Miracle-dz/UniFAR-FTUs huggingface dataset:
A Bridgeless Electrolytic Capacitor-Free LED Driver Based on Series Resonant Converter With Constant Frequency Control

High-brightness light-emitting diode (LED) is next generation of green lighting. However, the bulky electrolytic capacitor is required to compensate the difference of pulsating power, thereby restricting the lifetime. Meanwhile, the existing drivers based on pulse frequency modulation (PFM) is bound to arouse heavy electromagnetic interference. In this paper, the series resonant converter based LED drive is proposed to overcome the shortcomings of electrolytic capacitor and PFM control. The proposed method is to attenuate the low frequency ripples delivered from the power factor correction (PFC) to LEDs; therefore, the capacitance is reduced for energy storage of offline LED drivers. On account of the adoption of the switching controlled capacitor (SCC), the constant frequency control is achieved by the regulations of the equivalent capacitance. Half-bridge switches are shared by the bridgeless PFC and the resonant unit, therefore, single-stage LED drive is realized with high efficiency. In addition, SCC is simultaneously shared for the upper and lower half-bridge to reduce the cost and improve the power density. Operating principle, design considerations, and performance comparisons are examined in detail. Finally, all the superior performances of proposed LED driver are verified by simulation and experimental prototypes with two-channel LEDs, and rated output power of 80 W.
Wireless Coverage Analysis for Intra-Wagon Scenario at 60 GHz Band

Very high throughput (VHT) intensive applications which demand a high data rate of Gigabits per second, motivate the exploration of millimeter-wave (mmWave) band, such as 60 GHz. With the new era of “smart rail mobility”, surfing the Internet, in general, accessing the network has become the fundamental part of the rail traffic services. Hence, this paper discusses the wireless coverage, which is the core of network planning, for intra-wagon scenario at 60 GHz band. First, the three-dimensional (3D) high-speed train (HST) model is reconstructed according to the actual size of China Railways CRH5. Then, we utilize a self-developed reliable ray-tracing (RT) simulator and set various parameters proposed by IEEE 802.11ad, which is a typical standard operating at 60 GHz band. Our result indicates that the wireless coverage is maximized while maximizing the number of the transmitters and the combination of transmit power and antenna gain. By considering the balance of the wireless coverage and the cost of the transmitters, the deployment of two transmitters in one wagon is suggested.
Cost-Efficient Consolidating Service for Aliyun’s Cloud-Scale Computing

Server consolidation is critical for energy efficiency of cloud-scale computing. In production environments like Aliyun, which is one of the largest public cloud platforms in the world, server consolidation has several challenges. First, the widespread use of local storage remarkably increases the migration cost (time). Second, the resource utilization of service instances varies over time, which may result in migration oscillation. Third, server consolidation must follow practical constraints. E.g., instances can only be migrated within a maintenance window, and both the resource utilization and the number of instances on a server are bounded. This paper designs and implements C4, a Cost-Efficient Consolidating Service for Aliyun’s Cloud-Scale Computing, which Aliyun uses to consolidate servers. We analyze user pattern, resource utilization, and migration cost in Aliyun, showing that traditional utilization-based consolidation approaches cannot meet the needs in production environments, especially for the local-storage-based computing. This motivates us to propose the migration cost model, by which to select servers with the minimum migration time to release. We use the Worst-Fit heuristic to migrate instances to balance the load. Evaluation shows that C4 achieves cost-efficient, load-balanced, and oscillation-free consolidating service. We describe experience with over one year of C4 production deployment, lessons learned, and areas for future work.
Optimal Configuration of Multiphase AC Motors Under Squarewave and PWM Operations

In many applications such as electric vehicles, dual mode operation is needed. In the low speed region, PWM mode is chosen and squarewave mode is used for high speed region. In this paper, multiphase motor that is suitable for dual mode operation is investigated. Inverter input current ripple and rotor current ripple will be considered as the decision factors. This paper shows that nine-phase is suitable for such dual mode operation.
A New Algorithm for Attitude Detemination Based on Chicken Swarm Optimization

In this paper a new algorithm for global navigation satellite system attitude determination based on CSO (Chicken Swarm Optimization) was developed. A distinct feature of this algorithm is the new optimization. The traditional LAMBDA algorithm for integer ambiguity resolution is time-consuming due to the large search space and complex calculation, so the attitude determination time is longer correspondingly. The optimization algorithm without search space based on least squares proposed in this paper improves the efficiency of fixing the integer ambiguity and applied to solve attitude problems. In conclusion, it is proven to work, offering a very efficient method of attitude determination.
Neural Cross-Session Filtering: Next-Item Prediction Under Intra- and Inter-Session Context

