Miracle-dz/UniFAR-FTUs huggingface dataset:
Modeling and analysis of semiconductor manufacturing plants using time Petri net models: COT business case study

The radical change in the economical situation affected the semiconductor manufacturing business. Recently COT (customer oriented technology a.k.a. IC foundry) business is gaining popularity in many semiconductor manufacturing companies. In this field of semiconductor manufacturing business, predicting and controlling the fabrication cycle time, work in process status and in-line data with maintaining the high yields and quality of the products are the most important factors in the success of the whole business. Although semiconductor fabrication, excluding the design and assembly stages where these two stages are optional in COT business, is process oriented, just like the FMS in traditional assembly oriented manufacturing system, flexibility plays a major role. In this paper, the authors model and analyze the COT semiconductor fabrication from the customer order to the delivery stage using time Petri net models. Then a method to obtain turn around time, work in process status and capacity on-line by analyzing the behavior of the model is proposed by the authors.
Real-time planning and control for simulated bipedal locomotion

Understanding and reproducing the processes that give rise to purposeful human and animal motions has long been of interest in the fields of character animation, robotics and biomechanics. However, despite the grace and agility with which many living creatures effortlessly perform skilled motions, modeling motor control has proven to be a difficult problem. Building on recent advances, this thesis presents several approaches to creating control policies that allow physically-simulated characters to demonstrate skill and purpose as they interact with their virtual environments. We begin by introducing a synthesis-analysis-synthesis framework that enables physically-simulated characters to navigate environments with significant stepping constraints. First, an offline optimization method is used to compute control solutions for randomly-generated example problems. Second, the example motions and their underlying control patterns are analyzed to build a low-dimensional step-to-step model of the dynamics. Third, the dynamics model is exploited by a planner to solve new instances of the task in real-time. We then present a method for precomputing robust task-based control policies for physically simulated characters. This allows our characters to complete higher-level locomotion tasks, such as walking in a user specified direction, while interacting with the environment in significant ways. As input, the method assumes an abstract action vocabulary consisting of balance-aware locomotion controllers. A constrained state exploration phase is first used to define a dynamics model as well as a finite volume of character states over which the control policy will be defined. An optimized control policy is then computed using reinforcement learning. Lastly, we describe a control strategy for walking that generalizes well across gait parameters, motion styles, character proportions, and a variety of skills. The control requires no characterspecific or motion-specific tuning, is robust to disturbances, and is simple to compute. The method integrates tracking using proportional-derivative control, foot placement adjustments using an inverted pendulum model and Jacobian transpose control for gravity compensation and fine-level velocity tuning. We demonstrate a variety of walking-related skills such as picking up objects placed at any height, lifting, pulling, pushing and walking with heavy crates, ducking over and stepping under obstacles and climbing stairs.
Personalised Web Search using User Profile Incorporation

Search has become important aspect of day to day life. Search engines are the best way to get what user wants. But the necessity is to understand what user actually wants to search. Personalised search is best way to resolve ambiguity of the query terms given by the user. By considering three major parameters that will help to give the precise results to the user . User Profiling plays important role here, in the proposed idea we are considering user profile which contains details like interests, hobby's and educational background data. We will be also storing previous queries of the user as well.There are many other parameters taken into consideration while searching data on web for a given query. Hence we have to take into consideration user's profile and pattern for the search. As user enters the query the query will be re-formulated using re-formulated using the profile of the respected user. The context of user must be taken into consideration. This makes the search relevant to the user's requirements. Also the previously searched results will be taken into consideration for next probable search. This intelligence of search engine will help user to navigate towards the relevant or more precise search. We are providing easy way to search data on web by understanding user's context to provide more allied as well as personalized search. The result shows improvement in helping user's to find what they are looking for. More facilities like backup and restore are provided for enhancing searching experience. Relate download is the facility where user can find the related documents on the local machine. Also Backup and Restore facility is provided for facilitating fast search and a complete new search.
Backpropagation networks including time delay elements (BPD)

Multilayered neural networks are applied to many problems. These networks need many learning iterations and can not directly handle the time sequence signals. In this paper the authors propose the backpropagation network (BPD) with time delay elements in the feedback. The backpropagation network described does not need as many learning iterations and is usable as a nonlinear filter.
MPC extensions to feedback linearizable systems

We present and compare various extensions of model predictive control (MPC) to the class of both full state and input/output feedback linearizable systems. The approaches presented are valid even for systems with unstable zero dynamics, with the goal being a reduction of the burdensome computational costs typically associated with nonlinear MPC. Finally, these various approaches are tested on examples.
Analysis of the Temporal and Geographical Distribution of a Chinese Learning Website Visitors

