IEEE Access

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ISSN / EISSN : 21693536 / 21693536
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Bruce Davidson, M. Ali Akber Dewan, Vivekanandan S. Kumar, Maiga Chang, Brenda Liggett
IEEE Access pp 1-1; doi:10.1109/access.2020.3015467

Abstract:
Reducing costs and optimizing operations are major challenges in many large-scale organizations including healthcare authorities. Research shows that despite ongoing investments in healthcare information systems (IS), the promised benefits often are partially realized or not at all. Part of the solution is to ensure that evaluation methodologies are available to clearly identify the success of these initiatives and from here articulate and mitigate the deficiencies or move to an alternative technology. The literature asserts that few practitioners have implemented a standardized evaluation approach. Using an established model, namely Information System Impact (IS-impact) model, we proposed a modified evaluation model to assess and a visualization tool to visualize the success of an information systems from a healthcare perspective. The modified IS-Impact model includes six constructs – individual impact, organization impact, provincial alignment impact, system quality, information quality, and service quality. We applied the modified IS-impact model and the proposed visualization tool against an existing healthcare software solution. An empirical study was conducted at a healthcare authority, with responses from 150 participants who use the healthcare IS, which confirmed that the proposed model and the visualization tool are valid and reliable to measure healthcare systems success. The evaluation model and the visualization tool are found to be efficient to narrow down the scope of inquiry from the general to the specific and quickly identifying the gaps and successes within the established software solution for the healthcare authority. Healthcare or clinical informatics researchers will be benefited from this research in evaluating the ongoing or nearly established healthcare information systems.
Yu Guo, Yuxu Lu, Ryan Wen Liu, Meifang Yang, Kwok Tai Chui
IEEE Access pp 1-1; doi:10.1109/access.2020.3015217

Abstract:
Maritime images captured under low-light imaging condition easily suffer from low visibility and unexpected noise, leading to negative effects on maritime traffic supervision and management. To promote imaging performance, it is necessary to restore the important visual information from degraded low-light images. In this paper, we propose to enhance the low-light images through regularized illumination optimization and deep noise suppression. In particular, a hybrid regularized variational model, which combines L0-norm gradient sparsity prior with structure-aware regularization, is presented to refine the coarse illumination map originally estimated using Max-RGB. The adaptive gamma correction method is then introduced to adjust the refined illumination map. Based on the assumption of Retinex theory, a guided filter-based detail boosting method is introduced to optimize the reflection map. The adjusted illumination and optimized reflection maps are finally combined to generate the enhanced maritime images. To suppress the effect of unwanted noise on imaging performance, a deep learning-based blind denoising framework is further introduced to promote the visual quality of enhanced image. In particular, this framework is composed of two sub-networks, i.e., E-Net and D-Net adopted for noise level estimation and non-blind noise reduction, respectively. The main benefit of our image enhancement method is that it takes full advantage of the regularized illumination optimization and deep blind denoising. Comprehensive experiments have been conducted on both synthetic and realistic maritime images to compare our proposed method with several state-of-the-art imaging methods. Experimental results have illustrated its superior performance in terms of both quantitative and qualitative evaluations.
Tao Ye, Zhihao Zhang, Xi Zhang, Fuqiang Zhou
IEEE Access pp 1-1; doi:10.1109/access.2020.3015251

Abstract:
With the high growth rates of railway transportation, it is extremely important to detect railway obstacles ahead of the train to ensure safety. Manual and traditional feature-extraction methods have been utilized in this scenario. There are also deep learning-based railway object detection approaches. However, in the case of a complex railway scene, these object detection approaches are either inefficient or have insufficient accuracy, particularly for small objects. To address this issue, we propose a feature-enhanced single-shot detector (FE-SSD). The proposed method inherits a prior detection module of RON [1] and a feature transfer block of FB-Net [2]. It also employs a novel receptive field-enhancement module. Through the integration of these three modules, the feature discrimination and robustness are significantly enhanced. Experimental results for a railway traffic dataset built by our team indicated that the proposed approach is superior to other SSD-derived models, particularly for small-object detection, while achieving real-time performance close to that of the SSD. The proposed method achieved a mean average precision of 0.895 and a frame rate of 38 frames per second on a railway traffic dataset with an input size of 320 × 320 pixels. The experimental results indicate that the proposed method can be used for real-world railway object detection.
Fazli Wahid, M. Sultan Zia, Rao N. B. Rais, Muhammad Aamir, Umair Muneer Butt, Mubashir Ali, Adeel Ahmed, Imran Ali Khan, Osman Khalid
IEEE Access pp 1-1; doi:10.1109/access.2020.3015206

