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The distribution system's economic operation is significantly impacted by the management of distributed generation (DG) resources, energy storage (ES), and controllable loads. The paper employs a smart distribution system that incorporates dispatchable and non‐dispatchable DG resources, as well as battery storage, in addition to the demand response (DR) scheme. New modelling was performed in hourly steps to achieve the optimal unit commitment. In smart homes, appliances are prioritized and classified into four types: adjustable, interruptible, shiftable, and uncontrollable loads. Load reduction in smart homes is also considered based on load prioritization and customer participation in the DR scheme to achieve the proposed scheme's objectives. The proposed method considers costs associated with microturbines (MTs), including manufacturing, start‐up, shutdown, and pollution. Additionally, planning is conducted to purchase or sell electricity to the upstream network. The simulation is run on an IEEE 33‐bus system to demonstrate the proposed method's effectiveness. The system is assumed to be capable of operation in both island and grid‐connected modes. The results demonstrate the proposed approach's efficacy in load reduction, operation cost, and execution time. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The development of Smart Home Controllers has seen rapid growth in recent years, especially for smart devices, that can utilize the Internet of Things (IoT). However, a large portion of the household devices and appliances already in use, are not IoT enabled, and therefore, requires their default control mechanisms for the devices to operate. This paper proposes a smart appliance controller that uses Augmented Reality, MQTT, and other up-to-date platforms to control aftermarket home appliances in the most efficient manner. The proposed work integrates mobile AR with IoT, to control household appliances, with the help of infrared (IR) signals. The characteristics of the system are evaluated through a series of tests and performance measures. The results of the test highlight the quick response time of MQTT for the implementation of a Home Automation System, when compared to the request–reply protocol: CoAP (4 times as fast). [ABSTRACT FROM AUTHOR]

This paper proposes a smart home appliance scheduling management (HASM) system based on demand response. An IoT based HASM system is developed to provide an efficient electricity consumption scheme to residential consumers for scheduling their appliances in a cost-effective way and smartly throughout a day. This smart load scheduler takes residential electricity users' preferences to shift the appliances with deferrable characteristics at cost effective periods considering the user is aware of dynamic tarif f information. The user interaction to the scheduling of loads provides maximum comfort level as well as saving of total electricity bill, and the effective utilization of demand response strategies to reduce peak hour electricity consumption. The results of this development indicate a reduction of daily energy consumption confirming the consumer comfort. [ABSTRACT FROM AUTHOR]

This paper investigates the challenge of finding and analyzing security vulnerabilities among widely available low-budget Internet of Things smart home appliances. It considers the identification of security vulnerabilities within the appliances' embedded software and connectivity functions over wired and wireless channels in local networks and external communications with manufacturers' cloud services. To analyze the security of these appliances, a universal laboratory test bench is proposed and a set of methodologies for testing the security of smart home devices is described. The proposed testing platform offers a practical solution for security analysis of Internet of Things smart home devices and it can serve as a reference approach for future research. The results from the research indicated varying levels of susceptibility across different types of devices. A list of recommendations for manufacturers and others to improve the security level of these appliances is provided. The findings emphasize the need for regular security assessments of smart home devices, to maintain the protection of personal and sensitive information. [ABSTRACT FROM AUTHOR]

With the widespread adoption of applications and IoT devices, modern society has come to rely on them in various aspects of daily life. These applications and devices cover a range of needs, including home appliances and even medical devices for body monitoring. Because a significant amount of data generated by IoT devices and applications must be transmitted over networks, particularly the Internet, there is an increased risk of cyber-attacks on this transmitted data. This data can be text, images, sound, or other forms, and it is critical to ensure its protection, especially if it contains valuable information. Extensive research has been conducted on various encryption algorithms to ensure the security of transmitted data, whether through block ciphers or stream ciphers. In addition, efforts have been made to increase encryption efficiency by securing the transmission channels. The question of this research is “what is the state of the art in the lightweight encryption in terms of strength and speed up the process?”. This paper provides a comprehensive survey of research related to two main categories of lightweight encryption, and work focused on secure authentication between entities. The paper includes an analysis of the techniques used in each surveyed paper and highlights their main results. [ABSTRACT FROM AUTHOR]

