Home Appliances Research Articles

Sim-Based Home Tracheostomy Care: A Mixed Methods Study on Outcomes and Parental Preparedness.

To assess effects of a Simulation-Based Discharge Education Program (SDP) on long-term caregiver-reported satisfaction and to compare clinical outcomes for children with new tracheostomies whose caregivers completed SDP versus controls. The study employed a mixed methods approach: (1) a qualitative analysis of feedback from caregivers who previously completed SDP, and (2) a quantitative retrospective case-control study comparing outcomes between children with new tracheostomies whose caregivers completed SDP versus controls, matched on discharge disposition and age. The primary outcome was emergency department visits for tracheostomy-related issues within 1 year of discharge. Feedback from 18 interviews was coded into 5 themes: knowledge acquisition, active learning, comfort and preparedness, home application of skills, and overall assessment. Caregivers of 27 children (median age 26 months [interquartile range (IQR) 5.5 months-11 years]) underwent SDP training. Clinical outcomes of these children were compared with 27 matched children in the non-SDP group (median age 16 months [IQR 3.5 months-10 years]). There was no significant difference in ED visits for tracheostomy-related complications within 1 year of discharge between the SDP group and non-SDP group (2 [IQR 0-2] vs 1 [IQR 0-2], P = .2). Caregivers reported overwhelmingly positive experiences with SDP that persisted even 4 years after training. Caregiver participation in SDP did not yield a significant difference in ED visits within 1 year of discharge for tracheostomy-related complications compared with control counterparts. Future steps will identify more effective methods for comparing and analyzing clinical outcomes to further validate impacts of simulation-based programs.

The Influence of Digital Transformation on the Reconfigurability and Performance of Supply Chains: A Study of the Electronic, Machinery, and Home Appliance Manufacturing Industries in China

In this era of intense global competition, supply chains are facing challenges in coping with emerging market issues. Within diverse industries worldwide, supply chains are experiencing accelerated reconfiguration, with one of the most notable transformations being the digitalization of supply chain operations. But the literature lacks empirical evidence about how digital transformation effectively contributes to it. Thus, this paper delves into the implications of the supply chain digital transformation (SCDT) and supply chain reconfigurability (SCR) on its overall performance. Cross-sectional data from 379 respondents in the machinery, electronics, and home appliance manufacturing industries were collected through a closed questionnaire. Utilizing a hybrid approach involving the Partial Least Squares Structural Equation Model (PLS-SEM) and fuzzy-set Qualitative Comparative Analysis (fsQCA), this study employs a cross-validation of the conceptual model. Initially, the PLS-SEM is employed to unveil the direct impact of SCDT on supply chain performance (SCP), as well as the intermediary effect of SCR. Building on this foundation, the fsQCA method is further utilized to investigate the configuration paths that enable enterprises to achieve high SCP under the combined influence of SCR and SCDT. The research results affirm the significant influence of SCDT on SCP. Likewise, the findings highlight the crucial intermediary role of SCR between SCDT and SCP. Ultimately, three distinct configurations driving high supply chain performance are identified: technical, management, and flexible configurations, each playing a unique role.

Symbolic Aggregate Approximation-Based Clustering of Monthly Natural Gas Consumption

Natural gas is an indispensable non-renewable energy source for many countries. It is used in many different areas such as heating and kitchen appliances in homes, and heat treatment and electricity generation in industry. Natural gas is an essential component of the transportation sector, providing a cleaner alternative to traditional fuels in vehicles and fleets. Moreover, natural gas plays a vital role in boosting energy efficiency through the development of combined heat and power systems. These systems produce electricity and useful heat concurrently. As nations move towards more sustainable energy solutions, natural gas has gained prominence as a transitional fuel. This is due to its lower carbon emissions when compared to coal and oil, thus making it an essential component of the global energy framework. In this study, monthly natural gas consumption data of 28 different European countries between 2014 and 2022 are used. Symbolic Aggregate Approximation method is used to analyse the data. Analyses are made with different numbers of segments and numbers of alphabet sizes, and alphabet vectors of each country are created. These letter vectors are used in hierarchical clustering and dendrogram graphs are created. Furthermore, the elbow method is used to determine the appropriate number of clusters. Clusters of countries are created according to the determined number of clusters. In addition, it is interpreted according to the consumption trends of the countries in the determined clusters.

