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The semiconductor industry faces increasing pressure to improve energy efficiency while maintaining competitiveness and sustainability. Apart from more conventional energy efficiency measures look at equipment modernization and process and design optimization, this paper explores the potential of data-driven approaches to address these challenges and optimize energy consumption across both the facility and manufacturing space of a semiconductor manufacture plant. By harnessing advanced analytics, machine learning algorithms, and IoT technologies, semiconductor manufacturers can gain real-time insights into energy usage patterns, and identify areas of opportunities that leads to the implementation of targeted interventions to optimize performance. The paper first looks into the challenges and measures of enabling and enhancing data visibility which is the foundation of the data-driven approach, then it examines case studies, best practices and various systematic approaches, demonstrating the transformative impact of data-driven energy efficiency measures which leads to operational efficiency, cost reduction, and environmental sustainability. Ultimately, this paper aims to provide a fresh angle into the energy efficiency study for peers in semiconductor industries to leverage in their journey towards a more sustainable and energy efficient future. [ABSTRACT FROM AUTHOR]

Abstract-The escalating demand for secure communication in the Internet of Things (IoT), particularly in energy-sensitive devices such as smart meters, highlights a critical challenge: achieving robust security without excessive energy consumption. While various solutions have been proposed to minimize energy use, many fail to address the unique constraints of the IoT devices effectively. This article introduces an innovative approach by proposing a secure, lightweight wireless meter-bus (wM-Bus) protocol, specifically designed for the stringent resource constraints of the IoT environments. By incorporating the noise protocol framework (NPF), our protocol significantly reduces computational and power requirements without compromising security integrity. Through a methodical implementation that spanned five distinct phases, including a comparative analysis with the conventional transport layer security (TLS), our findings are compelling. The NPF, particularly with its NX and XX patterns, dramatically surpasses TLS in performance, extending operational lifetimes to approximately 9 and 7.88 years, respectively, in contrast to the 3.81 years offered by TLS. These results not only demonstrate the superior efficiency of the NPF in the IoT settings but also highlight its potential in striking an optimal balance between security and operational longevity. [ABSTRACT FROM AUTHOR]

Sea clutter modeling and parameter estimation/prediction are important basis of system design, performance assessment, and target detection of maritime radars. In addition to parameter estimation from measured data, parameter prediction from radar parameters, sea state, and the viewing geometry of the radar is an alternative. In this letter, a dual-polarimetric full-recorded sea clutter database measured by an island-based X-band experimental radar on the Yellow Sea of China is introduced. On this database, the model selection reveals that the compound-Gaussian model with inverse Gaussian (CGIG) texture is suited for HH high-resolution sea clutter data, the generalized Pareto (GP) intensity distributions are for HH moderate-resolution data, and the $K$ amplitude distributions are for high and moderate-resolution VV polarized data. Further, three five-parametric empirical formulas are constructed to predict the shape parameter of the CGIG, GP, and $K$ distributions of sea clutter from the area of spatial resolution cell, the grazing angle, the significant wave height (SWH), the average wave period, and the wave direction relative to the sight line of radar. The new empirical formulas attain more accurate prediction than existing empirical formula on the database. [ABSTRACT FROM AUTHOR]

Distributed energy resources enable efficient power response but may cause transformer overload in distribution grids, calling for recovering meter-transformer mapping to provide situational awareness, i.e., the transformer loading. The challenge lies in recovering meter-transformer (M.T.) mapping for two common scenarios, e.g., large distances between a meter and its parent transformer or high similarity of a meter’s consumption pattern to a non-parent transformer’s meter. Past methods either assume a variety of data as in the transmission grid or ignore the two common scenarios mentioned above. Therefore, we propose to utilize the above observation via spectral embedding by using the property that inter-transformer meter consumptions are not the same and that the data noise is limited so that all the $k$ smallest eigenvalues of the voltage-based Laplacian matrix are smaller than the next smallest eigenvalue of the ideal Laplacian matrix. We also provide a performance guarantee for Spectral Embedding-based M.T. mapping (SEMTM). Furthermore, we partially relax the assumption by utilizing location information to aid voltage information for areas geographically far away, but with similar voltages. Numerical simulations on the IEEE test systems and real feeders from our partner utility show that the proposed method correctly identifies the M.T. mapping.

Since there are many kinds of automobile instruments and the industrial environment is usually complex, automatic instrument detection during automobile production test becomes a challenging task. This article presents an automobile pointer instrument detection method based on prior information and fuzzy sets. The proposed method consists of two frameworks built around a pointer meter prior information model (PMPIM). The first one targets PMPIM construction to obtain the required prior information. With this purpose, a pointer-free template is obtained from a template generation algorithm and pointer positions are mapped into an energy function for optimization, using an energy function-based pointer positioning algorithm. The energy function is defined based on the distance between the crisp and fuzzy sets. The second framework targets PMPIM utilization to detect pointer meters during production test. A fuzzy-based image enhancement method is proposed to enhance test images and the template simultaneously. A prior information and energy function-based pointer positioning algorithm is also proposed to locate pointers in test images. Finally, the indicator value (the value the pointer points to) is calculated according to the positions of the pointer and scale marks. Experimental results show that the proposed method achieves better generalization and robustness than existing state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Inverter-based resources are proliferating in distribution systems across the globe, along with battery energy storage systems. This in part is attributed to a descending trend observed in their costs. These inverter-based resources, however, create new problems for electrical utilities planners and engineers. One such issue is how to measure, test, and manage load rejection overvoltage. This phenomenon takes place upon sudden islanding of a power system area such that it becomes supported by grid-following inverter-based resources only. This letter presents background, practical methods to test the behavior, as well as two case studies of utility-scale generation and energy storage connected to a distribution feeder. [ABSTRACT FROM AUTHOR]

