Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-Grained Deep Feature Learning for Robust Pedestrian Detection

In this paper, we address the challenging problem of detecting pedestrians, which are heavily occluded and/or far from cameras. Unlike most existing pedestrian detection methods which only use coarse-resolution feature maps with fixed receptive fields, our approach exploits multi-grained deep features to make the detector robust to visible parts of occluded pedestrians and small-size targets. Specifically, we jointly train a multi-scale network and a human parsing network in a weakly supervised manner with only bounding box annotations. We carefully design the multi-scale network to predict pedestrians of particular scales with the most appropriate feature maps, by matching their receptive fields with the target sizes. The human parsing network generates a fine-grained attention map, which helps guide the detector to focus on the visible parts of occluded pedestrians and small-size instances. Both networks are computed in parallel and form a unified single stage pedestrian detector, which assures a suitable tradeoff between accuracy and speed. Moreover, we introduce an adversarial hiding network to make our detector more robust to occlusion situations, which generates occlusions on pedestrians with the goal to fool the detector that in turn adapts itself to learn to localize these adversarial instances. Experiments on three challenging pedestrian detection benchmarks show that our proposed method achieves a state-of-the-art performance and executes $2\times $ faster than the competitive methods.
A Novel Feature Selection and Short-Term Price Forecasting Based on a Decision Tree (J48) Model

A novel feature selection method based on a decision tree (J48) for price forecasting is proposed in this work. The method uses a genetic algorithm along with a decision tree classifier to obtain the minimum number of features giving an optimum forecast accuracy. The usefulness of the proposed approach is established through the performance test of the forecaster using the feature selected by this approach. It is found that the forecast with the selected feature consistently out-performed than that having larger feature set.
Robust Fault Ride Through of Converter-Based Generation During Severe Faults With Phase Jumps

As grid-connected converters are at risk of losing synchronism with the grid when exposed to extreme voltage sags, this might jeopardize the stability during a fault and a converter's ability to comply with fault ride-through requirements. This article investigates the synchronization stability of grid-tied converters during severe symmetrical faults with phase jumps. To achieve zero-voltage ride-through capability, a frozen phase-locked loop (PLL) structure can be employed to guarantee stability during faults. However, as the frozen PLL approach is unaware of frequency drifts and phase-angle jumps in the grid voltage, its performance during nonconstant frequency and phase is unknown. Therefore, this article investigates and provides new insight into how the frozen PLL performs during phase jumps and reveals whether phase compensation should be utilized to improve the converter response during a severe symmetrical fault. It is disclosed, that even though phase compensation can improve the injected currents during a fault situation including large phase jumps, a noncompensated frozen PLL can inherently ensure stability and allow for zero-voltage ride-through capability at an acceptable current injection. Furthermore, the robustness of the frozen PLL has been analyzed through a comprehensive simulation study where three test cases have been experimentally verified, which confirms the presented findings.
Product Line Practice Workshop Report.

Abstract : The Department of Defense (DoD) Product Line Practice workshop, Product Lines: Bridging the Gap, Commercial Success to DoD Practice was a hands-on meeting held in March 1998. Its purpose was to identify industry wide best practices in software product lines, to share DoD product line experience, to explore the technical and non-technical issues involved, and to discuss ways in which the current gap between commercial best practice and DoD practice can be bridged. This report synthesizes the workshop presentations and discussions that described selected product line practices and identified barriers and enablers to achieving these practices within the DoD.
Accelerated testing for automated vehicles safety evaluation in cut-in scenarios based on importance sampling, genetic algorithm and simulation applications

Purpose It would take billions of miles’ field road testing to demonstrate that the safety of automated vehicle is statistically significantly higher than the safety of human driving because that the accident of vehicle is rare event. Design/methodology/approach This paper proposes an accelerated testing method for automated vehicles safety evaluation based on improved importance sampling (IS) techniques. Taking the typical cut-in scenario as example, the proposed method extracts the critical variables of the scenario. Then, the distributions of critical variables are statistically fitted. The genetic algorithm is used to calculate the optimal IS parameters by solving an optimization problem. Considering the error of distribution fitting, the result is modified so that it can accurately reveal the safety benefits of automated vehicles in the real world. Findings Based on the naturalistic driving data in Shanghai, the proposed method is validated by simulation. The result shows that compared with the existing methods, the proposed method improves the test efficiency by 35 per cent, and the accuracy of accelerated test result is increased by 23 per cent. Originality/value This paper has three contributions. First, the genetic algorithm is used to calculate IS parameters, which improves the efficiency of test. Second, the result of test is modified by the error correction parameter, which improves the accuracy of test result. Third, typical high-risk cut-in scenarios in China are analyzed, and the proposed method is validated by simulation.
The Fit Between Value Proposition Innovation and Technological Innovation in the Digital Environment: Implications for the Performance of Startups