An enhanced coati optimization algorithm for optimizing energy management in smart grids for home appliances

This research presents an innovative approach to energy management in smart homes, aiming to efficiently regulate energy demands while ensuring customer loyalty. The focus is on addressing the limitations of existing demand-side management (DSM) programs, which predominantly target the residential sector. The proposed solution introduces the Adaptive Coati Optimization algorithm, which optimizes device organization based on Critical-Peak-Price and Real-Time-Price power payment systems. By strategically managing energy consumption, the algorithm reduces electrical expenses and peaks without compromising user convenience. The study evaluates the effectiveness of the approach across three operational periods (60 minutes, 12 minutes, and 24 minutes) to align with varying customer needs. Overall, the Adaptive Coati Optimization algorithm offers a promising solution for cost-efficient energy management in smart homes, combining both financial benefits and enhanced user satisfaction. The results indicate a significant decrease in tariffs rates, up to 30%, leading to a 20% increase in user satisfaction and a 25% improvement in cost utilization.

Exercise Promotion System for Single Households Based on Agent-Oriented IoT Architecture.

People living alone encounter well-being challenges due to unnoticed personal situations. Thus, it is essential to monitor their activities and encourage them to adopt healthy lifestyle habits without imposing a mental burden, aiming to enhance their overall well-being. To realize such a support system, its components should be simple and loosely coupled to handle various internet of things (IoT)-based smart home applications. In this study, we propose an exercise promotion system for individuals living alone to encourage them to adopt good lifestyle habits. The system comprises autonomous IoT devices as agents and is realized using an agent-oriented IoT architecture. It estimates user activity via sensors and offers exercise advice based on recognized conditions, surroundings, and preferences. The proposed system accepts user feedback to improve status estimation accuracy and offers better advice. The proposed system was evaluated from three perspectives through experiments with subjects. Initially, we demonstrated the system's operation through agent cooperation. Then, we showed it adapts to user preferences within two weeks. Third, the users expressed satisfaction with the detection accuracy regarding their stay-at-home status and the relevance of the advice provided. They were also motivated to engage in exercise based on a subjective evaluation, as indicated by preliminary results.

Enhancing children's nutrition: the influence of rural household technology under China's home appliances going to the countryside policy.

This study explores the influence of household technological advancements on children's nutritional intake, specifically within the context of the Chinese government's "Home Appliances Going to the Countryside" (HAGC) initiative. Utilizing data from the China Health and Nutrition Surveys of 2006, 2009, and 2011, we employed a Propensity Score Matching Difference-in-Differences (PSM-DID) framework to ascertain the repercussions of enhanced household technology on the dynamics of children's nutritional consumption patterns. Our analysis reveals that the HAGC-inspired integration of household appliances, including color televisions, washing machines, and refrigerators, has beneficially reshaped the nutritional consumption patterns of children, with a pronounced effect among female children. This finding remains consistent even when employing alternate methodological robustness tests. A deeper examination of the HAGC policy's mechanisms underscores the pivotal roles of parental time allocation, improved food storage capabilities, and augmented information accessibility as significant drivers bolstering children's nutritional intake. These insights bear considerable significance for strategizing interventions aimed at elevating the nutritional wellbeing of children in rural settings, offering valuable input for shaping public health policies tailored for such demographies.

The Factors Affecting the Purchase Intention of Household Electrical Appliances (Home Appliances) That Are Good for Consumers’ Health in Chongqing City.

The objective of this study is to explore and examine the impact of these factors on the Purchase intention of household electrical appliances through the mediating role of Attitude. Survey data were collected from 329 consumers in Chongqing City. Results from the Partial Least Squares Structural Equation Modeling (SEM) using the Amos 20 program showed that Health concern, Environmental concern, Attitude, Perceived behavioral control, and Trust affect directly to purchasing intention of consumers. Results from the Process Macro using the SPSS 25 program have also identified the mediating role of Consumers’Attitudes in the relationship between Health Concern and Environmental Concern to Consumer’s Purchase intention. Finally, several suggested governance implications will assist manufacturers and marketers in developing targeted customer- focused strategies.

An economical and simple strategy for overcoming the Hall sensor faults in three phase brushless DC motor drive

SummaryBrushless DC motors (BLDCs) are highly efficient and have the least maintenance requirement due to which they have replaced many conventional AC motors in various applications like electric vehicles, fans, water pumps, and home appliances. However, like all conventional motors, performance degradation of the BLDC motors can occur due to faults like Hall sensors failure, commutation switch failure, and stator‐winding failure. Among all these failures, this paper focuses on Hall sensor failures and the execution of fault tolerant control. Hall sensors produce an output after every 60° of rotation of the rotor, generating six sequences in one electrical cycle. In this study, each sequence is defined as a sector, which corresponds to the rotor position. Based only on this sector information in each electrical cycle, this paper proposes an uncomplicated detection strategy for Hall sensor fault, which is independent of motor loading and high‐performance processing unit. Along with the diagnosis, an improved faulty signal reconstruction is implemented, which bypasses the fault and continues a smooth performance of the motor. The proposed detection and reconstruction strategies have been simulated in MATLAB Simulink 2020, and the experimental analysis has been conducted to validate the proposed strategies.

