Earticle

This study focuses on optimizing the uniform pressing process in precision manufacturing, addressing challenges posed by surface roughness and height differences between components. In real-world conditions, such irregularities can lead to non-uniform pressure distribution during pressing, negatively affecting product quality. To mitigate these issues, a buffer protection layer was introduced between the press and components. The optimization process was conducted through finite element analysis (FEA) to determine the ideal material properties, including elastic modulus, Poisson's ratio, and thickness of the buffer layer. Two surface roughness scenarios were examined to assess the impact of surface conditions on pressing uniformity. The results indicate that a higher elastic modulus, Poisson’s ratio, and thicker buffer layers are more effective in achieving uniform pressing, particularly under rougher surface conditions. This study provides a practical solution for improving the precision and reliability of pressing processes, ensuring better product consistency and enhancing overall manufacturing efficiency.

Liquid hydrogen, a promising energy carrier, necessitates robust storage and transportation systems due to its extremely low boiling point. Consequently, the development of reliable cryogenic adhesives and standardized testing protocols is crucial. This study focused on optimizing the design of a gripper used in single lap shear tests for evaluating cryogenic adhesives, specifically targeting the challenges posed by low-temperature conditions that induce slippage at the gripper interface. The optimal design was performed using a total of five variables, including the position and size of the gripper. By employing the genetic algorithm coupled with finite element analysis, we exhaustively searched through over 1000 models to identify the optimal gripper geometry. We successfully minimized stress concentration at the gripper region while maintaining a uniform stress distribution on the non-bonded surface. Furthermore, the study explored the impact of symmetric versus asymmetric gripper configurations on test results. The findings revealed that symmetric grippers generally yielded more consistent and reliable data. This study's results enable the accurate and stable execution of lap shear tests under the temperature conditions of liquefied hydrogen.

The purpose of this study was to develop an optimized hull-form of a coastal fishing vessel operating at high speed. In order to achieve the purpose of the study, HOTBOAT, that can perform an automatic hull-form optimal design of a coastal fishing vessel, was developed. HOTBOAT was composed of an objective function estimation algorithm and an optimization algorithm and a hull-form modification algorithm. In this study, the wave-making resistance was selected as an objective function and the potential-based panel method was applied to predict the objective function. SQP(sequential quadratic programming) method were adopted to predict the optimal direction and answer. Bell-shaped hull-form modification function method and NURBS(non-uniform rational B-spline) geometry modeling method were applied to modify the hull-form during the whole optimization process. HOTBOAT was applied to develop the optimal hull-form of a coastal fishing vessel with minimum wave resistance. The initial hull-form of the coastal fishing vessel was compared with the optimal hull-form. As a result of hull-form optimization, a coastal fishing vessel with a reduction of about 30% was developed compared with the initial hull-form and the displacement and the wetted surface area of the optimal hull-form was decreased to less than about 1% in comparison with the initial hull-form.

유비쿼터스 환경에서는 다양한 센서로 부터 원거리에 데이터를 전송해야 하는 문제가 제기되어져 왔다. 특히 서로 다른 위치에서 도착한 데이터를 통합하는 과정에서 데이터의 속성 값들이 상이하거나 데이터에 일부 손실이 있는 데이터들도 처리해야 하는 어려운 문제를 가지고 있었다. 본 논문은 이와 같은 데이터들을 대상으로 집락분석 하는 방법을 제시한다. 이 방법의 핵심은 문제에 적합한 목적함수를 정의하고, 이 목적함수를 최적화 할 수 있는 알고리즘을 개발하는데 있다. 목적함수는 OCS 목적함수를 변형하여 사용한다. 이진 값을 가지는 데이터 만을 처리할 수 있었던 MFA(Mean Field Annealing)을 연속 값을 가지는 분야에도 적용할 수 있도록 확장한다. 그리고 이를 CMFA이라 명하고 최적화 알고리즘으로 사용한다.

In the ubiquitous environment, there has been a problem of transmitting data from various sensors at a long distance. Especially, in the process of integrating data arriving at different locations, data having different property values of data or having some loss in data had to be processed. This paper present a method to analyze such data. The core of this method is to define an objective function suitable for the problem and to develop an algorithm that can optimize this objective function. The objective function is used by modifying the OCS function. MFA (Mean Field Annealing), which was able to process only binary data, is extended to be applicable to fields with continuous values. It is called CMFA and used as an optimization algorithm.

