A key success factor for the IoT adoption in Smart Buildings is to provide a loosely-coupled Web protocol stack allowing interoperation between all devices present in a building. We review in this chapter different strategies that are going in this direction. More specifically, we emphasise on several aspects issued from pervasive and ubiquitous computing like service discovery. Finally, making the assumption of seamless access to sensor data through IoT paradigms, we provide an overview of some of the most exciting enabling applications that rely on intelligent data analysis and machine learning for energy saving in buildings.

关键词：节能；建筑；自动化系统；网络协议

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Literature-Based Discovery (LBD) is a technique that can be used in translational research to connect the very sparse and huge information available in scientific publications in order to extract new knowledge. This paper presents an LBD system based on the open discovery paradigm exploiting NLP techniques and UMLS medical concepts mapping, to provide a set of tools useful to discover unknown relationships. The system has been evaluated on the problem of discovering new candidate genes potentially related to dilated cardiomyopathies (DCM), and can be used in any medical context to connect different type of concepts. The validation of the system involves reproducing the discovery of genes currently associated to DCM. Validation showed that the system is able to discover many gene-disease associations by using the literature available before their first publication in a scientific article.

关键词：医药；基于文献的发现；UMLS；NLP

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In many real-life situations, e.g., in medicine, it is necessary to process data while preserving the patients' confidentiality. One of the most efficient methods of preserving privacy is to replace the exact values with intervals that contain these values. For example, instead of an exact age, a privacy-protected database only contains the information that the age is, e.g., between 10 and 20, or between 20 and 30, etc. Based on this data, it is important to compute correlation and covariance between different quantities. For privacy-protected data, different values from the intervals lead, in general, to different estimates for the desired statistical characteristic. Our objective is then to compute the range of possible values of these estimates. Algorithms for effectively computing such ranges have been developed for situations when intervals come from the original surveys, e.g., when a person fills in whether his or her age is between 10 or 20, between 20 and 30, etc. These intervals, however, do not always lead to an optimal privacy protection; it turns out that more complex, computer-generated "intervalization" can lead to better privacy under the same accuracy, or, alternatively, to more accurate estimates of statistical characteristics under the same privacy constraints. In this paper, we extend the existing efficient algorithms for computing covariance and correlation based on privacy-protected data to this more general case of interval data.

关键词：医药；隐私保护；统计数据库；计算协方差；计算相关性

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Today, medical knowledge is expanding so rapidly that clinicians cannot follow all progress any more. This is one reason for making knowledge-based systems desirable in medicine. Such systems can give a clinician a second opinion and give them access to new experience and knowledge. Recent advances in Artificial Intelligence (AI) offers methods and techniques with the potential of solving tasks previously difficult to solve with computer-based systems in medical domains. This chapter is especially concerned with diagnosis of stress-related dysfunctions using AI methods and techniques. Since there are large individual variations between people when looking at biological sensor signals to diagnose stress, this is a worthy challenge. Stress is an inevitable part of our human life. No one can live without stress. However, long-term exposure to stress may in the worst case cause severe mental and/or physical problems that are often related to different kind of psychosomatic disorders, coronary heart disease etc. So, diagnosis of stress is an important issue for health and well-being. Diagnosis of stress often involves acquisition of biological signals for example finger temperature, electrocardiogram (ECG), electromyography (EMG) signal, skin conductance (SC) signals etc. and is followed by a careful analysis by an expert. However, the number of experts to diagnose stress in psycho-physiological domain is limited. Again, responses to stress are different for different persons. So, interpreting a particular curve and diagnosing stress levels is difficult even for experts in the domain due to large individual variations. It is a highly complex and partly intuitive process which experienced clinicians use when manually inspecting biological sensor signals and classifying a patient. Clinical studies show that the pattern of variation within heart rate i.e., HRV signal and finger temperature can help to determine stress-related disorders. This chapter presents a signal pre-processing and feature extraction approach based on electrocardiogram (ECG) and finger temperature sensor signals. The extracted features are used to formulate cases in a case-based reasoning system to develop a personalized stress diagnosis system. The results obtained from the evaluation show a performance close to an expert in the domain in diagnosing stress.

关键词：生理传感器；医药；信号分析；案例；诊断系统

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We present a methodology and associated analysis tool (LIGHTEnUP Lifecycle Industry GHgas, Technology and Energy through the Use Phase) that aims to capture both the manufacturing sector energy consumption and product life-cycle energy consumption implications of manufacturing innovation measures. The tool architecture incorporates U.S. national energy use data associated with manufacturing, building operations, and transportation. Inputs for technology assessment, both direct energy saving to the manufacturing sector, and indirect energy impacts to additional sectors are estimated through extensive literature review and engineering methods. The result is a transparent and uniform system of comparing manufacturing and use-phase impacts of technologies.

关键词：节能；工业工序；材料加工；经济影响；技术评估

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In this paper, we combined train performance calculation and power supply solver with our proposed multi-agent based condition monitoring under some specific conditions. The first part of this paper demonstrated the exploitation of the regenerative braking energy with on-board energy storage devices. With the proposed scheme applied in this paper, the energy consumed by the power substation can be reduced. This confirmed the attractive use in regenerative energy storage. The second part gave explanation of the train monitoring software using Java multi-agent integrating with VC++ GUI. The performances of in-service trains can be monitored online. The multi-agent system designed for this test is enhanced visually by the development of the VC++ GUI. The software tool is useful to help the system operators to improve their train service.

关键词：节能；直流电气铁路；可再生能源；多智能体系统

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In this work, principles of operation, advantages and disadvantages are presented for different detector technologies. An idea of a new detection and classification method for a single magnetic sensor based system is also discussed. It is important that the detection algorithm and the neural network classifier needs to be easily implementable in a microcontroller based system.

关键词：神经网络；车辆检测；磁性传感器；车辆分类；车辆检测技术

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关键词：节能；DSM；ESCO；节能市场

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The purpose of this project was to follow up on a Building America (BA) retrofit project to determine the actual savings. In addition, this project provided a perfect opportunity to look at the use of the Building Energy Optimization Program (BEopt) as it applies to multifamily retrofits. BEopt is a tool that is used by the BA Program to determine cost optimized packages in both new and retrofit construction. It also provides a consistent way to compare one BA project from another in various climate regions. BEopt was not originally built for multifamily, nor for retrofit projects. However, when Vice President Biden announced Recovery through Retrofit in 2009, BA took on the challenge of existing homes. BEopt was one component of the BA Program that added retrofits into its repertoire and has been continually improved since then.

关键词：节能；建筑；成本效益分析；改造

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This paper shows a method of odor-source localization by utilizing a sensor network. The network consists of two and three sensor nodes which measure odor intensities spatially. The measured data are then processed using an optimization technique to estimate the odor source position. The suitable configuration of sensor nodes for the reliable result were investigated.

关键词：气味源定位；气味传感器；羽模型；优化；模拟

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