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ISSN / EISSN : 2169-3536 / 2169-3536
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Jidong Wang,
In this paper, a novel fuzzy fault tolerance algorithm is proposed for quarter vehicle active suspensions based on multi-objective constrained optimizations, which improves the riding comfort and driving safety. The objective function approach the optimal solutions via integrating multi-objective constraints as cost functions. By introducing the barrier function, the nonlinear active suspensions with multi-objective constraints are transformed into pure feedback systems without constraints. The problem is rather complicated yet challenging if the actuator faults are taken into account in quarter vehicle. The actuator faults are solved by the utilization of proportional actuation method. The unknown smooth dynamics based on physical truth are identified by exploiting the fuzzy logic knowledge. Meanwhile, the signals received in the suspensions do not violate the constraint boundary. Finally, the simulation results show that the algorithm is effective.
Ahmad Musamih, , Khaled Salah, Haya Hasan, Ibrar Yaqoob, Yousof Al-Hammadi
Controlled drugs are open to abuse, misuse, and diversion. Therefore, they are regulated and tracked across the healthcare sector to protect the health of the general public which is a highly prioritized rule in the health professional’s code of ethics. Healthcare centers that provide controlled medication to patients are still using manual papers to record controlled drugs production, delivery, prescription, administration, and disposal which causes delays in the system. Moreover, instances of controlled drugs misuse, abuse, and diversion still exist, which shows how the currently used system is inefficient in detecting such activities. Therefore, to ensure that the public health is safe and secure, an end-to-end system that tracks the whole healthcare supply chain is necessary. In this paper, we introduce a private Ethereum blockchain-based solution for the management of controlled medication.We ensure transparency, accountability, security, and data provenance by developing smart contracts that record all actions on an immutable ledger. We utilize off-chain storage, which is represented in the IPFS to store content that is large in size such as images. We present algorithms of the different phases in the proposed solution to illustrate how each phase will be carried out. We showcase the functionality of the proposed solution by performing tests and validating the smart contracts. We assess the performance of the proposed solution by conducting privacy, security, and confidentiality analysis. Performance evaluation shows that our solution is secure against common attacks and vulnerabilities and preserves the privacy and confidentiality of the patients. The smart contracts code is made publicly available along with the testing scripts.
, Suchita Bhinge, QunFang Long, Tulay Adali
Functional magnetic resonance imaging (fMRI) is a powerful, noninvasive tool that has significantly contributed to the understanding of the human brain. FMRI data provide a sequence of whole-brain volumes over time and hence are inherently four dimensional (4D). Missing data in fMRI experiments arise from image acquisition limits, susceptibility and motion artifacts or during confounding noise removal. Hence, significant brain regions may be excluded from the data, which can seriously undermine the quality of subsequent analyses due to the significant number of missing voxels. We take advantage of the four dimensional (4D) nature of fMRI data through a tensor representation and introduce an effective algorithm to estimate missing samples in fMRI data. The proposed Riemannian nonlinear spectral conjugate gradient (RSCG) optimization method uses tensor train (TT) decomposition, which enables compact representations and provides efficient linear algebra operations. Exploiting the Riemannian structure boosts algorithm performance significantly, as evidenced by the comparison of RSCG-TT with state-of-the-art stochastic gradient methods, which are developed in the Euclidean space. We thus provide an effective method for estimating missing brain voxels and, more importantly, clearly show that taking the full 4D structure of fMRI data into account provides important gains when compared with three-dimensional (3D) and the most commonly used two-dimensional (2D) representations of fMRI data.
Sundarapandian Vaidyanathan, , Esteban Tlelo-Cuautle, Ahmed A. Abd El-Latif, Bassem Abd-El-Atty, Omar Guillen-Fernandez, Khaled Benkouider, Mohamad Afendee Mohamed, Mustafa Mamat, Mohd Asrul Hery Ibrahim
In this work, we describe the model of a new 4-D hyperchaotic system with no balance point and deduce that the new hyperchaotic system has a hidden attractor. We present a detailed bifurcation analysis for the new hyperchaotic dynamo system with respect to the system parameters and also exhibit that the new hyperchaotic system displays multistability with coexisting attractors. Using Multisim, we design an electronic circuit for the implementation of the new 4-D hyperchaotic system and present the circuit simulation results. We also show the implementation of the new 4-D hyperchaotic system by using a field programmable gate array (FPGA). The hardware resources are reduced by designing single-constant multipliers, adders, subtractors and multipliers. The FPGA design is done for three numerical methods, namely: Forward-Euler, Backward-Euler and fourth-order Runge-Kutta. We demonstrate that experimental chaotic attractors are in good agreement with theoretical simulations. To verify the ability of the presented hyperchaotic system for designing robust cryptosystems, we suggest a novel image cryptosystem using the proposed hyperchaotic system. Simulation outcomes confirm the effectiveness of the proposed image cryptosystem, and consequently, the effectiveness of the proposed 4-D hyperchaotic system in designing diverse cryptographic purposes.
, Umut Kucukaslan, Nassir Navab
Longitudinal analysis of a disease is an important issue to understand its progression and design prognosis and early diagnostic tools. From the longitudinal images where data is collected from multiple time points, both the spatial structural information and the longitudinal variations are captured. The temporal dynamics are more informative than static observations of the symptoms, particularly for neurodegenerative diseases such as Alzheimer’s disease, whose progression spans over the years with early subtle changes. In this paper, we propose a new generative framework to predict the lesion progression over time. Our method first encodes images into the structural and longitudinal state vectors, where interpolation or extrapolation of feature vectors in the time axis can be performed for the manipulation of these feature vectors. These processed feature vectors can be decoded into image space to predict the image at the time point which we are interested. During the training, we force the model to encode longitudinal changes into longitudinal state features and capture the structural information in a separate vector. Moreover, we introduce a personalized memory for the online update scheme, which adapts the model to the target subject, which helps the model preserve fine details of brain image structures in each subject. Experimental results on the public longitudinal brain magnetic resonance imaging dataset show the effectiveness of the proposed method.
