Search

SYN Flood Attack is one form of distributed denial of service attack that attains the handshake process of TCP. This attack consumes all available server resources and provokes legitimate traffic which aims to make the server unavailable. It causes serious damage to cloud server and networking protocols. The main objective of this research work is to train the neural network for detecting the attack and to secure network connection. A novel binary fruit fly optimization algorithm with deep learning is proposed to predict the syn flood attack. The proposed algorithm is implemented using the KDD cup dataset. DL- BFFA algorithm has achieved 99.96% detection accuracy for detecting the SYN Flood Attack. A comparison study is conducted to validate the proposed model.

The solution behavior of a second element in the primary phase (α(Mg)) is important in the design of high-performance alloys. In this work, three sets of features have been collected: a) interaction features of solutes and Mg obtained from first-principles calculation, b) intrinsic physical properties of the pure elements and c) structural features. Based on the maximum solid solubility values, the solution behavior of elements in α(Mg) are classified into four types, e.g., miscible, soluble, sparingly-soluble and slightly-soluble. The machine learning approach, including random forest and decision tree algorithm methods, is performed and it has been found that four features, e.g., formation energy, electronegativity, non-bonded atomic radius, and work function, can together determine the classification of the solution behavior of an element in α(Mg). The mathematical correlations, as well as the physical relationships among the selected features have been analyzed. This model can also be applied to other systems following minor modifications of the defined features, if required.

Identification of the most prominent bio-markers or genes with high classification accuracy in the high-dimensional cancerous data, is still remained as an emerging challenge for the machine learning researchers. As this challenge has two objectives, i.e. minimizing the number of genes (NoG) and maximizing the classification accuracy percentage (CAP), this problem can be modeled as binary multi-objective approach. In this work, a modified version of multi-objective Jaya algorithm, multi-objective chaotic Jaya (MOCJaya), is suggested to select the minimum NoG with high CAP. Initially, a filter approach, namely Fisher score is applied to pre-select the informative genes. Then, MOCJaya algorithm is employed for both selecting key genes and classifying the cancer data. To assess the efficacy of the designed algorithm, ten binary and multi-class cancerous datasets are considered. Here, the suggested algorithm has been compared with multi-objective chaotic Genetic Algorithm (MOCGA), multi-objective chaotic particle swarm optimization (MOCPSO), multi-objective Jaya (MOJaya), multi-objective PSO (MOPSO), and non-dominated sorting GA (NSGA-II) models. Moreover, a comparison of MOCJaya algorithm with other seventeen existing models, is also performed here. The experimental results and comparison analysis reveal that MOCJaya classifies both the positive and negative samples of the cancer datasets in high CAP with a smaller NoG.

Let Z be the set of integers and N the set of positive integers. For a nonempty set A of integers and any integer n, denote r A ( n ) by the number of representations of n of the form n = a + a ′ , where a ⩽ a ′ and a , a ′ ∈ A , and d A ( n ) by the number of ( a , a ′ ) with a , a ′ ∈ A such that n = a − a ′ . The binary support of a positive integer n is defined as the subset S ( n ) of nonnegative integers consisting of the exponents in the binary expansion of n, and S ( n ) = − S ( | n | ) for negative integers n. In 2004, Nesetřil and Serra (2004) [6] proved that there is a set A of integers satisfying r A ( n ) = 1 for all integers n and | S ( x ) ⋃ S ( y ) | ⩽ 4 | S ( x + y ) | for x , y ∈ A . In this paper, we obtain a stronger result by adding the restriction that d A ( n ) = 1 for all positive integers n. Furthermore, we also prove that, (i) there is a set A 1 of integers satisfying r A 1 ( n ) = 1 for all n ∈ Z , the set consisting of positive integers n with d A 1 ( n ) = 0 has density one, and | S ( x ) ⋃ S ( y ) | ⩽ 4 | S ( x + y ) | for x , y ∈ A 1 ; (ii) there is a set A 2 of integers satisfying d A 2 ( n ) = 1 for all n ∈ N , the set consisting of integers n with r A 2 ( n ) = 0 has density one, and | S ( x ) ⋃ S ( y ) | ⩽ 4 | S ( x + y ) | for x , y ∈ A 2 .

We consider the Pareto-scheduling of two competing agents on a single machine, in which the jobs of each agent have their “own equal processing times” (shortly, OEPT). In the literature, two special versions of the OEPT model, in which the jobs have either unit or equal processing times, have been well studied, where the criteria are given by various regular objective functions without including the late work criteria. However, for equal processing times, the exact complexity of three problems is still unaddressed. Two-agent scheduling related to late work criteria is also a hot topic in recent years. This inspires our research by also including the total (weighted) late work as criteria. We show that, for equal processing times, all the problems are binary NP-hard if the criterion of one agent is the total tardiness or the total late work and the criterion of the other agent is either the total tardiness or the total late work or the weighted number of tardy jobs or the total weighted completion time. As a result, complexity classification for equal processing times is completely addressed. We further show that all the problems under the OEPT model are either polynomially solvable or ordinary NP-hard, which results in a complete complexity classification for the OEPT model.

