CEE-Gr:A Global Router with Performance Optimization Under Multi-Constraints

A global routing algorithm with performance optimization under multi constraints is proposed,which studies RLC coupling noise,timing performance,and routability simultaneously at global routing level.The algorithm is implemented and the global router is called CEE Gr.The CEE Gr is tested on MCNC benchmarks and the experimental results are promising.

Using multi-class queuing network to solve performance models of e-business sites

Due to e-business' s variety of customers with different navigational patterns and demands, multiclass queuing network is a natural performance model for it. The open multi-class queuing network(QN) models are based on the assumption that no service center is saturated as a result of the combined loads of all the classes. Several formulas are used to calculate performance measures, including throughput, residence time, queue length, response time and the average number of requests. The solution technique of closed multi-class QN models is an approximate mean value analysis algorithm (MVA) based on three key equations, because the exact algorithm needs huge time and space requirement. As mixed multi-class QN models, include some open and some closed classes, the open classes should be eliminated to create a closed multi-class QN so that the closed model algorithm can be applied. Some corresponding examples are given to show how to apply the algorithms mentioned in this article. These examples indicate that multi-class QN is a reasonably accurate model of e-business and can be solved efficiently.

Multi‐objective particle swarm optimisation of complex product change plan considering service performance

Design change is an inevitable part of the product development process.This study proposes an improved binary multi‐objective PSO algorithm guided by problem char-acteristics(P‐BMOPSO)to solve the optimisation problem of complex product change plan considering service performance.Firstly,a complex product multi‐layer network with service performance is established for the first time to reveal the impact of change effect propagation on the product service performance.Secondly,the concept of service performance impact(SPI)is defined by decoupling the impact of strongly associated nodes on the service performance in the process of change affect propagation.Then,a triple‐objective selection model of change nodes is established,which includes the three indicators:SPI degree,change cost,and change time.Furthermore,an integer multi‐objective particle swarm optimisation algorithm guided by problem characteristics is developed to solve the model above.Experimental results on the design change problem of a certain type of Skyworth TV verify the effectiveness of the established optimisation model and the proposed P‐BMOPSO algorithm.

Effect of replacing antibiotics using multi-enzyme preparations on production performance and antioxidant activity in piglets

The study was conducted to investigate the effects of replacing antibiotics using multi-enzyme preparations on growth performance,coefficient of total tract apparent digestibility,digestive enzyme activity,and antioxidant property in piglets.A total of 160 piglets（（21.35±0.22）kg）were randomly assigned to five dietary treatments：1）basal diet supplemented with antibiotics（AC）,2）antibiotic diet supplemented with 0.5 g kg^-1 multi-enzyme preparations（AC＋0.5EP）,3）antibiotic diet supplemented with 1.5 g kg^-1 multi-enzyme preparations（AC＋1.5EP）,4）basal diet supplemented with a half dosage of antibiotics and 1.5 g kg^-1 multi-enzyme preparations（AH＋1.5EP）,and 5）basal diet supplemented with 1.5 g kg^-1 multienzyme preparations（BC＋1.5EP）.The results showed that AC＋1.5EP significantly improved the feed efficiency,apparent digestibility of ether extract（EE）and crude ash（CA）,lipase activity in pancreas and duodenum content,maltase and lactase activity in jejunum and ileum mucosa,glutathione peroxidase（GSH-Px）concentration in serum and liver,and decreased malondialdehyde（MDA）concentration in serum and liver compared with piglets receiving AC（P〈0.05）.Piglets receiving BC＋1.5EP showed no significant difference in growth performance（P〉0.05）but had lower MDA concentration than piglets receiving AC（P〈0.05）.The apparent digestibility of EE and crude fiber（CF）,duodenal lipase activity,jejunum mucosa maltase,and ileum mucosa lactase activity of piglets receiving AH＋1.5EP or BC＋1.5EP were significantly improved compared with piglets receiving AC（P〈0.05）.These results indicated an additive growth promotion effect between antibiotics and multi-enzyme preparations on piglets,and the multi-enzyme preparations may be used as substitutes for antibiotics for improving piglet production performance and health status.

