Knowledge Graph Completion to Solve University Campus Issues

2020 ◽  
Vol 1 (3) ◽  
pp. 333-350
Author(s):  
Yuto Tsukagoshi ◽  
Takahiro Kawamura ◽  
Yuichi Sei ◽  
Yasuyuki Tahara ◽  
Akihiko Ohsuga
Keyword(s):  
Data Integration ◽  
Conventional Method ◽  
Current Literature ◽  
Statistical Data ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Knowledge Graph ◽  
University Campus ◽  
Combining Data ◽  
Conventional Methods

A number of urban challenges are encountered by modern societies. Governments, businesses and public bodies need to make statistical data widely available in order to tackle these challenges. Nonetheless, current literature and data are problematic; they have inaccuracies which lead to less effective methods of resolving these issues. This research aims to solve this challenge by thinking of a university campus as a microcosm of society, implementing a data integration schema, and combining data into a knowledge graph. Existing completion methods will then be applied and updated. Especially in regards to bicycle environment, our knowledge graph was tailored and evaluated in line with conventional methods, and secondly with our proposed derivative methods. Roughly 650 pieces of parking data, with various dates and times, was contrasted with each time's mean absolute error. Our approach accurately projected 54.5 more bicycles than the conventional method.

scholarly journals Charuco Board-Based Omnidirectional Camera Calibration Method

Electronics ◽  
2018 ◽  
Vol 7 (12) ◽  
pp. 421 ◽  
Author(s):  
Gwon An ◽  
Siyeong Lee ◽  
Min-Woo Seo ◽  
Kugjin Yun ◽  
Won-Sik Cheong ◽  
...  
Keyword(s):  
Camera Calibration ◽  
Mean Absolute Error ◽  
Three Dimensional ◽  
Absolute Error ◽  
Calibration Method ◽  
Feature Points ◽  
Omnidirectional Camera ◽  
Reprojection Error ◽  
Conventional Methods ◽  
Better Than

In this paper, we propose a Charuco board-based omnidirectional camera calibration method to solve the problem of conventional methods requiring overly complicated calibration procedures. Specifically, the proposed method can easily and precisely provide two-dimensional and three-dimensional coordinates of patterned feature points by arranging the omnidirectional camera in the Charuco board-based cube structure. Then, using the coordinate information of the feature points, an intrinsic calibration of each camera constituting the omnidirectional camera can be performed by estimating the perspective projection matrix. Furthermore, without an additional calibration structure, an extrinsic calibration of each camera can be performed, even though only part of the calibration structure is included in the captured image. Compared to conventional methods, the proposed method exhibits increased reliability, because it does not require additional adjustments to the mirror angle or the positions of several pattern boards. Moreover, the proposed method calibrates independently, regardless of the number of cameras comprising the omnidirectional camera or the camera rig structure. In the experimental results, for the intrinsic parameters, the proposed method yielded an average reprojection error of 0.37 pixels, which was better than that of conventional methods. For the extrinsic parameters, the proposed method had a mean absolute error of 0.90° for rotation displacement and a mean absolute error of 1.32 mm for translation displacement.

Contactless Core-Temperature Monitoring by Infrared Thermal Sensor using Mean Absolute Error Analysis

2020 ◽  
Vol 15 ◽  
Author(s):  
Fahad Layth Malallah ◽  
Baraa T. Shareef ◽  
Mustafah Ghanem Saeed ◽  
Khaled N. Yasen
Keyword(s):  
Body Temperature ◽  
Embedded System ◽  
Core Temperature ◽  
Integrated Circuit ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Thermal Sensor ◽  
Human Face ◽  
Body Contact ◽  
Arduino Microcontroller

