scholarly journals Firewall Rule Set Inconsistency Characterization by Clustering

2008 ◽  
Keyword(s):  
Rule Set

Termination Analysis for Active Rule Set Based on Triggering Path

2012 ◽  
Vol 35 (1) ◽  
pp. 65-75 ◽  
Author(s):  
Wei XIONG ◽  
Ye WU ◽  
Zhen ZHANG ◽  
Qiu-Yun WU
Keyword(s):  
Termination Analysis ◽  
Rule Set ◽  
Active Rule

scholarly journals Optimal Control Strategies for Switchable Transparent Insulation Systems Applied to Smart Windows for US Residential Buildings

Energies ◽  
2021 ◽  
Vol 14 (10) ◽  
pp. 2917
Author(s):  
Mohammad Dabbagh ◽  
Moncef Krarti
Keyword(s):  
Energy Use ◽  
Control Strategies ◽  
Residential Buildings ◽  
Residential Building ◽  
Optimization Approach ◽  
Optimal Controls ◽  
Peak Demand ◽  
Smart Windows ◽  
Insulation Systems ◽  
Rule Set

This paper evaluates the potential energy use and peak demand savings associated with optimal controls of switchable transparent insulation systems (STIS) applied to smart windows for US residential buildings. The optimal controls are developed based on Genetic Algorithm (GA) to identify the automatic settings of the dynamic shades. First, switchable insulation systems and their operation mechanisms are briefly described when combined with smart windows. Then, the GA-based optimization approach is outlined to operate switchable insulation systems applied to windows for a prototypical US residential building. The optimized controls are implemented to reduce heating and cooling energy end-uses for a house located four US locations, during three representative days of swing, summer, and winter seasons. The performance of optimal controller is compared to that obtained using simplified rule-based control sets to operate the dynamic insulation systems. The analysis results indicate that optimized controls of STISs can save up to 81.8% in daily thermal loads compared to the simplified rule-set especially when dwellings are located in hot climates such as that of Phoenix, AZ. Moreover, optimally controlled STISs can reduce electrical peak demand by up to 49.8% compared to the simplified rule-set, indicating significant energy efficiency and demand response potentials of the SIS technology when applied to US residential buildings.

scholarly journals A modular graph transformation rule set for IFC‐to‐CityGML conversion

2021 ◽  
Vol 25 (1) ◽  
pp. 261-290
Author(s):  
Helga Tauscher ◽  
Joie Lim ◽  
Rudi Stouffs
Keyword(s):  
Graph Transformation ◽  
Transformation Rule ◽  
Rule Set ◽  
Graph Transformation Rule

Colonial Origins of Maoist Insurgency in India

2017 ◽  
Vol 62 (10) ◽  
pp. 2232-2274 ◽  
Author(s):  
Shivaji Mukherjee
Keyword(s):  
Ethnic Inequality ◽  
Long Term Effects ◽  
Security Threat ◽  
Data Set ◽  
Internal Security ◽  
New Instrument ◽  
Maoist Insurgency ◽  
Rule Set ◽  
Set Up ◽  
Indirect Rule

What are the long-term effects of colonial institutions on insurgency? My article shows the historical origins of insurgency by addressing the puzzle of why the persistent Maoist insurgency, considered to be India’s biggest internal security threat, affects some districts along the central eastern corridor of India but not others. Combining archival and interview data from fieldwork in Maoist zones with an original district-level quantitative data set, I demonstrate that different types of British colonial indirect rule set up the structural conditions of ethnic inequality and state weakness that facilitate emergence of Maoist control. I address the issue of selection bias, by developing a new instrument for the British choice of indirect rule through princely states, based on the exogenous effect of wars in Europe on British decisions in India. This article reconceptualizes colonial indirect rule and also presents new data on rebel control and precolonial rebellions.

scholarly journals Detection of Banana Plants Using Multi-Temporal Multispectral UAV Imagery

2021 ◽  
Vol 13 (11) ◽  
pp. 2123
Author(s):  
Aaron Aeberli ◽  
Kasper Johansen ◽  
Andrew Robson ◽  
David Lamb ◽  
Stuart Phinn
Keyword(s):  
Template Matching ◽  
Crop Production ◽  
Local Maximum ◽  
Banana Plant ◽  
Essential Information ◽  
Object Based ◽  
Agricultural Applications ◽  
Multi Temporal ◽  
Rule Set ◽  
Geographic Object