In recent years, an emerging stream of research suggests that firms should use technological innovation and business model innovation harmoniously to maximize firm performance. In order to enrich the scholarly conversation on the fit between technological innovation and business model innovation, drawing insights from the dynamic capabilities perspective, this paper examines the fit between value proposition innovation and technological innovation (exploitative versus explorative) for the performance of startups in the digital environment. Based on on-site survey data of 285 digital startups in one of the world's largest digital economies, we find that explorative innovation strengthens the positive impact of value proposition innovation on the performance of startups, whereas exploitative innovation weakens this positive effect. Furthermore, these moderating effects are magnified in a highly uncertain demand environment. These findings inject fresh insights into existing scholarly conversation on the fit between technological innovation and business model innovation by offering a dynamic capabilities perspective and by extending it to an increasingly digital business environment.
Robotic Manipulation Planning for Shaping Deformable Linear Objects WithEnvironmental Contacts

Humans use contacts in the environment to modify the shape of deformable objects. Yet, few papers have studied the use of contacts in robotic manipulation. In this letter, we investigate the problem of robotic manipulation of cables with environmental contacts. Instead of avoiding contacts, we propose a framework that allows the robot to use them for shaping the cable. We introduce an index to quantify the contact mobility of a cable with a circular contact. Based on this index, we present a planner to plan robot motions. The planner is aided by a vision-based contact detector. The framework is validated with robot experiments on different desired cable configurations.
Online Application of Local OOS Protection and Graph Theory for Controlled Islanding

Despite layers of protection at work, power systems have experienced major blackouts in the past due to cascading outages. Such outages can be prevented with controlled islanding, which consists of two problems: “when” and “where” to island. The majority of the existing offline and real-time methods for the “when” aspects are based on wide area measurement systems (WAMSs), which depend on the system topology to perform periodic offline simulations or real-time computation of dynamic system equivalents. In this paper, a simplified “when” approach is proposed based on local generator out-of-step (OOS) protection and generator coherency using fault location. The novelty of the work lies in the use of local OOS protection relays to identify the overall system instability, which eliminates the need for WAMS communication and the complex computation required in real-time methods to form dynamic system equivalents. As this approach does not require any equivalent networks, the methodology is independent of system topology. Unlike existing WAMS-based methods, the proposed methodology simplifies the communication between the central “when” unit and each generator’s OOS protection relay using status flags communicated with the IEC 61850 R-GOOSE protocol. The proposed “when” methodology is combined with the “where” method based on graph theory to test the overall controlled islanding scheme in the IEEE 39-bus system using the real-time digital simulator (RTDS).
Application of Fuzzy Theory to the Evaluation Model of Product Assembly Design and Usability Operation Complexity

In order to make people’s lives more convenient and enhance living standards, the composition of a product usually includes more than one component. However, a product is created by the joint endeavor of people from various territories and therefore one of the important considerations is making a product merge into consumers’ daily lives rather than simply fulfilling its functions. There might be conflicts with people’s existing life patterns or existing values, which should be taken into consideration during the manufacturing process. This study is an investigation of the process of assembly by considering the assembly operations and the assembly operating time. By determining the relationship between components, the assembly concept of most components was analyzed. A fuzzy comprehensive evaluation was carried out during the evaluation of the degree of complexity of user operations. Depending on the ranking of membership, the most appropriate assembly was determined and this serves as a reference for designers to select the optimal product assembly. By recording the consumer usage models, the optimal assembly and usage model of product design were also proposed. The goal of this study is to find the balance between assembly evaluation and user usage model by the process in order to allow designers to determine the new assembly concepts that meet consumer usage models. A case study of four bedside stereos was carried out by implementing the proposed approach in order to determine the evaluation principle of assembly. The purpose of this is to enhance the balance between assembly design and user operation complexity for making efficient decisions. A product design can comply with the spirit of concurrent engineering and the quality of a product design can be enhanced.
A Practical Core Loss Estimation Method for Three-Phase Three-Level Grid-Connected Inverters

