Home Appliances Research Articles

The Application of Touch Sensors

Touch sensors have a wide range of applications in smart homes, enabling the control of lighting, air conditioning, curtains, and other equipment. This paper summarizes the principles, applications, and future developments of touch sensors, including capacitive, resistive, and piezoelectric sensors. The capacitive touch sensor operates based on the capacitance formula, the resistive sensor on the resistivity formula, and the piezoelectric sensor on the piezoelectric effect. Capacitive touch sensors can detect the movement and position of objects and respond quickly, but they require power. Resistive sensors have a fast response time, long durability, low cost, and high sensitivity, but they also require power. Piezoelectric sensors do not require power, can detect dynamic physical quantities, but are unable to detect static ones. Both capacitive and piezoelectric sensors are used in smart mobile devices, while resistive sensors are used in all-in-one machines. Given their principles and current applications, touch sensors are expected to be utilized in the touch screens of operation interfaces in more machines and the bionic skin of androids.

Price-based demand response with renewable energy sources and peer-to-peer trading for residential microgrid with electric vehicle uncertainty

This article deals with the impact of demand response (DR) with peer-to-peer (P2P) energy trading for a residential smart home consisting of smart appliances, an electric vehicle, a battery energy storage system (BESS), and renewable energy-based distributed generation (DG) such as solar PV and wind. The proposed work deals with two stages, such as DR implementation and P2P energy trading. The first stage of implementation deals with the optimal scheduling of smart appliances in a residential home using a Binary Dragonfly Algorithm (BDA). Each appliance in the smart home is scheduled based on the slab tariff, Time of Day (ToD), and Real-Time Pricing (RTP) with DR to identify the most economical tariff structure for the residential home. Five different cases are examined to identify the best smart home for effectively utilizing DGs by reducing grid dependency and electricity costs.The RTP tariff is proved to be more beneficial than the slab tariff and ToD tariff because the electricity cost of the smart home is very minimal for all the cases with RTP, especially in case 5 cost is reduced by ₹12.0613 than slab tariff and ₹8.414 than ToD. Hence, the RTP tariff is chosen as the optimal tariff structure for the next stage of the proposed work.The second stage of implementation deals with the P2P trading between the smart homes with a proposed enhanced bidding strategy to reduce the grid dependency and electricity cost of the individual smart homes. The proposed enhanced bidding strategy involves a double auction mechanism to determine the trading decision and trading cost for the benefit of both consumers and prosumers. The electricity cost reduction achieved by consumer 1 and consumer 2 from the enhanced smart bidding strategy are ₹92.7615 and ₹15.1525, respectively. The results also proved that prosumer 1 and prosumer 2 obtained a profit of ₹78.2170 and ₹28.6970, respectively. The EV uncertainty analysis is also examined in smart homes to validate the effectiveness of the proposed bidding strategy. The simulation results proved that the proposed DR-based P2P trading with an enhanced bidding strategy reduces grid dependency and electricity costs, including EV uncertainty conditions.

Focus on the Role of Non-Invasive Respiratory Support (NRS) during Palliative Care in Patients with Life-Limiting Respiratory Disease.

The management of patients with life-threatening respiratory disease in the ICU and at home has become increasingly of interest over the past decades. Growing knowledge supports the use of NRS, aimed at improving patient comfort and improving quality of life. However, its role during palliative care is not well defined, and evidence of support remains limited. The aim of this narrative review is to examine the recent evidence relating to the use of non-invasive respiratory support at the end of life, in order to clarify who benefits and when. The literature research was conducted on PubMed, using MeSH words. A review of the relevant literature showed that non-invasive respiratory support techniques for patients with life-limiting respiratory disease vary (from high-flow oxygen therapy to conventional oxygen therapy, from CPAP to NPPV) and each has precise indications. To date, from the hospital to the home setting, the monitoring and application of these respiratory support techniques have varied widely. In conclusion, the choice of respiratory support in this category of patients should be based on the technique that will optimize the comfort of the patient and improve the quality of their life. On the other hand, regarding monitoring, both telemedicine and ultrasound diagnostics help to satisfy the patient's wish to spend the last period of his life in the home environment, to avoid inappropriately aggressive diagnostic interventions, and to reduce the high costs of hospitalized procedures in this category of patients.

