insideOut: A Bio-Inspired Machine Learning Approach to Estimating Posture in Robots Driven by Compliant Tendons

Estimates of limb posture are critical for controlling robotic systems. This is generally accomplished with angle sensors at individual joints that simplify control but can complicate mechanical design and robustness. Limb posture should be derivable from each joint's actuator shaft angle but this is problematic for compliant tendon-driven systems where (i) motors are not placed at the joints and (ii) nonlinear tendon stiffness decouples the relationship between motor and joint angles. Here we propose a novel machine learning algorithm to accurately estimate joint posture during dynamic tasks by limited training of an artificial neural network (ANN) receiving motor angles and tendon tensions, analogous to biological muscle and tendon mechanoreceptors. Simulating an inverted pendulum—antagonistically-driven by motors and nonlinearly-elastic tendons—we compare how accurately ANNs estimate joint angles when trained with different sets of non-collocated sensory information generated via random motor-babbling. Cross-validating with new movements, we find that ANNs trained with motor angles and tendon tension data predict joint angles more accurately than ANNs trained without tendon tension. Furthermore, these results are robust to changes in network/mechanical hyper-parameters. We conclude that regardless of the tendon properties, actuator behavior, or movement demands, tendon tension information invariably improves joint angle estimates from non-collocated sensory signals.

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Roboterbasierte spanende Bearbeitung*/Robot-Based Milling Operation. Machine Learning Algorithm for a model-based feed-forward torque control

wt Werkstattstechnik online ◽

10.37544/1436-4980-2019-05-54 ◽

2019 ◽

Vol 109 (05) ◽

pp. 352-357

Author(s):

C. Brecher ◽

L. Gründel ◽

L. Lienenlüke ◽

S. Storms

Keyword(s):

Machine Learning ◽

Position Control ◽

Learning Algorithm ◽

Milling Process ◽

Industrial Robots ◽

Torque Control ◽

Feed Forward ◽

Control Loops ◽

Model Based ◽

Machine Learning Approach

Die Lageregelung von konventionellen Industrierobotern ist nicht auf den dynamischen Fräsprozess ausgelegt. Eine Möglichkeit, das Verhalten der Regelkreise zu optimieren, ist eine modellbasierte Momentenvorsteuerung, welche in dieser Arbeit aufgrund vieler Vorteile durch einen Machine-Learning-Ansatz erweitert wird. Hierzu wird die Umsetzung in Matlab und die simulative Evaluation erläutert, die im Anschluss das Potenzial dieses Konzeptes bestätigt.   The position control of conventional industrial robots is not designed for the dynamic milling process. One possibility to optimize the behavior of the control loops is a model-based feed-forward torque control which is supported by a machine learning approach due to many advantages. The implementation in Matlab and the simulative evaluation are explained, which subsequently confirms the potential of this concept.

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AN IMPROVED AUTOMATIC POINTWISE SEMANTIC SEGMENTATION OF A 3D URBAN SCENE FROM MOBILE TERRESTRIAL AND AIRBORNE LIDAR POINT CLOUDS: A MACHINE LEARNING APPROACH

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-iv-4-w8-139-2019 ◽

2019 ◽

Vol IV-4/W8 ◽

pp. 139-146

Author(s):

X.-F. Xing ◽

M. A. Mostafavi ◽

G. Edwards ◽

N. Sabo

Keyword(s):

Machine Learning ◽

Urban Areas ◽

Learning Algorithm ◽

Semantic Segmentation ◽

Point Clouds ◽

Airborne Lidar ◽

Urban Scenes ◽

Machine Learning Approach ◽

Urban Scene ◽

Point Level

Abstract. Automatic semantic segmentation of point clouds observed in a 3D complex urban scene is a challenging issue. Semantic segmentation of urban scenes based on machine learning algorithm requires appropriate features to distinguish objects from mobile terrestrial and airborne LiDAR point clouds in point level. In this paper, we propose a pointwise semantic segmentation method based on our proposed features derived from Difference of Normal and the features “directional height above” that compare height difference between a given point and neighbors in eight directions in addition to the features based on normal estimation. Random forest classifier is chosen to classify points in mobile terrestrial and airborne LiDAR point clouds. The results obtained from our experiments show that the proposed features are effective for semantic segmentation of mobile terrestrial and airborne LiDAR point clouds, especially for vegetation, building and ground classes in an airborne LiDAR point clouds in urban areas.

