Articles de revues sur le sujet « Appliance classification »
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1
Faustine, Anthony, et Lucas Pereira. « Improved Appliance Classification in Non-Intrusive Load Monitoring Using Weighted Recurrence Graph and Convolutional Neural Networks ». Energies 13, no 13 (1 juillet 2020) : 3374. http://dx.doi.org/10.3390/en13133374.
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Appliance recognition is one of the vital sub-tasks of NILM in which a machine learning classier is used to detect and recognize active appliances from power measurements. The performance of the appliance classifier highly depends on the signal features used to characterize the loads. Recently, different appliance features derived from the voltage–current (V–I) waveforms have been extensively used to describe appliances. However, the performance of V–I-based approaches is still unsatisfactory as it is still not distinctive enough to recognize devices that fall into the same category. Instead, we propose an appliance recognition method utilizing the recurrence graph (RG) technique and convolutional neural networks (CNNs). We introduce the weighted recurrent graph (WRG) generation that, given one-cycle current and voltage, produces an image-like representation with more values than the binary output created by RG. Experimental results on three different sub-metered datasets show that the proposed WRG-based image representation provides superior feature representation and, therefore, improves classification performance compared to V–I-based features.
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Jiang, Lei, Jiaming Li, Suhuai Luo, Sam West et Glenn Platt. « Power Load Event Detection and Classification Based on Edge Symbol Analysis and Support Vector Machine ». Applied Computational Intelligence and Soft Computing 2012 (2012) : 1–10. http://dx.doi.org/10.1155/2012/742461.
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Energy signature analysis of power appliance is the core of nonintrusive load monitoring (NILM) where the detailed data of the appliances used in houses are obtained by analyzing changes in the voltage and current. This paper focuses on developing an automatic power load event detection and appliance classification based on machine learning. In power load event detection, the paper presents a new transient detection algorithm. By turn-on and turn-off transient waveforms analysis, it can accurately detect the edge point when a device is switched on or switched off. The proposed load classification technique can identify different power appliances with improved recognition accuracy and computational speed. The load classification method is composed of two processes including frequency feature analysis and support vector machine. The experimental results indicated that the incorporation of the new edge detection and turn-on and turn-off transient signature analysis into NILM revealed more information than traditional NILM methods. The load classification method has achieved more than ninety percent recognition rate.
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Matindife, Liston, Yanxia Sun et Zenghui Wang. « A Machine-Learning Based Nonintrusive Smart Home Appliance Status Recognition ». Mathematical Problems in Engineering 2020 (18 septembre 2020) : 1–21. http://dx.doi.org/10.1155/2020/9356165.
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In a smart home, the nonintrusive load monitoring recognition scheme normally achieves high appliance recognition performance in the case where the appliance signals have widely varying power levels and signature characteristics. However, it becomes more difficult to recognize appliances with equal or very close power specifications, often with almost identical signature characteristics. In literature, complex methods based on transient event detection and multiple classifiers that operate on different hand crafted features of the signal have been proposed to tackle this issue. In this paper, we propose a deep learning approach that dispenses with the complex transient event detection and hand crafting of signal features to provide high performance recognition of close tolerance appliances. The appliance classification is premised on the deep multilayer perceptron having three appliance signal parameters as input to increase the number of trainable samples and hence accuracy. In the case where we have limited data, we implement a transfer learning-based appliance classification strategy. With the view of obtaining an appropriate high performing disaggregation deep learning network for the said problem, we explore individually three deep learning disaggregation algorithms based on the multiple parallel structure convolutional neural networks, the recurrent neural network with parallel dense layers for a shared input, and the hybrid convolutional recurrent neural network. We disaggregate a total of three signal parameters per appliance in each case. To evaluate the performance of the proposed method, some simulations and comparisons have been carried out, and the results show that the proposed method can achieve promising performance.
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Kim, Hwan, et Sungsu Lim. « Temporal Patternization of Power Signatures for Appliance Classification in NILM ». Energies 14, no 10 (19 mai 2021) : 2931. http://dx.doi.org/10.3390/en14102931.
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Non-Intrusive Load Monitoring (NILM) techniques are effective for managing energy and for addressing imbalances between the energy demand and supply. Various studies based on deep learning have reported the classification of appliances from aggregated power signals. In this paper, we propose a novel approach called a temporal bar graph, which patternizes the operational status of the appliances and time in order to extract the inherent features from the aggregated power signals for efficient load identification. To verify the effectiveness of the proposed method, a temporal bar graph was applied to the total power and tested on three state-of-the-art deep learning techniques that previously exhibited superior performance in image classification tasks—namely, Extreme Inception (Xception), Very Deep One Dimensional CNN (VDOCNN), and Concatenate-DenseNet121. The UK Domestic Appliance-Level Electricity (UK-DALE) and Tracebase datasets were used for our experiments. The results of the five-appliance case demonstrated that the accuracy and F1-score increased by 19.55% and 21.43%, respectively, on VDOCNN, and by 33.22% and 35.71%, respectively, on Xception. A performance comparison with the state-of-the-art deep learning methods and image-based spectrogram approach was conducted.
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Kulkarni, Anand Sunil, Cindy K. Harnett et Karla Conn Welch. « EMF Signature for Appliance Classification ». IEEE Sensors Journal 15, no 6 (juin 2015) : 3573–81. http://dx.doi.org/10.1109/jsen.2014.2379113.
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Baptista, Darío, Sheikh Mostafa, Lucas Pereira, Leonel Sousa et Fernando Morgado-Dias. « Implementation Strategy of Convolution Neural Networks on Field Programmable Gate Arrays for Appliance Classification Using the Voltage and Current (V-I) Trajectory ». Energies 11, no 9 (17 septembre 2018) : 2460. http://dx.doi.org/10.3390/en11092460.
Texte intégralRésumé :
Specific information about types of appliances and their use in a specific time window could help determining in details the electrical energy consumption information. However, conventional main power meters fail to provide any specific information. One of the best ways to solve these problems is through non-intrusive load monitoring, which is cheaper and easier to implement than other methods. However, developing a classifier for deducing what kind of appliances are used at home is a difficult assignment, because the system should identify the appliance as fast as possible with a higher degree of certainty. To achieve all these requirements, a convolution neural network implemented on hardware was used to identify the appliance through the voltage and current (V-I) trajectory. For the implementation on hardware, a field programmable gate array (FPGA) was used to exploit processing parallelism in order to achieve optimal performance. To validate the design, a publicly available Plug Load Appliance Identification Dataset (PLAID), constituted by 11 different appliances, has been used. The overall average F-score achieved using this classifier is 78.16% for the PLAID 1 dataset. The convolution neural network implemented on hardware has a processing time of approximately 5.7 ms and a power consumption of 1.868 W.
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Faustine, Anthony, et Lucas Pereira. « Multi-Label Learning for Appliance Recognition in NILM Using Fryze-Current Decomposition and Convolutional Neural Network ». Energies 13, no 16 (11 août 2020) : 4154. http://dx.doi.org/10.3390/en13164154.
