Search

Power supply is the indispensable component for stable non-traditional machining based on electric power, such as electrical discharge machining (EDM), electrochemical machining (ECM), EDM-ECM combined machining, and EDM-ECM hybrid machining. The properties of power supply have significant effects on the machining performance, including efficiency, accuracy, and surface quality. This paper reviews the research progress and technical application in power supply of EDM, ECM, EDM-ECM combined machining, and EDM-ECM hybrid machining. The circuit principle, performance index, process effect of power supply are systematically analyzed. Moreover, the applications are summarized, and the future development are prospected.

Ni–Fe alloy coatings with Nb contents of 0, 4.2 at.%, 7.0 at.% and 9.0 at.% were electrodeposited on ferritic stainless steels for intermediate-temperature solid oxide fuel cell (SOFC) interconnect application. The oxidation behavior, scale-growth mechanisms and electrical performances of the Ni–Fe- and NiFe–Nb-coated steels were then investigated in air at 800 °C corresponding to the cathode environment of SOFC. It was found that the scales formed are mainly composed of an external NiFe2O4 layer and an inner Cr2O3-rich layer after oxidation, with some Nb-rich oxides also observed for the Nb-containing coatings. Doping 4.2 at.%Nb into the coating decreased the growth rate of Cr2O3 and suppressed the formation of voids at the scale/alloy interface, whereas excess Nb impaired the oxidation resistance of the steel. The Nb addition affects the oxide growth direction by suppressing the cation transport. All the coatings decreased the area-specific resistance (ASR) of the steel, with the NiFe–4.2 at.%Nb-coated sample exhibiting the lowest ASR value. The effects of Nb on the oxidation mechanism and electrical properties of the Ni–Fe-coated steels are discussed.

Fault diagnosis is essential for assuring the safety and dependability of rotating machinery systems. Several emerging techniques, especially artificial intelligence-based technologies, are used to overcome the difficulties in this field. In most engineering scenarios, machines perform in normal conditions, which implies that fault data may be hard to acquire and limited. Therefore, the data imbalance and the deficiency of labels are practical challenges in the fault diagnosis of machinery bearings. Among the mainstream methods, transfer learning-based fault diagnosis is highly effective, as it transfers the results of previous studies and integrates existing resources. The knowledge from the source domain is transferred via Domain Adversarial Training of Neural Networks (DANN) while the dataset of the target domain is partially labeled. A semi-supervised framework based on uncertainty-aware pseudo-label selection (UPS) is adopted in parallel to improve the model performance by utilizing abundant unlabeled data. Through experiments on two bearing datasets, the accuracy of bearing fault classification surpassed the independent approaches.

Numerical pattern synthesis (NPS) algorithm based on adaptive array theory is an effective spatial power pattern synthesis method for antenna arrays. In this letter, instead of using the artificial interferences weighted on the total power pattern in the conventional NPS algorithm, a generalized two-dimensional (2-D) NPS algorithm is proposed in which two weighting functions are assigned to control the copolarization and cross-polarization patterns, respectively. The weighting functions are iteratively updated until both the sidelobe and cross-polarization levels reach their prescribed ones. Two numerical examples for synthesizing the patterns of a linearly polarized planar array and a circularly polarized cylindrical array are provided to validate effectiveness and advantages of the proposed generalized 2-D NPS algorithm.

© 2002-2011 IEEE. A novel method called alternating convex optimization is presented to synthesize unequally spaced linear arrays with minimum element spacing constraint. In this method, the problem of synthesizing an unequally spaced array is formulated as a sequence of alternating convex optimization problems, and the excitation vector and auxiliary weighting vector are alternately chosen as the optimization variables. The minimum spacing constraint for considering the physical element antenna size can be easily imposed in this alternating optimization process. Two examples for synthesizing unequally spaced linear arrays with focused and shaped patterns are provided to validate the effectiveness and advantages of the proposed method.

Optimizing the Map process is important for the improvement of the MapReduce performance. Many efforts have been devoted into the problem to design more efficient scheduling strategies. However, there exists a kind of MapReduce applications, named imprecise applications, where the imprecise results based on part of map tasks can satisfy the requirements of imprecise applications and thus the job processes can be completed when enough map tasks are processed. According to the feature of imprecise applications, the authors propose an improved MapReduce model, named MapCheckReduce, which can terminate the map process when the requirements of an imprecise application is satisfied. Compared to MapReduce, a Check mechanism and a set of extended programming interfaces are added to MapCheckReduce. The Check mechanism receives and analyzes messages submitted by completed map tasks and then determines whether to terminate the map phase according to the analysis results. The programming interfaces are used by the programmers to define the termination conditions of the map process. A data-prefetching mechanism is designed and implemented in MapCheckReduce which can improve the performance of MapCheckReduce effectively. The MapCheckReduce prototype has been implemented and experiment results verify the feasibility and effectiveness of MapCheckReduce.

