• Title/Summary/Keyword: performance-based optimization

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The study of optimization of restraint systems for injuries of Q6 and Q10 child dummies (Q6, Q10 어린이 인체모형 상해치에 대한 안전 구속 시스템 최적화 연구)

  • Sun, Hongyul;Lee, Seul;Kim, Kiseok;Yoon, Ilsung
    • Journal of Auto-vehicle Safety Association
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    • v.7 no.3
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    • pp.7-13
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    • 2015
  • Occupant protection performance in frontal crashes has been developed and assessed for mainly front seat occupants over many years, and in recent years protection of rear seat occupants has also been extensively discussed. Unlike the front seats, the rear seats are often occupied by children seated in rear-facing or forward - facing child restraint systems, or booster seats. In the ENCAP, child occupant protection assessments using 18-month-old(P1.5) and 3-year-old(P3) test dummies in the rear seat have already been changed to new type of 18-month-old (Q1.5)and 3-year-old(Q3) test dummies. In addition, ENCAP are scheduled with the development and introduction of test dummies of 6-year-old (Q6) and 10.5-year-old children(Q10) starting 2016. In KNCAP, Q6 and Q10 child dummies will be introduced in 2017 as well. Automobile manufacturers need to develop safety performance for new child dummies closely. In this paper, we focused on Q6 and Q10 child dummies sitting in child restraint system. Offset frontal crash tests were conducted using two types of test dummies, Q6 and Q10 child dummies, positioned in the rear seat. Q6 and Q10 were used to compare dummy kinematics in rear seating positions between Q6 behind the driver's seat and Q10 behind the front passenger's seat. The full vehicle sled test results of both dummies were conducted with different restraint systems. It showed that several injury and image data was collected as the result of the full vehicle sled test. Based on the results of these investigations, this paper describes which factor is most important and combination is the best performance when evaluating rear seat occupant protection for Q6 and Q10 child dummies.

Prediction of Distillation Column Temperature Using Machine Learning and Data Preprocessing (머신 러닝과 데이터 전처리를 활용한 증류탑 온도 예측)

  • Lee, Yechan;Choi, Yeongryeol;Cho, Hyungtae;Kim, Junghwan
    • Korean Chemical Engineering Research
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    • v.59 no.2
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    • pp.191-199
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    • 2021
  • A distillation column, which is a main facility of the chemical process, separates the desired product from a mixture by using the difference of boiling points. The distillation process requires the optimization and the prediction of operation because it consumes much energy. The target process of this study is difficult to operate efficiently because the composition of feed flow is not steady according to the supplier. To deal with this problem, we could develop a data-driven model to predict operating conditions. However, data preprocessing is essential to improve the predictive performance of the model because the raw data contains outlier and noise. In this study, after optimizing the predictive model based long-short term memory (LSTM) and Random forest (RF), we used a low-pass filter and one-class support vector machine for data preprocessing and compared predictive performance according to the method and range of the preprocessing. The performance of the predictive model and the effect of the preprocessing is compared by using R2 and RMSE. In the case of LSTM, R2 increased from 0.791 to 0.977 by 23.5%, and RMSE decreased from 0.132 to 0.029 by 78.0%. In the case of RF, R2 increased from 0.767 to 0.938 by 22.3%, and RMSE decreased from 0.140 to 0.050 by 64.3%.

In-feed organic and inorganic manganese supplementation on broiler performance and physiological responses

  • de Carvalho, Bruno Reis;Ferreira Junior, Helvio da Cruz;Viana, Gabriel da Silva;Alves, Warley Junior;Muniz, Jorge Cunha Lima;Rostagno, Horacio Santiago;Pettigrew, James Eugene;Hannas, Melissa Izabel
    • Animal Bioscience
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    • v.34 no.11
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    • pp.1811-1821
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    • 2021
  • Objective: A trial was conducted to investigate the effects of supplemental levels of Mn provided by organic and inorganic trace mineral supplements on growth, tissue mineralization, mineral balance, and antioxidant status of growing broiler chicks. Methods: A total of 500 male chicks (8-d-old) were used in 10-day feeding trial, with 10 treatments and 10 replicates of 5 chicks per treatment. A 2×5 factorial design was used where supplemental Mn levels (0, 25, 50, 75, and 100 mg Mn/kg diet) were provided as MnSO4·H2O or MnPro. When Mn was supplied as MnPro, supplements of zinc, copper, iron, and selenium were supplied as organic minerals, whereas in MnSO4·H2O supplemented diets, inorganic salts were used as sources of other trace minerals. Performance data were fitted to a linearbroken line regression model to estimate the optimal supplemental Mn levels. Results: Manganese supplementation improved body weight, average daily gain (ADG) and feed conversion ratio (FCR) compared with chicks fed diets not supplemented with Mn. Manganese in liver, breast muscle, and tibia were greatest at 50, 75, and 100 mg supplemental Mn/kg diet, respectively. Higher activities of glutathione peroxidase and superoxide dismutase (total-SOD) were found in both liver and breast muscle of chicks fed diets supplemented with inorganic minerals. In chicks fed MnSO4·H2O, ADG, FCR, Mn balance, and concentration in liver were optimized at 59.8, 74.3, 20.6, and 43.1 mg supplemental Mn/kg diet, respectively. In MnPro fed chicks, ADG, FCR, Mn balance, and concentration in liver and breast were optimized at 20.6, 38.0, 16.6, 33.5, and 62.3 mg supplemental Mn/kg, respectively. Conclusion: Lower levels of organic Mn were required by growing chicks for performance optimization compared to inorganic Mn. Based on the FCR, the ideal supplemental levels of organic and inorganic Mn in chick feeds were 38.0 and 74.3 mg Mn/kg diet, respectively.

