با سلام خدمت کاربران عزیز، به اطلاع می رساند ترجمه مقالاتی که سال انتشار آن ها زیر 2008 می باشد رایگان بوده و میتوانید با وارد شدن در صفحه جزییات مقاله به رایگان ترجمه را دانلود نمایید.
An equine disease diagnosis expert system based on improved reasoning of evidence credibility
سیستم خبره تشخیص بیماری های اسب بر اساس استدلال بهبود یافته از اعتبار شواهد-2019
In China, there is a troubling shortage of well-trained equine veterinarians, leaving the needs of many equine farmers unmet. This is especially true with respect to the diagnosis of equine diseases. To solve this shortcoming, an equine disease diagnosis expert system was developed. For the aspect of knowledge representation, the structure of equine disease diagnosis knowledge was analyzed using an ontology system. Next, the clinical signs were described using an object-attribute-value (O-A-V) format, and the knowledge representation was then expressed using production rules.With respect to the reasoning mechanism, the weights of the clinical signs and promoted confidence factors (PCF) were combined to express information and rules pertaining to clinical signs with an associated level of uncertainty. The model was established based on improved reasoning of evidence credibility. Finally, using the ASP.Net platform and the SQL Server 2008 database, the equine disease diagnosis expert system based on the B/S structure has been developed, and is capable of reliably diagnosing 40 of the most common equine diseases. A functional evaluation of the system was conducted, and the diagnostic accuracy was observed to be 88%. This study demonstrates a bright prospect for the popularization and application of the system through continuous system maintenance and knowledge-based updates.
Keywords: Equine disease | Diagnosis | Expert system | Object-based ontology | Evidence credibility
Ontology-based approach for the provision of simulation knowledge acquired by Data and Text Mining processes
رویکرد مبتنی بر هستی شناسی برای ارائه دانش شبیه سازی به دست آمده توسط فرآیندهای داده و متن کاوی-2019
Numerical simulation techniques such as Finite Element Analyses are essential in todays engineering design practices. However, comprehensive knowledge is required for the setup of reliable simulations to verify strength and further product properties. Due to limited capacities, design-accompanying simulations are performed too rarely by experienced simulation engineers. Therefore, product models are not sufficiently verified or the simulations lead to wrong design decisions, if they are applied by less experienced users. This results in belated redesigns of already detailed product models and to highly cost- and time-intensive iterations in product development. Thus, in order to support less experienced simulation users in setting up reliable Finite Element Analyses, a novel ontology-based approach is presented. The knowledge management tools developed on the basis of this approach allow an automated acquisition and target-oriented provision of necessary simulation knowledge. This knowledge is acquired from existing simulation models and text-based documentations from previous product developments by Text and Data Mining. By offering support to less experienced simulation users, the presented approach may finally lead to a more efficient and extensive application of reliable FEA in product development
Keywords: Knowledge-based engineering | Simulation, Finite Element Analysis | Ontology-based knowledge representation | Text Mining | Data Mining
Semantic reasoning in service robots using expert systems
استدلال معنایی در روبات های کارگر با استفاده از سیستم های خبره-2019
This paper presents the semantic-reasoning module of VIRBOT, our proposed architecture for service robots. We show that by combining symbolic AI with digital-signal processing techniques this module achieves competitive performance. Our system translates a voice command into an unambiguous representation that helps an inference engine, built around an expert system, to perform action and motion planning. First, in the natural-language interpretation process, the system generates two outputs: (1) conceptual dependence, expressing the linguistic meaning of the statement, and (2) verbal confirmation, a paraphrase in natural language that is repeated to the user to confirm that the command has been correctly understood. Then, a conceptual-dependency interpreter extracts semantic role structures from the input sentence and looks for such structures in a set of known interpretation patterns. We evaluate this approach in a series of skill-specific semantic-reasoning experiments. Finally, we demonstrate our system in the general-purpose service robot test of the RoboCup-at-Home international competition, where incomplete information is given to a robot and the robot must recognize and request the missing information, and we compare our results with a series of baselines from the competition where our proposal performed best.
