• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.4
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• pp.641-648
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• 2023

Recently, the rapid growth of artificial intelligence among the 4th industrial revolution has progressed based on the performance improvement of semiconductor, and circuit integration. According to transistors, which help operation of internal electronic devices and equipment that have been progressed to be more complicated and miniaturized, the control of heat generation and improvement of heat dissipation efficiency have emerged as new performance indicators. The DUT(Device Under Test) Shell is equipment which detects malfunction transistor by evaluating the durability of transistor through heat dissipation in a state where the power is cut off at an arbitrary heating point applying the rating current to inspect the transistor. Since the DUT shell can test more transistor at the same time according to the heat dissipation structure inside the equipment, the heat dissipation efficiency has a direct relationship with the malfunction transistor detection efficiency. Thus, in this paper, we propose various method for PCB configuration structure to optimize heat dissipation of DUT shell and we also propose various transformation and thermal analysis of optimal DUT shell using computational fluid dynamics.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.6
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• pp.1151-1156
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• 2023

In this paper, we propose a deep learning model that utilizes charge/discharge data from initial lithium-ion batteries to predict the remaining useful life of lithium-ion batteries. We build the DMP using the PNP model. To demonstrate the performance of DMP, we organize DML using the LSTM model and compare the remaining useful life prediction performance of lithium-ion batteries between DMP and DML. We utilize the RMSE and RMSPE error measurement methods to evaluate the performance of DMP and DML models using test data. The results reveal that the RMSE difference between DMP and DML is 144.62 [Cycle], and the RMSPE difference is 3.37 [%]. These results indicate that the DMP model has a lower error rate than DML. Based on the results of our analysis, we have showcased the superior performance of DMP over DML. This demonstrates that in the field of lithium-ion batteries, the PNP model outperforms the LSTM model.

• Journal of the Korean Society of Industry Convergence
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• v.27 no.3
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• pp.609-618
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• 2024

System reliability prediction in the development stage is increasingly crucial to reliability growth management to satisfy its target reliability, since modern system usually takes a form of complex composition and various complicated functions. In most cases of development stage, however, the information available for system reliability prediction is very limited, making it difficult to predict system reliability more precisely as in the production and operating stages. In this study, a system reliability prediction process is considered when the reliability-related information such as SIL (Safety Integrity Level) and MTTFd (Mean Time to Dangerous Failure) is available in the development stage. It is suggested that when the SIL or MTTFd of a system component is known and the field operational data of similar system is given, the reliability prediction could be performed using the scaling factor for the SIL or MTTFd value of the component based on the similar system's field operational data analysis. Predicting a system reliability is then adjusted with the conversion factor reflecting the temperature condition of the environment in which the system actually operates. Finally, the case of applying the proposed system reliability prediction process to a high performance mooring platform is dealt with.

• Tunnel and Underground Space
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• v.31 no.5
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• pp.374-384
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• 2021

In this study, we propose a new approach for automatic fracture detection in CT scan images of rock specimens. This approach is built on top of two-stage object detection deep learning algorithm called Faster R-CNN with a major modification of using rotated bounding box. The use of rotated bounding box plays a key role in the future work to overcome several inherent difficulties of fracture segmentation relating to the heterogeneity of uninterested background (i.e., minerals) and the variation in size and shape of fracture. Comparing to the commonly used bounding box (i.e., axis-align bounding box), rotated bounding box shows a greater adaptability to fit with the elongated shape of fracture, such that minimizing the ratio of background within the bounding box. Besides, an additional benefit of rotated bounding box is that it can provide relative information on the orientation and length of fracture without the further segmentation and measurement step. To validate the applicability of the proposed approach, we train and test our approach with a number of CT image sets of fractured granite specimens with highly heterogeneous background and other rocks such as sandstone and shale. The result demonstrates that our approach can lead to the encouraging results on fracture detection with the mean average precision (mAP) up to 0.89 and also outperform the conventional approach in terms of background-to-object ratio within the bounding box.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.5
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• pp.477-484
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• 2019

