Data-Driven Quality Prediction of Batch Processes Based on Minimal-Redundancy-Maximal-Relevance Integrated Convolutional Neural Network

For batch processes that are extensively applied in modern industry and characterized by nonlinearity and dynamics, quality prediction is significant to obtain high-quality products and maintain production safety. However, some quality variables and key performance indicators are difficult to measure online. In addition, the mechanism-based model for batch processes is usually tough to acquire due to the strong nonlinearity and dynamics, which makes quality prediction a challenge. With the accumulation of historical process data, data-driven methods for quality prediction gain increasing attention, among which convolutional neural network (CNN) is quite successful for its automatic feature extraction of nonlinear features from raw data. Considering that most CNN-based methods mainly take the variety of extracted features into account and ignore the redundancy between them, this paper introduces the minimal-redundancy-maximal-relevance algorithm to select features obtained by original CNN and further improves it with a feature selection layer to form the proposed method referred as mRMR-CNN. Then, a quality prediction model is established based on mRMR-CNN and the effectiveness of it is verified on the penicillin fermentation process, where the proposed method shows remarkable performance.

The first troglobitic Pseudochthonius Balzan, 1892 (Pseudoscorpiones, Chthoniidae) from the karst area of Serra do Ramalho, Brazil: a threatened species

Pseudochthonius ramalhosp. nov. is described to Gruna do Vandercir cave, in the Serra do Ramalho karst area, southwestern Bahia, Brazil. This area has an extensive limestone outcrop, with several caves, and the occurrence of potential minerals that are financially attractive for mining projects. The new species shows troglomorphic characteristics such as the depigmentation of the carapace and absence or reduction of eyes. It is a rare troglobitic species, and following the criteria of IUCN, we categorized the species as Critically Endangered – CR, IUCN criteria B1ab(iii)+2ab(iii). According to Brazilian legislation, locations, where critically endangered species live, can be protected by law, and we consider this cave/region to be of maximal relevance for protection.

Study of Auditory Brain Cognition Laws-Based Recognition Method of Automobile Sound Quality

The research shows that subjective feelings of people, such as emotions and fatigue, can be objectively reflected by electroencephalography (EEG) physiological signals Thus, an evaluation method based on EEG, which is used to explore auditory brain cognition laws, is introduced in this study. The brain cognition laws are summarized by analyzing the EEG power topographic map under the stimulation of three kinds of automobile sound, namely, quality of comfort, powerfulness, and acceleration. Then, the EEG features of the subjects are classified through a machine learning algorithm, by which the recognition of diversified automobile sound is realized. In addition, the Kalman smoothing and minimal redundancy maximal relevance (mRMR) algorithm is used to improve the recognition accuracy. The results show that there are differences in the neural characteristics of diversified automobile sound quality, with a positive correlation between EEG energy and sound intensity. Furthermore, by using the Kalman smoothing and mRMR algorithm, recognition accuracy is improved, and the amount of calculation is reduced. The novel idea and method to explore the cognitive laws of automobile sound quality from the field of brain-computer interface technology are provided in this study.

State of health prediction based on multi-kernel relevance vector machine and whale optimization algorithm for lithium-ion battery

The state of health (SOH) reflects the health status of the lithium-ion battery and is expected to accurately predicted, so as the corresponding maintenance measures can be taken to ensure the safe operation of the battery. This paper proposed a SOH prediction method based on multi-kernel relevance vector machine (RVM) and whale optimization algorithm (WOA). Firstly, the original features were obtained from the battery voltage and temperature data in charging and discharging phases. Secondly, the minimal-redundancy-maximal-relevance (mRMR) algorithm was introduced to select the optimal feature set. Then, the online model and offline model based on multi-kernel RVM and WOA were constructed. Finally, a hybrid model which combines the online model and offline model was proposed to prediction the SOH of the lithium-ion battery. The performance of the proposed method was evaluated with two kinds of data sets. The experimental results showed that the proposed method obtained higher prediction accuracy in both long-term and short-term periods than other methods.

Analysis of Speech Humor in Crosstalk From Principles of Cooperation and Relevance Theory

Cross talk is one of the unique performing arts in China, and most crosstalk gives the audience happy feelings through the delivery of humorous language. This paper interprets the generation of humor in crosstalk through the cooperation principles and the relevance theory, and the generation of the humorous results comes from the contrast between ostensive-inference model and the contrast between maximal relevance and optimal relevance.

Performance Evaluation of Epileptic Seizure Prediction Using Time, Frequency, and Time–Frequency Domain Measures

The prediction of epileptic seizures is crucial to aid patients in gaining early warning and taking effective intervention. Several features have been explored to predict the onset via electroencephalography signals, which are typically non-stationary, dynamic, and varying from person-to-person. In the former literature, features applied in the classification have shared similar contributions to all patients. Therefore, in this paper, we analyze the impact of the specific combination of feature and channel from time, frequency, and time–frequency domains on prediction performance of disparate patients. Based on the minimal-redundancy-maximal-relevance criterion, the proposed framework uses a sequential forward selection approach to individually find the optimal features and channels. Trained models could discriminate the pre-ictal and inter-ictal electroencephalography with a sensitivity of 90.2% and a false prediction rate of 0.096/h. We also present the comparison between the classification accuracy obtained by the optimal features, several features summarized from optimal features, and the complete set of features from three domains. The results indicate that various patient interpretations have a certain specificity in the selection of feature-channel. Furthermore, the detailed list of optimal features and summarized features are proffered for reference to those who research the corresponding database.

Feature Selections Using Minimal Redundancy Maximal Relevance Algorithm for Human Activity Recognition in Smart Home Environments

In this paper, maximal relevance measure and minimal redundancy maximal relevance (mRMR) algorithm (under D-R and D/R criteria) have been applied to select features and to compose different features subsets based on observed motion sensor events for human activity recognition in smart home environments. And then, the selected features subsets have been evaluated and the activity recognition accuracy rates have been compared with two probabilistic algorithms: naïve Bayes (NB) classifier and hidden Markov model (HMM). The experimental results show that not all features are beneficial to human activity recognition and different features subsets yield different human activity recognition accuracy rates. Furthermore, even the same features subset has different effect on human activity recognition accuracy rate for different activity classifiers. It is significant for researchers performing human activity recognition to consider both relevance between features and activities and redundancy among features. Generally, both maximal relevance measure and mRMR algorithm are feasible for feature selection and positive to activity recognition.