In this paper, a robust fault detection methodology for complex analog CMOS integrated filters is presented. It is based on combining the two types of testing methodologies, Oscillation-Based Testing (OBT) and IDDQ te...
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In this paper, a robust fault detection methodology for complex analog CMOS integrated filters is presented. It is based on combining the two types of testing methodologies, Oscillation-Based Testing (OBT) and IDDQ testing, i.e., measuring of the power-supply current (I-DD). The proposed methodology is applied to the Bi-quad Sallen-Key band-pass (BP) filter cell with relatively complex, two-stage, class-AB-output, operational amplifier (opamp) topology. The filter is custom designed targeting the 180-nm CMOS technology. Hundreds of time-domain simulations and analyses of the circuit output signal are performed in order to obtain the fault dictionary. The presented results show that the proposed hybrid OBT-IDDQ methodology is significantly more efficient in the defects coverage than any of the particular test methodologies alone. Subsequently, the specific algorithm for the defects classification is proposed. Based on the classification, certain degree of diagnosis of the individual defect, or a group of defects, can be achieved.
Increasing number of deaf or hard-of-hearing individuals is a crucial problem since communication among and within the deaf population proves to be a challenge. Despite sign languages developing in various countries, ...
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Increasing number of deaf or hard-of-hearing individuals is a crucial problem since communication among and within the deaf population proves to be a challenge. Despite sign languages developing in various countries, there is still lack of formal implementation of programs supporting its needs, especially for the Filipino sign language (FSL). Recently, studies on FSL recognition explored deep networks. Current findings are promising but drawbacks on using deep networks still prevail. This includes low transparency, interpretability, need for big data, and high computational requirements. Hence, this article explores topological data analysis (TDA), an emerging field of study that harnesses techniques from computational topology, for this task. Specifically, we evaluate a TDA-inspired classifier called Persistent Homology classification algorithm (PHCA) to classify static alphabet signed using FSL and compare its result with classical classifiers. Experiment is implemented on balanced and imbalanced datasets with multiple trials, and hyperparameters are tuned for a comprehensive comparison. Results show that PHCA and support vector machine (SVM) performed better than the other classifiers, having mean Accuracy of 99.45% and 99.31%, respectively. Further analysis shows that PHCA's performance is not significantly different from SVM, indicating that PHCA performed at par with the best performing classifier. Misclassification analysis shows that PHCA struggles to classify signs with similar gestures, common to FSL recognition. Regardless, outcomes provide evidence on the robustness and stability of PHCA against perturbations to data and noise. It can be concluded that PHCA can serve as an alternative for FSL recognition, offering opportunities for further research.
Obtaining and storing large amounts of data have become easier with the rapidly developing information technologies (IT). However, the data generated and collected, which are irrelevant in and of themselves, become us...
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Obtaining and storing large amounts of data have become easier with the rapidly developing information technologies (IT). However, the data generated and collected, which are irrelevant in and of themselves, become useful only when they are analyzed for a specific reason. Data mining may transform raw data into useful information. In the present study, classification and analysis of denim fabric quality characteristics according to denim fabric production parameters were carried out. The present study proposes a new classification rule inference algorithm. The suggested approach is mostly based on Artificial Bee Colony Optimization (ABC), a swarm intelligence meta-heuristic. In each step of the algorithm, there are two phases called the employed bee phase and the onlooker bee phase. This algorithm has been compared with the classification algorithms in the related literature. This proposed algorithm is a new data mining tool that intelligently combines various metaheuristic and neural networks and can generate classification rules. The results indicate that the proposed data mining algorithms may be highly useful in determining weight and width in denim fabric manufacture.
The exponential growth in data volume has necessitated the adoption of alternative storage solutions, and DNA storage stands out as the most promising solution. However, the exorbitant costs associated with synthesis ...
