Miracle-dz/UniFAR-FTUs huggingface dataset:
Response Surface-Assisted Trust-Region Method for Optimization of Electromagnetic Devices

In this article, a trust-region method based optimization approach is proposed for optimization of electromagnetic devices using a reduced number of finite-element method (FEM) simulations. This approach relies on a computationally efficient approximation of the objective function using a response surface model. A second-order polynomial model is used because of its simplicity. The design of experiments technique is used to build this model. Moreover, this approach allows the use of coarser mesh in the FEM model because of its smoothing capabilities under noisy objective function. The algorithm is tested on two benchmark problems: Loney’s solenoid design optimization and TEAM workshop problem 22. The proposed method has shown clear advantage of reduced number of FEM simulations compared to the existing algorithms tested on these problems. Later, this methodology is used for optimization of a Halbach array-based magnetic levitation device where the objective is to maximize permanent magnet utilization. This device is considered for various numbers of Halbach segments per pole. Improvement in the range of 5%–10% is observed over the past optimized results from the literature. Furthermore, this improvement is verified using a commercial optimization software, and the proposed method is found competitive to this software.
Enhancing the Flexibility of Storage Integrated Power System by Multi-Stage Robust Dispatch

The ever-increasing integration of variable wind energy requests for a power system with high flexibility. In this paper, we formulate the real-time economic dispatch problem as a multi-stage robust program to leverage flexible resources, especially energy storage systems, in a broader timescale, and also to avoid the risk of high costs in critical scenarios. The multi-stage robust program is decomposed to the dynamic programming form and the fast robust dual dynamic programming (FRDDP) algorithm is developed to solve it efficiently. To tackle the nonconvexities caused by the inter-temporal constraints of ESSs, an estimation-correction method is developed, and measures of the approximation accuracy are presented. Comprehensive case studies comprising units up to 1371 are carried out to verify the effectiveness of the proposed model. Numerical results show that compared with the existing ones, the proposed multi-stage robust model achieves lower cost under the worst-case scenario and shows better scalability in large-scale systems.
Third Order Band Pass Filter With Double-Gate MOSFET: A Circuit Perspective

To achieve the signal of a particular range, a third order Band Pass Filter (BPF) has been realized for the Radio frequency (RF) applications. The mathematical analysis of the designed filter has been performed to determine the overall center frequency and bandwidth. Traditional components such as BJT and MOSFETs have increased power consumption and larger electronic package size. The application of Double-Gate (DG) MOSFET leads to performance and reliability improvement of the filter with a reduction in the device's size. The DG MOSFET has a smaller width and better gate control over the channel than the traditional MOSFET. This DG MOSFET also provides improved power efficiency and switching capabilities for the filter design.
Data-driven Iterative Learning Formation Control of Non-affine Multi-agent Systems Using Integral Sliding Mode Method

The problem of formation control for unknown nonlinear and non-affine multi-agent systems with external perturbations is addressed in this study based on a data-driven robust learning-based algorithm. Firstly, by using the iterative dynamic-linearization concept and pseudo-partial-derivatives, virtual linearized data models are established to represent the agents’ unknown dynamics. Subsequently, an iteration-dependent integral sliding variable is considered to design the proposed data-based iterative-learning integral sliding-mode formation control which is solely based upon the input/output information of the agents. Finally, the mathematical analysis proves the stability of the closed loop plant. A demonstrative example is also conducted to verify the proficiency of the designed method.
Evaluation of the Suitability of Natural Flower Dyes Spectra on Dye-Sensitized Solar Cell (DSSC) Containing TiO2 and I-/I-3 With Respect to Stability and Efficiency

Different dye-sensitized solar cells (DSSC) were fabricated using TiO2 as a semiconductor with two groups of flowers, nursery flowers from Duhok city and mountain flowers. The ultraviolet (UV)-visible absorption spectra of these dyes were examined. Current density–voltage (J–V) characteristics for each fabricated cell were recorded at ambient temperature. The stability of these cells has been examined through annealing time. The mountain flower dyes have been collected from Gara Mountain at a height about 2500 m above sea level for which they are believed to have resistivity to UV light. The efficiencies of the cells mostly depend on the visible absorption of the dye while their stabilities depend on the ultraviolet absorption of these dyes. As a compromise, the nursery flower dyes efficiencies were between 1.88% and 4.19%, while mountain flowers were between 1.83% and 4.82%. Although we get low efficiency in comparison to other workers, however, in this study we are after getting the best stability of natural flower dyes. The current findings are substantially added to our understanding of the stability of DSSC. Some suitable stable natural flowers were selected for our new program of DSSC research. The statistical P values show that the efficiencies of mountain flower dyes are much more stable than that of nursery flower dyes.
Comparative Study on various Storage Optimization Techniques in IoT-Cloud Ecosystem

