Non-Intrusive Load Monitoring for Residential Appliances with Ultra-Sparse Sample and Real-Time Computation

To achieve the goal of carbon neutrality, the demand for energy saving by the residential sector has witnessed a soaring increase. As a promising paradigm to monitor and manage residential loads, the existing studies on non-intrusive load monitoring (NILM) either lack the scalability of real-world cases or pay unaffordable attention to identification accuracy. This paper proposes a high accuracy, ultra-sparse sample, and real-time computation based NILM method for residential appliances. The method includes three steps: event detection, feature extraction and load identification. A wavelet decomposition based standard deviation multiple (WDSDM) is first proposed to empower event detection of appliances with complex starting processes. The results indicate a false detection rate of only one out of sixteen samples and a time consumption of only 0.77 s. In addition, an essential feature for NILM is introduced, namely the overshoot multiple (which facilitates an average identification improvement from 82.1% to 100% for similar appliances). Moreover, the combination of modified weighted K-nearest neighbors (KNN) and overshoot multiples achieves 100% appliance identification accuracy under a sampling frequency of 6.25 kHz with only one training sample. The proposed method sheds light on highly efficient, user friendly, scalable, and real-world implementable energy management systems in the expectable future.

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Streaming Social Event Detection and Evolution Discovery in Heterogeneous Information Networks

ACM Transactions on Knowledge Discovery from Data ◽

10.1145/3447585 ◽

2021 ◽

Vol 15 (5) ◽

pp. 1-33

Author(s):

Hao Peng ◽

Jianxin Li ◽

Yangqiu Song ◽

Renyu Yang ◽

Rajiv Ranjan ◽

...

Keyword(s):

Real Time ◽

Real World ◽

Event Detection ◽

Social Event ◽

Heterogeneous Information ◽

Heterogeneous Information Networks ◽

Social Events ◽

Meta Path ◽

Social Event Detection ◽

Event Based

Events are happening in real world and real time, which can be planned and organized for occasions, such as social gatherings, festival celebrations, influential meetings, or sports activities. Social media platforms generate a lot of real-time text information regarding public events with different topics. However, mining social events is challenging because events typically exhibit heterogeneous texture and metadata are often ambiguous. In this article, we first design a novel event-based meta-schema to characterize the semantic relatedness of social events and then build an event-based heterogeneous information network (HIN) integrating information from external knowledge base. Second, we propose a novel Pairwise Popularity Graph Convolutional Network, named as PP-GCN, based on weighted meta-path instance similarity and textual semantic representation as inputs, to perform fine-grained social event categorization and learn the optimal weights of meta-paths in different tasks. Third, we propose a streaming social event detection and evolution discovery framework for HINs based on meta-path similarity search, historical information about meta-paths, and heterogeneous DBSCAN clustering method. Comprehensive experiments on real-world streaming social text data are conducted to compare various social event detection and evolution discovery algorithms. Experimental results demonstrate that our proposed framework outperforms other alternative social event detection and evolution discovery techniques.

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Thwarting Nonintrusive Occupancy Detection Attacks from Smart Meters

Security and Communication Networks ◽

10.1155/2017/5350201 ◽

2017 ◽

Vol 2017 ◽

pp. 1-9 ◽

Cited By ~ 2

Author(s):

Dapeng Man ◽

Wu Yang ◽

Shichang Xuan ◽

Xiaojiang Du

Keyword(s):

Power Consumption ◽

Real Time ◽

Real World ◽

Side Effect ◽

Smart Meter ◽

Smart Meters ◽

Occupancy Detection ◽

Load Monitoring ◽

Privacy Issues ◽

Statistical Metrics

Occupancy information is one of the most important privacy issues of a home. Unfortunately, an attacker is able to detect occupancy from smart meter data. The current battery-based load hiding (BLH) methods cannot solve this problem. To thwart occupancy detection attacks, we propose a framework of battery-based schemes to prevent occupancy detection (BPOD). BPOD monitors the power consumption of a home and detects the occupancy in real time. According to the detection result, BPOD modifies those statistical metrics of power consumption, which highly correlate with the occupancy by charging or discharging a battery, creating a delusion that the home is always occupied. We evaluate BPOD in a simulation using several real-world smart meter datasets. Our experiment results show that BPOD effectively prevents the threshold-based and classifier-based occupancy detection attacks. Furthermore, BPOD is also able to prevent nonintrusive appliance load monitoring attacks (NILM) as a side-effect of thwarting detection attacks.