It is shown in this paper that the problem of reducing the number of elements for multiple- pattern arrays can be solved by a sequence of reweighted � 1 optimizations under multiple linear constraints. To do so, conjugate symmetric excitations are assumed so that the upper and lower bounds for each pattern can be formulated as linear inequality constraints. In addition, we introduce an auxiliary variable for each element to define the common upper bound of both the real and imaginary parts of multiple excitations for different patterns, so that only linear inequality constraints are required. The objective function minimizes the reweighted � 1-norm of these auxiliary variables for all elements. Thus, the proposed method can be efficiently implemented by the iterative linear programming. For multiple desired patterns, the proposed method can select the common elements with multiple set of optimized amplitudes and phases, consequently reducing the number of elements. The radiation characteristics for each pattern, such as the mainlobe shape, response ripple, sidelobe level and nulling region, can be accurately controlled. Several synthesis examples for linear array, rectangular/triangular-grid and randomly spaced planar arrays are presented to validate the effectiveness of the proposed method in the reduction of the number of elements.

Distributed storage systems, built on peer-to-peer networks, can provide large-scale data storage and high data reliability by redundancy. Data backup is the process to store data into a set of redundant storage nodes. Rapid completion of such a process is very critical to maintain system performance. In traditional data backup in distributed systems based on erasure codes, star-structured scheme is used, in which each redundant block is just sent to each target storage node from the source node directly, so the storage throughput and delay are limited by the bottleneck bandwidth, due to bandwidth heterogeneity. The recent “in-network” redundancy generation scheme uses locally repairable property of self-repairing codes to speed up data backup. However, such kind of code does not own maximum distance separable property, thus does not achieve optimal storage efficiency. We still lack a fast backup scheme in distributed systems based on general erasure coding. To this end, we proposed that instead of only focusing on bandwidths between the source node and target nodes, the bandwidths between target storage nodes should be fully taken into account. In our scheme, each redundant data block is divided into some parts according to different proportions and each part of the block is sent to the target storage node via other different storage nodes. The benefit is that spare bandwidths between target storage nodes are used to reduce backup time. We further show how this process can be modeled and derive a formula about the final backup time. We can achieve minimum backup time by solution for classical quadratic programming problem. We conduct both numerical analysis and experimental study. Our experiments shows, the delay reduces 59 %, compared with common star-structured scheme. Meanwhile, the throughput is increased significantly in backup process.

Cloud storage system provides reliable service to users by widely deploying redundancy schemes in its system - which brings high reliability to the data storage, but inversely introduces significant overhead to the system, consisting of storage cost and energy consumption. The core behind this issue is how to leverage the relationship between data redundancy and data reliability. To optimize both concurrently is apparently difficult. As such, to fix one as a constraint and then to reach another one becomes the consensus. We aim in the paper to pursue a storage allocation scheme that minimizes the data redundancy while achieving a given (high) data reliability. For this purpose, we have provided a novel model based on generating function. With this model, we have proposed a practical and efficient storage allocation scheme, which is proved to be able to minimize the data redundancy. We analytically demonstrate that the suggested solution brings several advantages, in particular the reduction of the search space and the acceleration to the computation. We also assess the improvement on the savings of data redundancy experimentally by adopting availability traces collected from real world - which encouragingly shows that the reduction of data redundancy by our solution can reach up to more than 30% as compared to the heuristic method recently proposed in the research community.

Quality of service (QoS) optimization for end-to-end (e2e) services always depends on performance analysis in cloud-based service delivery industry. However, performance analysis of e2e services becomes difficult as the scale and complexity of virtualized computing environments increase. In this paper, the authors present a novel hierarchical stochastic approach to evaluate the QoS of e2e virtualized cloud services using Quasi-Birth Death structures, where jobs arrive according to a stochastic process and request virtual machines (VMs), which are specified in terms of resources, i.e., VM-configuration. To reduce the complexity of performance evaluation, the overall virtualized cloud services are partitioned into three sub-hierarchies. The authors analyze each individual sub-hierarchy using stochastic queueing approach. Thus, the key performance metrics of e2e cloud service QoS, such as acceptance probability and e2e response delay incurred on user requests, are obtained.

