Miracle-dz/UniFAR-FTUs huggingface dataset:
Investigation of the Factors Influencing the Crash Frequency in Expressway Tunnels: Considering Excess Zero Observations and Unobserved Heterogeneity

The existing crash modeling techniques for expressway tunnels must overcome the following difficulties: 1) The collected risk factors contributing to the tunnel crashes include narrow ranges, especially the pavement conditions and weather conditions of the tunnels are rarely taken into account. 2) Most researchers ignored the estimation deviation caused by the excess zero observations of tunnel crash datasets. 3) No existing tunnel crash model can combine the random-parameters approach and spatial-temporal approach to solve the estimation deviation caused by the inter-samples and spatial-temporal heterogeneity. To address these problems, this study presents an investigation of the safety effects of risk factors of tunnel design features, traffic conditions, pavement conditions and weather conditions utilizing a 12-quarter period (3 years) of data as well as five crash frequency models: 1) a fixed parameters negative binomial model (FPNB), 2) a random parameters negative binomial model (RPNB), 3) a random parameters negative binomial Lindley model (RPNBL), 4) a spatial and random parameters negative binomial Lindley model (SP-RPNBL), and 5) a spatial-temporal and random parameters negative binomial Lindley model (ST-RPNBL). The results showed that the ST-RPNBL model solves the deviation that arises from excess zero observations by introducing the Lindley distribution and considers the unobserved heterogeneity by introducing both the random parameters and spatial-temporal parameters that provided better goodness of fit and offered more insights into the factors that contribute to tunnel safety. Furthermore, the ST-RPNBL model detected 16 variables that were significantly correlated with tunnel crash frequency, of which 12 variables were associated with a higher crash frequency and four variables were associated with a lower crash frequency. The random variables of the curvature, the steep downgrade indicator, the proportion of class 5 vehicle and the skidding resistance index (SRI) were identified, and the influence of each significant variable on the crash frequency was analyzed.
Object Detection for P&ID Images using various Deep Learning Techniques

Piping and Instrumentation Diagrams are detailed diagrams that show the piping and process equipment together with the instrumentation and control devices used in the process industry. These are updated during the plant life cycle to depict the latest changes and modifications done in the plant. These diagrams are detailed and are usually in the form of PDF, they are difficult to modify, and inferring details from these diagrams requires a deep understanding of plant process and engineering. To reduce the difficulty in finding and retrieving information from the diagrams, the proposal of automating the task of detection of the symbols and the texts embedded is explored in this paper. This paper compares various Deep Learning models on P&I Diagrams by using object detection for symbols using the Transfer Learning technique. Initially, the Connectionist Text Proposal Network algorithm is used for text detection in the images, then for the geometrically shaped objects, OpenCV library is used. The models trained by Transfer Learning are exported and the results are compared by performing symbol detection. This paper is an overall study of various deep learning methods implemented on P&I Diagrams.
Automated Sorter and Grading of Tomatoes using Image Analysis and Deep Learning Techniques

Many sorting machines were made in the past that classifies the ripeness of tomatoes. These machines do not classify the size and quality of tomatoes like physical damage, defects, and bruises of plants, resulting in the traditional checking of the quality and size estimation. Based on the Philippines Statistics Office, the Philippines produces many tomatoes that become the driving force to push through this study. This study aims to innovate the existing method of sorting and grading tomatoes depending on the size and quality, including the color, defects, physical damage, and bruises. Specifically, this study aims to create software and hardware that can analyze tomatoes' grades based on color and classify the fruit according to size. Also, to conduct a statistical analysis of the efficiency of the system. The process starts from capturing an image of a tomato, which will be analyzed by applying the image processing techniques such as deep learning algorithms to determine the quality and the Canny-Edge Detection to categorize the size in small, medium, large, and jumbo. The Conveyor is controlled by an Arduino microcontroller transporting the tomatoes into the spot where the tomatoes' image is captured. This study results in an overall efficiency of 88% after testing multiple batches of tomatoes of different sizes and quality.
Investigation of Smart Methodologies in Lyme Disease Detection

