Pervasive service computing: community coordinated multimedia, context awareness, and service composition

Abstract This thesis introduces a novel Web service-centric solution to pervasive computing, called Service-oriented Pervasive Computing (also called Pervasive Service Computing), which enables computer systems to deal with context in the user’s environment, to dynamically discover and compose existing services, and to develop Internet-scale multimedia applications that support users’ activities. First, this thesis introduces the concept of Pervasive Service Computing and its relation to community coordinated multimedia, context awareness, and service-oriented computing. It then investigates the state of the art, the practices, and techniques which have been developed to support such services. Building on these tools, this study adopts a service-oriented methodology to design a reference model for Pervasive Service Computing, for accommodating specified technical requirements. This model can serve as a guide for research and development towards Pervasive Service Computing. Second, the thesis examines the nature of community coordinated multimedia, and develops the concept of Community Coordinated Multimedia (CCM). To discover the potentials of discoverability and composability of multimedia applications, the thesis introduces a model for Service-oriented Community Coordinated Multimedia (SCCM), and demonstrates the idea of “multimedia application as a service.” Furthermore, the thesis presents a content annotation service and evaluates its feasibility as an end-user prototype. Third, the thesis investigates the nature of context awareness in Pervasive Service Computing, to broaden the definition of context and context-awareness. This research introduces context-aware pervasive service composition (CAPSC) applications, and specifies three-levels of context awareness. Building on this framework, the context-aware service composition prototype is implemented. Fourth, the author examines the overall potential of service composition in Pervasive Service Computing, distinguishes its two main functions as service collaboration, and service coordination, and then develops an ODPSC (Ontology-Driven Pervasive Service Composition) ontology. To address the availability and scalability of service composition, the thesis introduces options for dynamic service composition in the Cloud, and develops an accelerated Cloud architecture for service composition in the Cloud (namely CM4SC middleware). Last, the CM4SC middleware as a service prototype is implemented.Tiivistelmä Tässä työssä käsitellään uutta jokapaikan tietotekniikan Web-palvelukeskeistä ratkaisua, palveluorientoitunutta jokapaikan tietotekniikkaa (Pervasive Service Computing). Tämän avulla tietokonejärjestelmät voivat ottaa huomioon käyttäjän ympäristön tilanteen, löytää ja koota palveluja dynaamisesti, ja näin voidaan kehittää Internetin laajuisia käyttäjän toimintoja tukevia multimediasovelluksia. Ensiksi työssä esitellään jokapaikan tietotekniikan palvelujen käsite sekä tällaisten palveluiden suhde yhteisöllisesti koordinoituun multimediaan, tilannetietoisuuten ja palveluorientoituneeseen tietotekniikkaan. Tieteen nykytila sekä tällaisia palveluja tukemaan kehitetyt käytännöt ja tekniikat esitellään. Näihin työkaluihin pohjautuen työssä omaksutaan palveluorientoitunut metodiikka, kun jokapaikan tietotekniikan palveluille suunnitellaan referenssimalli, jonka avulla voidaan määritellä teknisiä vaatimuksia ja joka voi muutenkin toimia ohjenuorana jokapaikan tietotekniikan palvelujen tutkimukselle ja tuotekehitykselle. Toiseksi työssä tutkitaan yhteisöllisesti koordinoidun multimedian ominaispiirteitä ja määritellään yhteisöllisesti koordinoidun multimedian (Community Coordinated Multimedia, CCM) käsite. Multimediasovellusten löydettävyyden ja kokoamisen mahdollisuuksien kartoittamiseen luodaan palveluorientoitunut CCM-malli (Service-oriented Community Coordinated Multimedia, SCCM). Työssä esitellään ”multimediasovellus palveluna” -idea, jonka käyttökelpoisuutta arvioidaan sisältöpohjaisen annotoinnin prototyyppiratkaisun avulla. Kolmanneksi työssä tutkitaan jokapaikan tietotekniikan palvelujen tilannetietoisuutta laajentamalla tilanteen ja tilannetietoisuuden määritelmiä. Tutkimus esittelee tilannetietoiseen jokapaikan tietotekniikan palvelujen kokoamiseen (Context-Aware Pervasive Service Composition, CAPSC) perustuvia sovelluksia ja määrittelee kolme tasoa tilannetietoisuudelle. Tämän viitekehyksen avulla toteutetaan tilannetietoinen palvelujen kokoamisen prototyyppi. Neljänneksi työssä arvioidaan jokapaikan tietotekniikan palvelujen kokoamisen mahdollisuuksia, tunnistetaan sen kaksi keskeistä toiminnallisuutta, palvelujen yhteistoiminnallisuus (service collaboration) ja palvelujen koordinointi (service coordination), sekä kehitetään ODPSC (Ontology-Driven Pervasive Service Composition) -ontologia. Työssä esitetään saavutettavuuden ja laajennettavuuden haasteisiin ratkaisuksi dynaaminen palvelujen kokoaminen pilvipalveluna. Työssä kehitetään kiihdytetty pilviarkkitehtuuri (CM4SC-välikerrosohjelmisto) palvelujen kokoamiseen pilvessä. Lopuksi työssä toteutetaan CM4SC-välikerrosohjelmiston palveluprototyyppi