Abstract:
Firefly Algorithm (FA) is one of the most recently introduced stochastic, nature-inspired, meta-heuristic approaches used for solving optimization problems. The conventional FA use randomization factor during generation of solution search space and fireflies position changing, which results in imbalanced relationship between exploration and exploitation. This imbalanced relationship causes in incapability of FA to find the most optimum values at termination stage. In the proposed model, this issue has been resolved by incorporating PS at the termination stage of standard FA. The optimized values obtained from the FA are set as the initial starting points for the PS algorithm and the values are further optimized by PS to get the most optimal values or at least better values than the values obtained by conventional FA during its maximum number of iterations. The performance of the newly developed FA-PS model has been tested on eight minimization functions and six maximization functions by considering various performance evaluation parameters. The results obtained have been compared with other optimization algorithms namely genetic algorithm (GA), standard FA, artificial bee colony (ABC), ant colony optimization (ACO), differential equations (DE), bat algorithm (BA), grey wolf optimization (GWO), Self-Adaptive Step Firefly Algorithm (SASFA), and FA-Cross algorithm in terms of convergence rate and various numerical performance evaluation parameters. A significant improvement has been observed in the solution quality by embedding PS in the standard FA at the termination stage. The result behind this improvement is the better exploration and exploitation of the solution search space at this stage.
Pengwei Song, Hongyu Si, Hua Zhou, Rui Yuan, Enqing Chen, Zhendong Zhang
IEEE Access pp 1-1; doi:10.1109/access.2020.3015261

Abstract:
The detection and recognition of moving objects in image sequence images involve many aspects, such as pattern recognition, image processing, and computer vision. The main difficulties of target detection and recognition are complex background interference, local occlusion, real-time recognition, illumination changes, target size type changes, etc. However, it is very difficult to solve these problems in practical applications. This article introduces image pre-processing for the pre-processing of image sequences. Selectively we highlight the visually obvious features that are helpful for target detection in the image, weaken the image background and features that are not related to the target, and improve the quality of the image sequence. A multi-information integrated probability density estimation kernel integrating gray scale, spatial relationship and local standard deviation information is designed, and the multiinformation integrated kernel is used to extract the feature of the moving target. In terms of moving target recognition, Naive Bayes is used as a weak learner. In order to avoid the over-fitting of the classifier caused by high-noise moving image sequence features, the regularized Adaboost recognition model is introduced as a moving target recognition classifier. In order to completely separate the target and the background, we propose a moving target extraction method based on multi-information kernel density estimation, and input relevant target feature description vectors into the regularized Adaboost-based moving target recognition framework. Robust target recognition performance is obtained, and the reliability of target recognition under high noise data is improved.
Hayati Ture, Temel Kayikcioglu
IEEE Access pp 1-1; doi:10.1109/access.2020.3015286

Abstract:
Automatic detection of the pectoral muscle in mammograms is widely used in computer-aided diagnostic (CAD) systems for breast cancer. The pectoral muscle region has some prominent features such as the upper corner position, high density, and triangular shape. But, these features may be distorted due to the masses, artifacts, skin folds, and overlapping tissues, and other reasons. Despite recent developments in CAD technology, accurate detection of distorted pectoral muscle images remains a challenging task. In this study, we proposed an automatic method that uses a divided topographic representation to detect distorted pectoral muscle boundaries. After the preprocessing stage, firstly an isocontour map is generated and then divided into horizontal blocks. The contours of the pectoral muscle boundary in the blocks often reveal specific patterns in terms of location, geometric and topological features. We developed a new segmentation algorithm, rule-based contour detection (RBCD), to detect these specific patterned isocontours. The method applied to two datasets consisting of 84 and 201 mammogram images from MIAS and Inbreast databases respectively. Besides, some distorted pectoral muscle samples selected from these datasets were used to further analyze the performance of the proposed method. The mean False-Positive and the mean False-Negative rates of the proposed method for MIAS and Inbreast datasets were 0.92%, 1.26%, and 2.34%, 1.15%, respectively. The quantitative and qualitative results for the distorted pectoral muscle samples show that the proposed method outperformed the compared methods.
Kexiang Li, Zhiquan Deng, Cong Peng, Gucai Pang, Lei Mei, Chunmin Yu
IEEE Access pp 1-1; doi:10.1109/access.2020.3015502