Copyright of Romanian Journal of Civil Engineering / Revista Română de Inginerie Civilă is the property of Matrix Rom and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Energy harvesting models are the focus of most countries, given that governments are becoming aware of the limitations of natural resources and the need to optimize them. On the other hand, different systems used in everyday life and in industrial environments involve the use of heat cycles, but in most cases, their thermoelectric energy is not recovered from these processes. Accordingly, this paper proposes to implement a model based on a low-cost Peltier array that can be attached to commonly used devices with heat cycles involving small temperature differences (ΔT = 25 ℃). A maximum power point tracking (MPPT) method was used to extract the maximum power from this array. This device is thought to take advantage of home and industrial elements' heat to power low-power system applications. The results show that this technology allows acceptable use and represents an effective recovery mechanism. This work represents a new approximation of the energy harvesting solutions from thermoelectric energy with future benefits, especially on the Internet of Things (IoT) applications, which has been one of the technology areas of most significant expansion and growth in recent decades. The IoT has opened significant challenges in the scientific community, especially regarding the energy supply methods of the IoT elements or nodes, considering that these elements can be located in places where it is impossible to wire to supply power and that use of batteries is unsustainable in the long term, also generating a negative environmental impact. The proposed system harvests energy from the temperature difference generated at a window, considering that the device is controlled environment within a room. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Predicting errors in home appliances is crucial for maintaining the reliability and efficiency of smart homes. However, there is a significant lack of such data on appliance malfunctions that can be used in developing effective anomaly detection models. This research paper presents a novel approach for simulating errors of heterogeneous home appliance power consumption patterns. The proposed model takes normal consumption patterns as input and employs advanced algorithms to produce labeled anomalies, categorizing them based on the severity of malfunctions. One of the main objectives of this research involves developing models that can accurately reproduce anomaly power consumption patterns, highlighting anomalies related to major, minor, and specific malfunctions. The resulting dataset may serve as a valuable resource for training algorithms specifically tailored to detect and diagnose these errors in real-world scenarios. The outcomes of this research contribute significantly to the field of anomaly detection in smart home environments. The simulated datasets facilitate the development of predictive maintenance strategies, allowing for early detection and mitigation of appliance malfunctions. This proactive approach not only improves the reliability and lifespan of home appliances but also enhances energy efficiency, thereby reducing operational costs and environmental impact. [ABSTRACT FROM AUTHOR]

Research on automated domestic appliances, categorized as Smart Home Technology (SHT), has increased exponentially over the last decade and has taken various guises, from qualitative descriptive investigation to empirically based analysis. Given the unresolved uncertainties surrounding the SHT acceptance literature and concern regarding the relatively low smart home device uptake, there is a need to reappraise the existing literature to delve deeper and search for solutions. Based on the research method PRISMA, a systematic literature review on SHT acceptance was undertaken to evaluate its different models and develop a hypothetical model. Twenty-three papers were selected in the review, and the results indicate that the Technological Acceptance Model was the most applied model when investigating SHT acceptance. Moreover, the most significant variables used to measure SHT acceptance were compatibility and perceived usefulness. The systematic literature review also revealed some significant patterns including the uptake of non-Western research and the use of sales and market share as a metric of SHT acceptance. Future directions on how researchers, smart home developers and governmental agencies can utilize the findings conclude the systematic review. [ABSTRACT FROM AUTHOR]

Packages made of corrugated paper are fundamental to the protection, transportation and handling of the appliance product market. During the storage and sales stages of a product, the package must resist compressive loads in different directions beyond moderate impacts. In this context, the objective of this work is to develop and implement a post-processor that allows the simultaneous analysis of two of the most common failure modes of packages made of corrugated paper: failure due to tensile or compressive stress limit, and failure due to local buckling, when the buckling of the faces of the corrugated paper between two peaks of the fluting waves occurs. It was realized that the current procedure proposed by the literature expend a series of operations, what became the failure analysis for corrugated boxes too lengthy for the immediacy of the industry. Thus, it was chosen to apply a procedure to simplify the failure limit surface. Tensile tests were performed for the characterization of corrugated material used in numerical simulations. Experimental tests were performed on corrugated boxes used on household appliance products. The Tsai-Wu criteria for the material failure evaluation was applied and a modified Nyman-Gustafsson criteria for the local buckling analysis on the numerical simulations was proposed. A good correlation between experimental tests and numerical results was obtained. This work brings high expectations to the agility requested by the industry in the launching of new products. [ABSTRACT FROM AUTHOR]

To support the 2021 Manufacturing & Service Operations Management (M&SOM) Data-Driven Research Challenge, RiRiShun Logistics (a Haier group subsidiary focusing on logistics service for home appliances) provides MSOM members with logistics operational-level data for data-driven research. This paper provides a detailed description of the data associated with over 14 million orders from 149 clients (the consigners) associated with 4.2 million end consumers (the recipients and end users of the appliances) in China, involving 18,000 stock keeping units operated at 103 warehouses. Researchers are welcomed to develop econometric models, data-driven optimization techniques, analytical models, and algorithm designs by using this data set to address questions suggested by company managers. History: This paper has been accepted as part of the 2021 M&SOM Data-Driven Research Challenge. Funding: This work was supported by the National Natural Science Foundation of China [Grants 72091215/72091210, 71871207, 71991464/71991460, and 71921001]. Supplemental Material: Data and the online appendix are is available at https://doi.org/10.1287/msom.2021.0994. [ABSTRACT FROM AUTHOR]

