Abstract
This is the study of the thermal efficiency performance of a possible 1-ha solar pond located at Caota beach, in Benguela, Angola. During the first year of operation there was no energy extraction from the pond and the water temperature in the lowest and denser layer reached 93 degrees C. From the second year onwards thermal energy was extracted. Two scenarios were adopted, water heating from 40 to 60 degrees C or water heating from 50 to 70 degrees C. Yearly 1600 MWh and 700 MWh could be extracted, in the first and or the second scenario. Operating regimes of 8, 12 or 24 h/day were considered. Based on the total pond incident solar energy, the overall energy efficiency of the pond is in the 2 to 5 % range, while the exergy efficiency is in the 0.4 to 2.6 % range. The repartition of the pond energy losses is also quantified.

Abstract
The penetration of electric vehicles (EVs) in vehicle markets is increasing; however long charging time in battery charging stations is an obstacle for larger adoption of EVs. In order to address this problem, battery swap stations (BSSs) have been introduced to exchange near-empty EV batteries with fully charged batteries. Refilling an EV in BSS takes only a few minutes. With decentralization of power systems, BSSs are typically connected to the microgrid (MG) in their neighborhood. Although the location of BSS in MG affects MG operation cost, to the best knowledge of the author, optimal placement of BSS has not been done from the perspective of MG. Therefore, in this paper, the objective is to find optimal location of BSSs in a MG with micro pumped hydro storage (PHS), photovoltaic, wind and geothermal units, while reactive power dispatch and all network constraints are considered by AC optimal power flow. The effect of BSS capacity and maximum charging/discharging power, BSS to MG link capacity, PHS capacity and maximum power of PHS unit on MG operation and optimal BSS location are investigated. DICOPT solver in general algebraic mathematical system (GAMS) is used to solve the formulated mixed-integer nonlinear optimisation problem.

Abstract

Abstract
The world is living the fourth industrial revolution, marked by the increasing intelligence and automation of manufacturing systems. Nevertheless, there are types of tasks that are too complex or too expensive to be fully automated, it would be more efficient if the machines were able to work with the human, not only by sharing the same workspace but also as useful collaborators. A possible solution to that problem is on human-robot interaction systems, understanding the applications where they can be helpful to implement and what are the challenges they face. This work proposes the development of an industrial prototype of a human-machine interaction system through Augmented Reality, in which the objective is to enable an industrial operator without any programming experience to program a robot. The system itself is divided into two different parts: the tracking system, which records the operator's hand movement, and the translator system, which writes the program to be sent to the robot that will execute the task. To demonstrate the concept, the user drew geometric figures, and the robot was able to replicate the operator's path recorded.

Abstract
Understanding the distribution of people's transportation mode is a crucial facet of today's urban mobility for proper transportation planning. The penetration of smartphones combined with their sensing capability is an enabler for crowdsourcing large mobility data such as commuters' GPS records. In this paper, we leverage the GPS traces of commuters to infer five different transportation modes frequently used in urban areas including foot, bike, bus, car and metro. We compare three different approaches commonly reported in the literature for transportation mode detection from the family of machine learning algorithms (random forest -RF) and deep learning architectures (convolutional neural network -CNN and ensemble of autoencoders -EAE). By splitting the dataset into train-test by the period of data collection, as well as the conventional 80-20 split, we evaluate the impact of several data pre-processing decisions on overall classifiers' performance. Our results show RF and CNN performing better upon evaluation on classification metrics such as the f1 score and the area under the Receiver Operating Characteristics (ROC) curve.

Abstract
This experimental study focuses on the comparison between two different sensors for vibration signals: a magnetoresistive sensor and an accelerometer as a calibrated reference. The vibrations are collected from a variable speed inductor motor setup, coupled to a ball bearing load with adjustable misalignments. To evaluate the performance of the magnetoresistive sensor against the accelerometer, several vibration measurements are performed in three different axes: axial, horizontal and vertical. Vibration velocity measurements from both sensors were collected and analyzed based on spectral decomposition of the signals. The high cross-correlation coefficient between spectrum vibration signatures in all experimental measurements shows good agreement between the proposed magnetoresistive sensor and the reference accelerometer performances. The results demonstrate the potential of this type of innovative and non-contact approach to vibration data collection and a prospective use of magnetoresistive sensors for predictive maintenance models for inductive motors in Industry 4.0 applications.