Wellsite serviced power model for the CSG industry

2018 ◽  
Vol 58 (2) ◽  
pp. 715
Author(s):  
Gregory Edward Harris
Keyword(s):  
Power Supply ◽  
Superior Performance ◽  
Power Model ◽  
Reciprocating Engine ◽  
Field Service ◽  
Innovative Program ◽  
Solar Battery ◽  
High Level ◽  
Contracting Model ◽  
Hv Cable

CD Power (CDP) was challenged by a coal seam gas (CSG) operator to develop a wellsite power supply solution that would lower the operators running costs ($/kWh) whilst improving availability from low 90% to above 97% power availability. Furthermore, the operator sought a supply, install and maintain approach – resulting in a cheaper, more reliable outsourced energy solution. Typically, energy supply to wellsites in Australia consists of expensive inflexible and slow to install high voltage (HV) cable reticulated to each well. As wellsites deplete, these HV cables cannot be rerouted, resulting in expensive loss of capital. These limitations prompted operators to seek a highly mobile and cost focussed solution. CDP was contracted to deliver an innovative program of power solutions for a major CSG operator at 50 wellsites in 2017. The contract is to design, manufacture, install and maintain mobile 3-phase power supply units for wellsites. These units are powered by gas reciprocating engine generators and solar battery modules with synchronising switchboards. The full wellsite serviced power contracting model is a first of its kind for the CSG industry and is based on delivering high power availability and collaborative field service performance. CDP anticipates that providing a high level of technical innovation and superior performance, they will see the model extended across the industry and challenge the traditional economics around HV wellsite power. This paper will address the challenges faced in moving from a traditional segregated power model to a performance-based model at a time when the power industry is surrounded by innovation.

scholarly journals A Review of Algorithms and Hardware Implementations for Spiking Neural Networks

2021 ◽  
Vol 11 (2) ◽  
pp. 23
Author(s):  
Duy-Anh Nguyen ◽  
Xuan-Tu Tran ◽  
Francesca Iacopi
Keyword(s):  
Neural Networks ◽  
Computational Cost ◽  
Superior Performance ◽  
Training Algorithms ◽  
Current State ◽  
Hardware Implementations ◽  
Neuromorphic Hardware ◽  
High Level ◽  
Event Based ◽  
Hardware Platforms

Deep Learning (DL) has contributed to the success of many applications in recent years. The applications range from simple ones such as recognizing tiny images or simple speech patterns to ones with a high level of complexity such as playing the game of Go. However, this superior performance comes at a high computational cost, which made porting DL applications to conventional hardware platforms a challenging task. Many approaches have been investigated, and Spiking Neural Network (SNN) is one of the promising candidates. SNN is the third generation of Artificial Neural Networks (ANNs), where each neuron in the network uses discrete spikes to communicate in an event-based manner. SNNs have the potential advantage of achieving better energy efficiency than their ANN counterparts. While generally there will be a loss of accuracy on SNN models, new algorithms have helped to close the accuracy gap. For hardware implementations, SNNs have attracted much attention in the neuromorphic hardware research community. In this work, we review the basic background of SNNs, the current state and challenges of the training algorithms for SNNs and the current implementations of SNNs on various hardware platforms.

Performance on an Adaptive Automated Team Tracking Task with Human and Computer Teammates

1997 ◽  
Vol 41 (1) ◽  
pp. 551-555 ◽  
Author(s):  
Kristin Krahl ◽  
Mark W. Scerbo
Keyword(s):  
Adaptive Systems ◽  
High Performance ◽  
Vertical Axis ◽  
Tracking Task ◽  
Superior Performance ◽  
Performance Skills ◽  
Group Assignment ◽  
Pursuit Tracking ◽  
Human Participants ◽  
High Level