Copyright of Revista Foco (Interdisciplinary Studies Journal) is the property of Revista Foco and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Home appliances connected to Internet-of-Things (IoT) platforms have been extensively installed in smart homes. In this context, home energy management systems (HEMSs) have emerged as a viable solution for reducing energy costs. Although several studies have analyzed the implementation of HEMSs, a majority of these studies were based on the installation of numerous sensors. Owing to the complexity and costs associated with the installation of multiple sensors, implementation of HEMSs in smart homes is challenging. This paper presents an energy management scheme for an HEMS that minimizes the energy cost in a smart home with data obtained from IoT-based appliances typically used in a smart home. Case studies were conducted to demonstrate the effectiveness of the proposed method. Prototype software (version 11) based on the proposed method for a HEMS and a test house with IoT-based appliances were also implemented in the case studies. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

With the development of Non-Intrusive Load Monitoring (NILM), it has become feasible to perform device identification, energy consumption decomposition, and load switching detection using Deep Learning (DL) methods. Similar to other machine learning problems, the research and validation of NILM necessitate substantial data support. Moreover, different regions exhibit distinct characteristics in their electricity environments. Therefore, there is a need to provide open datasets tailored to different regions. In this paper, we introduce the Transient Dataset of Household Appliances with Intensive Switching Events (TDHA25). This dataset comprises switch instantaneous data from 10 typical household appliances in China. The TDHA dataset features a high sampling rate, accurate labelling, and realistic representation of actual appliance start-up waveforms. Additionally, appliance switching is achieved through precise control of relay switches, thus mitigating interference caused by mechanical switches. By furnishing such a dataset, we aim not only to enhance the recognition accuracy of existing NILM algorithms but also to facilitate the application of NILM algorithms in regions sharing similar electricity consumption characteristics to those of China. [ABSTRACT FROM AUTHOR]

Purpose: Considering the problems faced by the home appliance industry in Iran, such as the increase in waste, lack of information transparency and lack of traceability of manufactured products, etc. the companies in the home appliance industry are moving toward Industry 4.0 and have been prompted to use it. On the other hand, the adoption of Industry 4.0 is associated with challenges that may lead to the failure of the adoption project and the bankruptcy of home appliance manufacturers. This study identified the challenges in the implementation of Industry 4.0 on current status and provided suitable solutions to overcome the identified challenges. Design/methodology/approach: In this study, a review of the literature and background of Industry 4.0 identified the challenges that influence the adoption of Industry 4.0. To measure the current status of the identified challenges, the opinions of experts in the Iranian home appliance industry were used. To find solutions to the challenges in the adoption of Industry 4.0 in the Iranian home appliance industry, a fuzzy cognitive mapping and scenario design were used. Findings: The results of this study show that to face the challenge of data sharing, skilled personnel should be effectively promoted among workers in the home appliance industry. The results of this study also show that the barriers to cooperation should be removed to reduce the impact of the IT Security Concerns challenge. Originality/value: This paper is the first article that identifies the challenges and effective solutions for implementing Industry 4.0 in the home appliance industry. [ABSTRACT FROM AUTHOR]

This paper presents the development of advanced adaptive controller for dynamic voltage restorer (DVR) to regulate the power quality connected to a distribution system. Various voltage quality issues have been generated as distorted grid conditions are responsible for abnormal operation of the consumer electronics and domestic household appliances. Therefore dynamic voltage restorer (DVR) is employed for elimination of voltage quality issues in the distribution lines. To improve the performance and efficiency of DVR, an improved robust SRF (IR-SRF) controller has been developed in this paper for the mitigation of voltage quality issues under weak grid conditions. In the developed IR-SRF controller, moving average filter (MAF) has been utilized for better extraction of ripples from the sensed voltage. For better grid voltage signal synchronization with the proposed controller under highly dynamic condition, an enhanced Phase Locked Loop (PLL) scheme has been utilized instead of conventional PLL. For the regulation of DC link voltage under dynamic environmental conditions, MAF based fuzzy tuned PI controller has been utilized with the ability to adapt the situation. The developed controller has been simulated and results for various grid voltage conditions have been analyzed. The obtained simulation results are compared with conventional control algorithm to validate the efficacy. The efficiency of the proposed controller has been validated using the experimental results. [ABSTRACT FROM AUTHOR]

