Position control for asymmetrical hydraulic cylinder system using a single on/off valve

A single on/off valve is used to carry out the position control of the asymmetrical hydraulic cylinder. The influence of the nominal flow rate on the positional accuracy of piston is investigated and the proximate formula for calculating the nominal flow rate of on/off valve is introduced. The system structure proposed in this paper could avoid cavitation and hyper pressure in two chambers to some extent. The simulation results indicated that the control method in this paper could satisfy the expected control requirements.     

Subjective well-being related to satisfaction with daily travel

Previous research demonstrates an impact on subjective well-being (SWB) of affect associated with routine performance of out-of-home activities. A primary aim of the present study is to investigate whether satisfaction with daily travel has a positive impact on SWB, either directly or indirectly through facilitating the performance of out-of-home activities. A secondary aim is to determine whether emotional-symbolic or instrumental reasons for car use results in higher satisfaction with daily travel than other travel modes. A survey of a population-based sample of 1,330 Swedish citizens included measures of car access and use, satisfaction with daily travel, satisfaction with performance of out-of-home routine activities, and affective and cognitive SWB. The results confirmed that the effect on affective and cognitive SWB of satisfaction with daily travel is both direct and indirect via satisfaction with performance of activities. Percent weekly car use had a small effect on satisfaction with daily travel and on affective SWB, although fully mediating the effect of satisfaction with performance of the activities. This suggests that car use plays a minor role for satisfaction with daily travel and its effect on SWB. This role may be larger if investigated after a forced reduced car use.     

Modelling the dynamics of bus use in a changing travel environment using panel data

Panel data offers the potential to represent the influence on travel choices of changing circumstances, past history and persistent individual differences (unobserved heterogeneity). A four-wave panel survey collected data on the travel choices of residents before and after the introduction of a new bus rapid transit service. The data shows gradual changes to bus use over the four waves, implying time was required for residents to become aware of the new service and to adapt to it. Ordered response models are estimated for bus use over the survey period. The results show that the influence of level of service (LOS) is underestimated if unobserved heterogeneity is not taken into account. The delayed response to the new service is able to be well represented by including LOS as a lagged variable. Current bus use is found to be conditioned on past bus use, but with additional influence of lagged LOS and unobserved heterogeneity. It is shown how different model specifications generate different evolution patterns with the most realistic predictions arising from a model which takes into account lagged responses to change in LOS and unobserved heterogeneity. The paper demonstrates the feasibility of developing panel data models that can be applied to forecasting the effect of interventions in the travel environment. Longer panels—encompassing periods of both stability and change—are required to support future efforts at modelling travel choice dynamics.     

The validity of using activity type to structure tour-based scheduling models

A unique set of activity scheduling data is utilized in this paper to provide much needed empirical analysis of the sequence in which activities are planned in everyday life. This is used to assess the validity of the assumption that activities are planned in accordance to a fixed hierarchy of activity types: mandatory activities first (work/school), followed by joint maintenance, joint discretionary, allocated maintenance, and individual discretionary activities. Such an assumption is typical of current generation activity and tour-based travel demand models. However, the empirical results clearly do not support such assumptions. For instance, fewer than 50% of mandatory activities were actually planned first in related out-of-home tours; remaining activity types also did not take any particular precedence in the planning sequence. Given this, a search was made for the more salient attributes of activities (beyond activity type) that would better predict how they are planned within tours. Several ordered response choice models for different tour sizes were developed for this purpose, predicting the choice order of the 1st, 2nd, 3rd, etc. planned activity in the tour as a function of activity type, activity characteristics (duration, frequency, travel time, and involved persons), and individual characteristics. Activity duration played the most significant role in the models compared to any other single variable, wherein longer duration activities tended to be planned much earlier in tours. This strongly suggests that the amount of time-use, rather than the nature of the event as indicated by activity type, is a primary driver of within-tour planning order and offers potential for a much improved and valid fit.     