—As China developing in a rapid step, there is a tremendous growing in the foreigners’ interest of learning Chinese and exploring the Chinese culture. Along with this, more and more foreigners choose the Internet as an access to Chinese learning resources. In this paper we process an important Chinese learning website’s Web logs and analyze the users’ distribution. Based on deep statistics and analysis, we propose several conclusions about the temporal and geographical distribution characteristics of the Chinese learning website visitors. For the temporal distribution, we observe an obvious pattern: (i) it’s a cyclic pattern with a period of one week, (ii)Thursday holds the peak of user numbers within a specific week, (iii) there is a downward trend of the user numbers per day during weekdays. For the geographical distribution of the users, we present experiments on two levels: (a) countries and regions ,(b) continents. Understanding the temporal and geographical distribution characteristics of Chinese learning website visitors brings us a better knowledge of the users’ preference and gives us pointers for research, so that we can improve the Chinese learning website and then attain a better adaption of the users’ habits and need.
A comparable Wikipedia corpus: from wiki syntax to POS tagged XML

To build a comparable Wikipedia corpus of German, French, Italian, Norwegian, Polish and Hungarian for contrastive grammar research, we used a set of XSLT stylesheets to transform the mediawiki anntations to XML. Furthermore, the data has been amnntated with word class information using different taggers. The outcome is a corpus with rich meta data and linguistic annotation that can be used for multilingual research in various linguistic topics.
Assessing user experience with CASE tools: an exploratory analysis

The paper reports on the results of a survey of 19 large organizations in the Twin Cities of Minnesota. The survey gathered data on the experiences, expectations, realizations, and suggestions regarding the use of CASE tools. It focused on what they considered to be the critical success factors in the introduction and use of CASE. Most organizations had strong management support and a high ranking executive to champion the adoption and use of CASE. More organizations had made a commitment to use construction tools than design, and to use design tools than planning tools. Larger organizations were further along in their commitment to use CASE than smaller organizations. <<ETX>>
Visualization as a bridge between chemical and nuclear engineering simulations

Collaborating across disciplines can be a difficult task for a variety of reasons. One of which is the lack of common methodologies and tools for visualization and analysis. Tools developed in one group can rarely be applied directly to data from another group, due to differences in data formats, analysis methodologies, and learning curves associated with the advanced visualization systems necessary for modern scientific data. Despite these difficulties, multi-physics collaboration is key to developing and understanding the next generation of computational models, which couple the dynamic processes and scales of multiple disciplines. As a step towards integrative models, we propose a visualization and analysis system to serve as a common interconnect between chemical and nuclear engineering. This choice allows multiple organizations to fruitfully exchange data and example visualizations in a reproducible manner, providing a foundation for productive discussion. In this work, we demonstrate the utility of a visualization system as it applies to bridging simulation domains and providing for multidisciplinary research in nuclear and chemical engineering. ∗Scientific Computing and Imaging Institute, University of Utah †Radiation Protection, SLAC National Accelerator Laboratory ‡Utah Nuclear Engineering Program, University of Utah §Interactive Visualization and Data Analysis Group, DFKI Saarbrucken, Germany ¶Civil and Environmental Engineering, University of Utah
Design and Simulation of FPGA BasedDigitally Controlled Full Bridge DC-DCConverter

This paper explains Field-programmable gate array (FPGA) based digital control of DC-DC phase shifted full bridge converter operating at very high switching frequency. To achieve required high switching frequency, theoretically very high clock frequency is needed but how to achieve with less clock frequency has been shown here. The implementation in FPGA includes control algorithm and hybrid Digital Pulse Width Modulator (DPWM). Microsemi’s Proasic3E FPGA is used for implementation with 32 MHz clock frequency to achieve required 1 MHz switching frequency.
A Data Management Model For Large-Scale Bioinformatics Analysis

Bioinformatics has seen an extreme data growth in later years due to the reduction in cost per megabase of sequencing, which today is around 1/400,000th of the cost in 2001. This reduction in cost enables new types of studies, such as searching for novel enzymes in marine environments using metagenomic approaches. However, it also leads to an increase in volume of data, which shifts overall cost from sequencing to analysis and data management. In addition, the data growth means that the analysis must move from personal computers to cluster, cloud and supercomputer infrastructure, which further complicates data management and processing. This increase in data volume applies to both raw data produced as well as the size of reference databases. These reference databases are used in analysis to e.g. compare sequences to all the known sequences, so larger reference databases provide more accurate results. However, this increase in analysis accuracy also increases the volume of both input data and reference databases, which further increases analysis cost as well as the complexity of data management and processing. In this dissertation, we examine the challenge of data management, particularly how existing bioinformatics analysis pipelines can reduce the runtime and hence the cost of analysis through a better data management approach. We provide the file-based distributed data materialization (FDDM) approach and realize it as the GeStore system to provide data management for real-world bioinformatics pipelines. The commonly used bioinformatics analysis frameworks do not provide efficient large-scale data management, in particular, updating analysis results with updated reference databases and reproducing previously computed results are costly and time-consuming. Technologies such as distributed databases and processing systems can efficiently process large amounts of data, but such systems are not straightforward to integrate with existing bioinformatics workflows since these workflows typically comprise legacy tools that are costly and time-consuming to port to new frameworks. Our approach bridges the gap between these by providing a simple file-based interface that makes it simple to integrate workflows using legacy tools with modern distributed databases and data processing frameworks. We show the need for such a system through an evaluation of the tools of a bioinformatics pipeline that is provided as a data analysis service. Our results
How can we investigate citation behavior?: a study of reasons for citing literature in communication