Classic recommender systems (RSs) often repeatedly recommend similar items to user historical profiles or recent purchases. For this, session-based RSs (SBRSs) are extensively studied in recent years. Current SBRSs often assume a rigid-order sequence, which does not fit in many real-world cases. In fact, the next-item recommendation depends on not only current session context but also historical sessions which are often neglected by current SBRSs. Accordingly, an SBRS over relaxed-order sequences with both intra- and inter-context is more pragmatic. Inspired by the successful experience in modern language modeling, we design an efficient neural architecture to model both intra- and inter-context for next item prediction.
A Game Theoretic Approach for Demand-Side Management Considering Generation, Storage and the Combinatorial Nature of Load Scheduling

In this paper an energy consumption scheduling for residential area considering generation, storage and the combinatorial nature of shiftable device scheduling has been proposed. The smart grid consists of the traditional users as well as users with smart meter who participate in the day ahead optimization process to reduce their energy bill by storing, generating and shifting their device to non-peak hours. These entities by participating in optimization process reduce the per unit energy cost of the grid for all users. It is assumed that all of the participating users own a storing, generating or both devices having same characteristic. A billing scheme has been adopted to encourage the user to shift their load to non-peak hour. The users will compete to shift their load as well as to charge their storage device to low load period, therefore the resulting day ahead optimization problem has been formulated as non-cooperative game between the users. The iterative distributed algorithm can be run on user’s smart meters to solve the formulated problem. The strategy set for the scheduling of time shiftable devices are discrete and combinatorial in nature, hence particle swarm algorithm for optimizing the individual users pay off function has been used. The obtained results demonstrated the effectiveness of the proposed method in terms of reduced energy price and system peak while considering the user’s privacy and comfortability.
Design of Antenna Rapid Optimization Platform Based on Intelligent Algorithms and Surrogate Models

Optimizers in electromagnetic (EM) simulation software are facing challenges in optimization efficiency, supported optimization types and optimization results usability. Aiming to fill these gaps, a new antenna design exploration scheme is proposed, called Antenna Rapid Optimization Platform (AROP). The scheme adopts ANSOFT HFSS and MATLAB co-simulation method and provides a friendly graphical interface based on the antenna optimization process. State-of-the-art intelligent optimization algorithms and surrogate model methods are embedded, addressing efficient multi-objective antenna optimization problems and generating Pareto optimal solution sets. Design results show that the surrogate models assisted by evolutionary algorithms built into AROP can largely improve optimization capacity for high-performance antenna synthesis and deal with complex antenna designs with high-dimensional parameter space.
Design and Control for ZVS Constant Current and Constant Voltage Wireless Charging Systems

This paper proposes a design and control method for double sided LCC compensation network based wireless charging to obtain both constant voltage (CV) and multiple constant current (CC) characteristics which is suitable for stationary charging system without the need of additional electronic components requirement while maintaining input zero phase angle (ZPA) condition for a wide range of load variations. Proper control strategies are also presented to realize soft switching of power switches. The validity of the proposed concept is demonstrated by both theoretical analysis and computer simulations. Experimental results verify the feasibility of the proposed system.
Credit Scoring Using Information Fusion Technique

Banks frequently face massive credit risks, which might lead to opportunities lost or financial losses. Regarding to this, more and more data mining methods are used in bank credit scoring nowadays. However, different data mining methods for classification can produce different results. The aim of this paper is to fuse the different data mining results together to get one better solution by using the information fusion technique. In this study, information fusion technique is used to build the credit scoring models based on data mining methods such as SVM and Logistic regression model. Two real credit scoring data sets of UCI databases are used to demonstrate the effectiveness and feasibility of the method. The results show that the information fusion model has certain validity, reliability and a higher accuracy than those of the two methods obtained separately.
A Systematic Approach for Simulating Transient Stability of Multi-Machine Power System Using a Proposed Machine Template Procedure

This paper introduces a systematic approach incorporating MATLAB and SIMULINK for modeling transient stability of small and large scale multi-machine power systems with their associated controllers. Although there are different commercial and educational packages for power system simulations, this proposed approach introduces a step-by-step simple model starting by modeling a single machine and its controllers as a machine template. This machine template is suitable to be repeated and modified according to the simulated network. A multi-machine model is constructed systematically and easily by cloning the machine template to expand the power system. The regular and systematic expansion from small to larger system emphasizes the flexibility and the simplicity of the proposed approach for modeling different power systems for the purpose of research and educational study. This approach is tested and verified through the comparison of responses of two test systems with respect to their responses from the simulation package of power system toolbox (PST).
Representing Urban Forms: A Collective Learning Model with Heterogeneous Human Mobility Data

