Machine learning algorithm selection for windage alteration fault diagnosis of mine ventilation system

Machine learning algorithms have been widely used in mine fault diagnosis. The correct selection of the suitable algorithms is the key factor that affects the fault diagnosis. However, the impact of machine learning algorithms on the prediction performance of mine fault diagnosis models has not been fully evaluated. In this study, the windage alteration faults (WAFs) diagnosis models, which are based on K-nearest neighbor algorithm (KNN), multi-layer perceptron (MLP), support vector machine (SVM), and decision tree (DT), are constructed. Furthermore, the applicability of these four algorithms in the WAFs diagnosis is explored by a T-type ventilation network simulation experiment and the field empirical application research of Jinchuan No. 2 mine. The accuracy of the fault location diagnosis for the four models in both networks was 100%. In the simulation experiment, the mean absolute percentage error (MAPE) between the predicted values and the real values of the fault volume of the four models was 0.59%, 97.26%, 123.61%, and 8.78%, respectively. The MAPE for the field empirical application was 3.94%, 52.40%, 25.25%, and 7.15%, respectively. The results of the comprehensive evaluation of the fault location and fault volume diagnosis tests showed that the KNN model is the most suitable algorithm for the WAFs diagnosis, whereas the prediction performance of the DT model was the second-best. This study realizes the intelligent diagnosis of WAFs, and provides technical support for the realization of intelligent ventilation.     

Ensemble relation network with multi-level measure

Fine-grained few-shot learning is a difficult task in image classification. The reason is that the discriminative features of fine-grained images are often located in local areas of the image, while most of the existing few-shotlearning image classification methods only use top-level features and adopt a single measure. In that way, the localfeatures of the sample cannot be learned well. In response to this problem, ensemble relation network with multi-level measure (ERN-MM) is proposed in this paper. It adds the relation modules in the shallow feature space tocompare the similarity between the samples in the local features, and finally integrates the similarity scores from thefeature spaces to assign the label of the query samples. So the proposed method ERN-MM can use local details andglobal information of different grains. Experimental results on different fine-grained datasets show that the proposedmethod achieves good classification performance and also proves its rationality.     

Complexities of digital technology use and the teaching and learning of function

Many argue that digital technologies have the potential to enhance the teaching and learning of mathematics. However, the availability of technology is not sufficient to realise this potential. The study reported takes a detailed approach to investigate the utility of the particular offerings of the available technologies in the teaching and learning of a specific area of mathematics, functions. Sixteen affordances identified in the data are described. The complexity of the process involved in resolving a situation where particular affordances would be useful so as they are perceived and enacted is detailed. Finally, a grounded theory framework arising from the data analysis from this study that can be used to explain, predict and guide action in other digital environments is presented.     

A bi-layer optimization approach for a hybrid flow shop scheduling problem involving controllable processing times in the steelmaking industry

A steelmaking-continuous casting (SCC) scheduling problem is an example of complex hybrid flow shop scheduling problem (HFSSP) with a strong industrial background. This paper investigates the SCC scheduling problem that involves controllable processing times (CPT) with multiple objectives concerning the total waiting time, earliness/tardiness and adjusting cost. The SCC scheduling problem with CPT is seldom discussed in the existing literature. This study is motivated by the practical situation of a large integrated steel company in which the just-in-time (JIT) and cost-cutting production strategy have become a significant concern. To address this complex HFSSP, the scheduling problem is decomposed into two subproblems: a parallel machine scheduling problem (PMSP) in the last stage and an HFSSP in the upstream stages. First, a hybrid differential evolution (HDE) algorithm combined with a variable neighborhood decomposition search (VNDS) is proposed for the former subproblem. Second, an iterative backward list scheduling (IBLS) algorithm is presented to solve the latter subproblem. The effectiveness of this bi-layer optimization approach is verified by computational experiments on well-designed and real-world scheduling instances. This study provides a new perspective on modeling and solving practical SCC scheduling problems.     

WIRELESS BODY-AREA NETWORKS

The last decade has witnessed the convergence of three giant worlds:electronics,computer science and telecommunications.The next decade should follow this convergence in most of our activities with the generalization of sensor networks.In particular with the progress in medicine,people live longer and the aging of population will push the development of wireless person-     

Conventional and machine learning approaches as countermeasures against hardware trojan attacks

Every year, the rate at which technology is applied on areas of our everyday life is increasing at a steady pace. This rapid development drives the technology companies to design and fabricate their integrated circuits (ICs) in non-trustworthy outsourcing foundries to reduce the cost, thus, leaving space for a synchronous form of virus, known as Hardware Trojan (HT), to be developed. HTs leak encrypted information, degrade device performance or lead to total destruction. To reduce the risks associated with these viruses, various approaches have been developed aiming to prevent and detect them, based on conventional or machine learning methods. Ideally, any undesired modification made to an IC should be detectable by pre-silicon verification/simulation and post-silicon testing. The infected circuit can be inserted in different stages of the manufacturing process, rendering the detection of HTs a complicated procedure. In this paper, we present a comprehensive review of research dedicated to countermeasures against HTs embedded into ICs. The literature is grouped in four main categories; (a) conventional HT detection approaches, (b) machine learning for HT countermeasures, (c) design for security and (d) runtime monitor.     

