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Open accessProceedings ArticleDOI
01 Oct 2011
45 Citations
This sampling scheme minimizes the energy consumption of the sampling process, which is approximately proportional to sampling rate.
Open accessProceedings ArticleDOI
M. Schinkel, Wen-Hua Chen, Anders Rantzer 
08 May 2002
51 Citations
This results in a piecewise constant state feedback control law and guarantees stability regardless of the variations in sampling rate.
In other words, even traditional feedback controls involve open-loop controls through the process of sampling.
The trajectories resulting from this "sampling-feedback" are well-defined even when the feedback is discontinuous.
Open accessProceedings ArticleDOI
Jurgen van Zundert, Tom Oomen 
01 Jun 2018
9 Citations
Application of the framework in a case study demonstrates the use for feedback control design and the potential of non-equidistant sampling.
Experimental results based on the instrument platform show that the proposed algorithm achieves almost equivalent results with only a 2.5 Msamples/s feedback sampling rate.
The sampling-period T is an explicit parameter, and thus it is easy to observe and analyse the effect of the state feedback controller with different sampling periods.
This correspondence shows that the overshoot of response of a sampled data feedback control system sometimes tends to infinity as the sampling period tends to zero provided that the assigned poles are constant.

Related Questions

Why sampling is important in research?4 answersSampling is important in research because it allows researchers to select a subset of the population of interest for data collection and analysis. Sampling helps to reduce the cost, time, and workload of studying an entire population, while still providing high-quality information that can be extrapolated to the entire population. It is difficult and sometimes impossible to study every individual in a population, so sampling allows researchers to make inferences about the population based on observations made on the sample. Sampling also helps to ensure that the sample is a true representation of the population, which is crucial for making valid inferences. Different research approaches, such as quantitative and qualitative research, have different sampling considerations, but both rely heavily on non-probability samples. Probability sampling, where all members of the target population have a known and random chance of being selected, is ideal but often impractical. Therefore, careful planning and consideration of the sampling strategy is necessary to ensure the validity of research conclusions.
What is the Sampling?4 answersSampling is a statistical method used to select a subset or sample from a population for the purpose of making observations and drawing inferences about the entire population. It is a way to study a representative group of individuals when studying the entire population is not feasible. The sample should be a true representation of the population to ensure that the inferences made from the analysis can be applied to the population. Sampling techniques eliminate bias in choosing the subset. Sampling is integral to research and has implications on the quality of research findings. In quantitative research, sampling focuses on maximizing the statistical representativeness of a population, while in qualitative research, it focuses on complete representation of a phenomenon of interest. Probability sampling, where all individuals have an equal chance of being selected, is ideal but often not practical. Non-probability sampling, such as convenience sampling, is more common. Careful planning and consideration of sample size are important in both types of research. Sampling involves selecting a subset of the population for study due to time, cost, and other constraints. It allows for accurate and reliable information while reducing time and cost. However, there are disadvantages such as inadequate samples, bias, accuracy problems, and sampling errors. Different sampling techniques, such as non-probability and probability sampling, are used to make the samples representative of the population. Sampling is the selection of a subset of the population of interest in a research study. It is a practical way to collect data faster and at a lower cost than attempting to reach every member of the population. Understanding the means by which the data arrived in the database is important for analyzing and drawing conclusions from the data. A sampling device is a tool that includes update circuits and a selection circuit to select a set of values from a plurality of state variables. The update circuits calculate change values and update the values of the state variables and local fields. The selection circuit selects a set of values based on score values and outputs the selected set.
How to sampling method in survey?5 answersSampling methods in surveys involve selecting a subset of individuals from a larger population to gather data. Different sampling techniques are used to ensure the representativeness and generalizability of the results. Probability sampling methods, such as random selection, are commonly used to increase precision and allow for generalization to the population of interest. Other types of data collection, such as social media embeddedness relationships, require sampling techniques due to the high cost and inefficiency of using population networks. Stratified sampling based on areas is one approach that can improve representativeness by avoiding sampling defects caused by a single reference condition. Sampling errors can occur at different scales, affecting the reliability and validity of the sample. Evaluating different sampling methods and their pros and cons is important for selecting an appropriate sampling design. The combination of different sampling techniques can also enhance the representation of the population.
What type of sampling if using archieve samples?5 answersArchive samples are not specifically mentioned in the provided abstracts. However, the abstract by Fleischer discusses a double sampling procedure where a large simple random sample is taken from the whole population and then a second stratified sample is taken from the chosen units. The abstract by Gabor mentions non-probabilistic models of gathering information, including "snowball" sampling, which could potentially be applied to archive samples. The abstract by Huang et al. describes a sampling inspection method for a multi-product production line, which may not directly relate to archive samples. The abstract by Mao and Song discusses a sampling inspection method for pass-fail data distribution based on the sequential posterior odd test (SPOT) method, but does not mention archive samples. Therefore, based on the available information, it is not possible to determine the specific type of sampling that is used for archive samples.