Robot vacuum cleaners have gained widespread popularity as household appliances. One significant challenge in enhancing their functionality is to identify and classify small indoor objects suitable for safe suctioning and recycling during cleaning operations. However, the current state of research faces several difficulties, including the lack of a comprehensive dataset, size variation, limited visual features, occlusion and clutter, varying lighting conditions, the need for real-time processing, and edge computing. In this paper, I address these challenges by investigating a lightweight AI model specifically tailored for robot vacuum cleaners. First, I assembled a diverse dataset containing 23,042 ground-view perspective images captured by robot vacuum cleaners. Then, I examined state-of-the-art AI models from the existing literature and carefully selected three high-performance models (Xception, DenseNet121, and MobileNet) as potential model candidates. Subsequently, I simplified these three selected models to reduce their computational complexity and overall size. To further compress the model size, I employed post-training weight quantization on these simplified models. In this way, our proposed lightweight AI model strikes a balance between object classification accuracy and computational complexity, enabling real-time processing on resource-constrained robot vacuum cleaner platforms. I thoroughly evaluated the performance of the proposed AI model on a diverse dataset, demonstrating its feasibility and practical applicability. The experimental results show that, with a small memory size budget of 0.7 MB, the best AI model is L-w Xception 1, with a width factor of 0.25, whose resultant object classification accuracy is 84.37%. When compared with the most accurate state-of-the-art model in the literature, this proposed model accomplished a remarkable memory size reduction of 350 times, while incurring only a slight decrease in classification accuracy, i.e., approximately 4.54%. [ABSTRACT FROM AUTHOR]

Nowadays, large warehouses handle a huge number of products. Handling the enormity and different types of (range) products also demand complex warehouse processes. In this paper, the IKEA warehouse in Stavanger, Norway, is taken as an example, which stores, manages, and sells ready-to-assemble furniture, kitchen appliances and home accessories. The focus of this paper is to understand the warehouse processes that make the warehouse popular with the customers. Petri net is used to model the processes, and by simulation, the effect of the processes are understood. This paper shows how the processes integrate the large range of products, customer service, and the supply chain. The logistics flow is represented with a modular Petri Net model using the tool known as the General-purpose Petri Net Simulator (GPenSim). The goal of this paper is also to determine and propose any changes for a more efficient warehouse performance. [ABSTRACT FROM AUTHOR]

Reverse logistics networks reintroduce end-of-life products to remanufacturing, which is significant for sustainable development and environmental protection. In this paper, we investigate the current reverse logistics network of household appliances in China and redesign a new network that introduces necessary facilities: disassembly centres and three types of remanufacturing plants to improve recycling rates. A mixed-integer linear programming model with multi-level capacity choices is proposed to determine the locations and capacities for disassembly centres and remanufacturing plants. An efficient decomposition-and-expansion heuristic is developed to solve the model. The redesigned network is evaluated using a real case. The results indicate that the recycling rate is largely improved by the new network in which almost all useful materials can be recycled, whereas, in the traditional network, only metals are efficiently recycled. A sensitivity analysis of model parameters is conducted using the Taguchi method to identify their effects on recycling decisions. The proposed algorithm is further evaluated using a set of randomly generated instances. The results show that the algorithm can yield high-quality solutions within a short time. The best configuration of the algorithm is suggested via sensitivity analysis of parameters using the generated instances. [ABSTRACT FROM AUTHOR]

In globalised manufacturing and production environments, companies increasingly tend to exploit public contribution advantages within designing, manufacturing and marketing processes. To be benefited from outbound capabilities, this paper presents a tactical supply chain planning model to integrate the designing process in the form of open innovation within the supply chain main processes. In this regard, a fuzzy mathematical model is proposed to optimise the tactical decisions according to supply chain objectives and open innovation considerations. On the other hand, intellectual property issues and protecting the rights of innovators are significant concerns that sometimes avoid companies to be engaged in open innovation initiatives. Therefore, this paper proposes a registering mechanism in which the ideas and creative works are collected, refined and finally approved within a blockchain platform. Furthermore, in order to deal with epistemic environmental uncertainty, the fuzzy set theory is utilised. To investigate the applicability of the developed model, a case study in home appliances domain is employed. The results show that the company can achieve favourable designs by spending approximately 1% of the supply chain total cost. Additionally, benefiting the registering mechanism can decrease the cost of using non-original designs more than 41%. [ABSTRACT FROM AUTHOR]

