機械学習を用いたコグニティブ無線における変調方式識別に関する研究

The current spectrum allocation cannot satisfy the demand for future wireless communications, which prompts extensive studies in search of feasible solutions for the spectrum scarcity. The burden in terms of the spectral efficiency on the radio frequency terminal is intended to be small by cognitive radio (CR) systems that prefer low power transmission, changeable carrier frequencies, and diverse modulation schemes. However, the recent surge in the application of the CR has been accompanied by an indispensable component: the spectrum sensing, to avoid interference towards the primary user. This requirement leads to a complex strategy for sensing and transmission and an increased demand for signal processing at the secondary user. However, the performance of the spectrum sensing can be extended by a robust modulation classification (MC) scheme to distinguish between a primary user and a secondary user along with the interference identification. For instance, the underlying paradigm that enables a concurrent transmission of the primary and secondary links may need a precise measure of the interference that the secondary users cause to the primary users. An adjustment to the transmission power should be made, if there is a change in the modulation of the primary users, implying a noise oor excess at the primary user location; else, the primary user will be subject to interference and a collision may occur.Alternatively, the interweave paradigm that progresses the spectrum efficiency by reusing the allocated spectrum over a temporary space, requires a classification of the intercepted signal into primary and secondary systems. Moreover, a distinction between noise and interference can be accomplished by modulation classification, if spectrum sensing is impossible. Therefore, modulation classification has been a fruitful area of study for over three decades.In this thesis, the modulation classification algorithms using machine learning are investigated while new methods are proposed. Firstly, a supervised machine learning based modulation classification algorithm is proposed. The higher-order cumulants are selected as features, due to its robustness to noise. Stacked denoising autoencoders,which is an extended edition of the neural network, is chosen as the classifier. On one hand stacked pre-train overcomes the shortcoming of local optimization, on the other, denoising function further enhances the anti-noise performance. The performance of this method is compared with the conventional methods in terms of the classification accuracy and execution speed. Secondly, an unsupervised machine learning based modulation classification algorithm is proposed.The features from time-frequency distribution are extracted. Density-based spatial clustering of applications with noise (DBSCAN) is used as the classifier because it is impossible to decide the number of clusters in advance. The simulation reveals that this method has higher classification accuracy than the conventional methods. Moreover, the training phase is unnecessary for this method. Therefore, it has higher workability then supervised method. Finally, the advantages and dis-advantages of them are summarized.For the future work, algorithm optimization is still a challenging task, because the computation capability of hardware is limited. On one hand, for the supervised machine learning, GPU computation is a potential solution for supervised machine learning, to reduce the execution cost. Altering the modulation pool, the network structure has to be redesigned as well. On the other hand, for the unsupervised machine learning, that shifting the symbols to carrier frequency consumes extra computing resources.電気通信大学201

機械学習を用いたコグニティブ無線におけるOFDM信号認識に関する研究

電気通信大学201

Condition Monitoring Methods for Large, Low-speed Bearings

In all industrial production plants, well-functioning machines and systems are required for sustained and safe operation. However, asset performance degrades over time and may lead to reduced effiency, poor product quality, secondary damage to other assets or even complete failure and unplanned downtime of critical systems. Besides the potential safety hazards from machine failure, the economic consequences are large, particularly in offshore applications where repairs are difficult. This thesis focuses on large, low-speed rolling element bearings, concretized by the main swivel bearing of an offshore drilling machine. Surveys have shown that bearing failure in drilling machines is a major cause of rig downtime. Bearings have a finite lifetime, which can be estimated using formulas supplied by the bearing manufacturer. Premature failure may still occur as a result of irregularities in operating conditions and use, lubrication, mounting, contamination, or external environmental factors. On the contrary, a bearing may also exceed the expected lifetime. Compared to smaller bearings, historical failure data from large, low-speed machinery is rare. Due to the high cost of maintenance and repairs, the preferred maintenance arrangement is often condition based. Vibration measurements with accelerometers is the most common data acquisition technique. However, vibration based condition monitoring of large, low-speed bearings is challenging, due to non-stationary operating conditions, low kinetic energy and increased distance from fault to transducer. On the sensor side, this project has also investigated the usage of acoustic emission sensors for condition monitoring purposes. Roller end damage is identified as a failure mode of interest in tapered axial bearings. Early stage abrasive wear has been observed on bearings in drilling machines. The failure mode is currently only detectable upon visual inspection and potentially through wear debris in the bearing lubricant. In this thesis, multiple machine learning algorithms are developed and applied to handle the challenges of fault detection in large, low-speed bearings with little or no historical data and unknown fault signatures. The feasibility of transfer learning is demonstrated, as an approach to speed up implementation of automated fault detection systems when historical failure data is available. Variational autoencoders are proposed as a method for unsupervised dimensionality reduction and feature extraction, being useful for obtaining a health indicator with a statistical anomaly detection threshold. Data is collected from numerous experiments throughout the project. Most notably, a test was performed on a real offshore drilling machine with roller end wear in the bearing. To replicate this failure mode and aid development of condition monitoring methods, an axial bearing test rig has been designed and built as a part of the project. An overview of all experiments, methods and results are given in the thesis, with details covered in the appended papers.publishedVersio

