Emerging Trends of Nanotechnology towards Picotechnology: Energy and Biomolecules

In nature, metal oxide particles display their existence at the level of picomolecules in solution state and bioactive states in the body. We present evidence of picomolar behavior of molecules different than nanomolar behavior of particles. These particles can be encapsulated in polymers and can be functionalized with protein, nucleotides, and drugs to develop as smart intracellular targeting pico-devices. The preparation technique and physiological conditions decide the size and functionality of these pico-carrier devices. Their usable success rate, feasibility and potentials are yet to be proven or we do not know. The major difference between nanodevices and pico-devices is their intermolecular and intramolecular thermodynamics in medium and their molecular conformational interaction with molecular assembly in cytoarchitecture of the neurofilament, actin-myosin, microtubule proteins. Pico-carrier devices can be presumed as potential spears without interacting with host signal transduction and immunoprotection. In conclusion, the ultrafine size of newer picotechnology products may be better suited and easier to functionalize for design of particle based picodrugs, picochemicals, and pico-targeting molecules

Enhanced Industrial Machinery Condition Monitoring Methodology based on Novelty Detection and Multi-Modal Analysis

This paper presents a condition-based monitoring methodology based on novelty detection applied to industrial machinery. The proposed approach includes both, the classical classification of multiple a priori known scenarios, and the innovative detection capability of new operating modes not previously available. The development of condition-based monitoring methodologies considering the isolation capabilities of unexpected scenarios represents, nowadays, a trending topic able to answer the demanding requirements of the future industrial processes monitoring systems. First, the method is based on the temporal segmentation of the available physical magnitudes, and the estimation of a set of time-based statistical features. Then, a double feature reduction stage based on Principal Component Analysis and Linear Discriminant Analysis is applied in order to optimize the classification and novelty detection performances. The posterior combination of a Feed-forward Neural Network and One-Class Support Vector Machine allows the proper interpretation of known and unknown operating conditions. The effectiveness of this novel condition monitoring scheme has been verified by experimental results obtained from an automotive industry machine.Postprint (published version

Reliability enhanced EV using pattern recognition techniques

The following paper will contribute to the development of novel data transmission techniques from an IVHM perspective so that Electrical Vehicles (EV) will be able to communicate semantically by directly pointing out to the worst failure/threat scenarios. This is achieved by constructing an image-based data communication in which the data that is monitored by a vast number of different sensors are collected as images; and then, the meaningful failure/threat objects are transmitted among a number of EVs. The meanings of these objects that are clarified for each EV by a set of training patterns are semantically linked from one to other EVs through the similarities that the EVs share. This is a similar approach to wellknown image compression and retrieval techniques, but the difference is that the training patterns, codebook, and codewords within the different EVs are not the same. Hence, the initial image that is compressed at the transmitter side does not exactly match the image retrieved at the receiver's side; as it concerns both EVs semantically that mainly addresses the worst risky scenarios. As an advantage, connected EVs would require less number of communication channels to talk together while also reducing data bandwidth as it only sends the similarity rates and tags of patterns instead of sending the whole initial image that is constructed from various sensors, including cameras. As a case study, this concept is applied to DC-DC converters which refer to a system that presents one of the major problems for EVs

PharOS, a multicore OS ready for safety-related automotive systems: results and future prospects

International audienceAutomotive electrical/electronic architectures need to perform more and more functions that are mapped onto many different electronic control units (ECU) because of their different safety levels or different application domains (body, powertrain, multimedia, etc.). Freedom of interference is required to comply with the upcoming ISO 26262 standard for mixing different ASIL levels on the same ECU and is also required to cope with the safe integration of software from different suppliers. PharOS provides dedicated software partitioning mechanisms as well as controlled and efficient resource sharing by construction, from the design to the implementation stages. The main features of PharOS, contributing to this property, are presented in this paper as well as the results on its application an industry-driven case study and associated future prospects

A framework and methods for on-board network level fault diagnostics in automobiles

A significant number of electronic control units (ECUs) are nowadays networked in automotive vehicles to help achieve advanced vehicle control and eliminate bulky electrical wiring. This, however, inevitably leads to increased complexity in vehicle fault diagnostics. Traditional off-board fault diagnostics and repair at service centres, by using only diagnostic trouble codes logged by conventional onboard diagnostics, can become unwieldy especially when dealing with intermittent faults in complex networked electronic systems. This can result in inaccurate and time consuming diagnostics due to lack of real-time fault information of the interaction among ECUs in the network-wide perspective. This thesis proposes a new framework for on-board knowledge-based diagnostics focusing on network level faults, and presents an implementation of a real-time in-vehicle network diagnostic system, using case-based reasoning. A newly developed fault detection technique and the results from several practical experiments with the diagnostic system using a network simulation tool, a hardware- in-the- loop simulator, a disturbance simulator, simulated ECUs and real ECUs networked on a test rig are also presented. The results show that the new vehicle diagnostics scheme, based on the proposed new framework, can provide more real-time network level diagnostic data, and more detailed and self-explanatory diagnostic outcomes. This new system can provide increased diagnostic capability when compared with conventional diagnostic methods in terms of detecting message communication faults. In particular, the underlying incipient network problems that are ignored by the conventional on-board diagnostics are picked up for thorough fault diagnostics and prognostics which can be carried out by a whole-vehicle fault management system, contributing to the further development of intelligent and fault-tolerant vehicles

Continuous maintenance and the future – Foundations and technological challenges

High value and long life products require continuous maintenance throughout their life cycle to achieve required performance with optimum through-life cost. This paper presents foundations and technologies required to offer the maintenance service. Component and system level degradation science, assessment and modelling along with life cycle ‘big data’ analytics are the two most important knowledge and skill base required for the continuous maintenance. Advanced computing and visualisation technologies will improve efficiency of the maintenance and reduce through-life cost of the product. Future of continuous maintenance within the Industry 4.0 context also identifies the role of IoT, standards and cyber security