Appliance Control Research Articles

Occupancy-based Energy Management System in Campus Building: A Case Study in Electrical Engineering Department Building, ITN Malang

Building energy management systems provide an effective method for energy saving. An occupancy-based energy management system considers the occupants' behavior that affects energy consumption. This paper presents the investigation of different scenarios based on the occupancy to control the energy consumption in the Electrical Engineering Department Building, ITN Malang. The results show that switching off the appliances when the occupants go home in the afternoon reduces the monthly energy consumption by 33.95%. The result indicates that the occupants' awareness regarding energy saving has recently been low. Thus, the occupancy sensor and automatic time scheduling for the appliance control are suggested to be installed.

IoT-Based Smart Home Automation System Using NodeMCU

The Smart Home Automation System utilizes the NodeMCU ESP8266 microcontroller, integrated with key sensors and actuators, to enable intelligent, remote control and monitoring of home appliances. This project automates environmental regulation and appliance control by using a DHT11 sensor, relay modules, and the Blynk IoT platform, allowing users to manage devices such as fans and lights through both manual and automatic modes. Temperature and humidity data, collected by the DHT11 sensor, enables real-time monitoring and automated fan control based on predefined conditions. Data is transmitted wirelessly to the Blynk cloud, where it is stored, visualized, and accessible on user-friendly mobile dashboards. The system includes alert mechanisms and remote accessibility, promoting energy efficiency and enhancing user convenience. Designed with a modular architecture, this system supports easy customization and scalability for integration with additional devices and sensors, aligning with the broader objectives of smart home ecosystems. Overall, this project showcases the potential of IoT-driven home automation in achieving greater energy management, user comfort, and operational simplicity

Exploring the genetic expression of Sdf1, Foxc1 and histologic changes following mandibular advancement and recovery phase in Wistar rats.

PurposeThis study evaluated the impact of mandibular advancement on Sdf1 and Foxc1 gene expression in the mandibular condylar cartilage of young Wistar rats. By examining the changes that occur during a unique one-month recovery period, it highlights the critical role of gene expression and condylar adaptation during the recovery phase. The analysis focused on whether, during the recovery period, reversal changes occur when functional appliances are removed and whether genetic expression important for condyle growth and adaptation downregulates. Material and MethodsThe study involved 30 male Wistar rats divided into 2 control groups Appliance Control and Recovery Control groups, and 2 experimental groups, the Appliance group with mandibular advancement bite-jumping appliance for 30 days, and the Recovery group with appliance for 30 days followed by a 30-day recovery. Molecular analysis of condylar cartilage using real-time RT-PCR and histological assessments was conducted. ResultsSignificant genetic expression alterations were noted in both the experimental groups for Sdf1 (p < 0.05) and Foxc1 (p < 0.05). According to histological investigations, significant alterations with an increase in the proliferative and hypertrophic layer in condylar cartilage were seen. ConclusionMandibular advancement bite-jumping appliances induce proliferative and hypertrophic layer changes in mandibular condylar cartilage, shown by elevated Foxc1 levels and decreased Sdf1 levels. Post-appliance removal, persistent gene expression reveals a true joint stimulation.

Blind assistance system for appliance control and public transport safety using CNN, MobileNet V2 and Yolo V8

The purpose of this research is to address the challenges faced by visually impaired individuals, particularly in handling household appliances independently. With approximately 285 million visually impaired individuals worldwide, technological solutions are crucial to enhancing their accessibility and independence. This paper introduces a Smart Assistance System designed to empower visually impaired individuals to interact with household appliances in real-time without assistance. In this study, three Convolutional Neural Network (CNN) algorithms are compared to develop the system. The evaluation metrics include accuracy, precision, recall, F1 score, and hamming loss on validation images. The performance comparison reveals that the custom architecture CNN, MobileNetv2, and YOLO models achieve F1 scores of 0.43, 0.63, and 0.24, respectively. To enhance object detection and classification, the paper suggests implementing bounding box buttons categorization using YOLOv8, which demonstrates superior performance with a 95% classification accuracy on testing images of home appliance buttons. They face similar difficult while in public and accessing public property. Expanding upon the proposed system’s capabilities, the paper introduces the concept of panic button detection and activation in a bus environment tailored for blind individuals. This system relies on various factors such as the number of people onboard, heart rate monitoring, and the detection of distress signals or SOS sounds emitted by the user. By integrating advanced sensing technologies and intelligent algorithms, this panic button detection system aims to provide prompt assistance and ensure the safety of visually impaired passengers in public transportation settings.

