Abstract
Existing information systems are experiencing a bottleneck in dealing with complexity, dynamics, and uncertainties in their applications. After the technologies of integrated circuits, personal computers, and the Internet, Cloud computing is the latest information technology that is radically changing our daily life. With the fast development of cloud computing technology, various modern information network systems such as navigation system, fault Diagnosis system, indoor position system, resource management system, face detection system, and large-scale information system have achieved great advancement in recent years. The cloud and modern information systems should support data distribution, resource management, incomplete information processing, etc. This special issue brings together research articles covering advanced algorithms for cloud and modern information systems, including intelligent collision avoidance model for submarine in underwater navigation environment, fault diagnosis system for large diesel fuel engine, RFID-based three-dimensional (3D) indoor positioning system (IPS), QoS-aware mechanism in cloud, low-power mapping algorithm for 3D network on chip (NoC), new cascade classifier system for face detection, and improved genetic algorithm in the electroencephalographic (EEG)-based mental workload evaluation for miners. The papers in this special issue are arranged as follows.
Yan-hua Liang and Chengtao Chen in the first paper “Intelligent Collision Avoidance based on 2D Risk model” proposed a novel and efficient collision risk model for handling with the challenging and imperative issue. The concepts of detection domain and fuzzy logic are adopted for modelling the degree of collision risk. The detection domain is used as the submarine safety scope when navigating under water, while the fuzzy logic is used as the mathematical implement for the analysis and synthesis of relations between obstacles or other submarines that are met in the navigation environment. The collision risk is regarded as one effective evaluation function that reflects which exact action should be done when the threshold of the risk degree is met.
The second paper by Li et al. “Fault Diagnosis for Large Diesel Fuel Engine Based on Chaotic Fractal Method” investigates the chaotic fractal characteristics during the combustion process of the diesel engine fuel system to build an effective fault diagnosis method for large fuel engine based on chaotic fractal method. Considering the two kinds of typical failure modes, the two important characteristic parameters variation trend for chaotic system, namely, correlation dimensions, and the calculation methods of the maximum Lyapunov exponent are discussed. The results showed that the maximum Lyapunov exponent method is more proper to system fault detection.
In the third paper “A Novel RFID Three-Dimensional Indoor Positioning System based on Trilateral Positioning Algorithm,” Xu et al. design and implement the RFID IPS based on received signal strength indicator (RSSI), which obtains 3D coordinates orientation through multiple trilateral positioning algorithm on location tag. RFID is the key component of the internet of things (IoT) and many fusion application systems combining RFID and cloud computing are emerging. The authors' work in the IPS can deal with the problem that GPS system cannot provide indoor positioning services.
Ye et al. in the fourth paper “A Novel QoS-Aware Mechanism for Provisioning of Virtual Machine Resource in Cloud” focus on efficient low-level resource provisioning and QoS guaranteed. A novel adaptive QoS-aware virtual machine provisioning mechanism is proposed to ensure efficient utilization of the system low-level resources. The VM for similar type of requests has been recycled so that the VM creation time can be minimized and used to serve more user requests. In the proposed model, QoS is linked by low-level infrastructure resource and ensured by serving all the tasks within the requirements described in SLA.
The fifth paper “Low-Power Mapping Algorithm for 3D NoC based on DCQPSO” by Huang et al. discusses on the topic of 3D NoC. In order to improve the efficiency of power optimization with large-scale application characteristic graph, a low-power mapping algorithm for 3D NoC based on diversity-controlled quantum-behaved particle swarm optimization (PSO) is proposed. Simulation results show that the proposed algorithm is able to maintain a stable power optimization efficiency (4.08%–8.04%) and converges much faster for large-scale application characteristic graph.
In the sixth paper “Cascade Classifier for Face Detection,” Yang and Wang propose a cascade face detection method based on histograms of oriented gradients (HOG), using different kinds of features and classifier to exclude non-face step by step. The candidate feature set was constructed by HOG feature of different grain size; at different stages, the support vector machine (SVM) was used as the weak classifier with different parameters. Experimental results showed a better performance compared to the state-of-the-art on CMU/MIT datasets.
Finally, Li et al. in the final paper “Research on the Application of the Improved Genetic Algorithm in the EEG-based Mental Workload Evaluation for Miners” study on the EEG-based mental workload evaluation for miners. The PSO is introduced to improve the standard genetic algorithm (SCA), and the combined method integrating the genetic algorithm with PSO is used for achieving EEG-based measures of miners’ mental workload. The simulation results prove the effectiveness of the combined method.
We would like to thank all the authors for their valuable contributions and the reviewers for their time and constructive feedback during several rounds of review and revision. We would like to sincerely thank Prof. Choi-Hong Lai, the Editor-in-Chief of JA&CT journal, for the opportunity to edit this special issue and for his encouragement. The support by the Journal's Editorial office at Multi-Science Publishing Co. Ltd is highly appreciated.