A review of k-NN algorithm based on classical and Quantum Machine Learning

[EN] Artificial intelligence algorithms, developed for traditional computing, based on Von Neumann’s architecture, are slow and expen- sive in terms of computational resources. Quantum mechanics has opened up a new world of possibilities within this field, since, thanks to the basic properties of a quantum computer, a great degree of parallelism can be achieved in the execution of the quantum version of machine learning algorithms. In this paper, a study has been carried out on these proper- ties and on the design of their quantum computing versions. More specif- ically, the study has been focused on the quantum version of the k-NN algorithm that allows to understand the fundamentals when transcribing classical machine learning algorithms into its quantum versions

Algoritmos de machine learning y su aplicación al mantenimiento industrial en el sector agroalimentario

Las aplicaciones de Machine Learning, o aprendizaje automático, son soluciones que, tras su implementación, continúan mejorando con el tiempo y con una mínima intervención humana, lo que las hace muy adecuadas para ayudar en las labores de mantenimiento de cualquier industria. Se han analizado 10 algoritmos, de los más utilizados, para comprender los conceptos básicos del aprendizaje automático, los problemas que solucionan y seleccionar el mejor algoritmo para la aplicación al mantenimiento predictivo en una industria agroalimentaria española: Solán de Cabras

Aplicación móvil para la corrección automática de tests y encuestas con visión artificial

Memoria ID-197. Ayudas de la Universidad de Salamanca para la innovación docente, curso 2018-2019

Knowledge Management in the Fourth Industrial Revolution: Mapping the Literature and Scoping Future Avenues

Due to increased competitive pressure, modern organizations tend to rely on knowledge and its exploitation to sustain a long-term advantage. This calls for a precise understanding of knowledge management (KM) processes and, specifically, how knowledge is created, shared/transferred, acquired, stored/retrieved, and applied throughout an organizational system. However, since the beginning of the new millennium, such KM processes have been deeply affected and molded by the advent of the fourth industrial revolution, also called Industry 4.0, which involves the interconnectedness of machines and their ability to learn and share data autonomously. For this reason, the present study investigates the intellectual structure and trends of KM in Industry 4.0. Bibliometric analysis and a systematic literature review are conducted on a total of 90 relevant articles. The results reveal 6 clusters of keywords, subsequently explored via a systematic literature review to identify potential stream of this emergent field and future research avenues capable of producing meaningful advances in managerial knowledge of Industry 4.0 and its consequences

Diseño de un modelo para mantenimiento predictivo en motores de inducción utilizando técnicas de la Industria 4.0

La industria en el mundo está pasando por un constante cambio tecnológico. El Perú no es ajeno a tales cambios, actualmente se está fomentando lentamente, pero a pasos firmes el desarrollo de nuevas tecnologías, tales como la Inteligencia artificial, el trabajo colaborativo, el Big data, el internet de las cosas (loT), que nos están introduciendo a la cuarta revolución industrial o industria 4.0. La industria en general tiene como uno de sus activos principales al motor de inducción, y en ese sentido es necesario implementar un método de mantenimiento predictivo basado en la inteligencia artificial, el cual monitorizara el estado del motor, con el finde predecir el momento adecuado de cambio de sus elementos, y así evitar las paradas inesperadas que generan altos costes, haciendo a la industria local más competitiva. Es así que, el presente trabajo utilizara el aprendizaje supervisado de Machine Learning para predecir el estado de los rodamientos del motor de inducción. Se trabajará con datos experimentales de la base de datos de acceso público de la Case Western Reserve University (Case Western & University Reserve (CWRU), n.d.), los cuales obtuvieron mediante diversos ensayos controlados en laboratorio.The industry in the world is going through constant technological change. Perú is no stranger to such changes, currently the development of new technologies is being promoted slowly, but steadily, such as artificial intelligence, collaborative work, Big data, the internet of things (loT), which we They are ushering in the fourth industrial revolution or Industry 4.0. The industry in general has the induction motor as one of its main assets, and in that sense, it is necessary to implement a predictive maintenance method based on artificial intelligence, which will monitor the condition of the motor, in order to predict the right moment of operation. change of its elements, and thus avoid unexpected stops that generate high costs, making the local industry more competitive. Thus, the present work will use supervised Machine Learning to predict the state of the induction motor bearings. We will work with experimental data from the Case Western Reserve University public access database (Case Western & University Reserve (CWRU), n.d.), which were obtained through various controlled laboratory tests.Campus Lima Centr

