Harnessing IoT Data and Knowledge in Smart Manufacturing

In the modern digitalized era, the use of electronic devices is a necessity in daily life, with most end users requiring high product quality of these devices. During the electronics manufacturing process, environmental control, for monitoring ambient temperature and relative humidity, is one of the critical elements affecting product quality. However, the manufacturing process is complicated and involves numerous sections, such as processing workshops and storage facilities. Each section has its own specific requirements for environmental conditions, which are checked regularly and manually, such that the whole environmental control process becomes time-consuming and inefficient. In addition, the reporting mechanism when conditions are out of specification is done manually at regular intervals, resulting in a certain likelihood of serious quality deviation. There is a substantial need for improving knowledge management under smart manufacturing for full integration of Internet of Things (IoT) data and manufacturing knowledge. In this chapter, an Internet-of-Things Quality Prediction System (IQPS), which is a mission critical system in electronics manufacturing, is proposed in adopting the advanced IoT technologies to develop a real-time environmental monitoring scheme in electronics manufacturing. By deploying IQPS, the total intelligent environmental monitoring is achieved, while product quality is predicted in a systematic manner

An Intelligent Clinical Decision Support System for Assessing the Needs of a Long-Term Care Plan

With the global aging population, providing effective long-term care has been promoted and emphasized for reducing the hospitalizations of the elderly and the care burden to hospitals and governments. Under the scheme of Long-term Care Project 2.0 (LTCP 2.0), initiated in Taiwan, two types of long-term care services, i.e., institutional care and home care, are provided for the elderly with chronic diseases and disabilities, according to their personality, living environment and health situation. Due to the increasing emphasis on the quality of life in recent years, the elderly expect long-term care service providers (LCSP) to provide the best quality of care (QoC). Such healthcare must be safe, effective, timely, efficiently, diversified and up-to-date. Instead of supporting basic activities in daily living, LCSPs have changed their goals to formulate elderly-centered care plans in an accurate, time-efficient and cost-effective manner. In order to ensure the quality of the care services, an intelligent clinical decision support system (ICDSS) is proposed for care managers to improve their efficiency and effectiveness in assessing the long-term care needs of the elderly. In the ICDSS, artificial intelligence (AI) techniques are adopted to distinguish and formulate personalized long-term care plans by retrieving relevant knowledge from past similar records

Blockchain-driven IoT for food traceability with an integrated consensus mechanism

Food traceability has been one of the emerging blockchain applications in recent years, for improving the areas of anti-counterfeiting and quality assurance. Existing food traceability systems do not guarantee a high level of system reliability, scalability, and information accuracy. Moreover, the traceability process is time-consuming and complicated in modern supply chain networks. To alleviate these concerns, blockchain technology is promising to create a new ontology for supply chain traceability. However, most consensus mechanisms and data flow in blockchain are developed for cryptocurrency, not for supply chain traceability; hence, simply applying blockchain technology to food traceability is impractical. In this paper, a blockchain-IoT-based food traceability system (BIFTS) is proposed to integrate the novel deployment of blockchain, IoT technology, and fuzzy logic into a total traceability shelf life management system for managing perishable food. To address the needs for food traceability, lightweight and vaporized characteristics are deployed in the blockchain, while an integrated consensus mechanism that considers shipment transit time, stakeholder assessment, and shipment volume is developed. The data flow of blockchain is then aligned to the deployment of IoT technologies according to the level of traceable resource units. Subsequently, the decision support can be established in the food supply chain by using reliable and accurate data for shelf life adjustment, and by using fuzzy logic for quality decay evaluation

