Sensor placement determination for range-difference positioning using evolutionary multi-objective optimization

This paper focuses on the application of a decision support system based on evolutionary multi-objective optimization for deploying sensors in an indoor localization system. Our methods aim to provide the human expert who works as the sensor resource manager with a full set of Pareto efficient solutions of the sensor placement problem. In our analysis, we use five scalar performance measures as objective functions derived from the covariance matrix of the estimation, namely the trace, determinant, maximum eigenvalue, ratio of maximum and minimum eigenvalues, and the uncertainty in a given direction. We run the multi-objective genetic algorithm to optimize these objectives and obtain the Pareto fronts. The paper includes a detailed explanation of every aspect of the system and an application of the proposed decision support system to an indoor infrared positioning system. Final results show the different placement alternatives according to the objectives and the trade-off between different accuracy performance measures can be clearly seen. This approach contributes to the current state-of-the art in the fact that we point out the problems of optimizing a single accuracy measure and propose using a decision support system that provides the resource manager with a full overview of the set of Pareto efficient solutions considering several accuracy metrics. Since the manager will know all the Pareto optimal solutions before deciding the final sensor placement scheme, this method provides more information than dealing with a single function of the weighted objectives. Additionally, we are able to use this system to optimize objectives obtained from fairly complex functions. On the contrary, recent works that are referenced in this paper need to simplify the localization process to obtain tractable problem formulations.     

附网存储系统卷管理模块的性能优化分析

为了提高NAS系统的性能,设计一个良好的卷管理模块是关键。该文首先介绍了卷管理中的功能模块和相应的处理流程。然后针对卷管理模块中与系统性能相关的问题进行探讨和研究,通过理论和实验分析,得出针对不同应用的优化方案。通过在实际中使用表明,优化后的系统性能得到较大提高。     

Cognitive biases and decision support systems development: a design science approach

Abstract.  This paper presents design science research that aims to improve decision support systems (DSS) development in organizations. Evolutionary development has been central to DSS theory and practice for decades, but a significant problem for DSS analysts remains how to conceptualize the improvement of a decision task during evolutionary DSS development. The objective of a DSS project is to improve the decision process and outcome for a manager making an important decision. The DSS analyst needs to have a clear idea of the nature of the target decision task and a clear strategy of how to support the decision process. Existing psychological research was examined for help with the conceptualization problem, and the theory of cognitive bias is proposed as a candidate for this assistance. A taxonomy of 37 cognitive biases that codifies a complex area of psychological research is developed. The core of the project involves the construction of a design artefact – an evolutionary DSS development methodology that uses cognitive bias theory as a focusing construct, especially in its analysis cycles. The methodology is the major contribution of the project. The feasibility and effectiveness of the development methodology are evaluated in a participatory case study of a strategic DSS project where a managing director is supported in a decision about whether to close a division of a company.     

Time variance and defect prediction in software projects

It is crucial for a software manager to know whether or not one can rely on a bug prediction model. A wrong prediction of the number or the location of future bugs can lead to problems in the achievement of a project’s goals. In this paper we first verify the existence of variability in a bug prediction model’s accuracy over time both visually and statistically. Furthermore, we explore the reasons for such a high variability over time, which includes periods of stability and variability of prediction quality, and formulate a decision procedure for evaluating prediction models before applying them. To exemplify our findings we use data from four open source projects and empirically identify various project features that influence the defect prediction quality. Specifically, we observed that a change in the number of authors editing a file and the number of defects fixed by them influence the prediction quality. Finally, we introduce an approach to estimate the accuracy of prediction models that helps a project manager decide when to rely on a prediction model. Our findings suggest that one should be aware of the periods of stability and variability of prediction quality and should use approaches such as ours to assess their models’ accuracy in advance.     