The article presents the cross-sectional results of analyzes of authentic conducted disturbances introduced into public power networks by various types of loads installed at end users. The foundation for the research was a set of 260 identified cases of Power Line Communication (PLC) transmission disturbances based on the Open Smart Grid Protocol (OSGP) technology, which were recorded in the urban network of the distribution system operator covering over 400,000 smart meters. The paper presents a quantitative assessment of the share of loads that most often cause problems with transmission efficiency. Moreover, a qualitative assessment of the detected disturbance cases was carried out in terms of conditions relating to the structure of the power grid and real transmission levels and disturbance levels. The authors made a pioneering analysis in the so-called “communication path” where the PLC communication disturbance was identified, i.e., from the installation point of the PLC concentrator to the metering point where the energy meter, with which the connection was lost, was installed. [ABSTRACT FROM AUTHOR]

DC Water installed state‐of‐the‐art technology to detect hard‐to‐find leaks in water pipes and valves. [ABSTRACT FROM AUTHOR]

Choosing the first broadcaster to be the parent node as proposed by the leader-based routing (LBR) enables its Polling packet to be received earliest in a heterogeneous mobile sensor network (MSN) with short and intermittent availability of the mobile sink but may lead to an unreachable parent node. Furthermore, employing the three-way-handshaking as proposed by the link quality indicator (LQI)-based beaconless routing (LQI-BLR) may lead to a longer packet delivery delay. To avoid the aforementioned drawbacks simultaneously, the transmission power of the candidate parent node is included in the Polling packet in our proposed protocol. Along with the LQI, a mobile sensor can estimate its distance to the candidate parent node and its packet-receive ratio (PRR). If the PRR is higher than a preset threshold, the candidate can be chosen accordingly. Via simulations, we demonstrate that our proposed protocol can outperform LBR and LQI-BLR in a heterogeneous environment. In terms of the packet delivery ratio, our proposed protocol is able to achieve at least 35% and 20% of improvement when compared to LBR and LQI-BLR, respectively. Regarding the energy consumption and packet delivery delay, more than 20% improvement can be observed. [ABSTRACT FROM AUTHOR]

Due to the diversity of power consumption patterns, the false positive rate (FPR) of data-driven electricity theft detection (ETD) methods is too high to meet practical needs, which severely restricts the engineering application of data-based methods. To reduce FPR of ETD methods based on advanced metering infrastructure (AMI), a deep neural network with low FPR (LFPR-DNN) is proposed in this paper. First, a deep model is constructed based on one-dimensional convolution and residual network, which can automatically extract features from consumption data. Then, a two-stage training scheme is used to train the network. In the first stage, the conventional gradient descent algorithm is used to update the network weights. To minimize the impact of data imbalance on detection performance, focal loss is used. Besides, grid search is used to optimize the hyper-parameters of the model. In the second stage, with FPR as the optimization objective, the particle swarm optimization (PSO) algorithm is used to train the network. Finally, the proposed LFPR-DNN is verified by using the open Irish data set. Compared to other state-of-the-art classifiers, LFPR-DNN has the lowest FPR with 0.29% and the highest AUC with 99.42%. The FPR is reduced by an order of magnitude, which verifies the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Key Takeaways: Traditional cost‐of‐service structures for water rates have been based on customer classes, which can overlook the stresses put on a distribution system through peak usage and unfairly charge some customers. With more and better data, an individualized approach that ties rates to effects on the water system is possible, meaning utilities can capture each customer's water‐use patterns and tailor rates accordingly. Individualized rates enabled by smart meters can result in more equitable pricing for customers, help them understand their water use, and provide a budgetary benefit for utilities. [ABSTRACT FROM AUTHOR]

The advanced metering infrastructure in modern networked smart homes brings various advantages, such as multiple pricing and energy scheduling. However, smart homes are vulnerable to many cyberattacks, and the most striking one is energy theft. Researchers have developed many countermeasures, fostered by advanced machine learning (ML) techniques. Nevertheless, recent advances are not robust enough in practice, partially due to the vulnerabilities of employed ML algorithms. Given a group of smart homes, in this article, we present a covert electricity theft strategy through mimicking normal consumption patterns and compromising neighboring meters concurrently. Such attack is almost impossible to be detected by existing solutions as the manipulated data have little deviation against honest usage records. To address this threat, we initially identify and define two levels of consumption deviations: home-level and interpersonal-level, respectively. Then, we design a feature extraction scheme that can capture the correlation between attacks and honest customers. Finally, we develop a new deep learning-based detection model. Extensive experiments based on real-world datasets show that the presented attack could evade existing mainstream detectors but still gain high profits. In addition, the proposed countermeasure outperforms state-of-the-art detection methods. [ABSTRACT FROM AUTHOR]