The high-frequency band, crucial for supporting the 5G/6G system, faces challenges of signal obstruction by obstacles. This is attributed to significant path loss resulting from radio straightness and short radio distance. To address these challenges, there is a growing interest in leveraging non-terrestrial networks (NTNs) and reconfigurable intelligent surfaces (RISs), utilizing high-altitude satellites as base stations or terminals. Within a three-dimensional NTN system, the vulnerability of wireless signals to eavesdropping due to the open nature of the environment is a notable drawback. To mitigate this vulnerability, this paper introduces an algorithm designed to maximize the secrecy rate. The proposed algorithm optimizes security performance by fine-tuning the base station and RIS beamforming vectors. This optimization is achieved through successive convex approximation and minorization?maximization algorithms. Simulation results affirm the superiority of the proposed algorithm in terms of secrecy rate over existing techniques.

Unmanned aerial vehicles (UAVs) and reconfigurable intelligent surfaces (RISs) have garnered considerable research interest in the fields of 5G and 6G wireless communication due to their remarkable flexibility and cost-effectiveness. However, the inherent openness of wireless communication environments renders these technologies vulnerable to eavesdropping. This paper presents a penalty-based successive convex approximation algorithm and a minorize?maximization algorithm to optimize the transmission beamforming vector, RIS beamforming vector, and UAV?RIS trajectory. The objective of this study was to enhance the physical layer security performance of wireless communication systems using UAVs and RISs. Our simulation results demonstrate that the proposed technique achieves a higher security transmission rate compared to existing techniques.

The large amplitude variations of OFDM signals generate in-band distortion and out-of-band radiation. In recent years, cubic metric (CM) has been verified as a more accurate metric to measure the amplitude variations. In this paper, the PTS technique is used to decrease the CM of OFDM signals. To overcome the search complexity of an exhaustive search based PTS technique, we introduce an improved backtracking search (IBS) optimization algorithm. Simulations are conducted to show the advantages of the proposed IBS based PTS approach compared with the conventional OFDM, and several state-of-the-art methods in terms of search complexity and CM reduction performance.

High-risk pregnancy endangers both mother and baby, with one maternal death every two minutes in 2023. This study has proposed three Gradient Boosting models?GB-Base, GB-SMOTE, and ZOA-GB?using the West Lombok Pregnancy Risk Prediction Dataset. GB-Base and GB-SMOTE have achieved 90.46% and 90.28% accuracy, while ZOA-GB, using 10 selected features, has reached 88.89%. GB-SMOTE has shown the best performance with an F-score of 84.41%. SHAP has identified Maternal Age, Hemoglobin, and Parity as key features, and DiCE has validated feature-driven prediction control. The study is limited by a single-source dataset, the absence of external-validation, and unexplored optimizers.

The objective of cloud task scheduling is to partition tasks on shared resources to minimize energy consumption and makespan. Recently, several meta-heuristics for task scheduling were proposed and achieved encouraging results. However, their performance is far from the ideal state and needs more improvement. This paper introduces an enhanced sunflower optimization (ESFO) algorithm for improving the performance of existing task scheduling. It finds optimal scheduling in a polynomial time. The experiments show that ESFO outperformed its counterparts. The amount of improvement in comparison with the best counterpart is 0.73% and 2.24% respectively in terms of makespan and energy consumption.

The primary objective of task scheduling involves scheduling the task on resources and minimizing the objective of the schedule. In this study, we proposed mean grey wolf optimization algorithm to enhance the system performance there by depleting the scheduling issues. The main objective of this method is minimizing the makespan and energy consumption. The objective of the proposed algorithms has been evaluated using CloudSim toolkit for standard workload (left-skewed & right-skewed). The outcome of the simulation result shows that the proposed Mean GWO algorithm renders comparatively ample result than the other existing algorithms.

This paper proposes a new intrusion detection system (IDS) based on a combination of a multilayer perceptron (MLP) network, and artificial bee colony (ABC) and fuzzy clustering algorithms. Normal and abnormal network traffic packets are identified by the MLP, while the MLP training is done by the ABC algorithm through optimizing the values of linkage weights and biases. The CloudSim simulator and NSL-KDD dataset are used to verify the proposed method. Mean absolute error (MAE), root mean square error (RMSE), and the kappa statistic are considered as evaluation criteria. The obtained results have indicated the superiority of the proposed method in comparison with state-of-the-art methods.