Brain-computer interfaces (BCIs) have a potential to revolutionize humancomputer interaction by enabling direct links between the brain and computer systems. Recent studies are increasingly focusing on practical applications of BCIs--e.g., home appliance control just by thoughts. One of the non-invasive BCIs using electroencephalography (EEG) capitalizes on event-related potentials (ERPs) in response to target stimuli and have shown promise in controlling home appliance. In this paper, we present a comprehensive dataset of online ERP-based BCIs for controlling various home appliances in diverse stimulus presentation environments. We collected online BCI data from a total of 84 subjects among whom 60 subjects controlled three types of appliances (TV: 30, door lock: 15, and electric light: 15) with 4 functions per appliance, 14 subjects controlled a Bluetooth speaker with 6 functions via an LCD monitor, and 10 subjects controlled air conditioner with 4 functions via augmented reality (AR). Using the dataset, we aimed to address the issue of inter-subject variability in ERPs by employing the transfer learning in two different approaches. The first approach, "within-paradigm transfer learning," aimed to generalize the model within the same paradigm of stimulus presentation. The second approach, "cross-paradigm transfer learning," involved extending the model from a 4-class LCD environment to different paradigms. The results demonstrated that transfer learning can effectively enhance the generalizability of BCIs based on ERP across different subjects and environments. [ABSTRACT FROM AUTHOR]

This paper introduces the first completely unsupervised methodology for non-intrusive load monitoring that does not rely on any additional data, making it suitable for real-life applications. The methodology includes an algorithm to efficiently decompose the aggregated energy load from households in events and algorithms based on expert knowledge to assign each of these events to four types of appliances: fridge, dishwasher, microwave, and washer/dryer. The methodology was developed to work with smart meters that have a granularity of 1 min and was evaluated using the Reference Energy Disaggregation Dataset. The results show that the algorithm can disaggregate the refrigerator with high accuracy and the usefulness of the proposed methodology to extract relevant features from other appliances, such as the power use and duration from the heating cycles of a dishwasher. [ABSTRACT FROM AUTHOR]

Nowadays, various interfaces are used to control smart home appliances. The human and smart home appliances interaction may be based on input devices such as a mouse, keyboard, microphone, or webcam. The interaction between humans and machines can be established via speech using a microphone as one of the input modes. The Speech-based human and machine interaction is a more natural way of communication in comparison to other types of interfaces. Existing speech-based interfaces in the smart home domain suffer from some problems such as limiting the users to use a fixed set of pre-defined commands, not supporting indirect commands, requiring a large training set, or depending on some specific speakers. To solve these challenges, we proposed several approaches in this paper. We exploited ontology as a knowledge base to support indirect commands and remove user restrictions on expressing a specific set of commands. Moreover, Long Short-Term Memory (LSTM) has been exploited for detecting spoken commands more accurately. Additionally, due to the lack of Persian voice commands for interacting with smart home appliances, a dataset of speaker-independent Persian voice commands for communicating with TV, media player, and lighting system has been designed, recorded, and evaluated in this research. The experimental results show that the LSTM-based voice command detection system performed almost 1.5% and 13% more accurately than the Hidden Markov Model-based one, in scenarios 'with' and 'without ontology', respectively. Furthermore, using ontology in the LSTM-based method has improved the system performance by about 40%. [ABSTRACT FROM AUTHOR]

In the process of recycling, dismantling, and reusing household appliances, implementing extended producer responsibility (EPR) has become increasingly important. Designing a reasonable pricing mechanism for waste household appliance recycling is critical for the implementation of EPR. To address the problem of labor-intensive and experience-dependent traditional manual methods for assessing the value of waste household appliances, in this paper, we propose an evaluation method based on the subtractive clustering method and an adaptive neuro fuzzy inference system (SCM–ANFIS), which outperforms traditional neural networks such as LSTM, BP neural network, random forest and Takagi–Sugeno fuzzy neural network (T–S FNN). Moreover, in this paper, we combine the five aforementioned algorithms to design a combination evaluation model based on maximum ratio combination (CEM–MRC), which can achieve a performance improvement of 0.1% in terms of mean absolute percentage error (MAPE) compared to the suboptimal BP neural network. Furthermore, an enhanced evaluation model based on classification selection (EEM–CS) is designed to automatically select the evaluation results between the optimal SCM–ANFIS and the suboptimal CEM–MRC, resulting in a 0.73% reduction in MAPE compared to the optimal SCM–ANFIS and a 1.42% reduction compared to the suboptimal CEM–MRC. In this paper, we also validate the performance of the proposed algorithms using a dataset of waste television recycling, which demonstrates the high accuracy of the proposed value assessment mechanisms achieved without human intervention and a significant improvement in evaluation accuracy as compared to conventional neural-network-based algorithms. [ABSTRACT FROM AUTHOR]

