scholarly journals A Methodology for Generation of Performance Models for the Sizing of Analog High-Level Topologies

VLSI Design ◽  
2011 ◽  
Vol 2011 ◽  
pp. 1-17
Author(s):  
Soumya Pandit ◽  
Chittaranjan Mandal ◽  
Amit Patra
Keyword(s):  
Regression Function ◽  
Support Vector ◽  
Detailed Knowledge ◽  
Performance Models ◽  
Construction Time ◽  
Level Model ◽  
The Individual ◽  
High Level ◽  
Sequence Generator ◽  
Level Performance

This paper presents a systematic methodology for the generation of high-level performance models for analog component blocks. The transistor sizes of the circuit-level implementations of the component blocks along with a set of geometry constraints applied over them define the sample space. A Halton sequence generator is used as a sampling algorithm. Performance data are generated by simulating each sampled circuit configuration through SPICE. Least squares support vector machine (LS-SVM) is used as a regression function. Optimal values of the model hyper parameters are determined through a grid search-based technique and a genetic algorithm- (GA-) based technique. The high-level models of the individual component blocks are combined analytically to construct the high-level model of a complete system. The constructed performance models have been used to implement a GA-based high-level topology sizing process. The advantages of the present methodology are that the constructed models are accurate with respect to real circuit-level simulation results, fast to evaluate, and have a good generalization ability. In addition, the model construction time is low and the construction process does not require any detailed knowledge of circuit design. The entire methodology has been demonstrated with a set of numerical results.

Modeling and design of role engineering in development of access control for dynamic information systems

2013 ◽  
Vol 61 (3) ◽  
pp. 569-579 ◽  
Author(s):  
A. Poniszewska-Marańda
Keyword(s):  
Information Systems ◽  
Dynamic Information ◽  
Minimal Set ◽  
Distributed Information ◽  
Heterogeneous Information ◽  
System Protection ◽  
Level Model ◽  
Model Independent ◽  
Role Concept ◽  
High Level

Abstract Nowadays, the growth and complexity of functionalities of current information systems, especially dynamic, distributed and heterogeneous information systems, makes the design and creation of such systems a difficult task and at the same time, strategic for businesses. A very important stage of data protection in an information system is the creation of a high level model, independent of the software, satisfying the needs of system protection and security. The process of role engineering, i.e. the identification of roles and setting up in an organization is a complex task. The paper presents the modeling and design stages in the process of role engineering in the aspect of security schema development for information systems, in particular for dynamic, distributed information systems, based on the role concept and the usage concept. Such a schema is created first of all during the design phase of a system. Two actors should cooperate with each other in this creation process, the application developer and the security administrator, to determine the minimal set of user’s roles in agreement with the security constraints that guarantee the global security coherence of the system.

Territorial Distribution of Alcohol and Drug Addictions Mortality Concerning Regional Disparities in the Slovak Republic from Year 1996 to Year 2015

2020 ◽  
Keyword(s):  
Mortality Rate ◽  
Slovak Republic ◽  
Regional Disparities ◽  
Point Of View ◽  
Extreme Position ◽  
Data Set ◽  
Female Sex ◽  
Male Sex ◽  
The Individual ◽  
High Level

BACKGROUND: This paper deals with territorial distribution of the alcohol and drug addictions mortality at a level of the districts of the Slovak Republic. AIM: The aim of the paper is to explore the relations within the administrative territorial division of the Slovak Republic, that is, between the individual districts and hence, to reveal possibly hidden relation in alcohol and drug mortality. METHODS: The analysis is divided and executed into the two fragments – one belongs to the female sex, the other one belongs to the male sex. The standardised mortality rate is computed according to a sequence of the mathematical relations. The Euclidean distance is employed to compute the similarity within each pair of a whole data set. The cluster analysis examines is performed. The clusters are created by means of the mutual distances of the districts. The data is collected from the database of the Statistical Office of the Slovak Republic for all the districts of the Slovak Republic. The covered time span begins in the year 1996 and ends in the year 2015. RESULTS: The most substantial point is that the Slovak Republic possesses the regional disparities in a field of mortality expressed by the standardised mortality rate computed particularly for the diagnoses assigned to the alcohol and drug addictions at a considerably high level. However, the female sex and the male sex have the different outcome. The Bratislava III District keeps absolutely the most extreme position. It forms an own cluster for the both sexes too. The Topoľčany District bears a similar extreme position from a point of view of the male sex. All the Bratislava districts keep their mutual notable dissimilarity. Contrariwise, evaluation of a development of the regional disparities among the districts looks like notably heterogeneously. CONCLUSIONS: There are considerable regional discrepancies throughout the districts of the Slovak Republic. Hence, it is necessary to create a common platform how to proceed with the solution of this issue.

