Embedded Software Fault Prediction Based on Back Propagation Neural Network

Bastos-Filho

2022

Preprint

Quality control is a critical aspect of modern electronic circuit industry. In addition to being a prerequisite for proper functioning, circuit quality is closely related to safety, security, and economics. Deep learning models have been extensively used in embedded systems testing and anomaly detection. However, performance is heavily dependent on the data available for training. Often, the number of samples or even its quality is limited. This leads to poor training process and low performance. We present a solution to improve anomaly detection in embedded systems by transforming time signals acquired from the printed circuit board under test. The proposed approach is experimentally validated in two autoencoder-based anomaly detection systems. Hence, two types of signals are analyzed: electric current and thermographic signatures. In both cases, electrical or thermographic signals are pre-processed prior to being converted into spectrogram images, which are then used to train and test the autoencoder. The achieved anomaly detection accuracy improvement for the thermographic case is 71%, compared with the raw data. For the electric current case, we show how data transformations enable autoencoder training where, using raw data, training is not feasible. In this case, we find accuracy improvements of up to 98%. The results indicate that, even in a scenario where the available data are limited, it is possible to achieve an acceptable performance using the proposed technique.

Section: Related Workmentioning

confidence: 99%

Improving Autoencoder-Based Anomaly Detection in Embedded Systems using Data Transformation

Bastos-Filho

2022

Preprint

“…The second advantage is that in changing environments where delegates respond well to the mentioned changes, they change their behavior and adapt to them independently [6].…”

Section: Collective Intelligencementioning

confidence: 99%

Investigating and comparing the performance of meta-heuristic algorithms in feature selection and software fault prediction

Arshaghi

2022

Preprint

Meta‑Heuristic algorithms are optimization techniques that provide the optimal solution through processes of repeated exploration and exploitation of the entire search space. Feature selection is also an important and prominent process in the field of machine learning that reduces data dimensions. This paper examines and compares nature-inspired meta-heuristic algorithms for feature selection to increase the accuracy of software fault prediction. Researchers cannot easily select meta-heuristic algorithms as a suitable method for their research due to their great variety and multiplicity. In this paper, by describing the feature selection techniques and its methods, the application of meta-heuristic algorithms in different fields, such as swarm intelligence and binary methods of these algorithms has been investigated. Also, by introducing 18 meta-heuristic algorithms in 6 different categories and evaluating each of them, a suitable analysis has been provided to researchers so that they can easily and with the highest efficiency choose the appropriate algorithm and method of their work. In the papers presented so far, meta-heuristic algorithms have been studied from only one aspect, while in this article, while studying different types of research, they have tried to study and evaluate them from different aspects. The effectiveness of the combination of three meta-heuristic algorithms, developed butterfly flame, bee colony and developed wall, was tested on 20 data sets. the proposed method in 17 datasets was able to improve the result of 7 datasets.

“…Assuming that the input path of the cell is dendrites, the synapses connected between the cell bodies receive the signals from the surrounding cell bodies, and the output path of the cell bodies is equivalent to the axons. When the nerve cell is stimulated, the received information is transmitted to the brain through the neural network, and the brain recognizes or judges the information, and finally transmits the response to the nerve cell through the neural network, so that the nerve the cell performs the desired action [16][17].…”

Section: Neural Networkmentioning

confidence: 99%

“…Compared with the previous predicting models, the demand has been insufficient, and have begun to develop artificial intelligence-based predicting methods, and many artificial intelligence models have emerged as a result. Artificial intelligence refers to computer systems or computer programs with human-like behavior and knowledge, including problem reasoning solving, knowledge storage and learning, and the ability to recognize and interpret human language [16][17][18][19].…”

mentioning

confidence: 99%

“…Based on the electricity consumption data and the temperature and humidity data of various places in the Meteorological Bureau, the temperature is first analyzed by regression method, and then the backward transfer neural network analysis is used to calculate and solve according to different learning rates and inertia factors to construct the best the power predicting model allows the power dispatching unit to do the dispatching work in advance. Apply predicts to analyze the current development of the TFT panel industry and future development trends, and explore the relevance of the TFT panel industry's capacity utilization rate to the business performance of the TFT panel industry under the pressure of global market competition, and put forward specific suggestions to provide TFT The panel industry is planning production capacity in the future, the capacity utilization strategy adopted [16][17][18].…”

mentioning

confidence: 99%

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Using Artificial Intelligence in IC Substrate Production Predicting

Liu

2021

Preprint

Today's technology products are changing with each day, the purpose is to bring more convenience to people, but also the competition among the technology industries is more competitive. In such environment, whether the company's decision-making is correct or not will directly affect the future development of an enterprise. Therefore, how an enterprise can formulate and construct a set of appropriate decision-making systems to accurately predict the future market will be the first important issue for enterprises. This research proposed an artificial intelligence predicting system to estimate manufacturing capacities and client demands, and providing it to manufacturing managers as a reference for inventory arrangements so that inventory can be adjusted appropriately to avoid excessive inventory levels. In recent years, neural networks have been widely and effectively applied to many predicting problems. The main reason is that most of the predicting problems are nonlinear models. And the backward neural network has the ability to construct nonlinear models. In this study, a predicting model combining grey correlation and neural network will be used to establish a high-accuracy predition system for the production predict of IC product. First, grey correlation analysis will be used to screen out the most relevant factors among many factors. And then put these factors into the neural network prediction model for training and prediction. The results show that the training prediction error and the empirical error value are about 14%. This value indicates that the prediction ability is better, so the proposed prediction model can be applied to the prediction of IC substrate production. It provided a predictive reference material and provide decision making with a more accurate, convenient and a fast tool to enhance the company’s competitiveness.