Probability Vector Research Articles

A matrix-analytic method for the steady state analysis of a Markovian queueing system with scrap items

This paper studies a mathematical analysis of scrap item-based single server stochastic queueing, which consists of storage space for the scrap items collected from the customers. The primary customers arrive in the finite waiting hall to sell scrap or recyclable products. The server collects the customer’s scrap items and stores them in the storage space. If the waiting hall is full whenever the primary customers arrive, they can join an infinite orbit or permanently leave the system under the Bernoulli trial. Whenever the orbital customers find at least one free space in the waiting hall, they can enter it. The server begins vacation once the storage reaches its maximum capacity or no more customers exist in the waiting hall. The server switches to regular mode whenever at least one free space exists in the storage, and one customer is in the waiting hall. Otherwise, he is not working in vacation mode. Customers in the waiting hall may lose their patience after waiting for a long time and may either enter orbit or leave the system permanently. Whenever the storage level (scrap item level) reaches a certain limit, the server places the procurement order for sending the items to the recycling unit or re-manufacturer. The system’s steady-state probability vector and stability condition have been derived by truncating the orbit at a point. Various system performances, such as expected total cost, profit cost, and waiting time for customers in the waiting hall, are defined, and a numerical study is carried out to analyze the performance of the proposed model. The numerical findings indicate that disabling working in vacation mode increases waiting time and total cost while working in vacation mode results in minimized waiting time and total cost.

Distribution Characteristics of Wind Speed Relative Volatility and Its Influence on Output Power

The stochastic fluctuations of wind speed and wind power curve modeling are complex tasks due to fluctuations in the difference between actual and theoretical power output, leading to a reduction in the accuracy of wind-power curve models. To address this issue, this paper proposes a normal distribution-modeling method based on relative volatility, which extracts the wind-speed variation patterns from the onsite SCADA (Supervisory Control And Data Acquisition) data, analyzes the correlation between wind-speed relative volatility and power relative volatility, and establishes a wind-power volatility-curve model to provide a basis for evaluating the efficiency of wind turbines. First, the definitions of relative volatility and probability vectors are provided, and a probability vector volatility-assessment function is designed to calculate the volatility-assessment index of the probability vector. Then, the relative volatility and probability vectors of wind speed are modeled, and features extracted from the onsite SCADA data, and characteristic parameters such as mean, standard deviation, and confidence interval of wind-speed relative volatility are statistically analyzed, as well as the wide-window coefficient, volatility-assessment index, attribute features (volatility center and volatility boundary), normal distribution features (mean and standard deviation) of the probability vectors of wind-speed relative volatility with different periods. The visualization descriptions of six typical probability vector distributions show that there is a correlation between the volatility assessment index of the probability vector based on relative volatility and the standard deviation of its distribution. Finally, the correlation between wind-speed relative volatility and power relative volatility is analyzed: in the maximum wind-energy tracking area, the derivative of power is linearly related to the derivative of wind speed, while in the constant power area, the derivative of the wind-energy utilization coefficient is linearly related to the derivative of wind speed. The conclusions obtained in this paper will provide a method reference for data processing to mine the parameter variation patterns and interrelationships of wind farm SCADA data and provide a basis for evaluating the power generation efficiency of wind turbines.

Query-based image tagging model using ensemble learning with enhanced artificial bee colony optimization

Digital images make up most multimedia data and are analysed in computer vision applications. Daily uploads of millions of pictures to Internet archives such as satellite image repositories complicate multimedia content and image graphs. As feature vectors, content based image retrieval (CBIR) and image classification models represent high-level image viewpoints. Observing photos recognizes objects and evaluates their significance for image enhancement. To access the visual information of big datasets, efficiently retrieve and query picture graphs. The artificial bee colony (ABC) algorithm is inspired by the foraging behaviour of honeybee swarms. ABC is susceptible to laziness in convergence and local optimums, just like other optimization methods. This study created an enhanced ABC (EABC) model to enhance precision. This study presents query-based image tagging model using ensemble learning with EABC (QbITM-ELEABC) for CBIR for appropriately tagging images based on the query image. We examine a number of convolutional neural network (CNNs) with varying topologies that can be trained on the dataset with varying degrees of similarity. As representations, each network extracts class probability vectors from images. The final image representation is created by combining the ensemble's class probability vectors with image.