Texte intégralRésumé :
The advance in energy-sensing and smart-meter technologies have motivated the use of a Non-Intrusive Load Monitoring (NILM), a data-driven technique that recognizes active end-use appliances by analyzing the data streams coming from these devices. NILM offers an electricity consumption pattern of individual loads at consumer premises, which is crucial in the design of energy efficiency and energy demand management strategies in buildings. Appliance classification, also known as load identification is an essential sub-task for identifying the type and status of an unknown load from appliance features extracted from the aggregate power signal. Most of the existing work for appliance recognition in NILM uses a single-label learning strategy which, assumes only one appliance is active at a time. This assumption ignores the fact that multiple devices can be active simultaneously and requires a perfect event detector to recognize the appliance. In this paper proposes the Convolutional Neural Network (CNN)-based multi-label learning approach, which links multiple loads to an observed aggregate current signal. Our approach applies the Fryze power theory to decompose the current features into active and non-active components and use the Euclidean distance similarity function to transform the decomposed current into an image-like representation which, is used as input to the CNN. Experimental results suggest that the proposed approach is sufficient for recognizing multiple appliances from aggregated measurements.
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Matindife, L., Y. Sun et Z. Wang. « Few-Shot Learning for Image-Based Nonintrusive Appliance Signal Recognition ». Computational Intelligence and Neuroscience 2022 (23 août 2022) : 1–14. http://dx.doi.org/10.1155/2022/2142935.
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In this article, we present the recognition of nonintrusive disaggregated appliance signals through a reduced dataset computer vision deep learning approach. Deep learning data requirements are costly in terms of acquisition time, storage memory requirements, computation time, and dynamic memory usage. We develop our recognition strategy on Siamese and prototypical reduced data few-shot classification algorithms. Siamese networks address the 1-shot recognition well. Appliance activation periods vary considerably, and this can result in imbalance in the number of appliance-specific generated signal images. Prototypical networks address the problem of data imbalance in training. By first carrying out a similarity test on the entire dataset, we establish the quality of our data before input into the deep learning algorithms. The results give acceptable performance and show the promise of few-shot learning in recognizing appliances in the nonintrusive load-monitoring scheme for very limited data samples.
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Azizi, Elnaz, Mohammad T. H. Beheshti et Sadegh Bolouki. « Event Matching Classification Method for Non-Intrusive Load Monitoring ». Sustainability 13, no 2 (12 janvier 2021) : 693. http://dx.doi.org/10.3390/su13020693.
Texte intégralRésumé :
Nowadays, energy management aims to propose different strategies to utilize available energy resources, resulting in sustainability of energy systems and development of smart sustainable cities. As an effective approach toward energy management, non-intrusive load monitoring (NILM), aims to infer the power profiles of appliances from the aggregated power signal via purely analytical methods. Existing NILM methods are susceptible to various issues such as the noise and transient spikes of the power signal, overshoots at the mode transition times, close consumption values by different appliances, and unavailability of a large training dataset. This paper proposes a novel event-based NILM classification algorithm mitigating these issues. The proposed algorithm (i) filters power signals and accurately detects all events; (ii) extracts specific features of appliances, such as operation modes and their respective power intervals, from their power signals in the training dataset; and (iii) labels with high accuracy each detected event of the aggregated signal with an appliance mode transition. The algorithm is validated using REDD with the results showing its effectiveness to accurately disaggregate low-frequency measured data by existing smart meters.
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Massidda, Luca, Marino Marrocu et Simone Manca. « Non-Intrusive Load Disaggregation by Convolutional Neural Network and Multilabel Classification ». Applied Sciences 10, no 4 (21 février 2020) : 1454. http://dx.doi.org/10.3390/app10041454.
Texte intégralRésumé :
Non-intrusive load monitoring (NILM) is the main method used to monitor the energy footprint of a residential building and disaggregate total electrical usage into appliance-related signals. The most common disaggregation algorithms are based on the Hidden Markov Model, while solutions based on deep neural networks have recently caught the attention of researchers. In this work we address the problem through the recognition of the state of activation of the appliances using a fully convolutional deep neural network, borrowing some techniques used in the semantic segmentation of images and multilabel classification. This approach has allowed obtaining high performances not only in the recognition of the activation state of the domestic appliances but also in the estimation of their consumptions, improving the state of the art for a reference dataset.
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Yan, Da, Yuan Jin, Hongsan Sun, Bing Dong, Zi Ye, Zhaoxuan Li et Yanping Yuan. « Household appliance recognition through a Bayes classification model ». Sustainable Cities and Society 46 (avril 2019) : 101393. http://dx.doi.org/10.1016/j.scs.2018.12.021.
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Sadeghianpourhamami, N., J. Ruyssinck, D. Deschrijver, T. Dhaene et C. Develder. « Comprehensive feature selection for appliance classification in NILM ». Energy and Buildings 151 (septembre 2017) : 98–106. http://dx.doi.org/10.1016/j.enbuild.2017.06.042.
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Wójcik, Augustyn, Piotr Bilski, Robert Łukaszewski, Krzysztof Dowalla et Ryszard Kowalik. « Identification of the State of Electrical Appliances with the Use of a Pulse Signal Generator ». Energies 14, no 3 (28 janvier 2021) : 673. http://dx.doi.org/10.3390/en14030673.
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The paper presents the novel HF-GEN method for determining the characteristics of Electrical Appliance (EA) operating in the end-user environment. The method includes a measurement system that uses a pulse signal generator to improve the quality of EA identification. Its structure and the principles of operation are presented. A method for determining the characteristics of the current signals’ transients using the cross-correlation is described. Its result is the appliance signature with a set of features characterizing its state of operation. The quality of the obtained signature is evaluated in the standard classification task with the aim of identifying the particular appliance’s state based on the analysis of features by three independent algorithms. Experimental results for 15 EAs categories show the usefulness of the proposed approach.
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De Baets, Leen, Joeri Ruyssinck, Chris Develder, Tom Dhaene et Dirk Deschrijver. « Appliance classification using VI trajectories and convolutional neural networks ». Energy and Buildings 158 (janvier 2018) : 32–36. http://dx.doi.org/10.1016/j.enbuild.2017.09.087.
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Hegde, Rakshith M. D., et Harish H. Kenchannavar. « A Survey on Predicting Resident Intentions Using Contextual Modalities in Smart Home ». International Journal of Advanced Pervasive and Ubiquitous Computing 11, no 4 (octobre 2019) : 44–59. http://dx.doi.org/10.4018/ijapuc.2019100104.
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The Smart Home is an environment that enables the resident to interact with home appliances which provide resident intended services. In recent years, predicting resident intention based on the contextual modalities like activity, speech, emotion, object affordances, and physiological parameters have increased importance in the field of pervasive computing. Contextual modality is the feature through which resident interacts with the home appliances like TVs, lights, doors, fans, etc. These modalities assist the appliances in predicting the resident intentions making them recommend resident intended services like opening and closing doors, turning on and off televisions, lights, and fans. Resident-appliance interaction can be achieved by embedding artificial intelligence-based machine learning algorithms into the appliances. Recent research works on the contextual modalities and associated machine learning algorithms which are required to build resident intention prediction system have been surveyed in this article. A classification taxonomy of contextual modalities is also discussed.
