University of British Columbia
Assembly Line
Miru closes $27.4-million CAD Series A as smart windows startup prepares to launch in 2025
Vancouver-based startup Miru has announced $27.4 million CAD ($20 million USD) in Series A financing as it looks to accelerate the commercialization of its electronically tintable window technology.
The all-equity round, which closed July 2024, was co-led by BDC Capital, the investment arm of Crown corporation the Business Development Bank of Canada (BDC), and Angelo Paletta, president and CEO of Ontario-based TNG Capital Corp. The deal also saw participation from Toronto cleantech-focused venture capital firm Greensoil. Miru’s spokesperson noted there were other participants in the Series A round, but declined to disclose names.
Miru is led by CEO and co-founder Curtis Berlinguette, a professor of chemistry and biological engineering at the University of British Columbia (UBC). Berlinguette’s research group at UBC focuses on exploring and discovering advanced materials for high-performance, low-cost alternative energy technologies. He was inducted into the Royal Society of Canada for his work in 2021.
Miru is designing three demonstration plants in North America and Europe, with two additional plants currently in development. The new funding will be allocated to invest in Miru’s automotive glass roofs and support the construction of new pilot lines in North America and Europe. Pilot lines refer to small-scale industrial setups used to test and refine manufacturing processes before they are implemented on a full production scale.
A recommender system for human operators in industrial automation
This paper presents a recommender system to assist human operators in industrial automation applications, which suggests proper actions to take during process operation and those to avoid. The proposed recommender system is built based on a three-stage computational procedure. In the first stage, historical process data segments containing timestamped events are clustered using a k-medoids-based algorithm, where instead of distance metrics, structural similarity of the segments is used. This is achieved by using a Smith–Waterman-based algorithm to consider all events and their timestamps in the segments. In the second stage, a modified collaborative filtering technique applicable to time sequences is proposed, where not only operator ratings, but also the structural similarity of the segments are taken into account. In the third stage, possible actions are analyzed and accordingly, proper and improper actions are determined and presented to the human operators. The effectiveness of the proposed recommender system is examined through a power system operation example.