Unoccupied aerial vehicles (UAVs) have become increasingly commonplace in aiding planning and management decisions in agricultural and horticultural crop production. The ability of UAV-based sensing technologies to provide high spatial (<1 m) and temporal (on-demand) resolution data facilitates monitoring of individual plants over time and can provide essential information about health, yield, and growth in a timely and quantifiable manner. Such applications would be beneficial for cropped banana plants due to their distinctive growth characteristics. Limited studies have employed UAV data for mapping banana crops and to our knowledge only one other investigation features multi-temporal detection of banana crowns. The purpose of this study was to determine the suitability of multiple-date UAV-captured multi-spectral data for the automated detection of individual plants using convolutional neural network (CNN), template matching (TM), and local maximum filter (LMF) methods in a geographic object-based image analysis (GEOBIA) software framework coupled with basic classification refinement. The results indicate that CNN returns the highest plant detection accuracies, with the developed rule set and model providing greater transferability between dates (F-score ranging between 0.93 and 0.85) than TM (0.86–0.74) and LMF (0.86–0.73) approaches. The findings provide a foundation for UAV-based individual banana plant counting and crop monitoring, which may be used for precision agricultural applications to monitor health, estimate yield, and to inform on fertilizer, pesticide, and other input requirements for optimized farm management.

scholarly journals WordNet and Cosine Similarity based Classifier of Exam Questions using Bloom’s Taxonomy

2016 ◽  
Vol 11 (04) ◽  
pp. 142 ◽  
Author(s):  
Kithsiri Jayakodi ◽  
Madhushi Bandara ◽  
Indika Perera ◽  
Dulani Meedeniya
Keyword(s):  
Language Processing ◽  
Evaluation Process ◽  
Bloom's Taxonomy ◽  
Cosine Similarity ◽  
Stop Word ◽  
Bloom’S Taxonomy ◽  
Similarity Algorithm ◽  
Question Category ◽  
Rule Set ◽  
Derived Rules

Assessment usually plays an indispensable role in the education and it is the prime indicator of student learning achievement. Exam questions are the main form of assessment used in learning. Setting appropriate exam questions to achieve the desired outcome of the course is a challenging work for the examiner. Therefore this research is mainly focused to categorize the exam questions automatically into its learning levels using Bloom’s taxonomy. Natural Language Processing (NLP) techniques such as tokenization, stop word removal, lemmatization and tagging were used before generating the rule set to be used for this classification. WordNet similarity algorithms with NLTK and cosine similarity algorithm were developed to generate a unique set of rules to identify the question category and the weight for each exam question according to Bloom’s taxonomy. These derived rules make it easy to analyze the exam questions. Evaluators can redesign their exam papers based on the outcome of the evaluation process. A sample of examination questions of the Department of Computing and Information Systems, Wayamba University, Sri Lanka was used for the evaluation; weight assignment was done based on the total value generated from both WordNet algorithm and the cosine algorithm. Identified question categories were confirmed by a domain expert. The generated rule set indicated over 70% accuracy.

Two Approaches to the Induction of Graph-Rewriting Rules for Function-Based Design Synthesis

2016 ◽  
Author(s):  
Julian R. Eichhoff ◽  
Felix Baumann ◽  
Dieter Roller
Keyword(s):  
Engineering Design ◽  
Conceptual Design ◽  
Automatic Generation ◽  
Production Rules ◽  
Design Synthesis ◽  
Graph Rewriting ◽  
Conceptual Engineering ◽  
Frequent Subgraph ◽  
Rule Set ◽  
Rewriting Rules

In this paper we demonstrate and compare two complementary approaches to the automatic generation of production rules from a set of given graphs representing sample designs. The first approach generates a complete rule set from scratch by means of frequent subgraph discovery. Whereas the second approach is intended to learn additional rules that fit an existing, yet incomplete, rule set using genetic programming. Both approaches have been developed and tested in the context of an application for automated conceptual engineering design, more specifically functional decomposition. They can be considered feasible, complementary approaches to the automatic inference of graph rewriting rules for conceptual design applications.

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