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Zhang, Tingting
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Publications (10 of 87) Show all publications
Lindén, J., Wang, X., Forsström, S. & Zhang, T. (2019). Bilingual Auto-Categorization Comparison of two LSTM Text Classifiers. In: : . Paper presented at 8th International Congress on Advanced Applied Informatics, Toyama, Japan, July 7-11 (Main Event) & 12 (Forum), 2019.
Open this publication in new window or tab >>Bilingual Auto-Categorization Comparison of two LSTM Text Classifiers
2019 (English)Conference paper (Other academic)
Identifiers
urn:nbn:se:miun:diva-37261 (URN)
Conference
8th International Congress on Advanced Applied Informatics, Toyama, Japan, July 7-11 (Main Event) & 12 (Forum), 2019
Projects
SMART (Smarta system och tjänster för ett effektivt och innovativt samhälle)
Available from: 2019-09-19 Created: 2019-09-19 Last updated: 2019-09-19Bibliographically approved
Lavassani, M., Forsström, S., Jennehag, U. & Zhang, T. (2018). Combining Fog Computing with Sensor Mote Machine Learning for Industrial IoT. Sensors, 18(5), Article ID 1532.
Open this publication in new window or tab >>Combining Fog Computing with Sensor Mote Machine Learning for Industrial IoT
2018 (English)In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 18, no 5, article id 1532Article in journal (Refereed) Published
Abstract [en]

Digitalization is a global trend becoming ever more important to our connected and sustainable society. This trend also affects industry where the Industrial Internet of Things is an important part, and there is a need to conserve spectrum as well as energy when communicating data to a fog or cloud back-end system. In this paper we investigate the benefits of fog computing by proposing a novel distributed learning model on the sensor device and simulating the data stream in the fog, instead of transmitting all raw sensor values to the cloud back-end. To save energy and to communicate as few packets as possible, the updated parameters of the learned model at the sensor device are communicated in longer time intervals to a fog computing system. The proposed framework is implemented and tested in a real world testbed in order to make quantitative measurements and evaluate the system. Our results show that the proposed model can achieve a 98% decrease in the number of packets sent over the wireless link, and the fog node can still simulate the data stream with an acceptable accuracy of 97%. We also observe an end-to-end delay of 180 ms in our proposed three-layer framework. Hence, the framework shows that a combination of fog and cloud computing with a distributed data modeling at the sensor device for wireless sensor networks can be beneficial for Industrial Internet of Things applications.

Place, publisher, year, edition, pages
MDPI, 2018
Keywords
data mining, fog computing, IoT, online learning, monitoring
National Category
Communication Systems
Identifiers
urn:nbn:se:miun:diva-33609 (URN)10.3390/s18051532 (DOI)000435580300231 ()29757227 (PubMedID)2-s2.0-85047063861 (Scopus ID)
Projects
SMART (Smarta system och tjänster för ett effektivt och innovativt samhälle)
Funder
Knowledge Foundation, 20150363, 20140321, and 20140319
Available from: 2018-05-14 Created: 2018-05-14 Last updated: 2019-09-09Bibliographically approved
Lindén, J., Forsström, S. & Zhang, T. (2018). Evaluating Combinations of Classification Algorithms and Paragraph Vectors for News Article Classification. In: Maria Ganzha, Leszek Maciaszek, Marcin Paprzycki (Ed.), Proceedings of the 2018 Federated Conference on Computer Science and Information Systems: . Paper presented at 3rd International Workshop on Language Technologies and Applications (LTA'18) at 2018 Federated Conference on Computer Science and Information Systems, FedCSIS 2018; Poznan; Poland; 9 September 2018 through 12 September 2018 (pp. 489-495). Warzaw: Polskie Towarzystwo Informatyczne, Article ID 8511213.
Open this publication in new window or tab >>Evaluating Combinations of Classification Algorithms and Paragraph Vectors for News Article Classification
2018 (English)In: Proceedings of the 2018 Federated Conference on Computer Science and Information Systems / [ed] Maria Ganzha, Leszek Maciaszek, Marcin Paprzycki, Warzaw: Polskie Towarzystwo Informatyczne , 2018, p. 489-495, article id 8511213Conference paper, Published paper (Refereed)
Abstract [en]

