MGP-INTACTSKY: Multitree Genetic Programming-based learning of INTerpretable and ACcurate TSK sYstems for dynamic portfolio trading

In this paper, a Multitree Genetic Programming-based method is developed to learn an INTerpretable and ACcurate Takagi-Sugeno-Kang (TSK) fuzzy rule based sYstem (MGP-INTACTSKY) for dynamic portfolio trading. The MGP-INTACTSKY utilizes a TSK model with a new structure to develop a more interpretable and accurate system for dynamic portfolio trading. In the new structure of TSK, disjunctive normal form rules with variable structured consequent parts are developed in which the absence of some input variables is allowed. Input variables are the most influential technical indices which are selected by stepwise regression analysis. The technical indices are computed using wavelet transformed stock price series to eliminate the noise. The proposed system directly induces the preferred portfolio weights from the stock's technical indices through time. Here, genetic programming with the multitree structure is applied to learn the TSK fuzzy rule bases with the Pittsburgh approach. With this approach, the correlation of different stocks is properly considered during the evolutionary process. To evaluate the performance of the MGP-INTACTSKY for portfolio trading, the proposed model is implemented on the Tehran Stock Exchange as an emerging market as well as Toronto and Frankfurt Stock Exchanges as two mature markets. The experimental results show that the proposed model outperforms other methods such as the momentum strategy, the multitree genetic programming-based crisp system, the genetic algorithm-based first order TSK system, the buy and hold approach and the market's main index in terms of accuracy and interpretability.     

基于混合业务的OFDMA系统资源分配

针对存在混合业务的OFDMA系统的子载波和功率分配问题,提出了一种基于折衷因子的次优资源分配算法,在系统总发送功率的限制下使得系统的吞吐量最大,同时保证实时业务用户的QoS要求和非实时业务用户资源分配的比例公平.该算法将自适应资源分配与自适应调制相结合,并加入了比特调整和优化处理,对实时业务用户和非实时业务用户间的资源利用进行了有效的折衷.分析和仿真结果表明,SRATF算法在不增加复杂度的前提下减少了运行时间,保证了非实时业务用户间的公平性.     

基于自适应遗传算法的实现服务质量优化

服务质量优化问题是多维服务质量参数映射有限的资源问题,这个优化问题是一个NP－hard问题,我们采用通过改进流行遗传算法来解决这一棘手问题,于是提出了基于权的自适应遗传算法（weighted based adaptiv genetic algorithm) 通过实验说明这一算法优于目前为止最好的启示式算法和普通的遗传算法,算法运算时间短,鲁棒性强。     

确定性有限状态机的最小测试成本迁移覆盖准则

软件测试中的一个重要问题是测试成本和测试效率的平衡问题.依据程序中错误分布的2-8定律,将测试分为两个阶段,以解决该问题.第1阶段采用最小代价发现软件中的错误,第2阶段针对第1阶段中发现的错误补充设计测试用例,探测软件中潜在的错误.重点是第1阶段的实现.依据确定性有限状态机和集合划分的理论,提出了确定性有限状态机的最小...     

To be fair or efficient or a bit of both

Introducing a new concept of (α,β)-fairness, which allows for a bounded fairness compromise, so that a source is allocated a rate neither less than 0α1, nor more than β1, times its fair share, this paper provides a framework to optimize efficiency (utilization, throughput or revenue) subject to fairness constraints in a general telecommunications network for an arbitrary fairness criterion and cost functions. We formulate a non-linear program (NLP) that finds the optimal bandwidth allocation by maximizing efficiency subject to (α,β)-fairness constraints. This leads to what we call an efficiency–fairness function, which shows the benefit in efficiency as a function of the extent to which fairness is compromised. To solve the NLP we use two algorithms. The first is a well-known branch-and-bound-based algorithm called Lipschitz Global Optimization and the second is a recently developed algorithm called Algorithm for Global Optimization Problems (AGOP).We demonstrate the applicability of the framework to a range of examples from sharing a single link to efficiency fairness issues associated with serving customers in remote communities.     