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A machine learning approach to predicting short-term mortality risk in patients starting chemotherapy

10.1101/204081 ◽

2017 ◽

Cited By ~ 2

Author(s):

Aymen A. Elfiky ◽

Maximilian J. Pany ◽

Ravi B. Parikh ◽

Ziad Obermeyer

Keyword(s):

Machine Learning ◽

Mortality Risk ◽

Palliative Chemotherapy ◽

Learning Algorithm ◽

Cancer Center ◽

Short Term ◽

Machine Learning Model ◽

Machine Learning Approach ◽

Short Term Mortality ◽

And Performance

ABSTRACTBackgroundCancer patients who die soon after starting chemotherapy incur costs of treatment without benefits. Accurately predicting mortality risk from chemotherapy is important, but few patient data-driven tools exist. We sought to create and validate a machine learning model predicting mortality for patients starting new chemotherapy.MethodsWe obtained electronic health records for patients treated at a large cancer center (26,946 patients; 51,774 new regimens) over 2004-14, linked to Social Security data for date of death. The model was derived using 2004-11 data, and performance measured on non-overlapping 2012-14 data.Findings30-day mortality from chemotherapy start was 2.1%. Common cancers included breast (21.1%), colorectal (19.3%), and lung (18.0%). Model predictions were accurate for all patients (AUC 0.94). Predictions for patients starting palliative chemotherapy (46.6% of regimens), for whom prognosis is particularly important, remained highly accurate (AUC 0.92). To illustrate model discrimination, we ranked patients initiating palliative chemotherapy by model-predicted mortality risk, and calculated observed mortality by risk decile. 30-day mortality in the highest-risk decile was 22.6%; in the lowest-risk decile, no patients died. Predictions remained accurate across all primary cancers, stages, and chemotherapies—even for clinical trial regimens that first appeared in years after the model was trained (AUC 0.94). The model also performed well for prediction of 180-day mortality (AUC 0.87; mortality 74.8% in the highest risk decile vs. 0.2% in the lowest). Predictions were more accurate than data from randomized trials of individual chemotherapies, or SEER estimates.InterpretationA machine learning algorithm accurately predicted short-term mortality in patients starting chemotherapy using EHR data. Further research is necessary to determine generalizability and the feasibility of applying this algorithm in clinical settings.

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Using Supervised Machine Learning Algorithms for Automated Lithology Prediction from Wireline Log Data

10.2118/208559-ms ◽

2021 ◽

Author(s):

Marian Popescu ◽

Rebecca Head ◽

Tim Ferriday ◽

Kate Evans ◽

Jose Montero ◽

...

Keyword(s):

Machine Learning ◽

Learning Algorithm ◽

Machine Learning Algorithms ◽

Supervised Machine Learning ◽

Training Dataset ◽

Depth Interval ◽

Log Data ◽

Machine Learning Approach ◽

Lithology Prediction ◽

Logging While Drilling

Abstract This paper presents advancements in machine learning and cloud deployment that enable rapid and accurate automated lithology interpretation. A supervised machine learning technique is described that enables rapid, consistent, and accurate lithology prediction alongside quantitative uncertainty from large wireline or logging-while-drilling (LWD) datasets. To leverage supervised machine learning, a team of geoscientists and petrophysicists made detailed lithology interpretations of wells to generate a comprehensive training dataset. Lithology interpretations were based on applying determinist cross-plotting by utilizing and combining various raw logs. This training dataset was used to develop a model and test a machine learning pipeline. The pipeline was applied to a dataset previously unseen by the algorithm, to predict lithology. A quality checking process was performed by a petrophysicist to validate new predictions delivered by the pipeline against human interpretations. Confidence in the interpretations was assessed in two ways. The prior probability was calculated, a measure of confidence in the input data being recognized by the model. Posterior probability was calculated, which quantifies the likelihood that a specified depth interval comprises a given lithology. The supervised machine learning algorithm ensured that the wells were interpreted consistently by removing interpreter biases and inconsistencies. The scalability of cloud computing enabled a large log dataset to be interpreted rapidly; >100 wells were interpreted consistently in five minutes, yielding >70% lithological match to the human petrophysical interpretation. Supervised machine learning methods have strong potential for classifying lithology from log data because: 1) they can automatically define complex, non-parametric, multi-variate relationships across several input logs; and 2) they allow classifications to be quantified confidently. Furthermore, this approach captured the knowledge and nuances of an interpreter's decisions by training the algorithm using human-interpreted labels. In the hydrocarbon industry, the quantity of generated data is predicted to increase by >300% between 2018 and 2023 (IDC, Worldwide Global DataSphere Forecast, 2019–2023). Additionally, the industry holds vast legacy data. This supervised machine learning approach can unlock the potential of some of these datasets by providing consistent lithology interpretations rapidly, allowing resources to be used more effectively.