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Singh, Shikha, Emilie Chouzenoux, Giovanni Chierchia et Angshul Majumdar. « Multi-label Deep Convolutional Transform Learning for Non-intrusive Load Monitoring ». ACM Transactions on Knowledge Discovery from Data 16, no 5 (31 octobre 2022) : 1–6. http://dx.doi.org/10.1145/3502729.
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The objective of this letter is to propose a novel computational method to learn the state of an appliance (ON / OFF) given the aggregate power consumption recorded by the smart-meter. We formulate a multi-label classification problem where the classes correspond to the appliances. The proposed approach is based on our recently introduced framework of convolutional transform learning. We propose a deep supervised version of it relying on an original multi-label cost. Comparisons with state-of-the-art techniques show that our proposed method improves over the benchmarks on popular non-intrusive load monitoring datasets.
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Aina, Segun, Samuel Dayo Okegbile, Perfect Makanju et Adeniran Ishola Oluwaranti. « An Architectural Framework for Facebook Messenger Chatbot Enabled Home Appliance Control System ». International Journal of Ambient Computing and Intelligence 10, no 2 (avril 2019) : 18–33. http://dx.doi.org/10.4018/ijaci.2019040102.
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The need to remotely control home appliances is an important aspect of home automation and is now receiving lot of attentions in the literature. The works so far are still at a development level making further research necessary. This article presents a framework for chatbot-controlled home appliance control system and was implemented by programming a Raspberry Pi using the Python language while the chatbot server was also implemented using a Node.js on JavaScript. The Raspberry Pi was connected to the chatbot server via Wi-Fi using a websockets protocol. The chatbot server is linked to Facebook Messenger using the Messenger Application Protocol Interface. Messages received at the chatbot server are analyzed with RasaNLU to classify the user's intention and extract necessary information which are sent over websocket to the connected Raspberry pi. The system was evaluated using control precision and percentage correct classification with both producing a significant level of acceptance. This work produced a Facebook Messenger chatbot-based framework capable of controlling Home Appliances remotely.
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Nguyen, Vanh Khuyen, Wei Emma Zhang et Adnan Mahmood. « Semi-supervised Intrusive Appliance Load Monitoring in Smart Energy Monitoring System ». ACM Transactions on Sensor Networks 17, no 3 (21 juin 2021) : 1–20. http://dx.doi.org/10.1145/3448415.
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Intrusive Load Monitoring (ILM) is a method to measure and collect the energy consumption data of individual appliances via smart plugs or smart sockets. A major challenge of ILM is automatic appliance identification, in which the system is able to determine automatically a label of the active appliance connected to the smart device. Existing ILM techniques depend on labels input by end-users and are usually under the supervised learning scheme. However, in reality, end-users labeling is laboriously rendering insufficient training data to fit the supervised learning models. In this work, we propose a semi-supervised learning (SSL) method that leverages rich signals from the unlabeled dataset and jointly learns the classification loss for the labeled dataset and the consistency training loss for unlabeled dataset. The samples fit into consistency learning are generated by a transformation that is built upon weighted versions of DTW Barycenter Averaging algorithm. The work is inspired by two recent advanced works in SSL in computer vision and combines the advantages of the two. We evaluate our method on the dataset collected from our developed Internet-of-Things based energy monitoring system in a smart home environment. We also examine the method’s performances on 10 benchmark datasets. As a result, the proposed method outperforms other methods on our smart appliance datasets and most of the benchmarks datasets, while it shows competitive results on the rest datasets.
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Hur, Cheong-Hwan, Han-Eum Lee, Young-Joo Kim et Sang-Gil Kang. « Semi-Supervised Domain Adaptation for Multi-Label Classification on Nonintrusive Load Monitoring ». Sensors 22, no 15 (4 août 2022) : 5838. http://dx.doi.org/10.3390/s22155838.
Texte intégralRésumé :
Nonintrusive load monitoring (NILM) is a technology that analyzes the load consumption and usage of an appliance from the total load. NILM is becoming increasingly important because residential and commercial power consumption account for about 60% of global energy consumption. Deep neural network-based NILM studies have increased rapidly as hardware computation costs have decreased. A significant amount of labeled data is required to train deep neural networks. However, installing smart meters on each appliance of all households for data collection requires the cost of geometric series. Therefore, it is urgent to detect whether the appliance is used from the total load without installing a separate smart meter. In other words, domain adaptation research, which can interpret the huge complexity of data and generalize information from various environments, has become a major challenge for NILM. In this research, we optimize domain adaptation by employing techniques such as robust knowledge distillation based on teacher–student structure, reduced complexity of feature distribution based on gkMMD, TCN-based feature extraction, and pseudo-labeling-based domain stabilization. In the experiments, we down-sample the UK-DALE and REDD datasets as in the real environment, and then verify the proposed model in various cases and discuss the results.
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Wang, Zeyu, et Ravi Srinivasan. « Classification of Household Appliance Operation Cycles : A Case-Study Approach ». Energies 8, no 9 (22 septembre 2015) : 10522–36. http://dx.doi.org/10.3390/en80910522.
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Shafqat, Wafa, Kyu-Tae Lee et Do-Hyeun Kim. « A Comprehensive Predictive-Learning Framework for Optimal Scheduling and Control of Smart Home Appliances Based on User and Appliance Classification ». Sensors 23, no 1 (23 décembre 2022) : 127. http://dx.doi.org/10.3390/s23010127.
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Energy consumption is increasing daily, and with that comes a continuous increase in energy costs. Predicting future energy consumption and building an effective energy management system for smart homes has become essential for many industrialists to solve the problem of energy wastage. Machine learning has shown significant outcomes in the field of energy management systems. This paper presents a comprehensive predictive-learning based framework for smart home energy management systems. We propose five modules: classification, prediction, optimization, scheduling, and controllers. In the classification module, we classify the category of users and appliances by using k-means clustering and support vector machine based classification. We predict the future energy consumption and energy cost for each user category using long-term memory in the prediction module. We define objective functions for optimization and use grey wolf optimization and particle swarm optimization for scheduling appliances. For each case, we give priority to user preferences and indoor and outdoor environmental conditions. We define control rules to control the usage of appliances according to the schedule while prioritizing user preferences and minimizing energy consumption and cost. We perform experiments to evaluate the performance of our proposed methodology, and the results show that our proposed approach significantly reduces energy cost while providing an optimized solution for energy consumption that prioritizes user preferences and considers both indoor and outdoor environmental factors.
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Hu, Yu-Chen, Yu-Hsiu Lin et Harinahalli Lokesh Gururaj. « Partitional Clustering-Hybridized Neuro-Fuzzy Classification Evolved through Parallel Evolutionary Computing and Applied to Energy Decomposition for Demand-Side Management in a Smart Home ». Processes 9, no 9 (29 août 2021) : 1539. http://dx.doi.org/10.3390/pr9091539.