In this paper, numerical pattern synthesis (NPS) algorithm integrated with seeded region growing (SRG) method is proposed to synthesize the array in the presence of mutual coupling. In order to obtain the mutual coupling between the array elements, active pattern technique is used to find active element patterns. A 4 × 8 microstrip antenna array is employed to validate the effectiveness of the proposed algorithm including mutual coupling.

© The Institution of Engineering and Technology. Antenna arrays with shaped patterns have drawn significant attention for their wide applications in radar, sonar and communication systems. The combination of genetic algorithm (GA) and fast Fourier transform (FFT) has been used to synthesise shaped pattern of antenna array's factor without considering coupling effects. In this work, the GA-FFT method is generalised by integrating with a new virtual active element pattern (AEP) expansion method which approximates each AEP as the radiation pattern by exciting several equally spaced virtual elements surrounding the real element position. The generalised GA-FFT can be applied for the shaped pattern synthesis of unequally spaced linear arrays including mutual coupling and platform effect. Several synthesis examples with different pattern shapes and different antenna structures are given to demonstrate the effectiveness, accuracy and robustness of the proposed method.

© 2002-2011 IEEE. A novel method incorporating the least-squares active element pattern expansion (LS-AEPE) into the iterative Fourier transform (IFT) procedure is presented to efficiently synthesize linear arrays including mutual coupling and platform effects. The proposed method has better pattern synthesis accuracy than the original IFT, and is much faster than the combination of LS-AEPE and the genetic algorithm. Numerical examples are given for synthesizing the patterns of two microstrip antenna arrays mounted on inhomogeneous platforms. Synthesis results show the effectiveness and advantages of the proposed method.

A shaped power pattern synthesis with reduction of dynamic range ratio (DRR) is presented in this paper. Controlling the DRR of the excitations for the shaped power pattern synthesis is very useful since it can reduce the output power loss and also simplifies the complexity of the feeding network design. The synthesis of shaped pattern is typically a nonconvex optimization problem. To solve this problem efficiently, we formulate the synthesis as Semi-definite relaxation (SDR) form which allows to relax some non-convex constraints so as to formulate the problem into a convex form. Then it can be efficiently solved using readily available toolbox. Finally, numerical example is presented to show the effectiveness and advantages of the proposed method.

To provide timely results for 'Big Data Analytics', it is crucial to satisfy deadline requirements for MapReduce jobs in production environments. In this paper, we propose a deadline-oriented task scheduling approach, named Dart, to meet the given deadline and maximize the input size if only part of the dataset can be processed before the time limit. Dart uses an iterative estimation method which is based on both historical data and job running status to precisely estimate the real-time job completion time. By comparing the estimated time with the deadline constraint, a YARN-based task scheduler dynamically decides whether continuing or terminating the map phase. We have validated our approach using workloads from OpenCloud and Facebook on a cluster of 60 virtual machines. The results show that Dart can not only effectively meet the deadline but also process near-maximal data volumes even when the deadline is set to be extremely small and limited resources are allocated.

Distributed storage systems can provide large-scale data storage and high data reliability by redundant schemes, such as replica and erasure codes. Redundant data may get lost due to frequent node failures in the system. The lost data is needed to be regenerated as soon as possible so as to maintain data availability and reliability. The direct way for reducing regeneration time is to reduce network traffic in regeneration. Compared with that way, tree-structured regeneration achieves shorter regeneration time by constructing better tree-structured topology to increase transmission bandwidth. However, some bandwidth of many other edges beyond the tree is not utilized to speed up transmission in tree-structured regeneration. In this paper, we consider to use multiple edge-disjoint trees to parallel regenerate the lost data, and analyze the total regeneration time. We deduce the formula about optimal regeneration time, and propose an approximate construction algorithm with polynomial time complexity for the optimal multiple regeneration trees. Our experiments shows, the regeneration time reduces 62i?ź% compared with common tree---structured scheme, and the file availability reaches almost 99i?ź%.

