Kranthi ms d banu (32 risultati)

Condizione: New.

PAP. Condizione: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condizione: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Paperback. Condizione: new. Paperback. "Deep Learning-Based Predictive Maintenance and Anomaly Detection for Spacecraft Engines" presents an intelligent framework that uses deep learning techniques to monitor the health of spacecraft engines, identify abnormal operating conditions, and predict potential failures before they occur. By analyzing sensor data collected during engine operation, the proposed approach detects hidden patterns and early signs of degradation, enabling timely maintenance and reducing the risk of mission failure. Advanced neural network models, such as recurrent and convolutional architectures, improve fault detection accuracy and support real-time decision-making. The proposed system enhances spacecraft reliability, minimizes maintenance costs, extends engine lifespan, and contributes to safer and more efficient space missions. This research demonstrates the potential of artificial intelligence to transform predictive maintenance strategies in the aerospace industry. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Paperback. Condizione: new. Paperback. L'article Maintenance predictive et detection d'anomalies basees sur l'apprentissage profond pour les moteurs de vaisseaux spatiaux presente un cadre intelligent qui utilise des techniques d'apprentissage profond pour surveiller l'etat des moteurs de vaisseaux spatiaux, identifier les conditions de fonctionnement anormales et predire les defaillances potentielles avant qu'elles ne se produisent. En analysant les donnees des capteurs collectees pendant le fonctionnement des moteurs, l'approche proposee detecte des schemas caches et les premiers signes de degradation, ce qui permet d'effectuer une maintenance en temps opportun et de reduire le risque d'echec de la mission. Des modeles avances de reseaux neuronaux, tels que les architectures recurrentes et convolutives, ameliorent la precision de la detection des defauts et facilitent la prise de decision en temps reel. Le systeme propose renforce la fiabilite des engins spatiaux, minimise les couts de maintenance, prolonge la duree de vie des moteurs et contribue a des missions spatiales plus sures et plus efficaces. Cette recherche demontre le potentiel de l'intelligence artificielle pour transformer les strategies de maintenance predictive dans l'industrie aerospatiale. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

PAP. Condizione: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condizione: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Paperback. Condizione: new. Paperback. "Deep Learning-Based Predictive Maintenance and Anomaly Detection for Spacecraft Engines" presents an intelligent framework that uses deep learning techniques to monitor the health of spacecraft engines, identify abnormal operating conditions, and predict potential failures before they occur. By analyzing sensor data collected during engine operation, the proposed approach detects hidden patterns and early signs of degradation, enabling timely maintenance and reducing the risk of mission failure. Advanced neural network models, such as recurrent and convolutional architectures, improve fault detection accuracy and support real-time decision-making. The proposed system enhances spacecraft reliability, minimizes maintenance costs, extends engine lifespan, and contributes to safer and more efficient space missions. This research demonstrates the potential of artificial intelligence to transform predictive maintenance strategies in the aerospace industry. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…

Paperback. Condizione: new. Paperback. "Deep Learning-Based Predictive Maintenance and Anomaly Detection for Spacecraft Engines" presents an intelligent framework that uses deep learning techniques to monitor the health of spacecraft engines, identify abnormal operating conditions, and predict potential failures before they occur. By analyzing sensor data collected during engine operation, the proposed approach detects hidden patterns and early signs of degradation, enabling timely maintenance and reducing the risk of mission failure. Advanced neural network models, such as recurrent and convolutional architectures, improve fault detection accuracy and support real-time decision-making. The proposed system enhances spacecraft reliability, minimizes maintenance costs, extends engine lifespan, and contributes to safer and more efficient space missions. This research demonstrates the potential of artificial intelligence to transform predictive maintenance strategies in the aerospace industry. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Taschenbuch. Condizione: Neu. DEEP LEARNING-BASED PREDICTIVE MAINTENANACE | AND ANOMALY DETECTION FOR SPACECRAFT ENGINES | Ms. D. Banu Kranthi (u. a.) | Taschenbuch | Englisch | 2026 | LAP LAMBERT Academic Publishing | EAN 9786630259094 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu Print on Demand. …

