Miracle-dz/UniFAR-FTUs huggingface dataset:
A Deep Learning Method for Material Performance Recognition in Laser Additive Manufacturing

With the development of intelligent manufacturing and "Industry 4.0", the traditional methods of material mechanical properties evaluation cannot meet the needs of industrial production due to the shortcomings of wasted materials, tedious processes, and poor accuracy. This paper combines artificial intelligence technology to propose a new material performance evaluation method. The laser additive manufacturing is taken as the research background, three kinds of Ti6-Al-4V material microstructure images with different properties are used as data sets, based on DenseNet model, a deep convolution neural network NDenseNet is trained to optimize the network model memory and improve the recognition accuracy. The experimental results show that the accuracy of the model reaches 90.4%, loss value remains at 25%. Params and FLOPs are significantly reduced compared with DenseNet model. It only takes 0.1 seconds to process a microstructural image on a GPU processor. This method can greatly reduce the work of researchers, improve product development efficiency in industrial environment, reduce human errors, save production materials, and has guiding significance for the development of high-performance materials.
Ultra-High-Frequency Radio-Frequency-Identification Baseband Processor Design for Bio-Signal Acquisition and Wireless Transmission in Healthcare System

Healthcare systems have been an important part of the research on consumer electronics in recent years. To be a mass-market product, a healthcare system is combined with a home control system. In this article, an ultra-high-frequency radio-frequency-identification (UHF RFID) wireless interface is built and implemented for the bio-signal monitoring of the healthcare system. This interface is composed of a digital baseband processor with an application specific integrated circuit (ASIC) for the RFID reader and tag. All functions are implemented to fit the requirement of electronic product code class 1 generation 2 protocol, such as cyclic redundancy check/verify, pulse interval encoding, and bi-phase space coding. The system platform was built to obtain bio-signals, which can demonstrate the hand-shaking stage and communication between the reader and tag at the same time. The tag was fabricated under the 180-nm standard CMOS process with the average power consumption of only 73.62 $\mu \text{W}$ . The reader was implemented by a field programmable gate array in the system. The power consumption of the entire system combined with those in our previous works, which include analog front-end and radio-frequency front-end, is considered. It achieves 75 $\mu \text{W}$ , which is lower than those achieved with the existing state-of-the-art wireless interface. Moreover, the wearable device with ASICs is proposed to feature on relative low power and small size. Thus, it is more suitable for consumer electronics.
Near-Threshold L1 Data Cache for Yield Management Under Process Variations

Near-threshold computing (NTC) has recently emerged and been considered as a strong candidate for future energy-efficient computing. However, adverse impacts from process variation such as delay and power fluctuations within die as well as across dies are much more severe than the super-threshold regime. In particular, static random access memory (SRAM)-based components (e.g., cache memories) are easily affected by process variation in NTC, resulting in large delay fluctuations. It incurs a huge loss in the maximum clock frequencies of processors, which eventually leads to huge yield losses. In this paper, we first analyze L1 data cache yield in NTC and reveal an inefficiency of frequency binning for yield improvement in NTC. We then introduce a variable latency L1 data cache for NTC to obtain a sufficient yield. By allowing the higher cache access cycles, we can improve cache yield with only a little performance overhead. Moreover, we propose an adaptive line migration technique which improves performance and energy efficiency of variable latency caches. The cache line which is expected to be frequently accessed in the near future is dynamically migrated to the fastest way in a cache set. According to our evaluation, our cache architecture greatly improves cache yield with only a little performance, energy, and area overhead.
Spatio-Temporal Position Prediction Model for Mobile Users Based on LSTM

In Mobile Edge Computing (MEC), the services that a user receives change dynamically with location due to the user's mobility. If we mine the user's location data, predicting the user's next location, we can get the user's services to be used. It is convenient for the edge server to preload the user's services. When users reaches predicted location, the edge servers near users provide timely services. Therefore, this paper proposes a Spatio-temporal Position Prediction Model (SPPM) for Mobile Users Based on LSTM (Long Short-Term Memory) model in the mobile edge computing. Firstly, the time series feature extraction method is used to preprocess the historical location data of the mobile user. Next, the model uses the PCA data dimensionality reduction algorithm to process the data and then uses the LSTM model to predict the next spatiotemporal trajectory point of the mobile user. Finally, using the 17621 user trajectory data of the Geolife GPS trajectory data set, the algorithm is tested and verified. The experimental results show that the SPPM model proposed in this paper has higher prediction accuracy and more accurate prediction position.
Data-driven Distributionally Robust Energy Consumption Scheduling of HVAC based on Disjoint Layered Ambiguity Set

