Journal articles on the topic 'Featue location'
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Shum, Leia C., Reza Faieghi, Terry Borsook, Tamim Faruk, Souraiya Kassam, Hoda Nabavi, Sofija Spasojevic, James Tung, Shehroz S. Khan, and Andrea Iaboni. "Indoor Location Data for Tracking Human Behaviours: A Scoping Review." Sensors 22, no. 3 (February 5, 2022): 1220. http://dx.doi.org/10.3390/s22031220.
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Real-time location systems (RTLS) record locations of individuals over time and are valuable sources of spatiotemporal data that can be used to understand patterns of human behaviour. Location data are used in a wide breadth of applications, from locating individuals to contact tracing or monitoring health markers. To support the use of RTLS in many applications, the varied ways location data can describe patterns of human behaviour should be examined. The objective of this review is to investigate behaviours described using indoor location data, and particularly the types of features extracted from RTLS data to describe behaviours. Four major applications were identified: health status monitoring, consumer behaviours, developmental behaviour, and workplace safety/efficiency. RTLS data features used to analyse behaviours were categorized into four groups: dwell time, activity level, trajectory, and proximity. Passive sensors that provide non-uniform data streams and features with lower complexity were common. Few studies analysed social behaviours between more than one individual at once. Less than half the health status monitoring studies examined clinical validity against gold-standard measures. Overall, spatiotemporal data from RTLS technologies are useful to identify behaviour patterns, provided there is sufficient richness in location data, the behaviour of interest is well-characterized, and a detailed feature analysis is undertaken.
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Bichot, Narcisse R., Kyle R. Cave, and Harold Pashler. "Visual selection mediated by location: Feature-based selection of noncontiguous locations." Perception & Psychophysics 61, no. 3 (April 1999): 403–23. http://dx.doi.org/10.3758/bf03211962.
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Yang, Yang, Huiwen Zheng, Chunhua Wang, Wanyue Xiao, and Taigang Liu. "Predicting Apoptosis Protein Subcellular Locations based on the Protein Overlapping Property Matrix and Tri-Gram Encoding." International Journal of Molecular Sciences 20, no. 9 (May 11, 2019): 2344. http://dx.doi.org/10.3390/ijms20092344.
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To reveal the working pattern of programmed cell death, knowledge of the subcellular location of apoptosis proteins is essential. Besides the costly and time-consuming method of experimental determination, research into computational locating schemes, focusing mainly on the innovation of representation techniques on protein sequences and the selection of classification algorithms, has become popular in recent decades. In this study, a novel tri-gram encoding model is proposed, which is based on using the protein overlapping property matrix (POPM) for predicting apoptosis protein subcellular location. Next, a 1000-dimensional feature vector is built to represent a protein. Finally, with the help of support vector machine-recursive feature elimination (SVM-RFE), we select the optimal features and put them into a support vector machine (SVM) classifier for predictions. The results of jackknife tests on two benchmark datasets demonstrate that our proposed method can achieve satisfactory prediction performance level with less computing capacity required and could work as a promising tool to predict the subcellular locations of apoptosis proteins.
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Zhang, Ting-He, and Shao-Wu Zhang. "Advances in the Prediction of Protein Subcellular Locations with Machine Learning." Current Bioinformatics 14, no. 5 (June 28, 2019): 406–21. http://dx.doi.org/10.2174/1574893614666181217145156.
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Background: Revealing the subcellular location of a newly discovered protein can bring insight into their function and guide research at the cellular level. The experimental methods currently used to identify the protein subcellular locations are both time-consuming and expensive. Thus, it is highly desired to develop computational methods for efficiently and effectively identifying the protein subcellular locations. Especially, the rapidly increasing number of protein sequences entering the genome databases has called for the development of automated analysis methods. Methods: In this review, we will describe the recent advances in predicting the protein subcellular locations with machine learning from the following aspects: i) Protein subcellular location benchmark dataset construction, ii) Protein feature representation and feature descriptors, iii) Common machine learning algorithms, iv) Cross-validation test methods and assessment metrics, v) Web servers. Result & Conclusion: Concomitant with a large number of protein sequences generated by highthroughput technologies, four future directions for predicting protein subcellular locations with machine learning should be paid attention. One direction is the selection of novel and effective features (e.g., statistics, physical-chemical, evolutional) from the sequences and structures of proteins. Another is the feature fusion strategy. The third is the design of a powerful predictor and the fourth one is the protein multiple location sites prediction.
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Li, Da, and Zhao Niu. "A Wireless Fingerprint Positioning Method Based on Wavelet Transform and Deep Learning." ISPRS International Journal of Geo-Information 10, no. 7 (June 29, 2021): 442. http://dx.doi.org/10.3390/ijgi10070442.
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As the demand for location services increases, research on location technology has aroused great interest. In particular, signal-based fingerprint location positioning technology has become a research hotspot owing to its high positioning performance. In general, the received signal strength indicator (RSSI) will be used as a location feature to build a fingerprint database. However, at different locations, this feature distinction may not be obvious, resulting in low positioning accuracy. Considering the wavelet transform can get valuable features from the signals, the long-term evolution (LTE) signals were converted into wavelet feature images to construct the fingerprint database. To fully extract the signal features, a two-level hierarchical structure positioning system is proposed to achieve satisfactory positioning accuracy. A deep residual network (ResNet) rough locator is used to learn useful features from the wavelet feature fingerprint image database. Then, inspired by the transfer learning idea, a fine locator based on multilayer perceptron (MLP) is leveraged to further learn the features of the wavelet fingerprint image to obtain better localization performance. Additionally, multiple data enhancement techniques were adopted to increase the richness of the fingerprint dataset, thereby enhancing the robustness of the positioning system. Experimental results indicate that the proposed system leads to improved positioning performance in outdoor environments.
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Hanning, Nina M., Donatas Jonikaitis, Heiner Deubel, and Martin Szinte. "Oculomotor selection underlies feature retention in visual working memory." Journal of Neurophysiology 115, no. 2 (February 1, 2016): 1071–76. http://dx.doi.org/10.1152/jn.00927.2015.
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Oculomotor selection, spatial task relevance, and visual working memory (WM) are described as three processes highly intertwined and sustained by similar cortical structures. However, because task-relevant locations always constitute potential saccade targets, no study so far has been able to distinguish between oculomotor selection and spatial task relevance. We designed an experiment that allowed us to dissociate in humans the contribution of task relevance, oculomotor selection, and oculomotor execution to the retention of feature representations in WM. We report that task relevance and oculomotor selection lead to dissociable effects on feature WM maintenance. In a first task, in which an object's location was encoded as a saccade target, its feature representations were successfully maintained in WM, whereas they declined at nonsaccade target locations. Likewise, we observed a similar WM benefit at the target of saccades that were prepared but never executed. In a second task, when an object's location was marked as task relevant but constituted a nonsaccade target (a location to avoid), feature representations maintained at that location did not benefit. Combined, our results demonstrate that oculomotor selection is consistently associated with WM, whereas task relevance is not. This provides evidence for an overlapping circuitry serving saccade target selection and feature-based WM that can be dissociated from processes encoding task-relevant locations.
