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Bimetallic Pt-Ni with Pt on the outermost layer and an innermost layer enriched in Ni, referred to as Pt3Ni(Pt-skin), is a promising configuration of an electrocatalyst for the oxygen reduction reaction (ORR) in fuel cells. We prepare a core (Pd)/shell (Pt3Ni(Pt-skin)) catalyst (Pt3Ni(Pt-skin)/Pd/C) from Zn underpotential deposition (UPD) on a Ni UPD modified Pd/C catalyst, facilitating Pt atomic layer-by-layer growth on the Ni surface through the galvanic replacement process. Pt3Ni(Pt-skin)/Pd/C shows the best ORR performance, with a Pt specific activity of 16.7 mA cm-2 and Pt mass activity of 14.2 A mgPt-1, which are 90- and 156- fold improvements over commercial Pt/C catalysts. The Pt3Ni(Pt-skin) structure effectively inhibits Ni leaching to improve the durability in two accelerated durability test modes mimicking the catalyst lifetime and start-up/shut-down cycles.

The objective of this paper is to describe that carve 2D image on the 3D curved surface based on surface flattening using hybrid 3D printing approach. Customized logo or trademark is supposed to be displayed on the surface of products, which cannot be completed only by 3D printing techniques. Projection algorithm, which transfers the 2D image to 3D sticker that matches the surface of products, is one of the techniques to satisfy this demand. In this paper, we propose a novel surface flattening algorithm that combines conformal mapping with optimal adjuster based on edge-lengths of the original mesh. By using the proposed algorithm, the surface of 3D model is unfolded. A two dimensional data or figure is mapped onto the unfolded plane. After the plane is mapped onto the 3D surface, a path for carving is obtained to sculpture the indicated figure onto the model. Moreover, compared with the several surface flattening methods, the proposed algorithm can minimize distortion while transferring 2D figure to 3D sticker. Furthermore, the algorithm is physically experimented to proof of the concept on the gantry-type hybrid machine developed in our NTU lab which integrates the additive (3D printing) and the subtractive (carving) process.

Additive manufacturing (AM) technologies not only reduce the new product development cycle, but also allow users to design models of their own style. Fused Filament Fabrication is a method that builds a model of rapid prototyping and product in customization fashion. With path generation and planning methods for additive manufacturing, common 3D printers print an object layer by layer. However, when multiple objects are printed at the same time, the nozzle moves among objects and always increases enormous distance of the transition travel. In this paper, a novel trajectory generation and planning algorithm are proposed for printing multi-object structures simultaneously. The algorithm has two parts. In the first part, different textures of different objects are created respectively in the same batch. This process solves the problem that objects must have same texture in conventional processes. For the other process, trajectory planning optimizes motion paths of the printing head to reduce the period of fabrication. We have demonstrated the success of the proposed methods using the development of hybrid 3D printing machine consists of additive and subtractive processes in our NTU robotics and automation lab.

In this study, a multi-parameter constrained optimization procedure integrating the design of experiments (DOE), full factorial experimental design (FFED), genetic algorithm (GA) and computational fluid dynamics (CFD) is proposed to design two-dimensional wavy channel with nanofluids (Cu/water, Al 2 O 3 /water and CuO/water). The elliptical, coupled, steady-state, two-dimensional governing partial differential equations for laminar forced convection of nanofluids are solved numerically using the finite volume approach. Some important parameters for the influences of heat transfer enhancement such as the Reynolds number (250 ≦ Re ≦ 1000), the particle volume concentration (0% ≦ ϕ ≦ 5%), the wavy channel amplitude (0.1 ≦ α ≦ 0.3) and the wavy numbers (3 ≦ β ≦ 12) on the enhancement of nanofluid heat transfer have been investigated. The numerical results with a single-phase model are first validated with the available data in the literature. The maximum discrepancy is within 8%. Results of a further extension to a two phase model are also validated. The numerical results indicate that the thermal enhancement can achieve 15%, 24% in the wavy channel flow compared with pure fluid, with the particle volume concentration of ϕ = 3% and ϕ = 5% of Cu/water nanofluids. In addition, after the validation of the numerical results, the numerical optimization of this problem is also presented by using a full factorial experimental design and the genetic algorithm (GA) method. The objective function E which is defined as thermal performance factor has developed a correlation function with three design parameters. The predicting performance factor E (α = 0.278, β = 3, ϕ = 5%) of regression function is closely agreed with those from the CFD computational results within 4.6% difference. The combination of parameters is considered as the optimal solution.