Internet of Things is a pervasive network of physical entities called "people" and "Things". IoT data is increasing at a very fast rate, every connected device produces and exchanges a large amount of data on the internet. Therefore with limited storage-capacity and processing-power, IoT devices are unable to process or store this big data. To achieve more substantial growth in IoT as described in [1], there is a need to improve the performance of the system by managing storage of data with reduced latency and space-efficient storage. For optimizing the storage space, many tools and techniques are available in the literature. Some of these techniques are compared in this paper. The analysis of these techniques is performed on parameters like response time, access control, energy consumption, storage efficiency, etc. used by the respective researchers. At the end of this paper, all tools and techniques are summarized and the pros and cons of all of them are listed.
Stabilization of DC/DC Converter with Constant Power Load using Exact Feedback Linearization Method based on Backstepping Sliding Mode Control and Nonlinear Disturbance Observer

Qualitative advancement in DC microgrids (MGs) has provided the use of various loads with special conditions and characteristics in the power grid. Optimal control of such grids requires the introduction of new tools and methods in the fields of power electronics. In this research work, by merging sliding mode and backstepping control approaches, the stability of DC-MG system using boost converter and constant power load (CPL) with nonlinear disturbance observer (NDO) is guaranteed. In this regards, first, the linear model of the canonical brunovsky form of the system is extracted to solve the problem of nonlinearity of the system, due to the existence of CPL and the problem of non-minimum phase of the boost converter. Then, using a nonlinear observer, the disturbance is estimated. Also, by combining sliding mode control and backstepping control, the desired control technique is implemented, and the overall stability of the system is achieved. In order to verify the effectiveness of the proposed stabilizing technique, the simulation is performed in MATLAB software package and the results confirm the superiority of dynamic proficiency and higher robustness of the suggested controller compared to other controllers.
Understanding Network Characteristics of Spam Users in Social Media

Timely identification of spam users, usually regarded as users creating and spreading false information in social media, has experienced a resurgence of interest. Besides the linguistic and behavioral cues, the network among users has become an informative yet robust data source towards spam users detection. However, the characteristics of spam users’ behavior on networks have not been closely examined. Along this line, our study selects two datasets from e-commerce review system and social networking platform for systematically exploring network characteristics of spam users. We perform the network analysis from three aspects: homophily of edges, centrality of nodes and cohesive groups. Our analytical results yield several conclusions: (i) the homophily property is always satisfied on the e-commerce co-reviewing network, but it does not hold on multiple relations of social networking platform; (ii) spam users are likely to be on the periphery of the network on e-commerce platform, but on the innermost network core on social networking platform; (iii) spam users on e-commerce platform tend to form cohesive groups that are quite scarce on the social networking platform.
Representation Learning on Networks for Community Detection

Community detection is a fundamental problem in network analysis. In recent years, network representation learning has been leveraged to help the detection of potential communities. However, representation learning and community detection in these studies are usually optimized independently. In addition, they also have a strong assumption that the number of communities is prespecified before detection. To address those problems, we propose a joint representation learning and community detection (JRC) method. To allow the learned representations to be community-aware, JRC introduces two additional terms to the loss function for clustering. One is designed to getting node representations as close as possible to the exemplar of the community. Another is set up as a smoothness regularization to enforce the community properties of high cohesion and low coupling. Experimental results on multiple real-world network datasets show that JRC improves the quality of community detection and outperforms many competitive baselines.
A mobile application for crowdsourced road condition monitoring

An Android App has been developed to measure geo-tagged vehicle-induced vibrations with the use of the built-in accelerometer of a mobile phone. The architecture of the app is designed in such a way that it can be published as a crowdsourced vehicle-induced vibration measurement data collection app. A simple calibration method has been developed to calibrate each individual axis of the accelerometer to get rid of imperfections within the three axes. The system is evaluated for extracting data of vibrations for a known system (simple pendulum) and the same system was tested for vehicle induced-vibration capturing. The results produced from the system during the test run correctly classified different road conditions. Once the crowdsourced data is available, a more statistically significant prediction could be performed.
A Non-isolated High Step-up Soft Switching DC to DC Converter with Continues input Current and Low Switch Voltage Stress