Parallel computing can improve the data-processing efficiency significantly. However, the traditional approaches, such as MPI and MapReduce, need to program in the special environment. In this paper, a new distributed computing framework named MEX is proposed. Users just provides the input files and the name of an executable program to MEX. Then MEX will automatically process these files on a cluster of machines with the executable program. The MEX platform has been designed and implemented based on MapReduce and some key problems are addressed. An improved map function are designed for the start-up of the executable program. To support the improved map function, a data-conversion mechanism is added into MEX which generates the command texts as the parameter of the map function. A process-feedback mechanism is proposed for the fault-tolerance of the executable program. The mechanism also supports the synchronous execution between the map task and the executable program, which can avoid too many processes to be started on the same worknode. Comprehensive experiments are performed to verify the effectiveness of the MEX framework. According to the results, more computing worknodes can result in less job runtime in MEX. When 100 virtual machines are used for an OCR job with 1000 images in 400 dpi, the runtime is reduced 88.6% compared to a single machine.

Erasure code is an efficient redundancy solution to avoid permanent data losses in distributed storage system. By using erasure code, the original data is encoded into blocks which are stored on different nodes. When one node fails, the lost data block needs to be recovered by transmitting a large number of data from helper nodes, which is called repair. Intersecting zigzag sets codes (IZS codes) and hierarchical code are two kinds of codes which reduce the bandwidth overhead in repair. IZS codes reduce the amount of data uploaded by each helper node, while hierarchical codes reduce the number of nodes participating in repair. In this paper, we propose a class of code called intersecting zigzag sets-hierarchical code (IZ2SHC code) to unify them together. IZ2SHC code decreases repair degree as hierarchical code and reduces repair overhead compared with the latter. Besides, it realizes exact regeneration of the failed data, and need not encode in helper node during the repair, which reduces disk read cost for helper node when transferring data in repair.

The scheduling approach in MapReduce may result in the "long tail" problem because of the unreasonable task assignment and high scheduling overhead because of an amount of task scheduling operations. To address these problems, a new task scheduling approach for MapReduce, named "Iterative Task Scheduling Algorithm", is proposed. The new approach tries to schedule the map tasks according to the solution of the equation for the optimal task assignment. Thus the "long tail" problem can be mitigated effectively and the task scheduling operations can be significantly reduced. To support our new scheduling approach, two approaches are proposed: The first one is adopted to estimate task execution times of nodes and the second one is adopted to produce the optimal task assignment based on the known task execution times of nodes. Comprehensive experiments have been performed with the real log data from the Ali Cloud and the results verify the effectiveness of the new task scheduling approach. The map runtime of the job is reduced 23% in our experiments.

The problem of selecting antennas for synthesizing multiple-pattern linear arrays is considered. It is shown that this problem can be solved by an iterative linear programming procedure. Only few iterations are required for this procedure to reach the convergence. The synthesis results show that the proposed method can select the optimum element positions and consequently reduces the number of elements, while the synthesized multiple patterns can meet the specified multiple pattern bounds.

Due to high storage efficiency, erasure codes are recently used to provide high data reliability in distributed storage systems. When multiple data loses in system, regeneration time for them demands to be as short as possible so as to keep data availbility and reliability. Common way is to repair them one by one, which prolongs the regeneration time. Tree-structured regeneration may reduce regeneration time when regenerating one single node failure by relaying the network traffic, and is also extended to regenerate multiple data losses. In this paper, based on regenerating codes which achieve minimal network traffic during the regeneration, we consider reducing regeneration time by using multiple max-min trees to parallel regenerate multiple data losses. And we proposed an algorithm: bandwidth-sharing max-min algorithm (BSM2RC) to construct multiple parallel max-min trees. It realizes efficient bandwidth utilization by maximizing the minimal bottleneck edge weight of multiple regeneration trees, thus improve regeneration efficiency. Our simulation experiment shows that multiple parallel max-min trees reduce total regeneration time for multiple data losses significantly, and thus enhance system reliability, compared with existing regeneration scheme.

A new least-square active element pattern expansion (LS-AEPE) method is presented. With the help of this method, the array's pattern including the element mutual coupling and platform effect can be efficiently calculated by the fast Fourier transform (FFT). These techniques are then combined with the genetic algorithm (GA) to optimise the excitation phases for a 64-element linear array mounted on a trapezoid metal plate.

Wireless sensor networks (WSN) is a critical technology for information gathering covering many areas, including health-care, transportation, air traffic control and environment monitoring. Despite wide use, the fast increasing data emanating from WSN is not fully utilized due to the limitation for structure of WSN itself. Along with the further development of WSN, the data form which is not be efficiently managed and applied to supply information services for users. As the emerging IT technology, cloud computing supplies powerful utilization ability for IT resources, which makes many traditional applications migrate to cloud computing. In this paper, we propose a framework integrating cloud computing paradigm and WSN, which fully uses data process ability and service model for cloud computing. In the framework, data form WSN are efficiently utilized and managed, depending on which, information services for WSN are well provided to users.

