The availability of complex rotating machines is vital for the prevention of catastrophic failures in a significant number of industrial operations. Reliability engineering theories stipulate that optimising the mean-time-to-repair (MTTR) for failed machines can immensely boost availability. In practice, however, a significant amount of time is taken to accurately detect and classify rotor-related anomalies which often negate the drive to achieve a truly robust maintenance decision-making system. Earlier studies have attempted to address these limitations by classifying the poly coherent composite spectra (pCCS) features generated at different machine speeds using principal components analysis (PCA). As valuable as the observations obtained were, the PCA-based classifications applied are linear which may or may not limit their applicability to some real-life machine vibration data that are often associated with certain degrees of non-linearities due to faults. Additionally, the PCA-based faults classification approach used in earlier studies sometimes lack the capability to self-learn which implies that routine machine health classifications would be done manually. The initial parts of the current paper were presented in the form of a thorough search of the literature related to the general concept of data fusion approaches in condition monitoring (CM) of rotation machines. Based on the potentials of pCCS features, the later parts of the article are concerned with the application of the same features for the exploration of a simplified two-staged artificial neural network (ANN) classification approach that could pave the way for the automatic classification of rotating machines faults. This preliminary examination of the classification accuracies of the networks at both stages of the algorithm offered encouraging results, as well as indicates a promising potential for this enhanced approach during field-based condition monitoring of critical rotating machines.

This paper presents a novel data and timed control routing protocol which is Flying Adhoc Network (FANET) specific. The developed FANET specific routing protocol laid emphasis on the route connectivity in the network b y considering the captured data size, minimum allowab le distance b etween randomly moving nodes and connection time. The performance of the proposed FANET specific routing protocol was simulated using NS3. The ob tained throughput value for the routing protocol fluctuated b etween 742.064kb ps and 755.083kb ps as data are exchanged b etween nodes. This showed that when all the UAVs are on the network and communicating with one another, the throughput is flatline and not plummet. This implies consistency as nodes join and leave the network. The packet delivery ratio ob tained for the FSRP during simulation was 96.13%. These results implied that data is successfully transmitted b etween the UAV acting as server and UAV acting as client on the network.

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Abstract:The emergence of plug-in electric vehicles (PEVs) is unveiling new opportunities to de-carbonise the vehicle parcs and promote sustainability in different parts of the globe. As battery technologies and PEV efficiency continue to improve, the use of electric cars as distributed energy resources is fast becoming a reality. While the distribution network operators (DNOs) strive to ensure grid balancing and reliability, the PEV owners primarily aim at maximising their economic benefits. However, given that the PEV batteries have limited capacities and the distribution network is constrained, smart techniques are required to coordinate the charging/discharging of the PEVs. Using the economic model predictive control (EMPC) technique, this paper proposes a decentralised optimisation algorithm for PEVs during the grid-to-vehicle (G2V) and vehicle-to-grid (V2G) operations. To capture the operational dynamics of the batteries, it considers the state-of-charge (SoC) at a given time as a discrete state space and investigates PEVs performance in V2G and G2V operations. In particular, this study exploits the variability in the energy tariff across different periods of the day to schedule V2G/G2V cycles using real data from the university's PEV infrastructure. The results show that by charging/discharging the vehicles during optimal time partitions, prosumers can take advantage of the price elasticity of supply to achieve net savings of about 63%.

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In this paper, a new optimization algorithm called motion-encoded electric charged particles optimization (ECPO-ME) is developed to find moving targets using unmanned aerial vehicles (UAV). The algorithm is based on the combination of the ECPO (i.e., the base algorithm) with the ME mechanism. This study is directly applicable to a real-world scenario, for instance the movement of a misplaced animal can be detected and subsequently its location can be transmitted to its caretaker. Using Bayesian theory, finding the location of a moving target is formulated as an optimization problem wherein the objective function is to maximize the probability of detecting the target. In the proposed ECPO-ME algorithm, the search trajectory is encoded as a series of UAV motion paths. These paths evolve in each iteration of the ECPO-ME algorithm. The performance of the algorithm is tested for six different scenarios with different characteristics. A statistical analysis is carried out to compare the results obtained from ECPO-ME with other well-known metaheuristics, widely used for benchmarking studies. The results found show that the ECPO-ME has great potential in finding moving targets, since it outperforms the base algorithm (i.e., ECPO) by as much as 2.16%, 5.26%, 7.17%, 14.72%, 0.79% and 3.38% for the investigated scenarios, respectively.

The ease of use and advancements in drone technology is resulting in the widespread application of Unmanned Aerial Vehicles (UAVs) to diverse fields, making it a booming technology. Among UAVs' several applications, livestock agriculture is one of the most promising, where UAVs facilitate various operations for efficient animal management. But the field is characterized by multiple environmental, technical, economic, and strategic challenges. However, the use of advanced technological techniques like Artificial Intelligence (AI), Internet of Things (IoT), Machine Learning (ML), Deep Learning (DL), advanced sensors, etc. along with the assurance of animal welfare while operating the UAVs, can lead to widespread adoption of drone technology amongst livestock farmers. This paper discusses livestock management research where UAVs monitor farm animals via detection, counting, tracking animals, etc. In this article, an attempt has been made to elucidate different aspects and broader issues around livestock management while highlighting the associated challenges, opportunities, and prospects. This work is the first review paper on the subject matter with all the necessary information and analysis, to the best of our knowledge. Therefore, the article promises to provide interested researchers with detailed information about the field, guiding future research.