Nonlinear MPC based on a Volterra series model for greenhouse temperature control using natural ventilation

Suitable environmental conditions are a fundamental issue in greenhouse crop growth and can be achieved by advanced climate control strategies. In different climatic zones, natural ventilation is used to regulate both the greenhouse temperature and humidity. In mild climates, the greatest problem faced by far in greenhouse climate control is cooling, which, for dynamical reasons, leads to natural ventilation as a standard tool. This work addresses the design of a nonlinear model predictive control (NMPC) strategy for greenhouse temperature control using natural ventilation. The NMPC strategy is based on a second-order Volterra series model identified from experimental input/output data of a greenhouse. These models, representing the simple and logical extension of convolution models, can be used to approximate the nonlinear dynamic effect of the ventilation and other environmental conditions on the greenhouse temperature. The developed NMPC is applied to a greenhouse and the control performance of the proposed strategy will be illustrated by means of experimental results.     

EXPERIMENTAL AND NUMERICAL STUDY OF THE PARTICLE FLUCTUATING MOTION INDUCED BY COLLISIONS BETWEEN PARTICLES IN A VERTICAL GAS-SOLID FLOW

The effect of interparticle collisions on the gravitational motion of large particles in a vertical convergent channel is experimentally and numerically investigated. A probabilistic collision model is implemented in a three-dimensional Monte Carlo type Lagrangian simulation code. The numerical predictions are compared to the experimental results. It is shown that an interparticle collision model is necessary to reproduce the experimentally observed particle fluctuating motion characteristics. The simulation results using the present probabilistic collision model are found to yield satisfactory agreement with experimental observations, even though the collision frequency seems to be slightly overestimated. In particular, reduction of initial anisotropy of the particle fluctuating motion with increasing particulate mass flow rate is well reproduced by the simulation. A rather good agreement is also observed between experimental results and quantitative predictions of statistical properties of the flow such as particle axial and transverse velocity distributions and standard deviations.     

Advanced system‐level simulation paradigm for ultra‐wideband systems using SCERNE platform

In this article, a progressive system‐level simulation framework is developed based on Simulation de Chaînes d'Emission/Réception Nouvelle gEnération (SCERNE) platform to simulate an ultra‐wideband (UWB) impulse radar transmitter and accurately predict its performance. With the purpose of demonstrating the usefulness of the SCERNE ability in system‐level modeling, we present and simulate a simplified structure of UWB impulse radar transmitter. First, after simulation each component in different circuit‐level tools such as ADS, CST, and HFSS, each part has been modeled by using different modeling methods to transfer their data into MATLAB environment. Then, we duplicate the transmitter structure in SCERNE toolbox to validate the results. The advantage conferred by the proposed SCERNE toolbox is that fast and accurate bilateral modeling method is available at multi‐medium structures in contrast with conventional unilateral modeling, and so a lower memory and higher accuracy of the behavioral model is achieved. It can also be beneficial when the user is looking for system‐level, as the increased components amounts can help as a surrogate model. The system model can be easily extended to other UWB radar systems by simply changing the input pulse shape, UWB channel environment, transceiver topology, etc. Various effects such as signal quality, and pulse shape that can easily investigate and re‐optimize for high performance are using the developed model. To validate the practicality of the proposed paradigm, the simulations and predictions through model results are being outlined.     

A contribution to vision-based autonomous helicopter flight in urban environments

A navigation strategy that exploits the optic flow and inertial information to continuously avoid collisions with both lateral and frontal obstacles has been used to control a simulated helicopter flying autonomously in a textured urban environment. Experimental results demonstrate that the corresponding controller generates cautious behavior, whereby the helicopter tends to stay in the middle of narrow corridors, while its forward velocity is automatically reduced when the obstacle density increases. When confronted with a frontal obstacle, the controller is also able to generate a tight U-turn that ensures the UAV’s survival. The paper provides comparisons with related work, and discusses the applicability of the approach to real platforms.     

