Earticle

This paper describes the use of approximation in Collaborative Optimization (CO) method, one of the Multidisciplinary Design Optimization (MDO) techniques. The approximation is used to model the result of a disciplinary design, optimal discrepancy function value, as a function of the interdisciplinary target variables passed from system level to the discipline. The optimal discrepancy function value is used to examine the interdisciplinary compatibility constraint (discrepancy function =0) duringthe system level optimization. However, the peculiar shape of the compatibility constraint makes it difficult to exploit well–developed conventional approximation methods. This paper introduces the combination of neural network classification and kriging to resolve this problem. In addition, for the purpose of enhancing the accuracy of the approximation, the approximation is continuously updated using the information obtained from the system level optimization. This iterative process is continued until acceptable convergence is achieved.

Collaborative optimization (CO) is a multi-level decomposed methodology for a large-scale multidisciplinary design optimization (MDO). CO is known to have computational and organizational advantages. Its decomposed architecture removes a necessity of direct communication among disciplines, guaranteeing their autonomy. However, CO has several problems at convergence characteristics and computation time. In this study, such features are discussed and some suggestions are made to improve the performance of CO. Only for the system level optimization, genetic algorithm is used and gradient-based method is used for subspace optimizers. Moreover, response surface models are replaced as analyses in subspaces. In this manner, CO is applied to aero-structural design problems of the aircraft wing and its results are compared with the multidisciplinary feasible (MDF) method and the original CO. Through these results, it is verified that the suggested approach improves convergence characteristics and offers a proper solution.

We address the issues of difference in latency tolerance with heterogeneous IoT users from ground and aviation space by proposing a collaborative satellite terrestrial EDGE computing network. Based on the variability in latency tolerance we propose three offloading schemes under two distinctive scenarios. Optimization in resource sharing while offloading is carried out by adopting DDPG-based actor-critic framework which is suggested as a suitable algorithm by recent studies. We validated the schemes evaluating four performance parameters. Results showed that schemes that tolerate delays between 0.25 to 2.00 s outperformed other schemes in terms of reward, delay and energy consumption.

Multidisciplinary Design Optimization(MDO) method that considers principles in various fields affecting big scale structure and system design at the same time is used. Because most variables are connected many engineering phenomena under the classic optimized design method(all-in-one design approach), it is hard to judge the meaning of final design solution obtained, and there are cases where all variables converge before reaching the optimal design value in large-scale design problems with many variables. Collaborative Optimization (CO) method, the most advanced MDO approach, is used to efficiently solve these optimum problems, to efficiently analyze design problems involving numerous design variables and constraints and in which various engineering phenomena occur. However, the application of the MDO problem to CO introduces a number of numerical problems by destroying the numerical properties of the original optimal design problem. Therefore, this study researches one solution by listing the problems of CO after organizing various approaches of MDO.

Traditionally, cellular wide area networks like UMTS are used as Internet access networks for particular users but, in some cases, they can be employed to provide Internet access to other smaller networks as well. The main inconvenient is that cellular networks have not the same bandwidth than wired networks and therefore, the cellular channel becomes a network bottle-neck. To help to mitigate this situation and in order to improve the user’s experience different optimization techniques exist, especially in web traffic. This paper studies first the existing synergies at HTTP layer between device capabilities expression, content negotiation, channel optimization and content adaptation. And secondly, it presents a system where HTTP requests transmission is optimized, showing a significant improvement in response time by means of HTTP header reduction over the cellular channel. In order to obtain a successful browsing experience, headers should be restored when reaching the Internet. This dynamic header reconstruction allows giving enriched and more expressive information about user’s device and browser capabilities. Thus navigation speed and user’s QoE can be enhanced by means of dynamic content negotiation in order to obtain adapted (and lighter) content and responses from web servers and adaptation proxies alike.

