IoT Load Classification and Anomaly Warning in ELV DC Pico-grids using Hierarchical Extended k-Nearest Neighbors

The remote monitoring of electrical systems has progressed beyond the need of knowing how much energy is consumed. As the maintenance procedure has evolved from reactive to preventive to predictive, there is a growing demand to know what appliances reside in the circuit (classification) and a need to know whether any appliance requires attention and maintenance (anomaly warning). Targeting at the increasing penetration of dc appliances and equipment in households and offices, the described low-cost solution consists of multiple distributed slave meters with a single master computer for extra low voltage dc pico-grids. The slave meter acquires the current and voltage waveform from the cable of interest, conditions the data and extracts four features per window block that are sent remotely to the master computer. The proposed solution uses a hierarchical extended k-nearest neighbors (HE-kNN) technique that exploits the use of distance in kNN algorithm and considers a window block instead of individual data point for classification and anomaly warning to trigger the attention of the user. This solution can be used as an ad hoc standalone investigation of suspicious circuit or further expanded to several circuits in a building or vicinity to monitor the network. The solution can also be implemented as part of an Internet of Things application. This paper presents the successful implementation of HE-kNN technique in three different circuits: lightings, air-conditioning and multiple load dc pico-grids with accuracy of over 93%. Its performance is superior over other anomaly warning techniques with the same set of data

Impact of Exchange-Correlation Effects on the IV Characteristics of a Molecular Junction

The role of exchange-correlation effects in non-equilibrium quantum transport through molecular junctions is assessed by analyzing the IV curve of a generic two-level model using self-consistent many-body perturbation theory (second Born and GW approximations) on the Keldysh contour. For weak molecule-lead coupling we identify a mechanism which can lead to anomalously strong peaks in the dI/dV due to a bias-induced interplay between the position of the HOMO and LUMO levels. The effect is suppressed by self-interaction errors and is therefore unlikely to be observed in standard transport calculations based on density functional theory. Inclusion of dynamic correlations lead to substantial renormalization of the energy levels. In particular, we find a strong enhancement of quasi-particle (QP) scattering at finite bias which reduces the QP lifetimes significantly with a large impact on the IV curve.Comment: 4 pages, 3 figures. Phys. Rev. Lett. (accepted

van der Waals Bonded Co/h-BN Contacts to Ultrathin Black Phosphorus Devices

Due to the chemical inertness of 2D hexagonal-Boron Nitride (h-BN), few atomic-layer h-BN is often used to encapsulate air-sensitive 2D crystals such as Black Phosphorus (BP). However, the effects of h-BN on Schottky barrier height, doping and contact resistance are not well known. Here, we investigate these effects by fabricating h-BN encapsulated BP transistors with cobalt (Co) contacts. In sharp contrast to directly Co contacted p-type BP devices, we observe strong n-type conduction upon insertion of the h-BN at the Co/BP interface. First principles calculations show that this difference arises from the much larger interface dipole at the Co/h-BN interface compared to the Co/BP interface, which reduces the work function of the Co/h-BN contact. The Co/h-BN contacts exhibit low contact resistances (~ 4.5 k-ohm), and are Schottky barrier free. This allows us to probe high electron mobilities (4,200 cm2/Vs) and observe insulator-metal transitions even under two-terminal measurement geometry

pH-, thermo- and electrolyte-responsive polymer gels derived from a well-defined, RAFT-synthesized, poly(2-vinyl-4,4-dimethylazlactone) homopolymer via one-pot post-polymerization modification

Well-defined stimulus-responsive polymer gels were prepared from poly(2-vinyl-4,4-dimethylazlatone) (PVDMA) via one-pot post-polymerization modification. VDMA homopolymers were reacted with diamine crosslinking agents and functional 1° or 2° amines to form polymer gels that swelled in organic solvents and, in many cases, aqueous solutions. A series of functional amine reagents, including N,N-dimethylethylenediamine (DMEDA), N,N-diethylethylenediamine (DEEDA), morpholine, 3-morpholinopropylamine (MPPA) and tetrahydrofurfurylamine (THFA), were chosen as functional amines to produce polymer gels containing environmentally sensitive species. 13C solid-state NMR and FTIR spectroscopic measurements confirmed complete conversion of the reactive scaffolds. pH-dependent swelling behavior at ambient temperature was observed in DMEDA-, DEEDA- and MPPA-modified hydrogels. Kinetic studies showed the swelling behaviors of DMEDA-modified hydrogels were regulated by cross-linker type and concentration in acidic water (pH = 4) at ambient temperature. The swelling ratio of hydrogels modified by DEEDA, MPPA and THFA also depended strongly on temperature, indicating successful synthesis of thermoresponsive gels. Furthermore, the concentration of added sodium sulfate played a significant role with respect to the swelling properties of MPPA-modified hydrogels. These smart materials may be of interest in the biomedical field as well as in other applications