The one-machine sequencing problem with dependent jobs

The problem of minimizing the makespan on one machine with possible dependencies between jobs is considered. We enlarge the notion of precedence constraint by considering a minimal duration between the start times of each pair of dependent jobs. We propose an exact algorithm where this duration is taken into account. This algorithm is based on Carlier's algorithm [Carlier 82] for the One-Machine Sequencing (OMS) problem with independent jobs.     

The interpersonal privacy identity (IPI): development of a privacy as control model

The Internet and social computing technology have revolutionized our ability to gather information as well as enabled new modes of communication and forms of self-expression. As the popularity of social computing technologies has increased, our society has begun to witness modifications in socialization behaviors. Social psychology theory suggests that technological changes can influence an individual’s expectation of privacy, through adaptive behaviors resulting from use (Laufer and Wolfe in J Soc Issues 33(3): 22–42 (1977)). We adapt traditional privacy theory to explore the influence of developmental and environmental factors on the individual’s inner privacy identity, which is comprised of the individual’s belief in his or her right to control (1) personal information and (2) interactions with others, and is continuously shaped by privacy experiences. We then use the inner privacy identity to examine interpersonal behaviors in the online context. We find that individuals’ belief in their right to control their information impacts their information disclosure practices when consequences are implied and that their belief in their right to control the interaction impacts their online information sharing practices. We do not find support for a relationship between the interaction management component of the IPI and online interaction behavior, which considered in the presence of the relationship between interaction management and online information sharing, suggests that interaction behavior is more complicated in the online context. Insights from the model developed in this study can inform future studies of situational privacy behaviors.     

The EMISQ method and its tool support-expert-based evaluation of internal software quality

There is empirical evidence that internal software quality, e.g., the quality of source code, has great impact on the overall quality of software. Besides well-known manual inspection and review techniques for source code, more recent approaches utilize tool-based static code analysis for the evaluation of internal software quality. Despite the high potential of code analyzers the application of tools alone cannot replace well-founded expert opinion. Knowledge, experience and fair judgment are indispensable for a valid, reliable quality assessment, which is accepted by software developers and managers. The EMISQ method (Evaluation Method for Internal Software Quality), guides the assessment process for all stakeholders of an evaluation project. The method is supported by the Software Product Quality Reporter (SPQR), a tool which assists evaluators with their analysis and rating tasks and provides support for generating code quality reports. The application of SPQR has already proved its usefulness in various code assessment projects around the world. This paper introduces the EMISQ method and describes the tool support needed for an efficient and effective evaluation of internal software quality.     

Using targeted Bayesian network learning for suspect identification in communication networks

This paper proposes a machine learning application to identify mobile phone users suspected of involvement in criminal activities. The application characterizes the behavioral patterns of suspect users versus non-suspect users based on usage metadata such as call duration, call distribution, interaction time preferences and text-to-call ratios while avoiding any access to the content of calls or messages. The application is based on targeted Bayesian network learning method. It generates a graphical network that can be used by domain experts to gain intuitive insights about the key features that can help identify suspect users. The method enables experts to manage the trade-off between model complexity and accuracy using information theory metrics. Unlike other graphical Bayesian classifiers, the proposed application accomplishes the task required of a security company, namely an accurate suspect identification rate (recall) of at least 50% with no more than a 1% false identification rate. The targeted Bayesian network learning method is also used for additional tasks such as anomaly detection, distinction between “relevant” and “irrelevant” anomalies, and for associating anonymous telephone numbers with existing users by matching behavioral patterns.     

Tutoring executives online: what drives perceived quality?

Studies of learning and student satisfaction in the context of online university programmes have largely neglected programmes catering specifically to business executives. Such executives have typically been away from higher education for a number of years, and have collected substantial practical experience in the subject matters they are taught. Their expectations in terms of both content and delivery may therefore be different from non-executive students. We explore perceptions of the quality of tutoring in the context of an online executive MBA programme through participant interviews. We find that in addition to some of the tutor behaviours already discussed in the literature, executive students look specifically for practical industry knowledge and experience in tutors, when judging how effective a tutor is. This has implications for both the recruitment and training of online executive MBA tutors.