This paper proposes a distributionally robust optimization approach (DROA) based on a disjoint layered ambiguity set for scheduling the energy consumption of the heating, ventilation and air conditioning (HVAC) system. The uncertainties of the predicted outdoor temperature error and indoor temperature variations that are caused by human activities are taken into account in the energy consumption scheduling of the HVAC based on historical data. The maximum uncertainty set of outdoor temperature is divided into disjoint subintervals and the probabilistic information of these subintervals is obtained to construct a disjoint layered ambiguity set. A nonlinear HVAC's energy consumption problem is formulated by using the DROA method to deal with these two uncertainties based on a disjoint layered ambiguity set with distributionally robust chance constraints (DRCCs). In order to solve this nonlinear problem, these DRCCs are converted to a linear programming problem and solved by using linear programming. Simulation results illustrate the effectiveness of the proposed method and comparing them with the DROA based on a nest layered ambiguity set and the traditional robust approach (ROA), the proposed DROA based on a disjoint layered ambiguity set reduces the electricity cost and guarantees the thermal comfort level of users.
A Highly Integrated D-Band Multi-Channel Transceiver Chip for Radar Applications

This paper presents a low-power, highly-integrated D-Band transceiver chip realized in a 0.13 µm SiGe BiCMOS technology. The integration level includes three receiver (RX) channels, one transmitter (TX) channel, local oscillator (LO) signal generation and distribution network, frequency dividers and serial peripheral interface (SPI) for digital reconfigurability. The receiver achieves a peak gain of 14 dB at 118 GHz, while the transmitter achieves an output power of −6 dBm at 118 GHz. The VCO is realized in a push-push Colpitts topology. Additionally, its output is multiplied by two using a frequency doubler. Hence the transmitter output signal is continuously tunable in the frequency range 117 − 126 GHz, while achieving a measured phase noise of − 93.5 dBc /Hz at 1 MHz offset at 120 GHz. The entire transceiver draws 195 mA from a single 1.8 V supply. A single RX channel draws 19 mA, while a single TX consumes 25 mA. The circuit including pads occupies a chip area of only 3.5 mm × 2.75 mm, which is limited only by the separation necessary for isolation between the channels. The transceiver provides a competitive performance and is suitable for continuous-wave radar applications around 120 GHz.
Data-driven Robust Dispatch of Integrated Electricity-gas Energy Systems Considering Uncertainty of Wind Power

The interconnection of the electricity-gas integrated energy system is an effective way to improve renewable energy utilization efficiency. This paper constructs the optimal power flow(OPF) model and the natural gas network model. The main coupling parts of power system and natural gas system are gas-fired power unit (GFPU) and power to gas (P2G). In order to solve the problem, the power flow equation is transformed into a second-order cone and the natural gas network equation is linearized. Based on this, this paper proposes to apply robust optimization of data-driven to optimal power flow under the participation of wind power, and the model is improved on the basis of integrated 1-norm and $\infty$-norm. The final example analysis proves the validity and practicability of the model.
Mining Maximal Clique Summary with Effective Sampling

Maximal clique enumeration (MCE) is a fundamental problem in graph theory and is used in many applications, such as social network analysis, bioinformatics, intelligent agent systems, cyber security. Most existing MCE algorithms focus on improving the efficiency rather than reducing the size of the output, which could consist of a large number of maximal cliques. In this paper, we study how to report a summary of less overlapping maximal cliques. The problem was studied before, however, after examining the pioneer approach, we consider it still not satisfactory. To advance the research along this line, this paper attempts to make two contributions: (a) We propose a more effective sampling strategy, which produces a much smaller summary but still ensures that the summary can somehow witness all the maximal cliques and the expectation of each maximal clique witnessed by the summary is above a predefined threshold. (b) To verify experimentally, we tested ten real benchmark datasets that have a variety of graph characteristics. The results show that our new sampling strategy consistently outperforms the state-of-the-art method by producing smaller summaries and running faster on all the datasets.
On the Effectiveness of Tools to Support Infrastructure as Code: Model-Driven Versus Code-Centric