News companies have a need to automate and make the process of writing about popular and new events more effective. Current technologies involve robotic programs that fill in values in templates and website listeners that notify editors when changes are made so that the editor can read up on the source change on the actual website. Editors can provide news faster and better if directly provided with abstracts of the external sources and categorical meta-data that supports what the text is about. In this article, the focus is on the importance of evaluating critical parameter modifications of the four classification algorithms Decisiontree, Randomforest, Multi Layer perceptron and Long-Short-Term-Memory in a combination with the paragraph vector algorithms Distributed Memory and Distributed Bag of Words, with an aim to categorise news articles. The result shows that Decisiontree and Multi Layer perceptron are stable within a short interval, while Randomforest is more dependent on the parameters best split and number of trees. The most accurate model is Long-Short-Term-Memory model that achieves an accuracy of 71%.

Place, publisher, year, edition, pages
Warzaw: Polskie Towarzystwo Informatyczne, 2018
Series
Annals of Computer Science and Information Systems, ISSN 2300-5963
National Category
Computer Sciences
Identifiers
urn:nbn:se:miun:diva-34767 (URN)10.15439/2018F110 (DOI)000454652300071 ()2-s2.0-85057226648 (Scopus ID)978-83-949419-7-0 (ISBN)
Conference
3rd International Workshop on Language Technologies and Applications (LTA'18) at 2018 Federated Conference on Computer Science and Information Systems, FedCSIS 2018; Poznan; Poland; 9 September 2018 through 12 September 2018
Projects
SMART (Smarta system och tjänster för ett effektivt och innovativt samhälle)
Available from: 2018-10-23 Created: 2018-10-23 Last updated: 2019-09-09Bibliographically approved
Wang, B., Zhang, T., Chang, Z., Ristaniemi, T. & Liu, G. (2017). 3D Matrix-Based Visualization System of Association Rules. In: IEEE CIT 2017 - 17th IEEE International Conference on Computer and Information Technology: . Paper presented at 17th IEEE International Conference on Computer and Information Technology, CIT 2017, Helsinki, Finland, 21 August 2017 through 23 August 2017 (pp. 357-362). IEEE, Article ID 8031499.
Open this publication in new window or tab >>3D Matrix-Based Visualization System of Association Rules
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2017 (English)In: IEEE CIT 2017 - 17th IEEE International Conference on Computer and Information Technology, IEEE, 2017, p. 357-362, article id 8031499Conference paper, Published paper (Refereed)
Abstract [en]

With the growing number of mining datasets, it becomes increasingly difficult to explore interesting rules because of the large number of resultant and its nature complexity. Studies on human perception and intuition show that graphical representation could be a better illustration of how to seek information from the data using the capabilities of human visual system. In this work, we present and implement a 3D matrix-based approach visualization system of association rules. The main visual representation applies the extended matrix-based approach with rule-to-items mapping to general transaction data set. A novel method merging rules and assigning weight is proposed in order to reduce the dimension of the association rules, which will help users to find more important items in the new rule. Furthermore, several interactions such as sorting, filtering, zoom and rotation, facilitate decision-makers to explore the rules which are of interest in various aspects. Finally, extensive evaluations have been conducted to assess the system from a logical reasoning point of view. 

