Solution of free vibration equation of elastically supported Timoshenko columns with a tip mass by differential transform method

In this study free vibration analysis of a Timoshenko column with a tip mass having rotary inertia is carried out by both exact solution and differential transform method (DTM). The support of the system is modeled by an elastic spring against rotation. Fixity factor is used to define the relative stiffness of the elastic connection and the column. The governing equation of the column is solved by the separation of variables method including the rotatory inertia to get the exact solution. The same problem is also solved by DTM algorithm and the results are compared with the ones of exact solution. The comparison tables are presented in numerical analysis to show that a very good agreement is observed for DTM.     

Efficient selective replication of critical code regions for SDC mitigation leveraging redundant multithreading

The Journal of Supercomputing - Redundant multithreading (RMT) is an effective reliability solution that provides thread-level replication; however, it imposes additional overheads in terms of...     

Dynamic Fault-Tolerant three-dimensional cellular genetic algorithms

This paper proposes a new dynamic and algorithm-based approach to achieve fault tolerance using 3D cellular genetic algorithms (Dynamic Fault-Tolerant 3D-cGA). The proposed algorithm is an improved version of our previous algorithm (Fault-Tolerant 3D-cGA) that introduces and utilizes a dynamic adaptation feature to achieve further improvement. In Dynamic Fault-Tolerant 3D-cGA, faulty individuals are isolated and the maximum number of fitness evaluations is recalculated to adapt to faults encountered. To improve the performance of the algorithm, a mitigation technique is integrated into our algorithm by introducing an explicit migration operator. A benchmark of well-known real-world and test problems is used to test the effectiveness of the algorithm in order to investigate the influence of adaptation schemes and migration on algorithm performance. Faulty critical system data is tackled in conjunction with various fault ratios. To illustrate the improvement achieved, Dynamic Fault-Tolerant 3D-cGA is compared with Fault-Tolerant 3D-cGA, the previously proposed algorithm. The overall results demonstrate the ability of Dynamic Fault-Tolerant 3D-cGA to maintain system’s functionality despite an increasing number of faults with up to 40% of processing elements (PEs), and clearly illustrate the importance of migration. Significant improvements in the performance of the algorithm, measured as efficiency, efficacy, and speed, are achieved, especially when migration is employed.     

Lung nodules detection using semantic segmentation and classification with optimal features

Neural Computing and Applications - Lung cancer is a deadly disease if not diagnosed in its early stages. However, early detection of lung cancer is a challenging task due to the shape and size of...     

A MIP based heuristic for capacitated MRP systems

Although Material Requirements Planning (MRP) is the most widely used production planning tool in today’s manufacturing companies, its inability to perform an exhaustive capacity planning, lack of a comprehensive and integrated shop floor extension and using constant and inflated lead times necessitate intelligent methods for developing cost effective production plans. A single optimization model might be employed to overcome these limitations, but it would be intractable to use it in large manufacturing systems. Hence, in this paper, we propose a heuristic method called Capacity Allocater and Scheduler, CAS, to eliminate drawbacks of MRP systems and provide solutions for large-scale instances. The CAS procedure, based on iteratively solving relaxed Mixed Integer Programming (MIP) models, is built on a lot sizing and scheduling framework, which considers both supply alternatives and lot size restrictions simultaneously. Finally, we give a detailed numerical example to demonstrate how CAS may be used in practice, and provide our concluding remarks.     

Medical application of information gain based artificial immune recognition system (AIRS): Diagnosis of thyroid disease

In this paper, we have made medical application of a new artificial immune system named the information gain based artificial immune recognition system (IG-AIRS) which minimizes the negative effects of taking into account all attributes in calculating Euclidean distance in shape–space representation which is used in many artificial immune systems. For medical data, thyroid disease data set was applied in the performance analysis of our proposed system. Our proposed system reached 95.90% classification accuracy with 10-fold CV method. This result ensured that IG-AIRS would be helpful in diagnosing thyroid function based on laboratory tests, and would open the way to various ill diagnoses support by using the recent clinical examination data, and we are actually in progress.