Distortion-tolerant on–off keying preemphasis signal transmission for a 10 Gbps indoor visible light communications

Abstract The design and simulation of indoor visible light communication (VLC) system with preemphasis is proposed for 5 m transmission of signal at the data rate of 10 Gbps for receiving only applications like smart TV, smart speaker, and smart home appliance control. The preemphasis driver circuit is designed to reduce the intersymbol interference by pulse shaping the signal and transimpedance amplifier is used to amplify the signal for longer distance transmission. Intensity modulation at the sender side and direct detection at the receiver-side are adopted in this work for the improvement of the system’s overall performance and to decrease its implementation cost. The system performance is analyzed in terms of BER, data-rate, Q-factor, and eye diagrams. The setup is developed, which includes a nonreturn-to-zero (NRZ) generator, preemphasis driver, light-emitting-diode, VLC channel, and receiver with an avalanche photodiode and Gaussian LPF. At 10 Gbps data rate, the maximum transmission distance that can be obtained is 5 m with a BER of less than 10−31.

Segmental maxillary osteotomy in cleft lip and palate patients: a stepwise planning in the orthodontic pre-surgical preparation with Invisalign.

To show the potential of Invisalign in multi-segmental Le Fort I preparation with the aid of an occlusogram-like method. Nine patients treated with Invisalign before segmental osteotomy of the maxilla (study sample) were matched for diagnosis, surgical procedure, and complexity of orthodontic preparation with nine patients treated with fixed appliances (control sample). A superimposition technique was adapted to complement the Invisalign software to carry out an appropriate ClinCheck in segmental pre-surgical treatments. The correct pre-surgical positions of the maxillary teeth were guided with the use of virtual added curves that followed the lower arch. The number of impressions needed in both groups to reach adequate pre-surgical coordination of the segmented arches was compared. With the aid of the proposed superimposition method, during the digital simulation of presurgical movements, clear aligner therapy appears to be an effective treatment even in patients with skeletal discrepancies and craniofacial anomalies involving the need for segmental Le Fort I osteotomies. More sophisticated software will certainly be implemented in the future.

Home Automation System using OpenCV and Arduino

The research focuses on the integration of computer vision capabilities through OpenCV with Arduino-based hardware to create an efficient and versatile home automation system. The system employs cameras for image processing, allowing it to recognize and respond to various environmental changes within the home. The hardware setup utilizes Arduino microcontrollers as the central processing units, enabling seamless communication between the OpenCV algorithms and the connected devices. A user-friendly interface facilitates remote monitoring and control of home appliances through a mobile or web application. By combining the power of OpenCV for image processing and Arduino for real-time control, this home automation system provides a smart and adaptive solution for enhancing security, energy efficiency, and overall convenience within a household. The research’s modular design allows for future expansions and integration with emerging technologies, ensuring scalability and relevance in the rapidly evolving field of smart home automation.

An Intelligent Cryptographic Approach for Preserving the Privacy and Security of Smart Home IoT Applications

Background: Today, computer networks are everywhere, and we utilize the Internet to access our home network. IoT networks connect home appliances and provide remote instructions. Access to any tool over an uncertain network attracts assaults. User authentication might be password- or biometric-based. Data security across a secure network like the Internet is difficult when authenticating a device. Hashing is used for validation and confidentiality in several encryption and decryption schemes. Classic cryptographic security methods require a lot of memory, processing power, and power. They cannot work with low-resource IoT devices. Methods: Automatic Device-to-Device communiqué opens up new applications, yet network machines and devices have limited resources. A remote-access home device authentication mechanism is proposed in this research. A new, lightweight encryption approach based on Deoxyribonucleic- Acid (DNA) sequences is developed to make IoT device connections easy and secure. Home network and appliance controller devices use authentication tools. DNA sequences are random therefore we utilized them to create a secure secret key. Results: Efficiency and strength are advantages of the proposed method. Our method prevents replay, server spoofing, and man-in-the-middle attacks. The suggested method protects network users and devices. Conclusion: Meanwhile, we model the system and find that the network's delay, throughput, and energy consumption don't degrade considerably.