Intelligent sensing technology inspired by cell electrophoresis: Dielectric polarization sensor for monitoring the electrical equipment status of IoT nodes

The rapid development of the Internet of Things (IoT) era has led to the emergence of various types of information sensing devices. The operation and maintenance of IoT devices require a large-scale use of sensors at the expense of high energy consumption. In this work, we develop a self-powered voltage monitoring sensor (VSM-S) based on the principle of dielectric polarization, which is inspired by the cell electrophoresis phenomenon. This sensor imitates the structure of biological cells and takes advantage of the easier polarization characteristics of liquid medium with high dielectric constant in an external electric field. It collects electric field energy to achieve self-powering function and can monitor the position and status of the electric field source in real-time through output signals. Experiments show that the designed VSM-S has the characteristics of fast response (10−9-10−15 s), high sensitivity (0.3 nA/V), and wide application range. Furthermore, its integration with monitoring systems enables applications in smart industries, smart homes, and smart grids. This work has considerable development prospects in the fields of wearable sensors and the IoTs.

A Scheduler for Smart Home Appliances Based on a Novel Concept of Tariff Space.

The background of this work is related to the scheduling of household appliances, taking into account variations in energy costs during the day from official Brazilian domestic tariffs: constant and white. The white tariff can reach an average price of around 17% lower than the constant, but charges twice its value at peak hours. In addition to cost reduction, we propose a methodology to reduce user discomfort due to time-shifting of controllable devices, presenting a balanced solution through the analytical analysis of a new method referred to as tariff space, derived from white tariff posts. To achieve this goal, we explore the geometric properties of the movement of devices through the tariff space (geometric locus of the load), over which we can define a limited region in which the cost of a load under the white tariff will be equal to or less than the constant tariff. As a trial for the efficiency of this new methodology, we collected some benchmarks (such as execution time and memory usage) against a classic multi-objective algorithm (hierarchical) available in the language portfolio in which the project has been executed (the Julia language). As a result, while both methodologies yield similar results, the approach presented in this article demonstrates a significant reduction in processing time and memory usage, which could lead to the future implementation of the solution in a simple, low-cost embedded system like an ARM cortex M.

A Multi-Stream Deep Neural Network to Predict the Energy Consumption of Smart Home Appliances

A novel ensemble machine learning approach for predicting energy consumption in smart appliances is presented in this paper. The main objective is minimizing the number of necessary sensors and improving the prediction accuracy at the same time. The proposed method combines the Fuzzy C-Means method with a multi-stream deep neural network to achieve this goal. The method focuses on prediction accuracy and aims to extract the minimum number of essential features from the dataset corresponding to the required sensors. These selected features are then scatter-reduced and homogenized into subsets. Each subset is used to train a cluster-specific deep neural network designed exclusively for that subset. The final prediction is obtained by computing the fuzzy-weighted sum of these cluster-specific network outputs. Numerical results show that the proposed prediction method outperforms conventional methods in terms of root mean square error and mean absolute percentage error criteria, despite using fewer sensors. This improvement can be attributed to the reduced dataset scatter, which improves the learning speed and model performance. Furthermore, the fuzzy combination of the outputs improves the final prediction accuracy. Overall, the proposed approach provides a more cost-effective and accurate solution for predicting the energy consumed by smart appliances.

Net-zero, resilience, and agile closed-loop supply chain network design considering robustness and renewable energy.

The Net-zero, Resilience, and Agile Closed-Loop Supply Chain Network (NZRACLSCND) concept integrates net-zero, resiliency, and agility in a circular economy. Regarding net-zero, this research embeds renewable energy like solar energy and hybrid trucks to supply energy for facilities and transportation of goods and products between components. Applying redundancy, multi-source, and flexible capacity as resiliency strategies is suggested to cope with the demand disruption. Satisfaction demand level is utilized for the agile approach. This research proposes Robust Stochastic Optimization (RSO), including the weighted expected value and maximum CO2 for NZRACLSCND. This study locates and determines the flow of CLSC in the home appliance industry by considering NZRA, robustness, and risk against demand disruption. CO2 emission using the NZRA concept is 233.33% less than without considering NZRA concepts. In addition, the conservative coefficient, agile coefficient, decreased CO2 coefficient, and the model scale are analyzed. The results show that when the conservative coefficient increases, the risks of CO2 emission increase. In addition, when the agile coefficient increases, as a result, CO2 emission increases. Finally, when the decreased CO2 coefficient and the model scale increase, we can see that CO2 emission and cost are increased.

