New Knowledge Network Evaluation Method for Design Rationale Management

Current design rationale （DR） systems have not demonstrated the value of the approach in practice since little attention is put to the evaluation method of DR knowledge. To systematize knowledge management process for future computer-aided DR applications, a prerequisite is to provide the measure for the DR knowledge. In this paper, a new knowledge network evaluation method for DR management is presented. The method characterizes the DR knowledge value from four perspectives, namely, the design rationale structure scale, association knowledge and reasoning ability, degree of design justification support and degree of knowledge representation conciseness. The DR knowledge comprehensive value is also measured by the proposed method. To validate the proposed method, different style of DR knowledge network and the performance of the proposed measure are discussed. The evaluation method has been applied in two realistic design cases and compared with the structural measures. The research proposes the DR knowledge evaluation method which can provide object metric and selection basis for the DR knowledge reuse during the product design process. In addition, the method is proved to be more effective guidance and support for the application and management of DR knowledge.

A Performance Evaluation Platform for Mega Satellite Constellation Network

With the rapid development of satellite technology, mega satellite constellations have become a research hotspot. A large number of related techniques have been developed on orbit topology, network routing, energy balance and resource control. However, it is difficult to accurately compare the performance of similar studies due to differences in the means of validation. Especially for invulnerability studies in many military applications, a unified evaluation system is essential. This paper proposes a network evaluation system for mega satellite constellations. Evaluation parameters include orbit topology, communication network, energy balance and invulnerability. Different application algorithms and traffic models were used to validate the specific system. .

Image aesthetic quality evaluation using convolution neural network embedded learning

A way of embedded learning convolution neural network(ELCNN) based on the image content is proposed to evaluate the image aesthetic quality in this paper. Our approach can not only solve the problem of small-scale data but also score the image aesthetic quality. First, we chose Alexnet and VGG_S to compare for confirming which is more suitable for this image aesthetic quality evaluation task. Second, to further boost the image aesthetic quality classification performance, we employ the image content to train aesthetic quality classification models. But the training samples become smaller and only using once fine-tuning cannot make full use of the small-scale data set. Third, to solve the problem in second step, a way of using twice fine-tuning continually based on the aesthetic quality label and content label respective is proposed, the classification probability of the trained CNN models is used to evaluate the image aesthetic quality. The experiments are carried on the small-scale data set of Photo Quality. The experiment results show that the classification accuracy rates of our approach are higher than the existing image aesthetic quality evaluation approaches.

Network level pavement evaluation with 1 mm 3D survey system

The latest iteration of PaveVision3D Ultra can obtain true 1 mm resolution 3D data at full- lane coverage in all 3 directions at highway speed up to 60 mph. This paper introduces the PaveVision3D Ultra technology for rapid network level pavement survey on approximately 1280 center miles of Oklahoma interstate highways. With sophisticated automated distress analyzer （ADA） software interface, the collected 1 mm 3D data provide Oklahoma Department of Transportation （ODOT） with comprehensive solutions for automated eval- uation of pavement surface including longitudinal profile for roughness, transverse profile for rutting, predicted hydroplaning speed for safety analysis, and cracking and various surface defects for distresses. The pruned exact linear time （PELT） method, an optimal partitioning algorithm, is implemented to identify change points and dynamically deter- mine homogeneous segments so as to assist ODOT effectively using the available 1 mm 3D pavement surface condition data for decision-making. The application of 1 mm 3D laser imaging technology for network survey is unprecedented. This innovative technology allows highway agencies to access its options in using the 1 mm 3D system for its design and management purposes, particularly to meet the data needs for pavement management system （PMS）, pavement ME design and highway performance monitoring system （HPMS）.

Service-oriented network performance evaluation framework based on LA-FAHP

Since the different characteristics of various network services determine that their requirements for network are also disparate, the performance of one network varies according to the services running on it. However, most of previous network performance evaluation （NPE） researches conduct evaluations based on the network parameters, but without considering from the perspective of specific service running on the network. In view of this issue, a novel service-oriented NPE framework is proposed. First, the characteristics discrepancy among different types of services are investigated. Next, in order to conduct comprehensive evaluation of multiple services, an enhanced low-complexity adaptive （LA）-fuzzy analytical hierarchy process （FAHP） is introduced; meanwhile by applying the experts-construct-directly （ECD） algorithm proposed later, the consistency check required in previous studies can be omitted, thereby significantly reducing the computation complexity and assessment workload for experts. Then, in accordance with the features of each service, corrections are made to their respective membership functions, thus making the proposed LA-FAHP adaptive to various service evaluation scenarios. The subsequent comparison with other NPE methods well proves the effectiveness and high sensitivity of proposed framework, and the analysis verifies the low computation complexity of the proposed algorithms as well.

Research on Quality Markers of Moutan Cortex: Quality Evaluation and Quality Standards of Moutan Cortex

Objective To identify the quality markers of Moutan Cortex（MC） and establish the quality evaluation methods for multi-component assay and fingerprinting of MC. Methods The chemical constituents in MC were identified by HPLC-QTOF-MS. UPLC was employed for the multi-component assay and fingerprinting of MC. Furthermore, text mining was carried out to review the biosynthesis pathways and pharmacological and pharmacokinetic studies related to MC, and in silico target fishing was conducted to construct compound-target networks for MC. Results Sixteen compounds were clearly identified in MC and their structures were confirmed through comparison with literature data. In addition, the biosynthetic pathways and component specificities of the identified compounds were summarized and confirmed by text mining.Pharmacological activities, including traditional usage and modern pharmacological studies were summarized. A total of 282 targets from Homo sapiens were fished for 13 compounds. In addition, pharmacokinetic studies of different compounds were synopsized. Finally, multi-component assay and fingerprint of MC were established. Conclusion Eight major components are selected as quality markers of MC, such as oxypaeoniflorin, apiopaeonoside, albiflorin, paeonolide, paeoniflorin, 1,2,3,4,6-penta-O-galloyl-β-D-glucose, mudanpioside C and paeonol. These eight quality markers are successfully applied to the quality evaluation of MC, and could be useful in improving the current quality standards of MC.

A Hybrid Set of Complexity Metrics for Large-Scale Object-Oriented Software Systems

Large-scale object-oriented（OO） software systems have recently been found to share global network characteristics such as small world and scale free,which go beyond the scope of traditional software measurement and assessment methodologies.To measure the complexity at various levels of granularity,namely graph,class（and object） and source code,we propose a hierarchical set of metrics in terms of coupling and cohesion-the most important characteristics of software,and analyze a sample of 12 open-source OO software systems to empirically validate the set.Experimental results of the correlations between cross-level metrics indicate that the graph measures of our set complement traditional software metrics well from the viewpoint of network thinking,and provide more effective information about fault-prone classes in practice.