A visual digital twin framework based on residual-based fourier neural operator online simulation method

ABSTRACT To enjoy a wonderful cooking experience in a smart kitchen, users and designers need a visual interaction platform. This paper proposes a DT framework incorporating data processing, flow field online simulation, equipment monitoring, interaction and visualization. Specifically, the DT online simulation and visualization of the kitchen fume flow field serve as the foundation for appliance design and control. Additionally, users can gain deeper insight into the state and change trends of the kitchen. To address the online simulation of the flow field, a RFNO online simulation method is proposed. In addition, this paper proposes an Echarts-based 2D and UE5-based 3D flow field visualization method to enable dynamic visualization and interaction of the flow field. The proposed DT framework was successfully verified in the case of the smart kitchen, demonstrating its efficiency and effectiveness.

Modelling and Simulation of a Budget-Friendly, Secured and Intelligent Home Automation System Using Internet of Things for the Aged and Disabled

Intelligent Home Automation System is one of the subjects that is gaining traction and popularity due to its numerous benefits across the globe today. Home automation is the process of remotely monitoring and controlling household appliances and devices. With the Internet's and its applications' never-ending growth, there is a lot of promise and scope for remote access, control, and monitoring of network-enabled appliances and devices. This paper developed an easy-to-use, secured and intelligent home automation system; capable of remotely monitoring and controlling appliances in the home, by integrating it with an affordable, flexible, versatile, open-sourced, single-board microprocessor and yet scalable Arduino Uno. The work was carried out in the following stages; First, the Arduino Uno was set up with Bluetooth and Wi-Fi connections, and Android device interfacing. Secondly, upon successful connection and configurations, the system successfully worked with the home model developed. Lastly, a unique yet simple user interface was created using the Android MIT App inventor, thereby completing the system design. The intelligent home automation system was fully simulated given the above steps, ensuring that everyday use of home appliances is monitored and controlled for a more secure, seamless and intuitive user experience. The paper also examined a number of intelligent home automation systems and technologies from a variety of defined perspectives. It focused on the home automation idea, in which smart devices are used to integrate home automation systems and technologies with a central single-board – microprocessor (Arduino Uno); Life is becoming simpler and easier in all aspects due to advancements in automation technologies, particularly in homes and working environments.

Planning with the electricity market One day ahead for a smart home connected to the RES by the MILP method

This study proposes a novel framework for smart homes to optimize energy consumption and production, leading to reduced costs and a more reliable grid. The framework schedules the use of controllable appliances and renewable energy sources while considering uncertainties in production, real-time market prices, and uncontrollable household loads. By incorporating both incremental and real-time pricing models, the system discourages excessive consumption during peak hours. The core innovation lies in a two-stage scheduling approach implemented using GAMS software. This method minimizes the expected total cost while accounting for limitations on controllable loads, power supply, production resources, battery performance, and overall home energy balance. Additionally, the framework leverages the previous day’s bilateral contract and allows residents to adjust desired lighting levels based on current market fluctuations. Simulations demonstrate the program’s effectiveness in reducing both net energy costs and peak load on the electricity grid.

P2P power trading based on reinforcement learning for nanogrid clusters

In this paper, a novel peer-to-peer (P2P) power trading for power management of nanogrid clusters with renewable energy sources is presented. Unlike power management of smart grids in megawatt power scale, power management of nanogrids in kilowatt power scale requires individual control of electric appliances. P2P trading is characterized by complex elements such as decentralized architecture, participant behaviors, and distributed energy resources. To this end, multi-objective optimization providing Pareto-optimal solutions is applied to the power management of nanogrid clusters capable of P2P trading. The P2P power trading aims at maximizing the profit as well as minimizing the grid power consumption. However, due to the conventional rule for P2P trading and trade-off property of Pareto-optimal solution, the result of P2P trading is not optimal. To improve the performance of P2P trading in terms of total electricity cost, reinforcement learning (RL) algorithms are introduced. For RL, deep neural networks are applied to P2P power trading. The RL agent of each nanogrid cluster learns the behavior of P2P trading and then fulfills P2P trading. In the proposed RL technique, graph convolutional network is employed for analyzing graph-structured data, and bidirectional long short-term memory network is utilized for data prediction, enhancing the performance of P2P trading. It is found from simulations of power management for nanogrid clusters that the electricity cost with the proposed RL technique is the lowest among the RL algorithms considered for benchmark test. The electricity cost is reduced by 36.7%, averaged over ten nanogrid clusters, as compared to the electricity cost obtained from power management without RL algorithms.