The proliferation of smart meters has resulted in a large amount of data being generated. It is increasingly apparent that methods are required for allowing a variety of stakeholders to leverage the data in a manner that preserves the privacy of the consumers. The sector is scrambling to define policies, such as the so called ‘15/15 rule’, to respond to the need. However, the current policies fail to adequately guarantee privacy. In this paper, we address the problem of allowing third parties to apply $K$ -means clustering, obtaining customer labels and centroids for a set of load time series by applying the framework of differential privacy. We leverage the method to design an algorithm that generates differentially private synthetic load data consistent with the labeled data. We test our algorithm’s utility by answering summary statistics such as average daily load profiles for a 2-dimensional synthetic dataset and a real-world power load dataset. [ABSTRACT FROM AUTHOR]

This article addresses the problem of proper flicker severity assessment by smart energy meters. Flicker severity occurs for a typical voltage “clipped cosine” distortion caused by operation of power electronic devices in the power grid. The voltage distortion caused only by higher harmonics does not cause voltage fluctuations, and therefore does not cause flicker. This article presents the research results for some models of advanced metering infrastructure (AMI) smart meters, for which irregularities are observed in the voltage variation severity assessment if the typical voltage distortion occurs in the power grid. Possible mistakes in implementation of flickermeter signal chain as defined by International Electrotechnical Commission (IEC) in smart meters that influence the measurement results have been explained. The analysis of the influence of typical voltage distortion on the indicator $P_{st}$ measurement has been carried out analytically and in numerical simulation studies using the modeled flickermeter signal chain. [ABSTRACT FROM AUTHOR]

The opening up of the railway to competition in Europe has been accompanied by many technical challenges never before seen in the railway field. One of these challenges concerns precise energy metering. As European railways have been a monopoly until now, the current systems can only measure the energy consumption of a single operator. In accordance with European law, the installation of an energy meter has been mandatory in new or refurbished rolling stocks since 2014. Nevertheless, these energy meters are still not fully reliable, with misreadings and missing readings being common occurrences. To address the problem of energy metering data reliability, this paper presents a method for validating the reading measurement based on a physical model and validates it in two different case studies. For the issue of energy metering data availability, different estimators are developed, and their performance are shown with the aim of calculating the missing consumptions for both case studies. Finally, the paper suggests how these methods may be implemented in other types of trains. [ABSTRACT FROM AUTHOR]

Autonomous vehicles in intelligent transportation systems must be able to perform reliable and safe navigation. This necessitates accurate object detection, which is commonly achieved by high-precision depth perception. Existing stereo vision-based depth estimation systems generally involve computation of pixel correspondences and estimation of disparities between rectified image pairs. The estimated disparity values will be converted into depth values in downstream applications. As most applications often work in the depth domain, the accuracy of depth estimation is often more compelling than disparity estimation. However, at large distances (> 50m), the accuracy of disparity estimation does not directly translate to the accuracy of depth estimation. In the context of learning-based stereo systems, this is mainly due to biases imposed by the choices of the disparity-based loss function and the training data. Consequently, the learning algorithms often produce unreliable depth estimates of under-represented foreground objects, particularly at large distances. To resolve this issue, we first analyze the effect of those biases and then propose a pair of depth-based loss functions for foreground objects and background separately. These loss functions can be tuned and can balance the inherent bias of the stereo learning algorithms. The efficacy of our solution is demonstrated by an extensive set of experiments, which are benchmarked against state of the art. We show on the KITTI 2015 benchmark that our proposed solution yields substantial improvements in disparity and depth estimation, particularly for objects located at distances beyond 50 meters, outperforming the previous state of the art by 10%. [ABSTRACT FROM AUTHOR]

The $P_{st}$ indicator is one of the parameters used in the power quality evaluation. The International Electrotechnical Commission (IEC) flickermeter with a complex signal chain structure is used for $P_{st}$ measurements. The occurrence of $P_{st}$ values greater than the limit value indicates the occurrence of flicker severity caused by voltage fluctuation. This article presents the analysis of the influence of the sampling rate on $P_{st}$ measurement results. For analysis purpose, the analytical model of the IEC flickermeter signal chain has been prepared, considering the sampling rate of input voltage. Based on the prepared model, the possible effects of incorrect sampling rate selection in $P_{st}$ measurements have been presented. The model test results have been compared with laboratory measurement results for two advanced metering infrastructure energy meters with built-in IEC flickermeter. The obtained laboratory measurement results indicate the occurrence of aliasing for tested energy meters, which causes $P_{st}$ measurement disturbance. [ABSTRACT FROM AUTHOR]

The advent of Industry 4.0 facilitates the Int- ernet-of-Things-based-transactive energy system (IoTES), which enables innovative services with numerous independent distributed systems. These systems generate heterogeneous data in bulk, which become susceptible to cyber-attacks, particularly the stealthy false data injection attacks (FDIAs). The existing centralized FDIA detection algorithms often breach data privacy and fail to perform effectively in highly dynamic and distributed environments, such as IoTES. To resolve the issue, initially, a recurrent deep deterministic policy gradient is utilized to invent an experience-driven FDIA in a complex IoTES. The attacker intends to intelligently exploit the data integrity of smart energy meters with insufficient knowledge of the system. Subsequently, to countermove the stealth and enable independent clients to train a centralized model while keeping each client’s data privacy intact, a deep-federated-learning-based decentralized FDIA detection method using an attentive aggregation is exploited in this article. The proposed approach is capable of parallel computing and can reliably identify the stealthy FDIA on all the nodes simultaneously. Simulation results validate that the proposed scheme outperforms the state-of-the-art methods under a distributed environment with a significantly higher detection accuracy and lower computational complexity while keeping the data privacy intact. [ABSTRACT FROM AUTHOR]