What is sampling in research?3 answersSampling in research refers to the process of selecting a subset of the population of interest for data collection purposes. It is a practical approach used when it is not possible to include the entire population in the study. Sampling allows for faster and more cost-effective data collection, as compared to attempting to reach every member of the population. There are different sampling techniques used in research, including probability sampling techniques such as simple random sampling, systematic random sampling, and stratified random sampling, as well as non-probability sampling techniques such as quota sampling, self-selection sampling, convenience sampling, snowball sampling, and purposive sampling. The selection of the study sample is crucial as it can shape the impact of the research on both practice and theory. It involves considering various factors and making informed decisions to ensure the validity and generalizability of the research findings. Proper sampling design is essential for accurate inference and generalization to the population of interest, and it involves considerations such as response rate, margin of error, sample weighting, and adjustment.
Where are feedback amplifiers used?9 answers

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What is the definition of purposive sampling according to see different people?
5 answers
Purposive sampling is a non-probability sampling technique used in qualitative research to select specific individuals or cases that possess characteristics or experiences relevant to the research focus. It involves intentionally selecting participants based on the researcher's judgment of who can provide the most valuable information related to the phenomenon under study. This method is commonly employed in educational research studies to observe how people approach purposeful sampling, aiming to identify bias in sample selection. In health care literature, purposeful sampling is utilized in qualitative evidence syntheses to strategically select articles that align with the research purpose, enhancing theoretical diversity and conceptual alignment with the synthesis goal. Purposeful sampling strategies, such as criterion sampling, are widely used in implementation research to identify information-rich cases relevant to the research objectives.
What is the rationale behind determining the minimum sample size in phenomenological studies?
4 answers
Determining the minimum sample size in phenomenological studies is crucial for ensuring the adequacy and quality of research outcomes. Researchers face dilemmas in selecting a sample size that balances thorough findings with resource efficiency. The debate on sample size in qualitative research highlights the importance of reaching reliable outputs through proper sample size determination. Researchers must consider factors like theme analysis, data saturation, and understanding the complete dimensionality of themes when deciding on sample size. Adequate sample size is essential for achieving saturation, where no new information emerges, indicating a comprehensive exploration of the research topic. By determining the minimum sample size based on these considerations, researchers can enhance the rigor and credibility of phenomenological studies.
Purposeful and purposive sampling the same?
5 answers
"Purposeful sampling" and "purposive sampling" are often used interchangeably in research literature. Both terms refer to the intentional selection of samples based on specific criteria or purposes relevant to the study. Purposeful sampling involves selecting information-rich cases related to the phenomenon of interest, commonly used in qualitative research. It is emphasized that good sampling techniques are crucial for research design and interpretation, highlighting the importance of purposeful sampling strategies. Contrary to traditional beliefs that random sampling may introduce bias, recent studies suggest that both random and purposive sampling methods can generate consistent and unbiased estimators of population parameters. Therefore, while the terms may have slight nuances in specific contexts, they generally refer to the deliberate selection of samples based on defined criteria for research purposes.
What are the advantages of using qualitative research methods in comparison to quantitative research methods?
7 answers
Qualitative research methods offer several advantages over quantitative research methods, particularly in their ability to provide depth and nuance to the understanding of complex phenomena. Unlike quantitative methods, which focus on numerical data and statistical analysis, qualitative research delves into the intricacies of human experience, offering insights that are often missed by more structured, numerical approaches. Qualitative research begins with broad questions that can be refined as the study progresses, allowing for a more flexible and responsive approach to the subject matter. This adaptability is crucial for exploring the subtleties of how and why certain phenomena occur, as it can accommodate the evolving nature of research inquiries. One of the primary strengths of qualitative research is its capacity for in-depth exploration. Through methods such as interviews, focus groups, and observations, qualitative research gathers rich, detailed data that can reveal the complexities and contradictions within people's experiences and perspectives. This depth of understanding is particularly valuable in fields like implementation research, where knowing the nuances of how and why implementation efforts succeed or fail can inform more effective strategies. Qualitative research also excels in exploring the experiences of underrepresented or marginalized groups, whose perspectives might be overlooked in larger quantitative studies. By employing purposive sampling techniques, qualitative studies ensure that the research focuses on individuals who are most relevant to the research question, thereby generating more meaningful and applicable insights. Moreover, qualitative research can enhance mixed-methods research (MMR) by providing a qualitative counterpoint to quantitative data, enriching the analysis and discussion of the results. This integration of qualitative and quantitative findings can offer a more comprehensive understanding of the research topic. In the realm of specific fields such as cardiovascular outcomes research and spinal cord research, qualitative methods have been recognized for their unique contributions, including preimplementation evaluation and understanding patient and caregiver experiences. Furthermore, qualitative Comparative Analysis (QCA) demonstrates how qualitative research can systematically conceptualize and analyze complex phenomena, further illustrating the method's versatility and depth. In summary, the advantages of qualitative research methods lie in their flexibility, depth, and ability to provide nuanced insights into human experiences, making them an invaluable complement to quantitative methods in a wide range of research contexts.