Infrastructure as Code (IaC) is an approach for infrastructure automation that is based on software development practices. The IaC approach supports code-centric tools that use scripts to specify the creation, updating and execution of cloud infrastructure resources. Since each cloud provider offers a different type of infrastructure, the definition of an infrastructure resource (e.g., a virtual machine) implies writing several lines of code that greatly depend on the target cloud provider. Model-driven tools, meanwhile, abstract the complexity of using IaC scripts through the high-level modeling of the cloud infrastructure. In a previous work, we presented an infrastructure modeling approach and tool (Argon) for cloud provisioning that leverages model-driven engineering and supports the IaC approach. The objective of the present work is to compare a model-driven tool (Argon) with a well-known code-centric tool (Ansible) in order to provide empirical evidence of their effectiveness when defining the cloud infrastructure, and the participants’ perceptions when using these tools. We, therefore, conducted a family of three experiments involving 67 Computer Science students in order to compare Argon with Ansible as regards their effectiveness, efficiency, perceived ease of use, perceived usefulness, and intention to use. We used the AB/BA crossover design to configure the individual experiments and the linear mixed model to statistically analyze the data collected and subsequently obtain empirical findings. The results of the individual experiments and meta-analysis indicate that Argon is more effective as regards supporting the IaC approach in terms of defining the cloud infrastructure. The participants also perceived that Argon is easier to use and more useful for specifying the infrastructure resources. Our findings suggest that Argon accelerates the provisioning process by modeling the cloud infrastructure and automating the generation of scripts for different DevOps tools when compared to Ansible, which is a code-centric tool that is greatly used in practice.
Flexible Distribution Grid Demonstrator (FLEDGE): Requirements and Software Architecture

In the distribution grid, flexible resources are becoming an important tool to address challenges from uncertain renewable generation and increasing peak loads. To this end, there exists a need for a dedicated software framework to simulate the active distribution grid operation with flexible resources. The Flexible Distribution Grid Demonstrator (FLEDGE) is presented as a simulation tool which integrates active distribution grid operation with classic power flow studies. This paper outlines the requirements, software architecture and key capabilities of FLEDGE.
Research on Circulating Current Suppression Method of Paralleled Multi-bidirectional Power Converter Based on Unified Voltage Control

The use of paralleled converters is one of the most practical solutions to solve the problem of transmitting bulk power among the AC/DC hybrid microgrid. In this paper, the definition of the circulating currents of multiphase paralleled converters is presented, and the circulating current generating mechanism is clearly explained. It is seen from this model that the circulating current consists of not only the zero-sequence but also the positive and negative sequence components. With this understanding, a unified voltage control is then presented to reduce the circulating current. The method uses the fourth order generalized integrator to form the unbalanced voltage compensation strategy based on the fundamental normal component separation. The performances of these methods are verified by analyses and simulations. Finally, the simulation results are presented to verify the effectiveness of the proposed methods.
Probabilistic Power Transformer Condition Monitoring in Smart Grids

Power transformers are critical assets for the correct and reliable operation of the power grid. However, the use of power transformers in the context of smart grids creates new challenges for efficient lifetime management and maintenance planning. The use of intermittent sources of energy and dynamic loads increases the sources of uncertainty and causes non-linear operation dynamics. In addition, the increased use of probabilistic forecasting models for the estimation of influential parameters such as temperature or load, influences the uncertainty associated with the transformer lifetime estimation. These variable operation mechanisms influence the operation and lifetime planning of power transformers. Accordingly, this paper presents a novel probabilistic health state estimation framework to improve the lifetime management of power transformers operated in smart grids through the integration of probabilistic forecasting models with Monte Carlo based Bayesian filtering methods.
Quick Fire Sensing Model and Extinguishing by Using an Arduino Based Fire Protection Device

Fire protection is obvious for safety purposes in human life because mishandling of fire sources can be the cause of serious accidents. In this paper, an Arduino based automatic fire alarm provider with extinguishing device is proposed for fire protection. The thermal model of a house and explanation of the proposed system is presented where smoke detector and temperature sensor assist the flame sensor. The flame sensor generates electric signals and transmits via mobile phone to the corresponding person to get notified. Besides this, the signal also triggers the fire extinguishing system automatically to take quick action. Simulation results are obtained by using MATLAB software. An Arduino based prototype of the proposed device is built and tested by creating artificial smoke and flame. Test results prove that, the proposed fire protection system responses quickly and effectively.
Efficient State Update Exchange in a CPS Environment for Linked Data-based Digital Twins

This paper addresses the problem of reducing the number of messages needed to exchange state updates between the Cyber-Physical System (CPS) components that integrate with the rest of the CPS through Digital Twins in order to maintain uniform communication interface and carry out their tasks correctly and safely. The main contribution is a proposed architecture and the discussion of its suitability to support correct execution of complex tasks across the CPS. A new State Event Filtering component is presented to provide event-based communication among Digital Twins that are based on the Linked Data principles while keeping the fan-out limited to ensure the scalability of the architecture.
Case Study of a New Type of Ferroresonance in Solar Power Plants

