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Design of a Variable Stability Flight Control System
Sungsu Park,Joon Soo Ko 한국항공우주학회 2008 International Journal of Aeronautical and Space Sc Vol.9 No.1
A design objective for variable stability flight control system is to develop a controller of in-flight simulation capability that forces the aircraft being flown to follow the dynamics of other aircraft. This paper presents a model-following variable stability control system (VSS) for in-flight simulation which consists of feedforward and feedback control laws, the aircraft dynamic model to be simulated, and switching and fader logics to reduce the transient effect between two aircraft dynamics. The separate design techniques for feedforward and feedback control law proposals are based on model matching and augmented linear quadratic (LQ) techniques. The system allows pilots to select and engage VSS mode, and when deselected, the aircraft reverts to the baseline flight control system. Both the baseline flight control laws and VSS control laws are computed continuously during flight. Initialization of the state values are necessary to prevent instability, since VSS control laws have integrators and filters in longitudinal, and lateral/directional axes. This paper demonstrates and validates the effectiveness and quality of VSS with F-16 models embedded in T-50 in-flight simulation aircraft.
A Decision Tree Approach for Identifying Defective Products in the Manufacturing Process
Sungsu Choi,Lkhagvadorj Battulga,Aziz Nasridinov,Kwan-Hee Yoo 한국콘텐츠학회 2017 International Journal of Contents Vol.13 No.2
Recently, due to the significance of Industry 4.0, the manufacturing industry is developing globally. Conventionally, the manufacturing industry generates a large volume of data that is often related to process, line and products. In this paper, we analyzed causes of defective products in the manufacturing process using the decision tree technique, that is a well-known technique used in data mining. We used data collected from the domestic manufacturing industry that includes Manufacturing Execution System (MES), Point of Production (POP), equipment data accumulated directly in equipment, in-process/external air-conditioning sensors and static electricity. We propose to implement a model using C4.5 decision tree algorithm. Specifically, the proposed decision tree model is modeled based on components of a specific part. We propose to identify the state of products, where the defect occurred and compare it with the generated decision tree model to determine the cause of the defect.
Sungsue Rheem,Insoo Rheem,Sejong Oh 한국축산식품학회 2017 한국축산식품학회지 Vol.37 No.1
Response surface methodology (RSM) is a useful set of statistical techniques for modeling and optimizing responses in research studies of food science. In the analysis of response surface data, a second-order polynomial regression model is usually used. However, sometimes we encounter situations where the fit of the second-order model is poor. If the model fitted to the data has a poor fit including a lack of fit, the modeling and optimization results might not be accurate. In such a case, using a fullest balanced model, which has no lack of fit, can fix such problem, enhancing the accuracy of the response surface modeling and optimization. This article presents how to develop and use such a model for the better modeling and optimizing of the response through an illustrative re-analysis of a dataset in Park et al. (2014) published in the Korean Journal for Food Science of Animal Resources.
Stability of the Max-Weight Protocol in Adversarial Wireless Networks
Sungsu Lim,Kyomin Jung,Andrews, Matthew IEEE 2014 IEEE/ACM transactions on networking Vol.22 No.6
<P>In this paper, we consider the Max-Weight protocol for routing and scheduling in wireless networks under an adversarial model. This protocol has received a significant amount of attention dating back to the papers of Tassiulas and Ephremides. In particular, this protocol is known to be throughput-optimal whenever the traffic patterns and propagation conditions are governed by a stationary stochastic process. However, the standard proof of throughput optimality (which is based on the negative drift of a quadratic potential function) does not hold when the traffic patterns and the edge capacity changes over time are governed by an arbitrary adversarial process. Such an environment appears frequently in many practical wireless scenarios when the assumption that channel conditions are governed by a stationary stochastic process does not readily apply. In this paper, we prove that even in the above adversarial setting, the Max-Weight protocol keeps the queues in the network stable (i.e., keeps the queue sizes bounded) whenever this is feasible by some routing and scheduling algorithm. However, the proof is somewhat more complex than the negative potential drift argument that applied in the stationary case. Our proof holds for any arbitrary interference relationships among edges. We also prove the same stability of ε-approximate Max-Weight under the adversarial model. We conclude the paper with a discussion of queue sizes in the adversarial model as well as a set of simulation results.</P>
Sungsue Rheem,Sejong Oh 한국축산식품학회 2019 한국축산식품학회지 Vol.39 No.1
Response surface methodology (RSM) is a useful set of statistical techniques for modeling and optimizing responses in research studies of food science. As a design for a response surface experiment, a central composite design (CCD) with multiple runs at the center point is frequently used. However, sometimes there exist situations where some among the responses at the center point are outliers and these outliers are overlooked. Since the responses from center runs are those from the same experimental conditions, there should be no outliers at the center point. Outliers at the center point ruin statistical analysis. Thus, the responses at the center point need to be looked at, and if outliers are observed, they have to be examined. If the reasons for the outliers are not errors in measuring or typing, such outliers need to be deleted. If the outliers are due to such errors, they have to be corrected. Through a re-analysis of a dataset published in the Korean Journal for Food Science of Animal Resources, we have shown that outlier elimination resulted in the increase of the maximum possible R-square that the modeling of the data can obtain, which enables us to improve the quality of response surface analysis.
Sungsue Rheem,Insoo Rheem,Sejong Oh 한국축산식품학회 2019 한국축산식품학회지 Vol.39 No.2
This research was motivated by our encounter with the situation where an optimization was done based on statistically non-significant models having poor fits. Such a situation took place in a research to optimize manufacturing conditions for improving storage stability of coffee-supplemented milk beverage by using response surface methodology, where two responses are Y1=particle size and Y2=zeta-potential, two factors are F1=speed of primary homogenization (rpm) and F2=concentration of emulsifier (%), and the optimization objective is to simultaneously minimize Y1 and maximize Y2. For response surface analysis, practically, the second-order polynomial model is almost solely used. But, there exists the cases in which the second-order model fails to provide a good fit, to which remedies are seldom known to researchers. Thus, as an alternative to a failed second-order model, we present the heterogeneous third-order model, which can be used when the experimental plan is a two-factor central composite design having -1, 0, and 1 as the coded levels of factors. And, for multi-response optimization, we suggest a modified desirability function technique. Using these two methods, we have obtained statistical models with improved fits and multi-response optimization results with the predictions better than those in the previous research. Our predicted optimum combination of conditions is (F1, F2)=(5,000, 0.295), which is different from the previous combination. This research is expected to help improve the quality of response surface analysis in experimental sciences including food science of animal resources.