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Real-time gait event detection in a real-world environment using a laser-ranging sensor and gyroscope fusion method

Physiological Measurement ◽

10.1088/1361-6579/aae7ee ◽

2018 ◽

Vol 39 (12) ◽

pp. 125003

Author(s):

Qing Ji ◽

Lilin Yang ◽

Wang Li ◽

Congcong Zhou ◽

Xuesong Ye

Keyword(s):

Real Time ◽

Real World ◽

Event Detection ◽

Laser Ranging ◽

Fusion Method ◽

World Environment

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A Simple and Practical Method for Incorporating Augmented Reality into the Classroom and Laboratory

10.26434/chemrxiv.7137827.v1 ◽

2018 ◽

Author(s):

Kyle Plunkett

Keyword(s):

Organic Chemistry ◽

Augmented Reality ◽

Real Time ◽

Real World ◽

Smart Phone ◽

Practical Method ◽

Virtual Expert ◽

Video Projection

This manuscript provides two demonstrations of how Augmented Reality (AR), which is the projection of virtual information onto a real-world object, can be applied in the classroom and in the laboratory. Using only a smart phone and the free HP Reveal app, content rich AR notecards were prepared. The physical notecards are based on Organic Chemistry I reactions and show only a reagent and substrate. Upon interacting with the HP Reveal app, an AR video projection shows the product of the reaction as well as a real-time, hand-drawn curved-arrow mechanism of how the product is formed. Thirty AR notecards based on common Organic Chemistry I reactions and mechanisms are provided in the Supporting Information and are available for widespread use. In addition, the HP Reveal app was used to create AR video projections onto laboratory instrumentation so that a virtual expert can guide the user during the equipment setup and operation.

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A Novel Rail-Network Hardware Simulator for Embedded System Programming

Electronics ◽

10.3390/electronics10010013 ◽

2020 ◽

Vol 10 (1) ◽

pp. 13

Author(s):

Balaji M ◽

Chandrasekaran M ◽

Vaithiyanathan Dhandapani

Keyword(s):

Embedded Systems ◽

Embedded System ◽

Real Time ◽

Learning Outcomes ◽

Real World ◽

Time Constraints ◽

Network Simulator ◽

Practical Applications ◽

Rail Network ◽

Knowledge Enhancement

A Novel Rail-Network Hardware with simulation facilities is presented in this paper. The hardware is designed to facilitate the learning of application-oriented, logical, real-time programming in an embedded system environment. The platform enables the creation of multiple unique programming scenarios with variability in complexity without any hardware changes. Prior experimental hardware comes with static programming facilities that focus the students’ learning on hardware features and programming basics, leaving them ill-equipped to take up practical applications with more real-time constraints. This hardware complements and completes their learning to help them program real-world embedded systems. The hardware uses LEDs to simulate the movement of trains in a network. The network has train stations, intersections and parking slots where the train movements can be controlled by using a 16-bit Renesas RL78/G13 microcontroller. Additionally, simulating facilities are provided to enable the students to navigate the trains by manual controls using switches and indicators. This helps them get an easy understanding of train navigation functions before taking up programming. The students start with simple tasks and gradually progress to more complicated ones with real-time constraints, on their own. During training, students’ learning outcomes are evaluated by obtaining their feedback and conducting a test at the end to measure their knowledge acquisition during the training. Students’ Knowledge Enhancement Index is originated to measure the knowledge acquired by the students. It is observed that 87% of students have successfully enhanced their knowledge undergoing training with this rail-network simulator.

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A machine learning approach for accurate and real-time DNA sequence identification

BMC Genomics ◽

10.1186/s12864-021-07841-6 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Yiren Wang ◽

Mashari Alangari ◽

Joshua Hihath ◽

Arindam K. Das ◽

M. P. Anantram

Keyword(s):

Real Time ◽

Dna Sequence ◽

Single Molecule ◽

Identification Accuracy ◽

Measuring System ◽

Identification System ◽

Genetic Sequencing ◽

Sequence Identification ◽

Tree Classifier ◽

Single Mismatch

Abstract Background The all-electronic Single Molecule Break Junction (SMBJ) method is an emerging alternative to traditional polymerase chain reaction (PCR) techniques for genetic sequencing and identification. Existing work indicates that the current spectra recorded from SMBJ experimentations contain unique signatures to identify known sequences from a dataset. However, the spectra are typically extremely noisy due to the stochastic and complex interactions between the substrate, sample, environment, and the measuring system, necessitating hundreds or thousands of experimentations to obtain reliable and accurate results. Results This article presents a DNA sequence identification system based on the current spectra of ten short strand sequences, including a pair that differs by a single mismatch. By employing a gradient boosted tree classifier model trained on conductance histograms, we demonstrate that extremely high accuracy, ranging from approximately 96 % for molecules differing by a single mismatch to 99.5 % otherwise, is possible. Further, such accuracy metrics are achievable in near real-time with just twenty or thirty SMBJ measurements instead of hundreds or thousands. We also demonstrate that a tandem classifier architecture, where the first stage is a multiclass classifier and the second stage is a binary classifier, can be employed to boost the single mismatched pair’s identification accuracy to 99.5 %. Conclusions A monolithic classifier, or more generally, a multistage classifier with model specific parameters that depend on experimental current spectra can be used to successfully identify DNA strands.

