A cost-driven process planning method for hybrid additive–subtractive remanufacturing

Hybrid manufacturing combines additive manufacturing’s advantages of building complex geometries and subtractive manufacturing’s benefits of dimensional precision and surface quality. This technology shows great potential to support repairing and remanufacturing processes. Hybrid manufacturing is used to repair end-of-life parts or remanufacture them to new features and functionalities. However, process planning for hybrid remanufacturing is still a challenging research topic. This is because current methods require extensive human intervention for feature recognition and knowledge interpretation, and the quality of the derived process plans are hard to quantify. To fill this gap, a cost-driven process planning method for hybrid additive–subtractive remanufacturing is proposed in this paper. An automated additive–subtractive feature extraction method is developed and the process planning task is formulated into a cost-minimization optimization problem to guarantee a high-quality solution. Specifically, an implicit level-set function-based feature extraction method is proposed. Precedence constraints and cost models are also formulated to construct the hybrid process planning task as a mixed-integer programming model. Numerical examples demonstrate the efficacy of the proposed method.     

A strategic knowledge-based planning system for freight forwarding industry

In recent years, over half of the Hong Kong freight forwarding firms experienced a decline in business volume due to the growing challenge from the neighbour ports of Yantian and Shekou in Shenzhen, China, which operated in a much cheaper mode. In order to remain competitive, local freight forwarders in Hong Kong must establish a long-term union relationship with their customers such as the provision of customized logistics services. One of the ways is through the use of a co-loading shipment plan, which is a knowledge intensive and complex process involving multiple knowledge source and decision rules. This paper presents hybrid knowledge and model system, which integrates mathematical models with knowledge rules, in the formulation of such co-loading shipment plans. A strategic knowledge-based planning system, (SKPS) integrates knowledge rules with mathematical model for solving problems of co-loading shipment plans formulation and market constriction prevention, is proposed. The system was implemented in Elite World Logistics Service Limited (EW), a local freight forwarding company, for supporting the planning process of a co-loading shipping plan. The result reveals that both customer retention rate and resource utilization has increased significantly.     

基于神经索引实例与知识推理的混合型智能CAPP策略

提出了一种新的基于耦合神经网络实例与知识的混合推理策略,采用面向对象的方法表达实例和知识,将神经索引模型引入实例推理中,在此基础上实现了ＣＡＰＰ的变异设计。同时,建立了基于零件及其工艺数据知识的分层知识表达与层次式推理机制,从而实现了ＣＡＰＰ的创成式设计。在给出这种知识化的ＣＡＰＰ系统的总体结构之后,详细讨论了基于神经网络的实例索引模型及实例推理和知识基推理的实现过程,由于吸收了派生法的类比设计思     

Parallel Planning: A New Motion Planning Framework for Autonomous Driving

Motion planning is one of the most significant technologies for autonomous driving. To make motion planning models able to learn from the environment and to deal with emergency situations, a new motion planning framework called as "parallel planning" is proposed in this paper. In order to generate sufficient and various training samples, artificial traffic scenes are firstly constructed based on the knowledge from the reality. A deep planning model which combines a convolutional neural network (CNN) with the Long Short-Term Memory module (LSTM) is developed to make planning decisions in an end-toend mode. This model can learn from both real and artificial traffic scenes and imitate the driving style of human drivers. Moreover, a parallel deep reinforcement learning approach is also presented to improve the robustness of planning model and reduce the error rate. To handle emergency situations, a hybrid generative model including a variational auto-encoder (VAE) and a generative adversarial network (GAN) is utilized to learn from virtual emergencies generated in artificial traffic scenes. While an autonomous vehicle is moving, the hybrid generative model generates multiple video clips in parallel, which correspond to different potential emergency scenarios. Simultaneously, the deep planning model makes planning decisions for both virtual and current real scenes. The final planning decision is determined by analysis of real observations. Leveraging the parallel planning approach, the planner is able to make rational decisions without heavy calculation burden when an emergency occurs.      

A privacy-preserving model for multi-agent propositional planning

Abstract

Over the last years, the planning community has formalised several models and approaches to multi-agent (MA) propositional planning. One of the main motivations in MA planning is that some or all agents have private knowledge that cannot be communicated to other agents during the planning process and the plan execution. We argue that the existing models of the multi-agent planning task do not maintain the agents’ privacy when a (strict) subset of the involved agents share confidential knowledge, or when the identity/existence of at least one agent is confidential. In this paper, first we propose a model of the MA-planning tasks that preserves the privacy of the involved agents when this happens. Then we investigate an algorithm based on best first search for our model that uses some new heuristics providing a trade-off between accuracy and agents’ privacy. Finally, an experimental study compares the effectiveness of using the proposed heuristics.     

