Achieving Effective Innovation Based On TRIZ Technological Evolution

Organised by: Cranfield UniversityThis paper outlines the conception of effective innovation and discusses the method to achieve it. Effective Innovation is constrained on the path of technological evolution so that the corresponding path must be detected before conceptual design of the product. The process of products technological evolution is a technical developing process that the products approach to Ideal Final Result (IFR). During the process, the sustaining innovation and disruptive innovation carry on alternately. By researching and forecasting potential techniques using TRIZ technological evolution theory, the effective innovation can be achieved finally.Mori Seiki – The Machine Tool Compan

Evolutionary L∞ identification and model reduction for robust control

An evolutionary approach for modern robust control oriented system identification and model reduction in the frequency domain is proposed. The technique provides both an optimized nominal model and a 'worst-case' additive or multiplicative uncertainty bounding function which is compatible with robust control design methodologies. In addition, the evolutionary approach is applicable to both continuous- and discrete-time systems without the need for linear parametrization or a confined problem domain for deterministic convex optimization. The proposed method is validated against a laboratory multiple-input multiple-output (MIMO) test rig and benchmark problems, which show a higher fitting accuracy and provides a tighter L�¢���� error bound than existing methods in the literature do

Noisy pre-processing facilitating a photonic realisation of device-independent quantum key distribution

Device-independent quantum key distribution provides security even when the equipment used to communicate over the quantum channel is largely uncharacterized. An experimental demonstration of device-independent quantum key distribution is however challenging. A central obstacle in photonic implementations is that the global detection efficiency, i.e., the probability that the signals sent over the quantum channel are successfully received, must be above a certain threshold. We here propose a method to significantly relax this threshold, while maintaining provable device-independent security. This is achieved with a protocol that adds artificial noise, which cannot be known or controlled by an adversary, to the initial measurement data (the raw key). Focusing on a realistic photonic setup using a source based on spontaneous parametric down conversion, we give explicit bounds on the minimal required global detection efficiency.Comment: 5+16 pages, 4 figure

A primitive machine learning tool for the mechanical property prediction of multiple principal element alloys

Multi-principal element alloys (MPEAs) are produced by combining metallic elements in what is a diverse range of proportions. MPEAs reported to date have revealed promising performance due to their exceptional mechanical properties. Training a machine learning (ML) model on known performance data is a reasonable method to rationalise the complexity of composition dependent mechanical properties of MPEAs. This study utilises data from a specifically curated dataset, that contains information regarding six mechanical properties of MPEAs. A parser tool was introduced to convert chemical composition of alloys into the input format of the ML models, and a number of ML models were applied. Finally, Gradio was used to visualise the ML model predictions and to create a user-interactive interface. The ML model presented is an initial primitive model (as it does not factor in aspects such as MPEA production and processing route), however serves as a an initial user tool, whilst also providing a workflow for other researchers

Enhancing Big Data Security with Collaborative Intrusion Detection

As an asset of Cloud computing, big data is now changing our business models and applications. Rich information residing in big data is driving business decision making to be a data-driven process. Its security and privacy, however, have always been a concern of the owners of the data. The security and privacy could be strengthened via securing Cloud computing environments. This requires a comprehensive security solution from attack prevention to attack detection. Intrusion Detection Systems (IDSs) are playing an increasingly important role within the realm of a set of network security schemes. In this article, we study the vulnerabilities in Cloud computing and propose a collaborative IDS framework to enhance the security and privacy of big data

Intrusion detection using geometrical structure

We propose a statistical model, namely Geometrical Structure Anomaly Detection (GSAD) to detect intrusion using the packet payload in the network. GSAD takes into account the correlations among the packet payload features arranged in a geometrical structure. The representation is based on statistical analysis of Mahalanobis distances among payload features, which calculate the similarity of new data against precomputed profile. It calculates weight factor to determine anomaly in the payload. In the 1999 DARPA intrusion detection evaluation data set, we conduct several tests for limited attacks on port 80 and port 25. Our approach establishes and identifies the correlation among packet payloads in a network. © 2009 IEEE

Translating education neuroscience for teachers

Translating Neuroscience to education involves providing accurate and simplified information about neuroscience to teachers. The aim of this research was to understand if providing translated abstracts from neuroscientific articles helped teachers understand content more thoroughly. Surveys, experimental manipulation, and focus group discussions were conducted with thirty teachers from two primary schools in Singapore. Teachers shared their familiarity with neuroscience, self-rated their understanding of neuroscientific abstracts, and provided feedback on the abstracts’ translations. Results indicate that translated abstracts did not improve attitudes significantly; however, focus group discussions revealed that teachers were more interested in the applications of neuroscience research in classroom pedagogy. These findings highlight the importance of improving communication between neuroscientists and educators