the colour control system of an offset printing machine is one example, where modern information processing technologies allow an improved process control and higher resource efficiency. It is not possible to measure ...
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ISBN:
(纸本)9789898565709
the colour control system of an offset printing machine is one example, where modern information processing technologies allow an improved process control and higher resource efficiency. It is not possible to measure the printing quality during production start. So no regular closed loop control can be used. For better system behaviour a simulation model is integrated to calculate the printing quality at any time. To get an optimal process performance, a high simulation quality must be ensured, which includes a compensation of process simulation inaccuracies as well as variable influences. therefore a cognitive system is installed, which measures the most important influences like the used paper and many other process parameters. After each production the right model parameters will be calculated by identification algorithms. So a data set with influences and parameters is available. For the next production run the best-fitting parameters for the simulation model can be calculated by a Neural Network. Additionally wear and deposits, which change the machine's performance, can be compensated. the simulation accuracy and the process control quality rises, which enables a faster run-up. Savings of paper, ink, energy and time allow an economic application of this control concept.
Sequential verification is a well known research framework that has attracted many researchers in the academic and industrial worlds during the last few decades. In this framework, initialization of synchronous models...
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ISBN:
(纸本)0780376552
Sequential verification is a well known research framework that has attracted many researchers in the academic and industrial worlds during the last few decades. In this framework, initialization of synchronous models is one of the fundamental and challenging research topics that is difficult to solve, especially when talking about large industrial strengths hardware models. Many researchers in this domain such as [10] [8] [9] and others tried to analyze and propose solutions to this problem, however the majority of the,approaches used were based on BDDs and classical reachability analysis methods, which by nature suffer from capacity and complexity limits. When talking about hardware formal, equivalence verification, the initialization issue becomes even more complex especially when trying to verify the logic equivalence of two large industrial circuits. In this note we propose a new adaptive and iterative approach that combines various symbolic simulation techniques and bounded model checking algorithms to initialize sequential circuits for the alignability equivalence verification. the novelty of our method has been employed on complex real life sequential models from Intel lead Pentium processor designs. these methods are already implemented in Intel's sequential verification engine, Insight.
Withthe ever increasing volumes of electronic information generation, users of information systems are facing an information overload. It is desirable to support information filtering as a complement to traditional r...
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ISBN:
(纸本)0818654007
Withthe ever increasing volumes of electronic information generation, users of information systems are facing an information overload. It is desirable to support information filtering as a complement to traditional retrieval mechanism. the number of users, and thus profiles (representing users' long-term interests), handled by an information filtering system is potentially huge, and the system has to process a constant stream of incoming information in a timely fashion. the efficiency of the filtering process is thus an important issue. In this paper, we study what datastructures and algorithms can be used to efficiently perform large-scale information filtering under the vector space model, a retrieval model established as being effective. We apply the idea of the standard inverted index to index user profiles. We devise an alternative to the standard inverted index, in which we, instead of indexing every term in a profile, select only the significant ones to index. We evaluate their performance and show that the indexing methods require orders of magnitude fewer I/Os to process a document than when no index is used. We also show that the proposed alternative performs better in terms of I/O and CPU processing time in many cases.
Artificial Intelligence (AI) and Machine Learning (ML) provide a set of useful analytic and decision-making techniques that are being leveraged by an ever-growing community of practitioners, including many whose appli...
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ISBN:
(纸本)9781450349468
Artificial Intelligence (AI) and Machine Learning (ML) provide a set of useful analytic and decision-making techniques that are being leveraged by an ever-growing community of practitioners, including many whose applications have security-sensitive elements. However, while security researchers often utilize such techniques to address problems and AI/ML researchers develop techniques for Big data analytics applications, neither community devotes much attention to the other. Within security research, AI/ML components are usually regarded as black-box solvers. Conversely, the learning community seldom considers the security/privacy implications entailed in the application of their algorithms when they are designing them. While these two communities generally focus on different directions, where these two fields do meet, interesting problems appear. Researchers working in this intersection have raised many novel questions for both communities and created a new branch of research known as secure learning. the AISec workshop has become the primary venue for this unique fusion of research.
Automatic medical image segmentation is an important part of medical image analysis, and plays an indispensable role in computer-aided diagnosis. Recently, FCN (Fully Convolutional Network) and U-Net have become the m...
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though architects are increasingly using building simulation, limitations in the interoperability of simulation domains remain a challenge. Independent simulations often have unique database structures. Incompatibilit...
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ISBN:
(纸本)9780977170630
though architects are increasingly using building simulation, limitations in the interoperability of simulation domains remain a challenge. Independent simulations often have unique database structures. Incompatibilities between these structures render it difficult to share input and output data with other simulations, making it difficult for a designer to predict multiple aspects of building performance at the same time. the integration of algorithms provides one means of overcoming some of these limitations and of increasing the efficiency and prediction accuracy of performance tools. DeST (Designer's Simulation Toolkit) is a custom environment in which multiple simulation engines are integrated into one system. this paper presents a newly developed IFC (Industry Foundation Classes)-based common database, the advantages and limitations of integration with DeST database structure and its object expansion to various domain integration will be discussed.
the proceedings contain 23 papers. the special focus in this conference is on Similarity Search and Applications. the topics include: Cost models and scheduling policies for quality-controlled similarity queries;cache...
ISBN:
(纸本)9783319684734
the proceedings contain 23 papers. the special focus in this conference is on Similarity Search and Applications. the topics include: Cost models and scheduling policies for quality-controlled similarity queries;cache and priority queue based approximation technique for a stream of similarity search queries;a benchmarking tool for approximate nearest neighbor algorithms;sketches with unbalanced bits for similarity search;an extreme-value-theoretic foundation for similarity applications;multivariate analysis and distributional support;high-dimensional simplexes for supermetric search;improving k-NN graph accuracy using local intrinsic dimensionality;dynamic time warping and the (windowed) dog-keeper distance;fast similarity search withthe earth mover's distance via feasible initialization and pruning;a new perspective on the tree edit distance;good and bad neighborhood approximations for outlier detection ensembles;scalable similarity search for molecular descriptors;self-indexed motion planning;practical space-efficient datastructures for high-dimensional orthogonal range searching;semantic similarity group by operators for metric data;succinct quadtrees for road data;on competitiveness of nearest-neighbor-based music classification;dataset proximity mining for governing the data lake;similarity-based browsing through large lists (extended abstract).;malware discovery using behaviour-based exploration of network traffic and concepts and challenges for effective retrieval considering users, tasks, and data.
the integrated approach is a classifier established on statistical estimator and artificial neural network. this consists of preliminary data whitening transformation which provides good starting weight vector, and fa...
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Accurate prediction of protein structures is very important for many applications such as drug discovery and biotechnology. Building side chains is an essential to get any reliable prediction of the protein structure ...
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We improve the worst-case time complexity of non-dominated sorting, an operation frequently used in evolutionary multiobjective algorithms, to O(n·(log n)k-2 log log n), where n is the number of solutions, k is t...
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