Industry 4.0 technologies for manufacturing sustainability: A systematic review and future research directions

Recent developments in manufacturing processes and automation have led to the new industrial revolution termed “Industry 4.0”. Industry 4.0 can be considered as a broad domain which includes: data management, manufacturing competitiveness, production processes and efficiency. The term Industry 4.0 includes a variety of key enabling technologies i.e., cyber physical systems, Internet of Things, artificial intelligence, big data analytics and digital twins which can be considered as the major contributors to automated and digital manufacturing environments. Sustainability can be considered as the core of business strategy which is highlighted in the United Nations (UN) Sustainability 2030 agenda and includes smart manufacturing, energy efficient buildings and low-impact industrialization. Industry 4.0 technologies help to achieve sustainability in business practices. However, very limited studies reported about the extensive reviews on these two research areas. This study uses a systematic literature review approach to find out the current research progress and future research potential of Industry 4.0 technologies to achieve manufacturing sustainability. The role and impact of different Industry 4.0 technologies for manufacturing sustainability is discussed in detail. The findings of this study provide new research scopes and future research directions in different research areas of Industry 4.0 which will be valuable for industry and academia in order to achieve manufacturing sustainability with Industry 4.0 technologies

Research on incentive mechanisms for anti-heterogeneous federated learning based on reputation and contribution

An optimization algorithm for federated learning, equipped with an incentive mechanism, is introduced to tackle the challenges of excessive iterations, prolonged training durations, and suboptimal efficiency encountered during model training within the federated learning framework. Initially, the algorithm establishes reputation values that are tied to both time and model loss metrics. This foundation enables the creation of incentive mechanisms aimed at rewarding honest nodes while penalizing malicious ones. Subsequently, a bidirectional selection mechanism anchored in blockchain technology is developed, allowing smart contracts to enroll nodes with high reputations in training sessions, thus filtering out malicious clients and enhancing local training efficiency. Furthermore, the integration of the Earth Mover's Distance (EMD) mechanism serves to lessen the impact of non-IID (non-Independent and Identically Distributed) data on the global model, leading to a reduction in the frequency of model training cycles and an improvement in model accuracy. Experimental results confirm that this approach maintains high model accuracy in non-IID data settings, outperforming traditional federated learning algorithms

Maintenance optimization in industry 4.0

This work reviews maintenance optimization from different and complementary points of view. Specifically, we systematically analyze the knowledge, information and data that can be exploited for maintenance optimization within the Industry 4.0 paradigm. Then, the possible objectives of the optimization are critically discussed, together with the maintenance features to be optimized, such as maintenance periods and degradation thresholds. The main challenges and trends of maintenance optimization are, then, highlighted and the need is identified for methods that do not require a-priori selection of a predefined maintenance strategy, are able to deal with large amounts of heterogeneous data collected from different sources, can properly treat all the uncertainties affecting the behavior of the systems and the environment, and can jointly consider multiple optimization objectives, including the emerging ones related to sustainability and resilience

Torque Prediction Model of a CI Engine for Agricultural Purposes Based on Exhaust Gas Temperatures and CFD-FVM Methodologies Validated with Experimental Tests

A truly universal system to optimize consumptions, monitor operation and predict maintenance interventions for internal combustion engines must be independent of onboard systems, if present. One of the least invasive methods of detecting engine performance involves the measurement of the exhaust gas temperature (EGT), which can be related to the instant torque through thermodynamic relations. The practical implementation of such a system requires great care since its torque-predictive capabilities are strongly influenced by the position chosen for the temperature-detection point(s) along the exhaust line, specific for each engine, the type of installation for the thermocouples, and the thermal characteristics of the interposed materials. After performing some preliminary tests at the dynamometric brake on a compression-ignition engine for agricultural purposes equipped with three thermocouples at different points in the exhaust duct, a novel procedure was developed to: (1) tune a CFD-FVM-model of the exhaust pipe and determine many unknown thermodynamic parameters concerning the engine (including the real EGT at the exhaust valve outlet in some engine operative conditions), (2) use the CFD-FVM results to considerably increase the predictive capability of an indirect torque-detection strategy based on the EGT. The joint use of the CFD-FVM software, Response Surface Method, and specific optimization algorithms was fundamental to these aims and granted the experimenters a full mastery of systems’ non-linearity and a maximum relative error on the torque estimations of 2.9%