In a three-phase three-level grid-connected inverter, the ac output current ripple actually fluctuates four times per switching period with each fluctuation generating some core loss. However, only the two larger fluctuations are considered in most loss estimation methods, resulting in a smaller core loss evaluation. In this letter, an alternative method has, hence, been proposed, beginning with the deduction of fundamental and ripple equivalent circuits from the inverter. These circuits permit current ripple to be calculated, from which its four fluctuations are all considered for forming the dual current-ripple envelopes (DCREs). These DCREs, together with the fundamental current and a selected core loss model, can then be solved to give the estimated fundamental and ripple core loss values. The nonlinear characteristic of the filter inductor has also been considered in the calculation of the DCREs and the core loss. The estimated values have eventually been verified through testing with a 10-kW three-phase T-type three-level grid-connected inverter.
Novel Core Airgap Profiles Design Scheme for Winding and Thermal Loss Reduction in High-Frequency Current Transformer Sensors

This paper proposes two novel airgap profile designs and distributed airgap arrangement scheme for reduction of winding and thermal losses in high frequency current transformer (HFCT) sensors. The novel airgap profiles namely, zigzag and stepped airgap profiles are proposed to reduce fringing flux around the winding conductors. Moreover, to decrease winding and thermal losses, distributed airgap arrangement scheme is proposed. In distributed airgap arrangement, a single airgap is distributed to multiple equidistant smaller airgaps around the magnetic core. The analysis is performed using 3D finite element analysis (FEA) method. The 3D-finite element analysis results confirm that the HFCT sensors with the proposed airgap profiles and arrangement scheme can reduce winding and thermal losses ensuring high performance for HFCT sensors.
Design Considerations of Fault-Tolerant Permanent Magnet Vernier Machine

This article investigates some design considerations of fault-tolerant permanent magnet vernier (PMV) machines. The key of this article is to provide a holistic approach to analyze and design fault-tolerant permanent magnet vernier machines. The electromagnetic performance, including torque density, fault-tolerant capability, and power factor are taken into consideration by using air gap permeance and magnetic equivalent circuit models. Hence, the proper slot–pole combination and structure parameter of the fault-tolerant PMV machine can be selected. Then, the finite-element models of the typical machines are established to verify these analyses. Finally, the experiments on a prototype machine are conducted, verifying the effectiveness of theoretical analyses.
Short-Term Bidding Strategy for a Price-Maker Virtual Power Plant Based on Interval Optimization

A virtual power plant is proposed to aggregate various distributed renewable resources with controllable resources to overcome the uncertainty and volatility of the renewables so as to improve market involvement. As the virtual power plant capacity becomes remarkable, it behaves as a strategic price maker rather than price taker in the market for higher profit. In this work, a two-stage bi-level bidding and scheduling model is proposed to study the virtual power plant strategic behaviors as a price maker. A mathematical problem with an equilibrium constraints-based method is applied to solve the problem by transforming the two level problem into a single level multi-integer linear problem. Considering the deficiency of computational burden and implausible assumptions of conventional stochastic optimization, we introduce interval numbers to represent the predicted output of uncertainty resources in a real-time stage. The pessimism degree-based method is utilized to order the preferences of profit intervals and tradeoff between expected profit and uncertainty. An imbalance cost mitigation mechanism is proposed in this pessimism degree-based interval optimization manner. Results show that the bidding price directly affects the cleared day ahead of the locational marginal price for higher profit. Interior conventional generators, energy storage and interruptible loads are comprehensively optimized to cover potential power shortage or profit from market. Moreover, controllable resources can decrease or even wipe out the uncertainty through the imbalance cost mitigation mechanism when the negative deviation charge is high. Finally, a sensitivity analysis reveals the effect of interval parameter setting upon optimization results. Moreover, a virtual power plant operator with a higher pessimism degree pursues higher profit with higher uncertainty.
Insider Threat Detection Based on User Behavior Modeling and Anomaly Detection Algorithms

Insider threats are malicious activities by authorized users, such as theft of intellectual property or security information, fraud, and sabotage. Although the number of insider threats is much lower than external network attacks, insider threats can cause extensive damage. As insiders are very familiar with an organization’s system, it is very difficult to detect their malicious behavior. Traditional insider-threat detection methods focus on rule-based approaches built by domain experts, but they are neither flexible nor robust. In this paper, we propose insider-threat detection methods based on user behavior modeling and anomaly detection algorithms. Based on user log data, we constructed three types of datasets: user’s daily activity summary, e-mail contents topic distribution, and user’s weekly e-mail communication history. Then, we applied four anomaly detection algorithms and their combinations to detect malicious activities. Experimental results indicate that the proposed framework can work well for imbalanced datasets in which there are only a few insider threats and where no domain experts’ knowledge is provided.
Efficient Equalization of Time-Varying Channels in MIMO OFDM Systems

