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Review
Key Crowdsourcing Technologies for Product Design and Development
Xiao-Jing Niu, Sheng-Feng Qin, John Vines, Rose Wong, Hui Lu
2019, vol. 16, no. 1, pp. 1-15, doi: 10.1007/s11633-018-1138-7
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Abstract:
Traditionally, small and medium enterprises (SMEs) in manufacturing rely heavily on a skilled, technical and professional workforce to increase productivity and remain globally competitive. Crowdsourcing offers an opportunity for SMEs to get access to online communities who may provide requested services such as generating design ideas or problem solutions. However, there are some barriers preventing them from adopting crowdsourcing into their product design and development (PDD) practice. In this paper, we provide a literature review of key crowdsourcing technologies including crowdsourcing platforms and tools, crowdsourcing frameworks, and techniques in terms of open call generation, rewarding, crowd qualification for working, organization structure of crowds, solution evaluation, workflow and quality control and indicate the challenges of integrating crowdsourcing with a PDD process. We also explore the necessary techniques and tools to support the crowdsourcing PDD process. Finally, we propose some key guidelines for coping with the aforementioned challenges in the crowdsourcing PDD process.
A Survey of the Research Status of Pedestrian Dead Reckoning Systems Based on Inertial Sensors
Yuan Wu, Hai-Bing Zhu, Qing-Xiu Du, Shu-Ming Tang
2019, vol. 16, no. 1, pp. 65-83, doi: 10.1007/s11633-018-1150-y
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Abstract:
With the development of micro-electromechanical systems (MEMS), miniaturized, low-power and low-cost inertial measurement units (IMUs) have been widely integrated into mobile terminals and smart wearable devices. This provides the prospect of a broad application for the inertial sensor-based pedestrian dead-reckoning (IPDR) systems. Especially for indoor navigation and indoor positioning, the IPDR systems have many unique advantages that other methods do not have. At present, a large number of technologies and methods for IPDR systems are proposed. In this paper, we have analyzed and outlined the IPDR systems based on about 80 documents in the field of IPDR in recent years. The article is structured in the form of an introduction-elucidation-conclusion framework. First, we proposed a general framework to explore the structure of an IPDR system. Then, according to this framework, the IPDR system was divided into six relatively independent sub-problems, which were discussed and summarized separately. Finally, we proposed a graph structure of IPDR systems, and a sub-directed graph, formed by selecting a combined path from the start node to the end node, skillfully constitutes a technical route of one specific IPDR system. At the end of the article, we summarized some key issues that need to be resolved before the IPDR systems are widely used.
Research Article
Potential Bands of Sentinel-2A Satellite for Classification Problems in Precision Agriculture
Tian-Xiang Zhang, Jin-Ya Su, Cun-Jia Liu, Wen-Hua Chen
2019, vol. 16, no. 1, pp. 16-26, doi: 10.1007/s11633-018-1143-x
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Abstract:
Various indices are used for assessing vegetation and soil properties in satellite remote sensing applications. Some indices, such as normalized difference vegetation index (NDVI) and normalized difference water index (NDWI), are capable of simply differentiating crop vitality and water stress. Nowadays, remote sensing capabilities with high spectral, spatial and temporal resolution are available to analyse classification problems in precision agriculture. Many challenges in precision agriculture can be addressed by supervised classification, such as crop type classification, disease and stress (e.g., grass, water and nitrogen) monitoring. Instead of performing classification based on designated indices, this paper explores direct classification using different bands information as features. Land cover classification by using the recently launched Sentinel-2A image is adopted as a case study to validate our method. Four approaches of featured band selection are compared to classify five classes (crop, tree, soil, water and road) with the support vector machines (SVMs) algorithm, where the first approach utilizes traditional empirical indices as features and the latter three approaches adopt specific bands (red, near infrared and short wave infrared) related to indices, specific bands after ranking by mutual information (MI), and full bands of on-board sensors as features, respectively. It is shown that a better classification performance can be achieved by directly using the selected bands after MI ranking compared with the one using empirical indices and specific bands related to indices, while the use of all 13 bands can marginally improve the classification accuracy than MI based one. Therefore, it is recommended that this approach can be applied for specific Sentinel-2A image classification problems in precision agriculture.
Effective Crowd Anomaly Detection Through Spatio-temporal Texture Analysis
Yu Hao, Zhi-Jie Xu, Ying Liu, Jing Wang, Jiu-Lun Fan
2019, vol. 16, no. 1, pp. 27-39, doi: 10.1007/s11633-018-1141-z
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Abstract:
Abnormal crowd behaviors in high density situations can pose great danger to public safety. Despite the extensive installation of closed-circuit television (CCTV) cameras, it is still difficult to achieve real-time alerts and automated responses from current systems. Two major breakthroughs have been reported in this research. Firstly, a spatial-temporal texture extraction algorithm is developed. This algorithm is able to effectively extract video textures with abundant crowd motion details. It is through adopting Gabor-filtered textures with the highest information entropy values. Secondly, a novel scheme for defining crowd motion patterns (signatures) is devised to identify abnormal behaviors in the crowd by employing an enhanced gray level co-occurrence matrix model. In the experiments, various classic classifiers are utilized to benchmark the performance of the proposed method. The results obtained exhibit detection and accuracy rates which are, overall, superior to other techniques.
Multi-layer Contribution Propagation Analysis for Fault Diagnosis
Ruo-Mu Tan, Yi Cao
2019, vol. 16, no. 1, pp. 40-51, doi: 10.1007/s11633-018-1142-y
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Abstract:
