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Related Concept Videos

Reducing Line Loss01:18

Reducing Line Loss

208
In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss...
208
Boundary Conditions: Lossless Lines01:21

Boundary Conditions: Lossless Lines

161
Consider a single-phase, two-wire, lossless transmission line terminated by an impedance at the receiving end and a source with Thevenin voltage and impedance at the sending end. The line, with length, has a surge impedance and wave velocity determined by the line's inductance and capacitance.
At the receiving end, the boundary condition states that the voltage equals the product of the receiving-end impedance and current. This relationship is expressed as a function of the incident and...
161
Lossy Lines and Overvoltages01:22

Lossy Lines and Overvoltages

138
Transmission-line series resistance and shunt conductance cause three primary effects: attenuation, distortion, and power losses.
Attenuation
When constant series resistance and shunt conductance are present, voltage and current equations are modified. The propagation constant indicates that voltage and current waves consist of both forward and backward traveling components. These waves attenuate as they propagate, with the attenuation factor related to the resistance and conductance. In a...
138
Censoring Survival Data01:09

Censoring Survival Data

257
Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different...
257
Downsampling01:20

Downsampling

274
When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...
274
Lossless Lines01:23

Lossless Lines

184
In electrical engineering, a lossless transmission line is characterized by a purely imaginary propagation constant and a resistive characteristic impedance. The ABCD parameters, which describe the relationship between the input and output voltages and currents, indicate an equivalent π circuit with an imaginary series impedance and a shunt admittance. This results in a transmission line that, when the product of the phase constant (beta) and the length of the line is less than pi,...
184

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Ontology-Driven Data Cleaning Towards Lossless Data Compression.

Athanasios Kiourtis1, Argyro Mavrogiorgou1, George Manias1

  • 1Department of Digital Systems, University of Piraeus, Greece.

Studies in Health Technology and Informatics
|May 25, 2022
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Healthcare data exchange needs efficiency and interoperability. This study introduces an Ontology-driven Data Cleaning mechanism for lossless healthcare data compression, improving efficiency for diverse data types.

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Area of Science:

  • Health Informatics
  • Data Science
  • Computer Science

Background:

  • Healthcare data exchange requires interoperable and efficient solutions.
  • Current data security measures often overlook data complexity.
  • Efficient handling of diverse healthcare data (text, audio, image) is a challenge.

Purpose of the Study:

  • To present an Ontology-driven Data Cleaning mechanism.
  • To facilitate Lossless Healthcare Data Compression for various data types.
  • To address the need for efficient and secure healthcare data exchange.

Main Methods:

  • Development of an Ontology-driven Data Cleaning mechanism.
  • Implementation of Lossless Healthcare Data Compression techniques.
  • Evaluation using three distinct healthcare datasets (textual, audio, image).

Main Results:

  • The mechanism effectively compresses diverse healthcare data formats.
  • Demonstrated efficiency in lossless data compression.
  • Validated the added value of the Ontology-driven approach.

Conclusions:

  • The Ontology-driven Data Cleaning mechanism enhances healthcare data compression.
  • Lossless compression of varied healthcare data is achievable and valuable.
  • The proposed method improves efficiency in healthcare data exchange.