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The key advantage of smart meters over rotating-disc meters is their ability to transmit electric energy consumption data to power utilities’ remote data centers. Besides enabling the automated collection of consumers’ electric energy consumption data for billing purposes, data gathered by smart meters and analyzed through Artificial Intelligence (AI) make the realization of consumer-centric use cases possible. A smart meter installed in a domestic sector of an electrical grid and used for the realization of consumer-centric use cases is located at the entry point of a household/building’s electrical grid connection and can gather composite/circuit-level electric energy consumption data. However, it is not able to decompose its measured circuit-level electric energy consumption into appliance-level electric energy consumption. In this research, we present an AI model, a neuro-fuzzy classifier integrated with partitional clustering and metaheuristically optimized through parallel-computing-accelerated evolutionary computing, that performs energy decomposition on smart meter data in residential demand-side management, where a publicly available UK-DALE (UK Domestic Appliance-Level Electricity) dataset is used to experimentally test the presented model to classify the On/Off status of monitored electrical appliances. As shown in this research, the presented AI model is effective at providing energy decomposition for domestic consumers. Further, energy decomposition can be provided for industrial as well as commercial consumers.
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Chahine, Khaled. « Towards automatic setup of non intrusive appliance load monitoring – feature extraction and clustering ». International Journal of Electrical and Computer Engineering (IJECE) 9, no 2 (1 avril 2019) : 1002. http://dx.doi.org/10.11591/ijece.v9i2.pp1002-1011.
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<p style="-qt-block-indent: 0; text-indent: 0px; margin: 0px;">Given climate change concerns and incessantly increasing energy demands of the present time, improving energy efficiency becomes of significant environmental and economic impact. Monitoring household electrical consumption through a non-intrusive appliance load monitoring (NIALM) system achieves significant efficiency improvement by providing appliance-level energy consumption and relaying this information back to the user. This paper focuses on feature extraction and clustering, which constitute two of the four modules of the proposed automatic-setup NIALM system, the other two being labeling and classification. The feature extraction module applies the Estimation of Signal Parameters via Rotational Invariance Techniques (ESPRIT), a well-known parametric estimation technique, to the drawn electric current. The result is a compact representation of the signal in terms of complex numbers referred to as poles and residues. These complex numbers are then used to determine a feature vector consisting of the contribution of the fundamental, the third and the fifth harmonic currents to the maximum of the total load current. Once a signature is extracted, the clustering module applies distance-based rules inferred off-line from various databases and decides either to create a new class out of the new signature or to discard it and increase the count of an existing signature. As a result, the feature space is clustered without the a priori knowledge of the number of appliances into singleton clusters. Results obtained from a set of appliances indicate that these two modules succeed in creating an unlabeled database of signatures.</p>
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Liu, Hui, Haiping Wu et Chenming Yu. « A hybrid model for appliance classification based on time series features ». Energy and Buildings 196 (août 2019) : 112–23. http://dx.doi.org/10.1016/j.enbuild.2019.05.028.
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Cannas, Barbara, Sara Carcangiu, Daniele Carta, Alessandra Fanni et Carlo Muscas. « Selection of Features Based on Electric Power Quantities for Non-Intrusive Load Monitoring ». Applied Sciences 11, no 2 (7 janvier 2021) : 533. http://dx.doi.org/10.3390/app11020533.
Texte intégralRésumé :
Non-intrusive load monitoring (NILM) is a process of determining the operating states and the energy consumption of single electric devices using a single energy meter providing aggregate load measurements. Due to the large spread of power electronic-based and nonlinear devices connected to the network, the time signals of both voltage and current are typically non-sinusoidal. The effectiveness of a NILM algorithm strongly depends on determining a set of discriminative features. In this paper, voltage and current signals were combined to define, according to the definitions provided in Standard IEEE 1459, different power quantities, that can be used to distinguish different types of appliance. Multi-layer perceptron (MLP) classifiers were trained to solve the appliance detection problem as a multi-class event classification problem, varying the electric features in input. This allowed to select an optimal set of features guarantying good classification performance in identifying typical electric loads.
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Cannas, Barbara, Sara Carcangiu, Daniele Carta, Alessandra Fanni et Carlo Muscas. « Selection of Features Based on Electric Power Quantities for Non-Intrusive Load Monitoring ». Applied Sciences 11, no 2 (7 janvier 2021) : 533. http://dx.doi.org/10.3390/app11020533.
Texte intégralRésumé :
Non-intrusive load monitoring (NILM) is a process of determining the operating states and the energy consumption of single electric devices using a single energy meter providing aggregate load measurements. Due to the large spread of power electronic-based and nonlinear devices connected to the network, the time signals of both voltage and current are typically non-sinusoidal. The effectiveness of a NILM algorithm strongly depends on determining a set of discriminative features. In this paper, voltage and current signals were combined to define, according to the definitions provided in Standard IEEE 1459, different power quantities, that can be used to distinguish different types of appliance. Multi-layer perceptron (MLP) classifiers were trained to solve the appliance detection problem as a multi-class event classification problem, varying the electric features in input. This allowed to select an optimal set of features guarantying good classification performance in identifying typical electric loads.
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Kim, Jihyun, Thi-Thu-Huong Le et Howon Kim. « Nonintrusive Load Monitoring Based on Advanced Deep Learning and Novel Signature ». Computational Intelligence and Neuroscience 2017 (2017) : 1–22. http://dx.doi.org/10.1155/2017/4216281.
Texte intégralRésumé :
Monitoring electricity consumption in the home is an important way to help reduce energy usage. Nonintrusive Load Monitoring (NILM) is existing technique which helps us monitor electricity consumption effectively and costly. NILM is a promising approach to obtain estimates of the electrical power consumption of individual appliances from aggregate measurements of voltage and/or current in the distribution system. Among the previous studies, Hidden Markov Model (HMM) based models have been studied very much. However, increasing appliances, multistate of appliances, and similar power consumption of appliances are three big issues in NILM recently. In this paper, we address these problems through providing our contributions as follows. First, we proposed state-of-the-art energy disaggregation based on Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) model and additional advanced deep learning. Second, we proposed a novel signature to improve classification performance of the proposed model in multistate appliance case. We applied the proposed model on two datasets such as UK-DALE and REDD. Via our experimental results, we have confirmed that our model outperforms the advanced model. Thus, we show that our combination between advanced deep learning and novel signature can be a robust solution to overcome NILM’s issues and improve the performance of load identification.
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Piccialli, Veronica, et Antonio M. Sudoso. « Improving Non-Intrusive Load Disaggregation through an Attention-Based Deep Neural Network ». Energies 14, no 4 (5 février 2021) : 847. http://dx.doi.org/10.3390/en14040847.
