Nature is phenomenal. The achievements in, for example, evolution are everywhere to be seen: complexity, resilience, inventive solutions and beauty. Evolvable Hardware (EH) is a field of evolutionary computation (EC) that focuses on the embodiment of evolution in a physical media. If EH could achieve even a small step in natural evolution’s achievements, it would be a significant step for hardware designers. Before the field of EH began, EC had already shown artificial evolution to be a highly competitive problem solver. EH thus started off as a new and exciting field with much promise. It seemed only a matter of time before researchers would find ways to convert such techniques into hardware problem solvers and further refine the techniques to achieve systems that were competitive with or better than human designs. However, 15 years on—it appears that problems solved by EH are only of the size and complexity of that achievable in EC 15 years ago and seldom compete with traditional designs. A critical review of the field is presented. Whilst highlighting some of the successes, it also considers why the field is far from reaching these goals. The paper further redefines the field and speculates where the field should go in the next 10 years.

Electric motors find widespread application across various industrial fields. The pursuit of enhanced comprehensive electric motors performance has consistently drawn significant attention, prompting extensive research in this domain. With the rapid and profound integration of artificial intelligence (AI) technology into diverse sectors, an increasing array of AI-based methodologies is being employed in electric motors research. This review aims to comprehensively delineate the applications of AI within the realm of electric motors, specifically focusing into three pivotal stages: electric motors design, drive control, and health maintenance, covering a full-cycle research and development of electric motors. The review commences by outlining the elucidation of the fundamental principles and components of AI. Subsequently, the encompassed input signals, characteristic methodologies, and AI techniques are reviewed and analyzed as demonstrated by pertinent research cases. Furthermore, a synthesis of the distinctive attributes characterizing various techniques is presented. Conclusively, this article offers an unprecedented review of AI-based technology throughout the entirety of the electric motors research cycle. Considering the existing body of knowledge, this article stands as the pioneering endeavor to encapsulate the expansive domain of AI's influence in electric motors research.

Due to the importance of determining faulty bypass diodes in photovoltaic systems, faulty bypass diodes have been of widespread interest in recent years due to their importance to improving PV system durability, operation, and overall safety. This paper presents new work in developing an artificial intelligence (AI) based model using the principles of artificial neural networks (ANN) to detect short and open PV bypass diodes fault conditions. With only three inputs from the PV system, namely the output power, short-circuit current, and open-circuit voltage, the developed ANN model can determine whether the PV bypass diodes are defective. In the experimentally validated case of short and open bypass diodes, 93.6% and 93.3% of faulty bypass diodes can be detected. Furthermore, the developed ANN model has an average precision and sensitivity of 96.4% and 92.6%, respectively.

It is now known that astrocytes modulate the activity at the tripartite synapses where indirect signaling via the retrograde messengers, endocannabinoids, leads to a localized self-repairing capability. In this paper, a self-repairing spiking astrocyte neural network (SANN) is proposed to demonstrate a distributed self-repairing capability at the network level. The SANN uses a novel learning rule that combines the spike-timing-dependent plasticity (STDP) and Bienenstock, Cooper, and Munro (BCM) learning rules (hereafter referred to as the BSTDP rule). In this learning rule, the synaptic weight potentiation is not only driven by the temporal difference between the presynaptic and postsynaptic neuron firing times but also by the postsynaptic neuron activity. We will show in this paper that the BSTDP modulates the height of the plasticity window to establish an input-output mapping (in the learning phase) and also maintains this mapping (via self-repair) if synaptic pathways become dysfunctional. It is the functional dependence of postsynaptic neuron firing activity on the height of the plasticity window that underpins how the proposed SANN self-repairs on the fly. The SANN also uses the coupling between the tripartite synapses and ɣ-GABAergic interneurons. This interaction gives rise to a presynaptic neuron frequency filtering capability that serves to route information, represented as spike trains, to different neurons in the subsequent layers of the SANN. The proposed SANN follows a feedforward architecture with multiple interneuron pathways and astrocytes modulate synaptic activity at the hidden and output neuronal layers. The self-repairing capability will be demonstrated in a robotic obstacle avoidance application, and the simulation results will show that the SANN can maintain learned maneuvers at synaptic fault densities of up to 80% regardless of the fault locations.

The Rey Osterrieth Complex Figure (ROCF) is a widely used neuropsychological test for visual perception and long term visual memory. Many scoring systems are used to quantify the accuracy of the drawings; these are currently implemented by hand in a subjective manner. This paper gives details of the current progress of a novel technique to locate the scoring sections of the most common of these system (the Osterrieth Scoring System), with the ultimate goal to automating the scoring system. High levels of dis- tortion are possible making this an extremely difficult task; however, location and perceptual grading of the basic ge- ometric features (triangles, rectangles and diamonds) have been most successful. All but one section in the test data was located (99.3% success) and 78% of the perceptual grades calculated were within 5% of grades generated by indepen- dent raters. Unary spatial metrics have been implemented to reduce the possible section candidates by an average of 75% without the loss of a single section.

A long-term vision of evolutionary robotics is a technology enabling the evolution of entire autonomous robotic ecosystems that live and work for long periods in challenging and dynamic environments without the need for direct human oversight. Evolutionary robotics has been widely used due to its capability of creating unique robot designs in simulation. Recent work has shown that it is possible to autonomously construct evolved designs in the physical domain; however, this brings new challenges: the autonomous manufacture and assembly process introduces new constraints that are not apparent in simulation. To tackle this, we introduce a new method for producing a repertoire of diverse but manufacturable robots. This repertoire is used to seed an evolutionary loop that subsequently evolves robot designs and controllers capable of solving a maze-navigation task. We show that compared to random initialisation, seeding with a diverse and manufacturable population speeds up convergence and on some tasks, increases performance, while maintaining manufacturability.