The role of project management in ineffective decision making within Agile software development projects

Decision making in traditional software development lies with the project manager. In contrast, Agile software development teams are empowered to make decisions, while the role of project manager has changed from one of command and control (i.e. to make decisions and ensure they are implemented) to one of a facilitator. This article argues that decision making in software development is not characterised by a sequence of isolated or exclusive decisions; rather, decisions are inter-related, with each decision leading to further decisions, the chain of which often spans the entire duration of a project. Over this extended period, there are several potential factors that can negatively affect the efficacy of decision making by Agile teams. One of the findings of this exploratory longitudinal study is that the high level of empowerment of a cohesive software development team undertaking an Agile project may be one of these negative factors, as empowered, cohesive teams can exhibit problems such as groupthink or the Abilene Paradox. This article therefore argues that the role of project manager in Agile development initiatives needs to be reassessed, with project managers taking on the role of devil's advocate in the decision-making process.     

Software project management using PROMPT: A hybrid metrics, modeling and utility framework

In this paper, we present a ‘forward-looking’ decision support framework that integrates up-to-date metrics data with simulation models of the software development process in order to support the software project management control function. This forward-looking approach (called the PROMPT method) provides predictions of project performance and the impact of various management decisions. Tradeoffs among performance measures are accomplished using outcome based control limits (OBCLs) and are augmented using multi-criteria utility functions and financial measures of performance to evaluate various process alternatives. The decision support framework enables the program manager to plan, manage and track current software development activities in the short term and to take corrective action as necessary to bring the project back on track. The model provides insight on potential performance impacts of the proposed corrective actions. A real world example utilizing a software process simulation model is presented.     

Risk minimization in biometric sensor networks: an evolutionary multi-objective optimization approach

Biometric systems aim at identifying humans by their characteristics or traits. This article addresses the problem of designing a biometric sensor management unit by optimizing the risk, which is modeled as a multi-objective optimization (MO) problem with global false acceptance rate and global false rejection rate as the two objectives. In practice, when multiple biometric sensors are used, the decision is taken locally at each sensor and the data are passed to the sensor manager. At the sensor manager, the data are fused using a fusion rule and the final decision is taken. The optimization process involves designing the data fusion rule and setting of the sensor thresholds. In this work, we employ a fuzzy dominance and decomposition-based multi-objective evolutionary algorithm (MOEA) called MOEA/DFD and compare its performance with two state-of-the-art MO algorithms: MOEA/D and NSGA-II in context to the risk minimization task. The algorithm introduces a fuzzy Pareto dominance concept to compare two solutions and uses the scalar decomposition method only when one of the solutions fails to dominate the other in terms of a fuzzy dominance level. The MO algorithms are simulated on different number of sensor setups consisting of three, six, and eight sensors. The a priori probability of imposter is also varied from 0.1 to 0.9 to verify the performance of the system with varying degrees of threat. One of the most significant advantages of using the MO framework is that with a single run, just by changing the decision-making logic applied to the obtained Pareto front, one can find the required threshold and decision strategies for varying threats of imposter. However, with single-objective optimization, one needs to run the algorithms each time with change in the threat of imposter. Thus, multi-objective formulation of the problem appears to be more useful and better than the single-objective one. In all the test instances, MOEA/DFD performs better than all the other algorithms.     

电子文件管理系统在企业信息自动化中的应用

阐述电子文件管理系统在企业信息自动化中的应用,首先介绍在企业信息自动化中进行电子文件版本管理的核心方法,并阐述根据企业不同需求新建电子文件的两种不同方式,然后给出为确保电子文件的安全性而使用的独占式签入签出方法和完善的权限管理系统,在权限系统中,实现角色的复用,降低系统管理人员的工作量,最后针对电子文件管理系统的特点,改进电子文件管理的算法,提高了系统的工作效率。     

A decision support system for machine selection with load balancing

This paper presents a new approach towards the machine selection problem. This approach utilizes a decision support system (DSS) for advising the production manager which machine to select. The DSS is of special kind: its machine selection rules are modified to ensure equal load on the machines. The basic structure of the decision tree is defined by process experts and the rules threshold values are dynamically evaluated.

The implementation domain is the aluminum extrusion process, with the objective of aiding the manager in selecting the appropriate extrusion press machine from a set of similar machines. These machines are the most expensive equipment in the process, and are expected to be fully utilized.

The methodology presented in this paper was implemented in such a facility resulting in about 3% improvement on machine utilization.     