The rationale behind a green network is that it should effectively reduce energy consumption, while maintaining the level of services for data communications. In this paper, we propose an efficient approach, called the Compression Algorithm (CA), which is designed to solve the link on/off and weight assignment problems jointly so as to minimize a network's energy consumption. The problem is formulated as a mixed integer non-linear optimization problem. Because the problem is NP-hard, the CA utilizes a genetic algorithm to determine the link on/off schedule. In addition, it exploits the simulated annealing technique for link weight assignment so that the routing paths satisfy the link capacity constraints. By solving the link on/off and weight assignment problems sequentially, the CA scheme reduces the uncertainty about network energy consumption and yields a near optimal solution. To observe the relationship between network energy consumption and link load distributions, performance evaluations were conducted on three schemes, namely, the proposed CA, route construction without considering power savings, and route construction using minimum power saving without link capacity constraints. Numerical results demonstrate that the CA outperforms the other approaches on a network embedded with both uniform and non-uniform demand distributions.

In order to lessen the greenhouse effects and diminish environmental pollution, reducing energy usage is important in designing next generation networks. Shutting down the network devices that carry light load and redirecting their traffic flows to other routes is the most common way to reduce network energy consumption. Since traffic demands among node pairs vary in different time periods, an energy efficient network has to dynamically determine the optimal active links to adapt itself to network traffic changes. However, in current IP networks, shutting down and/or turning on links would trigger link state routing protocols to reconverge to a new topology. Since the convergence time would take tens of seconds, routing table inconsistencies among routers would result in network disconnection and even worse, generating traffic loops during the convergence interval. Removing routing images inconsistent among routers to prevent loops is a critical issue in energy efficient network and this issue is still not considered in the green network design yet. The contribution of the paper is presented in two parts. First, we propose a comprehensive approach to determine a network topology and a link metric for each time period. Traffic engineering is considered in our design such that flows going on the energy-aware network are within a predetermined percentage of the link capacity such that no congestion occurs in a statistical manner. Second, to avoid transient loops during time period changes, we propose a Distributed Loop-free Routing Update (DLRU) scheme to determine the correct sequence for updating the routing table. A scrupulous proof was also presented to ensure the loop-free property of the DLRU. In this paper, we formulate an integer linear programming to determine this multi-topology and link weight assignment problem. Due to its NP-hard property, we propose an efficient algorithm, termed Lagrangian Relaxation and Harmonic Series (LR&HS) heuristic. Numerical results demonstrate that the proposed LRHS approach outperforms the other approaches on several benchmark networks and random networks by providing up to 35%-50% additional energy saving in our experimental cases.

Protected Working Capacity Envelope (PWCE) has been proposed to simplify resource management and traffic control for survivable WDM networks. In a PWCE-based network, part of the link capacity is reserved for accommodating working routes, and the remaining capacity is reserved for backup routes. The shortest path routing is applied in PWCE-based networks. An arrival call is accepted only when each link along the shortest path has a free working channel. Such a working path routing scheme greatly simplifies the call admission control process for dynamic traffic, and it is especially suitable for implementation in a distributed manner among network nodes. In this article, we investigate two protection strategies: Bundle Protection (BP) and Individual Protection (IDP). In BP, only one backup path can be used to protect a failure component, whereas multiple backup paths can be used in IDP. We formulate four mixed integer non-linear programming (MINLP) problems using BP and IDP strategies for single link and single node failure protection. Each model is designed to determine link metrics for shortest working path routing, working and backup channel assignments, and backup path planning. Our objective is to minimize call-blocking probability on the bottleneck link. Since these models are highly non-linear and non-convex, it is difficult to obtain exact global optimal solutions. We propose a Simulated Annealing-based Heuristic (SAH) algorithm to obtain near optimal solutions. This SAH adopts the concepts of simulated annealing as well as the bi-section technique to minimize call-blocking probabilities. To evaluate the performance, we made simulation comparisons between SAH and the unity link weight assignment scheme. The results indicate that SAH can greatly reduce call-blocking probabilities on benchmark and the randomly generated networks.

