The paper illustrates how software can be described precisely using LD-relations, how these descriptions can be presented in a readable manner using tabular notations, and one way such descriptions can be used to test programs. The authors describe an algorithm that can be used to generate a test oracle from program documentation, and present the results of using a tool based on it to help test part of a commercial network management application. The results demonstrate that these methods can be effective at detecting errors and greatly increase the speed and accuracy of test evaluation when compared with manual evaluation. Such oracles can be used for unit testing, in situ testing, constructing self-checking software, and ensuring consistency between code and documentation.

In this paper, curve-fitting and intensity-level-selection (ILS)-based algorithms for wind parameter extraction from shipborne X-band nautical radar images are investigated. First, to exclude the rain cases and low-backscatter images, a data quality control process is designed for both algorithms. An additional process is then introduced for the ILS-based method to improve the accuracy of wind measurements, including the recognition of blockages and islands in the temporally integrated radar images. For the low sea states, a dual-curve-fitting is proposed. These wind algorithms are tested using radar images and shipborne anemometer data collected on the east coast of Canada. It is shown that the dual-curve-fitting algorithm produces improvements in the mean differences between the radar and the anemometer results for wind direction and speed of about 5.7° and 0.3 m/s, respectively, under sea states with significant wave height lower than 2.30 m. Also, a harmonic function that is least-squares fitted to the selected range distances vector as a function of antenna look direction is applied. Compared with the original ILS-based algorithm, the modified procedure reduces the standard deviation for wind direction and speed by about 4° and 0.2 m/s, respectively. Finally, the performance of these two modified methods are compared.

Before designing safety- or mission-critical real-time systems, a specification of the required behavior of the system should be produced and reviewed by domain experts. After the system has been implemented, it should be thoroughly tested to ensure that it behaves correctly. This is best done using a monitor, a system that observes the behavior of a target system and reports if that behavior is consistent with the requirements. Such a monitor can be used both as an oracle during testing and as a supervisor during operation. Monitors should be based on the documented requirements of the system. If the target system is required to monitor or control real-valued quantities, then the requirements, which are expressed in terms of the monitored and controlled quantities, will allow a range of behaviors to account for errors and imprecision in observation and control of these quantities. Even if the controlled variables are discrete valued, the requirements must specify the timing tolerance. Because of the limitations of the devices used by the monitor to observe the environmental quantities, there is unavoidable potential for false reports, both negative and positive, This paper discusses design of monitors for real-time systems, and examines the conditions under which a monitor will produce false reports. We describe the conclusions that can be drawn when using a monitor to observe system behavior.

This paper describes the design of an efficient parallel implementation of an ice simulator that simulates the behaviour of a ship operating in pack ice. The main idea of the method is to treat ice as a set of discrete objects with very simple properties, and to model the system mechanics mainly as a set of discrete contact and failure events. In this way it becomes possible to parallelize the problem, so that a very large number of ice floes can be modeled. This approach is called the Ice Event Mechanics Modeling (IEMM) method which builds a system solution from a large set of discrete events occurring between a large set of discrete objects. The simulator is developed using the NVIDIA Compute Unified Device Architecture (CUDA). This paper also describes the execution of experiments to evaluate the performance of the simulator and to validate the numerical modeling of ship operations in pack ice. Our results show speed up of 11 times, reducing simulation time for a large ice field (9,801 floes) from over 2 hours to about 12 minutes.

Stratigraphic identification from wire-line logs and core samples is a common method for lithology classification. This traditional approach is considered superior, despite its significant financial cost. Artificial neural networks and machine learning offer alternative, cost-effective means for automated data interpretation, allowing geoscientists to extract insights from data. At the same time, supervised and semi-supervised learning techniques are commonly employed, requiring a sufficient amount of labeled data to be generated through manual interpretation. Typically, there are abundant unlabeled geophysical data while labeled data are scarcer. Supervised and semi-supervised techniques partially address the cost issue. An underutilized class of machine-learning-based methods, unsupervised data clustering, can perform consonant classification by grouping similar data without requiring known results, presenting an even more cost-effective solution. In this study, we examine a state-of-the-art unsupervised data clustering algorithm called piecemeal clustering to identify lithofacies from wire-line logs, effectively addressing these challenges. The piecemeal clustering algorithm groups similar wire-log signatures into clusters, determines the number of clusters present in the data, and assigns each signature to one of the clusters, each of which represents a lithofacies. To evaluate the performance, we tested the algorithm on publicly released data from ten wells drilled in the Hugoton and Panoma fields of southwest Kansas and northwest Oklahoma, respectively. The data consist of two major groups: marine and non-marine facies. The study herein is centered around addressing two fundamental research questions regarding the accuracy and practicality of the piecemeal clustering algorithm. The algorithm successfully identified nine distinct clusters in our dataset, aligning with the cluster count observed in previously published works employing the same data. Regarding mapping accuracy, the results were notable, with success rates of 81.90% and 45.20% with and without considering adjacent facies, respectively. Further detailed analysis of the results was conducted for individual types of facies and independently for each well. These findings suggest the algorithm’s precision in characterizing the geological formations. To assess its performance, a comprehensive comparative analysis was conducted, encompassing other data clustering algorithms, as well as supervised and semi-supervised machine learning techniques. Notably, the piecemeal clustering algorithm outperformed alternative data clustering methods. Furthermore, despite its unsupervised nature, the algorithm demonstrated competitiveness by yielding results comparable to, or even surpassing, those obtained through supervised and semi-supervised techniques.