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Zhou, G., Q. Li, G. Deng, T. Yue, and X. Zhou. "MINING CO-LOCATION PATTERNS WITH CLUSTERING ITEMS FROM SPATIAL DATA SETS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3 (May 2, 2018): 2505–9. http://dx.doi.org/10.5194/isprs-archives-xlii-3-2505-2018.
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The explosive growth of spatial data and widespread use of spatial databases emphasize the need for the spatial data mining. Co-location patterns discovery is an important branch in spatial data mining. Spatial co-locations represent the subsets of features which are frequently located together in geographic space. However, the appearance of a spatial feature C is often not determined by a single spatial feature A or B but by the two spatial features A and B, that is to say where A and B appear together, C often appears. We note that this co-location pattern is different from the traditional co-location pattern. Thus, this paper presents a new concept called clustering terms, and this co-location pattern is called co-location patterns with clustering items. And the traditional algorithm cannot mine this co-location pattern, so we introduce the related concept in detail and propose a novel algorithm. This algorithm is extended by join-based approach proposed by Huang. Finally, we evaluate the performance of this algorithm.
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Szinte, Martin, Donatas Jonikaitis, Martin Rolfs, Patrick Cavanagh, and Heiner Deubel. "Presaccadic motion integration between current and future retinotopic locations of attended objects." Journal of Neurophysiology 116, no. 4 (October 1, 2016): 1592–602. http://dx.doi.org/10.1152/jn.00171.2016.
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Object tracking across eye movements is thought to rely on presaccadic updating of attention between the object's current and its “remapped” location (i.e., the postsaccadic retinotopic location). We report evidence for a bifocal, presaccadic sampling between these two positions. While preparing a saccade, participants viewed four spatially separated random dot kinematograms, one of which was cued by a colored flash. They reported the direction of a coherent motion signal at the cued location while a second signal occurred simultaneously either at the cue's remapped location or at one of several control locations. Motion integration between the signals occurred only when the two motion signals were congruent and were shown at the cue and at its remapped location. This shows that the visual system integrates features between both the current and the future retinotopic locations of an attended object and that such presaccadic sampling is feature specific.
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Wang, Xiao, Hui Li, Rong Wang, Qiuwen Zhang, Weiwei Zhang, and Yong Gan. "MultiP-Apo: A Multilabel Predictor for Identifying Subcellular Locations of Apoptosis Proteins." Computational Intelligence and Neuroscience 2017 (2017): 1–10. http://dx.doi.org/10.1155/2017/9183796.
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Apoptosis proteins play an important role in the mechanism of programmed cell death. Predicting subcellular localization of apoptosis proteins is an essential step to understand their functions and identify drugs target. Many computational prediction methods have been developed for apoptosis protein subcellular localization. However, these existing works only focus on the proteins that have one location; proteins with multiple locations are either not considered or assumed as not existing when constructing prediction models, so that they cannot completely predict all the locations of the apoptosis proteins with multiple locations. To address this problem, this paper proposes a novel multilabel predictor named MultiP-Apo, which can predict not only apoptosis proteins with single subcellular location but also those with multiple subcellular locations. Specifically, given a query protein, GO-based feature extraction method is used to extract its feature vector. Subsequently, the GO feature vector is classified by a new multilabel classifier based on the label-specific features. It is the first multilabel predictor ever established for identifying subcellular locations of multilocation apoptosis proteins. As an initial study, MultiP-Apo achieves an overall accuracy of 58.49% by jackknife test, which indicates that our proposed predictor may become a very useful high-throughput tool in this area.
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Costa Tomaz de Souza, Arthur, Georgy Ayzel, and Maik Heistermann. "Quantifying the Location Error of Precipitation Nowcasts." Advances in Meteorology 2020 (December 2, 2020): 1–12. http://dx.doi.org/10.1155/2020/8841913.
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In precipitation nowcasting, it is common to track the motion of precipitation in a sequence of weather radar images and to extrapolate this motion into the future. The total error of such a prediction consists of an error in the predicted location of a precipitation feature and an error in the change of precipitation intensity over lead time. So far, verification measures did not allow isolating the extent of location errors, making it difficult to specifically improve nowcast models with regard to location prediction. In this paper, we introduce a framework to directly quantify the location error. To that end, we detect and track scale-invariant precipitation features (corners) in radar images. We then consider these observed tracks as the true reference in order to evaluate the performance (or, inversely, the error) of any model that aims to predict the future location of a precipitation feature. Hence, the location error of a forecast at any lead time Δt ahead of the forecast time t corresponds to the Euclidean distance between the observed and the predicted feature locations at t + Δt. Based on this framework, we carried out a benchmarking case study using one year worth of weather radar composites of the German Weather Service. We evaluated the performance of four extrapolation models, two of which are based on the linear extrapolation of corner motion from t − 1 to t (LK-Lin1) and t − 4 to t (LK-Lin4) and the other two are based on the Dense Inverse Search (DIS) method: motion vectors obtained from DIS are used to predict feature locations by linear (DIS-Lin1) and Semi-Lagrangian extrapolation (DIS-Rot1). Of those four models, DIS-Lin1 and LK-Lin4 turned out to be the most skillful with regard to the prediction of feature location, while we also found that the model skill dramatically depends on the sinuosity of the observed tracks. The dataset of 376,125 detected feature tracks in 2016 is openly available to foster the improvement of location prediction in extrapolation-based nowcasting models.
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Zhu, Ye, Xiaoqian Shen, Shikun Liu, Xiaoli Zhang, and Gang Yan. "Image Splicing Location Based on Illumination Maps and Cluster Region Proposal Network." Applied Sciences 11, no. 18 (September 11, 2021): 8437. http://dx.doi.org/10.3390/app11188437.
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Splicing is the most common operation in image forgery, where the tampered background regions are imported from different images. Illumination maps are inherent attribute of images and provide significant clues when searching for splicing locations. This paper proposes an end-to-end dual-stream network for splicing location, where the illumination stream, which includes Grey-Edge (GE) and Inverse-Intensity Chromaticity (IIC), extract the inconsistent features, and the image stream extracts the global unnatural tampered features. The dual-stream feature in our network is fused through Multiple Feature Pyramid Network (MFPN), which contains richer context information. Finally, a Cluster Region Proposal Network (C-RPN) with spatial attention and an adaptive cluster anchor are proposed to generate potential tampered regions with greater retention of location information. Extensive experiments, which were evaluated on the NIST16 and CASIA standard datasets, show that our proposed algorithm is superior to some state-of-the-art algorithms, because it achieves accurate tampered locations at the pixel level, and has great robustness in post-processing operations, such as noise, blur and JPEG recompression.
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Schneegans, Sebastian, William J. Harrison, and Paul M. Bays. "Location-independent feature binding in visual working memory for sequentially presented objects." Attention, Perception, & Psychophysics 83, no. 6 (April 16, 2021): 2377–93. http://dx.doi.org/10.3758/s13414-021-02245-w.