An Optimization of Home Delivery Services in a Stochastic Modeling with Self and Compulsory Vacation Interruption

This study presents and discusses the home delivery services in stochastic queuing-inventory modeling (SQIM). This system consists of two servers: one server manages the inventory sales processes, and the other server provides home delivery services at the doorstep of customers. Based on the Bernoulli schedule, a customer served by the first server may opt for a home delivery service. If any customer chooses the home delivery option, he hands over the purchased item for home delivery and leaves the system immediately. Otherwise, he carries the purchased item and leaves the system. When the delivery server returns to the system after the last home delivery service and finds that there are no items available for delivery, he goes on vacation. Such a vacation of a delivery server is to be interrupted compulsorily or voluntarily, according to the prefixed threshold level. The replenishment process is executed due to the (s,Q) reordering policy. The unique solution of the stationary probability vector to the finite generator matrix is found using recursive substitution and the normalizing condition. The necessary and sufficient system performance measures and the expected total cost of the system are computed. The optimal expected total cost is obtained numerically for all the parameters and shown graphically. The influence of parameters on the expected number of items that need to be delivered, the probability that the delivery server is busy, and the expected rate at which the delivery server’s self and compulsory vacation interruptions are also discussed.

Better decision-making strategy with target seeking approach of humanoids using hybridized SOARANN-fuzzy technique

The prediction and decision-making features of the artificial neural network have been applied to several robotic related research works. Humanoid robot navigation is a challenging task for the researchers with best decision making. Simple artificial neural network (ANN) is the connection models which are poor in multistep decision making with long term planning and logical reasoning. So, in this study artificial neural network is improved in long term cognitive planning by SOAR (State Operator and Result) with powerful feature prediction through ANN (SANN) and hybridized with Fuzzy system for generation effective turning angle value, which will guide the humanoids to reach target location with better decision target seeking ability. An intelligent data fusion model is embedded between SOAR and ANN for converting the logical sequence information of SOAR to probabilistic vectors in multilayer ANNs. The obstacle distances are the input parameters to the SANN model and turning angle (TA) is the output from SANN. The obstacle distances along with the TA obtained from SANN is fed to the fuzzy system as input value and effective turning angle (ETA) is obtained to guide the humanoids towards reaching target. The proposed novel technique and ANN is used by both single and multiple humanoids in both simulation and experimental platforms. The novelty of the research focuses on the development of the new SOAR-ANN technique with better decision strategy for effective turning angle value, to obtain global optimal path by avoiding trapping in local optima and dead ends. The simulation and experimental results in terms of path length travelled and time spent to reach target are obtained from both ANN and novel SANN-Fuzzy technique. When the developed novel technique is compared with standalone ANN method during single humanoid navigation then, a significant improvement of 2–5% and 14–15% is observed in related to trajectory path span and trajectory time spent respectively. Similarly, during multiple humanoid navigation percentage improvement of 5–9% and 14–18% is obtained for humanoids in terms of navigational parameters respectively. The results clearly shows the developed technique requires less time and travels less path to reach target as compared to ANN and the percentage of error is also quite low. Statistical analysis is performed from the results of both the techniques and reflects the effectiveness of the proposed technique in comparison to ANN. Further the developed technique is compared against another existing methodologies like Fuzzy-ANN and Boundary Node Approach and, significant enhancements of 5.64% and 9.72% is attained in terms of track span respectively.

On the study of multistage stochastic vector quasi-variational problems

This paper focuses on the study of multistage stochastic vector generalized quasi-variational inequalities with a variable ordering structure. The proposed multistage stochastic vector quasi-variational problems are defined in a suitable functional setting relative to a finite set of final possible states and certain information fields; these formulations are a multicriteria extension of the multistage stochastic variational inequalities. A relevant aspect of these problems is the presence of the nonanticipativity constraints on the variables of the problem; stage by stage, these constraints impose the measurability with respect to the information field at that stage. Without requiring any assumption of monotonicity, we prove some existence results by using a nonlinear scalarization technique. On this basis, we analyze multistage stochastic vector Nash equilibrium problems: as an example, we focus on a suitable multistage stochastic bicriteria Cournot oligopolistic model.