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The exponential growth in data volume has necessitated the adoption of alternative storage solutions, and DNA storage stands out as the most promising solution. However, the exorbitant costs associated with synthesis and sequencing impeded its development. Pre-compressing the data is recognized as one of the most effective approaches for reducing storage costs. However, different compression methods yield varying compression ratios for the same file, and compressing a large number of files with a single method may not achieve the maximum compression ratio. This study proposes a multi-file dynamic compression method based on machine learning classification algorithms that selects the appropriate compression method for each file to minimize the amount of data stored into DNA as much as possible. Firstly, four different compression methods are applied to the collected files. Subsequently, the optimal compression method is selected as a label, as well as the file type and size are used as features, which are put into seven machine learning classification algorithms for training. The results demonstrate that k-nearest neighbor outperforms other machine learning algorithms on the validation set and test set most of the time, achieving an accuracy rate of over 85% and showing less volatility. Additionally, the compression rate of 30.85% can be achieved according to k-nearest neighbor model, more than 4.5% compared to the traditional single compression method, resulting in significant cost savings for DNA storage in the range of $0.48 to 3 billion/TB. In comparison to the traditional compression method, the multi-file dynamic compression method demonstrates a more significant compression effect when compressing multiple files. Therefore, it can considerably decrease the cost of DNA storage and facilitate the widespread implementation of DNA storage *** AbstractFile compression is an important step in DNA storage, and different types of files may have varyin
In order to address the issue of low accuracy rate of current orchid type classification methods due to their similarities in the characteristics of orchid types, an effective orchid type classification method using d...
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In order to address the issue of low accuracy rate of current orchid type classification methods due to their similarities in the characteristics of orchid types, an effective orchid type classification method using data enhancement is suggested in this work, whose contribution depends on the utilization of data enhancement technologies, which can efficiently enhance the orchid type classification accuracy rate by providing sufficient and balanced sample sets. Specifically, in our approach, firstly, an image set of 12 orchid types containing 12,227 images is established;secondly, the characteristics of the above orchid image dataset are analyzed and studied;thirdly, the reasons for the processing difficulties are identified based on the above orchid image set;at last, some data enhancement technologies are applied to improve the classification accuracy rate of orchid types, which can also enhance the whole performance of orchid type classification. The experimental results display that our suggested classification method using data enhancement in the article can achieve a classification accuracy of 92.65% compared with the one not using data enhancement under the condition of insufficient and unbalanced image datasets.
During the last decade, variable-selection-based (VS) control charts have gained much popularity for process monitoring and diagnosis. These charts have been proven efficient for the detection of sparse mean shifts in...
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During the last decade, variable-selection-based (VS) control charts have gained much popularity for process monitoring and diagnosis. These charts have been proven efficient for the detection of sparse mean shifts in high-dimensional processes. VS charts usually assume that in-control (IC) data are the only information used to determine the control limits. In modern industrial processes, however, out-of-control (OC) data can be easily collected. Detecting a specific shift in a data-rich environment without utilizing OC data information will limit the development of a process monitoring scheme. In this paper, a novel variable selection control chart that is combined with a classification algorithm is proposed, which is expected to benefit from both the classification and variable selection approaches. In contrast to alternative charts, the proposed sensitized variable selection chart can capture the potential shifted variables using both IC and OC information, which can improve the sensitivity of the chart in a specific direction. Extensive Monte Carlo simulations demonstrate that the proposed chart outperforms the alternatives in a data-rich and high-dimensional environment. A real-life example of cellular localization is also included to support the findings of our study.
CO_(2)huff and puff technology can enhance the recovery of heavy oil in high-water-cut ***,the effectiveness of this method varies significantly under different geological and fluid conditions,which leads to a high-di...
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CO_(2)huff and puff technology can enhance the recovery of heavy oil in high-water-cut ***,the effectiveness of this method varies significantly under different geological and fluid conditions,which leads to a high-dimensional and small-sample(HDSS)*** is difficult for conventional techniques that identify key factors that influence CO_(2)huff and puff effects,such as fuzzy mathematics,to manage HDSS datasets,which often contain nonlinear and irremovable abnormal *** accurately pinpoint the primary control factors for heavy oil CO_(2)huff and puff,four machine learning classification algorithms were *** algorithms were selected to align with the characteristics of HDSS datasets,taking into account algorithmic principles and an analysis of key control *** results demonstrated that logistic regression encounters difficulties when dealing with nonlinear data,whereas the extreme gradient boosting and gradient boosting decision tree algorithms exhibit greater sensitivity to abnormal *** contrast,the random forest algorithm proved to be insensitive to outliers and provided a reliable ranking of factors that influence CO_(2)huff and puff *** top five control factors identified were the distance between parallel wells,cumulative gas injection volume,liquid production rate of parallel wells,huff and puff timing,and heterogeneous Lorentz *** research find-ings not only contribute to the precise implementation of heavy oil CO_(2)huff and puff but also offer valuable insights into selecting classification algorithms for typical HDSS data.