Place, publisher, year, edition, pages
IEEE, 2017
Keywords
Association rule, Matrix-based apporach, Reduction, Visualization system
National Category
Computer Sciences
Identifiers
urn:nbn:se:miun:diva-32280 (URN)10.1109/CIT.2017.39 (DOI)000426119400055 ()2-s2.0-85032394793 (Scopus ID)9781538609583 (ISBN)
Conference
17th IEEE International Conference on Computer and Information Technology, CIT 2017, Helsinki, Finland, 21 August 2017 through 23 August 2017
Projects
SMART (Smarta system och tjänster för ett effektivt och innovativt samhälle)
Available from: 2017-12-06 Created: 2017-12-06 Last updated: 2019-09-09Bibliographically approved
Barac, F., Gidlund, M., Zhang, T. & Sisinni, E. (2017). Error Manifestation in Industrial WSN Communication and Guidelines for Countermeasures. In: H. F. Rashvand and A. Abedi (Ed.), Wireless Sensor Systems for Extreme Environments: Space, Underwater, Underground and Industrial. John Wiley & Sons
Open this publication in new window or tab >>Error Manifestation in Industrial WSN Communication and Guidelines for Countermeasures
2017 (English)In: Wireless Sensor Systems for Extreme Environments: Space, Underwater, Underground and Industrial / [ed] H. F. Rashvand and A. Abedi, John Wiley & Sons, 2017Chapter in book (Refereed)
Place, publisher, year, edition, pages
John Wiley & Sons, 2017
Keywords
IWSN, reliability, delay, PER, BER, LQI
National Category
Computer Engineering Communication Systems Telecommunications
Identifiers
urn:nbn:se:miun:diva-30238 (URN)STC (Local ID)978-1-119-12646-1 (ISBN)STC (Archive number)STC (OAI)
Projects
SMART (Smarta system och tjänster för ett effektivt och innovativt samhälle)
Funder
Knowledge Foundation
Available from: 2017-02-22 Created: 2017-02-22 Last updated: 2019-09-09Bibliographically approved
Lin, Y., Lavassani, M., Li, J. & Zhang, T. (2017). PixVid: Capturing Temporal Correlated Changes in Time Series. In: Proceedings - 5th International Conference on Advanced Cloud and Big Data, CBD 2017: . Paper presented at The Fifth International Conference on Advanced Cloud and Big Data, CBD, August 13-16, 2017, Shanghai, China (pp. 337-342). , Article ID 8026960.
Open this publication in new window or tab >>PixVid: Capturing Temporal Correlated Changes in Time Series
2017 (English)In: Proceedings - 5th International Conference on Advanced Cloud and Big Data, CBD 2017, 2017, p. 337-342, article id 8026960Conference paper, Published paper (Refereed)
Abstract [en]

Time series is one of the main research domains in variety of disciplines. Visualization is an important mechanism to present the raw data as well as the processed time series for further analysis. Many successful visualization techniques have been reported recently. However, most of these techniques display data statically, intending to show as much information as possible by one image or plot. We propose PixVid, a visualization technique which orders the dimensions by constructing a hierarchal dimension cluster tree, and then uses a pixel-oriented technique to form images and displays the data in video format.

Series
International Conference on Advanced Cloud and Big Data, ISSN 2573-301X
Keywords
Data Visualisation, Big Data
National Category
Computer Sciences Computer Engineering
Identifiers
urn:nbn:se:miun:diva-31487 (URN)10.1109/CBD.2017.65 (DOI)000426950400057 ()2-s2.0-85031717526 (Scopus ID)STC (Local ID)978-1-5386-1072-5 (ISBN)STC (Archive number)STC (OAI)
Conference
The Fifth International Conference on Advanced Cloud and Big Data, CBD, August 13-16, 2017, Shanghai, China
Available from: 2017-08-28 Created: 2017-08-28 Last updated: 2018-04-03Bibliographically approved
Yang, Q., Wang, S. & Zhang, T. (2017). Pruning and Summarizing the Discovered Time Series Association Rules from Mechanical Sensor Data Qing. In: PROCEEDINGS OF THE 3RD ANNUAL INTERNATIONAL CONFERENCE ON ELECTRONICS, ELECTRICAL ENGINEERING AND INFORMATION SCIENCE (EEEIS 2017): . Paper presented at 3rd Annual International Conference on Electronics, Electrical Engineering and Information Science (EEEIS 2017), Guangzhou, Guangdong, China, September 8-10, 2017 (pp. 40-45). Paris: Atlantis Press, 131
Open this publication in new window or tab >>Pruning and Summarizing the Discovered Time Series Association Rules from Mechanical Sensor Data Qing
2017 (English)In: PROCEEDINGS OF THE 3RD ANNUAL INTERNATIONAL CONFERENCE ON ELECTRONICS, ELECTRICAL ENGINEERING AND INFORMATION SCIENCE (EEEIS 2017), Paris: Atlantis Press, 2017, Vol. 131, p. 40-45Conference paper, Published paper (Refereed)
Abstract [en]