Lyme disease is one of the major vector-borne sicknesses most common in European countries as well as northern America. The medical diagnosis for such vector-borne diseases are effectively distinguished and for appropriate treatment. Still misinterpretation about the condition of the diseased case is continued to be exist. Recent advancement in the technology and modern medical facilities leads towards the appropriate recognition of the Lyme disease in a stage wise category. The motivation of this research article is to provide the complete overview about the Lyme disease, its diagnosis stages for an effective treatment and also highlight few intelligent approaches towards the detection of Lyme disease in the earlier stage.
Waste Sorting System Using Binarized Neural Network

Garbage classification is of great importance to reduce environmental pollution and resource waste. For the sake of realizing efficient waste sorting and treatment, we propose a low latency automatic waste sorting system, which can classify garbage into the four categories: recyclable, harmful, kitchen and other garbage, and put the classified garbage into a corresponding garbage can. The system is in a standby state and does not perform any action when there is no garbage. For classifying garbage accurately, we customize an appropriative garbage sorting model based on a binarized neural network (BNN), and it is a small and low complexity network structure deployed to resource-limited embedded devices. To improve the energy efficiency of the system, we design a coprocessor dedicated to accelerating BNN on FPGA. The experiment shows that the classification accuracy of the model reaches 96.8% in the dataset we collected. The coprocessor is 5x better performance and 285x higher energy efficiency than NVIDIA GTX 1060. The system can sort 20 kinds of garbage, and the average execution time of the system is 1.3 seconds.
Single Current Sensor-Based Vector Control of Solar PV-Battery Assisted Induction Motor Drive for Electric Vehicle

This paper presents the design and control of solar photovoltaic (PV) / battery-assisted induction motor (IM) drive train topology. To increase the driving range, solar PV panel is included. Based upon the power requirement, panel efficiency and available installation space the size of the panel is decided. Perturb and observe (PO) method is used to extract maximum power from the solar PV panel. The battery is charged either from the solar PV panel or regenerative operation of the motor. Single current sensor based vector control is implemented for propulsion induction motor drive in which, the three-phase motor currents are estimated from DC – link current using sinusoidal curve fitting observer. The speed of the motor is estimated by using a rotor flux based estimator. This topology is designed and implemented in Matlab / Simulink platform and analysis are performed for the various operating conditions, which shows inclusion of solar PV panel considerably reduces the discharging of the battery.
Hybrid Deep Learning for Dynamic Total Transfer Capability Control

This letter proposes a data-driven hybrid deep learning method for dynamic total transfer capability (TTC) control. It leverages deep learning (DL) to achieve fast prediction of TTC and reduce the problem complexity, while the deep reinforcement learning (DRL) method, e.g., proximal policy optimization (PPO), is enhanced by competitive learning (CL) to obtain a better generalization of the DRL agents. This also allows us to deal with system stochasticity. Comparison results with other model-based alternatives on the IEEE 39-bus system highlight the advantages of the proposed method for variable unseen and insecure scenarios.
Intrusion Detection Based on Sequential Information Preserving Log Embedding Methods and Anomaly Detection Algorithms

