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Full-Text Articles in Engineering
Reducing Carbon Emission Of Ocean Shipments By Optimizing Container Size Selection, Edwin Lik Ming Chong, Nang Laik Ma, Kar Way Tan
Reducing Carbon Emission Of Ocean Shipments By Optimizing Container Size Selection, Edwin Lik Ming Chong, Nang Laik Ma, Kar Way Tan
Research Collection School Of Computing and Information Systems
Human’s impact on earth through global warming is more or less an accepted fact. Ocean freight is estimated to contribute 4-5% of global carbon emissions and manufacturing companies can aid in reducing this amount. Many companies that ship goods through full container loads do not have the capabilities to ensure the containers they are using minimizes their carbon footprint. One of the reasons is the choice of non-ideal container sizes for their shipments. This paper provides a mathematical model to minimize companies’ shipping carbon footprints by selecting the ideal container sizes appropriate for their shipment volumes. Using data from a …
Scheduling And Resource Allocation In Wireless Sensor Networks, Yosef Alayev
Scheduling And Resource Allocation In Wireless Sensor Networks, Yosef Alayev
Dissertations, Theses, and Capstone Projects
In computer science and telecommunications, wireless sensor networks are an active research area. Each sensor in a wireless sensor network has some pre-defined or on demand tasks such as collecting or disseminating data. Network resources, such as broadcast channels, number of sensors, power, battery life, etc., are limited. Hence, a schedule is required to optimally allocate network resources so as to maximize some profit or minimize some cost. This thesis focuses on scheduling problems in the wireless sensor networks environment. In particular, we study three scheduling problems in the wireless sensor networks: broadcast scheduling, sensor scheduling for area monitoring, and …
Optimization Of Job Shop Scheduling Problems Using Modified Clonal Selection Algorithm, Yilmaz Atay, Hali̇fe Kodaz
Optimization Of Job Shop Scheduling Problems Using Modified Clonal Selection Algorithm, Yilmaz Atay, Hali̇fe Kodaz
Turkish Journal of Electrical Engineering and Computer Sciences
Artificial immune systems (AISs) are one of the artificial intelligence techniques studied a lot in recent years. AISs are based on the principles and mechanisms of the natural immune system. In this study, the clonal selection algorithm, which is used commonly in AISs, is modified. This algorithm is applied to job shop scheduling problems, which are one of the most difficult optimization problems. For applying application results to the optimum solution, parameter values giving the optimum solution are determined by analyzing the parameters in the algorithm. The obtained results are given in detail in the tables and figures. The best …
A Reduced Probabilistic Neural Network For The Classification Of Large Databases, Abdelhadi Lotfi, Abdelkader Benyettou
A Reduced Probabilistic Neural Network For The Classification Of Large Databases, Abdelhadi Lotfi, Abdelkader Benyettou
Turkish Journal of Electrical Engineering and Computer Sciences
The probabilistic neural network (PNN) is a special type of radial basis neural network used mainly for classification problems. Due to the size of the network after training, this type of network is usually used for problems with a small-sized training dataset. In this paper, a new training algorithm is presented for use with large training databases. Application to the handwritten digit database shows that the reduced PNN performs better than the standard PNN for all of the studied cases with a big gain in size and processing speed. This new type of neural network can be used easily for …
A Distributed Consensus Algorithm For Decision Making In Service-Oriented Internet Of Things, Shancang Li, George Oikonomou, Theo Tryfonas, Thomas M. Chen, Li Da Xu
A Distributed Consensus Algorithm For Decision Making In Service-Oriented Internet Of Things, Shancang Li, George Oikonomou, Theo Tryfonas, Thomas M. Chen, Li Da Xu
Information Technology & Decision Sciences Faculty Publications
In a service-oriented Internet of things (IoT) deployment, it is difficult to make consensus decisions for services at different IoT edge nodes where available information might be insufficient or overloaded. Existing statistical methods attempt to resolve the inconsistency, which requires adequate information to make decisions. Distributed consensus decision making (CDM) methods can provide an efficient and reliable means of synthesizing information by using a wider range of information than existing statistical methods. In this paper, we first discuss service composition for the IoT by minimizing the multi-parameter dependent matching value. Subsequently, a cluster-based distributed algorithm is proposed, whereby consensuses are …