602 research outputs found

    Topology of the Bend Loci of Convex Piecewise Linear Functions

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    This short article serves as the appendix for [Tran and Wang, 2022]. We prove that a complete intersection of nn generic polyhedral hypersurfaces in Rd\mathbb{R}^d is (dn1)(d-n-1)-connected for d2,d>nd\geq 2, d>n

    Robust Event-Triggered Energy-to-Peak Filtering for Polytopic Uncertain Systems over Lossy Network with Quantized Measurements

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    The event-triggered energy-to-peak filtering for polytopic discrete-time linear systems is studied with the consideration of lossy network and quantization error. Because of the communication imperfections from the packet dropout of lossy link, the event-triggered condition used to determine the data release instant at the event generator (EG) can not be directly applied to update the filter input at the zero order holder (ZOH) when performing filter performance analysis and synthesis. In order to balance such nonuniform time series between the triggered instant of EG and the updated instant of ZOH, two event-triggered conditions are defined, respectively, whereafter a worst-case bound on the number of consecutive packet losses of the transmitted data from EG is given, which marginally guarantees the effectiveness of the filter that will be designed based on the event-triggered updating condition of ZOH. Then, the filter performance analysis conditions are obtained under the assumption that the maximum number of packet losses is allowable for the worst-case bound. In what follows, a two-stage LMI-based alternative optimization approach is proposed to separately design the filter, which reduces the conservatism of the traditional linearization method of filter analysis conditions. Subsequently a codesign algorithm is developed to determine the communication and filter parameters simultaneously. Finally, an illustrative example is provided to verify the validity of the obtained results

    Influence of silicon and silicon/sulfur-containing additives on coke formation during steam cracking of hydrocarbons

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    The influence of the combination of two Si-containing additives, BTMS and TEOS, with DMDS on coke formation during steam cracking has been evaluated both on a laboratory scale and in a pilot plant unit. Under the optimal presulfidation conditions (T = 1023 K, H2O = 20 g h(-1), DMDS in H2O = 750 ppm wt, duration = 1 h), the combination of Si pretreatment + presulfidation + continuous addition of 2 ppm wt DMDS results in a decrease in the rate of coke formation up to 40% when hexane is cracked in the lab-scale unit. Under similar conditions in the pilot plant the coke formation is decreased by 70%, while the CO production decreases by more than 90%. Moreover, the suppressing effect on coke formation remains significant even after several coking/decoking cycles. Simulations of an industrial ethane cracker indicate that the application of Si- and S-containing compounds as additives for the suppression of coke formation can potentially double the run length of industrial steam crackers

    Combining stable carbon isotope analysis and petroleum-fingerprinting to evaluate petroleum contamination in the Yanchang oilfield located on loess plateau in China

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    This study evaluated petroleum contamination in the Yanchang (Shaanxi Yanchang Petroleum (Group) Co., Ltd.) oilfield, located in the loess plateau region of northern Shaanxi, China. Surface soil and sediment samples were collected from the wasteland, farmland, and riverbed in this area to assess the following parameters: total petroleum hydrocarbon (TPH), n-alkanes, polycyclic aromatic hydrocarbons (PAHs), and carbon isotope ratios (delta C-13). The results showed that TPH and PAH levels in the study area were 907-3447 mg/kg and 103.59-563.50 mu g/kg, respectively, significantly higher than the control samples (TPH 224 mg/kg, PAHs below method quantification limit, MQL). Tests using delta C-13 to detect modified TPH (2238.66 to 6639.42 mg/kg) in the wastelands adjacent to the oil wells revealed more significant contamination than tests using extraction gravimetric analysis. In addition, "chemical fingerprint" indicators, such as low to high molecular weight (LMW/HMW) hydrocarbons, carbon preference index (CPI), and pristine/phytane (Pr/Ph), further confirmed the presence of heavy petroleum contamination and weathering. This has resulted in a nutrient imbalance and unsuitable pH and moisture conditions for microbial metabolic activities. This study evaluates petroleum contamination, which can inform contamination remediation on a case by case basis

    HyperLink: Virtual Machine Introspection and Memory Forensic Analysis without Kernel Source Code

