In some methods for test generation, an analog device under test (DUT) is treated as a discrete-time digital system by placing it between a digital-to-analog converter and an analog-to-digital converter. Then the test patterns and responses can be performed and analyzed in the digital domain. We propose a novel test generation algorithm based on a support vector machine (SVM). This method uses test patterns derived from the test generation algorithm as input stimuli, and sampled output responses of the analog DUT for classification and fault detection. The SVM is used for classification of the response space. When the responses of normal circuits are similar to those of faulty circuits (i.e., the latter have only small parametric faults), the response space is mixed and traditional algorithms have difficulty in distinguishing the two groups. However, the SVM provides an effective result. This paper also proposes an algorithm to calculate the test sequence for input stimuli using the SVM results. Numerical experiments prove that this algorithm can enhance the precision of test generation.
Many methods have been presented for the testing and diagnosis of analog circuits. Each of these methods has its advantages and disadvantages. In this paper we propose a novel sensitivity analysis algorithm for the classical parameter identification method and a continuous fault model for the modern test generation algorithm, and we compare the characteristics of these methods. At present, parameter identification based on the component connection model (CCM) cannot ensure that the diagnostic equation is optimal. The sensitivity analysis algorithm proposed in this paper can choose the optimal set of trees to construct an optimal CCM diagnostic equation, and enhance the diagnostic precision. But nowadays increasing attention is being paid to test generation algorithms. Most test generation algorithms use a single value in the fault model. But the single values cannot substitute for the actual faults that may occur, because the possible faulty values vary over a continuous range. To solve this problem, this paper presents a continuous fault model for the test generation algorithm which has a continuous range of parameters. The test generation algorithm with this model can improve the treatment of the tolerance problem, including the tolerances of both normal and faulty parameters, and enhance the fault coverage rate. The two methods can be applied in different situations.
Field computation, an emerging computation technique, has inspired passion of intelligence science research. A novel field computation model based on the magnetic field theory is constructed. The proposed magnetic field computation (MFC) model consists of a field simulator, a non-derivative optimization algorithm and an auxiliary data processing unit. The mathematical model is deduced and proved that the MFC model is equivalent to a quadratic discriminant function. Furthermore, the finite element prototype is derived, and the simulator is developed, combining with particle swarm optimizer for the field configuration. Two benchmark classification experiments are studied in the numerical experiment, and one notable advantage is demonstrated that less training samples are required and a better generalization can be achieved.
The Performance Seeking Control (PSC) system for the Pratt & Whitney 1128 Engine requires estimates of engine performance; however, the current estimation method of Kalman Filtering imposes a heavy computation overhead. Thus fast on-line applications may be limited. A neural network approach to estimating engine performance is investigated as an alternative method. This neuro-estimator emulates the Kalman Filter based on off-line training. Good agreement between Kalman Filter output and the neuro-estimator output was achieved for steady state conditions.
Computerized Adaptive Testing (CAT) refers to an online system that adaptively selects the best-suited question for students with various abilities based on their historical response records. Compared with traditional CAT methods based on heuristic rules, recent data-driven CAT methods obtain higher performance by learning from large-scale datasets. However, most CAT methods only focus on the quality objective of predicting the student ability accurately, but neglect concept diversity or question exposure control, which are important considerations in ensuring the performance and validity of CAT. Besides, the students' response records contain valuable relational information between questions and knowledge concepts. The previous methods ignore this relational information, resulting in the selection of sub-optimal test questions. To address these challenges, we propose a Graph-Enhanced Multi-Objective method for CAT (GMOCAT). Firstly, three objectives, namely quality, diversity and novelty, are introduced into the Scalarized Multi-Objective Reinforcement Learning framework of CAT, which respectively correspond to improving the prediction accuracy, increasing the concept diversity and reducing the question exposure. We use an Actor-Critic Recommender to select questions and optimize three objectives simultaneously by the scalarization function. Secondly, we utilize the graph neural network to learn relation-aware embeddings of questions and concepts. These embeddings are able
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