This essay presents a non-isolated high gain boost soft switching DC to DC topology with persistent input and output power, light input current ripple and just an active switch with reduced voltage stress. In this low cost proposed converter the reverse recovery matter of the diodes is reduced via the leakage inductance of the coupled inductor. The output diode is acted in ZCS (zero current switching) situation in on or off states and so, switching loss is zero in this case. Other diodes and Switch are operated under ZCS condition in turning on or off. The operation steady-state examination and principle are introduced in detail. To show the privilege of this converter, the main specifications of the proposed topology have been checked with other previous high-gain (HG) converters. Finally, Simulation verifications is shown to prove the accuracy of the theoretical analysis for this high-gain converter.
Detailed Feature Guided Generative Adversarial Pose Reconstruction Network

Face frontalization is a critical and difficult task on face pose reconstruction. Previous researches use simple posture information as guidance, such as pose coding and facial landmarks. To explore the guidance effect of profile faces, we propose detailed features that provide much detailed information. In this paper, a Detailed Feature Guided Generative Adversarial Pose Reconstruction Network (DGPR) is proposed. Firstly, frontal pose coding and profile detailed features are fed into DGPR to generate detailed features of front face. Then, the second generator combines frontal detailed features and profile face to reconstruct front face. Besides, we propose a conditional enhancement loss to strengthen the guiding role of detailed features, and a smoothing loss to reduce edge sharpness in generated faces. Experimental results show that our method generates photorealistic front faces and outperforms state-of-the-art methods on M2FPA and CAS-PEAL. Specifically, DGPR improves the face recognition accuracy under pose angles of ±60°, ±75°, ±90° by 2%, 1%, and 6% respectively over the state-of-the-art methods on M2FPA, achieves the average rank-1 recognition rate to 99.95% and improves it by 0.05% on CAS-PEAL. These results demonstrate the effects of detailed features and corresponding modules.
Change Detection and Tracking using Synthetic Aperture Radar Videos

In this paper, a change detection technique which can be utilized in identifying important changes in SAR videos has been proposed. Even though most researches have done on change detection in multi temporal SAR images, implementing a methodology to identify changes in SAR videos in a near real time manner has unique challenges such as the inherent speckle noise which will increase the false positive rate of the detection, rotation of the reconstructed SAR video frames due to the movement of the airborne vehicle, dynamic background and the overlapping of the area in consecutive video frames, non-uniform backscattering of SAR pulses and shadowy modelling of objects in video frames which doesn’t provide much information about the appearance model of the objects. We propose an algorithm based on combination of optical flow calculation using Lucas Kanade method(LK method) and blob detection to detect the changes, and we feed the detected changes along with optical flow calculations to a centroid tracking algorithm to track the detected changes throughout the video.
Sub-Channel Scheduling, Task Assignment, and Power Allocation for OMA-Based and NOMA-Based MEC Systems

In this paper, sub-channel scheduling, task assignment and power allocation are investigated for orthogonal multiple access (OMA)-based and non-orthogonal multiple access (NOMA)-based mobile edge computing (MEC) systems. Based on different channel conditions and computational capacities, computational tasks are partially offloaded to the MEC server via OMA or NOMA protocols. In order to minimize the total energy consumption, an optimization problem under the task execution latency constraint is formulated and divided into two sub-problems. By utilizing matching theory, the formulated sub-channel allocation problem is solved by a proposed low-complexity algorithm, where the joint optimization of task assignment and power allocation is performed at each iteration. Based on the delay constraint, some insights are obtained, and the closed-form solutions of task assignment coefficients and transmit power are derived. Furthermore, the offloading strategy in both OMA and NOMA schemes is analyzed, which shows that the optimal task assignment coefficient is decided by the energy consumption efficiency (ECE). Simulation results indicate that: i) the proposed sub-channel allocation algorithm and derived closed-form solutions can significantly improve the MEC system in terms of the energy consumption; ii) the provided offloading strategy can be dynamically and efficiently employed with different channel conditions and computational capacities.
A 9-Switch 3-Level VSI-Based MPSC of PMSM Without Weighting Factors

