查看更多>>摘要：Cloud computing is widely preferred by various organizations due to its numerous advantages such as lower costs, better security, and faster processing times. The most critical data stored in the remote servers are protected by firewalls and encryption, at the same time, the user's essential data may be accessed from the remote cloud server without their control. This kind of data storage facility introduces various security complications. In order to prevent these issues, we have proposed a method that uses a Biometric authentication method with PSO to provide high-level security. The method combines a pseudo-integer authentication scheme with a secret OTP that can be used to authenticate an individual. A Tri-level security mechanism model is proposed to store three types of data classification namely, No Privacy, Privacy with Trusted Provider and Privacy with Non-PNR. The proposed method provides a uniform security scheme for all the classifications using Adaptive ElGamal with Menezes-Qu-Verstone, that prevents data loss and corruption caused by insiders in the cloud. Further Seed Block is utilized to recover and extract data without loss. It does so by effectively extracting the data from the formatted disks. It is then followed with the decryption process, as if the data are requested by the user. Our framework aims to provide a secure and authenticated environment for accessing data. It features an improved authentication process and reduced complexity.
查看更多>>摘要：In real conditions, the parameters of multi-objective nonlinear programming (MONLP) problem models can't be determined exactly. Hence in this paper, we concerned with studying the uncertainty of MONLP problems. We propose algorithms to solve rough and fully-rough-interval multi-objective nonlinear programming (RIMONLP and FRIMONLP) problems, to determine optimal rough solutions value and rough decision variables, where all coefficients and decision variables in the objective functions and constraints are rough intervals (RIs). For the RIMONLP and FRIMONLP problems solving methodology are presented using the weighting method and slice-sum method with Kuhn-Tucker conditions, We will structure two nonlinear programming (NLP) problems. In the first one of this NLP problem, all of its variables and coefficients are the lower approximation (LAI) it's RIs. The second NLP problems are upper approximation intervals (UAI) of RIs. Subsequently, both NLP problems are sliced into two crisp nonlinear problems. NLP is utilized because numerous real systems are inherently nonlinear. Also, rough intervals are so important for dealing with uncertainty and inaccurate data in decision-making (DM) problems. The suggested algorithms enable us to the optimal solutions in the largest range of possible solution. Finally, Illustrative examples of the results are given.
查看更多>>摘要：Deep learning has been widely used in medical image segmentation, such as breast tumor segmentation, prostate MR image segmentation, and so on. However, the labeling of the data set takes a lot of time. Although the emergence of unsupervised domain adaptation fills the technical gap, the existing domain adaptation methods for breast segmentation do not consider the alignment of the source domain and target domain breast mass structure. This paper proposes a hyperbolic graph convolutional network architecture. First, a hyperbolic graph convolutional network is used to make the source and target domains structurally aligned. Secondly, we adopt a hyperbolic space mapping model that has better expressive ability than Euclidean space in a graph structure. In particular, when constructing the graph structure, we added the completion adjacency matrix, so that the graph structure can be changed after each feature mapping, which can better improve the segmentation accuracy. Extensive comparative and ablation experiments were performed on two common breast datasets(CBIS-DDSM and INbreast). Experiments show that the method in this paper is better than the most advanced model. When CBIS-DDSM and INbreast are used as the source domain, the segmentation accuracy reaches 89.1% and 80.7%.
Gheitasi, MohsenFeylizadeh, Mohammad RezaAhari, Roya M.
29页
查看更多>>摘要：Nowadays, companies and financial institutions have different strategies and priorities aligned with their financial goals and digital maturity; however, they rapidly make efforts to meet customers' ever-changing expectations. One of the technologies that can meet the needs and expectations of customers faster and more accurately is the Omni-channel distribution system. Within this system, a set of distribution channels is defined. In addition to increasing the level of service and customer satisfaction, the amount of product demand and sales increases, resulting in more revenue and profit. The present study aimed to design a mathematical multi-objective Mixed Integer Linear Programming (MOMILP) model to investigate the relationship between different components of an Omni-Channel system and an innovative retail business model in the form of a financial approach for the optimization of profitability. In order to confirm the accuracy of the proposed mathematical model, numerical experiments were carried out using near-reality data based on an integration of Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and Red Deer Algorithm (RDA). To solve the problem, the input parameters of this algorithm and mathematical model were provided in the form of problems with three sizes: small, medium, and large. The problem with the small size provided a fast and highly precise optimized response.
查看更多>>摘要：The intelligent control strategy of electromagnetic clutch actuator is analyzed in detail in this paper. The start - stop control of the loom is realized by an electromagnetic clutch. The existing control method of electromagnetic clutch of loom is high and low pressure control strategy. The operator sets the braking advance angle according to experience, to realize the accurate braking of the spindle, but it is difficult to realize the fast and accurate control. In order to achieve good performance, it is very important to develop a fast and accurate loom braking system. Aiming at the fabric defects caused by the elongation of the warp when the loom is stopped, a method of stabilizing the excitation current of the electromagnetic clutch by using the neural adaptive PID (proportional integral differential) controller is proposed to improve the control precision of the actuator. The experimental results show that the proposed control algorithm is feasible and can effectively realize the adaptive control of the spindle braking Angle within the allowable error range.