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AbstractSpatial location is believed to have a privileged role in binding features held in visual working memory. Supporting this view, Pertzov and Husain (Attention, Perception, & Psychophysics, 76(7), 1914–1924, 2014) reported that recall of bindings between visual features was selectively impaired when items were presented sequentially at the same location compared to sequentially at different locations. We replicated their experiment, but additionally tested whether the observed impairment could be explained by perceptual interference during encoding. Participants viewed four oriented bars in highly discriminable colors presented sequentially either at the same or different locations, and after a brief delay were cued with one color to reproduce the associated orientation. When we used the same timing as the original study, we reproduced its key finding of impaired binding memory in the same-location condition. Critically, however, this effect was significantly modulated by the duration of the inter-stimulus interval, and disappeared if memoranda were presented with longer delays between them. In a second experiment, we tested whether the effect generalized to other visual features, namely reporting of colors cued by stimulus shape. While we found performance deficits in the same-location condition, these did not selectively affect binding memory. We argue that the observed effects are best explained by encoding interference, and that memory for feature binding is not necessarily impaired when memoranda share the same location.
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Chetverikov, Andrey, Gianluca Campana, and Árni Kristjánsson. "Binding feature distributions to locations and to other features." Journal of Vision 17, no. 10 (August 31, 2017): 78. http://dx.doi.org/10.1167/17.10.78.
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Annisa, Annisa, and Leni Angraeni. "Location Selection Query in Google Maps using Voronoi-based Spatial Skyline (VS2) Algorithm." Jurnal Online Informatika 6, no. 1 (June 17, 2021): 25. http://dx.doi.org/10.15575/join.v6i1.667.
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Google Maps is one of the popular location selection systems. One of the popular features of Google Maps is nearby search. For example, someone who wants to find the closest restaurants to his location can use the nearby search feature. This feature only considers one specific location in providing the desired place choice. In a real-world situation, there may be a need to consider more than one location in selecting the desired place. Assume someone would like to choose a hotel close to the conference hall, the museum, beach, and souvenir store. In this situation, nearby search feature in Google Maps may not be able to suggest a list of hotels that are interesting for him based on the distance from each destination places. In this paper, we have successfully developed a web-based application of Google Maps search using Voronoi-based Spatial Skyline (VS2) algorithm to choose some Point Of Interest (POI) from Google Maps as their considered locations to select desired place. We used Google Maps API to provide POI information for our web-based application. The experiment result showed that the execution time increases while the number of considered location increases.
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ZHANG, DAQING, CHAO CHEN, ZHANGBING ZHOU, and BIN LI. "IDENTIFYING LOGICAL LOCATION VIA GPS-ENABLED MOBILE PHONE AND WEARABLE CAMERA." International Journal of Pattern Recognition and Artificial Intelligence 26, no. 08 (December 2012): 1260007. http://dx.doi.org/10.1142/s0218001412600075.
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More and more location-based services become relying on the logical notion of a physical location, known as logical location (e.g. Starbucks, KFC). In this paper, we propose a new way to identify logical location using (1) a GPS-enabled mobile phone and (2) a wearable camera embedded in user's glasses. When a user with a wearable camera is detected paying attention to a certain physical location, all the logical locations within the error range of the GPS coordinates are considered as the matched candidates. We select the representative frames in the video stream corresponding to user's interested location in real-time and use multi-view images taken beforehand to represent each logical location. We then extract the Scale Invariant Feature Transform visual features from both the representative video frames and pre-stored images of candidate logical locations for video-image matching, the logical location that the user pays attention to can thus be identified. In order to differentiate the cases where users watch certain objects rather than a logical location in the street, we use Support Vector Machine to classify the two cases so that only the valid logical location is identified. Our proposed approach is proved weather and user independent, and it does not request additional user efforts compared with previous solutions. The results tested using a real-world dataset can achieve an average accuracy of 91.08%.
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Bindra, Supreet Singh, Sujit Vijay Sakpal, Sergey Cherneykin, and Ronald S. Chamberlain. "Location! Location!! Location!!! The Salient Clinical Feature of Atypical Lipomatous Tumors." Journal of Pelvic Medicine and Surgery 15, no. 6 (November 2009): 467–70. http://dx.doi.org/10.1097/spv.0b013e3181c62e50.
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YANG, YANG, and BAO-LIANG LU. "PROTEIN SUBCELLULAR MULTI-LOCALIZATION PREDICTION USING A MIN-MAX MODULAR SUPPORT VECTOR MACHINE." International Journal of Neural Systems 20, no. 01 (February 2010): 13–28. http://dx.doi.org/10.1142/s0129065710002206.
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Prediction of protein subcellular localization is an important issue in computational biology because it provides important clues for the characterization of protein functions. Currently, much research has been dedicated to developing automatic prediction tools. Most, however, focus on mono-locational proteins, i.e., they assume that proteins exist in only one location. It should be noted that many proteins bear multi-locational characteristics and carry out crucial functions in biological processes. This work aims to develop a general pattern classifier for predicting multiple subcellular locations of proteins. We use an ensemble classifier, called the min-max modular support vector machine (M3-SVM), to solve protein subcellular multi-localization problems; and, propose a module decomposition method based on gene ontology (GO) semantic information for M3-SVM. The amino acid composition with secondary structure and solvent accessibility information is adopted to represent features of protein sequences. We apply our method to two multi-locational protein data sets. The M3-SVMs show higher accuracy and efficiency than traditional SVMs using the same feature vectors. And the GO decomposition also helps to improve prediction accuracy. Moreover, our method has a much higher rate of accuracy than existing subcellular localization predictors in predicting protein multi-localization.
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Delooze, Molly A., Naomi Langerock, Robin Macy, Evie Vergauwe, and Candice C. Morey. "Encode a Letter and Get Its Location for Free? Assessing Incidental Binding of Verbal and Spatial Features." Brain Sciences 12, no. 6 (May 24, 2022): 685. http://dx.doi.org/10.3390/brainsci12060685.
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Previous studies have demonstrated that when presented with a display of spatially arranged letters, participants seem to remember the letters’ locations when letters are the focus of a recognition test, but do not remember letters’ identity when locations are tested. This strong binding asymmetry suggests that encoding location may be obligatory when remembering letters, which requires explanation within theories of working memory. We report two studies in which participants focused either on remembering letters or locations for a short interval. At test, positive probes were either intact letter–location combinations or recombinations of an observed letter and another previously occupied location. Incidental binding is observed when intact probes are recognized more accurately or faster than recombined probes. Here, however, we observed no evidence of incidental binding of location to letter in either experiment, neither under conditions where participants focused on one feature exclusively for a block, nor where the to-be-remembered feature was revealed prior to encoding with a changing pre-cue, nor where the to-be-remembered feature was retro-cued and therefore unknown during encoding. Our results call into question the robustness of a strong, consistent binding asymmetry. They suggest that while incidental location-to-letter binding may sometimes occur, it is not obligatory.
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Imran, Sajida, and Young-Bae Ko. "A Novel Indoor Positioning System Using Kernel Local Discriminant Analysis in Internet-of-Things." Wireless Communications and Mobile Computing 2018 (2018): 1–9. http://dx.doi.org/10.1155/2018/2976751.