Seismic fragility analysis of highway bridges considering multi-dimensional performance limit state

Fragility analysis for highway bridges has become increasingly important in the risk assessment of highway transportation networks exposed to seismic hazards. This study introduces a methodology to calculate fragility that considers multi-dimensional performance limit state parameters and makes a first attempt to develop fragility curves for a multi-span continuous (MSC) concrete girder bridge considering two performance limit state parameters: column ductility and transverse deformation in the abutments. The main purpose of this paper is to show that the performance limit states, which are compared with the seismic response parameters in the calculation of fragility, should be properly modeled as randomly interdependent variables instead of deterministic quantities. The sensitivity of fragility curves is also investigated when the dependency between the limit states is different. The results indicate that the proposed method can be used to describe the vulnerable behavior of bridges which are sensitive to multiple response parameters and that the fragility information generated by this method will be more reliable and likely to be implemented into transportation network loss estimation.

MULTI-SCALE AND MULTI-PHASE NANOCOMPOSITE CERAMIC TOOLS AND CUTTING PERFORMANCE

An advanced ceramic cutting tool material Al2O3/TiC/TiN （LTN） is developed by incorporation and dispersion of micro-scale TiC particle and nano-scale TiN particle in alumina matrix. With the optimal dispersing and fabricating technology, this multi-scale and multi-phase nanocomposite ceramic tool material can get both higher flexural strength and fracture toughness than that of A1203/TiC （LZ） ceramic tool material without nano-scale TiN particle, especially the fracture toughness can reach to 7.8 MPa . m^0.5. The nano-scale TiN can lead to the grain fining effect and promote the sintering process to get a higher density. The coexisting transgranular and intergranular fracture mode induced by micro-scale TiC and nano-scale TiN, and the homogeneous and densified microstructure can result in a remarkable strengthening and toughening effect. The cutting performance and wear mechanisms of the advanced multi-scale and multi-phase nanocomposite ceramic cutting tool are researched.

Dynamical analysis and performance evaluation of a biped robot under multi-source random disturbances

During bipedal walking,it is critical to detect and adjust the robot postures by feedback control to maintain its normal state amidst multi-source random disturbances arising from some unavoidable uncertain factors.The radical basis function（RBF）neural network model of a five-link biped robot is established,and two certain disturbances and a randomly uncertain disturbance are then mixed with the optimal torques in the network model to study the performance of the biped robot by several evaluation indices and a specific Poincar′e map.In contrast with the simulations,the response varies as desired under optimal inputting while the output is fluctuating in the situation of disturbance driving.Simulation results from noise inputting also show that the dynamics of the robot is less sensitive to the disturbance of knee joint input of the swing leg than those of the other three joints,the response errors of the biped will be increasing with higher disturbance levels,and especially there are larger output fluctuations in the knee and hip joints of the swing leg.

Multi-hazard performance assessment of a transfer-plate high-rise building

Many urban areas are located in regions of moderate seismicity and are subjected to strong wind. Buildings in these regions are often designed without seismic provisions. As a result, in the event of an earthquake, the potential for damage and loss of lives may not be known. In this paper, the performance of a typical high-rise building with a thick transfer plate （TP）, which is one type of building structure commonly found in Hong Kong, is assessed against both earthquake and wind hazards. Seismic- and wind-resistant performance objectives are first reviewed based on relevant codes and design guidelines for high-rise buildings. After a brief introduction of wind-resistant design of the building, various methodologies, including equivalent static load analysis （ESLA）, response spectrum analysis （RSA）, pushover analysis （POA）, linear and nonlinear time-history analysis （LTHA and NTHA）, are employed to assess the seismic performance of the building when subjected to frequent earthquakes, design based earthquakes and maximum credible earthquakes. The effects of design wind and seismic action with a common 50-year return period are also compared. The results indicate that most performance objectives can be satisfied by the building, but there are some objectives, such as inter-story drift ratio, that cannot be achieved when subjected to the frequent earthquakes. It is concluded that in addition to wind, seismic action may need to be explicitly considered in the design of buildings in regions of moderate seismicity.