This letter presents a combined model for appropriate radio coverage of mission-critical push-to-talk communications in mountainous vegetated terrains. The proposed model uses the irregular terrain model (ITM) with the detailed information of the ground topography to predict the excess loss caused by the terrain irregularities, along with the Lateral International Telecommunications Union of Radio model, to account for the excess loss caused by the presence of vegetation in the radio path. The performance of the proposed combined model was assessed against experimental results obtained from a measurement campaign conducted along unpaved paths and trails, usually taken by firefighters during firefighting procedures, in mountainous terrains with dense vegetation. The acquired experimental data was assessed against experimental results obtained from a measurement campaign conducted along unpaved paths and trails. [ABSTRACT FROM AUTHOR]

Nowadays, with the overconsumption of energy, researchers attempt to optimally manage energy consumption and prevent it from being wasted. One beneficial way to manage energy consumption is to integrate the energy grid with information technology, so that data can be exchanged between producers and consumers in a two-way communication network, which is called a smart grid. This data transfer must take place in a secure environment. However, many protocols have been designed to establish a secure channel to transfer data in the smart grid environment, each of which has some disadvantages that still keep the problem open for this field of research. Recently, Kumar et al. proposed a lightweight authentication and key agreement protocol based on elliptic curve cryptography (ECC). In this article, we show that the proposed protocol does not provide the anonymity of smart meters and is vulnerable to smart meter tracing attack. We also propose a new lightweight ECC-based authenticated key agreement protocol that is resistant against all known attacks including the attack presented in this article. Also, the proposed scheme is more efficient than the recent related scheme. We show that the proposed protocol is semantically secure and also we simulate our protocol using the AVISPA. [ABSTRACT FROM AUTHOR]

Any discontinuity in the power cords of electrical devices can lead to electric shock, injury, and even death. This article presents a portable device that, when placed in the proximity of handheld tools, prevents such injuries. The principal operation of this device is based on electric and magnetic field meters and display segment LEDs. When the grounding conductor of a portable tool with a metal enclosure is broken, the electric field around it is increased. In the same way, when the neutral conductor is broken, the magnetic field is increased. The device detects any variations in electric or magnetic fields and displays a warning signal for alert. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) generation is increasing in distribution systems following policies and incentives to promote zero-carbon emission societies. Most residential PV systems are installed behind-the-meter (BTM). Due to single meter deployment that measures the net load only, this PV generation is invisible to distribution system operators causing a negative impact on the distribution system planning and local supply and demand balance. This paper proposes a novel data-driven BTM PV generation disaggregation method using only net load and weather data, without relying on other PV proxies and PV panels’ physical models. Long Short-Term Memory (LSTM) is employed to build a generation difference fitted model (GDFM) and a consumption difference fitted model (CDFM) derived from weather data. Both difference fitted models are refined by a cross-iteration with mutual output. Finally, considering the photoelectric conversion properties, the disaggregated generation results are acquired by the refined GDFM of changing input. The proposed method has been tested with actual smart meter data of Austin, Texas and proves to increase the disaggregated accuracy as compared to current state-of-the-art methods. The proposed method is also applicable to disaggregate BTM PV systems of different manufacturing processes and types. [ABSTRACT FROM AUTHOR]

Today’s power distribution system is changing to a power-electronics-enabled distribution system, especially with the increasing penetration of distributed energy resources (DERs). To monitor and manage those electronic devices and DERs at the grid edge, the advanced metering infrastructure (AMI) with two-way communications presents great potential. At present, extensive research explores the upstream communication from smart meters to electric utilities (e.g., meter reading) but few examine the downstream communication from the utilities to smart meters (e.g., meter pinging). This paper discusses the AMI two-way communication and its recent industrial practice in the U.S., especially for applying the smart meter pinging functionality to monitor grid-edge devices and DERs. This paper then develops the two-way communication model and the network calculus method to quantify the impact of the two-way communication on the AMI network. In the end, the proposed method is validated with ns-3 simulation using the modified 13-node test feeder and real-world feeder systems. [ABSTRACT FROM AUTHOR]

Network slicing is an essential technology for 5G mobile networks. It partitions network resource logically into multiple isolated slices, each of which can satisfy a suite of network requirements for one specific service. However, it cannot be fulfilled by the current SDN (Software-Defined Networks), since the conventional SDN data-plane technology, OpenFlow, is not flexible enough to offer fine-grained network resource control or queue/packet scheduling. It leads to many research studies developing corresponding solutions on programmable switches. In this work, we focus on the support of the bandwidth guarantee and management for network slices. Although several studies with the similar goal have been proposed, they do not consider interference among different flow types or use the built-in meter for easy deployment on COTS (Commercial Off-The-Shelf) P4 switches. To this end, we first conduct a case study to examine the interference cases. We then propose a solution, designated as P4-TINS (P4-driven Traffic Isolation for Network Slicing), to resolve the interference by isolating different types of traffic flows in priority queues and set the P4 switch’s bucket size based on the time granularity of its bandwidth management operation. It cannot only ensure the guaranteed bandwidth for each slice but also enable coexistent slices to fairly share residual bandwidth. We have confirmed its effectiveness experimentally based on our prototype over an ONOS (Open Network Operating System) controller and a COTS P4 switch. [ABSTRACT FROM AUTHOR]