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WLAN based localization is a key technique of location-based services (LBS) indoors. However, the indoor environment is complex; received signal strength (RSS) is highly uncertain, multimodal, and nonlinear. The traditional location estimation methods fail to provide fair estimation accuracy under the said environment. We proposed a novel indoor positioning system that considers the nonlinear discriminative feature extraction of RSS using kernel local Fisher discriminant analysis (KLFDA). KLFDA extracts location features in a well-preserved kernelized space. In the new kernel featured space, nonlinear RSS features are characterized effectively. Along with handling of nonlinearity, KLFDA also copes well with the multimodality in the RSS data. By performing KLFDA, the discriminating information contained in RSS is reorganized and maximally extracted. Prior to feature extraction, we performed outlier detection on RSS data to remove any anomalies present in the data. Experimental results show that the proposed approach obtains higher positioning accuracy by extracting maximal discriminate location features and discarding outlying information present in the RSS data.
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Dowd, Emma Wu, and Julie D. Golomb. "Object-Feature Binding Survives Dynamic Shifts of Spatial Attention." Psychological Science 30, no. 3 (January 29, 2019): 343–61. http://dx.doi.org/10.1177/0956797618818481.
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Visual object perception requires integration of multiple features; spatial attention is thought to be critical to this binding. But attention is rarely static—how does dynamic attention impact object integrity? Here, we manipulated covert spatial attention and had participants (total N = 48) reproduce multiple properties (color, orientation, location) of a target item. Object-feature binding was assessed by applying probabilistic models to the joint distribution of feature errors: Feature reports for the same object could be correlated (and thus bound together) or independent. We found that splitting attention across multiple locations degrades object integrity, whereas rapid shifts of spatial attention maintain bound objects. Moreover, we document a novel attentional phenomenon, wherein participants exhibit unintentional fluctuations— lapses of spatial attention—yet nevertheless preserve object integrity at the wrong location. These findings emphasize the importance of a single focus of spatial attention for object-feature binding, even when that focus is dynamically moving across the visual field.
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Rosyadi, Ahmad Wahyu, Renest Danardono, Siprianus Septian Manek, and Agus Zainal Arifin. "A FLEXIBLE SUB-BLOCK IN REGION BASED IMAGE RETRIEVAL BASED ON TRANSITION REGION." Jurnal Ilmu Komputer dan Informasi 11, no. 1 (February 28, 2018): 42. http://dx.doi.org/10.21609/jiki.v11i1.471.
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One of the techniques in region based image retrieval (RBIR) is comparing the global feature of an entire image and the local feature of image’s sub-block in query and database image. The determined sub-block must be able to detect an object with varying sizes and locations. So the sub-block with flexible size and location is needed. We propose a new method for local feature extraction by determining the flexible size and location of sub-block based on the transition region in region based image retrieval. Global features of both query and database image are extracted using invariant moment. Local features in database and query image are extracted using hue, saturation, and value (HSV) histogram and local binary patterns (LBP). There are several steps to extract the local feature of sub-block in the query image. First, preprocessing is conducted to get the transition region, then the flexible sub-block is determined based on the transition region. Afterward, the local feature of sub-block is extracted. The result of this application is the retrieved images ordered by the most similar to the query image. The local feature extraction with the proposed method is effective for image retrieval with precision and recall value are 57%.
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Ak, Çiğdem, Alex D. Chitsazan, Mehmet Gönen, Ruth Etzioni, and Aaron J. Grossberg. "Spatial Prediction of COVID-19 Pandemic Dynamics in the United States." ISPRS International Journal of Geo-Information 11, no. 9 (August 30, 2022): 470. http://dx.doi.org/10.3390/ijgi11090470.
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The impact of COVID-19 across the United States (US) has been heterogeneous, with rapid spread and greater mortality in some areas compared with others. We used geographically-linked data to test the hypothesis that the risk for COVID-19 was defined by location and sought to define which demographic features were most closely associated with elevated COVID-19 spread and mortality. We leveraged geographically-restricted social, economic, political, and demographic information from US counties to develop a computational framework using structured Gaussian process to predict county-level case and death counts during the pandemic’s initial and nationwide phases. After identifying the most predictive information sources by location, we applied an unsupervised clustering algorithm and topic modeling to identify groups of features most closely associated with COVID-19 spread. Our model successfully predicted COVID-19 case counts of unseen locations after examining case counts and demographic information of neighboring locations, with overall Pearson’s correlation coefficient and the proportion of variance explained as 0.96 and 0.84 during the initial phase and 0.95 and 0.87 during the nationwide phase, respectively. Aside from population metrics, presidential vote margin was the most consistently selected spatial feature in our COVID-19 prediction models. Urbanicity and 2020 presidential vote margins were more predictive than other demographic features. Models trained using death counts showed similar performance metrics. Topic modeling showed that counties with similar socioeconomic and demographic features tended to group together, and some of these feature sets were associated with COVID-19 dynamics. Clustering of counties based on these feature groups found by topic modeling revealed groups of counties that experienced markedly different COVID-19 spread. We conclude that topic modeling can be used to group similar features and identify counties with similar features in epidemiologic research.
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Baydar, Mucahit, and Songul Albayrak. "Location prediction in location-based social networks." Global Journal of Information Technology: Emerging Technologies 7, no. 3 (December 24, 2017): 149–56. http://dx.doi.org/10.18844/gjit.v7i3.2835.
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AbstractDevelopments in mobile devices and wireless networks have led to the increasing popularity of location-based social networks. These networks allow users to explore new places, share their location, videos and photos and make friends. They give information about the mobility of users, which can be used to improve the networks. This paper studies the problem of predicting the next check-in of users of location-based social networks. For an accurate prediction, we first analyse the datasets that are obtained from the social networks, Foursquare and Gowalla. Then we obtain some features like place popularity, place popular time range, place distance to user’s home, user’s past visits, category preferences and friendships ,which are used for prediction and deeper understanding of the user behaviours. We use each feature individually, and then in combination, using the new method. Finally, we compare the acquired results and observe the improvement with the new method.Keywords: Location prediction, location-based social network, check-in data.
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M. Zaki, Salim, M. A. Ngadi, Maznah Kamat, and Shukor A. Razak. "A Review of Location Prediction Techniques in Mobile Ad Hoc Networks." Al-Qadisiyah Journal Of Pure Science 25, no. 2 (April 14, 2020): 17–28. http://dx.doi.org/10.29350/qjps.2020.25.2.974.
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Predicting future locations of mobile objects has received a lot of attention in research due to its importance in mobile ad hoc networks. The precise location of a mobile node is essential in determining the location of the destination node for the purpose of communication. High mobility of nodes and delay in sending current location affect the accuracy of mobile nodes’ locations. Providing accurate location needs well-designed location prediction technique considers a number of factors that assist in retrieving up-to-date locations. This paper reviews available models: mathematical models and models with neural network and address the problems in location prediction techniques and provides a deep analysis of the good features for improved prediction techniques.