Keywords: Service robots | Semantic reasoning | Knowledge representation
Knowledge representation using non-parametric Bayesian networks for tunneling risk analysis
نمایش دانش با استفاده از شبکه های بیزی غیر پارامتری برای تجزیه و تحلیل ریسک تونل زنی-2019
Knowledge capture and reuse are critical in the risk management of tunneling works. Bayesian networks (BNs) are promising for knowledge representation due to their ability to integrate domain knowledge, encode causal relationships, and update models when evidence is available. However, the model development based on classic BNs is challenging when expert opinions are solicited due to the discretization of variables and quantification of large conditional probability tables. This study applies non-parametric BNs, which only require the elicitation of the marginal distribution corresponding to each node and correlation coefficient associated with each edge, to develop a knowledge-based expert system for tunneling risk analysis. In particular, we propose to use the pairwise Pearsons linear correlations to parameterize the model because the assessment is intuitive and experts in the engineering domain are more familiar and comfortable with this notion. However, when Spearmans rank correlation is given, the method can also be used by modification of the marginals. The method is illustrated with a tunnel case in the Wuhan metro project. The expert knowledge of risk assessment for common failures in shield tunneling is integrated and visualized. The developed model is validated by real documented accidents. Potential applications of the model are also explored, such as decision support for risk-based design.
Keywords: Non-parametric Bayesian networks | Structured expert judgment | Expert system | Risk analysis | Tunneling
An evolutionary framework for machine learning applied to medical data
یک چارچوب تکاملی برای یادگیری ماشین که برای داده های پزشکی کاربرد دارد-2019
Supervised learning problems can be faced by using a wide variety of approaches supported in machine learning. In recent years there has been an increasing interest in using the evolutionary computation paradigm as a search method for classifiers, helping the applied machine learning technique. In this context, the knowledge representation in the form of logical rules has been one of the most accepted machine learning approaches, because of its level of expressiveness. This paper proposes an evolutionary framework for rule-based classifier induction. Our proposal introduces genetic programming to build a search method for classification-rules (IF/THEN). From this approach, we deal with problems such as, maximum rule length and rule intersection. The experiments have been carried out on our domain of interest, medical data. The achieved results define a methodology to follow in the learning method evaluation for knowledge discovery from medical data. Moreover, the results compared to other methods have shown that our proposal can be very useful in data analysis and classification coming from the medical domain.
Keywords: Machine learning | Logical rule induction | Data mining | Supervised learning | Evolutionary computation | Genetic programming | Ensemble classifier | Medical data
Optimising virtual networks over time by using Windows Multiplicative DEA model
بهینه سازی شبکه های مجازی در طول زمان با استفاده از مدل تحلیل پوششی داده ها ویندوز ضربی-2019
Recently, the prediction of the most efficient configuration of a vast set of devices used for mounting an optimised cloud computing services and virtual networks environments have attracted growing atten- tion. This paper proposes a paradigm shift in modelling transmission control protocol (TCP) behaviour over time in virtual networks by using data envelopment analysis (DEA) models. Firstly, it proves that self-similarity with long-range dependency is presented differently in every network device. This study implements a novel fractal dimension concept on virtual networks for prediction, where this key in- dex informs if the transport layer forwards services with smooth or jagged behaviour over time. Another substantial contribution is proving that virtual network devices have a distinct fractal memory, TCP band- width performance, and fractal dimension over time, presenting themselves as important factor for fore- casting of spatiotemporal data. Thus, a continuous stepwise fractal performance evaluation framework methodology is developed as an expert system for virtual network assessment and performs a fractal analysis as a knowledge representation. In addition, due to the limitations of classical DEA models, the windows multiplicative data envelopment analysis (WMDEA) model is used to dynamically assess the fractal time series from virtual network hypervisors. For knowledge acquisition, 50 different virtual net- work hypervisors were appraised as decision-making units (DMU). Finally, this expert system also acts as a math hypervisor capable of determining the correct fractal pattern to follow when delivering TCP services in an optimised virtual network.