Authors' motivations for citing documents are addressed through a literature review and an empirical study. Replicating an investigation in psychology, the works of two highly‐cited authors in the discipline of communication were identified, and all of the authors who cited them during the period 1995–1997 were surveyed. The instrument posed 32 questions about why a certain document was cited, plus questions about the citer's relationship to the cited author and document. Most findings were similar to the psychology study, including a tendency to cite “concept markers” representing a genre of work. Authors in communication were more likely to have an interpersonal connection to cited authors, and to cite literature reviews—their most common reason for citation. Three types of judgments about cited works were found to best predict citation: (1) that the work was novel, well‐known, and a concept‐marker; (2) that citing it might promote the authority of one's own work; and (3) that the work deserved criticism. Suggestions are made for further research, especially regarding the anomalous role of creativity in cited works.
Joint Detection-Estimation of Brain Activity in fMRI using Graph Cuts

The functional Magnetic Resonance Imaging (fMRI) is a technique with increasing applications in studying the brain function. The blood-oxygenation-level-dependent (BOLD) is a fMRI method that allows the detection of brain activated regions after the application of an external stimulus, e.g., visual or auditive. This technique is based on the assumption that the metabolism increases in activated areas as well as the oxygen uptake. Analysing this information is a challenging problem because the BOLD signal is very noisy and its changes due to the application of a stimulus are very weak. Therefore, the detection of temporal correlations with the applied stimulus requires sophisticated statistical algorithms to understand if the changes on the BOLD signal are pure noise or are related with the applied stimulus, called paradigm in the fMRI scope. The traditional approach to detect activated regions is based on the general linear model (GLM) to describe the BOLD signal using statistical inference techniques to infer the activation. Unfortunately, this technique requires the tuning of parameters by a clinician which makes it impossible to be completely automatic. In this paper we propose a new technique, here called SPM-GC, designed in a Bayesian framework where the linear coefﬁcients of the explanatory variables (EV’s) that characterize the activation status of a given region are considered to be binary. In this approach the classiﬁcation task is modeled as a huge combinatorial optimization task that is optimally solved by using a recent optimization technique based on Graph Cuts . Exhaustive tests using synthetic data are presented and the error probability, P e , of the algorithm is characterized. Examples using real data are also presented to illustrate the application and performance of the algorithm.
VLSI Implementation of Fault Tolerance Multiplier based on Reversible Logic Gate

Multiplier is one of the essential component in the digital world such as in digital signal processing, microprocessor, quantum computing and widely used in arithmetic unit. Due to the complexity of the multiplier, tendency of errors are very high. This paper aimed to design a 2×2 bit Fault Tolerance Multiplier based on Reversible logic gate with low power consumption and high performance. This design have been implemented using 90nm Complemetary Metal Oxide Semiconductor (CMOS) technology in Synopsys Electronic Design Automation (EDA) Tools. Implementation of the multiplier architecture is by using the reversible logic gates. The fault tolerance multiplier used the combination of three reversible logic gate which are Double Feynman gate (F2G), New Fault Tolerance (NFT) gate and Islam Gate (IG) with the area of 160μm x 420. 3μm (67.25 mm2). This design achieved a low power consumption of 122. 85μW and propagation delay of 16.99ns. The fault tolerance multiplier proposed achieved a low power consumption and high performance which suitable for application of modern computing as it has a fault tolerance capabilities.
Switching adaptive filters for enhancing noisy and reverberant speech from microphone array recordings

Switching adaptive filters, suitable for speech beamforming, with no prior knowledge about the speech source are presented. The filters have two sections, of which only one section at any given time is allowed to adapt its coefficients. The switch between both is controlled by a speech detection function. The first section implements an adaptive look direction and cues in on the desired speech. This section only adapts when speech is present. The second section acts as a multichannel adaptive noise canceller. The obtained noise references are typically very bad; hence, adaptation must be restricted to silence-only periods. Several ideas were explored for the first section. The most robust solution, and the one with the best sound quality, was given by the simplest solution, i.e. a delay and sum beamformer that cues in on the direct path only and neglects all multipath contributions. Tests were performed with a four-microphone array in a highly reverberant room with both music and fan type noise as jammers, SNR improvements of 10 dB were typical with no audible distortion. >
Extracting Social Networks from Language Statistics