The present study examined team performance on an adaptive pursuit tracking task with human-human and human-computer teams. The participants were randomly assigned to one of three team conditions where their partner was either a computer novice, computer expert, or human. Participants began the experiment with control over either the horizontal or vertical axis, but had the option of taking control of their teammate's axis if they achieved superior performance on the previous trial. A control condition was also run where a single participant controlled both axes. Performance was assessed by RMSE scores over 100 trials. The results showed that performance along the horizontal axis improved over the session regardless of the experimental condition, but the degree of improvement was dependent upon group assignment. Individuals working alone or paired with an expert computer maintained a high level of performance throughout the experiment. Those paired with a computer-novice or another human performed poorly initially, but eventually reached the level of those in the other conditions. The results showed that team training can be as effective as individual training, but that the quality of training is moderated by the skill level of one's teammate. Moreover, these findings suggest that task partitioning of high performance skills between a human and a computer is not only possible but may be considered a viable option in the design of adaptive systems.

Compensation Committees and CEO Compensation Incentives in U.S. Entrepreneurial Firms

2004 ◽  
Vol 16 (1) ◽  
pp. 35-56 ◽  
Author(s):  
Martin J. Conyon ◽  
Lerong He
Keyword(s):  
Ceo Compensation ◽  
Theoretical Models ◽  
Power Model ◽  
Entrepreneurial Firms ◽  
Compensation Committee ◽  
Ceo Incentives ◽  
Optimal Contracting ◽  
Managerial Power ◽  
Ceo Pay ◽  
Contracting Model

This study uses a sample of IPO firms to investigate the relation between the compensation committee, CEO compensation, and CEO incentives. We investigate two theoretical models: the three-tier optimal contracting model and the managerial power model. We find support for the three-tier agency model. The presence of significant shareholders on the compensation committee (i.e., those with share stakes in excess of 5 percent) is associated with lower CEO pay and higher CEO equity incentives. Firms with higher paid compensation committee members are associated with greater CEO compensation and lower incentives. The managerial power model receives little support. We find no evidence that insiders or CEOs of other firms serving on the compensation committee raise the level of CEO pay or lower CEO incentives.

Design and Development of a Hybrid DC-DC Converter for Solar-Battery-Based Standalone Milk Vending Machine

2022 ◽  
pp. 110-144
Author(s):  
Aneeja K. J. ◽  
Bekkam Krishna ◽  
V. Karthikeyan
Keyword(s):  
Power Supply ◽  
Rural Areas ◽  
Power Flow ◽  
Power Conversion ◽  
Rural Families ◽  
Secondary Source ◽  
Optimum Level ◽  
Control Scheme ◽  
Solar Battery ◽  
And Performance

Dairying has become a major secondary source of income for several rural families. The easily perishable nature of milk increases the spoilage of the product and reduces the dairy farms' productivity in rural areas due to power supply shortage issues. In order to overcome the inaccessibility of proper preservation strategies, this chapter proposed a hybrid DC-DC converter for a solar battery-powered milk vending machine. This proposed system can work continuously and provides an uninterrupted power supply to maintain the milk quality at an optimum level. Moreover, the proposed system utilized a novel converter to reduce the number of power conversion stages and compact the system. Besides, the proposed converter can achieve a higher gain ratio with fewer components. Furthermore, a proper algorithmic-based control scheme has been implemented to maintain effective power flow management. Finally, to verify the feasibility and performance of the system, detailed results are obtained at different dynamic conditions, and various case studies are presented in this chapter.

Magnetorquer Based Vertical Damping Method for Microsatellite Attitude Control

2012 ◽  
Vol 263-266 ◽  
pp. 584-587
Author(s):  
Xu Guang Hou ◽  
Jian Yan ◽  
Jin Jin ◽  
Shun Liang Mei
Keyword(s):  
Power Supply ◽  
Attitude Control ◽  
Control Method ◽  
The Sun ◽  
Extreme Situation ◽  
Control Scheme ◽  
Solar Battery ◽  
Simulation Results ◽  
Electrical Source

Aiming at a three-axis stabilized microsatellite, a novel attitude control method, called magnetorquer based vertical damping, is proposed to avoid the occurrence of the worst situation that the non-solar-battery-plane spins towards the sun. DSP based simulation results based on DSP show that the vertical damping method outperforms the simple damping method when no orbit information is available, simultaneously the whole attitude control scheme is simple and effective. The proposed solution guarantees a stable power supply from the electrical source even under the extreme situation, which improves the reliability of the whole microsatellite system.