Previous methods for system intrusion detection have mainly consisted of those based on pattern matching that employs prior knowledge extracted from experts’ domain knowledge. However, pattern matching-based methods have a major drawback that it can be bypassed through various modified techniques. These advanced persistent threats cause limitation to the pattern matching-based detecting mechanism, because they are not only more sophisticated than usual threats but also specialized in the targeted attacking object. The defense mechanism should have to comprehend unusual phenomenons or behaviors to successfully handles the advanced threats. To achieve this, various security techniques based on machine learning have been developed recently. Among these, anomaly detection algorithms, which are trained in unsupervised fashion, are capable of reducing efforts of security experts and securing labeled dataset through post analysis. It is further possible to distinguish abnormal behaviors more precisely by training classification models if sufficient amounts of labeled dataset is obtained through post analysis of anomaly detection results. In this study, we proposed an end-to-end abnormal behavior detection method based on sequential information preserving log embedding algorithms and machine learning-based anomaly detection algorithms. Contrary to other machine learning based system anomaly detection models, which borrow domain experts’ knowledge to extract significant features from the log data, raw log data are transformed into a fixed size of continuous vector regardless of their length, and these vectors are used to train the anomaly detection models. Experimental results based on a real system call trace dataset, our proposed log embedding method with unsupervised anomaly detection model yielded a favorable performance, at most 0.8708 in terms of AUROC, and it can be further improved up to 0.9745 with supervised classification algorithms if sufficient labeled attack log data become available.
Research on Interpolation Method of Missing Traffic Flow Data Based on Improved Historical Trend Method

Traffic flow data is the basis of traffic prediction, traffic behavior analysis, and traffic facility settings and so on. The data integrity of traffic flow data is very important, however, there is a phenomenon of missing data in the data collected by the monitor. In order to solve this problem, based on a variety of imputation methods for missing data, an improved historical trend method (IHTM) using nearest neighbor monitor data is proposed, and compared with a variety of imputation methods, the analysis shows that the method is for traffic flow missing data interpolation The application of the method provides theoretical basis and practical basis.
Increasing the Efficiency of Solar PV Array Using Sudoku Configuration

Sun energy is the best possibility for power age as it is available in abundance and is permitted to work. Concealing is one of the main viewpoints that impact presentations of ‘PV systems. Changing the PV bunch interconnections is an elective strategy for abridging the aftermath of the fractional concealing and growing the yield power. This work revolves around looking at assorted daylight-based show structures with sun-based cells and finding the ideal interconnection of PV modules in a PV bunch to reduce incomplete concealing mishaps. Various designs examined here are "Series Parallel (SP), Total Cross Tied (TCT), Bridge Linked (BL) and Honey Comb (HC)". Differentiating shows of the different topographies a best one is proposed it is Sudoku pattern configuration.
Slotted Tree-Shaped Microstrip Patch Antenna Using SSRR for Wide-band Applications

This paper presents a new design of a multiband antenna for multiband applications. To enhance the bandwidth and return loss characteristics, three Square split ring resonators (SSRRs) and two inverted U-slots on a partial ground were applied in this design. The antenna has a size of $40 \times 41 \times 1.6$ mm3, printed on FR-4 dielectric material with height (h) of 1.6 mm, loss tangent () of 0.02 and dielectric constant () of 4.4. Antenna parameters such as return loss (), gain, voltage standing wave ratio (VSWR), radiation pattern were simulated and analyzed. The antenna has two frequency bands. The first band (2.74GHz - 7.48GHz) in which the antenna resonates at frequencies, 3.37 GHz, 5.15 GHz, 6.64 GHz, 7.4 GHz and the second band (8.23 GHz - 9.8 GHz) with a center frequency at 9 GHz. Fractional bandwidths (< -10 dB) of 140%, 91.8%, 71.7% and 63.9% in the first band and 15.9% in the second band were achieved respectively. Furthermore, 3.3 dBi, 3.5 dBi, 3.244 dBi, 3.437 dBi and 3.937 dBi were provided at the resonant frequencies respectively. The simulation and numerical analysis were carried out using HFSS software.
A Boost Switched-Capacitor Multilevel Inverter Using Quasi-Resonant Inductor

Through this paper, a novel multilevel inverter (MLI) is proposed. The suggested topology composed of switch- capacitor quasi-resonance boosted module (SCQRB) and H-bridge. Each module exploits only one DC voltage source, which is mentioned as one of the superiorities of the proposed topology. Also, in this topology, quasi-resonant inductors are utilized to suppress the spikes on the charging current of capacitors. The suggested MLI generates a 9-level voltage waveform in the output. Besides, to reach higher voltage levels, more SCQRB with H-bridge units could be cascaded. Also, in the suggested MLI, the capacitors are kept stable without any sophisticated control system, which is one of the great privileges of the suggested inverter. Furthermore, the merit and superiority of the presented MLI are illustrated by a comprehensive comparison among the number of elements such as capacitors and power switches. Since the suggested topology can increase the value of voltage, the voltage gain is another factor of comparison. Moreover, to show the feasibility and availability of the designed inverter, the MATLAB/Simulink environment is used to simulate a case-study circuit.
A Hybrid Approach to Joint Estimation of MIMO Channel and Antenna Impedance Matrices

