Among redundant arrays of independent disks (RAID)-6 codes, maximum distance separable (MDS) based RAID-6 codes are popular because they have the optimal storage efficiency. Although vertical MDS codes exhibit better load balancing compared to horizontal MDS codes in partial stripes, an I/O unbalancing problem still exists in some vertical codes. To address this issue, we propose a novel efficient data layout, uniform P-code (UPC), to support highly balanced I/Os among P-coded disk arrays (i.e., PC). In UPC, the nonuniformly distributed information symbols in each parity chain of P-code are moved along their columns to other rows, thus enabling the parity chain to keep original parity relationships and tolerate double disk failures. The UPC scheme not only achieves optimal storage efficiency, computational complexity, and update complexity, but also supports better I/O balancing in the context of large-scale storage systems. We also conduct a performance study on reconstruction algorithms using an analytical model. Besides extensive theoretical analysis, comparative performance experiments are conducted by replaying real-world workloads under various configurations. Experimental results illustrate that our UPC scheme significantly outperforms the PC scheme in terms of average user response time. In particular, in the case of a 12-disk array, the UPC scheme can improve the access performance of the RAID-6 storage system by 29.9% compared to the PC scheme.

This paper presents a novel formulation for detecting objects with articulated rigid bodies from high-resolution monitoring images, particularly engineering vehicles. There are many pixels in high-resolution monitoring images, and most of them represent the background. Our method first detects object patches from monitoring images using a coarse detection process. In this phase, we build a descriptor based on histograms of oriented gradient, which contain color frequency information. Then we use a linear support vector machine to rapidly detect many image patches that may contain object parts, with a low false negative rate and a high false positive rate. In the second phase, we apply a refinement classification to determine the patches that actually contain objects. In this stage, we increase the size of the image patches so that they include the complete object using models of the object parts. Then an accelerated and improved salient mask is used to improve the performance of the dense scale-invariant feature transform descriptor. The detection process returns the absolute position of positive objects in the original images. We have applied our methods to three datasets to demonstrate their effectiveness.

Emotion-based features are critical for achieving high performance in a speech emotion recognition (SER) system. In general, it is difficult to develop these features due to the ambiguity of the ground-truth. In this paper, we apply several unsupervised feature learning algorithms (including K-means clustering, the sparse auto-encoder, and sparse restricted Boltzmann machines), which have promise for learning task-related features by using unlabeled data, to speech emotion recognition. We then evaluate the performance of the proposed approach and present a detailed analysis of the effect of two important factors in the model setup, the content window size and the number of hidden layer nodes. Experimental results show that larger content windows and more hidden nodes contribute to higher performance. We also show that the two-layer network cannot explicitly improve performance compared to a single-layer network.

To promote the development of the intangible cultural heritage of the world, shadow play, many studies have focused on shadow puppet modeling and interaction. Most of the shadow puppet figures are still imaginary, spread by ancients, or carved and painted by shadow puppet artists, without consideration of real dimensions or the appearance of human bodies. This study proposes an algorithm to transform 3D human models to 2D puppet figures for shadow puppets, including automatic location of feature points, automatic segmentation of 3D models, automatic extraction of 2D contours, automatic clothes matching, and animation. Experiment proves that more realistic and attractive figures and animations of the shadow puppet can be generated in real time with this algorithm.

We propose a new constructive algorithm, called HAPE3D, which is a heuristic algorithm based on the principle of minimum total potential energy for the 3D irregular packing problem, involving packing a set of irregularly shaped polyhedrons into a box-shaped container with fixed width and length but unconstrained height. The objective is to allocate all the polyhedrons in the container, and thus minimize the waste or maximize profit. HAPE3D can deal with arbitrarily shaped polyhedrons, which can be rotated around each coordinate axis at different angles. The most outstanding merit is that HAPE3D does not need to calculate no-fit polyhedron (NFP), which is a huge obstacle for the 3D packing problem. HAPE3D can also be hybridized with a meta-heuristic algorithm such as simulated annealing. Two groups of computational experiments demonstrate the good performance of HAPE3D and prove that it can be hybridized quite well with a meta-heuristic algorithm to further improve the packing quality.

The continuous emerging of peer-to-peer (P2P) applications enriches resource sharing by networks, but it also brings about many challenges to network management. Therefore, P2P applications monitoring, in particular, P2P traffic classification, is becoming increasingly important. In this paper, we propose a novel approach for accurate P2P traffic classification at a fine-grained level. Our approach relies only on counting some special flows that are appearing frequently and steadily in the traffic generated by specific P2P applications. In contrast to existing methods, the main contribution of our approach can be summarized as the following two aspects. Firstly, it can achieve a high classification accuracy by exploiting only several generic properties of flows rather than complicated features and sophisticated techniques. Secondly, it can work well even if the classification target is running with other high bandwidth-consuming applications, outperforming most existing host-based approaches, which are incapable of dealing with this situation. We evaluated the performance of our approach on a real-world trace. Experimental results show that P2P applications can be classified with a true positive rate higher than 97.22% and a false positive rate lower than 2.78%.

Short-term hydrothermal scheduling (STHTS) is a non-linear and complex optimization problem with a set of operational hydraulic and thermal constraints. Earlier, this problem has been addressed by several classical techniques; however, due to limitations such as non-linearity and non-convexity in cost curves, artificial intelligence tools based techniques are being used to solve the STHTS problem. In this paper an improved chaotic hybrid differential evolution (ICHDE) algorithm is proposed to find an optimal solution to this problem taking into account practical constraints. A self-adjusted parameter setting is obtained in differential evolution (DE) with the application of chaos theory, and a chaotic hybridized local search mechanism is embedded in DE to effectively prevent it from premature convergence. Furthermore, heuristic constraint handling techniques without any penalty factor setting are adopted to handle the complex hydraulic and thermal constraints. The superiority and effectiveness of the developed methodology are evaluated by its application in two illustrated hydrothermal test systems taken from the literature. The transmission line losses, prohibited discharge zones of hydel plants, and ramp rate limits of thermal plants are also taken into account. The simulation results reveal that the proposed technique is competent to produce an encouraging solution as compared with other recently established evolutionary approaches.

An islanding operation of a distribution network is a topic of interest due to the significant penetration of distributed generation (DG) in a power system network. However, controlling the frequency of an islanded distribution system remains an unresolved issue, especially when the load exceeds the generation. This paper presents a new technique for a successful islanding operation of a distribution network connected with multiple mini hydro based DGs. The proposed technique is based on three main parts. The first part uses an islanding detection technique to detect the islanding event correctly. The second part consists of a power imbalance estimation module (PIEM), which determines the power imbalance between the generation and load demand. The third part consists of a load shedding controller, which receives the power imbalance value and performs load shedding according to load priority. The proposed technique is validated on an 11 kV existing Malaysia distribution network. The simulation results show that the proposed technique is effective in performing a successful islanding operation by shedding a significant number of loads.

In the original article, Fig. 6 was incorrect. The correct version is given.