빅데이터 기반의 추천시스템 모델링에서 바이어스, 분산, 오류 및 학습은 성능에 중요한 요소이다. 이러한 시스템 에서는 추천 모델이 설명도를 유지하면서 복잡도를 줄여야 한다. 또한 데이터의 희소성과 시스템의 예측은 서로 반비례 의 속성을 가지기 마련이다. 따라서 희소성의 데이터를 인수분해 방법을 활용하여 상품간의 유사성을 학습을 통한 상품 추천모델이 제안되어 왔다. 본 논문에서는 이 모델의 손실함수에 대한 최적화 방안으로 max-norm 규제를 적용하여 모델의 일반화 능력을 향상시키고자 한다. 해결방안은 기울기를 투영하는 확률적 투영 기울기 강하법을 적용하는 것이 다. 많은 실험을 통하여 데이터가 희박해질수록 기존의 방법에 비해 제안된 규제 방법이 상대적으로 효과가 있다는 것을 확인하였다.

Bias, variance, error and learning are important factors for performance in modeling a big data based recommendation system. The recommendation model in this system must reduce complexity while maintaining the explanatory diagram. In addition, the sparsity of the dataset and the prediction of the system are more likely to be inversely proportional to each other. Therefore, a product recommendation model has been proposed through learning the similarity between products by using a factorization method of the sparsity of the dataset. In this paper, the generalization ability of the model is improved by applying the max-norm regularization as an optimization method for the loss function of this model. The solution is to apply a stochastic projection gradient descent method that projects a gradient. The sparser data became, it was confirmed that the propsed regularization method was relatively effective compared to the existing method through lots of experiment.

본 논문에서는 다분야 통합 설계최적화(MDO : Multidisciplinary Design Optimization)를 적용한 지구관측위성의 개념설계 과정 및 결과를 기술하였다. 현재까지 구축된 지구관측 위성의 데이터베이스를 기반으로 주요 파라미터에 대한 개념설계식을 정립하였으며, 다분야 통합 최적설계 아키텍처 중 CO(Collaborative Optimization) 기반을 이용하여 지구관측 위성 시스템의 최적 개념설계를 수행할 수 있는 설계 도구를 개발하였다. 주어진 제약조건을 만족시키면서 위성의 총 질량을 최소화하는 것을 설계 목표로 설정하였으며, 최적화 알고리즘으로는 SQP(Sequential Quadratic Programming)를 이용하였다. 다분야 통합 최적설계를 적용한 개념설계 결과와 ASNARO-1 및 IKONOS-2 위성 규격의 비교를 통해 해당설계도구의 유용성을 검증하였다.

In this paper, the conceptual design procedure and results of Earth observation satellite through Multidisciplinary Design Optimization (MDO) are described. The conceptual design equations for major parameters are developed based on the established database of Earth observation satellite so far. The MDO conceptual design tool for Earth observation satellite was developed by applying the Collaborative Optimization (CO) architecture amongst several MDO architecture techniques available today. The objective for this research was set to minimize the total mass of satellite as well as satisfy all design constraints by utilizing the Sequential Quadratic Programming (SQP) algorithm. Eventually the effectiveness of MDO conceptual design tool was verified through proposing a comparison between the conceptual design results with MDO applied and the design specification of ASNARO-1 & IKONOS-2 Earth observation satellite.

A Collaborative Optimization (CO) methodology for ring-stiffened composite material pressure hull of underwater vehicle is proposed. Structural stability and material strength are both examined. Lamination parameters of laminated plates are introduced to improve the optimization efficiency. Approximation models are established based on the Ellipsoidal Basis Function (EBF) neural network to replace the finite element analysis in layout optimizers. On the basis of a two-level optimization, the simultaneous structure material collaborative optimization for the pressure vessel is implemented. The optimal configuration of metal liner and frames and composite material is obtained with the comprehensive consideration of structure and material performances. The weight of the composite pressure hull decreases by 30.3% after optimization and the validation is carried out. Collaborative optimization based on the lamination parameters can optimize the composite pressure hull effectively, as well as provide a solution for low efficiency and non-convergence of direct optimization with design variables.