Evaluation of multi-cycle performance of chemical looping dry reforming using CO_2 as an oxidant with Fe–Ni bimetallic oxides

Chemical looping dry reforming（CLDR） is an innovative technology for CO2 utilization using the chemical looping principle.The CLDR process consists of three stages,i.e.CH4 reduction,CO2 reforming,and air oxidation.Spinel nickel ferrite（NiFe2O4） was prepared and its multi-cycle performance as an oxygen carrier for CLDR was experimentally investigated.X-ray diffraction（XRD） and Laser Raman spectroscopy showed that a pure spinel crystalline phase（NiFe2O4） was obtained by a parallel flow co-precipitating method.NiFe2O4was reduced into Fe-Ni alloy and wustite（FexO） during the CH4 reduction process.Subsequent oxidation of the reduced oxygen carrier was performed with CO2 as an oxidant to form an intermediate state：a mixture of spinel Ni（1-x）Fe（2＋x）O4,Fe（2＋y）O4 and metallic Ni.And CO was generated in parallel during this stage.Approximate 185 mL of CO was generated for 1 g spinel NiFe2O4 in a single cycle.The intermediate oxygen carrier was fully oxidized in the air oxidation stage to form a mixture of Ni（1＋x）Fe（2-x）O4 and Fe2O3.Although the original state of oxygen carrier（NiFe2O4） was not fully regenerated and agglomeration was observed,a good recyclability was shown in 10 successive redox cycles.

Machine Learning Assisted Design of Natural Rubber Composites with Multi⁃Performance Optimization

Multi⁃performance optimization of tread rubber composites is a key issue of great concern in automotive industry.Traditional experimental design approach via“trial and error”or intuition is ineffective due to mutual inhibition among multiple properties.A“Uniform design⁃Machine learning”strategy for performance prediction and multi⁃performance optimization of tread rubber composites was proposed.The wear resistance,rolling resistance,tensile strength and wet skid resistance were simultaneously optimized.A series of feasible optimization designs were screened via statistical analysis and machine learning analysis,and were experimentally prepared.The verification experiments demonstrate that the optimization design via machine learning analysis meets the optimization requirements of all target performance,especially for Akron abrasion and 60℃tanδ(about 21%and 9%lower than the design targets,respectively)due to the inhibition of mechanical degradation and good dispersion of fillers.

Hydrodynamic Performance Analysis of the Ducted Propeller Based on the Combination of Multi-Block Hybrid Mesh and Reynolds Stress Model

In order to analyze the hydrodynamic performance of the ducted propeller with high precision, this paper proposes a new method which combines Multi-Block Hybrid Mesh and Reynolds Stress Model (MBHM & RSM). The calculation errors of MBHM & RSM and standard two-equation model (standard k-ε model) on the ducted propeller JD7704 +Ka4-55 are compared. The maximum error of the total thrust coefficient KT, the duct thrust coefficient KTN, the torque coefficient KQ and the open-water efficiency η0 of MBHM & RSM are 2.98%, 4.01%, 1.46%, and 0.89%, respectively, which are lower than those of standard k-ε model. Indeed, the pressure distribution on the propeller surfaces, the pressure and the velocity vector distribution of the flow field are also analyzed, which are consistent with the theory. It is demonstrated that MBHM & RSM on the thruster dynamics analysis are feasible. This paper provides reference in the thruster designing of underwater robot.

Learning Performance of Linear and Exponential Activity Function with Multi-layered Neural Networks

This paper presents a study on the improvement of MLNNs(multi-layer neural networks)performance by an activity function for multi logic training patterns.Our model network has L hidden layers of two inputs and three,four to six output training using BP(backpropagation)neural network.We used logic functions of XOR(exclusive OR),OR,AND,NAND(not AND),NXOR(not exclusive OR)and NOR(not OR)as the multi logic teacher signals to evaluate the training performance of MLNNs by an activity function for information and data enlargement in signal processing(synaptic divergence state).We specifically used four activity functions from which we modified one and called it L&exp.function as it could give the highest training abilities compared to the original activity functions of Sigmoid,ReLU and Step during simulation and training in the network.And finally,we propose L&exp.function as being good for MLNNs and it may be applicable for signal processing of data and information enlargement because of its performance training characteristics with multiple training logic patterns hence can be adopted in machine deep learning.