Antenna impedance matching significantly affects the channel capacity of compact MIMO receivers. When antenna impedance is known to the receiver, channel capacity can be optimized. However, channel capacity may diminish, when antenna impedance varies due to time-varying near-field loading. This motivates impedance estimation in real-time. In this paper, we derive joint MAP/ML estimators for channel and impedance matrices in closed-form. As one result, we develop a design principle leveraging a trade-off between channel and impedance estimation, which depends on transmit diversity.
Evaluation of the Root Zone Water Quality Model (RZWQM) for Southern Ontario: Part I. Sensitivity Analysis, Calibration, and Validation

This study focuses on the performance of the Root Zone Water Quality Model (RZWQM) for corn production in southern Ontario. The model was used to simulate the amount of subsurface tile drainage, residual soil nitrate-nitrogen (NO 3 -N), NO 3 -N in subsurface drainage water, and crop yield. A precalibration sensitivity analysis of the model was conducted for several key parameters using fi eld data collected at the study site. The RZWQM’s hydrology component was most sensitive to the Brooks and Corey fi tting parameters and saturated hydraulic conductivity (K s ), while the tile drain fl ow and the water table depth were sensitive to the Brooks and Corey fi tting parameters of bubbling pressure (ψ bp ) and pore-size-distribution index (λ). The fraction of dead-end pores had relatively little effect on tile drain N loss. The crop yield is most affected by N uptake, age, and evapotranspiration rate. RZWQM simulated evapotranspiration was within the range (568 ± 55 mm) of the observed evapotranspiration. The model simulated corn yield very well (-0.1% difference) at the calibration site; however, it underestimated yield (-14.1%) at the validation site. Overall, the RZWQM simulated tile drain fl ow, NO 3 -N loss to tile drainage water, and crop yield with reasonable accuracy, but tended to underestimate the amount of soil NO 3 -N (mean deviation, -0.971). The inability of the model to handle the spatial and temporal variability of the soil may have affected its prediction accuracy. The model also needs improvement in simulating early spring snowmelt hydrology.
A Ku-Band CMOS Power Amplifier With Series-Shunt LC Notch Filter for Satellite Communications

This article presents a <inline-formula> <tex-math notation="LaTeX">$Ku$ </tex-math></inline-formula>-band power amplifier with a series-shunt <inline-formula> <tex-math notation="LaTeX">$LC$ </tex-math></inline-formula> notch filter in 65-nm CMOS. The notch filter is integrated into the inter-stage matching network to attenuate the receiver-band noise, thereby reducing transmitter-to-receiver interference. A comprehensive analysis of the series and the shunt notch filters, as well as the position to apply the notch filter is discussed. Besides, a systematic method of optimizing passive devices in the notch filter is proposed to further improve network <inline-formula> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> and minimize the influence on the power amplifier. Fabricated in 65-nm CMOS technology, the power amplifier prototype delivers a measured gain of 21.9 dB with 3-dB bandwidth from 13.7 GHz to 16.7 GHz at the nominal state. At 14.2 GHz, it can offer a saturated output power of 14.5 dBm with peak power added efficiency of 24.1%. The notch frequency is adjustable from 10.3 to 11.9 GHz to offer the best attenuation at the receiver band. From 10 to 12 GHz, a maximal attenuation of 30 dB is achieved. The design occupies a core area of <inline-formula> <tex-math notation="LaTeX">$0.35\times 0.85$ </tex-math></inline-formula> mm<sup>2</sup>.
Optimal Operation Strategy of Virtual Power Plant Considering Real-Time Dispatch Uncertainty of Distributed Energy Resource Aggregation

