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Research Article
Energy Efficient Scheduler of Aperiodic Jobs for Real-time Embedded Systems
Hussein El Ghor, El-Hadi M. Aggoune
2020, vol. 17, no. 5, pp. 733-743, doi: 10.1007/s11633-016-0993-3
Energy consumption has become a key metric for evaluating how good an embedded system is, alongside more performance metrics like respecting operation deadlines and speed of execution. Schedulability improvement is no longer the only metric by which optimality is judged. In fact, energy efficiency is becoming a preferred choice with a fundamental objective to optimize the system's lifetime. In this work, we propose an optimal energy efficient scheduling algorithm for aperiodic real-time jobs to reduce CPU energy consumption. Specifically, we apply the concept of real-time process scheduling to a dynamic voltage and frequency scaling (DVFS) technique. We address a variant of earliest deadline first (EDF) scheduling algorithm called energy saving-dynamic voltage and frequency scaling (ES-DVFS) algorithm that is suited to unpredictable future energy production and irregular job arrivals. We prove that ES-DVFS cannot attain a total value greater than \begin{document}$ C/\widehat{S}^{\alpha} $\end{document}, where \begin{document}$ \widehat{S} $\end{document} is the minimum speed of any job and \begin{document}$ C $\end{document} is the available energy capacity. We also investigate the implications of having in advance, information about the largest job size and the minimum speed used for the competitive factor of ES-DVFS. We show that such advance knowledge makes possible the design of semi-on-line algorithm, ES-DVFS\begin{document}$ ^{**} $\end{document}, that achieved a constant competitive factor of \begin{document}$ 0.5 $\end{document} which is proved as an optimal competitive factor. The experimental study demonstrates that substantial energy savings and highest percentage of feasible job sets can be obtained through our solution that combines EDF and DVFS optimally under the given aperiodic jobs and energy models.
A Novel Self-adaptive Circuit Design Technique Based on Evolvable Hardware
Jun-Bin Zhang, Jin-Yan Cai, Ya-Feng Meng, Tian-Zhen Meng
2020, vol. 17, no. 5, pp. 744-751, doi: 10.1007/s11633-016-1000-8
Since traditional fault tolerance methods of electronic systems are based on redundant fault tolerance technique, and their structures are fixed when circuits are designed, the self-adaptive ability is limited. In order to solve these problems, a novel circuit self-adaptive design technique based on evolvable hardware (EHW) is proposed. It features robustness, self-organization and self-adaption. It can be adapted to a complex environment through dynamic configuration of the circuit. In this paper, the proposed technique simulated. The consumption of hardware resources and the number of convergence iterations researched. The effectiveness and superiority of the proposed technique are verified. The designed circuit has the ability of resistible redundant-state interference (RRSI). The proposed technique has a broad application prospect, and it has great significance.
A Study on Performance and Reliability of Urethral Valve Driven by Ultrasonic-vaporized Steam
Zhen Hu, Xiao Li, Ting Guan
2020, vol. 17, no. 5, pp. 752-762, doi: 10.1007/s11633-016-1026-y
The aim of this study is to investigate the performance and reliability of urethral valve driven by ultrasonic-vaporized steam. The performance model of urethral valve is established to analyze the driving and opening/closing performances of urethral valve. The reliability model of urethral valve is obtained, and the reliability simulation algorithm is proposed to calculate the reliability index of urethral valve. The numerical simulation and experimental results show that urethral valve has a good opening/closing performance, the driving performance can be improved by increasing ultrasonic intensity, radiation area and ultrasonic frequency, and the corrosion and aging of driving bags are the weak links of urethral valve.
Item Response Theory Based Ensemble in Machine Learning
Ziheng Chen, Hongshik Ahn
2020, vol. 17, no. 5, pp. 621-636, doi: 10.1007/s11633-020-1239-y
In this article, we propose a novel probabilistic framework to improve the accuracy of a weighted majority voting algorithm. In order to assign higher weights to the classifiers which can correctly classify hard-to-classify instances, we introduce the item response theory (IRT) framework to evaluate the samples′ difficulty and classifiers′ ability simultaneously. We assigned the weights to classifiers based on their abilities. Three models are created with different assumptions suitable for different cases. When making an inference, we keep a balance between the accuracy and complexity. In our experiment, all the base models are constructed by single trees via bootstrap. To explain the models, we illustrate how the IRT ensemble model constructs the classifying boundary. We also compare their performance with other widely used methods and show that our model performs well on 19 datasets.