This paper presents a detailed study of the energy savings that can be achieved through residential appliances by implementing conservation voltage reduction (CVR). The computed energy savings are derived from the ZIP coefficients of a load model (static) under varying voltage conditions that represent the power consumed by each appliance tested. Multiple studies have shown energy savings at the substation level but not at the level of a specific residence and appliance; hence, the latter is the focus of this analysis. The appliances are determined based on the type of heating and cooling loads, and the daily duration of use for each appliance, which contributes to the total monthly consumption that is billed for a household are included in this study. Energy savings in Malaysia has been a focus of many current studies, and this study is the first attempt to achieve energy savings through the implementation of CVR. No published work has compared individual appliances that truly benefits from CVR, and this is addressed in this study. The main contribution of this study is that it provides very detailed and measured data that are used to analyze and generate energy savings for very specific residential appliances intended to meet every 1% voltage drop. The load model is developed for the common household appliances in Malaysia, and then a voltage reduction study is applied to the appliances as well as the Malaysian distribution networks. Here, we also provide insight for performing voltage reduction at an electric vehicle (EV) charging station in Malaysia. The results of this study should serve as a foundation for all practicing utilities engineers to address the issue of CVR in Malaysia. The CVR factors for residential, commercial, and industrial networks in Malaysia are also published in this paper. [ABSTRACT FROM AUTHOR]

The advancements in the field of materials and force gadgets, coupled with the increased affordability of high-performance processors, have led to a widespread adoption of Brushless Direct Current (BLDC) motors across various industries. These applications range from household appliances to automotive, aerospace, and medical sectors. The widespread use of may be attributed to its manifold advantages over various types of engines, such as its superior efficiency, robust power output, extended operational lifespan, relatively quiet operation, and broader range of achievable speeds. Due to the increasing adoption of Brushless Direct Current (BLDC) motors in many real-life applications as a replacement for traditional motors, this paper aims to compile and evaluate a comprehensive list of control algorithms employed for BLDC motor control. This study discusses several ways for managing speed and current, including hysteresis band control, variable DC-link voltage control, and Pulse Width Modulation (PWM) control schemes. The optimization of Proportional-Integral-Derivative (PID) gains for these controlling techniques is achieved through the utilization of the particle swarm optimization (PSO) algorithm. By employing Fast Fourier Transform (FFT) analysis to examine the controller behavior through frequency analysis of the output signals and calculating the Total Harmonic Distortion (THD), a more advantageous control method may be determined. [ABSTRACT FROM AUTHOR]

The first aim of this paper was to determine the variability in the signal characteristics and psychoacoustic data of canister-type vacuum cleaners. Fifteen vacuum cleaners with different sound power levels, provided by the manufacturers, were selected as test units to calculate their acoustic and psychoacoustic parameters. The selection of the devices was based on an even distribution of the reported sound power levels. The investigated variability in the acoustic and psychoacoustic parameters on different vacuum cleaners was discussed to derive the common characteristics of canister-type vacuum cleaner noise. The derived common characteristics were compared with the those in the available literature on the noise generation mechanisms of vacuum cleaners. Based on these characteristics, prototypical vacuum cleaner noise was defined. The second aim of this paper was to understand the annoyance perception of vacuum cleaner noise. Annoyance assessments were obtained from two sets of listening experiments. The first listening experiment was conducted to find the correlates of annoyance evaluations. Loudness, sharpness and tonal components at lower and higher frequencies were found to be dominant correlates of vacuum cleaner noise annoyance estimations. In the second listening experiment, a possible interaction between loudness and sharpness was investigated in different listening test methods. The selected loudness and sharpness values for this experiment were consistent with the observed ranges in the first part. No significant interaction between loudness and sharpness was observed, although each separately correlated significantly positively with annoyance. [ABSTRACT FROM AUTHOR]

This paper utilizes an IoT platform called IoTtalk to shape "intelligence" into a house to make it a smart home. The developed project is called HomeTalk that serves as a platform to accommodate various smart applications in a house. We describe the following HomeTalk applications. The PlantTalk application takes care of house plants. The FishTalk application provides fish comfortable life in the fish tank at home. The BreathTalk application detects the number of people in a room, which also purifies the air. The TheaterTalk application uses home and special appliances to create the effects for a 4D experience theater at home. The FrameTalk application allows a painting frame to interact with people in a house. The GardenTalk application provides smart gardening. By examining these projects, this paper explicitly identifies and generalizes the features of HomeTalk that provides flexibility to reuse these IoT devices by sharing them among various smart home applications simultaneously. Specifically, we show how these applications share the sensors and actuators in the house. In the future, we will integrate them through an award winning project called Orchid House. [ABSTRACT FROM AUTHOR]