From the dawn of the computer age, man has mass produced binary components for computers, due to which ternary or higher radix computers are not yet commercially used. It has been proved that ternary logic can be more efficient than binary logic and there are many devices in development that operate on more than two internal states. Therefore an efficient method to produce multi-valued logic on the basis of binary input provided is needed. In this article, an effective, simple, flexible, and low power consumption implementation has been proposed that can convert any binary number to a number of chosen radix. The proposed design is implemented in 32 nm TSMC CMOS. The proposed design is then evaluated in power/delay space and its performances are compared with the latest state of art designs. The proposed circuit exhibits power saving up to 92 % over previous models.

Nowadays the majority of retail customers use multiple channels. We investigate the assortment, space and inventory problem for an omni-channel retailer operating with interconnected bricks-and-mortar stores and an online shop. For this problem it becomes essential to consider customers’ demand interactions across channels. Current literature mainly focuses on single-channel assortments and ignores cross-channel substitution. We contribute the first integrated omni-channel model that determines assortments for the online and bricks-and-mortar channel with stochastic, space-elastic and out-of-assortment and out-of-stock demand both for in-channel substitution and cross-channel substitution. A specialized heuristic is developed that is based on an iterative solution of a binary problem and demand updates. Our approach achieves near-optimal results for small instances and higher objective values as an alternative heuristic for larger instances. With the full integration of channels, omni-channel retailers can realize a profit increase that mainly depends on the magnitude of substitution rates. We further show numerically that in-channel substitution has a stronger impact on profits than cross-channel substitution when costs are equal across channels.

Boolean network is a modeling tool that describes a dynamic system with binary variables and their logical transition formulas. Recent studies in precision medicine use a Boolean network to discover critical genetic alterations that may lead to cancer or target genes for effective therapies to individuals. In this paper, we study a logical inference problem in a Boolean network to find all such critical genetic alterations in a minimal (parsimonious) way. We propose a bilevel integer programming model to find a single minimal genetic alteration. Using the bilevel integer programming model, we develop a branch and bound algorithm that effectively finds all of the minimal alterations. Through a computational study with eleven Boolean networks from the literature, we show that the proposed algorithm finds solutions much faster than the state-of-the-art algorithms in large data sets.

We propose a heuristic approach for solving binary Multiple Instance Learning (MIL) problems, whose objective is to categorize bags of instances. Considering the case with two classes of instances, on the basis of the standard MIL assumption, a bag is classified positive if it contains at least a positive instance and negative if all its instances are negative. Inspired by a well-established MIL Support Vector Machine type approach, our technique is based on iteratively separating the bags by means of successive maximum-margin spheres. Such spheres, whose number is automatically determined, are generated by computing, for each of them, the optimal radius in correspondence to a prefixed center. Numerical results are presented on a set of benchmark test problems, showing the effectiveness of our approach.

Homogeneity, which plays an essential role in decision making, economics and image processing, reflects the regularity of aggregation functions with respect to the inputs with the same ratio. Quasi-homogeneity is a relaxed homogeneity that reflects the original output as well as the same ratio. This paper is devoted to the characterization of all homogeneous/quasi-homogeneous binary aggregation functions in terms of single-argument functions.

With the increasing demand for surveillance applications, pedestrian detection has been a topic of interest for many researchers in recent time. The quality of a pedestrian detector is decided in terms of detection accuracy and rate of detection. This paper presents new pedestrian detectors based on two types of classifiers, linear support vector machine and cascade of boosted classifier. These classifiers are trained by using a feature set comprising of the histogram of oriented gradients and dense local difference binary features. Both the image pyramid and non-linear scale space are used to detect pedestrians of various sizes. In order to combine the benefits of the two classifiers, a new two-stage detection scheme is also presented. The detection accuracies of the proposed detectors are studied in terms of miss-rate versus false positive per image and miss-rate versus false positive per window. The performances of the detectors are also compared with the performances of existing detectors of similar type.

Digital images used in business, government and medical sectors insist the need for image security against various threats. An exclusive system that can make use of color spaces has been proposed to process sensitive images based on the binary curve. The proposed Encryption systeM fOr proTecting imagEs has been coined as EMOTE. This multilayered system works on binary curves as binary operations have no carries. Since binary squaring is lightweight, it is claimed to be smaller and faster in hardware than prime-field ones. Logistic mapping has been done to perform chaotic masking, which is then followed by DNA encoding in the next layer. ECC over GF(2m) that works on the spatial domain has been chosen as it is one of the proven technique for its mathematical strength. This proposed system can facilitate both symmetric and asymmetric combination and hence hybrid. Experimental results and the histogram analysis obtained comprehend this proposed system for its extensive deployment to process sensitive images, especially in the medical domain. Ideal measures attained for MSE, PSNR values for the standard benchmark images taken from “The whole brain atlas” database from Harvard University, substantiate the proposed EMOTE system as an expedient choice for processing sensitive images.