Deep Learning and Polar Transformation to Achieve a Novel Adaptive Automatic Modulation Classification Framework

Automatic modulation classification (AMC) is an approach that can be leveraged to identify an observed signal\u27s most likely employed modulation scheme without any a priori knowledge of the intercepted signal. Of the three primary approaches proposed in literature, which are likelihood-based, distribution test-based, and feature-based (FB), the latter is considered to be the most promising approach for real-world implementations due to its favorable computational complexity and classification accuracy. FB AMC is comprised of two stages: feature extraction and labeling. In this thesis, we enhance the FB approach in both stages. In the feature extraction stage, we propose a new architecture in which it first removes the bias issue for the estimator of fourth-order cumulants, then extracts polar-transformed information of the received IQ waveform\u27s samples, and finally forms a unique dataset to be used in the labeling stage. The labeling stage utilizes a deep learning architecture. Furthermore, we propose a new approach to increasing the classification accuracy in low signal-to-noise ratio conditions by employing a deep belief network platform in addition to the spiking neural network platform to overcome computational complexity concerns associated with deep learning architecture. In the process of evaluating the contributions, we first study each individual FB AMC classifier to derive the respective upper and lower performance bounds. We then propose an adaptive framework that is built upon and developed around these findings. This framework aims to efficiently classify the received signal\u27s modulation scheme by intelligently switching between these different FB classifiers to achieve an optimal balance between classification accuracy and computational complexity for any observed channel conditions derived from the main receiver\u27s equalizer. This framework also provides flexibility in deploying FB AMC classifiers in various environments. We conduct a performance analysis using this framework in which we employ the standard RadioML dataset to achieve a realistic evaluation. Numerical results indicate a notably higher classification accuracy by 16.02% on average when the deep belief network is employed, whereas the spiking neural network requires significantly less computational complexity by 34.31% to label the modulation scheme compared to the other platforms. Moreover, the analysis of employing framework exhibits higher efficiency versus employing an individual FB AMC classifier. Advisor: Hamid R. Sharif-Kashan

Health condition estimation of bearings with multiple faults by a composite learning-based approach

Bearings are critical components found in most rotating machinery; their health condition is of immense importance to many industries. The varied conditions and environments in which bearings operate make them prone to single and multiple faults. Widespread interest in the improvements of single fault diagnosis meant limited attention was spent on multiple fault diagnosis. However, multiple fault diagnosis poses extra challenges due to the submergence of the weak fault by the strong fault, presence of non-Gaussian noise, coupling of the frequency components, etc. A number of existing convolutional neural network models operate on a distinct feature that is not enough to assure reliable results in the presence of these challenges. In this paper, extended feature sets in three homogenous deep learning models are used for multiple fault diagnosis. This ensures a measure of diversity is introduced to the health management dataset to obtain complementary solutions from the models. The outputs of the models are fused through blending ensemble learning. Experiments using vibration datasets based on bearing multiple faults show an accuracy of 98.54%, with an improvement of 2.74% in the overall effectiveness over the single models. Compared with other technologies, the results show that this approach provides an improved generalized diagnostic capability

