Sentence examples for some common problems of from inspiring English sources

The combined model-based and kinematic-based estimation structure mitigates some common problems of the widely used kinematic-based estimators such as the spikes and drifting issues.

Another study by Fontana et al., (2015) applied 16 different machine-learning algorithms in 74 software systems to detect four code smells in an attempt to avoid some common problems of code smell detectors.

However, some common problems of textile dyeing industries include uneven or faulty dyeing and formation of color patches on the fabric surface during dyeing and downstream processing of textiles materials (Fono and Montclair 1980; Ogulata and Balci 2007).

Some common problems of textile dyeing industries include uneven or faulty dyeing and formation of color patches on the fabric surface during dyeing and downstream processing of textiles materials.

The AEC part of the proposed AENC scheme may suffer from some common problems of adaptive filter-based algorithms, such as slow convergence rate and fluctuation around the desired estimates, especially in practical cases where the assumption on negligibility of cross-correlation terms (stated in the previous section) may not strictly hold.

They share some common problems: a lot of beautiful (but superfluous) words in long introductions that include unfamiliar and boring proper names; and they rarely mention where the idea comes from or why it became the driving passion behind the work.

Some common problems still in need of technology solutions include water desalination, low-cost and low-energy refrigeration, minimal electricity access, and community-level sanitization.

The objective is to demonstrate that GAs can successfully address some common problems in the application of NNs to classification problems, such as training the networks, choosing appropriate network topologies, and selecting relevant features.

Here are some common problems in the lexicon of abortion.

For example, the study towards coordinated planning has been undertaken in the United States [55, 56], and some common problems related to the development of natural gas and electricity networks (operation costs, network expansion costs, optimal placement of gas-fired generation plants) have been analyzed in Europe [57].

Applying machine learning or statistical methods to epistatic interaction detection will encounter some common problems, e.g., very limited number of samples, an extremely high search space, a large number of false positives, and ways to measure the association between disease markers and the phenotype.