Mining fuzzy local periodic activity pattern for Smart home applications

Perceiving resident movements from ubiquitous sensor devices can aid health and safety management in smart home environment. One can find resident movement patterns to get notification about what should be aware of to prepare for possible accidents. The goal of this work is to propose a method to search for periodic residents’ activity patterns given smart home applications. Specifically, the proposed method is aimed to capture fuzzy local periodic activity patterns (FLPAP) from the smart home data. A FLPAP is an activity pattern that periodically occurs for a particular duration in fuzzy time intervals. We construct an algorithm to help to find the FLPAP called Fuzzy Local Periodic Activity Pattern Miner (FLoPMiner). On the side, a parameter called k-scale period is well-chosen to reorganize the data to make it suitable for the need of periodic activity pattern mining. The proposed method was demonstrated for its effectiveness on a public smart home dataset. After all, we successfully retrieved periodic resident activity patterns, which can be applied to self-health management systems, especially when focusing on a precise scale period. Overall, the proposed method shows descent pattern representation against the state-of-the-art methods of the same category.

Safe Home from Fire and Control Home Appliance Based on Internet of Things (IOT)

Safe Home from Fire and Control Home Appliance Based on Internet of Things (IOT)

Security-enhanced firmware management scheme for smart home IoT devices using distributed ledger technologies

With the increase of IoT devices generating large amounts of user-sensitive data, improper firmware harms users’ security and privacy. Latest home appliances are integrated with features to assure compatibility with smart home IoT. However, applying complex security mechanisms to IoT is limited by device hardware capabilities, making them vulnerable to attacks. Such attacks have recently become frequent. To address this issue, we developed a secure verification mechanism for firmware released by the device’s manufacturer. We proposed an IoT gateway for secure firmware verification and updating for smart home IoT devices utilizing the IOTA MAM (Masked Authenticated Messaging) protocol and a distributed file system with IPFS (Inter-Planetary File System) protocol. These two communication protocols ensure decentralized communication and firmware file distribution between the IoT device vendor and the IoT end device. The proposed scheme securely shares latest firmware content over IOTA and IPFS networks, performs a secure firmware update on IoT end devices and ensures authenticity and integrity of the firmware. Two types of validation methods were proposed for firmware updating and validation. We implemented the proposed scheme using three entities, Vendor, IoT gateway, and IoT end device. Our system yielded promising results in performing secure automated firmware updates on IoT end devices with very low computational power. The system’s functionality was implemented using IOTA’s MAM run on Raspberry Pi as an IoT gateway along with an ESP8266 Wi-Fi microcontroller, demonstrating the effectiveness of our approach. Our proposed methodology can be used for secure firmware distribution on home IoT applications.

AI Bedroom

Purpose: Now, artificial intelligence (AI) is booming. Day by day, AI is introduced into the new field. We have lots of expectations for the advancement of AI. In our modern busy schedule, we all expect our everyday monotonous homework to be executed by AI. We are introducing more and more smart devices to do our work smartly. But at the end of the day, all our smart home devices are operated manually. We are not fully satisfied with smart devices. Knowing this, the smart device manufacturer is adding AI features inside their devices. Here, we demonstrate how to build an AI bedroom for better living. Design/Methodology/Approach: We install three devices inside the bedroom. The first is a surveillance PTZ camera, the second is the CPU, and the third is the action module. The camera will capture the events and is transferred to the central processing unit or CPU. It will process the image and then detect the event. Once the event is detected, then through the action module, we trigger the electrical or electronic equipment. Findings/Result: the performance of the centralized system is better than that of distributed individually operated smart devices. Here, we account for two types of performance: the event detection and the action module on the specific action. The event detection module takes much more time due to the processing overhead of the image. We get the result within a couple of milliseconds. Due to the dedicated CPU, the processing is faster than on a cloud-based server, which depends on the bandwidth of the internet. Originality/Value/ Novelty: We studied several research documents on smart homes and artificial intelligence-integrated homes. Most AI homes are built using several smart home appliances operated manually. And there is no centralized control. Without central control, the system could not deliver the best performance. Here, the complete system is nicely controlled by a centralized CPU, which makes it a unique approach to this project. Type of Paper: Conceptual Research.