A High-Speed Acoustic Echo Canceller Based on Grey Wolf Optimization and Particle Swarm Optimization Algorithms.

Currently, the use of acoustic echo cancellers (AECs) plays a crucial role in IoT applications, such as voice control appliances, hands-free telephony and intelligent voice control devices, among others. Therefore, these IoT devices are mostly controlled by voice commands. However, the performance of these devices is significantly affected by echo noise in real acoustic environments. Despite good results being achieved in terms of echo noise reductions using conventional adaptive filtering based on gradient optimization algorithms, recently, the use of bio-inspired algorithms has attracted significant attention in the science community, since these algorithms exhibit a faster convergence rate when compared with gradient optimization algorithms. To date, several authors have tried to develop high-performance AEC systems to offer high-quality and realistic sound. In this work, we present a new AEC system based on the grey wolf optimization (GWO) and particle swarm optimization (PSO) algorithms to guarantee a higher convergence speed compared with previously reported solutions. This improvement potentially allows for high tracking capabilities. This aspect has special relevance in real acoustic environments since it indicates the rate at which noise is reduced.

ESmart Home: AI and IoT Integration for Energy Efficient Solar Powered Homes

The eSmart Home project aims to revolutionize sustainable living by integrating Artificial Intelligence (AI) and Internet of Things (IoT) technologies into solar-powered home automation systems. Using Arduino as the core microcontroller platform, the project focuses on enhancing energy management and optimizing solar power utilization. By leveraging real-time monitoring and automated control of electrical appliances, the system minimizes energy waste and lowers utility bills. Advanced AI algorithms enable predictive maintenance and adaptive energy allocation based on usage patterns, ensuring efficient operation and proactive problem-solving. Additionally, the eSmart Home project emphasizes user-friendliness, offering homeowners intuitive interfaces to interact with and control their smart home environment. The system includes various sensors for monitoring temperature, humidity, light, motion, current, and voltage, combined with actuators for appliance control. Communication modules facilitate seamless connectivity, while AI modules process data for intelligent decision-making. By integrating these technologies, the eSmart Home not only enhances energy efficiency but also provides a scalable and adaptive solution for modern smart homes.

Folded Narrow-Band and Wide-Band Monopole Antennas with In-Plane and Vertical Grounds for Wireless Sensor Nodes in Smart Home IoT Applications

This article presents two monopole antennas with an endfire radiation pattern in the UHF band that can be installed on dry walls or metallic cabinets as a part of wireless sensor nodes, making them a suitable choice for smart home applications, such as the wireless remote control of house appliances. Two different antennas are proposed to cover the RFID bands of North America (902–928 MHz) and worldwide (860–960 MHz). The antennas have wide horizontal radiation patterns that provide great reading coverage in their communication with a base station placed at a certain distance from the antennas. The structures have two ground planes, one in-plane and the other vertical. The vertical ground helps the antenna to have a directive radiation and also makes it easily installed on walls. The antenna feeding line lies over the vertical ground substrate. The maximum dimensions of the narrow-band antenna are L × W = 0.3λ × 0.14λ, and those for the wide-band antenna are L × W = 0.39λ × 0.14λ. The measured results show that the bandwidth of the proposed antennas for the North America and worldwide RFID bands are from 902 MHz to 939 MHz and 822 MHz to 961 MHz, with maximum gains of 4.2 dBi and 4.9 dBi, respectively.

Secure and User-Friendly Smart Home Automation: A Mobile-Centric IoT Approach

Home automation systems (HAS), leveraging the power of the Internet of Things (IoT), are gaining significant attention for their potential to reduce costs, labor, and human error. As the demand for smart home technologies grows, effective energy management and cost reduction become increasingly important. This research explores the technical landscape of IoT-based HAS, focusing on remote control and management of home appliances through smartphones and dedicated applications. A comparative analysis of various HAS solutions will be conducted to evaluate their functionality, usability, and energy efficiency. Additionally, the study will examine the security and privacy implications of IoT-based home automation and potential barriers to adoption such as cost and complexity. By addressing these key issues, this research aims to contribute to the development of more effective and accessible smart home solutions.