Aims: Normally, the temperature increase of individuals leads to the possibility of getting a type of disease, which might be risky to other people such as coronavirus. Traditional techniques for tracking core-temperature require body contact either by oral, rectum, axillary, or tympanic, which are unfortunately considered intrusive in nature as well as causes of contagion. Therefore, sensing human core-temperature non-intrusively and remotely is the objective of this research. Background: Nowadays, increasing level of medical sectors is a necessary targets for the research operations, especially with the development of the integrated circuit, sensors and cameras that made the normal life easier. Methods: The solution is by proposing an embedded system consisting of the Arduino microcontroller, which is trained with a model of Mean Absolute Error (MAE) analysis for predicting Contactless Core-Temperature (CCT), which is the real body temperature. Results: The Arduino is connected to an Infrared-Thermal sensor named MLX90614 as input signal, and connected to the LCD to display the CCT. To evaluate the proposed system, experiments are conducted by participating 31-subject sensing contactless temperature from the three face sub-regions: forehead, nose, and cheek. Conclusion: Experimental results approved that CCT can be measured remotely depending on the human face, in which the forehead region is better to be dependent, rather than nose and cheek regions for CCT measurement due to the smallest

scholarly journals Towards Tracking of Deep Brain Stimulation Electrodes Using an Integrated Magnetometer

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2670
Author(s):  
Thomas Quirin ◽  
Corentin Féry ◽  
Dorian Vogel ◽  
Céline Vergne ◽  
Mathieu Sarracanie ◽  
...  
Keyword(s):  
Deep Brain Stimulation ◽  
Brain Stimulation ◽  
Mean Absolute Error ◽  
Tracking System ◽  
Three Dimensional ◽  
Absolute Error ◽  
Magnetic Tracking ◽  
Magnetic Camera ◽  
Magnetic Resonance Imaging Mri ◽  
Deep Brain

This paper presents a tracking system using magnetometers, possibly integrable in a deep brain stimulation (DBS) electrode. DBS is a treatment for movement disorders where the position of the implant is of prime importance. Positioning challenges during the surgery could be addressed thanks to a magnetic tracking. The system proposed in this paper, complementary to existing procedures, has been designed to bridge preoperative clinical imaging with DBS surgery, allowing the surgeon to increase his/her control on the implantation trajectory. Here the magnetic source required for tracking consists of three coils, and is experimentally mapped. This mapping has been performed with an in-house three-dimensional magnetic camera. The system demonstrates how magnetometers integrated directly at the tip of a DBS electrode, might improve treatment by monitoring the position during and after the surgery. The three-dimensional operation without line of sight has been demonstrated using a reference obtained with magnetic resonance imaging (MRI) of a simplified brain model. We observed experimentally a mean absolute error of 1.35 mm and an Euclidean error of 3.07 mm. Several areas of improvement to target errors below 1 mm are also discussed.

scholarly journals A Review of Deep Learning-Based Contactless Heart Rate Measurement Methods

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3719
Author(s):  
Aoxin Ni ◽  
Arian Azarang ◽  
Nasser Kehtarnavaz
Keyword(s):  
Heart Rate ◽  
Deep Learning ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Video Camera ◽  
Measurement Methods ◽  
Rate Measurement ◽  
Learning Methods ◽  
The Public ◽  
Heart Rate Measurement

The interest in contactless or remote heart rate measurement has been steadily growing in healthcare and sports applications. Contactless methods involve the utilization of a video camera and image processing algorithms. Recently, deep learning methods have been used to improve the performance of conventional contactless methods for heart rate measurement. After providing a review of the related literature, a comparison of the deep learning methods whose codes are publicly available is conducted in this paper. The public domain UBFC dataset is used to compare the performance of these deep learning methods for heart rate measurement. The results obtained show that the deep learning method PhysNet generates the best heart rate measurement outcome among these methods, with a mean absolute error value of 2.57 beats per minute and a mean square error value of 7.56 beats per minute.

scholarly journals Detection and Severity Evaluation of Combined Rail Defects Using Deep Learning

Vibration ◽  
2021 ◽  
Vol 4 (2) ◽  
pp. 341-356
Author(s):  
Jessada Sresakoolchai ◽  
Sakdirat Kaewunruen
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Machine Learning Techniques ◽  
Rolling Stock ◽  
Raw Data ◽  
Learning Techniques ◽  
Combined Defects