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    Virtual Machine Introspection (VMI) is an approach to inspecting and analyzing the software running inside a virtual machine from the hypervisor. Similarly, memory forensics analyzes the memory snapshots or dumps to understand the runtime state of a physical or virtual machine. The existing VMI and memory forensic tools rely on up-to-date kernel information of the target operating system (OS) to work properly, which often requires the availability of the kernel source code. This requirement prevents these tools from being widely deployed in real cloud environments. In this paper, we present a VMI tool called HyperLink that partially retrieves running process information from a guest virtual machine without its source code. While current introspection and memory forensic solutions support only one or a limited number of kernel versions of the target OS, HyperLink is a one-for-many introspection and forensic tool, i.e., it supports most, if not all, popular OSes regardless of their versions. We implement both online and offline versions of HyperLink.We validate the efficacy of HyperLink under different versions of Linux, Windows, FreeBSD, and Mac OS X. For all the OSes we tested, HyperLink can successfully retrieve the process information in one minute or several seconds. Through online and offline analyses, we demonstrate that HyperLink can help users detect real-world kernel rootkits and play an important role in intrusion detection. Due to its version-agnostic property, HyperLink could become the first introspection and forensic tool that works well in autonomic cloud computing environments

    Boric Acid Cross-linked 3D Polyvinyl Alcohol Gel Beads by NaOH-Titration Method as a Suitable Biomass Immobilization Matrix

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    Granule-base immobilization of biomass is a potential method for a decent quality granular sludge cultivation. In this study, 3D polyvinyl alcohol (PVA) gel beads were chemically cross-linked via a simple NaOH-titration method. The PVA gel beads’ porous morphology was characterized using scanning electron microscope (SEM) and Brunauer–Emmette–Teller (BET), and their mechanical properties were evaluated by swelling rate and compressive stress tests. When cross-linking time was 10 min, high quality gel beads (P10) were synthesized, which demonstrated a homogeneous porous structure, good swelling rate, and high compressive strength. A mechanism for synthesis of the gel beads was proposed based on the results of Fourier transform infrared (FTIR) and X-ray diffractometer (XRD) analysis. Briefly, the intermolecular hydrogen bonds of PVA were firstly broken by NaOH to generate active bond of O–Na, which easily reacted with B(OH)4 − to produce the PVA-boric acid gel beads. P10 showed excellent biocompatibility for anaerobic ammonia oxidation (anammox) biomass’ immobilization. After incubation for three months, well granule-base immobilized sludge on P10 was developed in up-flow reactor. The sludge had high abundance of anammox biomass and was in balance with other functional bacteria. This work provides a simple method for the rapid preparation of 3D PVA gel beads and verifies their potential in granule-base immobilization of biomass.</p

    Optimal scheduling of industrial task-continuous load management for smart power utilization

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    In the context of climate change and energy crisis around the world, an increasing amount of attention has been paid to developing clean energy and improving energy efficiency. The penetration of distributed generation (DG) is increasing rapidly on the user’s side of an increasingly intelligent power system. This paper proposes an optimization method for industrial task-continuous load management in which distributed generation (including photovoltaic systems and wind generation) and energy storage devices are both considered. To begin with, a model of distributed generation and an energy storage device are built. Then, subject to various constraints, an operation optimization problem is formulated to maximize user profit, renewable energy efficiency, and the local consumption of distributed generation. Finally, the effectiveness of the method is verified by comparing user profit under different power modes

    A Prediction Model for Ultra-Short-Term Output Power of Wind Farms Based on Deep Learning

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    The output power prediction of wind farm is the key to effective utilization of wind energy and reduction of wind curtailment. However, the prediction of output power has long been a difficulty faced by both academia and the wind power industry, due to the high stochasticity of wind energy. This paper attempts to improve the ultra-short-term prediction accuracy of output power in wind farm. For this purpose, an output power prediction model was constructed for wind farm based on the time sliding window (TSW) and long short-term memory (LSTM) network. Firstly, the wind power data from multiple sources were fused, and cleaned through operations like dimension reduction and standardization. Then, the cyclic features of the actual output powers were extracted, and used to construct the input dataset by the TSW algorithm. On this basis, the TSW-LSTM prediction model was established to predict the output power of wind farm in ultra-short-term. Next, two regression evaluation metrics were designed to evaluate the prediction accuracy. Finally, the proposed TSW-LSTM model was compared with four other models through experiments on the dataset from an actual wind farm. Our model achieved a super-high prediction accuracy 92.7% as measured by d_MAE, an evidence of its effectiveness. To sum up, this research simplifies the complex prediction features, unifies the evaluation metrics, and provides an accurate prediction model for output power of wind farm with strong generalization ability