Radar Based Secure Contactless Fall Detection Using Hybrid Optimizer with Convolution Neural Network

Introduction: senior citizens can lead to severe injuries. Existing wearable fall-alert sensors are often ineffective as seniors tend to avoid using them, highlighting the need for non-contact sensor applications in smart homes. This study proposes a CNN-based fall detection system using time-frequency analyses. A unique hybrid optimizer, GWO-ABC, combining Artificial Bee Colony (ABC) and Grey Wolf Optimizer (GWO), is employed to optimize CNN architectures. Radar return signals are transformed into spectrograms and binary images for training the HOCNN with fall and non-fall data.Methods: Radar signals are processed using short-time Fourier transformation to create time-frequency spectrograms, converted into binary images. These images are fed into a CNN optimized by the GWO-ABC algorithm. The CNN is trained on labelled fall and non-fall instances, focusing on high-level feature extraction.Results: The HOCNN showed superior accuracy in fall detection, successfully extracting critical high-level features from radar signals. Performance metrics, including precision, recall, and F1-score, demonstrated significant improvements over traditional methods.Conclusion: This study introduces a non-contact, automatic fall detection system for smart homes using GWO-ABC optimized CNNs, offering a promising solution for enhancing geriatric care and ensuring senior citizen safety. Index Terms—Grey Wolf Optimizer, Artificial Bee Colony algorithm, Convolutional neural network, fall detection, time-frequency analysis, ultra-wideband (UWB) radar

A secured deep learning based smart home automation system

With the expansion of modern technologies and the Internet of Things (IoT), the concept of smart homes has gained tremendous popularity with a view to making people’s lives easier by ensuring a secured environment. Several home automation systems have been developed to report suspicious activities by capturing the movements of residents. However, these systems are associated with challenges such as weak security, lack of interoperability and integration with IoT devices, timely reporting of suspicious movements, etc. Therefore, the given paper proposes a novel smart home automation framework for controlling home appliances by integrating with sensors, IoT devices, and microcontrollers, which would in turn monitor the movements and send notifications about suspicious movements on the resident’s smartphone. The proposed framework makes use of convolutional neural networks (CNNs) for motion detection and classification based on pre-processing of images. The images related to the movements of residents are captured by a spy camera installed in the system. It helps in identification of outsiders based on differentiation of motion patterns. The performance of the framework is compared with existing deep learning models used in recent studies based on evaluation metrics such as accuracy (%), precision (%), recall (%), and f-1 measure (%). The results show that the proposed framework attains the highest accuracy (98.67%), thereby surpassing the existing deep learning models used in smart home automation systems.

Construction of Ensemble Learning Model for Home Appliance Demand Forecasting

Given the increasing competition among household appliance enterprises, accurately predicting household appliance demand is crucial for enterprise supply chain management and marketing. This paper proposes a combined model integrating deep learning and ensemble learning—LSTM-RF-XGBoost—to assist enterprises in identifying customer demand, thereby addressing the complexity and uncertainty of the household appliance market demand. In this study, Long Short-Term Memory Network (LSTM), Random Forest (RF), and Extreme Gradient Boosting (XGBoost) models are established separately. Then, the three individual algorithms are used as the base models in the first layer, with the multiple linear regression (MLR) algorithm serving as the meta-model in the second layer, merging the demand prediction model based on the hybrid model into the overall demand prediction model. This study demonstrates that the accuracy and stability of demand prediction using the LSTM–RF–XGBoost model significantly outperform traditional single models, highlighting the significant advantages of using a combined model. This research offers practical and innovative solutions for enterprises seeking rational resource allocation through demand prediction.