Quantitative indicators of air traffic controllers’ attitude to the danger of errors

2020 ◽  
pp. 68-78
Author(s):  
O. M. Reva ◽  
V. V. Kamyshin ◽  
S. P. Borsuk ◽  
V. A. Shulhin ◽  
A. V. Nevynitsyn
Keyword(s):  
Human Factor ◽  
Differential Method ◽  
Air Traffic ◽  
Professional Activity ◽  
Aviation Accidents ◽  
Air Traffic Controllers ◽  
The Difference ◽  
Level Of Significance ◽  
The Individual ◽  
High Level

The negative and persistent impact of the human factor on the statistics of aviation accidents and serious incidents makes proactive studies of the attitude of “front line” aviation operators (air traffic controllers, flight crewmembers) to dangerous actions or professional conditions as a key component of the current paradigm of ICAO safety concept. This “attitude” is determined through the indicators of the influence of the human factor on decision-making, which also include the systems of preferences of air traffic controllers on the indicators and characteristics of professional activity, illustrating both the individual perception of potential risks and dangers, and the peculiarities of generalized group thinking that have developed in a particular society. Preference systems are an ordered (ranked) series of n = 21 errors: from the most dangerous to the least dangerous and characterize only the danger preference of one error over another. The degree of this preference is determined only by the difference in the ranks of the errors and does not answer the question of how much time one error is more dangerous in relation to another. The differential method for identifying the comparative danger of errors, as well as the multistep technology for identifying and filtering out marginal opinions were applied. From the initial sample of m = 37 professional air traffic controllers, two subgroups mB=20 and mG=7 people were identified with statisti-cally significant at a high level of significance within the group consistency of opinions a = 1%. Nonpara-metric optimization of the corresponding group preference systems resulted in Kemeny’s medians, in which the related (middle) ranks were missing. Based on these medians, weighted coefficients of error hazards were determined by the mathematical prioritization method. It is substantiated that with the ac-cepted accuracy of calculations, the results obtained at the second iteration of this method are more ac-ceptable. The values of the error hazard coefficients, together with their ranks established in the preference systems, allow a more complete quantitative and qualitative analysis of the attitude of both individual air traffic controllers and their professional groups to hazardous actions or conditions.

scholarly journals Automatic Labeled Dialogue Generation for Nursing Record Systems

2020 ◽  
Vol 10 (3) ◽  
pp. 62
Author(s):  
Tittaya Mairittha ◽  
Nattaya Mairittha ◽  
Sozo Inoue
Keyword(s):  
Data Augmentation ◽  
Short Term Memory ◽  
Generative Models ◽  
Abstract Knowledge ◽  
Augmentation Techniques ◽  
Nursing Record ◽  
Long Short Term Memory ◽  
The Individual ◽  
High Level ◽  
Embedding Methods

The integration of digital voice assistants in nursing residences is becoming increasingly important to facilitate nursing productivity with documentation. A key idea behind this system is training natural language understanding (NLU) modules that enable the machine to classify the purpose of the user utterance (intent) and extract pieces of valuable information present in the utterance (entity). One of the main obstacles when creating robust NLU is the lack of sufficient labeled data, which generally relies on human labeling. This process is cost-intensive and time-consuming, particularly in the high-level nursing care domain, which requires abstract knowledge. In this paper, we propose an automatic dialogue labeling framework of NLU tasks, specifically for nursing record systems. First, we apply data augmentation techniques to create a collection of variant sample utterances. The individual evaluation result strongly shows a stratification rate, with regard to both fluency and accuracy in utterances. We also investigate the possibility of applying deep generative models for our augmented dataset. The preliminary character-based model based on long short-term memory (LSTM) obtains an accuracy of 90% and generates various reasonable texts with BLEU scores of 0.76. Secondly, we introduce an idea for intent and entity labeling by using feature embeddings and semantic similarity-based clustering. We also empirically evaluate different embedding methods for learning good representations that are most suitable to use with our data and clustering tasks. Experimental results show that fastText embeddings produce strong performances both for intent labeling and on entity labeling, which achieves an accuracy level of 0.79 and 0.78 f1-scores and 0.67 and 0.61 silhouette scores, respectively.