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ViFin

Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies ◽

10.1145/3448119 ◽

2021 ◽

Vol 5 (1) ◽

pp. 1-25

Author(s):

Wenqiang Chen ◽

Lin Chen ◽

Meiyi Ma ◽

Farshid Salemi Parizi ◽

Shwetak Patel ◽

...

Keyword(s):

Real Time ◽

Real World ◽

Wearable Devices ◽

User Studies ◽

Finger Movement ◽

Writing System ◽

Time System ◽

Real Time System ◽

Input Devices ◽

A New Technique

Wearable devices, such as smartwatches and head-mounted devices (HMD), demand new input devices for a natural, subtle, and easy-to-use way to input commands and text. In this paper, we propose and investigate ViFin, a new technique for input commands and text entry, which harness finger movement induced vibration to track continuous micro finger-level writing with a commodity smartwatch. Inspired by the recurrent neural aligner and transfer learning, ViFin recognizes continuous finger writing, works across different users, and achieves an accuracy of 90% and 91% for recognizing numbers and letters, respectively. We quantify our approach's accuracy through real-time system experiments in different arm positions, writing speeds, and smartwatch position displacements. Finally, a real-time writing system and two user studies on real-world tasks are implemented and assessed.

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OFCOD: On the Fly Clustering Based Outlier Detection Framework

Data ◽

10.3390/data6010001 ◽

2020 ◽

Vol 6 (1) ◽

pp. 1

Author(s):

Ahmed Elmogy ◽

Hamada Rizk ◽

Amany M. Sarhan

Keyword(s):

Data Mining ◽

Image Processing ◽

Intrusion Detection ◽

Real Time ◽

Outlier Detection ◽

Real World ◽

Medical Data ◽

Experimental Results ◽

Real Time Applications ◽

Real World Datasets

In data mining, outlier detection is a major challenge as it has an important role in many applications such as medical data, image processing, fraud detection, intrusion detection, and so forth. An extensive variety of clustering based approaches have been developed to detect outliers. However they are by nature time consuming which restrict their utilization with real-time applications. Furthermore, outlier detection requests are handled one at a time, which means that each request is initiated individually with a particular set of parameters. In this paper, the first clustering based outlier detection framework, (On the Fly Clustering Based Outlier Detection (OFCOD)) is presented. OFCOD enables analysts to effectively find out outliers on time with request even within huge datasets. The proposed framework has been tested and evaluated using two real world datasets with different features and applications; one with 699 records, and another with five millions records. The experimental results show that the performance of the proposed framework outperforms other existing approaches while considering several evaluation metrics.

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Assessing reinforcing versus aversive consequences in a real-time secondhand smoke intervention

Translational Behavioral Medicine ◽

10.1093/tbm/ibab004 ◽

2021 ◽

Author(s):

Vincent Berardi ◽

John Bellettiere ◽

Benjamin Nguyen ◽

Neil E Klepeis ◽

Suzanne C Hughes ◽

...

Keyword(s):

Real Time ◽

Real World ◽

Secondhand Smoke ◽

Relative Effectiveness ◽

Feedback System ◽

Control Group ◽

Sensing Technology ◽

Control Versus ◽

Particle Counts ◽

Post Hoc

Abstract Few studies have examined the relative effectiveness of reinforcing versus aversive consequences at changing behavior in real-world environments. Real-time sensing devices makes it easier to investigate such questions, offering the potential to improve both intervention outcomes and theory. This research aims to describe the development of a real-time, operant theory-based secondhand smoke (SHS) intervention and compare the efficacy of aversive versus aversive plus reinforcement contingency systems. Indoor air particle monitors were placed in the households of 253 smokers for approximately three months. Participants were assigned to a measurement-only control group (N = 129) or one of the following groups: 1.) aversive only (AO, N = 71), with aversive audio/visual consequences triggered by the detection of elevated air particle measurements, or 2.) aversive plus reinforcement (AP, N = 53), with reinforcing consequences contingent on the absence of SHS added to the AO intervention. Residualized change ANCOVA analysis compared particle concentrations over time and across groups. Post-hoc pairwise comparisons were also performed. After controlling for Baseline, Post-Baseline daily particle counts (F = 6.42, p = 0.002), % of time >15,000 counts (F = 7.72, p < 0.001), and daily particle events (F = 4.04, p = 0.02) significantly differed by study group. Nearly all control versus AO/AP pair-wise comparisons were statistically significant. No significant differences were found for AO versus AP groups. The aversive feedback system reduced SHS, but adding reinforcing consequences did not further improve outcomes. The complexity of real-world environments requires the nuances of these two contingency systems continue to be explored, with this study demonstrating that real-time sensing technology can serve as a platform for such research.

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