Vector-valued fractal functions: Fractal dimension and fractional calculus

There are many research available on the study of a real-valued fractal interpolation function and fractal dimension of its graph. In this paper, our main focus is to study the dimensional results for a vector-valued fractal interpolation function and its Riemann–Liouville fractional integral. Here, we give some results which ensure that dimensional results for vector-valued functions are quite different from real-valued functions. We determine interesting bounds for the Hausdorff dimension of the graph of a vector-valued fractal interpolation function. We also obtain bounds for the Hausdorff dimension of the associated invariant measure supported on the graph of a vector-valued fractal interpolation function. Next, we discuss more efficient upper bound for the Hausdorff dimension of measure in terms of probability vector and contraction ratios. Furthermore, we determine some dimensional results for the graph of the Riemann–Liouville fractional integral of a vector-valued fractal interpolation function.

A probabilistic framework with the gradient-based method for multi-objective land use optimization

Land use planning seeks to outline the future location and type of development activity. The planning process should reconcile development with environmental conservation and other concerns pertaining to sustainability; hence multi-objective spatial optimization is considered an effective tool to serve this purpose. However, as the number of social, economic, and environmental objectives increases, especially when numerous spatial units exist, the curse of dimensionality becomes a serious problem, making previous methods unsuitable. In this paper, we formulate a probabilistic framework based on the gradient descent algorithm (GDA) to search for Pareto optimal solutions more effectively and efficiently. Under this framework, land use as decision parameter(s) in each cell is represented as a probability vector instead of an integer value. Thus, the objectives can be designed as differentiable functions such that the GDA can be used for multi-objective optimization. An initial experiment is conducted using simulation data to compare the GDA with the genetic algorithm, with the results showing that the GDA outperforms the genetic algorithm, especially for large-scale problems. Furthermore, the outcomes in a real-world case study of Shenzhen demonstrate that the proposed framework is capable of generating effective optimal scenarios more efficiently, rendering it a pragmatic tool for planning practices.

An Application of the Feature Selection Method Based on Pairwise Correlation for Diagnosis of Ovarian Cancer with Machine Learning

Many machine learning classification problems have high dimensions, and efficient and effective feature selection algorithms are needed to determine the relatively essential features in the dataset. Gene data is often preferred in feature selection applications because it contains many features due to its structure. In addition, it is known from studies in the literature that gene selection plays a significant role in cancer detection. One of the cancer types with very high treatment success in the early period is ovarian cancer. For this purpose, it was aimed to select genes with high descriptiveness in cancer diagnosis by using the ovarian cancer dataset, which is a publicly available dataset. In this study, the feature selection method based on pairwise correlation, which is very new in the literature, was used for classification. Firstly, a feature selection application was made, and 38 genes with the highest cancer descriptors were determined. Then, the classification process was carried out using eight different classification algorithms. After the classification process, the lowest success was for the Extra Tree classification algorithm (with 96.44% accuracy), while the highest was for the Multi-Layer Perceptron, Stochastic Gradient Descent, Logistic Regression, and Support Vector Machine (with 100% accuracy). Although there are many studies on feature selection in the literature, this study is the first application of the current method. In this sense, it is thought that it will contribute to the literature.

Full characterization of building energy factor significance by novel integrated stochastic level-based sensitivity analysis with support vector network and multivariate clustering

Effective planning and targeted implementation of building energy efficiency programs explicitly require robust knowledge on significance pattern of involving factors for precision model-based estimates of potential energy savings. Though extensively adopted for factor significance characterization, prior sensitivity analysis (SA) frameworks fail to capture typical influences attributed to commonly used categorical factors. Moreover, they often assume the assessed factors are independent which can be invalid in many cases to generate misleading outcomes. To overcome these observable defects, this paper develops a novel integrated stochastic level-based sensitivity analysis approach for exhaustively patterning effectual significance of all available building factors of varied type while incorporating diverse factor interactions on validated reliability. While the integration of categorical factors is achieved through factor-type dependent variation levelling, the random factor interactions are apprehended by stochastic sampling and nonlinear support vector network computing to accommodate collinearity trap. Multivariate clustering identifies significance pattern via a composite multi-statistic sensitivity metric derived from stochastic energy responses. Case results of 289 U.S. residential houses validate its utility. This presented approach benefits to perform reliable full sensitivity investigations and analyses under collinearity for building energy systems with verifiable robustness.