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Pang, Jun, and Yang Zhang. "DeepCity: A Feature Learning Framework for Mining Location Check-Ins." Proceedings of the International AAAI Conference on Web and Social Media 11, no. 1 (May 3, 2017): 652–55. http://dx.doi.org/10.1609/icwsm.v11i1.14906.
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Online social networks being extended to geographical space has resulted in large amount of user check-in data. Understanding check-ins can help to build appealing applications, such as location recommendation. In this paper, we propose DeepCity, a feature learning framework based on deep learning, to profile users and locations, with respect to user demographics and location category prediction. Both of the predictions are essential for social network companies to increase user engagement. The key contribution of DeepCity is the proposal of task-specific random walk which uses the location and user properties to guide the feature learning to be specific to each prediction task. Experiments conducted on 42M check-ins in three cities collected from Instagram have shown that DeepCity achieves a superior performance and outperforms state-of-the-art models significantly.
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Störmer, Viola S., Michael A. Cohen, and George A. Alvarez. "Tuning Attention to Object Categories: Spatially Global Effects of Attention to Faces in Visual Processing." Journal of Cognitive Neuroscience 31, no. 7 (July 2019): 937–47. http://dx.doi.org/10.1162/jocn_a_01400.
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Feature-based attention is known to enhance visual processing globally across the visual field, even at task-irrelevant locations. Here, we asked whether attention to object categories, in particular faces, shows similar location-independent tuning. Using EEG, we measured the face-selective N170 component of the EEG signal to examine neural responses to faces at task-irrelevant locations while participants attended to faces at another task-relevant location. Across two experiments, we found that visual processing of faces was amplified at task-irrelevant locations when participants attended to faces relative to when participants attended to either buildings or scrambled face parts. The fact that we see this enhancement with the N170 suggests that these attentional effects occur at the earliest stage of face processing. Two additional behavioral experiments showed that it is easier to attend to the same object category across the visual field relative to two distinct categories, consistent with object-based attention spreading globally. Together, these results suggest that attention to high-level object categories shows similar spatially global effects on visual processing as attention to simple, individual, low-level features.
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Bessell, Nicola, and Eimear Mulhall. "What’s in an Accent? Perceptions of Young Adult Listeners in Cork and Kilkenny." Journal of Clinical Speech and Language Studies 21, no. 1 (September 1, 2014): 63–85. http://dx.doi.org/10.3233/acs-2014-21106.
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Objective: To investigate the perceptions of Irish listeners concerning geographical origin, occupation and socio-economic class, based on speech samples from County Cork; to relate this information to phonetic features of each speaker’s accent and recent changes in Irish English. Methods: Recordings of speakers from three locations in County Cork were analyzed for features of supra-regional and advanced Irish English (Hickey, 1998, 2003, 2010). These recordings were played to young adult listeners from counties Cork and Kilkenny. Listeners completed a questionnaire assessing the location, occupation and socio-economic class of each speaker. The results of the questionnaire were compared with the phonetic features of the speakers. Main results: Diphthongization of FACE and GOAT vowels, [𝜃, ð] for TH, GOOSE-fronting and emerging velarised /l/ in syllable final position tilt listener judgements towards non-regional, professional and upper middle class. Cork City listeners are most accurate in terms of locating Cork city speakers. Kilkenny listeners are least accurate in identifying speaker location. Conclusions: Irish English speech varies depending on location and gender. Perceptions of class and occupation are closely tied to gender of speaker and type of phonetic features present. Supra-regional features are increasingly used by young adults in County Cork, and recognized by young adult listeners.
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Kretzschmar, William A., Ilkka Juuso, and C. Thomas Bailey. "Computer Simulation of Dialect Feature Diffusion." Journal of Linguistic Geography 2, no. 1 (March 2014): 41–57. http://dx.doi.org/10.1017/jlg.2014.2.
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This paper describes the independent construction and implementation of two cellular automata that model dialect feature diffusion as the adaptive aspect of the complex system of speech. We show how a feature, once established, can spread across an area, and how the distribution of a dialect feature as it stands in Linguistic Atlas data could either spread or diminish. Cellular automata use update rules to determine the status of a feature at a given location with respect to the status of its neighboring locations. In each iteration all locations in a matrix are evaluated, and then the new status for each one is displayed all at once. Throughout hundreds of iterations, we can watch regional distributional patterns emerge as a consequence of these simple update rules. We validate patterns with respect to the linguistic distributions known to occur in the Linguistic Atlas Project.
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Sapkota, Raju P., Ian van der Linde, Nirmal Lamichhane, Tirthalal Upadhyaya, and Shahina Pardhan. "Patients with Mild Cognitive Impairment Show Lower Visual Short-Term Memory Performance in Feature Binding Tasks." Dementia and Geriatric Cognitive Disorders Extra 7, no. 1 (March 20, 2017): 74–86. http://dx.doi.org/10.1159/000455831.
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Background: Early cognitive changes in people at risk of developing dementia may be detected using behavioral tests that examine the performance of typically affected brain areas, such as the hippocampi. An important cognitive function supported by the hippocampi is memory binding, in which object features are associated to create a unified percept. Aim: To compare visual short-term memory (VSTM) binding performance for object names, locations, and identities between a participant group known to be at higher risk of developing dementia (mild cognitive impairment [MCI]) and healthily aging controls. Methods: Ten MCI and 10 control participants completed five VSTM tests that differed in their requirement of remembering bound or unbound object names, locations, and identities, along with a standard neuropsychological test (Addenbrooke’s Cognitive Examination [ACE]-III). Results: The performance of the MCI participants was selectively and significantly lower than that of the healthily aging controls for memory tasks that required object-location or name-location binding. Conclusion: Tasks that measure unimodal (object-location) and crossmodal (name-location) binding performance appear to be particularly effective for the detection of early cognitive changes in those at higher risk of developing dementia due to Alzheimer’s disease.
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Tomášek, Ivo. "Sensorial evaluation genuineness of wine." Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis 56, no. 2 (2008): 309–18. http://dx.doi.org/10.11118/actaun200856020309.
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The qualitative indicators of wine are also sensoric properties besides analytic properties. The specimens were evaluated immediately after their stabilization. Of course, by the time the sensoric properties are changing and can influence later evaluation, even customers in their desicion for repeating purchase. Specialists evaluated specimen of white wines such as: rhine Riesling, Sauvignon blanc and gruner Veltliner from three locations of Znojmo winery region. All specimen weren´t fermented to dry and they weren´t procesed the same technology, in spite of this, the speciments were evaluated objectively as possilble. The common parameters of vineyards were: exhibition, evaluation above sea-level and average annual temperatrature. The climatic factors had minimum differences in both monitoring vintages of growing season. A different parametr had soils, their geological origin, type of soil, structure and power of topsoil. The acquired results were evaluated and graphically displayed.Gruner Veltliner – specimen No. 1 – this variety was covered in smell and taste by used technology. An outstanding location was a vineyard Weinperky with paleozoic sediments of neogene and higher pH and deeper arable level provides this location incommutable feature in contrast to from other recognizing vineyards of future wine. More likely geological-soil features have even specimens No. 3 and 4, which showed balance characteristic features in recognizing vintage. The specimens No. 2 and 1 had quantity untypical variety shades and they showed balance large differences both in evaluating committees and in recognizing vintages.Sauvignon blanc – the most suitable location was a vineyard Knížecí vrch – a specimen No. 6, which lies on lighter limy soils of Dyje massif together with higher pH created nice feature of variety. A specimen No. 8 had more likely characteristics of location than a specimen No. 6. That express in evaluation. A specimen No. 7 seems less typical and characteristic substitute in evaluation.Riesling rhine – the most suitable location was chosen vineyard Šobes by judges, which gives incommutable features to smell and taste by sandy soils of Dyje massif above river Dyje. A specimen No. 9 represented the smell; specimens No. 10 and 11 were evaluated as average and untypical. They had quite different features in recognizing vintages.The authenticity was extended by sensorial evaluation and at the same time the outstanding locations were chosen, which can give wines of unusual quantity every year in connecting certain variety. The most suitable locations for singular type of wine with extending authenticity are Riesling rhine – vineyard Šobes, Sauvignon blanc – vineyard Knížecí vrch, Veltliner grun – vineyard Weinperky.
