人工神经网络外文翻译.docx

附录二：外文翻译原文 The Kahnawake Survival School (KSS), located in the Kahnawake Mohawk Territory on the south shore of the St. Lawrence River across from Montreal, is more than a high school. It is a teaching tool for students, a community gathering place and a shelter in case of disaster. The 5500 m2 (60,000 ft2) building is composed of a central block with two wings, spread partially on two floors. The school opened in August 2008. After a thorough inspection of the land owned by the Kahnawake Education Center (KEC), the design team chose a location in an existing clearing where the building would be adjacent to a forest but still accessible from the main highway of the reserve. The proposed location was analyzed by all building professionals to optimize the orientation/layout of the building. Considering the age and history of the site, the decision was made to design a building and make it fit the site, preventing major site modifications and unnecessary cutting down of trees. This consideration was even more important since an endangered tree species (Butternut) was present. Also, all soil removed from the site for excavation was piled near the highway to create a natural wall of dirt, grass and trees, to reduce the noise from the highway.Although building the facility in a clearing was a major construction challenge, it had many advantages for mechanical and electrical engineering. About the AuthorNicolas Lemire, P.Eng., is a principal at Pageau Morel and Associates, Montreal, QC, Canada.Table 1: Average energy consumption for teaching facilities in 2006 (per Natural Resources Canada, OEE).Average Teaching Facility in Quebec1.90 GJ/m2/yrAverage Teaching Facility in Canada1.70 GJ/m2/yrTable 2: Energy comparison of schools in Quebec, Ontario and Canada名 称GJ/m2/yrReduction Average Teaching Facility in Quebec1.90Average Teaching Facility in Canada1.70Reference Building (School) as Per MNECB of Canada for Quebec）1.300Reference Building (School) as Per MNECB of Canada for Ontario1.272.4KSS (Actual Data for 2008 2009)0.6351.4Table 3: Construction costs in Canadian dollars.cost$/m2$/ft2Mechanical1459555265.3724.33Electrical1059676192.6717.66Whole Building85912791562.05143.19For example, trees surrounding the path provide shade for the south fade of the facility during summer. With leaves shed in the winter, the resulting solar gains are used as an auxiliary heating source. Table 1 shows data published by Natural Resources Canada on the average energy consumption of teaching facilities in 2006. Table 2 shows an energy comparison between the average facilities presented in Table 1, the reference building as per the Model National Energy Code for Buildings (MNECB) of Canada for a teaching facility including geothermal energy in the Province of Quebec1 and in the Province of Ontario,2 and the new Kahnawake Survival School. Table 2 shows that KSS performs well at 