How public conversation management integrated with local business process management

In order for enterprises to collaborate at the business-process level, they must deal with two kinds of processes: the public conversation processes specifying inter-enterprise document flows, and the private business processes specifying local workflows of document manipulation and other related tasks. The provisioning, interaction and integration of conversation management and business process management, have become the common interest of the e-business industry. In this paper we discuss the relationship and interaction between conversation management and business process management; point out the difference between public conversation processes (e.g. BPSS processes) and peer-conversation processes (e.g. BPEL4WS processes). We then illustrate our collaborative process management system that has functionally separated conversation manager and business process manager. The conversation manager is based on the ebXML BPSS standard; it is used for validating document exchange at run-time and for activating corresponding process tasks. We have also proposed the conversation model driven asynchronous task activation mechanism for interaction between a conversation process and the coupled business process dynamically. With this mechanism, generic APIs between the conversation manager and the business process manager can be easily defined and used by multiple plugged-in conversation managers.     

A decision support tool for evaluating customer intentions

It is crucial for any manager to keep a close watch on customer satisfaction, customer loyalty and the customer’s intention to recommend the company. In this article, a new decision support tool is developed to support a manager with this task. This tool has been developed with companies in mind that posses limited customer satisfaction data. It uses model-based knowledge discovery to extract the customer’s expectation and the expectation-performance compatibility from the data. Two hypotheses are formulated which posit that compatibility between product performance and customer expectation have a positive influence on the customer’s intentions. Both hypotheses are supported by the data. Finally, a decision support tool is developed which visualizes the impact of customer satisfaction, product performance and expectation-performance compatibility on the customer’s intentions. The decision support tool contains three views which offer the manager important information at a glance.     

A fuzzy-based military officer performance appraisal system

In this paper, we developed a computer-based group decision support system—fuzzy group decision support system (FGDSS), which include three ranking methods (intuition ranking, Lee and Li's fuzzy mean/spread and metric distance) to provide more transparent information and help manager to make better decision under fuzzy circumstance. First, we derive metric distance to rank fuzzy numbers, and we execute some simulation experiments to validate the proposed method. A practical numerical example is introduced to illustrate those three methods and compared with different alternatives by FGDSS.     

A new marketing strategy map for direct marketing

Direct marketing is one of the most effective marketing methods with an aim to maximize the customer’s lifetime value. Many cost-sensitive learning methods which identify valuable customers to maximize expected profit have been proposed. However, current cost-sensitive methods for profit maximization do not identify how to control the defection probability while maximizing total profits over the customer’s lifetime. Unfortunately, optimal marketing actions to maximize profits often perform poorly in minimizing the defection probability due to a conflict between these two objectives. In this paper, we propose the sequential decision making method for profit maximization under the given defection probability in direct marketing. We adopt a Reinforcement Learning algorithm to determine the sequential optimal marketing actions. With this finding, we design a marketing strategy map which helps a marketing manager identify sequential optimal campaigns and the shortest paths toward desirable states. Ultimately, this strategy leads to the ideal design for more effective campaigns.     

基于实时操作系统uC/OS-II的通信管理机的研发

为了解决变电站自动化系统中,不同生产厂家的设备由于采用不同的通信规约和通信介质,从而无法通信的问题;为了实现设备间的互换性和相互操作性,本文在介绍实时操作系统µC/OS-II和通讯规约的基础上,提出了基于嵌入式实时操作系统µC/OS-II的通信管理机。从硬件选型、软件实现流程和约定的通信协议上阐述通信管理机的软硬件设计。该管理机具有多种通信接口,支持多种规约的相互转换,能使不同种类的网络相互通信,从而大大提高变电站自动化系统对设备选择的自由度,具有广阔的应用前景。     

An examination of the decision styles of project managers: Evidence of significant diversity

It has been suggested that the manner in which a project manager makes decisions can significantly influence his or her effectiveness and ultimately the design of systems under his or her direction. Arguments for a structured, systematic approach, as well as arguments for a more well-rounded, ‘whole-brained’ approach have been made. However, it is perhaps more important to examine what project managers actually are. We surveyed a group of over 200 project managers from across the United States, attempting to measure their decision making styles, especially as they relate to project management activity. The survey not only identifies a person’s propensity towards a particular style of decision making, but also his or her propensity towards brain dominance, an idea or action orientation, and a preferred management level of decision making. The results of this study indicate that though each project manager is unique, as a group project managers are well-rounded and tend to solve problems utilizing a ‘whole-brained’ approach.     

