Operational Reliability and Modernization of Refineries in Mexico: How?, Why? and Where?

2016 ◽  
Author(s):  
Luis Ivan Ruiz Flores ◽  
Rafael Castellanos Bustamante ◽  
Jorge Guillermo Calderon Guizar
Keyword(s):  
Distribution Systems ◽  
State Of The Art ◽  
Electrical Equipment ◽  
Operational Reliability ◽  
International Standards ◽  
Energy Deficit ◽  
Short Term ◽  
Electrical Systems ◽  
New Distribution

This article presents the “Why?” to modernize the electrical systems in the six refineries in Mexico, derived that since 1979 there has not built a new refinery, and the primary electrical equipment at each refinery to process fuel requires of imperative upgrading. It also presents the “How?” partial changes are being implemented in some refineries such as replacing electrical equipment in the short-term and implementation of new distribution systems in the long term. Also, it comes in “Where?” They are making the necessary changes including the integration of new electrical generators to supply the energy deficit. The state of the art in operational reliability today in Mexico is presented as part of a projection to fulfill the following objectives: a) optimize the security of personnel integration of new electrical equipment meeting international standards, b) Contribute the least damage to the primary electrical equipment in each refinery and c) allow tangible service continuity in the process of oil production. The result of this work is to show conditions change in Mexican refineries with an electric approach and culture in the safety of staff and the facilities themselves.

scholarly journals Learning Long- and Short-Term User Literal-Preference with Multimodal Hierarchical Transformer Network for Personalized Image Caption

2020 ◽  
Vol 34 (05) ◽  
pp. 9571-9578 ◽  
Author(s):  
Wei Zhang ◽  
Yue Ying ◽  
Pan Lu ◽  
Hongyuan Zha
Keyword(s):  
State Of The Art ◽  
Natural Extension ◽  
Target Image ◽  
Short Term ◽  
Image Representations ◽  
High Level ◽  
Image Descriptions ◽  
Shed Light ◽  
Image Caption

Personalized image caption, a natural extension of the standard image caption task, requires to generate brief image descriptions tailored for users' writing style and traits, and is more practical to meet users' real demands. Only a few recent studies shed light on this crucial task and learn static user representations to capture their long-term literal-preference. However, it is insufficient to achieve satisfactory performance due to the intrinsic existence of not only long-term user literal-preference, but also short-term literal-preference which is associated with users' recent states. To bridge this gap, we develop a novel multimodal hierarchical transformer network (MHTN) for personalized image caption in this paper. It learns short-term user literal-preference based on users' recent captions through a short-term user encoder at the low level. And at the high level, the multimodal encoder integrates target image representations with short-term literal-preference, as well as long-term literal-preference learned from user IDs. These two encoders enjoy the advantages of the powerful transformer networks. Extensive experiments on two real datasets show the effectiveness of considering two types of user literal-preference simultaneously and better performance over the state-of-the-art models.

scholarly journals Learning from Interventions Using Hierarchical Policies for Safe Learning

2020 ◽  
Vol 34 (06) ◽  
pp. 10352-10360
Author(s):  
Jing Bi ◽  
Vikas Dhiman ◽  
Tianyou Xiao ◽  
Chenliang Xu
Keyword(s):  
Reaction Time ◽  
State Of The Art ◽  
The State ◽  
Policy Framework ◽  
Asymptotic Performance ◽  
Short Term ◽  
Learning From Demonstrations ◽  
Hierarchical Levels ◽  
Long Term Behavior

Learning from Demonstrations (LfD) via Behavior Cloning (BC) works well on multiple complex tasks. However, a limitation of the typical LfD approach is that it requires expert demonstrations for all scenarios, including those in which the algorithm is already well-trained. The recently proposed Learning from Interventions (LfI) overcomes this limitation by using an expert overseer. The expert overseer only intervenes when it suspects that an unsafe action is about to be taken. Although LfI significantly improves over LfD, the state-of-the-art LfI fails to account for delay caused by the expert's reaction time and only learns short-term behavior. We address these limitations by 1) interpolating the expert's interventions back in time, and 2) by splitting the policy into two hierarchical levels, one that generates sub-goals for the future and another that generates actions to reach those desired sub-goals. This sub-goal prediction forces the algorithm to learn long-term behavior while also being robust to the expert's reaction time. Our experiments show that LfI using sub-goals in a hierarchical policy framework trains faster and achieves better asymptotic performance than typical LfD.

scholarly journals Long-term target tracking combined with re-detection

2020 ◽  
Author(s):  
Juanjuan Wang ◽  
HaoRan Yang ◽  
Ning Xu ◽  
Chengqin Wu ◽  
ZengShun Zhao ◽  
...  
Keyword(s):  
Correlation Energy ◽  
State Of The Art ◽  
Tracking Algorithm ◽  
Correlation Filters ◽  
Short Term ◽  
Tracking Method ◽  
Tracking Tasks ◽  
Svm Model ◽  
Confidence Degree

