Tsurumi Island will be the last to be added to the Inazuma region and is billed as the toughest for explorers to conquer, with dense fog a solid deterrent for outsiders. Genshin fans will have to find their own way in, with the low visibility causing issues when it comes to navigating the new locations and points of interest.
A message from Mihoyo adds: "New monstrous beasts also lurk in the gloomy mist. The Rifthounds and the Rifthound Whelps are stealthy monsters from the Abyss which can turn invisible, sneaking around for the best opportunity to land a strike in combat.
But what makes this species really dangerous is their ability to apply the Corrosion status to enemies. So beware of the hounds. Developers Mihoyo has confirmed that Genshin Impact Maintenance will start at 11pm BST on October 12, and last a total of five hours if everything goes to schedule. Delays are always possible and the company is known for adding more Primogems to the overall bounty if gamers are stuck waiting longer than they were told for maintenance to end.
A system of balanced planning and execution is essential for a seamless backlog. You also want to plan generously for reactive maintenance during this four-week backlog planning period. You never know when reactive maintenance repairs will come up, so we suggest scheduling for hours less than your regular workweek to accommodate these changes.
With these tips in mind, your facility can effectively reduce downtime and create a process for backlog management. To learn more about our system at MaintenX, contact your local team today! Your email address will not be published. Save my name, email, and website in this browser for the next time I comment.
Skip to content Very few facility managers want to admit how intimidating their backlog can be. Today we will introduce some of the challenges in creating tangible value with AI in industrial applications and how to overcome them.
We will also present our approach to predictive maintenance, its benefits and the advantages that we offer to our customers. There are two types of domain knowledge needed to develop ML-based solutions successfully.
Jungle is mainly composed of electrical engineers that speak machine who are also true data science professionals. This is a rare and powerful combination to have, but a crucial one in delivering meaningful AI-based solutions. But be aware! There is also a pitfall involved here that can be easily missed: human bias and pre-existing beliefs.
Even experts can inject too much prior knowledge and preclude better solutions from being created. Later on, we explain how we have avoided falling into this pitfall. Most companies are now sitting on multiple years of untapped operational data.
However, ML models were not envisioned as a direct application when these systems were built. Unfortunately, annotated examples are crucial to train AI models to identify failures. Most common AI strategies overcome this drawback by requiring experts to annotate periods in which the assets were behaving unexpectedly. This, however, may quickly become a Herculean task since end-users would be necessary to comb several years of highly non-linear and correlated multivariate time-series data.
Furthermore, this task assumes that the end-users could identify precisely the periods in which the assets are working unexpectedly. If this was quickly done, would AI models be required in the first place? Furthermore, most of the available data is of regular operation. If we focus on the data leading to failures, we will end up using only a small subset of the available data.
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