Mechano-responsive polymer solutions based on CO2 supersaturation: shaking-induced phase transitions and self-assembly or dissociation of polymeric nanoparticles

Mechanical stimulation of supersaturated aqueous CO2 solutions is accompanied by a pH increase within seconds. In solutions of tailored homo- and AB diblock copolymers this is exploited to induce micelle formation, or, taking advantage of an aqueous upper critical solution temperature transition, nanoparticle disassembly

Effect of Oxygen and Nitrogen Sparging during Grape Fermentation on Volatile Sulphur Compounds

Elemental sulphur is a common fungicide applied in vineyards before harvest, and has been found toincrease the production of desirable polyfunctional mercaptans, but also H2S and unwanted reductivesulphur aroma compounds. This paper investigates the effectiveness of oxygen and nitrogen sparging,applied during fermentation, on the removal of volatile sulphur compounds in Sauvignon blanc wines.Increasing the amount of elemental sulphur added to grapes after pressing, from nil to 10 to 100 mg/L,led to an increase in the formation of 3-mercaptohexanol (3MH), of 3-mercaptohexyl acetate (3MHA) forthe 10 mg/L additions only, and of some unwanted reductive compounds. Few changes were observed inthe concentrations of aroma compounds when the juices were sparged with nitrogen during fermentation.Additions of oxygen during fermentation led to some decrease in the concentration of polyfunctionalmercaptans for the 10 mg/L sulphur additions, but did not significantly remove reductive aroma compounds.Few differences were observed in the concentration of wine phenolics or of further wine aroma familieswith any of the treatments

573 IOS Press A refined numerical simulation on dynamic behavior of roller chain drives

Abstract. A refined numerical analysis of the dynamic behavior of roller chain drives is performed considering the roller assembly as a three-layer structure with mechanical clearance between each two of the mechanical components. Instead of using analytical method, explicit finite element technique is utilized for modeling and simulating the dynamic behavior of chain drives. The complete standard geometry of sprockets and all components of chain links are used in the developed model with minor geometry simplification. A primary goal is to achieve a more complete understanding of the dynamic behavior of chain drives especially in the transient vibration response of the engaging rollers, which is crucial for noise emission calculation. The simulated velocity response of the engaging rollers and roller-sprocket contact forces achieved using the full model are compared with what found by the simple model which has been adopted in analytical study of chain roller dynamics

The Role of Federated Learning in a Wireless World with Foundation Models

Foundation models (FMs) are general-purpose artificial intelligence (AI) models that have recently enabled multiple brand-new generative AI applications. The rapid advances in FMs serve as an important contextual backdrop for the vision of next-generation wireless networks, where federated learning (FL) is a key enabler of distributed network intelligence. Currently, the exploration of the interplay between FMs and FL is still in its nascent stage. Naturally, FMs are capable of boosting the performance of FL, and FL could also leverage decentralized data and computing resources to assist in the training of FMs. However, the exceptionally high requirements that FMs have for computing resources, storage, and communication overhead would pose critical challenges to FL-enabled wireless networks. In this article, we explore the extent to which FMs are suitable for FL over wireless networks, including a broad overview of research challenges and opportunities. In particular, we discuss multiple new paradigms for realizing future intelligent networks that integrate FMs and FL. We also consolidate several broad research directions associated with these paradigms.Comment: 8 pages, 5 figures, 1 tabl

An objective based classification of aggregation techniques for wireless sensor networks

Wireless Sensor Networks have gained immense popularity in recent years due to their ever increasing capabilities and wide range of critical applications. A huge body of research efforts has been dedicated to find ways to utilize limited resources of these sensor nodes in an efficient manner. One of the common ways to minimize energy consumption has been aggregation of input data. We note that every aggregation technique has an improvement objective to achieve with respect to the output it produces. Each technique is designed to achieve some target e.g. reduce data size, minimize transmission energy, enhance accuracy etc. This paper presents a comprehensive survey of aggregation techniques that can be used in distributed manner to improve lifetime and energy conservation of wireless sensor networks. Main contribution of this work is proposal of a novel classification of such techniques based on the type of improvement they offer when applied to WSNs. Due to the existence of a myriad of definitions of aggregation, we first review the meaning of term aggregation that can be applied to WSN. The concept is then associated with the proposed classes. Each class of techniques is divided into a number of subclasses and a brief literature review of related work in WSN for each of these is also presented