Data and knowledge modeling for design–process planning integration of sheet metal components

Realizing design–process planning integration is vital to the competitiveness of manufacturing organization and its ability to respond rapidly to market changes. Many attempts have been made in the past proposing the integration of the two activities based on product data models. However, both design and process planning activities are knowledge intensive. An effective integration is possible only if both data and knowledge models form a basis for integration. This paper presents key issues related to data and knowledge modeling for integration of design (CAD) and process planning (CAPP) activities for sheet metal components. Previous attempts to model data and knowledge have concentrated only on either design or process planning and not from an integration point of view. Moreover, in these attempts data and knowledge models have been proposed without attempting to relate the two. The same has been overcome in the present work. An integration framework based on data and knowledge is proposed at the end and discussed for domain of design–process planning integration of sheet metal components.     

一种利用工作流模型的分层任务网络规划领域建模方法

为了有效地获取和利用领域知识,提高规划效率,分析了工作流模型和分层任务网络(HTN)规划领域模型的相似性,提出了一种采用工作流模型进行规划领域建模,对领域知识进行获取和表达的方法.工作流模型中的行动和工作流模式,转换为HTN规划中的行动和任务分解;另外,引入了循环(Loop)工作流模式,转换为HTN规划中的递归调用,扩展了工作流模式对规划领域知识的表达能力.在典型的几个规划领域中,引入领域知识后大大提高了规划器的求解效率,从而验证了应用工作流模型进行规划领域建模的有效性.     

Novel approach to group multi-criteria decision making based on interval rough numbers: Hybrid DEMATEL-ANP-MAIRCA model

This paper presents a novel approach for treating uncertainty in the multi-criteria decision making process by introducing interval rough numbers (IRN). The IRN approach enables decision making using only the internal knowledge incorporated in the data provided by the decision maker. A hybrid multi-criteria model was developed based on IRN, and demonstrated using the example of the bidder selection process in the state administration public procurement procedure. The first segment of the hybrid model deals with the rough interval DEMATEL-ANP (IR'DANP) model, which enables more objective expert evaluation of criteria in a subjective environment than the traditional/crisp approach. In the second segment, the evaluation is enabled by applying the new rough interval MAIRCA method, which introduces mathematical tools and shows high stability concerning changes in the nature and characteristics of the criteria. The results of the hybrid IR'DANP-MAIRCA model were analyzed using 36 scenarios of sensitivity analysis, which showed high stability of the results. The results of the interval rough method were compared with the fuzzy extensions of the TOPSIS, VIKOR, MABAC, TODIM, ELECTRE I and DEMATEL-ANP models.     

Generic form feature recognition and operation selection using connectionist modelling

For the purpose of enhancing the adaptability of computer-aided process planning systems, two connectionist modelling methods, namely neocognitron (i.e. neural network modelling for pattern recognition) and fuzzy associative memories (FAM), are applied to the phases of feature recognition and operation selection respectively in order to provide the system with the ability of self-learning and the ability to integrate traditional expert planning systems with connectionism-based models. In this paper, the attributed adjacency graph (AAG) extracted from a (B-Rep) solid model is converted to attributed adjacency matrices (AAM) that can be used as input data for the neocognitron to train and recognize feature patterns. With this technique, the system can not only self-reconstruct its recognition abilities for new features by learning without a priori knowledge but can also recognize and decompose intersection features. A fuzzy connectionist model, which is created using the Hebbian fuzzy learning algorithm, is employed subsequently to map the features to the appropriate operations. As the algorithm is capable of learning from rules, it is much easier to integrate the proposed model with conventional expert CAPP systems so that they become more generic in dealing with uncertain information processing and perform knowledge updating. mg]Keywords mw]Computer-aided process planning mw]feature recognition mw]neural networks mw]fuzzy neural networks mw]operation selection mw]connectionist model mw]fuzzy associative memories     

Process structure-based recurrent neural network modeling for model predictive control of nonlinear processes

In this work, physics-based recurrent neural network (RNN) modeling approaches are proposed for a general class of nonlinear dynamic process systems to improve prediction accuracy by incorporating a priori process knowledge. Specifically, a hybrid modeling method is first introduced to integrate first-principles models and RNN models. Subsequently, a partially-connected RNN modeling method that designs the RNN structure based on a priori structural process knowledge, and a weight-constrained RNN modeling method that employs weight constraints in the optimization problem of the RNN training process are developed. The proposed physics-based RNN models are utilized in model predictive controllers and applied to a chemical process network example to demonstrate their improved approximation performance compared to the fully-connected RNN model that is developed as a black box model.     

