• Structural Engineering and Mechanics
• /
• 제42권3호
• /
• pp.335-352
• /
• 2012

Particle Swarm Optimization (PSO) is a stochastic population based optimization algorithm which has attracted attentions of many researchers. This method has great potentials to be applied to many optimization problems. Despite its robustness the standard version of PSO has some drawbacks that may reduce its performance in optimization of complex structures such as laminated composites. In this paper by suggesting a new variation scheme for acceleration parameters and inertial weight factors of PSO a novel optimization algorithm is developed to enhance the basic version's performance in optimization of laminated composite structures. To verify the performance of the new proposed method, it is applied in two multi-objective design optimization problems of laminated cylindrical. The numerical results from the proposed method are compared with those from two other conventional versions of PSO-based algorithms. The convergancy of the new algorithms is also compared with the other two versions. The results reveal that the new modifications inthe basic forms of particle swarm optimization method can increase its convergence speed and evade it from local optima traps. It is shown that the parameter variation scheme as presented in this paper is successful and can evenfind more preferable optimum results in design of laminated composite structures.

• Nuclear Engineering and Technology
• /
• 제43권6호
• /
• pp.573-582
• /
• 2011

The in-cabinet response spectrum is used to define the input motion in the seismic qualification of instruments and devices mounted inside an electrical cabinet. This paper presents a procedure for generating the in-cabinet response spectrum for electrical equipment based on in-situ testing by an impact hammer. The proposed procedure includes an algorithm to build the relationship between the impact forces and the measured acceleration responses of cabinet structures by estimating the state-space model. This model is used to predict seismic responses to the equivalent earthquake forces. Three types of structural model are analyzed for numerical verification of the proposed method. A comparison of predicted and simulated response spectra shows good convergence, demonstrating the potential of the proposed method to predict the response spectra for real cabinet structures using vibration tests. The presented procedure eliminates the uncertainty associated with constructing an analytical model of the electrical cabinet, which has complex mass distribution and stiffness.

• 한국기계가공학회지
• /
• 제20권9호
• /
• pp.90-96
• /
• 2021

As real-time data of factories can be collected using various sensors, the adaptation of intelligent unmanned processing systems is spreading via the establishment of smart factories. In intelligent unmanned processing systems, data are collected in real time using sensors. The equipment is controlled by predicting future situations using the collected data. Particularly, a technology for the prediction of tool wear and for determining the exact timing of tool replacement is needed to prevent defected or unprocessed products due to tool breakage or tool wear. Directly measuring the tool wear in real time is difficult during the cutting process in milling. Therefore, tool wear should be predicted indirectly by analyzing the cutting load of the main spindle, current, vibration, noise, etc. In this study, data from the current and acceleration sensors; displacement data along the X, Y, and Z axes; tool wear value, and shape change data observed using Newroview were collected from the high-speed, two-edge, flat-end mill machining process of SKD11 steel. The support vector machine technique (machine learning technique) was applied to predict the amount of tool wear using the aforementioned data. Additionally, the prediction accuracies of all kernels were compared.

• International journal of advanced smart convergence
• /
• 제8권1호
• /
• pp.9-17
• /
• 2019

In this article, we present a performance enhancement scheme for mobile applications by adopting persistent memory. The proposed scheme supports the deadline guarantee of real-time applications like a video player, and also provides reasonable performances for non-real-time applications. To do so, we analyze the program execution path of mobile software platforms and find two sources of unpredictable time delays that make the deadline-guarantee of real-time applications difficult. The first is the irregular activation of garbage collection in flash storage and the second is the blocking and time-slice based scheduling used in mobile platforms. We resolve these two issues by adopting high performance persistent memory as the storage of real-time applications. By maintaining real-time applications and their data in persistent memory, I/O latency can become predictable because persistent memory does not need garbage collection. Also, we present a new scheduler that exclusively allocates a processor core to a real-time application. Although processor cycles can be wasted while a real-time application performs I/O, we depict that the processor utilization is not degraded significantly due to the acceleration of I/O by adopting persistent memory. Simulation experiments show that the proposed scheme improves the deadline misses of real-time applications by 90% in comparison with the legacy I/O scheme used in mobile systems.

• 인터넷정보학회논문지
• /
• 제22권3호
• /
• pp.9-16
• /
• 2021

This study aims to develop a human activity recognition (HAR) system as a Deep-Learning (DL) classification model, distinguishing various human activities. We solely rely on the signals from a wristband accelerometer worn by a person for the user's convenience. 3-axis sequential acceleration signal data are gathered within a predefined time-window-slice, and they are used as input to the classification system. We are particularly interested in developing a Deep-Learning model that can outperform conventional machine learning classification performance. A total of 13 activities based on the laboratory experiments' data are used for the initial performance comparison. We have improved classification performance using the Convolutional Neural Network (CNN) combined with an auto-encoder feature reduction and parameter tuning. With various publically available HAR datasets, we could also achieve significant improvement in HAR classification. Our CNN model is also compared against Recurrent-Neural-Network(RNN) with Long Short-Term Memory(LSTM) to demonstrate its superiority. Noticeably, our model could distinguish both general activities and near-identical activities such as sitting down on the chair and floor, with almost perfect classification accuracy.