Federated learning enables collaborative model training across devices while preserving user privacy. However, the imbalanced class distribution in local datasets presents a challenge to global model accuracy. To address this problem, this paper proposes SmartFed, a novel optimization method integrating algorithm optimization and data enhancement. It introduces a confidence scoring mechanism to evaluate the divergence between global and local models, guiding local training to retain high-confidence knowledge and acquire low-confidence knowledge from local data. Additionally, SmartFed uses a variational autoencoder to generate virtual data features for fine-tuning the classifier layers of both models, complemented by feature distillation. Experiments show that SmartFed improves accuracy by 16.3%, reduces convergence time overhead by 22.2%, and decreases communication overhead by 33.1%, compared with the accuracy of state-of-the-art methods. The results show that SmartFed provides an effective solution for addressing class imbalance in federated learning, leading to enhanced model performance and more efficient training processes.

Ship design process requires lots of complicated analyses for determining a large number of design variables. Due to its complexity, the process is divided into several tractable designs or analysis problems. The interdependent relationship requires repetitive works. This paper employs collaborative optimization (CO), one of the multidisciplinary design optimization (MDO) techniques, for treating such complex relationship. CO guarantees disciplinary autonomy while maintaining interdisciplinary compatibility due to its bi-level optimization structure. However, the considerably increased computational time and the slow convergence have been reported as its drawbacks. This paper proposes the use of an approximation model in place of the disciplinary optimization in the system-level optimization. Neural network classification is employed as a classifier to determine whether a design point is feasible or not. Kriging is also combined with the classification to make up for the weakness that the classification cannot estimate the degree of infeasibility. For the purpose of enhancing the accuracy of a predicted optimum and reducing the required number of disciplinary optimizations, an approximation management framework is also employed in the system-level optimization.

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Multidisciplinary design optimization(MDO) can yield optimal design considering all the disciplinary requirements concurrently. A method to implement the collaborative optimization(CO) approach, one of the MDO methodologies, is developed using a pre-compiler “EzpreCompiler”, a design optimization library “EzOptimizer”, and a common object request broker architecture(CORBA) in distributed computing environment. The CO approach is applied to a mathematical example to show its applicability and equivalence to standard optimization(SO) formulation. In a realistic engineering problem such as optimal design of a two-member hub frame, optimal design of a speed reducer and initial design of a bulk carrier, the CO yields better results than the SO. Furthermore, the CO allows the distributed processing using the CORBA, which leads to reduction of overall computation time.

In practical design studies, most of designers solve multidisciplinary problems with complex design structure. These multidisciplinary problems have hundreds of analysis and thousands of variables. The sequence of process to solve these problems affects the speed of total design cycle. Thus it is very important for designer to reorder original design processes to minimize total cost and time. This is accomplished by decomposing large multidisciplinary problem into several multidisciplinary analysis subsystem (MDASS) and processing it in parallel. This paper proposes new strategy for parallel decomposition of multidisciplinary problem to raise design efficiency by using genetic algorithm and shows the relationship between decomposition and multidisciplinary design optimization (MDO) methodology.

In this study, large-scale distributed design approach for a life cycle cost (LCC) optimization of steel box girder bridges was implemented. A collaborative optimization approach is one of the multidisciplinary design optimization approaches and it has been proven to be best suited for distributed design environment. The problem of optimum LCC design of steel box girder bridges is formulated as that of minimization of the expected total LCC that consists of initial cost maintenance cost expected retrofit costs for strength, deflection and crack. To discuss the possibility of the application for the collaborative optimization of steel box girder bridges, the results of this algorithm are compared with those of single level algorithm. From the numerical investigations, the collaborative optimization approach proposed in this study may be expected to be new concepts and design methodologies associated with the LCC approach.

Nowadays. risk management in the enterprise is considered as the important activity. Risk management ran be defined as the activity which is the analysis of risk factors related to damages, the estimation of the magnitude of risk, and the determination of investment to protect damage in a company. Initially, risk management was originated in financial areas. But the concept of risk has been expanded in the enterprise. Most companies have extended their activities in various areas. In this tendency, most activities must be considered in supply chain So, risk management must be ronsidered as the concept in the viewpoint of supply chain. The framework of risk management in supply chain and the related mathematical model are represented in this paper. Risk management in supply chain ran provide a positive opportunity not only to protect various damages, but also to improve the relationship between partners.