DiscoStyle: Multi-level Logistic Ranking for Personalized Image Style Preference Inference
Zhen-Wei He, Lei Zhang, Fang-Yi Liu
2020, vol. 17, no. 5, pp. 637-651, doi: 10.1007/s11633-020-1244-1
Learning based on facial features for detection and recognition of people′s identities, emotions and image aesthetics has been widely explored in computer vision and biometrics. However, automatic discovery of users′ preferences to certain of faces (i.e., style), to the best of our knowledge, has never been studied, due to the subjective, implicative, and uncertain characteristic of psychological preference. Therefore, in this paper, we contribute to an answer to whether users′ psychological preference can be modeled and computed after observing several faces. To this end, we first propose an efficient approach for discovering the personality preference related facial features from only a very few anchors selected by each user, and make accurate predictions and recommendations for users. Specifically, we propose to discover the style of faces (DiscoStyle) for human′s psychological preference inference towards personalized face recommendation system/application. There are four merits of our DiscoStyle: 1) Transfer learning is exploited from identity related facial feature representation to personality preference related facial feature. 2) Appearance and geometric landmark feature are exploited for preference related feature augmentation. 3) A multi-level logistic ranking model with on-line negative sample selection is proposed for on-line modeling and score prediction, which reflects the users′ preference degree to gallery faces. 4) A large dataset with different facial styles for human′s psychological preference inference is developed for the first time. Experiments show that our proposed DiscoStyle can well achieve users′ preference reasoning and recommendation of preferred facial styles in different genders and races.
Robust Object Tracking via Information Theoretic Measures
Wei-Ning Wang, Qi Li, Liang Wang
2020, vol. 17, no. 5, pp. 652-666, doi: 10.1007/s11633-020-1235-2
Object tracking is a very important topic in the field of computer vision. Many sophisticated appearance models have been proposed. Among them, the trackers based on holistic appearance information provide a compact notion of the tracked object and thus are robust to appearance variations under a small amount of noise. However, in practice, the tracked objects are often corrupted by complex noises (e.g., partial occlusions, illumination variations) so that the original appearance-based trackers become less effective. This paper presents a correntropy-based robust holistic tracking algorithm to deal with various noises. Then, a half-quadratic algorithm is carefully employed to minimize the correntropy-based objective function. Based on the proposed information theoretic algorithm, we design a simple and effective template update scheme for object tracking. Experimental results on publicly available videos demonstrate that the proposed tracker outperforms other popular tracking algorithms.
Ground-level Ozone Prediction Using Machine Learning Techniques: A Case Study in Amman, Jordan
Maryam Aljanabi, Mohammad Shkoukani, Mohammad Hijjawi
2020, vol. 17, no. 5, pp. 667-677, doi: 10.1007/s11633-020-1233-4
Air pollution is one of the most serious hazards to humans′ health nowadays, it is an invisible killer that takes many human lives every year. There are many pollutants existing in the atmosphere today, ozone being one of the most threatening pollutants. It can cause serious health damage such as wheezing, asthma, inflammation, and early mortality rates. Although air pollution could be forecasted using chemical and physical models, machine learning techniques showed promising results in this area, especially artificial neural networks. Despite its importance, there has not been any research on predicting ground-level ozone in Jordan. In this paper, we build a model for predicting ozone concentration for the next day in Amman, Jordan using a mixture of meteorological and seasonal variables of the previous day. We compare a multi-layer perceptron neural network (MLP), support vector regression (SVR), decision tree regression (DTR), and extreme gradient boosting (XGBoost) algorithms. We also explore the effect of applying various smoothing filters on the time-series data such as moving average, Holt-Winters smoothing and Savitzky-Golay filters. We find that MLP outperformed the other algorithms and that using Savitzky-Golay improved the results by 50% for coefficient of determination (R2) and 80% for root mean square error (RMSE) and mean absolute error (MAE). Another point we focus on is the variables required to predict ozone concentration. In order to reduce the time required for prediction, we perform feature selection which greatly reduces the time by 91% as well as shrinking the number of features required for prediction to the previous day values of ozone, humidity, and temperature. The final model scored 98.653% for R2, 1.016 ppb for RMSE and 0.800 ppb for MAE.