Non-intrusive load monitoring (NILM) technology, crucial for intelligent electricity management, has gained considerable attention in residential electricity usage studies. NILM enables monitoring of total electrical current and voltage in homes, offering insights vital for enhancing safety and preventing domestic electrical accidents. Despite its importance, accurately discerning the operational status of appliances using non-intrusive methods remains a challenging area within this field. This paper presents a novel methodology that integrates an advanced clustering algorithm with a Bayesian network for the identification of appliance operational states. The approach involves capturing the electrical current signals during appliance operation via NILM, followed by their decomposition into odd harmonics. An enhanced clustering algorithm is then employed to ascertain the central coordinates of the signal clusters. Building upon this, a three-layer Bayesian network inference model, incorporating leak nodes, is developed. Within this model, harmonic signals are used as conditions for node activation. The operational states of the appliances are subsequently determined through probabilistic reasoning. The proposed method's effectiveness is validated through a series of simulation experiments conducted in a laboratory environment. The results of these experiments (low mode 89.1%, medium mode 94.4%, high mode 92.0%, and 98.4% for combination) provide strong evidence of the method's accuracy in inferring the operational status of household electrical appliances based on NILM technology. [ABSTRACT FROM AUTHOR]

Whereas the literature on rebates has focused on single-product firms, this paper analyses the role of rebates offered by a multiproduct firm Rebates have long been recognized as a price discrimination mechanism. When firms offer an extensive product line, an important managerial question concerns the choice of which products to promote. This paper finds that a multiproduct firm leverages rebates to screen consumers on the basis of their redemption costs, which enables it to exercise price discrimination via the product line more effectively. In particular, because consumers who use rebates tend to have lower valuations, the classic cannibalization concern is less severe in the product line offered to rebate users. Therefore, the lowest-quality product that is promoted with a rebate is of higher quality than the lowest-quality product that is offered without rebate. Hence, the firm does not promote its lowest-quality products. Instead, by promoting some products with rebates, the firm drives some low-valuation consumers to trade up to higher-quality products. Notably, it is found that, contrary to the commonly held view, rebates can serve a price discrimination role even when redeemed by all consumers. Therefore, this paper proposes a novel price discrimination explanation for large rebates, sometimes in the order of several hundreds of dollars, that are frequently used in industries such as consumer electronics and household appliances. History: Yuxin Chen served as the senior editor for this article. Supplemental Material: The e-companion is available at https://doi.org/10.1287/mksc.2023.1430. [ABSTRACT FROM AUTHOR]

Deep drawing is an operation to transform flat or hollow blanks to create hollow parts of the required shape and size. Deep drawing is an essential operation in sheet-forming technology to manufacture hollow parts. These parts are commonly used in the automobile, aviation, and household appliances industries. To prevent wrinkles on the rim part, a blank holder will be used to compress and flatten the workpiece before the metal is pulled into the die by the punch. Deep drawing without using workpiece holding force is applied to form hollow parts with low depth. In this case, the mould has a simple structure, helping to reduce manufacturing time and costs and making it easier to maintain and repair. The radius of the die is an essential parameter in deep drawing; it greatly affects the quality of products (making wrinkles and tears). However, this study has shown that, in deep drawing without using a blank holder, the cone angle of the die α has a major influence on the quality of the body and rim of the products, such as the products may or may not have wrinkles, either being thinned or thickened. This cone angle also affects the drawing force. This study has determined that, with α>120°, wrinkling begins to appear in the part wall, and wrinkling tends to increase as the angle α increases. The cone angle of the die for quality products is in the range of 100° < α ≤ 120°. The taper angle smaller than 120° simultaneously increases the quality of the products and the cost of manufacturing die, so the most optimal value is determined by simulation and experimental verification as α = 120°. The results of this study can be applied to fabricate hollow cylindrical parts with similar shapes when using the deep drawing method without using the blank holder. [ABSTRACT FROM AUTHOR]

Purpose: models of reverse logistics (RL) that consider the sustainable development goals (SDG) and circular economy activities (CEA) are increasingly appearing in the literature. Intention of the article is to show at what stage of implementation of the SDG and CEA the home appliance industry (HAI) is currently at and what challenges it faces in terms of implementing the RL model. Design/methodology/approach: The paper shows that the information, data, industry positions and reports presented on the website of the Association of Appliance Manufacturers (APPLiA) show that the HAI is too slow in moving towards the SDG and CEA. It also prevents the implementation of theoretical models of RL. Findings: The article shows that the current actions taken by HAI in Europe and Poland are insufficient to meet the SDG, CEA, and the implementation of full models of RL. Research limitations/implications: The presented research results refer to the data presented by the HAI of Europe and Poland, as well as the information contained on the websites of the APPLiA Europe and Poland. The presented research results can be related to the information of other relevant industries such as automotive, aerospace and others, so that the hypothesis that the industry insufficiently pursues the SDG and CE and does not take strategic steps towards the implementation of models of RL with special consideration of all CE activities. Practical implications: The paper points out that for the home appliance industry to realize the SDG and the CE through the implementation of RL, it must use Design for eXcellence (DfX) tools now with a focus on Design for Circular Economy (DfCE) and Design for Logistics (DfL). Social implications: no social implication. Originality/value: The article presents the author's model of RL and formulates challenges for the industry that are associated with its implementation. It should draw the attention of various industries to the need to take strategic actions for implementation SDG and CEA. [ABSTRACT FROM AUTHOR]

