Design of monitoring system for hazardous chemicals logistics based on Internet of Things

Guo, Lanzhong; Jin, Chun; Liu, Gang

doi:10.1109/compcomm.2017.8322727

Cited by 2 publications

(2 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the text modeling process using LDA, the total number of documents is denoted as M, and the total number of topics is denoted as K. In addition, α is the Dirichlet prior parameter of the distributions of multiple topics for each document, and β is the Dirichlet prior parameter of the distributions of multiple feature words under each topic [21]- [22]. Topics are generated for each document independently; thus, the probability of topic generation in the document library can be calculated using equation (1).…”

Section: ) Text Analysis Based On Lda Modelmentioning

confidence: 99%

“…As the quantity of hazardous chemicals in circulation increases, accidental leakage, fires, and explosions are likely to occur during production, storage, transportation, use, and waste disposal, causing casualties, economic losses, and ecological damage [1]- [3]. For example, two explosions occurred in a company's hazardous chemical warehouse in 2015.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Hazardous Chemical Accident Prevention Based on K-Means Clustering Analysis of Incident Information

Deng

Zeng

et al. 2020

IEEE Access

View full text Add to dashboard Cite

Hazardous chemicals are inflammable, explosive, and/or toxic and are prone to accidental leakage, fire, and explosion during production, storage, and transportation. It is timeconsuming and laborious to study the properties of hazardous chemicals individually for systematic accident prevention because of the wide variety of hazardous chemicals and conditions resulting in accidents. Moreover, accidents have numerous causes, and the relationships among the causative factors are complex. It is a problem that is difficult to accurately identify the effects of correlations among accident factors and determine the laws governing accident occurrence. In this paper, we propose a generic method of hazardous chemical accident prevention based on K-means clustering analysis of incident information to illustrate how to solve the problems. A database of hazardous chemical incidents was constructed, and a K-means clustering algorithm was adopted to classify the incidents. The numbers of occurrences and frequencies of the words in the textual descriptions of the consequences, processes, and causes of hazardous chemical incidents were counted and calculated using a class-based method. For words with a high frequency, risk scenarios were constructed, checklist items of newly revealed dangers were developed, and a system for systematic risk assessment and accident prevention was established. Finally, the information on hazardous material transportation incidents in the Pipeline and Hazardous Materials Safety Administration database of the U.S. Department of Transportation from 2009 to 2018 had been taken as an example to illustrate the method application. The results demonstrate that the proposed method of hazardous chemical accident prevention can be used to improve accident classification. The classification results make it possible to determine the optimal sequence of key targets on which to focus and the requirements for accident prevention and formulate preventive measures. Thus, they provide a technical basis for accident prevention.

show abstract

Section: ) Text Analysis Based On Lda Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hazardous Chemical Accident Prevention Based on K-Means Clustering Analysis of Incident Information

Deng

Zeng

et al. 2020

IEEE Access

View full text Add to dashboard Cite

show abstract

Ontology for pervasive traceability of agrochemicals

Monteiro¹,

Righi²,

Rigo³

et al. 2023

Ontology of Designing

View full text Add to dashboard Cite

The growth of the world population forces a more outstanding and more efficient food production, forc-ing agribusiness into the race for greater productivity. Thus, agrochemicals as a tool for increasing and defending production have become more critical with each harvest. This work presents an ontology that describes the knowledge involved in the need for Agrochemical Pervasive Traceability Model (APTM). This proposed ontology is called ontology for pervasive traceability of agrochemicals (OntoPTA). We present classes and their relationships in a hierarchical way and a visualization from the OWL ontology language. This ontology fills the gap in the understanding and modeling of this type of agribusiness pro-cess. This modeling helps farm administrators and software developers to perform better analysis for the development, use and maintenance of systems in agribusiness.

show abstract

Design of monitoring system for hazardous chemicals logistics based on Internet of Things

Cited by 2 publications

References 5 publications

Hazardous Chemical Accident Prevention Based on K-Means Clustering Analysis of Incident Information

Hazardous Chemical Accident Prevention Based on K-Means Clustering Analysis of Incident Information

Ontology for pervasive traceability of agrochemicals

Contact Info

Product

Resources

About