scholarly journals Non-Intrusive Load Monitoring Based on Deep Pairwise-Supervised Hashing to Detect Unidentified Appliances

Processes ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 505
Author(s):  
Qiang Zhao ◽  
Yao Xu ◽  
Zhenfan Wei ◽  
Yinghua Han
Keyword(s):  
Data Stream ◽  
Deep Neural Networks ◽  
Feature Learning ◽  
Identification Accuracy ◽  
Power System Monitoring ◽  
Hash Code ◽  
Load Monitoring ◽  
New Codes ◽  
Public Datasets ◽  
Hash Codes

Non-intrusive load monitoring (NILM) is a fast developing technique for appliances operation recognition in power system monitoring. At present, most NILM algorithms rely on the assumption that all fluctuations in the data stream are triggered by identified appliances. Therefore, NILM of identifying unidentified appliances is still an open challenge. To pursue a scalable solution to energy monitoring for contemporary unidentified appliances, we propose a voltage-current (V-I) trajectory enabled deep pairwise-supervised hashing (DPSH) method for NILM. DPSH performs simultaneous feature learning and hash-code learning with deep neural networks, which shows higher identification accuracy than a benchmark method. DPSH can generate different hash codes to distinguish identified appliances. For unidentified appliances, it generates completely new codes that are different from codes of multiple identified appliances to distinguish them. Experiments on public datasets show that our method can get better F1-score than the benchmark method to achieve state-of-the-art performance in the identification of unidentified appliances, and this method maintains high sustainability to identify other unidentified appliances through retraining. DPSH can be resilient against appliance changes in the house.

scholarly journals Unsupervised Hashing with Gradient Attention

Symmetry ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 1193
Author(s):  
Shaochen Jiang ◽  
Liejun Wang ◽  
Shuli Cheng ◽  
Anyu Du ◽  
Yongming Li
Keyword(s):  
Gradient Descent ◽  
Network Models ◽  
Image Features ◽  
Similarity Matrix ◽  
Hash Code ◽  
Unsupervised Training ◽  
Public Datasets ◽  
Cosine Distance ◽  
Hash Codes ◽  
Trained Network

The existing learning-based unsupervised hashing method usually uses a pre-trained network to extract features, and then uses the extracted feature vectors to construct a similarity matrix which guides the generation of hash codes through gradient descent. Existing research shows that the algorithm based on gradient descent will cause the hash codes of the paired images to be updated toward each other’s position during the training process. For unsupervised training, this situation will cause large fluctuations in the hash code during training and limit the learning efficiency of the hash code. In this paper, we propose a method named Deep Unsupervised Hashing with Gradient Attention (UHGA) to solve this problem. UHGA mainly includes the following contents: (1) use pre-trained network models to extract image features; (2) calculate the cosine distance of the corresponding features of the pair of images, and construct a similarity matrix through the cosine distance to guide the generation of hash codes; (3) a gradient attention mechanism is added during the training of the hash code to pay attention to the gradient. Experiments on two existing public datasets show that our proposed method can obtain more discriminating hash codes.

scholarly journals CDL-GAN: Contrastive Distance Learning Generative Adversarial Network for Image Generation

2021 ◽  
Vol 11 (4) ◽  
pp. 1380
Author(s):  
Yingbo Zhou ◽  
Pengcheng Zhao ◽  
Weiqin Tong ◽  
Yongxin Zhu
Keyword(s):  
Distance Learning ◽  
Feature Learning ◽  
Image Synthesis ◽  
Image Feature ◽  
Generative Adversarial Networks ◽  
Image Generation ◽  
Generative Adversarial Network ◽  
Feature Representations ◽  
Adversarial Network ◽  
Public Datasets

While Generative Adversarial Networks (GANs) have shown promising performance in image generation, they suffer from numerous issues such as mode collapse and training instability. To stabilize GAN training and improve image synthesis quality with diversity, we propose a simple yet effective approach as Contrastive Distance Learning GAN (CDL-GAN) in this paper. Specifically, we add Consistent Contrastive Distance (CoCD) and Characteristic Contrastive Distance (ChCD) into a principled framework to improve GAN performance. The CoCD explicitly maximizes the ratio of the distance between generated images and the increment between noise vectors to strengthen image feature learning for the generator. The ChCD measures the sampling distance of the encoded images in Euler space to boost feature representations for the discriminator. We model the framework by employing Siamese Network as a module into GANs without any modification on the backbone. Both qualitative and quantitative experiments conducted on three public datasets demonstrate the effectiveness of our method.

