Exploring the costs of technical debt management – a case study

Technical debt is a metaphor for delayed software maintenance tasks. Incurring technical debt may bring short-term benefits to a project, but such benefits are often achieved at the cost of extra work in future, analogous to paying interest on the debt. Currently technical debt is managed implicitly, if at all. However, on large systems, it is too easy to lose track of delayed tasks or to misunderstand their impact. Therefore, we have proposed a new approach to managing technical debt, which we believe to be helpful for software managers to make informed decisions. In this study we explored the costs of the new approach by tracking the technical debt management activities in an on-going software project. The results from the study provided insights into the impact of technical debt management on software projects. In particular, we found that there is a significant start-up cost when beginning to track and monitor technical debt, but the cost of ongoing management soon declines to very reasonable levels.     

Hybrid immune algorithm based on greedy algorithm and delete-cross operator for solving TSP

This paper first introduces the fundamental principles of immune algorithm (IA), greedy algorithm (GA) and delete-cross operator (DO). Based on these basic algorithms, a hybrid immune algorithm (HIA) is constructed to solve the traveling salesman problem (TSP). HIA employs GA to initialize the routes of TSP and utilizes DO to delete routes of crossover. With dynamic mutation operator (DMO) adopted to improve searching precision, this proposed algorithm can increase the likelihood of global optimum after the hybridization. Experimental results demonstrate that the HIA algorithm is able to yield a better solution than that of other algorithms, which also takes less computation time.     

Decomposing the user-preference in multiobjective optimization

Preference information (such as the reference point) of the decision maker (DM) is often used in multiobjective optimization; however, the location of the specified reference point has a detrimental effect on the performance of multiobjective evolutionary algorithms (MOEAs). Inspired by multiobjective evolutionary algorithm-based decomposition (MOEA/D), this paper proposes an MOEA to decompose the preference information of the reference point specified by the DM into a number of scalar optimization subproblems and deals with them simultaneously (called MOEA/D-PRE). This paper presents an approach of iterative weight to map the desired region of the DM, which makes the algorithm easily obtain the desired region. Experimental results have demonstrated that the proposed algorithm outperforms two popular preference-based approaches, g-dominance and r-dominance, on continuous multiobjective optimization problems (MOPs), especially on many-objective optimization problems. Moreover, this study develops distinct models to satisfy different needs of the DM, thus providing a new way to deal with preference-based multiobjective optimization. Additionally, in terms of the shortcoming of MOEA/D-PRE, an improved MOEA/D-PRE that dynamically adjusts the size of the preferred region is proposed and has better performance on some problems.     

Generalized incremental frequency method for topological designof continuum structures for minimum dynamic compliance subject to forced vibration at a prescribed low or high value of the excitation frequency

The application of multidisciplinary design optimisation is mostly confined to bi-disciplinary systems such as fluid-structure interaction problems. High fidelity models of three disciplines involving electromagnetic-thermal-structural designs are rare. Here, the multidisciplinary optimisation of such a design is presented. The device comprises a C-shaped iron core and a single coil. The problem is decomposed using a monolithic multidisciplinary feasible architecture. The multidisciplinary analyses involve a single three-dimensional finite element mesh for transient non-linear electromagnetic, non-linear-static thermal, and linear-static structural models. During each multidisciplinary iteration the mesh is linearly morphed. A gradient based optimisation algorithm in combination with a multi-start routine is applied to the constrained mass minimisation problem. Multidisciplinary feasibility is ensured by convergence of a single coupling parameter i.e. air-gap deformation. In conclusion, some multidisciplinary optimisation, analyses, and decomposition considerations are discussed.     

矿山物联网服务承载平台与矿山购买服务

矿山综合自动化系统由于没有考虑容纳第三方的服务,逐步显露出系统封闭、标准不一、信息应用困难、对矿山技术人员要求高等问题,阻碍了其发展。明确了矿山物联网的本质为服务承载平台,矿山物联网应具有相应的公共服务能力,包括基于位置的服务、基于时间的服务、基于信息的服务、基于云的服务、基于大数据的服务、基于语义的服务等,重点论述了矿山物联网应具备的服务能力和矿山购买服务,指出这些服务能力会随着技术和需求的发展而不断充实与增加,使矿山物联网逐步向智慧矿山迈进。     

一种基于字典学习的压缩感知视频编解码模型

无线多媒体传感器网络中针对视频信号处理的两个重要的问题是如何高效编解码和如何抵抗信道误码。结合压缩感知和字典学习理论,提出了一种应用于无线传感器网络的基于字典学习的压缩感知视频编解码模型。模型整体采用压缩感知理论以降低编码端复杂度,提高系统抗误码性。编码端应用差分编码和跳帧模式大大减少了信道传输数据量;解码端采用字典学习算法增强图像的稀疏表示能力,从而提高视频重构精度。本模型在实现高效编码的同时将计算复杂度从编码端转移到解码端,从而满足编码端资源受限的应用场合。理论分析和仿真实验表明该模型可行并且有效。     

基于iPhone手机的数字码实时识别与应用

根据苹果手机拍摄防伪标签数字实时识别的需要,针对防伪数字字号较小的因素和苹果手机因拍摄距离的原因造成的图像缩小、数字模糊、背景复杂等问题进行处理,提高识别精度。首先通过人工选取数字码区域,并进行背景数字分离,定位获取数字图像;其次采用灰度化和二值化得到黑白图像;然后通过投影对数字码图像进行分割,并对每个数字图像进行归一化、锐化和细化;基于统计学抽取数字码的特征,采用最近邻域判别函数进行数字码识别,取得很好的识别效果。