Paperback. Condizione: new. Paperback. Manutencao preditiva e detecao de anomalias baseadas em aprendizagem profunda para motores de naves espaciais apresenta uma estrutura inteligente que utiliza tecnicas de aprendizagem profunda para monitorizar o estado dos motores de naves espaciais, identificar condicoes de funcionamento anormais e prever potenciais falhas antes que estas ocorram. Atraves da analise dos dados dos sensores recolhidos durante o funcionamento do motor, a abordagem proposta deteta padroes ocultos e sinais precoces de degradacao, permitindo uma manutencao atempada e reduzindo o risco de falha da missao. Modelos avancados de redes neurais, tais como arquiteturas recorrentes e convolucionais, melhoram a precisao na detecao de falhas e apoiam a tomada de decisoes em tempo real. O sistema proposto aumenta a fiabilidade das naves espaciais, minimiza os custos de manutencao, prolonga a vida util dos motores e contribui para missoes espaciais mais seguras e eficientes. Esta investigacao demonstra o potencial da inteligencia artificial para transformar as estrategias de manutencao preditiva na industria aeroespacial. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Paperback. Condizione: new. Paperback. W artykule "Konserwacja predykcyjna i wykrywanie anomalii w silnikach statkow kosmicznych oparte na uczeniu glebokim" przedstawiono inteligentna platforme wykorzystujaca techniki uczenia glebokiego do monitorowania stanu technicznego silnikow statkow kosmicznych, identyfikowania nietypowych warunkow pracy oraz przewidywania potencjalnych awarii, zanim do nich dojdzie. Dzieki analizie danych z czujnikow zebranych podczas pracy silnika proponowane podejscie pozwala wykrywac ukryte wzorce i wczesne oznaki pogorszenia stanu technicznego, umozliwiajac terminowe przeprowadzenie konserwacji i zmniejszajac ryzyko niepowodzenia misji. Zaawansowane modele sieci neuronowych, takie jak architektury rekurencyjne i konwolucyjne, zwiekszaja dokladnosc wykrywania usterek i wspieraja podejmowanie decyzji w czasie rzeczywistym. Proponowany system zwieksza niezawodnosc statkow kosmicznych, minimalizuje koszty konserwacji, wydluza zywotnosc silnikow oraz przyczynia sie do bezpieczniejszych i bardziej wydajnych misji kosmicznych. Badania te pokazuja potencjal sztucznej inteligencji w zakresie transformacji strategii konserwacji predykcyjnej w przemysle lotniczym i kosmicznym. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Paperback. Condizione: new. Paperback. "Manutenzione predittiva e rilevamento delle anomalie basata sul deep learning per i motori dei veicoli spaziali" presenta un quadro di riferimento intelligente che utilizza tecniche di deep learning per monitorare lo stato di salute dei motori dei veicoli spaziali, identificare condizioni operative anomale e prevedere potenziali guasti prima che si verifichino. Analizzando i dati dei sensori raccolti durante il funzionamento del motore, l'approccio proposto rileva modelli nascosti e segni precoci di degrado, consentendo una manutenzione tempestiva e riducendo il rischio di fallimento della missione. Modelli avanzati di reti neurali, quali le architetture ricorrenti e convoluzionali, migliorano l'accuratezza nel rilevamento dei guasti e supportano il processo decisionale in tempo reale. Il sistema proposto migliora l'affidabilita dei veicoli spaziali, riduce al minimo i costi di manutenzione, prolunga la durata di vita dei motori e contribuisce a rendere le missioni spaziali piu sicure ed efficienti. Questa ricerca dimostra il potenziale dell'intelligenza artificiale nel trasformare le strategie di manutenzione predittiva nel settore aerospaziale. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Paperback. Condizione: new. Paperback. "Deep Learning-Based Predictive Maintenance and Anomaly Detection for Spacecraft Engines" praesentiert ein intelligentes Framework, das Deep-Learning-Techniken nutzt, um den Zustand von Raumfahrzeugtriebwerken zu ueberwachen, abnormale Betriebsbedingungen zu identifizieren und potenzielle Ausfaelle vor ihrem Auftreten vorherzusagen. Durch die Analyse von Sensordaten, die waehrend des Triebwerksbetriebs gesammelt werden, erkennt der vorgeschlagene Ansatz verborgene Muster und fruehe Anzeichen von Verschleiss, was eine rechtzeitige Wartung ermoeglicht und das Risiko eines Missionsfehlers verringert. Fortschrittliche neuronale Netzwerkmodelle wie rekursive und konvolutive Architekturen verbessern die Genauigkeit der Fehlererkennung und unterstuetzen die Entscheidungsfindung in Echtzeit. Das vorgeschlagene System erhoeht die Zuverlaessigkeit von Raumfahrzeugen, minimiert die Wartungskosten, verlaengert die Lebensdauer der Triebwerke und traegt zu sichereren und effizienteren Weltraummissionen bei. Diese Forschung demonstriert das Potenzial kuenstlicher Intelligenz, praediktive Wartungsstrategien in der Luft- und Raumfahrtindustrie zu transformieren. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