Evaluation of the Optimal Grouser Shape Ratio of Dozer Considering the Ground Conditions (지반 특성을 고려한 도저의 최적 그라우저 형상비 평가)

  • Baek, Sung-Ha;Kwak, Tae-Young;Choi, Changho;Lee, Seong-Hwan
    • Journal of the Korean Geotechnical Society
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    • v.37 no.3
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    • pp.31-41
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    • 2021
  • A dozer is a construction machinery used to move soil mass along large open tracts of land. Soil thrust generated on the soil-track interface determines the performance of the dozer; to improve the tractive performance of the dozer, the outer surface of the continuous-track is designed to protrude with grousers. In this study, we calculated soil thrust of the dozer equipped with grousers with various shape ratios, and evaluated the optimal grouser shape ratio considering ground conditions. Grouser generated additional soil thrust on the side of the continuous-track (e.g., side soil thrust) and converted the shearing surface (e.g., from soil-track interface to soil-soil interface), increasing the soil thrust of dozer by about 1.3 to 1.6 times. The effect of grouser's shape ratio on the soil thrust of dozer differed with the relative density of the ground. As the shape ratios of grouser increased, soil thrust of dozer decreased at the relative density of 40% and increased at the relative density of 80%. Based on these results, it can be concluded that the shape ratio of grouser severely affects the dozer's performance; thus, careful consideration of the optimal shape ratio of grouser is of great importance in the mechanical design, evaluation, and optimization of the undercarriage of dozers.

A Development of Hydrological Model Calibration Technique Considering Seasonality via Regional Sensitivity Analysis (지역적 민감도 분석을 이용하여 계절성을 고려한 수문 모형 보정 기법 개발)

  • Lee, Ye-Rin;Yu, Jae-Ung;Kim, Kyungtak;Kwon, Hyun-Han
    • KSCE Journal of Civil and Environmental Engineering Research
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    • v.43 no.3
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    • pp.337-352
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    • 2023
  • In general, Rainfall-Runoff model parameter set is optimized using the entire data to calculate unique parameter set. However, Korea has a large precipitation deviation according to the season, and it is expected to even worsen due to climate change. Therefore, the need for hydrological data considering seasonal characteristics. In this study, we conducted regional sensitivity analysis(RSA) using the conceptual Rainfall-Runoff model, GR4J aimed at the Soyanggang dam basin, and clustered combining the RSA results with hydrometeorological data using Self-Organizing map(SOM). In order to consider the climate characteristics in parameter estimation, the data was divided based on clustering, and a calibration approach of the Rainfall-Runoff model was developed by comparing the objective functions of the Global Optimization method. The performance of calibration was evaluated by statistical techniques. As a result, it was confirmed that the model performance during the Cold period(November~April) with a relatively low flow rate was improved. This is expected to improve the performance and predictability of the hydrological model for areas that have a large precipitation deviation such as Monsoon climate.

Performance Evaluation of Loss Functions and Composition Methods of Log-scale Train Data for Supervised Learning of Neural Network (신경 망의 지도 학습을 위한 로그 간격의 학습 자료 구성 방식과 손실 함수의 성능 평가)

  • Donggyu Song;Seheon Ko;Hyomin Lee
    • Korean Chemical Engineering Research
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    • v.61 no.3
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    • pp.388-393
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    • 2023
  • The analysis of engineering data using neural network based on supervised learning has been utilized in various engineering fields such as optimization of chemical engineering process, concentration prediction of particulate matter pollution, prediction of thermodynamic phase equilibria, and prediction of physical properties for transport phenomena system. The supervised learning requires training data, and the performance of the supervised learning is affected by the composition and the configurations of the given training data. Among the frequently observed engineering data, the data is given in log-scale such as length of DNA, concentration of analytes, etc. In this study, for widely distributed log-scaled training data of virtual 100×100 images, available loss functions were quantitatively evaluated in terms of (i) confusion matrix, (ii) maximum relative error and (iii) mean relative error. As a result, the loss functions of mean-absolute-percentage-error and mean-squared-logarithmic-error were the optimal functions for the log-scaled training data. Furthermore, we figured out that uniformly selected training data lead to the best prediction performance. The optimal loss functions and method for how to compose training data studied in this work would be applied to engineering problems such as evaluating DNA length, analyzing biomolecules, predicting concentration of colloidal suspension.

