Flight path optimization passing through waypoints for autonomous flight control systems

Trajectory optimization is performed to generate a flight path passing specified waypoints. To deal with the unspecified time of passing through a waypoint, an auxiliary variable is introduced. Normalization of the time variable by the auxiliary variable transforms the waypoint optimization problem into the conventional optimization problem. The condition for passing through the waypoints can be relaxed, so that the vehicle passes specified waypoints within a certain acceptable range. Sequential quadratic programming is used to solve the optimization problem. As a numerical example, six degree-of-freedom vehicle dynamics is considered. Two-dimensional and three-dimensional trajectory optimization problems with several waypoints are solved to verify the effectiveness of the proposed algorithm.     

Machine Learning-Based Two-Stage Data Selection Scheme for Long-Term Influenza Forecasting

One popular strategy to reduce the enormous number of illnesses and deaths from a seasonal influenza pandemic is to obtain the influenza vaccine on time. Usually, vaccine production preparation must be done at least six months in advance, and accurate long-term influenza forecasting is essential for this. Although diverse machine learning models have been proposed for influenza forecasting, they focus on short-term forecasting, and their performance is too dependent on input variables. For a country’s long-term influenza forecasting, typical surveillance data are known to be more effective than diverse external data on the Internet. We propose a two-stage data selection scheme for worldwide surveillance data to construct a long-term forecasting model for influenza in the target country. In the first stage, using a simple forecasting model based on the country’s surveillance data, we measured the change in performance by adding surveillance data from other countries, shifted by up to 52 weeks. In the second stage, for each set of surveillance data sorted by accuracy, we incrementally added data as input if the data have a positive effect on the performance of the forecasting model in the first stage. Using the selected surveillance data, we trained a new long-term forecasting model for influenza and perform influenza forecasting for the target country. We conducted extensive experiments using six machine learning models for the three target countries to verify the effectiveness of the proposed method. We report some of the results.     

Vibration suppression of cart-pendulum system by combining the input-shaping control and the position-input position-output feedback control

Journal of Mechanical Science and Technology - This study is concerned with the active vibration control of a cart-pendulum system. The input-shaping control alone is not sufficient to suppress...     

Crumple zone design for pedestrian protection using impact analysis

This paper describes the design process for an automobile crumple zone for pedestrian protection. The impact load and bending moments predicted by impact analysis were used to design a plastic structure that may help reduce pedestrian injuries to the thigh area. The fracture effect was incorporated into the model by calculating the damage to the plastic material during impact, and the analysis was conducted under the European New Car Assessment Program (Euro NCAP) test conditions, using the upper legform developed by ESI Corporation. In addition, the values predicted by the analysis were validated by comparison with results of actual impact tests.     

Application of in vitro gut fermentation models to food components: A review

This review introduces recent changes regarding measurement of preferences brought about by the discovery of false preferences that occur with paired preference testing. Testing protocols are examined critically. False preferences result from a response bias elicited by factors distinct from sensory properties of products being tested that can distort results. Statistical approaches to circumventing this problem are examined. Alternative methods are being developed to eliminate false preferences based on understanding information processing in the brain. This has led to development of disruptive experimental preference testing protocols based on hypothesized use of processing subroutines in the brain to protect the central processing system from overload. These disruptive protocols suppress response bias. Paired preference testing is designed to predict consumer behavior. A paucity of studies addressing whether they do is put into perspective.     

Effect of contact angle and drop spacing on the bulging frequency of inkjet-printed silver lines on FC-coated glass

The morphologies of inkjet-printed narrow silver lines on fluorocarbon film-coated glass substrates were measured with varying contact angles and drop spacing to study the actual stability of line printing by using a practical inkjet system. From a practical stability point of view, three types of the lines were observed: stable, unstable, and meta-stable. The stable lines were free from any bulging or breaking; the unstable lines had repetitive and periodic instabilities; and the meta-stable lines had no repetitive instability but had irregular bulges that appear sparsely. Unstable line printing resulted from either the dynamic or static instability of bead flow, which arose when the pressure-driven bead flow was too large or too small compared with droplet deposition rate, respectively. Whether the printing would be stable or meta-stable was determined by the anti-bulging stability of the flow against other disturbances. The anti-bulging stability increased when the bead flow rate was balanced with the printing rate, whereas it decreased for the present system when the flow-balance became sensitive to drop spacing.