Virtual Laboratory in Mixed Reality with 3D Modelling for Sustainable Design

Immersive experiences through Mixed Reality (MR) are revolutionizing the way people connect and interact with each other and things around them. MR is the umbrella term that binds all spatial computing technologies which blends real and virtual environments. It holds the potential to provide things beyond imagination while simultaneously making deeper and more meaningful engagement possible. A new engineer to be equipped with the know-how of an OP (Operational Procedure) of machinery, this virtual laboratory helps to know the intricate details of the machinery and tools. The virtual laboratory combines the use of MR technologies, such as augmented reality (AR) and virtual reality (VR), with advanced 3D modeling tools. This virtual laboratory has the potential to revolutionize the sustainable design process by providing an immersive and flexible platform for experimentation, analysis, and collaboration. This project focuses on helping the user to visualize the machine (i.e., Single Drum Piston) and its functionalities through an immersive experience and understand the visual empathy of it. Through 3D Modelling technology, Plane detection and algorithms written with C# script we built this solution with the incorporation of Autodesk Maya and Unity Game Engine. The built solution can be experienced immersive by viewing it through a mobile placed in a headset (Aryzon Headset). This MR experience provides realistic virtual presentations which eases learning of complex modules while analytics ensure understanding of outcomes. It can empower designers and researchers to explore innovative design solutions, optimize resource utilization, and ultimately contribute to the development of more sustainable built environments. This can be incorporated in various other fields like automobile manufacturing, machine industries, construction tool manufacturing, real estate buildings & landscapes, home appliances and furniture, interior design, medical institution, and educational institutions.

Design and Implementation of Intelligent Water Heater Based on STC89C52

Smart home appliances have penetrated into every household, and the water heater industry is undergoing new changes. At this time, it is urgent to develop an automatic thermostatic system to ensure that the water temperature remains stable. Intelligent water heater using the STC89C52 can automatically adjust the water temperature, and the system control is achieved through the keyboard. The system can set the temperature accurately to 0.1°C and display the temperature value on the display screen. When the temperature set by the system is higher than the actual temperature, the single chip microcomputer controls the relay to power on and heat it up. When the collected water temperature is higher than the set temperature, the relay will turn off, stopping the heating of the water in the water tank. After experimental verification, it is proved that the system has sufficient stability and reliability, with the advantages of convenience, safety, and small size.

Using IoT and AI to replenish household food supplies: A systematic review

Food wastage because of the lack or incompletion of a household replenishment system is an essential topic to be addressed. An appropriate utilization of Internet of Things (IoT) and Artificial Intelligence (AI) technologies with particular components is needed to design a smart household replenishment system to reduce food wastage. Therefore, this systematic review is dedicated to survey papers utilizing IoT and AI tools for perishable items storage compartments, as they are always full of items that need to be monitored. This study was conducted by following the PRISMA search strategy. It examined 70 papers in chronological order starting from 2000 when LG Electronics invented the first smart refrigerator, and research on technology involvement in food storage compartments increased. This comprehensive research aims to point out the approaches, contributions, used components and limitations of the reviewed papers to develop a unified framework for a household replenishment system. The analysis resulted in 43 approaches using IoT technology, 27 using AI, and recently the use of AIoT has been trending in the past two years. This systematic review provides future directions for researchers acquired from the limitations of the reviewed papers to enhance the household replenishment system by developing and adding required features in smart food storage compartments. Further investigation into smart home appliances would lead to extensive approaches like smart shops, industries, and eventually smart cities.

CHALLENGES WITH INTERNET OF THINGS (IOT) SECURITY

The phrase "Internet of Things Security" refers to a broad category of tactics, equipment, procedures, frameworks, and techniques utilized to safeguard every facet of the internet of things. The safeguarding of the hardware, software, data, and network connections to guarantee the availability, confidentiality, and integrity of IoT ecosystems is all part of IoT security. There are several security issues due to the large number of vulnerabilities that are frequently found in IoT systems. All aspects of protection, including as component hardening, monitoring, firmware updates, access control, threat response, and vulnerability remediation, are included in robust IoT security. IoT security is essential since these systems are widely dispersed and susceptible to attack, making them a highly focused attack vector. By preventing unwanted access, IoT devices can be protected from becoming entry points into the network and from leaking private data. Vulnerabilities in IoT security can be identified in watches, smart home appliances, cars, and smart grids.