An Investigation of The Relationship Between Organizational Trust and Loyalty in China's Largest Manufacturing Company

Trust is widely regarded as a potent factor in attaining favorable individual and collective results. Prior studies have commonly categorised trust in business organizations into three distinct dimensions: trust in the organization itself, trust in managers, and trust in colleagues. The primary outcome variables that determine organizational trust are employee job satisfaction, organizational commitment, positive organizational behaviour, and high performance levels. Employee trust can foster emotional loyalty, continuous loyalty, and moral loyalty. We contend that trust exerts a significant influence on employee satisfaction, thereby fostering employee loyalty. This study aimed to examine the correlation between organizational trust and employee loyalty by conducting an employee loyalty survey in a prominent Chinese home appliance company. The statistical analysis conducted using SPSS software validated the correlation between organizational trust and employee loyalty, highlighting the significance of organizational trust in fostering employee loyalty particularly in manufacturing industry.

Countermeasures of Chinese Home Appliance Enterprises Under the Background of "Belt and Road" to Develop the International Market

Countermeasures of Chinese Home Appliance Enterprises Under the Background of "Belt and Road" to Develop the International Market

Research on the influence of the digital economy on the development of China’s small household appliance industry and enhancement countermeasures

Purpose The implementation of the digital economy has had a considerable influence on the manufacturing industry, and this paper aims to address the important issues of how to capture the opportunities presented by digital innovation and promote the transformation and upgrading of the manufacturing industry, as well as the improvement of quality and efficiency. Design/methodology/approach Using panel data from 30 Chinese provinces and cities between 2010 and 2021, this study establishes the panel vector autoregression (PVAR) model and uses impulse response function analysis to evaluate the influence of the digital economy on the high-quality transformation and upgrading of China's small home appliance industry across five dimensions under the digital economy. Findings The development of digital infrastructure has not demonstrated a noteworthy capacity for advancing the transformation and upgrading of the small home appliance industry. Furthermore, digital industrialization has exerted a minimal restraining influence on this process. Nevertheless, digital governance has consistently exhibited a substantial impact on facilitating the transformation and upgrading of the small home appliance industry. While both industrial digitization and digital innovation hold significant potential for promoting the transformation and upgrading of the small home appliance industry, their sustainability remains limited. Practical implications The organization should logically join independent innovation and open innovation, construct an industrial ecosystem for the profound convergence of the digital economy and compact household appliances, use digital-wise science and technology to empower the establishment of brand effects, strengthen the portrayal of the digital standard framework for the intelligent compact household appliance industry, advance the development of a public stage for computerized administrations in the compact household appliance industry and develop a strategy ecosystem for computerized assets in the compact household appliance industry. Originality/value This study offers systematic evidence of the relationship between the digital economy and the development of the small home appliance industry. The results of this research contribute to the literature on the impact of the digital economy on the manufacturing sector and provide a logical explanation for the transformation and upgrading of the small home appliance industry within the context of the digital economy.

IoT based Smart Electricity Monitoring and Control Using ESP-Mesh Networking

The "IoT-Based Smart Electricity Monitoring and Control System Using ESP-MESH Networking" is a state-of- the-art project that aims to transform the way we manage and consume electricity in our homes. In today's world, our homes are equipped with a wide range of electrical appliances, each with varying levels of power consumption. Without an effective system to monitor and control these appliances, they can lead to excessive electricity usage. This not only results in high energy bills but also contributes to a significant carbon footprint, which is harmful to our environment. The Power consumption by the overall house can be monitored using this device and control the other electronic devices, able to monitor the power consumption in different rooms and able to monitor the current power by specific devices. User can check their Power usage from anywhere and at any time interval. The information can be monitored from mobile applications and also be monitored even on a web page. The Main can be turned on/off using a relay from anywhere. The Home application can also be controlled from anywhere through the mobile application. Keywords— ESP-MESH Networking, Power Consumption, Energy efficiency, Carbon footprint, Relay control, Energy management

Willingness-to-pay for low-carbon residential heating systems: A discrete choice experiment among Dutch households