The recent development of feature extraction algorithms with multiple layers in machine learning and pattern recognition has inspired many applications in multivariate statistical process monitoring. In this work, two existing multi-layer linear approaches in fault detection are reviewed and a new one with extra layer is proposed in analogy. To provide a general framework for fault diagnosis in succession, this work also proposes the contribution propagation analysis which extends the original definition of contribution of variables in multivariate statistical process monitoring. In fault diagnosis stage, the proposed contribution propagation analysis for multi-layer linear feature extraction algorithms is compared with the fault diagnosis results of original contribution plots associated with single layer feature extraction approach. Plots of variable contributions obtained by the aforementioned approaches on the data sets collected from a simulated benchmark case study (Tennessee Eastman process) as well as an industrial scale multiphase flow facility are presented as a demonstration of the usage and performance of the contribution propagation analysis on multi-layer linear algorithms.
A Reliability Aware Protocol for Cooperative Communication in Cognitive Radio Networks
Munam Ali Shah, Si-Jing Zhang, Hong-Ji Yang
2019, vol. 16, no. 1, pp. 84-92, doi: 10.1007/s11633-016-0995-1
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Abstract:
One of the challenging tasks in cognitive radio (CR) networks is to agree on a common control channel to exchange control information. This paper presents a novel medium access control (MAC) protocol for CR network which efficiently and intelligently establishes a common control channel between CR nodes. The proposed protocol is the first CR MAC protocol which is hybrid in nature and lies between global common control channel (GCCC) and non-GCCC family of MAC protocols. The dynamic nature of the protocol makes the CR nodes converge on a newly found control channel quicker whenever the interference from a licensed user is sensed. The analytical results show that the dynamic, hybrid and adaptive nature of proposed protocol yields higher throughputs when compared with other CR MAC protocols.
Virtual Plate Based Controlling Strategy of Toy Play for Robot's Communication Development in JA Space
Wei Wang, Xiao-Dan Huang
2019, vol. 16, no. 1, pp. 93-101, doi: 10.1007/s11633-016-1022-2
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Abstract:
Toy play is a basic skill for a humanoid robot after it has joint attention (JA) ability. Because such skill is helpful for human-robot interaction and cooperation, we must realize this skill to enhance the robot's communication ability with person. In this paper, we researched a toy play controlling strategy in JA space based on a virtual plate with a serial robot arm, which has five degrees of freedom (5-DoF). For this purpose, a reachable space of joint attention was constructed firstly. And then the toy play controlling strategy was proposed in details. Here we used a virtual plate to enhance the toy play effect. In order to realize this skill better, toy play energy and some restraining relations were analyzed. By contrasting the audio waveform in the experiments, good performance effect of toy play was demonstrated.
Iterative Selection of GOB Poles in the Context of System Modeling
Anis Khouaja, Hassani Messaoud
2019, vol. 16, no. 1, pp. 102-111, doi: 10.1007/s11633-016-0984-4
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Abstract:
This paper is concerned with the problem of system identification using expansions on generalized orthonormal bases (GOB). Three algorithms are proposed to optimize the poles of such a basis. The first two algorithms determine a GOB with optimal real poles while the third one determines a GOB with optimal real and complex poles. These algorithms are based on the estimation of the dominant mode associated with a residual signal obtained by iteratively filtering the output of the process to be modelled. These algorithms are iterative and based on the quadratic error between the linear process output and the GOB based model output. They present the advantage to be very simple to implement. No numerical optimization technique is needed, and in consequence there is no problem of local minima as is the case for other algorithms in the literature. The convergence of the proposed algorithms is proved by demonstrating that the modeling quadratic error between the process output and the GOB based model is decreasing at each iteration of the algorithm. The performance of the proposed pole selection algorithms are based on the quadratic error criteria and illustrated by means of simulation results.
Special Spectral Approach to Solutions of SISO LTI H-Optimization Problems
Evgeny I. Veremey
2019, vol. 16, no. 1, pp. 112-128, doi: 10.1007/s11633-017-1110-y
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Abstract:
The paper is devoted to \begin{document}$H$\end{document}-optimization problems for linear time invariant (LTI) systems with scalar control, external disturbance and measurement noise. All these problems can be numerically solved with the help of the well-known universal approaches based on Riccati equations, linear matrix inequalities (LMI) or maximum entropy technique. Nevertheless, in our opinion there exists a possibility to increase the computational efficiency of synthesis using a special spectral approach to the above mentioned problems in frequency domain. Some relevant details are discussed and efficient numerical algorithms are proposed for the practical implementation of spectral approach. One of its virtues is a possibility to present optimal solutions in a specific form, which is convenient for investigation.
Research Article
Optimization RFID-enabled Retail Store Management with Complex Event Processing
Shang-Lian Peng, Ci-Jian Liu, Jia He, Hong-Nian Yu, Fan Li
2019, vol. 16, no. 1, pp. 52-64, doi: 10.1007/s11633-018-1164-5
Abstract SpringerLink
Abstract:
Radio frequency identification (RFID) enabled retail store management needs workflow optimization to facilitate real-time decision making. In this paper, complex event processing (CEP) based RFID-enabled retail store management is studied, particularly focusing on automated shelf replenishment decisions. We define different types of event queries to describe retailer store workflow action over the RFID data streams on multiple tagging levels (e.g., item level and container level). Non-deterministic finite automata (NFA) based evaluation models are used to detect event patterns. To manage pattern match results in the process of event detection, optimization algorithm is applied in the event model to share event detection results. A simulated RFID-enabled retail store is used to verify the effectiveness of the method, experiment results show that the algorithm is effective and could optimize retail store management workflow.
Current Issue

2019 Vol.16 No.1

Table of Contents

ISSN 1476-8186

E-ISSN 1751-8520

CN 11-5350/TP

Editors-in-chief
Tieniu TAN, Chinese Academy of Sciences Guoping LIU, University of South Wales Huosheng HU, University of Essex

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