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Energy disaggregation, known in the literature as Non-Intrusive Load Monitoring (NILM), is the task of inferring the power demand of the individual appliances given the aggregate power demand recorded by a single smart meter which monitors multiple appliances. In this paper, we propose a deep neural network that combines a regression subnetwork with a classification subnetwork for solving the NILM problem. Specifically, we improve the generalization capability of the overall architecture by including an encoder–decoder with a tailored attention mechanism in the regression subnetwork. The attention mechanism is inspired by the temporal attention that has been successfully applied in neural machine translation, text summarization, and speech recognition. The experiments conducted on two publicly available datasets—REDD and UK-DALE—show that our proposed deep neural network outperforms the state-of-the-art in all the considered experimental conditions. We also show that modeling attention translates into the network’s ability to correctly detect the turning on or off an appliance and to locate signal sections with high power consumption, which are of extreme interest in the field of energy disaggregation.
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Fedorova, E. A., L. E. Khrustova et D. V. Chekrizov. « Industry characteristic of bankruptcy prediction models appliance ». Strategic decisions and risk management, no 1 (25 mai 2018) : 64–71. http://dx.doi.org/10.17747/2078-8886-2018-1-64-71.
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The aim of the research is to develop the methodology of bankruptcy prediction applying the specified statutory values of the existing models with a glance to company’s industry and developing the author’s prediction model. Initially authors estimated the forecast accuracy of the existing models for the enterprises of 8 industries. Using CART (Classification And Regression Tree) methodology the original statutory values of the models were specified for every industry under research. The calculated statutory values demonstrated the high level of prediction accuracy and balanced the indicators of accuracy for bankrupt and non-bankrupt companies. The indicators with the maximum level of significance for bankruptcy prediction were selected from all the models. They formed a basis for a new developed model, which has demonstrated the high level of prediction accuracy on a sample under research. The statutory values for the new model were also developed.The implementation of the research’s results will increase the efficiency of bankruptcy prediction and low the number of bankrupt companies.
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Matviichuk, A. D. « BICYCLES CLASSIFICATION APPLIANCE BY JUDICIAL EXPERT DURING CARRYING OUT THE TRADE EXAMINATION ». Uzhhorod National University Herald. Series : Law 58, no 2 (2019) : 151–55. http://dx.doi.org/10.32782/2307-3322.58-2.33.
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Jodeh, Diana S., Stephen Ruso, Randy Feldman, Ernesto Ruas et S. Alex Rottgers. « Clinical Outcomes Utilizing a “Modified Latham” Appliance for Presurgical Infant Orthopedics in Patients With Unilateral Complete Cleft Lip and Palate ». Cleft Palate-Craniofacial Journal 56, no 7 (9 décembre 2018) : 929–35. http://dx.doi.org/10.1177/1055665618816892.
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Presurgical infant orthopedic manipulation is utilized prior to cleft lip/nasal repair to facilitate a gingivoperiosteoplasty (GPP) and primary nasolabial repairs. The Latham dentomaxillary advancement appliance uses a screw that must be tightened daily to approximate the cleft segments in unilateral complete clefts. Our cleft center has been utilizing a “modified Latham” appliance since 1987, including an orthodontic elastic power chain to close the gap in a shorter amount of time. We performed a retrospective chart review of all patients undergoing treatment at Johns Hopkins All Children's Hospital (JHACH) with a unilateral complete cleft lip and palate between 1987 and 2017. Patients were identified by the International Classification of Diseases, Ninth Revision code (749.21). The majority of the patients represent the experience of the senior authors (E.R. and R.F.). Two hundred and eighty-one patients with unilateral complete cleft lip/palate were identified. Seventy-five patients were treated with a “modified Latham” appliance prior to their lip repair. The “modified Latham” appliance remained in place on average 20.6 days (range: 4-82), and average hospital stay after placement was 1.18 days. Nearly 96% of patients underwent a successful GPP at the time of nasolabial repair. Modification of the Latham appliance by utilizing an elastic power chain and eliminating the screw allows rapid closure of the alveolar cleft with limited need for adjustments and outpatient visits. Direct approximation of the palatal segments allows successful completion of a GPP in 95.9% of patients with limited dissection.
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Yoana, Y., Eka Chemiawan et Arlette Suzy Setiawan. « Dentoalveolar changes in post-twin block appliance orthodontic treatment class II dentoskeletal malocclusion ». Dental Journal (Majalah Kedokteran Gigi) 50, no 4 (30 décembre 2017) : 211. http://dx.doi.org/10.20473/j.djmkg.v50.i4.p211-215.
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Background: The analysis of cephalometric radiographs provides information about facial skeletal structures, jaw bone-base relationships, incisive-axial inclination relationships, soft tissue morphology, growth direction and pattern, malocclusion classification and the limitations of orthodontic treatments. In class II malocclusion, the mesiobuccal cusp of the permanent maxillary first molar rests between the first mandibular molar and the second premolar. A twin block appliance is recommended to treat Class II dentoskeletal malocclusion with retrognathic mandible characteristics. Purpose: The aim of this study was to analyze the dentoalveolar alterations in class II dentoskeletal malocclusion with retrognathic mandible characteristics after orthodontic treatment with twin block appliance based on a Steiner analysis. Methods: This research constitutes a retrospective study using secondary data derived from the lateral cephalometric radiographs of patients with Class II malocclusion treated with twin block appliance at the Pediatric Dentistry Department of the Oral and Dental Hospital, Universitas Padjajaran, Bandung. The data was analyzed using a T-test for normally distributed paired data. In cases where data was not normally distributed, a Wilcoxon test was employed. Results: The average measurements showed statistically significant dentoalveolar changes among class II malocclusion patients after twin block appliance treatment when analyzed using the paired t-test based on Steiner method cephalometric radiograph analysis (p < 0.05). Conclusion: It is concluded that a twin block appliance is effective in treating class II dentoskeletal malocclusion with a retrognathic mandible based on dentoalveolar changes resulting from Steiner analysis.
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Rafiq, Hasan, Xiaohan Shi, Hengxu Zhang, Huimin Li et Manesh Kumar Ochani. « A Deep Recurrent Neural Network for Non-Intrusive Load Monitoring Based on Multi-Feature Input Space and Post-Processing ». Energies 13, no 9 (2 mai 2020) : 2195. http://dx.doi.org/10.3390/en13092195.
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Non-intrusive load monitoring (NILM) is a process of estimating operational states and power consumption of individual appliances, which if implemented in real-time, can provide actionable feedback in terms of energy usage and personalized recommendations to consumers. Intelligent disaggregation algorithms such as deep neural networks can fulfill this objective if they possess high estimation accuracy and lowest generalization error. In order to achieve these two goals, this paper presents a disaggregation algorithm based on a deep recurrent neural network using multi-feature input space and post-processing. First, the mutual information method was used to select electrical parameters that had the most influence on the power consumption of each target appliance. Second, selected steady-state parameters based multi-feature input space (MFS) was used to train the 4-layered bidirectional long short-term memory (LSTM) model for each target appliance. Finally, a post-processing technique was used at the disaggregation stage to eliminate irrelevant predicted sequences, enhancing the classification and estimation accuracy of the algorithm. A comprehensive evaluation was conducted on 1-Hz sampled UKDALE and ECO datasets in a noised scenario with seen and unseen test cases. Performance evaluation showed that the MFS-LSTM algorithm is computationally efficient, scalable, and possesses better estimation accuracy in a noised scenario, and generalized to unseen loads as compared to benchmark algorithms. Presented results proved that the proposed algorithm fulfills practical application requirements and can be deployed in real-time.