Hybrid neural modeling for groundwater level prediction

The accurate prediction of groundwater level is important for the efficient use and management of groundwater resources, particularly in sub-humid regions where water surplus in monsoon season and water scarcity in non-monsoon season is a common phenomenon. In this paper, an attempt has been made to develop a hybrid neural model (ANN-GA) employing an artificial neural network (ANN) model in conjunction with famous optimization strategy called genetic algorithms (GA) for accurate prediction of groundwater levels in the lower Mahanadi river basin of Orissa State, India. Three types of functionally different algorithm-based ANN models (viz. back-propagation (GDX), Levenberg–Marquardt (LM) and Bayesian regularization (BR)) were used to compare the strength of proposed hybrid model in the efficient prediction of groundwater fluctuations. The ANN-GA hybrid modeling was carried out with lead-time of 1 week and study mainly aimed at November and January months of a year. Overall, simulation results suggest that the Bayesian regularization model is the most efficient of the ANN models tested for the study period. However, a strong correlation between the observed and predicted groundwater levels was observed for all the models. The results reveal that the hybrid GA-based ANN algorithm is able to produce better accuracy and performance in medium and high groundwater level predictions compared to conventional ANN techniques including Bayesian regularization model. Furthermore, the study shows that hybrid neural models can offer significant implications for improving groundwater management and water supply planning in semi-arid areas where aquifer information is not available.     

基于Tropos~+需求模型的软件自适应方法

在模型驱动的软件自适应控制过程中,监测、分析、决策和执行等活动均基于共享的知识模型。为便于知识重用和运行时维护,常采用抽象级别较高的需求模型来表示知识。为建模软件的适应性需求,针对传统的Tropos及其扩展方法不能用于软件对异常事件适应性需求建模问题,对Tropos进行上下文和异常条件扩展,记为Tropos+。在此基础上,提出一种由Tropos+需求模型驱动的软件自适应方法,该方法能够用于软件运行环境和异常事件监测以及软件对环境变化和异常事件的自适应处理。最后通过一个案例说明了软件自适应过程。     

Feature representations and classification procedures for Slovene phoneme recognition

In this paper the comparison of performances of different feature representations of the speech signal and comparison of classification procedures for Slovene phoneme recognition are presented. Recognition results are obtained on the database of continuous Slovene speech consisting of short Slovene sentences spoken by female speakers. MEL-cepstrum and LPC-cepstrum features combined with the normalized frame loudness were found to be the most suitable feature representations for Slovene speech. It was found that determination of MEL-cepstrum using linear spacing of bandpass filters gave significantly better results for speaker dependent recognition. Comparison of classification procedures favours the Bayes classification assuming normal distribution of the feature vectors (BNF) to the classification based on quadratic discriminant functions (DF) for minimum mean-square error and subspace method (SM), which does not confirm the results obtained in some previous studies for German and Finn speech. Additionally, classification procedures based on hidden Markov models (HMM) and the Kohonen Self-Organizing Map (KSOM) were tested on a smaller amount of speech data (1 speaker only). Classification results are comparable with classification using BNF.     

A parallel execution model of logic programs

A parallel-execution model that can concurrently exploit AND and OR parallelism in logic programs is presented. This model employs a combination of techniques in an approach to executing logic problems in parallel, making tradeoffs among number of processes, degree of parallelism, and combination bandwidth. For interpreting a nondeterministic logic program, this model (1) performs frame inheritance for newly created goals, (2) creates data-dependency graphs (DDGs) that represent relationships among the goals, and (3) constructs appropriate process structures based on the DDGs. (1) The use of frame inheritance serves to increase modularity. In contrast to most previous parallel models that have a large single process structure, frame inheritance facilitates the dynamic construction of multiple independent process structures, and thus permits further manipulation of each process structure. (2) The dynamic determination of data dependency serves to reduce computational complexity. In comparison to models that exploit brute-force parallelism and models that have fixed execution sequences, this model can reduce the number of unification and/or merging steps substantially. In comparison to models that exploit only AND parallelism, this model can selectively exploit demand-driven computation, according to the binding of the query and optional annotations. (3) The construction of appropriate process structures serves to reduce communication complexity. Unlike other methods that map DDGs directly onto process structures, this model can significantly reduce the number of data sent to a process and/or the number of communication channels connected to a process     

A novel hybrid model for short-term wind power forecasting

Wind energy prediction has a significant effect on the planning, economic operation and security maintenance of the wind power system. However, due to the high volatility and intermittency, it is difficult to model and predict wind power series through traditional forecasting approaches. To enhance prediction accuracy, this study developed a hybrid model that incorporates the following stages. First, an improved complete ensemble empirical mode decomposition with adaptive noise technology was applied to decompose the wind energy series for eliminating noise and extracting the main features of original data. Next, to achieve high accurate and stable forecasts, an improved wavelet neural network optimized by optimization methods was built and used to implement wind energy prediction. Finally, hypothesis testing, stability test and four case studies including eighteen comparison models were utilized to test the abilities of prediction models. The experimental results show that the average values of the mean absolute percent errors of the proposed hybrid model are 5.0116% (one-step ahead), 7.7877% (two-step ahead) and 10.6968% (three-step ahead), which are much lower than comparison models.     