Keywords: Cloud computing | Windows multiplicative data envelopment | analysis | Fractal expert system | Virtual Networks | Network Optimisation | Stepwise Performance Evaluation
Semantic hyper-graph-based knowledge representation architecture for complex product development
معماری نمایندگی دانش مبتنی بر گرافیک معنایی برای توسعه محصول پیچیده-2018
More and more manufacturing companies are facing challenges in knowledge refining and reusing in stage of product development. To resolve this problem and make the knowledge convenient for acquisition, machine understandable and human-understandable, this paper proposes a framework of semantic hyper-graph-based knowledge representation to support the knowledge sharing for the product development. A case study of car headlamp development is given to validate the feasibility and effectiveness of the proposed method. The results bring out that it can help engineers to rapidly and accurately acquire knowledge. In future research, the knowledge recommendation service based on product development process should be considered.
Keywords: Product development knowledge ، Knowledge representation ، Knowledge service ، XML topic map ، Ontology
Computational narrative mapping for the acquisition and representation of lessons learned knowledge
نگاشت محاسباتی برای کسب و ارائه دانش یادگیرنده-2018
Lessons learned knowledge is traditionally gained from trial and error or narratives describing past experiences. Learning from narratives is the preferred option to transfer lessons learned knowledge. However, learners with insufficient prior knowledge often experience difficulties in grasping the right information from narratives. This paper introduces an approach that uses narrative maps to represent lessons learned knowledge to help learners understand narratives. Since narrative mapping is a time-consuming, labor-intensive and knowledge intensive process, the proposed approach is supported by a computational narrative mapping (CNM) method to automate the process. CNM incorporates advanced technologies, such as computational linguistics and artificial intelligence (AI), to identify and extract critical narrative elements from an unstructured, text-based narrative and organize them into a structured narrative map representation. This research uses a case study conducted in the construction industry to evaluate CNM performance in comparison with existing paragraph and concept mapping approaches. Among the results, over 90% of respondents asserted that CNM enhanced their understanding of the lessons learned. CNM’s performance in identifying and extracting narrative elements was evaluated through an experiment using real-life narratives from a reminiscence study. The experiment recorded a precision and recall rate of over 75%.
Keywords: Knowledge management ، Lessons learned ، Knowledge acquisition ، Knowledge representation ، Human learning ، Computational narrative mapping
Ontologies for transportation research: A survey
هستی شناسی برای تحقیقات حمل و نقل: یک نظرسنجی-2018
Transportation research relies heavily on a variety of data. From sensors to surveys, data supports day-to-day operations as well as long-term planning and decision-making. The challenges that arise due to the volume and variety of data that are found in transportation research can be effectively addressed by ontologies. This opportunity has already been recognized – there are a number of existing transportation ontologies, however the relationship between them is unclear. The goal of this work is to provide an overview of the opportunities for ontologies in transpor tation research and operation, and to present a survey of existing transportation ontologies to serve two purposes: (1) to provide a resource for the transportation research community to aid in understanding (and potentially selecting between) existing transportation ontologies; and (2) to identify future work for the development of transportation ontologies, by identifying areas that may be lacking.
Keywords: Transportation ontology ، Knowledge representation ، Reasoning ، Interoperability ، Formal logic ، Semantic Web
Unsupervised data analytics in mining big building operational data for energy efficiency enhancement: A review
تحلیل داده های محافظت نشده در کاوش داده های عملیاتی ساختمان های بزرگ برای افزایش بهره وری انرژی: یک مرور-2018
Building operations account for the largest proportion of energy use throughout the building life cycle. The energy saving potential is considerable taking into account the existence of a wide variety of building operation deficiencies. The advancement in information technologies has made modern buildings to be not only energy-intensive, but also information-intensive. Massive amounts of building operational data, which are in essence the reflection of actual building operating conditions, are available for knowledge discovery. It is very promising to extract potentially useful insights from big building operational data, based on which actionable measures for energy efficiency enhancement are devised. Data mining is an advanced technology for analyzing big data. It consists of two main types of data analytics, i.e., supervised and unsupervised analytics. Despite of the power of supervised ana lytics in predictive modeling, unsupervised analytics are more practical and promising in discovering novel knowledge given limited prior knowledge. This paper provides a comprehensive review on the current utilization of unsupervised data analytics in mining massive building operational data. The com monly used unsupervised analytics are summarized according to their knowledge representations and applications. The challenges and opportunities are elaborated as guidance for future research in this multi-disciplinary field.
Keywords: Unsupervised data mining ، Big data ، Building operational performance ، Building energy management ، Building energy efficiency