In many practical applications, the data are class imbalanced. Accordingly, it is very meaningful and valuable to investigate the classification of imbalanced data. In the framework of binary imbalanced data classification, the synthetic minority oversampling technique (SMOTE) is the best-known oversampling method. However, for each positive sample, SMOTE generates only k synthetic samples on the lines between the positive sample and its k -nearest neighbors, resulting in three drawbacks: (1) SMOTE cannot effectively extend the training field of positive samples; (2) the generated positive samples lack diversity; (3) SMOTE does not accurately approximate the probability distribution of the positive samples. Therefore, two binary imbalanced data classification methods named BIDC1 and BIDC2 based on diversity oversampling by generative models are proposed. The BIDC1 and BIDC2 conduct diversity oversampling using extreme learning machine autoencoder and generative adversarial network, respectively. Extensive experiments on 26 data sets are conducted to compare the two methods with 14 state-of-the-art methods using five metrics: F-measure, G-means, AUC-area, MMD-score, and Silhouette-score. The experimental results demonstrate that the two methods outperform the other 14 methods.

The problem of constructing the most powerful test for random number generators (RNGs) is considered, where the generators are modelled by stationary ergodic processes. At present, RNGs are widely used in data protection, modelling and simulation systems, computer games, and in many other areas where the generated random numbers should look like binary numbers of a Bernoulli equiprobable sequence. Another problem considered is that of constructing effective statistical tests for random number generators (RNG). Currently, effectiveness of statistical tests for RNGs is mainly estimated based on experiments with various RNGs. We find an asymptotic estimate for the p -value of an optimal test in the case where the alternative hypothesis is a known stationary ergodic source, and then describe a family of tests each of which has the same asymptotic estimate of the p -value for any (unknown) stationary ergodic source. This model appears to be acceptable for binary sequences generated by physical devices that are used in cryptographic data protection systems.

Slip status of drill string is system generated binary value computed by comparison of sensor generated real time hook load value with a minimum threshold value of hook load stored in measurement system. This research article describes a novel method of slip status detection by machine vision technology which helps overcome the constraints of slip status detection with legacy measurement method. It also helps improve the real time drilling data quality and optimize and automate drilling operations. A method to detect drill string slip status with high-resolution digital camera installed on mast near rig floor is described along with backend vision processing and communication modules, which generate binary values of slip status. The binary values are transferred in real time to drilling measurement system of rig to compute other drilling parameters like bit depth, hole depth and stand counters. This method includes deploying active optical sensors at the rig floor, obtaining 1-D, 2-D, or 3D image data, and processing it to obtain the status of drill string. Reliable measurement of slip status by machine vision helps reduce non-productive time (NPT) by reliable real time surveillance of drilling operations.

Although eutectic high entropy alloys (EHEAs) display homogeneously fine microstructure, outstanding castability and promising industrial application potential, how to search for the eutectic compositions in multicomponent high-entropy alloys (HEAs) remains to be challenging. Herein, the strategy which was proposed for pinpointing good glass formers in the field of bulk metallic glasses was utilized for quickly locating EHEA compositions in Nb-(Fe, Co, Ni), Zr-(Fe, Co, Ni) and Hf-(Fe, Co, Ni) HEA systems based on the constituent binary eutectics. As a demonstration, the eutectic compositions in the Nb-(Fe, Co, Ni), Zr-(Fe, Co, Ni) and Hf-(Fe, Co, Ni) HEA systems were quickly determined using this new approach. The newly designed EHEAs showed ultrafine eutectic microstructures, good castability and decent compressive mechanical properties. Current findings clearly demonstrated that this proposed approach is effective for searching for eutectic compositions in HEAs, which will expedite alloy design of EHEAs with strong solid solution strengthening effects.

Since last few years, microarray technology has got tremendous application in many bio-medical researches. However, in order to efficiently recognize and apply this technology into the bio-medical areas is still very difficult and expensive. There are many metaheuristic approaches has been developed with different biological interpretation. Despite the existence of several approaches, there is always a requirement of development of more robust and efficient approach. In this work a new metaheuristic approach is proposed implementing binary shuffled frog leaping algorithm (BSFLA) for gene selection. To obtain an optimal gene subset, 20 different combination of gene subset is extracted from the original dataset. Out of which the optimal gene subset is identified implementing KNN classifier. Superiority of these gene set is shown using few other classifiers such as ANN and SVM. The model performance is also compared with few other metaheuristic approaches such as particle swarm optimization, differential evolution and genetic algorithm.

R1234yf and R1243zf have received extensive attention as environmentally refrigerants. However, the application of the pure R1234yf and R1243zf are limited due to their flammability and low volumetric refrigerating capacity. The mixed refrigerants are expected to achieve excellent thermophysical performance, safety, and environmental protection. Vapor-liquid equilibrium (VLE) is an extremely important thermophysical property of mixtures. By using a recirculation-analytical type VLE apparatus, the VLE data of the (R125+R1234yf) and (R125+R1243zf) binary systems were measured along six isotherms from 263.15 K to 313.15 K. The PR+vdW model is used to correlate the VLE data. The overall average relative deviation of pressure (ARD(p)) and the average absolute deviation of vapor phase mole fraction (AAD(y)) are 0.15 % and 0.0011 for R125+R1234yf system; and the ARD(p) and AAD(y) are 0.16 % and 0.0011 for R125+R1243zf system. The model has a good description of the two binary systems.

