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نتیجه جستجو - تلفیق اطلاعات

تعداد مقالات یافته شده: 4
ردیف عنوان نوع
1 A novel biometric recognition method based on multi kernelled bijection octal pattern using gait sound
یک روش جدید بیومتریک شناختی مبتنی بر الگوی هشت جداره چند هسته ای با استفاده از صدای راه رفتن-2021
Background: Many gait based methods have been presented about biometric identification in the literature. Gait recognition methods have generally used images and sensors signals. In this work, a novel gait based biometric recognition method is presented. A novel Multi Kernelled Bijection Octal Pattern (MK- BOP) is presented in this study. Object: The main aim of the proposed MK-BOP is to extract distinctive and comprehensive features from a signal (gait sound). By using the proposed MK-BOP, a novel biometric recognition method is proposed. Gait sounds are collected, and two novel datasets are collected. The first dataset is a noisy and heterogeneous dataset. The second dataset is a clear and homogenous dataset. A multileveled method is presented to authenticate subjects from these datasets. One dimensional discrete wavelet transform (1D-DWT) is applied to sound signal with Symlet 6 (sym6) filter, and levels are calculated. Conclusion: The proposed MK-BOP generates features from each level signals, and the generated features are concatenated. A hybrid feature selector (RFNCA) selects the most discriminative feature, and selected most discriminative features are forwarded to classifiers. 0.980 and 0.949 success rates were achieved for clear and noisy datasets, respectively.© 2020 Elsevier Ltd. All rights reserved.
Keywords: Gait recognition | Biometrics | Multi kernelled bijection octal pattern | Information fusion | Sound recognition
مقاله انگلیسی
2 Risk assessment and management via multi-source information fusion for undersea tunnel construction
ارزیابی و مدیریت ریسک از طریق تلفیق اطلاعات چند منبع برای ساخت تونل زیر زمینی -2020
The construction of undersea tunnels is an extremely risky endeavor that is vulnerable to water seepage and gushing due to the high water pressure, complex geological conditions, and pore water trapped in unstable rocks. This risk can lead to the collapse of tunnels under construction and disastrous consequences of fatalities and injuries as well as project delays and financial losses. The current risk management practices for tunnel construction projects in China are static and rely on the subjective judgement of experts and practitioners and do not incorporate real-time monitoring data during the construction process at this time. This paper presents a new method and system to assess and manage the risks during the construction process by coupling the risk management system and the quality management system and integrating jobsite monitoring data, design data, and environmental data. In this new method and system, the risk factors are categorized into (hu)man, material, machine, method, and environment, or 4M1E, and are quantitatively measured. The Dempster-Shaffer (D-S) theory was adopted in this method to both fuse the 4M1E data and to compute the aggregate risk index. This new method and system was tested during the Xiamen Metro Line No. 3 project when a shield machine cutter accident occurred. The results show that, before the accident, the individual risk measures in all five dimensions (4M1E) and the aggregate risk index were extremely high, which clearly illustrated the feasibility and capability of the newly developed method and system.
Keywords: Undersea tunnel construction | Multi-source information fusion | Construction risk | D-S evidence theory | Fuzzy matter element
مقاله انگلیسی
3 iFusion: Towards efficient intelligence fusion for deep learning from real-time and heterogeneous data
iFusion: به سمت تلفیق اطلاعاتی کارآمد برای یادگیری عمیق از داده های واقعی و ناهمگن-2019
Deep learning has shown great strength in many fields and has allowed people to live more conveniently and intelligently. However, deep learning requires a considerable amount of uniform training data, which introduces difficulties in many application scenarios. On the one hand, in real-time systems, training data are constantly generated, but users cannot immediately obtain this vast amount of training data. On the other hand, training data from heterogeneous sources have different data formats. Therefore, existing deep learning frameworks are not able to train all data together. In this paper, we propose the iFusion framework, which achieves efficient intelligence fusion for deep learning from real-time data and heterogeneous data. For real-time data, we train only newly arrived data to obtain a new discrimination model and fuse the previously trained models to obtain the discrimination result. For heterogeneous data, different types of data are trained separately; then, we fuse the different discrimination models so that it is not necessary to consider heterogeneous data formats. We use a method based on Dempster-Shafer theory (DST) to fuse the discrimination models. We apply iFusion to the deep learning of medical image data, and the results of the experiments show the effectiveness of the proposed method.
Keywords: Information| fusion | Real-time data | Heterogeneous data | Deep learning
مقاله انگلیسی
4 Video on demand recommender system for internet protocol television service based on explicit information fusion
ویدیو بر روی سیستم توصیه گر تقاضا برای خدمات تلویزیونی پروتکل اینترنت بر اساس تلفیق اطلاعات صریح-2019
Internet protocol television (IPTV) provides video on demand (VOD), internet service, and real-time broadcasting to users as a service that combines broadcasting and communication technology. Among various services, the sales of VOD are profitable because VODs offer relatively strong direct revenue mod- els in IPTV services. However, the development of a VOD recommender system for IPTV service is highly challenging owing to the lack of explicit preference information of users in an IPTV environment. Previous studies for IPTV VOD recommender systems have attempted to solve the data sparsity problem through implicit preference information; however, it is better to utilize explicit preference information to im- prove the performance of system. Recently, IPTV service providers have provided their own over-the-top (OTT) services such that explicit preference information of users for items can be combined. Therefore, we proposed a novel information fusion method for an IPTV VOD recommender system that integrates the explicit information of both IPTV and OTT services. In addition, we utilized the probabilistic matrix factorization, that guarantees high performance in most recommender systems, as a recommender algo- rithm in this study. Finally, we conducted comparative evaluations based on various metrics and validated that the information fusion of IPTV and OTT services contribute to the IPTV VOD recommender system
Keywords: Internet protocol television | Over-the-top | Video-on-demand | Video on demand recommender system | Data sparsity problem | Information fusion
مقاله انگلیسی
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