Most studies on decarbonizing space and water heating focus on the potential of low-carbon heating technologies from a top-down perspective, while there has been limited research on consumer preferences for low-carbon heat. To address this gap, we conduct a discrete-choice experiment among 1797 individuals in the Netherlands to estimate the willingness-to-pay (WTP) for residential-heating systems. Our results from the latent-class logit (LCL) model indicate that, on average, Dutch households are willing to pay a premium on their current monthly energy bill of 33% for electricity and 29.4% for hydrogen relative to using gas for heat production, 15.3% for reducing the amount of CO2 emissions per kWh of heat with 100 g and 16% for having the option to switch between energy retailers. In contrast, they require a discount of 28.4% for district-heating systems relative to standalone home appliances. Additionally, the class-profiling analysis indicates that individuals with higher education and income levels in the Northern Netherlands have the highest WTP for low-carbon electricity and hydrogen. These findings suggest that policymakers should favor decentralized heating solutions over district-heating systems to facilitate an energy transition in the residential sector. If district-heating systems are implemented, combining them with electricity or hydrogen instead of natural gas is recommended to realize higher consumer welfare.

Heuristics for home appliances scheduling problems with energy consumption bounds

In this paper we consider the problem of scheduling two types of home appliances: ones with fixed power and flexible operation time and others with fixed operation time and flexible power. We consider energy consumption bounds that depend on the time of the day, which modify considerably the computational complexity of the appliance scheduling problems. Additionally, we also take into account the possibility that appliances with flexible operation time could be interrupted. The goal of the decision-making process is to minimize the electricity bill paid by the users and the discomfort perceived by them, according to a desired-tradeoff, subject to the operation constraints involved.Since these problems turn out to be NP-hard, we describe several heuristics we designed, explaining their foundations. Finally, we analyze the computational results obtained for instances we generated and present the best algorithms to employ according to different priorities the users might have.

Role-Based Access Control (RBAC) Enabled Secure and Efficient Data Processing Framework for IoT Networks

Internet of Things (IoT) has the potential to significantly impact various domains e.g. health, transportation, automation, and emergency response to both man-made and natural disasters, particularly in scenarios where human decision is challenging. In this research a Role-Based Access Control (RBAC) Enabled Secure and Efficient Data Processing Framework for IoT Networks has been proposed. This framework ensures robust security and optimized data handling through granular access control mechanisms based on predefined roles. By leveraging RBAC, it mitigates unauthorized access risks, thereby safeguarding sensitive IoT data during transmission and storage. Our approach emphasizes efficiency by streamlining data processing workflows, reducing latency, and optimizing resource utilization. The framework is designed to scale with IoT network expansions and adapt to evolving security needs, promising enhanced reliability and trustworthiness in data operations for contemporary IoT environments. According to this research work current security effectiveness is 99 percent.Home area network (HAN) can be used for smart connectivity of different home appliances using IoT and automatic start and stop feature may be possible.Access control server (ACS) is used to control access and provide permission for different operations.

Rancang Bangun Green Portable Cooler Box

Freon is one of the refrigerants used in refrigeration systems. The refrigeration system has a very important role, namely to preserve food ingredients so that food can last longer. The use of freon is not only used in ships but is widely found in home appliances in refrigeration systems used in freezers, air conditioners, refrigerators, and dispensers. If more freon is used, the negative impact that arises is greater, namely the depletion of the ozone layer and global warming which can have a bad influence on health, the life of living things and the environment. The purpose of this researcher is to create a tool to reduce the negative impact of freon use, namely by using peltier as a cooling medium with environmentally friendly thermoelectric modules. This study uses trial and error methods. Where the trial process is carried out by turning on one of the working push buttons and observing the temperature value. Then it is processed by the control system, namely Arduino Mega which will later provide a PWM output signal to the IRF540 mosfet when the output temperature value is in line with the reference value and vice versa. The test of this tool was carried out by comparing measurements between the author's prototype tool and the standard measuring tool, namely the digital thermometer. Based on the test results, this device can produce a minimum temperature of 140C at 30-minute intervals without any media. The trial using 330ml bottled water can reach a temperature of 170C within 30 minutes in the cooler box. This tool is also equipped with an automatic monitoring system using a PWM signal in the form of a change in fan speed, which is 255 RPS maximum speed and when getting a PWM signal it changes to 50 RPS. The percentage of errors resulting from the comparison of standard measuring instruments and sensor values had the highest and lowest percentages of 2.37% and 0.84% in cold dispersion conditions without charge. Then the highest and lowest error percentages were 1.69% and 0.72% in the state of containing a load of 1 330ml mineral bottle. This ptototype uses data recording using Micro-SD as a data logger.