Imbalanced data can always be observed in our daily life and various practical tasks. A lot of well-constructed machine learning methodologies may produce ineffective performance, when conducted on this kind of data. This originates from the produced high training biases that towards the majority class instances. Among all the solutions of this problem, data generation of the minority class is always considered the most effective approach. However, in all the previous works, data are always processed sample-wisely and the distribution of each single data attribute is never noticed. So, in this paper, to estimate the mechanism of how each attribute contributes to its label, we explore the potential connection between the two items by Conditional Generative Adversarial Networks (CGAN) separately and individually. Then, the constructed new instances are purified by a designed attribute-based minimax filter and the survivors are concatenated to form the eventual generated data. In other words, different from the CGAN based data generation way, the proposed approach improves it by additionally considering all the single attribute patterns of the data that to construct new instances. In addition, we extend the binary class imbalanced learning framework to multiple class one. In the experimental part, the improved model is compared against GAN, CGAN and some other standard multiple-class oversampling algorithms on several widely used datasets. Results, in terms of four common measurements, have shown that the proposed approach can produce comparable and always superior performance when compared with the competitors.

Feature selection is a significant task in the workflow of predictive modeling for data analysis. Recent advanced feature selection methods are using the power of optimization algorithms for choosing a subset of relevant features to get better classification results. Most of the optimization algorithms like genetic algorithm use many controlling parameters which need to be tuned for better performance. Tuning these parameter values is a challenging task for the feature selection process. In this paper, we have developed a new wrapper-based feature selection method called binary teaching learning based optimization (FS-BTLBO) algorithm which needs only common controlling parameters like population size, and a number of generations to obtain a subset of optimal features from the dataset. We have used different classifiers as an objective function to compute the fitness of individuals for evaluating the efficiency of the proposed system. The results have proven that FS-BTLBO produces higher accuracy with a minimal number of features on Wisconsin diagnosis breast cancer (WDBC) data set to classify malignant and benign tumors.

Manually picking regularly and densely distributed first breaks (FBs) are critical for shallow velocity-model building in seismic data processing. However, it is time consuming. We employ the fully-convolutional SegNet to address this issue and present a fast automatic seismic waveform classification method to pick densely-sampled FBs directly from common-shot gathers with sparsely distributed traces. Through feeding a large number of representative shot gathers with missing traces and the corresponding binary labels segmented by manually interpreted fully-sampled FBs, we can obtain a well-trained SegNet model. When any unseen gather including the one with irregular trace spacing is inputted, the SegNet can output the probability distribution of different categories for waveform classification. Then FBs can be picked by locating the boundaries between one class on post-FBs data and the other on pre-FBs background. Two land datasets with each over 2000 shots are adopted to illustrate that one well-trained 25-layer SegNet can favorably classify waveform and further pick fully-sampled FBs verified by the manually-derived ones, even when the proportion of randomly missing traces reaches 50%, 21 traces are missing consecutively, or traces are missing regularly.

This paper formulates a fuzzy logic neuron that uses n-uninorms to construct uni-nullneurons. A fuzzy neural network (FNN) composed of these neurons is easy to operate with nullnorms and uninorms at different times, which results in high accuracy of the model outputs and increases the flexibility in connecting the rule antecedents (enabling AND and OR connections within one rule). This, in turn, may allow experts/operators to extract more knowledge from data. The FNN uses a one-versus-rest classifier learning scheme for multi-class classification problems, where neuron activation levels construct the (indicator) regression matrix; this results in a non-linear regression by indicator, which can resolve the inherent class masking problem apparent in the linear case. We propose a specific neuron-selection strategy in the learning stage that applies Lasso to bootstrap replications in order to ensure that the rule base is as compact as possible and induced by a low number of neurons. To evaluate the new neuron acting in FNNs, we performed pattern classification, and regression tests. Compared with traditional FNN in the literature, our variant showed improved model accuracies for several high-dimensional real-world datasets in binary and multi-class classification and regression problems. Combined with the ability to generate human-readable rules, this offers the ability to generate parsimonious responses with a high degree of confidence.

Thermal error compensation is a simple and efficient method to reduce thermal errors of machine tools, and the compensation effect largely depends on the temperature-sensitive points screened for the thermal error modeling. In this paper, 5 spindle heating experiments are carried out, and a new temperature-sensitive point screening method based on the Improved Binary Grasshopper Optimization Algorithm (IBGOA) feature selection is proposed. Firstly, an optimal approximation criterion is added to the Binary Grasshopper Optimization Algorithm (BGOA) for ensuring the convergence of the algorithm. And the temperature measuring points are regarded as the feature of the thermal error. Then these feature temperature point subsets are generated by IBGOA. Next, the stepwise regression analysis is carried out to remove non-significant temperature points from these subsets. And each subset is evaluated to search the temperature-sensitive points based on the cross-validation result of the multiple linear regression (MLR). Finally, for further testing the applicability of the proposed temperature-sensitive points screening method, 3 common thermal error models are established with MLR , support vector regression, and back propagation neural network respectively. Compared with the traditional fuzzy C-means clustering (FCM) temperature-sensitive points screening method, the RMSE of these models could decrease by 30–50% in the thermal drift error of X-direction, 10%–30% in the thermal tilt error of Y-direction, generally 40%–60% in the thermal elongation of Z-direction and the thermal drift error of Y-direction. The results show the superiority of the proposed IBGOA-feature selection method.