Virtual power plant (VPP) technologies continue to develop to embrace various types of distributed energy resources (DERs) that have inherent real-time uncertainty. To prevent side effects on the power system owing to the uncertainty, the VPP should manage its internal resources’ uncertainty as a whole. This paper proposes an optimal operation strategy for a VPP participating in day-ahead and real-time energy market so that a distributed energy resource aggregation (DERA) can cope with real-time fluctuation due to uncertainties while achieving its maximum profit. The proposed approach has bidding models of the DERAs including microgrid, electric vehicle aggregation, and demand response aggregation, as well as the VPP. The VPP determines internal prices applied to the DERA by evaluating its real-time responses to the day-ahead schedule and updating proposed pricing function parameters, and the DERA adjusts its energy reserves. By repeating this coordination process, the VPP can establish an optimal operation strategy to manage real-time uncertainty on the DERA’s own. The effectiveness of the proposed strategy is verified by identifying a capability of the DERA to cope with real-time fluctuation through scenario-based simulations. The result shows that the VPP can reduce 1.6% of cost while the internal price applied to the DERA is close to the maximum.
Impact of Single-Event Upsets on Convolutional Neural Networks in Xilinx Zynq FPGAs

Convolutional neural networks (CNNs) are quickly becoming an attractive solution for autonomous vehicles, military weapons, and space exploration. Thanks to their reconfiguration ability, design flexibility, and low power consumption, field-programmable gate arrays (FPGAs) have become a promising candidate for CNN accelerators. However, FPGAs have been proven to be susceptible to radiation-induced single-event upsets (SEUs). One goal of this article is to analyze the impact of quantization on the reliability of CNNs in FPGAs. Therefore, we performed quantization on ZynqNet without affecting its classification accuracy. Meanwhile, we implemented the triple modular redundancy (TMR) version of ZynqNet and we also evaluated the effects of SEUs on these CNNs through both fault injections and neutron exposures. Fault injection results show that TMRed ZynqNet reduces the soft error rate (SER) by 33.59% with a circuit area increase of 111.92% when compared with the standard ZynqNet. The experimental results demonstrate that the quantized ZynqNet reduces the SER by 71.36% with a circuit area reduction of 44.76% when compared with the standard ZynqNet. These results confirm that quantization does contribute to SER reduction of the neural networks. In addition, the operating system on the processing system (PS) side was also found to be highly sensitive to SEUs, and, thus, mitigation techniques should be applied.
Classification of student success using Random Forest and Neural Networks

In recent years online education has overgrown be-cause of the many advantages it offers. During courses, different institutions collect and analyze student performance data to improve their educational experience. One of the main challenges of online education is being able to detect those students who have difficulties completing the course. This paper presents an approach to classify student success based on Random Forests and Neural Networks. One of the characteristics of Random Forests is that the algorithm selects the best feature to split the data. The selection of the most relevant features were used to train the Neural Network models.
Single to Multi: Data-Driven High Resolution Calibration Method for Piezoresistive Sensor Array

Accurately detecting multiple simultaneous touches is crucial for various applications using piezoresistance sensor arrays. However, calibrating them is difficult due to their nonlinearity and hysteresis. While data-driven deep learning approaches could model complex sensor patterns, the required amount of labeled data increases exponentially as the number of contact points or sensor subelements increases. In this letter, we propose a novel supervised learning framework, Local Message Passing Network, that only needs single touch data to calibrate multiple contact points into a high resolution pressure map. The individual sub-local networks eliminate domain shift problems, while a message passing mechanism enables them to correctly learn correlations between neighboring sensor subelements. The performances of the proposed model were tested on labeled single- and double-pressure data and compared with previous deep learning calibration methods. Experimental results show that our framework can expand prior knowledge of single touch data to calibrate multi-touch sensor inputs into high resolution pressure maps.