Market response to the public display of energy performance rating at property sales

Energy labels have generally received positive response from consumers and have moved the market for white goods and cars in the direction of more energy-efficient products. On the real estate market, it was expected that an energy label, rating the energy performance of a property based on a national energy performance certificate (EPC) might receive similar response. However, in Denmark no response to the energy performance rating was observed for 15 years. This was a surprise considering that Denmark was the first country to implement an A to G rating of the energy performance of buildings. A statistical examination of data on property sales prices and energy performance ratings was carried out. All relevant property transaction data from 2007 till 2012 were examined and they showed that energy performance ratings had an impact on property sales prices. However, before June 2010, the impact was modest, whereas after June 2010 the impact of energy performance ratings on property sales prices increased significantly as a result of an EU requirement to display the energy performance rating in connection with property sales. On this background, it was concluded that a public display of the energy performance rating is fundamental for market response.     

Integration framework for simulations and SCADA systems

Stricter requirements on the quality of industrial plant operation together with environmental limits and decreasing energy consumption bring more complex automation systems. The intelligent control techniques, which are based on approaches from diverse disciplines including statistics, artificial intelligence or signal processing, have been widely used during the last years and their benefits have been proved. They cannot be developed and tested without simulation models and access to online and historical data. This article proposes a platform for the integration of simulations and industrial SCADA systems supporting complex data access and simulation code re-use.The idea of the presented framework is to connect simulations, data sources, optimizers, other calculations and SCADA systems into one integrated environment seamlessly. A technical level of the framework provides integration of stakeholders and a semantic level captures engineering knowledge in inter-mapped ontologies and configures the technical level, which is often called model-driven configuration. The semantic level utilizes a formal model implemented as set of ontologies. The major contribution of the article are the layered model of the integration architecture and formulation of the integration requirements in the industrial automation domain. The proposed solution has been implemented and tested on a software prototype level. It is demonstrated on two use-cases covering both design and integration of simulation models from the industrial perspective. The proposed architecture is intended to be as general as possible, however it has been tested on signal-oriented simulators only. It is the main limitation of this contribution and it should be addressed in upcoming work.     

TSPN: Term-Based Semantic Peer-to-Peer Networks

In this paper,we propose Term-based Semantic Peerto-Peer Networks (TSPN) to achieve semantic search.For each peer,TSPN builds a full text index of its documents.Through the analysis of resources,TSPN o...     

Weak signal identification with semantic web mining

We investigate an automated identification of weak signals according to Ansoff to improve strategic planning and technological forecasting. Literature shows that weak signals can be found in the organization’s environment and that they appear in different contexts. We use internet information to represent organization’s environment and we select these websites that are related to a given hypothesis. In contrast to related research, a methodology is provided that uses latent semantic indexing (LSI) for the identification of weak signals. This improves existing knowledge based approaches because LSI considers the aspects of meaning and thus, it is able to identify similar textual patterns in different contexts. A new weak signal maximization approach is introduced that replaces the commonly used prediction modeling approach in LSI. It enables to calculate the largest number of relevant weak signals represented by singular value decomposition (SVD) dimensions. A case study identifies and analyses weak signals to predict trends in the field of on-site medical oxygen production. This supports the planning of research and development (R&D) for a medical oxygen supplier. As a result, it is shown that the proposed methodology enables organizations to identify weak signals from the internet for a given hypothesis. This helps strategic planners to react ahead of time.     

Find truth in the hands of the few: acquiring specific knowledge with crowdsourcing

Crowdsourcing has been a helpful mechanism to leverage human intelligence to acquire useful knowledge.However, when we aggregate the crowd knowledge based on the currently developed voting algorithms, it often results in common knowledge that may not be expected. In this paper, we consider the problem of collecting specific knowledge via crowdsourcing. With the help of using external knowledge base such as WordNet, we incorporate the semantic relations between the alternative answers into a probabilisticmodel to determine which answer is more specific. We formulate the probabilistic model considering both worker’s ability and task’s difficulty from the basic assumption, and solve it by the expectation-maximization (EM) algorithm. To increase algorithm compatibility, we also refine our method into semi-supervised one. Experimental results show that our approach is robust with hyper-parameters and achieves better improvement thanmajority voting and other algorithms when more specific answers are expected, especially for sparse data.     

Mining temporal explicit and implicit semantic relations between entities using web search engines

In this paper, we study the problem of mining temporal semantic relations between entities. The goal of the studied problem is to mine and annotate a semantic relation with temporal, concise, and structured information, which can release the explicit, implicit, and diversity semantic relations between entities. The temporal semantic annotations can help users to learn and understand the unfamiliar or new emerged semantic relations between entities. The proposed temporal semantic annotation structure integrates the features from IEEE and Renlifang. We propose a general method to generate temporal semantic annotation of a semantic relation between entities by constructing its connection entities, lexical syntactic patterns, context sentences, context graph, and context communities. Empirical experiments on two different datasets including a LinkedIn dataset and movie star dataset show that the proposed method is effective and accurate. Different from the manually generated annotation repository such as Wikipedia and LinkedIn, the proposed method can automatically mine the semantic relation between entities and does not need any prior knowledge such as ontology or the hierarchical knowledge base. The proposed method can be used on some applications, which proves the effectiveness of the proposed temporal semantic relations on many web mining tasks.     

Quantitative cross impact analysis with latent semantic indexing

Cross impact analysis (CIA) consists of a set of related methodologies that predict the occurrence probability of a specific event and that also predict the conditional probability of a first event given a second event. The conditional probability can be interpreted as the impact of the second event on the first. Most of the CIA methodologies are qualitative that means the occurrence and conditional probabilities are calculated based on estimations of human experts. In recent years, an increased number of quantitative methodologies can be seen that use a large number of data from databases and the internet. Nearly 80% of all data available in the internet are textual information and thus, knowledge structure based approaches on textual information for calculating the conditional probabilities are proposed in literature. In contrast to related methodologies, this work proposes a new quantitative CIA methodology to predict the conditional probability based on the semantic structure of given textual information. Latent semantic indexing is used to identify the hidden semantic patterns standing behind an event and to calculate the impact of the patterns on other semantic textual patterns representing a different event. This enables to calculate the conditional probabilities semantically. A case study shows that this semantic approach can be used to predict the conditional probability of a technology on a different technology.     

IATA 58版变化和2017年锂电池运输规则要求

具体地描述了IATA 58版变化的主要概况,如启用两个有关锂电池的标签、不再需要托运人的声明和额外的说明文件、启用PI910的包装导则和张贴电池操作标记;分析了2017年锂电池运输规则要求的主要内容.