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Chahine, Khaled, et Khalil El Khamlichi Drissi. « A Novel Feature Extraction Method for Nonintrusive Appliance Load Monitoring ». Applied Computational Intelligence and Soft Computing 2013 (2013) : 1–7. http://dx.doi.org/10.1155/2013/686345.
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Improving energy efficiency by monitoring household electrical consumption is of significant importance with the climate change concerns of the present time. A solution for the electrical consumption management problem is the use of a nonintrusive appliance load monitoring (NIALM) system. This system captures the signals from the aggregate consumption, extracts the features from these signals and classifies the extracted features in order to identify the switched-on appliances. This paper focuses solely on feature extraction through applying the matrix pencil method, a well-known parametric estimation technique, to the drawn electric current. The result is a compact representation of the current signal in terms of complex numbers referred to as poles and residues. These complex numbers are shown to be characteristic of the considered load and can thus serve as features in any subsequent classification module. In the absence of noise, simulations indicate an almost perfect agreement between theoretical and estimated values of poles and residues. For real data, poles and residues are used to determine a feature vector consisting of the contribution of the fundamental, the third, and the fifth harmonic currents to the maximum of the total load current. The result is a three-dimensional feature space with reduced intercluster overlap.
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Kim, Jin-Gyeom, et Bowon Lee. « Appliance Classification by Power Signal Analysis Based on Multi-Feature Combination Multi-Layer LSTM ». Energies 12, no 14 (21 juillet 2019) : 2804. http://dx.doi.org/10.3390/en12142804.
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The imbalance of power supply and demand is an important problem to solve in power industry and Non Intrusive Load Monitoring (NILM) is one of the representative technologies for power demand management. The most critical factor to the NILM is the performance of the classifier among the last steps of the overall NILM operation, and therefore improving the performance of the NILM classifier is an important issue. This paper proposes a new architecture based on the RNN to overcome the limitations of existing classification algorithms and to improve the performance of the NILM classifier. The proposed model, called Multi-Feature Combination Multi-Layer Long Short-Term Memory (MFC-ML-LSTM), adapts various feature extraction techniques that are commonly used for audio signal processing to power signals. It uses Multi-Feature Combination (MFC) for generating the modified input data for improving the classification performance and adopts Multi-Layer LSTM (ML-LSTM) network as the classification model for further improvements. Experimental results show that the proposed method achieves the accuracy and the F1-score for appliance classification with the ranges of 95–100% and 84–100% that are superior to the existing methods based on the Gated Recurrent Unit (GRU) or a single-layer LSTM.
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Cuñado, J. R., et N. B. Linsangan. « A Supervised Learning Approach to Appliance Classification Based on Power Consumption Traces Analysis ». IOP Conference Series : Materials Science and Engineering 517 (26 avril 2019) : 012011. http://dx.doi.org/10.1088/1757-899x/517/1/012011.
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Le, Thi-Thu-Huong, Hyoeun Kang et Howon Kim. « Household Appliance Classification Using Lower Odd-Numbered Harmonics and the Bagging Decision Tree ». IEEE Access 8 (2020) : 55937–52. http://dx.doi.org/10.1109/access.2020.2981969.
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Wali, S., M. H. U. Haq, M. Kazmi et S. A. Qazi. « An End-to-End Machine Learning based Unified Architecture for Non-Intrusive Load Monitoring ». Engineering, Technology & ; Applied Science Research 11, no 3 (10 juin 2021) : 7217–22. http://dx.doi.org/10.48084/etasr.4142.
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Non-Intrusive Load Monitoring (NILM) or load disaggregation aims to analyze power consumption by decomposing the energy measured at the aggregate level into constituent appliances level. The conventional load disaggregation framework consists of signal processing and machine learning-based pipelined architectures, respectively for explicit feature extraction and decision making. Manual feature selection in such load disaggregation frameworks leads to biased decisions that eventually reduce system performance. This paper presents an efficient End-to-End (E2E) approach-based unified architecture using Gated Recurrent Units (GRU) for NILM. The proposed approach eliminates explicit feature engineering and has a unified classification and prediction model for appliance power. This eventually reduces the computational cost and enhances response time. The performance of the proposed system is compared with conventional algorithms' with the use of recall, precision, accuracy, F1 score, the relative error in total energy and Mean Absolute Error (MAE). These evaluation metrics are calculated on the power consumption of top priority appliances of Reference Energy Disaggregation Dataset (REDD). The proposed architecture with an overall accuracy of 91.2 and MAE of 25.23 outperforms conventional methods for all electrical appliances. It has been showcased through a series of experiments that feature extraction and event-based approaches for NILM can readily be replaced with E2E deep learning techniques allowing simpler and cost-efficient implementation pathways.
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Rogers, Kim, Phillip M. Campbell, Larry Tadlock, Emet Schneiderman et Peter H. Buschang. « Treatment changes of hypo- and hyperdivergent Class II Herbst patients ». Angle Orthodontist 88, no 1 (10 octobre 2017) : 3–9. http://dx.doi.org/10.2319/060117-369.1.
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ABSTRACT Objectives: To determine the relative effects of Herbst appliance therapy in hypo- and hyperdivergent patients. Materials and Methods: The treated group included 45 growing Class II, division 1, patients treated with stainless steel crown Herbst appliances, followed by fixed edgewise appliances. The untreated control group consisted of 45 Class II, division 1, subjects, matched to the treated sample based on Angle classification, age, sex, and pretreatment mandibular plane angle (MPA). Subjects were categorized as hypo- or hyperdivergent based on their MPAs. Pre- and posttreatment cephalograms were traced and superimposed on cranial base and mandibular structures. Results: The primary effect of the Herbst in terms of maxillomandibular correction was in the maxilla. It significantly restricted maxillary growth, producing a “headgear effect.” Mandibular treatment changes depended on divergence. Hyperdivergent patients experienced a deleterious backward true mandibular rotation with Herbst treatment. Hypodivergent patients, as well as untreated hypo- and hyperdivergent controls, underwent forward true mandibular rotation. However, hypodivergent chins did not advance any more than expected for untreated hypodivergent Class II patients. Conclusions: Hypo- and hyperdivergent patients benefit from the Herbst's headgear effect. While the mandibular growth of hypodivergent patients overcomes the negative rotational effects, hyperdivergent patients undergo a deleterious backward mandibular rotation and increases in facial height.
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Liu, Yu, Yan Wang, Yu Hong, Qianyun Shi, Shan Gao et Xueliang Huang. « Toward Robust Non-Intrusive Load Monitoring via Probability Model Framed Ensemble Method ». Sensors 21, no 21 (1 novembre 2021) : 7272. http://dx.doi.org/10.3390/s21217272.