Effect of Phase Change Materials on the Thermal Protective Performance of the Multi-layered Fabrics Examined by TPP Tester under Flash Fire

Cotton fabrics treated with phase change materials( PCMs)were used in multi-layered fabrics of the fire fighter protective clothing to study its effect on thermal protection. The thermal protective performance( TPP) of the multi-layered fabrics was measured by a TPP tester under flash fire. Results showed that the utilization of the PCM fabrics improved the thermal protective performance of the multi-layered fabrics. The fabric with a PCM add on of 41. 9% increased the thermal protection by 50. 6% and reduced the time to reach a second degree burn by 8. 4 s compared with the reference fabrics( without PCMs). The employment of the PCM fabrics also reduced the blackened areas on the inner layers. The PCM fabrics with higher PCM melting temperature could bring higher thermal protective performance.

Effects of A Multi-Enzyme Complex or Probiotic Supplementation on Performance and Nutrient Digestibility of Broiler Chicks

This experiment was conducted to determine whether the performance of broilers fed diets based on corn and soybean meal could be enhanced with enzymes or probiotics. A total of 120 male broilers, three days of age, were assigned to one of four treatments in a completely randomized design, and housed in groups of five with six cages per treatment. The control diet was based on corn and soybean meal while the three experimental diets consisted of the basal diet supplemented with 0.1% of enzyme I, enzyme II, or probiotic. Enzyme I provided α-galactosidase and fl-mannanase, while enzyme II provided protease, amylase, α-galactosidase, xylanase, and cellulase. The probiotic was composed of Bacillus coagulance, Bacillus lichenformis , Bacillus subtilis , and Clostridium butyricum. Over the 28 day experiment, the weight gain of birds fed the probiotic treatment was superior （P = 0.03 ） to the control, while gains for the enzyme treatments were intermediate to those of the control and probiotic. Feed intake and feed conversion did not differ among treatments （P 〉 0.05 ）. Ammonia production was significantly （ P 〈 0.01 ） higher in the control compared with either of the enzyme or probiotic treatments. Compared with the control, supplementation with enzyme H significantly reduced the digestibility of arginine, isoleucine, and lysine （P 〈 0.05 ）. In contrast, the digestibility of energy was higher （P 〈 0.01 ） for birds supplemented with enzyme II than the control. Digestibility coeffi- cients did not differ for any other parameter with the exception of energy which was significantly higher for birds fed the probiotic treatment than the control （P 〈 0.01 ）. In summary, the performance of broilers was significantly enhanced by the addition of a probiotic to the diet. However, under the conditions of this experi- ment, supplementation with a multi-enzyme complex containing either α-galactosidase and fl-mannanase or the combination of protease, amylase, galactosidase, xylanase, and cellulase failed to improve broiler performance.

Multi-user Motion JPEG2000 over wireless LAN: run-time performance-energy optimization with application-aware cross-layer scheduling

This paper introduces a video application-aware cross-layer framework for joint performance-energy optimization,considering the scenario of multiple users upstreaming real-time Motion JPEG2000 video streams to the access point of a WiFi wireless local area network and extends the PHY-MAC run-time cross-layer scheduling strategy that we introduced in (Mangharam et al., 2005; Pollin et al., 2005) to also consider congested network situations where video packets have to be dropped. We show that an optimal solution at PHY-MAC level can be highly suboptimal at application level, and then show that making the cross-layer framework application-aware through a prioritized dropping policy capitalizing on the inherent scalability of Motion JPEG2000 video streams leads to drastic average video quality improvements and inter-user quality variation reductions of as much as 10 dB PSNR, without affecting the overall energy consumption requirements.

A NOVEL CROSS-LAYER STRATEGY OF VIDEO TRANSMISSION TO SOLVE PERFORMANCE ANOMALY IN MULTI-RATE IEEE 802.11 NETWORKS

When wireless hosts use different rates to transmit data in IEEE 802.11 networks, it will take on the state of performance anomaly which will severely decrease the throughputs of all the higher rate hosts. Hence, it is bad for video service transmission. Considering that video is very sensitive to packet delivery delay but can tolerate some packet losses, we propose a novel cross-layer scheme which takes these two characteristics into consideration. Firstly, the maximum number of retransmissions for a video Medium Access Control (MAC) frame is computed in MAC layer according to video frame rate requirement of application layer and current access delay of MAC layer. Secondly, within the margin of the tolerant Packet Loss Rate (PLR) of application layer, several video MAC frames are allowed to drop so that we can adaptively select the transmission rate as high as possible for the rest of video MAC frames in terms of current channel quality and the maximum number of retransmissions. Experiment results show that the proposed method can reduce the delay and jitter of video service and improve the throughputs of fast hosts. Therefore, it increases the quality of reconstructed video to a certain extent and relieves the performance anomaly of network effectively.