Human mobility data refers to records of human movements, such as cellphone traces, vehicle GPS trajectories, geo-tagged posts, and photos. While successfully mining human mobility data can benefit many applications such as city planning, transportation, urban economics, and public safety, it is very challenging to model large-scale Heterogeneous Human Mobility Data (HHMD) that are generated from different resources. In this paper, we develop a general collective learning approach to model HHMD at an individual level towards identifying and quantifying the urban forms of residential communities. Specifically, our proposed method exploits two geographic regularities among HHMD. First, we jointly capture the correlations among residential communities, urban functions, temporal effects, and user mobility patterns by analogizing communities as documents and mobility patterns as words. Also, we further combine explicit LASSO analysis and significant testing into latent representation learning as a regularization term by analogizing compatible Point-of-Interests (POIs) as the meta-data of communities. In this way, we can learn the urban forms, including a mix of functions and corresponding portfolios, of residential communities from HHDM and POIs. We further leverage these learned results to address two application problems: real estate ranking and restaurant popularity prediction. Finally, we conduct intensive evaluations with a variety of real-world data, where experimental results demonstrate the effectiveness of our proposed modeling method and its successful applications for other problems.
Analysis and Design of a Novel High Step-Down DC-DC Converter for Battery Applications

In this paper, the low voltage stress of switches, high voltage gain DC-DC converter is proposed. The operating principle of the converter is similar to that of the conventional buck converter. Therefore, the proposed converter can be operated in the discontinuous conduction mode, continuous conduction mode, depending on the design. Moreover, the controller is also similar to that of the traditional buck converter, results in the simple controller. Furthermore, the voltage stress of every switch is reduced significantly compared with the conventional converter. Therefore, the use of the low voltage rating switches reduces the losses significantly. A 200W converter prototype is successfully built and tested to verify the feasibility of the proposed converter.
A Secure Authentication Protocol for Multi-Server-Based E-Healthcare Using a Fuzzy Commitment Scheme

Smart card-based remote authentication schemes are widely used in multi-medical-server-based telecare medicine information systems (TMISs). Biometric is one of the most trustworthy authenticators and is presently being advocated to use in the remote authentication of TMIS. However, most of the existing TMISs consider a single-server-environment-based authentication system. Therefore, patients need to register and log into every server separately for different services. Furthermore, these schemes do not employ error correction technique to remove the errors from biometric data. Also, biometrics are inherent and demand diversification to generate a revocable template from inherent biometric data. In this paper, we propose a mutual authentication and key agreement scheme for a multi-medical server environment to overcome the limitations of the existing schemes. In the proposed scheme, a cancelable transformation of the raw biometric data is used to provide the privacy and the diversification of biometric data. The errors of the biometric data are corrected with error-correction techniques under the fuzzy commitment mechanism. A formal security analysis using the widely accepted real-or-random model, the Burrows–Abadi–Needham logic, and the automated validation of Internet security protocols and applications tool concludes that the proposed scheme is safe against known attacks. We also compare the computation and communication costs of our scheme to evaluate the performance with the others.
Applying Blockchain to Automate Installments Payment between Supply Chain Parties

Motivated by the recent disruption in shaping industries through blockchain, this paper examines the effect of integrating blockchain and Enterprise Resource Planning systems on the relation among the different parties of the supply chain. For a seamless integration, distributors seek to automate payments with retailers under specific conditions, which can be achieved through smart contracts in a blockchain network. In this paper, we study the utilization of QR codes to handle such cases in the supply chain industry between the distributors and retailers in particular. Then, we analyze the contribution of using blockchain to handle these cases. Furthermore, to emphasize the advantages of blockchain on a supply chain, a prototype is implemented and developed using Hyperledger Composer. This prototype highlights the added value blockchain can have on the relation between the two supply chain parties, while providing additional features to build the needed trust between both parties.
Routing in the Vicinity of Multiple Holes in WSNs

In disaster management wireless sensor networks, routing holes may occur due to various reasons such as the presence of obstacles (e.g., lakes, rivers, mountains) or external forces (caused by fire, earthquake, flood, The traditional scheme for bypassing holes is to forward packets along the holes' perimeter. However, this scheme leads to two serious problems: data concentration around the hole boundaries and routing path enlargement. Recently, several approaches have been proposed to address these two problems, wherein a common idea is to form forbidden areas around the holes from which packets are kept to stay away. Unfortunately, most of the protocols proposed so far address only special cases where either the network contains only one hole or the sources and the destinations stay fairly far from the holes. In this paper, we focus on the general case where the network may contain multiple holes and the sources/destinations may stay in the vicinity of the holes. We then propose a hole bypassing protocol which can balance the traffic over the network while ensuring the constant stretch property of the routing path. The theoretical analysis proves that the routing path stretch of the proposed protocol can be controlled to be as small as $1+\epsilon$ (for any predefined $\epsilon\gt0$), and the simulation experiments show that our protocol strongly outperforms state-of-the-art protocols in terms of load balancing.
A Kernel-Based Approach for Content-Based Image Retrieval