This paper proposes utilizing a 9-switch 3-level voltage source inverter (VSI) in model predictive speed control (MPSC) of a permanent magnet synchronous motor (PMSM) without weighting factors. MPSC, through which PMSM speed is controlled directly, is an impressive method which enjoys a faster dynamic response compared to the other predictive approaches, such as model predictive current control (MPCC). However, switching frequency is lower in MPSC which, subsequently, leads to high steady-state torque and speed ripples. In this regard, utilizing the proposed VSI, which has fewer switches in its structure compared to other 3-level VSIs, provides more active voltage vectors compared to the conventional 2-level VSI, and will decreases the speed, torque and d-axis stator current ripples of PMSM. Besides, cost function of MPSC consists of different components with dissimilar natures and dimensions; thus, proper tuning of the weighting factors of its cost function can be problematic. With this in mind, in this paper, weighting factors are eliminated through rating the different components of the cost function relatively. Simulation results applied in MATLAB/Simulink environment validates the effectiveness of the proposed method.
High Performance and Energy Efficient Integer Matrix Multiplication for Deep Learning

We present a multi-threaded implementation of the matrix multiplication for deep learning on ARM multicore processors. Following standard practice for inference with convolutional neural networks, our GEMM kernel operates with 16-bit integer arithmetic, yielding significant performance acceleration and cutting the memory requirements with respect to IEEE (floating point) single precision by half, allowing the deployment of larger neural network models on low power devices with limited storage capacity.
The Empirical Analysis of the Relationship among Environmental Hostility, Manufacturing Flexibility

This study investigates the impact of individual absorptive capacity on manufacturing flexibility as well as the role of organizational absorptive capacity to improve individual absorptive capacity in environmental hostility. It mainly focuses on the operational absorptive capacity to identify the interplay between environmental hostility and operational absorptive capacity to improve flexibility in the manufacturing sector of South Korea. For the structural equation models(SEM) analysis, we used a random sample of 428 manufacturing firms in shipbuilding, automotive and general machinery industries from the 2013 survey drawn from the Korea Productivity Center. Based on the results of analysis, it shows various findings regarding the relation of environmental hostility and individual and organizational absorptive capacity to manufacturing flexibility. This research proposed important theoretical and practical implications for the relationship between environmental hostility and operational absorptive capacity to improve manufacturing flexibility.
Intelligent Monitoring System of Residential Environment Based on Cloud Computing and Internet of Things

With the development of society and the improvement of living standards, people pay more and more attention to their own living environment and life safety, and more people are gradually paying attention to the impact of the quality of the living environment on their health and work efficiency, and to meet this demand of people, it is necessary to effectively monitor and control the living environment. Based on this, this article applies cloud computing and Internet of things technologies that have developed rapidly in recent years, and proposes to design a residential environment intelligent monitoring system based on cloud computing and Internet of things, and use Internet of things and sensor technologies to achieve target connection and communication. It then uses distributed computing, a cloud computing technology, to implement integrated, standardized management of this system for truly intelligent monitoring. In this article, we first gathered a large amount of information through literature search methods, systematically introduced cloud computing and Internet of Things technologies, and introduced two applications in environmental intelligent monitoring. Next, we propose a design experiment of a residential environment intelligent monitoring system based on cloud computing and the Internet of Things, and propose an overall concept of system design, system hardware and software design requirements, and device selection and comfort evaluation. And living environment data integration is introduced in detail. We then tested the performance of the system in a specific application of the system in a real residential environment, using data parameters collected at different points in time as experimental data. Finally, it is concluded that the fuzzy close fusion algorithm used in the experiment can obtain data parameter values that are in good agreement with the real values, and the error range is controlled within 0–0.01; the indoor environment comfort is judged by 1 as the standard. $PMV>1$ indicates that the comfort level is excellent, and in the test experiment, each parameter value at 5 time points is all > 1, indicating that the indoor environment comfort level detected by the system is excellent.
Optimizing Parallel Applications via Dynamic Concurrency Throttling and Turbo Boosting

With the increasing number of cores in modern systems, dynamic concurrency throttling (DCT) and turbo-boosting techniques are becoming a solution to better use the hardware resources. While DCT techniques tune the number of running threads, boosting techniques speed up sequential phases or unbalanced threads. However, as each region of an application may behave differently, optimizing both knobs is not straightforward. Hence, we propose two strategies that apply DCT and turbo-boosting: DBF, which aims to find an ideal configuration for each parallel/sequential region, and DBC, which considers the combination of parallel/sequential regions during the optimization. We show that DBF and DBC improve the EDP by up to 19% and 27% compared to a DCT-only strategy and by up to 95% and 96% compared to a Boost-only technique. We also show that DBF is more suitable for applications with high variability in the CPU workload, while DBC is better when there is low workload variability.