, Yufu Ning, Bo Li, Fengming Liu, Chunhua Gao, Yichang Gao
Social media marketing is a new mode of marketing industry. KOL (Key Opinion Leader) marketing is a popular way of social media marketing, which is profit-oriented. During the brand building in the early stage of marketing, the product side generally carries out corresponding advertising promotion, so as to achieve the purpose of promoting marketing. As decision-makers, different KOLs selection affects the final promotion effect. Therefore, to understand the advertising promotion effect of social media, this paper considers the instability of the network environment and the uncertainty of a KOL’s promotion ability, solves the advertising promotion problem in the absence of historical data, and provides meaningful insights for decision-makers. First, this paper takes the advertising promotion effect of the KOL belonging to different levels and the cost of advertisers as uncertain variables and constructs an uncertain KOL selection model considering the constraints of income (promotion effect), cost and risk. Second, based on the relevant algorithm of uncertainty theory, the uncertainty of the model is eliminated, the uncertainty model is transformed into a corresponding clear model, and the KOL’s optimal choice at each level is calculated. Finally, the effectiveness and practicability of the model and the algorithm are verified.
, Lau Bee Theng, Almon Chai Weiyen, McCarthy Christopher
Automatic text recognition in natural scene images is essential for accessing information and understanding our surroundings. Scene text orientations include horizontal scene texts, arbitrarily oriented scene texts, curved scene texts, and vertically oriented scene texts. While attention has been given to horizontal, arbitrarily oriented, and curved text, limited research has been carried out on vertically oriented scene text recognition. To this end, we propose Vertical Text Interpreter, an autonomous vertically oriented scene text recognizer model. Vertical Text Interpreter detects and recognizes vertically oriented scene texts in natural scenes, including vertically-stacked texts, bottom-to-top vertical texts, and top-to-bottom vertical texts. It consists of a shared convolutional neural network, a Vertical Text Spotter, and a Vertical Text Reader. Addressing the need for a dataset for this category of scene texts, we developed a dataset, namely Vertically Oriented Scene Text 1250 Dataset, created as part of this research. The performance of the Vertical Text Interpreter is evaluated using benchmark datasets and the VOST-1250 dataset. Results show that Vertical Text Interpreter can detect and recognize different types of vertically oriented scene texts simultaneously. For future work, we can explore Vertical Text Interpreter for the contexts such as reading assistance and visual navigation systems.
Dora Gazivoda, , Ivan Novko, Tomislav Zupan
Relevant international instrument transformer standards specify internal arc testing to prove the transformer behavior under internal fault conditions. However, the test is defined in a way that does not recognize that it is possible to limit and reduce the total fault energy. For such instances, testing, as currently defined, is mostly inapplicable. The purpose of this paper is to address this issue by presenting a testing sequence that is applicable for verifying the behavior of transformers with sectioned active parts that contain energy-limiting features. Furthermore, the acceptance criteria for the successful completion of the test are also introduced. Every step of the proposed test sequence is discussed in detail and presented on a 145 kV inductive transformer, selected specifically for this purpose. This paper is a part of a continuous broad research with the aim of developing and specifying adequate routine, type and special testing sequences for qualifying paper-oil insulation systems that limit internal arc energy, with the aim of improving the performance of such systems and introducing test methods and criteria that exceed the practices of current standards.
Jiaxiu Dong, Zhaonan Li, Zibin Wang, NianNian Wang, Wentong Guo, Duo Ma, Haobang Hu, Shan Zhong
Regular damage detection plays an important role in timely pavement maintenance. However, the existing detection methods struggle to efficiently and accurately identify the category and contour of the damage. Therefore, this paper proposes a Road-Mask R-CNN mobile damage detection model to automatically segment and measure multiple pavement damages. First, the optimized k-means clustering algorithm is used to intelligently determine the size and ratio of the anchor. Subsequently, the traditional nonmaximum suppression (NMS) algorithm is replaced by the distance intersection over union nonmaximum suppression (DIoU-NMS) algorithm, which improves the detection accuracy of multiple damages in the same image with a mean average precision (mAP) value of 0.934. Then, a comparative experiment with U-Net, the unimproved Mask R-CNN, MSNet and the unsupervised domain adaptation network (UDA) is carried out to verify the effectiveness of the proposed model. And combined with the segmentation and measurement results, the damage is quantitatively evaluated. Moreover, a webcam damage detection system combined with a workstation and an automatic damage detection system for smartphones is developed to quickly detect multiple types of pavement damage. In addition, on-site experiments are carried out on real pavements to verify the feasibility and effectiveness of the proposed method.
, Yongning Guo
Image encryption is often used to protect private images during transmission on a public channel. A high dimensional chaotic map has a greater secret key space, better ergodicity and dynamic property than a low-dimensional chaotic system. A seven-dimensional (7D) hyperchaotic map is used to produce chaotic sequences. Given secret keys and SHA-512 function are employed to generate initial values for iteration. Many stochastic signals are injected into one of the variables during iteration to transfer trajectory and increase dynamic behavior of a chaotic system. Three matrices are constructed with generated chaotic sequences. Permutation is performed on the basis of a control matrix. It keeps the pixel far from its neighboring pixels. Cycle shift is executed during bit-level permutation. Characteristic values of scrambling image are calculated and temporary values are achieved successfully. Two dynamic values are also applied during the diffusion process. Experimental results display the effects of the proposed algorithm. Security analysis reveals that the proposed method has some special advantages.
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