Sensitivity and Efficiency of the Frequency Shift Coefficient Based on the Damage Identification Algorithm: Modeling Uncertainty on Natural Frequencies

Health surveillance in industries is an important prospect to ensure safety and prevent sudden collapses. Vibration Based Structure Health Monitoring (VBSHM) is being used continuously for structures and machine diagnostics in industry. Changes in natural frequencies are frequently used as an input parameter for VBSHM. In this paper, the Frequency Shift Coefficient (FSC) is used for the assessment of various numerical damaged cases. An FSC-based algorithm is employed in order to estimate the positions and severity of damages using only the natural frequencies of healthy and unknown (damaged) structures. The study focuses on cantilever beams. By considering the minimization of FSC, damage positions and severity are obtained. Artificially damaged cases are assessed by changes in its positions, the number of damages and the size of damages along with the various parts of the cantilever beam. The study is further investigated by considering the effect of uncertainty on natural frequencies (0.1%, 0.2% and 0.3%) in damaged cases, and the algorithm is used to estimate the position and severity of the damage. The outcomes and efficiency of the proposed FSC based method are evaluated in order to locate and quantify damages. The efficiency of the algorithm is demonstrated by locating and quantifying double damages in a real cantilever steel beam using vibration measurements.

Parameter Adaptive Sliding Mode Trajectory Tracking Strategy with Initial Value Identification for the Swing in a Hydraulic Construction Robot

A novel trajectory tracking strategy is developed for a double actuated swing in a hydraulic construction robot. Specifically, a nonlinear hydraulic dynamics model of a double actuated swing is established, and a robust adaptive control strategy is designed to enhance the trajectory tracking performance. When an object is grabbed and unloaded, the inertia of a swing considerably changes, and the performance of the estimation algorithm is generally inadequate. Thus, it is necessary to establish an algorithm to identify the initial value of the moment of inertia of the object. To this end, this paper proposes a novel initial value identification algorithm based on a two-DOF robot gravity force identification method combined with computer vision information. The performance of the identification algorithm is enhanced. Simulations and experiments are performed to verify the effect of the novel control scheme.

Payload Identification and Gravity/Inertial Compensation for Six-Dimensional Force/Torque Sensor with a Fast and Robust Trajectory Design Approach

In the robot contact operation, the robot relies on the multi-dimensional force/torque sensor installed at the end to sense the external contact force. When the effective load and speed of the robot are large, the gravity/inertial force generated by it will have a non-negligible impact on the output of the force sensor, which will seriously affect the accuracy and effect of the force control. The existing identification algorithm time is often longer, which also affects the efficiency of force control operations. In this paper, a self-developed multi-dimensional force sensor with integrated gravity/inertial force sensing function is used to directly measure the resultant force. Further, a method for the rapid identification of payload based on excitation trajectory is proposed. Firstly, both a gravity compensation algorithm and an inertial force compensation algorithm are introduced. Secondly, the optimal spatial recognition pose based on the excitation trajectory was designed, and the excitation trajectory of each joint is represented by a finite Fourier series. The least square method is used to calculate the identification parameters of the load, the gravity, and inertial force. Finally, the experiment was verified on the robot. The experimental results show that the algorithm can quickly identify the payload, and it is faster and more accurate than other algorithms.

Single base station hybrid TOA/AOD/AOA localization algorithms with the synchronization error in dense multipath environment

Mobile station (MS) localization in a cellular network is appealing to both industrial community and academia, due to the wide applications of location-based services. The main challenge is the unknown one-bound (OB) and multiple-bound (MB) scattering environment in dense multipath environment. Moreover, multiple base stations (BSs) are required to be involved in the localization process, and the precise time synchronization between MS and BSs is assumed. In order to address these problems, hybrid time of arrival (TOA), angle of departure (AOD), and angle of arrival (AOA) measurement model from the serving BS with the synchronization error is investigated in this paper. In OB scattering environment, four linear least square (LLS), one quadratic programming and data fusion-based localization algorithms are proposed to eliminate the effect of the synchronization error. In addition, the Cramer-Rao lower bound (CRLB) of our localization model on the root mean-square error (RMSE) is derived. In hybrid OB and MB scattering environment, a novel double identification algorithm (DIA) is proposed to identify the MB path. Simulation results demonstrate that the proposed algorithms are capable to deal with the synchronization error, and LLS-based localization algorithms show better localization accuracy. Furthermore, the DIA can correctly identify the MB path, and the RMSE comparison of different algorithms further prove the effectiveness of the DIA.