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Multi-Step-Ahead Forecasting of Wave Conditions Based on a Physics-Based Machine Learning (PBML) Model for Marine Operations

Journal of Marine Science and Engineering ◽

10.3390/jmse8120992 ◽

2020 ◽

Vol 8 (12) ◽

pp. 992

Author(s):

Mengning Wu ◽

Christos Stefanakos ◽

Zhen Gao

Keyword(s):

Machine Learning ◽

Learning Algorithm ◽

High Reliability ◽

Computational Cost ◽

Model Performance ◽

The North ◽

The North Sea ◽

Wave Conditions ◽

Wave Models ◽

Artificial Neural Network Ann

Short-term wave forecasts are essential for the execution of marine operations. In this paper, an efficient and reliable physics-based machine learning (PBML) model is proposed to realize the multi-step-ahead forecasting of wave conditions (e.g., significant wave height Hs and peak wave period Tp). In the model, the primary variables in physics-based wave models (i.e., the wind forcing and initial wave boundary condition) are considered as inputs. Meanwhile, a machine learning algorithm (artificial neural network, ANN) is adopted to build an implicit relation between inputs and forecasted outputs of wave conditions. The computational cost of this data-driven model is obviously much lower than that of the differential-equation based physical model. A ten-year (from 2001 to 2010) dataset of every three hours at the North Sea center was used to assess the model performance in a small domain. The result reveals high reliability for one-day-ahead Hs forecasts, while that of Tp is slightly lower due to the weaker implicit relationships between the data. Overall, the PBML model can be conceived as an efficient tool for the multi-step-ahead forecasting of wave conditions, and thus has great potential for furthering assist decision-making during the execution of marine operations.

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An Entropy-Based Machine Learning Algorithm for Combining Macroeconomic Forecasts

Entropy ◽

10.3390/e21101015 ◽

2019 ◽

Vol 21 (10) ◽

pp. 1015 ◽

Cited By ~ 3

Author(s):

Carles Bretó ◽

Priscila Espinosa ◽

Penélope Hernández ◽

Jose M. Pavía

Keyword(s):

Machine Learning ◽

Gross Domestic Product ◽

Maximum Entropy ◽

Simulation Study ◽

Learning Algorithm ◽

Predictive Ability ◽

Learning Approach ◽

Machine Learning Algorithm ◽

Machine Learning Approach ◽

Maximum Entropy Inference

This paper applies a Machine Learning approach with the aim of providing a single aggregated prediction from a set of individual predictions. Departing from the well-known maximum-entropy inference methodology, a new factor capturing the distance between the true and the estimated aggregated predictions presents a new problem. Algorithms such as ridge, lasso or elastic net help in finding a new methodology to tackle this issue. We carry out a simulation study to evaluate the performance of such a procedure and apply it in order to forecast and measure predictive ability using a dataset of predictions on Spanish gross domestic product.

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Reconstructive derivational analogy: A machine learning approach to automating redesign

Artificial intelligence for engineering design analysis and manufacturing ◽

10.1017/s0890060400001359 ◽

1996 ◽

Vol 10 (2) ◽

pp. 115-126 ◽

Cited By ~ 3

Author(s):

B.D. Britt ◽

T. Glagowski

Keyword(s):

Machine Learning ◽

Learning Algorithm ◽

Design Tool ◽

Design Reuse ◽

Learning Approach ◽

Machine Learning Algorithm ◽

Machine Learning Approach ◽

Design Plan ◽

Primary Focus ◽

Different Parts

AbstractThis paper describes current research toward automating the redesign process. In redesign, a working design is altered to meet new problem specifications. This process is complicated by interactions between different parts of the design, and many researchers have addressed these issues. An overview is given of a large design tool under development, the Circuit Designer's Apprentice. This tool integrates various techniques for reengineering existing circuits so that they meet new circuit requirements. The primary focus of the paper is one particular technique being used to reengineer circuits when they cannot be transformed to meet the new problem requirements. In these cases, a design plan is automatically generated for the circuit, and then replayed to solve all or part of the new problem. This technique is based upon the derivational analogy approach to design reuse. Derivational Analogy is a machine learning algorithm in which a design plan is saved at the time of design so that it can be replayed on a new design problem. Because design plans were not saved for the circuits available to the Circuit Designer's Apprentice, an algorithm was developed that automatically reconstructs a design plan for any circuit. This algorithm, Reconstructive Derivational Analogy, is described in detail, including a quantitative analysis of the implementation of this algorithm.