The proximal policy optimization (PPO) algorithm is a promising algorithm in reinforcement learning. In this paper, we propose to add an action mask in the PPO algorithm. The mask indicates whether an action is valid or invalid for each state. Simulation results show that, when compared with the original version, the proposed algorithm yields much higher return with a moderate number of training steps. Therefore, it is useful and valuable to incorporate such a mask if applicable.

Task scheduling is one of the most important issues in heterogeneous environments when high efficiency is required. Because task scheduling is a Nondeterministic Polynomial (NP)-hard problem, many evolutionary algorithms have been adopted to solve this problem. Since the convergence speed of solutions in population-based algorithms is low, they are integrated with local search algorithms. Thus, in this paper, to optimize the task scheduling makespan, a hybrid particle swarm optimization and hill climbing algorithm is proposed. The experimental results on random and scientific Directed Acyclic Graph (DAG) showed that the proposed algorithm performs effectively in terms of the makespan compared to the current well-known heuristic and particle swarm optimization algorithms.

고객의 요구에 대한 빠른 대응과 유연하고 효율적으로 새로운 제품을 적기에 개발하기 위해서는 제품 플랫폼에 기초한 대량 맞춤이 절실히 요구된다. 이러한 목적을 달성하기 위하여 기업들은 상대적으로 생산비용을 낮게 유지하면서 대량생산의 이점을 유지하고 동시에 고객의 요구사항을 만족시키기 위해, product family를 도입하고 가능하면 작은 변화를 통하여 제품의 다양성을 유지하고자 한다. Product family를 설계할 때 중요한 이슈 중에 하나는 제품의 공통성과 차별성간의 절충점을 찾아내는 것인데, 본 연구에서는 설계자들이 product family 재설계를 용이하게 하기 위한 방법론을 제안한다. 이를 위하여 본 연구에서는 ant colony 알고리즘과 product family의 공통성 평가지수를 이용하여 product family 재설계 방법론을 개발한다. 제안한 방법론은 복잡하고 반복적인 많은 계산과정을 가지고 있는 다른 방법과 달리 메타 휴리스틱 알고리즘을 적용하여 인간의 간섭을 줄이고, 실험결과의 정확도, 반복성 및 강건성을 향상시킨다. 본 연구에서는 컴퓨터 마우스 제품군을 대상으로 제안한 방법의 타당성을 검증하였고, 추가적으로 product family 레벨과 부품 레벨의 product family 재설계 추천방안도 제시하였다.

신체영역무선통신망(WBAN)에서 클러스터 헤드(CH) 선출 및 최적경로의 라우팅은 에너지 효율 향상과 네트워크 노드 운영수명 연장을 위해 해결해야 할 이슈이다. 이러한 연구를 위해 본 논문에서는 BKOA알고리즘과 그리드 기반 멀티홉 라우팅 프레임워크를 결합한 하이브리드 BKOA-GRID를 제안한다. 시뮬레이션 수행결과 제안된 BKOA-GRID는 PSO, LEACH, EEUC 등 기존 알고리즘보다 노드생존율 90%, 잔류에너지 지속성은 총 에너지의 약 60%를 유지하여 높은 에너지 효율을 보였다.

Cluster head(CH) election and optimal path routing in a Wireless Body Area Network(WBAN) are issues that must be addressed to improve energy efficiency and extend the operating life of network nodes. To address these issues, this paper proposes a hybrid BKOA-GRID (Black Kite Optimization Algorithm-GRID) framework, which integrates the Black Kite Optimization Algorithm with a grid-based multi-hop routing structure. Simulation results demonstrate that the proposed BKOA-GRID exhibits superior energy efficiency compared to existing algorithms such as PSO, LEACH, and EEUC, maintaining a node survival rate of 90% and preserving approximately 60% of the total residual energy.

An UHF () RFID tag antenna is optimized and designed using a genetic algorithm (GA). The tag antenna impedance should be matched to the conjugate of the impedance of the tag IC Chip. The chip impedance has real and capacitive imaginary parts due to the parasitic capacitance of the RFID chip. A GA linked with a commercially available antenna simulation program optimizes the UHF tag antenna to match a commercially available RFID chip. This method shows that any RFID antenna can be designed for any commercial RFID chip with any impedance.