PAP. Condizione: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condizione: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condizione: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condizione: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condizione: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condizione: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condizione: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condizione: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Paperback. Condizione: new. Paperback. L'article Maintenance predictive et detection d'anomalies basees sur l'apprentissage profond pour les moteurs de vaisseaux spatiaux presente un cadre intelligent qui utilise des techniques d'apprentissage profond pour surveiller l'etat des moteurs de vaisseaux spatiaux, identifier les conditions de fonctionnement anormales et predire les defaillances potentielles avant qu'elles ne se produisent. En analysant les donnees des capteurs collectees pendant le fonctionnement des moteurs, l'approche proposee detecte des schemas caches et les premiers signes de degradation, ce qui permet d'effectuer une maintenance en temps opportun et de reduire le risque d'echec de la mission. Des modeles avances de reseaux neuronaux, tels que les architectures recurrentes et convolutives, ameliorent la precision de la detection des defauts et facilitent la prise de decision en temps reel. Le systeme propose renforce la fiabilite des engins spatiaux, minimise les couts de maintenance, prolonge la duree de vie des moteurs et contribue a des missions spatiales plus sures et plus efficaces. Cette recherche demontre le potentiel de l'intelligence artificielle pour transformer les strategies de maintenance predictive dans l'industrie aerospatiale. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt.