Sensors are widely used in all aspects of our daily life including factories, hospitals and even our homes. Discovering time series association rules from sensor data can reveal the potential relationship between different sensors which can be used in many applications. However, the time series association rule mining algorithms usually produce rules much more than expected. It's hardly to understand, present or make use of the rules. So we need to prune and summarize the huge amount of rules. In this paper, a two-step pruning method is proposed to reduce both the number and redundancy in the large set of time series rules. Besides, we put forward the BIGBAR summarizing method to summarize the rules and present the results intuitively.

Place, publisher, year, edition, pages
Paris: Atlantis Press, 2017
Series
Advances in Engineering Research, ISSN 2352-5401
Keywords
Sensor Time Series, Association Rules, Rules Pruning, Rules Summarizing, BIGBAR
National Category
Engineering and Technology
Identifiers
urn:nbn:se:miun:diva-32364 (URN)10.2991/eeeis-17.2017.7 (DOI)000416098500007 ()978-94-6252-400-2 (ISBN)
Conference
3rd Annual International Conference on Electronics, Electrical Engineering and Information Science (EEEIS 2017), Guangzhou, Guangdong, China, September 8-10, 2017
Projects
DAWNSMART (Smarta system och tjänster för ett effektivt och innovativt samhälle)
Available from: 2017-12-11 Created: 2017-12-11 Last updated: 2019-09-09Bibliographically approved
Dobslaw, F., Zhang, T. & Gidlund, M. (2016). End-to-End Reliability-aware Scheduling for Wireless Sensor Networks. IEEE Transactions on Industrial Informatics, 12(2), 758-767
Open this publication in new window or tab >>End-to-End Reliability-aware Scheduling for Wireless Sensor Networks
2016 (English)In: IEEE Transactions on Industrial Informatics, ISSN 1551-3203, E-ISSN 1941-0050, Vol. 12, no 2, p. 758-767Article in journal (Refereed) Published
Abstract [en]

Wireless Sensor Networks (WSN) are gaining popularity as a flexible and economical alternative to field-bus installations for monitoring and control applications. For missioncritical applications, communication networks must provide endto- end reliability guarantees, posing substantial challenges for WSN. Reliability can be improved by redundancy, and is often addressed on the MAC layer by re-submission of lost packets, usually applying slotted scheduling. Recently, researchers have proposed a strategy to optimally improve the reliability of a given schedule by repeating the most rewarding slots in a schedule incrementally until a deadline. This Incrementer can be used with most scheduling algorithms but has scalability issues which narrows its usability to offline calculations of schedules, for networks that are rather static. In this paper, we introduce SchedEx, a generic heuristic scheduling algorithm extension which guarantees a user-defined end-to-end reliability. SchedEx produces competitive schedules to the existing approach, and it does that consistently more than an order of magnitude faster. The harsher the end-to-end reliability demand of the network, the better SchedEx performs compared to the Incrementer. We further show that SchedEx has a more evenly distributed improvement impact on the scheduling algorithms, whereas the Incrementer favors schedules created by certain scheduling algorithms.