To test electric devices such as household appliances, electric vehicle and other loads, there is need for emulators instead of real devices, which decreases the cost of testing. This paper presents a dynamically programmable load emulator that imitates the current–time characteristic of different types of the loads. The exact mathematical model of various types of electric loads is firstly extracted. These mathematical models are implemented by LabVIEW software. A non-ideal buck converter is used for the hardware part of the emulator, which is controlled by LabVIEW. The proposed emulator can mimic the types of loads given by LabVIEW software with great accuracy and is an example of an actual load simulator with good approximation. Manufacturers or laboratories of electric equipment can use the proposed emulator to perform their tests instead of using the actual device. This reduces the cost of tests and speeds them up so that all types of electric loads can be modeled with an emulator. In addition, because a simple power electronic converter structure is used, the proposed emulator has low complexity and low manufacturing cost. Simulation and laboratory results prove the efficiency of this emulator in making models of different types of loads. [ABSTRACT FROM AUTHOR]

Washing machines (WMs) are common household appliances that help to save time and effort used in brushing and washing clothes. It is a common practice to use manually operated WMs, and based on their uses, WMs are classified into top and front open cloth washing machines, which operate based on an automatic control mechanism. In this paper, we present the design and simulation of an Arduino fuzzy logic-based WM control system, with an emphasis on improvement in its operating algorithm. Simulations were performed to determine the optimal time the WM takes to wash clothes, the maximum number of clothes the WM can wash per time, the acceptable dirtiness level of cloths, and the type of clothes that the machine can wash. The number of clothes to be washed, the degree of dirtiness, and the type of clothes govern the fuzzy logic control process adopted by the WM. The output voltage of the WM varies as the degree of dirtiness of the water varies from 0 to 1.95 V for very dirty water output from washed clothes and varies from 4 to 4.89 V for low-contaminated water. Another constraint considered was the load current, which increased as the number of clothes increased. The WM's operation time is determined by the amount of voltage and load current used during its operation. As a result, the control of the WM is dependent on the dirtiness level of the clothes and the amount of load. [ABSTRACT FROM AUTHOR]

Given the longevity of investments in energy-consuming products (such as household appliances, vehicles, and properties), underinvestment in energy efficiency can have long-lasting negative economic and environmental consequences. Previous research has indicated that underinvestment may be due to imperfect information in relation to the long-term benefits of investing in energy efficiency. This paper presents the results of a cluster randomised controlled trial examining an intervention which aims to overcome this information deficit by providing long-term energy cost information on appliances in an electrical retail chain in Ireland. Two treatments are considered: a label showing 10-year energy cost information based on typical usage for four appliance categories (fridge freezers, dishwashers, washing machines, and tumble dryers); and a second treatment which supplements this label with a QR code where consumers can gain personalised cost estimates based on their expected appliance usage. Results indicate that neither of the treatments resulted in an increase in the average energy efficiency of appliances sold. Also, engagement of customers with the QR code was extremely low. Given that the newly designed EU energy labels incorporate QR codes for personalisation, this low usage suggests that this element of the new labels may be ineffective in increasing the uptake of energy efficiency. Finally, a customer survey suggests that while the treatment increased the stated importance of energy efficiency in decision-making, this did not translate into an increase in efficiency of products purchased, i.e., stated preferences for energy efficiency did not translate into revealed purchasing preferences. [ABSTRACT FROM AUTHOR]

The use of electronic equipment and other dc-compatible appliances in homes and offices is increasing at a very fast rate, as well as the distributed generation of energy. These two statements will most likely trigger, in a near future, the adoption of district-scale dc grids, connecting dc microgeneration plants and consumers, in an effort to cut the number of conversion steps required to deliver power to dc appliances. DC–DC converters will be fundamental in these systems, allowing the establishment of several voltage levels. The reliability of these converters plays a critical role, ensuring service continuity of the loads connected to them. With the increment of reliability in mind, this paper proposes a simple yet effective open-circuit fault diagnostic method suitable for interleaved dc–dc boost converters, but equally functional for other dc–dc converter topologies. The improvements introduced in this paper greatly simplify the diagnostic action, while making the correct diagnostic of faults independent of the switching duty cycle. Moreover, an alternative reconfiguration scheme is proposed to mitigate the adverse impacts of such faults. To prove the effectiveness of these strategies, a three-phase interleaved boost converter prototype was used in the experiments. [ABSTRACT FROM AUTHOR]