Revisiting industry 4.0 with a case study

Abstract The Industry 4.0 is becoming a hot topic. In this paper, we revisit Industry 4.0 from the perspectives of its purposes, features, and key performance indicators. We present a reference roadmap for advancing an Industry 4.0 project from plan to implementation. We present the case study of steel industry 4.0 and lessons. In addition, we give suggestions directing the advancement of Industry 4.0

Surface defect detection using hierarchical features

Abstract In this paper, we propose an instance level hierarchical features based convolution neural network model (H-CNN) for detecting surface defects. The H-CNN uses different convolutional layers’ extracted features to generate defect masks. The H-CNN first generates proposal regions. Then, it proposes a fully convolutional neural network to extract different level’s convolutional features and detect instance level defects. We applied the H-CNN model in freight train detection system for detecting oil-leaks, and the results demonstrate that the H-CNN can effectively identify and generate defect masks. It achieves 92% accuracy on the large reflective oil-leak stain, 86% on the large non-reflective oil-leak stain, 89% on the small reflective oil-leak stain and 74% on the small non-reflective oil-leak stain. Its image process speed is 0.467 s per frame

CNN4GCDD:a one-dimensional convolutional neural network-based model for gear crack depth diagnosis

Abstract Gear crack is one of the common failures in transmission systems. With the gradual expansion of cracks, it may cause tooth fracture. Therefore, it is of great significance to study the fault diagnosis of gear cracks. Vibration signals with time sequence are widely used in gear fault diagnosis. Extracting key fault features from vibration signals determines the accuracy of fault diagnosis models. This paper takes spur gears as research objects, and proposes a model for diagnosing gear crack depth based on one-dimensional convolutional neural network (short for CNN4GCDD). In order to identify crack depths, we collect the vibration signals from three gears with various crack depths and a normal gear without cracks. CNN4GCDD uses the original vibration signal as the input, adaptively extracts features, and makes crack depth diagnosis through the convolutional neural network. The experimental results demonstrate that CNN4GCDD can directly use the original time-domain signal for crack depth diagnosis, and make a high accurate prediction

A scalable and efficient multi-label cnn-based license plate recognition on spark

Abstract Surveillance cameras are being rapidly deployed for facilitating smart transportation. Recognizing the vehicle license plate from massive videos becomes a challenge in context of system scalability and efficiency. This paper proposes a novel algorithm for scalable and efficient license plate recognition (SELPR). The SELPR algorithm first locates the license plate using a YOLO (You Look Only Once) network and recognizes the license plate using multi-label convolutional neural network (Multi-label CNN). We deploy the SELPR algorithm to the Apache Spark framework to evaluate its scalability and efficiency in parallel processing. The results demonstrates that SELPR can achieve synthesized performance with 95% recognition accuracy, better processing efficiency and scalability on a Spark cluster

INDICS:an industrial internet platform

Abstract The industrial Internet integrates the Internet, big data, artificial intelligence, and the real economy. We introduce China’s first industrial Internet platform INDICS — one of the world’s first industrial Internet platforms. We present the INDICS system architecture, and examine three successful INDICS application cases

Emotion recognition from Chinese speech for smart affective services using a combination of SVM and DBN

Abstract Accurate emotion recognition from speech is important for applications like smart health care, smart entertainment, and other smart services. High accuracy emotion recognition from Chinese speech is challenging due to the complexities of the Chinese language. In this paper, we explore how to improve the accuracy of speech emotion recognition, including speech signal feature extraction and emotion classification methods. Five types of features are extracted from a speech sample: mel frequency cepstrum coefficient (MFCC), pitch, formant, short-term zero-crossing rate and short-term energy. By comparing statistical features with deep features extracted by a Deep Belief Network (DBN), we attempt to find the best features to identify the emotion status for speech. We propose a novel classification method that combines DBN and SVM (support vector machine) instead of using only one of them. In addition, a conjugate gradient method is applied to train DBN in order to speed up the training process. Gender-dependent experiments are conducted using an emotional speech database created by the Chinese Academy of Sciences. The results show that DBN features can reflect emotion status better than artificial features, and our new classification approach achieves an accuracy of 95.8%, which is higher than using either DBN or SVM separately. Results also show that DBN can work very well for small training databases if it is properly designed

Parallel-education-blockchain driven smart education:challenges and issues

Abstract Recently education blockchain driven smart education has become focus of attention, and related system frameworks and key technologies are presented. However, problems of difficult to model, difficult to experiment, and difficult to optimize in education blockchain need to be further solved, and driving mechanisms, application scenarios and other issues need further analysis. This paper first introduces education blockchain, challenges and issues, then based on introduction of parallel intelligence theory and parallel blockchain, it proposes parallel education blockchain, and its driven mechanism, function distribution, data transfer, application scenarios and related issues are elaborated; At last, several questions are raised for discussion