Large antenna arrays and high-frequency bands are two key features of future wireless communication systems. The combination of large-scale antennas with high transmission frequencies often results in the communicating devices operating in the near-field (Fresnel) region. In this paper, we study the potential of beam focusing, feasible in near-field operation, in facilitating high-rate multi-user downlink multiple-input multiple-output (MIMO) systems. As the ability to achieve beam focusing is dictated by the transmit antenna, we study near-field signalling considering different antenna structures, including fully-digital architectures, hybrid phase shifter-based precoders, and the emerging dynamic metasurface antenna (DMA) architecture for massive MIMO arrays. We first provide a mathematical model to characterize near-field wireless channels as well as the transmission pattern for the considered antenna architectures. Then, we formulate the beam focusing problem for the goal of maximizing the achievable sum-rate in multi-user networks. We propose efficient solutions based on the sum-rate maximization task for fully-digital, (phase shifters based-) hybrid and DMA architectures. Simulation results show the feasibility of the proposed beam focusing scheme for both single- and multi-user scenarios. In particular, the designed focused beams provide a new degree of freedom to mitigate interference in both angle and distance domains, which is not achievable using conventional far-field beam steering, allowing reliable communications for uses even residing at the same angular direction. [ABSTRACT FROM AUTHOR]

In this letter, we experimentally demonstrate a long-distance, large-capacity and photonics-aided wireless terahertz (THz) transmission system without THz amplifier. Using dielectric lenses and advanced digital signal processing (DSP), we have achieved 124 Gbit/s signal transmission over 54 meters wireless distance and 44 Gbit/s signal transmission over 104 meters wireless distance. For all we know, this is the longest wireless distance that can be achieved by using dielectric lenses without any THz amplifiers. [ABSTRACT FROM AUTHOR]

Common clamp-on ultrasonic flow meters consist of two single-element transducers placed on the pipe wall. Flow speed is measured noninvasively, i.e., without interrupting the flow and without perforating the pipe wall, which also minimizes safety risks and avoids pressure drops inside the pipe. However, before metering, the transducers have to be carefully positioned along the pipe axis to correctly align the acoustic beams and obtain a well-calibrated flowmeter. This process is done manually, is dependent on the properties of the pipe and the liquid, does not account for pipe imperfections, and becomes troublesome on pipelines with an intricate shape. Matrix transducer arrays are suitable to dynamically steer acoustic beams and realize self-alignment upon reception, without user input. In this work, the design of a broadband $37\times 17$ matrix array (center frequency of 1 MHz) to perform clamp-on ultrasonic flow measurements over a wide range of liquids (${c}=1000-2000$ m/s, $\alpha \leq 1$ dB/MHz $\cdot $ cm) and pipe sizes is presented. Three critical aspects were assessed: efficiency, electronic beam steering, and wave mode conversion in the pipe wall. A prototype of a proof-of-concept flowmeter consisting of two 36-element linear arrays (center frequency of 1.1 MHz) was fabricated and placed on a 1-mm-thick, 40-mm inner diameter stainless steel pipe in a custom-made flow loop filled with water. At resonance, simulated and measured efficiencies in water of the linear arrays compared well: 0.88 and 0.81 kPa/V, respectively. Mean flow measurements were achieved by electronic beam steering of the acoustic beams and using both compressional and shear waves generated in the pipe wall. Correlation coefficients of ${R}^{2}>0.99$ between measured and reference flow speeds were obtained, thus showing the operational concept of an array-based clamp-on ultrasonic flowmeter. [ABSTRACT FROM AUTHOR]

As residential photovoltaic (PV) system installations continue to increase rapidly, utilities need to identify the locations of these new components to manage the unconventional two-way power flow and maintain sustainable management of distribution grids. But, historical records are unreliable and constant re-assessment of active residential PV locations is resource-intensive. To resolve these issues, we propose to model the solar detection problem in a machine learning setup based on labeled data, e.g., supervised learning. However, the challenge for most utilities is limited labels or labels on only one type of users. Therefore, we design new semi-supervised learning and one-class classification methods based on autoencoders, which greatly improve the nonlinear data representation of human behavior and solar behavior. The proposed methods have been tested and validated not only on synthetic data based on a publicly available data set but also on real-world data from utility partners. The numerical results show robust detection accuracy, laying down the foundation for managing distributed energy resources in distribution grids. [ABSTRACT FROM AUTHOR]

Event cameras, offering extremely high temporal resolution and high dynamic range, have brought a new perspective to addressing common object detection challenges (e.g., motion blur and low light). However, how to learn a better spatio-temporal representation and exploit rich temporal cues from asynchronous events for object detection still remains an open issue. To address this problem, we propose a novel asynchronous spatio-temporal memory network (ASTMNet) that directly consumes asynchronous events instead of event images prior to processing, which can well detect objects in a continuous manner. Technically, ASTMNet learns an asynchronous attention embedding from the continuous event stream by adopting an adaptive temporal sampling strategy and a temporal attention convolutional module. Besides, a spatio-temporal memory module is designed to exploit rich temporal cues via a lightweight yet efficient inter-weaved recurrent-convolutional architecture. Empirically, it shows that our approach outperforms the state-of-the-art methods using the feed-forward frame-based detectors on three datasets by a large margin (i.e., 7.6% in the KITTI Simulated Dataset, 10.8% in the Gen1 Automotive Dataset, and 10.5% in the 1Mpx Detection Dataset). The results demonstrate that event cameras can perform robust object detection even in cases where conventional cameras fail, e.g., fast motion and challenging light conditions. [ABSTRACT FROM AUTHOR]