A Creative Computing Approach to Film-story Creation: A Proposed Theoretical Framework
Hong-Wei Liu, Hong-Rui Liu, Hong-Ji Yang, En-Ze Yu
2020, vol. 17, no. 5, pp. 678-690, doi: 10.1007/s11633-020-1238-8
The film industry is currently witnessing a severe shortage of good stories and a decline in storytelling art. Meanwhile, creative computing has been employed successfully in the humanities, especially in the fields of art. Seeing the similarities between the process of film-story creation and that of creative computing, we propose a theoretical framework across the two domains, where the exploratory, the combinational and the transformational rules are jointly utilized to generate new ideas and provide potential options in film-story creation. The framework consists of a film knowledge library, a creative computing system, an evaluation model and an output module. The combination of creative computing and film story creation not only helps to produce novel storylines, shorten the creation cycle, and speed up film industry, but also contributes to the novelty and specificity of interdisciplinary studies.
Optimization of Grasping Efficiency of a Robot Used for Sorting Construction and Demolition Waste
Yue-Dong Ku, Jian-Hong Yang, Huai-Ying Fang, Wen Xiao, Jiang-Teng Zhuang
2020, vol. 17, no. 5, pp. 691-700, doi: 10.1007/s11633-020-1237-0
The recycling of construction and demolition waste (CDW) remains an urgent problem to be solved. In the industry, raw CDW needs to be manually sorted. To achieve high efficiency and avoid the risks of manual sorting, a sorting robot can be designed to grasp and sort CDW on a conveyor belt. But dynamic grasping on the conveyor belt is a challenge. We collected location information with a three-dimensional camera and then evaluated the method of dynamic robotic grasping. This paper discusses the grasping strategy of rough processed CDW on the conveyor belt, and implements the function of grasping and sorting on the recycling line. Furthermore, two new mathematical models for a robotic locating system are established, the accuracy of the model is tested with Matlab, and the selected model is applied to actual working conditions to verify the sorting accuracy. Finally, the robot kinematics parameters are optimized to improve the sorting efficiency through experiments in a real system, and it was concluded that when the conveyor speed was kept at around 0.25 m/s, better sorting results could be achieved. Increasing the speed and shortening the acceleration/deceleration time would reach the maximum efficiency when the load would allow it. Currently, the sorting efficiency reached approximately 2 000 pieces per hour, showing a high accuracy.
Adaptive Equivalent-input-disturbance Approach to Improving Disturbance-rejection Performance
Ze-Wen Wang, Jin-Hua She, Guang-Jun Wang
2020, vol. 17, no. 5, pp. 701-712, doi: 10.1007/s11633-020-1230-7
This paper presents an adaptive equivalent-input-disturbance (AEID) approach that contains a new adjustable gain to improve disturbance-rejection performance. A linear matrix inequality is derived to design the parameters of a control system. An adaptive law for the adjustable gain is presented based on the combination of the root locus method and Lyapunov stability theory to guarantee the stability of the AEID-based system. The adjustable gain is limited in an allowable range and the information for adjusting is obtained from the state of the system. Simulation results show that the method is effective and robust. A comparison with the conventional EID approach demonstrates the validity and superiority of the method.
Novel Non-monotonic Lyapunov-Krasovskii Based Stability Analysis and Stabilization of Discrete State-delay System
Younes Solgi, Alireza Fatehi, Ala Shariati
2020, vol. 17, no. 5, pp. 713-732, doi: 10.1007/s11633-020-1222-7
This paper proposes a novel less-conservative non-monotonic Lyapunov-Krasovskii stability approach for stability analysis of discrete time-delay systems. In this method, monotonically decreasing requirements of the Lyapunov-Krasovskii method are replaced with non-monotonic ones. The Lyapunov-Krasovskii functional is allowed to increase in some steps, but the overall trend should be decreasing. The model of practical systems used for stability analysis usually contain uncertainty. Therefore, firstly a non-monotonic stability condition is derived for certain discrete time-delay systems, then robust non-monotonic stability conditions are proposed for uncertain systems. Finally, a novel stabilization algorithm is derived based on the introduced non-monotonic stability condition. The Lyapunov-Krasovskii functional and the controller are obtained by solving a set of linear matrix inequalities (LMI) or iterative LMI based nonlinear minimization. The proposed theorems are first evaluated by some numerical examples, and then by simulation and implementation on the pH neutralizing process plant.