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Daniel, Nati, Felix Goldberg, and Itzik Klein. "Smartphone Location Recognition with Unknown Modes in Deep Feature Space." Sensors 21, no. 14 (July 14, 2021): 4807. http://dx.doi.org/10.3390/s21144807.
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Smartphone location recognition aims to identify the location of a smartphone on a user in specific actions such as talking or texting. This task is critical for accurate indoor navigation using pedestrian dead reckoning. Usually, for that task, a supervised network is trained on a set of defined user modes (smartphone locations), available during the training process. In such situations, when the user encounters an unknown mode, the classifier will be forced to identify it as one of the original modes it was trained on. Such classification errors will degrade the navigation solution accuracy. A solution to detect unknown modes is based on a probability threshold of existing modes, yet fails to work with the problem setup. Therefore, to identify unknown modes, two end-to-end ML-based approaches are derived utilizing only the smartphone’s accelerometers measurements. Results using six different datasets shows the ability of the proposed approaches to classify unknown smartphone locations with an accuracy of 93.12%. The proposed approaches can be easily applied to any other classification problems containing unknown modes.
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Bayrak, Ahmet Engin, and Faruk Polat. "Effective feature reduction for link prediction in location-based social networks." Journal of Information Science 45, no. 5 (November 6, 2018): 676–90. http://dx.doi.org/10.1177/0165551518808200.
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In this study, we investigated feature-based approaches for improving the link prediction performance for location-based social networks (LBSNs) and analysed their performances. We developed new features based on time, common friend detail and place category information of check-in data in order to make use of information in the data which cannot be utilised by the existing features from the literature. We proposed a feature selection method to determine a feature subset that enhances the prediction performance with the removal of redundant features by clustering them. After clustering features, a genetic algorithm is used to determine the ones to select from each cluster. A non-monotonic and feasible feature selection is ensured by the proposed genetic algorithm. Results depict that both new features and the proposed feature selection method improved link prediction performance for LBSNs.
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N, Vinaykumar, Sai Kumar Kodumunja, Akshitha Ramavarapu, and Sushma Ch. "Application Based Bus Tracking System." International Journal for Research in Applied Science and Engineering Technology 10, no. 6 (June 30, 2022): 1451–57. http://dx.doi.org/10.22214/ijraset.2022.44007.
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Abstract: Buses are available to transport people to a variety of locations, although few passengers are aware of their existence. Complete information, such as the verity of buses those travel those the required end of location, bus numbers, bus time to, bus route data, and the time instant it will take for the vehicle to arrive at its end location, will assist passengers with various routes, track the present location of the bus, and provide the correct time for the bus to arrive at its end location. The proposed system is designed to address the concerns listed above. The system is an web app that offers critical bus data for all of Hyderabad. Because the Android Operating System provides a lot of features, it was picked for this type of gadget.It has only lately been released. It has grown to enormous proportions, with about every second person owning a piece of it. Since its launch, an everincreasing number of Android apps have been created on a massive scale. A vehicle tracking system can be used to track a vehicle's location and movement at any time and from any location. A fastest Google locations and GPS_module-based car tracks object are used in this project. These are some examples of similar ideas that have been attempted to be implemented in engineering and technology literature. To create a smartphone application that allows bus riders to track their bus's location? Users would be able to look for a specific bus by inputting its number, and the programme would reveal the bus's current location.Essentially, this programme provides a brief overview of bus locations, routes, and expected travel time with an online attendance feature, and it is entirely based on Google Maps and its API. Keywords: API, Web-Server, Google Maps,Android Studio ,Android SDK, GPS Tracking unit
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Jia, Xing-Zhao, Chang-Lei DongYe, Yan-Jun Peng, Wen-Xiu Zhao, and Tian-De Liu. "MRBENet: A Multiresolution Boundary Enhancement Network for Salient Object Detection." Computational Intelligence and Neuroscience 2022 (October 10, 2022): 1–11. http://dx.doi.org/10.1155/2022/7780756.
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Salient Object Detection (SOD) simulates the human visual perception in locating the most attractive objects in the images. Existing methods based on convolutional neural networks have proven to be highly effective for SOD. However, in some cases, these methods cannot satisfy the need of both accurately detecting intact objects and maintaining their boundary details. In this paper, we present a Multiresolution Boundary Enhancement Network (MRBENet) that exploits edge features to optimize the location and boundary fineness of salient objects. We incorporate a deeper convolutional layer into the backbone network to extract high-level semantic features and indicate the location of salient objects. Edge features of different resolutions are extracted by a U-shaped network. We designed a Feature Fusion Module (FFM) to fuse edge features and salient features. Feature Aggregation Module (FAM) based on spatial attention performs multiscale convolutions to enhance salient features. The FFM and FAM allow the model to accurately locate salient objects and enhance boundary fineness. Extensive experiments on six benchmark datasets demonstrate that the proposed method is highly effective and improves the accuracy of salient object detection compared with state-of-the-art methods.
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Guo, Chong Ying, Jian Hua Liu, Ke Jiang, and Hai Bo Liu. "Evaluate Planar Feature with a Material Condition Using Convex Hull." Applied Mechanics and Materials 727-728 (January 2015): 185–91. http://dx.doi.org/10.4028/www.scientific.net/amm.727-728.185.
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In order to obtain an orientation error of planar feature accurate, a method to evaluate it using convex hull is proposed. Firstly, a convex hull is established based on a set of measured points on the planar datum feature by the coordinate measurement machine (CMM). Then, the datum simulator is determined by the vertices of the convex hull with taking maximum material requirements (MMC) into consideration. Afterwards, allowable variation range of the locating features is derived with relative to the datum simulator. Finally, orientation error is evaluated based on convex hull constructed by measuring points of the location feature.
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Hilchey, Matthew D., Jason Rajsic, Greg Huffman, Raymond M. Klein, and Jay Pratt. "Dissociating Orienting Biases From Integration Effects With Eye Movements." Psychological Science 29, no. 3 (January 3, 2018): 328–39. http://dx.doi.org/10.1177/0956797617734021.