Semantic text-based image retrieval with multi-modality ontology and DBpedia

2017 ◽  
Vol 35 (6) ◽  
pp. 1191-1214 ◽  
Author(s):  
Yanti Idaya Aspura M.K. ◽  
Shahrul Azman Mohd Noah
Keyword(s):  
Image Retrieval ◽  
Semantic Distance ◽  
Image Features ◽  
Superior Performance ◽  
Semantic Retrieval ◽  
Content Type ◽  
Modality Approach ◽  
High Level ◽  
Image Collection ◽  
Practical Implications

Purpose The purpose of this study is to reduce the semantic distance by proposing a model for integrating indexes of textual and visual features via a multi-modality ontology and the use of DBpedia to improve the comprehensiveness of the ontology to enhance semantic retrieval. Design/methodology/approach A multi-modality ontology-based approach was developed to integrate high-level concepts and low-level features, as well as integrate the ontology base with DBpedia to enrich the knowledge resource. A complete ontology model was also developed to represent the domain of sport news, with image caption keywords and image features. Precision and recall were used as metrics to evaluate the effectiveness of the multi-modality approach, and the outputs were compared with those obtained using a single-modality approach (i.e. textual ontology and visual ontology). Findings The results based on ten queries show a superior performance of the multi-modality ontology-based IMR system integrated with DBpedia in retrieving correct images in accordance with user queries. The system achieved 100 per cent precision for six of the queries and greater than 80 per cent precision for the other four queries. The text-based system only achieved 100 per cent precision for one query; all other queries yielded precision rates less than 0.500. Research limitations/implications This study only focused on BBC Sport News collection in the year 2009. Practical implications The paper includes implications for the development of ontology-based retrieval on image collection. Originality value This study demonstrates the strength of using a multi-modality ontology integrated with DBpedia for image retrieval to overcome the deficiencies of text-based and ontology-based systems. The result validates semantic text-based with multi-modality ontology and DBpedia as a useful model to reduce the semantic distance.

scholarly journals A High-Level Power Model for MPSoC on FPGA

2012 ◽  
Vol 11 (1) ◽  
pp. 13-16 ◽  
Author(s):  
R. Piscitelli ◽  
A. Pimentel
Keyword(s):  
Power Model ◽  
High Level

Combining the Kernel Collaboration Representation and Deep Subspace Learning for Facial Expression Recognition

2018 ◽  
Vol 27 (08) ◽  
pp. 1850121 ◽  
Author(s):  
Zhe Sun ◽  
Zheng-Ping Hu ◽  
Raymond Chiong ◽  
Meng Wang ◽  
Wei He
Keyword(s):  
Facial Expression ◽  
Facial Expression Recognition ◽  
Principal Component ◽  
Subspace Learning ◽  
Superior Performance ◽  
Expression Recognition ◽  
Learning Networks ◽  
Linear Discriminant ◽  
High Level ◽  
Relationship Of

Recent research has demonstrated the effectiveness of deep subspace learning networks, including the principal component analysis network (PCANet) and linear discriminant analysis network (LDANet), since they can extract high-level features and better represent abstract semantics of given data. However, their representation does not consider the nonlinear relationship of data and limits the use of features with nonlinear metrics. In this paper, we propose a novel architecture combining the kernel collaboration representation with deep subspace learning based on the PCANet and LDANet for facial expression recognition. First, the PCANet and LDANet are employed to learn abstract features. These features are then mapped to the kernel space to effectively capture their nonlinear similarities. Finally, we develop a simple yet effective classification method with squared [Formula: see text]-regularization, which improves the recognition accuracy and reduces time complexity. Comprehensive experimental results based on the JAFFE, CK[Formula: see text], KDEF and CMU Multi-PIE datasets confirm that our proposed approach has superior performance not just in terms of accuracy, but it is also robust against block occlusion and varying parameter configurations.

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