As the residence time in the vehicle increases, the passenger desires a pleasant and stable riding environment in addition to the high driving performance of the vehicle. The windshield defrosting performance is one of the performance requirements that is essential for driver's safe driving. In order to improve the defrosting performance of the windshield of a vehicle, relevant elements such as the shape of the defrost nozzle should be appropriately designed. In this paper, CFD based numerical analysis is conducted to improve defrost performance of small electric vehicles. The defrost performance analysis was performed by changing the angle of the defrost nozzle and the guide vane that spray hot air to the windshield of the vehicle. Numerical simulation results show that the defrosting performance is best when the defrost nozzle angle is $70^{\circ}$ and the guide vane installation angle is $60^{\circ}$. Based on the analytical results, the defrosting experiment was performed by fabricating the defrost nozzle and the guide vane. As a result of the experiment, it is confirmed that the frost of windshield is removed by 80% within 20 minutes, and it is judged that the defrost performance satisfying the FVMSS 103 specification is secured.

• Proceeding of EDISON Challenge
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• 2017.03a
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• pp.665-671
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• 2017

Iks 칼륨 전류에 관여하는 KCNQ1유전자의 S140G 돌연변이는 심방세동에 영향을 미치는 대표적인 돌연변이 유전자로, 심방세동과 S140G 돌연변이의 상관관계를 밝히기 위한 연구들이 많이 진행되어 왔다. 하지만 S140G 돌연변이 유전자가 심방 세동 환자의 심실 반응에 영향을 미칠 수 있다는 선행연구를 비롯하여 심방과 심실의 활동전위에 영향을 미칠 수 있는 가능성이 있음에도 불구하고, KCNQ1 S140G 돌연변이 유전자의 심실세동에 대한 영향과 그 메커니즘에 대한 연구는 부족하다. 따라서 본 연구는 KCNQ1 S140G 돌연변이 유전자가 심실세동에 미치는 영향에 대한 컴퓨터 시뮬레이션 연구를 통해 그 상관관계를 밝히고자 하였다. 이를 위해 1차원 세포 모델을 비롯하여 2차원 심실세동 반응과 3차원 전기 생리학 및 기계적 수축 시뮬레이션을 진행하였다. 3차원의 전기생리학 및 기계적 수축 시뮬레이션에서는 심실의 박출 활동을 확인하기 위한 정상 박동 시뮬레이션과 심실 세동 발생시의 심실의 변화를 확인하기 위한 세동 시뮬레이션을 각각 진행하였다. 그 결과 KCNQ1 S140G 돌연변이로 인해 심실의 Iks가 증가되었으며, 그로 인해 심실의 활동 전위기간(APD)과 불응기(ERP)가 단축되는 것을 확인할 수 있었다. 또한 활동전위 지속 곡선(APDr)과 불응기 지속 곡선(ERPr)이 완만하게 나타났으며, 심근세포의 전도파장이 감소하였다. 3차원 정상 박동 시뮬레이션의 결과 표준형에서 보다 KCNQ1 S140G 돌연변이형에서 심실이 소모하는 ATP의 양과 박출계수가 감소하였다. 3차원 세동 시뮬레이션 결과 표준형에서는 심실세동이 종결되었으나, S140G 돌연변이 형에서는 심실세동이 종결되지않고 유지되었으며, 심실세동이 빠르게 발생하였다. 결론적으로, KCNQ1 S140G 돌연변이로 인해 증가된 심실의 Iks는 심실의 박출 효율을 감소시키고 심실세동을 발생시키고 유지시키며, 부정맥 발생의 위험성을 높일 수 있다.

• Proceeding of EDISON Challenge
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• 2014.03a
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• pp.237-249
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• 2014

Deposition of amyloid-${\beta}$ ($A{\beta}$) proteins is the conventional pathological hallmark of Alzheimer's disease (AD). The $A{\beta}$ protein formed from the amyloid precursor protein is predominated by the 40 residue protein ($A{\beta}40$) and by the 42 residue protein ($A{\beta}42$). While $A{\beta}40$ and $A{\beta}42$ differ in only two amino acid residues at the C-terminal end, $A{\beta}42$ is much more prone to aggregate and exhibits more neurotoxicity than $A{\beta}40$. Here, we investigate the molecular origin of the difference in the aggregation propensity of these two proteins by performing fully atomistic, explicit-water molecular dynamics simulations. Then, it is followed by the solvation thermodynamic analysis based on the integral-equation theory of liquids. We find that $A{\beta}42$ displays higher tendency to adopt ${\beta}$-sheet conformations than $A{\beta}40$, which would consequently facilitate the conversion to the ${\beta}$-sheet rich fibril structure. Furthermore, the solvation thermodynamic analysis on the simulated protein conformations indicates that $A{\beta}42$ is more hydrophobic than $A{\beta}40$, implying that the surrounding water imparts a larger thermodynamic driving force for the self-assembly of $A{\beta}42$. Taken together, our results provide structural and thermodynamic grounds on why $A{\beta}42$ is more aggregation-prone than $A{\beta}40$ in aqueous environments.

