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Occupant feedback is crucial for healthy, comfortable and productive offices. Existing facility management (FM) systems are limited in effective use of occupant feedback, as they fail to collect the vital contextual information (e.g. related building element, space) associated with the feedback. The purpose of this study is to formalise the contextual information requirements for structured collection of occupant feedback for rapid diagnosis and resolution of problems and integrating occupant feedback with building information modelling (BIM) for making use of its visualisation and analysis capabilities, and eventually for effective use of occupant feedback in FM operations.

A mixed-methods approach was conducted in four steps: (1) identifying occupant feedback types (e.g. echo in meeting room) in office buildings, (2) examining the current practice in collecting and processing occupant feedback via use cases, (3) determining the contextual information requirements via expert interviews and (4) validation of the information requirements via a BIM-integrated prototype.

The findings present the contextual information requirements for 107 occupant feedback types grouped under thermal comfort, indoor air quality, acoustic comfort, visual comfort, building design and facility services.

Feedback-specific contextual information items enable structured data collection and help to avoid missing data and minimise the time lost in manual data entry and recursive interaction with the occupants during FM operations.

The contextual information requirements determined are expected to enhance occupant satisfaction and FM performance in office buildings by better use of the occupant feedback and integration into BIM-enabled FM and can be extended to other building types in future studies by using the proposed methodology.

This article takes into account object identification, enhanced visual feature optimization, cost effectiveness and speed selection in response to terrain conditions. Neither supervised machine learning nor manual engineering are used in this work. Instead, the OTV educates itself without instruction from humans or labeling. Beyond its link to stopping distance and lateral mobility, choosing the right speed is crucial. One of the biggest problems with autonomous operations is accurate perception. Obstacle avoidance is typically the focus of perceptive technology. The vehicle's shock is nonetheless controlled by the terrain's roughness at high speeds. The precision needed to recognize difficult terrain is far higher than the accuracy needed to avoid obstacles.

Robots that can drive unattended in an unfamiliar environment should be used for the Orbital Transfer Vehicle (OTV) for the clearance of space debris. In recent years, OTV research has attracted more attention and revealed several insights for robot systems in various applications. Improvements to advanced assistance systems like lane departure warning and intelligent speed adaptation systems are eagerly sought after by the industry, particularly space enterprises. OTV serves as a research basis for advancements in machine learning, computer vision, sensor data fusion, path planning, decision making and intelligent autonomous behavior from a computer science perspective. In the framework of autonomous OTV, this study offers a few perceptual technologies for autonomous driving in this study.

One of the most important steps in the functioning of autonomous OTVs and aid systems is the recognition of barriers, such as other satellites. Using sensors to perceive its surroundings, an autonomous car decides how to operate on its own. Driver-assistance systems like adaptive cruise control and stop-and-go must be able to distinguish between stationary and moving objects surrounding the OTV.

One of the most important steps in the functioning of autonomous OTVs and aid systems is the recognition of barriers, such as other satellites. Using sensors to perceive its surroundings, an autonomous car decides how to operate on its own. Driver-assistance systems like adaptive cruise control and stop-and-go must be able to distinguish between stationary and moving objects surrounding the OTV.