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Temperature effects on luminescent properties of Sr2CeO4:Eu3+ nanophosphor: A machine learning approach

Tehnika ◽

10.5937/tehnika2003279s ◽

2020 ◽

Vol 75 (4) ◽

pp. 279-283

Author(s):

Dragutin Šević ◽

Ana Vlašić ◽

Maja Rabasović ◽

Svetlana Savić-Šević ◽

Mihailo Rabasović ◽

...

Keyword(s):

Machine Learning ◽

Learning Algorithm ◽

Solution Combustion Synthesis ◽

Principal Component ◽

Optical Emission ◽

Luminescent Properties ◽

Point Of View ◽

Solution Combustion ◽

Machine Learning Approach ◽

Prepared Material

In this paper we analyze possibilities of application of Sr2CeO4:Eu3+ nanopowder for temperature sensing using machine learning. The material was prepared by simple solution combustion synthesis. Photoluminescence technique has been used to measure the optical emission temperature dependence of the prepared material. Principal Component Analysis, the basic machine learning algorithm, provided insight into temperature dependent spectral data from another point of view than usual approach.

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Obfuscated computer virus detection using machine learning algorithm

Bulletin of Electrical Engineering and Informatics ◽

10.11591/eei.v8i4.1584 ◽

2019 ◽

Vol 8 (4) ◽

Author(s):

Tan Hui Xin ◽

Ismahani Ismail ◽

Ban Mohammed Khammas

Keyword(s):

Machine Learning ◽

Learning Algorithm ◽

Virus Detection ◽

Computer Virus ◽

Machine Learning Technique ◽

Memory Space ◽

Alternative Approach ◽

Machine Learning Approach ◽

Learning Technique ◽

String Feature

Nowadays, computer virus attacks are getting very advanced. New obfuscated computer virus created by computer virus writers will generate a new shape of computer virus automatically for every single iteration and download. This constantly evolving computer virus has caused significant threat to information security of computer users, organizations and even government. However, signature based detection technique which is used by the conventional anti-computer virus software in the market fails to identify it as signatures are unavailable. This research proposed an alternative approach to the traditional signature based detection method and investigated the use of machine learning technique for obfuscated computer virus detection. In this work, text strings are used and have been extracted from virus program codes as the features to generate a suitable classifier model that can correctly classify obfuscated virus files. Text string feature is used as it is informative and potentially only use small amount of memory space. Results show that unknown files can be correctly classified with 99.5% accuracy using SMO classifier model. Thus, it is believed that current computer virus defense can be strengthening through machine learning approach.

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An Analytical Model for Prediction of Heart Disease using Machine Learning Classifiers

10.36227/techrxiv.14867175 ◽

2021 ◽

Author(s):

Diti Roy ◽

Md. Ashiq Mahmood ◽

Tamal Joyti Roy

Keyword(s):

Machine Learning ◽

Heart Disease ◽

Random Forest ◽

Learning Algorithm ◽

Modern Technology ◽

Learning Approach ◽

Data Sets ◽

Machine Learning Classifiers ◽

Machine Learning Approach ◽

Day By Day

Heart Disease is the most dominating disease which is taking a large number of deaths every year. A report from WHO in 2016 portrayed that every year at least 17 million people die of heart disease. This number is gradually increasing day by day and WHO estimated that this death toll will reach the summit of 75 million by 2030. Despite having modern technology and health care system predicting heart disease is still beyond limitations. As the Machine Learning algorithm is a vital source predicting data from available data sets we have used a machine learning approach to predict heart disease. We have collected data from the UCI repository. In our study, we have used Random Forest, Zero R, Voted Perceptron, K star classifier. We have got the best result through the Random Forest classifier with an accuracy of 97.69.

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