Abstract:
For thrust magnetic bearings (TMB), the stators and thrust disks are commonly made of nonlaminated material. Therefore, eddy currents are inevitable in solid ferromagnetic cores. However, the mechanisms of eddy-current effects on switching power amplifiers have never been investigated. Firstly, the fractional-order model of effective reluctance of a nonlaminated TMB is built. And the time domain solution of current ripples of switching power amplifiers is calculated. Then we define the effective inductance and present the equivalent circuit of current drive mode. Through model reduction, the main impact factor of eddy currents is found, and the simplified model as well as simplified equivalent circuit is obtained. Furthermore, the effects of eddy currents on two current control modes and control parameter design are analyzed. Through finite element method (FEM) and experiments, the established models are verified, and the current tracking performance is compared between laminated and nonlaminated TMB with different control parameters, which demonstrates the theoretical results.
Fayed F. M. Ghaleb, Azza A. Taha, Maryam Hazman, Mahmoud Abd Ellatif, Mona Abbass
IEEE Access pp 1-1; doi:10.1109/access.2020.3015654

Abstract:
The notion of hypergraph cyclicity is important in numerous fields of application of hypergraph theory in computer science and relational database theory. The database scheme and query can be represented as a hypergraph. The database scheme (or query) has a cycle if the corresponding hypergraph has a cycle. An Acyclic database has several desired computational properties such as making query optimization easier and can be recognized in linear time. In this paper, we introduce a new type of cyclicity in hypergraphs via the notions of Quasi α-cycle(s) and the set of α-nodes in hypergraphs, which are based on the existence of an α–cycle(s). Then, it is proved that a hypergraph is acyclic if and only if it does not contain any α-nodes. Moreover, a polynomial-time algorithm is proposed to detect the set of α-nodes based on the existence of Quasi α-cycle(s), or otherwise claims the acyclicity of the hypergraph. Finally, a systematic discussion is given to show how to use the detected set of α-nodes to convert the cyclic hypergraph into acyclic one if the conversion is possible. The acyclic database and acyclic query enjoy time and/or space-efficient access paths for answering a query.
Muhammad Shahid Anwar, Jing Wang, Wahab Khan, Asad Ullah, Sadique Ahmad, Zesong Fei
IEEE Access pp 1-1; doi:10.1109/access.2020.3015556

Abstract:
360-degree video provides an immersive experience to end-users through Virtual Reality (VR) Head-Mounted-Displays (HMDs). However, it is not trivial to understand the Quality of Experience (QoE) of 360-degree video since user experience is influenced by various factors that affect QoE when watching a 360-degree video in VR. This manuscript presents a machine learning-based QoE prediction of 360-degree video in VR, considering the two key QoE aspects: perceptual quality and cybersickness. In addition, we proposed two new QoE-affecting factors: user’s familiarity with VR and user’s interest in 360-degree video for the QoE evaluation. To aim this, we first conduct a subjective experiment on 96 video samples and collect datasets from 29 users for perceptual quality and cybersickness. We design a new Logistic Regression (LR) based model for QoE prediction in terms of perceptual quality. The prediction accuracy of the proposed model is compared against well-known supervised machine-learning algorithms such as k-Nearest Neighbors (kNN), Support Vector Machine (SVM), and Decision Tree (DT) with respect to accuracy rate, recall, f1-score, precision, and mean absolute error (MAE). LR performs well with 86% accuracy, which is in close agreement with subjective opinion. The prediction accuracy of the proposed model is then compared with existing QoE models in terms of perceptual quality. Finally, we build a Neural Network-based model for the QoE prediction in terms of cybersickness. The proposed model performs well against the state of the art QoE prediction methods in terms of cybersickness.
Sangkeum Lee, Hojun Jin, Luiz Felipe Vecchietti, Junhee Hong, Dongsoo Har
IEEE Access pp 1-1; doi:10.1109/access.2020.3015243

Abstract:
Optimization of power management of nanogrid based on short-term prediction of PV power production and consequent EV charging/discharging is proposed. Goal of power management is to reduce time-based electricity cost and total delay. To achieve the goal, efficiency in the combined use of PV power and EV charging/discharging power is important. Unlike the PV power used ahead of costly grid power and entirely dependent on weather condition, timing of EV charging/discharging depends on power management scheme. In order to find out the timing for EV charging/discharging, short-term prediction of PV power production is considered as a key contributor. When PV power production is predicted to decrease in short-term, e.g., 10minutes, discharging power of EVs can compensate the loss and, when predicted to increase in short-term, EVs are charged to capitalize on the gain. Short-term prediction of PV power production is performed by long short-term memory (LSTM) network trained and validated by dataset of PV power production over 1 year. In addition, variation of outdoor temperature in relation to indoor temperature is factored in to determine the timing for EV charging/discharging. Our work is comprehensive in that various electric appliances as well as PV source and EVs are taken into account for power management of nanogrid. Simulation results show the cost benefit obtained from the short-term prediction of PV power production and consequent EV charging/discharging while managing peak demand below maximum allowed level.
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