Information technology adoption for sustainable development: green e-books as an example*

ABSTRACT

In recent years, due to increasing awareness of the need for environmental protection, there have been significant efforts to improve consumers’ acceptance of green information technology(IT) products for sustainable development. This study first investigated how knowledge about a green IT product (e-books) influences consumers’ planning processes by using the technology acceptance model and the theory of planned behavior. Data from 320 respondents were analyzed using structural equation modeling to examine the hypothesized relationships in the research model. Results show that perceived usefulness, attitudes, subjective norms, and perceived behavioral control have a significant and positive impact on the intention to purchase e-books. Results also show the moderating effect of consumers’ knowledge about the environmental friendliness of e-books on their intention to buy e-books. Finally, important implications of the findings are discussed, and directions for future research are also provided in this paper.     

C反编译控制流恢复的形式描述及算法

反编译是软件逆向工程的重要组成部分。控制流恢复是Ｃ反编译的重要组成部分。本文首先描述了验证反编译结果与原程序功能等价的模型；其次从数学角度提出了Ｃ编译和反编译控制结构的数学模型并给出其性质；再次根据对Ｃ控制语句编译结果的分析，以扩展的ＢＮＦ形式描述了Ｃ控制语句反编译的约束属性方法；最后给出并说明了反编译控制流恢复的算法及其运行示例。     

Using participatory modelling to compensate for data scarcity in environmental planning: A case study from India

Participatory modelling has provided a new approach to overcome the problem of data scarcity which formerly interfered with the environmental planning for the restoration of the Kolleru-Upputeru wetland ecosystem on the east coast of Andhra Pradesh in South India. New ways had to be found to address the shortcomings of traditionally validated simulation models. The traditional validation process was replaced by joint plausibility discussions and shared vision building in order to improve the understanding of cause-effect relationships and proposals for restoration measures. This study has aimed to match the tacit knowledge of the local stakeholders with explicit scientific knowledge in order to create (i) a mutual basis for an integrated approach as opposed to single-issue measures and (ii) a mutual agreement on follow-up steps needed to sustain both the livelihood of the people as well as the wetland ecosystem. The challenge was to address the hydrological and social complexity. On the basis of a literature review, input data for model simulations were generated from the location-specific knowledge of stakeholders and a rapid field appraisal. The model simulations were used to predict the effects of a number of restoration options. In two workshops, these restoration options were discussed with the stakeholders in order to improve the mutual understanding of the complexity of the wetland system and to reach an agreement on the outlines of an integrated action plan. The participatory modelling approach proved to be a useful tool to obtain a consensus of opinions among the stakeholders.     

Support planning and controlling of early quality assurance by combining expert judgment and defect data—a case study

Planning quality assurance (QA) activities in a systematic way and controlling their execution are challenging tasks for companies that develop software or software-intensive systems. Both require estimation capabilities regarding the effectiveness of the applied QA techniques and the defect content of the checked artifacts. Existing approaches for these purposes need extensive measurement data from historical projects. Due to the fact that many companies do not collect enough data for applying these approaches (especially for the early project lifecycle), they typically base their QA planning and controlling solely on expert opinion. This article presents a hybrid method combining commonly available measurement data and context-specific expert knowledge. To evaluate the method’s applicability and usefulness, we conducted a case study in the context of independent verification and validation activities for critical software in the space domain. A hybrid defect content and effectiveness model was developed for the software requirements analysis phase and evaluated with available legacy data. One major result is that the hybrid model provides improved estimation accuracy when compared to applicable models based solely on data. The mean magnitude of relative error (MMRE) determined by cross-validation is 29.6% compared to 76.5% obtained by the most accurate data-based model.     

A BLACKBOARD DATABASE MODEL OF THE DESIGN PLANNING PROCESS IN CONCURRENT ENGINEERING

The basis for an intelligent decision support system for design process planning within a concurrent engineering (CE) environment is the efficient utilization and coordination of planning knowledge that resides within computerized workgroups of multidisciplinary experts. A systems approach may be taken to derive, represent, and utilize the many models of reasoning that might support a human-centric view of planning in a distributed environment. The blackboard database (BB) provides a suitable framework for utilizing these models in a structured manner by representing the planning problem as a loosely coupled hierarchy of partial problems along with the knowledge needed to progressively solve different parts of this problem. This article discusses the development of such a BB system, which is intended to provide the ability to experiment with various control and domain strategies in order to yield insight into more developed and intelligent methods to assist humans in planning the CE design process.