Geoscience Language Processing for Exploration

Deep learning has recently been providing step-change capabilities, particularly using transformer models, for natural language processing applications such as question answering, query-based summarization, and language translation for general-purpose context. We have developed a geoscience-specific language processing solution using such models to enable geoscientists to perform rapid, fully-quantitative and automated analysis of large corpuses of data and gain insights. One of the key transformer-based model is BERT (Bidirectional Encoder Representations from Transformers). It is trained with a large amount of general-purpose text (e.g., Common Crawl). Use of such a model for geoscience applications can face a number of challenges. One is due to the insignificant presence of geoscience-specific vocabulary in general-purpose context (e.g. daily language) and the other one is due to the geoscience jargon (domain-specific meaning of words). For example, salt is more likely to be associated with table salt within a daily language but it is used as a subsurface entity within geosciences. To elevate such challenges, we retrained a pre-trained BERT model with our 20M internal geoscientific records. We will refer the retrained model as GeoBERT. We fine-tuned the GeoBERT model for a number of tasks including geoscience question answering and query-based summarization. BERT models are very large in size. For example, BERT-Large has 340M trained parameters. Geoscience language processing with these models, including GeoBERT, could result in a substantial latency when all database is processed at every call of the model. To address this challenge, we developed a retriever-reader engine consisting of an embedding-based similarity search as a context retrieval step, which helps the solution to narrow the context for a given query before processing the context with GeoBERT. We built a solution integrating context-retrieval and GeoBERT models. Benchmarks show that it is effective to help geologists to identify answers and context for given questions. The prototype will also produce a summary to different granularity for a given set of documents. We have also demonstrated that domain-specific GeoBERT outperforms general-purpose BERT for geoscience applications.

Combining the Advantages of Different Processing Solutions Using a Novel Motion Processing Approach

In processing machines, technological tasks are implemented using suitable processing solutions. Those processing solutions can in turn have very different characteristics and specific advantages and disadvantages, e.g., concerning sensitivity to changing operating speed. In state-of-the-art processing machine controls, executing one single processing solution is supported. The execution of various processing solutions together and the combination of their advantages is currently not supported at all. In this article, a motion control approach is discussed that allows combining seemingly incompatible process solutions for a given technological task into a hybrid process solution, using the example of processing machines. The objective of this approach is to increase the achievable process window of the machine in terms of operating speed. It is shown that combining different process solutions can merge their advantages and compensate for their disadvantages. The article brings together the lessons learned from previous work in a new application to exploit advantages and compensate for disadvantages.

Resolving Gas Bubbles Ascending in Liquid Metal from Low-SNR Neutron Radiography Images

We demonstrate a new image processing methodology for resolving gas bubbles travelling through liquid metal from dynamic neutron radiography images with an intrinsically low signal-to-noise ratio. Image pre-processing, denoising and bubble segmentation are described in detail, with practical recommendations. Experimental validation is presented—stationary and moving reference bodies with neutron-transparent cavities are radiographed with imaging conditions representative of the cases with bubbles in liquid metal. The new methods are applied to our experimental data from previous and recent imaging campaigns, and the performance of the methods proposed in this paper is compared against our previously achieved results. Significant improvements are observed as well as the capacity to reliably extract physically meaningful information from measurements performed under highly adverse imaging conditions. The showcased image processing solution and separate elements thereof are readily extendable beyond the present application, and have been made open-source.

Lightweight Blockchain Processing. Case Study: Scanned Document Tracking on Tezos Blockchain

To bridge the current gap between the Blockchain expectancies and their intensive computation constraints, the present paper advances a lightweight processing solution, based on a load-balancing architecture, compatible with the lightweight/embedding processing paradigms. In this way, the execution of complex operations is securely delegated to an off-chain general-purpose computing machine while the intimate Blockchain operations are kept on-chain. The illustrations correspond to an on-chain Tezos configuration and to a multiprocessor ARM embedded platform (integrated into a Raspberry Pi). The performances are assessed in terms of security, execution time, and CPU consumption when achieving a visual document fingerprint task. It is thus demonstrated that the advanced solution makes it possible for a computing intensive application to be deployed under severely constrained computation and memory resources, as set by a Raspberry Pi 3. The experimental results show that up to nine Tezos nodes can be deployed on a single Raspberry Pi 3 and that the limitation is not derived from the memory but from the computation resources. The execution time with a limited number of fingerprints is 40% higher than using a classical PC solution (value computed with 95% relative error lower than 5%).

