Pub Date : 2021-09-02DOI: 10.1201/9781003138341-3-3
Suchitra Kumari, Arijit Ghosh, S. Mondal, A. Ahmadian, S. Salahshour
Digital Darwinism or Internet of things is the minute integration of Artificial intelligence and machine learning to transform the world into sensors where nothing will just “stop at a screen”. Internet of things refers to devices that normally do not require connectivity but are connected to each other via the internet and function smoothly without human action. Usage of this in businesses will depend on the implementation, efficiency and agility with which the systems are put into place. Industries 4.0 are adding sensors to their products so that the usage statistics can be reported back and any glitch can be cured before the object malfunctions and gives a bad name to the company. To have a reliable Internet of things network the most important thing is the compatibility standards which refers to the connected devices being able to talk and share data and recordings. In case all the devices run on different standards then they will be unable to match with each other and hence the interconnected system will break down at one or more links. Security is one of the key factors for its widespread use since the sensors collect extremely sensitive data. Once businesses adopt the advanced use of technology it might lead to critical attacks on the infrastructure or industrial espionage if there is no encryption done to sensors, gateways and company networks. The Internet of things bridges the gap between the digital world and the physical world, therefore hacking will lead to catastrophic real-world consequences.
{"title":"Industrial Internet of Things and Industry 4.0","authors":"Suchitra Kumari, Arijit Ghosh, S. Mondal, A. Ahmadian, S. Salahshour","doi":"10.1201/9781003138341-3-3","DOIUrl":"https://doi.org/10.1201/9781003138341-3-3","url":null,"abstract":"Digital Darwinism or Internet of things is the minute integration of Artificial intelligence and machine learning to transform the world into sensors where nothing will just “stop at a screen”. Internet of things refers to devices that normally do not require connectivity but are connected to each other via the internet and function smoothly without human action. Usage of this in businesses will depend on the implementation, efficiency and agility with which the systems are put into place. Industries 4.0 are adding sensors to their products so that the usage statistics can be reported back and any glitch can be cured before the object malfunctions and gives a bad name to the company. To have a reliable Internet of things network the most important thing is the compatibility standards which refers to the connected devices being able to talk and share data and recordings. In case all the devices run on different standards then they will be unable to match with each other and hence the interconnected system will break down at one or more links. Security is one of the key factors for its widespread use since the sensors collect extremely sensitive data. Once businesses adopt the advanced use of technology it might lead to critical attacks on the infrastructure or industrial espionage if there is no encryption done to sensors, gateways and company networks. The Internet of things bridges the gap between the digital world and the physical world, therefore hacking will lead to catastrophic real-world consequences.","PeriodicalId":143757,"journal":{"name":"Soft Computing Approach for Mathematical Modeling of Engineering Problems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133515191","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-02DOI: 10.1201/9781003138341-12-12
A. Saif, Z. R. Mahayuddin, H. Arshad
The Fourth industrial revolution (IR 4.0) saw the emergence of computer vision and artificial intelligence in creating smart imaging systems that can replace human vision and decision making especially to predict models for autonomous vehicles. In this context, advanced prediction of probable collision in real time scenario is an unsolved problem especially in the use of artificial intelligence and computer vision for autonomous vehicles. This research proposed an efficient collision avoidance model to avoid collision in real time scenario. Proposed model differs from other methods in a way that it does not require any other equipment like sensors for measuring distance between the vehicles. Proposed collision avoidance model estimates the relation between distance and size of the vehicle in real time scenario to generate an approximate notion of distance between the vehicles. Then, the ratio of distance between vehicles and size of the vehicle was used to depict vehicles that are in potentially dangerous positions for probable collision. Proposed collision avoidance model was experimented in the real-time traffic and experimental results showed that the model could detect vehicles in order to avoid the probable collisions efficiently. Proposed model is expected to be a possible tool in dealing with future demand of autonomous vehicles with the increase of 4IR technologies.
{"title":"Vision-Based Efficient Collision Avoidance Model Using Distance Measurement","authors":"A. Saif, Z. R. Mahayuddin, H. Arshad","doi":"10.1201/9781003138341-12-12","DOIUrl":"https://doi.org/10.1201/9781003138341-12-12","url":null,"abstract":"The Fourth industrial revolution (IR 4.0) saw the emergence of computer vision and artificial intelligence in creating smart imaging systems that can replace human vision and decision making especially to predict models for autonomous vehicles. In this context, advanced prediction of probable collision in real time scenario is an unsolved problem especially in the use of artificial intelligence and computer vision for autonomous vehicles. This research proposed an efficient collision avoidance model to avoid collision in real time scenario. Proposed model differs from other methods in a way that it does not require any other equipment like sensors for measuring distance between the vehicles. Proposed collision avoidance model estimates the relation between distance and size of the vehicle in real time scenario to generate an approximate notion of distance between the vehicles. Then, the ratio of distance between vehicles and size of the vehicle was used to depict vehicles that are in potentially dangerous positions for probable collision. Proposed collision avoidance model was experimented in the real-time traffic and experimental results showed that the model could detect vehicles in order to avoid the probable collisions efficiently. Proposed model is expected to be a possible tool in dealing with future demand of autonomous vehicles with the increase of 4IR technologies.","PeriodicalId":143757,"journal":{"name":"Soft Computing Approach for Mathematical Modeling of Engineering Problems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124172698","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-02DOI: 10.1201/9781003138341-4-4
Arijit Ghosh, Suchitra Kumari, S. Mondal, A. Ahmadian
Industry 4.0 comprises an optimum mix of speed, flexibility, quality, technology, business efficiency and environmental efficiency which leads to a safer and secured future. The previous revolutions have all been designed on the idea of creating newer technologies and have thereby chanced upon editing the flaws which comes with creating but the newest Industry 4.0 deals with taking whatever technology is already present in the market and actively removing all the clutter associated with it which eventually makes this process an innovation through and through. In spite of innovating upon the base technology to build processes for a better industrial future, there are some problems which do not have a clear-cut solution and this is where Fuzzy mathematical modeling comes into play. Moreover, with talks of Industry 5.0 surfacing, which is considered as the age of robots, a robot is given clear cut instructions as to a manufacturing process even in an uncertain environment. Thus, the process is slightly modified; a fuzzy modeling will be capable of handling such imprecise situations.