66.8% more efficient than the average teaching facility in the Province of Quebec and 51.4% more efficient than the reference building of the MNECB of Canada for a teaching facility in Quebec using geothermal energy.Heat recovery is applied to fresh air and exhaust using an enthalpy wheel. Coils and filters in the systems are selected at 1.8 m/s (350 fpm) to reduce static pressure loss. Variable frequency drives are installed on all fans and motors and are directly coupled to avoid belt losses and reduce maintenance. Motors are high efficiency. A closed loop geothermal heat exchanger (15 vertical boreholes of 137 m2 450 ft) is used to supply tempered glycol to local geothermal water-to-air heat pumps. A water-to-water heat pump produces hot or cold glycol to supply a coil in the main fresh air ventilation unit that provides outside air to all building areas. Additional energy efficient measures implemented are high efficiency lighting fixtures and fresh air control using CO2 sensors for the gym.Ducting was designed to group the classrooms in four zones, with a fifth zone for the offices. As soon as all classrooms in one zone are unoccupied, that ventilation zone is shut down. When all zones are closed, the main system is turned off.Physical separations are present between classrooms and offices, giving a means of controlling IAQ and energy consumption depending on occupation modes (offices are used in summer, while classes are not; classes can be used at night throughout the year while offices are empty at night, etc.).Commissioning was performed with emphasis on performances of primary air-handling units (AHUs) and ventilation strategies. To deal with the amount of fresh air injected into the gymnasium, CO2 sensors were installed in the return duct leading to the air-handling units, analyzing CO2 quantities contained in return air. Fresh air is injected in the mixing box of the AHU to maintain CO2 levels at 800 ppm. The special variable air volume (VAV) system with predetermined outside air rate and terminal reheat is an efficient way of providing effective indoor environmental quality to the users. All zones in the building can maintain effective temperature within the ASHRAE comfort zone as defined in ASHRAE Standard 55. A minimum humidity level of 30% is maintained during winter using electrical steam generator humidifiers installed in the air-Image: . 2009 DigitalGlobeImage: . 2009 DigitalGlobe technology award case studiesBuilding at a GlanceName: Kahnawake Survival SchoolLocation: Kahnawake, QC, CanadaOwner: Kahnawake Education CenterPrincipal Use: SchoolIncludes: High School, Community Center, Public AssemblyEmployees/Occupants: 450 studentsGross Square Footage: 60,000 ft2Substantial Completion/Occupancy: August 2008Occupancy: 100%handling units. During summer, a maximum of 60% is allowed (design criteria for offices).Considering the many types of activity occurring