Perancangan stasiun kerja instruktur laboratorium desain produk dan inovasi menggunakan metode ergonomic function deployment

The Product Design and Innovation Laboratory (Desprin), Faculty of Technology, Institute of Technology is a necessary facility in an effort to support the implementation of an educational process that implements a competency-based curriculum. This study aims to produce an ergonomic instructor workstation design in the laboratory by applying the Ergonomic Function Deployment (EFD), 12 ergonomic principles, and anthropometric data with the 5-95th percentile with selecting concepts from several concepts that have been designed. The final result of this research is a workstation design in the form of a drawing table, computer desk, and instructor chair designed using Solidworks 2018 software. The drawing table can be folded when not in use and attached to a computer table so that user movement is more effective and the selected chair is able to work synergistically with the two tables.

Multitier Service Migration Framework Based on Mobility Prediction in Mobile Edge Computing

Mobile edge computing (MEC) pushes computing resources to the edge of the network and distributes them at the edge of the mobile network. Offloading computing tasks to the edge instead of the cloud can reduce computing latency and backhaul load simultaneously. However, new challenges incurred by user mobility and limited coverage of MEC server service arise. Services should be dynamically migrated between multiple MEC servers to maintain service performance due to user movement. Tackling this problem is nontrivial because it is arduous to predict user movement, and service migration will generate service interruptions and redundant network traffic. Service interruption time must be minimized, and redundant network traffic should be reduced to ensure service quality. In this paper, the container live migration technology based on prediction is studied, and an online prediction method based on map data that does not rely on prior knowledge such as user trajectories is proposed to address this challenge in terms of mobility prediction accuracy. A multitier framework and scheduling algorithm are designed to select MEC servers according to moving speeds of users and latency requirements of offloading tasks to reduce redundant network traffic. Based on the map of Beijing, extensive experiments are conducted using simulation platforms and real-world data trace. Experimental results show that our online prediction methods perform better than the common strategy. Our system reduces network traffic by 65% while meeting task delay requirements. Moreover, it can flexibly respond to changes in the user’s moving speed and environment to ensure the stability of offload service.

A Predictive Vehicle Ride Sharing Recommendation System for Smart Cities Commuting

Smart Cities (or Cities 2.0) are an evolution in citizen habitation. In such cities, transport commuting is changing rapidly with the proliferation of contemporary vehicular technology. New models of vehicle ride sharing systems are changing the way citizens commute in their daily movement schedule. The use of a private vehicle per single passenger transportation is no longer viable in sustainable Smart Cities (SC) because of the vehicles’ resource allocation and urban pollution. The current research on car ride sharing systems is widely expanding in a range of contemporary technologies, however, without covering a multidisciplinary approach. In this paper, the focus is on performing a multidisciplinary research on car riding systems taking into consideration personalized user mobility behavior by providing next destination prediction as well as a recommender system based on riders’ personalized information. Specifically, it proposes a predictive vehicle ride sharing system for commuting, which has impact on the SC green ecosystem. The adopted system also provides a recommendation to citizens to select the persons they would like to commute with. An Artificial Intelligence (AI)-enabled weighted pattern matching model is used to assess user movement behavior in SC and provide the best predicted recommendation list of commuting users. Citizens are then able to engage a current trip to next destination with the more suitable user provided by the list. An experimented is conducted with real data from the municipality of New Philadelphia, in SC of Athens, Greece, to implement the proposed system and observe certain user movement behavior. The results are promising for the incorporation of the adopted system to other SCs.

Redirected walking for virtually expanded play area

We describe a method of achieving redirected walking by modulating subjective translation and rotation. In a real space, a user walks around without leaving a 5 m2 area, but we have built a system that allows virtual movement around a larger area than the real space. This system is realized by translating and rotating the apparent ground in response to user movement.