Detecting events from web resources has attracted increasing research interests in recent years. Our focus in this paper is to detect events from photos on Flickr, an Internet image community website. The results can be used to facilitate user searching and browsing photos by events. The problem is challenging considering: (1) Flickr data is noisy, because there are photos unrelated to real-world events; (2) It is not easy to capture the content of photos. This paper presents our effort in detecting events from Flickr photos by exploiting the tags supplied by users to annotate photos. In particular, the temporal and locational distributions of tag usage are analyzed in the first place, where a wavelet transform is employed to suppress noise. Then, we identify tags related with events, and further distinguish between tags of aperiodic events and those of periodic events. Afterwards, event-related tags are clustered such that each cluster, representing an event, consists of tags with similar temporal and locational distribution patterns as well as with similar associated photos. Finally, for each tag cluster, photos corresponding to the represented event are extracted. We evaluate the performance of our approach using a set of real data collected from Flickr. The experimental results demonstrate that our approach is effective in detecting events from the Flickr photo collection.
The traditional bearing fault diagnosis method is achieved often by sampling the bearing vibration data under the Shannon sampling theorem. Then, the information of the bearing state can be extracted from the vibration data, which will be used in fault diagnosis. A long-term and continuous monitoring needs to sample and store large amounts of raw vibration signals, which will burden the data storage and transmission greatly. For this problem, a new bearing fault diagnosis method based on compressed sensing is presented, which just needs to sample and store a small amount of compressed observation data and uses these data directly to achieve the fault diagnosis. First, several over-complete dictionaries are trained by dictionary learning method using the historical operating data of the bearings. Each of these dictionaries can be effective in signal sparse decomposition for a particular state, while the signals corresponding to other states cannot be decomposed sparsely. According to this difference, the bearing states can be identified finally. The fault diagnosis results of the proposed method with different parameters are analyzed. The effectiveness of the method is validated by experimental tests.
Purpose -The main purpose of this paper is to investigate two key elements of localization and mapping of Autonomous Underwater Vehicle (AUV), i.e. to overview various sensors and algorithms used for underwater localization and mapping, and to make suggestions for future research. Design/methodology/approach -The authors first review various sensors and algorithms used for AUVs in the terms of basic working principle, characters, their advantages and disadvantages. The statistical analysis is carried out by studying 35 AUV platforms according to the application circumstances of sensors and algorithms. Findings -As real-world applications have different requirements and specifications, it is necessary to select the most appropriate one by balancing various factors such as accuracy, cost, size, etc. Although highly accurate localization and mapping in an underwater environment is very difficult, more and more accurate and robust navigation solutions will be achieved with the development of both sensors and algorithms. Research limitations/implications -This paper provides an overview of the state of art underwater localisation and mapping algorithms and systems. No experiments are conducted for verification. Practical implications -The paper will give readers a clear guideline to find suitable underwater localisation and mapping algorithms and systems for their practical applications in hand. Social implications -There is a wide range of audiences who will benefit from reading this comprehensive survey of autonomous localisation and mapping of UAVs. Originality/value -The paper will provide useful information and suggestions to research students, engineers and scientists who work in the field of autonomous underwater vehicles.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.