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As a pivotal technological foundation for smart home implementation, non-intrusive load monitoring is emerging as a widely recognized and popular technology to replace the sensors or sockets networks for the detailed household appliance monitoring. In this paper, a probability model framed ensemble method is proposed for the target of robust appliance monitoring. Firstly, the non-intrusive load disaggregation-oriented ensemble architecture is presented. Then, dictionary learning model is utilized to formulate the individual classifier, while the sparse coding-based approach is capable of providing multiple solutions under greedy mechanism. Furthermore, a fully probabilistic model is established for combined classifier, where the candidate solutions are all labelled with probability scores and evaluated via two-stage decision-making. The proposed method is tested on both low-voltage network simulator platform and field measurement datasets, and the results show that the proposed ensemble method always guarantees an enhancement on the performance of non-intrusive load disaggregation. Besides, the proposed approach shows high flexibility and scalability in classification model selection. Therefore, by initializing the architecture and approach of ensemble method-based NILM, this work plays a pioneer role in using ensemble method to improve the robustness and reliability of non-intrusive appliance monitoring.
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Rimskaya, O. N., et I. V. Anokhov. « DIGITAL TWINS AND THEIR APPLIANCE IN TRANSPORT ECONOMICS ». Strategic decisions and risk management 12, no 2 (14 décembre 2021) : 127–37. http://dx.doi.org/10.17747/2618-947x-2021-2-127-137.
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Today digitalization increasingly affects the economy, including the transport industry. The consequence of this is the emergence of digital twins that allow modeling and predicting the behavior of both individual processes and enterprises as a whole.The aim of the article is to investigate the process of digitalization in the transport industry. The theoretical basis of the article was the universal organizational science of A. Bogdanov.The article offers a definition of information, and its classification in relation to the economy at three levels: applied information (technological information), information about algorithms of the owners of factors production behavior (behavioral information) and information, with which the impact on the owners of production factors and the real economy in general (directive information). The totality of these levels of information from the macroeconomic point of view forms an information economy, and from the microeconomic point of view – a digital twin of a particular subject of the real economy.It is proved that the digital economy is a subsystem of the information economy, differs in a binary way of presenting information and is maximally oriented to the management of the real economy.Information precedes all activity, so the real economy is a product of the information economy. Consequently, the technological division of labor is based on a prior informational division of labor. This theoretically allows us to judge the adequacy of the digital twin through the analysis of individual technological levels of the transport enterprise. This hypothesis was applied to the analysis of the Russian railway transport, which gave reason to consider this approach promising for use at macro- and micro-levels both.
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Larasati, Astari, Pinta Marito, Laura Susanti Himawan et Ira Tanti. « Migraine and temporomandibular disorder triggered by stress-induced bruxism : a case report ». Makassar Dental Journal 11, no 3 (19 décembre 2022) : 286–90. http://dx.doi.org/10.35856/mdj.v11i3.643.
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Migraine is the most common primary headache that associated with temporomandibular disorders (TMD) that is known as number of clinical problemsinvolving the masticatory muscles, temporomandibular joint and associated structures. Bruxism may be considered as an important factor of initiation of TMDand stress might be the contributory factor. This article is aimed to discuss the management of migraine and TMD patient with stress-induced bruxism as aprecipitating factor. A 42-year-old male patient visited RSKGM FKGUI due to throbbing pain, headache and discomfort around ear and face. Patient was diag-nosed myalgia based on DC/TMD with no articular joint disorder and migraine according to orofacial pain classification axis-1 following with anxiety disorder and stress. A stabilization appliance was designed followed with patient education, physical therapy and stress management. It was concluded that treatment combination of stabilization appliance, stress management and significantly improve the patient’s condition with lessen migraine attack and TMD symptoms.
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Koutroumpina, Christina, Spyros Sioutas, Stelios Koutroubinas et Kostas Tsichlas. « Evaluation of Features Generated by a High-End Low-Cost Electrical Smart Meter ». Algorithms 14, no 11 (25 octobre 2021) : 311. http://dx.doi.org/10.3390/a14110311.
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The problem of energy disaggregation is the separation of an aggregate energy signal into the consumption of individual appliances in a household. This is useful, since the goal of energy efficiency at the household level can be achieved through energy-saving policies towards changing the behavior of the consumers. This requires as a prerequisite to be able to measure the energy consumption at the appliance level. The purpose of this study is to present some initial results towards this goal by making heavy use of the characteristics of a particular din-rail meter, which is provided by Meazon S.A. Our thinking is that meter-specific energy disaggregation solutions may yield better results than general-purpose methods, especially for sophisticated meters. This meter has a 50 Hz sampling rate over 3 different lines and provides a rather rich set of measurements with respect to the extracted features. In this paper we aim at evaluating the set of features generated by the smart meter. To this end, we use well-known supervised machine learning models and test their effectiveness on certain appliances when selecting specific subsets of features. Three algorithms are used for this purpose: the Decision Tree Classifier, the Random Forest Classifier, and the Multilayer Perceptron Classifier. Our experimental study shows that by using a specific set of features one can enhance the classification performance of these algorithms.
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Houidi, Sarra, Dominique Fourer, François Auger, Houda Ben Attia Sethom et Laurence Miègeville. « Comparative Evaluation of Non-Intrusive Load Monitoring Methods Using Relevant Features and Transfer Learning ». Energies 14, no 9 (10 mai 2021) : 2726. http://dx.doi.org/10.3390/en14092726.
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Non-Intrusive Load Monitoring (NILM) refers to the analysis of the aggregated current and voltage measurements of Home Electrical Appliances (HEAs) recorded by the house electrical panel. Such methods aim to identify each HEA for a better control of the energy consumption and for future smart grid applications. Here, we are interested in an event-based NILM pipeline, and particularly in the HEAs’ recognition step. This paper focuses on the selection of relevant and understandable features for efficiently discriminating distinct HEAs. Our contributions are manifold. First, we introduce a new publicly available annotated dataset of individual HEAs described by a large set of electrical features computed from current and voltage measurements in steady-state conditions. Second, we investigate through a comparative evaluation a large number of new methods resulting from the combination of different feature selection techniques with several classification algorithms. To this end, we also investigate an original feature selection method based on a deep neural network architecture. Then, through a machine learning framework, we study the benefits of these methods for improving Home Electrical Appliance (HEA) identification in a supervised classification scenario. Finally, we introduce new transfer learning results, which confirm the relevance and the robustness of the selected features learned from our proposed dataset when they are transferred to a larger dataset. As a result, the best investigated methods outperform the previous state-of-the-art results and reach a maximum recognition accuracy above 99% on the PLAID evaluation dataset.
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Shin, Changho, Seungeun Rho, Hyoseop Lee et Wonjong Rhee. « Data Requirements for Applying Machine Learning to Energy Disaggregation ». Energies 12, no 9 (5 mai 2019) : 1696. http://dx.doi.org/10.3390/en12091696.
