Accomplishments
- CAD modeling & assembly: Completed CAD assembly for testbed
- Block diagram: Finalized block diagram with added power distribution components and software
- Analysis & Research: Conducted thermal analysis and research to justify use case requirements
- ML model research to choose specific model and plan inputs/outputs
Significant Risks
- Dependency on off the shelf parts for fabrication: Same as last week, cannot finalize or fabricate custom mounting plates until we have real parts to verify mounting hole dimensions
- Cannot start testing of code until fan/pump, SSRs, etc. arrive
- Plan to start ordering parts on Monday
Design Changes
- Block diagram finalized with added power components and sensor / control separation. No costs affiliated with this design change.
- CAD assembly finished to also include power components. No costs affiliated with this design change.
Schedule Changes
- The schedule was updated this week to account for delays in parts purchasing. Fabrication and code development all depend on receiving components.
- Adjusted task split for parallel development

Public Health, Safety, or Welfare
Kristina
Part A: One consideration of public health could be the system’s effect on the operator’s mental wellbeing. Frequent false alerts may cause stress or alert fatigue while reliable detection can reduce anxiety and improve confidence in system performance.
In terms of welfare, maintaining uptime prevents data loss and disruption for users who rely on the server for work. This could help the public’s mental health if people do not have to worry about losing unsaved work.
Social Factors
Aidan
Part B: Our design and product considers social factors by allowing for versatile configuration and tuning towards a multitude of real life applications. This allows the product to be used in different social and economic settings, given that each group has a different set of computing needs, and our product can be fine tuned and adapted to fit each of these groups. Additionally, our product considers alert fatigue with regards to the user and ensures that false positives are minimized to ensure the product works as intended across social groups. Lastly, the ML models and fine tuning are exposed and customizable to the specific user depending on their setup to encourage transparency in the product and solidify trust in AI-based anomaly detection systems.
Economic Factors
Jacob
Part C: For economic considerations of the project, the first thing that catches my mind is components. Our project is intended to help extend the lifetime of the system. With a longer life, the components no longer need to purchased and replaced as frequently. This in turn should save the user money.
Another consideration is the real time cost of running the system. For our use case in the datacenter or servers, they use masses of energy to power themselves. Running AnomAIy should use extremely marginal amount of power compared to the heavy load of the data centers.


