Generalizing a graph-theoretical result of Maffray to binary matroids, Oxley and Wetzler proved that a connected simple binary matroid M has no odd circuits other than triangles if and only if M is affine, M is isomorphic to M ( K 4 ) or F 7 , or M is the cycle matroid of a graph consisting of a collection of triangles all sharing a common edge. In this paper, we show that if M is a 3-connected binary matroid having a 5-element circuit but no larger odd circuit, then M has rank less than six; or M has rank six and is one of nine sporadic matroids; or M can be obtained by attaching together, via generalized parallel connection across a common triangle, a collection of copies of F 7 and M ( K 4 ) and then possibly deleting up to two elements of the common triangle. From this, we deduce that a 3-connected simple graph with a 5-cycle but no larger odd cycle is obtained from K 3 , n for some n ≥ 3 by adding one, two, or three edges between the vertices in the 3-vertex class.

A key issue for moth search algorithm (MS) is the ability to maintain sufficient diversity in the population so as to be able to track a dynamically changing landscape. However, for the original MS algorithm, the implicit learning mechanism of flight straightly operator focuses mainly on the global best individual, which inevitably causes a deteriorated evolution. To increase the population diversity and enhance the global search ability of MS, a binary moth search algorithm based on self-learning (SLMS) is proposed to solve an NP-hard combinatorial optimization problem with many diverse applications, the 0–1 multidimensional knapsack problem (MKP). In SLMS, a self-learning flight straightly operator is proposed to make each individual learn from any one better than itself, not just the global best individual. The performance of the proposed SLMS is validated by testing 89 different widely used benchmark instances. Experimental results show the effectiveness of the proposed SLMS in solving the MKP problem and the self-learning flight straightly operator can enhance population diversity and global search ability. The effect of transfer functions on the performance of SLMS is also investigated.

The study of refractive index of liquids becoming increasingly popular in diverse areas to monitor chemical, pharmaceutical, food, scientific, biochemical, medical, etc. processes and procedures. In the present paper a low cost, simple, compact, reliable, robust, miniaturized and sturdy Refractive Index sensor is reported. It is configured by using a set of two multimode PCS fiber of 50 cm length in each, connected one to the source of wavelength 660 nm and other to a light detector. A glass rod drawn in the form of ‘U’, of compatible dimensions to that of two multimode fibers, was connected between source and detector by jointing to the remaining ends of the fibers, thus creating a sensing zone. Maintaining the binary liquids of Toluene and t-Butanol at the sensing zone at various temperatures, the light launched from source was monitored in the detector at the detector end. By forming relationship between index of refraction and power output at the temperature range of 10 °C to 60 °C, the sensor expected to be used to detect the index of refraction of liquids with maximum resolution of the order of 10-5 in the operating range from 1.36312nD to 1.50915nD.

This paper investigates the consensus of a multiagent system (MAS) under binary-valued communication with directed topology. An improved two-time-scale algorithm that alternates between control and estimation is proposed. Compared with the existing two-time-scale algorithm for the undirected topology case, we relax the conditions on the algorithm parameters, thereby providing more choices for practical implementations. The asymptotic property of the algorithm is analyzed by analyzing the convergence of the explicit closed-loop solution. To reduce the effect of binary communication on the consensus value for a single-sample path, the selection method for obtaining suitable parameters is given. To demonstrate their effectiveness, the selection method and algorithm are applied to the reputation management of a public welfare platform.

Many embedding approaches of drugs have been proposed for the downstream task of drug-drug interaction (DDI) prediction in a DDI-derived network where drugs are considered nodes, and interactions are represented as edges. One of the most popular approaches is learning the representation of a drug from the DDI network by aggregating the features or information of its neighboring drugs. However, existing methods do not consider the specific type of the relation between the drugs, leading to an incomplete embedding learning process. Given that different relations between drugs may have different effects on drug embedding, the combination of multirelational embedding and relation-aware network structure embedding of drugs can be helpful to improve the prediction of DDIs. Therefore, in this paper, a relation-aware network embedding model for the prediction of drug-drug interactions (RANEDDI) is proposed. RANEDDI not only considers the multirelational information between drugs but also integrates the relation-aware network structure information in the topology of a multirelational DDI network to obtain the drug embedding. Under evaluation metrics such as AUC, AUPR and F1, the experimental results show that RANEDDI is superior to several state-of-the-art methods and can be used in the prediction of binary and multirelational DDIs. We also perform ablation studies that demonstrate that RANEDDI is effective and that it is robust in the task of binary DDI prediction, even in the case of a scarcity of labeled DDIs. The source code is freely available at https://github.com/DongWenMin/RANEDDI.

The quasi-lattice theory based on the joint effect of size ratio and the energetic (or enthalpic) contribution as well as entropic effect has been applied to study the concentration dependence of the thermodynamic and structural properties of AgCu liquid alloys at 1423 K. The analytical expressions for the thermodynamic properties such as excess free energy of mixing, free energy of mixing, entropy of mixing, heat of mixing and the structural properties like the concentration fluctuations in the long wavelength limit and short range order parameter for a binary liquid alloy have been derived in the framework of quasi-lattice theory. The analytical expressions have been used to compute the various thermodynamic and structural properties of liquid AgCu alloys at 1423 K. The theoretical data of thermodynamic and structural properties have been compared with the available experimental data. The present theoretical investigation successfully explains the observed symmetries in the properties of mixing of liquid AgCu alloys at 1423 K.Further the energy parameter is found to be temperature dependent.

