Radio-frequency identification technology, based on the reader/tag paradigm, is quickly permeating several aspects of everyday life. The electromagnetic research mainly concerns the design of tag antennas having high efficiency and small size, and suited to complex impedance matching to the embedded electronics. Starting from the available but fragmented open literature, this paper presents a homogeneous survey of relevant methodologies for the design of UHF passive tag antennas. Particular care is taken to illustrate, within a common framework, the basic concepts of the most-used design layouts. The design techniques are illustrated by means of many noncommercial examples.

/pdf/the-art-of-uhf-rfid-antenna-design-impedance-matching-and-8181kp44t2.pdf

The art of UHF RFID antenna design: impedance-matching and size-reduction techniques

Mass-customization production (MCP) companies must fight with shop-floor uncertainty and complexity caused by wide variety of product components. The research is motivated by a typical MCP company that has experienced inefficient scheduling due to paper-based identification and manual data collection. This paper presents an RFID-enabled real-time manufacturing execution system (RT-MES). RFID devices are deployed systematically on the shop-floor to track and trace manufacturing objects and collect real-time production data. Disturbances are identified and controlled within RT-MES. Planning and scheduling decisions are more practically and precisely made and executed. Online facilities are provided to visualize and manage real-time dynamics of shop-floor WIP (work-in-progress) items. A case study is reported in a collaborating company which manufactures large-scale and heavy-duty machineries. The efficiency and effectiveness of the proposed RT-MES are evaluated with real-life industrial data for shop-floor production management in terms of workers, machines and materials.

RFID-enabled real-time manufacturing execution system for mass-customization production

http://www.relialab.org/Upload/files/On%20the%20concept%20of%20e-maintenance%20Review%20and%20current%20research.pdf

On the concept of e-maintenance: Review and current research

In this paper, we discuss scheduling problems in semiconductor manufacturing. Starting from describing the manufacturing process, we identify typical scheduling problems found in semiconductor manufacturing systems. We describe batch scheduling problems, parallel machine scheduling problems, job shop scheduling problems, scheduling problems with auxiliary resources, multiple orders per job scheduling problems, and scheduling problems related to cluster tools. We also present important solution techniques that are used to solve these scheduling problems by means of specific examples, and report on known implementations. Finally, we summarize some of the challenges in scheduling semiconductor manufacturing operations.

A survey of problems, solution techniques, and future challenges in scheduling semiconductor manufacturing operations

Advanced internet of things for personalised healthcare systems

ervice is broadly considered as an application of specialized knowledge, skill, and experience, performed for co-creation of respective values of both consumer and provider. Services are engineered and delivered through a heterogeneous service system. Compared to physical goods in manufacturing, resources, largely people (end users as the service consumer and employees as the service provider) - the main focus of a service system, cannot be held and are more complex to model and manage as people participating in service production and consumption have physiological and psychological issues, cognitive capability, and sociological constraints, etc. As the world becomes more complex and uncertain socially and economically, this research proposes a computational thinking approach to modeling of the dynamics and adaptiveness of a service system, aimed at fully leveraging today’s ubiquitous digitalized information, computing capability and computational power so that the service system can be studied qualitatively and quantitatively. Ultimately, with this foundation we will successively and successfully develop the following mechanisms to implement and enhance service systems: • A mechanism to timely capture end users’ requirements, changes, expectation and satisfaction in a variety of technical, social, and cultural aspects; • A mechanism to efficiently and cost-effectively provide employees right means and assistances to engineer services while promptly responding the changes; • A mechanism to allow involved people consciously infuse as much intelligence as possible into all levels and aspects of decision-making to assure necessary system adaptiveness for smarter operations.

https://pubsonline.informs.org/doi/pdf/10.1287/serv.1.1.42

Computational Thinking of Service Systems: Dynamics and Adaptiveness Modeling

In recognition of the need for more effective and efficient factory system integration solutions, this paper improves the method proposed in our previous work, focusing on new mechanisms to bridge the gap between shop floor automation and factory information systems. As one of the mainstream identification technologies, radio frequency identification (RFID) technology makes possible all products uniquely identifiable across factories. By taking advantage of data capacity stored in an RFID tag, critical manufacturing information on a product can be locally stored with the product. RFID technology provides a means for a product to rapidly retrieve its needed information as it advances through shop floors. A corresponding framework to enable the instant delivery of pertinent data and information on a uniquely identifiable job/product at point-of-need across factories is proposed. A small-scale manufacturing application is presented to show how the proposed approach can be implemented on the shop floor.