Application of Interval Multi-attribute Decision-Making Method to Aeroengine Performance Ranking

In view of the uncertainty of the monitored performance parameters of aeroengines, the fluctuating scope of the monitored infurmation during a period is taken as interval numbers, and the interval multi-attribute decision-making method is employed to predict the performance of aeroengine, The synthetic weights of interval numbers are obtained by calculating deviation degree and possibility degree. As an example of application, 5 performance parameters monitored on 10 CF6 aeroengines of China Eastern Airlines Co., Ltd are adopted as decision attributes to verify the algorithm. The obtained synthetic ranking result shows the effectiveness and rationality of the proposed method in reflecting the performance stares of aeroengins.

Multi-powder dam concrete with high performance and low adiabatic temperature rise

Based on the fluidity, strength, heat of hydration and loop crack resistance experiment of multi-powder paste, the components and proportion of multi-powder were optimized and the concrete properties were studied. The multi-powder consists of limestone powder, slag, fly ash and moderate heat Portland cement (PMH cement). The results show that the compressive strength of the multi-powder paste and mortar is close to those of PMH cement, fly ash paste and mortar currently used in dam concrete, yet the flexural strength is relatively higher. The multi-powder paste is featured by larger fluidity, lower heat of hydration and delayed cracking time. In comparison, less unit water consumption and cement is used in multi-powder concrete, and under premise of equal mechanical performance, deformation, thermal performance and durability, the adiabatic temperature rise at 28 d is reduced by 2 ℃. In this way, the crack resistance is improved and it is feasible both technically and economically to produce HPC for dam concrete.

Identification of Issues in Predicting Multi-Robot Performance through Model-Based Simulations

Predicting the performance of intelligent multi-robot systems is advantageous because running physical experiments with teams of robots can be costly and time consuming. Controlling for every factor can be difficult in the presence of minor disparities (i.e. battery charge). Access to a variety of environmental configurations and hardware choices is prohibitive in many cases. With the eminent need for dependable robot controllers and algorithms, it is essential to understand when real robot performance can be accurately predicted. New prediction methods must account for the effects of digital and physical interaction between the robots that are more complex than just collision detection of 2D or physics-based 3D models. In this paper, we identify issues in predicting multi-robot performance and present examples of statistical and model-based simulation methods and their applicability to multi-robot systems. Even when sensor noise, latency and environmental configuration are modeled in some complexity, multi-robot systems interject interference and messaging latency, causing many prediction systems to fail to correlate to absolute or relative performance. We support this supposition by comparing results from 3D physics-based simulations to identical experiments with a physical robot team for a coverage task.

Performance Evaluation Approach for Multi-Tier Cloud Applications

Complex multi-tier applications deployed in cloud computing environments can experience rapid changes in their workloads. To ensure market readiness of such applications, adequate resources need to be provisioned so that the applications can meet the demands of specified workload levels and at the same time ensure that service level agreements are met. Multi-tier cloud applications can have complex deployment configurations with load balancers, web servers, application servers and database servers. Complex dependencies may exist between servers in various tiers. To support provisioning and capacity planning decisions, performance testing approaches with synthetic workloads are used. Accuracy of a performance testing approach is determined by how closely the generated synthetic workloads mimic the realistic workloads. Since multi-tier applications can have varied deployment configurations and characteristic workloads, there is a need for a generic performance testing methodology that allows accurately modeling the performance of applications. We propose a methodology for performance testing of complex multi-tier applications. The workloads of multi-tier cloud applications are captured in two different models-benchmark application and workload models. An architecture model captures the deployment configurations of multi-tier applications. We propose a rapid deployment prototyping methodology that can help in choosing the best and most cost effective deployments for multi-tier applications that meet the specified performance requirements. We also describe a system bottleneck detection approach based on experimental evaluation of multi-tier applications.