Various techniques have been developed to detect railway defects. One of the popular techniques is machine learning. This unprecedented study applies deep learning, which is a branch of machine learning techniques, to detect and evaluate the severity of rail combined defects. The combined defects in the study are settlement and dipped joint. Features used to detect and evaluate the severity of combined defects are axle box accelerations simulated using a verified rolling stock dynamic behavior simulation called D-Track. A total of 1650 simulations are run to generate numerical data. Deep learning techniques used in the study are deep neural network (DNN), convolutional neural network (CNN), and recurrent neural network (RNN). Simulated data are used in two ways: simplified data and raw data. Simplified data are used to develop the DNN model, while raw data are used to develop the CNN and RNN model. For simplified data, features are extracted from raw data, which are the weight of rolling stock, the speed of rolling stock, and three peak and bottom accelerations from two wheels of rolling stock. In total, there are 14 features used as simplified data for developing the DNN model. For raw data, time-domain accelerations are used directly to develop the CNN and RNN models without processing and data extraction. Hyperparameter tuning is performed to ensure that the performance of each model is optimized. Grid search is used for performing hyperparameter tuning. To detect the combined defects, the study proposes two approaches. The first approach uses one model to detect settlement and dipped joint, and the second approach uses two models to detect settlement and dipped joint separately. The results show that the CNN models of both approaches provide the same accuracy of 99%, so one model is good enough to detect settlement and dipped joint. To evaluate the severity of the combined defects, the study applies classification and regression concepts. Classification is used to evaluate the severity by categorizing defects into light, medium, and severe classes, and regression is used to estimate the size of defects. From the study, the CNN model is suitable for evaluating dipped joint severity with an accuracy of 84% and mean absolute error (MAE) of 1.25 mm, and the RNN model is suitable for evaluating settlement severity with an accuracy of 99% and mean absolute error (MAE) of 1.58 mm.

Trust and fuzzy inference based cross domain serendipitous item recommendations (TFCDSRS)

2021 ◽  
pp. 1-13
Author(s):  
Richa ◽  
Punam Bedi
Keyword(s):  
Fuzzy Inference ◽  
Mean Absolute Error ◽  
Information Filtering ◽  
Absolute Error ◽  
User Acceptance ◽  
Decision Making Process ◽  
End User ◽  
Cross Domain ◽  
F Measure ◽  
Filtering Approach

Recommender System (RS) is an information filtering approach that helps the overburdened user with information in his decision making process and suggests items which might be interesting to him. While presenting recommendation to the user, accuracy of the presented list is always a concern for the researchers. However, in recent years, the focus has now shifted to include the unexpectedness and novel items in the list along with accuracy of the recommended items. To increase the user acceptance, it is important to provide potentially interesting items which are not so obvious and different from the items that the end user has rated. In this work, we have proposed a model that generates serendipitous item recommendation and also takes care of accuracy as well as the sparsity issues. Literature suggests that there are various components that help to achieve the objective of serendipitous recommendations. In this paper, fuzzy inference based approach is used for the serendipity computation because the definitions of the components overlap. Moreover, to improve the accuracy and sparsity issues in the recommendation process, cross domain and trust based approaches are incorporated. A prototype of the system is developed for the tourism domain and the performance is measured using mean absolute error (MAE), root mean square error (RMSE), unexpectedness, precision, recall and F-measure.

scholarly journals Safety and Efficacy of All-In-One Percutaneous Pedicle Screw System

2021 ◽  
pp. 219256822110114
Author(s):  
Atsushi Kojima ◽  
Takato Aihara ◽  
Makoto Urushibara ◽  
Kenji Hatakeyama ◽  
Tomonori Sodeyama
Keyword(s):  
Pedicle Screw ◽  
Conventional Method ◽  
Surgical Time ◽  
Computed Tomography Images ◽  
Spine Stabilization ◽  
Percutaneous Pedicle Screw ◽  
Minimally Invasive Spine ◽  
Conventional Methods ◽  
Speed And Accuracy ◽  
Minimally Invasive Spine Stabilization

