The green design of electromechanical products is a pivotal link to manufacturing industry. The question on how to design green products must be answered by excellent designers using both advanced design methods and effective assessment techniques of design alternatives. Making an objective and precise assessment of green designs is of increasing importance to ensure sustainable development. This work proposes a framework based on the combination of analytical hierarchy process (AHP), gray correlation (GC), and technique for order performance by similarity to ideal solution (TOPSIS) to evaluate the performance of design alternatives. AHP is used to determine the weights of performance indices and a nonlinear programming model with constraints is proposed to obtain the integrated closeness index based on the similarity closeness index from GC and distance closeness index from TOPSIS. A case study, i.e., three kinds of refrigerators, is illustrated to verify the proposed method. By comparing with existing methods, i.e., AHP-TOPSIS and AHP-GC, the effectiveness of the proposed method has been confirmed. In addition, sensitivity analysis is also provided in order to assess the robustness of the proposed method. Also, the following implication can be obtained from our results: 1) chloro–fluoro–carbons (mg/ $\text{m}^{3}$ ) (C1), production noise (dB) (C6), and environmental cost (C16) have a large impact on refrigerator’s green design since these factors carry relatively larger weights. Results of the sensitivity analysis from different cases demonstrate that the best alternative may change when different weights are assigned to the evaluation criteria. This finding means the importance of establishing a qualified group of experts/designers in design evaluation and 2) the selection of design alternatives to produce a green product is critical to product development. The main contribution of this paper is the definition and development of an effective evaluation framework to guide managers to assess product design alternatives. The results show that it overcomes the one-sidedness of AHP-TOPSIS and AHP-GC, and makes the evaluation results more objective and realistic. It provides an accurate, effective, and systematic decision support tool for green performance evaluation of product design alternatives.

AHP, Gray Correlation, and TOPSIS Combined Approach to Green Performance Evaluation of Design Alternatives

The Internet of Things is the natural continuity of the Ambient Intelligence where smart and ambient environments are built mainly by integrating a large number of interconnected smart objects (sensors, actuators, Smartphone, appliances, etc.) with heterogeneous capabilities abstracted as software services. These services can be composed on the fly and provided, all the time and everywhere, to assist users in their daily activities. A key issue in user-centered services composition is to intelligently and effectively discover and select the most relevant services that best match the users’ requirements and closely meet the specified quality-of-service level. Monitoring seamlessly the provided services and enhancing their quality, is still a challenging issue due mainly to the dynamicity and uncertainty characterizing ambient environments. In this paper, we propose a new service-oriented, user-centered and event-aware Framework capable of performing services monitoring to handle automatically events that may occur in ambient environments. This monitoring is based on a dynamic services discovery and selection process to enhance self-adaptation to unpredicted changes, and ensure services continuity with best quality. The overall proposed Framework has been implemented and validated through a scenario dedicated to daily activity recognition in an Ambient-Assisted Living environment. In addition, the obtained performances from extensive tests show clearly the efficiency and feasibility of the proposed approach in the case of a large-scale environment.

Event-Aware Framework for Dynamic Services Discovery and Selection in the Context of Ambient Intelligence and Internet of Things

Internet of Things (IoT) applications introduce a set of stringent requirements (e.g., low latency, high bandwidth) to network and computing paradigm. 5G networks are faced with great challenges for supporting IoT services. The centralized cloud computing paradigm also becomes inefficient for those stringent requirements. Only extending spectrum resources cannot solve the problem effectively. Mobile edge computing offers an IT service environment at the Radio Access Network edge and presents great opportunities for the development of IoT applications. With the capability to reduce latency and offer an improved user experience, mobile edge computing becomes a key technology toward 5G. To achieve abundant sharing, complex IoT applications have been implemented as a set of lightweight micro-services that are distributed among containers over the mobile edge network. How to produce the optimal collocation of suitable micro-service for an application in mobile edge computing environment is an important issue that should be addressed. To address this issue, we propose a latency-aware micro-service mashup approach in this paper. Firstly, the problem is formulated into an integer nonlinear programming. Then, we prove the NP-hardness of the problem by reducing it into the delay constrained least cost problem. Finally, we propose an approximation latency-aware micro-service mashup approach to solve the problem. Experiment results show that the proposed approach achieves a substantial reduction in network resource consumption while still ensuring the latency constraint.