With the help of measured values of ultrasonic Velocity (U), density (ρ) and viscosity (η) excess acoustic and thermodynamic properties like velocity (UE), density (ρE), viscosity (ηE), adiabatic compressibility (βEa), free length (LEf), free volume (VEf), acoustic impedance (ZE), internal pressure (πEi), relaxation time (τE), Gibb’s free energy (GE) have been calculated for the binary mixture of isopropyl alcohol with CCl4, at various temperatures at a fixed frequency of 2 MHz for different concentration ranges. The graphs were plotted against the concentration of the mixture at different temperatures, from that the strength of interaction between like and unlike molecules has been discussed.

This paper presents the study of binary component analysis subjected to distillation process using Aspen Plus. Binary components namely Isopropyle alcohol and water were considered for the study and their characteristics are analyzed at certain temperature and pressure. Aspen Plus provides virtual exposure of physical process, gives quantitative measure of mole fraction of liquid phase, vapor phase with changes in temperature, pressure and activity coefficients. Steady state and dynamic simulation results depicts the behavior of the system and enables the user to understand how the system reacts in virtual environment, to realize the system behavior in real-time environment.

The simulation-based artificial neural networks (ANN) program is one of the suitable candidates from artificial intelligence simulation which can work to predict important ultrasonic and mechanical parameters in the glass field. This research is focused on exploring the validity of this system by comparing the prediction values from ANN with the experimental measurements and other theoretical models. The ANN simulation was effectively applied to a binary zinc-borate glass system with the composition of zZnO−(100-z)B2O3 where z = 0, 40, 45, 50, 55, and 60 mol%, which was fabricated by using the melt-quenching techniques. The increase of ZnO content caused the ultrasonic velocity and elastic moduli of the glasses to exhibit a decreasing trend. The bond compression theoretical calculation compared with the experimental measurement was considered to be satisfactory with the value of the coefficient R2 being around 0.92452 to 0.98492. The Makishima-Mackenzie calculation model concerning the experimental measurement of the elastic moduli and Poisson's ratio were between 0.86628 to 0.99786. The coefficient R2 value from the ANN simulation displayed on the density, ultrasonic velocity, and elastic moduli graph is around 0.9999 to 1.0000, which is considered to be very reasonable. The values predicted by this remarkable model proved that ANN simulation is suitable for use in glass research.

Using chaotic system for image encryption is a feasible and popular research interest. In this paper, a compound Sine-Piecewise Linear Chaotic Map is proposed to improve the chaotic characteristics of the original chaotic map. Compared with the original chaotic system, the proposed compound chaotic system has better dynamics performances, larger parameter space, which is more suitable for cryptographic applications. Furthermore, an image encryption algorithm based on this chaotic model and varying DNA coding is proposed. In this encryption algorithm, a novel method is provided to convert decimal numbers to binary numbers. Several numerical experiments show that the proposed encryption algorithm has good security performances, and is competitive with other chaos-based image encryption algorithms.

We revisit the single-machine preemptive scheduling problem to minimize total completion time with release times and deadlines. Du and Leung (1993) showed that the problem is binary NP-hard. But the exact complexity (unary NP-hardness or pseudo-polynomial-time solvability) of the problem is a long standing open problem. We show in this paper that the problem is unary NP-hard.

Gas fluidized beds with bi-dispersed particles of different sizes, densities, and shapes are encountered in many industrial processes spanning the manufacture of energy, material and resources to greenhouse emissions mitigation and renewable resources utilization. The design and operation of such systems heavily rely on the prediction of minimum fluidization velocity of the mixture (Umf(mixture)) to avoid particle segregation and maximize mixing. Many correlations exist for predicting Umf(mixture); however, they exhibit limitations in accuracy and/or applicability. The limitations of these correlations are often expressed in terms of particles properties (e.g. effective size and density), flow conditions, and solids composition. This study has critically assessed highly cited correlations, delivered a comparative analysis of their applicability and predictability against a large pool of experimental data, and deepened the understanding of parameters that affect the prediction of Umf(mixture) using different correlations.

Discrete element method (DEM) simulations of size-driven segregation of binary particles in a rotary drum were conducted to investigate the influence of filling level, size ratio, and rotation speed on the segregation performance. Segregation experiments with different filling levels were used to verify the DEM model and analyze the influence of filling levels on segregation. The granular bed was divided into five layers to study the axial segregation in the rotary drum. The total velocity fluctuation was used to discuss the granular behavior from a mesoscopic perspective. A segregation index was adopted to quantify the segregation performance with different parameters. It was found that binary particles in the drum with different filling levels have different segregation patterns. A core of small particles was formed in the middle of granular bed for the case with a higher filling level, while there is no core formed for the case with a lower filling level. Results obtained indicate that the size-driven segregation in the drum with lower filling level increases with the increase of size ratio. Within the rolling regime, the rotation speed has little influence on the final segregation index of particles.