This paper presents a new type of ferroresonance issue that caused the failure of substation surge arresters at a solar power plant. The transient overvoltages that occurred are similar in phenomena to classic ferroresonance cases, except that they occurred without any single-phasing conditions by fuse operation or any other switching means. A detailed transient model of the solar plant was developed using an ElectroMagnetic Transient (EMT) software program and the phenomenon was studied. From the simulations, it was determined that the combination of transformer grounding schemes used resulted in these high overvoltage conditions and surge arrester failures.
A Heavy-Ion Detector Based on 3-D NAND Flash Memories

The feasibility of a 3-D-NAND-Flash-based heavy-ion detector is explored. The possibility of measuring the angle of incidence and the linear energy transfer (LET) of impinging particles by studying the pattern of the threshold voltage shifts along the track of the affected cells is discussed. The results of the experiments with different beams (both directly and indirectly ionizing) are illustrated. A set of Monte Carlo simulations is also presented, to study sensitive volumes on 3-D NAND floating gate cells and explore the possibility of distinguishing the effects generated by ionizing particles with different features, such as LET and angle of incidence.
Real-Time Cloud Visual Simultaneous Localization and Mapping for Indoor Service Robots

Unlike traditional industrial robots, indoor service robots are usually required to possess high intelligence, such as the skills of flexible moving, precise spacial perceiving. And high intelligence is always accompanied by consuming complicated and expensive computation resources. One solution for indoor service robots is centralization of expensive computation resource so that it is possible to design a low cost client with a high-intelligence brain. However, as a fundamental intelligence function for mobile indoor robots, if a real-time visual Simultaneously Localization and Mapping (vSLAM) system is split into client and brain, it will be confronted with new challenges, such as the barrier of instant data sharing and performance degradation brought by network delay inbetween. To solve the problem, we focus on a framework and approach of cloud-based visual SLAM in this paper, and provide an efficient solution to offload the expensive computation and reduce the cost of robot clients. The integrated system is distributed in a 3-level Cloud with light-weight tracking, high precision dense mapping, and map sharing. Based on recent excellent algorithms, our system is able to run a real-time sparse tracking on the client, and a real-time dense mapping on the cloud server, which outputs an explicit 3D dense map. Only keyframes are sent to the local cloud center to reduce the network bandwidth requirement. Dense geometric pose estimation besides feature-based methods is computed to make the system resistant to feature-less indoor scenes. The camera poses associated with keyframes are optimized on the local computing cloud center, and are sent back to the client to decrease the trajectory drift. We evaluate the system on the Technical University of Munich (TUM) datasets, Imperial College London and National University of Ireland Maynooth (ICL-NUIM) datasets, and the real data captured by our robot in terms of visual odometry on the client side and dense maps generated on the server cloud. Qualitative and quantitative experiments show our cloud visual SLAM system is able to bear the network delay in Local Area Network (LAN), and it is an efficient vSLAM solution for indoor service robots with high intelligence from a centric brain.
Online Classroom Attendance System Based on RFID Technology and Cloud Computing

Students attendance in the classroom is one of instrument for marking in the end of class, several students are cheating they attendance while manual system used which is signed the form or sheet. Furthermore, manual sheet attendance is ineffective way while digital technology available and widely used in today. This research proposed on online attendance system for students and lecturers, where every student before entering the classroom have to tap their student card on RFID reader available in front of classroom. Attendance for time in and out set to complete of attendance in a lecture. Time tolerance for late and early out is set 10 minutes before and after the schedule time. Similar to students, every lecture required to tap as well before and after teaching in the classroom, besides that lecturer required to hold his card on RFID reader to on electricity in the classroom else no electricity and no power supply provided. The data of students and lecturer attendance with room number is set and send to a database for student's attendance record and honorarium for lecturer. This system tested in a classroom of Faculty of Engineering, Islamic University of Riau with the number of students is 40. Data collected by RFID reader passed to the cloud server through Arduino Microcontroller for filtering in case student double tapped in RFID scanner. In order to match and avoid cheating by students who tap double students card thus a camera installed in the classroom for record and match student face by image recognition system to picture taken as in the card. The system gives effective and efficiency in administration, paperless and efficiency for staff to control and check in manual attendance is one of the advantages of this system.
Room sound field characterization and clustering using global multifractal parameters

This paper introduces one method for room sound field characterization and clustering and it presents part of the research carried out for the purpose of the doctoral dissertation. Room impulse response is the basic source of information in room acoustics. The algorithm provides impulse response characterization and clustering using global multifractal parameters. Large Deviation method is used in calculating the multifractal spectrum. The correlation between different multifractal parameters and room acoustic characteristics is determined. Clusters of impulse responses with identical acoustic characteristics are formed. The test results verify the usability of the proposed algorithm.