无线携能网络中一种非线性能量收集与信息传输均衡策略

在无线携能(SWIPT)网络中,节点通常采用非线性能量收集。论文首先建立了非线性能量收集模型,提出了时隙切换与静态功率分割相结合的智能协同SWIPT传输方案(CoTP),推导了该传输方案的速率-能量(R-E)域,研究了接收端存在电路功耗情况下CoTP方案的速率-能量(R-E)均衡策略。同时,论文将所提的CoTP策略与动态功率分割(DPS)、开-关功率分割(OPS)、静态功率分割(SPS)与时隙切换策略(TS)的R-E域性能进行了比较,仿真分析了噪声以及接收端电路功耗对R-E域性能的影响。数值结果表明,与其他方案相比,若不考虑电路功耗情况,所提CoTP策略在每比特收集能量为0.6 mJ~2。4 mJ时具有较高的可达速率。若考虑电路功耗情况,所提CoTP策略在每比特收集能量为0 mJ~15 mJ时具有较高的可达速率,可以实现能量收集与信息传输的均衡。      

Bagged ensembles with tunable parameters

Ensemble learning is a popular classification method where many individual simple learners contribute to a final prediction. Constructing an ensemble of learners has been shown to often improve prediction accuracy over a single learner. Bagging and boosting are the most common ensemble methods, each with distinct advantages. While boosting methods are typically very tunable with numerous parameters, to date, the type of flexibility this allows has been missing for general bagging ensembles. In this paper, we propose a new tunable weighted bagged ensemble methodology, resulting in a very flexible method for classification. We explore the impact tunable weighting has on the votes of each learner in an ensemble and compare the results with pure bagging and the best known bagged ensemble method, namely, the random forest.     

Integrated time–cost tradeoff and resources leveling problems with allowed activity splitting

Resource leveling and time–cost tradeoff are among the most challenging optimization problems in project management. These two problems are usually addressed separately because each problem optimizes different objective functions. In this paper, we develop an integrated model that addresses both problems when activities are allowed to split for better utilization of resources. The formulated mixed integer linear program (MILP) model considers the tradeoff between the crashing‐dependent costs; direct and indirect costs, and resource utilization related costs; acquiring, releasing, and splitting costs. The model can be used as a decision tool to determine whether crashing is recommended when decision makers are also concerned with the better utilization of project's resources. A one‐way sensitivity analysis was conducted to assess total cost savings achieved through the integration of time–cost tradeoff and resource leveling problems. Another experimental study was undertaken to evaluate the performance of the MILP runtime.     

IMC–PID Design: Analytical Optimization for Performance/Robustness Tradeoff Tuning for Servo/Regulation Mode

This paper addresses the analytical design of a one‐degree‐of‐freedom (1‐DoF) robust proportional–integral–derivative (PID) controller for the servo/regulation mode in line with the enhanced internal model control (IMC) principle based on a first order plus dead time (FOPDT) model. The proposed enhanced IMC–PID can provide acceptable performance both for set‐point tracking and for load disturbance rejection. The design procedure is formulated as a constrained optimization problem, in which the robustness of the system is adequately considered. Therefore, the resulting PID controller gives optimal performance with an exact selected robustness degree. Illustrative examples are given to show the effectiveness and merits of the analytical tuning rules for a wide range of plants.     

具有沟槽阳极短路的新型沟槽栅场截止绝缘栅双极晶体管

文章提出了一种新型的具有沟槽阳极短路的槽栅场截止型绝缘栅双极晶体管结构。通过引入沟槽短路阳极结构,器件的击穿电压得到了明显提高。仿真结果显示,相比于传统的场截止型绝缘栅双极晶体管,新结构提高了19.5 %的击穿电压,而且新结构具有更小的漏电流。在电流密度为150 A/cm2 时,新结构虽然提高了近百分之九的导通压降,但是关断时间只有传统结构的一半。此外,新结构导通时没有负阻效应。因此,新结构具有更好的关断功耗与导通压降的折中关系。