RFID-enabled automation in support of factory integration

Tracking patients, charts and equipment in hospitals and across integrated health delivery networks is mostly done manually on white boards or manual entry within health information systems. Some health delivery networks have adopted systems using a combination of infrared and radio frequency (RF) technology to help manage the tracking process. This paper proposes an approach that can improve the operational efficiency of a health delivery network by automating this process through the use of RF identification (RFID) tags. RFID tags are low power communication devices that can be embedded in a patient's ID bracelet, inside a patient chart and medical equipment, and can help track their location and status.

Using RFID tags for tracking patients, charts and medical equipment within an integrated health delivery network

The number of service visits of Alzheimer’s disease (AD) patients is different from each other and their visit time intervals are non-uniform. Although the literature has revealed many approaches in disease progression modeling, they fail to leverage these time-relevant part of patients’ medical records in predicting disease’s future status. This paper investigates how to predict the AD progression for a patient’s next medical visit through leveraging heterogeneous medical data. Data provided by the National Alzheimer’s Coordinating Center includes 5432 patients with probable AD from August 31, 2005 to May 25, 2017. Long short-term memory recurrent neural networks (RNN) are adopted. The approach relies on an enhanced “many-to-one” RNN architecture to support the shift of time steps. Hence, the approach can deal with patients’ various numbers of visits and uneven time intervals. The results show that the proposed approach can be utilized to predict patients’ AD progressions on their next visits with over 99% accuracy, significantly outperforming classic baseline methods. This study confirms that RNN can effectively solve the AD progression prediction problem by fully leveraging the inherent temporal and medical patterns derived from patients’ historical visits. More promisingly, the approach can be customarily applied to other chronic disease progression problems.

/pdf/predictive-modeling-of-the-progression-of-alzheimer-s-3n5ti5k3s6.pdf

Predictive Modeling of the Progression of Alzheimer’s Disease with Recurrent Neural Networks

Buildings account for roughly 40% of the total energy consumption in the world, out of which heating, ventilation, and air conditioning are the major contributors. Traditional heating controllers are inefficient due to lack of adaptability to dynamic conditions such as changing user preferences and outside temperature patterns. Therefore, it is necessary to design energy-efficient controllers that can improvise occupant thermal comfort (deviation from setpoint temperature) while reducing energy consumption. This research presents a Deep Reinforcement Learning (DRL)-based heating controller to improve thermal comfort and minimize energy costs in smart buildings. We perform extensive simulation experiments using real-world outside temperature data. The results show that the DRL-based smart controller outperforms a traditional thermostat controller by improving thermal comfort between 15% and 30% and reducing energy costs between 5% and 12% in the simulated environment. A second set of experiments is then performed for the case of multiple buildings, each having its own heating equipment. The performance is compared when the buildings are controlled centrally (using a single DRL-based controller) versus decentralized control, where each heater is controlled independently and has its own DRL-based controller. We observe that as the number of buildings and differences in their setpoint temperatures increase, decentralized control performs better than a centralized controller. The results have practical implications for heating control, especially in areas with multiple buildings such as residential complexes with multiple houses.

Robin G. Qiu

Papers

Computational Thinking of Service Systems: Dynamics and Adaptiveness Modeling

RFID-enabled automation in support of factory integration

Using RFID tags for tracking patients, charts and medical equipment within an integrated health delivery network

Predictive Modeling of the Progression of Alzheimer’s Disease with Recurrent Neural Networks

Energy-efficient heating control for smart buildings with deep reinforcement learning