The iconic pipette is integrated with an electrochemical pH sensor for preventing cross-contamination while still enabling pH measurements in industrial fermentation reactors. Contamination is a big financial cost source of the fermentation industry and periodic parameter (such as pH, dissolved oxygen, temperature, etc.) measurements are crucial for a successful fermentation run. Yet, the very act of measuring poses contamination risks to the fermentation reactors. To resolve the dichotomy between the need for pH monitoring and cross-contamination prevention, we develop a pH sensing pipette (composed of an integrated electrochemical polyaniline-based pH sensitive electrode, flowcell, and readout circuit) with a disposable pipette tip for cross-contamination-free pH measurements. This pH sensing pipette achieves a system-level sensitivity of −134.8 mV/pH, a limit of detection of pH 0.029 × 10−3, and an average response time of 2.6–6.0 s, which is good enough for pH measurements in industrial fermentation reactors. It is then demonstrated to monitor the pH of ethanol fermentation across two days successfully, implying that this device may be used to further prevent cross-contamination risks in industrial fermentation processes. [ABSTRACT FROM AUTHOR]

Real-time parking availability prediction is of great value to optimize the on-street parking resource utilization and improve traffic conditions, while the expensive costs of the existing parking availability sensing systems have limited their large-scale applications in more cities and areas. This paper presents the MePark system to predict real-time citywide on-street parking availability at fine-grained temporal level based on the readily accessible parking meter transactions data and other context data, together with the parking events data reported from a limited number of specially deployed sensors. We design an iterative mechanism to effectively integrate the aggregated inflow prediction and individual parking duration prediction for adequately exploiting the transactions data. Meanwhile, we extract discriminative features from the multi-source data, and combine the multiple-graph convolutional neural network (MGCN) and the long short-term memory (LSTM) network for capturing complex spatio-temporal correlations. The extensive experimental results based on a four-month real-world on-street parking dataset in Shenzhen, China demonstrate the advantages of our approach over various baselines. [ABSTRACT FROM AUTHOR]

Web services often impose inter-parameter dependencies that restrict the way in which two or more input parameters can be combined to form valid calls to the service. Unfortunately, current specification languages for web services like the OpenAPI Specification (OAS) provide no support for the formal description of such dependencies, which makes it hardly possible to automatically discover and interact with services without human intervention. In this article, we present an approach for the specification and automated analysis of inter-parameter dependencies in web APIs. We first present a domain-specific language, called Inter-parameter Dependency Language (IDL), for the specification of dependencies among input parameters in web services. Then, we propose a mapping to translate an IDL document into a constraint satisfaction problem (CSP), enabling the automated analysis of IDL specifications using standard CSP-based reasoning operations. Specifically, we present a catalogue of seven analysis operations on IDL documents allowing to compute, for example, whether a given request satisfies all the dependencies of the service. Finally, we present a tool suite including an editor, a parser, an OAS extension, a constraint programming-aided library, and a test suite supporting IDL specifications and their analyses. Together, these contributions pave the way for a new range of specification-driven applications in areas such as code generation and testing. [ABSTRACT FROM AUTHOR]

The generalized adoption of power-electronic-based loads in residential networks is leading to increasing concern about harmonic distortion levels. At the same time, residential distribution systems are insufficiently monitored to provide a reliable analysis of harmonic limits compliance at the point of common coupling of customers. To address this problem, this paper proposes a practical method to estimate voltage harmonic levels in real time at all connection points of residential consumers. The proposed methodology assumes unknown network parameters and topology according to the limited data availability which is usual in low voltage residential networks. The proposed harmonic estimation method uses information gathered from conventional smart meters typically installed in the network (which register fundamental voltage and power) and from a reduced number of installed power quality monitors. A penetration of power quality meters below 10% has proved to provide accurate estimations of voltage harmonic levels at the whole network. The paper also proposes a methodology to optimally locate these power quality meters in the network. Validation of the methodology is performed in two different European distribution test networks with 55 buses and 471 obtaining satisfactory results. The method’s robustness and the parameters affecting accuracy are also analyzed in the article. [ABSTRACT FROM AUTHOR]

Extensive studies have revealed that smart grid is vulnerable to cyber-physical attacks. However, these strategies only focus on the cascading initiation phase to induce single-stage failures with multiple branch tripping, lacking of exploring the attack effectiveness in the propagation phase so that the deeply-hidden cascading failures are underestimated. In this paper, we propose a novel false load injection attack strategy that can intentionally penetrate into the cascading propagation phase to drive multi-stage dynamical failures with a cascading process. Specifically, we formulate a bi-level optimization problem to model the adversarial game between operator and attacker. The former is in charge of security-constrained economic dispatching to minimize the generation cost, and the latter aims to maximize the cumulative number of tripped branches. Further, we reformulate this NP-hard bi-level problem as a mixed integer linear program for tractable computation. Finally, we perform numerical simulations on different-scale IEEE test systems to validate our strategy in driving the dynamical failures. [ABSTRACT FROM AUTHOR]