Electrochemical growth and characterization of iron doped cadmium sulfide thin films

Cadmium Sulfide and Ferrous doped Cadmium Sulfide thin films have been prepared on different substrates using an electrodeposition technique. Linear sweep voltammetric analysis has been carried out to determine deposition potential of the prepared films. X-ray diffraction analysis showed that the prepared films possess polycrystalline nature with hexagonal structure. Surface morphology and film composition have been analyzed using Scanning electron microscopy and Energy dispersive analysis by X-rays. Optical absorption analysis showed that the prepared films are found to exhibit Band gap value in the range between 2.3, 2.8 eV for Cadmium Sulfide and Ferrous doped Cadmium Sulfide.     

iM-SIMPLE: iMproved stable increased-throughput multi-hop link efficient routing protocol for Wireless Body Area Networks

Wireless Body Area Networks (WBANs) are envisaged to play crucial role in psychological, medical and non-medical applications. This paper presents iM-SIMPLE; a reliable, and power efficient routing protocol with high throughput for WBAN. We deploy sensor nodes on human body to measure the physiological parameters such as blood pressure, temperature, glucose, lactic acid, EMG, acceleration, pressure, and position. Data from sensors is forwarded to intermediate node, from where it is transmitted to sink. An end user can access the required information available at sink via internet. To minimize energy consumption of the network, we utilize multi-hop mode of communication. A cost function is introduced to select the forwarder; node with high residual energy and least distance to sink has minimum cost function value and is selected. Residual energy parameter balances the energy consumption among the sensor nodes, and least distance improves packet delivery to sink because of reduced less path loss. We formulate the minimum energy consumption and high throughput problems as an Integer Linear Program. In order to support mobility, we also consider two body postures. Simulation results confirm the performance advantage of iM-SIMPLE compared to contemporary schemes in terms of maximizing stability period and throughput of the network.     

A Novel Beam Search to Improve Neural Machine Translation for English-Chinese

Neural Machine Translation (NMT) is an end-to-end learning approach for automated translation, overcoming the weaknesses of conventional phrase-based translation systems. Although NMT based systems have gained their popularity in commercial translation applications, there is still plenty of room for improvement. Being the most popular search algorithm in NMT, beam search is vital to the translation result. However, traditional beam search can produce duplicate or missing translation due to its target sequence selection strategy. Aiming to alleviate this problem, this paper proposed neural machine translation improvements based on a novel beam search evaluation function. And we use reinforcement learning to train a translation evaluation system to select better candidate words for generating translations. In the experiments, we conducted extensive experiments to evaluate our methods. CASIA corpus and the 1,000,000 pairs of bilingual corpora of NiuTrans are used in our experiments. The experiment results prove that the proposed methods can effectively improve the English to Chinese translation quality.     

An efficient video summarization for surveillance system using normalized k-means and quick sort method

There has been a vast augmentation in quantity of Video Content (VC) generated amid the last some years. The Video Summarization (VS) approach is introduced for managing the VC. Prevailing VS techniques have endeavored to render the VS but the systems have Execution Time (ET) as well as condensing the video's content in domain specific manner. To triumph over such disadvantages, this paper proposed efficient VS for surveillance system using normalized k-means along with quick sort method. The proposed technique comprises eight stages, like split video into frames, pre-sampling, provide ID number, feature extraction, Feature Selection (FS), clustering, extract frames, video summary. Initially, the video frames are pre-sampled utilizing the proposed Three Step Cross Searching Algorithm (TSCS) technique. Then, give the ID number for every frame. Next, the features are extracted as of the frames. Then, the necessary features are selected using Entropy based Spider Monkey Algorithms (ESMA). In next stage, the features are grouped using Normalized K-Means (N-Kmeans) algorithm for identifying best candidate frames. Then select the minimum distance value based cluster set is the Key Frame (KF) selection. At last, the video is orderly summarized using quick sort method. Finally, in experimental evaluation the proposed work is compared with the prevailing methods. The proposed VS gave better outcome than the existing approaches.