Content-based image retrieval (CBIR)is a popular approach to retrieve images based on a query. In CBIR, retrieval is executed based on the properties of image contents (e.g. gradient, shape, color, texture)which are generally encoded into image descriptors. Among the various image descriptors, histogram-based descriptors are very popular. However, they suffer from the limitation of coarse quantization. In contrast, the use of kernel descriptors (KDES)is proven to be more effective than histogram-based descriptors in other applications, e.g. image classification. This is because, in the KDES framework, instead of the quantization of pixel attributes, each pixel equally takes part in the similarity measurement between two images. In this paper, we propose an approach for how the conventional KDES and its improved version can be used for CBIR. In addition, we have provided a detailed insight into the effectiveness of improved kernel descriptors. Finally, our experiment results will show that kernel descriptors are significantly more effective than histogram-based descriptors in CBIR.
Comprehensive Investigations on Degradations of Dynamic Characteristics for SiC Power MOSFETs Under Repetitive Avalanche Shocks

In this work, degradations of dynamic characteristics for silicon carbide (SiC) power metal-oxide-semiconductor field-effect transistors under repetitive avalanche shocks are investigated in details. With the help of Silvaco TCAD simulations, gate capacitance versus gate voltage (<italic>C<sub>g</sub></italic>–<italic>V<sub>g</sub></italic>) measurement, and three-terminal charge pumping test, the main damaged position is demonstrated to be the SiC/SiO<sub>2</sub> interface along junction FET (JFET) region instead of the body diode where most of the avalanche current passes through. Dominant avalanche degradation mechanism is then confirmed to be the injection of holes into the gate oxide above the JFET region. Since the channel region and the main junction of body diode are not seriously damaged by the avalanche stress, static parameters all remain stable. Meanwhile, due to the injection of holes, the depletion layer beneath the JFET region gets thinner, resulting in the increase of gate-drain capacitance (<italic>C</italic><sub>gd</sub>) under low drain-source voltage (<italic>V</italic><sub>ds</sub>) bias condition. It further takes responsibilities for the increments in input capacitance (<italic>C<sub>iss</sub></italic>), output capacitance (<italic>C<sub>oss</sub></italic>), and reverse transfer capacitance (<italic>C<sub>rss</sub></italic>). Moreover, it results in the extension of Miller plateau. Therefore, the increase of gate charge and delay of turn-<sc>off</sc> time after being stressed by repetitive avalanche shocks are monitored. Moreover, turn-<sc>on</sc> and turn-<sc>off</sc> dissipated energies after different unclamped-inductive-switching stress cycles are extracted. They are rarely influenced by the stress for the overlapping areas of voltage and current during switching procedures are relatively stable.
Performance evaluation of LoraWan physical layer integration on IoT devices

Due to the constant motion of wireless devices in the Internet of Things $(\mathrm {I}\mathrm {o}\mathrm {T})$, current wireless networks cannot provide connectivity in each case using altering topolgoies, in comparison with ad hoc networks. LoraWan, as a Long Range Wide Area Network recommended by the LoRa Alliance, is an upcoming candidate for low power and long distance communication $\mathrm {I}\mathrm {o}\mathrm {T}$ environments and applications to different concepts such as tracking, healthcare agriculture. This $\mathrm {I}\mathrm {o}\mathrm {T}$ concept is gaining a rapid growth on the $\mathrm {I}\mathrm {o}\mathrm {T}$ market and is simultaneously improving our living environment. In this paper, we first briefly introduce LoRa as an efficient solution of physical layer integration on the $\mathrm {I}\mathrm {o}\mathrm {T}$ devices. We then conduct a performance evaluation taking into consideration values such as bit error rate, time on air transmission based on Signal to Noise Ratio (SNR) and Spreading Factors modifications for different bandwidth values.
TAOF: Traffic Aware Objective Function for RPL-based Networks

Within the context of the Internet of Things (IoT), the distance-vector IPv6 Routing Protocol for Low-power and Lossy Networks (RPL) is one of the most popular choices for the network routing layer. Within RPL, distance calculation is abstracted by the Objective Function (OF) and two OF implementations have been standardized, namely OF0 and MRHOF. However, these OFs build network topologies where bottleneck nodes may suffer from excessive unbalanced traffic load. The load distribution problem is a major issue for existing OFs defined in RPL because it decreases network performance and the network's lifetime. In this paper, we propose a new OF called the Traffic Aware Objective Function (TAOF), which balances the traffic load that each node processes in order to ensure node lifetime maximization. To implement this OF, we altered the DIO message format, introduced a new RPL metric, named Traffic Rate, and used a new parent selection algorithm. Simulation experiments have been conducted to examine the performance of our proposal. The results show that TAOF achieves enhanced performance in terms of Packet Delivery Ratio (PDR) and that it builds more stable networks with fewer parent changes.