This letter proposes a novel low complexity shortest path tree restoration scheme to address link cost perturbation problem for link state routing in IP Networks. The proposed scheme can reduce the number of unnecessary shortest path tree recomputations and also provides a low-complexity computation when the Shortest Path Tree (SPT) restoration process executes. The proposed scheme has been designed based on the Network Simplex Method (NSM) and Sensitivity Analysis (SA) techniques to perform the optimality test for the current shortest path tree. The computational overhead is compared with various dynamic schemes over six network benchmarks.

In cognitive radio (CR) networks, upon the request of a primary user (PU) to utilize the licensed spectrum, all the secondary users (SUs) using the same licensed spectrum must terminate the spectrum usage immediately and switch their data transmissions to unoccupied spectra to avoid interference with the PU. The spectrum switching may lead to severe throughput degradation in SU networks. To mitigate the impact of spectrum usage termination and switching, Minimum Interference Robust Topology Construction (MIRTC) is a critical problem. In this paper, we formulate the problem as an integer programming problem and propose a genetic-algorithm-based channel assignment (GACA) scheme to construct a robust CR topology while minimizing interference. Our proposed formulation maintains the connectivity of each source-destination pair under the interruption of any single channel. A Bisearch algorithm is further proposed to approach the optimal solution of the problem. Simulation results demonstrate that the proposed scheme can reduce network interference and enhance network throughput efficiently.

In this paper, two critical issues, i.e., energy efficiency and throughput, are jointly considered for non-infrastructure based cognitive radio networks. We formulated a Mixed Integer Non-linear Programming (MINP) Problem to determine the power control and channel assignment so as to minimize energy consumption and simultaneously maximize the network throughput. Due to the NP-hard property of the MINP, the MINP is decomposed into |N| problems so that each problem can be distributedly and locally solved at each node. Furthermore, a Lagrangian Relaxation based Heuristic (LRH) was proposed to approximate the solution of each problem. In simulations, we perform LRH on five randomly generated networks to observe the influence of transmission power on the network throughput.

In order to mitigate the greenhouse effects and reduce environmental pollution, energy saving has become an important issue in designing the next generation Internet. Shutting down the network devices carrying light loads and redirecting their traffic flows to other routes is a way to decrease energy consumption. An energy efficient network has to dynamically determine the optimal active links to adapt itself to network traffic changes. However, in current IP networks, shutting down and/or turning on links would trigger link state routing protocols to reconverge to a new topology. Since the convergence time would take tens of seconds, routing table inconsistencies among routers would result in network disconnection and even worse, generating traffic loops during the convergence interval. In this paper, we propose a comprehensive solution to resolve such problems. The contribution of the paper is presented in three parts. First, we propose a distributed energy-aware link management algorithm to dynamically determine the link states for a router. Routers performing the algorithm obtain a pair of high and low thresholds in order to compare flow amount to determine the link states. Those thresholds are automatically adapted to network traffic so that network load and power saving are balanced. The second contribution of the paper comes from the flows being immediately redirected to their new next hop nodes when a link's state is changed. We borrow link metric from link state routing protocols to determine potential for each node pair. The potential is used to determine loop free next hop routing nodes. This technique enables us to redirect flows on the fly without suffering from long convergence of link state routing protocols. The third contribution of the work comes from our proposal of an optimization model to determine a link metric and a most power saving network topology. The obtained link metric is used in our distributed link management algorithm to determine node potential. We have evaluated the performance of the proposed algorithm through extensive simulations. The numerical results reveal that the proposed distributed link management algorithm can effectively save network energy in three benchmark networks.

