4,667 research outputs found
SciTech News Volume 71, No. 1 (2017)
Columns and Reports From the Editor 3
Division News Science-Technology Division 5 Chemistry Division 8 Engineering Division Aerospace Section of the Engineering Division 9 Architecture, Building Engineering, Construction and Design Section of the Engineering Division 11
Reviews Sci-Tech Book News Reviews 12
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A Model that Predicts the Material Recognition Performance of Thermal Tactile Sensing
Tactile sensing can enable a robot to infer properties of its surroundings,
such as the material of an object. Heat transfer based sensing can be used for
material recognition due to differences in the thermal properties of materials.
While data-driven methods have shown promise for this recognition problem, many
factors can influence performance, including sensor noise, the initial
temperatures of the sensor and the object, the thermal effusivities of the
materials, and the duration of contact. We present a physics-based mathematical
model that predicts material recognition performance given these factors. Our
model uses semi-infinite solids and a statistical method to calculate an F1
score for the binary material recognition. We evaluated our method using
simulated contact with 69 materials and data collected by a real robot with 12
materials. Our model predicted the material recognition performance of support
vector machine (SVM) with 96% accuracy for the simulated data, with 92%
accuracy for real-world data with constant initial sensor temperatures, and
with 91% accuracy for real-world data with varied initial sensor temperatures.
Using our model, we also provide insight into the roles of various factors on
recognition performance, such as the temperature difference between the sensor
and the object. Overall, our results suggest that our model could be used to
help design better thermal sensors for robots and enable robots to use them
more effectively.Comment: This article is currently under review for possible publicatio
Developing Methods and Algorithms for Cloud Computing Management Systems in Industrial Polymer Synthesis Processes
To date, the resources and computational capacity of companies have been insufficient to evaluate the technological properties of emerging products based on mathematical modelling tools. Often, several calculations have to be performed with different initial data. A remote computing system using a high-performance cluster can overcome this challenge. This study aims to develop unified methods and algorithms for a remote computing management system for modelling polymer synthesis processes at a continuous production scale. The mathematical description of the problem-solving algorithms is based on a kinetic approach to process investigation. A conceptual scheme for the proposed service can be built as a multi-level architecture with distributed layers for data storage and computation. This approach provides the basis for a unified database of laboratory and computational experiments to address and solve promising problems in the use of neural network technologies in chemical kinetics. The methods and algorithms embedded in the system eliminate the need for model description. The operation of the system was tested by simulating the simultaneous statement and computation of 15 to 30 tasks for an industrially significant polymer production process. Analysis of the time required showed a nearly 10-fold increase in the rate of operation when managing a set of similar tasks. The analysis shows that the described formulation and solution of problems is more time-efficient and provides better production modes. Doi: 10.28991/esj-2021-01324 Full Text: PD
A hybrid algorithm for Bayesian network structure learning with application to multi-label learning
We present a novel hybrid algorithm for Bayesian network structure learning,
called H2PC. It first reconstructs the skeleton of a Bayesian network and then
performs a Bayesian-scoring greedy hill-climbing search to orient the edges.
