Battery Efficiency in Outdoor Sports Environments for Mobile Pervasive Augmented Reality Systems

This work analyses energy expenditure in outdoor sport environments with augmented reality technology. Battery efficiency is becoming a relevant topic in the context of the varied outdoor end-user services, among other realms. It is a key to the acceptance and use of mobile technology. In outdoor environments, battery efficiency can be low, especially when information based on close-to-real-time requires internet access and the use of sensors. Such requirement is today evident with the growth of internet dependence and multiple sensors, which perform both actively and passively via fitness gadgets, smartphones, pervasive systems, and other personal mobile gadgets. In this context, it is relevant to understand how energy is spent with the accelerometer, global position system, and internet access (Wi-Fi or mobile data) providing smart data for outdoor sports activities. Through a prototype, an analysis is made based on the current battery autonomy, and an algorithm model for better battery efficiency is proposed.

Impact of Sensor Data Characterization with Directional Nature of Fault and Statistical Feature Combination for Defect Detection on Roll-to-Roll Printed Electronics

Gravure printing, which is a roll-to-roll printed electronics system suitable for high-speed patterning of functional layers have advantages of being applied to flexible webs in large areas. As each of the printing procedure from inking to doctoring followed by ink transferring and setting influences the quality of the pattern geometry, it is necessary to detect and diagnose factors causing the printing defects beforehand. Data acquisition with three triaxial acceleration sensors for fault diagnosis of four major defects such as doctor blade tilting fault was obtained. To improve the diagnosis performances, optimal sensor selection with Sensor Data Efficiency Evaluation, sensitivity evaluation for axis selection with Directional Nature of Fault and feature variable optimization with Feature Combination Matrix method was applied on the raw data to form a Smart Data. Each phase carried out on the raw data progressively enhanced the diagnosis results in contents of accuracy, positive predictive value, diagnosis processing time, and data capacity. In the case of doctor blade tilting fault, the diagnosis accuracy increased from 48% to 97% with decreasing processing time of 3640 s to 16 s and the data capacity of 100 Mb to 5 Mb depending on the input data between raw data and Smart Data.

Street life and pedestrian activities in smart cities: opportunities and challenges for computational urban science

Ongoing efforts among cities to reinvigorate streets have encouraged innovations in using smart data to understand pedestrian activities. Empowered by advanced algorithms and computation power, data from smartphone applications, GPS devices, video cameras, and other forms of sensors can help better understand and promote street life and pedestrian activities. Through adopting a pedestrian-oriented and place-based approach, this paper reviews the major environmental components, pedestrian behavior, and sources of smart data in advancing this field of computational urban science. Responding to the identified research gap, a case study that hybridizes different smart data to understand pedestrian jaywalking as a reflection of urban spaces that need further improvement is presented. Finally, some major research challenges and directions are also highlighted.

Control Strategies for Reactive Extrusion of Polypropylene by Peroxide Degradation A Brief Review and an Experimental Study

Industry 4.0 and digitalization are widely argued for the future success of numerous industrial solutions. Big data management might lead to the assumption that every issue can be solved numerically without any physical background. To some extent, this strategy will help within the plastics industry in general and in the extrusion technology in particular. However, a deep process knowledge together with process-relevant sensors, as well as the right process arrangements within the processing chain combined with smart data mining methods will be still the key success of industry 4.0. This presentation illustrates, based on a brief review on existing control strategies (Part 1), including sensory and predictive control models for reactive extrusion applied at a real-life on-site best practice project (Part 2), possibilities in combination of process tasks with digitalization approaches for PP-Polymer production. Specifically, rheological research conducted with a novel, patented multi-point rheometer (part 3), will provide a deeper insight into dynamic processes such as reactive extrusion. With those results and derivations thereof, improvements in predictive process control in addition to artificial control systems are made and might even lead to further interesting opportunities.

AI in Healthcare: medical and socio-economic benefits and challenges

The objective of this research was to investigate benefits and challenges of AI in healthcare. Wedivided the benefits into two subcategories: benefits related to the medical domain, and the benefitsrelated to economic and social lives domain. The findings are: 1) Smart data inclusion contributessignificantly and help to improve decision-making quality. 2) Surgical robots have improved theprecision and predictability of the surgery. 3) Intraoperative guidance via video pictures andcommunication systems has proven to be beneficial, particularly in situations when there is a pooraccess to clinics, travel limitations, or pandemic. 4) Sentiment analysis analyzes, interprets, andresponds to verbal expressions of human emotions. 6)Data scientists have been able to create algorithmsthat can comprehend human feeling from written textwith unique combination of NLP and sentimentanalysis. 7) AI could re-balance a clinician's workload,providing them more time to connect with patientsand thereby improve care quality. The majorchallenges are: 1) the data reflects sometimesinherent biases and disparities in the healthcare system. 2) The demand for huge datasets incentivizesdevelopers to acquire data from a large number of patients. Some patients may be worried that thisdata collection would infringe on their confidentiality. 3) AI systems may occasionally be incorrect,resulting in patient damage or other health-care issues. It is not assumed that a new technology willalways be good; it has the potential to be detrimental. There are some improvements that benefit andthere are some challenges that may harm, and these challenges must be responded by futureresearch.