Efficient human resource management (HRM) is essential for company achievement in today's fast-paced corporate world. Businesses must find effective ways to retrieve and categorise the ever-growing amount of HR-re...
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Efficient human resource management (HRM) is essential for company achievement in today's fast-paced corporate world. Businesses must find effective ways to retrieve and categorise the ever-growing amount of HR-related knowledge. This work presents a new method for retrieving and classifying HRM data using machine learning. Modern natural language processing (NLP) and CNN methods are used by the algorithm we developed to handle unorganized human resources (HR) information, including worker records, job postings, and certificates. HR decision-making is facilitated by the system's ability to derive insightful information from the information using sophisticated text mining and machine learning *** two main parts of the method are classification and data extraction. HR workers can more easily obtain the required knowledge thanks to information retrieval, which makes it possible to search HR data quickly and accurately. Contrarily, categorisation optimises the division of human resources information into pertinent classifications, including job positions, competencies, and achievement grades. We assess our algorithm's effectiveness on various datasets from actual HR datasets. The outcomes show how well our strategy works to streamline HRM procedures, cut down on manual labour, and increase the precision of decisions. Furthermore, our technology is compatible with corporate human resources offices and educational settings because it complies with worldwide university *** study belongs to the growing body of knowledge in HRM. It provides a useful tool for businesses looking to improve employee relations, simplify HR procedures, and attract and retain talent. The suggested method is a valuable resource for academics and businesses alike because of its versatility and compliance with global educational norms.
This study explores the integration of supervised machine learning using decision tree algorithms within the framework of the Structure of Observed Learning Outcomes for diagnosing and customizing learning pathways in...
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ISBN:
(纸本)9798350379068;9798350379051
This study explores the integration of supervised machine learning using decision tree algorithms within the framework of the Structure of Observed Learning Outcomes for diagnosing and customizing learning pathways in middle school mathematics education. Focusing on seventh-grade students' proficiency in numbers and operations, the research employs a large dataset of student responses to develop a real-time adaptive diagnostic tool. The tool classifies students into five proficiency levels-starter, basic, medium, high, and advanced-based on their responses. Initial findings demonstrate an overall classification accuracy of 83%, with further analysis revealing specific strengths and weaknesses across different proficiency levels. This research underscores the potential of supervised machine learning to enhance educational diagnostics and contribute to personalized learning experiences, suggesting that such technology can significantly improve educational outcomes by dynamically adjusting to individual student needs.
Soil texture is the most important soil physical property that determines water holding capacity, nutrient availability and crop growth. Spatial distribution of soil texture at a higher spatial resolution at regional ...
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Soil texture is the most important soil physical property that determines water holding capacity, nutrient availability and crop growth. Spatial distribution of soil texture at a higher spatial resolution at regional and national level is essential for crop planning and management. In the present study, we mapped the soil textural classes over 16.2 M ha area of Andhra Pradesh state, India, at 250 m spatial resolution up to 2 m depth using the digital soil mapping approach. A total of 2,272 profile observations were used for the prediction of soil textural classes using the Random Forest (RF) classification algorithm. To estimate textural classes at six standard soil depth intervals (0-5, 5-15, 15-30, 30-60, 60-100 and 100-200 cm), we used continuous depth function of texture distribution using average sand, silt and clay content of different textural classes. Depth-wise spline outputs were then transformed into textural classes as per USDA textural classification. Sixteen environmental variables including Landsat-8 data, digital elevation model attributes and climatic variables were used for modelling. For model building, 75% of data was used and 25% of data was used for validation. Overall classification accuracy index and kappa index were calculated for validation data sets using 100 RF models. We recorded overall accuracy of 50%-65% and kappa index of 35%-47% for various depths. We found that equal-area quadratic splines of average sand, silt and clay are useful for soil profile depth harmonization of soil textural classes and random forest classification algorithm is a promising tool for spatial prediction of texture classes at the regional level. The present high-resolution (250 m) maps of soil texture classes are useful for different hydrological studies and preparation of proper land-use plans.
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