Keywords
Mission-Critical, Industrial Wireless Sensor Net- works, Reliable Packet Delivery, TDMA
National Category
Computer Systems
Identifiers
urn:nbn:se:miun:diva-24017 (URN)10.1109/TII.2014.2382335 (DOI)000373949100030 ()2-s2.0-84963878040 (Scopus ID)STC (Local ID)STC (Archive number)STC (OAI)
Projects
ASIS
Funder
Knowledge Foundation
Available from: 2014-12-23 Created: 2014-12-23 Last updated: 2017-08-15Bibliographically approved
Lavassani, M., Barac, F., Gidlund, M. & Zhang, T. (2016). Handling Event-Triggered Traffic of Safety and Closed-Loop Control Systems in WSANs. In: 14th IEEE International Conference on Industrial Informatics (INDIN'16): . Paper presented at 14th IEEE International Conference on Industrial Informatics (INDIN'16), Poitiers, France, July 18-21, 2016 (pp. 631-636). IEEE, Article ID 7819237.
Open this publication in new window or tab >>Handling Event-Triggered Traffic of Safety and Closed-Loop Control Systems in WSANs
2016 (English)In: 14th IEEE International Conference on Industrial Informatics (INDIN'16), IEEE, 2016, p. 631-636, article id 7819237Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
IEEE, 2016
Keywords
WSN, QoS, Scheduling
National Category
Communication Systems
Identifiers
urn:nbn:se:miun:diva-28557 (URN)10.1109/INDIN.2016.7819237 (DOI)000393551200096 ()2-s2.0-85012891393 (Scopus ID)STC (Local ID)978-150-902-870-2 (ISBN)STC (Archive number)STC (OAI)
Conference
14th IEEE International Conference on Industrial Informatics (INDIN'16), Poitiers, France, July 18-21, 2016
Projects
TIMELINESS
Funder
Knowledge Foundation
Available from: 2016-08-18 Created: 2016-08-18 Last updated: 2017-06-30Bibliographically approved
Dobslaw, F., Zhang, T. & Gidlund, M. (2016). QoS-Aware Cross-layer Configuration for Industrial Wireless Sensor Networks. IEEE Transactions on Industrial Informatics, 12(5), 1679-1691, Article ID 7485858.
Open this publication in new window or tab >>QoS-Aware Cross-layer Configuration for Industrial Wireless Sensor Networks
2016 (English)In: IEEE Transactions on Industrial Informatics, ISSN 1551-3203, E-ISSN 1941-0050, Vol. 12, no 5, p. 1679-1691, article id 7485858Article in journal (Refereed) Published
Abstract [en]

In many applications of Industrial Sensor Networks, stringentreliability and maximum delay constraints paired with priority demands ona sensor-basis are present. These QoS requirements pose tough challenges forIndustrial Wireless Sensor Networks that are deployed to an ever largerextent due to their flexibility and extendibility.In this paper, we introduce an integrated cross-layer framework, SchedEx-GA, spanning MAC layer and networklayer. SchedEx-GA attempts to identify a network configuration that fulfills all application-specific process requirements over a topology including the sensorpublish rates, maximum acceptable delay, service differentiation, and eventtransport reliabilities. The network configuration comprisesthe decision for routing, as well as scheduling.

For many of the evaluatedtopologies it is not possible to find a valid configuration due to the physicalconditions of the environment. We therefore introduce a converging algorithm on top of the frameworkwhich configures a given topology by additional sink positioning in order tobuild a backbone with the gateway that guaranteesthe application specific constraints.The results show that, in order to guarantee a high end-to-end reliability of 99.999% for all flows in a network containing emergency, control loop, andmonitoring traffic, a backbone with multiple sinks is often required for thetested topologies. Additional features, such as multi-channel utilization andaggregation, though, can substantially reduce the demand for required sinks.In its present version, the framework is used for centralized control, butwith the potential to be extended for de-centralized control in future work.

Place, publisher, year, edition, pages
IEEE, 2016
Keywords
Quality if Service, Priority, Wireless Sensor Networks
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:miun:diva-26294 (URN)10.1109/TII.2016.2576964 (DOI)000389219800005 ()2-s2.0-85012039893 (Scopus ID)STC (Local ID)STC (Archive number)STC (OAI)
Available from: 2015-11-24 Created: 2015-11-24 Last updated: 2017-06-30Bibliographically approved
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