LMM: latency-aware micro-service mashup in mobile edge computing environment

The automatic web services composition plays an important role in the area of semantic web services. In fact, in a real life it is not usual to discover atomicweb services matching with the user’s query. So composing services fulfilling the query is needed. This paper discusses the web services composition model based on an oriented graph. It is based on Bcov algorithm (Benatallah.B et al., 2005a), initially defined for web services discovery and considered as the best cover problem, which it was mapped as the problem of computing the minimal transversals with minimum cost of a weighted hypergraph. We propose two algorithms handling the oriented graph reasoning for web service composition. At first glance, a basic composition version for Bcov algorithm is given. The algorithm composes services over the induced subgraphs of each transversal provided by Becov algorithm, and gives a covering ratio satisfying the query. The second algorithm used a composed services graph maintained by a web services provider. The composition in that case is based on Dijkstra algorithm, where for each two consecutive services in the best minimal transversal provided by Bcov algorithm, a shortest path is computed regarding the composed services graph.

A graph-based approach for web services composition

Formal verification approaches in the web service composition: A comprehensive analysis of the current challenges for future research

By arranging multiple existing web services into workflows to create value-added services, automatic web service composition has received much attention in service-oriented computing. A large number of methods have been proposed for it although most of them are merely based on the matching of input-output parameters of services. Besides these parameters, some other elements can affect the execution of services and their composition, such as the preconditions and service execution results. In particular, the execution effects of some services are often uncertain because of the complex and dynamically changing application environments in the real world, and this can cause the emergence of nondeterministic choices in the workflows of composite services. However, the previous methods for automatic service composition mainly rely on sequential structures, which make them difficult to take into account uncertain effects during service composition. In this paper, Graphplan is employed and extended to tackle this problem. In order to model services with uncertain effects, we first extend the original form of Graphplan. Then, we propose a novel approach that can introduce branch structures into composite solutions to cope with such uncertainty in the service composition process. Extensive experiments are performed to evaluate and analyze the proposed methodology.

Automatic Web Service Composition Based on Uncertainty Execution Effects

The invention discloses a data visualizing system for monitoring user and software behaviors during network transaction. The data visualizing system is deployed in a Windows or Linux server system and deployed on an Apache server; a middleware module comprises a real-time date invoking service module, a data cleaning module, a data processing module and a data authority module; a visual presentation platform, comprising platform software behavior monitoring, platform transaction data visualization and platform user behavior monitoring visualization, of a monitoring system platform is arranged above the middleware module; user-oriented monitoring visual display comprises indoor monitoring and external equipment monitoring; during outdoor monitoring, a server externally provides service, monitoring information can be checked in real time directly through hand-held equipment and other equipment with an Internet access function and a Web browser function. The data visualizing system presents the software and user behaviors and transaction data during the network transaction intuitionally and visually so as to help business people to understand the data better; on-line real-time checking and preliminary abnormality analysis can be performed on multiple terminals.

Data visualizing system for monitoring user and software behaviors during network transaction

Semi-supervised video object segmentation is a fundamental yet Challenging task in computer vision. Embedding matching based CFBI series networks have achieved promising results by foreground-background integration approach. Despite its superior performance, these works exhibit distinct shortcomings, especially the false predictions caused by little appearance instances in first frame, even they could easily be recognized by previous frame. Moreover, they suffer from object's occlusion and error drifts. In order to overcome the shortcomings , we propose Collaborative Attention Memory Network with an enhanced segmentation head. We introduce a object context scheme that explicitly enhances the object information, which aims at only gathering the pixels that belong to the same category as a given pixel as its context. Additionally, a segmentation head with Feature Pyramid Attention(FPA) module is adopted to perform spatial pyramid attention structure on high-level output. Furthermore, we propose an ensemble network to combine STM network with all these new refined CFBI network. Finally, we evaluated our approach on the 2021 Youtube-VOS challenge where we obtain 6th place with an overall score of 83.5\%.

YuWei Zheng

Papers

Automatic Web Service Composition Based on Uncertainty Execution Effects

Data visualizing system for monitoring user and software behaviors during network transaction

Collaborative Attention Memory Network for Video Object Segmentation