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ردیف | عنوان | نوع |
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1 |
Deep unsupervised methods towards behavior analysis in ubiquitous sensor data
روش های عمیق بدون نظارت برای تجزیه و تحلیل رفتار در داده های حسگر همه جا حاضر-2022 Behavioral analysis (BA) on ubiquitous sensor data is the task of finding the latent distribution of
features for modeling user-specific characteristics. These characteristics, in turn, can be used for a
number of tasks including resource management, power efficiency, and smart home applications.
In recent years, the employment of topic models for BA has been found to successfully extract the
dynamics of the sensed data. Topic modeling is popularly performed on text data for mining
inherent topics. The task of finding the latent topics in textual data is done in an unsupervised
manner. In this work we propose a novel clustering technique for BA which can find hidden
routines in ubiquitous data and also captures the pattern in the routines. Our approach efficiently
works on high dimensional data for BA without performing any computationally expensive
reduction operations. We evaluate three different techniques namely Latent Dirichlet Allocation
(LDA), the Non-negative Matrix Factorization (NMF), and the Probabilistic Latent Semantic
Analysis (PLSA) for comparative study. We have analyzed the efficiency of the methods by using
performance indices like perplexity and silhouette on three real-world ubiquitous sensor datasets
namely, the Intel Lab, Kyoto, and MERL. Through rigorous experiments, we achieve silhouette
scores of 0.7049 over the Intel Lab dataset, 0.6547 over the Kyoto dataset, and 0.8312 over the
MERL dataset for clustering. In these cases, however, it is di cult to validate the results obtained as
the datasets do not contain any ground truth information. Towards that, we investigate a self-
supervised method that will be capable of capturing the inherent ground truths that are avail-
able in the dataset. We design a self-supervised technique which we apply on datasets containing
ground truth and also without. We see that our performance on data without ground truth differs
from that with ground truth by approximately 8% (F-score) hence showing the efficacy of self-
supervised techniques towards capturing ground truth information. keywords: تحلیل داده های فراگیر | تحلیل رفتار | یادگیری خود نظارتی | Ubiquitous data analysis | Behavior analysis | Self supervised learning |
مقاله انگلیسی |
2 |
In-home Health Monitoring using Floor-based Gait Tracking
نظارت بر سلامت در خانه با استفاده از ردیابی راه رفتن مبتنی بر کف-2022 Gait assessments are commonly used for clinical evaluations of neurocognitive disease progression and general wellness. However, gait measurements in clinical settings do not accurately
reflect gait in daily life. We present a non-wearable and unobtrusive method of detecting
gait parameters in the home through the vibrations in the floor created by footfalls. Gait
characteristics and gait asymmetry are estimated despite a low sensor density of 6.7 m2/sensor.
Features from each footfall vibration signal is extracted and used to estimate gait parameters
with gradient boosting regression and probabilistic models. Temporal gait asymmetry, locations
of the footfalls, and peak tibial acceleration asymmetry can be predicted with a root mean
square error of 0.013 s, 0.42 m, and 0.34 g respectively. This system allows for continuous
at-home monitoring of gait which aids in early detection of gait anomalies.
keywords: Gait monitoring | Smart home | Signal processing | Localization | Ground reaction force |
مقاله انگلیسی |
3 |
Text mining of industry 4:0 job advertisements
استخراج متن آگهی های شغلی صنعت 4:0-2020 Since changes in job characteristics in areas such as Industry 4.0 are rapid, fast tool for analysis of job advertisements
is needed. Current knowledge about competencies required in Industry 4.0 is scarce. The goal of this
paper is to develop a profile of Industry 4.0 job advertisements, using text mining on publicly available job
advertisements, which are often used as a channel for collecting relevant information about the required
knowledge and skills in rapid-changing industries. We searched website, which publishes job advertisements,
related to Industry 4.0, and performed text mining analysis on the data collected from those job advertisements.
Analysis of the job advertisements revealed that most of them were for full time entry; associate and mid-senior
level management positions and mainly came from the United States and Germany. Text mining analysis resulted
in two groups of job profiles. The first group of job profiles was focused solely on the knowledge related to
Industry 4.0: cyberphysical systems and the Internet of things for robotized production; and smart production
design and production control. The second group of job profiles was focused on more general knowledge areas,
which are adapted to Industry 4.0: supply change management, customer satisfaction, and enterprise software.