본 연구의 목적은 건자재 물류시장의 효율적인 협업적 차량경로관리를 위한 개미군 최적화 방법을 제시하고자 한다. 건자재 물류는 출발지 석산에서 모래, 자갈을 덤프트럭을 이용하여 목적지 레미콘공장까지 운송함에 있어 덤프트럭의 운송이 단위 석산별로 개별 운송방식을 취하고 있다. 보통의 경우 대부분의 차량들이 출발지로 복귀 시 공차운행을 하게 되어 운송효율이 저하되는 원인이 되고 있다. 따라서 본 연구에서는 협업적 차량경로관리를 위한 개미군 최적화 방법을 적용하는 방법을 제시하고 파라미터 설정에 따른 개미군 최적화 방법의 결과를 비교 분석하였다. 개미군 최적화 방법의 파라미터 중 α값과 β값의 변화에 따른 결과를 실험하였으며 가장 좋은 결과를 보여준 α값과 β값을 제시하였다.

This paper proposes Ant Colony Optimization (ACO) for Collaborative Vehicle Routing Problem (CVRP) in the aggregate industry. The objective of the method is to minimize total traveling distances. In the aggregate industry the individual companies are main operators, which resulted in low utilization of vehicles because of lack of collaboration. Dump trucks carry the sand and gravels from the origin to the destination in one direction and they can carry waste materials when returning. More efficient operation can reduce empty returning vehicles. The data we used in the analysis is a part of real data in Seoul Metropolitan Areas in 2006. In this paper, Ant Colony Optimization Algorithm is adopted for simulation. We found the best value of α and β to this case.

Today enterprises are bringing forward the strong needs for the Global Work Space that is able to realize the collaboration of the virtual enterprise in order to achieve the rapid entrance to market, the quality improvement as well as the cost reduction of their new products. Especially, they are building the efficient Product Management Infrastructure in parallel with the real-time knowledge management for the information generated in the course of a product lifecycle and the Process Innovation making the Concurrent Engineering possible. Building a system in the web environment cannot be the entire effort to realize the Global Work Space within an enterprise that is an essential factor for the reduction of a product development period which in turn contributes to Time to Market. Various work models and processes are found in enterprises and many different application programs are developed and utilized to support these. This study proposes a scheme for the optimized Collaborative Workflow System Architecture that is able to take in and apply various application programs accompanied by the product development work process. Through this, we are to examine various limits and problems existing in the real-time collaborative system between enterprises and to reform these.

협력 필터링 기반의 추천시스템에서 유사도 측정은 시스템의 성능에 큰 영향을 미치는데, 이는 유사한 다른 사용자들로부터 항목을 추천받기 때문이다. 본 연구에서는 전통적인 유사도 측정 방법의 가장 큰 문제인 데이터 희소성을 극복하기 위해, 기존의 유사도 측정값과 공통평가항목수의 반영값을 최적으로 결합하는 새로운 유사도 측정방식을 제안한다. 제안 방식의 성능 평가를 위해 다양한 조건으로 실험한 결과 기존 방식들보다 우수한 예측 정확도를 나타냈으며, 구체적으로 전통적인 피어슨 상관보다 최대 약 7%, 코사인 유사도보다는 최대 약 4% 향상된 결과를 보였다.

Measuring similarity in collaborative filtering-based recommender systems greatly affects system performance. This is because items are recommended from other similar users. In order to overcome the biggest problem of traditional similarity measures, i.e., data sparsity problem, this study suggests a new similarity measure that is the optimal combination of previous similarity and the value reflecting the number of co-rated items. We conducted experiments with various conditions to evaluate performance of the proposed measure. As a result, the proposed measure yielded much better performance than previous ones in terms of prediction qualities, specifically the maximum of about 7% improvement over the traditional Pearson correlation and about 4% over the cosine similarity.