Paperback. Condizione: new. Paperback. Manutencao preditiva e detecao de anomalias baseadas em aprendizagem profunda para motores de naves espaciais apresenta uma estrutura inteligente que utiliza tecnicas de aprendizagem profunda para monitorizar o estado dos motores de naves espaciais, identificar condicoes de funcionamento anormais e prever potenciais falhas antes que estas ocorram. Atraves da analise dos dados dos sensores recolhidos durante o funcionamento do motor, a abordagem proposta deteta padroes ocultos e sinais precoces de degradacao, permitindo uma manutencao atempada e reduzindo o risco de falha da missao. Modelos avancados de redes neurais, tais como arquiteturas recorrentes e convolucionais, melhoram a precisao na detecao de falhas e apoiam a tomada de decisoes em tempo real. O sistema proposto aumenta a fiabilidade das naves espaciais, minimiza os custos de manutencao, prolonga a vida util dos motores e contribui para missoes espaciais mais seguras e eficientes. Esta investigacao demonstra o potencial da inteligencia artificial para transformar as estrategias de manutencao preditiva na industria aeroespacial. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Paperback. Condizione: new. Paperback. "Deep Learning-Based Predictive Maintenance and Anomaly Detection for Spacecraft Engines" praesentiert ein intelligentes Framework, das Deep-Learning-Techniken nutzt, um den Zustand von Raumfahrzeugtriebwerken zu ueberwachen, abnormale Betriebsbedingungen zu identifizieren und potenzielle Ausfaelle vor ihrem Auftreten vorherzusagen. Durch die Analyse von Sensordaten, die waehrend des Triebwerksbetriebs gesammelt werden, erkennt der vorgeschlagene Ansatz verborgene Muster und fruehe Anzeichen von Verschleiss, was eine rechtzeitige Wartung ermoeglicht und das Risiko eines Missionsfehlers verringert. Fortschrittliche neuronale Netzwerkmodelle wie rekursive und konvolutive Architekturen verbessern die Genauigkeit der Fehlererkennung und unterstuetzen die Entscheidungsfindung in Echtzeit. Das vorgeschlagene System erhoeht die Zuverlaessigkeit von Raumfahrzeugen, minimiert die Wartungskosten, verlaengert die Lebensdauer der Triebwerke und traegt zu sichereren und effizienteren Weltraummissionen bei. Diese Forschung demonstriert das Potenzial kuenstlicher Intelligenz, praediktive Wartungsstrategien in der Luft- und Raumfahrtindustrie zu transformieren. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Paperback. Condizione: new. Paperback. W artykule "Konserwacja predykcyjna i wykrywanie anomalii w silnikach statkow kosmicznych oparte na uczeniu glebokim" przedstawiono inteligentna platforme wykorzystujaca techniki uczenia glebokiego do monitorowania stanu technicznego silnikow statkow kosmicznych, identyfikowania nietypowych warunkow pracy oraz przewidywania potencjalnych awarii, zanim do nich dojdzie. Dzieki analizie danych z czujnikow zebranych podczas pracy silnika proponowane podejscie pozwala wykrywac ukryte wzorce i wczesne oznaki pogorszenia stanu technicznego, umozliwiajac terminowe przeprowadzenie konserwacji i zmniejszajac ryzyko niepowodzenia misji. Zaawansowane modele sieci neuronowych, takie jak architektury rekurencyjne i konwolucyjne, zwiekszaja dokladnosc wykrywania usterek i wspieraja podejmowanie decyzji w czasie rzeczywistym. Proponowany system zwieksza niezawodnosc statkow kosmicznych, minimalizuje koszty konserwacji, wydluza zywotnosc silnikow oraz przyczynia sie do bezpieczniejszych i bardziej wydajnych misji kosmicznych. Badania te pokazuja potencjal sztucznej inteligencji w zakresie transformacji strategii konserwacji predykcyjnej w przemysle lotniczym i kosmicznym. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Paperback. Condizione: new. Paperback. "Manutenzione predittiva e rilevamento delle anomalie basata sul deep learning per i motori dei veicoli spaziali" presenta un quadro di riferimento intelligente che utilizza tecniche di deep learning per monitorare lo stato di salute dei motori dei veicoli spaziali, identificare condizioni operative anomale e prevedere potenziali guasti prima che si verifichino. Analizzando i dati dei sensori raccolti durante il funzionamento del motore, l'approccio proposto rileva modelli nascosti e segni precoci di degrado, consentendo una manutenzione tempestiva e riducendo il rischio di fallimento della missione. Modelli avanzati di reti neurali, quali le architetture ricorrenti e convoluzionali, migliorano l'accuratezza nel rilevamento dei guasti e supportano il processo decisionale in tempo reale. Il sistema proposto migliora l'affidabilita dei veicoli spaziali, riduce al minimo i costi di manutenzione, prolunga la durata di vita dei motori e contribuisce a rendere le missioni spaziali piu sicure ed efficienti. Questa ricerca dimostra il potenziale dell'intelligenza artificiale nel trasformare le strategie di manutenzione predittiva nel settore aerospaziale. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Taschenbuch. Condizione: Neu. MANUTENÇÃO PREDITIVA BASEADA EM DEEP LEARNING | E DETEÇÃO DE ANOMALIAS PARA MOTORES DE NAVES ESPACIAIS | Ms. D. Banu Kranthi (u. a.) | Taschenbuch | Portugiesisch | 2026 | Edições Nosso Conhecimento | EAN 9786630117967 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu Print on Demand. …

Taschenbuch. Condizione: Neu. MANUTENZIONE PREDITTIVA BASATA SUL DEEP LEARNING | E RILEVAMENTO DELLE ANOMALIE PER I MOTORI DELLE NAVICELLI SPAZIALI | Ms. D. Banu Kranthi (u. a.) | Taschenbuch | Italienisch | 2026 | Edizioni Sapienza | EAN 9786630171136 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu Print on Demand.…