본 연구의 목적은 주석 필터를 사용한 high-pitch 기반 저선량 프로토콜이 적용된 전산화단층영상에서 발생하는 화질 저하 문제를 해결하기 위해 융합형 노이즈 제거 알고리즘을 모델링하고 그 kernel size를 최적화하는 것이다. CT 영상을 획득하기 위하여 AAPM CT performance phantom을 사용하였으며, 고주파 신호 손실 저감을 위하여 median-modified Wiener filter에 Richardson-Lucy 복원 알고리즘이 혼합된 융합형 노이즈 제거 알고리즘을 모델 링하였다. 알고리즘 적용 후의 노이즈 개선 정도를 객관적으로 평가하기 위하여 정량적 평가인자인 coefficient of variation (COV), contrast to noise ratio (CNR), 그리고 natural image quality evaluator (NIQE)를 계산하였다. 그 결과, 7 × 7의 kernel size에서 가장 우수한 영상특성을 나타내었고, 최적화된 융합형 노이즈 제거 알고리즘이 적용된 주석 필터를 사용한 high-pitch 기반 저선량 프로토콜이 적용된 영상을 알루미늄 필터 및 저선량 프로토콜이 적용되지 않은 주석 필터 영상과 비교하였을 때 COV 값은 각각 약 2.36배 및 3.95배, CNR 값은 4.36배 및 7.31배, 그리고 NIQE 값은 1.43배 및 1.45배 향상되었다. 결론적으로, 본 연구를 통해 kernel size가 7 × 7으로 최적화 된 융합형 노이즈 제거 알고리즘을 적용함으로써 주석 필터를 사용한 high-pitch 기반 저선량 프로토콜은 CT 영상의 화질 저하 문제를 해결하는 데에 있어 효과적임을 확인하였다.

In this study, computed tomography (CT) images were obtained using American Association of Physicists in Medicine CT performance phantom to quantitatively evaluate changes in image quality due to the application of high-pitch-based low-dose protocols using tin filters. Median modified Wiener filter algorithm and the Richardson-Lucy restoration algorithm were used fusion noise reduction algorithm, and kernel size was set from 3 × 3 to 15 × 15. We applied a fusion noise reduction algorithm from the acquired images, and quantitatively evaluated the effectiveness of reducing radiation dose and improving image quality. In addition, tin filter images without aluminum filters and low-dose protocols were obtained for comparative evaluation. As a result, the kernel size of 7 × 7 showed the best image quality, it was confirmed that images with low-dose protocols applied with optimized algorithms improved coefficient of variation values by 2.36 times and 3.95 times, contrast to noise ratio values by 4.36 times and 7.31 times, and natural image quality evaluator values by 1.43 times and 1.45 times. In conclusion, if high-pitch based low-dose protocols using tin filters can be stably used by applying the fusion noise reduction algorithm, it is expected that to reduce the exposure dose and provide patient-centered care services.

이 논문은 다목적 최적화 알고리듬을 이용하여 오일샌드 저류층에서 생산성이 높으면서도 최적의 에너지효 율을 갖는 SAGD(Steam Assisted Gravity Drainage) 공법의 운영조건을 선정하는 기법을 제안한다. 기존의 SAGD 공 법 최적화 연구들은 경제성인자가 고정된 단목적 최적화에 집중하였다. 이 연구에서는 회수율 최대화와 누적증기오 일비 최소화 관점에서 비지배관계에 놓이는 최적 생산운영 시나리오를 선정하였다. 이를 통하여 특정 목적함수에 편 향된 하나의 최종해만을 도출하는 기존 방법의 한계를 개선하였다. 제안한 방법으로 선정된 최적운영 시나리오들은 기존 방법과 달리 유가상황 및 비용 변화에 따라 추가적인 최적화 작업 없이 광구운영 의사결정에 유용하게 활용할 수 있다.

This paper proposes multi-objective optimization of the Steam Assisted Gravity Drainage (SAGD) process for improving the energy efficiency and recovery factor of oil sand reservoirs. Previous studies conducted on optimizing the SAGD process have focused on single-objective optimization with fixed economic factors. In this study, multiple trade-off operating scenarios were selected by applying a multi-objective optimization algorithm that aims at maximizing the recovery factor and minimizing the cumulative steam-oil ratio for efficiently addressing volatile market conditions. Thus, the proposed method can overcome the limitations of conventional optimization methods that not only yield a single solution based on a particular objective function but also are hard to adapt to fluctuating oil prices. Furthermore, the proposed method can provide optimum trade-off operating scenarios, and hence can aid in planning operating (i.e., marketing) strategies according to the variation in oil prices and operating costs, without the need for an additional optimization process.