Abstract The long-term visual tracking undergoes more challenges and is closer to realistic applications than short-term tracking. However, the performances of most existing methods have been limited in the long-term tracking tasks. In this work, we present a reliable yet simple long-term tracking method, which extends the state-of-the-art Learning Adaptive Discriminative Correlation Filters (LADCF) tracking algorithm with a re-detection component based on the SVM model. The LADCF tracking algorithm localizes the target in each frame and the re-detector is able to efficiently re-detect the target in the whole image when the tracking fails. We further introduce a robust confidence degree evaluation criterion that combines the maximum response criterion and the average peak-to correlation energy (APCE) to judge the confidence level of the predicted target. When the confidence degree is generally high, the SVM is updated accordingly. If the confidence drops sharply, the SVM re-detects the target. We perform extensive experiments on the OTB-2015 and UAV123 datasets. The experimental results demonstrate the effectiveness of our algorithm in long-term tracking.

scholarly journals Neural Architecture Search for a Highly Efficient Network with Random Skip Connections

2020 ◽  
Vol 10 (11) ◽  
pp. 3712
Author(s):  
Dongjing Shan ◽  
Xiongwei Zhang ◽  
Wenhua Shi ◽  
Li Li
Keyword(s):  
State Of The Art ◽  
Cell Structure ◽  
Frequency Discrimination ◽  
Search Space ◽  
Short Term ◽  
Cell Parameters ◽  
Neural Architecture ◽  
Proposed Model ◽  
Initialization Scheme

Regarding the sequence learning of neural networks, there exists a problem of how to capture long-term dependencies and alleviate the gradient vanishing phenomenon. To manage this problem, we proposed a neural network with random connections via a scheme of a neural architecture search. First, a dense network was designed and trained to construct a search space, and then another network was generated by random sampling in the space, whose skip connections could transmit information directly over multiple periods and capture long-term dependencies more efficiently. Moreover, we devised a novel cell structure that required less memory and computational power than the structures of long short-term memories (LSTMs), and finally, we performed a special initialization scheme on the cell parameters, which could permit unhindered gradient propagation on the time axis at the beginning of training. In the experiments, we evaluated four sequential tasks: adding, copying, frequency discrimination, and image classification; we also adopted several state-of-the-art methods for comparison. The experimental results demonstrated that our proposed model achieved the best performance.

The IMO Initial Strategy for the Reduction of GHGs from International Shipping: A Commentary

2019 ◽  
Vol 34 (3) ◽  
pp. 482-512
Author(s):  
Aldo Chircop
Keyword(s):  
International Standards ◽  
Shipping Industry ◽  
Short Term ◽  
International Convention ◽  
International Shipping ◽  
The Road ◽  
Initial Strategy ◽  
On The Road

AbstractIn 2018 the IMO adopted the initial Strategy for the international shipping industry’s reduction of global greenhouse gas emissions towards achieving the goal set in the 2015 Paris Agreement. At this time the Strategy is no more than a preliminary structure to frame the measures that will need to be adopted for the short, medium and long terms. In the short term (2018–2023) a first suite of measures will be adopted, and the initial Strategy will be revised and adopted as changed in 2023 with proposed measures for the medium term (2023–2030) and long term (2030–2050 and beyond). New international standards, tools and best practices will be needed to supplement the existing energy efficiency management rules in the International Convention on the Prevention of Pollution from Ships, 1973/78. This article discusses the Strategy and the role of the IMO in leading the shipping industry on the road to decarbonization.

scholarly journals Where to Go Next: Modeling Long- and Short-Term User Preferences for Point-of-Interest Recommendation

2020 ◽  
Vol 34 (01) ◽  
pp. 214-221 ◽  
Author(s):  
Ke Sun ◽  
Tieyun Qian ◽  
Tong Chen ◽  
Yile Liang ◽  
Quoc Viet Hung Nguyen ◽  
...  
Keyword(s):  
State Of The Art ◽  
User Preferences ◽  
Short Term ◽  
Preference Modeling ◽  
Point Of Interest ◽  
Proposed Model ◽  
Poi Recommendation ◽  
Novel Method ◽  
Real World Datasets

Point-of-Interest (POI) recommendation has been a trending research topic as it generates personalized suggestions on facilities for users from a large number of candidate venues. Since users' check-in records can be viewed as a long sequence, methods based on recurrent neural networks (RNNs) have recently shown promising applicability for this task. However, existing RNN-based methods either neglect users' long-term preferences or overlook the geographical relations among recently visited POIs when modeling users' short-term preferences, thus making the recommendation results unreliable. To address the above limitations, we propose a novel method named Long- and Short-Term Preference Modeling (LSTPM) for next-POI recommendation. In particular, the proposed model consists of a nonlocal network for long-term preference modeling and a geo-dilated RNN for short-term preference learning. Extensive experiments on two real-world datasets demonstrate that our model yields significant improvements over the state-of-the-art methods.