Entropy regularization for weakly supervised object localization

The goal of weakly-supervised object localization (WSOL) is to train a localization model without the location information of the object(s). Recently, most existing WSOL methods capture the object with an attention map extracted from a classification network. However, it has been observed that we need to sacrifice classification performances to achieve the best WSOL score. We conjecture that this is because the objective of classification training, minimizing entropy between one-hot ground truth and predicted class probability, is not entirely consistent with that of localization. In this paper, we investigate how the entropy of predicted class probability affects localization performances, where we conclude that there is a sweet spot for localization with respect to entropy. Hence, we propose a new training strategy that adopts entropy regularization for finding the optimal point effectively. Specifically, we add an additional term to the loss function, which minimizes the entropy between a uniform distribution and the predicted class probability vector. The proposed method is easy to implement since we do not need to modify the architecture or data pipeline. In addition, our method is efficient in that almost zero additional resources are required. Most importantly, our method improves WSOL scores significantly, which has been shown through extensive experiments.

Performance analysis of a queueing system based on working vacation with repairable fault in the P2P network

In recent years, peer-to-peer (P2P) as a means of content distribution and sharing service are receiving extensive attention. In this paper, the hybrid P2P network structure model is combined with the knowledge of queueing theory. In order to address the problem generated by unnecessary online behavior of nodes, the working sleep mechanism for the serving nodes is introduced, and the synchronous multiple working vacation strategy is also adopted to decrease the system energy consumption. Then a queueing model with working vacation and repairable fault is built, which used to simulate the resource transmission process of P2P network and to study the performance of P2P sharing system. The steady-state probability vector is derived by employing quasi-birth-and-death process and matrix-geometric solution method. And the expressions for system performance indicators, such as the average delay, are obtained by employing Gauss–Seidel iteration method. Through numerical experiments, the performance indicators of the P2P resource sharing system are studied, which provides a theoretical basis to decrease online energy consumption of nodes in the hybrid P2P network. And through employing Nash equilibrium and social optimal strategy, the value of the social maximum benefit under the social optimization is obtained.

Epistemic uncertainty aware semantic localization and mapping for inference and belief space planning

We investigate the problem of autonomous object classification and semantic SLAM, which generally exhibits a tight coupling between classification, metric SLAM, and planning under uncertainty. We contribute a unified framework for inference and belief space planning (BSP) that addresses prominent sources of uncertainty in this context: classification aliasing (classifier cannot distinguish between candidate classes from certain viewpoints), classifier epistemic uncertainty (classifier receives data “far” from its training set), and localization uncertainty (camera and object poses are uncertain). Specifically, two methods are developed for maintaining a joint distribution over robot and object poses, and over a posterior class probability vector that considers epistemic uncertainty in a Bayesian fashion. The first approach is Multi-Hybrid (MH), where multiple hybrid beliefs over poses and classes are maintained to approximate the joint belief over poses and posterior class probability. The second approach is Joint Lambda Pose (JLP), where the joint belief is maintained directly using a novel JLP factor. Furthermore, we extend both methods to BSP, while reasoning about future posterior epistemic uncertainty indirectly or directly via a novel information-theoretic reward function. Both inference methods utilize a novel viewpoint-dependent classifier uncertainty model that leverages the coupling between poses and classification scores and predicts the epistemic uncertainty from certain viewpoints. In addition, this model is used to generate predicted measurements during planning. To the best of our knowledge, this is the first work that reasons about classifier epistemic uncertainty within semantic SLAM and BSP. We extensively evaluate our inference and BSP approaches in simulation using real image data from the Active Vision Dataset. Results clearly indicate superior classification performance of our methods compared to an approach that is not epistemic uncertainty aware.

Polarization singularities: Topological and dynamical aspects

The review describes general principles and characteristics of inhomogeneously polarized paraxial optical fields and, especially, the polarization singularities (PSs). Main parameters of the optical vector waves are discussed, with the emphasis on the physical relevance and topological distinctiveness of the PSs. Special features of the stochastic vector fields are considered in the context of the PSs’ genericity and structural stability. A detailed attention is paid to interrelations between the PSs and the phase singularities of scalar fields formed by the orthogonal polarization projections of the total field, and their derivatives (complex Stokes fields, phase-difference fields, etc.). On this base, the practical approaches are discussed for the experimental PS identification and characterization. A particular examination of the internal energy flows associated with the PSs, and accompanying distributions of the optical momentum and angular momentum, reveals meaningful dynamical features of PSs and supplies additional physically transparent and informative means for their studies and characterization.