This letter discusses pointing errors in underwater optical communication caused by environmental disturbances and uncertainties that cannot be well measured and controlled in previous optical alignment methods. The bore-sight and jitter effects are identified in the motion model of the mobile platform. We propose to use the sensor suite that includes a pressure sensor, super short baseline (SSBL) acoustic system, Doppler velocity log (DVL), and fiber optic gyro (FOG) to observe and estimate pointing errors during communication. The pointing errors updated by the particle filter can be shared with the pointing, acquisition, and tracking (PAT) system and thruster system. The sea experiments reveal that the proposed method can measure pointing errors and limit error growth by maneuvering the mobile platform. [ABSTRACT FROM AUTHOR]

The article analyzes and classifies the meters used in Thai and Burmese poetry. Though both poetic traditions have a rich literary heritage and an advanced repertoire of poetic meters, their structural properties have not received significant scholarly attention. Along with summarizing preexisting scholarly research on the subject, the article provides description of meters used in traditional Thai poetry and their typological classification. The analysis of meters in Burmese poetry is based on the 18th century long drama “Maniket” by Padethayaza as well as on a collection of short Burmese classical poems. The article describes and classifies the five meters of Thai classical poetry (klon, chan, khlong, kap and rai). Burmese traditional poetry used one or two syllabic meters as well as other meters, which have not yet been classified in the scholarly literature. The article concludes with a comparative analysis of rhyme in Thai and Burmese poetry.

International Fusion Materials Irradiation Facility–DEMO-Oriented NEutron Source (IFMIF-DONES) is an accelerator based on deuterium–lithium (D-Li) neutron source designed to qualify materials at the irradiation conditions of the reactor at the DEMO nuclear fusion power station. This article presents the shut-down dose rate (SDDR) calculations performed with AutomateD VAriaNce reducTion Generator (ADVANTG) and Monte Carlo N-Particle (MCNP) codes, which were used for the variance reduction and particle transport accordingly. Application of such coupled computational method led to the SDDR map production, where the obtained IFMIF-DONES estimates verify the access cell (AC) containment building shielding after the relocation of irradiated high flux test module with target assembly. In addition, this article provides dose rate distributions for the safe operation of the device for the entire AC surroundings of DEMO-Oriented NEutron Source (DONES) building. SDDR calculations were performed using the R2Smesh code system. The dose rates were determined, where 1 Sv/h close to the source location, 1 mSv/h at the farthest wall, and 10–100- $\mu $ Sv/h leakage through the walls at certain positions at AC were identified, and dose rate maps were produced. [ABSTRACT FROM AUTHOR]

The use of helium recycling systems for magnetoencephalography (MEG) has rapidly expanded in recent years. Placing the cryocooler close to the MEG cryostat is expected to aid in achieving a high helium-recycling efficiency. However, the cryocooler typically introduces a large amount of magnetic interference in the MEG system owing to mechanical vibration, and electric and magnetic noise. In this study, we proposed a mobile reference sensor method using fluxgate magnetometers and vibration meters to reduce the noise originating from the cryocooler. We placed the MEG cryostat, which included superconducting quantum interference device (SQUID)-based MEG sensors and reference magnetometers, and a pulse-tube cryocooler inside a magnetically shielded room. We recorded the vibration and magnetic noise during the operation of the cryocooler using the vibration meters and fluxgate magnetometers placed on the cryocooler and reference SQUID magnetometers as the reference noise data. A noise reduction algorithm named time-shift principal component analysis (TSPCA) was applied to the recorded MEG sensor data by employing the reference noise data. A noise reduction ratio of –39 dB was achieved without temporal and spatial distortion of the MEG signal using an MEG phantom. Thus, we conclude that the mobile reference sensor method with the TSPCA algorithm is an effective technique for reducing the magnetic noise originating from the cryocooler. [ABSTRACT FROM AUTHOR]

Spatial colocation patterns are subsets of spatial feature sets that are frequently “neighboring” in space. In the majority of existing mining methods, neighboring is determined by a single distance threshold and does not take the neighboring degree into consideration causing many neighbor relationships to be lost and they also cannot objectively describe the correlation between spatial features. In addition, existing methods ignore the instance-sharing problem of spatial neighbor relationships when calculating the prevalence of colocation patterns. In order to overcome these weaknesses, this article introduces the fuzzy theory into the spatial colocation pattern discovery. Through the fuzziness of spatial neighbor relationships between spatial instances, we develop a reasonable fuzzy proximity metric, which takes the instance-sharing problem into account and which measures the similarity between different spatial features. Three effective fuzzy clustering methods are proposed, which ensure that colocation patterns can be extracted accurately and quickly. Finally, extensive experiments on five synthetic datasets and five real-world datasets prove the practicability and efficiency of the proposed methods. [ABSTRACT FROM AUTHOR]