in the building (teaching, administration, community activities, shows, sporting events, community meetings and shelter), two basic options were analyzed: dedicated systems and centralized systems. After analysis, it was decided to combine both strategies. The use of a centralized system to condition the amount of outside air required and send it in all zones was the best solution. The capability to operate at variable flow was a major aspect of this system since classrooms and teaching areas are not used 24/7 while offices and administrative areas operate throughout the year.Because classrooms are not used during the hotter months of the year (from mid-June to the end of August), cooling the classrooms and the gym was questioned. It was decided to use local geothermal water-to-air heat pumps into the administrative and office areas and in the cafeteria/student lounge as those areas were more likely to be used throughout the year or during summer for events. The fresh air system was equipped with a geothermal water-to-water heat pump to allow heating/cooling/dehumidification of fresh air supplied to all areas including classrooms and the gym. For the gym, a provision has been made to allow installation of cooling capacity in the system in the future by adding a water-water heat pump to supply a coil. Natural ventilation is available for all classrooms and the gym using operable windows. The main central corridor is open on two stories and continues higher (almost three floors) to act as a natural chimney. All classrooms are opened to the central corridor (using operable panels). When the outside conditions are adequate, a special green light shows teachers/users it is a good day to use natural ventilation. Operable windows located at the top of the natural chimney are opened, and teachers/users can decide if they want to open them.If very hot days occur during the academic year, a provision for two propeller fans, located at each end of the main corridor, was planned (at the top of the natural chimney). This would force air movement through the building (using natural ventilation openings in classrooms but closing the windows at the top of the natural chimney). The same strategy was applied to the gym, allowing it to be naturally cooled. Also, a dedicated air-handling unit was installed into the gym for ventilation and heating purposes. Variable frequency drives were installed on each fan. Heat recovery was implemented on exhaust fans (washrooms, janitor rooms, etc).A centralized building automation system (BAS) links all mechanical components through a centralized DDC network. A central panel is located in the main mechanical room and is simple to use so that occupants who are present outside of normal business hours can start/stop different features of the building (natural ventilation, forced natural ventilation fans, primary fresh air system, gym ventilation system