This paper proposes a novel two-layer monitoring and rescheduling approach designed for vessels navigating natural inland waterways, addressing diverse scenarios such as arrival delays, speed reductions, and machinery failures. Firstly, a methodology for incident detection and classification is proposed, which compares the planned trajectory of the vessels with their actual position provided by an automatic identification system. Secondly, in case an incident is detected, the plan for each vessel is rescheduled. To achieve this, various rescheduling strategies are proposed, taking into account the particularities of each incident. The aim is not only to ensure the safety and efficiency of the operation but also to minimize the impact of rescheduling on the original plan, thus averting any negative repercussions on the port’s reputation. Different simulations have been carried out taking the Guadalquivir River as an example, demonstrating the efficiency of the proposed methodology in preventing incidents and ensuring optimality.

Despite inland ports play an essential role in today’s global logistics chain, many problems still remain unsolved. One of the main challenges that inland waterways face is the trip planning of cargo vessels. A correct trip planning does not only improve the efficiency and prestige of the port, but is also crucial to ensure the safety of operations. This issue is particularly critical in the case of natural waterways whose depth and width are conditioned by natural phenomena that, in principle, cannot be controlled. This is the case of the Guadalquivir river, a waterway that connects the Atlantic Ocean and the inland Port of Seville in the south of Spain. In this context, this work proposes a two-step solution to optimize the time needed for vessels to complete their journey through the waterway while considering the constraint imposed by the time-varying depth and encountering situations. The outcome is the set of times when vessels are required to cross a series of boundaries delimiting the different sections of the waterway. The advantages of the proposed approach are studied in contrast to a first-arrived first-served scheduling solution in terms of optimality and feasibility.

Data centers are facilities with large number of servers providing cloud services. The increasing number of data centers in the last years has generated environmental concern due to the large amount of energy consumed by them. This also includes some auxiliary services such as the cooling equipment which is known to be very costly. For that reason, efficient data center strategies are needed in order to provide an acceptable quality of service (QoS) and suitable temperature for every server while using the least amount of resources possible. This article presents some strategies to deal with the unified workload and temperature problem that appears in the data center. As the system is modeled as a queue and the control variables have an hybrid nature, some highly parallelizable particle-based optimization algorithms are proposed to solve the optimization problem. Numerical simulations are provided in order to illustrate the effectiveness of the strategy. These simulations also show the improvements obtained from the GPU computing. Finally, a probabilistic evaluation approach is developed in order to provide certificates on the probability of constraint satisfaction without increasing the computational burden of the online problem. Note to Practitioners—This article addresses the problem of deciding in real-time the number of active servers in a data center that is required to meet the quality of service (QoS) demands while keeping energy consumption at a minimum. The temperature set point of the cooling equipment must also be taken into account, as it is advisable to use the minimum cooling that keeps the servers running in safe conditions. The management strategy proposed is based on predictive control. In this way, the number of active servers and temperature set point will be chosen so that the future energy consumption is minimized while guaranteeing that QoS and safety demands are met under different possible operating conditions. Furthermore, the proposed management strategy can be tuned depending on the QoS that it is desirable to provide. The proposed strategy will lead to energy-consumption improvements while having guarantees on the data center performance.

Enthusiasm in speech has a huge impact on listeners. Students of enthusiastic teachers show better performance. Leaders that are enthusiastic influence employee's innovative behavior and can also spark excitement in customers. We, at TalkMeUp, want to help people learn how to talk with enthusiasm in order to spark creativity among their listeners. In this work we want to present a multimodal speech analysis platform. We provide feedback on enthusiasm by analyzing eye contact, facial expressions, voice prosody, and text content.

This paper presents a control scheme in order to manage a laboratory-scale microgrid in a economic way. The main aim of this paper is the comparison between different control strategies. This microgrid is located in Seville (Spain) and incorporates an electrolyzer, metal hybrid storage, fuel cell and a battery set, as well as electronic load and generator as main components. This allows us to study smartgrids with different renewable sources (emulated by the electronic generator) and energy storage based on hydrogen under realistic scenarios. The control objective is to regulate the plant fulfilling the energy demand. This is posed as a tracking problem to be solved. A first approach is a two layer controller composed by a scheduler layer and an offset free LQR layer with output feedforward. This controller provides nice results and although its control layer does not take into account the operational limits of the plant, it gets the best performance from a optimal point of view. A one layer model predictive control for tracking has also been designed and tested in order to increase the performance of the first approach. The obtained results show that this last approach is a good control technique for this class of plants.

The production of hot water for use in a Hospital can use solar energy as a main energy source. The hot water production system can also use fossil energy in cases were the demand is too high to be satisfied with solar power. The resulting system is hybrid and its control poses some problems. In this paper the work in progress towards the control of this kind of hybrid system is shown. The model used belongs to a general category of models that can be applied to various types of renewable energy plants. The abstract representation receives the name of RESCUE model. In this paper it is applied to a particular hot water system installed at the Virgen del Rocío hospital in Seville (Spain). The RESCUE model allows to identify niches of inefficiency in the operation. The model is a first step towards the improving of operation by means of adequate controllers.

This paper presents a way of implementing a model-based predictive controller (MBPC) for mobile robot navigation using genetic algorithms to perform online nonlinear optimization. Obstacle avoidance is considered by the controller. Constraints on the maximum attainable speeds are also considered by the algorithm. Experimental results obtained when applying the controller to a TRC LABMATE mobile platform are given in the paper.