scholarly journals A Mobile-Oriented System for Integrity Preserving in Audio Forensics

2019 ◽  
Vol 9 (15) ◽  
pp. 3097 ◽  
Author(s):  
Diego Renza ◽  
Jaime Andres Arango ◽  
Dora Maria Ballesteros
Keyword(s):  
Computational Cost ◽  
Digital Evidence ◽  
Audio Signals ◽  
Audio Forensics ◽  
Audio Recordings ◽  
Hash Code ◽  
Chain Of Custody ◽  
Code Changes ◽  
Hash Codes

This paper addresses a problem in the field of audio forensics. With the aim of providing a solution that helps Chain of Custody (CoC) processes, we propose an integrity verification system that includes capture (mobile based), hash code calculation and cloud storage. When the audio is recorded, a hash code is generated in situ by the capture module (an application), and it is sent immediately to the cloud. Later, the integrity of the audio recording given as evidence can be verified according to the information stored in the cloud. To validate the properties of the proposed scheme, we conducted several tests to evaluate if two different inputs could generate the same hash code (collision resistance), and to evaluate how much the hash code changes when small changes occur in the input (sensitivity analysis). According to the results, all selected audio signals provide different hash codes, and these values are very sensitive to small changes over the recorded audio. On the other hand, in terms of computational cost, less than 2 s per minute of recording are required to calculate the hash code. With the above results, our system is useful to verify the integrity of audio recordings that may be relied on as digital evidence.

scholarly journals Cycle-Consistent Adversarial GAN: The Integration of Adversarial Attack and Defense

2020 ◽  
Vol 2020 ◽  
pp. 1-9 ◽  
Author(s):  
Lingyun Jiang ◽  
Kai Qiao ◽  
Ruoxi Qin ◽  
Linyuan Wang ◽  
Wanting Yu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Deep Neural Networks ◽  
State Of The Art ◽  
Small Magnitude ◽  
Defense Strategies ◽  
Adversarial Examples ◽  
Adversarial Attack ◽  
Public Datasets ◽  
Attack And Defense

In image classification of deep learning, adversarial examples where input is intended to add small magnitude perturbations may mislead deep neural networks (DNNs) to incorrect results, which means DNNs are vulnerable to them. Different attack and defense strategies have been proposed to better research the mechanism of deep learning. However, those researches in these networks are only for one aspect, either an attack or a defense. There is in the improvement of offensive and defensive performance, and it is difficult to promote each other in the same framework. In this paper, we propose Cycle-Consistent Adversarial GAN (CycleAdvGAN) to generate adversarial examples, which can learn and approximate the distribution of the original instances and adversarial examples, especially promoting attackers and defenders to confront each other and improve their ability. For CycleAdvGAN, once the GeneratorA and D are trained, GA can generate adversarial perturbations efficiently for any instance, improving the performance of the existing attack methods, and GD can generate recovery adversarial examples to clean instances, defending against existing attack methods. We apply CycleAdvGAN under semiwhite-box and black-box settings on two public datasets MNIST and CIFAR10. Using the extensive experiments, we show that our method has achieved the state-of-the-art adversarial attack method and also has efficiently improved the defense ability, which made the integration of adversarial attack and defense come true. In addition, it has improved the attack effect only trained on the adversarial dataset generated by any kind of adversarial attack.

scholarly journals Non-Intrusive Load Disaggregation by Linear Classifier Group Considering Multi-Feature Integration

2019 ◽  
Vol 9 (17) ◽  
pp. 3558 ◽  
Author(s):  
Jinying Yu ◽  
Yuchen Gao ◽  
Yuxin Wu ◽  
Dian Jiao ◽  
Chang Su ◽  
...  
Keyword(s):  
Identification Accuracy ◽  
Data Set ◽  
Linear Discriminant ◽  
Practical Applications ◽  
Core Technology ◽  
Open Source Data ◽  
Source Data ◽  
Load Monitoring ◽  
Global Similarity ◽  
Linear Discriminant Classifier

Non-intrusive load monitoring (NILM) is a core technology for demand response (DR) and energy conservation services. Traditional NILM methods are rarely combined with practical applications, and most studies aim to disaggregate the whole loads in a household, which leads to low identification accuracy. In this method, the event detection method is used to obtain the switching event sets of all loads, and the power consumption curves of independent unknown electrical appliances in a period are disaggregated by utilizing comprehensive features. A linear discriminant classifier group based on multi-feature global similarity is used for load identification. The uniqueness of our algorithm is that it designs an event detector based on steady-state segmentation and a linear discriminant classifier group based on multi-feature global similarity. The simulation is carried out on an open source data set. The results demonstrate the effectiveness and high accuracy of the multi-feature integrated classification (MFIC) algorithm by using the state-of-the-art NILM methods as benchmarks.