Study Design: Retrospective study of the prospectively collected outcomes data. Objective: The indications for PPS placement during minimally invasive spine stabilization (MISt) procedures have increased in recent years. To the best of our knowledge, no studies have documented the outcomes of PPS insertion using the all-in-one PPS system. This study compared the conventional methods and the use of all-in-one percutaneous pedicle screw (PPS) system with respect to the speed and accuracy of PPS placement. We also determined the advantages associated with the use of the all-in-one PPS system. Methods: We evaluated 54 patients who underwent PPS insertion using the conventional method and the all-in-one PPS system during MISt procedures. We also assessed the number of implanted PPSs, the time taken to implant PPSs, and the accuracy of PPS placement based on postoperative computed tomography images. Results: A total of 254 PPSs were inserted (126 using the conventional method and 128 using the all-in-one PPS system). The PPS insertion time with the all-in-one PPS system (mean, 25.3 ± 9.1 s) was significantly shorter than that using the conventional method (mean, 63.1 ± 13.0 s; P < 0.01). With respect to the accuracy of PPS insertion, ≥ 2 mm pedicle breach was noted in one case each in both groups. Conclusions: PPS placement using the all-in-one PPS system is as safe as conventional methods and has the potential to save the surgical time of MISt procedures.

Explanation Plus Prediction—The Logical Focus of Project Management Research

2021 ◽  
pp. 875697282199994
Author(s):  
Joseph F. Hair ◽  
Marko Sarstedt
Keyword(s):  
Project Management ◽  
Statistical Models ◽  
Predictive Power ◽  
Mean Absolute Error ◽  
Explanatory Power ◽  
Absolute Error ◽  
Model Parameters ◽  
Mean Square ◽  
Management Research ◽  
The Mean

Most project management research focuses almost exclusively on explanatory analyses. Evaluation of the explanatory power of statistical models is generally based on F-type statistics and the R 2 metric, followed by an assessment of the model parameters (e.g., beta coefficients) in terms of their significance, size, and direction. However, these measures are not indicative of a model’s predictive power, which is central for deriving managerial recommendations. We recommend that project management researchers routinely use additional metrics, such as the mean absolute error or the root mean square error, to accurately quantify their statistical models’ predictive power.

scholarly journals Analog Ensemble Methods for Improving Satellite-Based Intensity Estimates of Tropical Cyclones

Atmosphere ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 830
Author(s):  
William E. Lewis ◽  
Timothy L. Olander ◽  
Christopher S. Velden ◽  
Christopher Rozoff ◽  
Stefano Alessandrini
Keyword(s):  
North Atlantic ◽  
Mean Absolute Error ◽  
Ensemble Methods ◽  
Absolute Error ◽  
Eastern Pacific ◽  
Intensity Maximum ◽  
The North ◽  
The University ◽  
Meteorological Satellite ◽  
Fold Cross Validation

Accurate, reliable estimates of tropical cyclone (TC) intensity are a crucial element in the warning and forecast process worldwide, and for the better part of 50 years, estimates made from geostationary satellite observations have been indispensable to forecasters for this purpose. One such method, the Advanced Dvorak Technique (ADT), was used to develop analog ensemble (AnEn) techniques that provide more precise estimates of TC intensity with instant access to information on the reliability of the estimate. The resulting methods, ADT-AnEn and ADT-based Error Analog Ensemble (ADTE-AnEn), were trained and tested using seventeen years of historical ADT intensity estimates using k-fold cross-validation with 10 folds. Using only two predictors, ADT-estimated current intensity (maximum wind speed) and TC center latitude, both AnEn techniques produced significant reductions in mean absolute error and bias for all TC intensity classes in the North Atlantic and for most intensity classes in the Eastern Pacific. The ADTE-AnEn performed better for extreme intensities in both basins (significantly so in the Eastern Pacific) and will be incorporated in the University of Wisconsin’s Cooperative Institute for Meteorological Satellite Studies (UW-CIMSS) workflow for further testing during operations in 2021.

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