Synthesis and properties of new poly(amide imide)s containing trimellitic rings and hydantoin moieties in the main chain under microwave irradiation

Trimellitic anhydride was reacted with 4,4′‐diaminodiphenyl ether in a mixture of acetic acid and pyridine (3 : 2) at room temperature and was refluxed at 90–100°C, and N,N′‐(4,4′‐diphenylether) bistrimellitimide (3) was obtained in a quantitative yield. 3 was converted into N,N′‐(4,4′‐diphenylether) bistrimellitimide diacid chloride (4) by a reaction with thionyl chloride. Then, six new poly(amide imide)s were synthesized under microwave irradiation with a domestic microwave oven through the polycondensation reactions of 4 with six different derivatives of 5,5‐disubstituted hydantoin in the presence of a small amount of a polar organic medium such as o‐cresol. The polycondensation proceeded rapidly and was completed within 7–10 min, producing a series of new poly(amide imide)s in high yields with inherent viscosities of 0.27–0.66 dL/g. The resulting poly(amide imide)s were characterized by elemental analysis, viscosity measurements, differential scanning calorimetry, thermogravimetric analysis, derivative thermogravimetry, solubility testing, and Fourier transform infrared spectroscopy. All the polymers were soluble at room temperature in polar solvents such as N,N‐dimethylacetamide, N,N‐dimethylformamide, dimethyl sulfoxide, tetrahydrofuran, and N‐methyl‐2‐pyrrolidone. © 2004 Wiley Periodicals, Inc. J Appl Polym Sci 92: 3447–3453, 2004     

A GMM/HMM model for reconstruction of missing speech spectral components for continuous speech recognition

This paper presents a method for reconstructing unreliable spectral components of speech signals using the statistical distributions of the clean components. Our goal is to model the temporal patterns in speech signal and take advantage of correlations between speech features in both time and frequency domain simultaneously. In this approach, a hidden Markov model (HMM) is first trained on clean speech data to model the temporal patterns which appear in the sequences of the spectral components. Using this model and according to the probabilities of occurring noisy spectral component at each states, a probability distributions for noisy components are estimated. Then, by applying maximum a posteriori (MAP) estimation on the mentioned distributions, the final estimations of the unreliable spectral components are obtained. The proposed method is compared to a common missing feature method which is based on the probabilistic clustering of the feature vectors and also to a state of the art method based on sparse reconstruction. The experimental results exhibits significant improvement in recognition accuracy over a noise polluted Persian corpus.     

Concept learning games

In this paper, we intend to have a game theoretic study on the concept learning problem in a multi-agent system. Concept learning is a very essential and well-studied domain of machine learning when it is studied under the characteristics of a multi-agent system. The most important reasons are the partiality of the environment perception for any agent and also the communication holdbacks, resulting into a deep need for a collaborative protocol in favor of multi-agent transactions. Here we wish to investigate multi-agent concept learning with the help of its components, thoroughly with a game theoretic taste, esp. on the pre-learning processes. Based on two standard notations, we address the non-unanimity of concepts, classification of objects, voting and communicating protocol, and also the learning itself. In such a game of concept learning, we consider a group of agents, communicating and consulting to upgrade their ontologies based on their conceptualizations of the environment. For this purpose, we investigate the problem in two separate and standard distinctions of game theory study, cooperation and competition. Several solution concepts and innovative ideas from the multi-agent realm are used to produce an approach that contains the reasoning process of the agents in this system. Some experimentations come at the end to show the functionality of our approach. These experimentations come distinctly for both cooperative and competitive views.     

Optimal Deep Learning Based Intruder Identification in Industrial Internet of Things Environment

With the increased advancements of smart industries, cybersecurity has become a vital growth factor in the success of industrial transformation. The Industrial Internet of Things (IIoT) or Industry 4.0 has revolutionized the concepts of manufacturing and production altogether. In industry 4.0, powerful Intrusion Detection Systems (IDS) play a significant role in ensuring network security. Though various intrusion detection techniques have been developed so far, it is challenging to protect the intricate data of networks. This is because conventional Machine Learning (ML) approaches are inadequate and insufficient to address the demands of dynamic IIoT networks. Further, the existing Deep Learning (DL) can be employed to identify anonymous intrusions. Therefore, the current study proposes a Hunger Games Search Optimization with Deep Learning-Driven Intrusion Detection (HGSODL-ID) model for the IIoT environment. The presented HGSODL-ID model exploits the linear normalization approach to transform the input data into a useful format. The HGSO algorithm is employed for Feature Selection (HGSO-FS) to reduce the curse of dimensionality. Moreover, Sparrow Search Optimization (SSO) is utilized with a Graph Convolutional Network (GCN) to classify and identify intrusions in the network. Finally, the SSO technique is exploited to fine-tune the hyper-parameters involved in the GCN model. The proposed HGSODL-ID model was experimentally validated using a benchmark dataset, and the results confirmed the superiority of the proposed HGSODL-ID method over recent approaches.     

F-LEACH: a fuzzy-based data aggregation scheme for healthcare IoT systems

The Journal of Supercomputing - Internet of Things (IoT) is an emerging paradigm that consists of numerous connected and interrelated devices with embedded sensors, exchanging data with each other...     

Trends in intelligent manufacturing research: a keyword co-occurrence network based review

Journal of Intelligent Manufacturing - In recent years, driven by Industry 4.0 wave, academic research has focused on the science, engineering, and enabling technologies for intelligent and cyber...     

ETAS: predictive scheduling of functions on worker nodes of Apache OpenWhisk platform

The Journal of Supercomputing - Fast execution of functions is an inevitable challenge in the serverless computing landscape. Inefficient dispatching, fluctuations in invocation rates, burstiness...     

The Effect of Dilute Charged Impurity on the Electronic Heat Capacity and Magnetic Susceptibility of Ferromagnetic MoS<Subscript>2</Subscript>

In this paper, the effects of dilute charged impurity doping on electronic heat capacity (EHC) and magnetic susceptibility (MS) of a two-dimensional material ferromagnetic gapped graphene-like, MoS₂, are investigated within the Green’s function approach by using the Kane-Mele Hamiltonian and self-consistent Born approximation (SCBA) at Dirac points. Our findings show that there is a critical impurity concentration (IC) and scattering strength (ISS) for each valley in EHC and MS curves. Also, we have found that the spin band gap decreases with impurity only for valley K, ↑ and \(K^{\prime }, \downarrow \) due to the existence of inversion symmetry between valleys. On the other hand, a magnetic phase transition from ferromagnetic to antiferromagnetic and paramagnetic has been observed. The increase of scattering rate of carriers in the presence of impurity is the main reason of these behaviors.