Based on literature review and measurements performed on electric power stations which considered adjusted acoustic pressure level, generated during operation of high power transformer units with integrated on load ta...
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The subject area regards realization of scientific research works considering technical condition estimation of electric power transformer cores based on vibroacoustic measurements performed during their normal operat...
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Non-invasive estimation of chlorophyll content in plants plays an important role in precision agriculture. This task may be tackled using hyperspectral imaging that acquires numerous narrow bands of the electromagneti...
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The optimal stochastic approximation procedure (OSAP) is applied to the parameter identification problem of distributed parameter system (DPS) driven by random disturbances and observed through noisy measurements. Thi...
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The optimal stochastic approximation procedure (OSAP) is applied to the parameter identification problem of distributed parameter system (DPS) driven by random disturbances and observed through noisy measurements. This procedure is a stochastic approximation procedure (SAP) with an optimal gain sequence and an optimal transformation on the gradient of the objective function: these optimal values accelerate the convergence rate by minimizing the mean squared parameter estimation error, under the assumption that the density functions of the system and observation noises are known, or can be easily estimated. An example of parameter identification of a stochastic parabolic DPS is simulated on the digital computer. A comparison is made among the results of the optimal, the modified, the nominal first-order, and the nominal second-order SAP. It is shown that the OSAP gives higher accuracy and faster rate of convergence as compared to the nominal SAP
Data collected from the environment in computerengineering may include missing values due to various factors, such as lost readings from sensors caused by communication errors or power outages. Missing data can resul...
Data collected from the environment in computerengineering may include missing values due to various factors, such as lost readings from sensors caused by communication errors or power outages. Missing data can result in inaccurate analysis or even false alarms. It is therefore essential to identify missing values and correct them as accurately as possible to ensure the integrity of the analysis and the effectiveness of any decision-making based on the data. This paper presents a new approach, the Gap Imputing Algorithm (GMA), for imputing missing values in time series data. The Gap Imputing Algorithm (GMA) identifies sequences of missing values and determines the periodic time of the time series. Then, it searches for the most similar subsequence from historical data. Unlike previous work, GMA supports any type of time series and is resilient to consecutively missing values with different gaps distances. The experimental findings, which were based on both real-world and benchmark datasets, demonstrate that the GMA framework proposed in this study outperforms other methods in terms of accuracy. Specifically, our proposed method achieves an accuracy score that is 5 to 20% higher than that of other methods. Furthermore, the GMA framework is well suited to handling missing gaps with larger distances, and it produces more accurate imputations, particularly for datasets with strong periodic patterns.
This paper addresses the development of an automatic segmentation technique for detecting cell nuclei. The technique uses a new approach for segmenting nuclei in images taken from tissues with colon carcinoma. The seg...
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Today's circuit design process is become a complex task. Sequential logic synthesis is an important part of the circuit design flow. The logic synthesis phase is a bottleneck during the overall design process sinc...
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In this paper, the authors do a comparative study of two methods to eliminate the static hazards from logical functions. The first method consists in determining the coupled terms and getting the term that masks the s...
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Evolutionary algorithms have become popular in the recent years as a general, simple, and robust technique that can be used when other optimization methods cannot be applied. Presently, there are a number of evolution...
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