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Despite decades of research, the conditions under which shifts of attention to prior target locations are facilitated or inhibited remain unknown. This ambiguity is a product of the popular feature discrimination task, in which attentional bias is commonly inferred from the efficiency by which a stimulus feature is discriminated after its location has been repeated or changed. Problematically, these tasks lead to integration effects; effects of target-location repetition appear to depend entirely on whether the target feature or response also repeats, allowing for several possible inferences about orienting bias. To parcel out integration effects and orienting biases, we designed the present experiments to require localized eye movements and manual discrimination responses to serially presented targets with randomly repeating locations. Eye movements revealed consistent biases away from prior target locations. Manual discrimination responses revealed integration effects. These data collectively revealed inhibited reorienting and integration effects, which resolve the ambiguity and reconcile episodic integration and attentional orienting accounts.
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Abozaid, V., H. Arif Abdulrahman, and D. Ayoub Ibrahim. "IMPACT OF REGIONAL DISTRIBUTION AND AIR POLLUTION ON INTERNAL STRUCTURE OF MELIA AZEDARACH L. LEAVES." IRAQI JOURNAL OF AGRICULTURAL SCIENCES 52, no. 6 (December 22, 2021): 1326–33. http://dx.doi.org/10.36103/ijas.v52i6.1472.
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This study was performed to investigate the impact of air pollution on leaf area and anatomical features of Melia azedarach L. trees, in urban areas with three demographical classes: location (I) industrial area, location (II) roadside area and free parts (control area) as a location (III) of Duhok city/Kurdistan Region-Iraq, during July 2021. The results demonstrated that the leaf area of selected plants' leaves in location I had reduced with no noticeable change in the average stomata density in the three locations I, II and Ⅲ. Meanwhile, the results of the most anatomical features of the blade (blade, lower cuticle, epidermis (both upper and lower) thickness, palisade layer height and spongy parenchyma width) in addition to midrib parameters (epidermis thickness (upper and lower), collenchyma and parenchyma layer width, phloem and xylem width and pith diameter) were decreased in both locations I, II, and with well-developed anatomical features in location III.
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Mackintosh, Christopher, Richard Butterfield, Nan Zhang, Julia Lorence, Piotr Zlomanczuk, Bernard R. Bendok, Richard S. Zimmerman, Kristin Swanson, Alyx Porter, and Maciej M. Mrugala. "Does location matter? Characterisation of the anatomic locations, molecular profiles, and clinical features of gliomas." Neurologia i Neurochirurgia Polska 54, no. 5 (October 30, 2020): 456–65. http://dx.doi.org/10.5603/pjnns.a2020.0067.
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Yu, Chen, Baiyun Xiao, Dezhong Yao, Xiaofeng Ding, and Hai Jin. "Using check-in features to partition locations for individual users in location based social network." Information Fusion 37 (September 2017): 86–97. http://dx.doi.org/10.1016/j.inffus.2017.01.006.
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Sun, Xiaobing, Congying Xu, Bin Li, Yucong Duan, and Xintong Lu. "Enabling Feature Location for API Method Recommendation and Usage Location." IEEE Access 7 (2019): 49872–81. http://dx.doi.org/10.1109/access.2019.2910732.
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Li, Xuan, Dunant Halim, and Xiaoling Liu. "Assessment of delamination location in composite laminates based on a chaotic oscillator method." INTER-NOISE and NOISE-CON Congress and Conference Proceedings 263, no. 2 (August 1, 2021): 4709–16. http://dx.doi.org/10.3397/in-2021-2806.
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The work aims to study the assessment of delamination location in composite laminates using vibration measurement with a chaotic oscillator method. Delamination is a type of damage that commonly occurs in composite laminates, which can cause a severe degradation of their material properties. The traditional vibration-based methods can encounter difficulties in detecting and locating these delamination-type damages especially when the size of delamination is relatively small and there is a significant level of noise in its vibration measurement. With this particular consideration, a vibration-based method using a non-linear chaotic oscillator was used in this study due to its sensitivity to the change in vibration signal characteristics. A numerical model of composite laminates with delamination damage under harmonic excitation was developed and the vibration signal obtained from composite laminates was processed using the chaotic oscillator method. A feature named Lyapunov Exponent (LE) was used as a delamination damage index to describe the characteristics of the chaotic oscillator for cases with delamination at varying structural locations. The effects of delamination locations on the developed damage index were analyzed in this work. The results showed that there was a strong correlation between the delamination location and the LE feature, even for the case with a relatively high level of measurement noise. The results demonstrated the effectiveness of the method to identify delamination in composite laminates, which has also the potential to be used to detect other types of damages.
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Li, Du, Rose Zhou, and Rob Zanoya. "Cross-Sectional Transmission Electron Microscopy Sample Preparation Using Focus Ion Beam Machine and Wedge Technique." Microscopy and Microanalysis 5, S2 (August 1999): 894–95. http://dx.doi.org/10.1017/s1431927600017797.
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As features on an IC chip become smaller than the resolution power of an optical microscope and of the size of the grinding particles, the trend for preparing cross-sectional transmission electron microscopy (TEM) samples at specific locations (bits) is moving towards using a focused ion beam (FIB) machine. Details on how to use a FIB machine to prepare cross-sectional TEM samples have been outlined in many references.The general procedure is to first mark the specific location (bit) in the FIB machine and then grind the sample down to about 20 microns, 10 microns on each side of the feature of interest. After grinding, the sample is mounted on a pre-cut TEM grid and thinned with the FIB to about 0.1 micron in the region containing the feature of interest. There are several disadvantages to this method. First, the sample goes into the FIB machine at least twice—once for FIB marks on the location and once again for the final thinning.
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Liu, Xing, Huai-Xin Chen, and Bi-Yuan Liu. "Dynamic Anchor: A Feature-Guided Anchor Strategy for Object Detection." Applied Sciences 12, no. 10 (May 12, 2022): 4897. http://dx.doi.org/10.3390/app12104897.
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The majority of modern object detectors rely on a set of pre-defined anchor boxes, which enhances detection performance dramatically. Nevertheless, the pre-defined anchor strategy suffers some drawbacks, especially the complex hyper-parameters of anchors, seriously affecting detection performance. In this paper, we propose a feature-guided anchor generation method named dynamic anchor. Dynamic anchor mainly includes two structures: the anchor generator and the feature enhancement module. The anchor generator leverages semantic features to predict optimized anchor shapes at the locations where the objects are likely to exist in the feature maps; by converting the predicted shape maps into location offsets, the feature enhancement module uses the high-quality anchors to improve detection performance. Compared with the hand-designed anchor scheme, dynamic anchor discards all pre-defined boxes and avoids complex hyper-parameters. In addition, only one anchor box is predicted for each location, which dramatically reduces calculation. With ResNet-50 and ResNet-101 as the backbone of the one-stage detector RetinaNet, dynamic anchor achieved 2.1 AP and 1.0 AP gains, respectively. The proposed dynamic anchor strategy can be easily integrated into the anchor-based detectors to replace the traditional pre-defined anchor scheme.