The algorithm is based on divide-and-conquer constraint-based subroutines to
learn the local structure around a target variable. We conduct two series of
experimental comparisons of H2PC against Max-Min Hill-Climbing (MMHC), which is
currently the most powerful state-of-the-art algorithm for Bayesian network
structure learning. First, we use eight well-known Bayesian network benchmarks
with various data sizes to assess the quality of the learned structure returned
by the algorithms. Our extensive experiments show that H2PC outperforms MMHC in
terms of goodness of fit to new data and quality of the network structure with
respect to the true dependence structure of the data. Second, we investigate
H2PC's ability to solve the multi-label learning problem. We provide
theoretical results to characterize and identify graphically the so-called
minimal label powersets that appear as irreducible factors in the joint
distribution under the faithfulness condition. The multi-label learning problem
is then decomposed into a series of multi-class classification problems, where
each multi-class variable encodes a label powerset. H2PC is shown to compare
favorably to MMHC in terms of global classification accuracy over ten
multi-label data sets covering different application domains. Overall, our
experiments support the conclusions that local structural learning with H2PC in
the form of local neighborhood induction is a theoretically well-motivated and
empirically effective learning framework that is well suited to multi-label
learning. The source code (in R) of H2PC as well as all data sets used for the
empirical tests are publicly available.Comment: arXiv admin note: text overlap with arXiv:1101.5184 by other author
Currency security and forensics: a survey
By its definition, the word currency refers to an agreed medium for exchange, a nation’s currency is the formal medium enforced by the elected governing entity. Throughout history, issuers have faced one common threat: counterfeiting. Despite technological advancements, overcoming counterfeit production remains a distant future. Scientific determination of authenticity requires a deep understanding of the raw materials and manufacturing processes involved. This survey serves as a synthesis of the current literature to understand the technology and the mechanics involved in currency manufacture and security, whilst identifying gaps in the current literature. Ultimately, a robust currency is desire
Graduate Catalog, 1999-2002, New Jersey Institute of Technology
https://digitalcommons.njit.edu/coursecatalogs/1004/thumbnail.jp
Low cost angular displacement sensors for biomechanical applications - a review
In the general scientific quest for increased quality of life a natural ambition is to know more about human body kinematics. Varied knowledge can be extracted from sensors placed on human body and through associated biomechanical parameter evaluation the causal connection between different biomechanical parameters and medical conditions can be inferred. From a biomechanical point of view, one of the most important parameters within the human body is the amplitude of angular movements of joints. Although many angular sensors are used in industry, particular characteristics such as small size, flexibility and appropriate attachment methods must be taken into consideration when estimating the amplitude of movement of human joints. This paper reviews the existing low cost easy to manipulate angular sensors listed in the scientific literature, which currently are or could be used in rehabilitation engineering, physiotherapy or biomechanical evaluations in sport. The review is carried out in terms of a classification based on the sensors’ working principles and includes resistive, capacitive, magnetic and piezoresistive sensors
A free customizable tool for easy integration of microfluidics and smartphones
The integration of smartphones and microfluidics is nowadays the best possible route to achieve effective point-of-need testing (PONT), a concept increasingly demanded in the fields of human health, agriculture, food safety, and environmental monitoring. Nevertheless, efforts are still required to integrally seize all the advantages of smartphones, as well as to share the developments in easily adoptable formats. For this purpose, here we present the free platform appuente that was designed for the easy integration of microfluidic chips, smartphones, and the cloud. It includes a mobile app for end users, which provides chip identification and tracking, guidance and control, processing, smart-imaging, result reporting and cloud and Internet of Things (IoT) integration. The platform also includes a web app for PONT developers, to easily customize their mobile apps and manage the data of administered tests. Three application examples were used to validate appuente: a dummy grayscale detector that mimics quantitative colorimetric tests, a root elongation assay for pesticide toxicity assessment, and a lateral flow immunoassay for leptospirosis detection. The platform openly offers fast prototyping of smartphone apps to the wide community of lab-on-a-chip developers, and also serves as a friendly framework for new techniques, IoT integration and further capabilities. Exploiting these advantages will certainly help to enlarge the use of PONT with real-time connectivity in the near future.Fil: Schaumburg, Federico. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; ArgentinaFil: Vidocevich, Juan Pablo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; ArgentinaFil: Gerlero, Gabriel Santiago. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Centro de Investigaciones en Métodos Computacionales. Universidad Nacional del Litoral. Centro de Investigaciones en Métodos Computacionales; ArgentinaFil: Pujato, Nazarena. Universidad Nacional del Litoral. Facultad de Bioquímica y Ciencias Biológicas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; ArgentinaFil: Macagno, Joana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; ArgentinaFil: Kler, Pablo Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Centro de Investigaciones en Métodos Computacionales. Universidad Nacional del Litoral. Centro de Investigaciones en Métodos Computacionales; ArgentinaFil: Berli, Claudio Luis Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; Argentin
JTEC/WTEC annual report and program summary: 1993/94
The JTEC/WTEC (Japanese Technology Evaluation Center/World Technology Evaluation Center) Program at Loyola College is overviewed. A review of activities for 1993 and early 1994 is discussed along with plans for the following year. The bulk of the report consists of the summaries of completed projects in Information and Communication Technology; Materials; Manufacturing and Construction; Aeronautics, Space, and Ocean Technology; Energy; and Biotechnology
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