多チャンネル表面筋電図を用いたend-plateと伝播波の解析に関する研究

電気通信大学201

The mediating role of meta-cognitive beliefs between alexithymia and chronic pain intensity

 Aims and background: chronic pain isn’t always psychosomatic. Chronic pain, is a disorder that has a lot of psychological components and onethat a lot of people have at some point in  their life. The aim of this study was to determine the role meta-cognitive beliefs play in mediating between alexithymia and the intensity of pain that is perceived percipience by the patients with chronic pain. Materials and Methods: This study evaluated patients aged 20-60 with chronic pain who had been referred to the Mahan clinic and the physical medicine and rehabilitation clinic of Arman in Tehran from the spring of 1396 to autumn of 1396. During this time frame 440 patients who had at least 3 months of musculoskeletal pain, were chosen.  Theyanswered the Toronto Alexithymia Scale (TAS-20) the Meta-cognition Questionnaire (MCQ-30), and the Numeric Rating Scale (NRS). Findings: The intensity of pain was coorelated positively with with alexithymia (p< 0.001) and meta-cognitive beliefs (p< 0.001). Alexithymia had a positive coorelationwith meta-cognitive beliefs (p< 0.001). Alexithymia (t=6.68, β= 0.29), and meta-cognitive beliefs (t= 2.42, β= 0.11) could clarify the variance of the pain intensity. Alexithymia could also clarify the meta-cognitive beliefs (t= 9.48, β= 0.40). Conclusion: Based on the findings, the relation between alexithymia and the intensity of pain, was not a simple linear relationship, but meta-cognitive beliefs, could affect this relationship

Anomaly Detection on Gas Turbine Time-series’ Data Using Deep LSTM-Autoencoder

Anomaly detection with the aim of identifying outliers plays a very important role in various applications (e.g., online spam, manufacturing, finance etc.). An automatic and reliable anomaly detection tool with accurate prediction is essential in many domains. This thesis proposes an anomaly detection method by applying deep LSTM (long short-term memory) especially on time-series data. By validating on real-worlddata at Siemens Industrial Turbomachinery (SIT), the proposed methods hows promising performance, and can be employed in different data domains like device logs of turbine machines to provide useful information on abnormal behaviors. In detail, our proposed method applies an auto encoder to have feature selection by keeping vital features, and learn the time series’s encoded representation. This approach reduces the extensive input data by pulling out the auto encoder’s latent layer output. For prediction, we then train a deep LSTM model with three hidden layers based on the encoder’s latent layer output. Afterwards, given the output from the prediction model, we detect the anomaly sensors related to the specific gas turbine by using a threshold approach. Our experimental results show that our proposed methods perform well on noisy and real-world data set in order to detect anomalies. Moreover, it confirmed that making predictions based on encoding representation, which is under reduction, is more accurate. We could say applying autoencoder can improve both anomaly detection and prediction tasks. Additionally, the performance of deep neural networks would be significantly improved for data with high complexity

Anomaly Detection on Gas Turbine Time-series’ Data Using Deep LSTM-Autoencoder

Anomaly detection with the aim of identifying outliers plays a very important role in various applications (e.g., online spam, manufacturing, finance etc.). An automatic and reliable anomaly detection tool with accurate prediction is essential in many domains. This thesis proposes an anomaly detection method by applying deep LSTM (long short-term memory) especially on time-series data. By validating on real-worlddata at Siemens Industrial Turbomachinery (SIT), the proposed methods hows promising performance, and can be employed in different data domains like device logs of turbine machines to provide useful information on abnormal behaviors. In detail, our proposed method applies an auto encoder to have feature selection by keeping vital features, and learn the time series’s encoded representation. This approach reduces the extensive input data by pulling out the auto encoder’s latent layer output. For prediction, we then train a deep LSTM model with three hidden layers based on the encoder’s latent layer output. Afterwards, given the output from the prediction model, we detect the anomaly sensors related to the specific gas turbine by using a threshold approach. Our experimental results show that our proposed methods perform well on noisy and real-world data set in order to detect anomalies. Moreover, it confirmed that making predictions based on encoding representation, which is under reduction, is more accurate. We could say applying autoencoder can improve both anomaly detection and prediction tasks. Additionally, the performance of deep neural networks would be significantly improved for data with high complexity

the fundamental principles of the constitution law to describe the concept of citizenship

The paper is trying to show on the existing potential of Articles 2nd and 3rd of Iranian Constitution as ones of basic principles for defining the issue of citizenship and in order to representing the capacity of above mentioned articles for forming construction of the system of citizenship rights – encompassing general guaranties of recognition, improvement, protection and ensuring the rights and freedoms for every citizen. we examine if the theoretical model of citizenship cube and its doctrinal basis is applicable to explain and interpret the aforesaid articles and also, to comprehend some complex layers and dimensions thereof. According to the citizenship-oriented theories, one side of the cube is consisted from five elements of citizenship, i.e. civil and legal, social, political, virtue-related and identity-related aspects. The other side of the cube is composed from functional networks or, in other words, the geographical levels of citizenship including local, provincial, statistical, regional and universal levels. And the last side is shaped from educating citizenship capacities covering levels of knowledge and skill. It is to argue that some contents of the Articles can be creatively employed to propose citizenship cube and to provide the legal capacity for understanding the model within the Constitution