ABSTRACT Knowledge regarding social information is commonly believed to be derived from sources such as formal relationships and interviews and can be plotted as complex networks. We explored whether social networks can also be extracted through other means by using language statistics. In three computational studies we computed first-order and higher-order (latent semantic analysis) co-occurrences of story characters in three novels. These statistical linguistic frequencies entered in a multidimensional scaling analysis yielded a two-dimensional solution that correlated with the two-dimensional networks of characters generated by experts. An experimental study in which participants were asked to estimate social networks showed that human estimates are similar to computational estimates. These results demonstrated that language statistics based on texts can be used to generate social networks.
Interactive Visual Clutter Management in Scientific Visualization

Scientists visualize their data and interact with them on computers in order to thoroughly understand them. Nowadays, data become so large and complex that it is impossible to display the entire data on a single image. Scientific visualization often suffers from visual clutter problem because of high spacial resolution/dimension and temporal resolution. Interacting with the visualizations of large data, on the other hand, allows users to dynamically explore different parts of the data and gradually understand all information in the data. Information congestion and visual clutter exist in visualizations of different kinds of data, such as flow field data, tensor field data, and time-varying data. Occlusion presents a major challenge in visualizing 3D flow and tensor fields using streamlines. Displaying too many streamlines creates a dense visualization filled with occluded structures, but displaying too few streams risks losing important features. Glyph as a powerful multivariate visualization technique is used to visualize data through its visual channels. Placing large number of glyphs over the entire 3D space results in occlusion and visual clutter that make the visualization ineffective. To avoid the occlusion in streamline and glyph visualization, we propose a view-dependent interactive 3D lens that removes the occluding streamlines/glyphs by pulling the them aside through animations. High resolution simulations are capable of generating very large
Quantification and Analysis on Animal Social Behavior

我々がこの世界の現象を理解しようとするとき,対象を 観察し,定量的に計測することは最も重要な作業の一つで あり,科学の進展は,観測手法の発達によって支えられて きた. 例えば,天体観測による膨大なデータからケプラー の法則が発見され,二重スリット実験によって量子力学の 基礎である光の波と粒子の性質が明らかになった. では,動物行動を対象にした観測はどうだろうか? 古く から動物行動学者は,独自の勘と根気強さに基づいた目視 観察によって動物の行動を理解してきた. それにより,ミ ツバチの 8の字ダンスなど様々な発見をもたらし成功を収 めてきた. しかし,目視観察は人の目で捉えられる速度の 動きや,比較的短い時間の定性的な観察に限定される. 近 年,動物行動のデータを人の目ではなく画像解析などに基 づいて観測する手法が発展し,大規模な定量データを得る ことが可能になってきた [1]. 本論文では,その観測手法の 概要と,定量データを解析する手法をまとめ,特に社会性 動物への適用例を紹介し,動物行動学の今後の展開につい て議論する.
Research on Encryption Key Extraction From Iris Feature

The current encryption algorithm has some problems that key length is long, which is difficult to be memorized and kept so that a potential threat is caused to information security. The encryption key extracted from the biological feature is used for the encryption method, which has become a hotspot of the research. The Haar wavelet decomposition is carried out to the iris image, to extract the third-layer high frequency coefficient as the iris feature code. The random mapping function is used to generate a 128 - bit key. chi-square (χ 2 ) test is used to analyze the key safety extracted. The results show that the key extracted from iris feature can meet requirements of the randomness and security of the encryption algorithm.
A PASSIVE UPPER LIMB EXOSKELETON FOR MACAQUES IN A BMI STUDY - KINEMATIC DESIGN, ANALYSIS, AND CALIBRATION

Integrating an exoskeleton as an external apparatus for a brain-machine interface has the advantage of providing mul tiple contact points to determine body segment postures and allow ing control to and feedback from each joint. When using macaques as subjects to study neural control of movement, a singulari tyfree upper limb exoskeleton is required to guarantee safe an d accurate tracking of joint angles over all possible range ofmotion. In addition, the compactness of a design is of more impo rtance considering macaques’ significantly smaller body dim ensions than humans’. Proposed in this paper is a 6-degree-offreedom (DOF) passive upper limb exoskeleton with 4 DOFs at the shoulder complex. System kinematic analysis is investi gated in terms of its singularity and manipulability. A real-timedata acquisition system is set up, and system kinematic calibrat ion is conducted.