In this paper, error performance of a communication link subjected to a non-Gaussian noise model known as symmetric alpha-stable (S α S) noise and α − μ fading environment is analyzed with selection combining at the receiver. To this end, using binary phase-shift keying (BPSK), closed-form expressions of exact and asymptotic bit-error-rate (BER) are obtained. Furthermore, closed-form expressions of outage probability and n -th moment are obtained. From the n -th moment, the average signal-to-noise ratio (SNR) and channel quality estimation index (CQEI) are calculated. The effect of the number of antennas (L) and the fading parameters on the BER is studied for different impulsive settings. Monte-Carlo simulations are performed to validate the derived results.

In the field of fuzzy set theory, whether from the perspective of theory or practical application, as an important property, the migrativity has an extraordinary significance for the research of binary aggregation functions. There have been a lot of contributions on migrativity properties for some binary aggregation functions, for instance, t-norms, t-operators, uninorms, mayor's operators, copulas and so on, but few for 2-uninorms. Therefore, we make a deep study on the migrativity of uninorms over 2-uninorms in this article. According to whether the absorbing element of a 2-uninorm is consistent with the neutral element of a uninorm or not, we study the migrativity properties of uninorms in the usual classes over the three different subclasses of 2-uninorms. In addition, all solutions of the migrative equations are respectively analyzed and characterized for all possible combinations of uninorms in the usual classes and the three different subclasses of 2-uninorms. The usual classes of uninorms consist of U min e 1 , U max e 1 , U r e p e 1 , U i d e e 1 , U c o s , min e 1 and U c o s , max e 1 , while the three different subclasses of 2-uninorms are made up of C 0 , C 1 and C k .

It is well-known that there are instances of the binary paint shop problem for which the recursive greedy heuristic is better than the greedy heuristic. In this note, we give an example of a family of instances where the greedy heuristic is better than the recursive greedy heuristic, thus showing that these heuristics are uncorrelated.

A complete pipeline is presented for accurate and efficient partial 3D object retrieval based on Quick Intersection Count Change Image (QUICCI) binary local descriptors and a novel indexing tree. It is shown how a modification to the QUICCI query descriptor makes it ideal for partial retrieval. An indexing structure called Dissimilarity Tree is proposed which can significantly accelerate searching the large space of local descriptors; this is applicable to QUICCI and other binary descriptors. The index exploits the distribution of bits within descriptors for efficient retrieval. The retrieval pipeline is tested on the artificial part of SHREC'16 dataset with near-ideal retrieval results., Comment: 19 pages, 17 figures, to be published in Computers & Graphics

Mixture working fluids have the advantages of active design and adjustable physical properties, therefore it has a promising future in many power and refrigeration applications. Studies on the evaporation process of mixture nanodroplets are helpful to reveal the mechanism of heat transfer at the interface of mixture working fluids. However, at present, there are few studies on the evaporation of mixture droplet, and the conclusions on the pure fluids evaporation cannot be directly applied to the mixture working fluids, thus it is necessary to carry out specific research on the evaporation of mixtures. In this study, molecular dynamics simulation was used to study the evaporation process of R32/R1234yf nanodroplets with different compositions (xR32=0, 0.2, 0.4, 0.6, 0.8, 1) in a large space. The dynamic changes of evaporation rates of droplets with different components were compared, and the evaporation characteristics of droplet with different concentrations were analyzed in combination with the density, temperature and concentration field distribution near the liquid-vapor interface during the evaporation process. The results show that the evaporation rate of the mixture nanodroplets is lower than the linear interpolation with those of the two components, which is similar to the heat transfer deterioration of the mixture working fluids in pool or flow boiling. The prediction deviation of R32/R1234yf mixture nanodroplet evaporation by the D2 law is around 80%. Considering the scale effect and the interfacial concentration gradient, the prediction accuracy of the D2 law can be improved.

In tradition, surface mounted devices (SMD) are classified and registered manually in surface mount technology (SMT). Manual classification is inefficient and relies on the experience of the operators. We propose a novel SMD classification method based on a mixture neural network. The network contains two modules, one is a convolutional neural network with a gray image as input, and the other is a fully connected network with a Hand-Crafted feature as input. The lighting environment of chip has a great impact on chip imaging, and the classification method should maintain sufficient robustness against light changes in SMT. However, there is a lack of a robust feature extraction method for SMT classification against illumination changes. A novel feature extraction method based the characteristics of the binary chip image is first proposed, which is composed of the mathematical statistical pin parameters of SMD. As the light changes, the binary image is relatively stable, making the proposed feature is invariant to light changes. An SMD dataset in SMT contains 61 kinds of chips is built for training and testing. Experiments on this dataset demonstrate that our method is more effective than the state-of-the-art methods in terms of performance measures.

Binarization has always been a very challenging task for degraded document images because of the variety and complexity of degradations. This paper proposed a nonlinear diffusion equation with selective source for restoration of degraded document images, followed by a binary projection for binarization. The source is composed of two parts: one is used for the restoration of contaminated background, and another is responsible for the fidelity of texts. The evolution equation is numerically solved by the simplest finite differencing. The proposed method is tested on publicly available datasets (DIBCO (Document Image Binarization Competition) 2009–2014, 2016) and is compared with five models based on partial differential equation (PDE). The experimental results show that the proposed method is effective for degraded document images and has achieved generally the best performance of binarization compared to the other five PDE methods.