Semantic Based Weighted Web Session Clustering Using Adapted K-Means and Hierarchical Agglomerative Algorithms

The WWW has a big number of pages and URLs that supply the user with a great amount of content. In an intensifying epoch of information, analysing users browsing behaviour is a significant affair. Web usage mining techniques are applied to the web server log to analyse the user behaviour. Identification of user sessions is one of the key and demanding tasks in the pre-processing stage of web usage mining. This paper emphasizes on two important fallouts with the approaches used in the existing session identification methods such as Time based and Referrer based sessionization. The first is dealing with comparing of current request’s referrer field with the URL of previous request. The second is dealing with session creation, new sessions are created or comes in to one session due to threshold value of page stay time and session time. So, authors developed enhanced semantic distance based session identification algorithm that tackles above mentioned issues of traditional session identification methods. The enhanced semantic based method has an accuracy of 84 percent, which is higher than the Time based and Time-Referrer based session identification approaches. The authors also used adapted K-Means and Hierarchical Agglomerative clustering algorithms to improve the prediction of user browsing patterns. Clusters were found using a weighted dissimilarity matrix, which is calculated using two key parameters: page weight and session weight. The Dunn Index and Davies-Bouldin Index are then used to evaluate the clusters. Experimental results shows that more pure and accurate session clusters are formed when adapted clustering algorithms are applied on the weighted sessions rather than the session obtained from traditional sessionization algorithms. Accuracy of the semantic session cluster is higher compared with the cluster of sessions obtained using traditional sessionization.

A deep learning model based anomalous behavior detection for supporting verifiable access control scheme in cloud servers

Due to the growth of Big Data (BD) storage and access in cloud computing infrastructure, the detection of anomalies for Cloud Servers (CSs) is essential to ensure data confidentiality. Over the past decades, different security systems have been designed based on various methods like encryption, Access Policy (AP) control schemes, signcryption and so on. Among many security systems, a new Improved NTRU (INTRU) decryption based on the AP strategy has been suggested to secure the BD processed by the CSs. Also, the shared secret data was authenticated to defend the clients from anomalies in the cloud. But, the AP upgrade must not degrade the confidentiality of storing information, reveal trust in the CS or cause any different security challenges. It is not considered that such security challenges occur when the data owner shares its data with many CSs. Hence in this article, an INTRU with Detecting Anomalous in CS (INTRU-DACS) system is proposed that employs a deep learning-based Anomaly Detection System (ADS) to handle and secure the BD stored in the CSs. The main goal of this method is to effectively identify the abnormalities in the real world by the conduct utilization, i.e., the System Call Identifier Sequences (SCISs) created from CSs in which these conducts are associated with BD. Initially, effective data summarization is constructed via different feature states to analyze the SCISs of specific durations. After that, an anomaly identification algorithm is proposed to train and test the streaming of raw SC sequences. This observable SCs execution task of CSs is gathered from log files. The variations of such SCISs having a specified duration are random for usual and unusual sequences. So, the fact of current normal and abnormal services is recognized regarding their SCISs. Such normal and abnormal behavioral states are learned from Convolutional Neural Network-Hidden Markov Model (CNNHMM) classifier to identify the anomalies in CSs. But, it is still a challenging process because of the patterns of usual and unusual events. The performance is not effective since it models only the conduct of a huge number of SCISs created from a single CS. As a result, a Secure Access Control Scheme with DACS (SACS-DACS) system is proposed in which a Multidimensional Feature Misbehavior Server Detection method (MFMSD) is introduced for detecting anomalies in multiple CSs. In this method, large-scale SCISs of multiple CSs are extracted, including different features such as network traffic sequence features, CPU energy usage and memory usage from host logs. These extracted multidimensional features are fed to the CNNHMM that identifies the anomalies and maximizes the detection accuracy. At last, the simulation results demonstrate the effectiveness of the SACS-DACS and INTRU-DACS as compared to the INTRU.