The orthogonality of the cyclic prefix orthogonal frequency division multiplexing (CP-OFDM) modulation is ensured as long as the channel can be assumed constant across the duration of one CP-OFDM symbol period. Unfortunately, this assumption may not hold anymore for a large variety of emerging scenarios with mobility, high carrier frequency and multiple carrier frequency offsets. To tackle this issue, we propose a novel equalization structure. In contrast to existing works in the literature, the equalizer is obtained by considering a Taylor approximation of the ideal time-varying channel equalizer function. This results in an extremely simple implementation only consisting of per-subcarrier multiplications and FFT/IFFT operations. The general form of the equalizer is particularized to two specific cases: zero forcing and linear minimum mean squared error. Furthermore, the implementation complexity of the equalizers is computed and an analytical formula is proposed to efficiently evaluate their performance. Finally, numerical results demonstrate the efficiency of the proposed receivers as compared to the ideal one and previous works.
on-Time Mismatch Based System Identification Technique for Buck Converters

This article introduces a fast and low-complexity natural frequency estimation concept for power converters, enabling their adaptive control. Neither a high-resolution analog-to-digital converter nor complex arithmetic operations, such as multiplications, are required by the proposed approach. Instead, the mismatch between the on-time of the control signal and the effective on-time at the switching node, occurring under light load conditions, is exploited. This mismatch is caused by the parasitic drain–source capacitance and the parasitic body diode of the power MOSFETs. It is not necessary that the value of the parasitic capacitance is known nor is it required to stay constant, e.g., over temperature. Due to its simplicity, the proposed method is well-suited for practical on-chip realizations. The introduced concept is experimentally verified on the example of a buck converter with different output filter sets. A digital controller is adaptively tuned based on the estimated natural frequency, resulting in an improved dynamic performance. For the presented converter, the magnitude of the output voltage transient in response to a load change was reduced by 30%.
Functional properties of okra protein products containing different levels of mucilage

The functional properties of okra protein products low-mucilage protein isolate (LMPI), low-mucilage protein concentrate (LMPC) and high mucilage protein concentrate (HMPC) and whole okra flour (WOF) prepared from fresh okra fruits were studied. The okra protein products differed significantly (p<0.05) from each other in moisture, protein, fat, carbohydrate and energy content. The WOF and HMPC samples had significantly better (p<0.05) emulsifying properties, foaming capacity, water absorption, viscosity, nitrogen solubility and yield composition but significantly (p<0.05) lower oil absorption than LMPC and LMPI. They can be ingredients of choice in foods requiring gelation.
Evaluation of Feasibility of Using the Bacteriophage T4 Lysozyme to Improve the Hydrolysis and Biochemical Methane Potential of Secondary Sludge

Anaerobic digestion (AD) of secondary sludge is a rate-limiting step due to the bacterial cell wall. In this study, experiments were performed to characterize secondary sludges from three wastewater treatment plants (WWTPs), and to investigate the feasibility of using bacteriophage lysozymes to speed up AD by accelerating the degradation of bacterial cell walls. Protein was the main organic material (67.7% of volatile solids in the sludge). The bacteriophage T4 lysozyme (T4L) was tested for hydrolysis and biochemical methane potential. Variations in the volatile suspended solid (VSS) concentration and biogas production were monitored. The VSS reduction efficiencies by hydrolysis using T4L for 72 h increased and ranged from 17.8% to 26.4%. Biogas production using T4L treated sludges increased and biogas production was increased by as much as 82.4%. Biogas production rate also increased, and the average reaction rate coefficient of first-order kinetics was 0.56 ± 0.02/d, which was up to 47.5% higher compared to the untreated samples at the maximum. Alphaproteobacteria, Betaproteobacteria, Flavobacteriia, Gammaproteobacteria, and Sphingobacteriia were major microbial classes in all sludges. The interpretation of the microbial community structure indicated that T4L treatment is likely to increase the rate of cell wall digestion.
A Spectral Clustering Algorithm Improved by P Systems

Using spectral clustering algorithm is diffcult to find the clusters in the cases that dataset has a large difference in density and its clustering effect depends on the selection of initial centers. To overcome the shortcomings, we propose a novel spectral clustering algorithm based on membrane computing framework, called MSC algorithm, whose idea is to use membrane clustering algorithm to realize the clustering component in spectral clustering. A tissue-like P system is used as its computing framework, where each object in cells denotes a set of cluster centers and velocity-location model is used as the evolution rules. Under the control of evolutioncommunication mechanism, the tissue-like P system can obtain a good clustering partition for each dataset. The proposed spectral clustering algorithm is evaluated on three artiffcial datasets and ten UCI datasets, and it is further compared with classical spectral clustering algorithms. The comparison results demonstrate the advantage of the proposed spectral clustering algorithm.