{"title":"Industry 4.0 and Its Practice in Terms of Fuzzy Uncertain Environment","authors":"Arijit Ghosh, Suchitra Kumari, S. Mondal, A. Ahmadian","doi":"10.1201/9781003138341-4-4","DOIUrl":"https://doi.org/10.1201/9781003138341-4-4","url":null,"abstract":"Industry 4.0 comprises an optimum mix of speed, flexibility, quality, technology, business efficiency and environmental efficiency which leads to a safer and secured future. The previous revolutions have all been designed on the idea of creating newer technologies and have thereby chanced upon editing the flaws which comes with creating but the newest Industry 4.0 deals with taking whatever technology is already present in the market and actively removing all the clutter associated with it which eventually makes this process an innovation through and through. In spite of innovating upon the base technology to build processes for a better industrial future, there are some problems which do not have a clear-cut solution and this is where Fuzzy mathematical modeling comes into play.\u0000 Moreover, with talks of Industry 5.0 surfacing, which is considered as the age of robots, a robot is given clear cut instructions as to a manufacturing process even in an uncertain environment. Thus, the process is slightly modified; a fuzzy modeling will be capable of handling such imprecise situations.","PeriodicalId":143757,"journal":{"name":"Soft Computing Approach for Mathematical Modeling of Engineering Problems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122434558","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-02DOI: 10.1201/9781003138341-5-5
Azadeh Zahedi Khameneh, A. Kılıçman, F. Ali
This study investigates the consistency problem of m-polar fuzzy preference relations, presented by m-polar fuzzy digraphs, during the consensus phase in group decision making. At first, a conjunction-based framework is presented to generalize the concept of m-polar fuzzy relation on an m-polar fuzzy set. Consequently, the definition of m-polar fuzzy graphs is developed by using an arbitrary conjunctive aggregation operator rather than the minimum. This change enables us to measure the strength of the relation between each pair of objects in an m-polar fuzzy graph based on the membership values of both not necessarily the lowest one. Next, by using the aggregation functions, new types of reflexivity, symmetry, antisymmetry and transitivity are given on an m-polar fuzzy relation. Then, m-polar fuzzy preference relation is derived, where the preferences are in the form of aggregation-based transitivite, and modeled by the m-polar fuzzy digraph. A theorem is given to consider the sufficient conditions for preservation of the consistency of aggregation-based m-polar fuzzy preferences during the consensus process. Lastly, an algorithm is designed to model the final consistence priority by using digraphs. A numerical example is also given to illustrate the proposed method.
{"title":"Consistency of Aggregation Function-Based m-Polar Fuzzy Digraphs in Group Decision Making","authors":"Azadeh Zahedi Khameneh, A. Kılıçman, F. Ali","doi":"10.1201/9781003138341-5-5","DOIUrl":"https://doi.org/10.1201/9781003138341-5-5","url":null,"abstract":"This study investigates the consistency problem of m-polar fuzzy preference relations, presented by m-polar fuzzy digraphs, during the consensus phase in group decision making. At first, a conjunction-based framework is presented to generalize the concept of m-polar fuzzy relation on an m-polar fuzzy set. Consequently, the definition of m-polar fuzzy graphs is developed by using an arbitrary conjunctive aggregation operator rather than the minimum. This change enables us to measure the strength of the relation between each pair of objects in an m-polar fuzzy graph based on the membership values of both not necessarily the lowest one. Next, by using the aggregation functions, new types of reflexivity, symmetry, antisymmetry and transitivity are given on an m-polar fuzzy relation. Then, m-polar fuzzy preference relation is derived, where the preferences are in the form of aggregation-based transitivite, and modeled by the m-polar fuzzy digraph. A theorem is given to consider the sufficient conditions for preservation of the consistency of aggregation-based m-polar fuzzy preferences during the consensus process. Lastly, an algorithm is designed to model the final consistence priority by using digraphs. A numerical example is also given to illustrate the proposed method.","PeriodicalId":143757,"journal":{"name":"Soft Computing Approach for Mathematical Modeling of Engineering Problems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132381482","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-02DOI: 10.1201/9781003138341-1-1
M. Pakdaman, A. Ahmadian, S. Salahshour
{"title":"Soft Computing Techniques: An Overview","authors":"M. Pakdaman, A. Ahmadian, S. Salahshour","doi":"10.1201/9781003138341-1-1","DOIUrl":"https://doi.org/10.1201/9781003138341-1-1","url":null,"abstract":"","PeriodicalId":143757,"journal":{"name":"Soft Computing Approach for Mathematical Modeling of Engineering Problems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133084013","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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