Most of the existing research focuses on electricity theft cyberattacks in the consumption domain. On the contrary, a high penetration level of distributed generators (DGs) may result in increased electricity theft cyberattacks in the distributed generation domain, which is the focus of this article. In these attacks, malicious customers can hack into the smart meters monitoring their DG units, which are usually photovoltaic, and manipulate their readings to report higher injected energy to the grid and claim more profit under feed-in tariff programs. This article proposes a data-driven approach based on machine learning to detect such thefts. We adopt an anomaly detection approach where a theft detection unit (TDU) based on a regression tree model is designed to detect suspicious data. Historical records of solar irradiance, temperature, and smart meter readings are utilized in the training stage of the detector. The probability density function of the error between the actual readings from DG meters and the predicted generation by the regression model is utilized as a metric to detect suspicious data. Several theft scenarios are used to assess the performance of the TDU. Furthermore, a comparison study with other detectors is presented to demonstrate the superiority of the proposed TDU. [ABSTRACT FROM AUTHOR]

The solutions recommended by international roadmaps and technical reports on smart metering refer to interval metering with time resolutions higher than 15 min to 1 h as traditionally used. Based on the characteristics of the users’ power patterns in distribution networks, this article shows that in practical cases the resolutions of the traditional metering are not sufficient to assess peak power and network losses effectively. Effective interval metering solutions should have resolutions of one minute or less. Moreover, this article shows the advantages of assessing the average power peak (amplitude and duration) and estimating the network losses through innovative solutions beyond interval metering, based on event-driven energy metering. The use of EDM significantly enhances the quality of pattern representation and reduces the amount of data required with respect to high-resolution interval metering. Based on the Pareto analysis of conflicting objectives, a novel procedure to set up the EDM thresholds is presented. The applications shown use real data and refer to a single user, some users connected to a distribution network feeder, and many users connected to a large distribution system. The EDM benefits are quantified using specific indicators that consider energy losses and peak power. [ABSTRACT FROM AUTHOR]

In this article, we investigate a novel multisensor odor detection system (electronic nose) for low-concentration volatile organic compounds (VOCs). In order to break through the limitation for detecting low concentration VOCs, we design a conformal symmetric preconcentration unit structure based on the finite element method. A typical application for exhaled breath is developed to investigate the proposed scheme. The designed preconcentration system can increase the concentration of the concentrated substance, so that the sensor can detect it. Besides, we choose the alveolar gas at the bottom of the human lung, which can better represent the health status of the human body, to carry out the effectiveness test of the system. In order to combine the gas acquisition system with the preconcentration system, this article further designs a cost-effective alveolar gas collection device by a doubled adaptive pulsewidth modulation (PWM) operation. Finally, the effectiveness of the preconcentration system is judged by the detection results of gas chromatography-mass spectrometry (GC-MS). We also use the classical algorithm to classify lung cancer and nonlung cancer patients, which proves the effectiveness of our proposed integrated design system and shows the potential of practical application of the system. [ABSTRACT FROM AUTHOR]

Electricity meters are devices that record the electrical energy consumed by the customer. Recently, the electricity meter has developed as a result of technological progress, and it transformed from an electro-mechanical energy meter to become an electronic meter. The electronic energy meter consists of electronic components which may change its properties due to exposure to different climatic environments, so climatic tests must be conducted on the electricity meter [1]. Solar radiation testing is considered most important for an outdoor electrical energy meter, performed to ensure that the meter does not change its characteristics or that its insulation does not fail during exposure to solar radiation [2]. Electricity meters are classified according their installation for either indoor or outdoor energy measurements [3]. Outdoor electricity meters are mostly protective class two which means that the insulation level is higher than that used in indoor energy meters and can be more exposed to direct sunlight. Solar tests are carried out on electricity meters according to the international standard of IEC62052-11:2020, and IEC60068-2-5:2010 [3], [4] at an irradiance value equal to 1120 W/ m2± 10%. [ABSTRACT FROM AUTHOR]

The location and identification of harmonic sources in radial distribution feeders are essential tasks since they improve the power quality monitoring and assist in mitigating issues resulting from harmonic distortions. Thus, this article presents an approach based on particle swarm optimization algorithm with adaptive and individual inertia, which was used to locate and estimate the parameters (harmonic magnitudes, real and reactive power) of the harmonic source with the highest contribution. The proposed approach needs only 20 cycles in steady state, considering the harmonic source pre- and postconnection as input data. In addition, this approach uses few meters to reduce the cost of investment. As a result, it was possible to locate the harmonic source, in the IEEE 34-bus test feeder, with absolute errors less than 1.78 km, using only 3 m. Moreover, the harmonic source identification algorithm reached mean percentage relative errors of less than 3.7%. [ABSTRACT FROM AUTHOR]

The technological upgrade of power utilities to smart metering is a process that can take several years. Meanwhile, smart meters coexist with previous generations of digital and electromechanical power meters. While the smart meters provide high-resolution power measurements, electromechanical meters are typically read by an operator once a month. The coexistence of these two technologies poses the challenge of monitoring non-technical losses (NTL) and fraud where some customers’ consumption is sampled every 15 minutes, while others are sampled once a month. In addition, since companies already have years of monthly historical consumption, it is natural to reflect how the past data can be leveraged to predict and improve NTL on smart grids. This work addresses both problems by proposing a multi-resolution deep learning architecture capable of simultaneously training and predicting input consumption curves sampled 1 a month or every 15 minutes. The proposed algorithms are tested on an extensive data set of users with and without fraudulent behaviors collected from the Uruguayan utility company UTE and on a public access data set with synthetic fraud. Results show that the multi-resolution architecture performs better than algorithms trained for a specific type of meters (i.e., for a particular resolution). [ABSTRACT FROM AUTHOR]