Dynamic Accuracy of GPS Receivers for Use in Health Research: A Novel Method to Assess GPS Accuracy in Real-World Settings

The cornerstone to achieve the development of autonomous ground driving with the lowest possible risk of collision in real traffic environments is the movement estimation obstacle. Predicting trajectories of multiple obstacles in dynamic traffic scenarios is a major challenge, especially when different types of obstacles such as vehicles and pedestrians are involved. According to the issues mentioned, in this work a novel method based on Bayesian dynamic networks is proposed to infer the paths of interest objects (IO). Environmental information is obtained through stereo video, the direction vectors of multiple obstacles are computed and the trajectories with the highest probability of occurrence and the possibility of collision are highlighted. The proposed approach was evaluated using test environments considering different road layouts and multiple obstacles in real-world traffic scenarios. A comparison of the results obtained against the ground truth of the paths taken by each detected IO is performed. According to experimental results, the proposed method obtains a prediction rate of 75% for the change of direction taking into consideration the risk of collision. The importance of the proposal is that it does not obviate the risk of collision in contrast with related work.

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A Novel Method based on Gaussianity and Sparsity for Signal Separation Algorithms

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v7i4.pp1906-1914 ◽

2017 ◽

Vol 7 (4) ◽

pp. 1906 ◽

Cited By ~ 2

Author(s):

Abouzid Houda ◽

Chakkor Otman

Keyword(s):

Independent Component Analysis ◽

Blind Source Separation ◽

Real World ◽

Source Separation ◽

Real Life ◽

Signal Separation ◽

Mixing Process ◽

The Real ◽

Input Signals ◽

Novel Method

Blind source separation is a very known problem which refers to finding the original sources without the aid of information about the nature of the sources and the mixing process, to solve this kind of problem having only the mixtures, it is almost impossible , that why using some assumptions is needed in somehow according to the differents situations existing in the real world, for exemple, in laboratory condition, most of tested algorithms works very fine and having good performence because the nature and the number of the input signals are almost known apriori and then the mixing process is well determined for the separation operation. But in fact, the real-life scenario is much more different and of course the problem is becoming much more complicated due to the the fact of having the most of the parameters of the linear equation are unknown. In this paper, we present a novel method based on Gaussianity and Sparsity for signal separation algorithms where independent component analysis will be used. The Sparsity as a preprocessing step, then, as a final step, the Gaussianity based source separation block has been used to estimate the original sources. To validate our proposed method, the FPICA algorithm based on BSS technique has been used.

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Feedback-Driven Refinement of Mandarin Speech Recognition Result Based on Lattice Modification and Rescoring

Cognitive Analytics ◽

10.4018/978-1-7998-2460-2.ch062 ◽

2020 ◽

pp. 1237-1247

Author(s):

Xiangdong Wang ◽

Yang Yang ◽

Hong Liu ◽

Yueliang Qian ◽

Duan Jia

Keyword(s):

Speech Recognition ◽

Real World ◽

Computational Cost ◽

User Feedback ◽

Recognition Result ◽

Novel Method ◽

Mandarin Speech Recognition ◽

Recognition Errors ◽

Dynamic Updating ◽

Manual Correction

In real world applications of speech recognition, recognition errors are inevitable, and manual correction is necessary. This paper presents an approach for the refinement of Mandarin speech recognition result by exploiting user feedback. An interface incorporating character-based candidate lists and feedback-driven updating of the candidate lists is introduced. For dynamic updating of candidate lists, a novel method based on lattice modification and rescoring is proposed. By adding words with similar pronunciations to the candidates next to the corrected character into the lattice and then performing rescoring on the modified lattice, the proposed method can improve the accuracy of the candidate lists even if the correct characters are not in the original lattice, with much lower computational cost than that of the speech re-recognition methods. Experimental results show that the proposed method can reduce 24.03% of user inputs and improve average candidate rank by 25.31%.

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A Novel Method for Assessing the Spatial Presence of Omnidirectional Videos: Comparing Virtual and Real World

IEEE Access ◽

10.1109/access.2019.2920443 ◽

2019 ◽

Vol 7 ◽

pp. 183405-183413 ◽

Cited By ~ 2

Author(s):

Wenjie Zou ◽

Wei Zhang ◽

Jiarun Song ◽

Fuzheng Yang ◽

Patrick Le Callet

Keyword(s):

Real World ◽

Spatial Presence ◽

Novel Method

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Evaluating the Ability of Global Positioning System Receivers to Measure a Real-World Operating Mode for Emissions Research

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/0361198105194100106 ◽

2005 ◽

Vol 1941 (1) ◽

pp. 43-50 ◽

Cited By ~ 4

Author(s):

Eric Jackson ◽

Lisa Aultman-Hall ◽

Britt A. Holmén ◽

Jianhe Du

Keyword(s):

Global Positioning System ◽

Real World ◽

Vehicle Emissions ◽

Transportation Network ◽

Operating Mode ◽

Positioning System ◽

Gps Receivers ◽

The Real ◽

Acceleration Data ◽

Global Positioning

This paper evaluates the ability of Global Positioning System (GPS) receivers to determine accurately the second-by-second operating mode of a vehicle in the real-world transportation network. GPS offers the ability to obtain second-by-second velocity directly and to obtain acceleration data indirectly from a vehicle traveling in the real-world traffic network. Although GPS has been used successfully in travel behavior and route choice surveys, the uncertainty in accuracy of velocity and acceleration data obtained from the GPS warrants further investigation to gain a better understanding of the range and spatial distribution of vehicle emissions. In this study, data from two GPS receivers and a ScanTool were collected over five repetitions of a 65-mi route. The results indicate that GPS receivers perform as well as the ScanTool when measuring velocity. Furthermore, the GPS receivers determined the 1-s operating mode of the vehicle successfully when measured against the ScanTool. These results will aid in the future development of vehicle emissions models and allow for an analysis of real-world emissions based on real-world operating mode data.