Topic mining was conducted on the extracted phrases generating various multidisciplinary job profiles. Higher
educational institutions, human resources professionals, as well as experts that are already employed or aspire to
be employed in Industry 4.0 organizations, would benefit from the results of our analysis. Keywords: Human resource management | Text mining | Job profiles | Big data analytics | Industry 4.0 | Education | Smart factory |
مقاله انگلیسی |
4 |
A collaborative energy management among plug-in electric vehicle, smart homes and neighbors’ interaction for residential power load profile smoothing
مدیریت انرژی مشارکتی بین خودروهای برقی پلاگین ، خانه های هوشمند و تعامل همسایگان برای هموار کردن مشخصات بار توان مسکونی-2020 With the modernization of the smart grid, Plug-in Electric Vehicles (PEVs) have attracted attention thanks to the
effective energy support through the bi-directional power flow exchanging. In particular, vehicle-to-home
technology has drawn a significant interest in PEVs’ parked at smart home to enhance the power consumption
profile. This paper proposes a collaborative energy management among PEVs, smart homes and neighbors’
interaction. For that, a new supervision strategy based on PEVs power scheduling for smoothing the residential
power load profile is developed. The objective of this study is to improve the power demand profile by controlling
the PEV power charging/discharging amount to fill the valley of the power consumption curve or by
providing power to home especially during peak periods to shave peak. The home energy management for
achieving a flattened power load profile is divided into two parts: a local control according to the base demand
profile of the considering home, the availability of their PEVs, their arrival and departure times and their initial
state of charge (SOC) values. A global control according to the power demand of the specific home, the total
power demand of neighbors and the availability of PEVs’ neighbors (arrival and departure times, initial energy of
the battery). The simulation results of the power load profile of such smart homes highlights the interaction
between PEVs, smart home and their neighbors in order to flatten the power demand curve to the greatest extent
possible. Keywords: Plug-in electric vehicles (PEVs) | Smart home | Neighbors’ interaction | Collaborative energy management | Fill the valley | Shave peak | Smooth |
مقاله انگلیسی |
5 |
A method of NC machine tools intelligent monitoring system in smart factories
روش ابزار و ماشین آلات NC سیستم نظارت هوشمند در کارخانه های هوشمند-2020 The construction of effectual connection to bridge the gap between physical machine tools and upper software
applications is one of the inherent requirements for smart factories. The difficulties in this issue lies in the lack of
effective and appropriate means for real-time data acquisition, storage and processing in monitoring and the post
workflows. The rapid advancements in Internet of things (IoT) and information technology have made it possible
for the realization of this scheme, which have become an important module of the concepts such as “Industry
4.0”, etc. In this paper, a framework of bi-directional data and control flows between various machine tools and
upper-level software system is proposed, within which several key stumbling blocks are presented, and corresponding
solutions are subsequently deeply investigated and analyzed. Through monitoring manufacturing big
data, potential essential information are extracted, providing useful guides for practical production and enterprise
decision-making. Based on the integrated model, an NC machine tool intelligent monitoring and data
processing system in smart factories is developed. Typical machine tools, such as Siemens series, are the main
objects for investigation. The system validates the concept and performs well in the complex manufacturing
environment, which will be a beneficial attempt and gain its value in smart factories.. Keywords: CNC | Monitoring system | Data analysis | Machine tool | Smart factory |
مقاله انگلیسی |
6 |
Intensive quadratic programming approach for home energy management systems with power utility requirements
رویکرد برنامه نویسی درجه دوم فشرده برای سیستم های مدیریت انرژی خانه با نیازهای ابزار برق-2020 This paper proposes a model of a home energy management system (HEMS) to meet utility requirements while
maximizing home profit. It contributes to intensify the flattening effects on the exchanging power pattern with a
constraint of a fair profit reduction among households. The proposed method first uses a normal mixed-integer
linear programming approach to find out the highest profit a household can get under a condition of a generous
power limitation. It is highly possible that the resulted power aggregated from numerous homes may negatively
affect power system operation such as violating voltage limits and overloading transformers. Based on that
highest profit, the utility proposes the same percentage number of profit reduction for all households. Then, each
HEMS performs an intensive mixed-integer quadratic programming optimization to flatten the selling and
buying profiles whilst constraining the home profit reduction to the percentage set by the utility. A simulation
shows that the peak power demand at the substation transformer would reduce about 44% if each household
suffered a reduction of just 10% of the highest possible home profit. Since the flattening effects are improved if
increasing the home profit reduction, our method is a basis for the utility to determine a compensation or
alternative incentives to shave the peak-load and flatten the demand curve. Keywords: Home Energy Management System | Peak-load shaving | Smart household | Smart home | Rooftop solar |
مقاله انگلیسی |
7 |
Integration of Big Data analytics embedded smart city architecture with RESTful web of things for efficient service provision and energy management
ادغام تجزیه و تحلیل داده های بزرگ جاسازی شده معماری شهر هوشمند با وب سایت RESTful برای ارائه خدمات کارآمد و مدیریت انرژی-2020 Emergence of smart things has revolutionized the conventional internet into a connected network of
things, maturing the concept of Internet of Things (IoT). With the evolution of IoT, many attempts were
made to realize the notion of smart cities. However, demands for processing enormous amount of data
and platform incompatibilities of connected smart things hindered the actual implementation of smart
cities. Keeping it in view, we proposed a Big Data analytics embedded smart city architecture, which