The Performance Bottleneck of Subsequence Matching in Time-Series Databases: Observation, Solution, and Performance Evaluation (시계열 데이타베이스에서 서브시퀀스 매칭의 성능 병목 : 관찰, 해결 방안, 성능 평가)

  • 김상욱
    • Journal of KIISE:Databases
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    • v.30 no.4
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    • pp.381-396
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    • 2003
  • Subsequence matching is an operation that finds subsequences whose changing patterns are similar to a given query sequence from time-series databases. This paper points out the performance bottleneck in subsequence matching, and then proposes an effective method that improves the performance of entire subsequence matching significantly by resolving the performance bottleneck. First, we analyze the disk access and CPU processing times required during the index searching and post processing steps through preliminary experiments. Based on their results, we show that the post processing step is the main performance bottleneck in subsequence matching, and them claim that its optimization is a crucial issue overlooked in previous approaches. In order to resolve the performance bottleneck, we propose a simple but quite effective method that processes the post processing step in the optimal way. By rearranging the order of candidate subsequences to be compared with a query sequence, our method completely eliminates the redundancy of disk accesses and CPU processing occurred in the post processing step. We formally prove that our method is optimal and also does not incur any false dismissal. We show the effectiveness of our method by extensive experiments. The results show that our method achieves significant speed-up in the post processing step 3.91 to 9.42 times when using a data set of real-world stock sequences and 4.97 to 5.61 times when using data sets of a large volume of synthetic sequences. Also, the results show that our method reduces the weight of the post processing step in entire subsequence matching from about 90% to less than 70%. This implies that our method successfully resolves th performance bottleneck in subsequence matching. As a result, our method provides excellent performance in entire subsequence matching. The experimental results reveal that it is 3.05 to 5.60 times faster when using a data set of real-world stock sequences and 3.68 to 4.21 times faster when using data sets of a large volume of synthetic sequences compared with the previous one.

Development of the Drift Design Method of High-rise Buildings using Weight Control Factors (중량 조절계수를 이용한 고층 건물 변위설계법 개발)

  • Park, Hyo Seon;Seo, Ji Hyun
    • Journal of Korean Society of Steel Construction
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    • v.17 no.3 s.76
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    • pp.285-294
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    • 2005
  • Drift design is one of the core techniques in the structural design of high-rise buildings and resizing technique is regarded as a practical drift design method for high-rise buildings. In the resizing technique, the structural weight is re-distributed to minimize the target displacement without a change in structural weights. However, the structural weight determined from resizing algorithm is bound to the structural weight based on the preliminary design. Therefore, in this paper, a drift design method that can control the weight of the structure without causing drift control performance to deteriorate is proposed by incorporating the weight control factor in the formulation of resizing algorithm. The proposed drift design method is applied to the drift design of two frame-shear wall systems. The proposed drift design method, in this study, makes it possible to control both the drift and weight of a high-rise building.

A Design Methodology for CNN-based Associative Memories (연상 메모리 기능을 수행하는 셀룰라 신경망의 설계 방법론)

  • Park, Yon-Mook;Kim, Hye-Yeon;Park, Joo-Young;Lee, Seong-Whan
    • Journal of KIISE:Software and Applications
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    • v.27 no.5
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    • pp.463-472
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    • 2000
  • In this paper, we consider the problem of realizing associative memories via cellular neural network(CNN). After introducing qualitative properties of the CNN model, we formulate the synthesis of CNN that can store given binary vectors with optimal performance as a constrained optimization problem. Next, we observe that this problem's constraints can be transformed into simple inequalities involving linear matrix inequalities(LMIs). Finally, we reformulate the synthesis problem as a generalized eigenvalue problem(GEVP), which can be efficiently solved by recently developed interior point methods. Proposed method can be applied to both space varying template CNNs and space-invariant template CNNs. The validity of the proposed approach is illustrated by design examples.

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Series-Type Hybrid Electric Bus Fuel Economy Increase with Optimal Component Sizing and Real-Time Control Strategy (최적용량매칭 및 실시간 제어전략에 의한 직렬형 하이브리드 버스의 연비향상)

  • Kim, Minjae;Jung, Daebong;Kang, Hyungmook;Min, Kyoungdoug
    • Transactions of the Korean Society of Mechanical Engineers B
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    • v.37 no.3
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    • pp.307-312
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    • 2013
  • The interest in reducing the emissions and increasing the fuel economy of ICE vehicles has prompted research on hybrid vehicles, which come in the series, parallel, and power-split types. This study focuses on the series-type hybrid electric vehicle, which has a simple structure. Because each component of a series hybrid vehicle is larger than the corresponding component of the parallel type, the sizing of the vehicle is very important. This is because the performance may be greater or less than what is required. Thus, in this research, the optimal fuel economy was determined and simulated in a real-world system. The optimal sizing was achieved based on the motor, engine/generator, and battery for 13 cycles, where DP was used. The model was developed using ASCET or a Simulink-Amisim Co-simulation platform on the rapid controller prototype, ES-1000.