scholarly journals AVBH: Asymmetric Learning to Hash with Variable Bit Encoding

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Yanduo Ren ◽  
Jiangbo Qian ◽  
Yihong Dong ◽  
Yu Xin ◽  
Huahui Chen
Keyword(s):  
Low Frequency ◽  
Large Data ◽  
Data Retrieval ◽  
Query Point ◽  
Compact Binary ◽  
Hash Code ◽  
Asymmetric Learning ◽  
Learning To Hash ◽  
Public Datasets ◽  
The Cost

Nearest neighbour search (NNS) is the core of large data retrieval. Learning to hash is an effective way to solve the problems by representing high-dimensional data into a compact binary code. However, existing learning to hash methods needs long bit encoding to ensure the accuracy of query, and long bit encoding brings large cost of storage, which severely restricts the long bit encoding in the application of big data. An asymmetric learning to hash with variable bit encoding algorithm (AVBH) is proposed to solve the problem. The AVBH hash algorithm uses two types of hash mapping functions to encode the dataset and the query set into different length bits. For datasets, the hash code frequencies of datasets after random Fourier feature encoding are statistically analysed. The hash code with high frequency is compressed into a longer coding representation, and the hash code with low frequency is compressed into a shorter coding representation. The query point is quantized to a long bit hash code and compared with the same length cascade concatenated data point. Experiments on public datasets show that the proposed algorithm effectively reduces the cost of storage and improves the accuracy of query.

scholarly journals Nonintrusive Appliance Load Monitoring: An Overview, Laboratory Test Results and Research Directions

Sensors ◽  
2019 ◽  
Vol 19 (16) ◽  
pp. 3621 ◽  
Author(s):  
Augustyn Wójcik ◽  
Robert Łukaszewski ◽  
Ryszard Kowalik ◽  
Wiesław Winiecki
Keyword(s):  
Smart Home ◽  
State Of The Art ◽  
Measurement Data ◽  
Electricity Consumption ◽  
Identification Accuracy ◽  
Test Results ◽  
Research Directions ◽  
Laboratory Test Results ◽  
New Directions ◽  
Load Monitoring

Nonintrusive appliance load monitoring (NIALM) allows disaggregation of total electricity consumption into particular appliances in domestic or industrial environments. NIALM systems operation is based on processing of electrical signals acquired at one point of a monitored area. The main objective of this paper was to present the state-of-the-art in NIALM technologies for the smart home. This paper focuses on sensors and measurement methods. Different intelligent algorithms for processing signals have been presented. Identification accuracy for an actual set of appliances has been compared. This article depicts the architecture of a unique NIALM laboratory, presented in detail. Results of developed NIALM methods exploiting different measurement data are discussed and compared to known methods. New directions of NIALM research are proposed.

scholarly journals Self-Supervised Learning for Generalizable Out-of-Distribution Detection

2020 ◽  
Vol 34 (04) ◽  
pp. 5216-5223 ◽  
Author(s):  
Sina Mohseni ◽  
Mandar Pitale ◽  
JBS Yadawa ◽  
Zhangyang Wang
Keyword(s):  
Neural Networks ◽  
Autonomous Vehicles ◽  
Deep Neural Networks ◽  
State Of The Art ◽  
Feature Learning ◽  
Detection Methods ◽  
Training Set ◽  
Safety Critical ◽  
Multiple Image ◽  
A New Technique

The real-world deployment of Deep Neural Networks (DNNs) in safety-critical applications such as autonomous vehicles needs to address a variety of DNNs' vulnerabilities, one of which being detecting and rejecting out-of-distribution outliers that might result in unpredictable fatal errors. We propose a new technique relying on self-supervision for generalizable out-of-distribution (OOD) feature learning and rejecting those samples at the inference time. Our technique does not need to pre-know the distribution of targeted OOD samples and incur no extra overheads compared to other methods. We perform multiple image classification experiments and observe our technique to perform favorably against state-of-the-art OOD detection methods. Interestingly, we witness that our method also reduces in-distribution classification risk via rejecting samples near the boundaries of the training set distribution.

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