is further integrated with the web via a smart gateway. Integration with the web provides a universal
communication platform to overcome the platform incompatibilities of smart things. We introduced Big
Data analytics to enhance data processing speed. Further, we evaluated authentic datasets to determine
the threshold values for intelligent decision-making and to present the performance improvement gained
in data processing. Finally, we presented a representational state transfer (RESTful) web of things (WoT)
integrated smart building architecture (smart home) to reveal the performance improvements of the
proposed smart city architecture in terms of network performance and energy management of smart
buildings. Keywords: Smart city | Big Data analytics | Smart home | Web of things | RESTful architecture |
مقاله انگلیسی |
8 |
A-SEM: An adaptive smart energy management testbed for shiftable loads optimisation in the smart home
A-SEM: یک تست مدیریت انرژی هوشمند سازگار برای بهینه سازی بارهای قابل تغییر در خانه هوشمند-2020 Managing the increment in energy demand can be solved by involving the demand side in order to
increase energy consumption efficiency. However, increasing energy consumption efficiency tends to
influence the user level of comfort. A research gap exists in the study of the energy efficiency and user
comfort trade-off and in particular in providing low cost testbeds to study this phenomenon. This research
discusses a low-cost testbed hardware design and the potential of the proposed Adaptive-Smart Energy
Management Tool (A-SEM tool) to balance the level of comfort and energy consumption efficiency. We
implement an adaptive energy limitation algorithm which uses up to 30 days historical data. The energy
consumption is influenced by user behaviour which is monitored by the system sensors and energy limits
are adapted for the provision of comfort. A smart home user is able to set the monthly energy consumption
budget which determines the initial level of daily energy limitation. A-SEM performs real time monitoring
and controlling, mainly considering shiftable loads evaluated at the testing stage. We test 3 possible conditions
(possible modes of operation) and prove that our ‘‘adaptive limit” energy limitation algorithm is
the most successful in balancing the level of comfort and energy efficiency. For a fixed budget and energy
price, the proposed adaptive approach meets user level of comfort (our main priority) as well as achieving
some energy savings. In our test the A-SEM tool provides user comfort, meets the monthly budget constraint
and yet shows an energy saving of 2.62% which can increase or decrease depending on user behaviour.
We present results showing energy saving levels, comfort levels and efficiency levels. The proposed
A-SEM tool is low cost, implements an uncomplicated adaptive algorithm and therefore has the potential
to be an affordable smart energy management system in future smart homes. Keywords: Home energy management system | HEMS | Adaptive-A-SEM tool | Smart home | User comfort | Demand side management |
مقاله انگلیسی |
9 |
A customized transition towards smart homes: A fast framework for economic analyses
خانه های هوشمندبه سمت انتقال سفارشی : یک چارچوب سریع برای تحلیل های اقتصادی-2020 Smart homes allow optimized energy usage, allowing households to reduce electricity bills or even make profits.
By 2020, 20% of all households in Europe will be expected to become smart homes. Although smart homes seem
to be the future for homes, many customers have the perception that a transition from current homes to smart
ones is unprofitable. Adopting a smart home concept requires investments for which the households desire a
positive return. A question in this context is the following: for a given household, when and/or what set of home
appliances/technologies should be acquired so that the investment made by householder has a positive financial
return? The available tool to answer that question can be time-consuming from a practical perspective. Based on
our previous work, this paper proposes a framework to help the transition from current houses to smart homes
considering customized electricity usage and economic measures. A tree algorithm is developed to decrease the
time needed by an economic analysis of each possible acquisition combination of smart appliances or equipment
for a given user. The proposed framework is tested on 40 cases covering all Brazilian capital cities, whose results
are available online and may be used directly as an approximation for economic analyses. An example of one
case is described in detail. Results show that the proposed tree algorithm is able to reduce days of CPU time to
solve the problem and Net Present Value should be used as an economic measure to answer the aforementioned
question. Keywords: Smart home | Economic analysis | Energy management system | Interior point | Optimization |
مقاله انگلیسی |
10 |
An assessment of opinions and perceptions of smart thermostats using aspect-based sentiment analysis of online reviews
ارزیابی نظرات و برداشت از ترموستات هوشمند با استفاده از تحلیل احساسات مبتنی بر جنبه های بررسی های آنلاین-2020 Smart thermostats have been on the market for nearly a decade, with an estimated adoption rate of 7% in 2018.
With many regions of the U.S. having a heating and/or cooling system in nearly 100% of households, there is
significant opportunity for further adoption, which can help support energy savings and building-grid interactions.
However, more insight is needed to provide a better understanding of their utilization and user
opinions. In this study, online reviews are used to evaluate users’ perceptions and attitudes towards smart
thermostats. 26,372 product reviews were collected for five commercially-available smart thermostats and were
analyzed with a confirmatory aspect-based opinion mining technique. An analysis of this dataset shows that the
characteristics of the current user population show substantial differences compared to the more widely studied
early adopters. When comparing the most commonly discussed topics, users generally do not discuss the energy
and cost savings related features of their devices in comparison to other topics such as control, ease of use, and
installation. In addition, comfort is discussed nearly twice as much as energy efficiency. The results of this work
can help product manufacturers and utility providers to push towards more widespread adoption and efficient
use. Keywords: Energy savings | Smart thermostats | Smart home technology | Home energy management | Opinion mining |
مقاله انگلیسی |