To create a future society based on cyber-physical systems, we need real-time digital twins made with cameras and sensors. The challenge is making this energy-efficient. A model in [1] suggests dividing video analysis tasks among terminals, edge servers, and cloud servers to minimize power consumption. This paper addresses energy optimization in dynamic environments with a two-level approach: detecting and categorizing environmental changes (LEC and SEC) and using a two-level adaptive evolutionary algorithm (TAEA) to make corresponding adjustments. A case study with a differential evolution algorithm demonstrates its effectiveness in minimizing energy costs and improving processing accuracy and latency violations.

The development of Internet of Things and smart building technologies will accelerate many real-world entities (e.g., equipment, people flow, comfort levels) to be managed as digital data. This enables a Software-Defined Building in which advanced and complex applications realize a broad range of value propositions for smart buildings. To enable a scalable platform that can easily add and integrate a wide range of data and applications, the main issue addressed is the difficulty in unifying existing building equipment data models used in BACnet or others, or any related information in the building. Based on the Web of Things architecture, we propose a data model that accommodates both the information within buildings and the broad range of information expected to increase in the future. To enable the proposed data model to be efficiently managed by a building OS, we also propose a hierarchical mechanism that incorporates modules to convert the group of devices connected by different protocols into a unified data model. We develop a prototype building OS platform using Eclipse Ditto to evaluate the proposed model and mechanism. We confirm that the platform can correctly manage device information and that its performance is sufficient.

To achieve efficient human-robot cooperation, it is necessary to work in close proximity while ensuring safety. However, in conventional robot control, maintaining a certain distance between humans and robots is required for safety, owing to control uncertainties and unexpected human actions, which can limit the efficiency of robot operations. Therefore, this study aims to establish a human-robot cooperation aiding system that concerns both safety and efficiency in a close proximity situation. We propose two Augmented Reality (AR) interfaces to display robot information via AR glasses, allowing workers to see the robot information while focusing on their task and avoiding collisions with the robot. AR glasses can give hands-free communication required for a work environment like warehouses or convenience store backyards, and multiple information levels, simple or informative, to balance accuracy and easiness of human recognition ability. We conducted a comparative evaluation experiments with 24 participants and found that both safety and efficiency were improved using the proposed user interfaces (UIs). We also collected the position, head motion, and eye-tracking data from the AR glasses to gain insight into human behavior during the tasks for each UI. Consequently, we clarified the behavior of the participants under each condition and how they contributed to safety and efficiency.

For the future cyber-physical system (CPS) society, it is necessary to construct digital twins (DTs) of a real world in real time using a lot of cameras and sensors. Hence, the energy efficiency of both networks and computers for largescale distributed video analysis is a major challenge for the full-scale spread of CPSs and DTs. Toward this goal, we first propose a model to arbitrarily split and distribute the video analysis task to terminals, edge servers, and cloud servers and dynamically assign appropriate CNN models to them. System-wide optimization of such distributed processing can reduce overall system power consumption by reducing network bandwidth and efficiently utilizing distributed CPU/GPU resources. To realize this optimization in a real system, we also propose a model to estimate the GPU load, processing time, and power consumption of these devices based on massive experimental measurements. Since such a large-scale optimization is difficult because of the dynamic and multi-objective nature of the problem, we propose a new optimization algorithm composed of Genetic Algorithm and Bayesian Attractor Model. Finally, simulation evaluations are performed to demonstrate that the proposed method can minimize system power consumption and satisfy latency and recognition accuracy requirements of each video analysis, even under changing environmental conditions.

In educational settings such as art galleries or museums, Augmented Reality (AR) has the potential to provide detailed information about exhibits. However, dealing with items that contain information in multiple sections or areas is still a significant challenge. For example, a large painting may contain many minute details, which requires a system that can explain its broader features rather than just a generic description. To address this challenge, we introduce an AR guidance system that uses an embodied agent to point out items and explain each piece and part of exhibit items in detail. We also designed and tested 3 different pointing interfaces for the embodied agent: gesture only, gesture with a dot laser, and gesture with line laser. To evaluate this interface, we conducted a user experiment simulating painting guidance to test interest and exhibit memory. During the experiment, the agent pointed to various areas of interest in the painting and provided a detailed description to participants. The result shows that the search times for target positions were the fastest with the line laser. However, no particular interface outperformed others in memory recall of exhibit content.