AN INTELLIGENT HYBRID SCHEDULING ALGORITHM FOR COMPUTER AIDED PROCESS CONTROL OF MANUFACTURING SYSTEM

In recent times, maintaining stable and efficient operation, industrial automation and control systems that quickly respond to change is become a tedious task. Although the purpose of process scheduling is different according to the classes of process, the conventional methods have scheduled every process equivalently because they do not know the classes of process. To overcome this limitation, intelligent process scheduling method has to be developed to help the complexity associated with industries. In this paper, an intelligent algorithm is developed to do process scheduling of manufacturing system. Here, the proposed method utilizes a recent soft computing algorithm called, cuckoo search and traditional algorithm, called genetic algorithm.  These two algorithms are effectively combined to do intelligent process scheduling. Initially, solutions are encoded effectively by considering the sequential order, set up selection and machine selection. Solution is nothing but the order of process to be carried out sequentially by considering machine availability, set up condition and predefined order of machine ordering. Then, the fitness of the solution are found out using the fitness that considers machine cost of doing task, set up cost and machine change cost. After designing the solution coding and fitness function, the intelligent scheduling will be done with the help of HCGA algorithm which is developed by combining cuckoo search and genetic algorithm. The experimental results showed that, the proposed approach gives fitness rate of 0.82 and which helps to achieve the scheduling in limited time, listed as 22000 sec on an average.

Total Design in the Design and Development Process of a Remotely Operated Vehicle (ROV) with Particular Consideration of Sensorization

This paper provides a methodological proposal for the design and development process of a remotely operated vehicle (ROV). The design core and product design specifications (PDS) of Pugh’s Total Design model are considered, with a focus on the early stages of the product design and development process. A modularization of the functional groups of an ROV is proposed, focusing attention on the sensor system. The main concepts regarding ROVs are presented, Pugh’s Total Design model is explained, justifying the application interest in technological projects, a methodological proposal adapted to ROV projects is provided, based on Pugh’s Total Design model, with special interest in the early stages of the new product development process (NPD), the suitability of applying our own model of industrial design engineering in an ROV system is analyzed, and the contribution of this study is evaluated, proposing future work and lines of research

A Review and Comparative Study of Firefly Algorithm and its Modified Versions

Firefly algorithm is one of the well-known swarm-based algorithms which gained popularity within a short time and has different applications. It is easy to understand and implement. The existing studies show that it is prone to premature convergence and suggest the relaxation of having constant parameters. To boost the performance of the algorithm, different modifications are done by several researchers. In this chapter, we will review these modifications done on the standard firefly algorithm based on parameter modification, modified search strategy and change the solution space to make the search easy using different probability distributions. The modifications are done for continuous as well as non-continuous problems. Different studies including hybridization of firefly algorithm with other algorithms, extended firefly algorithm for multiobjective as well as multilevel optimization problems, for dynamic problems, constraint handling and convergence study will also be briefly reviewed. A simulation-based comparison will also be provided to analyse the performance of the standard as well as the modified versions of the algorithm

Applications of Artificial Intelligence in Construction Industry: A Review

Construction is probably the most seasoned calling as individuals have been building safe houses and structures for centuries. In any case, the business has advanced a lot in the manner they configuration, plan, and assemble structures. As of late, development organizations have progressively begun utilizing AI in a scope of approaches to make development more effective and imaginative. From advancing work routines to improving work environment wellbeing to keeping a protected watch on development offices, AI in the development business is as of now demonstrating its worth. Development supervisors have been discovering an incentive with AI and psychological innovations to help mechanize a significant number of the everyday except fundamental assignments to running their tasks. They are discovering AI accommodating with booking related assignments so as to forestall postponements, clashes, and different issues. This is both on the staff level of planning and on the undertaking and materials side. For little scope ventures people may have the option to oversee entangled development calendars and procedures physically. Nonetheless, enormous scope, multi-year ventures require the coordination of many convoluted errands and moving parts, for example, plans and outlines, licenses, and unforeseen postponements and changes that rapidly gain out of power for people to oversee without the help of innovation. The AI can screen hardware, devices and supplies and convey cautions in the event that anybody endeavours to take something from the site. In view of the mind-boggling results AI has conveyed, it's nothing unexpected that the development business is receiving different AI advancements. The advantages that AI can give are still moderately early. In the coming years AI will keep on driving cost reserve funds, time investment funds, and generally enhancements and efficiencies to the development business

Biopsychosocial Assessment and Ergonomics Intervention for Sustainable Living: A Case Study on Flats

This study proposes an ergonomics-based approach for those who are living in small housings (known as flats) in Indonesia. With regard to human capability and limitation, this research shows how the basic needs of human beings are captured and analyzed, followed by proposed designs of facilities and standard living in small housings. Ninety samples were involved during the study through in- depth interview and face-to-face questionnaire. The results show that there were some proposed of modification of critical facilities (such as multifunction ironing work station, bed furniture, and clothesline) and validated through usability testing. Overall, it is hoped that the proposed designs will support biopsychosocial needs and sustainability

Methodology of Algorithm Engineering

Research on algorithms has drastically increased in recent years. Various sub-disciplines of computer science investigate algorithms according to different objectives and standards. This plurality of the field has led to various methodological advances that have not yet been transferred to neighboring sub-disciplines. The central roadblock for a better knowledge exchange is the lack of a common methodological framework integrating the perspectives of these sub-disciplines. It is the objective of this paper to develop a research framework for algorithm engineering. Our framework builds on three areas discussed in the philosophy of science: ontology, epistemology and methodology. In essence, ontology describes algorithm engineering as being concerned with algorithmic problems, algorithmic tasks, algorithm designs and algorithm implementations. Epistemology describes the body of knowledge of algorithm engineering as a collection of prescriptive and descriptive knowledge, residing in World 3 of Popper's Three Worlds model. Methodology refers to the steps how we can systematically enhance our knowledge of specific algorithms. The framework helps us to identify and discuss various validity concerns relevant to any algorithm engineering contribution. In this way, our framework has important implications for researching algorithms in various areas of computer science

Efficient Implementation of Stochastic Inference on Heterogeneous Clusters and Spiking Neural Networks

Neuromorphic computing refers to brain inspired algorithms and architectures. This paradigm of computing can solve complex problems which were not possible with traditional computing methods. This is because such implementations learn to identify the required features and classify them based on its training, akin to how brains function. This task involves performing computation on large quantities of data. With this inspiration, a comprehensive multi-pronged approach is employed to study and efficiently implement neuromorphic inference model using heterogeneous clusters to address the problem using traditional Von Neumann architectures and by developing spiking neural networks (SNN) for native and ultra-low power implementation. In this regard, an extendable high-performance computing (HPC) framework and optimizations are proposed for heterogeneous clusters to modularize complex neuromorphic applications in a distributed manner. To achieve best possible throughput and load balancing for such modularized architectures a set of algorithms are proposed to suggest the optimal mapping of different modules as an asynchronous pipeline to the available cluster resources while considering the complex data dependencies between stages. On the other hand, SNNs are more biologically plausible and can achieve ultra-low power implementation due to its sparse spike based communication, which is possible with emerging non-Von Neumann computing platforms. As a significant progress in this direction, spiking neuron models capable of distributed online learning are proposed. A high performance SNN simulator (SpNSim) is developed for simulation of large scale mixed neuron model networks. An accompanying digital hardware neuron RTL is also proposed for efficient real time implementation of SNNs capable of online learning. Finally, a methodology for mapping probabilistic graphical model to off-the-shelf neurosynaptic processor (IBM TrueNorth) as a stochastic SNN is presented with ultra-low power consumption