Internet of Things (IoT) Based Home Automation Using ESP-32

Abstract: In the era of rapidly advancing technology, automated home systems have emerged as an innovative solution to enhance convenience, efficiency, and security in residential environments. This project focuses on the execution of a automated home system utilizing the ESP32 microcontroller, a versatile and powerful platform renowned for its connectivity capabilities. The system integrates various sensors, actuators, and communication protocols to enable seamless control and monitoring of household devices and appliances. The ESP32 serves as the central hub of the home automation network, facilitating communication between different components and providing connectivity to the internet for access and control. Sensors such as motion detectors, temperature and humidity sensors, and light sensors gather environmental data to inform automated actions and trigger responses based on predefined conditions. Actuators such as relays, servo motors, and LED lights enable the system to communicate with and control appliances, lights, locks, and other devices within the home. Through a user- friendly interface, residents can remotely monitor and manage their home environment, adjust settings, schedule tasks, and receive notifications/alerts via a mobile application or web interface

Smart Power Monitoring System Using IoT

The "Smart Power Monitoring System using IoT" constitutes a pioneering solution poised to revolutionize the landscape of power management in both residential and industrial settings. Anchored by the ESP8266 NODEMCU microcontroller, this system seamlessly integrates an array of state-of-the-art sensors and modules, all working in concert to deliver real-time monitoring, controlling capabilities. The predominant objective of this innovative system is multifaceted: to elevate energy efficiency, bolster security measures and enable remote monitoring through the dynamic fabric of the IoT. The ESP8266 NODEMCU, functioning as the system's central hub, orchestrates a symphony of components, including a current sensor for precise power consumption insights and a PIR sensor to intelligently detect human motion for enhanced automation. The inclusion of a GSM module ensures timely communication of critical alerts to users via SMS, underscoring the system's responsiveness. Additionally, a relay module facilitates the remote control of appliances, promoting user-friendly power management. A half-horsepower induction motor acts as a tangible load indicator, providing users with a visual representation of power fluctuations. The system, powered by a stable 9V 1 Amp DC supply, culminates its capabilities by leveraging the “ThingSpeak” platform to graphically display power consumption trends, empowering users to make informed decisions for sustainable and efficient energy practices. In essence, the "Smart Power Monitoring System using IoT" emerges as a comprehensive and forward-thinking solution, poised to redefine the paradigm of intelligent power management. Keywords: IoT technology, real-time monitoring, energy consumption analysis.

GSM BASED SMART SWITCH

This review paper delves into the design and im- plementation of GSM-based smart switches, which offer users the ability to remotely control electrical appliances using GSM networks. These devices incorporate a SIM card to facilitate com- munication through SMS commands and phone calls, enabling remote appliance control with ease. The fundamental components of this system include a microcontroller, a GSM module, a relay, and a dedicated power supply. The microcontroller, programmed in the preferred language, plays a crucial role in command processing and relay management. It serves as the system’s central processing unit, orchestrating relay operations to facilitate appliance on/off control. This approach provides users with a cost-effective, efficient, and accessible solution for home automa- tion and appliance management. The practical applications of this technology extend beyond convenience to include enhanced security and energy efficiency. Users can remotely activate or deactivate appliances, including lighting, security gates, and doors. This review paper not only fosters a deeper understanding of electronics and GSM networks but also highlights their potential to contribute to an improved quality of life, making it a subject of great interest within the realm of IoT, home automation, and agriculture firm. Index Terms—Keywords: GSM, SMS Commands, Motor, Smart Switch, Microcontroller.

HOME AUTOMATION SYSTEM

The project presents the development of an advanced home automation system aimed at revolutionizing residential living through smart technologies. Leveraging Internet of Things (IoT) principles, the system integrates various sensors, actuators, and communication protocols to enable remote monitoring and control of home appliances and devices. Through a user-friendly interface accessible via mobile or web platforms, homeowners can effortlessly manage their home environment, ensuring convenience, security, and energy efficiency. The project emphasizes scalability and interoperability, paving the way for future expansion and integration with emerging smart technologies. Key Words: Home Automation System, Internet of Things (IOT), Sensors, Actuators, Remote Monitoring