Texte intégralRésumé :
Energy disaggregation, or nonintrusive load monitoring (NILM), is a technology for separating a household’s aggregate electricity consumption information. Although this technology was developed in 1992, its practical usage and mass deployment have been rather limited, possibly because the commonly used datasets are not adequate for NILM research. In this study, we report the findings from a newly collected dataset that contains 10 Hz sampling data for 58 houses. The dataset not only contains the aggregate measurements, but also individual appliance measurements for three types of appliances. By applying three classification algorithms (vanilla DNN (Deep Neural Network), ML (Machine Learning) with feature engineering, and CNN (Convolutional Neural Network) with hyper-parameter tuning) and a recent regression algorithm (Subtask Gated Network) to the new dataset, we show that NILM performance can be significantly limited when the data sampling rate is too low or when the number of distinct houses in the dataset is too small. The well-known NILM datasets that are popular in the research community do not meet these requirements. Our results indicate that higher quality datasets should be used to expedite the progress of NILM research.
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Sales Mendes, André, Gabriel Villarrubia González, Juan Francisco De Paz, Alberto López Barriuso et Álvaro Lozano Murciego. « Coin Recognition Approach in Social Environments Using Virtual Organizations of Agents ». Applied Sciences 9, no 6 (25 mars 2019) : 1252. http://dx.doi.org/10.3390/app9061252.
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Social systems have gained relevance during the last decade, trying to provide intelligent environments where humans and machines collaborate to resolve a social problem. The main objective of this paper is to obtain an intelligent system specifically designed to help dependent and/or visually disabled people to count money more easily by using a mobile phone camera. The proposed system incorporates an image recognition system for classifying coins by using homography to transform images previously for classification tasks. The main difficulty in the appliance of these techniques relies on the fact that camera position and height are unknown. This process allows changing the perspective of the images in order to calculate different meaningful variables such as diameter and colour employed later to perform classification and counting tasks. The system uses the information of the variables as inputs for classification algorithms that allow us to identify the amount and type of coins. The system has been tested with euro coins. This paper presents the results obtained.
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Sankhye, Sidharth, et Guiping Hu. « Machine Learning Methods for Quality Prediction in Production ». Logistics 4, no 4 (21 décembre 2020) : 35. http://dx.doi.org/10.3390/logistics4040035.
Texte intégralRésumé :
The rising popularity of smart factories and Industry 4.0 has made it possible to collect large amounts of data from production stages. Thus, supervised machine learning methods such as classification can viably predict product compliance quality using manufacturing data collected during production. Elimination of uncertainty via accurate prediction provides significant benefits at any stage in a supply chain. Thus, early knowledge of product batch quality can save costs associated with recalls, packaging, and transportation. While there has been thorough research on predicting the quality of specific manufacturing processes, the adoption of classification methods to predict the overall compliance of production batches has not been extensively investigated. This paper aims to design machine learning based classification methods for quality compliance and validate the models via case study of a multi-model appliance production line. The proposed classification model could achieve an accuracy of 0.99 and Cohen’s Kappa of 0.91 for the compliance quality of unit batches. Thus, the proposed method would enable implementation of a predictive model for compliance quality. The case study also highlights the importance of feature construction and dataset knowledge in training classification models.
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Nasser, Dr Najim O. « The effect of design on Removable Partial Dentures ». Mustansiria Dental Journal 11, no 1 (26 février 2018) : 43–47. http://dx.doi.org/10.32828/mdj.v11i1.212.
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The importance of properly designed removable partial denture cannot beoveremphasized, the execution of removable partial denture design may determine thesuccess or failure of the appliance inadequate design assures its facility.This study was done to confirm the effect of Kennedy classification and clinicalexamination on the removable partial design of group (A) dental technician and group(B) the dentists, and to be solved in future.The result show 36% of the cases were modified and changed according to thecases related variables this high and significant number of munificent reinforces theposition that RPD design should be decided and guided by the dentists.The study conducted the effects of Kennedy classification and clinicalexamination on the RPD design marked by the dentists after providing the clinicalexamination played a very important role in changing the RPD designs.
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Ujager, Farhan Sabir, et Azhar Mahmood. « A Context-Aware Accurate Wellness Determination (CAAWD) Model for Elderly People Using Lazy Associative Classification ». Sensors 19, no 7 (3 avril 2019) : 1613. http://dx.doi.org/10.3390/s19071613.
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Wireless Sensor Network (WSN) based smart homes are proving to be an ideal candidate to provide better healthcare facilities to elderly people in their living areas. Several currently proposed techniques have implementation and usage complexities (such as wearable devices and the charging of these devices) which make these proposed techniques less acceptable for elderly people, while the behavioral analysis based on visual techniques lacks privacy. In this paper, a context-aware accurate wellness determination (CAAWD) model for elderly people is presented, where behavior monitoring information is extracted by using simple sensor nodes attached to household objects and appliances for the analysis of daily, frequent behavior patterns of elderly people in a simple and non-obtrusive manner. A contextual data extraction algorithm (CDEA) is proposed for the generation of contextually comprehensive behavior-training instances for accurate wellness classification. The CDEA presents an activity’s spatial–temporal information along with behavioral contextual correlation aspects (such as the object/appliance of usage and sub-activities of an activity) which are vital for accurate wellness analysis and determination. As a result, the classifier is trained in a more logical manner in the context of behavior parameters which are more relevant for wellness determination. The frequent behavioral patterns are classified using the lazy associative classifier (LAC) for wellness determination. The associative nature of LAC helps to integrate spatial–temporal and related contextual attributes (provided by CDEA) of elderly behavior to generate behavior-focused classification rules. Similarly, LAC provides high accuracy with less training time of the classifier, includes minimum-support behavior patterns, and selects highly accurate classification rules for the classification of a test instance. CAAWD further introduces the ability to contextually validate the authenticity of the already classified instance by taking behavioral contextual information (of the elderly person) from the caregiver. Due to the consideration of spatial–temporal behavior contextual attributes, the use of an efficient classifier, and the ability to contextually validate the classified instances, it has been observed that the CAAWD model out-performs currently proposed techniques in terms of accuracy, precision, and f-measure.
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Desai, Sanket, Rabei Alhadad, Abdun Mahmood, Naveen Chilamkurti et Seungmin Rho. « Multi-State Energy Classifier to Evaluate the Performance of the NILM Algorithm ». Sensors 19, no 23 (28 novembre 2019) : 5236. http://dx.doi.org/10.3390/s19235236.
Texte intégralRésumé :
With the large-scale deployment of smart meters worldwide, research in non-intrusive load monitoring (NILM) has seen a significant rise due to its dual use of real-time monitoring of end-user appliances and user-centric feedback of power consumption usage. NILM is a technique for estimating the state and the power consumption of an individual appliance in a consumer’s premise using a single point of measurement device such as a smart meter. Although there are several existing NILM techniques, there is no meaningful and accurate metric to evaluate these NILM techniques for multi-state devices such as the fridge, heat pump, etc. In this paper, we demonstrate the inadequacy of the existing metrics and propose a new metric that combines both event classification and energy estimation of an operational state to give a more realistic and accurate evaluation of the performance of the existing NILM techniques. In particular, we use unsupervised clustering techniques to identify the operational states of the device from a labeled dataset to compute a penalty threshold for predictions that are too far away from the ground truth. Our work includes experimental evaluation of the state-of-the-art NILM techniques on widely used datasets of power consumption data measured in a real-world environment.