AN ACCURATE REAL-TIME UAV MAPPING SOLUTION FOR THE GENERATION OF ORTHOMOSAICS AND SURFACE MODELS

UAVs have become an indispensable tool for a variety of mapping applications. Not only in the area of surveying, infrastructure planning and environmental monitoring tasks but also in time-critical applications, such as emergency and disaster response. Although UAVs enable rapid data acquisition per se, data processing usually relies on offline workflows. This contribution presents an accurate real-time data processing solution for UAV mapping applications as well as an extensive experimental and comparative study to the commercial offline solution Pix4D on the absolute accuracy of orthomosaics and digital surface models. We show that our procedure achieves an absolute horizontal and vertical accuracy of about 1 m without the use of ground control. The code will be made publicly available.

Village Community Empowerment Through Oyster Mushroom Processing Training for Economic Independence

Madiun Merdeka University with Higher Education Leading Community Empowerment Program did technology transfer with theme processing oyster mushroom products. The aim of this activities is to solve the weakness of oyster mushroom which is wither quickly, easily rotten, and cheap selling price, so it can improve welfare of the people at Bodag Village, Kare District, Madiun Regency. The method is: the amount of participants are 26 people that member of Tani Rejo farmers amn Mulyo farmers. The activities by following 3M healthy protocol. The activity consist of processed oyster mushroom practices, problem discussion and processing solution, business management, marketing, packaging, and product serving. The results of this activities is oyster mushroom processing training gives positive value to the people. The people give best response into this activities. The output of this activities is the people can make crispy mushroom, shredded mushroom, rendang mushroom, ca mushroom, satay mushroom, ice cream mushroom, risol mushroom, nuggets mushroom, mushroom fried rice, mushroom fried rice, mushrood tomyam, and mushroom garang asem.

PREPROCESSING ARABIC DIALECT FOR SENTIMENT MINING: STATE OF ART

Sentiment Analysis concerns the analysis of ideas, emotions, evaluations, values, attitudes and feelings about products, services, companies, individuals, tasks, events, titles and their characteristics. With the increase in applications on the Internet and social networks, Sentiment Analysis has become more crucial in the field of text mining research and has since been used to explore users’ opinions on various products or topics discussed on the Internet. Developments in the fields of Natural Language Processing and Computational Linguistics have contributed positively to Sentiment Analysis studies, especially for sentiments written in non-structured or semi-structured languages. In this paper, we present a literature review on the pre-processing task on the field of sentiment analysis and an analytical and comparative study of different researches conducted in Arabic social networks. This study allowed as concluding that several works have dealt with the generation of stop words dictionary. In this context, two approaches are adopted: first, the manual one, which gives rise to a limited list, and second, the automatic, where the list of stop words is extracted from social networks based on defined rules. For stemming two, algorithms have been proposed to isolate prefixes and suffixes from words in dialects. However, few works have been interested in dialects directly without translation. The Moroccan dialect in particular is considered as the 5th dialect studied among Arabic dialects after Jordanian, Egyptian, Tunisian and Algerian dialects. Despite the significant lack in studies carried out on Arabic dialects, we were able to extract several conclusions about the difficulties and challenges encountered through this comparative study, as well as the possible ways and tracks to study in any dialects sentiment analysis pre-processing solution.

Fault section location for distribution network containing DG based on IBQPSO

To meet the requirement of the efficiency and accuracy for fault section location introduced by the construction and development of smart distribution network containing DG, a switching function model which can adapt itself to the change caused by switching of multiple DG is built and a network regional processing solution is raised. A fault section location for distribution network containing DG based on improved binary quantum particle swarm optimization (IBQPSO), which can effectively overcome the problem of global and local search capability imbalance in binary particle swarm optimization (BPSO), is proposed. The fault tolerance, rapidity and accuracy of this method are verified by simulation analysis of IEEE33 node system containing DG.