基于DSP嵌入式技术的无刷直流电动机智能控制系统的研究

本文以TI公司的数字信号处理器TMS320F2812作为核心控制芯片,设计出了一款低成本具有足够功能的无刷直流电动机的调速控制系统实验平台。详细介绍了该控制系统的软硬件结构及控制策略。通过对该控制系统实验结果的分析,表明该系统的动态性能和稳态性能好,抗干扰能力强的特点,且在该实验平台上可进行一些先进控制算法的研究。     

Enhanced Photoelectrochemical Performance of Hematite Photoanode by Decorating NiCoP Nanoparticles Through a Facile Spin Coating Method

Catalysis Letters - Hematite (α-Fe2O3) is a potential photoanode material for photoelectrochemical (PEC) water splitting, but its short hole diffusion length and low water oxidation kinetics...     

碳纳米管及稀土元素钇(Y)对高Ni含量Ti(C,N)基金属陶瓷组织与性能的影响

研究了不同含量的碳纳米管及0.8%稀土Y对高Ni含量Ti(C,N)基金属陶瓷组织性能的影响.将不同成分的试样分别于1400℃、1410 ℃、1420℃真空烧结,测试抗弯强度、洛氏硬度,观察背散射电子形貌、断口形貌并做能谱分析.结果表明,加入质量分数(下同)为0.8%Y及0.3%～1.0%的碳纳米管时,碳纳米管含量为0.5%的组织较均匀,芯壳结构明显,抗弯强度及硬度均较高.     

Individual empowerment of agile and non-agile software developers in small teams

ContextEmpowerment of employees at work has been known to have a positive impact on job motivation and satisfaction. Software development is a field of knowledge work wherein one should also expect to see these effects, and the idea of empowerment has become particularly visible in agile methodologies, in which proponents emphasise team empowerment and individual control of the work activities as a central concern.ObjectiveThis research aims to get a better understanding of how empowerment is enabled in software development teams, both agile and non-agile, to identify differences in empowering practices and levels of individual empowerment.MethodTwenty-five interviews with agile and non-agile developers from Norway and Canada on decision making and empowerment are analysed. The analysis is conducted using a conceptual model with categories for involvement, structural empowerment and psychological empowerment.ResultsBoth kinds of development organisations are highly empowered and they are similar in most aspects relating to empowerment. However, there is a distinction in the sense that agile developers have more possibilities to select work tasks and influence the priorities in a development project due to team empowerment. Agile developers seem to put a higher emphasis on the value of information in decision making, and have more prescribed activities to enable low-cost information flow. More power is obtained through the achievement of managing roles for the non-agile developers who show interest and are rich in initiatives.ConclusionAgile developers have a higher sense of being able to impact the organisation than non-agile developers and have information channels that is significantly differently from non-agile developers. For non-agile teams, higher empowerment can be obtained by systematically applying low-cost participative decision making practices in the manager–developer relation and among peer developers. For agile teams, it is essential to more rigorously follow the empowering practices already established.     

Agent-based architecture for context-aware and personalized event recommendation

In order to offer context-aware and personalized information, intelligent processing techniques are necessary. Different initiatives considering many contexts have been proposed, but users preferences need to be learned to offer contextualized and personalized services, products or information. Therefore, this paper proposes an agent-based architecture for context-aware and personalized event recommendation based on ontology and the spreading algorithm. The use of ontology allows to define the domain knowledge model, while the spreading activation algorithm learns user patterns by discovering user interests. The proposed agent-based architecture was validated with the modeling and implementation of eAgora? application, which was illustrated at the pervasive university context.     

Applying a student modeling with non-monotonic diagnosis to Intelligent Virtual Environment for Training/Instruction

We present a student modeling approach that has been designed to be part of an Intelligent Virtual Environment for Training and/or Instruction (IVET). In order to provide the proper tutoring to a student, an IVET needs to keep and update dynamically a student model taking into account the student’s behaviour in the Virtual Environment. For that purpose, the proposed student model employs a student ontology, a pedagogic diagnosis module and a Conflict Solver module. The goal of the pedagogic diagnosis module is to infer which learning objectives have been acquired or not by the student. Nevertheless, the diagnosis process can be complicated by the fact that while learning the student will not only acquire new knowledge, but he/she may also forget some previously acquired knowledge, or he/she may have some oversights that could mislead the tutor about the true state of the student’s knowledge. All of these situations will lead to contradictions in the student model that must be solved so that the diagnosis can continue. Thus, our approach consists in applying diagnosis rules until a contradiction arises. At that moment, a conflict solver module is responsible of classifying and solving the contradiction. Next, the student ontology is updated according to the resolution adopted by the Conflict Solver and the diagnosis can continue. This paper mainly focuses on the design of the proper mechanisms of the student model to deal with the non monotonic nature of the pedagogic diagnosis.     

Alcohol consumption detection through behavioural analysis using intelligent systems

We describe in this paper a new methodology for blood alcohol content (BAC) estimation of a subject. Rather than using external devices to determine the BAC value of a subject, we perform a behaviour analysis of this subject using intelligent systems. We monitor the user’s actions in an ordinary task and label those data to various measured BAC values. The obtained data-set is then used to train learning systems to detect alcoholic consumption and perform BAC estimation. We obtain good results on a mono-user base, and lower results with multiple users. We improve the results by combining multiple classifiers and regression algorithms.     

Greedy-search-based multi-objective genetic algorithm for emergency logistics scheduling

To enable the immediate and efficient dispatch of relief to victims of disaster, this study proposes a greedy-search-based, multi-objective, genetic algorithm capable of regulating the distribution of available resources and automatically generating a variety of feasible emergency logistics schedules for decision-makers. The proposed algorithm dynamically adjusts distribution schedules from various supply points according to the requirements at demand points in order to minimize unsatisfied demand for resources, time to delivery, and transportation costs. The proposed algorithm was applied to the case of the Chi–Chi earthquake in Taiwan to verify its performance. Simulation results demonstrate that under conditions of a limited/unlimited number of available vehicles, the proposed algorithm outperforms the MOGA and standard greedy algorithm in ‘time to delivery’ by an average of 63.57% and 46.15%, respectively, based on 10,000 iterations.