Wireless sensor networks (WSN) is a key technology extensively applied in many fields, such as transportation, health-care and environment monitoring. Despite rapid development, the exponentially increasing data emanating from WSN is not efficiently stored and used. Besides, the data from multiple different types and locations of WSN needs to be well analyzed, fused and supplied to various types of clients, such as PC, workstation and smart phone. The emerging cloud computing technology provides scalable data process and storage power and some types of connectable services, which can helpfully utilize sensor data from WSN. In this paper, we propose an integration framework of cloud computing with WSN, in which sensor data is transmitted from WSN to cloud, and processed and stored in cloud, then mined and analyzed so as to be supplied to various clients. By applying virtualization and cloud storage technology, and Infrastructure as a Service (IaaS) and Software as a Service (SaaS) of cloud service model, the framework can fully process and store mass sensor data from multiple types of WSN. Besides, it efficiently mines and analyzes sensor data, based on which the data applications are well supplied to various types of clients in form of services.

Recently, Cloud computing, as one of the hottest words in IT world, has drawn great attention. Many IT companies such as IBM, Google, Amazon, Microsoft, Yahoo and others vigorously develop cloud computing systems and related products to customers. However, there are still some difficulties for customers to adopt cloud computing, in which many security issues exist, because data for a customer is stored and processed in cloud, not in a local machine. This paper briefly introduces cloud computing and its key concepts. In particularly, we intend to discuss security requirements and security issues involving data, application and virtualization in cloud computing, as well as current solutions to these issues.

The risk of transportation of flammable and explosive goods by road transport cannot be ignored in the transport system. The author has established a model on route choice of flammable and explosive goods by road transport. Firstly, the author analyzed the interaction between transportation of flammable and explosive goods and sensitive targets of lines. Quantitative safety assessment method is used to calculate the extent of injury and scope of an accident during transport, which are used to assess injury severity and exposure of circuit sensitive targets (including the number of sensitive targets, type and distance and so on). Count and select sensitive targets in transport routes, make selection decisions on road transport routes of the inflammable and explosive goods provide reference for the road transport enterprises of the inflammable and explosive goods.

Although multi-touch technology is currently a research focus in the field of Human-Computer Interaction, its relative research, however, is still comparatively few in China. In this paper, several foreign multi-touch technologies based on senor and computer vision are introduced and the advantages and disadvantages of these technologies are analyzed briefly. It is important for studying the technology of detection and tracking touch-point in multi-touch. Furthermore the FTIR (Frustrated Total Internal Reflection) and DI (Diffused Illumination) which are based on computer vision multi-touch technology are highlighted. Finally, several crucial techniques in the field of multi-touch technology are also discussed.

Recently, urban traffic congestion is becoming one of the most serious problems all over the world, which badly affects city's sustainable development and people's daily life. The measures of traffic flow leading is used in abate traffic congestions. However, the present measures are hardly to cope with the routine traffic congestion because they are instituted basically by experience. In response to this problem, the congestion and flow control method of Data Communication Network were put forward to traffic control to resolve this problem. Utilize the existing transport information collection system for real-time information collection, and the pre-leading model core devices -Controller, to carry out the similar algorithm and achieve the similar function. Controller using a combination of centralized dynamic routing with distributed dynamic routing strategy. This model is considered to be able to realize urban traffic flow pre-leading, its method for resolving congestion is effective, and it can achieve the goal of intellectual traffic control.

On the basis of catastrophe theory predecessors studied, the paper uses catastrophe theory to talk over accidents causation models of dangerous goods transport, analyzes basic elements of accident and their interrelations, all factors are concluded to be within the dangerous goods transport accident system of "human-machine-environment". According to the exploration, the risk degree of dangerous goods transport system is divided into safety, critical, dangerous state, and establish model of safety decision-making and safety measures. Construct mathematical model of safety degree and safety state. Catastrophe theory can be used to reveal safety state of dangerous goods transport and identify key risk factors, to take measures to the key risk factors which can improve the safety situation. So as to enhance the system's safety status and avoid accidents of dangerous goods transport.

The least-square active element pattern expansion (LS-AEPE) has been introduced for linear array synthesis including mutual coupling and platform effect. A new method based on combination of LS-AEPE and iterative Fourier transform (IFT) is proposed to optimize the excitation phases for sidelobe reduction and null control. Simulation results illustrate the new method outperforms the GA-FFT method base on LS-AEPE in terms of computational efficiency.