To alleviate the impact of network component failures, many fast IP local recovery schemes have been proposed to reroute traffic in the event of failure. Tunnel-based fast IP local recovery is one of the most commonly adopted techniques. In IP-in-IP tunneling, when a failure occurs, the nodes adjacent to a failure are activated to encapsulate and reroute the affected traffic to the endpoint along the shortest path. Once the endpoint receives the affected traffic, it decapsulâtes and delivers the packets to original destination along the shortest path. These shortest paths are computed based on link weights. Therefore the design of link weights for tunnel-based fast IP local recovery is a critical issue. The goal of this paper is to determine a set of link weights in a tunnel-based fast IP local recovery system to jointly achieve: (1) load balance in the normal state (i.e., non-failure state) and (2) protection of any single link failure without incurring link overload during the failure recovery. We first formulate this problem as a mixed integer programming (MIP). Due to the NP-hard property of the MIP, a Simulated Annealing based Tunneling (SATu) scheme is proposed to obtain the solution of the MIP. In SATu, only the nodes adjacent to a failure are activated to encapsulate the affected packets and forward them to endpoint without disturbing regular traffic. Numerical results show that the proposed scheme improves tunnel-based IP fast reroute approach to achieve high rate of fault recovery in protecting single link failures without incurring link congestion in the non-failure state.

As a failure occurs, the affected traffic is quickly rerouted to backup paths for a network performing a fast protection scheme. Such a prompt reaction is aimed to reduce the damages caused by a failure. However, in some cases, the rerouted traffic may cause congestion along the backup paths which would lead to more packet losses than purely discarded affected flows. In this paper, we propose a load-balanced fast local protection scheme called Non-Weighted Interface Specific Routing (NISR) for determining the working and backup routing tables of IP routers. We jointly consider protection switching time, network survivability, and traffic load distribution together in the proposed scheme. In NISR, once a failure occurs, only the nodes adjacent to a failure divert affected traffic to backup paths. This local reaction process guarantees fast protection switching and reduces failure recovery time. Unlike the conventional IP routing, our approach relaxes the shortest path routing in computing working and backup routing tables. Most importantly, each interface in a router has its own routing tables. Combining the interface specific routing with the shortest path relaxation provides greater routing flexibility to enhance network survivability and load balancing. We formulate this as a mixed integer programming problem in which the traffic load on the most congested link is to be minimized. Since this problem is intractable by its NP-hard nature, we further decompose it into several sub-problems which are solved optimally and their solutions combined to provide a solution to the original problem. We perform experiments on some benchmark networks and compare the proposed scheme to several well-known schemes (including LFA, ECMP, OSPF, and NLB) on survivability ratio, link load distribution, and average path length for both normal and failure states. Through numerical results, we delineate that the proposed scheme achieves a sub-optimal solution, which is better for its high survivability and load balancing at the expense of slightly raising the average path hop count.

In order to reduce the greenhouse effects and environmental pollution, energy saving has become important in designing next the generation networks. Shutting down the network devices carrying light loads and redirecting the traffic flows to other routes is a way to decrease network energy consumption. Since traffic demands among node pairs vary in different time periods, the energy efficient network design is to determine the optimal network topologies for them. However, in current IP network, flows are governed by shortest path routing, thus purely determining the network topology is not enough. A set of link weight metric has to be derived in company with the network topology such that the flows on the active links can meet the physical capacity constraints. Although applying adaptive network topology could save energy consumption, however, the lack of stringent synchronization among routers would cause undesired loops during the transient period of topology changes. Removing routing images inconsistent among routers to prevent loops is a critical issue in energy efficient network and this issue is still not yet considered in the green network design. In this paper, we propose a comprehensive approach that determines network topology and link metric for each time period. Traffic engineering is considered in our design such that flows going on the energy aware network are within a predetermined percentage of the link capacity such that no congestion occurs in a statistical manner. To avoid accurate synchronization among routers, we propose using RFC 4915 Multi-Topology Routing, to resolve the transient loop problem. By controlling the update sequence in MTR, there is no transient loop during the period of topology changes. We formulate an integer linear programming to jointly determine this multi-topology and link weight assignment problem. Due to its NP-hard property, we propose an efficient algorithm, termed Lagrangean Relaxation and Harmonic Series (LR&HS) heuristic. Numerical results demonstrate that the proposed LR&HS approach outperforms the other approaches on four benchmark networks and provides up to 35%–50% energy saving in our experimental cases.