• International Journal of Internet, Broadcasting and Communication
• /
• 제13권3호
• /
• pp.92-103
• /
• 2021

With the continuous acceleration of economic and social development, people gradually pay attention to their health, improve their living environment, diet, strengthen exercise, and even conduct regular health examination, to ensure that they always understand the health status. Even so, people still face many health problems, and the number of chronic diseases is increasing. Recently, COVID-19 has also reminded people that public health problems are also facing severe challenges. With the development of artificial intelligence equipment and technology, medical diagnosis expert systems based on big data have become a topic of concern to many researchers. At present, there are many algorithms that can help computers initially diagnose diseases for patients, but they want to improve the accuracy of diagnosis. And taking into account the pathology that varies from person to person, the health diagnosis expert system urgently needs a new algorithm to improve accuracy. Through the understanding of classic algorithms, this paper has optimized it, and finally proved through experiments that the combined classification algorithm improved by latent factors can meet the needs of medical intelligent diagnosis.

• 한국항공우주학회지
• /
• 제50권6호
• /
• pp.393-400
• /
• 2022

고고도 교전 시 사용되는 궤도수정 및 자세제어 시스템(Divert and Attitude Control System, DACS)은 고가이며 복잡하다. 본 논문에서는 비교적 단순하고 저가인 추력벡터제어(Thrust Vector Control, TVC)를 탑재한 유도탄의 고고도 종말 유도조종 루프를 제안한다. 본 유도조종 루프는 쿼터니언 피드백 제어기법을 이용하여 진 비례항법유도로 산출된 가속도 명령으로부터 변환된 추력 자세각 명령을 추종하며 유도를 수행한다. 고고도에서 탄도탄에 대한 교전 시뮬레이션을 통하여 제안한 유도조종 루프의 성능을 분석한다.

• 문화기술의 융합
• /
• 제7권4호
• /
• pp.801-806
• /
• 2021

레일 파상마모는 레일표면 요철 관리기준의 부재로 인해 지속적으로 증가하는 추세에 있다. 레일파상마모는 승차감 저하와 궤도 유지관리 물량증대 등과 같은 다양한 문제점을 야기 시키고 있다. 본 논문에서는 레일 파상마모 발생구간을 대상으로 레일 연마 전, 후에 대한 레일요철 측정 및 궤도계측(동적 윤중, 변위, 가속도)을 수행하여 레일 파상마모가 궤도부담력에 미치는 영향을 분석하였다. 또한 레일 파상마모 저감을 위해 실시한 레일연마는 레일 파상마모로 인해 발생되는 추가적인 궤도부담력 저감에 매우 효과적이었음을 실험적으로 입증하였다.

• Nuclear Engineering and Technology
• /
• 제54권5호
• /
• pp.1712-1725
• /
• 2022

For improving the seismic performance of the nuclear power plant (NPP) piping system, attempts have been made to apply a dynamic absorber (DA). However, the current piping DA design method is limited because it cannot provide the globally optimum values for the target design seismic loading. Therefore, this study proposes a seismic time history analysis-based DA optimal design method for piping. To this end, the Kriging approach is introduced to reduce the numerical cost required for seismic time history analyses. The appropriate design of the experiment method is used to increase the efficiency in securing response data. A gradient-based method is used to efficiently deal with the multi-dimensional unconstrained optimization problem of the DA optimal design. As a result, the proposed method showed an excellent response reduction effect in several responses compared to other optimal design methods. The proposed method showed that the average response reduction rate was about 9% less at the maximum acceleration, about 5% less at the maximum value of the response spectrum, about 9% less at the maximum relative displacement, and about 4% less at the maximum combined stress compared to existing optimal design methods. Therefore, the proposed method enables an effective optimal DA design method for mitigating seismic response in NPP piping in the future.

• Smart Structures and Systems
• /
• 제29권4호
• /
• pp.617-624
• /
• 2022

A new intelligent adaptive control scheme was proposed that combines the control based on interference observer and fuzzy adaptive s-curve for flight path tracking control of unmanned aerial vehicle (UAV). The most important contribution is that the control configurations don't need to know the uncertainty limit of the vehicle and the influence of interference is removed. The proposed control law is an integration of fuzzy control estimator and adaptive proportional integral (PI) compensator with input. The rated feedback drive specifies the desired dynamic properties of the closed control loop based on the known properties of the preferred acceleration vector. At the same time, the adaptive PI control compensate for the unknown of perturbation. Additional terms such as s-surface control can ensure rapid convergence due to the non-linear representation on the surface and also improve the stability. In addition, the observer improves the robustness of the adaptive fuzzy system. It has been proven that the stability of the regulatory system can be ensured according to linear matrix equality based Lyapunov's theory. In summary, the numerical simulation results show the efficiency and the feasibility by the use of the robust control methodology.