Identification of Communication Cables Based on Scattering Parameters and a Support Vector Machine Algorithm

Bader, Oumaima; Haddad, Dhia; Kallel, Ahmed Yahia; Hassine, Tarek; Amara, Najoua Essoukri Ben; Kanoun, Olfa

doi:10.1109/lsens.2021.3087539

Cited by 11 publications

(3 citation statements)

References 11 publications

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“…The advantages of this platform are reflected in its inherent powerful cross-platform features and its natively provided high efficiency. on the ability of distributed application development [3][4]. All result data obtained after data preprocessing, data analysis intermediate processing result data, and final result data will be saved to the HDFS file system.…”

Section: Dsmentioning

confidence: 99%

Campus Distributed System Platform Considering Support Vector Machine Algorithm

2021

DPS

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With the increase of enrollment scale, the traditional management model of the school has brought huge pressure, and various business processes are flooded with all aspects of education and teaching management. In order to solve these serious problems, the introduction of information technology and the use of network technology, Web technology, to standardize various business processes has become an imperative task. Distributed processing technology decouples and splits the business content in the original single software system. According to business requirements, these split subtasks are distributed to processing nodes with different business processing capabilities for processing, realizing the task processing. Efficiency, perfect for handling campus business. Therefore, this paper designs a distributed system(DS) for the business management of the campus, builds a network topology map through the campus network, and then classifies the campus data according to the characteristics of the support vector machine algorithm classification to achieve a unified and orderly information management. In this paper, the data collection efficiency of the DS is tested under different nodes, and the results show that with the multiplication of collection nodes, the average number of web pages per second is multiplied, and the total time consumption is multiplied.

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Section: Dsmentioning

confidence: 99%

Campus Distributed System Platform Considering Support Vector Machine Algorithm

2021

DPS

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“…For quality detection of palletized packaged food, reference [10] used a deep neural network based on a principal component analysis network. Experiments were conducted using support vector machines (SVM) [11] as well as K-nearest neighbours (KNN) [12] for experimental comparison, which showed that the study has advantages not only in terms of detection accuracy but also speed. From the above studies, it can be seen that for food quality inspection, more and more scholars are introducing intelligent algorithms such as machine learning algorithms [13][14][15] and deep learning [16][17][18] algorithms.…”

Section: Introductionmentioning

confidence: 99%

Application of Intelligent Taste Analysis Based on Random Forest Algorithm in Food Quality Inspection

Zhang

Sun

2022

Computational Intelligence and Neuroscience

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Food safety is a major concern that has an impact on the national economy and people’s lives. The food industry has grown in quality and innovation in tandem with the rapid development of the economy and society. The emergence of new food technologies, as well as changes in dietary habits, has increased public concern about food safety. With the emergence of various counterfeit and substandard products, food quality and safety testing have become even more important. Traditional testing methods rely on sensory analysis and physical and chemical analysis. This approach is subjective and poorly adapted to the general public. It requires a high level of technical operation and is difficult to carry out on a large scale. To address this situation, this paper proposes an intelligent approach to food safety quality testing. The core idea is, first, to use sensors to collect data on the various components of the sample to be tested. Second, the random forest (RF) model used in this paper is trained. Third, the trained model is used to classify and identify the test samples. Based on the classification results, a conclusion is drawn as to whether the food product is a variant or a counterfeit. The advantage of this study is that the training model used is a weighted RF algorithm based on mutual information. The correlation between any two decision trees is calculated using mutual information, and for the more correlated decision trees, only the one with the highest evaluation accuracy is retained to form a new RF, and the evaluation accuracy is converted into voting weights, resulting in an RF model with less redundancy and higher evaluation accuracy. The experimental results show that the method used in this paper can successfully identify spoiled or counterfeit products and has good practicality.

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“…Impedance Spectroscopy is a powerful method for several applications, such as electrical material characterization [1,2], body tissue diagnosis [3,4], cancer detection [5], cable fault diagnosis [6] and identification [7]. It is suitable for the determination of state-ofcharge [8] and state-of-health and aging of battery cells, such as lithium-ion and lead-acid batteries [9][10][11], as well as fuel-cells [12].…”

Section: Introductionmentioning

confidence: 99%

Crest Factor Optimization for Multisine Excitation Signals with Logarithmic Frequency Distribution Based on a Hybrid Stochastic-Deterministic Optimization Algorithm

Kallel

Kanoun

2022

Batteries

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For diagnosis of batteries and fuel cells based on impedance spectroscopy, excitation signals are required, including low frequencies down to the mHz range. This leads to a long measurement time and compromises the stability condition for impedance spectroscopy. Multisine excitation signals with logarithmic frequency distribution can significantly reduce the measurement time but need optimization of the crest factor to realize a high signal-to-noise ratio at all excitation frequencies and maintain at the same time the linearity and stability conditions of impedance spectroscopy. Crest factor optimization is challenging, as the obtained results strongly depend on the initial phase values and many trials are necessary. It takes a very long time and can not be easily performed automatically up to now. In this paper, we propose a time-efficient hybrid stochastic-deterministic crest factor optimization method for multisine signals with logarithmic frequency distribution. A sigmoid transform on the multisine signal gradually transforms the multi-frequency signal into a binary-alike signal. The crest factor is significantly decreased, but the phases of the singular frequency signals remain sub-optimal. Further optimization based on the Gauss-Newton algorithm can determine the final phases, realizing a lower crest factor. The proposed method is less sensitive to initial phase values and provides more reasonable results in a reasonable time. The validation on a Samsung INR-18650-25R Lithium-ion battery cell shows that the crest factor of the optimized multisine signals has a median of 3.62 ± 0.7 within 6 min of run time, which is significantly better than the best previous work in the state-of-the-art of 3.85 ± 0.11 for the same run time.

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Identification of Communication Cables Based on Scattering Parameters and a Support Vector Machine Algorithm

Cited by 11 publications

References 11 publications

Campus Distributed System Platform Considering Support Vector Machine Algorithm

Campus Distributed System Platform Considering Support Vector Machine Algorithm

Application of Intelligent Taste Analysis Based on Random Forest Algorithm in Food Quality Inspection

Crest Factor Optimization for Multisine Excitation Signals with Logarithmic Frequency Distribution Based on a Hybrid Stochastic-Deterministic Optimization Algorithm

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