Evaluating indoor positioning systems in a shopping mall : the lessons learned from the IPIN 2018 competition

The Indoor Positioning and Indoor Navigation (IPIN) conference holds an annual competition in which indoor localization systems from different research groups worldwide are evaluated empirically. The objective of this competition is to establish a systematic evaluation methodology with rigorous metrics both for real-time (on-site) and post-processing (off-site) situations, in a realistic environment unfamiliar to the prototype developers. For the IPIN 2018 conference, this competition was held on September 22nd, 2018, in Atlantis, a large shopping mall in Nantes (France). Four competition tracks (two on-site and two off-site) were designed. They consisted of several 1 km routes traversing several floors of the mall. Along these paths, 180 points were topographically surveyed with a 10 cm accuracy, to serve as ground truth landmarks, combining theodolite measurements, differential global navigation satellite system (GNSS) and 3D scanner systems. 34 teams effectively competed. The accuracy score corresponds to the third quartile (75th percentile) of an error metric that combines the horizontal positioning error and the floor detection. The best results for the on-site tracks showed an accuracy score of 11.70 m (Track 1) and 5.50 m (Track 2), while the best results for the off-site tracks showed an accuracy score of 0.90 m (Track 3) and 1.30 m (Track 4). These results showed that it is possible to obtain high accuracy indoor positioning solutions in large, realistic environments using wearable light-weight sensors without deploying any beacon. This paper describes the organization work of the tracks, analyzes the methodology used to quantify the results, reviews the lessons learned from the competition and discusses its future

Fusing East and West Leads a Way to Global Competitiveness in Emerging Economy: Source of China’s Conspicuous Strength in Solar Industry

In light of a conspicuous strength in China’s solar industry in recent years this paper analyzes an institutional source of its strength. Empirical analysis was conducted focusing on the interaction between indigenous semiconductor industry (“East”) and newly emerging solar industry in absorption of global best practices (“West”) thereby fusion between them was demonstrated. Success of this fusion can be attributed to a joint work between industry’s intensive effort in learning global best practices for exploring new business and government’s catalytic role for the attainment of decarbonisation society for nation’s sustainability. This suggests a new insight for growing economy for its development of global competitive industry

Sensor based real-time process monitoring for ultra-precision manufacturing processes with non-linearity and non-stationarity

This research investigates methodologies for real-time process monitoring in ultra-precision manufacturing processes, specifically, chemical mechanical planarization (CMP) and ultra-precision machining (UPM), are investigated in this dissertation.The three main components of this research are as follows: (1) developing a predictive modeling approaches for early detection of process anomalies/change points, (2) devising approaches that can capture the non-Gaussian and non-stationary characteristics of CMP and UPM processes, and (3) integrating multiple sensor data to make more reliable process related decisions in real-time.In the first part, we establish a quantitative relationship between CMP process performance, such as material removal rate (MRR) and data acquired from wireless vibration sensors. Subsequently, a non-linear sequential Bayesian analysis is integrated with decision theoretic concepts for detection of CMP process end-point for blanket copper wafers. Using this approach, CMP polishing end-point was detected within a 5% error rate.Next, a non-parametric Bayesian analytical approach is utilized to capture the inherently complex, non-Gaussian, and non-stationary sensor signal patterns observed in CMP process. An evolutionary clustering analysis, called Recurrent Nested Dirichlet Process (RNDP) approach is developed for monitoring CMP process changes using MEMS vibration signals. Using this novel signal analysis approach, process drifts are detected within 20 milliseconds and is assessed to be 3-7 times faster than traditional SPC charts. This is very beneficial to the industry from an application standpoint, because, wafer yield losses will be mitigated to a great extent, if the onset of CMP process drifts can be detected timely and accurately.Lastly, a non-parametric Bayesian modeling approach, termed Dirichlet Process (DP) is combined with a multi-level hierarchical information fusion technique for monitoring of surface finish in UPM process. Using this approach, signal patterns from six different sensors (three axis vibration and force) are integrated based on information fusion theory. It was observed that using experimental UPM sensor data that process decisions based on the multiple sensor information fusion approach were 15%-30% more accurate than the decisions from individual sensors. This will enable more accurate and reliable estimation of process conditions in ultra-precision manufacturing applications

Fusing indigenous knowledge and global best practices leads a way to global competitiveness in emerging economies: Source of China?s conspicuous strength in solar and wind industry

Ph.DDOCTOR OF PHILOSOPH

Segmenting Markets by Bagged Clustering: Young Chinese Travelers to Western Europe.

Market segmentation is ubiquitous in marketing. Hierarchical and nonhierarchical methods are popular for segmenting tourism markets. These methods are not without controversy. In this study, we use bagged clustering on the push and pull factors of Western Europe to segment potential young Chinese travelers. Bagged clustering overcomes some of the limitations of hierarchical and nonhierarchical methods. A sample of 403 travelers revealed the existence of four clusters of potential visitors. The clusters were subsequently profiled on sociodemographics and travel characteristics. The findings suggest a nascent young Chinese independent travel segment that cannot be distinguished on push factors but can be differentiated on perceptions of the current independent travel infrastructure in Western Europe. Managerial implications are offered on marketing and service provision to the young Chinese outbound travel market

An artificial intelligence driven multi-feature extraction scheme for big data detection

© 2019 IEEE. The Internet improves the speed of information dissemination, and the scale of unstructured text data is expanding and increasingly being used for mass communication. Although these large amounts of data meet the infinite demand, it is difficult to find public focus in a timely manner. Therefore, information extraction from big data has become an important research issue, and there are many published studies on big data processing at home and abroad. In this paper, we propose a multi-feature keyword extraction method, and based on this, an artificial intelligence driven big data MFE scheme is designed, then an application example of the general scheme is expanded and detailed. Taking news as the carrier, this scheme is applied to the algorithm design of hot event detection. As a result, a multi-feature fusion clustering algorithm is proposed based on user attention with two main stages. In the first stage, a multi-feature fusion model is developed to evaluate keywords, and this model combines the term frequency and part of speech features. We use it to extract keywords for representing news and events. In the second stage, we perform clustering and detect hot events in accordance with the procedure, and during the composition of news clusters, we analyze several variadic parameters in order to explore the optimal effectiveness. Then, experiments on the news corpus are conducted, and the results show that the approach presented herein performs well

Innovative Tokyo

This paper compares and contrasts Tokyo's innovation structure with the industrial districts model and the international hub model in the literature on urban and regional development. The Tokyo model embraces and yet transcends both industrial districts and international hub models. The paper details key elements making up the Tokyo model-organizational knowledge creation, integral and co-location systems of corporate R&D and new product development, test markets, industrial districts and clusters, participative consumer culture, continuous learning from abroad, local government policies, the national system of innovation, and the historical genesis of Tokyo in Japan's political economy. The paper finds that the Tokyo model of innovation will continue to evolve with the changing external environment, but fundamentally retains its main characteristics. The lessons from the Tokyo model is that openness, a diversified industrial base, the continuing development of new industries, and an emphasis on innovation, all contribute to the dynamism of a major metropolitan region.Labor Policies,Environmental Economics&Policies,Public Health Promotion,ICT Policy and Strategies,Agricultural Knowledge&Information Systems,ICT Policy and Strategies,Environmental Economics&Policies,Health Monitoring&Evaluation,Agricultural Knowledge&Information Systems,Innovation