METHODOLOGY AND ARCHITECTURE FOR SAFETY MANAGEMENT
Matthieu Carré1,2, Ernesto Exposito1 and Javier Ibañez-Guzmán1,2 1Univ Pau & Pays Adour, E2S UPPA, LIUPPA France 2Renault S.A.S, 1 av. du Golf, France
The design of complex systems, as in the case of autonomous vehicles, requires a specialized systems engineering methodology and an adapted modelling framework. In particular, the integration of non-functional requirements, as important as the Safety, requires from this methodological framework the well-adapted semantic expression of constraints as well as their traceability during all phases of analysis, design and implementation. This paper focuses on the study of model-based autonomous system design and investigates the design flows and initiatives grasping with this complex computational model. The specialization of the ARCADIA methodology will be illustrated in a real industrial case.
Model Based System Engineering, Safety, Autonomous vehicles, System Engineering analysis, System Engineering design.
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AN INTELLIGENT MOBILE APPLICATION TO AUTOMATE FOOD HEALTH RECOMMENDATION USING DEEP LEARNING
Peiqi Gu1, Yu Sun1 and Fangyan Zhang2 1California State Polytechnic University,CA
As the global health condition declines, people have started to be more conscious about their health. In addition, the development of deep learning, especially in the sector of image recognition, proliferates, provides more convenience for people to monitor their health. Even though some food recognition applications appear on the internet, most of them are inaccurate, and there aren’t any researches that focus on the correlation between the accuracy of the model and attribute of the model. In addition, it is still inconvenient for people to gather information about how the food they eat everyday affects their health. Hence, in this project, the advanced development of deep learning was utilized for making an app which could be used to recognize a picture of the food taken by a phone and to display the food’s effect on a person’s certain health conditions. This project, or the application, has two main components: a model that can recognize the actual food through the camera of the phone and a database that stores the effects of the foods toward different kinds of health problems. After taking the photo, the application will display the effect of the foods to certain health problems that the user wants to see.
The experiment part of this project was inclined more on the optimization of the image recognition model. The result of this experiment indicated that more pictures in one category, less categories in total, and higher image resolution can improve the accuracy of the recognition model. This finding will be used on optimizing both the model and the application.
Deep Learning, Food Health Recommendation
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AUTOMATION REGRESSION SUITE CREATION FOR HEALTH CARE SOLUTION
Anjali Rawat and Shahid Ali, AGI Institute, New Zealand
Regression testing is very important for dynamic verification. It helps to simulate a suite of test cases periodically and after major changes in the design or its environment, in order to check that no new bugs were introduced. Evidences regarding benefit of implementing automation testing which includes saves of time and cost as it can re-run test scripts again and again and hence is much quicker than manual testing, providing more confidence in the quality of the product and increasing the ability to meet schedules and significantly reducing the effort that automation requires from testers are provided on the basis of survey of 115 software professionals. In addition to this, automated regression suite has an ability to explore the whole software every day without requiring much of manual effort. Also, bug identification is easier after the incorrect changes have been made. Occupational Health Management Solution (OHMS) is going through continuous development and requires testing again and again to check if new feature implementation has affected the existing functionality. In addition to this, The Company is facing issue in validation of the OHMS installation at client site since it requires availability of testers to check the critical functionality of the software manually. Company wants to create an automated regression suite for OHMS which can be executed at client site for checking the functionality of the software. In addition to this, this suite will also help the testing team to validate if the new features which have been added to the existing software are affecting the existing system or not. Visual studio, Selenium Webdriver, Visual SVN and Trello are the tools which have been used to achieve the creation of automation regression suite. The current research will provide guidelines to the future researchers on how to create an automated regression suite for any web application using open source tools.
Automation testing, Regression testing, Visual Studio, C#, Selenium Webdriver, Agile- Scrum
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QUALITY MODEL TO THE ADAPTIVE GUIDANCE
Hamid Khemissa1 and Mourad Oussala2, 1USTHB: University of Science and Technology Houari Boumediene, Algeria 2Nantes University, France
The need for adaptive guidance systems is now recognized for all software development processes. The new needs generated by the mobility context for software development led these guidance systems to both quality and ability adaptation to the possible variations of the development context. This paper deals with the adaptive guidance quality to satisfy the developer’s guidance needs. We propose a quality model to the adaptive guidance. This model offers a more detailed description of the quality factors of guidance service adaptation. This description aims to assess the quality level of each guidance adaptation factor and therefore the evaluation of the adaptive quality guidance services.
Quality model, Guidance System Quality, Adaptive Guidance, Plasticity
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AN IRREGULAR SPATIAL CLUSTER DETECTION COMBINING THE GENETIC ALGORITHM
Tao Wang1, Yitong Zhao2, Yonglin Lei3, Mei Yang4 and Shan Mei5, 1,3,4,5National University of Defence Technology, China
Spatial cluster detection is widely used for disease surveillance, prevention and containment. However, the commonly used clustering methods cannot resolve the conflicts between the accuracy and efficiency of the detection. This paper proposes an improved method for flexiblyshaped spatial scanning, which can identify irregular spatial clusters more accurately and efficiently. By using a genetic algorithm, we also accelerate the detection process. We convert geographic information to a network structure, in which nodes represent the regions and edges represent the adjacency relationship between regions. According to Kulldorff’s spatial scan statistics, we set the objective function. A constraint condition based on the spectral graph theory is employed to avoid disconnectedness or excessive irregularity of clusters. The algorithm is tested by analysing the simulation data of H1N1 influenza in Beijing. The results show that compared with the previous spatial scan statistic algorithms, our algorithm performs better with shorter time and higher accuracy.
Spatial cluster detection, flexibly-shaped spatial scanning, H1N1 influenza in Beijing
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