A Statistical Comparison of Call Volume Uniformity Due to Mailing Strategy

Abstract For operations such as a decennial census, the U.S. Census Bureau sends mail to potential respondents inviting a self-response. It is suspected that the mailing strategy affects the distribution of call volumes to the U.S. Census Bureau's telephone helplines. For staffing purposes, more uniform call volumes throughout the week are desirable. In this work, we formulate tests and confidence intervals to compare uniformity of call volumes resulting from competing mailing strategies. Regarding the data as multinomial observations, we compare pairs of call volume observations to determine whether one mailing strategy has multinomial cell probabilities closer to the uniform probability vector compared to another strategy. A motivating illustration is provided by call volume data recorded in three studies which were carried out in advance of the 2020 Decennial Census.

Enhancer-FRL: Improved and Robust Identification of Enhancers and Their Activities Using Feature Representation Learning.

Enhancers are crucial for precise regulation of gene expression, while enhancer identification and strength prediction are challenging because of their free distribution and tremendous number of similar fractions in the genome. Although several bioinformatics tools have been developed, shortfalls in these models remain, and their performances need further improvement. In the present study, a two-layer predictor called Enhancer-FRL was proposed for identifying enhancers (enhancers or nonenhancers) and their activities (strong and weak). More specifically, to build an efficient model, the feature representation learning scheme was applied to generate a 50D probabilistic vector based on 10 feature encodings and five machine learning algorithms. Subsequently, the multiview probabilistic features were integrated to construct the final prediction model. Compared with the single feature-based model, Enhancer-FRL showed significant performance improvement and model robustness. Performance assessment on the independent test dataset indicated that the proposed model outperformed state-of-the-art available toolkits. The webserver Enhancer-FRL is freely accessible at http://lab.malab.cn/∼wangchao/softwares/Enhancer-FRL/, The code and datasets can be downloaded at the webserver page or at the Github https://github.com/wangchao-malab/Enhancer-FRL/.

Using Deep Reinforcement Learning for Assessing the Consequences of Cyber Mitigation Techniques on Industrial Control Systems

This paper discusses an in-progress study involving the use of deep reinforcement learning (DRL) to mitigate the effects of an advanced cyber-attack against industrial control systems (ICS). The research is a qualitative, exploratory study which emerged as a gap during the execution of two rapid prototyping studies. During these studies, cyber defensive procedures, known as “Mitigation, were characterized as actions taken to minimize the impact of ongoing advanced cyber-attacks against an ICS while enabling primary operations to continue. To execute Mitigation procedures, affected ICS components required rapid isolation and quarantining from “healthy” system segments. However today, with most attacks leveraging automation, mitigation also requires rapid decision-making capabilities operating at the speed of automation yet with human-like refinement. The authors settled on the choice of DRL as a viable solution to this problem due to the algorithm’s designs which involves “intelligent” decisions based upon continuous learning achieved through a rewards system. The primary theory of this study posits that processes informed by data sources relative to the execution path of an advanced cyber-attack as well as the consequences of deploying a particular Mitigation procedure evolve the system into an ever-improving defensive capability. This study seeks to produce a defensive DLR based software agent trained by a DRL based offensive software agent that generates policy refinements based upon extrapolations from a corrupted network state as reported by an IDS and baseline data. Results include an estimation rule that would quantify impacts of various mitigation actions while protecting the operational critical path and isolating an in-progress attack. This study is in a conceptual phase and development has not started.
 This research questions for this study are:
 RQ1: Can this software agent categorize correctly an in-progress cyber-attack and extrapolate the potential ICS assets affected?
 RQ2: Can this software agent categorize novel cyber-attacks and extrapolate a probable attack vector while enumerating affected assets?
 RQ3: Can this software agent characterize how operations are affected by quarantine actions?
 RQ4: Can this software agent generate a set of ranked recommended courses of action by effectiveness, and least negative effects on the operational critical path?

Molecular Detection of Leishmania Infection in Phlebotomine Sand Flies from an Endemic Focus of Zoonotic Cutaneous Leishmaniasis in Iran.