Surveillance multisensor management with fuzzy evaluation of sensor task priorities

In this contribution a fuzzy management scheme is proposed to cope with the evaluation of multisensor tasks priority in defence surveillance applications. Based on all fused track and sector data, a reasoning system determines the priority of each surveillance task to perform during the decision cycle, by means of a symbolic inference process inspired in the behaviour of human operators. This approach allows to integrate high-level information (possibly subjective concepts, considering also their uncertainty) with conventional numeric representations in the decision process. The elected formal method to represent the variables involved in this decision process is the theory of possibility and fuzzy sets, since it offers a unified framework to represent uncertainty knowledge. In this sense, to obtain the priority for each task, the reasoning process relies on a decision tree whose nodes are linguistic variables representing intermediate concepts used by a human operator to determine the tasks priorities. The validity of the fuzzy reasoning approach is supported by the fact that it has been able to manage environmental situations in a similar way as experienced human operators do. Included results illustrate how the importance of the tasks, measured through their time-varying priorities, allows the manager to timely adapt sensor operation to changing situations.     

一种多线程轻核机器中进程管理的硬件实现

提出了一种多线程轻核处理器的进程管理器硬件设计。为了得到更好的效果,该进程管理器拥有一个内建的事件管理器来监测等待进程的触发条件,进程的调度也采用硬件实现。所设计的并行轻核处理器的任务管理器由ALU、存储系统和内置路由器构成,用来处理进程。     

A resource management and fault tolerance services in grid computing

In grid computing, resource management and fault tolerance services are important issues. The availability of the selected resources for job execution is a primary factor that determines the computing performance. In this paper, we propose a resource manager for optimal resource selection. Our resource manager automatically selects the set of optimal resources among candidate resources that achieves optimal performance using a genetic algorithm. Typically, the probability of a failure is higher in the grid computing than in a traditional parallel computing and the failure of resources affects job execution fatally. Therefore, a fault tolerance service is essential in computational grids. And grid services are often expected to meet some minimum levels of Quality of Service (QoS) for a desirable operation. To address this issue, we also propose a fault tolerance service that satisfies QoS requirements. We extend the definition of failures from the conventional notion of failures in distribute systems in order to provide a fault tolerance service that deals with various types of resource failures, which include process failures, processor failures, and network failures. We also design and implement a fault detector and a fault manager. The implementation and simulation results indicate that our approaches are promising in that (1) the resource manager finds the optimal set of resources that guarantees efficient job execution, (2) the fault detector detects the occurrence of resource failures and (3) the fault manager guarantees that the submitted jobs complete and the performance of job execution is improved due to job migration even if some failures occur.     

A process knowledge representation approach for decision support in design of complex engineered systems

Process knowledge is of considerable significance to the digitalization and intelligentization of the manufacturing industry. Current research on the process knowledge representation of decision-making in engineering design has predominantly focused on either mathematical models of individual decisions at the micro-level or organizational models of group decision consensus at the macro-level. However, the management of complexity and uncertainty in the model-based realization of engineered systems is critical to achieving rational, comprehensive, and robust decisions, especially in terms of knowledge-intensive design. The efficiency and effectiveness of decisions in system design are intrinsically linked to the process, knowledge, and system concepts involved, necessitating a more flexible and systematic decision process representation scheme that supports both the management of complexity and uncertainty. Hence, in this paper, we propose a decision-centric design process representation scheme named the Phase-Event-Information X (PEI-X) diagram and its corresponding systematic design guidance method for designing decision workflows. Using the proposed method, designers have the ability to (1) model hierarchical decision processes that cover vertical and horizontal interaction patterns, and (2) exploit the synthesis of the “Formulating-Identifying-Reusing-Exploring” iterative process to extend the understanding and prediction of decision process behaviors in design. We achieve the aforesaid abilities through the implementation of a knowledge-based design guidance system for collaborative decision support and we demonstrate the efficacy by adopting a specific multi-stage manufacturing process design problem, hot rod rolling system design, and carry out an integrated design of materials, products, and related manufacturing processes.