Hong Kong Renal Registry Report 2012

SummaryThis report examined the characteristics and trends of dialysis and renal transplant patients among the resident population of Hong Kong who were managed by hospitals or dialysis centers of the Hospital Authority, and accounted for approximately 95% of all patients receiving renal replacement therapies (RRTs) in the territory. Patients receiving RRTs solely in the private sector were not included in this report. Data trends from 1996 to 2011 are presented. In 2011, 1115 new patients were accepted into RRT programs, and the incident rate was 157 patients per million populations (pmp). An increasing trend was noted. The incident rate was 95.1 pmp at the commencement of the annual report in 1996. The point prevalence on December 31, 2012 was 8197 with a prevalence rate of 1152.5 pmp. Overall, there were 3573 patients (43.6%) on peritoneal dialysis (PD) and 1246 patients (15.2%) on hemodialysis (HD), and 3378 patients (41.2%) were living with a functioning renal transplant. The PD/HD ratio was 74.2:25.8. The “PD First” policy was continued. The overall mortality rate among RRT patients was 9.95 patients per 100 patient-years exposed. There was a decreasing trend in mortality among PD patients. Infection and cardiovascular complications were the most common causes of death. Renal transplant was the modality with the best survival rates. The 5 years cumulative patient survival rate for patients on transplant treatment was 89.6%, whereas the corresponding patient survival rates for PD and HD patients were 50.7% and 55.7%, respectively. More than 70% of RRT patients with reports on rehabilitation were active and had normal daily activities

An intelligent risk management model for achieving smart manufacturing on Internet of Things

To adapt to the ever-changing environment, Internet of Things (IoT) has emerged for supporting manufacturing plants to better manage the quality of products. Since the application of IoT is relatively new to the manufacturing industry, increasing attention has been paid on how to manage the planning and implementation process so as to achieve smart manufacturing. However, IoT applications in each manufacturing plant are varied due to different specifications, such as the product types, product nature, plant layout, production flow, machine and equipment settings. Hence, it is essential to perform risk analysis to ensure that any possible situation and uncertainty is being considered before the implementation process. Risk management plays an important role since disruption can cause significant financial and reputational loss, especially for electronics products, which are environmental-sensitive. In this study, an electronic manufacturing risk management model (EM-RMM) is designed to assess the risk faced by manufacturing plants for IoT applications. By identifying the risks faced by manufacturing plants for IoT applications, the likelihood and consequences of the risks are analyzed by using fuzzy analytical hierarchy process (FAHP) to calculate the weighting of the risks. Through a case study in a plant which manufactures environmental-sensitive electronics products, the results provide a systematic procedure for risk assessment in IoT implementation, with the aim of achieving smart manufacturing

A Computer Vision-Based Roadside Occupation Surveillance System for Intelligent Transport in Smart Cities

In digital and green city initiatives, smart mobility is a key aspect of developing smart cities and it is important for built-up areas worldwide. Double-parking and busy roadside activities such as frequent loading and unloading of trucks, have a negative impact on traffic situations, especially in cities with high transportation density. Hence, a real-time internet of things (IoT)-based system for surveillance of roadside loading and unloading bays is needed. In this paper, a fully integrated solution is developed by equipping high-definition smart cameras with wireless communication for traffic surveillance. Henceforth, this system is referred to as a computer vision-based roadside occupation surveillance system (CVROSS). Through a vision-based network, real-time roadside traffic images, such as images of loading or unloading activities, are captured automatically. By making use of the collected data, decision support on roadside occupancy and vacancy can be evaluated by means of fuzzy logic and visualized for users, thus enhancing the transparency of roadside activities. The CVROSS was designed and tested in Hong Kong to validate the accuracy of parking-gap estimation and system performance, aiming at facilitating traffic and fleet management for smart mobility

An adaptive clinical decision support system for serving the elderly with chronic diseases in healthcare industry

With the increasing ageing population worldwide, providing effective nursing care planning in nursing homes is important in meeting the expectations of elderly patients and in streamlining the healthcare information process, hence maintaining high-quality services. Instead of the traditional manual nursing care planning formulation based on expert experience and subjective judgement, this paper describes an adaptive decision support system, namely, the cloud-based nursing care planning system, to enable decision making in formulating nursing care strategies. By integrating cloud computing technology and the case-based reasoning (CBR) technique, medical records and documents pertaining to the elderly can be captured in real time, whereas appropriate treatment plans based on past similar treatment records can be formulated. However, the current case adaptation processes in CBR rely on domain experts to modify retrieved cases, which may not satisfy the needs of the elderly. Therefore, text mining is integrated in the case adaptation process of CBR for extracting up-to-date medical information from the Internet so that its efficiency can be improved. By conducting a pilot study in a nursing home, it was shown that the time for formulating applicable treatment plans for elderly patients can be reduced, and the service satisfaction level can be enhanced