38 research outputs found
Predicate Generation for Learning-Based Quantifier-Free Loop Invariant Inference
We address the predicate generation problem in the context of loop invariant
inference. Motivated by the interpolation-based abstraction refinement
technique, we apply the interpolation theorem to synthesize predicates
implicitly implied by program texts. Our technique is able to improve the
effectiveness and efficiency of the learning-based loop invariant inference
algorithm in [14]. We report experiment results of examples from Linux,
SPEC2000, and Tar utility
Ion trap with gold-plated alumina: substrate and surface characterization
We describe a complete development process of a segmented-blade linear ion
trap. Alumina substrate is characterized with an X-ray diffraction and
loss-tangent measurement. The blade is laser-micromachined and polished,
followed by the sputtering and gold electroplating. Surface roughness is
examined at each step of the fabrication via both electron and optical
microscopies. On the gold-plated facet, we obtain a height deviation of tens of
nanometers in the vicinity of the ion position. Trapping of laser-cooled
Yb ions is demonstrated.Comment: 7 pages, 6 figure
Predicate Generation for Learning-Based Quantifier-Free Loop Invariant Inference
PETITION FOR ORIGINAL WRIT OF MANDAMUS DIRECTED TO THE HONORABLE DAVID L. MOWER DISTRICT JUDGE OF SEVIER COUNTY, STATE OF UTA
Low Power Sensor Location Prediction Using Spatial Dimension Transformation and Pattern Recognition
A method of positioning a location on a specific object using a wireless sensor has been developed for a long time. However, due to the error of wavelengths and various interference factors occurring in three-dimensional space, accurate positioning is difficult, and predicting future locations is even more difficult. It uses IoT-based node pattern recognition technology to overcome positioning errors or inaccurate predictions in wireless sensor networks. It developed a method to improve the current positioning accuracy in a sensor network environment and a method to learn a pattern of position data directly from a wavelength receiver. The developed method consists of two steps: The first step is a method of changing location data in 3D space to location data in 2D space in order to reduce the possibility of positioning errors in 3D space. The second step is to reduce the range of the moving direction angle in which the data changed in two dimensions can be changed in the future and to predict future positions through pattern recognition of the position data. It is to calculate the expected position in the future. In conclusion, three-dimensional positioning accuracy was improved through this method, and future positioning accuracy was also improved. The core technology was able to reduce inevitable errors by changing the spatial dimension from 3D to 2D and to improve the accuracy of future location prediction by reducing the range of the movable direction angle of the location data changed to 2D. It was also possible to obtain the result that the prediction accuracy increases in proportion to the amount of data accumulated in the wavelength receiver and the learning time. In the era of the Fourth Industrial Revolution, this method is expected to be utilized in various places, such as smart cities, autonomous vehicles, and disaster prediction
Low Power Sensor Location Prediction Using Spatial Dimension Transformation and Pattern Recognition
A method of positioning a location on a specific object using a wireless sensor has been developed for a long time. However, due to the error of wavelengths and various interference factors occurring in three-dimensional space, accurate positioning is difficult, and predicting future locations is even more difficult. It uses IoT-based node pattern recognition technology to overcome positioning errors or inaccurate predictions in wireless sensor networks. It developed a method to improve the current positioning accuracy in a sensor network environment and a method to learn a pattern of position data directly from a wavelength receiver. The developed method consists of two steps: The first step is a method of changing location data in 3D space to location data in 2D space in order to reduce the possibility of positioning errors in 3D space. The second step is to reduce the range of the moving direction angle in which the data changed in two dimensions can be changed in the future and to predict future positions through pattern recognition of the position data. It is to calculate the expected position in the future. In conclusion, three-dimensional positioning accuracy was improved through this method, and future positioning accuracy was also improved. The core technology was able to reduce inevitable errors by changing the spatial dimension from 3D to 2D and to improve the accuracy of future location prediction by reducing the range of the movable direction angle of the location data changed to 2D. It was also possible to obtain the result that the prediction accuracy increases in proportion to the amount of data accumulated in the wavelength receiver and the learning time. In the era of the Fourth Industrial Revolution, this method is expected to be utilized in various places, such as smart cities, autonomous vehicles, and disaster prediction
A Study on an Object oriented Modeling for the Satelite Control System Development reusing Structured Analysis and Design Approach
The object-oriented approach is a difficult method for engineers who are accustomed to other software development methods although it is an excellent software development approach. This paper presents a method for object-orineted modeling re-using DFD(Data Flow Diagram) and SC(Structure Chart) of structured analysis and design approach. This paper suggests an easy method for analysis and design using structured approach for object abstraction, which is one of the most difficult things in object-oriented approach