and gym forced natural ventilation fans).Commissioning was done on the BAS, which helped improve energy efficiency. This process continued after delivery of thebuilding and will continue for a few years to perfectly tune the building to the desired operation.Capital costs were controlled by providing simple systems that rely on well-established, low-cost technologies and by optimizing equipment selection for dependability, low maintenance and maximum efficiency. A major advantage of the VAV systems with terminal reheat is that, despite different load requirements, a comfortable environment can be maintained in all rooms. This makes the systems flexible enough to adapt, at low cost, to any layout modification.Designing complicated systems is not always a guarantee for energy efficiency. In fact, the guiding principle is that simpler systems (as long as energy efficiency is not compromised) are understood better by maintenance personnel, which lowers operation costs.The decrease of energy consumption led to a reduction of CO2 emissions by about 192 Mg (212 tons) per year. The total energy consumption reduction per year corresponds approximately to the energy consumption of 92 average houses.外文翻译中文 本文研究人工神经网络（ANN）的适用性，如汽车空调系统（AAC）的适用性能预测。制冷剂使用的是HFC134a。为了这个目的，实验厂原始组件从乘用车空调系统的一个标准的尺寸制造。实验系统是在稳定条件下操作，同时改变压缩机的转速，冷却能力和冷凝温度。然后，使用了一些实验数据进行模拟，系统的人工神经网络模型，基于标准的反向传播算法的开发。该模型被用于各种性能参数的预测系统，即压缩机的功率，在冷凝器的散热率，制冷剂的质量流量，压缩机排出温度和性能系数。这些参数的人工神经网络预测通常约定好在0.968-0.999的范围内，相关系数的实验值，在范围内的平均相对误差1.52-2.5非常低的根均方误差。这项研究表明，空调系统，即使是那些采用可变速度压缩机，如AAC系统，也可以使用具有高度准确性的人工神经网络建模准确度。 2005年爱思唯尔有限公司保留所有权 1 介绍 汽车空调系统（AAC），通常采用蒸气压缩式制冷电路，目前使用的HFC134a作为制冷剂，在乘客车厢内实现夏季舒适。AAC系统由于压缩机是由发动机驱动的皮带带动，压缩机的转速与发动机的转速成正比，这将影响该系统的制冷能力，以改变发动机转速的函数。因此，这些系统不同于国内的空调系统，由于不同的压缩机转速和散热能力以及不稳定的制冷负荷，这些复杂特性影响AAC系统建模，因此采用经典的技术。 AAC系统公开发表的文献是非常有限的，因为这个行业是一个竞争激烈的行业。 专家 发表过的文章。 Jung 1一些混合制冷剂电脑分析，如HFC134a，HCFC142b，RE170，HC290和HC600a尽可能替代CFC12在现有的AAC系统。他们的分析结果初步筛选替代品。然后，他们根据实验的结果提出的替代制冷剂的混合物。通过计算机分析，发现一个HFC134a/RE170的混合物是最好的混合物替代CFC12。但是，他并没有报告理论和实际之间的任何实验比较结果。李和Yoo 2 对制冷剂为HFC134a的AAC系统的各组成部分进行了分析，并开发了一个仿真模型，开发出整个系统相结合的组件的单独性能分析程序。他们的计划用于研究蒸发器的性能是基于实验结果，该程序用于研究冷凝器性能是假设没有过冷冷凝器出口。他们发现整个系统的仿真模型和实验结果之间的误差是在7以内。 ratts和Brown 3实验对性能的影响进行了分析，制冷剂为四氟乙烷（HFC134a）装料水平的AAC的系统。为了这个目标，他们确定了单个组件在一个AAC系统的损失作为一个功能的制冷剂，对他的的电荷使用第二定律。他们发现，压缩机和冷凝器部件造成的总损失的比例最大，而蒸发器和扩展设备的损失占一个较小比例的损失。 Rabghi和尼牙孜 4加装一个的CFC12 的AAC系统使用的制冷剂为HFC134a，并确定了实验压缩机速度的函数的系统参数，在各制冷剂的性能系数（COP）下，他们发现使用CFC12 AAC系统比使用四氟乙烷（HFC134a）的系统有一个更好的COP。jabardo等。 5 开发了一种含有一个可变的AAC系统的稳态仿真模型容量压缩机，微通道平行流冷凝器，恒温膨胀阀和板翅片管式蒸发器。他们测试了一个实验单元上的模型的有效性。他们发现，仿真和实验结果之间的偏差的制冷量，COP和制冷剂质量为压缩机速度的函数的流量分别为5以内。然而，相同的性能参数，作为相对于蒸发器的回风温度对模拟结果的偏差的函数实验高达18。joudi等。 6模拟了一个理想的AAC系统的工作性能与几个制冷剂确定最合适的替代制冷剂为CFC12。他们的模型预测的混合物HC290/HC600a的最佳替代CFC12。之后，他们比较了各种性能使用CFC12的实验AAC系统的参数和的混合物HC290/HC600a为工作液体。他们观察到，压缩机的功率消耗在HC290/HC600a的情况下小幅走高比中的CFC12的情况下为相同的冷却能力。然而，他们没有报告任何比较仿真和实验结果。Kaynakli Horuz 7分析了HFC134a的AAC系统性能的实验，以找到最佳的工作条件。