Evaluating the Trade-offs between Wireless Security and Performance in IoT Networks: A Case Study of Web Applications in AI-Driven Home Appliances

Evaluating the Trade-offs between Wireless Security and Performance in IoT Networks: A Case Study of Web Applications in AI-Driven Home Appliances

Enhancing energy-efficient home appliance adoption: Media publicity strategies and planned behavior theory

Enhancing energy-efficient home appliance adoption: Media publicity strategies and planned behavior theory

Impact of Delay in ZigBee WSNs for Smart Home Applications

Impact of Delay in ZigBee WSNs for Smart Home Applications

Enhancing Visual Homing in Robotics: A Study on Blockchain Integration and Consensus Algorithms

Creating an immutable repository for vital robot and environmental data, ensuring long-term accessibility, and functioning in the absence of GPS or mapping are crucial for visual homing navigation systems. We focus on the intersection of blockchain and robotics, particularly in visual homing. Our research involves an in-depth analysis of various blockchain consensus mechanisms, highlighting their suitability for visual homing applications. The heart of blockchain functionality lies in its consensus mechanism, which facilitates agreement among network nodes. In our first study part, we conduct a comprehensive comparative analysis of key consensus algorithms, emphasizing visual homing's decentralization, fault tolerance, latency, and throughput requirements. This analysis serves as a valuable reference for researchers and developers, emphasizing the importance of aligning the chosen consensus mechanism with specific blockchain application needs. The second part of our work involves extensive experiments exploring the connection between blockchain and visual homing. We assess prominent consensus mechanisms like Proof of Work (PoW), Proof of Stake (PoS), Delegated Proof of Stake (DPoS), and Proof of Authority (PoA) within a virtual environment in Gazebo, leveraging Wide Area Visual Navigation (WAVN). Our research implementation is grounded in the ROS framework and the Gazebo simulation environment.

The Relationship between the Working Environment and Quality of Life among Home Health Aides: Focusing on the Mediation Role of Burnout

ABSTRACT As South Korea’s population rapidly ages, there is an increasing demand for home aides. However, little is known about how the caregiving environment affects HHAs. Guided by the environment comfort model, we examined the association between care recipients’ home environment and HHA’s quality of life, focusing on how burnout mediates this relationship. Our data came from a national survey of home health aides in 2020 (N = 786). We conducted an exploratory factor analysis to identify six factors related to the care environment in three dimensions: physical (1. space; 2. indoor/outdoor conditions), functional (3. home appliances; 4. heating/air conditioning), and psychological (5. satisfaction with the home environment; 6. relationships with care recipients and their families). We then used a path analysis to examine the relationship between these factors, burnout, and quality of life. Our findings show that safe indoor/outdoor conditions and positive relationships with care recipients and their families are associated with lower levels of burnout, leading to a higher quality of life (p < .05). This highlights the importance of considering both physical and psychological aspects of the caregiving environment to prevent burnout and improve the quality of life for HHAs, ultimately contributing to high-quality services for care recipients.

Beyond the Urban Kitchen: Refrigeration and Domesticity across Australia and the Pacific Islands, 1920s–1940s

Abstract Histories of the rise of domestic refrigeration conventionally situate the appliance within the urban household. An emphasis on the spread of electrification and notions of idealized femininity, abundance, and scientific home management together attribute an emblematic “Americanness” to the refrigerator. Moving beyond the confines of the urban kitchen to rural and tropical regions of Australia and across the island Pacific, this article examines the appeal and influence of the home appliance in areas remote from reliable or affordable electricity, as well as from accessible supplies of perishable foods produced elsewhere. In such locations the refrigerator was understood differently, centering ideals of white settler self-sufficiency, masculine mastery of the environment, and notions of colonizing capacity. Examining the early history of this appliance in these places also reveals how technological influences and cultural aspirations moved between Australia and the islands and vice versa, intensifying in the immediate aftermath of World War II. In repositioning the refrigerator through such engagements and entanglements, this article offers new insights into patterns and practices of colonial domesticity, provisioning, and consumption.