Physicochemical, antioxidant and sensory properties of Mango Sorbet containing L-theanine as a potential functional food product

The non-proteinous amino acid L-theanine (L-THE) is associated with a range of health benefits including improvements in immune function, cardiovascular outcomes and cognition. The aims of this study were to develop a food product (mango sorbet; ms-L-THE) containing physiologically relevant doses of L-THE (0.2/100 g w/w) and determine its antioxidant, physicochemical and sensory properties in comparison to a mango sorbet without L-THE (ms). Total phenolic and flavanol content, and antioxidant analysis (DPPH, FRAP and ABTS) were determined spectrophotometrically. Both products were also evaluated for acceptability and likeability in healthy participants using the 9-point hedonic scale. Any differences that could be caused by the addition of L-THE were examined using the triangle test. Results indicated no significant differences between ms-L-THE and ms in taste of the products (p > 0.05), and the ms-L-THE was well received and accepted as a potential commercial product. Findings of the DPPH assay indicated significant difference between the two products (p < 0.05). In conclusion, we have successfully created a mango sorbet that contains a potentially physiologically relevant concentration of L-THE with antioxidant properties that could be used as a novel method of L-THE delivery to clinical and healthy populations

Sensors Fault Diagnosis Trends and Applications

Fault diagnosis has always been a concern for industry. In general, diagnosis in complex systems requires the acquisition of information from sensors and the processing and extracting of required features for the classification or identification of faults. Therefore, fault diagnosis of sensors is clearly important as faulty information from a sensor may lead to misleading conclusions about the whole system. As engineering systems grow in size and complexity, it becomes more and more important to diagnose faulty behavior before it can lead to total failure. In the light of above issues, this book is dedicated to trends and applications in modern-sensor fault diagnosis

Channel Activity Statistics Estimation in Spectrum Sharing Systems Based on Imperfect Spectrum Sensing

As we are stepping into the era of beyond 5G, the demand for frequency bands will increase significantly to accommodate the fast growing tendency in wireless communications technology. Spectrum sharing is one of the promising solutions to overcome the frequency scarcity problem and maximise spectrum utilisation efficiency. Its consideration can be seen in the recent ongoing deployment of 5G as in 5G New Radio Unlicensed (5G NR-U). The harmonious coexistence of several wireless systems in a shared frequency spectrum is highly dependent on making effective decisions for the utilisation of such spectrum. These decisions are usually based on the users’ traffic activity within the channel and their statistical information. Therefore, it is crucial for a spectrum sharing system to accurately obtain channel activity statistics. Although spectrum sensing is used in such systems to determine the instantaneous state of the channel, sensing decisions can further be exploited to provide a broad range of statistical information of the channel activity. However, spectrum sensing is imperfect in real world which therefore leads to inaccurate estimation of the channel activity statistics. In this context, this thesis studies the problem of estimating the channel activity statistics under (realistic) Imperfect Spectrum Sensing (ISS) and it finds mathematical relationships (in closed-form expressions) between the observed channel activity statistics under ISS and their corresponding actual statistical information, as a function of relevant operating parameters including the probability of sensing errors, the employed sensing period and the sample size. Such problem is poorly addressed in the literature, without deep and rigorous mathematical analyses taking into account all the factors that would influence the estimation accuracy of the channel activity statistics. Then, the thesis investigates different approaches that can be used to improve the estimation of the channel activity statistics under ISS, namely the closed-form expression approach, which is based on the obtained mathematical expressions for these statistics; the algorithmic approach, which is based on reconstruction algorithms; and finally a Traffic Learning (TL) approach, which is based on deep learning techniques. It is shown that the proposed estimation methods in this thesis outperform the existing methods in the literature without requiring any prior knowledge of the channel activity. The correctness of the obtained analytical expressions and proposed estimation methods are corroborated with both simulation and experimental results, for which a USRP-based prototype is developed as an experimental platform to validate the theoretical analyses conducted for the estimation of the channel activity statistics in spectrum sharing systems