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Community detection via closure extension

International Journal of Modern Physics C ◽

10.1142/s012918311850119x ◽

2018 ◽

Vol 29 (12) ◽

pp. 1850119

Author(s):

Jingming Zhang ◽

Jianjun Cheng ◽

Xiaosu Feng ◽

Xiaoyun Chen

Keyword(s):

Community Structure ◽

Computational Complexity ◽

Community Detection ◽

Network Structure ◽

Real World ◽

Prior Information ◽

State Of The Art ◽

Local Information ◽

Second Step ◽

Novel Method

Identifying community structure in networks plays an important role in understanding the network structure and analyzing the network features. Many state-of-the-art algorithms have been proposed to identify the community structure in networks. In this paper, we propose a novel method based on closure extension; it performs in two steps. The first step uses the similarity closure or correlation closure to find the initial community structure. In the second step, we merge the initial communities using Modularity [Formula: see text]. The proposed method does not need any prior information such as the number or sizes of communities, and it is able to obtain the same resulting communities in multiple runs. Moreover, it is noteworthy that our method has low computational complexity because of considering only local information of network. Some real-world and synthetic graphs are used to test the performance of the proposed method. The results demonstrate that our method can detect deterministic and informative community structure in most cases.

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SEMANTIC FIELD: A THEORETICAL PERSPECTIVE OF MODELING INFORMATION RETRIEVAL

International Journal of Artificial Intelligence Tools ◽

10.1142/s0218213009000421 ◽

2009 ◽

Vol 18 (06) ◽

pp. 825-851

Author(s):

KUN YUE ◽

WEI-YI LIU

Keyword(s):

Information Retrieval ◽

Theoretical Model ◽

Electrostatic Field ◽

Real World ◽

Field Model ◽

Theoretical Perspective ◽

Keyword Extraction ◽

Semantic Field ◽

Novel Method ◽

Interpretable Model

Information retrieval has been paid much attention and it is widely studied and applied in real world paradigms. For various aspects of information retrieval, various approaches have been proposed from various perspectives. It is necessary to provide a formally-unified and physically-interpretable model for classical problems in information retrieval (e.g., document classification, authority-page selection, and keyword extraction, etc.). In this paper we propose a theoretical model, called semantic field, inspired by the theories of lexical semantics and electrostatic field. Based on this physical model, information retrieval can be viewed from a theoretical perspective and interpreted by people's physical intuitions and natural heuristics. Centered on the concept of semantic field, we give some relevant properties, including semantic affinity, semantic coacervation degree and radiation of a semantic source. As the representative application of the proposed semantic field model, a novel method for automatic keyword extraction is discussed, and the feasibility is verified by corresponding experiments.

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Coarsening optimization for differentiable programming

Proceedings of the ACM on Programming Languages ◽

10.1145/3485507 ◽

2021 ◽

Vol 5 (OOPSLA) ◽

pp. 1-27

Author(s):

Xipeng Shen ◽

Guoqiang Zhang ◽

Irene Dea ◽

Samantha Andow ◽

Emilio Arroyo-Fang ◽

...

Keyword(s):

Real World ◽

Control Flow ◽

Algorithmic Differentiation ◽

Symbolic Reasoning ◽

Single Operation ◽

Real World Applications ◽

Novel Method ◽

Expression Swell

This paper presents a novel optimization for differentiable programming named coarsening optimization. It offers a systematic way to synergize symbolic differentiation and algorithmic differentiation (AD). Through it, the granularity of the computations differentiated by each step in AD can become much larger than a single operation, and hence lead to much reduced runtime computations and data allocations in AD. To circumvent the difficulties that control flow creates to symbolic differentiation in coarsening, this work introduces phi-calculus, a novel method to allow symbolic reasoning and differentiation of computations that involve branches and loops. It further avoids "expression swell" in symbolic differentiation and balance reuse and coarsening through the design of reuse-centric segment of interest identification. Experiments on a collection of real-world applications show that coarsening optimization is effective in speeding up AD, producing several times to two orders of magnitude speedups.

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Exploring Periodicity and Interactivity in Multi-Interest Framework for Sequential Recommendation

Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2021/197 ◽

2021 ◽

Author(s):

Gaode Chen ◽

Xinghua Zhang ◽

Yanyan Zhao ◽

Cong Xue ◽

Ji Xiang

Keyword(s):

Real World ◽

Information Overload ◽

State Of The Art ◽

Recommendation Systems ◽

Time Interval ◽

Interest Representation ◽

Novel Method ◽

Real World Datasets ◽

Item Representation ◽

Global And Local

Sequential recommendation systems alleviate the problem of information overload, and have attracted increasing attention in the literature. Most prior works usually obtain an overall representation based on the user’s behavior sequence, which can not sufficiently reflect the multiple interests of the user. To this end, we propose a novel method called PIMI to mitigate this issue. PIMI can model the user’s multi-interest representation effectively by considering both the periodicity and interactivity in the item sequence. Specifically, we design a periodicity-aware module to utilize the time interval information between user’s behaviors. Meanwhile, an ingenious graph is proposed to enhance the interactivity between items in user’s behavior sequence, which can capture both global and local item features. Finally, a multi-interest extraction module is applied to describe user’s multiple interests based on the obtained item representation. Extensive experiments on two real-world datasets Amazon and Taobao show that PIMI outperforms state-of-the-art methods consistently.

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