Wooden Trusses With Nodal Joints On Glued Flat Rods

2020 ◽  
pp. 9-15
Keyword(s):  
Bearing Capacity ◽  
Test Procedure ◽  
Experimental Studies ◽  
Operational Reliability ◽  
Design Solution ◽  
Short Term ◽  
High Degree ◽  
Wooden Structures ◽  
Node Type

Wooden trusses are widely used in construction and differ in a variety of structural forms. In general, their bearing capacity and stiffness are determined by the design solution of the node joints. In order to accept significant loads and reduce the overall deformation of trusses, it is necessary to develop new types of nodes that would also be characterized by low labor intensity of manufacturing and a high degree of operational reliability. Proposed by the authors nodes of wooden trusses based on steel glued flat rods are met the above requirements. The article describes the results of experimental studies of a wooden truss with nodal joints on glued flat rods under the short-term loads. The layout principles of the proposed node type are given; test procedure of experimental structures and results of experimental studies are presented: features of operation of steel connecting plates glued into wood in the nodes are revealed. It is shown that the adopted design solution of nodes refers to the joints of wooden structures of a rigid type and provides sufficient load-bearing capacity of the trusses and their increased rigidity. The nature of the destruction and the value of the destructive load confirmed the operational reliability of the proposed type of wooden trusses, including under the action of long-term loads. The analysis of the results revealed the directions of further improvement of wooden trusses nodes with steel glued flat rods.

“The Enthusiasm Expressed by Our Industry Friends”

Vulcan ◽  
2020 ◽  
Vol 8 (1) ◽  
pp. 100-124
Author(s):  
Adam Givens
Keyword(s):  
Long Range ◽  
State Of The Art ◽  
The State ◽  
Next Generation ◽  
Short Term ◽  
Us Army ◽  
The Face ◽  
Long Term Consequences ◽  
Fait Accompli

Abstract This article analyzes the groundbreaking 1952 plan by US Army leadership to develop a sizeable cargo helicopter program in the face of interservice opposition. It examines the influence that decision had in the next decade on the Army, the helicopter industry, and vtol technology. The Army’s procurement of large helicopters that could transport soldiers and materiel was neither a fait accompli nor based on short-term needs. Rather, archival records reveal that the decision was based on long-range concerns about the postwar health of the helicopter industry, developing the state of the art, and fostering new doctrinal concepts. The procurement had long-term consequences. Helicopters became central to Army war planning, and the ground service’s needs dictated the next generation of helicopter designs. That technology made possible the revolutionary airmobility concept that the Army took into Vietnam and also led to a flourishing commercial helicopter field.

scholarly journals Real-Time Inland CCTV Ship Tracking

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Lei Xiao ◽  
Minghai Xu ◽  
Zhongyi Hu
Keyword(s):  
Real Time ◽  
State Of The Art ◽  
Random Projection ◽  
Feedback Mechanism ◽  
Face Tracking ◽  
Great Success ◽  
Short Term ◽  
Corner Points ◽  
Full Occlusion

The predator algorithm is a representative pioneering work that achieves state-of-the-art performance on several popular visual tracking benchmarks and with great success when commercially applied to real-time face tracking in long-term unconstrained videos. However, there are two major drawbacks of predator algorithm when applied to inland CCTV (closed-circuit television) ship tracking. First, the LK short-term tracker within predator algorithm easily tends to drift if the target ship suffers partial or even full occlusion, mainly because the corner-points-like features employed by LK tracker are very sensitive to occlusion appearance change. Second, the cascaded detector within the predator algorithm searches for candidate objects in a predefined scale set, usually including 3-5 elements, which hampers the tracker to adapt to the potential diverse scale variations of the target ship. In this paper, we design a random projection based short-term tracker which can dramatically ease the tracking drift when the ship is under occlusion. Furthermore, a forward-backward feedback mechanism is proposed to estimate the scale variation between two consecutive frames. We prove that these two strategies gain significant improvements over the predator algorithm and also show that the proposed method outperforms several other state-of-the-art trackers.

scholarly journals An End-to-End Review-Based Aspect-Level Neural Model for Sequential Recommendation

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Yupeng Liu ◽  
Yanan Zhang ◽  
Xiaochen Zhang
Keyword(s):  
Semantic Information ◽  
State Of The Art ◽  
Neural Model ◽  
Sequential Patterns ◽  
Short Term ◽  
Attention Model ◽  
Individual Level ◽  
End To End

Users’ reviews of items contain a lot of semantic information about their preferences for items. This paper models users’ long-term and short-term preferences through aspect-level reviews using a sequential neural recommendation model. Specifically, the model is devised to encode users and items with the aspect-aware representations extracted globally and locally from the user-related and item-related reviews. Given a sequence of neighbor users of a user, we design a hierarchical attention model to capture union-level preferences on sequential patterns, a pointer model to capture individual-level preferences, and a traditional attention model to balance the effects of both union-level and individual-level preferences. Finally, the long-term and short-term preferences are combined into a representation of the user and item profiles. Extensive experiments demonstrate that the model substantially outperforms many other state-of-the-art baselines substantially.

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