Optimal design and benefits of a short turning strategy for a bus corridor

We develop a short turning model using demand information from station to station within a single bus line-single period setting, aimed at increasing the service frequency on the more loaded sections to deal with spatial concentration of demand considering both operators’ and users’ costs. We find analytical expressions for optimal values of the design variables, namely frequencies (inside and outside the short cycle), capacity of vehicles and the position of the short turn limit stations. These expressions are used to analyze the influence of different parameters in the final solution. The design variables and the corresponding cost components for operators and users (waiting and in-vehicle times) are compared against an optimized normal operation scheme (single frequency). Applications on actual transit corridors exhibiting different demand profiles are conducted, calculating the optimal values for the design variables and the resulting benefits for each case. Results show the typical demand configurations that are better served using a short turn strategy.     

An SNN Ontology Based Environment Monitoring Method for Intelligent Irrigation System

In order to realize high precision of environment parameters detection in irrigation applications, a sensor and sensor network (SSN) ontology based data fusion method is proposed. An SSN sub-ontology for soilstate monitoring is revised, which includes the sensing devices hierarchies and measurement properties selection according to the detection feature interests. As for sensor data processing, a tuning data method by data pool filtering and clustering is adopted, as well as a useful data fusion method for multi-sensor system. The testing results show that both the accuracy and efficiency of the proposed method are higher after related filtering and clustering process, which enables a thorough monitoring for intelligent irrigation systems and can be extended into environment monitoring and control applications.     

Discretization Algorithm Based on Particle Swarm Optimization and Its Application in Attributes Reduction for Fault Data

In order to increase the fault diagnosis efficiency and make the fault data mining be realized, the decision table containing numerical attributes must be discretized for further calculations. The discernibility matrix-based reduction method depends on whether the numerical attributes can be properly discretized or not. So a discretization algorithm based on particle swarm optimization (PSO) is proposed. Moreover, hybrid weights are adopted in the process of particles evolution. Comparative calculations for certain equipment are completed to demonstrate the effectiveness of the proposed algorithm. The results indicate that the proposed algorithm has better performance than other popular algorithms such as class-attribute interdependence maximization (CAIM) discretization method and entropy-based discretization method.     

Inspection-Based Policy Considering Human Errors for Three-Stage Delay Time Degradation Systems

A periodic inspection policy for a single component system based on a three-stage failure process is proposed, and two different kinds of failures covering “hard” and “human” are considered in the proposed policy. The system is periodically inspected and inspections are perfect so that they can identify the intended defect. If the severe defect is detected by an inspection, an immediate repair is needed. However, once the system is identified to be in the minor defective state, there are two options. The first is to do nothing till the arrival of identifying the severe defect or hard failure, and the second is to repair immediately. Repair for any defect can renew the system with a limited probability such that the system may fail after repair due to human errors, which is common in many industrial applications. Two models are constructed by minimizing the expected cost per unit time and compared. We provide a numerical example to demonstrate the proposed model.     

Reliability Analysis of Systems with Common Cause Failure Based on Stress-Strength Interference Model

Many mechanical systems have the characteristics of multiple failure modes and complex failure mechanisms. On the basis of stress-strength interference (SSI) model, this paper takes the mechanical system with common cause failure (CCF) as the research object. The relationship between the stress distribution and the strength distribution is studied, and the failures of components are independent of each other under the deterministic stress. Then, the concept of conditional reliability is introduced to build the system reliability models under the action of one-stress and multi-stress for both series and parallel systems. Finally, the corresponding properties of the proposed methods are discussed to show their advantages.     

A New Fusion Chemical Reaction Optimization Algorithm Based on Random Molecules for Multi-Rotor UAV Path Planning in Transmission Line Inspection

A fusion chemical reaction optimization algorithm based on random molecules (RMCRO) is proposed to meet the special demand of power transmission line inspection. This new algorithm improves the shortcomings of chemical reaction algorithm by merging the idea of repellent-attractant rule and accelerates convergence by using difference algorithm. The molecules in this algorithm avoid obstacles and search optimal path of transmission line inspection by using sensors on multi-rotor unmanned aerial vehicle (UAV). The option of optimal path is based on potential energy of molecules and cost function without repeated parameter adjustment and complicated computation. By compared with an improved particle swarm optimization (IMPSO) in different circumstances of simulation, it can be concluded that the new algorithm presented not only can obtain more optimal path and avoid to trap in local minimum, but also can keep related sensors in a more stable status.