One of the most significant advances in the development of computer science, information and communication technologies is represented by the cyber-physical systems (CPS). They are systems of collaborating computational entities which are in intensive connection with the surrounding physical world and its on-going processes, providing and using, at the same time, data-accessing and data-processing services available on the Internet. Cyber-physical production systems (CPPS), relying on the latest, and the foreseeable further developments of computer science, information and communication technologies on one hand, and of manufacturing science and technology, on the other, may lead to the 4th industrial revolution, frequently noted as Industrie 4.0. The paper underlines that there are significant roots in general – and in particular to the CIRP community – which point towards CPPS. Expectations towards research in and implementation of CPS and CPPS are outlined and some case studies are introduced. Related new R&D challenges are highlighted.

Cyber-physical systems in manufacturing

There have been many recent advances in wireless communication technologies, particularly in the area of wireless sensor networks, which have undergone rapid development and been successfully applied in the consumer electronics market. Therefore, wireless networks (WNs) have been attracting more attention from academic communities and other domains. From an industrial perspective, WNs present many advantages including flexibility, low cost, easy deployment and so on. Therefore, WNs can play a vital role in the Industry 4.0 framework, and can be used for smart factories and intelligent manufacturing systems. In this paper, we present an overview of industrial WNs (IWNs), discuss IWN features and related techniques, and then provide a new architecture based on quality of service and quality of data for IWNs. We also propose some applications for IWNs and IWN standards. Then, we will use a case from our previous achievements to explain how to design an IWN under Industry 4.0. Finally, we highlight some of the design challenges and open issues that still need to be addressed to make IWNs truly ubiquitous for a wide range of applications.

A review of industrial wireless networks in the context of Industry 4.0

Machine learning in energy economics and finance: A review

Breast cancer is the second leading cause of death for women, so accurate early detection can help decrease breast cancer mortality rates. Computer-aided detection allows radiologists to detect abnormalities efficiently. Medical images are sources of information relevant to the detection and diagnosis of various diseases and abnormalities. Several modalities allow radiologists to study the internal structure, and these modalities have been met with great interest in several types of research. In some medical fields, each of these modalities is of considerable significance. This study aims at presenting a review that shows the new applications of machine learning and deep learning technology for detecting and classifying breast cancer and provides an overview of progress in this area. This review reflects on the classification of breast cancer utilizing multi-modalities medical imaging. Details are also given on techniques developed to facilitate the classification of tumors, non-tumors, and dense masses in various medical imaging modalities. It first provides an overview of the different approaches to machine learning, then an overview of the different deep learning techniques and specific architectures for the detection and classification of breast cancer. We also provide a brief overview of the different image modalities to give a complete overview of the area. In the same context, this review was performed using a broad variety of research databases as a source of information for access to various field publications. Finally, this review summarizes the future trends and challenges in the classification and detection of breast cancer.

Deep and machine learning techniques for medical imaging-based breast cancer: A comprehensive review

Deep learning approach for microarray cancer data classification

A support vector machine-based ensemble algorithm for breast cancer diagnosis

Cloud-based Materials Tracking System Prototype Integrated with Radio Frequency Identification Tagging Technology

Design and application of task administration protocols for collaborative production and service systems

Artificial pancreas system (APS) is a viable option to treat diabetic patients. Researchers, however, have not conclusively determined the best control method for APS. Due to intra-/inter-variability of insulin absorption and action, an individualized algorithm is required to control blood glucose level (BGL) for each patient. To this end, we developed model predictive control (MPC) based on artificial neural networks (ANNs), which combines ANN for BGL prediction based on inputs and MPC for BGL control based on the ANN (NN-MPC). First, we developed a mathematical model for diabetic rats, which was used to identify individual virtual subjects by fitting to empirical data collected through an APS, including BGL data, insulin injection, and food intake. Then, the virtual subjects were used to generate datasets for training ANNs. The NN-MPC determines control actions (insulin injection) based on BGL predicted by the ANN. To evaluate the NN-MPC, we conducted experiments using four virtual subjects under three different scenarios. Overall, the NN-MPC maintained BGL within the normal range about 90% of the time with a mean absolute deviation of 4.7 mg/dl from a desired BGL. Our findings suggest that the NN-MPC can provide subject-specific BGL control in conjunction with a closed-loop APS.

Neural network-based model predictive control for type 1 diabetic rats on artificial pancreas system

Customer-focused and concurrent engineering service systems process tasks more effectively as a result of the power of collaboration among multiple participants. In such environments, however, complex situations might arise that require decisions beyond simple coordination.Task Administration Protocols (TAPs) are designed as a control mechanism to manage complex situations in collaborative task environments. This article presents the design of TAPs for collaborative production systems in which tasks are performed by the collaboration of multiple agents. Three component protocols are found to constitute TAPs and are triggered at appropriate stages in task administration: 1) Task Requirement Analysis Protocol, 2) Shared Resource Allocation Protocol, and 3) Synchronization & Time-Out Protocol. A case study with TAPs metrics for task allocation in a collaborative production system is investigated to compare performance under TAPs, and under a non-TAP coordination protocol (which is considered to be simpler). In terms of task allocation ratio, the case study indicates that performance under TAPs is significantly better (up to 10.6%) than under the non-TAP coordination protocol, especially under medium or high load conditions. The advantage of TAPs can be explained by their design with relatively higher level of collaborative intelligence, addressing more complex control logic compared with non-TAP coordination protocols.

/pdf/design-of-protocols-for-task-administration-in-collaborative-24657r46n7.pdf

Hoo Sang Ko

Papers

A support vector machine-based ensemble algorithm for breast cancer diagnosis

Cloud-based Materials Tracking System Prototype Integrated with Radio Frequency Identification Tagging Technology

Design and application of task administration protocols for collaborative production and service systems

Neural network-based model predictive control for type 1 diabetic rats on artificial pancreas system

Design of Protocols for Task Administration in Collaborative Production Systems