Network coding has been proven with the capability to improve the network bandwidth efficiency for multicast communications. Streaming based IPTV services provided by network operators match the paradigm of using network coding. However, the major research works in the literature focus on the selection of network codes under given routing paths. Few research work jointly considers routing and network coding together to obtain survivable multicast routing and minimum bandwidth provision. In this paper, we investigate this new research topic and propose mathematical models that can be used to obtain an optimal routing for survivable multicast communications against any single link/node failure. To clarify the gain from applying network coding, we also made performance comparisons between the network-coding-based protection schemes and those without using network coding. We observe that the network coding scheme holds the best performance for all cases we had simulated. The trade-offs between the network protection schemes with and without using network coding are also identified in this paper.

Network failures often cause service interruptions as well as packet losses. To mitigate the impact of failures, many IP fast local recovery schemes have been proposed to reroute traffic in the event of failure in past literatures. However, the rerouted traffic may cause congestion or overload on pre-computed backup routes if the link capacity on those backup routes is not considered in recovery schemes. In this paper, we propose a Simulated Annealing based Load Balanced (SALB) fast IP local protection scheme to construct backup routing tables for protecting link failures. In the proposed SALB scheme, we jointly consider protection switching time, network survivability, and traffic load distribution. The proposed SALB scheme is capable of mitigating other kinds of failures, such as node failures or SRLG failures. In the proposed scheme, upon a failure, only the nodes adjacent to a failure are activated to divert affected traffic to backup paths. This local reaction process guarantees fast protection switching and reduces failure recovery time. Besides, we formulate this problem as a mixed integer programming problem, in which the working traffic load on the most congested link is to be minimized while protecting link failures. Numerical results delineate that the proposed scheme achieves high survivability and load balancing at the expense of slightly increasing the average path hop count and the entries of backup routing table.

In this paper, we propose a non-weighted load balanced fast local protection scheme for survivable IP networks. Unlike conventional shortest path based routing, we relax shortest path constraints in computing working and backup routes to achieve load balancing purposes. Once a failure occurs, those nodes adjacent to the failure point use their backup routing tables to achieve fast local failure recovery. Only the affected traffic is diverted to bypass a failure. The approach adopts failure set concept such that the proposed scheme can be used to handle various kinds of scenarios including single link, single node or even shared risk link group (SRLG) failure. We formulate this problem as a mixed integer linear programming (MILP) to minimize the traffic load on the most congested link. Since this problem is not tractable by its NP-hard property, we further decompose it into several sub-problems. Each sub-problem is solved optimally and the original problem is approximated by combining the solutions of those sub-problems. We perform experiments on benchmark networks and compare the proposed scheme to well-known schemes on survivability, path lengths and link load distribution for normal and rerouted states. Through numerical results and comparisons, we delineate that the proposed approach achieves a sub-optimal solution, which is profoundly to its high survivability and load balancing at the expense of slightly longer average path hop count.

Data collection is the most important stage in network forensics; but under the resource constrained situations, a good evidence collection mechanism is required to provide effective event collections in a high network traffic environment. In literatures, a few network forensic tools offer MSN-messenger behavior reconstruction. Moreover, they do not have classification strategies at the collection stage when the system becomes saturated. The emphasis of this paper is to address the shortcomings of the above situations and pose a solution to select a better classification in order to ensure the integrity of the evidences in the collection stage under high-traffic network environments. A system-awareness decision classifier (SADC) mechanism is proposed in this paper. MSN-shot sensor is able to adjust the amount of data to be collected according to the current system status and to keep evidence integrity as much as possible according to the file format and the current system status. Analytical results show that proposed SADC to implement selective collection (SC) consumes less cost than full collection (FC) under heavy traffic scenarios. With the deployment of the proposed SADC mechanism, we believe that MSN-shot is able to reconstruct the MSN-messenger behaviors perfectly in the context of upcoming next generation network.