scholarly journals Opportunities and Challenges of Green-Lean: An Integrated System for Sustainable Construction

2020 ◽  
Vol 12 (11) ◽  
pp. 4460 ◽  
Author(s):  
Mohammadsoroush Tafazzoli ◽  
Ehsan Mousavi ◽  
Sharareh Kermanshachi
Keyword(s):  
Cost Savings ◽  
Integrated Approach ◽  
Integrated System ◽  
Waste Minimization ◽  
Sustainable Construction ◽  
Future Research ◽  
Safety Improvement ◽  
The Individual ◽  
High Level ◽  
Industry Professionals

Although the two concepts of lean and sustainable construction have been developed due to different incentives, and they do not pursue the same exact goals, there exists considerable commonality between them. This paper discusses the potentials for integrating the two approaches and their practices and how the resulting synergy from combining the two methods can potentially lead to higher levels of fulfilling the individual goals of each of them. Some limitations and challenges to implementing the integrated approach are also discussed. Based on a comprehensive review of existing papers related to sustainable and lean construction topics, the commonality between the two approaches is discussed and grouped in five categories of (1) cost savings, (2) waste minimization, (3) Jobsite safety improvement, (4) reduced energy consumption, and (5) customers’ satisfaction improvement. The challenges of this integration are similarly identified and discussed in the four main categories of (1) additional initial costs to the project, (2) difficulty of providing specialized expertise, (3) contractors’ unwillingness to adopt the additional requirements, and (4) challenges to establish a high level of teamwork. Industry professionals were then interviewed to rank the elements in each of the two categories of opportunities and challenges. The results of the study highlight how future research can pursue the development of a new Green-Lean approach by investing in the communalities and meeting the challenges of this integration.

scholarly journals Extraction of Arecanut Planting Distribution Based on the Feature Space Optimization of PlanetScope Imagery

Agriculture ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 371
Author(s):  
Yu Jin ◽  
Jiawei Guo ◽  
Huichun Ye ◽  
Jinling Zhao ◽  
Wenjiang Huang ◽  
...  
Keyword(s):  
Random Forest ◽  
Satellite Imagery ◽  
Feature Space ◽  
Kappa Coefficient ◽  
Classification Model ◽  
Support Vector ◽  
Textural Feature ◽  
Monitoring Accuracy ◽  
Areca Catechu ◽  
High Level

The remote sensing extraction of large areas of arecanut (Areca catechu L.) planting plays an important role in investigating the distribution of arecanut planting area and the subsequent adjustment and optimization of regional planting structures. Satellite imagery has previously been used to investigate and monitor the agricultural and forestry vegetation in Hainan. However, the monitoring accuracy is affected by the cloudy and rainy climate of this region, as well as the high level of land fragmentation. In this paper, we used PlanetScope imagery at a 3 m spatial resolution over the Hainan arecanut planting area to investigate the high-precision extraction of the arecanut planting distribution based on feature space optimization. First, spectral and textural feature variables were selected to form the initial feature space, followed by the implementation of the random forest algorithm to optimize the feature space. Arecanut planting area extraction models based on the support vector machine (SVM), BP neural network (BPNN), and random forest (RF) classification algorithms were then constructed. The overall classification accuracies of the SVM, BPNN, and RF models optimized by the RF features were determined as 74.82%, 83.67%, and 88.30%, with Kappa coefficients of 0.680, 0.795, and 0.853, respectively. The RF model with optimized features exhibited the highest overall classification accuracy and kappa coefficient. The overall accuracy of the SVM, BPNN, and RF models following feature optimization was improved by 3.90%, 7.77%, and 7.45%, respectively, compared with the corresponding unoptimized classification model. The kappa coefficient also improved. The results demonstrate the ability of PlanetScope satellite imagery to extract the planting distribution of arecanut. Furthermore, the RF is proven to effectively optimize the initial feature space, composed of spectral and textural feature variables, further improving the extraction accuracy of the arecanut planting distribution. This work can act as a theoretical and technical reference for the agricultural and forestry industries.

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