Research on artificial intelligence safety prediction and intervention model based on ship driving habits

Based on the analysis of the causes of ship accidents, the development prospect and development direction of ship intelligent safe driving, the artificial intelligence safety prediction and intervention model is put forward. This model solves the problem of ship intelligent safety prediction by using intelligent analysis technology and network technology, and promotes the development of ship intelligence and ship safety navigation technology. Additionally, it expands the channels of obtaining information, connects the ship's mechanical and electrical equipment, collects, stores and analyzes the data reasonably, and constructs the intelligent analysis and processing platform of ship small-world data processing to implement intelligent intervention. What is impressive is that it makes ship navigation safer, more economical, more reasonable and optimized, and accelerates the development of ship artificial intelligence safe navigation.

Compressor Upgrade on Existing Platform: A Study on Vibration from Structural Perspective

Due to an increase in gas lift demand on an existing field in Sarawak, an existing Gas Lift Compressor (GLC) on the processing platform requires to be upgraded to meet incremental oil production requirement. These sets of compressors consist of 2x100% reciprocating compressors that were designed for 1.5 MMscfd each, with discharge pressure of 55.1 barg (800psig). The gas from these compressors is used mainly for gas lift at the processing platform as well as gas lift, instrument gas and utility gas at adjacent wellhead platforms. From the Conceptual Study, the existing compressors are not able to be retrofit for upgrade and is to be replaced with 2 × 100 % new gas engine driven compressor that capable of delivery 3.0 MMscfd of compressed gas each. During the engineering stage of GLC package, Skid Dynamic Analysis has been carried out to evaluate the GLC skid structural design due to the operating dynamic load cases. The study recommended that the skid to be welded to the platform where the compressor is located to prevent the risk of high vibration. With the recommendation from Contractor's study, project team proceeded to carry out Structural Dynamic Analysis to assess the dynamic effect of the GLC skids to the platform deck. The Finite Element Analysis (FEA) results revealed that there are several modal modes mainly at the drilling deck and extension deck non-compliance to PTS guideline. Structural Dynamic Modification (SDM) and optimization was performed to dynamically stiffens the structures to shift the modal modes away from the operating range to fulfil PTS criteria. However, the SDM results was still unable to comply thus the need of Anti-Vibration Mounts (AVMs) is considered. Prior to application of AVMs, Structural Forced Response Analysis needs to be carried out to evaluate the risk of the system and confirm the requirement of the AVMs. Without the forced response analysis, the effect of AVMs, locations and numbers of AVMs cannot be addressed during the design study. This paper will discuss the issues concerning vibration from reciprocating compressors upgrade on an existing platform, changes in the existing operating and design philosophy, challenges in addressing compressor installation and utilization of AVM from the perspective of Project Team. The paper will also provide key lessons learn and recommendation for future considerations in Compressor upgrades on existing facilities from a Structural Engineering point of view. The project is currently at its detail design finalization and installation is expected to be completed by November 2021.

Design of Remote Sensing Image Data Analysis and Processing Platform Based on Environmental Monitoring

In the continuous innovation of modern technology concept, remote sensing technology as an advanced and practical comprehensive detection technology has been widely used in many fields. Especially for environmental monitoring, the rational use of remote sensing image data analysis and processing platform can not only obtain valuable environmental information, but also provide effective management decisions for climate changeable natural disasters and other issues. Therefore, on the basis of understanding the design scheme of remote sensing image data analysis and processing platform system, this paper makes clear the positive role of remote sensing image processing technology in the development of environmental monitoring based on the application of the platform.

Real time Sentiment Analysis of Tweets using Apache Spark and Scala

Sentiment Analysis is becoming the field of focus with time considering the user experience weighs much more for the business to grow and for the studies as well. The sentimental expressions refers to the emotions or feeling of a person across certain point of focus or issues. So, in this project, with the assistance of Apache Spark Framework, an open source data streaming and processing platform, sentiment evaluation is done on the tweets from Twitter by the means of real time processing as well as an Ad-hoc Run. Some preprocessing of the textual data has been done upon for better characteristics extraction thus resulting in greater accuracy. The validation of this has been done for achieving better result by comparing the other processes when Naive Bayes algorithm is used.

SSVEP-based active control of an upper limb exoskeleton using a low-cost brain–computer interface

Purpose For the robot-assisted upper limb rehabilitation training process of the elderly with damaged neuromuscular channels and hemiplegic patients, bioelectric signals are added to transform the traditional passive training mode into the active training mode. Design/methodology/approach This paper mainly builds a steady-state visual stimulation interface, an electroencephalography (EEG) signal processing platform and an exoskeleton robot verification platform. The target flashing stimulation blocks provide visual stimulation at the specified position according to the specified frequency and stimulate EEG signals of different frequency bands. The EEG signal-processing platform constructed in this paper removes the noise by using Butterworth band-pass filtering and common average reference filtering on the obtained signals. Further, the features are extracted to identify the volunteer’s active movement intention through the canonical correlation analysis (CCA) method. The classification results are transmitted to the upper limb exoskeleton robot control system, combined with the position and posture of the exoskeleton robot to control the joint motion of robot. Findings Through a large number of experimental studies, the average accuracy of offline recognition of motion intention recognition can reach 86.1%. The control strategy with a three-instruction judgment method reduces the average execution error rate of the entire control system to 6.75%. Online experiments verify the feasibility of the steady-state visual evoked potentials (SSVEP)-based rehabilitation system. Originality/value An EEG signal analysis method based on SSVEP is integrated into the control of an upper limb exoskeleton robot, transforming the traditional passive training mode into the active training mode. The device used to record EEG is of very low cost, which has the potential to promote the rehabilitation system for further widely applications.

MARITIME BIG DATA ANALYSIS WITH ARLAS

The maritime industry has become a major part of globalization. Political and economic actors are meeting challenges regarding shipping and people transport. The Automatic Identification System (AIS) records and broadcasts the location of numerous vessels and delivers a huge amount of information that can be used to analyze fluxes and behaviors. However, the exploitation of these numerous messages requires tools based on Big Data principles.Acknowledgement of origin, destination, travel duration and distance of each vessel can help transporters to manage their fleet and ports to analyze fluxes and focus their investigations on some containers based on their previous locations. Thanks to the historical AIS messages provided by the Danish Maritime Authority and ARLAS PROC/ML, an open source and scalable processing platform based on Apache SPARK, we are able to apply our pipeline of processes and extract this information from millions of AIS messages. We use a Hidden Markov Model (HMM) to identify when a vessel is still or moving and we create “courses”, embodying the travel of the vessel. Then we derive the travel indicators. The visualization of results is made possible by ARLAS Exploration, an open source and scalable tool to explore geolocated data. This carto-centered application allows users to navigate into the huge amount of enriched data and helps to take benefits of these new origin and destination indicators. This tool can also be used to help in the creation of Machine Learning algorithms in order to deal with many maritime transportation challenges.