Otoacoustic Protection In Biologically-Inspired Systems

In this method an autonomic entity manages a system through the generation of one or more stay alive signals by a hierarchical evolvable synthetic neural system. The generated signal is based on the current functioning status and operating state of the system and dictates whether the system will stay alive, initiate self-destruction, or initiate sleep mode. This method provides a solution to the long standing need for a synthetic autonomous entity capable of adapting itself to changing external environments and ceasing its own operation upon the occurrence of a predetermined condition deemed harmful.

Highly distributed next-generation computer-based systems require self-managing environments that feature a range of autonomic computing techniques. This functionality is provided by collaborating agents, and includes an apoptotic (self-destruct) mechanism, autonomic quiescence (self-sleep), and others. The apoptotic feature is necessary to maintain system security and integrity when a component endangers the overall operation and viability of the entire system. However, the self-destruction of an agent/component may remove a key piece of functionality. The novel autopoietic functionality provides the capability to duplicate or substitute a new agent that provides the functionality of the self-destructed component.

A method for predicting the audibility of an arbitrary time-varying noise (signal) in the presence of masking noise is described in "An Algorithm for Statistical Audibility Prediction (SAP) of an Arbitrary Signal in the Presence of Noise" published in the Journal of the Audio Engineering Society (Vo. 69, No. 9, September 2021). The SAP method relies on the specific loudness, or loudness perceived through the individual auditory filters, for accurate statistical estimation of audibility vs. time. As such, this work investigated a new hypothesis that audibility is more accurately discerned within individual auditory filters by a higher-level, decision-making process. Audibility prediction vs. time is intuitive since it captures changes in audibility with time as it occurs, critical for the study of human response to noise. Concurrently, time-frequency prediction of audibility may provide valuable information about the root cause(s) for audibility useful for the design and operation of sources of noise. Empirical data, gathered under a three-alternative forced-choice (3AFC) test paradigm for low-frequency sound, has been used to examine the accuracy of SAPs. Future work should involve additional studies to examine the performance of SAP with realistic ambient noise and signals with higher-frequency content.

Some of the current challenges faced by research in artificial intelligence and autonomous control systems include providing self control, resilience, adaptability, and stability for intelligent systems, especially over a long period of time, in changing environments. The Evolvable Neural Software System (ENSS), Formulation for Emotion Embedding in Logic Systems (FEELS), Stability Algorithm for Neural Entities (SANE), and the Logic Expansion for Autonomously Reconfigurable Neural Systems (LEARNS) are foundations for tackling some of these challenges, by providing the basic algorithms evolvable systems could use to manage its own behavior. These algorithms would allow networks to self regulate, noticing unusual behavior and the circumstances that may have caused that behavior, and then correcting to behave more predictably when similar circumstances are encountered. The process is similar to how psychology in organisms evolved iteratively, eventually finding and keeping better responses to given stimuli.

Among the tests, landing gear cavities, a known cause of airframe noise, were evaluated. These are the regions where the landing gear deploys from the main body of an aircraft, typically leaving a large cavity where airflow can get pulled in, creating noise. NASA applied two concepts to these sections, including a series of chevrons placed near the front of the cavity with a sound-absorbing foam at the trailing wall, as well as a net that stretched across the opening of the main landing gear cavity. This altered the airflow and reduced the noise resulting from the interactions between the air, the cavity walls, and its edges.