Due to the outbreak of zoonotic cutaneous leishmaniasis (ZCL), a disease caused by Leishmania major and mainly transmitted by Phlebotomus papatasi, in Damghan City, Semnan Province, the probable vectors of the disease were investigated in the city from 20 March 2016 to 20 January 2018. Sand flies were collected from indoors and outdoors biweekly by sticky traps in different parts of the city. The trapped sand flies were stored in 70% ethanol. They were identified and checked for Leishmania infections using nested-PCR method and specific primers; CSB1XR, CSB2XF, LiR, and 13Z. Overall, 1862 phlebotomine sand flies of Ph. papatasi (48.8%), Ph. andrejevi (8.3%), Ph. caucasicus (7.7), Ph. mongolensis (2%), Ph. sergenti (1.2%), Ph. alexandri (0.7%), Sergentomyia murgabiensis sintoni (29.3%), and Se. sumbarica (2%) were collected indoors (31.1%) and outdoors (68.9%). The highest and lowest numbers of collected sand flies were belonging to Ph. papatasi (48.8%) and Ph. alexandri (0.7%) respectively. 2.2% of the examined sand flies were shown to be infected with L. major and all were belonging to Ph. papatasi. This study confirms the report of Ph. papatasi infection with L. major and also the existence of Ph. sergenti and Ph. alexandri, the potential vectors of L. tropica and L. infantum respectively, in Damghan City. According to the findings, it is necessary for health officials to plan and take action to prevent the occurrence of ZCL epidemic in the city as well as the occurrence of other forms of leishmaniasis.

DESCRIPTION, BIOLOGY, AND MEDICAL SIGNIFICANCE OF LEISHMANIA (MUNDINIA) CHANCEI N. SP. (KINETOPLASTEA: TRYPANOSOMATIDAE) FROM GHANA AND LEISHMANIA (MUNDINIA) PROCAVIENSIS N. SP. (KINETOPLASTEA: TRYPANOSOMATIDAE) FROM NAMIBIA.

Genetic and phylogenetic analysis was performed on 2 isolates of Leishmania using DNA sequence data from the RNA polymerase II large subunit gene and the ribosomal protein L23a intergenic sequence. This showed the isolates to represent 2 new species within the subgenus Leishmania (Mundinia). The addition of Leishmania (Mundinia) chancei and Leishmania (Mundinia) procaviensis creates a total of 6 named species to date within this recently described subgenus of parasitic protozoa, containing both human pathogens and nonpathogens. Their widespread geographical distribution, basal phylogenetic position within the genus Leishmania, and probable non-sand fly vectors make these L. (Mundinia) species of significant medical and biological interest.

Discovery of potential biomarkers for lung cancer classification based on human proteome microarrays using Stochastic Gradient Boosting approach.

Early identification of lung cancer (LC) will considerably facilitate the intervention and prevention of LC. The human proteome micro-arrays approach can be used as a "liquid biopsy" to diagnose LC to complement conventional diagnosis, which needs advanced bioinformatics methods such as feature selection (FS) and refined machine learning models. A two-stage FS methodology by infusing Pearson's Correlation (PC) with a univariate filter (SBF) or recursive feature elimination (RFE) was used to reduce the redundancy of the original dataset. The Stochastic Gradient Boosting (SGB), Random Forest (RF), and Support Vector Machine (SVM) techniques were applied to build ensemble classifiers based on four subsets. The synthetic minority oversampling technique (SMOTE) was used in the preprocessing of imbalanced data. FS approach with SBF and RFE extracted 25 and 55 features, respectively, with 14 overlapped ones. All three ensemble models demonstrate superior accuracy (ranging from 0.867 to 0.967) and sensitivity (0.917to 1.00) in the test datasets with SGB of SBF subset outperforming others. The SMOTE technique has improved the model performance in the training process. Three of the top selected candidate biomarkers (LGR4, CDC34, and GHRHR) were highly suggested to play a role in lung tumorigenesis. A novel hybrid FS method with classical ensemble machine learning algorithms was first used in the classification of protein microarray data. The parsimony model constructed by the SGB algorithm with the appropriate FS and SMOTE approach performs well in the classification task with higher sensitivity and specificity. Standardization and innovation of bioinformatics approach for protein microarray analysis need further exploration and validation.