This article presents the information concerning aspects of the autonomous underwater vehicle (AUV) mission planning process, emphasizing maritime security monitoring and surveillance, and using side-looking sonars as a primary data source. The paper describes characteristic mission plan phases and gives suggestions for the operators, mainly concerning the safety and effectiveness of the AUV mission. The article describes the coverage path planning algorithm, which could be used to create an effective AUV mission plan, considering AUV manoeuvrability, sonar characteristics, and environmental factors. The results of the algorithms have been verified during the real mission of the AUV vehicle.

The number of scanner stations used to acquire point cloud data is limited, resulting in poor data registration. As a result, a cloud point block registration approach was proposed that took into account the distance between the point and the surface. When registering point cloud data, the invariant angle, length, and area of the two groups of point cloud data were affine transformed, and then the block registration parameters of point cloud data were determined. A finite hybrid model of point cloud data was created based on the coplane four-point nonuniqueness during the affine translation. On this basis, the point cloud data block registration algorithm was designed. Experimental results prove that the proposed method has great advantages in texture alignment, registration accuracy and registration time, so it is able to effectively improve the registration effect of point cloud data. The point cloud data block registration algorithm was built on this foundation. Experiments show that the suggested method has significant improvements in texture alignment, registration accuracy, and registration time, indicating that it can significantly improve point cloud data registration.

Information fusion approaches have been commonly used in multi sensor environments for the fusion and grouping of data from various sensors which is used further to draw a meaningful interpretation of the data. Traditional information fusion methods have limitations such as high time complexity of fusion processes and poor recall rate. In this work, a new multi-channel nano sensor information fusion method based on a neural network has been designed. By analyzing the principles of information fusion methods, the back propagation based neural network (BP-NN) is devised in this work. Based on the design of the relevant algorithm flow, information is collected, processed, and normalized. Then the algorithm is trained, and output is generated to achieve the fusion of information based on multi-channel nano sensor. Moreover, an error function is utilized to reduce the fusion error. The results of the present study show that compared with the conventional methods, the proposed method has quicker fusion (integration of relevant data) and has a higher recall rate. The results indicate that this method has higher efficiency and reliability. The proposed method can be applied in many applications to integrate the data for further analysis and interpretations.

In recent years there has been an increasing demand for electric vehicles due to their attractive features including low pollution and increase in efficiency. Electric vehicles use electric motors as primary motion elements and permanent magnet machines found a proven record of use in electric vehicles. Permanent magnet synchronous motor (PMSM) as electric propulsion in electric vehicles supersedes the performance compared to other motor types. However, in order to eliminate the cumbersome mechanical sensors used for feedback, sensorless control of motors has been proposed. This paper proposes the design of sliding mode observer (SMO) based on Lyapunov stability for sensorless control of PMSM. The designed observer is modeled with a simulated PMSM model to evaluate the tracking efficiency of the observer. Further, the SMO is coded using MATLAB/Xilinx block models to investigate the performance at real-time.

The protection and use of historic buildings is a difficult and costly task. Most often, these objects are under conservatory protection and any interference in their structure requires appropriate consent. On the other hand, conducting construction works on historic buildings carries a high risk of their damage or even destruction. Therefore, proper prior diagnostics is an extremely important factor affecting the scope and manner of works to be conducted. The paper presents the use of 3D scanning to determine the deflection of the ceiling under the Column Hall of the historic Palace, the floor of which showed elasticity, recorded during changing service loads. After identifying the places with the greatest deflections, based on data from 3D laser scanning, test holes were made and wood samples from the ceiling were taken to perform moisture content and mycological tests. An endoscopic inspection camera was inserted into test holes, providing the basis for recognizing the structure of the ceiling, i.e. arrangement of layers as well as dimensions and spacing of ceiling beams. Strength calculations were made with the limit state method resulted in the determination of the maximum permissible service load on the ceiling. The presented course of action in diagnostics of the analysed historic building may be an example of a preliminary procedure to be taken before deciding on changes in the manner of use of historic buildings or the functionalities of their individual parts.

The conventional port distribution power system is being disrupted by increasing distributed generation (DG) levels based on integrated energy. Different new energy resources combine with conventional generation and energy storage to improve the reliability of the systems. Reliability assessment is one of the key indicators to measure the impact of the distributed generation units based on integrated energy. In this work, an analytical method to investigate the impacts of using solar, wind, energy storage system (ESS), combined cooling, heating and power (CCHP) system and commercial power on the reliability of the port distribution power system is improved, where the stochastic characteristics models of the major components of the new energy DG resources are based on Markov chain for assessment. The improved method is implemented on the IEEE 34 Node Test Feeder distribution power system to establish that new energy resources can be utilized to improve the reliability of the power system. The results obtained from the case studies have demonstrated efficient and robust performance. Moreover, the impacts of integrating DG units into the conventional port power system at proper locations and with appropriate capacities are analyzed in detail.