查看更多>>摘要：Fuzzy logic controllers can handle complex systems by incorporating expert's knowledge in the absence of formal mathematical models. Further, fuzzy logic controllers can effectively capture and accommodate uncertainties that are inherent in real-world controlled systems. On the other hand, Robot Operating System (ROS) has been widely used for many robotic applications due to its modular structure and efficient message-passing mechanisms for the integration of system's components. For this reason, Robot Operating System is an ideal tool for developing software stacks for robotic applications. This paper develops a generic and configurable Robot Operating System package for the implementation of fuzzy logic controllers, particularly type-1 and interval type-2, which are based on either Mamdani or Takagi-Sugeno-Kang fuzzy inference mechanisms. This is achieved by employing a systematic object-oriented approach using the Unified Model Language (UML) to implement the fuzzy inference system as a single class that is composed of fuzzifier, inference, and defuzzifier classes. The deployment of the developed Robot Operating System package is demonstrated by implementing an interval type-2 fuzzy logic control of an Unmanned Aerial Vehicle (UAV).
查看更多>>摘要：Wood dyeing technology is of great significance to improve the utilization rate of inferior wood resources. The challenge to imitating precious wood species by inferior wood is to quickly and accurately obtain the dyeing formula of precious wood species. This study uses Genetic Algorithm (GA) to optimize Extreme learning machine (ELM), and then a predictive model based on GA-ELM is proposed for predicting the dyeing formula of precious wood species. The sum of the relative deviations of the three dyes between the predicted formula and the actual formula, that is, the relative deviation of the formula, is calculated to evaluate the model's prediction accuracy. The simulation results show that the average relative deviations of the formula predicted by Back Propagation (BP) neural network, Radial Basis Function (RBF) neural network, ELM, and GA-ELM are 0.808, 0.717, 0.708, and 0.262. The prediction deviation of the GA-ELM is much smaller than that of other traditional neural networks, which can achieve good results in wood production.
查看更多>>摘要：As an extension of traditional information systems, interval-set information systems have a strong expressive ability to describe uncertain information. Study of the rough set theory and the attribute reduction of interval-set information system are worth discussing. Here, the granularity structure of similar equivalence classes in an interval-set information system is mined, and an attribute reduction algorithm is constructed. The upper and lower approximation operators in the interval-set information system are defined. The accuracy and roughness are determined by these operators. At the same time, using rough sets, a concept of three branches of rough sets on the interval-set information system is constructed. The concepts of attribute dependency and attribute importance are induced by the positive number domain of the three branch domains, and they then lead to the attribute reduction algorithm. Experiments on the UCI datasets show that the uncertainty measure proposed in this paper is sensitive to the attributes and can effectively reduce redundant information of the interval-set information system.
查看更多>>摘要：When a high-speed loom is suddenly stopped or is driven again in a faulty state, it is easy to cause defects such as brake marks and driving marks on the fabric. This is because the brake system of the loom has insufficient control of the accuracy of the parking angle. To improve the control accuracy of the braking system, this paper analyzes the dynamic process of the braking system when braking, builds a mathematical model of the electromagnetic clutch of the braking system, and proposes to regulate the excitation current of the system by fuzzy PID algorithm. The rule table of this PID controller is established by expert experience, and the center of gravity method is used for defuzzification. In addition, this article combines internet communication and storage technology to build a cloud platform for the brake system, and realizes the monitoring, storage and diagnosis of the brake system data. Finally, hardware testing and on-site braking experiments (including parking angle data acquisition experiment and spindle speed change experiment during braking) verify that the design can achieve high-precision parking angle positioning at different speeds, and has a fast dynamic response and a smooth braking process.
查看更多>>摘要：The Wireless Sensor Networks (WSNs) contain a significant quantity of sensor nodes that computes and communicates for data transmission. The data packet sensed and transmitted contains various cross layer feature set that includes many important information. Many essential aspects, which include storage capability, consumption of energy, and, computational power should be taken into account while dealing with the data packets. On the other hand, many past researchers have carried out their work in order to detect intrusion utilizing cross-layer packets but fail in detecting them at the same time. Cross-layer and feature selection techniques play a key role in building an efficient Intrusion Detection System (IDS). An advantage of using the cross-layer technique is to achieve a higher correlation among different layers of the protocol so that one layer can use the parametric information of the other layer by breaking the traditional layer barriers. In this work, we propose a cross-layer based multi-feature selection model for intrusion detection in WSNs. Firstly, an optimized multi-feature selection algorithm is proposed for selecting efficient and useful features from the cross-layered architecture of the network. Secondly, a multi class intrusion detection model is proposed for the classification of different cross-layer based intrusion in the network. The proposed algorithm is developed for providing total security to cross-layer based networks by selecting prominent features and detecting intrusion at the same time. The simulation results are utilizing on real-time intrusive data from the network by analyzing the proposed model.
NSTL
Ios Press