The success of Artificial Intelligence at solving real-world problems poses the need for interpretable models, especially in human-centered applications. The multi-class scenario is often present in these environments; however, the majority of research on interpretability has focused on binary classification. In this work, a novel method based on hierarchical decompositions to obtain interpretable multi-class models is introduced. The proposal, named Threshold Control for Nested Dichotomies (TC-ND) method, creates a binary-based hierarchical class structure. Then, it discards meta-classes at each dichotomy of the structure according to a certain level of confidence, pursuing a modular and more comprehensible decomposition of the multi-class problem. The approach presents internal parameters that are optimized using the Differential Evolution algorithm. The goodness of our proposal is assessed using a twofold approach: performance is evaluated by comparing against other state-of-the-art multi-class methods; interpretability is supported by practical examples with counterfactual explanations and a discussion of the advantages that the TC-ND method presents regarding its transparency and auditability.

The capability accurately and timely detect a resident space object (RSO) maneuver is a critical task for monitoring space activities. This paper presents a data-driven Gaussian Binary RSO Maneuver Detection (GaBRSOMD) method to detect whether or not there is a maneuver between two tracks of the same RSO on different orbital paths. Using an in-house simulated space catalog environment, the Gaussian Binary Classification (GBC) method is used to detect three types of maneuvers: the maneuver is due to a small impulsive velocity change; a low thrust, and the maneuver is due to either an impulsive or a low thrust which is unknown a priori. Numerical results demonstrate that the proposed GBC model can achieve high accuracy in detecting all three cases of maneuvers. The paper further demonstrates that the GBC approach is robust to noisy data and has advantages over an AutoEncoder method and the classical Principal Component Analysis (PCA) method. Hence, the proposed GBC has great potential for making quick detection decision with both high accuracy and precision.

Mixing and heat transfer among particles in rotating drums are widely applied in numerous industrial processes. Generally, mixing and heat transfer occur simultaneously. Consequently, the interrelationship between mixing and heat transfer must be investigated for industrial applications. In this study, the radial mixing and heat transfer of spherical granular materials (3 mm) with various properties in batch rotating drums are investigated numerically using the discrete element method. The evolution of mixing and heat transfer characteristics with rotation speed is analyzed from the perspective of time and number of revolutions, respectively. The results indicate that, depending on the physical parameters and thermal properties of particles, the mixing quality does not always accurately reflect the heat transfer effect. In binary granular beds of monodispersed spherical particles with different densities and thermal diffusivities, the main heat transfer mechanism is related to the ratio of the thermal conductivity of the particles to that of the fluid.

Viscosity is one of the important thermophysical properties of liquid aluminum alloys, which influences the characteristics of mold filling and solidification and thus the quality of castings. In this study, 315 sets of experimental viscosity data collected from the literatures were used to develop the viscosity prediction model. Back-propagation (BP) neural network method was adopted, with the melt temperature and mass contents of Al, Si, Fe, Cu, Mn, Mg and Zn solutes as the model input, and the viscosity value as the model output. To improve the model accuracy, the influence of different training algorithms and the number of hidden neurons was studied. The initial weight and bias values were also optimized using genetic algorithm, which considerably improve the model accuracy. The average relative error between the predicted and experimental data is less than 5%, confirming that the optimal model has high prediction accuracy and reliability. The predictions by our model for temperature- and solute content-dependent viscosity of pure Al and binary Al alloys are in very good agreement with the experimental results in the literature, indicating that the developed model has a good prediction accuracy.

In ranking applications, AUCROC is widely used in measuring the performance of a discriminative model. But this is possible only if the labels are binary, like in { 0 , 1 } , as AUCROC is undefined for non-binary labels. In modelling applications where the labels are not from { 0 , 1 } but are probabilities of membership ( p , 1 − p ) to the binary classes, we generally use other metrics. In this paper, we propose a metric ARatio which can be used on binary as well as probabilistic labels. We prove that it is exactly equal to AUCROC for binary labels. We also prove that it extends the same semantics as AUCROC for probabilistic labels. We extend the confusion matrix for probabilistic labels and redefine metrics like precision, recall and F1-score. We define AccRatioand show that it is equivalent to area under the precision–recall curve for non-binary probabilistic labels.

In this paper, support vector machines (SVMs), least squares SVMs (LSSVMs), relevance vector machines (RVMs), and probabilistic classification vector machines (PCVMs), are compared on sixteen binary and multiclass medical datasets. Particular emphasis is put on the comparison among the commonly used Gaussian radial basis function (GRBF) kernel, and the relatively new generalized min–max (GMM) kernel and exponentiated-GMM (eGMM) kernel. Since most medical decisions involve uncertainty, a postprocessing approach based on Platt’s method and pairwise coupling is employed to produce probabilistic outputs for prediction uncertainty assessment. The extensive empirical study illustrates that the SVM classifier using the tuning-free GMM kernel (SVM-GMM) shows good usability and broad applicability, and exhibits competitive performance against some state-of-the-art methods. These results indicate that SVM-GMM can be used as the first-choice method when selecting an appropriate kernel-based vector machine for medical diagnosis. As an illustration, SVM-GMM efficiently achieves a high accuracy of 98.92% on the thyroid disease dataset consisting of 7200 samples.