performance for NVH, while as the same time satisfying eco-constraints. Product innovation has also become increasingly dependent on the adoption of electronics and software, which drives for new solutions for NVH engineering that can be applied for NVH performance optimization of mechatronic products. Finally, relentless pressure to shorten time to market while maintaining overall product quality and reliability, mandates that the practice and solutions for NVH engineering can be optimally applied in all phases of product development. The presentation will first review the afore trends for product and process innovation, and discuss the challenges they represent for NVH engineering. Next, the presentation discusses new solutions for NVH engineering of products, so as to meet target brand values, while at the same time meeting ever more strict eco constraints, and this within a context of increasing adoption of electronics and controls to drive product innovation. NVH being very much defined by system level performance, these solutions implement the approach of "Model Based System Engineering" to increase the impact of system level analysis for NVH in all phases of product development: - At the Concept Phase, to be able to do business case analysis of new product concepts; to arrive at an optimized and robust product architecture (e.g. to hybrid powertrain lay-out, to optimize fuel economy); to enable target cascading, to subsystem and component level. - In Development Phase, to increase realism and productivity of simulation, so as to frontload virtual validation of components and subsystems and to further reduce reliance on physical testing. - During the final System Testing Phase, to enable subsystem testing by a combination of physical testing and simulation: using simulation models to simulate the final integration context when testing a subsystem, enabling to frontload subsystem testing before final system integration is possible. - To interconnect Mechanical, Electronical and Controls engineering, in all phases of development, by supporting model driven controls engineering (MIL, SIL, HIL). Finally, the presentation reviews examples of how LMS is implementing such new applications for NVH engineering with lead customers in Europe, Asia and US, with demonstrated benefits both in terms of shortening development cycles, and/or enabling a simulation based approach to reduce reliance on physical testing.