Why choosing purposeful sampling on research "Understanding Resilience in Indonesian Chronic Illness Employee"?
5 answers
Purposeful sampling is chosen in research on "Understanding Resilience in Indonesian Chronic Illness Employee" to select information-rich cases related to the phenomenon of interest, such as resilience and subjective well-being among employees working from home in South Jakarta. This sampling method allows for the identification of specific individuals who can provide valuable insights into the relationship between resilience and work stress among employees of PT Muramoto Elektronika Indonesia. By using purposeful sampling, researchers can strategically select participants based on their experiences with chronic illness and work-related stress, ensuring a comprehensive understanding of resilience in the context of Indonesian employees facing health challenges. Additionally, purposeful sampling helps mitigate bias in sample selection, enhancing the quality and relevance of the research findings.
What should be the sample size on the basis average ridership data?
5 answers
Determining the appropriate sample size for collecting average ridership data is crucial for transportation management studies. Research suggests that sample sizes of 250or even as low as two round trips per weekcan be effective in achieving the desired precision levels. Extending data collection to route-specific information can significantly improve the precision of ridership estimates, potentially reducing sample sizes by up to 45%. The issue of sample size in surveying for reliable data on Origin-Destination matrices is also highlighted, emphasizing the importance of sample size in producing accurate results. Therefore, a balance between data collection costs, computing time, and precision requirements must be considered when determining the sample size for average ridership data in transportation management studies.
How to remove particules from ultra pure water?
5 answers
To remove particles from ultra-pure water, various methods can be employed based on the specific requirements of the application. One approach involves utilizing a particle removing device that applies strong electric and ferromagnetic fields to the water, effectively collecting pulverous particles through a filter. Another method includes treating the water with cavitation in a tank equipped with an acoustic antenna emitting at the excitation frequency of water molecules, surpassing the cavitation threshold. Additionally, a sampling device for ultra-pure water can prevent contamination by impurities in the air during collection, ensuring accurate measurement of impurity concentrations in the sample water. These methods collectively contribute to maintaining the purity of ultra-pure water by effectively removing particles and impurities.
How does price affect guests revisit intention and satisfaction on service provided?
4 answers
Price plays a significant role in influencing guests' revisit intention and satisfaction with the service provided in the hospitality industry. Research indicates that perceived price fairness positively impacts customer satisfaction, leading to increased revisit intention. Additionally, compared to service quality, price has a stronger influence on customer satisfaction, ultimately affecting repurchase intention. Furthermore, in the context of airline services, price perception does not significantly impact repurchase intention, but service quality does positively influence it through customer satisfaction as an intervening variable. Moreover, in the hospitality sector, price, facilities, and service quality collectively have a positive impact on guests' intention to stay again, highlighting the importance of pricing strategies in enhancing revisit intentions.
What is the definition for active sampling in analytical chmistry?
5 answers
Active sampling in analytical chemistry refers to a strategic approach that involves iteratively selecting optimal subsamples for data collection based on machine learning predictions on unseen data. This method aims to overcome computational and economic constraints by improving the efficiency of parameter estimation in processes with shared and private parameters. It entails dynamic decision-making on which processes to sample, determining stopping criteria for data collection, and employing estimators for all parameters involved. By utilizing data-driven sampling decisions and conditional estimation cost functions, active sampling enables the sequential estimation of parameters with fewer samples while ensuring reliable estimates. This approach has shown significant performance enhancements compared to traditional sampling methods, particularly in scenarios like safety assessment of advanced driver assistance systems.
What are the most effective methods for assessing the impact of biodiversity conservation efforts?
5 answers
The most effective methods for assessing the impact of biodiversity conservation efforts involve enhancing biodiversity monitoring, utilizing modern tools like remote sensing and bioacoustics. Additionally, analyzing human-induced habitat quality changes can serve as a proxy for measuring the effectiveness of management efforts in biodiversity conservation. Monitoring biodiversity through various methods, such as roving surveys, belt transects, and citizen scientist-led surveys, can provide comparable measurements of species diversity and help in detecting changes in biodiversity effectively. Furthermore, considering counterfactuals in social-ecological systems can aid in evaluating the impact of conservation interventions by capturing the range of impact estimates associated with different actors, thus providing insights into the effectiveness of conservation measures.
Why is sampling resorted to in survey research?
4 answers
Sampling is utilized in survey research to efficiently study a subset of a population, enabling cost-effective and timely data collection while ensuring representativeness. By selecting a sample from a larger population, researchers can draw accurate inferences about the entire group. This method is crucial when studying attributes like disease prevalence, where studying the entire population is impractical. Sampling techniques, such as probability sampling, help control statistical properties of estimators, ensuring the findings are reliable and applicable to the broader population. Additionally, sampling allows for the integration of multiple data sources, enhancing the robustness and efficiency of inference in survey research.