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Chu, Tianyou, Yumin Chen, Liheng Huang, Zhiqiang Xu, and Huangyuan Tan. "A Grid Feature-Point Selection Method for Large-Scale Street View Image Retrieval Based on Deep Local Features." Remote Sensing 12, no. 23 (December 4, 2020): 3978. http://dx.doi.org/10.3390/rs12233978.
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Street view image retrieval aims to estimate the image locations by querying the nearest neighbor images with the same scene from a large-scale reference dataset. Query images usually have no location information and are represented by features to search for similar results. The deep local features (DELF) method shows great performance in the landmark retrieval task, but the method extracts many features so that the feature file is too large to load into memory when training the features index. The memory size is limited, and removing the part of features simply causes a great retrieval precision loss. Therefore, this paper proposes a grid feature-point selection method (GFS) to reduce the number of feature points in each image and minimize the precision loss. Convolutional Neural Networks (CNNs) are constructed to extract dense features, and an attention module is embedded into the network to score features. GFS divides the image into a grid and selects features with local region high scores. Product quantization and an inverted index are used to index the image features to improve retrieval efficiency. The retrieval performance of the method is tested on a large-scale Hong Kong street view dataset, and the results show that the GFS reduces feature points by 32.27–77.09% compared with the raw feature. In addition, GFS has a 5.27–23.59% higher precision than other methods.
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Alzahrani, Abdulaziz Saleh, and Ahmad Al Hanbali. "Maximum Coverage Location Model for Fire Stations with Top Corporate Risk Locations." International Journal of Industrial Engineering and Operations Management 03, no. 02 (December 2021): 58–74. http://dx.doi.org/10.46254/j.ieom.20210201.
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The fire station location is a critical decision to optimize the coverage level as measured in terms of the response time. This paper focuses on optimizing the coverage problem, especially in the fire protection field, with new model features to incorporate realistic business challenges such as location criticality and secondary coverage. We extend the deterministic Maximum Coverage Location Problem to account for Top Corporate Risk locations being covered by different fire stations as primary and secondary coverage. To deal with the response time uncertainty arising in practice, we propose a new binary linear problem based on the Maximum Expected Covering Location Problem. By exploiting the model structural characteristics, we prove that the model complexity can be substantially reduced to yield an efficient solution. In the numerical experiments, we use a real case study with five years of historical data. The optimization results of the models yield a priority ranking of the fire stations to open and show the value of incorporating the coverage uncertainty. Finally, we also compare our model with uncertainty with the standard scenario-based optimization to extend the numerical results.
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Davis, Stuart. "The location of the feature [continuant] in feature geometry." Lingua 78, no. 1 (May 1989): 1–22. http://dx.doi.org/10.1016/0024-3841(89)90002-8.
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Zhao, Beidi, Shuai Li, Yanbo Gao, Chuankun Li, and Wanqing Li. "A Framework of Combining Short-Term Spatial/Frequency Feature Extraction and Long-Term IndRNN for Activity Recognition." Sensors 20, no. 23 (December 7, 2020): 6984. http://dx.doi.org/10.3390/s20236984.
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Smartphone-sensors-based human activity recognition is attracting increasing interest due to the popularization of smartphones. It is a difficult long-range temporal recognition problem, especially with large intraclass distances such as carrying smartphones at different locations and small interclass distances such as taking a train or subway. To address this problem, we propose a new framework of combining short-term spatial/frequency feature extraction and a long-term independently recurrent neural network (IndRNN) for activity recognition. Considering the periodic characteristics of the sensor data, short-term temporal features are first extracted in the spatial and frequency domains. Then, the IndRNN, which can capture long-term patterns, is used to further obtain the long-term features for classification. Given the large differences when the smartphone is carried at different locations, a group-based location recognition is first developed to pinpoint the location of the smartphone. The Sussex-Huawei Locomotion (SHL) dataset from the SHL Challenge is used for evaluation. An earlier version of the proposed method won the second place award in the SHL Challenge 2020 (first place if not considering the multiple models fusion approach). The proposed method is further improved in this paper and achieves 80.72% accuracy, better than the existing methods using a single model.
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PETEK, METIN. "Effects of Housing Locations on Feather Damages of Laying Hens in a Free-Range Housing System." Journal of the Hellenic Veterinary Medical Society 71, no. 4 (January 25, 2021): 2525. http://dx.doi.org/10.12681/jhvms.25931.
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This study was made to evaluate the effects of different locations of a free range housing system on feather damages of laying hens. The experimental house consisted of three different locations as closed indoor plastic slats, closed indoor litter and outdoor range area. The birds were able to move freely between the locations of the experimental house and they had continuous access to outdoor range during the day. The feather damages of the birds was evaluated with a distance scoring system at 64 weeks of age. Five area in each location of the experimental house were determined at first and then feather damages of five body parts of ten birds in each location were scored to measure plumage quality. Total feather score was defined as the sum of the scores of five body parts of the birds. Best plumage quality was measured in neck in all housing locations (P<0.01, P<0.05 and P<0.01) and total feather score of the birds was significantly greatest (worst) in slats (P<0.05).
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Salehzadeh Nobari, Amin Ebrahim, and M. H. Ferri Aliabadi. "A Multilevel Isolation Forrest and Convolutional Neural Network Algorithm for Impact Characterization on Composite Structures." Sensors 20, no. 20 (October 19, 2020): 5896. http://dx.doi.org/10.3390/s20205896.
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In this paper, a Deep Learning approach is proposed to classify impact data based on the type of impact (Hard or Soft Impacts), via obtaining voltage signals from Piezo-Electric sensors, mounted on a composite panel. The data is processed further to be classified based on their energy, location and material. Minimalistic and Automated feature extraction and selection is achieved via a deep learning algorithm. Convolutional Neural Networks (CNN) are employed to extract and select important features from the voltage data. Once features are selected the impacts, are classified based on either, Hard Impacts (simulated from steel impactors in a lab setting), Soft Impacts (simulated from silicon impactors in a lab setting) and their corresponding location and energy levels. Furthermore, in order to use the right data for training they are obtained from the signals as anomalies via Isolation Forests (IF) to speed up the process. Using this approach Hard and Soft Impacts, their corresponding locations and respective energies are identified with high accuracy.
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Arwan, Achmad, and Denny Sagita Rusdianto. "Maintenance Web Based Applications Using Feature Location." Journal of Information Technology and Computer Science 5, no. 2 (July 29, 2020): 115. http://dx.doi.org/10.25126/jitecs.202052180.
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Maintenance web applications are a complex set of efforts. The FilkomApps are the web application used by the Faculty of Computer Science of Universitas Brawijaya to arrange the academic, theses of students, assignment of faculty, inventory, presence, honorarium. It has about 6K number of files(HTML, PHP, JS, CSS). The feature location was able to help the maintenance of the web applications by locating specific features on the files. The process comprises of preprocessing (tokenizing, web language syntax removal, splitting, stopword and stemming), indexing (VSM Lucene), and evaluations (precision and recall). The experiments were done by querying the keywords originate from previous maintenance modification effort and feature of a system. The results of precision were 86% and recall were 47%. The precision was better 374% than the conventional method (using the IDE search feature)