• Proceeding of EDISON Challenge
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• 2014.03a
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• pp.63-74
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• 2014

Predicting protein loop structures is an important modeling problem since protein loops are often involved in diverse biological functions by participating in enzyme active sites, ligand binding sites, etc. However, loop structure prediction is difficult even when structures of homologous proteins are known due to large sequence and structure variability among loops of homologous proteins. Therefore, an ab initio approach is necessary to solve loop modeling problems. One of the difficulties in the development of ab initio loop modeling method is to derive an accurate scoring function that closely approximates the true free energy function. In particular, entropy as well as energy contribution have to be considered adequately for loops because loops tend to be flexible compared to other parts of protein. In this study, the contribution of conformational entropy is considered in scoring loop conformations by employing "colony energy" which was previously proposed to estimate the free energy for an ensemble of conformations. Loop conformations were generated by using two EDISON_Chem programs GalaxyFill and GalaxySC, and colony energy was designed for this sampling by tuning relevant parameters. On a test set of 40 loops, the accuracy of predicted loop structure improved on average by scoring with the colony energy compared to scoring by energy alone. In addition, high correlation between colony energy and deviation from the native structure suggested that more extensive sampling can further improve the prediction accuracy. In another test on 6 ligand-binding loops that show conformational changes by ligand binding, both ligand-free and ligand-bound states could be identified by using colony energy when no information on the ligand-bound conformation is used.

• Proceeding of EDISON Challenge
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• 2015.03a
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• pp.14-23
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• 2015

Alzheimer's disease is one of the most common types of degenerative dementia. As a considerable cause of Alzheimer's disease, neurotoxic plaques composed of 39 to 42 residue-long amyloid beta($A{\beta}$) fibrils have been found in the patient's brain in large quantity. A previous study found that erythrosine B (ER), a red color food dye approved by FDA, inhibits the formation of amyloid beta fibril structures. Here, in an attempt to elucidate the inhibition mechanism, we performed molecular dynamics simulations to demonstrate the conformational change of $A{\beta}40$ induced by 2 ERs in atomistic detail. During the simulation, the ERs bound to the surfaces of both N-terminus and C-terminus regions of $A{\beta}40$ rapidly. The observed stacking of the ERs and the aromatic side chains near the N-terminus region suggests a possible inhibition mechanism in which disturbing the inter-chain stacking of PHEs destabilizes beta-sheet enriched in amyloid beta fibrils. The bound ERs block water molecules and thereby help stabilizing alpha helical structure at the main chain of C-terminus and interrupt the formation of the salt-bridge ASP23-LYS28 at the same time. Our findings can help better understanding of the current and upcoming treatment studies for Alzheimer's disease by suggesting inhibition mechanism of ER on the conformational transition of $A{\beta}40$ at the molecular level.

• Proceeding of EDISON Challenge
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• 2016.03a
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• pp.246-255
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• 2016

When spring of the suspension is exerted by an external load, a car should be designed to prevent predictable damages and designed for a ride comfort. We used experiments design to design VON-MISES STRESS and K, a constant, of spring of suspension which is installed in a car as a goal level. We analyzed the result from Edison's Elastic - Plastic Analysis SW(CSD_EPLAST) by setting D, d, n as external diameter of coil, internal diameter of coil, the number of total coil respectively. The experiment design let the outcome be as Full-second order by using Box-Behnken which is one of response surface methods. Experimented and analyzed results based on the established experiments design, We found out design parameter which has desired VON-MISES STRESS and the constant K. Additionally, we predicted life time of when the external load was exerted by repeated load by using fatigue equation, and verification of plastic deformation has also been made. Additionally we interpreted a model, which is formed by optimized design parameter, with linear analysis and non-linear analysis, at the same time we also analyzed plastic deformation with the values from the both models. Finally, we predicted fatigue life of optimized model by using fatigue estimation theory and also evaluated a ride comfort with oscillation analysis.