他们提出了一些性能参数，如冷却能力，压缩机功率，总功率消耗，制冷剂的质量流率和COP作为冷凝温度，蒸发器的回流空气温度的函数，环境温度和压缩机转速。最近的研究旨在降低全球气候变暖源于AAC系统设计或者修改的系统需要较少量的HFC134a的或新颖的系统使用不同的制冷剂如CO2和碳氢化合物。巴蒂处理潜在的HFC134a增强的AAC系统，降低全球变暖的影响8为此，他调查增加了压缩机的等熵效率的影响，增加冷凝器的有效性，在蒸发器中的空气侧压降降低，增加的冷凝器空气流和降低系统的COP的制冷负载。从基线四氟乙烷（HFC134a）系统获得的实验结果，从切实增强四氟乙烷（HFC134a）系统的比较的基础上，他指出的增强的系统可能是最实际的方法来处理AAC 系统等引起的全球变暖。 9研究了CO2和HFC134a AAC系统使用半理论周期模型的性能优劣。在除了标准制冷回路组件，即压缩机，冷凝器，膨胀装置和蒸发器，其二氧化碳的系统配备的液体线/吸气管道热交换器。10他们确定四氟乙烷（HFC134a）具有更好的COP比二氧化碳的COP差距是依赖于压缩机的转速和环境温度为了找到一个合适的烃的替代，具有较低的全球变暖潜力比四氟乙烷（HFC134a），Ghodbane模拟性能AAC使用碳氢制冷剂，即HC152a，HC270，HC290 HC600a的，在COP和压缩机排气温度的系统。他们决定将HC152a和HC270制冷剂的HFC134a系统，分别为11和15，而HC290显示，只有轻微改善。然而，由于其潜在的易燃性，AAC系统使用碳氢制冷剂在被认为是不安全的，除非一些额外的设计注意事项。taken.Tian和Li 11 开发了一个数学模型的HFC134a AAC系统与可变容量压缩机，模拟其稳定状态下的性能。他们的模型中确定的压缩机速度，环境温度和蒸发压力上的蒸发器的空气流率的影响，冷凝压力，冷却能力，并表示压缩机的功率。他们证实了一个实验单元上的模型的结果，发现，在11之内的模拟和测量的参数之间的偏差。Hosoz和Direk 12处理的操作与该特征的四氟乙烷（HFC134a）AAC系统的性能特征为空气，以空气能热泵。为了这个目的，他们开发了一个实验系统，并测试了它在空调和热泵模式，改变压缩机的转速和入口空气温度的室外和室内的线圈。他们评估了性能的集成系统中的制冷和制热能力，COP，压缩机的排气温度和速度破坏该系统的每个组件。他们决定，通常热泵运行产生了较高的COP和更低的价格相比，每单位容量（火用）破坏的空调操作，虽然不足heating.From，这个简短的文献综述，可以观察到，一些研究者倾向于开发数学模型，以确定AAC系统的各种性能参数，而其他人为了同样的目的进行彻底和昂贵的实验研究。在传统的建模方法，所采用的计算机模拟是复杂和耗时的，由于其处理复杂的微分方程的解。此外，该数学模型，需要大量的几何参数，定义系统，这可能不是可用的，并在许多情况下，他们的预测可能不够准确。这两种方法的替代，AAC系统可以模拟人工神经网络（ANN），大大减少工程工作。这种新的建模技术是基于模仿人类大脑的结构和机制，被用在越来越多的工程应用中的经典方法失败或过于复杂，是used.ANNs允许的造型在复杂系统中的物理现象，而不需要明确的数学表达式。有此功能，人工神经网络时，可以估算出所需的输出参数的系统足够的实验数据是provided.ANN能源系统的建模，通过大量的调查，审查通过Kalogirou最近的研究 14研究了用于制冷应用中的翅片管式换热器的传热的ANN建模。 bechtler等 15 处理的人工神经网络模型预测的各种制冷剂蒸汽压缩式热泵的稳态性能。他们还蒸气压缩式冷水机组的动态过程建模，预测COP和压缩机的功率16 Prieto的等 17 开发了一个人工神经网络模型预测的电厂冷凝器的性能。 chouai等。 18研究了几种制冷剂 19比较了人工神经网络和一些经验为基础的稳态模型建模的蒸汽压缩式冷水机组的热力学性质的人工神经网络建模。 sozen等 20开发了一种喷射器吸收式制冷系统的分析和人工神经网络模型相比，其预测的预测分析功能 21采用人工神经网络模型的蒸汽压缩式制冷系统的COP和总的不可逆性。 arcaklioglu等。 22提出了一种人工神经网络模型的性能的蒸汽使用不同比例的CFC12/HCFC22混合制冷剂的压缩式热泵。伊斯兰奥卢23研究了神经网络，建模的吸入管线的出口温度和用于家用冰箱的毛细管/吸气管道热交换器的制冷剂的质量流率。 ertunc Hosoz 24预测使用ANNs.In本研究用蒸发式冷凝器的蒸气压缩式制冷系统的各种性能参数，神经网络的方法已被用于调查一个AAC系统的四氟乙烷（HFC134a）的蒸气压缩式制冷电路的性能。利用从实验获得的稳定的状态数据AAC系统，一个系统的神经网络模型已经研制成功。与使用该模型，各种性能参数的系统，即压缩机的功率，在冷凝器的散热率，制冷剂的质量流量，压缩机排出温度和COP，已预测和与实际的相比。 2 人工神经网络 人工神经网络在电脑的方式诉诸人的行为为基础的学习机制，以反映大脑的功能。利用 样品从实验中，神经网络可以应用于不带算法的解决方案，或与太复杂的算法以找到的解决方案的问题。他们的学习能力的例子人工神经网络更灵活，更强大的比参数的方法25。 人工神经网络是由大量被称为神经元相互连接的处理节点。每个神经元接受输入和第一形式的加权组的总和以偏置的加权输入定义的26 (1) (2)其中P和的元素数目和输入向量，分别和b的互连权重是对神经元的偏置。请注意，作为偏压的互连权重的一组神经元的知识被存储在。然后，神经元的输出响应。为此，总和的加权输入以偏置处理通过激活函数，由f表示，和计算的输出基本上是，神经元模型模拟生物神经元，将触发时，显着激发，即神经元的输入，n，是足够大的。有许多方法来定义，如阈值函数的激活函数，S型函数和双曲正切函数。 使用一个合适的学习方法中，人工神经网络训练的处理节点之间的连接，即加权系数的值，通过调整，以执行特定的功能。训练过程继续进行，直到网络的输出相匹配的目标，即所需的输出。网络之间的误差输出和所需的输出最小化，通过修改的权重和偏见。当误差低于一个确定的值或超过历元的最大数目，在训练过程被终止。然后，将该训练的网络可以使用模拟系统的输出的输入，但没有之前引入。 通常分为三个部分：一个输入层，隐层和输出层的人工神经网络的架构。在输入层中包含的信息被映射到通过隐藏layers.Each神经元的输出层，可以只将其输出发送到上级层的神经元上，并只从接收其输入端的较低层的神经元。基于人工神经网络预测的性能评价的网络输出之间的回归分析，即预测的参数，和相应的目标，即实验值。该标准用于测量网络性能的相关系数，平均相对误差和均方根方误差。相关系数评估预测和实验结果之间的关系的强度。这个系数之间的实际值与预测输出27 (3)其中，是a和p集指的实际输出（实验）和预测的输出集合，分别之间的协方差，和27被定义 (4)其中E是期望值，la是平均的值的一组和